Microbial Phosphorus Transformation Pathways in Piggery ...€¦ · covered anaerobic piggery wastewater treatment systems. This thesis sought to characterise taxa involved in P transformation

Microbial Phosphorus Transformation Pathways in Piggery Waste Treatment

Systems

MLMAW Weerasekara

M.Sc. in Applied Biology, Saga University, Japan, 2009 M.Sc. in Environmental Soil Science, Postgraduate Institute of Agriculture,

University of Peradeniya, Sri Lanka, 2007 B.Sc. in Agriculture (Spp), University of Peradeniya, Sri Lanka, 2005

This thesis is presented for the degree of

Doctor of Philosophy at The University of Western Australia,

School of Earth and Environment,

Faculty of Science

2015

ii

DEDICATION

I dedicate this thesis to my loving husband, son,

mother, father, and the family whose love is boundless.

This work is the recompense for you standing beside me

like a pillar of strength, giving me warm

encouragement on the way to success.

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DECLARATIONS

I, MLMAW Weerasekara, declare that this thesis was composed by me and the research

detailed was conducted by me, except for the instances detailed and quoted in the text

and acknowledgments.

MLMAW Weerasekara

iv

ACKNOWLEDGEMENT

The entire work of this study has endowed me with much knowledge and experience.

Completing this was a testing task as I was in an entirely new environment outside my

home country, Sri Lanka. I believe this work would not have been a reality without the

help and support of many individuals along the way. So take this opportunity to

acknowledge everyone for their contributions with great pleasure.

I wish to express my first and foremost gratitude to my supervisors, Winthrop Professor

Lynette Abbott, Dr. Sasha Jenkins, and Winthrop Professor Anthony O'Donnell, for

offering me a scholarship to conduct my postgraduate studies under their supervision. I

am forever indebted to them for formulating and framing a very useful research theme,

and also for their intellectual suggestions, precious advice, constant supervision,

encouragement and most importantly for offering their valuable time during this study,

despite their crowded schedules. I also owe a depth of gratitude to Professor Abbott and

Dr Jenkins for the kindness and enormous help they have extended towards me in

making my life in Australia welcome and comfortable. My deepest appreciation is

extended to Winthrop Professor Andy Whiteley, who helped me to meet the challenges

posed by bioinformatics, thesis corrections and manuscript preparation. Also, I am

forever indebted to him for his intellectual suggestions, advice, encouragement, and

most importantly for offering his valuable time during this study.

I owe a special word of gratitude to Professor Richard Allcock and his research group

for performing sequencing and providing me facilities for analysing bioinformatics.

Special thanks to Dr Ela Eroglu for her valuable comments on my thesis. I deeply

acknowledge the support and time devoted by Ian Waite, my laboratory scientific

officer in helping me understand the molecular techniques, carrying out DNA library

preparations, assisting with field sampling, and other laboratory work. I owe a special

word of gratitude to the staff at the Centre for Microscopy at the University of Western

Australia, especially T Lee-Pullen, P Rigby, Irma Larma, and M Linden for their

assistance with flow cytometry, and epi-fluorecence microscopic analysis. I would also

like to acknowledge Andy Gulliver and John Barton at the Cwise for their advice and

providing me compost.

My sincere appreciation also goes to Dr Zakaria Solaiman, Dr Falko Mathes, Dr Yoshi

Sawada, Dr Suman Geroge, Dr.Matthias Leopold, Associate Professor Louise Barton

v

and Professor Dan Murphy for their advice during various events throughout my

research.

I am grateful for the sponsorship provided by The University of Western Australia

through the International Research Fees (SIRF) programme for my studies in Australia

and to the people of this lovely university for their kindness towards me. I would also

like to acknowledge the Australian Pork Limited (APL) for partially funding my study.

I owe a special word of gratitude to Michael Smirk, Darryl Roberts, Kim Duffecy, for

their valuable time on technical assistance. I thank administration staff in the Soil

Science office of the School of Earth and Environment (SEE), Margaret Pryor, Gail

Ware, Karen Newnham, and Julia Carless for their assistances during my study. I would

also like to thank the Soil Biology and Molecular Ecology group members SEE. I

extend my heartfelt appreciation to my colleagues in SEE, especially Dr Vanesa Glez-

quiñones, Dr Basu Dev Regmi, Dr Khalil Kariman, Dr Hazal Gaza, Laila Harvard,

Joginder Gill, Bede Mickan, and Jing-wei Fan for their kindness and help. I especially

thank Lalith and Tamara and all my Sri Lankans friends for making my life in Perth

welcome and comfortable for my family. Special thanks also to my friends, Shezmin

Zavahir and Nilusha Henakaarchchi.

I owe my heartfelt gratitude to my parents, son, sisters, brother, and father-in-law for

their support, encouragement and for being with me to share my happiness and sorrow

at all times. I owe a special word of gratitude to my mother and father for their endless

support, help and love throughout my studies. Last but not least, I am lost for words to

thank my husband, Kandula, for his everlasting love, encouragement and support.

vi

Abstract

Agricultural wastewaters arising from the pork industry are often high in phosphorus

(P) and require treatment to control environmental loading of soluble P and to improve

efficiency of its re-use in agriculture. Knowledge of the taxa mediating P transformation

pathways and the factors that regulate bacterial activities in the piggery wastewater

treatment processes can be used to optimise the recovery of P form wastewater.

However, current knowledge is limited concerning P transformation pathways in

covered anaerobic piggery wastewater treatment systems. This thesis sought to

characterise taxa involved in P transformation pathways (i.e. P mineralisation, P

solubilisation, and polyphosphate accumulation) and their metabolic functions in a

model covered anaerobic piggery wastewater treatment systems using novel molecular

techniques. Further, the effect of value added products from piggery waste remediation

on plant growth, soil nutrient improvement and fungal-bacterial community

composition in soil was demonstrated.

The first objective was the baseline characterisation of all compartments involved in a

model covered anaerobic piggery wastewater treatment system in terms of physico-

chemical properties, microbial community composition and P cycling potential

(Chapter 3). Physico-chemical characteristics of samples taken from all the

compartments (pit, holding tank, covered anaerobic pond digester, and aerobic

pond/evaporation pond) were done. Bacterial community composition of the whole

system was assessed using 16S rRNA Ion Tag sequencing and putative genetic potential

of P metabolisms in terms of P mineralisation, P solubilisation, and polyphosphate

(polyP) accumulation was assessed by assigning functional annotations to shotgun

metagenomic sequences. This study identified the key components of the bacterial

community involved in the whole process of piggery waste treatment system. Both 16S

rRNA Ion Tag sequencing and metagenome analyses showed that bacterial community

composition of the initial facultative anaerobic stages (i.e. pits and holding tank) and the

covered anaerobic digester was relatively similar but remarkably varied to that of the

aerobic stage (i.e. evaporation pond). Resource availability and environmental factors

between the anaerobic stages and the aerobic stage were the key drivers in shaping the

bacterial community dynamics among these compartments of piggery wastewater

treatment system. Piggery wastewater was high in both organic and soluble P and its

distribution varied among the stages. Genes responsible for P mineralisation were

highest in the covered anaerobic pond digester and polyP accumulation was greatest in

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the treated piggery wastewater contained within the aerobic pond/evaporation pond.

These findings identified the critical treatment stages for further study to understand P

solubilisation, P mineralisation and polyP accumulation in the model piggery

wastewater treatment process.

In order to assess population and cellular level processes within key treatment stages,

microscopy, cell sorting and high throughput DNA sequencing approaches were used to

determine the extent to which P mineralisation contributed to P cycling within the

piggery wastewater treatment system (Chapter 4). P mineralisation was comparatively

higher in anaerobic ponds, where organic P was higher, when compared to the aerobic

pond. Bacteroidales, Clostridiales, Campylobacterales, and Synergistales were the most

dominant groups of P mineralising bacteria in each stage of the wastewater treatment

process occupying stable community compositions, with different degrees of

abundance, along the waste treatment process. The knowledge gained from the

composition of P mineralising microbial community serve as a basis for controlling

their function in the piggery waste treatment system.

The third objective was to identify key microbes involved in polyP accumulation and

the potential for its enhancement under imposed acidic treatments, as a novel strategy

for enhanced biological P removal (Chapter 5). Abundance, identity and functionality

of active polyP accumulating organisms (PAOs) under two pH environments (pH 5.5

and 8.5) were assessed using a range of high throughput single cell and next generation

sequencing methods. Significantly higher polyP accumulation was observed at pH 5.5

compared to pH 8.5, with enrichment of polyphosphate kinase and exopolyphosphatase

genes at pH 5.5. Functionally active polyP accumulating bacteria were identified as

Aeromonas hydrophila, Aeromonas salmonicida, Acinetobacter baumannii, Bordetella

pertussis, Citrobacter koseri, Escherichia coli, Enterobacter sp. Klebsiella,

Pseudomonas aeruginosa, Salmonella enterica, and Shigella flexneri. These findings

serve as a basis to understand and manipulate PAOs community diversity and

functionality to enhance P uptake by altering the pH in the waste treatment process.

The fourth objective was to demonstrate the use of value added products from piggery

waste remediation through addition of pelletised piggery compost within the root zone.

Pelletised fertilisers derived from remediated piggery waste were applied with a low

rate of inorganic fertiliser to assess plant growth, soil nutrient improvement and fungal-

bacterial community composition (Chapter 6). Banding of a pelletized composted

viii

piggery waste (Balance®) with a granulated inorganic fertiliser (Agras®) was most

effective on wheat growth and soil fertility compared to the other soil amendments

tested. The combined Balance-Agras soil amendment application resulted in an increase

in soil available P, plant P uptake, and shoot and root dry weights and decrease

percentage colonisation of arbuscular mycorrhizal (AM) fungi. Those positive effects

are most likely to reflect the plants and bacterial community responses to changes in

soil nutrient levels due to the application of soil amendments.

Ultimately, this research demonstrated that knowledge of the taxonomic and functional

identities of P mediating bacteria at each stage in the piggery wastewater treatment

process, together with exploitation of P mineralising bacteria and polyphosphate

accumulating organisms, provided a novel strategy for improving the waste treatment

process and developing value added fertilisers for land application. Critically, this

research impacts directly upon sustainable agricultural practices by a more effective

management of phosphorus resources through the potential development of piggery

waste by-products (e.g. pelletised piggery compost). From an environmental

perspective, the recycling of nutrients from piggery waste and adoption of new practices

(e.g. covered anaerobic pond digesters) will reduce waste accumulation and minimise

nutrient leaching, both of which are significant risk factors which currently contribute to

global eutrophication of water bodies and increased greenhouse gas emissions.

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TABLE OF CONTENTS Page

DEDICATION II

DECLARATIONS III

ACKNOWLEDGEMENT IV

ABSTRACT VI

TABLE OF CONTENTS IX

LIST OF FIGURES XV

LIST OF TABLES

XIX

1 GENERAL INTRODUCTION 1

1.1 Background, research gaps, and expected outcomes 1

1.2 Thesis objectives and hypotheses 6

1.2 Thesis structure 7

2 LITERATURE REVIEW 10

2.1 Overview 10

2.2 Recycling piggery waste: general background 11

2.2.1 Risks and benefits of re-using pig waste 11

2.2.2 Recycling piggery waste by-product: Best management practices for agriculture

12

- Anaerobic digestion 14

- Composting and pelletising 16

- Removal of excess P in piggery waste 17

2.3 Enhanced Biological P removal (EBPR) for removing excess P in piggeries

18

2.4 P transformation in piggery waste and knowledge gaps in P cycling in wastewater

22

2.4.1 P mineralisation 23

2.4.2 P precipitation/ P solubilisation 24

2.5 Role of P mediating microorganisms and their diversity in soil 25

2.6 Current molecular and microscopy techniques for identifying P cycling microbes and their advantages and limitations

27

2.7 New advances in molecular and microscopy technology to resolve problems encounter with P cycling microorganisms

36

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TABLE OF CONTENTS continued Page

2.7.1 Enzyme-labeled fluorescence (ELF) coupled to epi-fluorescent microscopy, flow cytometry, and cell sorting

37

2.7.2 Ion Torrent sequencing 39

2.7.3 Community metagenomic 39

2.8 Rationale 40

3 MICROBIAL COMMUNITY COMPOSITION AND PHOSPHORUS CYCLING POTENTIAL WITHIN A COVERED ANAEROBIC POND SYSTEM TREATING PIGGERY WASTE

42

3.0 Abstract 42

3.1 Introduction 43

3.2 Material and Methods 45

3.2.1 Farm description and sampling 45

3.2.2 Physico-chemical characterization of pig waste samples 46

3.2.3 Isolation and identification of P mineralising bacteria and P solubilising bacteria

47

3.2.4 DNA extraction and 16S rRNA Ion Tag sequencing 48

3.2.5 Whole-genome-shotgun sequencing 49

3.2.6 Multivariate statistical analyses 49

3.3 Results 50

3.3.1 Physico-chemical characteristics of a piggery waste treatment system

50

3.3.2 Isolation and identification of P mineralising bacteria (PMB) and P solubilising bacteria (PSB)

51

3.3.3 Dynamics of bacterial populations in different stages of waste treatment

52


3.3.5 Functional hierarchical classification analysis 61

3.3.6 Distribution of metabolic functions in relation to P cycling 62

3.3.6.1 Distribution of metabolic functions in relation to P mineralisation

62

3.3.6.2 Distribution of metabolic functions in relation to P solubilisation

65

3.3.6.3 Distribution of metabolic functions in relation to polyP accumulation

66

3.4 Discussion 68

3.4.1 Characterisation of microbial community composition and diversity in the wastewater treatment process

68

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3.4.2 P mineralising and solubilising potential as revealed by the culture dependant detection

70

3.4.3 Distribution of metabolic functions in relation to P mineralisation

70

3.4.4 Distribution of metabolic functions in relation to polyp accumulation

71

3.4.5 Distribution of metabolic functions in relation to P solubilisation

72

3.4.6 Recycling potential of the piggery wastewater 73

3.5 Conclusions 75

4 PHOSPHORUS MINERALISING BACTERIA FOR NUTRIENT RECOVERY FROM HIGH PHOSPHORUS PIGGERY WASTEWATER EFFLUENTS

76

4.0 Abstract 76

4.1 Introduction 77

4.2 Materials and Methods 79

4.2.1 Field sample collection and preparation 79

4.2.2 Culture conditions and ELF staining 79

4.2.3 Optimisation of incubation time necessary for ELF labelling 80

4.2.4 Field Sample preparation for epi-fluorescence microscopy 80

4.2.5 Field Sample preparation for flow cytometry 80

4.2.6 Cell sorting 82

4.2.7 Data analysis 82

4.2.8 DNA extraction and 16S rRNA tag sequencing 83

4.3 Results 84

4.3.1 Assessment of PO4ase activity of pure cultures using ELF®97 phosphate

84

4.3.2 Optimisation of incubation time necessary for ELF-labeling 84

4.3.3 Optimisation of dual staining protocol for epi-fluorescence microscopic and flow cytometric detection of ELFA labeled cells

86

4.3.4 Accuracy of ELF labeling and defining the gating strategy with ELF+SYTO9

88

4.3.5 In situ applications 90

4.3.6 Community structure of PMBs within the piggery waste treatment process

91

4.4 Discussion

94

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4.4.1 Optimisation of incubation time necessary for ELF labeling 94

4.4.2 Optimisation of dual staining protocol in epi-fluorescence microscopy and flow cytometric detection of ELFA labeled cells

94

4.4.3 In situ applications 95

4.4.4 Community structure of PMB within the piggery waste treatment process

96

4.5 Conclusions 97

5 ANALYSIS OF POLYPHOSPHATE ACCUMULATING ORGANISMS IN HIGH PHOSPHORUS PIGGERY WASTEWATER

99

5.0 Abstract 99

5.1 Introduction 100

5.2 Materials and Methods 102

5.2.1 Sampling site and lab-scale incubation experiment 102

5.2.2 Bacterial Strain, Culture Conditions 102

5.2.3 Sample preparation for epi-fluorescence microscopy and flow cytometry

103

5.2.4 Titration of DAPI concentration, epi-fluorescence microscopy, and flow cytometry

104

5.2.5 DNA extraction and 16S rRNA tag sequencing 105


5.3 Results 106

5.3.1 Titration of DAPI concentration required for epi-fluorescence and flow analyses of polyP accumulation

106

5.3.2 PolyP accumulation in high Pi loaded lab microcosm experiments

108

5.3.3 Community structure of PAOs in piggery waste 111

5.3.4 Metagenomic analysis of piggery wastewater samples treated at pH 5.5

113

5.4. Discussion 122

5.5 Conclusions 125

6 EFFECT OF LOW RATE APPLICATION OF BANDED PELLETISED PIG COMPOST ON PLANT GROWTH AND SOIL MICROBIAL COMMUNITY COMPOSITION

126

6.0 Abstract 126

6.1 Introduction

127

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6.2. Materials and Methods 129

6.2.1 Experimental design 129

6.2.2 Soil collection and potting 132

6.2.3 Soil and plant analyses 132

6.2.4 Determination of root length and arbuscular mycorrhizal (AM) colonisation

133

6.2.5 DNA extraction and Ion Tag sequencing 133

6.2.6 ANOVA and multivariate statistical analysis 135

6.3. Results 136

6.3.1 Effect of soil amendments on plant growth, P uptake and AM colonization

136

6.3.2 Effect of different soil amendments on soil properties 139

6.3.3 Effect of soil amendments on rhizosphere and root colonising bacterial population dynamics

144

6.3.3.1 Changes in the rhizosphere bacterial community profile of the different treatments to the measured plant and soil variables

148

6.3.3.2 Changes in the root colonising bacterial community profile of the different treatments to the measured plant and soil variables

151

6.4 Discussion 155

6.4.1 Effects of soil amendments on plant growth and soil fertility 155

6.4.2 Effect of soil amendments on beneficial bacterial associated with rhizosphere soil and root surface

156

6.4.3 Effect of soil amendments on AM fungal colonisation 158

6.5 Conclusions 159

7 GENERAL DISCUSSION AND CONCLUSION 160

7.1 Summary of the work performed 160

7.1.1 Overview 160

7.1.2 Specific objectives 160

7.2. Key factors driving the P cycling bacterial diversity and activity in the piggery waste treatment process

161

7.2.1. Abiotic factors 164

7.2.2. Biotic factors 166

7.2.3 Management practices 167

7.3 Methodological Considerations 167

7.3.1 Sampling strategy

168

xiv


7.3.2. Methodological considerations in fluorescence staining, flow cytometry, and cell sorting

169

7.3.3 Methodological considerations to 16S rRNA Ion Tag sequencing

171

7.3.4. Discrepancy of degree of P mineralisation as revealed by ELF coupled to flow cytometry and metagenomics

172

7.3.5 Limitations in the pot trial 173

7.4 Underlying mechanisms in P cycling and proposed pathways for the piggery waste system

173

7. 5 Research Perspectives 177

7.5.1 Relevance to scientific community 177

7.5.2 Relevance to small scale and large scale pig farmers 178

7.6 Future research directions 178

7.6.1 Research directions for methodological development in tracking P cycling in environments

179

7.6.2 Research directions for improving the current piggery waste treatment process

180

7.6.3 Research direction for enhancing the low rate application of pelletised pig compost

181

APPENDICES 182

REFERENCES 189

xv

LIST OF FIGURES Page

1.1 Approach for minimising environment loading of inorganic P (Pi) in piggery waste effluent through manipulation of microbial activities to improve its recycling potential.

5

1.2 Structure of the thesis and the relationship between chapters. 8

2.1 (a) Growth of pork industry over the last decade (b) and meat production by type (Source FAO, 2013).

12

2.2 A process of best management practices for piggery waste. 13

2.3 The process of anaerobic digestion. Modified from Batstone et al. (2000). 15

2.4 Covered anaerobic pond (CAP) (a) at initiation, and (b) under operation, which captures biogas produced for odour and GHG emission control at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA).

15

2.5 Metabolism of PolyP accumulating organisms under anaerobic and aerobic conditions and resulting by-products. Modified from Kulakovskaya et al. (2012).

19

2.6 Probable P transformation pathways in piggery waste. 23

2.7 Proposed integrated approach for understanding P cycling pathways. 38 3.1 Ability of P mineralization and P solubilisation among isolates from the

waste treatment system at Medina Research Station. Ability of P solubilisation and mineralisation was assessed based on diameter of the clear zones around the colonies. a) high and low P mineralising ability, b) low solubilising ability(+), and c) high solubilising ability(+++).

52

3.2 Alpha diversity rarefaction plots of observed species for different stages in the wastewater samples (a). Microbial diversity indicated by Shannon’s index (b) (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7340).

54

3.3 Identities and % composition of the bacteria, at class level, from stages in the waste treatment system at Medina Research Station.

55

3.4 CCA biplot showing the relationship between a) microbial community composition or b) individual bacterial taxa and environmental variables in each sampling point of piggery wastewater treatment process. Plots on the graph represent the community composition at each sampling point () and individual taxa (▲). Arrows represent the environmental variables (EC, VS, TN, TC, Pi, C:N ratio, TS, TP, OP, Ca, Mg, K, pH, Ammonia).

56

3.5 Community DNA composition of piggery waste treatment process based upon functional gene phylogenies (a). Microbial community composition obtained by taxonomic identity linked to functional gene content by MG-RAST analysis (b).

59

3.6 Relationships between (a) the abundance of alkaline phosphatase gene involved in regulation of P mineralisation and the respective organic P concentration, and (b) the abundance of alkaline phosphatase gene and organic P concentration.

64

xvi

LIST OF FIGURES continued Page

3.7 Abundance of gene involved in PolyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) at the different stages of the piggery waste treatment process.

66

4.1 Preparation of the piggery waste effluent samples for flow cytometry. 81

4.2 Emission and excitation spectrums of ELF, DAPI and PI and filter settings for the Flow Cytometry (BD Influx). ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters. DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.

83

4.3 The Pseudomonas sp. (positive strain of P mineralization) grown in the P-limited PSM liquid medium was able to form clear zone around the colonies on PSM solid medium confirming their ability to mineralise organic P in the selective medium (a) whereas no E. coli (negative strain of P mineralization) colonies appeared on PSM solid medium (b). Epi-fluorescence microscopic images of DAPI stained cells of Pseudomonas sp. grown in P-limited PSM liquid medium (c) and that of ELFA stained cells (d).

85

4.4 Ratio of ELF-labeled cells (%) with respect to the incubation time (min). Error bars represent the standard deviation between triplicate measuerements.

86

4.5 Detection of PO4ase activity of piggery effluent using epi-fluorescence microscopy (a, b, and c), and flow cytometry (d, e, and f) after staining with DAPI (a and d), SYTO9 (b, and e), and PI (c, and f). Sample was gated on single cells and deployed is the percentage of ELF+ cells to the total bacteria. X Axes of the cytograms are ELF, DAPI, SYTO9 or PI fluorescence intensity in arbitrary units (a.u.).

87

4.6 Flow cytograms showing (a) cells pre-fixed with paraformaldehyde and ELF-stained, (b) cells pre-fixed with paraformaldehyde and ELF + SYTO9, (c) unstained sample, (d) first single stained sample (SYTO9 only), (e) second single stained sample (ELF only), and (f) dual stained sample (ELF + SYTO9). Y axis represents the fluorescence intensity of ELFA, while X axis shows the fluorescence intensity of SYTO9.

89

4.7 The percentages of ELF+ve cells (▲) and respective Pi levels (grey columns) at different stages of piggery waste treatment process.

90

4.8 (a) Alpha diversity rarefaction plots of OTUs for different wastewater samples. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7396).

92

4.9 Diversity of PMB communities within the piggery waste treatment process at (a) Phylum level, and (b) Order level.

93

5.1 Microcosm set-up and subsequent sample preparation for epi-fluorescence microscopy, and flow cytometry.

103

xvii


5.2 DAPI staining of pure culture of Pseudomonas syringe cells for polyP analysed by epi-fluorescence microscopy (a-f) and flow cytometry (g-l). Cells were subsequently stained with (a/ g) 0.25; (b/ h) 0.5; (c/ i) 1; (d/ j) 5; (e, k) 7; and (f/ l) 15 µg/mL of DAPI. In epi-fluorescence micrograms (a-f), intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. In flow cytograms (g-l), sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyP (DAPI-DNA).

107

5.3 Aerobic pond samples stained for polyP. Cells were incubated with (a) 1 mg/L-P, (b) 10 mg/L-P, and (c) 50 mg/L-P; and were stained with 15 µg/L of DAPI followed by the flow cytometric analysis. Sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyp (DAPI-DNA).

108

5.4 Overall phosphate removals from the pond water at different pH treatments (3a). Percentage of the cellular content in the form of DAPI-PolyP and DAPI-DNA complex at pH 5.5 and 8.5 (control), for both filtered and unfiltered samples (3b).

109

5.5 PolyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (a and b, respectively) and unfiltered (c and d, respectively) samples viewed under epi-fluorescence microscopy. Intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. Flow cytograms of polyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (e and f, respectively), and unfiltered (g and h, respectively) samples.

110

5.6 (a) Alpha diversity rarefaction plots of phylogenetic diversity of 3 EBPR systems. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth, the lowest one of total sequencing reads; 5200).

112

5.7 Identities and relative abundance (%) of the bacteria in 3 EBPR systems (a) at class level. Composition of the main polyP accumulators, Gammaproteobacteria under (b) pH 5.5 unfiltered, and (c) pH 5.5 filtered samples.

113

5.8 Abundance of genes involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) in the three EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

115

6.1 Effect of treatments on (a) shoot dry weight, (b) root dry weight, and (c) root length from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.

138

6.2 Effect of treatments on (a) P uptake (mg/pot), and (b) P concentration (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.

140

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6.3 Effect of treatments on (a) arbuscular mycorrhizal fungi colonised root length (m/pot), and their colonisation (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.

141

6.4 Relationship between (a) soil available P (mg/kg) and plant P uptake (mg/kg), and (b) soil available P (mg/kg) and AM fungal colonization (%). All treatments were done in triplicate and error bars indicate the standard error where n=3.

143

6.5 Alpha diversity rarefaction plots of phylogenetic diversity for (a) rhizosphere soil bacteria, and (b) root colonising bacteria. Value represents the mean of triplicate determinations.

145

6.6 Relative abundance of (a) rhizosphere bacteria and, (b) root colonised bacteria at phylum level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored).

147

6.7 Canonical correspondence analysis (CCA) biplot showing the relationship between (a) different soil amendment and measured plant and soil variables b) individual taxa distributions with measured plant and soil variables (b) for rhizosphere soil taken from pot experiment under different fertiliser treatments () at 6 weeks. Arrows represent the measured variables [pH, NH3, Colwell P, Plant P uptake, electrical conductivity (EC), Shoot and root DW, AM colonised root length (RL), and AM colonisation %]. Triangles (▲) on the graph (b) represent individual bacterial taxa. Taxonomic identities for the bacterial sequences are given in Table 6.10.

150

6.8 Canonical correspondence analysis (CCA) biplot showing the relationship between (a) different soil amendment and measured plant and soil variables b) individual taxa distributions with measured plant and soil variables (b) for root colonising bacteria in soil taken from pot experiment under different fertiliser treatments () at 6 weeks. Arrows represent the measured variables [pH, NH3, Colwell P, Plant P uptake, electrical conductivity (EC), Shoot and root DW, AM colonised root length (RL), and AM colonisation %]. Triangles (▲) on the graph (b) represent individual bacterial taxa. Taxonomic identities for the bacterial sequences are given in Table 6.11.

153

7.1 General diagram showing some of the factors influence of the P cycling microbial diversity and activity in wastewater treatment plants.

165

7.2 Probable mechanisms of P transformations in the CAP digester and Evaporation Pond under its natural states (a). A proposed method for improvement of the current waste treatment process (b).

175

xix

LIST OF TABLES Page

2.1 Examples of microorganisms involved in polyP accumulation and their optimal conditions.

21

2.2 Examples of microorganisms involved in P transformation in soil. 28

2.3 Common methods used in identifying P mediating microorganisms highlighting their advantages and disadvantages.

32

2.4 Enzymes and their encoding genes in P metabolism. 37

3.1 Physical and chemical characteristics of different wastewater treatment compartments at Medina Research Station, Western Australia.

50

3.2 Genetic characterisation of the isolated P mineralising and P solubilising bacteria.

53

3.3 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for different stages of waste treatment system.

58

3.4 Comparison of relative abundance (%) of the top 10 most abundant bacterial groups within the CAP-Bottom and Evaporation Pond as revealed by tag sequencing and metagenomic analyses.

60

3.5 Metabolic profiles based upon metagenomic functional classification within different compartments of waste treatment process

61

3.6 P mineralising potentials at different stages of piggery waste treatment process.

63

3.7 P solubilising potentials at different stages of piggery waste treatment process.

65

3.8 PolyP accumulating potentials at different stages of piggery waste treatment process.

67

5.1 Summary of the analysis of MG-RAST of the 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

114

5.2 Phylogenetic taxonomic composition of 3 EBPR systems based on metagenomics analysis (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered)

116

5.3 Most abundant gene sequences involved in P metabolism in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

120

5.4 Functional affiliations of PAOs in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

121

6.1 Soil amendments used in this experiment and their corresponding abbreviations.

130

6.2 Typical characteristics of the pelletised compost, Balance®. 131

6.3 Typical analysis of the granulated fertiliser, Agras®. 131

6.4 Relative N and P application rates of each 3 fertiliser treatments applied to wheat. Rates are shown in both kg ha-1 and mg/pot basis.

131

6.5 Soil properties at the field sampling site, Pingelly.

132

xx

LIST OF TABLES continued Page

6.6 Effect of different soil amendments on measured plant properties (shoot and root dry weight, total root length, shoot P concentration, AM colonised root length, and AM colonisation (%)) after each harvesting time. Values presented are means ± standard error of the mean, n = 3.

137

6.7 Effect of different soil amendments on soil physico-chemical parameters after each harvesting time (4, 6, and 8 weeks). Values presented are means ± standard error of the mean, n = 3.

142

6.8 Bacterial diversity of rhizosphere soil bacteria and plant roots colonising bacteria indicated by phylogenetic diversity, Chao1 richness, and Shannon’s index. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth; the lowest one of total sequencing reads: 5000).

146

6.9 Relative abundance of (a) rhizosphere bacteria and, (b) root colonising bacteria up to genus level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored).

149

6.10 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for rhizosphere bacteria.

152

6.11 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for root colonising bacteria.

154

7.1 The specific contributions of this thesis in relation to the P transformation in the model piggery waste treatment process.

162

Chapter 1: General Introduction

1

CHAPTER 1

General Introduction

1.1 Background, research gaps, and expected outcomes

The demand on agriculture to feed the world’s population continues to increase with

speculations of the global population rising to 8.9 billion by 2050 (Alexandratos et al.

2006). Australia, a major wheat producing country, contributes to food security for

future generations with 80% of the wheat it exports (Asseng et al. 2011). This

productivity however, is constrained by phosphorus (P) availability in Australian soils,

particularly those in the wheatbelt of Western Australia, which are among the most P

deficient soils in the World in their natural state (Guppy and McLaughlin 2009; Kirono

et al. 2011). Globally, P plays an important role as a primary plant-growth limiting

nutrient in both natural and agricultural systems (Hammond et al. 2004; Guppy and

McLaughlin 2009; Clair and Lynch 2010).

The limited availability of P in soil to plants is mainly due to inorganic P (Pi) adsorption

to soil surfaces, precipitation with soil minerals, and incorporated into the microbial

biomass (Guppy and McLaughlin 2009: Clair and Lynch 2010; Stamm et al. 2011).

Generally, less than 1% of total P is immediately available for plant uptake as

dihydrogen phosphate (H2PO4-) and hydrogen phosphate (HPO4

2-) (Richardson and

Simpson 2011). This has necessitated regular applications of P fertilisers to achieve and

maintain high levels for crop productivity and profitability. Phosphate rock is a finite

resource (Hammond et al. 2004), highlighting the need for more sustainable P fertiliser

use without compromising crop performance. P resources can be conserved through two

major processes – by recycling waste materials, and by more efficient use of inorganic P

fertilisers in agriculture.

The possibility of recycling livestock wastes, which are characteristically high in P, is

gaining increased attention as an alternative P source for agriculture (Güngör and

Karthikeyan 2008). Animal waste can be manipulated to form valuable by-products

such as liquid P-fertilisers (digested effluent), algal biomass, slow release P-fertilisers

(e.g. struvite) and soil stabilisers (compost, digestate, sludge) (Westerman et al. 2010).


2

Recycling of waste by-products also helps to reduce the environmental burden of waste

accumulation caused by intensive meat production (FAO, 2013). Crop performance, soil

quality and microbial activity can be enhanced by the application of animal waste by-

products (Colvan et al. 2001; Jenkins et al. 2009). However, significant risks associated

with their direct application to soil such as odour, greenhouse gas emissions, leaching,

toxicity and pathogen survival may counterbalance the benefits provided by these waste

by-products (Westerman et al. 2010). An opportunity to reduce these risks can be

achieved through anaerobic digestion of the waste prior to application on land. This can

be achieved using anaerobic digestion processes that help to reduce odour, greenhouse

gas emissions and pathogens, and stabilizes organic solids. This process also serves to

improve the versatility and quality of by-products including biogas (renewable energy),

P-fertilisers and soil improvers (Supaphol et al. 2011). However, the high expense

associated with installation and operation of anaerobic digestion technologies, together

with the lack of guaranteed return, has prevented this technology from being widely

adopted by the agricultural sector (Supaphol et al. 2011).

Low cost anaerobic digestion facilities, in the form of covered anaerobic pond digesters,

are increasing in popularity among Australian livestock industries (e.g. dairies and

piggeries) for treatment of slurry, biogas capture and recycling of nutrients (Davidson et

al. 2013). A covered anaerobic pond digester is a pond covered with an impermeable

cover (geosynthetic material) which captures the biogas produced (carbon dioxide and

methane) and maintains the anaerobic environment. This technology offers the

possibility for reduced odour and greenhouse gas (GHG) emission, pathogen removal,

and generation of biogas. However, the recovery of nutrients for production of value-

added products from these systems is largely unexplored.

Agricultural wastewater arising from piggeries is often high in P (Poulsen 2000).

Therefore, piggery waste by-products derived from covered anaerobic digesters and

composting can be effective sources of P nutrients for crop production. However, the

forms of P in these by-products have to be considered before their application in

agriculture. For example, the concentration of soluble P in treated piggery effluent is

often too high to permit its direct reuse in agriculture as liquid fertilisers (Obaja et al.

2003). One reason for the high P concentration in pig waste is likely to be due to the

high abundance of phytate-bound P (phytate-P). Phytate, found in many cereal grains, is

commonly included in pig feed (Selle and Ravindran 2008) and pigs are unable to fully

digest phytate in phosphorus-enriched food supplements. Both phytate and excess


3

phosphorus from supplements become concentrated in animal manure, increasing the

potential for eutrophication. On the other hand, obligatory anaerobic treatment of

wastewater releases large amounts of phosphorus and nitrogen into wastewater, the

major agents of eutrophication (De-Bashan and Bashan 2004). The high level of P in

anaerobically digested piggery waste would be a problem in sandy-textured soils which

accelerate P loss to surface- and ground-water bodies. Thus, there is justification for

reducing the concentration of soluble P in piggery waste by-products before it is used as

liquid fertiliser, or otherwise disposed of in the environment. The expected outcome of

this research is to develop an environmentally sound approach for minimising

environment loading of soluble P in piggery waste effluent through microbial activities,

while recovering more stable and effective P fertilisers for use in agriculture.

Effluent or slurry arising from the piggery waste treatment process consists of both

inorganic (orthophosphates, mineral phosphates such as stuvite) and organic forms of P

(phytates, polyphosphates and microbially-derived P such as phospholipid and

nucleotides). Microorganisms are involved in P transformation by P mineralisation, P

solubilisation and P accumulation. These processes play an important role in

determining the quantity and forms of P present in piggery waste by-products. P

mineralisation microorganisms are involved in degradation of organic P compound into

orthophosphates and P solubilising microorganisms are involved in solubilising mineral

P compounds into orthophosphates. Further, some microorganisms are capable of

accumulating excess orthophosphates inside their cells as chains of phosphate ions

(PolyP). Despite the importance of these P cycling processes, there is little information

related to taxonomy and functional identity of P cycling microorganisms in piggery

waste treatment processes. Knowledge of the taxa that mediate P transformation

pathways, and the factors that regulate their activities in piggery waste treatment

processes, could be used to optimise the recovery of P for more effective use in

fertilisers while reducing the environmental loading of soluble P. However, current

understanding of P transformation in piggery waste treatment processes is not sufficient

for full implementation.

This research focused on understanding the abundance, diversity and metabolic function

of P solubilising, P accumulating, and P mineralising microorganism in a model

covered anaerobic pond digester system utilised for treating piggery waste. The model

piggery waste treatment process consisted of several stages (pit, holding tank, covered

anaerobic pond digester and aerobic pond) and was located in south-western Australia


4

(Appendix 1). A detailed description of this system is found in the Chapter 2, Section

2.2.2. Knowledge of the taxa mediating P transformation pathways can be used in

planning cost effective and environmentally sound means for removing excess P from

piggery waste effluents (Figure 1.1) by enhancing the activity of P solubilising bacteria,

P mineralising bacteria, and P accumulating bacteria. The treated effluent with low

soluble P can then be used as liquid fertiliser with irrigation water or by mixing with

separated solids/slurry for preparation of novel soil improvers such as pelletised

compost. Pelletised compost can supply both organic and inorganic forms of P when

applied to land. However, the effect these have on soil and plant nutrition is not well

documented. Guppy and McLaughlin (2009) stated that the relative importance of each

pool depends on the soil microbial community. Therefore, it is crucial to gain a more

comprehensive understanding of the taxa involved and how they function in mediating

P cycling in soil.

The main objective was to characterise taxa involved in P transformation pathways (P

mineralisation, P solubilisation, and polyphosphate accumulation) and their specific

functions in pig waste by-products. In addition, soil amendment with piggery by-

products was investigated in association with mycorrhizal fungi. While knowledge of

the diversity, abundance and activity of microorganisms involved in P transformation is

critical, it has been constrained by the methods used to date. Therefore, emphasis was

placed on developing more effective approaches for characterising microorganisms

involved in the P cycle. To this end, a combined approach using epi-fluorescence

microscopy, flow cytometry, cell sorting and next generation sequencing was used to

quantify the abundance, taxonomic and functional diversity of P cycling

microorganisms in piggery waste.


5

Figure 1.1 Approach for minimising environment loading of inorganic P (Pi) in piggery waste effluent through manipulation of microbial activities to improve its recycling potential.


6

1.2 Thesis objectives and hypotheses

The aims and hypotheses were as follows:

Chapter 3

Objective: To characterise the piggery waste treatment process in terms of

physico-chemical properties, bacterial community composition, and P cycling

potential.

Chapter 4

Objective: To quantify the abundance and diversity of P mineralising bacteria

(the fraction of cells that expressed phosphatase activity) during the piggery waste

treatment process by developing an integrated approach using the enzyme-labeled

fluorescence technique coupled with epi-fluorescence microscopy, cell sorting,

and next generation sequencing (16S rRNA ion tag sequencing).

Hypothesis: A diverse and highly abundant P mineralising bacterial community

will be observed in piggery wastes which are high in organic P substrate.

Chapter 5

Objective: To identify key microbes involved in polyphosphate accumulation and

its enhancement under acidic conditions for assessing the efficacy of enhanced

biological P removal technology applied in high P loaded waste remediation.

Hypothesis: Under acidic conditions in a high inorganic P system there will be

an increase in abundance of polyP accumulating bacteria and a concomitant

increase in polyphosphate accumulation by these bacteria.

Chapter 6

Objective: To demonstrate the impact of application of pelletised piggery

compost to soil on plant growth, soil nutrient improvement, and changes in

bacterial and fungal community composition when banded with a reduced rate of

synthetic fertiliser.

Hypotheses:

1. Banding pelletised piggery compost at low rates in combination with inorganic

fertiliser in the root zone of wheat facilitates nutrient uptake by plant roots in a

P deficient agricultural soil, alters the abundance and community composition


7

of bacterial involved in increasing P availability in soil, and enhances plant

growth.

2. The increase in P in soil following application of inorganic P fertiliser, in the

presence or absence of compost, will decrease the percentage of root length

colonised by arbuscular mycorrhizal (AM) fungi but increase the length of root

colonised by AM fungi grown in this soil in line with the availability of soil P

and root growth.

1.3 Thesis Structure

The structure of the thesis and the relationship between chapters is shown in the Figure

1.2.

Chapter 1 introduces the background, justification, research questions and aims.

Chapter 2 reviews the literature associated with recycling piggery waste for sustainable

agriculture and highlights knowledge gaps and constraints in microbial P cycling in

piggery waste treatment and soils amended with piggery wastes. Application of novel

molecular and microscopy approaches were identified to fill the gaps in detecting P

cycling microorganisms.

Chapter 3 is the first experimental chapter, and it characterises a model piggery waste

treatment process in terms of physico-chemical properties, bacterial community

composition, and P cycling potentials using P chemistry, conventional plate culturing,

and next generation sequencing approaches.

Chapter 4 is the second experimental chapter, and it determines the abundance, activity

and diversity of P mineralising bacteria during the pig waste treatment process by

integrating an enzyme labelled fluorescence technique coupled with epi-fluorescence

microscopy, cell sorting, and next generation sequencing (16 S taq sequencing and

community metagenomics).

Chapter 5 is the third experimental chapter, and it identifies key microbes involved in

polyP accumulation and its enhancement under acidic conditions for assessing the

efficacy of enhanced biological P removal technology applied to high P loading waste

remediation.


8

Figure 1.2 Structure of the thesis and the relationship between chapters.

CHAPTER 1 General Introduction

-Background, research gaps, and expected outcomes -Thesis objectives and hypotheses -Thesis structure

CHAPTER 2 Literature Review

- Recycling piggery waste - Gaps and constraints in microbial

P cycling in piggery wastewater - Molecular and microscopy techniques for P cycling microbes

CHAPTER 3 Assessment of P cycling potential during the piggery

wastewater treatment process (P mineralisation, P solubilisation, and polyphosphate accumulation)

CHAPTER 4 Identify key microbes involved in P mineralisation and their abundance, diversity during the waste treatment process

CHAPTER 5 Identify key microbes involved in polyphosphate accumulation and its enhancement under acidic conditions

CHAPTER 6 Demonstrate the applicability of treated piggery waste by-product as a pelletised pig compost at low rate and its effect on plant growth, soil and plant P nutrient improvement, and soil microbial community composition (a pot experiment)

P transformation during the piggery waste treatment process

CHAPTER 7 GENERAL DISCUSSION

Summary of the thesis, conclusions, and recommendations

Applicability of research and knowledge transfer


9

Chapter 6 is the fourth experimental chapter, and it investigates the application of

pelletised piggery compost to soil and its impact on plant growth, soil nutrient

improvement, and changes in bacterial and fungal community composition when

banded with a lower rate of synthetic fertiliser.

Chapter 7 is a general discussion of the research, and identifies areas for future

research.

The four experimental chapters (Chapter 3, 4, 5, and 6) are presented in the format of

scientific papers that can be read individually or as a part of the whole thesis. This

‘thesis as a series of papers’ format results in some unavoidable repetition, especially in

the Materials and Methods sections of each experimental chapter. I have tried to keep

such repetition to a minimum.

Chapter 2:Literature Review

10

CHAPTER 2

Literature Review

2.1 Overview

Recycling of piggery waste through best management practices such as anaerobic

digestion, composting and removal of excess nutrients aids in minimising negative

environmental impacts associated with direct use of untreated piggery wastewater.

Recovery of phosphorus (P) from piggery wastewater is gaining increased attention as

an effective source of P fertiliser for agriculture. Piggery wastewater are generally high

in both organic and inorganic forms of P and treated piggery waste by-products can be

manipulated as liquid P-fertilisers (digested effluent), slow release P-fertilisers (e.g.

struvite), and soil stabilisers for crop production (Yang et al. 2006; Chen et al. 2009;

Westerman et al. 2010).

The concentration of soluble P in treated piggery effluent is often too high to be used as

liquid fertiliser in free draining sandy textured soils (Obaja et al. 2003). Failure to

reduce the amount of soluble P during the waste treatment process can result in

increased soil P runoff and leaching if effluent is used directly by irrigation (Jaiswal

2010; Nielsen et al. 2010). In order to reduce the concentration of P in wastewater and

to maximise the recovery of P with solid waste by-products, knowledge of

microorganisms that govern the P cycling is important. An understanding how

management practices can be altered to make the conditions more favourable to enhance

their activities is also vital to optimisation of P nutrient management in piggeries. This

requires insight into microbial P cycling pathways.

This review introduces current piggery waste treatment systems, risks and benefits of

using piggery waste, management of excess soluble P, by-products arising from piggery

waste for agriculture, and knowledge gaps and constraints in microbial P cycling in

piggeries and in soils amended with piggery wastes. Current molecular and microscopy

techniques for identifying P cycling microbes and their advances to fill the gaps are

further discussed.


11

2.2 Recycling piggery waste: general background

2.2.1 Risks and benefits of re-using pig waste

The pork industry is increasing rapidly and has become the most popular meat type

around the World (Figure 2.1 a and b). Most pig farms generate large quantities of

waste, generally in the form of wastewater. Typical pig waste is characterised by a high

content of suspended solids and organic matter, high biochemical oxygen demand, and

high phosphorus (P) and nitrogen (N) contents, odours caused by gases produced by

decomposing waste, as well as high levels of microbial populations including human

pathogens (Chelme-Ayala et al. 2011). Ineffective recycling has created environmental

problems such as odour generation, greenhouse gas emissions (GHG), pathogenicity,

leaching and runoff of N and P nutrients into water bodies (Giusti 2009; Westerman et

al. 2010; Chelme-Ayala et al. 2011). Unprocessed piggery waste is also a reservoir of

human pathogenic microorganisms (Chelme-Ayala et al. 2011). Some of the

predominant pathogens in piggery waste are Cryptosporidium, Campylobacter,

Salmonella, Escherichia, Enterococcus and spore-forming bacteria, Bacillus and

Clostridium (Chelme-Ayala et al. 2011; Mc Carthy et al. 2011). Faecal coliform

bacteria can persist in soils amended with untreated pig slurry especially at higher

application rates (Rufete et al. 2006). In addition to pathogens, piggery feed is often

supplemented with antimicrobial resulting in manures containing antimicrobials and

antimicrobial-resistant microorganisms. If the manure is applied to soil, this could lead

to the prevalence of antibiotic resistance genes in the environment (Zhou et al. 2010;

Graves et al. 2011).

Piggery manure has been shown to increase salinity and there is a possibility of

phytotoxicity though Cu and Zn contamination in cultivated soils following application

of pig slurry (Asada et al. 2010; Doelsch et al. 2010; Shi et al. 2011). Direct addition of

fresh or immature manure to soil can also give rise to plant toxicity problems, such as

those derived from nitrogen-rich feed-stocks which are often high in ammonium and

can be toxic to plant growth (Bernal et al. 2009). Nutrient leaching (especially N and P

leaching) into ground- and surface water systems through runoff from manure storage

facilities is another concern caused by improper pig manure management (Chelme-

Ayala et al. 2011). It has also been shown that under organic management, the amount


12

Figure 2.1 (a) Growth of pork industry over the last decade (b) and meat production by type (Source FAO, 2013)

of mobile organic P lost from soil through leaching and run-off was greater than the

amount of inorganic P (Ross et al. 1999) which could potentially lead to groundwater

contamination and eutrophication of waterways, especially in sandy textured soils

(Edmeades, 2003; O' Flynn et al. 2011). Therefore, fresh pig slurry is a form of animal

manure which requires treatment to minimise negative effects on soil, water and crop

associated with bacterial and heavy metal contamination, nitrite accumulation, gaseous

losses, nutrient leaching and crop wilting.

2.2.2 Recycling piggery waste by-products: Best management practices for

agriculture

The disposal of piggery waste without pathogenic contamination, odour generation,

greenhouse gas emission, and excessive release of nutrients into the environment is

essential. This requires more environmentally-sound methods for treating and disposing

of piggery waste (Girard et al. 2009), the best management practices for piggery waste

are summarised in Figure 2.2. The major form of piggery waste is slurry that comprises

a mixture of faeces, residual foodstuff and undigested feed items, pig urine,

antimicrobial drug residues and washing-down water. Separation of solids from liquid


13

waste is a common practice; the resulting liquid faction is usually treated through

anaerobic digestion and the solid faction is treated through composting (Vanotti et al.

2008). After anaerobic digestion, further processes are required to remove excess P

(also N) and to enhance the quality of any by-products derived from the piggery wastes.

The treated liquid faction can then be used as liquid fertiliser/irrigation water and for

on-farm recycling. The separated solids can be composted and further processed into

pellets for use in agriculture.

Figure 2.2 A process for best management practices for piggery waste.

(Mixture of faeces, residual foodstuff, urine, and washdown water)

Liquid fraction Separated Solids

Anaerobic digestion Composting

Solid liquid

separation

Excess P removal

On farm uses

Agricultural

application Treated waste water

Sludge

Irrigate as liquid fertilisers

Pelletalised

compost


14

Anaerobic digestion

Anaerobic digestion is an effective method of treating piggery wastes which are high in

suspended solids, organic matter, biochemical oxygen demand, odours and human

pathogens. It involves microbial break down of complex waste materials into simple

forms in the absence of oxygen (Carrère et al. 2010) and the liquid waste can be

converted into a biogas and digestate (Figure 2.3). Anaerobic digestion offers the

possibility of reducing risks of using pig waste, through the reduction of odour,

destroying pathogens, stabilization of P, while recovering valuable by-products such as

biogas, manures and soil improvers (Heubeck and Craggs 2010; Supaphol et al. 2011).

However, anaerobic digestion technologies have not been widely adopted by the

agricultural sector primarily because of the high cost associated with installation and

operation without a guaranteed return (Supaphol et al. 2011). As a low-cost alternative,

anaerobic digestion systems such as covered anaerobic pond (CAP) digesters are being

practiced by Australian livestock industries (e.g. dairies and piggeries) for the treatment

of slurry, biogas capture, and recycling of nutrients (Davidson et al. 2013).

A covered anaerobic pond is an anaerobic pond covered with an impermeable cover

which maintains anaerobic conditions and captures biogases and thus controls odour

and GHG emissions (Figure 2.4). Waste treatment systems with covered anaerobic

ponds generally combine pre- (solid settlement and mechanical solid separation) and

post processes (aerobic digestion) to accelerate the waste treatment process. A model

covered anaerobic digestion system is shown in Appendix 1. This system is separated

into 5 stages: pits in the pig shed, solid separation screens, holding tank, covered

anaerobic pond and finally a secondary evaporation pond (aerobic pond) (Appendix 1).

First, the waste is washed down from the pig shed and collected in the pits. The waste is

collected in pits is then pumped over a static run-down screen (solid separator) that

removes about 10-15% the total solids. The remaining wastewater is transferred to the

holding tank prior to being pumped into the covered anaerobic pond (CAP) digester on

a weekly basis (75,000 L/week). The wastewater enters the CAP at one end and the

treated effluent is removed at the other end (CAP-outlet). The biogas is collected and

transported through pipes. After anaerobic digestion, the treated waste is collected in an

aeration pond, which is the final stage of waste treatment process under aerobic

conditions whilst also providing a dewatering step by evaporation.


15

Figure 2.3 The process of anaerobic digestion. Modified from Batstone et al. (2000).

Figure 2.4 Covered anaerobic pond (CAP) (a) at initiation, and (b) under operation, which captures biogas produced for odour and GHG emission control at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA).

Anaerobic digested effluents are not recommended for direct use in agriculture as they

are too wet and may contain a significant amount of volatile fatty acids which may

cause phytotoxicity and, if the digestion is incomplete, the finished product may not be

hygienic (Mata-Alvarez et al. 2000). Therefore, post processes (i.e aerobic digestion)

after anaerobic digestion are necessary to obtain a high-quality product. By-products

arising from the covered anaerobic waste treatment process can be a valuable resource

a b


16

for renewable energy production and a source of nutrients for agriculture after post

treatment processes. The gaseous end products of anaerobic digestion, in the forms of

CH4 and CO2, can be processed into renewable natural gas (energy source for electricity

or heat) and transportation fuels (Holm-Nielsen et al. 2009). The digested solids at the

bottom of a covered anaerobic pond (sludge) is generally high in a precipitated form of

P, mainly as stuvite (NH4MgPO4·6H2O), and can be processed as compost (Bustamante

et al. 2013) or slow releasing P fertiliser for agriculture (De-Bashan and Bashan 2004).

The treated effluent in the evaporation pond can be recycled as a liquid fertiliser via

irrigation or for onsite non-potable uses such as cleaning. Overall, the covered anaerobic

technology is promising because the liquid and solid P phases can be easily and

inexpensively separated, allowing for the recovery of valuable by-products such as,

liquid P-fertilisers (digested effluent), slow release P-fertilisers (struvite) and soil

stabilisers (compost, digestate, sludge) (Westerman et al. 2010). These on-farm waste

management practices can help farmers recycle the waste while making profits via

production of cost effective fertilisers and reducing negative environmental impacts.

Composting and pelletising: Pig farms generate large quantities of manure, rich in

organic matter and nutrients (Imbeah 1998). Composting has potential as an effective

method of treating pig manure prior to land application (Imbeah 1998; Ros et al. 2006).

Pig manure generally includes a considerable amount of water; if it is too wet to be

composted directly, a solid separation prior to composting facilitates the composting

process (Imbeah 1998). The solid and colloidal parts of the digested slurry can be

inexpensively separated from the wastewater by mechanical screening (Hjorth et al.

2010). The separated solids can be further processed as compost which can be used as

valuable fertilisers for the farm and the domestic potting mix market (Bloxham and

Colclough 1996; Atiyeh et al. 2001; Rao et al. 2007). Co-composting of pig manure

with other biodegradable wastes such as saw dust (Zhang and He 2006), leaves (Huang

et al. 2001), rice straw (Zhu 2007), mushroom waste (Lee et al. 2010) has been reported.

Compost can be used in a defined agricultural management program to improve plant

growth, soil health, soil structure and water holding capacity. Fully matured compost

has been used as an alternative to chemical fertilizers, primarily due to positive effects

of compost on plant growth and soil quality, increased soil biological activity, increased

water holding capacity, suppression of plant pathogens, and reducing the risk of


17

environmental damage and human health (Bibi et al. 2010; Ojeda et al. 2010; Cytryn et

al. 2011; Martínez-Blanco et al. 2013). Compost can also stimulate root growth (Bibi et

al. 2010), leading to increased soil exploration for water and nutrients and increased

root exudates, which further stimulates soil biological processes (Broeckling et al.

2008). As for other animal wastes, composted pig waste contains useful nutrients which

can be recycled into agricultural land (Choudhary et al. 1996). Pig manure

vermicomposts with a range in concentrations (0%, 5%, 10%, 25%, 50% and 100% by

volume) increased bulk density, electrical conductivity, overall microbial activity and

nitrate concentrations of potting medium and increased the root growth of tomato

seedlings (Atiyeh et al. 2001).

Compost is considered to be an uneconomical soil amendment in some countries mainly

due to difficulties in transport and cost of application at the rates required (Quilty and

Cattle 2011). The usual method for applying compost is to spread it on the soil surface

or incorporate it into the top layers of soil (Stieg et al. 1997). This can result in large

amounts of compost that can be removed by water and/or wind erosion, intercepted by

weeds or otherwise lost before it reaches the root zone (Halvorson et al. 1997). These

problems may be overcome by precision placement of compost in the root zone such as

through pelletising (Blackshaw et al. 2005) enabling easy access by roots. Compost

pellets have been investigated (Yan et al. 2001; Rao et al. 2007) and several businesses

are producing pelletised compost in Australia (Quilty and Cattle, 2011), including the

use of piggery waste by-products (http://www.cwise.com.au/). Banding of compost or

the Placement of fertiliser/compost in a concentrated band beneath the seed at sowing

could also allow a reduction in the application rate of compost to an economically

viable level for broad-acre agriculture.

Removal of excess P in piggery waste: P management is an integral part of recycling

pig waste. The concentration of Pi in treated piggery effluent is often too high to permit

its direct re-use as a liquid fertiliser (such as during irrigation) (Obaja et al. 2003)

especially in areas where free-draining sandy-textured soils are common. This justifies

removal of Pi to a more appropriate level before it is used in agriculture or otherwise

released into the environment. There are several ways to reduce the concentration of P

in wastewater; they involve both chemical and biological processes, and are used at

either a large or small scale. Chemical removal of P is the most common and reliable


18

method and involves use of ferric, ferrous, aluminium, or calcium salts (Yeoman et al.

1988). However, the high cost and disposal of metal contamination reduces the

economic and environmental sustainability (Powell et al. 2008). Chemical precipitation

techniques (e.g. stuvite crystallisation) have been used to remove P from animal waste

including piggery waste (Çelen et al. 2007, Huang et al. 2011) but are not economically

feasible for low P concentration waste streams (<50 mg-P/L) (Wong et al. 2013). In

contrast, biological P removal methods known as enhanced biological P removal

(EBPR) can be economically viable (Günther et al. 2009).

2.3 Enhanced Biological P removal (EBPR) for removing excess P in

piggeries Enhance biological phosphorus removal (EBPR), on the basis of the accumulation of

excess Pi as polyP in bacterial cells, is extensively practiced for biological P removal

from wastewater (De-Bashan and Bashan 2004; Oehmen et al. 2007; Gebremariam et al.

2011; Sun et al. 2014; Zheng et al. 2014; Chen et al. 2015). Some microorganisms are

capable of taking up soluble phosphate in excess of their normal metabolic

requirements, and accumulate it as intracellular polyphosphate (McGrath and Quinn

2000; Blackall et al. 2002). EBPR is a P removal method based on the selective

enrichment of those P microorganisms that accumulate inorganic polyphosphate in their

microbial biomass. The EBPR processes in wastewater treatment have received

increased attention because EBPR processes afford the following benefits

(Kawaharasaki et al. 1999; Blackall et al. 2002):

1. Reduce operating costs

2. Lower sludge production and reduce energy costs

3. Minimise effluent salinity problems experienced during chemical processing

4. Enable significantly higher reuse potential of sludge than do conventional

chemical processes.

EBPR favours the initially carbon-rich, strictly anaerobic incubation, followed by

carbon-poor, aerobic incubation (De-Bashan and Bashan 2004) and is documented in

Figure 2.5. During the anaerobic incubation, microorganisms deplete organic matter and

carbon and accumulate biopolymers such as PHAs and glycogen. This requires the

energy released during the degradation of polyp, which in turn leads to the release of Pi


19

from the sludge. During the aerobic phase, PHAs and glycogen serve as energy and

carbon sources for taking up a larger amount of Pi than the amount released during the

anaerobic phase, leaving phosphate-reduced conditions in the aeration pond (De-Bashan

and Bashan 2004). The P enriched bacteria and microalgae biomass is then separated

from the treated effluent wastewater, and the separated biomass can be further used as

slow releasing P fertilisers, while remaining effluent with lower Pi concentration can be

re-used as a liquid fertilizer (Yoon et al. 2004).

Figure 2.5 Metabolism of PolyP accumulating organisms under anaerobic and aerobic conditions and resulting by-products. Modified from Kulakovskaya et al. (2012).

Many countries set 1 mg/L and 2 mg/L as the limit for total P concentrations in

discharges of wastewater treatment plants (Jiang et al. 2004) where influents from

domestic wastewaters are in the range 10 – 15 mg/L (Blackall et al. 2002; Powell et al.

2008). If no particular P removal methods are applied, growth of activated sludge

microorganisms usually removes 1–2 mg/L of influent P, leaving more than 10 mg/L in

the effluent. EBPR processes can accomplish P levels as low as 0.1–0.2 mg/L.

However, EBPR processes are difficult to control and are sometimes ineffective in

phosphate removal (Kawaharasaki et al. 1999). It has been reported that P removal in

waste stabilisation ponds is highly variable with more effective P uptake occurring at

high temperature (25 °C) and low light intensity (60 μE/m2s) (Powell et al. 2008), and


20

low pH (5.5) (McGrath and Quinn 2000; McGrath et al. 2001; Mullan et al. 2002b).

This demonstrates that the abundance and community structure of polyP accumulating

microorganisms can be highly variable with environmental conditions, serving to

increase or decrease the performance of the EBPR system. Accumulation of polyP in a

wide variety of microorganisms has been reported and the conditions favouring their

activity differ markedly, examples of some microorganisms involved in polyP

accumulation and their optimal conditions are highlighted in Table 2.1.

Although enhanced removal of biological P from wastewater has been widely studied,

an understanding of the microbial and environmental factors affecting enhanced P

accumulation efficiency is less well understood mainly due to the lack of understanding

of the microbiology of EBPR (Gebremariam et al. 2011; Chen et al. 2015; Sun et al.

2014; Zheng et al. 2014). Furthermore, the metabolic capacity of polyphosphate

accumulating organisms is still not adequately understood (Sun et al. 2014) due to the

absence of any isolates of the key agents of polyphosphate accumulating organisms, and

the lack of tools for their quantification and cellular level parameters in the EBPR

system. This can be overcome by using a combination of culture-dependent and culture-

independent techniques to characterize microbial composition and quantitative

evaluation of intracellular functional polymers in key populations of polyphosphate

accumulating organisms (Gebremariam et al. 2011; Majed et al. 2012). Therefore,

further research is essential for a more thorough understanding of P accumulation,

microbial identity and functionality under different environmental settings.

Ch

ap

ter

2:L

iter

atu

re R

evie

w

21

Tab

le 2

.1 E

xam

ples

of m

icro

orga

nism

s inv

olve

d in

pol

yP a

ccum

ulat

ion

and

thei

r opt

imal

con

ditio

ns

Bac

teri

al g

ener

a or

spec

ies

Evi

denc

e of

pol

yP a

ccum

ulat

ion

un

der

diff

eren

t con

ditio

ns

Ref

eren

ce

Esch

eric

hia

coli

Exte

nsiv

e ac

cum

ulat

ion

of p

olyP

in re

spon

se to

osm

otic

stre

ss

or to

nut

ritio

nal s

tress

(R

ao e

t al.

1998

)

Pseu

dom

onas

aer

ugin

osa

Intra

cellu

lar p

olyP

acc

umul

atio

n in

resp

onse

to p

hosp

hate

and

am

ino

acid

lim

itatio

ns

(Kim

et a

l. 19

98)

Ephy

datia

mue

lleri

(fre

shw

ater

spon

ge)

Poly

P ac

cum

ulat

ion

upon

exp

osur

e to

som

e or

gani

c po

lluta

nts

(Im

siec

ke e

t al.

1996

)

Can

dida

hum

icol

a G

-1 (e

nviro

nmen

tal

yeas

t) Po

lyP

accu

mul

atio

n as

a c

onse

quen

ce o

f gro

wth

at a

cid

pH

(McG

rath

and

Qui

nn 2

000)

Kle

bsie

lla a

erog

enes

Po

lyP

accu

mul

atio

n by

cel

ls o

f Kle

bsie

lla a

erog

enes

und

er

acid

con

ditio

ns (p

H 4

.0–5

.0)

(Dug

uid

et a

l. 19

54; D

ugui

d an

d W

ilkin

son

1956

)

Burk

hold

eria

cep

acia

R

emov

al o

f pho

spha

te a

nd a

ccum

ulat

ion

of p

olyP

occ

urre

d at

pH

5.5

(M

ulla

n et

al.

2002

a)

Acin

etob

acte

r St

rain

s acc

umul

ate

poly

phos

phat

e an

d PH

As u

nder

aer

obic

co

nditi

ons

Play

an

impo

rtant

role

in th

e EB

PR p

roce

sses

bas

ed o

n cu

lture

m

edia

gro

wth

. Gen

eral

ly c

ultu

re m

edia

con

ditio

ns a

re fa

vour

ed

(Min

o et

al.

1998

; Kaw

ahar

asak

i et

al. 1

999)

Mic

rolu

natu

s pho

spho

voru

s

Can

dida

tus A

ccum

ulib

acte

r pho

spha

tis

Bac

teria

acc

umul

ate

larg

e am

ount

s of p

olyp

hosp

hate

und

er

aero

bic

cond

ition

s.

EBPR

was

ach

ieve

d at

rel

ativ

ely

high

tem

pera

ture

s of

24

°C,

28 °C

and

32

°C (P

rem

oval

eff

icie

ncy

of >

95%

)

(Esc

henh

agen

et a

l. 20

03)

(Ong

et a

l. 20

14)


22

2.4 P transformation in piggery waste and knowledge gaps in P

cycling in wastewater In the context of P removal from wastewater, major emphasis has been placed on the

processes of EBPR that are governed by the polyP accumulating microorganisms.

However, there are other important P transformation processes (i.e. P mineralisation, P

solubilisation, and P precipitation) which directly or indirectly affect the efficiency of P

removal processes from wastewater. The content of soluble P in wastewater is a net

effect of all of those P transformation pathways. Hypothesised P transformation

pathways in piggery waste are shown in Figure 2.6.

Typically, piggery waste can be characterized as high in organic matter and soluble P

(Chelme-Ayala et al. 2011), one reason for a high level of P in pig waste being the

abundance of phytate-bound P (phytate-P). Phytate, the mixed salt of phytic acid (myo-

inositol hexaphosphate) is one of the major organic P forms derived from plant-sourced

feed ingredients and is commonly present in piggery feed (Selle and Ravindran 2008).

However, phytate-P is only partially utilised by pigs because they do not generate

sufficient endogenous phytase activity. Therefore, farmers often add inorganic P to the

feed to improve animal health but this further increases the P level in pig waste. The

excess phytate returned to the environment can be a major organic P substrate for P

mineralising microorganisms (Kerovuo et al. 1998; Cho et al. 2005), with bacteria

mineralising phytate to release soluble P (Golterman et al. 1997). Obligatory anaerobic

treatment of wastewater releases large amounts of phosphorus (P) and nitrogen (N) into

wastewater (De-Bashan and Bashan 2004), another major pathway for liberation of

soluble P in wastewater. Fundamental to the design of an efficient P removal process

from high organic P loading systems such as the pig industry is knowledge of the

identity and functionality of polyP accumulating microorganisms and knowledge of

other P transformation pathways. However current knowledge is limited in these P

transformation pathways in wastewater treatment streams. The following sections focus

on the main P transformation pathways and current knowledge and gaps in determining

microbial mediated P cycling in the environment.


23

Figure 2.6 Probable P transformation pathways in piggery waste.

2.4.1 P mineralisation

P mineralisation can be defined as the hydrolysis of Pi from organic or other complex P

compounds (e.g. polyP), soluble or particulate, in which the hydrolysed Pi is released

outside the cells (Kloeke and Geesey 1999). Most biologically-mediated P

transformations in activated sludge are carried out by bacteria (Kloeke and Geesey

1999) and are mediated by phosphomonoesterase and phosphodiesterase activity

(Anupama et al. 2008). Phosphomonoesterases are classified as either alkaline (pH>7;

EC 3.1.3.1) or acid (pH<6; EC 3.1.3.2) phosphatases depending upon their pH optima

(Kloeke and Geesey 1999; Anupama et al. 2008). Phosphatase (PO4ase) is a unique

extracellular, hydrolytic enzyme which catalyses the hydrolysis of Pi from organic

bound form of P (Kloeke and Geesey 1999; Anupama et al. 2008). The phosphatase

enzyme is also known to hydrolyze inorganic polyphosphates (polyP), the linear

polymers of phosphate stored by P accumulating microorganisms (Chrost 1991;

Golterman et al. 1997), which is known for Pi regeneration in wastewater. While

considerable work has been done on EBPR (Blackall et al. 2002; Malamis et al. 2013),

little is known about P mineralisation or Pi regeneration in wastewater which could


24

affect the overall efficiency or control of the P removal process (Kloeke and Geesey

1999; Li and Chróst 2006; Whiteley et al. 2002).

Phosphatase activity has been detected in activated sludge (Lemmer et al. 1994; Kloeke

and Geesey 1999; Li and Chróst 2006) and anaerobic reactors (Whiteley et al. 2002;

Anupama et al. 2008). Furthermore, PO4ase activity can be used as a rapid biochemical

test for anaerobic digester instability (Ashley and Hurst 1981; Zhenglan et al. 1990;

Yamaguchi et al. 1991). Therefore, monitoring PO4ase activity is important for planning

cost-effective and sustainable P removal systems in wastewater. There is limited

information on PO4ase activity in complex and diverse environments such as piggery

wastewater effluent. This is mainly due to methodological limitations in detecting P

mineralisation in highly diverse environments such as piggery waste. Consequently, P

mineralising bacteria in piggery waste treatment process are poorly characterised and

little is known of their diversity, abundance or activity, making it difficult to fully

optimise the process. Moreover, microbes involved in P mineralisation, the molecular

mechanisms controlling phosphorus metabolism and the ecological interactions

controlling mineralisation process and rates in wastewater are poorly characterised

(McMahon and Read 2013). Therefore, adequate information on identification,

classification, enrichment, and metabolic capacity of P mineralising microorganisms in

piggery wastewater is required.

2.4.2 P precipitation and solubilisation

When wastewater is rich in soluble P and in the presence of some ions, soluble P tends

to precipitate as insoluble Ca/Al/Fe/Mg-phosphates or other P complexes (e.g. Struvite).

These precipitates are often deposited in sludge and reduce phosphate availability in the

wastewater (Mehta and Batstone 2013). Therefore, recovery of soluble P (PO43-) in

waste as precipitated mineral P is commonly practiced using chemical methods

(Yeoman et al. 1988) or air stripping CO2 from anaerobic effluents (Kalyuzhnyi et al.,

2000) followed by removal of sludge.

Solubilisation of mineral phosphates can be mediated by P-solubilising microorganisms.

These microorganisms have the capability to produce organic acids which are strong

enough to dissolve less-soluble mineral phosphate products (e.g. struvite and

hydroxyapatite) recovered from wastewater. Surprisingly, almost no research has been


25

focused on P solubilising bacteria in wastewater where mineral phosphate precipitates

are highly abundant. P solubilising bacteria (Xanthobacter, Kluyvera and

Chryseomonas) have been isolated from an arid mangrove ecosystem in Mexico

(Holguin et al. 2001). Potentially they play an important role in releasing P that is

largely unavailable to plants in mangroves where precipitated forms of P are common

due to the higher abundance of cations (Holguin et al. 2001). The authors predicted that

root oxygen translocation plays an important role in solubilizing phosphate by bacteria

near the roots in mangroves where sediments are not always completely anoxic. This

could be the reason for lack of research on biological P solubilising activities in

wastewater treatment plant where the environment is basically completely anaerobic.

Alternatively it has been proposed that P solubilising bacteria and fungi can be

employed for solubilising precipitated phosphate recovered from wastewater (De-

Bashan and Bashan 2004). Finding suitable P solubilising microorganisms (bacteria,

fungi, or archea) which can be employed to solubilise precipitated forms of P (eg.

struvite and hydroxyapatite) in situ in anaerobic digesters would be breakthrough in P

removal from effluents and sludge. The activities of such microorganisms are also

beneficial in reducing the frequency of sludge withdrawal and blocking of wastewater

pipes with mineral P crystals. This would be a significant economic and environmental

advantage for the water management industries.

2.5 Role of P mediating microorganisms and their diversity in soil Microorganisms play a central role in cycling P in soil. They contribute to P

solubilisation, mineralisation, and immobilisation, thus controlling the plant available P

pool (Richardson and Simpson 2011). Plants can use P only in the forms of soluble

orthophosphate ions (e.g. H2PO4-1, HPO4

-2 and PO4-3) and the type of orthophosphate

ion present in the soil depends upon the soil environment, mainly pH and the form of

organic or inorganic P present. The P cycling microbes have the ability to convert

insoluble phosphorus (P) to an accessible form, such as orthophosphate, and this is an

important trait for increasing plant yields. However, their widespread utilisation remains

limited by a poor understanding of the microbial ecology and population dynamics in

soil, and by inconsistent performances over a range of environments (Richardson 2001).


26

Inorganic P in soil is present in the form of insoluble Ca/Al/Fe phosphates. The form of

inorganic P in soil will depend on the type of soil (alkaline, acidic, or organic-rich)

which dictates the variability of Fe-P, Al-P, and Ca-P (Bashan et al. 2013). P

solubilising microorganisms solubilize these insoluble P forms and liberate more P than

their cellular requirement, rendering surplus P for uptake by plants. Solubilisation of

Ca–P complexes is a dominant process among P solubilising microbes, whereas the

release of P by Fe–P or Al–P is very scarce (Fatima 2014). The major mechanism used

by P solubilising microorganisms are based on decreasing pH (Zhu et al. 2011), through

the synthesis of low molecular weight organic acids (gluoconic, malate, acetate, citrate,

oxalate, and lactate) (Rodriguez et al. 2004; Ogut et al. 2010) or indirectly by the

production of exo-polysaccharides (Fatima 2014). The dominant P solubilising

microorganisms belong to the genera Bacillus, Pseudomonas, Rhizobium, Burkholderia,

Enterobacter, Streptomyces, Penicillium and Aspergillus (Rodríguez and Fraga 1999;

Tao et al. 2008).

Organic P in soil is present mainly in form of inositol phosphate (soil phytate),

phosphomonoesters, phosphodiesters and phosphotriester (Jorquera et al. 2008a;

Richardson and Simpson 2011). Some microorganisms can hydrolyse Pi from organic

forms, and this is carried out by diverse groups of enzymes including

phosphomonoesterases, phosphodiesterases, nucleases, nucleotidases, and phytases

(Mackey and Paytan 2009). Bacteria such as Bacillus megaterium, Burkholderia

caryophylli, and Pseudomonas syringae demonstrate P mineralization activity (Jorquera

et al. 2008b; Jorquera et al. 2013).

Microorganisms that are capable of removal and sequestration of reactive P from the

environment for a period of time are known as P immobilising microorganisms. This

can occur as cellular assimilation or through intracellular phosphorus containing

minerals such as polyP granules. However, knowledge of P immobilising

microorganisms in soil is limited compared to P mineralising microorganisms and P

solubilising microorganisms.

Another important group of P mediating soil organisms are arbuscular mycorrhizal

(AM) fungi. AM fungi form symbioses with roots of nearly all vascular plants (Roy-

Bolduc and Hijri 2011). In exchange for plant assimilated carbon, AM fungi provide a

range of benefits (Roy-Bolduc and Hijri 2011) such as contributing to soil structure,


27

exploration of soil pores too small for plant roots and increasing the total volume of soil

explored for both nutrients and water (Al-Karaki 1998; Kaya et al. 2003; Rillig et al.

2003; Garg and Chandel 2010). AM fungi are well known to improve the efficiency of

plant P uptake and a range of other nutrients, including organic N (Hodge and Storer

2015). An increase in mycorrhiza formation when P is limiting can result in increases in

plant growth (Ryan et al. 2000) whereas when P is not limiting, an increase in AM

fungal colonisation of roots may lead to a reduction in growth due to an increased cost

in photosynthesis by the plant for little return from the fungi (Graham 2000; Jakobsen et

al. 2002).

Mechanisms for the increase in uptake of P by mycorrhizal plants can be characterised

as exploration of a larger soil volume, faster movement of P into mycorrhizal hyphae,

and solubilisation of soil phosphorus (Bolan 1991). AM fungi increase the explored

volume of soil by decreasing the distance that P ions must diffuse to plant roots and by

increasing the surface area for absorption of nutrients (particularly P) in exchange for

photosynthates (Garg and Chandel 2010; Hodge et al. 2010). Solubilisation of soil P by

mycorrhizal fungi is achieved by the release of organic acids and phosphatase enzymes.

Mycorrhizal plants have been shown to increase the uptake of poorly soluble P sources,

such as iron and aluminium phosphate and rock phosphates (Bolan 1991). Overall, P

mediating microorganisms could be manipulated for more efficient use of plant poorly-

available forms of P in soil (Gyaneshwar et al. 2002; Richardson 2001) and there is a

wide range of soil bacteria and fungi involved in P mineralisation and solubilisation

(Table 2.2).

2.6 Current molecular and microscopy techniques for identifying P

cycling microbes and their advantages and limitations A better understanding of the diversity, abundance and function of P cycling

microorganisms (P solubilisation, immobilisation, and mineralisation) is vital to

increasing their capacity to mobilize P to plants in piggery waste and in soil to which

piggery by-products have been applied.

Ch

ap

ter

2:L

iter

atu

re R

evie

w

28

Tab

le 2

.2 E

xam

ples

of m

icro

orga

nism

s inv

olve

d in

P tr

ansf

orm

atio

n in

soil.

Rol

e M

icro

orga

nism

s R

emar

ks a

nd r

efer

ence

s Ph

ytat

e-de

grad

ing

mic

roor

gani

sms

Pseu

dom

onal

spp.

, Bac

illus

subt

ilis,

Kleb

siel

la sp

p, E

sche

rich

ia c

oli,

Mits

uoke

lla sp

p., A

sper

gillu

s, Pe

nici

llium

spp.

, Arth

roba

cter

, St

aphy

loco

ccus

From

a v

arie

ty o

f env

ironm

ent

(Hill

et a

l. 20

07)

Org

anic

P m

iner

alis

ing

mic

roor

gani

sm

Pseu

dom

onas

fluo

resc

ens,

Pseu

dom

onas

sp. B

urkh

olde

ria

cepa

cia,

En

tero

bact

er a

erog

enes

, Ent

erob

acte

r clo

acae

, Citr

obac

ter f

reun

di,

Prot

eus m

irab

alis

, Ser

ratia

mar

cens

cens

, Bac

illus

subt

ilis,

Pseu

dom

onas

put

ida,

Pse

udom

onas

men

doci

na, B

acill

us

liche

nifo

rmis

, Kle

bsie

lla a

erog

enes

Phos

phat

e m

iner

aliz

atio

n fr

om

P-su

bstra

tes b

y so

me

soil

bact

eria

l spe

cies

(Rod

rígue

z an

d Fr

aga

1999

)

P so

lubi

lisin

g m

icro

orga

nism

s Ba

cillu

s meg

ater

ium

, Bur

khol

deri

a ca

ryop

hylli

, Pse

udom

onas

ci

chor

ii, P

seud

omon

as sy

ring

ae, B

acill

us, P

seud

omon

as, E

rwin

ia,

Agro

bact

eriu

m, S

erra

tia, F

lavo

bact

eriu

m, E

nter

obac

ter,

Mic

roco

ccus

, Azo

toba

cter

, Bra

dyrh

izob

ium

, Sal

mon

ella

, Alc

alig

enes

, C

hrom

obac

teri

um, A

rthr

obac

ter,

Stre

ptom

yces

,Thi

obac

illus

, Es

cher

ichi

a, P

enic

illiu

m, A

sper

gillu

s, Rh

izop

us, F

usar

ium

, Sc

lero

tium

, Ent

erob

acte

r, Pa

ntoe

a, a

nd K

lebs

iella

Isol

ated

from

soil

(Tao

et a

l. 20

08)

From

a v

arie

ty o

f env

ironm

ent

(Zha

o an

d Li

n 20

01)

From

rhiz

osph

e (C

hung

et a

l. 20

05)

(Rod

rígue

z an

d Fr

aga

1999

)


29

The diversity of P mediating microorganisms varies with environment and only about

1% of can be cultured successfully in the laboratory (Wasaki and Maruyama 2011).

Furthermore, cultivation of AM fungi in artificial media remains difficult since they are

obligate symbionts (Franz Lang and Hijri 2009). Findings derived from culture

dependant techniques are likely to provide a biased and inaccurate assessment of P

cycling microbes (Torsvik and Ovreas, 2002). Therefore, culture-independent methods

are generally required to study the function and ecology of microorganisms.

The majority of culture independent techniques are polymerase chain reaction (PCR)

dependant. With the discovery of PCR, a more rapid and better representation of

microbial community structure and diversity was obtained from a variety of

environments (Amann, 1995). DNA ‘fingerprinting’ technologies such as denaturing

gradient gel electrophoresis (DGGE) and terminal restriction fragment length

polymorphism (T-RFLP) were used to assess how microbial communities respond to

changes (eg. environmental parameters) (Fromin et al. 2002; Jenkins et al. 2009;

Jenkins et al. 2010). Analysing clone libraries with traditional Sanger sequencing

methods provide better resolution and accuracy in molecular sequencing as it is capable

to provide longer sequencing reads (Medvedev et al. 2009). However this method is not

always practicable, depending on the scale of a study due to the effort, time and costs

involved. Quantitative PCR (qPCR) has also been shown to be important for monitoring

gene amplification in real-time because it provides abundance of gene copy number of a

specific target microbial group in the environment (Jenkins et al. 2009; Jenkins et al.

2010; Supaphol et al. 2011).

While these molecular techniques provide considerable input into discovery of

microbial ecology, understanding of microbial diversity and function in the environment

has been further expanded with the introduction of next generation sequencing. Next

generation sequencing (NGS) approaches provide higher throughput solutions for

investigations of microbial ecology than ever before, allowing scientists to understand

the microbial ecology and function at greater depth. NGS techniques include

pyrosequencing (Margulies et al., 2005), barcoding and multiplex analyses (Hamady et

al. 2008), ultra-high-throughput sequencing (Bartram et al. 2011; Caporaso et al. 2012)

and improved storage, computational processing and sequence analysis (Lozupone and

Knight, 2005; Meyer et al. 2008; Caporaso et al. 2010).


30

In addition to PCR-based approaches, metagenomic sequencing (the analysis of whole

community genomes extracted from natural environments) is a more powerful approach

for characterising microbial community structure and its putative metabolic potential

based on genes, without a PCR bias (Meyer et al. 2008). Shotgun metagenomic

sequencing also provides information on both taxonomic and functional identity of

microbial community even although some taxa are in low abundance (Martin et al.

2006). While metagenomic sequencing is useful in identifying genes that could be

involved in a given metabolic pathway, metatranscriptomics (the collective RNA from

all the microorganisms in a community) provides functionally relevant groups or

individuals more precisely by assessing the expression of RNA transcripts (Bastida et

al. 2009). To date such studies have identified novel microorganisms and functional

genes encoding for metabolic pathways from a variety of environments (Martin et al.

2006; Xu, 2006), expanding the current databases for microbial ecology analysis.

Greater insight to visualisation, cellular location and characterisation of microorganisms

in the environment can be obtained when molecular tools are combined with

microscopy/flow cytometry. For example, fluorescence in situ hybridisation (FISH) can

be used to detect nucleic acid sequences by a fluorescently labelled probe that

hybridizes specifically to its complementary target sequence within intact cells. Various

probes have been used for specific detection of diverse levels of phylogenic groups

(Amann et al. 1990; Glöckner et al. 1999). FISH has been widely applied for

investigating bacterial population dynamics as well as community analysis in a range of

ecosystems without the need for isolation. This is a great advantage over most other

molecular detection techniques. Moreover, fluorescent probes can be labelled with dyes

of different emission wavelengths, enabling detection of several target sequences within

a single hybridization step (Moter and Göbel, 2000). Therefore, FISH is a powerful

technique for not only visualization and identification of individual microbial cells, but

also for investigating bacterial community compositions within their natural micro-

habitat. However, microscopy techniques are time-consuming and can be biased in

quantification. Thus, fluorescence techniques can be coupled to flow cytometry to

obtain higher accuracy and faster analysis.

Flow cytometers can be used to quantify fluorescence intensity data and provide

distinctive information about microbial cell populations (Schwartz and Fernandez-


31

Repollet 2001). This process is less time-consuming than epi-fluorescence microscopy

with automatic image analysis, and a larger number of cells and samples can be

analysed (Nedoma et al. 2003).

The application of molecular techniques and some nucleic acid fluorescence staining

techniques in detecting P cycling microbes, their advantages and disadvantages are

listed in Table 2.3. The majority of molecular methods described in Table 2.3 are PCR-

dependant and primers and probes targeting P mediating microbes are limited, non-

specific or poorly developed (Wasaki and Maruyama 2011). For example, primer sets

based on published Bacillus phytase genes successfully amplified the positive control

but failed to detect these genes in isolated phytate-mineralizing Bacillus strains (Hill et

al. 2007). This suggests that more genes are involved in P mineralization/solubilisation

and that the current databases are too small to provide adequate coverage (Jorquera et

al. 2008b; Lim et al. 2007). Therefore, the application of molecular methods to P

transformation is further limited by the availability of sequences in the current databases

for genes involved in P mineralization/solubilisation (Lim et al. 2007; Wasaki and

Maruyama 2011).

Owing to the complexity of their life cycles, it is difficult to study AM fungi in either

soil or roots, and conventional methods of assessing AM fungi diversity rely on

morphological identification of spores, or fungi in roots extracted directly from the field

or trap cultures (Leal et al. 2009; Oehl et al. 2009). Molecular assays for measuring the

abundance and identity of AM fungi have also been limited by suitability of specific

primers (Redecker 2000). Consequently, there are gaps in understanding P

transformations in biogeochemical P cycling.

Cha

pter

2:L

itera

ture

Rev

iew

32

Tab

le 2

.3 C

omm

on m

etho

ds u

sed

in id

entif

ying

P-m

edia

ting

mic

roor

gani

sms h

ighl

ight

ing

thei

r adv

anta

ges a

nd d

isad

vant

ages

.

Mol

ecul

ar

tech

niqu

es

Des

crip

tion

Adv

anta

ges

Dis

adva

ntag

es

App

licat

ion

to P

cy

clin

g 1.

FIS

H

(Flu

ores

cenc

e in

si

tu h

ybrid

isat

ion)

FISH

use

s flu

ores

cent

ly la

bele

d ol

igon

ucle

otid

e pr

obes

to d

etec

t an

d lo

caliz

e w

hole

-bac

teria

l cel

ls o

r th

e pr

esen

ce o

f tar

get D

NA

se

quen

ce.

-Loc

aliz

atio

n of

spec

ific

mic

robe

s or

gene

s can

be

visu

aliz

ed

-Allo

ws d

etec

tion

and

spat

ial

dist

ribut

ion

of m

ore

than

one

spec

ies

at th

e sa

me

time.

-Non

-spe

cific

labe

lling

or

auto

-fluo

resc

ence

of

mic

roor

gani

sms

-Am

plifi

catio

n of

sign

als i

s re

quire

d fo

r fun

ctio

nal g

enes

-A

ccur

acy

and

relia

bilit

y is

hi

ghly

dep

ende

nt o

n sp

ecifi

city

of p

robe

s.

(Cro

cetti

et a

l. 20

00;

Kon

g et

al.

2005

)

2. E

LF

(Enz

yme-

labe

led

fluor

esce

nce)

A m

etho

d ut

ilize

s ELF

®97

ph

osph

ate,

whi

ch y

ield

an

inte

nsel

y flu

ores

cent

yel

low

-gre

en p

reci

pita

te

at th

e si

te o

f pho

spha

tase

act

ivity

.

-Loc

aliz

atio

n of

pho

spha

tase

-T

aggi

ng m

etho

d fo

r pho

spha

tase

ac

tivity

.

-App

licat

ion

is sp

ecifi

c.

(Duh

amel

et a

l. 20

08;

Was

aki e

t al.

2008

; D

uham

el e

t al.

2009

)

3. F

low

cyt

omet

ry

Flow

cyt

omet

ry is

a la

ser-

base

d,

biop

hysi

cal t

echn

olog

y em

ploy

ed

in c

ell c

ount

ing,

cel

l sor

ting,

bi

omar

ker d

etec

tion

and

prot

ein

engi

neer

ing,

by

susp

endi

ng c

ells

in

a st

ream

of f

luid

and

pas

sing

them

by

an

elec

troni

c de

tect

ion

appa

ratu

s.

-Qua

ntify

P c

yclin

g m

icro

orga

nism

s -S

imul

tane

ous u

mul

tipar

amet

ric

anal

ysis

of t

he p

hysi

cal a

nd c

hem

ical

ch

arac

teris

tics o

f up

to th

ousa

nds o

f pa

rticl

es p

er se

cond

. -T

arge

ted

cells

can

be

sepa

rate

d fr

om

the

non-

targ

et c

ells

and

the

sorte

d ce

lls c

an b

e us

ed fo

r dow

nstre

am

mol

ecul

ar a

naly

sis.

-App

licat

ion

is sp

ecifi

c (D

uham

el e

t al.

2008

; D

uham

el e

t al.

2009

; G

ünth

er e

t al.

2009

)

Cha

pter

2:L

itera

ture

Rev

iew

33

Tab

le 2

.3 C

omm

on m

etho

ds u

sed

in id

entif

ying

P-m

edia

ting

mic

roor

gani

sms h

ighl

ight

ing

thei

r adv

anta

ges a

nd d

isad

vant

ages

(con

tinue

d….).

Mol

ecul

ar

tech

niqu

es

Des

crip

tion

Adv

anta

ges

Dis

adva

ntag

es

App

licat

ion

to P

cy

clin

g 4.

Phos

phat

e re

porte

r bac

teria

Q

uant

itativ

e co

lorim

etric

ess

ay o

f β-

gala

ctos

idas

e, in

dica

tes w

heth

er

the

bact

eria

hav

e be

en g

row

ing

unde

r pho

spha

te-li

miti

ng o

r su

ffic

ient

con

ditio

ns.

-Can

be

used

to a

sses

s whe

ther

su

ffic

ient

pho

spha

te is

ava

ilabl

e to

th

e ba

cter

ia.

-Use

ful t

ool f

or st

udyi

ng th

e pl

ant–

mic

robe

inte

ract

ions

invo

lved

in P

cy

clin

g.

-App

licat

ion

is sp

ecifi

c.

(De

Weg

er e

t al.

1994

; K

rage

lund

et a

l. 19

97)

5.PL

FA

(pho

spho

lipid

fatty

ac

id)

All

mic

robe

s hav

e m

embr

anes

w

hich

con

sist

mai

nly

of

phos

phol

ipid

fatty

aci

ds (P

LFA

). PL

FA a

naly

sis i

s a te

chni

que

wid

ely

used

for e

stim

atio

n of

the

tota

l bio

mas

s and

to o

bser

ve b

road

ch

ange

s in

the

com

mun

ity

com

posi

tion

of th

e liv

ing

mic

robe

s in

soil

and

aque

ous e

nviro

nmen

ts.

-Acc

urat

e qu

antif

icat

ion,

rapi

d an

d se

nsiti

ve m

etho

d to

det

ect c

hang

es in

th

e m

icro

bial

com

mun

ity

-Inex

pens

ive

and

repr

oduc

ible

-P

LFA

is D

NA

or R

NA

inde

pend

ent,

-Use

ful i

nfor

mat

ion

on th

e dy

nam

ics o

f via

ble

bact

eria

.

-Los

ses o

f pho

spho

lipid

s du

ring

the

sepa

ratio

n st

ep

and

durin

g m

ethy

latio

n -T

ime

cons

umin

g -L

ow n

umbe

r of s

ampl

es c

an

be tr

eate

d at

the

sam

e tim

e.

(Tsc

herk

o et

al.

2004

; H

e et

al.

2013

)

6. q

-PC

R

(qua

ntita

tive

poly

mer

ase

chai

n re

actio

n)

q-PC

R is

com

mon

ly u

sed

to

quan

tify

the

targ

eted

gen

e us

ing

spec

ific

prim

ers.

-The

met

hod

is u

sefu

l for

func

tiona

l ge

nes t

hat a

re d

irect

ly in

volv

ed in

P

cycl

ing

such

as p

hosp

hata

ses,

phyt

ases

, and

nuc

leas

es

-Qua

ntifi

catio

n of

spec

ific

gene

s -Q

uick

, acc

urat

e an

d hi

ghly

se

nsiti

ve m

etho

d fo

r seq

uenc

e qu

antif

icat

ion.

-Can

onl

y be

use

d fo

r ta

rget

ing

of k

now

n se

quen

ces.

-L

ack

of sp

ecifi

c pr

imer

s ca

use

for h

inde

ring

its w

ide

appl

icat

ions

-D

NA

impu

ritie

s and

ar

tifac

ts m

ay c

reat

e fa

lse-

posi

tives

or i

nhib

it am

plifi

catio

n.

(Alk

an e

t al.

2006

)

Cha

pter

2:L

itera

ture

Rev

iew

34

Tab

le 2

.3 C

omm

on m

etho

ds u

sed

in id

entif

ying

P-m

edia

ting

mic

roor

gani

sms h

ighl

ight

ing

thei

r adv

anta

ges a

nd d

isad

vant

ages

(con

tinue

d….).

Mol

ecul

ar

tech

niqu

es

Des

crip

tion

Adv

anta

ges

Dis

adva

ntag

es

App

licat

ion

to P

cyc

ling

7. D

GG

E/TG

GE

(den

atur

ant

grad

ient

gel

el

ectro

phor

esis

)

DG

GE

is fr

eque

ntly

app

lied

for

com

parin

g th

e m

icro

bial

co

mm

uniti

es o

f var

ious

en

viro

nmen

ts. T

arge

t gen

es

ampl

ified

by

PCR

are

sepa

rate

d on

po

lyac

ryla

mid

e ge

ls c

onta

inin

g gr

adie

nts o

f eith

er a

tem

pera

ture

or

chem

ical

to d

enat

ure

the

DN

A a

s it

mov

es a

cros

s an

acry

lam

ide

gel.

-Diff

eren

t sam

ples

can

be

com

pare

d w

ithou

t DN

A se

quen

ce

dete

rmin

atio

n -B

ands

can

be

iden

tifie

d by

clo

ning

an

d se

quen

cing

. The

refo

re D

GG

E ca

n vi

sual

ize

diff

eren

ces u

p to

sp

ecie

s lev

els.

-At h

ighe

r div

ersi

ty, t

he

band

s sep

arat

ed in

the

DG

GE

are

poor

ly re

solv

ed.

-Uns

uita

ble

for d

etec

tion

of

diff

eren

ces a

t the

gen

us o

r an

y hi

gher

leve

ls.

-Tim

e co

nsum

ing

-Onl

y fo

r sho

rt fr

agm

ents

-M

ultip

le b

ands

for a

sing

le

spec

ies c

an b

e ge

nera

ted

due

to m

icro

-het

erog

enei

ty.

(Onu

ki e

t al.

2002

; W

asak

i et a

l. 20

05;

Wan

g et

al.

2009

)

8. C

lone

Lib

rarie

s Se

quen

cing

of c

lone

libr

arie

s is

mos

t com

mon

met

hod

for t

he

anal

ysis

of t

arge

t gen

e se

quen

ces.

The

sequ

ence

pro

vide

s use

ful

info

rmat

ion

on th

e ph

ylog

enet

ic

posi

tion

of th

e m

icro

be.

-Info

rmat

ion

of D

NA

sequ

ence

for

each

clo

ne b

ecom

es a

vaila

ble

-T

he se

quen

ce p

rovi

des u

sefu

l in

form

atio

n on

the

phyl

ogen

etic

po

sitio

n of

the

mic

robe

.

Rel

ativ

ely

high

exp

ense

Ti

me

cons

umin

g.

(He

et a

l. 20

06;

Kim

et a

l. 20

10)

9. R

FLP

(R

estri

ctio

n fr

agm

ent l

engt

h po

lym

orph

ism

)

RFL

P vi

sual

izes

mic

robi

al

com

mun

ities

as p

atte

rns o

f re

stric

tion

frag

men

t len

gth.

The

re

stric

tion

frag

men

ts a

re se

para

ted

by e

lect

roph

ores

is.

RFL

P is

a u

sefu

l met

hod

for

dete

ctin

g re

lativ

ely

subs

tant

ial

diff

eren

ces (

high

er th

an

genu

s lev

el).

RFL

P ca

nnot

reso

lve

smal

l di

ffer

ence

s bet

wee

n th

e se

quen

ces c

ompa

red.

(Kaw

ahar

asak

i et a

l. 20

02; S

late

r et a

l. 20

10)

10. S

IP (s

tabl

e is

otop

e pr

obin

g)

Sepa

ratio

n an

d m

olec

ular

ana

lysi

s of

labe

led

nucl

eic

acid

s (13

C- o

r 15

N-la

bele

d) re

veal

s phy

loge

netic

an

d fu

nctio

nal i

nfor

mat

ion

abou

t th

e m

icro

orga

nism

s res

pons

ible

for

the

met

abol

ism

of a

par

ticul

ar

subs

trate

.

-Hig

h se

nsiti

vity

-P

rovi

des e

vide

nce

on th

e fu

nctio

n of

mic

roor

gani

sms i

n a

cont

rolle

d ex

perim

enta

l set

up

- SIP

is a

met

hod

for i

dent

ifyin

g ac

tive

mic

robe

s in

the

envi

ronm

ent.

Stab

le is

otop

e fo

r P is

ab

sent

. 13

C-S

IP c

ould

be

appl

ied

for s

tudy

ing

the

effe

cts o

f ro

ot e

xuda

tes o

n m

icro

bes

invo

lved

in P

cyc

ling

but

not y

et e

luci

date

d (W

asak

i and

Mar

uyam

a 20

11)

Cha

pter

2:L

itera

ture

Rev

iew

35

Tab

le 2

.3 C

omm

on m

etho

ds u

sed

in id

entif

ying

P-m

edia

ting

mic

roor

gani

sms h

ighl

ight

ing

thei

r adv

anta

ges a

nd d

isad

vant

ages

(con

tinue

d….).

Mol

ecul

ar

tech

niqu

es

Des

crip

tion

Adv

anta

ges

Dis

adva

ntag

es

App

licat

ion

to P

cyc

ling

11. M

icro

arra

y A

pow

erfu

l tec

hnol

ogy

can

be u

sed

to m

easu

re th

e le

vel o

f act

ivity

of

thou

sand

s gen

es si

mul

tane

ousl

y.

The

amou

nt o

f mR

NA

for e

ach

gene

in a

giv

en sa

mpl

e ca

n be

m

easu

red.

-Hig

h-th

roug

hput

, -L

arge

-sca

le,

-Gen

omic

-sca

le

-Allo

ws f

or th

e co

mpa

rison

of

thou

sand

s of g

enes

at o

nce.

-Exp

ensi

ve,

-Lim

ited

by th

e pr

esen

ce o

f pr

obes

on

the

arra

y Is

sues

w

ith R

NA

ext

ract

ion

from

so

il, u

nive

rsal

mic

robe

ar

rays

are

not

yet

app

lied.

(Was

aki e

t al.

2003

; Zhu

et

al.

2010

)

12. N

ext

gene

ratio

n se

quen

cing

(NG

S)

NG

S is

a P

CR

bas

ed h

igh-

thro

ughp

ut se

quen

cing

tech

nolo

gy

perf

orm

ed b

y us

ing

diff

eren

t m

oder

n se

quen

cing

tech

nolo

gies

in

clud

ing,

Illu

min

a (S

olex

a)

sequ

enci

ng, R

oche

454

sequ

enci

ng,

Ion

torr

ent:

Prot

on /

PGM

se

quen

cing

, SO

LiD

sequ

enci

ng.

-Mul

tiple

xing

ena

bles

larg

e sa

mpl

e nu

mbe

rs to

be

sim

ulta

neou

sly

sequ

ence

d du

ring

a si

ngle

ex

perim

ent.

-T

his a

dvan

ce e

nabl

es ra

pid

sequ

enci

ng.

-Diff

icul

ty g

ettin

g th

roug

h ho

mop

olym

ers

-Rel

ativ

ely

expe

nsiv

e -C

ompu

tatio

nal i

nten

sive

-T

ime

cons

umin

g in

term

s of

dat

a an

alys

is.

(And

erso

n et

al.

2011

; A

lber

tsen

et a

l. 20

13)

13. M

etag

enom

ics

An

appr

oach

of d

irect

sequ

enci

ng

of e

nviro

nmen

tal D

NA

with

out

PCR

am

plifi

catio

ns. M

etag

enom

ics

will

pro

vide

the

taxo

nom

ic

dive

rsity

of t

he w

hole

mic

roflo

ra in

an

env

ironm

ent,

toge

ther

with

the

func

tions

of t

he o

rgan

ism

s inv

olve

d an

d th

e is

olat

ion

of b

enef

icia

l gen

es

from

unc

ultu

red

mic

roor

gani

sms.

-Rev

eals

the

pres

ence

of t

hous

ands

of

mic

robi

al g

enom

es

sim

ulta

neou

sly

-Pro

vide

s inf

orm

atio

n ab

out t

he

func

tions

of m

icro

bial

com

mun

ities

in

a g

iven

env

ironm

ent

-Und

erst

andi

ng th

e w

hole

m

icro

flora

in th

e so

il, th

e fu

nctio

ns

of th

e or

gani

sms i

nvol

ved.

-Mas

sive

sequ

enci

ng

proj

ects

are

still

exp

ensi

ve.

-Dat

a an

alys

is is

ch

alle

ngin

g an

d tim

e co

nsum

ing

-Diff

icul

t to

use

for l

ow-

abun

danc

e C

omm

uniti

es.

(Mar

tin e

t al.

2006

; A

lber

tsen

et a

l. 20

12)


36

2.7 New advances in molecular and microscopy technology to resolve

problems encounter with P cycling microorganisms

There is increasing interest in developing PCR-independent molecular tools in relation

to P cycling. P-cycling microbes are known to produce a range of enzymes, including

phytases and phosphoesterases (phosphatases), which release inorganic P (Pi) from

organic P sources and dehydrogenases, which release inorganic P from mineral P

sources via production of organic acids such as, gluconic acid (Lim et al. 2007; Jorquera

et al. 2008a). Genes controlling the expression of these enzymes are potential candidates

for molecular biomarkers and the development of monitoring tools (Rondon et al. 2000;

Torsvik and Øvreås 2002; Cardon and Gage, 2006) (Table 2.4). Expressed protein-

encoding genes (‘functional genes’) as molecular genetic indicators of relevant P

mediating microorganisms can serve as indicators for their specific role in ecosystems

(Gamper et al. 2010). A number of mineral phosphate solubilising genes and organic P

mineralising genes involved in the turnover of phosphonates, phosphoesters and phytic

acid have been identified in specific bacterial or fungal strains, providing new

opportunities to target them to designing more reliable primers (Rodriguez et al. 2006).

For phosphoester mineralizing microorganisms primers can be designed to target a

range of genes including the phytase genes (phyA) alkaline phosphoesterase genes

(phoA/X) and acid phosphoesterase genes (acpA). The production of organic acids is

considered the principal mechanism for mineral phosphate solubilisation and any gene

involved in organic acid synthesis can be used for designing molecular markers.

Therefore, for mineral phosphate solubilising microorganisms, primers can be

developed to target the genes involved in P solubilizing pathways (e.g. pqq genes)

involved in gluconic acid, glucose dehydrogenase, malate dehydrogenase genes, any

genes involved in production of acids that solubilise mineral P.

The other option for understanding P cycling microbial diversity and functional

activities in detail is integration of suitable techniques listed in Table 2.3. For example,

a combined approach of epi-fluorescence microscopy (for co-location), flow cytometry

(quantification), cell sorting (separation of target cells) and Ion Tag sequencing (for

taxonomy), and community metagenomic (for putative functional) can be used to get

more reliable and more detailed understanding of P cycling microorganisms in

environmental samples. The schematic representation of the proposed integrated

approach is shown in Figure 2.7 and application of these techniques individually or in


37

combinations in detecting P cycling bacteria in a piggery waste treatment process is

assessed throughout this thesis. Here, details of each method are discussed briefly.

Table 2.4 Enzymes and their encoding genes in P metabolism.

Function Enzyme Genes PolyP accumulation -uptake of inorganic phosphate and its transport across the cytoplasmic membrane

-PolyP synthesis

-PolyP utilisation

1) The low affinity Pit (phosphate inorganic transport) system, 2) The high affinity Pst (phosphate specific transport) system 3) Pi linked antiport systems of sn-glycerol-3-phosphate 4) Glucose-6-phosphate

1) polyphosphate kinase polyP dependent nucleoside diphosphate kinase 2) polyP dependent nucleoside diphosphate kinase

1) Exopolyphosphatase

PitA PstB GlpT UhpT PPK1 PPK2 PPX

P mineralisation -phosphate synthesis

-phosphate starvation

-inducible

-Phytase, alkaline phosphoesterase, acid phosphoesterase -Phosphate sensor PHO regulon activator, -Polyanionic specific outer membrane porin

-High and low affinity phosphate specific transport and phosphate inorganic transport

phyA, phoA/X, acpA PhoR PhoB Pst, Pit

P solubilisation Glucose dehydrogenase and its co-factor, pyrroloquinoline quinone, gluconic acid, malate dehydrogenase genes, and other genes involved in production of acids that solubilise mineral P

GHD, pqq, mps, gabY, pkkY, pk1M10, PKG3791, pcc, and gcd

2.7.1 Enzyme-labeled fluorescence (ELF) coupled to epi-fluorescent microscopy,

flow cytometry, and cell sorting

Enzyme-labeled fluorescence (ELF) or fluorescence labeled enzyme assay (FLEA) has

been used to co-locate phosphatase activity at the single-cell level in a range of

environments (Duhamel et al. 2008). The principle behind ELF is that the fluorogenic

substrate, ELF®97 phosphate (C14H7Cl2N2Na2O5P, Invitrogen or Molecular Probes,

E6589) reacts with cell surface-bound phosphatases and is cleaved into inorganic P (Pi)

and fluorescent product called ELF alcohol (ELFA), thereby forming a fluorescent


38

precipitate at or near the site of phosphatase activity. ELFA fluorescent signal is bright

and photochemically stable, allowing a sensitive quantification of PO4ase activity either

by epi-fluorescent microscopy or rapid flow cytometry (Nedoma et al. 2003; Dignum et

al. 2004).

Figure 2.7 Proposed integrated approach for understanding P cycling pathways.

The detection of the ELFA signal by epi-fluorescence microscopy has been shown to be

efficient for collocating phosphatases activity in phytoplankton (Nedoma et al. 2003),

and bacterioplankton (Nedoma and Vrba 2006), aquatic bacteria (Duhamel et al. 2008).

Compared to epi-fluorescence microscopic techniques, flow cytometry is fast, and

highly accurate in quantification analysis. Therefore both techniques are often used

together to identify, visualise, and quantify PO4ase activity in microorganisms using

ELF®97 phosphate. ELF has been applied in various environments such as marine

(Nedoma et al. 2003; Dignum et al. 2004; Meseck et al. 2009), fresh water (Duhamel et

al. 2009), activated sludge (Kloeke and Geesey, 1999). To date, there is limited

information on PO4ase activity in complex and diverse environments such as piggery


39

wastewater effluent due to lack of methods and limitations of applying ELF in these

diverse microbial habitats.

Many questions remain unresolved while more than ten years of research on ELF

application using flow cytometry and epi-fluorescence microscopy has brought new

understanding about P mineralisation by microorganisms, (Duhamel et al. 2009),

particularly in relation to the identity and function of P mineralising bacteria. Therefore,

downstream molecular sequencing (Ion Tag sequencing and community metagenomic)

of sorted phosphatase active cells obtained from flow cytometric analysis allows to

understand identity and function of P mineralising bacteria even they are low in

abundance in an environment.

2.7.2 Ion Torrent sequencing

Recently, a light independent, Ion sequencing method has been developed and

commercialised with the introduction of the Ion Torrent Personal Genome Machine

(PGM) by the Life Technologies. During Ion sequencing, DNA sequence composition

is determined by measuring slight changes in pH as hydrogen ions are released when

nucleotides are incorporated during DNA strand synthesis (Rothberg et al. 2011).

Compared to pyrosequencing, Ion sequencing is a more affordable sequencing option

due to the substantially reduced costs since a light detection system and associated

reagents are not required as other sequencing analysis (Glenn, 2011). Utilising different

output PGM chips, an average of ca. 350,000 (314 PGM chip) or 1.2 million reads (316

PGM chip) or more (318 PGM chip) van be generated within 8 h and the output is

satisfactory in quality for downstream data analysis pipelines such as QIIME (Caporaso

et al. 2010). Recently, it was demonstrated that the Ion Torrent platform was a suitable

low cost next generation sequencing platform for studying microbial community

dynamics and function associated with a covered anaerobic piggery waste treatment

system (Whiteley et al. 2012). Whiteley et al. (2012) developed Ion Torrent protocols

for both PCR amplified 16S rRNA or metagenomic community sequencing analysis and

used these protocols to assess community structure, temporal stability and key taxa

during the waste treatment process. This included the development of Golay barcoded

Ion Tags for multiplex analyses of microbial communities which allow sequencing of

large number of sample at low cost.

2.7.3 Community metagenomics


40

Metagenomic analysis, the analysis of whole community genomes, provides

information on the whole microbial community in a given environment, the functions of

the organisms involved in their metabolic pathways, and even isolation of beneficial

genes from uncultured microorganisms (Wasaki and Maruyama 2011). Metagenomic

analysis helps to reveal the abundance of gene copies of functional genes as putative

molecular genetic indicators of relevant P mediating microorganisms and their specific

role in an environment (Table 2.4). Metagenomic analysis has been used for screening

functional pathways in relation to novel phosphonate utilization pathways in marine

bacteria (Martinez et al. 2010), key metabolic processes involved in soil phytic acid

utilization (Unno and Shinano 2013), distribution and diversity of phytate-mineralizing

bacteria (Lim et al. 2007), and community structure and genetic potential of EBPR

(Albertsen et al. 2012; Albertsen et al. 2013). With the progression of such studies of

functional details of a community (such as metatranscriptomics, metagenomic,

proteomics, metabolomics), a considerable number of sequences encoding for P cycling

genes are now available and these can be used to design molecular monitoring tools for

P cycling in environment.

2.8 Rationale It is well known that AM fungi, P solubilising, P accumulating and P mineralising

microorganism play crucial roles in P cycling via direct and indirect mechanisms. Given

that the most piggery wastes are high in both organic and inorganic forms of P, it can be

hypothesised that these systems are more diverse and abundant in P solubilising

microbial communities, P mineralising microbial communities, and P uptake/

immobilising microbial communities. However, despite the importance of these

processes for sustaining plant growth in natural and agricultural systems, little is known

about the specific microbes responsible for these transformation processes in piggery

waste treatment processes. It is therefore important to assess microbial abundance,

diversity and activity in piggery waste as a basis for recovery of environmentally and

economically sound P fertilisers. Knowledge gained can be applied to improving crop

production by amending soils with piggery waste by-products. In terms of

methodological advances in detecting P cycling microorganisms, further improvement

of available methods and the combination of molecular approaches is crucial for more

comprehensive understanding of microbes involved in P transformations.


41

Finally, key gaps in knowledge for P cycling in the piggery waste management can be

summarised as:

(1) Lack of understanding of the diversity and metabolic function of P

solubilising, P accumulating, and P mineralising microorganism in piggery

waste.

2) Lack of methods to unravel both functional and taxonomical identities of P

cycling microorganism in piggery waste.

3) Lack of application of efficient methodologies for reducing the level of

inorganic P in piggeries using low cost enhanced biological P accumulation.

4) Lack of understanding of interactions between P cycling microbes in soils

amended with piggery waste for an optimised P reutilisation efficiency.

Chapter 3: Characterisation of the piggery waste treatment process

42

CHAPTER 3 Microbial Community Composition and Phosphorus Cycling

Potential within a Covered Anaerobic Pond System Treating

Piggery Waste

3.0 Abstract

Uncovering the taxonomic composition and functional capacity of phosphorus (P)

cycling bacteria within the piggery wastewater treatment process is of great importance

for developing effective strategies for P recovery from piggeries. The primary goal of

this study was baseline characterisation of all compartments involved in a model

covered anaerobic piggery wastewater treatment plant according to physico-chemical

properties, microbial community composition and P cycling potential. Sampling was

carried out at all stages of the treatment process (pit, holding tank, covered anaerobic

pond digester, and aerobic pond/evaporation pond). 16S rRNA Tag sequencing was

used to determine bacterial community composition. As revealed by high throughput

16S rRNA Ion Tag sequencing, culturing techniques and metagenomic analysis,

bacterial community composition was spatially distributed among different stages of the

piggery waste treatment process. Overall, there were clear shifts in bacterial community

composition between the anaerobic and aerobic stage. Total populations of the covered

anaerobic digester mainly comprised Bacteroidia, Clostridia, Cloacamonae and

Synergistia. The aerobic pond was dominated by Proteobacteria and Actinobacteria.

Chemical characterisation highlighted the need to reduce soluble P concentration in the

piggery wastewater before either its use in agriculture or disposal back into

environment. Functional analysis in relation to P cycling revealed that genes responsible

for P mineralisation were higher in number in the covered anaerobic digester, and polyP

accumulation was greater in the treated piggery wastewater in the aerobic pond. These

findings aid in identifying the key processing stages in relation to reducing soluble P

and recovering valuable by-products for re-use in agriculture.


43

3.1 Introduction

Intensive pork production has created serious waste disposal problems, notably in

relation to the environmental loading of soluble phosphorus (orthophosphates), a major

agent of eutrophication. Piggery wastewater are generally high in inorganic P (e.g.

orthophosphates), mineral P (e.g. struvite and hydroxyapatite), and a variety of organic

P forms (e.g. phytates, polyphosphates, and microbial derived P such as phospholipid

and nucleotides) (Güngör and Karthikeyan 2008; Westerman et al. 2010). Although the

occurrence of high concentrations of soluble P in piggery waste is unavoidable, it can be

reduced if sound recycling strategies are developed and applied.

Recycling of piggery waste can yield a wide range of P fertilizers; liquid P-fertilisers

(digested effluent), algal biomass (separated biomass), slow release P-fertilisers

(struvite) and soil stabilisers (compost, digestate, sludge) (Westerman et al. 2010).

Moreover, it has also been previously proposed that precipitated phosphate recovered

from wastewater (e.g. stuvite and hydroxyl apatite) can be manipulated as efficient P

fertiliser when applied together with common P solubilising bacteria and fungi (De-

Bashan and Bashan 2004). On the other hand, P recovered from enhanced biological P

removal (EBPR) from waste sludge (i.e. P accumulated in microbial cells as chains of

phosphate called polyP granules) can be used as raw material in the fertilizer industry

(Hirota et al. 2010). Recent development of bioprocesses for expanded use of polyP has

been reviewed by Hirota et al. (2010) illustrating the wider application of polyP in

industry, agriculture and medicine while reducing the threats of water pollution via

eutrophication. Nevertheless, further research is needed to enhance the efficiency and

consistency of developing these by-products as P-fertilisers, and to ensure their

application is cost effective, environmental sound and practical from an operations

perspective.

Understanding P cycling in high P loaded waste streams like piggeries will assist

development of effective strategies for P nutrient management. P cycling in wastewater

is often determined by the activity of microbial communities present (McMahon and

Read 2013). They play a central role in transforming one form P to another by P uptake

and immobilisation in biomass (P immobilisation), they liberate orthophosphates from

organic matter (P mineralisation), and they alter redox conditions that affect solubility

of mineral P (P solubilisation). Understanding these pathways is the key to control

excess orthophosphate in piggery waste and to recycle them as valuable by-products for


44

agriculture. Consequently, monitoring P transformation within piggeries is useful for

making decisions about P removal strategies in a cost effective manner.

To date, the major emphasis on microbial mediated P removal in wastewater was put on

the process of EBPR which is governed by polyP accumulating microorganisms. The

most common explanation for P transformation pathways in wastewater is the

occurrence of polyP degradation during the anaerobic digestion and poly P formation

during the subsequent aerobic digestion (McGrath and Quinn, 2003; De-Bashan and

Bashan, 2004). During the anaerobic conditions, microorganisms deplete organic matter

and carbon and accumulate biopolymers such as polyhydroxyalkanoate (PHA) and

glycogen using the energy released during the degradation of polyP which in turn leads

to release of Pi from the sludge. During the aerobic phase, these biopolymers serve as

energy and carbon sources for taking up larger amounts of Pi than the amount released

during the anaerobic phase, leaving phosphate-reduced conditions in the aeration pond

(De-Bashan and Bashan 2004). However, there are other important P transformation

pathways (i.e. P mineralisation, P solubilisation) which directly or indirectly affect the

efficiency of P removal processes from wastewater. Organic forms of P in piggery

waste are transformed by P mineralising microorganism (PMM), liberating soluble P.

When wastewater is rich in soluble P in the presence of some ions (e.g.

Ca,Al,Fe,Mg,NH4), soluble P tends to precipitate as insoluble Ca/Al/Fe/Mg-phosphates

or other P complexes (e.g. struvite; MgNH4PO4.6H2O). These precipitates are often

deposited in sludge and reduce phosphate availability in the wastewater. Solubilisation

of mineral phosphates can be mediated by microorganisms known as P solubilising

microorganisms (PSM). Therefore, the amount of soluble P in wastewater is the gross

balance of all of those P transformation pathways (Figure 2.6). However, these

processes are not adequately defined in wastewater treatment processes and further

research on efficient P removal from the wastewater is required. In particular,

uncovering the taxonomic composition, diversity and functional capacity of

microorganisms mediating P transformation and how this community influences P

mineralisation, polyP formation and solubilisation requires further investigation during

the recycling of piggery waste.

The limited information available on P transformation in waste treatment processes

could be due to methodological constraints. Effective methodological approaches are

needed to identify P cycling bacteria and to identify their physiological role in

controlling P metabolism and factors controlling P cycling in the piggery wastewater


45

treatment processes. Recently, sequence analysis of the 16S rRNA Ion Tag sequencing

has shed new light on the diversity and composition of microbial communities within a

covered anaerobic pond of piggery waste treatment processes over a period of time at a

lower cost (Whiteley et al. 2012). 16S rRNA gene-based techniques reveal high

microbial diversity. However, this approach offers only limited information about the

functional role of microorganisms within a given environment. In this context, shotgun

metagenomic analysis is a powerful technique for revealing the putative metabolic

potential within different environments, while providing information on diversity and

composition of microorganisms. Community metagenomics, the direct genetic analysis

of genomes in an environmental sample, is increasingly being used to reveal genetic

diversity, population structure and ecological role of microorganisms. Metagenomic

technology has been successfully applied to studying, functional gene expression in

microbial systems (Wilmes and Bond, 2006), functional capacity of the swine gut

(Lamendella et al. 2011), subcellular location of marine bacterial alkaline phosphatases

and bacterial (Luo et al. 2009) and archaeal community dynamics in a covered

anaerobic pond (Whiteley et al. 2012). While characterisation of microbial diversity in

anaerobically treated piggery waste has been reported (Cardinali-Rezende et al. 2012;

Whiteley et al. 2012; Tuan et al. 2014), the functional genetic potential of P cycling of

piggery water treatment processes has not been studied using metagenomics.

The primary goal of this study was to characterize the piggery waste treatment process

in terms of physico-chemical properties, microbial community composition and P

cycling potentials. It was expected that this would identify where reduction in soluble P

in piggery waste and its recovery as by-products for agriculture could be achieved.

Based on this characterisation, the objectives and hypothesis for the rest of this thesis

were developed.

3.2 Material and Methods

3.2.1 Farm description and sampling

Piggery waste by-products were collected from a covered anaerobic pond digester

system at Medina Research Station, Department of Agriculture and Food Western

Australia (GPS geocoder: latitude -32.223000, Longitude 115.805801). The layout of

the sampling site and the process of wastewater treatment are shown in Appendix 1.

Piggery waste had been treated in five consecutive stages in this waste management


46

system: pits in the pig shed; solid separation screens, holding tank, the covered

anaerobic pond (CAP) and finally a secondary evaporation pond. Effluents generated

from pig pens were collected into a pit tank. Solids were then separated by mechanical

screening and the remaining waste effluent was collected into a storage holding tank.

Afterwards, wastewater was transferred to a covered anaerobic pond (CAP) at a rate of

75,000 L per week for downstream biological remediation. Finally, the anaerobically

treated wastewater was pumped into an aeration pond, as the final stage of waste

treatment under aeration conditions while also yielding a dewatering stage by

evaporation. Sampling was done during Spring 2012.

Sampling points in the piggery waste treatment process were: 1) pit (effluent; facultative

anaerobic), 2) holding tank (effluent; facultative anaerobic), 3) CAP-Bottom (slurry;

anaerobic), 4) CAP-Top (digested effluent: anaerobic), and 5) evaporation pond (treated

wastewater; aerobic). Sampling from the covered anaerobic pond was performed by

suction collection using a 12 V marine grade bilge pump connected to a PVC hosepipe.

The hosepipe was placed into the access port of the covered anaerobic pond and was run

for 5 min to flush the sampling line, followed by a collection of the sample (1 L) into

autoclaved containers. Samples from CAP were collected from the surface (0.5 m) and

bottom (4 m). Samples from other places were also collected using the suction collection

method. There were no true replicates for each sample as there was only one pond for

the each waste treatment stage at that particular time. Therefore, samples from each

point were collected into several sampling bottles and then corresponding samples were

mixed together to make a composite sample for each stage of the waste treatment

process.

3.2.2 Physico-chemical characterization of pig waste samples

Physico-chemical parameters monitored were phosphorus (total P, organic P, and

orthophosphate), total carbon (TC), total nitrogen (TN), ammonia (NH4+), total solids

(TS), volatile solids (VS), electrical conductivity (EC), temperature and pH. All

chemical analyses were assessed according to standard methods for examination of

water and wastewater (American Public Health Association 2005). The NH4+ content,

EC (salinity) and pH were measured on a Thermo Scientific Orion 5 Star meter

(Thermo Fisher Scientific Australia Pty Ltd, Vic 3179) using specific probes for each

variable measurement, following the manufactures protocol. Total carbon (TC) and total

nitrogen (TN) were determined by combustion using elementar (vario Macro CNS;


47

Elementar, Germany). TS and VS were determined for a 50 mL sample. An evaporation

dish was cleaned in an acid wash and placed in a furnace oven at 550°C for 30 minutes

to burn off any volatiles. The dish was then weighed prior to and after the sample was

added allowing for the weight of the sample to be calculated by subtraction. The

evaporation dish was placed in a drying room at 45ºC overnight and then transferred to

into an oven at 105 ºC for 3 hours. Afterwards, the dish was placed in a desiccator to

cool before being reweighed. The weight of total solids in the sample was then

recorded. To measure the amount of volatile solids in the samples, the evaporation dish

containing the TS was placed in a furnace at 550°C for twenty minutes and then cooled

in a desiccator. Once cooled, the final weight of each sample was subtracted from the

TS weight (after 105ºC) to determine the VS of the sample.

3.2.3 Isolation and identification of P mineralising bacteria and P solubilising

bacteria

P mineralising bacteria (PMB) and P solubilising bacteria (PSB) were selectively

isolated at each stage of the piggery waste treatment process compartments, using a

selective medium supplemented with phytate and tri-calcium phosphate respectively

(Kerovuo et al. 1998; Jorquera et al. 2008). Each waste sample was diluted in sterile

phosphate-buffered saline (PBS) and plated in triplicate on agar medium. Two media

were used, one containing phytate for selective isolation of PMB (10 g/L D-glucose, 4

g/L Na-phytate, 2 g/L CaCl2, 5 g/L NH4NO3, 0.5 g/L KCl, 0.5 g/L MgSO4·7H2O, 0.01

g/L FeSO4·7H2O, 0.01 g/L MnSO4·H2O, 15 g/L agar) and the second as tri calcium

phosphate for PSB (10 g/L D-glucose, 5 g/L Ca3(PO4)2, 5 g/L MgCl2·6H2O, 0.25 g/L

MgSO4·7H2O, 0.2 g/L KCl, 0.1 g/L,(NH4)2SO4, 15 g/L agar). Plates were incubated for

4 days at 27°C and any colonies that formed clear zones around them on their respective

plates were selected and purified as PMB or PSB.

Each recovered PMB or PSB isolate was characterized to genus level using partial

sequencing of the 16S ribosomal RNA gene. The DNA was extracted from pure

cultures by the phenol/chloroform method (Griffiths et al. 2000). PCR was carried out

using a primer set pA (5'-AGA GTT TGA TCC TGG CTC AG-3') and pH (5'-

AAGGAGGTG ATC CAG CCG CA-3') (Edwards et al. 1989). The 30 μL PCR

mixtures contained: 0.75 μL of each primer (10 µM), 0.8 μL of 10 mM dNTP’s, 3 μL of

10X buffer (contains 1.5 mM MgCl2), 2 μL of Dynazyme EXT DNA Polymerase (1 U/


48

μL -Thermo Scientific), 2 μL of template DNA and 20.7 μL molecular grade water. The

reaction conditions were 94 °C for 10 min (initial denaturation) followed by 30 cycles

of 93°C 1 min (denaturation); 58 °C, 1 min (annealing); 72 °C, 2 min (extension); and a

final extension step at 72°C for 10 min using a thermocycler (Techgene, Techne Inc,

New Jersey, USA). After the reaction, 8 μL of the PCR reaction was analysed on a 1.5%

agarose gels containing 1 μg mL−1 of ethidium bromide to ascertain PCR fragment size

and quality. Suitable quality PCR products were sent to Macrogen Inc, Korea for

sequencing (Sanger sequencing) and sequences obtained in return were analysed by

BLASTn to find the closest related bacterial sequences within the publicly available

database.

3.2.4 DNA extraction and 16S rRNA Ion Tag sequencing

Genomic DNA of piggery waste samples from each sampling point was extracted using

the MoBio Powersoil DNA isolation kit (Geneworks, Australia), utilising beat beating

and column purification, according to the manufacturer's guidelines. Extracted DNA

was quantified and checked for purity at A260/280 nm (Nanodrop, Thermo Fisher

Scientific, USA) prior to storage at −20 °C. Bacterial 16S ribosomal RNA genes were

amplified by polymerase chain reaction (PCR) from the DNA samples using

oligonucleotide primers specific for bacteria. All forward primers were modified by the

addition of an Ion Torrent sequencing adaptor, ‘GT’ spacer and unique error correcting

Golay barcode (Hamady et al. 2008), to allow multiplex analyses. Multiplexed samples

were subject to 16S ribosomal RNA gene amplification by PCR using Golay barcoded

primers 341F and 518R (Muyzer et al. 1993, Whiteley et al., 2012) with amplification

conditions described previously (Jenkins et al. 2010; Supaphol et al. 2011).

Following amplification, all PCR products were checked for size and specificity by

electrophoresis on 2.5% w/v agarose, gel purified and adjusted to 10 ng/μL using

molecular grade water and pooled equally for subsequent sequencing. Sequencing was

performed on the Ion Torrent Personal Genome Machine (Life technologies, USA)

using 200 base-pair chemistry as described in Whiteley et al. (2012). All the PGM

quality filtered data were exported as FastQ files and the results were split into fasta and

qual files and analysed using the QIIME pipeline (Caporaso et al. 2010). Assigning the

multiplexed reads to samples was performed using default parameters (minimum

quality score = 25, minimum/maximum length = 130/220, no ambiguous base calls,

remove reverse primers and no mismatches allowed in the forward and reverse primer


49

sequences). Chimera checking was done using usearch61 and only non-chimeric

sequences were considered for assigning operational taxonomic units (OTUs,

Greengenes (GG) reference database clustered at 97% identity). Singletons were

removed and taxonomy for the sample sequences was assigned to the representative

sequence of each OTU.

3.2.5 Whole-genome-shotgun sequencing

In order to gain an insight into the microbial functional capacities within different

compartments of the waste treatment process, genomic DNA derived from each

compartment was used for PCR independent whole genome shotgun sequencing on the

Life technologies Proton system. Approximately 150 ng of DNA was used to generate a

whole genome shotgun library using a NEBnext Ultra library preparation kit (New

England Biosciences). Fragments of 320-330bp were selected from the final library by

gel-excision and sequenced for 520 flows on an Ion Torrent Proton sequencer (Life

Technologies), yielding reads of 230-240bp modal length. Quality filtering and

trimming were performed on the instrument using TorrentSuite 4.0. Sequencing data

sets were uploaded to the Metagenome Rapid Annotation using Subsystem Technology

(MG-RAST) server (http://metagenomics.nmpdr.org/). Sequences were aligned to

sequences stored in a number of public databases (Appendix 2). Metagenomic data sets

for samples derived from pits, holding tank, CAP-Bottom, CAP-Top, CAP-Outlet and

evaporation pond are publicly available in the MG-RAST system under project

identifiers 4553572.3, 4553568.3, 4553570.3, 4553571.3, 4553569.3, and 4553566.3

respectively. Assignment of metabolic function and phylogenetic identification were

performed as described previously (Meyer et al. 2008). Functional classifications were

computed by using SEED FIGfams and subsystems. The Min. % Identity Cutoff was

60%, Min. Alignment Length Cutoff was 15 nucleotides and the Max. e-Value Cutoff

1e-5.

3.2.6 Multivariate statistical analyses

Canonical Correspondence Analysis (CCA) was used to model the changes in the

bacteria community profile of the wastewater treatment compartments to the measured

environmental variables (Jongman et al. 1995) to explore how the microbial community

is structured relative to the environmental variables at each stage of wastewater

treatment process. A sequencing data matrix of relative taxon abundances and


50

corresponding matrix of the environmental variables (Section 3.2.2) for each waste

treatment point was prepared as the method described previously (Jenkins et al. 2010).

Canonical correspondence analysis (CCA) was performed using the software package

Canoco v4.55 (Plant Research International © 2006). The data were analysed to

ascertain which covariates best explained changes in bacterial community profiles.

3.3 Results

3.3.1 Physico-chemical characteristics of a piggery waste treatment system

The chemical composition of waste in different stages varied in pH (6.9-8.1), EC (3.8-

6.9 mS/cm), Total Solids (TS: 0.13- 2.61 %), Volatile Solids (VS: 25.8-76.1 %), Total

N (TN: 6.3-7.9 %), Total C (TC: 45.2-46.5 %), Total P (TP: 13.6-147.6 mg/ L) and

orthophosphate (Pi: 12.2-26.3 mg/L) (Table 3.1).

Table 3.1 Physical and chemical characteristics of different piggery wastewater treatment compartments at Medina Research Station, Western Australia.

Sample Pits Holding Tank

CAP- Bottom

CAP- Top

CAP- Outlet

Evaporation Pond

pH 7.1 6.9 7.2 7.1 7.6 8.1 EC (mS/cm) 3.8 3.7 6.8 6.9 6.6 5.2 TS % 0.3 0.9 2.6 0.3 0.4 0.1 VS % 35.8 75.4 76.1 39.6 60.1 25.9 TN% 4.3 4.3 6.1 7.9 6.8 NT TC% 45.0 45.9 46.5 45.2 45.9 NT C:N ratio 10.5 10.7 7.6 5.8 6.7 NT Ammonia (ppb) 180.0 241.0 348.0 403.0 410.0 440 Total P (mg/L) 41 42.9 147.6 40.9 34.1 13.6 Orthophosphate (mg/L) 26.3 25.1 10.8 21.8 20.5 12.2 Organic P (mg/L) 14.7 17.8 136.8 19.1 13.6 1.3 Ca (mg/L) 112 58 1666 62 NT 21 Fe (mg/L) 0.6 1 205.2 1.6 NT 0.1 K (mg/L) 167 202 289 269 NT 681 Mg (mg/L) 62 46 144 58 NT 148 Cd (mg/L) 0.0 0.0 0.0 0.0 NT 0.0 Co (mg/L) 0.0 0.0 0.3 0.0 NT 0.0 Cu (mg/L) 0.1 0.1 56.1 0.4 NT 0.1 Pb (mg/L) 0.0 0.0 0.3 0.0 NT 0.0 Cr (mg/L) 0.0 0.0 0.4 0.0 NT 0 Zn (mg/L) 0.3 0.3 159.6 1.1 NT 0 VS is shown as a percent of TS. NT stands for not tested.


51

The highest TP/OP and the lowest orthophosphate were observed in CAP-Bottom slurry

at 147.6/136.8 mg/L and 10.8 mg/L respectively showing that the majority of P in the

CAP-Bottom was organic or insoluble forms of P (Table 3.1). In contrast to CAP-

Bottom, orthophosphate was high in CAP-Top (21.8 mg/L) implying that the

orthophosphate generate during the anaerobic digestion is released to the CAP Top

effluent (digestate) (Table 3.1). The level of orthophosphate in the CAP-Outlet was 20.5

mg/L and when it was transferred to the aerobic stage, the level of orthophosphate

decreased to 12.2 mg/L. Considerably higher amounts of ammonia were observed in

CAP-Bottom slurry (348 ppb) and CAP-Top effluents (403 ppb) and increased in

concentration towards the latter part of the waste treatment processes, reaching the

highest levels in the treated wastewater in the evaporation pond (440 ppb).

3.3.2 Isolation and identification of P mineralising bacteria (PMB) and P

solubilising bacteria (PSB)

Bacteria capable of producing a clearing zone due to the mineralisation of organic P

(phytate) and the inorganic P (tri-calcium phosphate) during agar isolations were

recovered as a basic isolation strategy to characterise the cultivable PMB and PSB

fraction of the community (Figure 3.1). Genetic characterisation of the isolated PMB

and PSB, based on partial sequencing of the 16S ribosomal RNA gene, are documented

in Table 3.2. The ability of P mineralization or solubilisation among isolates was

assessed based upon the diameter of the clear zones formed around the colonies as

shown in Figure 3.1 (+++ indicates high, ++ indicates medium, and + indicates low).

The majority of isolates showed both P solubilising and mineralising abilities and

belonged to the genera Pseudomonas, Enterobacter, Escherichia coli, Bacillus, and

Cronobacter. Amongst them, Pseudomonas aeruginosa and Enterobacter were

dominant, both of which often isolated at all the stages of the waste treatment process.


52

Figure 3.1 Ability of P mineralization and P solubilisation among isolates from the waste treatment system at Medina Research Station. Ability of P solubilisation and mineralisation was assessed based on diameter of the clear zones around the colonies. a) high and low P mineralising ability, b) low solubilising ability (+), and c) high solubilising ability (+++). 3.3.3 Bacterial community structure by culture independent means within different

stages of the waste treatment system

Bacterial communities assessed by culture independent methods comprised of a range

of taxa at each stage of the waste treatment system (Figure 3.2). According to the

rarefaction analyses (Figure 3.2a), the overall qualitative operational units (OTUs; 97%

sequence similarity) richness of each wastewater treatment stage appeared to be high as

rarefaction curves did not reach asymptote, even after 7000 sequences were examined.

This implies that the wastewater bacteria community is highly diverse. Microbial

diversity assessed by Shannon’s Index (Figure 3.2b) indicated that the species richness

and diversity of the bacterial populations was greatest in the facultative

anaerobic/anaerobic stages (Pit effluent, Holding Tank effluent, CAP-Bottom slurry,

CAP-Top digested effluent, and CAP-Outlet effluents) and lowest in the aerobic stage

(Evaporation Pond treated wastewater).

In terms of diversity during waste remediation as revealed by 16S rRNA Tag

sequencing, samples taken from facultative anaerobic/anaerobic ponds (Pit effluent,

Holding Tank effluent, CAP-Bottom slurry, CAP-Top digested effluent, CAP-Outlet

effluents) were considerably different from those of the aerobic pond (Evaporation Pond

treated wastewater) (Figure 3.3). The facultative anaerobic/anaerobic ponds maintained

a relatively stable community composition, as assessed by mean abundances of

dominant community members of these special sampling points e.g. Bacteroidia

(43±7.0), Clostridia (19.7±2.5), Cloacamonae (6.8±3.8), and Synergistia (6.3±4.8)


53

(Figure 3.3). The other minor taxa were Epsilonproteobacteria, Alphaproteobacteria,

Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria, Mollicutes,

Spirochaetes, Tenericutes, Firmicutes, Cyanobacteria and Bacilli.

Table 3.2 Genetic characterisation of the isolated P mineralising bacteria and P solubilising bacteria at the different stages of piggery was treatment process.

Genus Accession no%

identityAbility of P

mineralisation*Ability of P

solubilisation*

Pits Enterobacter ludwigii JQ308612.1 94% ₊ ₊

Pseudomonas aeruginosa JQ579643.1 99% ₊₊₊ ₊₊₊

Enterobacter sp. JF690889.1 95% ₊₊ ₊₊₊

Enterobacter sp. GU086159.1 99% ₊ ₊

Escherichia coli AB548580.1 99% ₊ noHolding Tank Cronobacter sakazakii GU727682.1 99% ₊ ₊₊₊

Escherichia coli EF560783.1 98% ₊₊ noBacillus subtilis JF905698.1 99% ₊₊₊ ₊₊

Pseudomonas aeruginosa JQ659909.1 99% ₊₊₊ ₊₊

Cronobacter turicensis FN401357.1 99% ₊ ₊

Escherichia fergusonii HQ259940.1 99% ₊₊₊ noPseudomonas aeruginosa JN999891.1 99% ₊₊₊ ₊₊₊

Pseudomonas aeruginosa JQ659966.1 100% ₊₊₊ ₊₊₊

Escherichia coli EF560776.1 98% ₊₊₊ noEnterobacter sp. FN401343.1 98% ₊ ₊₊₊

CAP-Top Pseudomonas citronellolis JQ659858.1 98% ₊₊₊ noPseudomonas aeruginosa JQ579643.1 99% ₊₊₊ ₊₊₊

Pseudomonas aeruginosa JN999830.1 99% ₊₊₊ ₊₊₊

Escherichia fergusonii HQ259940.1 99% ₊₊₊ noPseudomonas aeruginosa JQ659909.1 99% ₊₊₊ ₊₊₊

CAP-Bottom Pseudomonas sp. JQ595498.1 92% ₊₊₊ noPseudomonas aeruginosa JN999891.1 99% ₊₊₊ ₊₊₊

Klebsiella pneumoniae HQ907956.1 99% ₊₊₊ ₊₊

Escherichia coli JN811622.1 98% ₊₊ noEvapotation Pond Enterobacter sp. JQ398852.1 94% ₊₊₊ ₊₊

Enterobacter asburiae JQ659874.1 94% ₊₊₊ ₊₊

Enterobacter sp EU430753.1 92% ₊₊ ₊₊

Enterobacter asburiae JQ659874.1 99% ₊₊₊ ₊₊

Enterobacter sp. EU430753.1 91% ₊₊ ₊₊

Enterobacter sp. JQ398852.1 95% ₊₊₊ ₊₊

Pseudomonas sp JQ595498.1 92% ₊₊₊ noBacterium B28 FJ628394.1 92% ₊₊ ₊₊₊

Enterobacter sp. JQ398852.1 95% ₊₊ ₊



Enterobacter sp. HM107175.1 91% ₊₊ no

*Ability of P mineralization or solubilisation (+++ indicates high, ++ indicates medium, and + indicates low)


54

Figure 3.2 Alpha diversity rarefaction plots of observed species for different stages in the piggery wastewater samples. (a) Microbial diversity indicated by Shannon’s index, (b) Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7340).


55

Figure 3.3 Identities and % composition of the bacteria, at class level, from stages in the piggery waste treatment system at Medina Research Station.

Conversely, the community composition of the aerobic pond (evaporation pond) was

markedly different from that of the anaerobic/anaerobic ponds (Figure 3.3). The

evaporation pond was dominated by Actinobacteria (50.7%), Betaproteobacteria

(19.9%) with other taxa including Erysipelotrichi (8.2 %), TM7 (5.1%),

Gammaproteobacteria (2.9%), Sphingobacteriia (1.5%), Flavobacteriia (1.3%),

Alphaproteobacteria (0.9%), Clostridia (0.7%), OD1 (0.7%), Epsilonproteobacteria

(0.7%), and Acidimicrobiia (0.6%).

Multivariate statistical analyses revealed that the bacterial communities showed a

unimodal response to the physicochemical parameters (Table 3.1) making these data

suitable for analysis using CCA. The first two axes of the CCA analysis explained 85 %

of the total variance for the bacterial communities (Figure 3.4) indicating a good sample

separation along the axis. For construction of the bacterial CCA plots, the sample scores

(community structure) and environmental variable (arrows) were plotted. Using these

analyses, the evaporation pond and CAP-Outlet were distinct from the other sampling

points of the water treatment process. Differences in the bacterial community structure

(Figure 3.4a) between pits, holding tank, CAP-Bottom and CAP-Top were mainly


56

Figure 3.4. CCA biplot showing the relationship between, a) microbial community composition or b) individual bacterial taxa and environmental variables in each sampling point of piggery wastewater treatment process. Plots on the graph represent the community composition at each sampling point () and individual taxa (▲). Arrows represent the environmental variables (EC, VS, TN, TC, Pi, C:N ratio, TS, TP, OP, Ca, Mg, K, pH, Ammonia).


57

related to differences in C:N ratio, Total Solids (TS), Total P (TP), Organic P (OP), and

Ca. Conversely, differences in bacterial community structure in the aerobic evaporation

pond were mainly related to differences in pH, ammonium nitrogen, Mg and K.

A second biplot (Figure 3.4b) was constructed using the individual taxa scores

(phylogenetic identities of the taxa are shown in Table 3.3 to assess the contribution of

individual taxa (▲) to the scatter seen in Figure 3.4a. This enabled the key components

of the bacterial communities responsible for driving waste degradation process to be

identified. High taxa scores (length of the arrow) indicated the importance of

environmental variables in determining the community composition whilst the angle

between arrows indicates the degree of correlation between the variables. The position

of points (taxa) relative to the arrows indicates the environmental conditions at each

sample site. Thus, there were marked changes in the relative abundance of some

bacterial taxa between waste treatment points whose distributions and responses were

particularly closely correlated with the environmental conditions of those waste

treatment points (Figure 3.4b, Table 3.3).

Some taxa, whose distributions and responses were particularly closely correlated with

the environmental variables (pH, NH4+, Mg and K) within the evaporation pond were:

Aeromonadaceae (#45), Flavobacteriia (#14), Fluviicola (#13), Xanthomonadaceae

(#48), Rhodocyclaceae (# 40), Actinomycetales (#1), Microbacteriaceae (#2),

Agrococcus (#3), Candidatus (#4), Leucobacter (#5), Pedobacter (#15),

Erysipelotrichaceae (#30), OD1 (#32), TM6 (#54), Alcaligenaceae (#37) and TM7-1

(#55).

Distribution and response of Bacteroides (#8), TM7-3 (#56), Porphyromonadaceae

(#10), Lachnospiraceae (#24), Comamonadaceae (#38), Pseudomonadaceae (#47)

were closely correlated to the environmental variables of CAP-Outlet. The other taxa

presented in Table 3.3 were distributed amongst the pits, holding tank, CAP-Bottom,

CAP-Top where TP, OP, TS, C:N ratio and Ca were higher.

3.3.4 Whole-genome-shotgun sequencing

Phylogenetic reconstruction, based upon functional gene phylogenies within MGRAST,

revealed that community DNA within the different stages of the piggery waste treatment

process was dominated by bacterial DNA (97.2%- 82.2%) followed by Archaeal (0.4%-

16.6%) and Eukaryotes (0.3%-0.2%) (Figure 3.5a). Furthermore, metagenome analyses


58

indicated that considerably higher DNA was attributed to the archaea in the CAP-top

(6.8 %) and CAP-bottom (15.6%), with Methanomicrobia being more prevalent and, in

general, dominated by members of the Methanosarcinaceae (data not shown).

Table 3.3 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for different stages of the piggery waste treatment system.

Codes Taxa Codes Taxa1 Actinobacteria; Actinomycetales 32 Bacteria; OD12 Actinobacteria; Microbacteriaceae 33 Alphaproteobacteria;Other3 Actinobacteria; Agrococcus 34 Alphaproteobacteria; BD7-34 Actinobacteria; Candidatus Aquiluna 35 Alphaproteobacteria; Acetobacteraceae5 Actinobacteria; Leucobacter 36 Alphaproteobacteria; Rickettsiales6 Bacteroidetes; Bacteroidales 37 Betaproteobacteria; Alcaligenaceae7 Bacteroidetes; Bacteroidaceae 38 Betaproteobacteria; Comamonadaceae8 Bacteroidetes; Bacteroides 39 Betaproteobacteria; MWH-UniP19 Bacteroidetes; Marinilabiaceae 40 Betaproteobacteria; Rhodocyclaceae10 Bacteroidetes; Porphyromonadaceae 41 Deltaproteobacteria; Geobacter11 Bacteroidetes; Rikenellaceae 42 Deltaproteobacteria; Syntrophaceae12 Bacteroidetes; SB-1 43 Deltaproteobacteria; Syntrophus13 Bacteroidetes; Fluviicola 44 Epsilonproteobacteria; Helicobacteraceae14 Bacteroidetes; Flavobacteriia 45 Gammaproteobacteria; Aeromonadaceae15 Bacteroidetes; Pedobacter 46 Gammaproteobacteria; Methylomonas16 Chlorobi; OPB56 47 Gammaproteobacteria; Pseudomonadaceae17 Chloroflexi; Dehalococcoidaceae 48 Gammaproteobacteria; Xanthomonadaceae18 Cyanobacteria 49 Spirochaetes; PL-11B1019 Fibrobacteres 50 Spirochaetes; Sphaerochaeta20 Firmicutes; Lactobacillales 51 Spirochaetes; Treponema21 Firmicutes; Clostridiales 52 Synergistetes; Dethiosulfovibrionaceae22 Firmicutes; Clostridium 53 Synergistetes; Synergistaceae23 Firmicutes; Proteiniclasticum 54 TM624 Firmicutes; Lachnospiraceae 55 TM7; TM7-125 Firmicutes; Ruminococcaceae 56 TM7; TM7-326 Firmicutes; Syntrophomonas 57 Tenericutes;Acholeplasmataceae27 Firmicutes; Veillonellaceae 58 Tenericutes; Mollicutes28 Firmicutes; Mogibacteriaceae 59 Tenericutes; ML615J-2829 Firmicutes; Tissierellaceae 60 Verrucomicrobia;Puniceicoccaceae30 Firmicutes; Erysipelotrichaceae 61 Verrucomicrobia; Pedosphaerales31 Lentisphaerae; Victivallaceae 62 WWE1; Cloacamonales


59

Figure 3.5 Community DNA composition of the piggery waste treatment process based upon functional gene phylogenies (a) Microbial community composition obtained by taxonomic identity linked to functional gene content by MG-RAST analysis (b).

Bacterial identity derived from functional gene phylogenies (Figure 3.5b) further

supported the stability of the CAP system and revealed that Clostridia, Bacteroidia,

Actinobacteria, Deltaproteobacteria, Bacilli, Gammaproteobacteria, Flavobacteriia,


60

Alphaproteobacteria, Cytophagia, and Betaproteobacteria, were the 10 most abundant

bacterial groups within the CAP-Bottom. In contrast, Betaproteobacteria,

Actinobacteria, Gammaproteobacteria, Alphaproteobacteria, Clostridia, Bacilli,

Deltaproteobacteria, Flavobacteriia, Sphingobacteriia, and Bacteroidia were more

prevalent community members in the evaporation pond.

The diversity of the bacterial community profile derived by assignment of protein-

encoding genes via metagenomic analysis was closely matched with PCR-based 16S

rRNA diversity. However, their relative abundance varied markedly between the two

methods. For example, comparison of relative abundance using the two methods

indicated that the diversity of the bacterial profile was similar but their relative

abundance considerably varied between for both CAP-Bottom (anaerobic pond) and

evaporation pond (aerobic pond) (Table 3.4).

Table 3.4 Comparison of relative abundance (%) of the top 10 most abundant bacterial groups within the CAP-Bottom and evaporation pond as revealed by tag sequencing and metagenomic analyses.

PCR dependant tag sequencing PCR independent (metagenomic) CAP-Bottom

Bacteroidia 40.3 Clostridia 18.1 Clostridia 18.5 Bacteroidia 15.2 Synergistia 11.7 Actinobacteria 6.6 WWE1; [Cloacamonae] 9.2 Deltaproteobacteria 5.7 Deltaproteobacteria 2.0 Bacilli 5.0 Mollicutes 1.9 Gammaproteobacteria 4.9 Gammaproteobacteria 1.7 Flavobacteriia 3.6 Epsilonproteobacteria 1.6 Alphaproteobacteria 2.4 RF3 1.3 Cytophagia 2.0 Erysipelotrichi 1.2 Betaproteobacteria 1.9

Evaporation Pond Actinobacteria 50.7 Betaproteobacteria 39.6 Betaproteobacteria 19.9 Actinobacteria 27.1 Erysipelotrichi 8.2 Gammaproteobacteria 10.0 TM7-1 5.1 Alphaproteobacteria 6.3 Gammaproteobacteria 2.9 Clostridia 2.3 Sphingobacteriia 1.5 Bacilli 1.8 Flavobacteriia 1.3 Deltaproteobacteria 1.7 Alphaproteobacteria 0.9 Flavobacteriia 1.3 Clostridia 0.7 Sphingobacteriia 0.8 OD1 0.7 Bacteroidia 0.7


61

3.3.5 Hierarchical classification analysis of functional genes

Distribution of metabolic functions showed that most of the piggery microbiome linked

to known subsystems, including metabolism of organic macromolecules such as

proteins, carbohydrates and nucleic acids (Table 3.5). Clustering-based subsystems was

the most abundant SEED subsystem representing 14.6 %-16.1% of the total sequences

amongst the different stages of piggery waste process followed by genes associated with

protein metabolism (10.8-12.0%), carbohydrates (9.3-11.1%), miscellaneous (7.4-

7.7%), amino acids and derivatives (7.1-8.3%), DNA metabolism (6.0-6.4%) and RNA

metabolism (4.8-5.4%) were the most abundant metabolic functions in these systems

(Table 3.5). The high levels of genes associated with protein and nucleic acids

metabolism suggested that the cells in piggery waste were highly active. Carbohydrate

metabolism was particularly elevated in the CAP-Bottom compared to other points of

the waste treatment system, indicating that breakdown of carbohydrate is a predominant

function within CAP-Bottom.

Table 3.5 Metabolic profiles based upon metagenomic functional classification within

different compartments of waste treatment process

Metabolic potential Pits Holding Tank

CAP-Bottom

CAP-Top

CAP-outlet

Evaporation Pond

Clustering-based subsystems 16.0 16.1 14.6 16.0 16.2 15.6Protein Metabolism 10.8 12.0 10.8 11.7 12.0 10.6Carbohydrates 9.3 9.8 11.1 10.5 9.8 9.3Miscellaneous 7.4 7.7 7.6 7.5 7.5 7.4Amino Acids and Derivatives 7.1 7.7 8.3 8.2 8.2 8.6DNA Metabolism 6.4 6.7 6.1 6.0 6.1 5.0Cofactors, Vitamins, Prosthetic Groups, Pigments 5.4 5.1 5.9 5.3 5.0 6.9RNA Metabolism 5.2 5.4 4.8 5.0 5.0 4.4Phages, Prophages, Transposable elements, Plasmids 4.4 2.2 1.9 2.2 2.4 3.3Nucleosides and Nucleotides 3.5 3.3 3.5 3.5 3.5 3.5Cell Wall and Capsule 3.4 3.7 3.4 3.3 3.5 3.6Virulence, Disease and Defense 3.0 2.7 2.7 2.6 2.8 2.5Membrane Transport 2.9 2.7 2.9 3.1 3.1 2.6Respiration 2.5 2.8 3.6 3.0 2.8 3.1Stress Response 2.5 2.4 2.2 2.3 2.3 2.3Fatty Acids, Lipids, and Isoprenoids 2.1 1.9 2.0 1.9 1.9 2.5Cell Division and Cell Cycle 1.6 1.8 1.7 1.7 1.7 1.7Regulation and Cell signaling 1.2 1.1 1.2 1.2 1.2 1.2Metabolism of Aromatic Compounds 0.9 0.7 0.7 0.6 0.6 0.9Nitrogen Metabolism 0.9 0.9 1.1 1.0 0.9 1.4Sulfur Metabolism 0.8 0.7 0.9 0.7 0.7 0.7Iron acquisition and metabolism 0.7 0.4 0.6 0.4 0.4 0.3Phosphorus Metabolism 0.6 0.7 0.8 0.7 0.7 1.0Motility and Chemotaxis 0.6 0.5 0.6 0.5 0.6 0.4Dormancy and Sporulation 0.4 0.4 0.3 0.4 0.4 0.2Secondary Metabolism 0.3 0.3 0.4 0.3 0.3 0.4Potassium metabolism 0.2 0.2 0.3 0.2 0.2 0.2Photosynthesis 0.1 0.0 0.0 0.1 0.1 0.3


62

3.3.6 Distribution of metabolic functions in relation to P cycling

Community shotgun metagenomic analysis was used to identify the functionality of

microorganisms involved in P cycling in the each compartments of the piggery

wastewater treatment process. Functional assignments of P metabolisms were

conducted through the SEED subsystems of the MG-RAST, using an e-value of

minimum 1 e-5. The analyses showed that genes linked to P metabolism ranged among

the compartments from 0.6 % of the total sequences to 1.0 % in the pits and evaporation

pond respectively (data not shown). Putative genetic potential of P metabolisms in terms

of P mineralisation, P solubilisation, and polyP accumulation was assessed based on the

enrichment of the functional gens involved in these P transformation pathways by

assigning functional annotations to shotgun metagenomic sequences.

3.3.6.1 Distribution of metabolic functions in relation to P mineralisation

The genetic potential for P metabolism showed enrichment of genes involved in

assimilation and regulation of phosphatase metabolism within the piggery waste

treatment process (Table 3.6). Mainly the abundance of gene sequence numbers for

alkaline phosphatase, which is the primary enzyme responsible for P mineralisation,

was relatively higher in CAP-Bottom followed by CAP-Top, holding tank, pits and

evaporation pond stages. The alkaline phosphatase is regulated by PHO regulon and the

PHO regulon is central to assimilation of phosphate and regulation of phosphate

metabolism. Metagenomic analysis of the piggery waste treatment process revealed the

presence of a number of PHO regulon such as phosphate starvation-inducible genes

(PhoR, PhoB, Pst, Pit). This suggests that the piggery waste treatment stages are

fluctuating in their activity for the assimilation and regulation of phosphate metabolism.

The alkaline phosphatase gene sequence number was assessed to compare the

fluctuation of P mineralisation physiology within different compartments (Figure 3.6a).

The alkaline phosphatase gene sequence number fluctuated within the compartments

and corresponding organic P concentration fluctuated in the similar fashion. This

implies that P mineralisation was gradually increased during the early stage of waste

degradation and reached to the maximum at CAP-Bottom where the highest organic P

was available. Thereafter, the P mineralisation gradually decreased towards the end of

the waste treatment where the lowest level of organic P was available.


63

Table 3.6 P mineralising potentials at the different stages of the piggery waste treatment process.

Sample point function Abundance # hitsPits Phosphate starvation-inducible protein PhoH 71 49

Alkaline phosphatase (EC 3.1.3.1) 48 31Predicted ATPase related to phosphate starvation-inducible protein PhoH 48 24Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 40 33Phosphate regulon transcriptional regulatory protein PhoB (SphR) 40 32Phosphate transport system regulatory protein PhoU 23 18Phosphate transport regulator (distant homolog of PhoU) 14 12Phosphate starvation-inducible ATPase PhoH with RNA binding motif 10 9Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 4 3

Holding Tank Phosphate starvation-inducible protein PhoH 93 55Alkaline phosphatase (EC 3.1.3.1) 65 44Phosphate regulon transcriptional regulatory protein PhoB (SphR) 60 41Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 45 40Exopolyphosphatase (EC 3.6.1.11) 39 21Phosphate transport system regulatory protein PhoU 33 20Phosphate transport regulator (distant homolog of PhoU) 21 12Alkaline phosphatase like protein 19 5response regulator in two-component regulatory system with PhoQ 4 4Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 3 3

CAP-Bottom Alkaline phosphatase (EC 3.1.3.1) 137 48Phosphate starvation-inducible protein PhoH 101 62Phosphate starvation-inducible protein PhoH, predicted ATPase 101 62Predicted ATPase related to phosphate starvation-inducible protein PhoH 88 29Phosphate transport system regulatory protein PhoU 79 28Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 60 44Phosphate regulon transcriptional regulatory protein PhoB (SphR) 55 52Phosphate transport regulator (distant homolog of PhoU) 33 14Alkaline phosphatase like protein 16 4Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 8 8PhoQ 6 6

CAP-Top Phosphate starvation-inducible protein PhoH 139 75Phosphate starvation-inducible protein PhoH, predicted ATPase 139 75Phosphate transport system regulatory protein PhoU 84 34Phosphate regulon transcriptional regulatory protein PhoB (SphR) 77 65Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 68 50Alkaline phosphatase (EC 3.1.3.1) 66 28Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 11 8PhoQ 9 7Alkaline phosphatase like protein 8 5response regulator in two-component regulatory system with PhoQ 7 5secreted alkaline phosphatase 2 1

CAP-Outlet Phosphate starvation-inducible protein PhoH 109 57Phosphate transport system regulatory protein PhoU 49 27Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 46 34Phosphate regulon transcriptional regulatory protein PhoB (SphR) 44 36Predicted ATPase related to phosphate starvation-inducible protein PhoH 33 19Alkaline phosphatase (EC 3.1.3.1) 24 21Phosphate transport regulator (distant homolog of PhoU) 15 13Phosphate starvation-inducible ATPase PhoH with RNA binding motif 12 10Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 7 7

Evaporation pond Predicted ATPase related to phosphate starvation-inducible protein PhoH 254 66Phosphate regulon transcriptional regulatory protein PhoB (SphR) 151 51Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 111 43Phosphate starvation-inducible protein PhoH 108 50Phosphate transport system regulatory protein PhoU 84 28Alkaline phosphatase (EC 3.1.3.1) 14 13PhoQ 7 7Alkaline phosphatase synthesis transcriptional regulatory protein PhoP 1 1


64

Figure 3.6 Relationships between (a) the abundance of alkaline phosphatase gene involved in regulation of P mineralisation and the respective organic P concentration, and (b) the abundance of alkaline phosphatase gene and organic P concentration.

A positive correlation (R2= 85) was observed between organic P availability and

alkaline phosphatase gene sequence number (Figure 3.6b). This confirms that organic P

availability is one of the key drivers for the P mineralising capacity in the piggery waste

treatment process. Furthermore, abundance of those genes involved in phosphatase

metabolism and the presence of microorganisms assigning the alkaline PO4ase activity

suggests that both bacteria and archaea (mainly Methanosarcina sp.) play an important

role in the piggery waste treatment process during P mineralisation (data not shown).


65

The most abundant PMB identified using 16S rRNA Ion Tag sequencing of sorted cells

(Bacteroidales, Clostridiales, Campylobacterales, Synergistales) were also commonly

found in the metagenomic analysis of gene encoding for alkaline phosphatase

(Bacterioides, Parabacteroides, Flavobacterium, Clostridium, Desulfitobacterium).

3.3.6.2 Distribution of metabolic functions in relation to P solubilisation

Genes involved in the P solubilizing pathways (e.g. pqq genes involved in gluconic

acid, glucose dehydrogenase, malate dehydrogenase genes and genes involved in

production of acids) gave an indication of the putative function of mineral phosphate

solubilising microbial activity. Table 3.7 shows the relative abundance of some of those

potential genes within the piggery waste treatment process. Glucose dehydrogenase,

pqq-dependent (EC 1.1.5.2), the primary enzyme that governs the P solubilisation

pathway, was not detected except at very low abundance within the collection pits (7

copies), CAP-Bottom (2 copies) and outlet (1 copy). However, genes involved in

production of acids such as citrate synthase (si) (EC 2.3.3.1) and malate dehydrogenase

(EC 1.1.1.37) were commonly found at all the stages of the waste treatment with

markedly higher abundance in CAP-Bottom and the evaporation pond, suggesting that

P solubilisation could potentially occur in those stages. Apart from this, there was no

direct functional gene evidence to demonstrate categorically P solubilising capacity,

except for culture isolates obtained from tri calcium selective media.

Table 3.7 P solubilising potentials at the different stages of the piggery waste treatment process.

Sample function abundance # hitsPits Citrate synthase (si) (EC 2.3.3.1) 139 82

Malate dehydrogenase (EC 1.1.1.37) 95 51Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 7 4Gluconate dehydratase (EC 4.2.1.39) 11 9

Holding Tank Citrate synthase (si) (EC 2.3.3.1) 159 59Malate dehydrogenase (EC 1.1.1.37) 101 41

CAP-Bottom Citrate synthase (si) (EC 2.3.3.1) 226 79Malate dehydrogenase (EC 1.1.1.37) 135 48Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 2 2

CAP-Top Citrate synthase (si) (EC 2.3.3.1) 217 96Malate dehydrogenase (EC 1.1.1.37) 71 43

CAP-Outlet Citrate synthase (si) (EC 2.3.3.1) 139 65Malate dehydrogenase (EC 1.1.1.37) 46 31Glucose dehydrogenase, PQQ-dependent (EC 1.1.5.2) 1 1

Evaporation Pond Citrate synthase (si) (EC 2.3.3.1) 355 82Malate dehydrogenase (EC 1.1.1.37) 216 41


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3.3.6.3 Distribution of metabolic functions in relation to polyP accumulation

The abundance of genes of polyphosphate kinase and exopolyphosphatase, the primary

enzymes involved in polyP synthesis and degradation respectively, was assessed to

compare the activities of the polyP accumulation physiology within different

compartments (Figure 3.7). Enrichment of both polyphosphate kinase and

exopolyphosphatase was observed in the evaporation pond (aerobic pond) compared to

other stages (facultative anaerobic/anaerobic). This implies that under the aerobic

condition, poly P synthesis and degradation is highly active compared to the facultative/

anaerobic conditions.

Figure 3.7 Abundance of gene involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) at the different stages of the piggery waste treatment process.

High affinity Pst (phosphate specific transport) systems (PstA, PstB, and PstC) are

involved in the uptake and transport of Pi across the cytoplasmic membrane and

enrichment of those genes is an indication of phosphate assimilation to cells. The

genetic potential for P metabolism also showed enrichment of those genes in the

metagenome of piggery waste treatment process (Table 3.8). The higher abundance of

polyphosphate kinase and exopolyphosphatase further confirmed that assimilated

orthophosphate had subsequently converted to polyp granules. In general, the highest

community representation of genes that regulate polyP metabolism was found in the

evaporation pond. This implies that the reduction of orthophosphate at the evaporation


67

pond was due to polyP formation and not by just P assimilation inside the microbial

biomass.

Table 3.8 PolyP accumulating potentials at the different stages of piggery waste treatment process.

Pits function abundance # hitsPhosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 79 57Polyphosphate kinase (EC 2.7.4.1) 77 51Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 67 49Sodium-dependent phosphate transporter 62 31Probable low-affinity inorganic phosphate transporter 59 33Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 52 44Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 43 38Exopolyphosphatase (EC 3.6.1.11) 28 18

Holding Tank Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 126 87Sodium-dependent phosphate transporter 119 36Probable low-affinity inorganic phosphate transporter 113 40Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 107 63Polyphosphate kinase (EC 2.7.4.1) 91 45Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 86 54Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 83 43Exopolyphosphatase (EC 3.6.1.11) 39 21Low-affinity inorganic phosphate transporter 3 3

CAP-Bottom Probable low-affinity inorganic phosphate transporter 179 65Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 172 113Polyphosphate kinase (EC 2.7.4.1) 158 69Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 135 54Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 121 62Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 119 65Sodium-dependent phosphate transporter 103 38Exopolyphosphatase (EC 3.6.1.11) 59 17Low-affinity inorganic phosphate transporter 4 4

CAP-Top Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 179 111Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 161 69Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 158 66Sodium-dependent phosphate transporter 148 44Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 136 71Probable low-affinity inorganic phosphate transporter 114 36Polyphosphate kinase (EC 2.7.4.1) 110 55Exopolyphosphatase (EC 3.6.1.11) 45 17Putative periplasmic phosphate-binding protein PstS (Catenulisporaceae type) 1 1

CAP-Outlet Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 138 93Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 147 76Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 116 57Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 91 53Sodium-dependent phosphate transporter 142 36Probable low-affinity inorganic phosphate transporter 74 41Polyphosphate kinase (EC 2.7.4.1) 91 53Exopolyphosphatase (EC 3.6.1.11) 16 13

Evaporation Pond Pyrophosphate-energized proton pump (EC 3.6.1.1) 557 80Polyphosphate kinase (EC 2.7.4.1) 453 92Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 251 86Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 205 47Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 168 42Inorganic pyrophosphatase (EC 3.6.1.1) 155 38Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 137 38Exopolyphosphatase (EC 3.6.1.11) 127 40


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3.4 Discussion

3.4.1 Characterisation of microbial community composition and diversity in the

wastewater treatment process

This study identified the key components of the bacterial community involved in the

whole process of piggery waste degradation in a model covered anaerobic pond digester

system. Both 16S rRNA Ion Tag sequencing and metagenome analyses showed that

bacterial community composition of the initial facultative anaerobic stages (i.e. pits and

holding tank) and the covered anaerobic digester (i.e. CAP-Bottom and CAP-Top) was

relatively similar but remarkably varied to that of the aerobic stage (i.e. evaporation

pond) (Figure 3.3 and 3.5b). Physico-chemical characterisation (Table 3.1) and CCA

analysis (Figure 3.4) supported that both resource availability and environmental factors

between the anaerobic stages and the aerobic stage are the key drivers in shaping the

bacterial community dynamics among these compartments of piggery wastewater

treatment system.

16S rRNA Ion Tag sequencing indicated that the dominant bacteria within the

facultative anaerobic/anaerobic ponds were Bacteroidia, Clostridia, Cloacamonae, and

Synergistia, with considerable fluctuation in their abundance between compartments.

These taxa are commonly found in piggery waste treatment systems and other anaerobic

digesters (Cook et al. 2010; Patil et al. 2010; Talbot et al. 2010; Kampmann et al. 2012;

Supaphol et al. 2011; Whiteley et al. 2012) and known to participate in one or more of

stage of the AD process (hydrolysis, acidogenesis, acetogenesis) (Müller et al. 2010;

Supaphol et al. 2011). For example, previous studies have highlighted that the

Firmicutes phylum (Clostridia) is of significant importance in cellulose degradation

within biogas generating microbial communities. Further, members of the Bacteriodia

are common where degradable organic material is to be found and the Clostridia are

noted for their highly effective cellulose degradation potential (Wirth et al. 2012). It has

been reported that Clostridia are important components of the swine gut community and

play significant roles in fermenting lipids, sugars and amino acids (Zhu 2000).

Therefore, it appeared that the microbial communities in facultative anaerobic/anaerobic

stages are likely to be ubiquitous in all AD systems and play an important role in waste

degradation and biogas production.

In contrast to the facultative anaerobic/anaerobic stages, Proteobacteria and

Actinobacteria were the most abundant taxa in the aerobic stage, They are commonly


69

found in industrial wastewater evaporation ponds (Ben‐Dov et al. 2008) and industrial

waste gas biofilter systems (Friedrich et al. 2003). CCA analyses indicated that the

community composition in the evaporation pond is shaped mainly by specific

environmental conditions (pH, K, Mg, ammonium). Previously, factors such as pH,

Mg2+ and K+ were shown to be important for polyP formation (McGrath et al. 2001;

Günther et al. 2009). This together with the significant reduction of orthophosphate in

the evaporation pond suggests that the microbial community in the evaporation pond

may play an important role in P removal via polyP formation.

Using metagenomic analyses to reconstruct diversity assessments, a higher abundance

of archaea was observed in CAP-Bottom (16.7 %) and CAP-Top (8%) compared to the

other sampled compartments. A previous study has shown that about 10% of the

identified microbes in the biogas producing community belong to the Archaea (Wirth et

al. 2012) and, here, we can also assume that archaea play an important role in the

methanogenic biogas production in the CAP.

When comparing the 16S rRNA Ion Tag sequencing with phylogenies derived from

metagenomic analysis, apparent differences were observed between the bacterial

community compositions (Table 3.4). Nevertheless, there was reasonably good

agreement between the approaches with many of the same groups recovered in both the

PCR based and metagenome analyses, including all classes of Clostridia, Bacteroidia,

Actinobacteria, Gammaproteobacteria, Betaproteobacteria, Alphaproteobacteria,

Sphingobacteriia, and Flavobacteriia. However, the observed differences are likely to

be attributed to bias in the both methods (e.g. unequal amplification of 16S rRNA genes

of bacteria in PCR based sequencing). However, it is still premature to make

conclusions about similarity or dissimilarity of data between metagenomic and PCR-

based assessments for microbial diversity because few studies have directly addressed

the issue. While some studies have showed that these two approaches give largely

similar species profiles (Kalyuzhnaya et al. 2008a, 2008b) others have observed

significantly different community structures (Shah et al. 2011). With the availability of

low cost sequencing platform such as Ion Toront PGM, it is now possible to minimise

the any misinterpretation of microbial community analyses by generating co-incident

datasets for both MG-RAST phylogenetic based analyses and by extracting 16S rRNA

sequences directly from the metagenome (Whiteley et al. 2012).


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3.4.2 P mineralising and solubilising potential as revealed by the culture dependant

detection

P cycling potential was based on both taxonomic identity and functionality during the

waste treatment process. It was assessed by comparing sequences of isolates of P

mineralising bacteria (PMB) and P solubilising bacteria (PSB) with community

metagenome analyses. Genetic analyses of 16S rDNA sequences derived from the

isolated PMB and PSB revealed a high similarity with bacteria belonging to the genus

Pseudomonas, Enterobacter, Escherichia coli, Bacillus and Cronobacter. These

findings were consistent with previous studies, where the genera Pseudomonas,

Enterobacter and Bacillus have previously been identified as PMB (Rodríguez and

Fraga, 1999; Barik et al. 2001; Konietzny and Greiner 2004; Jorquera et al. 2008).

Members of the Bacillus spp. are also an important group in the mineralization of P in

aquatic and terrestrial environments (Hill et al. 2007) and play an important role in

organic P degradation (Kerovuo et al. 1998). Furthermore, the genera Pseudomonas,

Enterobacter and Bacillus have previously been identified as PSB (Chung et al. 2005;

Tao et al. 2008; Zhu et al. 2011). Therefore the finding of this study is in consistency

with the previous studies. However, these isolates only represented the minor taxa (<0.1

%) recovered from the PCR and metagenome approaches. The lower diversity observed

for PMB and PSB by culturing compared to diversity estimates obtained by PCR and

metagenomic approaches, reflects the inefficiency of culture methods to assess

functional diversity. Although culture-dependant techniques are useful for describing

the cultivable fraction of the population with defined physiologies (Whitehead et al.

2005), they often underestimate and/or bias total diversity estimates (Iannotti et al.

1982). For example, activities observed under culture conditions and in situ may not

reflect the same activity, as there are a large number of factors that affect phosphatase

activity (Barik et al. 2001).

3.4.3 Distribution of metabolic functions in relation to P mineralisation

Assessment of putative metabolic potential of P cycling in this study using

metagenomic analysis seemed to be a good approach for screening the P cycling genetic

potential in situ in the highly diverse environment of piggery wastewater. Putative

genetic potential of P metabolisms in terms of P mineralisation, P solubilisation, and

polyP accumulation was assessed based on the enrichment of the functional gens

involved in these P transformation pathways.


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The putative functional potential in relation to P mineralising indicated that activity in

CAP-Bottom was dominant compared to other stages where the availability of substrate

(organic P) for P mineralising bacteria was high. Alkaline phosphatase is regulated by

PHO regulon (Willsky and Malamy 1976) and the PHO regulon is central to

assimilation of phosphate and regulation of phosphate metabolism (Wanner and

Metcalf, 1992; Wanner, 1993). Considerable abundance of these genes was found at all

the stages of the piggery waste treatment system with a higher abundance in CAP-

Bottom and CAP-Top (Table 3.6). This was further supported by the observation of

higher abundance of Pi at the CAP-Top (ca. 21.8 mg/L) indicating that P mineralising

capacity in CAP digester was high. P mineralisation can be defined as the hydrolysis of

Pi from organic P or other complex P compounds (e.g. polyP), in which the hydrolysed

Pi is released outside the cells (Kloeke and Geesey 1999). This process is catalysed by

acid or alkaline phosphatases (Kloeke and Geesey 1999; Anupama et al. 2008). There

was a direct relationship between the number of alkaline phosphatase gene sequences

and organic P concentration in the piggery waste treatment system (Figure 3.6b). The

higher abundance of alkaline phosphatase gene sequences at the higher organic P

availability led to the hypothesis that a diverse P mineralisation bacterial community

can be found in piggery wastes which are characteristically high in organic P which

acts as a substrate for P mineralising microorganisms.

Uncovering the taxonomic identity of PMB is important for enhancing P mineralisation

in the CAP digester, so that low carbon digestates can be produced for removal of

soluble P post processing (within the evaporation pond) via Enhanced Biological P

Removal as polyP accumulation. Other culture independent techniques, i.e Enzyme

labelled fluorescence (ELF) labelling of single cells of PMB, coupled with rapid flow

cytometric analyses, cell sorting and next generation sequencing approaches to assign

phylogenetic and functional affiliations of PMB in the piggery waste treatment system

were investigated in Chapter 4.

3.4.4 Distribution of metabolic functions in relation to polyP accumulation

The community polyP accumulation potential was assessed in terms of the abundance

of genes involved in polyP formation to understand the capacity to enhance biological P

removal during the piggery wastewater treatment process. A remarkable enrichment of

Polyphosphate kinase and exopolyphosphatase was observed in the evaporation pond

(aerobic pond) compared to other stages (facultative anaerobic/ anaerobic). This verified

that the evaporation pond is a considerable site of activity for polyP formation.


72

In general, polyP formation favours carbon-rich, strictly anaerobic conditions, followed

by carbon-poor, aerobic incubation (De-Bashan and Bashan 2004). These data

suggested that wastewater in the evaporation pond has already undergone suitable

anaerobic processing which meets carbon-low, aerobic conditions for effective polyP

accumulation within the evaporation pond. The reduction of the Pi level in the

evaporation pond (12.2 mg/L), compared to the outlet of the CAP digestion (20.5

mg/L), verifies that a considerable degree of Pi immobilisation happens within the

evaporation pond. This could be either due to the consumption of Pi for cellular

requirements or to accumulation of Pi as polyP inside the microbial population.

Enrichment of polyphosphate kinase verified that the reduction of orthophosphate is

mainly due to the formation of polyP granules inside the microbial biomass and not

solely due to the consumption of Pi for cellular requirements.

The reduction of Pi in the evaporation pond down to 12.2 mg/L under this natural

condition is not sufficient to recycle the treated waste with the irrigation waste as a

liquid fertiliser, especially for sandy soils in south-western Australia where Pi leaching

and subsequent Pi pollution of water bodies can occur, and therefore EBPR technology

is required. Biological P removal process known as ‘enhanced biological P removal

(EBPR)’ is more efficient and economically viable (Majed 2011). There is evidence that

the EPBR process could be enhanced under acidic conditions (McGrath et al. 2001;

Mullan et al. 2002; Moriarty et al. 2006). Therefore, it was hypothesised that under

acidic conditions in a high inorganic P system there will be an increase in abundance of

polyP accumulating bacteria and a concomitant increase in polyphosphate

accumulation by these bacteria facilitating further reduction of Pi level in the

evaporation pond.

As polyP accumulation was dominant in the evaporation pond /aerobic pond and,

Chapter 5 investigated a mechanistic understanding of polyP accumulation dynamics,

and the organisms involved under acidic pH compared to the natural pH level in the

evaporation pond, by applying single-cell analyses coupled with next generation

sequencing approaches.

3.4.5 Distribution of metabolic functions in relation to P solubilisation

While polyP accumulation was predominant under aerobic conditions (evaporation

pond), and P mineralisation was high in anaerobic ponds (CAP-Bottom and CAP-Top),

there was no direct functional evidence of P solubilising activities in this system, apart


73

from the culture dependant identification of PSB. The main organic acids known to

solubilise P are gluconic, 2-ketogluconic, oxalic, citric, malic and succinic acid (Patel et

al. 2008; Panhwar et al. 2014). Only genes involved in the production of acids such as

citrate synthase (EC 2.3.3.1) and malate dehydrogenase (EC 1.1.1.37) were found in

this system and P solubilisation could potentially occurs in those stages. Evidence

obtained from P solubilising activity on the tri-calcium selective media confirmed that

Enterobacter sp. (facultative aerobes) and Pseudomonas aeruginosa (aerobic organism

or facultative anaerobe) were the main two groups that could potentially perform P

solubilising activities from a cultivable standpoint.

P solubilising bacteria have been previously isolated from mangroves which represent

an anaerobic environment (Vazquez et al. 2000). However, it has been predicted that

root oxygen translocation plays an important role in solubilizing phosphate by bacteria

near roots in mangroves where sediments are not always completely anoxic (Holguin et

al. 2001). This implies that oxygen translocation plays an important role in P

solubilisation and could be the reason a higher abundance of functional genes related to

P solubilising activities was not observed in the wastewater treatment plant where the

environment is completely anaerobic. Further studies are needed to confirm this. By

mimicking this, it is worth investigating the P solubilising activity in the sludge of an

aeration pond where the precipitated form of P is high (e.g. stuvite and hydroxyl apatite)

under oxygen bubbling condition.

3.4.6 Recycling potential of the piggery wastewater

Based on the physicochemical and microbial community characterisation, and the P

cycling potential, it appears that by-products arising from the CAP digester (CAP-

Bottom sludge) and the evaporation pond (pond bottom sludge and treated effluent)

could potentially enhance soil quality and crop productivity in terms of the nutritional

supply value, beneficial microbes, and lower risk of pathogenicity and heavy metal

content. Recycling these piggery by-products, could potentially introduce some

beneficial bacteria to the soil such as Actinobacteria, Bacteriodetes and Synergistetes

that have a role in nutrient recycling (Jenkins et al. 2009; Mclnerney et al. 2009).

Furthermore, those piggery wastewater samples did not contain significant quantities of

heavy metals and therefore soil contamination can be avoided following their

application to land. Any risk associated with recycling of piggery waste can be further

reduced using suitable management practices as discussed below.


74

The wastewater samples had relatively high EC levels which imply the possibility of

creating high salinity in the field. Accumulation of salt in soils could reduce the water

availability and limit plant growth (Munns and Termaat 1986; Munns 2002). Hence, it

is recommended that the wastewater is diluted before application as liquid fertiliser or

for direct irrigation to saline tolerant cultivars of cereals (e.g. barley, wheat).

The pH of the wastewater samples was slightly basic and could provide buffering

capacity in acid soils. High amounts of more soluble forms of N and P were found in

the piggery wastewater samples. However, any potential runoff or leaching can be

avoided by selecting appropriate application rates based on crop demand and avoiding

application at times when heavy rainfall is forecast.

The presence of pathogenic bacteria was relatively low with the anaerobic digestion and

has a great potential to recycle the by-product arising from piggeries (e.g. CAP-sludge

and treated wastewater). However, a number of bacteria involved in methane, nitrous

oxide and odour emission were found in some stages of the waste treatment process

(e.g. CAP-Bottom sludge). Therefore, it is recommended that aerobic conditions are

maintained during storage and land application to avoid greenhouse gas emission or

odour generation which occur largely under anaerobic conditions.

Any risk associated with recycling of piggery waste can be further reduced by

composting, pelletising and avoiding application at times when heavy rainfall is

forecast. If managed well, by-products could improve soil fertility by supplying

beneficial soil microorganisms and crops with plant nutrients either singly or in

combination with synthetic fertilisers thereby reducing reliance on fertilisers.

Furthermore, this study has shown that piggery waste by-products formed at different

stages of waste management can be high in both inorganic and organic P forms and

microorganism harbouring in piggery waste could potentially play an important role in

P-mineralisation, P-solubilisation, and P-immobilisation which are generally assumed to

be the main contributor of P turnover in soils. This led to the hypothesis that pelletised

piggery compost at low rates in combination with inorganic fertiliser in the root zone of

wheat facilitates nutrient uptake by plant roots in a P deficient agricultural soil, alters

the abundance and community composition of bacterial involved in increasing P

availability in soil, and enhances plant growth. Chapter 6 examined this hypothesis and

using application of pelletised piggery compost to soil and its impact on plant growth

promotion, soil nutrient improvement, and changes in bacterial and fungal community

composition.


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3.5 Conclusions

This study identified the key microbial community composition in stages of waste

degradation and their putative metabolic potential in terms of P transformation within a

covered anaerobic pond system treating piggery waste. Bacterial community

composition was spatially distributed among the different stages of piggery waste

treatment process and there were clear shifts in bacterial community composition

between the anaerobic and aerobic stages. The piggery waste water treatment system

was dominated by both soluble and organic form of P. Therefore, P cycling potential in

terms of P mineralisation and polyP accumulation was highly evident. Finally,

microbial community can be manipulated in the piggery waste treatment system to

enable efficient P nutrient re-use. From the economic and environmental perspectives,

this knowledge can contribute to increasing the efficiency of recycling of P from

piggery waste. Reduction in piggery waste accumulation and minimisation of nutrient

leaching could help avoid eutrophication of water bodies.

Chapter 4: Phosphorus mineralising bacteria

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CHAPTER 4

Phosphorus Mineralising Bacteria for Nutrient Recovery from

High Phosphorus Piggery Wastewater Effluents

4.0 Abstract

Phosphorus mineralising bacteria (PMB) play an important role in phosphorus (P)

mineralisation or P regeneration within high P containing wastewater treatment such as

piggery waste remediation, but little is known of their diversity, abundance or activity in

these treatment systems. PMB are the cells that express phosphatase (PO4ase) activity.

Enzyme-labeled fluorescence (ELF) is a tool for detecting PO4ase activity, and thereby

P mineralisation at a single-cell level. Developing an integrated approach after coupling

the ELF-labeling technique with cell sorting and next generation sequencing methods

allowed enumeration and identify the active fraction of PMB expressing the phosphatase

(PO4ase) activity within the piggery waste remediation process. The ELF-labeling

protocol was optimised for flow cytometric detection of ELF-labeled cells in piggery

waste. A small fraction of total bacterial cells (5.5 %-0.3 % v/v) in wastewater samples

displayed PO4ase activity and the respective inorganic P (Pi) levels were high. Sorted

ELF-labeled cells were used for downstream 16S rRNA Ion Tag Sequencing to assign

phylogenetic identity of PMB. Sequence data revealed that ELF-labeled cells mainly

belonged to Bacteroidales followed by Clostridiales, Campylobacterales and

Synergistales, occupying stable community compositions, with differences in abundance

along the waste treatment process. Coincident single cell and population approaches

used in this study are promising for determining the taxonomic identity of active PMB in

piggery wastewater. Knowledge of the composition of the PMB microbial community

facilitates understanding of their function in P mineralisation and the development of

effective strategies for P management within high P containing wastes.


77

4.1 Introduction

With global population expected to reach between 8 and 10.5 billion by 2050, there is

increasing pressure to develop innovative wastewater treatment technologies for

effective bioenergy, water and nutrient recovery. Agricultural wastewaters arising from

cropping, livestock and meat processing are often high in phosphorus. Recently, new

low-cost anaerobic digestion systems offer the possibility for reduced odour and

greenhouse gas (GHG) emission, pathogen removal and generation of biogas but the

recovery of water and nutrients is largely unexplored. Physical separation methods

(membrane separation) are often economically unfeasible for smaller operations or low

P waste treatment systems. Enhanced biological P removal (EBPR) systems are

attractive low-cost alternative where phosphorus and carbon can be readily accumulated

and separated as polyphosphate (poly-P) and polyhydroxyalkanoates. Another possible

way of reducing high levels of Pi in this system is to reduce or control the rate of P

mineralisation or regeneration of Pi especially in the last stage of the piggery waste

treatment process. While considerable work has been done on EBPR (Blackall et al.

2002; Malamis et al. 2013), little is known about P mineralisation or Pi regeneration in

wastewater which could affect the overall efficiency or control of the P removal process

(Kloeke and Geesey 1999; Whiteley et al. 2002; Li and Chróst 2006).

P regeneration is the hydrolysis of Pi from organic or other complex P compounds (e.g.

polyphosphates), either in soluble or particulate forms, in which the hydrolysed Pi is

released from the cells (Kloeke and Geesey 1999). Microorganisms play an important

role in P mineralisation in both terrestrial and aquatic systems. Most of the biologically

mediated P transformations that occur during activated sludge processes are carried out

by bacteria (Kloeke and Geesey 1999). Organic-P mineralisation is mediated by PMB

via activity of phosphomonoesterase and phosphodiesterases (Anupama et al. 2008).

Phosphomonoesterases are classified as either alkaline (pH>7; EC 3.1.3.1) or acidic

(pH<6; EC 3.1.3.2) phosphatases depending upon their optimum pH (Kloeke and

Geesey 1999; Anupama et al. 2008). Phosphatase (PO4ase) is a unique extracellular,

hydrolytic enzyme which catalyses the hydrolysis of Pi from organic bound form of P

(Kloeke and Geesey 1999; Anupama et al. 2008). Therefore, the role of bacterial

phosphatase should also be considered for increasing the P removal efficiency from

wastewater. Phosphatase activity has been previously detected in activated sludge

(Lemmer et al. 1994; Kloeke and Geesey 1999; Li and Chróst 2006) and anaerobic

reactors (Whiteley et al. 2002; Anupama et al. 2008). Furthermore, PO4ase activity can


78

be used as a rapid biochemical test for detecting instabilities in anaerobic digesters

(Ashley and Hurst 1981; Zhenglan et al. 1990; Yamaguchi et al. 1991). Therefore,

monitoring PO4ase activity is an essential step for the development of cost-effective and

sustainable P removal systems from high P loaded wastes such as piggery wastewater.

To date, information about PO4ase activity in these systems is lacking, mainly due to

methodological limitations for detecting P mineralisation in a highly diverse

environment such as piggery waste. Consequently, PMB involved in P mineralisation in

piggery waste treatment processes are poorly characterised and little is known of their

diversity, abundance or activity, so it is difficult to fully optimise the process.

Moreover, microbes involved in P mineralisation, molecular mechanisms controlling

phosphorus metabolism, ecological interactions, and factors controlling mineralisation

process and rates in wastewater are poorly characterised (McMahon and Read 2013).

It may be possible to enhance P mineralisation and removal through targeted

management by manipulating the resident microbial pathways. It was expected that

advances in anaerobic digestion technologies and waste treatment that encompass the

manipulation of microbial community dynamics will enable facilitate nutrient re-use.

However, microorganisms involved must first be identified so that the environmental

conditions can be modified for effective management of waste treatment systems.

Recent improvements in next generation sequencing, including the use of the low-cost

Ion Torrent Personal Genome Machine (PGM), provides high throughput analysis of

community structure and function linking microbial ecology with process stability and

efficiency (Whiteley et al. 2012). Enzyme-labeled fluorescence (ELF) is a useful tool for

detecting PO4ase activity, and therefore the detection of P mineralisation at the single-

cell level. Combined with flow cytometry and cell sorting approaches followed by

downstream molecular sequencing of sorted phosphatase active cells, this would enable

more precise assessment of the identity and function of PMB.

It was hypothesised that a diverse and highly abundant P mineralising bacterial

community can be found in piggery wastes which are characteristically high in organic

P substrate. The aim was to quantify the abundance, diversity, and putative metabolic

potential of P mineralising bacteria (the fraction of cells that expressed phosphatase

activity) during the pig waste treatment process by developing an integrated approach

using the enzyme-labeled fluorescence technique coupled with epi-fluorescence

microscopy, cell sorting, and next generation sequencing (16S rRNA Ion Tag

sequencing and community metagenomics). The investigation also sought to understand


79

and identify the key mechanisms, anaerobic digestion pathways and microorganisms

involved. A greater understanding of microbial P cycling and the factors that regulate it

in wastewater treatment will assist in capturing P from piggery wastewater for use as

fertiliser.

4.2 Materials and Methods

4.2.1 Field sample collection and preparation

Piggery waste samples were collected from waste treatment tanks at Medina Research

Station as described in Chapter 3 (see Table 3.1 for physical and chemical characteristics

of the different piggery waste treatment compartments). Samples for ELF were prepared

immediately after sampling from the field in Spring 2012. In order to facilitate the

filtration process, effluent wastewater samples (500 mL) were consecutively filtered

through 100 µm, 60 µm and 3 µm mesh filters, and the final extract was used for

analysis.

4.2.2 Culture conditions and ELF staining

Effective PMB previously isolated and taxonomically identified from different stages of

waste samples (Chapter 3) were used as positive controls (Pseudomonas sp.), while E.

coli K12 was used as a negative control to assess the PO4ase activity using ELF®97

phosphate. These strains were grown in P-limited conditions by reducing Na-phytate

level up to ¼ (i.e. 0.5 g/L) of its initial value (i.e. 2 g/L) in PSM liquid medium. Cultures

were grown in autoclaved 250 mL filter cap cell culture flasks in 50 mL P-limited PSM

at 27oC and 150 rpm in a rotary incubator until the cell number reached to 108. P

mineralising ability was tested on agar medium containing phytate, which was used for

selective isolation of PMB in Chapter 3 (Kerovuo et al. 1998).

For ELF-P staining, an aliquot (1 mL) of culture flask was taken and centrifuged at 3000

x g for 5 min to form a cell pellet (performed in triplicates). The pellets were washed 3

times with autoclaved distilled water by centrifuging at 3000 x g for 5 min. ELF staining

was done after some modifications to the previously described method (Duhamel et al.

2008). Briefly, cell pellets were incubated with 100 µL of ELF-P (ELF®97 phosphate,

Invitrogen, E6589, 20 µM final concentration) in the dark for 2 h at room temperature

and ELF activity was stopped by adding 4 % (w/v) paraformaldehyde. The fixed cells

were washed with phosphate buffer saline (PBS) and then with distilled water. Fifty

microliter (50 µL) of cells was placed in the middle of a microscope slide and the cells


80

were air dried. Just prior to the microscopic examination the slides were stained with

DAPI (2.5 µg/µL) and cells were visualised at x100 magnification using a Zeiss

Axioplan epi-fluorescence microscope under UV excitation (DAPI filter block) and blue

light (ELF).

4.2.3 Optimisation of incubation time necessary for ELF labeling

The incubation time necessary to reach a stable percentage of ELFA-labeled cells was

checked by labeling piggery effluent with ELF-P substrate over an incubation period of

210 min. A 1 mL aliquot of each filtered piggery effluent sample was placed in 2 mL

Eppendorf tubes and centrifuged at 16,000 x g for 20 min to have a concentrated cell

pellet. To find the optimum incubation time for ELF-labeling of field samples, cell

pellets were incubated with 100 µL of ELF-P (ELF®97 phosphate, Invitrogen, E6589, 20

µM final concentration) in the dark for a series of time intervals (0 min to up to 210 min)

at room temperature. The reaction was stopped by adding 4 % (w/v) paraformaldehyde

(PFA) overnight at 4 oC and percentage of ELF+ve cells were analysed using flow

cytometry. Based on the results, 2 h of incubation time for ELF-labeling was chosen for

the rest of analysis.

4.2.4 Field sample preparation for epi-fluorescence microscopy

A 1 mL aliquot of each filtered piggery effluent sample was placed in 2 mL Eppendorf

tubes and centrifuged at 16,000 x g for 20 min to concentrate cells. Incubation of

ELF®97 phosphates and preparation of slides for the epi-fluorescence microscopic

analysis was as described in Section 4.2.2. Slides were stained with DAPI (2.5 µg/µL),

PI (1 µg/µL) or SYTO9 (1 µg/µL) and placed in the dark for 10 min. The slides were

then rinsed with autoclaved distilled water and air dried. Cells were visualised at x100

magnification using the Zeiss Axioplan epi-fluorescence microscope.

4.2.5 Field sample preparation for flow cytometry

Piggery waste effluent sample preparation for flow cytomery is shown in Figure 4.1. For

each samples, positive and negative controls were used to gate ELFA-labelled bacteria

(ELF+) from negative bacteria (ELF-) and also avoid any background noise. The final

batches include (1) unstained control cells (cells not stained with ELF or DNA-binding

dyes), (2) single stained control cells (cells stained with only ELF), (3) single stained

control cells (cells stained with only DNA-binding dye), and (4) dual stained control

cells (cells stained with both ELF and DAPI/PI/SYTO9). As for the abiotic controls, a


81

filtered piggery waste effluent sample was fixed with 4 % (w/v) paraformaldehyde prior

to ELF staining.

Figure 4.1 Preparation of the piggery waste effluent samples for flow cytometry.

Before flow cytometric analysis, overnight fixed samples were passed through 3 µm

filter mesh to prevent cell clumps which could otherwise congest the BD Influx. Each

sample was diluted 10 times and DAPI staining was applied where necessary (2.5


82

µg/µL), and then incubated for 15 min prior to flow analysis. Other than DAPI, PI (1

µg/µL) and SYTO9 (1 µg/µL) were evaluated to find the most suitable dyes in

separating ELF+ cell clusters from the ELF- cells. Analysis was carried out on a BD

Influx cell sorter at the Centre for Microscopy, Characterisation and Analysis (CMCA)

at The University of Western Australia. The BD Influx cell sorter was equipped with

355 nm, 488 nm, 561 nm and 640 nm excitation lasers, 12 fluorescence parameters, and

a small particle detector for the detection of particles with sizes of 200 nm – 60 µM, and

allows simultaneous separation of up to four pure populations from a heterogeneous

suspension sample at a speed of up to 90,000 cells per second. ELF and DAPI were both

excited by UV and were separated by using different filter configurations (Figure 4.2).

ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters.

DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green

lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.

Dual stained samples (cells stained with both ELF and DAPI/PI/SYTO9) were used in

triplicate to quantify ELF+ cells (%) in each stage of the piggery waste treatment

process.

4.2.6 Cell sorting

For cell sorting, drop drive frequency was set at approximately 27 kHz, 3 drops were

simultaneously deflected, and droplet delay was set between 12 and 15. PBS was used as

a sheath fluid. Sorting criteria were defined as gating ELF+ and ELF- microbial

communities. ELF+ cells were sorted after keeping the flow rate lower than 3000 events

per second to avoid doublets and cell clumps. Triplicates of each sample, used for the

aforementioned flow analysis, were pooled and minimum of 50,000 ELF+ cells were

sorted in 100 µL of PBS, and then stored at -20oC until the extraction of DNA.

4.2.7 Data analysis

Flow cytometry data were analysed using Flow Jo software (version 7.6.5). Three

replicates per sample were taken for each analysis. Compensation was applied where

necessary to avoid the fluorescence spillover (spectral overlap) between ELFA and

DNA binding dyes. Data are presented as mean ± standard deviation. ANOVA was

performed using the Statistical Analysis System (SAS) version 9.2 software package

(SAS Institute, Inc. Cary, NC, USA). Means were separated using least significant

difference (LSD) at 5 % significance level.


83

Figure 4.2 Emission and excitation spectrums of ELF, DAPI and PI and filter settings for the Flow Cytometry (BD Influx). ELF97 was excited by 355nm UV laser, and detected using 550LP and 585/29BP filters. DAPI, Syto9 and PI were excited by UV 355nm, 488nm Blue and 561nm Yellow-Green lasers and emission collected with 450/50BP, 520/15BP, 670/30BP filters respectively.

4.2.8 DNA extraction and 16S rRNA Ion Tag sequencing

Sorted ELF+ve cells were pre-treated prior to DNA extraction to facilitate the DNA

extraction process from the PFA fixed cells. The sorted cells were concentrated by

centrifuging them at 16,000 x g for 20 min, and removing the supernatant carefully. The

remaining cells in the Eppendorf tubes were pre-incubated with freshly prepared 10 %

SDS (70 µL) plus 20 mg mL-1 proteinase K (10 µL) for 1 hr at 65oC inside a waterbath.

The treated cells were used for DNA extraction using the MoBio UltraClean® Microbial

DNA Isolation Kit (Geneworks, Australia), utilising beat beating and column

purification, in accordance with the manufacturer’s guidelines. The extracted DNA was

quantified and checked for its purity at A260/280 nm (Nanodrop, Thermofisher

Scientific, USA) prior to its storage at -20oC. A nested PCR approach was used to

amplify 16S rRNA genes due to low target abundance of the DNA extract. The first

round PCR was performed using pA and pH primers and the PCR conditions described

in 2.2 section. One microliter (1 μL) aliquot from the first round PCR was used as the

template for the second round PCR. The second round PCR was performed using Golay

barcode and Ion Torrent adapter modified core primers 341F and 518R (Whiteley et al.

2012). Following the amplification, all PCR products were checked for their sizes and

specificities by electrophoresis on 2.5% w/v agarose, gel purified and adjusted to 10

ng/μL using molecular grade water and pooled equally for subsequent sequencing. The


84

sequencing was performed on a PGM (Life technologies, USA) using 200 base-pair

chemistry as described in Whiteley et al. (2012). Analyses of the taxonomic identity and

abundance were done as described in the Chapter 3 (section 3.2.4).

4.3 Results

4.3.1 Assessment of PO4ase activity of pure cultures using ELF®97 phosphate

The tested positive controls (Pseudomonas sp.) formed clear zones around the colonies

(Figure 4.3a) proving their ability to mineralise organic P in the selective medium. In

contrast, no colonies of the negative control (E. coli K 12) formed clear zones on the

selective medium (Figure 4.3b) confirming their inability to mineralise organic P in the

selective medium. Also, the tested positive controls exhibited ELFA fluorescence when

the cells were grown in phosphate depleted medium, whereas no signals were detected

for the negative controls (data not shown). Epi-fluorescence microscopic images of

DAPI stained cells (blue) show the total cells of Pseudomonas sp. (Figure 4.3c), whereas

ELFA stained cells (yellow/green) show the ELF+ cells of Pseudomonas sp. (Figure

4.3d). Comparison of the epi-fluorescence microscopic images of DAPI stained cells of

Pseudomonas sp. (Figure 4.3c) and that of ELFA stained cells (Figure 4.3d) shows that

nearly all of the cells grown in P-limited conditions expressed PO4ase activity. These

data indicated the successful detection of ELFA stained cells of ELF positive cultures at

the single cell level.

4.3.2 Optimisation of incubation time necessary for ELF-labeling

The incubation time necessary to reach a stable percentage of ELFA-labeled cells within

the wastewater samples was checked by labelling piggery effluent with the ELF-P

substrate over an incubation period of 210 min at room temperature. Percentages of

ELFA-labeled cells linearly increased with incubation time (Figure 4.4), until reaching

an approximate plateau after the 120th min of incubation. Statistical differences between

samples incubated for different times (P < 0.05) were tested and no significant

difference was found after 120 min. Therefore, 120 min of incubation period was

adopted for ELF-labeling of the targeted piggery waste effluents.


85

Figure 4.3 The Pseudomonas sp. (positive strain of P mineralization) grown in the P-limited PSM liquid medium was able form clear zone around the colonies on PSM solid medium confirming their ability to mineralise organic P in the selective medium (a) whereas no E. coli (negative strain of P mineralization) colonies appeared on PSM solid medium (b). Epi-fluorescence microscopic images of DAPI stained cells of Pseudomonas sp. grown in P-limited PSM liquid medium (c) and that of ELFA stained cells (d).


86

Figure 4.4 Ratio of ELF-labeled cells (%) with respect to the incubation time (min). Error bars represent the standard deviation between triplicate measurements.

4.3.3 Optimisation of dual staining protocol for epi-fluorescence microscopic and

flow cytometric detection of ELFA-labeled cells

ELF-labeled piggery waste samples were examined for the presence of ELFA

precipitates using epi-fluorescence microscopy to co-locate PO4ase activity at each

sampling point within the piggery waste treatment process. Three different DNA binding

dyes (DAPI/SYTO9/PI) with different excitations/emissions were tested to distinguish

ELF+ cells (ELFA-labeled cells) from ELF- cells (cells only stained by

DAPI/SYTO9/PI). Dual stained samples with ELF+DAPI/SYTO9/PI taken from CAP-

Top were compared (Figure 4.5a,b,c). ELFA crystals produced a green/yellow

fluorescence in areas where PO4ase activity had occurred. The cells that did not express

PO4ase activity were distinguished in blue (only stained by DAPI; Figure 4.5a), green

(only stained by SYTO9; Figure 4.5b), and red (only stained by PI; Figure 4.5c) colours

for dual strained samples. While having different colour contrasts, all three DNA-

binding dyes can be used to discriminate ELFA labelled cells from the ELFA non-

labeled cells by epi-fluorescence microscopy.


87

Figure 4.5 Detection of PO4ase activity of piggery effluent using epi-fluorescence microscopy (a, b, and c), and flow cytometry (d, e, and f) after staining with DAPI (a and d), SYTO9 (b, and e), and PI (c, and f). Sample was gated on single cells and deployed is the percentage of ELF+ cells to the total bacteria. X Axes of the cytograms are ELF, DAPI, SYTO9 or PI fluorescence intensity in arbitrary units (a.u.).

In order to obtain a clear separation between ELF+ cells from ELF- cells in flow

cytometric analysis, DAPI, SYTO9, and PI were further evaluated. Based on the spectral

set-up in our flow cytometer, a significant spectral spillover of DAPI and PI into the

ELF emission detector was observed initially. Spectral spillover of DNA binding dyes

caused a difficulty in separating ELF+ cells from other nucleated cells (i.e ELF- cells)

and noise. Therefore, software compensation was applied for separating the signals of

DAPI and PI from ELFA signals. There were no significant differences in the proportion

of ELF+ cells (p<0.05) when stained with ELF+DAPI, ELF+SYTO9 and ELF+PI

(Figure 4.5d,e,f respectively) with spectral compensation. While these three DNA

binding dyes provided usable data, the separation between ELFA-labeled cells from both

ELF-non-labeled cells and background was more easily achieved using the SYTO9

dyes, even without applying spectral compensation (Figure 4.5e). Therefore, the

ELF+SYTO9 dual staining protocol was chosen for defining the gating strategy for

quantifying and sorting ELFA-labeled cells.


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4.3.4 Accuracy of ELF-labeling and defining the gating strategy with ELF+SYTO9

Replicate subsamples were assessed for defining the abiotic contributions to the

fluorescence reactions of the ELF-P substrate by pre-fixing piggery waste pond samples

with 4 % (w/v) paraformaldehyde (1h) before their ELF-labeling, and analysing by flow

cytometry (abiotic control). This allowed determination of the false positive ELFA

fluorescence due to non-biological reactions. As expected, no ELFA signals were

observed in the abiotic control samples, and suggested that there is no background of

ELF-labeling within non-living particles (Figure 4.6a,b). Several background tests were

also performed to check the signals derived from the chemicals used during ELF

labeling protocol (PBS, paraformaldehyde, distilled water), also did not affect the

signals of ELFA and SYTO9 (data not shown). The signals derived from the background

samples fell into the typical noise area of the cytogram (i.e. 0 to 10 arbitrary units of

fluorescence).

Four controls were used for the gating of ELFA+ cells (Figure 4.6 c,d,e,f): (i) unstained

control cells (cells not stained with ELF or DNA-binding dyes); (ii) Single stained

control cells (cells stained with only ELF); (iii Single stained control (cells stained with

only DNA-binding dye); and (iv) Dual stained control cells (cells stained with both ELF

and DAPI/PI/SYTO9). For the unstained sample (Figure 4.6c), the signals were located

in the area corresponding to the noise (ELF <10 a.u.; SYTO9 < 10 a.u.). There were no

signals in ELF channel (ELF >10 A.U) or SYTO9 channel (SYTO9 >10 a.u.),

suggesting that there was no interference from noise upon ELF or SYTO9 fluorescence

signals. For the single stained sample labelled with SYTO9 (Figure 4.6d), two

distinguished populations were observed in the area corresponding to the noise and

SYTO +ve region (SYTO9 > 10 A.U).

On the other hand, for the single stained samples labelled with only ELF (Figure 4.6e),

two distinguished populations were observed in the area corresponding to the noise and

ELF +ve region (ELF >10 A.U). For the dual stained samples with ELF + SYTO9

(Figure 4.6f), three distinguished populations were observed in the area corresponding to

the noise, ELF+ (confirming that ELF+ cells were being labeled both by SYTO9 and

ELFA) and SYTO9+ region (confirms that ELF- cells are being labeled only with

SYTO9). Therefore, instrument set-up was found to be sufficient to separate ELF+ cells

from the ELF- and the background noise for the quantitative detection of ELFA-labeled

cells in these waste samples. Dual staining configurations for each waste pond samples


89

were then used to differentiate ELF+ population from the ELF- population in order to

quantify and categorize the ELF-labeled cells.

Figure 4.6 Flow cytograms showing (a) cells pre-fixed with paraformaldehyde and ELF-stained, (b) cells pre-fixed with paraformaldehyde and ELF + SYTO9, (c) unstained sample, (d) first single stained sample (SYTO9 only), (e) second single stained sample (ELF only), and (f) dual stained sample (ELF + SYTO9). Y axis represents the fluorescence intensity of ELFA, while X axis shows the fluorescence intensity of SYTO9.


90

4.3.5 In situ applications

The developed method was used to quantify the distribution of alkaline-phosphate

activity of the waste treatment process (1 mL of effluents from each stage in triplicates)

using ELF®97 phosphate. The percentage (%) abundance of PMB (ELF+ cells) was

determined as a proportion of the total bacterial cells in 1 mL effluent samples. The

percentages of ELF+ cells and respective Pi levels at different stages of piggery waste

treatment process are shown in Figure 4.7. It was observed that the percentages of ELF+

cells in the evaporation pond (5.5 %), and pits (1.9%) were significantly higher (p<

0.05) than the other sampling points. The lowest percentages of ELF+ cells (Ca. 0.4%)

were observed for the samples taken from holding tank, CAP-Top and CAP-Bottom,

where no significant difference (p< 0.05) was observed between those samples. Overall,

small fractions of the total bacterial cells in the waste samples displayed PO4ase activity

(0.3 %- 5.5 %), which could be due to the potential inhibition of this enzyme at high Pi

levels (10.8 - 26.3 mg/L). However, there was no direct relationship (coefficient of

determination, R² = 0.15) between ELF activity and Pi level among the samples (data

not shown). Due to the complex nature of the waste samples, variations observed in

ELF+ cell percentages and Pi levels among the different waste treatment stages are not

easy to elucidate. The level of PO4ase activity among different stages of the waste

treatment could be a cumulative effect of both biological and physicochemical dynamics

at each waste treatment stage.

Figure 4.7 The percentages of ELF+ve cells (▲) and respective Pi levels (grey columns) at different stages of piggery waste treatment process.


91

4.3.6 Community structure of PMBs within the piggery waste treatment process

Genomic DNA was extracted from the sorted ELF+ cells and the V3 region of the 16S

rRNA gene was amplified and sequenced. Averages of 50,000 reads were obtained after

QIIME quality filtering and library splitting and all the samples were normalised to a

sequence number of 7396. The overall qualitative operational taxonomic unit (OTUs;

97% sequence similarity) richness from the ELF+ cells versus number of sequences per

sample were plotted and rarefaction curves for each sampling points were obtained. The

rarefaction curves appear to be reached to a plateau after 7000 sequences per sample

(Figure 4.8a), indicating that an overall excellent OTU coverage obtained for all the

samples.

The Shannon’s index indicated that all the stages of piggery wastewater treatment were

generally comprised of a higher diversity of PMB (Figure 4.8b). Comparison of 16S

rRNA gene sequences rarefaction curves showed that the α-diversity was lower at

Evaporation Pond and higher at CAP-Bottom.

The 16S rRNA Ion Tag sequencing revealed the contribution of the abundant ELF+

bacterial communities to each stage of waste treatment process at phylum level (Figure

4.9a) and class level (Figure 4.9b). Bacterial community composition of ELF+ bacteria

were dominated by the phyla Bacteroidetes (38.4-69.3 %), Firmicutes (13.1-28.7%),

Proteobacteria (3.1-21.9), Synergistetes (1.9-14.8), Tenericutes (0.7-4.9), Spirochaetes

(0.3-1.9), Cyanobacteria (0.3-1.4), Chloroflexi (0.2-0.8%) and Actinobacteria (0.1-0.5

%) with a minimum and maximum abundance between stages (Figure 4.9a). Across all

the stages, the most abundant classes of the ELF+ bacteria were Bacteroidales, followed

by Clostridiales, Campylobacterales, and Synergistales (Figure 4.9b). However, the

relative abundance of these groups differed between each stage of the wastewater

treatment process.


92

Figure 4.8 (a) Alpha diversity rarefaction plots of OTUs for different wastewater samples. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarified to the same sequencing depth, the lowest one of total sequencing reads; 7396).

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94

4.4 Discussion

4.4.1 Optimisation of incubation time necessary for ELF-labeling

The optimum incubation time (2 hours) found in this study is in good agreement with

previous time course studies in aquatic systems (Duhamel et al. 2009). Previous studies

using epi-fluorescence microscopy to detect ELFA-labeled cells highlighted the

difficulty in determining a threshold between ELFA-labeled and non-ELFA-labeled

cells (Dyhrman and Ruttenberg 2006; Duhamel et al. 2008; Wambeke et al. 2008).

Kloeke and Geesey (1999) suggested that time kinetics of ELF-P incubation is required

for each environment as a prerequisite for the estimation of PO4ase activity at the single

cell level. Two hours (2 h) of incubation was sufficient for the piggery waste samples for

labeling all of the phosphatase active cells, and also for observing a consistency in

ELFA signals.

4.4.2 Optimisation of dual staining protocol in epi-fluorescence microscopy and

flow cytometric detection of ELFA-labeled cells

Enzymatic dephosphorylation of the ELF®-phosphate (ELF-P) yielded a highly

green/yellow water insoluble product called ELF®97alchohol (ELFA) at the site of

enzymatic activity. It is necessary to choose a proper DNA-binding dye for

counterstaining the ELFA-labeled cells as the majority of bacteria (e.g. heterotrophic

bacteria) are not naturally fluorescent. However, with counterstaining, flow cytometry is

a promising tool to quantify the ELF-labeling of the heterotrophic bacteria expressing

PO4ase activity (Duhamel et al. 2008). Detection of ELFA-labeled bacteria in

environmental samples has generally been tested for naturally fluorescent

phytoplankton, which does not require any extra counterstaining steps (Gonzalez-Gil et

al. 1998; Dyhrman and Palenik 1999; Meseck et al. 2009). For non-auto-fluorescent

microorganisms, DAPI usage has been most common (Kloeke and Geesey 1999;

Duhamel et al. 2008), whereas acridine orange, SYTO9 (Kloeke and Geesey 1999), and

PI (Kloeke and Geesey 1999; Duhamel et al. 2008) were used less often. Applying epi-

fluorescence microscopy or flow cytometric detection of ELFA-labeled cells into a

complex and highly diverse environment such as piggery waste effluent, indicated

difficulties for discriminating ELF+ cells from the ELF- cells and debris. Therefore, it is

important to experimentally rationalise the best protocol for each environment type,

using the most appropriate nucleic-acid-binding dye to stain ELF- bacteria and

discriminate them from the ELF+ cells and debris (noise). Excitation and emission

wavelength of the counterstaining dye should be within the range of the excitation and


95

emission wave length of the ELF97® (345-530 nm) with minimum spectral spillover

between each fluorescence channels. In order to capture the maximum fluorescence

from each channel, appropriate filters were selected according to the type of the flow

cytometer (explained in Figure 4.2). To obtain a clear separation between ELF+ cells

(cells expressed phosphatase activity) and ELF- cells (other nucleated cells) in flow

cytometric analysis, 3 suitable fluorescent dyes, DAPI (358-461 nm), SYTO9 (485-498

nm), and PI (535-616 nm) were evaluated. SYTO9 was the most appropriate dye based

on the spectral set-up used. Additionally, SYTO9 has very low excitation efficiency by

UV and therefore exhibits low spectral cross talk in the ELF emission channel. It has

been shown that SYTO9 is one of the most appropriate dyes for bacterial enumeration in

non-saline waters and can be applied for both live and dead bacteria in activated sludge

flocs (Lebaron et al. 1998; Kloeke and Geesey 1999).

For other stains tested, the separation between nucleated and non-nucleated cells using

PI was less satisfactory, possibly due to its poor penetration into cells. Many bacteria

may not have been permeabilised adequately and this can affect the determination of

ELF+ cells as a frequency of nucleated cells, especially for membrane impermeable

stains. Further, DAPI and SYTO9 have advantages over PI to discriminate DNA stained

cells from naturally fluorescent microbial cells. Propidium iodide, as for chlorophyll a,

emits in the red wavelength making the discrimination of autotrophic cells from

heterotrophic ones almost impossible when PI is used in complex ecosystems

harbouring autotrophs (Duhamel et al. 2008).

4.4.3 In situ applications

As revealed by ELF detection, community differences in PO4ase activity along the

piggery waste treatment process is likely to be due to the cumulative effect of biological

and physicochemical dynamics of each waste treatment stage. For example, alkaline

PO4ase synthesis and activity in aquatic bacteria appeared to be controlled by the

concentrations and types of external organic-P, temperature, ionic strength, pH, presence

of any metal ions (Güngör and Karthikeyan 2008), internal N:P ratio, P demand of the

cell (Espeland and Wetzel 2001), and composition of wastewater (Li and Chróst 2006).

Moreover, it has been shown that starvation, salinity, presence of primary substrate, pH,

and volatile fatty acids (VFA) caused different expression of total PO4ase activity in

anaerobic sludge (Anupama et al. 2008).

There was no direct relationship between the percentages of ELF+ cells (0.3-5.5 %) and

Pi levels (10.8-26.3 mg/L). It is generally understood that elevated Pi concentrations


96

inhibit the PO4ase activity (Dignum et al. 2004). However, alkaline PO4ase synthesis in

many bacteria was not inhibited by elevated Pi (MH and HJ 1961; Chrost et al. 1986;

Kloeke and Geesey 1999). PO4ase in activated sludge has been attributed to microbial

cells (cell bound), extra cellular polymeric substance (EPS) and bulk liquid (cell-free

form) of sludge (Anupama et al. 2008). The ELF®97 phosphate provides cell surface-

associated PO4ase activity (Kloeke and Geesey 1999), and it has been reported that cell

surface-associated PO4ase was not regulated by environmental PO4 level (Braibant

2001). Therefore, the observed PO4ase activities in this study were more likely to

represent the fraction of cells that are not inhibited by higher Pi levels. Furthermore,

there might be some species-specific differences in association with Pi and PO4ase

activity. Meseck et al. (2009) explained that expression of PO4ase activity at a highly

soluble reactive P could be attributed to the ratio of DNA to protein or more of an

individual response, rather than a population response. This implies that further research

is necessary to elucidate the mechanisms responsible for PO4ase activity and subsequent

Pi regeneration under different environmental conditions.

This is the first study to demonstrate fluctuation and distribution of phosphatase activity

in the entire process of piggery waste treatment by using an integrated approach using

the enzyme labeled fluorescence technique coupled with epi-fluorescence microscopy,

cell sorting, and next generation sequencing. Previous investigation of phosphatase

activity in wastewater treatment systems is limited but phosphatase activity has been

detected in activated sludge (Lemmer et al. 1994; Kloeke and Geesey 1999; Li and

Chróst 2006) and anaerobic reactors (Ashley and Hurst 1981; Zhenglan et al. 1990;

Yamaguchi et al. 1991; Whiteley et al. 2002; Anupama et al. 2008).

4.4.4 Community structure of PMB within the piggery waste treatment process

Cell sorting of ELFA-labeled bacteria from the piggery waste provided efficient

separation of P mineralising bacteria with a high degree of purity. Therefore, molecular

sequencing of sorted cells represented the fraction of P mineralising bacteria that

expressed the phosphate activity, demonstrating the link between microbial identity and

P mineralisation activity. The most abundant P mineralising bacteria identified using

16S rRNA tag sequencing of sorted cells were represented by Bacteroidales,

Clostridiales, Campylobacterales and Synergistales. The dominant PMB found in this

study were consistent with the total bacterial community diversity for the initial stages

of piggery waste treatment process as revealed by the 16 S rRNA Ion Tag sequencing in

Chapter 3. Therefore, the identified PMB bacterial diversity was ecologically significant


97

and has been previously identified in piggery waste treatment systems (Cook et al. 2010;

Patil et al. 2010). Furthermore, the identified PMB also matched some of the homologs

identified in the metagenomics analysis (Bacterioides, Parabacteroides,

Flavobacterium, Clostridium, Desulfitobacterium) in Chapter 3 in relation to the

presence of the alkaline-phosphatase genes. This confirmed that the taxonomic identity

of PMB in this study is linked to the functional identity of the community P mineralising

bacteria in this piggery waste treatment process (Chapter 3).

The abundance of alkaline phosphatase, as revealed by using metagenomics (Chapter 3),

was not positively correlated to the % of ELF+ bacteria revealed by ELF. For example,

the highest % of PMB in the CAP digesters was expected to correspond with genes

encoding for alkaline phosphatase which was the highest in the CAP digester (Chapter

3). However, the % of ELF+ bacteria in the CAP digester was low according to the ELF

analysis. This observed discrepancy might be because the number of alkaline

phosphatase reads found in anaerobic ponds was mainly associated with activity of

Methanosarcina, an anaerobic methanogen, which was not expected to be detected by

either ELF or bacterial 16S rRNA Tag sequencing. This discrepancy may indicate that

archaea are playing an important role for the PO4ase activity in the wastewater treatment

process. Anupama et al. (2008) showed that both archaea and bacteria played equal roles

for PO4ase activity in anaerobic bioreactors. Therefore, further studies are required to

understanding the contribution of archaea, or specifically methanogens, in

mineralisation of P in waste treatment process as this environment significantly favoured

anaerobic microorganisms. Overall, ELF followed by cell sorting and sequencing used

in this study is reliable for determining the taxonomic identity of active PMB in piggery

waste. Although the identified PMB have been widely recovered from wastewater

plants, their role in piggery waste process and in P transformations in particular, has not

been elucidated until now.

4.5 Conclusions

This study aimed to determine the P mineralising bacterial abundance in a piggery waste

treatment process and their taxonomic and functional identity. ELF was useful in direct

localisation and quantification of P mineralising microbes at a single cell level by

applying epi-fluorescence microscopy and flow cytometric analysis in piggery waste

after 2 h of incubation. The importance of discrimination between ELFA-labeled cells

from non-labelled cells and background when the PO4ase activity was represented as a


98

frequency of the total number of bacteria in the samples was demonstrated. Moreover,

detection of PO4ase activity at a single cell level by flow cytometry followed by cell

sorting allowed an intended and defined selection of phosphatase active cells even at

their low abundance. Subsequent downstream next generation sequencing provided

insight into the microbial identity of active P mineralisers in the piggery waste.

Therefore, coincident single cell and population approaches used in this study are

promising for determining the taxonomic identity of active phosphorus mineralising

bacteria in piggery waste. Further studies should include investigation of detailed

approaches for flow cytometric detection of phosphatase activity, and monitoring of P

generation capacity at different operational or environmental conditions. Routine

analysis of PMB in the piggery waste stream will be useful for managing P levels in

piggeries by adding phytase enzyme to the pig feeds in order to reduce the accumulation

of organic P sources in the system. On the other hand, knowledge gained in taxa

mediating P mineralisation will aid for controlling the P generation in the system by

altering the P mineralising bacterial community. For example, the identified PMB could

employ as potential inocula (“seeds”) for enhancing the P mineralisation in the early

stages of piggery waste treatment process and reducing the Pi in the system at the later

stages by applying enhanced biological P removal (EBPR).

Chapter 5: PolyP accumulating bacteria in piggery waste

99

CHAPTER 5

Analysis of Polyphosphate Accumulating Organisms in

High Phosphorus Piggery Wastewater Effluents

5.0 Abstract

Phosphorus is a key agent of environmental impact in liquid fertilisers, its removal

being possible through enhanced biological phosphorus removal (EBPR) technologies

applied during wastewater treatment. Using a range of high throughput single cell and

next generation sequencing methods, active polyphosphate accumulating organisms

(PAOs) were identified and functionally analysed in two pH environments (pH 5.5 and

8.5) to assess the efficacy of EBPR technology applied to high P loading waste

remediation. A significant positive effect on polyphosphate accumulation was observed

at pH 5.5 compared to pH 8.5, with significant enrichment of polyphosphate kinase and

exopolyphosphatase genes at pH 5.5. Functionally active PAO accumulators at pH 5.5

were identified as Aeromonas hydrophila, Aeromonas salmonicida, Acinetobacter

baumannii, Bordetella pertussis, Citrobacter koseri, Escherichia coli, Enterobacter sp.

Klebsiella, Pseudomonas aeruginosa, Salmonella enterica and Shigella flexneri. These

findings serve as a basis to understand and manipulate PAOs community diversity and

functionality to enhance P uptake by altering the pH for improving the EBPR waste

treatment process and develop high value/low environmental risk products from a range

of effluents.


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5.1 Introduction

Anaerobic treatment of wastewater streams releases large amounts of phosphorus (P)

and nitrogen (N) compounds into the wastewater bodies, both being major agents of

eutrophication problem in their surrounding environment (De-Bashan and Bashan

2004). For example, failure to remove soluble P (Pi) during the wastewater treatment

process can result in increased soil P runoff and leaching during irrigation if treated

effluents are used as liquid fertilisers (Jaiswal 2010; Nielsen et al. 2010). In high P-

producing waste production systems, such as pig husbandry (Poulsen 2000), the

concentration of Pi in treated effluents is often too high to permit its re-use as a liquid

fertiliser for agricultural soils (Obaja et al. 2003), unless effective P removal systems

can be applied to reduce Pi loading.

Traditionally, Pi is removed from wastewater treatment plants by chemical precipitation

techniques before land application, but this can be both expensive and not

environmentally friendly (Günther et al. 2009). Specifically, chemical precipitation

techniques (e.g. stuvite crystallisation) are not economically feasible for low P

concentration wastewater streams (< 50 mg-P/L)(Wong et al. 2013), which can increase

the volume of sludge by up to 20 % (Cooper et al. 1994; Cooper et al. 1995).

Alternatively, Pi can be removed using a biological process called enhanced biological

phosphorus removal (EBPR) (Oehmen et al. 2007). In this process specific bacteria and

microalgae, called polyphosphate accumulating organisms (PAOs), accumulate large

quantities of P within their cells and are selectively enriched within the community. The

enriched bacteria and microalgae biomass is then separated from the treated effluent

wastewater, as the separated biomass can be further used as slow releasing P fertilisers,

whilst the remaining effluent can be re-used as a liquid fertilizer (Yoon et al. 2004;

Hirota et al. 2010).

Fundamental to the design of effective P removal systems is the knowledge of the

identity and functionality of PAO accumulating microorganisms (Nielsen et al. 2010;

Mehlig et al. 2013). Although the enhanced biological P removal from wastewater has

been widely studied, an understanding of the microbial identity and environmental

factors affecting enhanced P accumulation efficiency of PAO is less understood. Also,

this information is lacking in systems associated with high P-loaded piggery industry.

Specifically, enhanced biological phosphorus removal processes can be difficult to

control and sometimes ineffective (Kawaharasaki et al. 1999; Oehmen et al. 2007)


101

mainly due to the competition amongst microorganisms within the community, or

variation in parameters such as temperature, light intensity, pH, redox potential, and

biological activity (Crocetti et al. 2000; Oehmen et al. 2007). Therefore, for a consistent

performance, a better understanding of the types of microorganisms involved, their

interactions, optimum process conditions for their activity, and the possible causes of

environmental stresses is required.

Low-pH stimulated polyphosphate accumulation due to acid stress on environmental

microorganisms was first observed by McGrath and Quinn (2000). The study reported

10.5-fold increase in intracellular polyphosphate accumulation in Candida humicola G-

1, grown at pH 5.5 in a medium containing glucose as the carbon source, compared to

pH 7.5. Phosphate uptake from culture medium with activated sludge inocula increased

between 50% and 143% when pH was 5.5 rather than 7.5 (McGrath et al. 2001).

Approximately 34% of the activated sludge microflora was capable of acid-stimulated

luxury phosphate uptake. This is evidence that the EPBR process could be enhanced

under acidic conditions (McGrath et al. 2001; Mullan et al. 2002a; Moriarty et al. 2006)

and it has been claimed to be economically feasible (Mullan et al. 2006) for low P

concentration wastewater streams (< 50 mg-P/L). However, information on what

microbial communities in wastewater respond to acidic conditions and their

physiological role with respect to polyP accumulation is less well studied.

It was hypothesised that there would be an increase in polyphosphate uptake,

abundance, and functional activities of PAOs under acidic conditions in an aerobic

pond within a piggery waste treatment system. In order to address this hypothesis,

coincident single cell and population approaches were used to quantify and identify

functionally relevant polyphosphate accumulating organisms (PAOs) under two

different pH environments (pH 5.5 and pH 8.5). Epi-fluorescence microscopy, flow

cytometry, cell sorting and next-generation sequencing approaches were combined in

order to better link variation of environmental conditions and taxonomic/functional

capacities of PAO populations. Ultimately, this approach provides a ‘cell to population’

mechanistic understanding to enhance the consistency of EBPR systems.


102

5.2 Materials and Methods

5.2.1 Sampling site and lab-scale incubation experiment

Samples for laboratory incubation experiments for EBPR were collected at the final

stage of waste treatment pond (i.e. the aerobic pond) from the covered anaerobic pond

digester system at Medina Research Station as described in Chapter 3. Initially the

wastewater had a pH of 8.5 and phosphate concentration (Pi) of 12.2 mg/L. Half of the

samples were filtered (3 µm filtered) and the rest was left unfiltered. The Pi

concentration was adjusted to 25 mg/L using KH2PO4 (25mM) to simulate a high P

loaded wastewater system for both filtered and unfiltered samples. The experimental

design comprised either filtered or unfiltered samples over five different pH treatments

(5.5, 6.0, 6.5, 7.0 and 8.5 [control]) in triplicate to determine the best pH level for polyp

accumulation. Filtered or unfiltered pond samples (300 mL) were put into autoclaved

jam jars (500 mL) and left under aerobic conditions at room temperature (25oC) for 48 h

under the natural light/dark illumination cycle of the laboratory conditions. Samples

from each triplicated microcosm were combined to get a composite sample for

downstream analysis. Phosphate concentrations at the beginning and after the 48th h of

incubation were determined according to standard methods described previously (Eaton

et al. 2005). The microcosm set-up and subsequent sample preparation for epi-

fluorescence microscopy, and flow cytometry are schematically shown in Figure 5.1.

5.2.2 Optimisation of polyP staining: bacterial strain, culture conditions

A bacterial culture of Pseudomonas syringae grown in P-limited medium was used for

the titration of DAPI (4',6-diamidino-2-phenylindole) stain concentration required for

detecting and quantifying PAOs using epi-fluorescence microscopy and flow cytometry.

P-limited medium was prepared by modifying a previously reported PSM medium by

Jorquera et al. (2008b), by replacing Na-phytate with 0.41 ml of 0.1M K2HPO4 (42

uM) as the sole source of P. The cultures were grown in UV sterilized filter cap cell

culture flasks (180 mL) in 50 mL P-limited PSM by mixing at a rate of at 150 rpm in an

orbital shaking incubator at 27oC until the cell number reached approximately 108.

Subsequently, aliquots of cultured cells were supplemented with 1 ppm, 10 ppm or 50

ppm of K2HPO4 as a P pulse and incubated for 48 h. After incubation, cells were

prepared for epi-fluorescence and flow cytometric detection of polyP accumulation as

discussed below.


103

Figure 5.1 Microcosm set-up and subsequent sample preparation for epi-fluorescence microscopy, and flow cytometry. 5.2.3 Sample preparation for epi-fluorescence microscopy and flow cytometry

Aliquots (1 mL) of each pure culture sample were incubated at different P levels (1, 10,

and 50 mg/L) and collected by centrifugation at 3000 x g for 5 min. The cell pellet was

washed with phosphate buffered saline (PBS) and then resuspended in PBS medium and

fixed with 4% (w/v) paraformaldehyde fixative solution (PFA) and incubated for 1 h at

room temperature. PFA fixed cells were subsequently washed with PBS and finally with

distilled water, before being resuspended in 1 mL distilled water prior to the DAPI


104

staining for investigation of polyP cellular inclusion and further analysis at the Centre

for Microscopy, Characterisation and Analysis (CMCA) at the University of Western

Australia.

5.2.4 Titration of DAPI concentration, epi-fluorescence microscopy, and flow

cytometry

To verify the reliability of polyP staining, the stained pure cultures and environmental

samples were observed using epi-fluorescence microscopy. Fifty microliter (50 µL) of

cell culture was placed in the middle of a microscope slide and the cells were air dried.

Slides were stained with DAPI immediately prior to microscopic examination. For the

optimisation of polyP staining, pure culture samples were stained with 0.25, 0.5, 1, 5,

7.5 and 15 ug/mL of DAPI for 20 mins in the dark. Based on the DAPI titration results

of the pure culture, a 15 ug/mL DAPI concentration was selected for polyP staining of

the environmental samples. Apart from the pure cultures, the staining efficiency of

polyP-DAPI for environmental wastewater samples was also demonstrated under 3

levels of polyP accumulation using the wastewater samples incubated at three different

concentrations of P (1 mg/L, 10 mg/L, and 50 mg/L). The slides were then rinsed with

distilled water and air dried. Cells were visualised at x100 magnification using a Zeiss

Axioplan epifluorescence microscope under UV excitation (DAPI filter block). DAPI

stains both DNA (present in all microbial cells), and polyP (found in just PAO cells).

Both DAPI-DNA and DAPI-PolyP complexes are excited by UV light (355 nm) but

emit light at different wavelengths for PolyP (570-600 nm, yellow-green light) and

DNA (435-485 nm, blue light) (Kulakova et al. 2011a).

Flow cytometric analyses were performed using a BD Influx cell sorter. DAPI was

excited with a 355 nm (UV) laser, the standard DAPI emission was collected with a

460/50 nm band pass filter, and the DAPI-PolyP emission was collected with a 585/29

bandpass filter. Measurements for DAPI and polyP were acquired on a logarithmic scale

and post-acquisition analysis was performed using Flow Jo software version 7.6.5.

Briefly, single cells were gated on forward scatter area (FSC-A) vs forward scatter

height (FSC-H) to exclude any doublets, and DAPI-DNA and DAPI-polyP were gated

to determine proportions of bacteria accumulating polyP.


105

5.2.5 DNA extraction and 16S rRNA tag sequencing

DNA from the microcosm experiments was extracted using the MoBio Powersoil DNA

isolation kit (Geneworks, Australia), utilising bead beating and column purification

following the manufacturer's guidelines. Extracted DNA was quantified and checked for

its purity at A260/280 nm (Nanodrop, Thermo Fisher Scientific, USA) prior to storage

at −20°C. Fragments of the 16S ribosomal RNA gene were amplified by polymerase

chain reaction (PCR) from the DNA samples using Golay barcode and Ion Torrent

adapter modified core primers 341F and 518R (Muyzer et al. 1993), using amplification

conditions described previously (Jenkins et al. 2010). All PCR products were checked

for size and specificity by electrophoresis on 2.5% w/v agarose. Samples were purified

by gel excision and adjusted to a concentration of 10 ng/μL in molecular grade water,

and then pooled equally for sequencing. Sequencing was performed using an Ion

Torrent Personal Genome Machine (Life technologies, USA) using 200 base-pair

chemistry as described in Whiteley et al. (2012). All the PGM quality filtered data were

exported as FastQ files and split into fasta and qual files and analysed using the QIIME

pipeline (Caporaso et al. 2010). Sequencing data analyses performed as described in the

Chapter 3 (section 3.2.4).

5.2.6 Whole genome shotgun sequencing

DNA was extracted from the microcosm experiments (pH 5.5 filtered, pH 5.5 un-

filtered and pH 8.5 unfiltered) using the MoBio Powersoil DNA isolation kit

(Geneworks, Australia), by following the manufacturer's guidelines. Sequencing of the

genomic DNA derived from these samples was done by whole genome shotgun

sequencing. Approximately 150 ng of DNA-preparation was used to generate a whole

genome shotgun library using a NEBnext Ultra library preparation kit (New England

Biosciences). Fragments of 320-330bp were selected from the final library by gel-

excision and sequenced for 520 flows on an Ion Torrent Proton sequencer (Life

Technologies), yielding reads of 230-240bp modal length. Quality filtering and

trimming were performed on instrument using TorrentSuite 4.0. Metagenomic data sets

are publicly available in the MG-RAST system under project identifiers 4553565.3,

4553566.3, and 4553565.3. Assignment of metabolic function and phylogenetic

identification were done as described previously (Meyer et al. 2008).


106

5.3. Results

5.3.1 Titration of DAPI concentration required for epi-fluorescence and flow

analyses of polyP accumulation

Pseudomonas syringae cells grown in P-limited medium were stained with increasing

concentrations of DAPI (0.25, 0.5, 1, 5, 7 and 15 ug/mL) to ascertain the best

concentration for an effective staining of polyP granules by flow analyses and for

defining polyP gates within flow cytograms. Both DAPI-DNA and DAPI-PolyP

complexes were excited by UV light (355 nm) but emit light at different wavelengths

for PolyP (570-600 nm, yellow-green light) and DNA (435-485 nm, blue light).

Polyphosphate accumulating organisms with polyP granules (excitation/emission:

415/550 nm; DAPI-polyP) appeared bright yellow-green and were easily differentiated

from non-accumulating bacteria which stained blue due to DAPI-DNA binding

(excitation/emission: 358/461 nm; DAPI-DNA) under epi-fluorescence microscopy

(Figure 5.2a-f) and within two dimensional cytograms (Figure 5.2g-l). According to the

epi-fluorescence microscopy, the staining efficiency of polyP was increased from the

lowest (at 0.25 ug/mL- DAPI) to the highest (at 15 ug/mL- DAPI) within the range of

DAPI concentration tested on the Pseudomonas syringae (0.25, 0.5, 1, 5, 7 and 15

ug/mL) (Figure 5.2a-f). The culture condition was P limited and it can be assumed that

there is a higher affinity of cells to accumulate Pi as polyP granules due to the long-time

incubation of cells in the P limited medium. This was proven further by quantitative

analysis of the samples for the abundance of polyP cells by using flow cytometry

(Figure 5.2g-l). The lowest abundance of polyP cells (64.5 %) was observed at 0.25

ug/mL-DAPI as two clusters of cells (DAPI-polyP and DAPI-DNA) (Figure 5.2g) due

to the poor staining efficiency attributed to the lower DAPI concentration. The highest

abundance of polyP cells (99.9 %) was observed at 15 ug/mL-DAPI (Figure 5.2l),

where all the cells were concentrated to a single cluster (DAPI-polyP).

PolyP accumulation was detected in almost all cells when a minimum DAPI

concentration of 5 ug/mL (Figure 5.2j) was used, which was also selected to be the

optimum concentration for the pure cultures of Pseudomonas syringae that would yield

good signal to noise ratios. Having seen that majority of cells contained polyP granules

at a DAPI concentration of 15 ug/mL, we can assume that it is important to choose

appropriate concentrations of DAPI for staining the polyP granule to avoid partial or


107

poor staining of polyP granules as we observed under lower DAPI concentrations

(Figure 5.2g-h).

Figure 5.2 DAPI staining of pure culture of Pseudomonas syringe cells for polyP analysed by epi-fluorescence microscopy (a-f) and flow cytometry (g-l). Cells were subsequently stained with (a/ g) 0.25; (b/ h) 0.5; (c/ i) 1; (d/ j) 5; (e, k) 7; and (f/ l) 15 µg/mL of DAPI. In epi-fluorescence micrograms (a-f), intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. In flow cytograms (g-l), sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyP (DAPI-DNA).

Although 5 ug/mL was found to be the optimum concentration in pure cultures of

Pseudomonas syringae for good signal to noise ratios, due to the heterogeneous matrix

of natural samples, 15 ug/mL of DAPI was selected as the optimum concentration for

analysing the wastewater samples by using flow cytometry.


108

The efficiency of 15 ug/mL of DAPI concentration for the staining of polyP

accumulation in the diverse groups of bacteria was further assessed. Samples taken from

the aerobic pond were incubated in 1, 10, 50 mg/L P containing media, before

investigating the degree of polyP accumulation. Specifically, the titration of P

availability against polyP accumulation, using 15 ug/mL DAPI, revealed significantly

lower polyP accumulation at 1 mg/L-P (0.59 %, Figure 5.3a), compared to those of 10

and 50 mg/L-P (56.6 and 64%, Figure 5.3b and 5.3c, respectively). These results

confirmed that 15 ug/mL of DAPI was sufficient for staining of polyP accumulation in

diverse groups of microorganisms at high levels of available P which increased polyP

accumulation within the cellular biomass.

Figure 5.3 Aerobic pond samples stained for polyP. Cells were incubated with (a) 1 mg/L-P, (b) 10 mg/L-P, and (c) 50 mg/L-P; and were stained with 15 µg/L of DAPI followed by the flow cytometric analysis. Sample was gated on single cells and deployed is the percentage of cells with (DAPI-polyP) and without accumulated polyp (DAPI-DNA).

5.3.2 PolyP accumulation in high Pi loaded lab microcosm experiments

Phosphate concentrations at the beginning and after the 48th h of incubation were

determined and percentage P removal was calculated. A greater P removal (63 %) was

observed at pH 5.5 with respect to pH 8.5 (44 %) (Figure 5.4a). Based on this

preliminary observations, both filtered and unfiltered samples at pH 5.5 and pH 8.5 (pH

in the aerobic pond) were selected for assessing the polyP formation using flow

cytometry and epi-fluorescence microscopy. There was an increase in polyP formation

under acidic conditions for both filtered and unfiltered samples (Figure 5.4b). The

highest percentage content of the DAPI-polyP positive population was observed in pH

5.5 unfiltered samples and was almost 2 times greater than the pH 8.5 unfiltered


109

samples. The greatest polyP formation observed in the unfiltered samples at pH 5.5

suggested microalgae could be a key component of the polyP accumulating population

in piggery effluent ponds.

Figure 5.4 Overall phosphate removals from the pond water at different pH treatments (3a). Percentage of the cellular content in the form of DAPI-PolyP and DAPI-DNA complex at pH 5.5 and 8.5 (control), for both filtered and unfiltered samples (3b).

Under epi-fluorescence microscopy, DAPI staining confirmed the presence of

intracellular polyP granules inside bacterial and microalgal cells at pH 5.5 for both

filtered (Figure 5.5 a), and unfiltered samples (Figure 5.5c).


110

Figure 5.5 PolyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (a and b, respectively) and unfiltered (c and d, respectively) samples viewed under epi-fluorescence microscopy. Intracellular polyP granules form DAPI-polyP complexes appear yellow-green, whilst DAPI bound to DNA appears blue. Flow cytograms of polyP stained cells from aerobic pond at pH 5.5 and 8.5 for filtered (e and f, respectively), and unfiltered (g and h, respectively) samples.

In contrast, there were less polyP granules within cells maintained at pH 8.5 for both

filtered (Figure 5.5b), and unfiltered (Figure 5.5d) samples. These data suggest the

presence of intracellular polyP granules inside bacterial and microalgal cells at pH 5.5

implying the increased uptake of P in these samples (Figure 5.4 a) is due to the activity


111

of polyphosphate accumulating organisms and not just P assimilation. In the unfiltered

samples the microalgal cells were the predominant PAOs, suggesting that they

outcompete bacterial PAOs for phosphate. Filtering the aerobic pond sample prior to pH

adjustment appears to favour the growth of bacterial PAOs in the absence of

microalgae.

Flow cytograms (Figure 5.5e-h) provide further verification that two distinct DAPI

populations are formed: a DAPI-bound PolyP cell population (polyP positive) that

forms a distinct cluster in the PolyP 570-600 nm range and DAPI bound DNA cell

population (polyP negative) that forms a cluster in the DAPI 435-465 nm range. In the

pH 5.5 filtered samples these two populations were well defined showing the presence

of bacterial PAO (ca. 52 %) (Figure 5.5e). In the pH 5.5 unfiltered samples the PolyP

stained cells increased to 70 % (Figure 5.5g). Under more alkaline conditions (pH 8.5)

for both the filtered (Figure 5.5f) and unfiltered (Figure 5.5h) samples, the polyP

population significantly declined to 15 % and 36 %, respectively.

5.3.3 Community structure of PAOs in piggery waste

Based on these data above, only three systems of EBPR (pH 5.5 filtered, pH 5.5

unfiltered, and pH 8.5 unfiltered) were selected for downstream molecular analysis.

DNA extracted from incubated microcosms was used to assess the abundance and

phylogenetic affiliation of bacterial taxa present under the 3 microcosms. The V3 region

of 16S rRNA gene was assessed by sequencing on a PGM seminconductor sequencer.

An average of 42,000 reads after QIIME quality filtering and library splitting were

suitable for subsequent phylogenetic analysis. All the samples were normalised and

rarefied to 5200 sequences and the rarefaction curve analysis showed that the overall

bacterial diversity was well represented (Figure 5.6a). Microbial diversity indicated by

Shannon’s index (Figure 5.6b) showed that the richness of species and their diversity

were highest in the control (pH 8.5, unfiltered) followed by pH 5.5 unfiltered and pH

5.5 filtered microcosms. These data also showed that the diversity of the microbial

community at pH 5.5 was less diverse compared to the unfiltered sample at pH 8.5

(natural pH level of the aerobic pond), indicating that only specific microorganisms

favour the acidic pH of 5.5.

Bacterial community compositions under control conditions (pH 8.5, unfiltered) were

dominated by Actinobacteria (51.3 %), followed by Betaproteobacteria (19.9 %),

Firmicutes (8.2 %), TM7 (5.1 %), Gammaproteobacteria (2.9 %), Bacteroidetes (3.1%),


112

Alphaproteobacteria (0.9 %), Epsilonproteobacteria (0.7 %), Tenericutes (0.4 %), and

TM6 (0.4 %).

Figure 5.6 (a) Alpha diversity rarefaction plots of phylogenetic diversity of 3 EBPR systems. (b) Microbial diversity indicated by Shannon diversity. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth, the lowest one of total sequencing reads; 5200). In comparison, Gammaproteobacteria taxa dominated both filtered and unfiltered

microcosms maintained at pH 5.5, with relatively high abundances in both filtered and

unfiltered microcosms (ca. 93 % and 89 %, respectively) when compared to their

abundance under alkaline conditions (ca. 2.9 %) (Figure 5.7a). Within the acidic

microcosms, the Gammaproteobacteria, in filtered (Figure 5.7b) samples were

dominated by the genus Alteromonadales (59 %) followed by Aeromonadaceae (26 %),

Shewanella (9 %), Pseudomonas (3 %), Enterobacteriaceae (1 %) and Citrobacter (1

%). Conversely, in unfiltered samples (Figure 5.7c), Gammaproteobacteria community

members were aligned to the genus Aeromonadaceae (73 %) followed by


113

Enterobacteriaceae (16 %), Alteromonadales (4 %), Citrobacter (3 %), Pseudomonas

(3 %), and Acinetobacter (1 %).

Figure 5.7 Identities and relative abundance (%) of the bacteria in 3 EBPR systems as revealed by 16S rRNA Tag sequencing at class level (a). Composition of the main polyP accumulators, Gammaproteobacteria under (b) pH 5.5 unfiltered, and (c) pH 5.5 filtered samples.

5.3.4 Metagenomic analysis of piggery wastewater samples treated at pH 5.5

Shotgun metagenomics was used to confirm community diversity and the presumptive

functional genes involved in polyP accumulation. Whole genome shotgun libraries were

sequenced and normalised to 3,000,000 reads per sample for each of the three EBPR

systems. The data were analysed using the “Metagenome Rapid Annotation using a

Subsystem Technology” (MG-RAST) server (http://metagenomics.nmpdr.org/). A

summary of the sequencing analyses are shown in Table 5.1.

http://metagenomics.nmpdr.org/

Cha

pter

5: P

oly

P a

ccu

mu

lati

ng

ba

cter

ia in

pig

ge

ry w

ast

e

114

Tab

le 5

.1 S

umm

ary

of th

e an

alys

es o

f MG

-RA

ST o

f the

3 E

BPR

sys

tem

s (pH

5.5

filte

red,

pH

5.5

un-

filte

red,

and

pH

8.5

un-

filte

red)

.

Sam

ple

char

acte

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s pH

5.5

filte

red

pH 5

.5 u

n-fil

tere

d pH

8.5

un-

filte

red

U

ploa

d: b

p C

ount

53

8,37

0,13

0 bp

53

9,59

3,63

9 bp

53

1,18

0,68

4 bp

U

ploa

d: S

eque

nces

Cou

nt

3000

000

3000

000

3000

000

Upl

oad:

Mea

n Se

quen

ce L

engt

h 17

9 ±

58 b

p 17

9 ±

57 b

p 17

7 ±

59 b

p U

ploa

d: M

ean

GC

per

cent

54

± 9

%

51 ±

10

%

47 ±

11

%

Arti

ficia

l Dup

licat

e R

eads

: Seq

uenc

e C

ount

23

0711

33

7432

32

4612

Po

st Q

C: b

p C

ount

22

4,08

3,38

2 bp

20

2,10

4,70

5 bp

19

4,62

9,42

7 bp

Po

st Q

C: S

eque

nces

Cou

nt

1978

095

1806

120

1773

619

Post

QC

: Mea

n Se

quen

ce L

engt

h 11

3 ±

44 b

p 11

1 ±

44 b

p 10

9 ±

44 b

p Po

st Q

C: M

ean

GC

per

cent

54

± 9

%

51 ±

10

%

48 ±

11

%

Proc

esse

d: P

redi

cted

Pro

tein

Fea

ture

s 15

3741

0 13

8204

1 13

1965

4 Pr

oces

sed:

Pre

dict

ed rR

NA

Fea

ture

s 40

5435

40

7414

41

0162

A

lignm

ent:

Iden

tifie

d Pr

otei

n Fe

atur

es

8396

93

7410

95

2651

92

Alig

nmen

t: Id

entif

ied

rRN

A F

eatu

res

1604

2 16

533

1463

A

nnot

atio

n: Id

entif

ied

Func

tiona

l Cat

egor

ies

7573

51

6617

55

2292

15


115

Taxonomic analysis of metagenomic reads of all three EBPR systems (Table 5.2)

indicated that most of the sequences belonged to unclassified bacteria indicating that our

knowledge of polyP accumulating taxa under acid stimulation is lacking from a

taxonomic standpoint. However, amongst the known bacteria within the MG-RAST

database, taxonomic identity of the pH 5.5 filtered and unfiltered samples were

relatively similar to each other when compared to that of the control. Similar to the 16S

rRNA sequence dataset, the most dominant group in pH 5.5 samples (both filtered and

unfiltered) was Gammaproteobacteria (eg. Aeromonas, Pseudomonas, Xanthomonas,

Enterobacter, Klebsiella, Acinetobacter, Castellaniella, Pantoea, Edwardsiella,

Escherichia, Shewanella, Tolumonas, Citrobacter, Flavobacterium, Burkholderia,

Cronobacter, Pasteurella). In contrast, whole community shotgun sequencing showed

that the community composition of the control microcosms was comprised of

Glaciibacter, Candidatus Aquiluna, Candidatus Rhodoluna, Burkholderia,

Microbacterium and Agrococcus. Metabolic phosphorus potential of the community, in

terms of the abundance of genes involved in polyP metabolism, were compared by

assigning functional annotations to metagenomic sequences with subsequent sequence

assignment to subsystems. The genetic potential for P metabolism showed enrichment

of Polyphosphate kinase (EC 2.7.4.1) and Exopolyphosphatase (EC 3.6.1.11) genes,

which are essential in polyP synthesis and hydrolysis respectively (Figure 5.8).

Figure 5.8 Abundance of genes involved in polyP synthesis (polyphosphate kinase) and hydrolysis (exopolyphosphatase) in the three EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

Cha

pter

5: P

oly

P a

ccu

mu

lati

ng

ba

cter

ia in

pig

ge

ry w

ast

e

116

Tab

le 5

.2 P

hylo

gene

tic ta

xono

mic

com

posi

tion

of 3

EB

PR s

yste

ms

base

d on

met

agen

omic

s an

alys

is (p

H 5

.5 fi

ltere

d, p

H 5

.5 u

n-fil

tere

d,

and

pH 8

.5 u

n-fil

tere

d).

Cla

ss

Ord

er

Fam

ily

Gen

us

Abu

ndan

ce

Avg

%

iden

tity

# Hits

pH

5.5

filte

red

uncl

assi

fied

(Bac

teri

a)

uncl

assi

fied

un

clas

sifie

d

uncl

assi

fied

19

94

100.

0 25

1 G

amm

apro

teob

acte

ria

Aero

mon

adal

es

Aero

mon

adac

eae

Aero

mon

as

655

99.8

88

G

amm

apro

teob

acte

ria

Pseu

dom

onad

ales

Ps

eudo

mon

adac

eae

Pseu

dom

onas

11

5 99

.9

33

Gam

map

rote

obac

teri

a Xa

ntho

mon

adal

es

Xant

hom

onad

acea

e Xa

ntho

mon

as

96

100.

0 6

unas

sign

ed

unas

sign

ed

unas

sign

ed

unas

sign

ed

89

100.

0 36

G

amm

apro

teob

acte

ria

uncl

assi

fied

un

clas

sifie

d

uncl

assi

fied

72

99

.9

13

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Ente

roba

cter

55

99

.9

29

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Kle

bsie

lla

55

100.

0 23

G

amm

apro

teob

acte

ria

Pseu

dom

onad

ales

M

orax

ella

ceae

Ac

inet

obac

ter

53

100.

0 2

Beta

prot

eoba

cter

ia

Burk

hold

eria

les

Alca

ligen

acea

e C

aste

llani

ella

33

10

0.0

4 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Pa

ntoe

a 30

99

.4

15

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Edw

ards

iella

25

10

0.0

3 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Es

cher

ichi

a 23

10

0.0

10

Beta

prot

eoba

cter

ia

uncl

assi

fied

un

clas

sifie

d

uncl

assi

fied

20

10

0.0

10

Gam

map

rote

obac

teri

a Al

tero

mon

adal

es

Shew

anel

lace

ae

Shew

anel

la

20

99.7

4

Gam

map

rote

obac

teri

a Ae

rom

onad

ales

Ae

rom

onad

acea

e To

lum

onas

19

99

.9

3 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

C

itrob

acte

r 19

99

.9

10

Flav

obac

teri

ia

Flav

obac

teri

ales

Fl

avob

acte

riac

eae

Flav

obac

teri

um

16

99.9

9

Beta

prot

eoba

cter

ia

Burk

hold

eria

les

Burk

hold

eria

ceae

Bu

rkho

lder

ia

16

99.9

6

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Cro

noba

cter

15

99

.9

9 G

amm

apro

teob

acte

ria

Past

eure

llale

s Pa

steu

rella

ceae

Pa

steu

rella

15

10

0.0

1 Th

ese

data

are

bas

ed o

n R

DP

(min

imum

alig

nmen

t len

gth,

≥50

bp;

E-v

alue

cut

off

s, 0.

01).

Num

ber o

f seq

uenc

e fe

atur

es w

ith a

hit

no le

ss

than

20

is ig

nore

d.

Cha

pter

5: P

oly

P a

ccu

mu

lati

ng

ba

cter

ia in

pig

ge

ry w

ast

e

117

Tab

le 5

.2 P

hylo

gene

tic ta

xono

mic

com

posi

tion

of 3

EB

PR sy

stem

s bas

ed o

n m

etag

enom

ics a

naly

sis (

cont

inue

d….)

Cla

ss

Ord

er

Fam

ily

Gen

us

Abu

ndan

ce

Avg

%

iden

tity

# H

its

pH 5

.5 u

nfilt

ered

un

clas

sifie

d (B

acte

ria)

un

clas

sifie

d

uncl

assi

fied

uncl

assi

fied

19

17

99.3

22

4 G

amm

apro

teob

acte

ria

Aero

mon

adal

es

Aero

mon

adac

eae

Aero

mon

as

475

99.8

85

G

amm

apro

teob

acte

ria

uncl

assi

fied

uncl

assi

fied

un

clas

sifie

d

391

99.9

13

G

amm

apro

teob

acte

ria

Pseu

dom

onad

ales

Ps

eudo

mon

adac

eae

Pseu

dom

onas

21

1 99

.8

41

unas

sign

ed

unas

sign

ed

unas

sign

ed

unas

sign

ed

178

100.

0 43

G

amm

apro

teob

acte

ria

Aero

mon

adal

es

Aero

mon

adac

eae

Tolu

mon

as

156

99.8

4

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Ente

roba

cter

10

1 10

0.0

19

Gam

map

rote

obac

teri

a Xa

ntho

mon

adal

es

Xant

hom

onad

acea

e Xa

ntho

mon

as

86

100.

0 5

Gam

map

rote

obac

teri

a Al

tero

mon

adal

es

Shew

anel

lace

ae

Shew

anel

la

56

99.8

9

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Kle

bsie

lla

51

99.9

25

G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Sa

lmon

ella

42

99

.9

6 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Pa

ntoe

a 35

10

0.0

20

Gam

map

rote

obac

teri

a En

tero

bact

eria

les

Ente

roba

cter

iace

ae

Man

grov

ibac

ter

29

100.

0 1

Gam

map

rote

obac

teri

a Pa

steu

rella

les

Past

eure

llace

ae

Past

eure

lla

28

100.

0 1

Gam

map

rote

obac

teri

a Ps

eudo

mon

adal

es

Mor

axel

lace

ae

Acin

etob

acte

r 27

10

0.0

1 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Es

cher

ichi

a 19

10

0.0

7 G

amm

apro

teob

acte

ria

Ente

roba

cter

iale

s En

tero

bact

eria

ceae

Ed

war

dsie

lla

18

100.

0 2

Beta

prot

eoba

cter

ia

Burk

hold

eria

les

Com

amon

adac

eae

Com

amon

as

17

100.

0 3

Thes

e da

ta a

re b

ased

on

RD

P (m

inim

um a

lignm

ent l

engt

h, ≥

50 b

p; E

-val

ue c

ut o

ffs,

0.01

). N

umbe

r of s

eque

nce

feat

ures

with

a h

it no

less

th

an 2

0 is

igno

red.

Cha

pter

5: P

oly

P a

ccu

mu

lati

ng

ba

cter

ia in

pig

ge

ry w

ast

e

118

Tab

le 5

.2 P

hylo

gene

tic ta

xono

mic

com

posi

tion

of 3

EB

PR sy

stem

s bas

ed o

n m

etag

enom

ics a

naly

sis (

cont

inue

d….)

Cla

ss

Ord

er

Fam

ily

Gen

us

Abu

ndan

ce

Avg

%

iden

tity

# H

its

pH 8

.5 u

nfilt

ered

un

clas

sifie

d (B

acte

ria)

un

clas

sifie

d

uncl

assi

fied

un

clas

sifie

d

185

100.

0 89

Be

tapr

oteo

bact

eria

un

clas

sifie

d

Beta

prot

eoba

cter

ia)

uncl

assi

fied

47

99

.9

10

Actin

obac

teri

a (c

lass

) Ac

tinom

ycet

ales

M

icro

bact

eria

ceae

G

laci

ibac

ter

41

100.

0 2

Actin

obac

teri

a (c

lass

) Ac

tinom

ycet

ales

M

icro

bact

eria

ceae

C

andi

datu

s Aqu

iluna

35

99

.8

3 Ac

tinob

acte

ria

(cla

ss)

Actin

omyc

etal

es

Mic

roba

cter

iace

ae

Can

dida

tus R

hodo

luna

27

10

0.0

3 Be

tapr

oteo

bact

eria

Bu

rkho

lder

iale

s Bu

rkho

lder

iace

ae

Burk

hold

eria

22

99

.8

8 Ac

tinob

acte

ria

(cla

ss)

Actin

omyc

etal

es

Mic

roba

cter

iace

ae

Mic

roba

cter

ium

18

99

.3

7 Ac

tinob

acte

ria

(cla

ss)

Actin

omyc

etal

es

Mic

roba

cter

iace

ae

Agro

cocc

us

16

100.

0 4

Thes

e da

ta a

re b

ased

on

RD

P (m

inim

um a

lignm

ent l

engt

h, ≥

50 b

p; E

-val

ue c

ut o

ffs,

0.01

). N

umbe

r of s

eque

nce

feat

ures

with

a h

it no

less

th

an 2

0 is

igno

red.


119

Abundances of these genes within these datasets were higher in pH 5.5 filtered and

unfiltered systems compared to the control microcosms at pH 8.5 (unfiltered). In both

pH 5.5 filtered and unfiltered, polyphosphate kinase (EC 2.7.4.1) was the most abundant

gene sequence compared to other genes involved in P metabolism (Table 5.3). Before

polyP synthesis, Pi is required to be taken up and transported across the cytoplasmic

membrane. We found that high affinity Pst (phosphate specific transport) systems

(PstA, PstB, and PstC), which are involved in the uptake and transport of Pi across the

cytoplasmic membrane, were also highly abundant under pH 5.5 compared to pH 8.5

(Table 5.3). These data indicate that a selective enrichment of Pi uptake and polyP

synthesis pathways were more pronounced at an acidic pH of 5.5 compared to pH 8.5.

In terms of phylogenetic and functional diversity, the presence of the ppk genes were

mainly related to homologs from Aeromonas hydrophila, Aeromonas salmonicida,

Enterobacter sp, Pseudomonas aeruginosa, Klebsiella variicola, Citrobacter koseri,

Salmonella enterica, and Bordetella parapertussis (Table 5.4). Burkholderia like ppk

sequences were dominant among the bacteria expressing Polyphosphate kinase at pH

5.5, whereas under pH 8.5 conditions homologs relating to Bordetella parapertussis,

Bordetella avium, Burkholderia mallei, Kribbella flavida, Kineococcus radiotolerans,

Cellulomonas flavigena and Chromobacterium were the most abundant (Table 5.4).


120

Table 5.3 Most abundant gene sequences involved in P metabolism in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered).

Function abundance

Polyphosphate kinase (EC 2.7.4.1) 762Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 511Alkaline phosphatase (EC 3.1.3.1) 460NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 435Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 433NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 380Low-affinity inorganic phosphate transporter 366Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 347Predicted ATPase related to phosphate starvation-inducible protein PhoH 344Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 342Guanosine-5'-triphosphate,3'-diphosphate pyrophosphatase (EC 3.6.1.40) 340Exopolyphosphatase (EC 3.6.1.11) 326Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 272Inorganic pyrophosphatase (EC 3.6.1.1) 240PhoQ 237Sodium-dependent phosphate transporter 226Probable low-affinity inorganic phosphate transporter 222

Polyphosphate kinase (EC 2.7.4.1) 671NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 447Phosphate transport system permease protein PstC (TC 3.A.1.7.1) 436NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 415Low-affinity inorganic phosphate transporter 397Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 374Phosphate ABC transporter, periplasmic phosphate-binding protein PstS (TC 3.A.1.7.1) 365Guanosine-5'-triphosphate,3'-diphosphate pyrophosphatase (EC 3.6.1.40) 347Alkaline phosphatase (EC 3.1.3.1) 333Phosphate transport system permease protein PstA (TC 3.A.1.7.1) 323Predicted ATPase related to phosphate starvation-inducible protein PhoH 317Exopolyphosphatase (EC 3.6.1.11) 299Inorganic pyrophosphatase (EC 3.6.1.1) 271Phosphate regulon sensor protein PhoR (SphS) (EC 2.7.13.3) 255Phosphate regulon transcriptional regulatory protein PhoB (SphR) 229Phosphate transport system regulatory protein PhoU 222

Pyrophosphate-energized proton pump (EC 3.6.1.1) 557Polyphosphate kinase (EC 2.7.4.1) 453NAD(P) transhydrogenase subunit beta (EC 1.6.1.2) 347NAD(P) transhydrogenase alpha subunit (EC 1.6.1.2) 290Predicted ATPase related to phosphate starvation-inducible protein PhoH 254Phosphate transport ATP-binding protein PstB (TC 3.A.1.7.1) 251Number of sequence less than 200 is ignored

pH 5.5 filtered

pH 8.5 unfiltered

pH 5.5 unfiltered


121

Table 5.4 Functional affiliations (homologs) of PAOs in 3 EBPR systems (pH 5.5 filtered, pH 5.5 un-filtered, and pH 8.5 un-filtered) based on the presence of the ppk genes

Organism Reference Seq. ID Identity

Aeromonas hydrophila subsp. hydrophila ATCC 7966 YP_857333.1 61/61 (100%)Aeromonas salmonicida subsp. salmonicida A449 YP_001141341.1 71/71 (100%)Enterobacter sp. 638 YP_001177705.1 79/79 (100%)Pseudomonas aeruginosa 2192 ZP_04937198.1 31/31 (100%)Klebsiella variicola (strain At-22) YP_003438168.1 75/75 (100%)Citrobacter koseri ATCC BAA-895 YP_001451886.1 31/31 (100%)Salmonella enterica subsp. enterica serovar Typhi Ty2 ZP_06546958.1 64/64 (100%)Bordetella avium 197N YP_785463.1 36/41 (88%)Bordetella parapertussis 12822 NP_884330.1 26/27 (96%)Burkholderia YP_559732.1 49/65 (75%)

Aeromonas hydrophila subsp. hydrophila ATCC 7966 YP_857333.1 73/73 (100%)Aeromonas salmonicida subsp. salmonicida A449 YP_001141341.1 22/22 (100%)Pseudomonas aeruginosa 2192 ZP_04937198.1 49/49 (100%)Enterobacter sp. 638 YP_001177705.1 38/38 (100%)Shigella flexneri 2457T (serotype 2a) NP_838046.1 29/29 (100%)Klebsiella variicola (strain At-22) YP_003438168.1 39/39 (100%)Salmonella enterica subsp. enterica serovar Typhi Ty2 ZP_06546958.1 28/28 (100%)Bordetella avium 197N YP_785463.1 50/58 (86%)Bordetella parapertussis 12822 NP_884330.1 44/50 (88%)Citrobacter koseri ATCC BAA-895 YP_001451886.1 74/74 (100%)

Bordetella parapertussis 12822 NP_884330.1 57/61 (93%)Bordetella avium 197N YP_785463.1 26/29 (90%)Burkholderia mallei NCTC 10247 YP_001026565.1 27/32 (84%)Kribbella flavida DSM 17836 YP_003378870.1 44/50 (88%)Kineococcus radiotolerans SRS30216 YP_001360629.1 29/35 (83%)Cellulomonas flavigena DSM 20109 Unclassified. YP_003637899.1 66/72 (92%)Chromobacterium violaceum ATCC 12472 Unclassified NP_903027.1 45/57 (79%)Dechloromonas aromatica RCB YP_286024.1 38/42 (90%)Beutenbergia cavernae DSM 12333 Unclassified. YP_002883432.1 61/74 (82%)Jonesia denitrificans DSM 20603 YP_003162175.1 57/63 (90%)

pH 5.5 filtered

pH 5.5 unfiltered

pH 8.5 unfiltered (control)


122

5.4. Discussion

This study provides a comprehensive understanding of key PAOs and their diversity in

identity and functionality under two different pH conditions (pH 5.5 and pH 8.5) of high

P loaded wastewater. The results obtained using coincident single cell and population

approaches verified that maintaining pH of the wastewater at 5.5 would be a good

strategy to remove P from the high P loaded wastewater systems.

Quantification of the polyP formation seems to be a more accurate way for the

assessment of the performances of EBPR systems. The presence of intracellular polyP

granules inside PAOs is a good marker to prove the reduction of Pi level after EBPR,

due to the activity of PAOs rather than by the P assimilation alone. DAPI staining at a

higher concentration allows the quantification of intracellular polyP without the

requirement for prior polyP biopolymer isolation (Kulakova et al. 2011).

Using both epi-fluorescence microscopy and flow cytometry methods, we demonstrated

that 15 ug/mL concentration of DAPI was sufficient to obtain good signal to noise ratios

for the wastewater samples tested here when staining for the detection of polyP

accumulation. This concentration provided a good cytometric discrimination of polyP

containing bacteria from other non-target bacteria and is in good agreement with

previous findings (Kawaharasaki et al. 1999). Due to the fact that the environmental

matrices vary depending upon sample type, impurities, cellular abundance, and machine

sensitivity, we also suggest performing titration analyses of DAPI for the new

environments to obtain the optimum cytometric discrimination. Moreover, among the

different ways of quantitative visualization of polyP granules in microorganisms

(Serafim et al. 2002; Günther et al. 2009), DAPI staining is easily adaptable for a direct

application on environmental samples.

In order to test the hypothesis of enhanced P uptake at reduced pH (pH 5.5) in high P

environments, we applied the optimised polyP staining protocols to acidified and

control microcosms and subsequently challenged them with phosphorus amendments.

Specifically, we observed enhanced P accumulation, as measured by numbers of

accumulating cells, and determined around 70% of cells under acidic conditions (pH

5.5, unfiltered) were actively accumulating P, almost twice the number under control

(pH 8.5, unfiltered) (ca. 36%). Therefore, our findings support the hypothesis that

polyP accumulation in high P containing systems, such as piggery wastewater, can be

significantly enhanced by acidic manipulation. This result is consistent with previous


123

findings in other treatment systems, where growth of PAOs were enhanced and the

aerobic uptake of phosphate reached a maximum at pH 5.5 (McGrath et al. 2001;

Mullan et al. 2002a; Moriarty et al. 2006). Epi-fluorescence microscopy revealed that

polyP accumulating microalgae were highly abundant in unfiltered samples treated at

pH 5.5 with flow cytometry also confirming the differences of polyP accumulation

between filtered and unfiltered samples as ca. 20 %, presumably due to the additional

uptake by microalgae within the community. The formation of polyP during growth at

pH 5.5 could infer that pH regulates intracellular phosphate levels in bacteria and

microalgae (McGrath et al., 2001). Therefore, we surmise that, in addition to bacterial

PAOs, a number of microalgae play a significant role in polyP accumulation in this

waste system, an observation that is also consistent with previous findings (Powell et al.

2011; Powell et al. 2008).

In addition, here we addressed the question of identity and function of key PAO

diversity and functionality under two different pH conditions using next generation

sequencing approaches applied to the EBPR systems. We observed that both 16S rRNA

gene and shotgun metagenomic approaches yielded uniform taxonomic identities of the

key PAO organisms within the EBPR systems tested. Specifically, 16S rRNA Ion Tag

sequencing for microcosms at pH 5.5 indicated different phylogenetic composition of

the communities when compared to pH 8.5. PolyP accumulating communities

maintained at pH 5.5 were dominated by Gammaproteobacteria, represented by the

Aeromonadaceae, Enterobacteriaceae, Alteromonadales, Citrobacter, Pseudomonas,

Acinetobacter and, Shewanella. These taxa are known polyphosphate accumulating

bacteria and have been detected in other wastewater treatment systems (Sidat et al.

1999; Nielsen et al. 2010), with polyP synthesis being widely studied in Escherichia

coli, Pseudomonas aeruginosa and Acinetobacter spp. (Kornberg 1999). Indeed, based

upon the current literature, a broad spectrum of microbial phyla is able to accumulate

polyP including Actinobacteria, Bacteroidetes, and Proteobacteria

(Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria), indicating that

PAOs are ubiquitous, with only relative proportions varying upon the differences in the

types of wastewater treatment plant (Mehlig et al. 2013).

Although substantial progress has been made towards identifying the species of PAOs

involved in EBPR, knowledge gaps still exist in understanding the linked functional and

phylogenetic gene content of EBPRs, especially for high P remediating systems. In

order to address this, we performed shotgun sequencing of total DNA and grouped


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sequences at the 97% sequence similarity level. Using a PCR independent approach,

we found that the Gammaproteobacteria were at a consistently higher abundance when

employing shotgun sequence analysis from microcosms at pH 5.5. Specifically, both

filtered and unfiltered microcosms comprised of Aeromonas, Pseudomonas,

Xanthomonas, Enterobacter, Klebsiella, Acinetobacter, Castellaniella, Pantoea,

Edwardsiella, Escherichia, Shewanella, Tolumonas, Citrobacter, Flavobacterium,

Burkholderia, Cronobacter and Pasteurella.

PolyP synthesis is catalyzed by a polyphosphate kinase in prokaryotes, whilst

hydrolysis of the terminal phosphate residues from polyP to form orthophosphate is

catalyzed by exo- and endo-polyphosphatases. Exo-polyphosphatases are considered as

the central regulatory enzymes in polyP metabolism (Espiau et al. 2006), with the

bacterial ppk1 gene, encoding for the enzyme polyphosphate kinase responsible for

polyP synthesis in many bacteria (Mielczarek et al. 2013). For this reason, they can be

used as reliable indicators for the assessment of the performances of EBPR systems.

Comparing the functional genes involved in polyP metabolism, we observed a higher

abundance of genes involved in phosphorus metabolism under pH 5.5, indicating a

specific environmental selection for more polyP genes under acidic conditions. We

found that high affinity Pst (phosphate specific transport) systems (PstA, PstB, and

PstC) which are involved in the uptake and transportation of Pi across the cytoplasmic

membrane were highly abundant under pH 5.5 compared to pH 8.5. The exact

physiological role of the polyP accumulation in sludge microorganisms at pH 5.5 is

currently unknown (McGrath et al. 2001). However, it was reported that phosphate is

transported by the Pst system in the form of H2PO4- and HPO4

2- and the proportion of

H2PO4- increases with decreasing pH (van Veen et al. 1994). Therefore, we could

assume that under pH 5.5, uptake of Pi is enhanced and subsequently increases the

accumulation of Pi in cells as polyP. McGrath et al. (2001) have suggested that growth

at an external pH close to the phosphate transport optimum (pH 5.0-6.5) may well result

in an increased phosphate uptake and the elevation of intracellular phosphate

concentrations.

Functional affiliations of the ppk gene suggested that Aeromonas hydrophila,

Acinetobacter baumannii, Bordetella pertussis, Escherichia coli, Klebsiella,

Pseudomonas aeruginosa, Salmonella enterica, Shigella flexneri type species were

responsible for enhancing the polyP activity of the acid induced EBPR systems. The

revealed functional diversity was also in good agreement with a recent listing of species


125

possessing sequences homologous to polyphosphate kinase (ppk) (Rao et al. 1998).

Oehmen et al. (2007) have also showed the involvement of ppk in poly-P synthesis

within EBPRs, but there are still few studies which demonstrate the genetic potential of

the PAO community by metagenomic analysis (Martin et al. 2006; Temperton et al.

2011; Albertsen et al. 2012) or by chemical fingerprinting (Majed et al. 2012). We

believe this is the first study that investigates taxonomic and functional identity of

PAOs in acid induced EBPR system by DNA based metagenomic analyses.

5.5 Conclusions

In essence, this study has developed a practical and consistent approach to identify

polyP accumulating bacteria populations in EBPR processes in piggery waste effluent

and resolved key diversity and pathways present within a high P wastewater treatment

system. Specifically, flow cytometric and tag sequencing data supported our hypothesis

that there is an increase in polyP uptake and a greater activity, and abundance of PAOs

under more acidic conditions.

These findings indicate that polyphosphate accumulation within EBPRs could be

enhanced for P removal in piggery waste effluent and other EBPR systems where the

treatment of effluent wastewater is a problem. The information gained here by altering

the pH provides a basis of a novel strategy for improving the waste treatment process

and developing high value fertilisers for land application. Subsequent investigations

should therefore focus on assessing the economic feasibility of incorporating EBPR

systems into existing water treatment systems by lowering the pH of the aerobic pond.

Secondly, new methods and technologies should be explored for enhancing

polyphosphate accumulating organisms. Choosing one of the established separation

technologies (such as filtration, sedimentation, or air flotation) for the subsequent

harvesting stages would then remove the phosphorus rich accumulated microorganisms

from the system, which can further be used as slow releasing P fertilisers, and leave a

treated effluent with lower P concentration in the system. The application of an

integrated EBPR to the on-farm waste treatment systems, including covered anaerobic

ponds (CAPs), would also give the farmers an opportunity to recycle this treated

effluent during irrigation.

Chapter 6: Application of piggery waste compost to soil

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CHAPTER 6

Effect of Low Rate Application of Banded Pelletised Pig

Compost on Plant Growth and Soil Microbial Community

Composition

6.0 Abstract

Recycling composted piggery waste, which is generally high in phosphorus (P), is

gaining interest to augment inorganic P fertiliser. Precise placement of compost in the

root zone by pelletising and mixing with a low rate of inorganic fertiliser could support

application of compost at an economically viable level in broad-acre agriculture and

horticulture. This study compared the effects of a low application rate (50 kg ha-1) of

banded pelletised pig compost (Balance®) in combination with an inorganic fertiliser

(Agras®) with single applications applied at a commercial rate (100 kg ha-1) on plant

growth, soil fertility and changes in bacterial and fungal communities. Wheat was

grown in pots with 4 treatments: (1) Agras® 100 kg ha-1, (2) Balance® 100 kg ha-1, (3)

Balance® 50 kg ha-1 + Agras® 50 kg ha-1, and (4) a nil application. The experiment was

harvested 4, 6, and 8 weeks from sowing. Shoot and root dry weights, plant P uptake,

and arbuscular mycorrhizal (AM) colonisation of roots were assessed. Soils were

analysed for electrical conductivity (EC), pH, total carbon (TC), total nitrogen (TN),

available P, nitrate nitrogen (NH3-N), and (ammonium-nitrogen) NH4+-N. The diversity

of bacteria was analysed using 16 S rRNA Ion Tag sequencing for both rhizosphere soil

and plant root colonising bacteria. There was a positive correlation between soil

available P and plant P uptake, and a strong negative relationship between soil available

P level and AM colonisation, irrespective of the source of P. Banding of Balance®50 kg

ha-1+Agras®50 kg ha-1 was the most effective treatment for wheat growth and soil

fertility. Changes in bacterial community composition for this soil amendment were

associated with an increase in soil available P, plant P uptake, and shoot and root dry

weights. The blend of the reduced rate of inorganic P fertiliser and pelletised piggery

compost (50 kg ha-1 each) placed in close proximity to seeds at sowing could be more

effective than application of fertiliser applied alone at a rate of 100 kg ha-1.


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6.1 Introduction

Potential difficulties in maintaining food security are linked to future shortages of

phosphorus (P) fertiliser due to limitations in energy and resources (Hammond et al.

2004; Shafiee and Topal 2010; Beardsley 2011). Lower quality ores and a reduction in

the supply of new deposits will further impede P extraction (Beardsley 2011; Cordell et

al. 2011). Thus, there is an ongoing interest in finding more sustainable P fertiliser

supplies. Two major opportunities exist for conserving the world's phosphate resources:

(1) recycling waste materials, and (2) more efficient use of inorganic P fertilisers in

agriculture (Cordell et al. 2009; Güngör and Karthikeyan 2008; Beddington 2010;

Childers et al. 2011).

By-products arising from treated animal waste can be a valuable resource for renewable

energy production and an economical source of P for agriculture (De-Bashan and

Bashan 2004; Güngör and Karthikeyan 2008; Westerman et al. 2010). It has been

previously shown that the treatment of animal waste using anerobic digestion recovers

wide range of P fertilizers; liquid P-fertilisers (digested effluent), slow release P-

fertilisers (struvite) and soil stabilisers (digestate, sludge) (Westerman et al. 2010).

Animal waste, including piggery waste, can be high in P (Poulsen 2000; Güngör and

Karthikeyan 2008) and the application of animal manure to soil can enhance plant P

nutrition, crop performance, soil quality and microbial activity (Greaves et al. 1999;

Motavalli and Miles 2002; Parham et al. 2002). However, despite these benefits there

are considerable risks associated with application of animal manure to soil such as,

odour, greenhouse gas emissions (GHG), leaching, toxicity, and pathogen survival.

Composting waste prior to land application offers the possibility of reducing these risks

(Vanotti et al. 2009).

Composting has not been widely adopted by the agricultural sector in Australia

primarily because it is uneconomical and impractical in relation to transport and its

application to larger land areas. It is not very common in Australia, as the low gross

margins per hectare, large farms and relatively long distances from compost suppliers

generally make compost an uneconomical soil amendment (Quilty and Cattle 2011).

Preparation of compost as a pellet is of interest due to the ease of handling and the

opportunity to band compost at seeding using air-seeding equipment. Placement of

fertiliser in a concentrated band beneath the seed at sowing is more efficient than


128

surface broadcasting of fertiliser which can be removed by water and/or wind erosion,

intercepted by weeds or otherwise lost before it reaches the root zone (Halvorson et al.

1997). Application of pelletised compost using air-seeding equipment allows placement

of compost in the root zone for efficient nutrient supply for crops (Barton et al.

unpublished).

The second opportunity for conserving the world's phosphate resources is more efficient

use of synthetic P fertilisers in agriculture. A range of soil amendments based on

organic materials has been used successfully to decrease the amount of synthetic

fertiliser used per yield. Biochar is an example of a soil amendment which has been

banded in this manner (Blackwell et al. 2010; Solaiman et al. 2010). Banding biochar in

broad-acre crops allowed the application rate to be reduced from a range of 20-60 t/ha

for wheat (Castaldi et al. 2011; Prendergast-Miller et al. 2011) to 1 t/ha (Blackwell et al.

2010) for wheat. Banding compost could produce similar reductions in average broad-

acre rates of application which are 5 t/ha in Australia (Quilty and Cattle 2011). These

rates can be reduced even further by pelletising the compost for efficacy (Barton et al.

unpublished). Pelletising allows compost materials to be mixed with granulated

synthetic fertilisers and sown directly into the fertiliser band, while compost cannot be

used in conventional seeding equipment used in broad-acre agriculture when it is in its

natural loose form as it can block the delivery tubes. In this manner, a sufficient level of

compost could be achieved in the root zone with significantly less applied overall.

Pelletised compost has been trialled (Rao et al. 2007; Yan et al. 2001), and several

businesses produce this form of compost in Australia (Quilty and Cattle 2011).

Nevertheless, further research is needed to develop these pellets as P-fertilisers and to

ensure their application is economical, sustainable and practical from an operations

perspective.

The present study investigated whether piggery compost as pellets in combination with

a low rate of synthetic P fertilisers could contribute to plant growth with more

sustainability use of P fertiliser. Pelletised compost derived from remediated piggery

waste was applied with a low rate of inorganic fertiliser to assess the effect on plant

growth, soil nutrient improvement and fungal-bacterial community composition in soil.

Accurate placement of compost in the root zone following pelletising and mixing with

fertiliser when sowing cereal crops has been observed a in recent trial to allow an order

of magnitude reduction in compost and a 50% reduction in phosphorus fertiliser rates


129

(Barton et al. unpublished). However, the mechanisms involved in increasing yield with

small inputs of compost and lower fertiliser inputs are not fully explained. On the other

hand, it has been shown throughout this thesis that piggery wastes can be high in both

inorganic and organic P forms and P cycling microorganisms (Chapters 3 to 5).

Recycling piggery by-products as pelletised compost could potentially introduce some

beneficial bacteria into the soil and they could play an important role in P turnover in

soils. However, the identity of the microorganisms and mechanisms involved in P

transformation is largely unresolved in soils amended with piggery compost. Without

this knowledge the potential to further increase yield or reduce fertiliser application may

be overlooked.

In order to understand the effect of the reduced rate of organic-synthetic combination in

agriculture the following hypothesis were tested.

1. Banding pelletised piggery compost at low rates in combination with inorganic

fertiliser in the root zone of wheat facilitates nutrient uptake by the roots of the

plant in a P deficient agricultural soil alters the abundance and community

composition of bacteria involved in increasing P availability in soil, and

enhances plant growth.

2. The increase in P in soil following application of synthetic P fertiliser, in the

presence or absence of compost, will decrease the percentage of root length

colonised by arbuscular mycorrhizal (AM) fungi but increase the length of root

colonised by AM fungi grown in this soil in line with the availability of soil P

and root growth.

6.2. Materials and Methods

6.2.1 Experimental design

Wheat (Triticum aestivum L. cv. Wyalkatchem) was grown in pots (1 kg soil) for 8

weeks (from the 12th of October 2013 to 7th of December 2013) in a glasshouse at The

University of Western Australia, Crawley, Australia. The experiment was set up in a

complete randomised design with 4 treatments and 3 replicates were carried out for each

batch. Two different fertilisers were used (organic and synthetic). A commercial

pelletised compost product was used as the organic fertiliser. The pelletised compost


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product, Balance® (http://www.cwise.com.au), was derived from a blend of piggery

and other wastes, fully composted to meet ‘maturity index 3’ in the AS4454 (2012)

standard for composts (http://www.recycledorganics.com/lab/). Balance® was used in

combination with nitrogen/phosphorus/sulphur (NPS) synthetic fertiliser (Agras®)

(http://www.csbp-fertilisers.com.au). The application rate for the experiment was

decided on the basis of a previous observation (Barton et al. unpublished). Treatments

(Table 6.1) were applied to pots at the following equivalent rates: (1) Agras® 100 kg ha-

1, (2) Balance® 100 kg ha-1, (3) Balance® 50 kg ha-1+Agras® 50 kg ha-1 (4) control (no

nutrients were added, nil application). Treatment 1 simulates seeding practice of

conventional N and P chemical fertiliser (100 kg ha-1) in Western Australian wheat

growing regions. Agras® has been used as a fertiliser under Western Austrian conditions

for more than 30 years and adequate levels of nitrogen are supplied at seeding for

situations requiring low total nitrogen inputs. Agras® has relatively high nitrogen to

phosphorus ratio and is claimed to be an ideal starter fertiliser for canola and cereals

(http://www.csbp-fertilisers.com.au). Treatment 2 simulates the seeding practice

recommended by Cwise (http://www.cwise.com.au/) when using their Balance®

product. Treatment 3 simulates the reduction in the amount of fertiliser recommended

by Cwise compared to the conventional practice. Treatment 4 is a control (no nutrients

or compost were added). Fertilisers were placed beneath the soil surface in close

proximity to the seed to simulate application through an air-seeder. Typical

characteristics of Balance® and Agras®, and comparative nutrient contents of each

treatment, are shown in Table 6.2, Table 6.3, and Table 6.4 respectively.

Table 6.1 Soil amendments used in this experiment and their corresponding abbreviations. Treatments Abbreviations

Agras® 100 kg ha-1 Agras100

Balance® 100 kg ha-1 Balance100

Balance® 50 kg ha-1 + Agras® 50 kg ha-1 Balance50/Agras50

Control (a nil application) Control

http://www.recycledorganics.com/lab/


131

Table 6.2 Typical characteristics of the pelletised compost, Balance® . pH 8.1

Mg (mg/kg) 3500

EC (dS/m) 8

Mn (mg/kg) 280

TC (%) 37.1

Mo (mg/kg) 3.6

Organic C (%) 34.6

NO3-N (mg/kg) 24

TN (%) 2.55

NH4-N (mg/kg) 205

C:N 15

Na (mg/kg) 4200

TP (mg/kg) 7300

S (mg/kg) 10000

Ca (mg/kg) 360000

Se (mg/kg) 1

Co (mg/kg) 3.2

Cl (mg/kg) 0.95

B (mg/kg) 18

Zn (mg/kg) 330

Fe (mg/kg) 5100

Si (mg/kg) <0.1

K (mg/kg) 11000 CEC (cmol (+)/kg) 45

Source: Barton et al. unpublished

Table 6.3 Typical analysis of the granulated fertiliser, Agras®.

N (w/w %)

16.1

P (w/w %)

9.1

S (w/w %)

14.3

Ca (w/w %)

0.5

Zn (w/w %) 0.06

Source: http://www.csbp-fertilisers.com.au

Table 6.4 Relative N and P application rates of three fertiliser treatments applied to wheat. Rates are shown in both kg ha-1 and mg/pot basis. Treatments*

1 2 3

N (kg ha-1) 16.1 2.6 9.3

N (mg/pot) 28.5 4.5 16.5

P (kg ha-1) 9.1 0.7 4.9

P (mg/pot) 16.1 1.3 8.7

*1) Agras® 100 kg ha-1 2) Balance® 100 kg ha-1 3) Balance® 50 kg ha-1 + Agras® 50 kg ha-1


132

6.2.2 Soil collection and potting

Soil with low available P was collected for the experiment (0-10 cm depth) from a

broadacre cereal farm at The University of Western Australia Farm, Pingelly WA

(UTM 50H. 498440 m E, 6406561 m S). The soil properties are shown in Table 6.5. At

the time of sampling vegetation was dominated by subterranean clover and Wimmera

ryegrass, with low input management. The soil was sieved to 2 mm before potting. Six

wheat seeds were planted in each pot at a depth of 30 mm and seedlings were thinned to

3 per pot after germination. The pots were watered to and maintained at 80 % water

holding capacity.

Table 6.5 Soil properties at the field sampling site, Pingelly.

Soil characteristics Texture class Loamy Sand C (%) 1.2 N (%) 0.06 C/N ratio 19 pH (in water) 6.26 pH (in CaCl2) 5.06 EC (mS/m) 7.42 Exchangeable ions (meq/100g) CEC 4 Ca 1.6 Mg 1.6 Na 0.6 P retention index 3.7 Bicarbonate extractable P (mgP/kg) 9.53 Bicarbonate extractable K (mgK/kg) 126

6.2.3 Soil and plant analyses

At the end of each harvest (4, 6, and 8 weeks), roots were carefully lifted out of the soil

and shaken vigorously to remove loose adhering soil. The tightly adhering rhizosphere

soil was collected and used for subsequent soil analyses. Fresh shoot weight was taken

and oven-dried at 60°C for 72 h and total shoot dry weights per pot for each treatment

was calculated. The roots were washed well with water to remove the remaining

adhering soil particles, blotted dry, weighed, cut into 1 cm segments and mixed

thoroughly. Known weights of subsamples were taken for DNA extraction and root

staining (AM colonisation). The root fragments for DNA extractions were further cut


133

into segments several mm long at the time of harvesting and stored at -80oC for

molecular analysis. The remaining roots were oven-dried at 60°C for 72 h and total root

dry weights per pot for each treatment was calculated taking to consideration the weight

taken for DNA extraction and root staining.

Oven-dried shoots were ground and digested (HNO3–HClO4) and the P concentration in

the digest was measured by the molybdovanadophosphate method. Basic soil chemical

parameters were measured (EC, pH, soil available P, total carbon and total nitrogen,

NH4+ and NO3

–). Available P from the soil was extracted with 0.5 M aqueous NaHCO3-

(pH 8.5) and measured colorimetrically (Murphy and Riley 1962). The soil EC was

measured in water at 1: 5 (w/v) ratios. Soil pH was also measured in CaCl2 at 1:5 (w/v)

ratios. Total carbon and total N in ground soil and plant were assessed using combustion

analysis using an Elementar analyser (vario Macro CNS; Elementar, Germany). Soil

NH4+ and NO3

– were measured by extracting 20 g with 80 mL 0.5 M K2SO4 and

analysing the extracts colorimetrically for NH4+ using the salicylate–nitroprusside

method (Searle 1984) and NO3– concentration using the hydrazine reduction method

(Kempers and Luft 1988) on an automated flow injection Skalar AutoAnalyser (San

plus, Skalar Analytical, The Netherlands). All measurements were completed in

triplicate.

6.2.4 Determination of root length and arbuscular mycorrhizal (AM) colonisation

The root sub-samples (0.20-0.50 g fresh weight) taken for staining were used to assess

AM fungal colonisation. Roots were cleared in 10% KOH, acidified, and stained with

Trypan blue (0.05%) in lactoglycerol (1:1:1.2 lactic acid:glycerol:water) and de-stained

in lactoglycerol (Abbott and Robson 1981). Root length and root length colonised by

AM fungi were assessed by using the gridline intercept method scoring more than 100

intercepts per pot under a microscope at 100× magnification (Giovannetti and Mosse

1980).

6.2.5 DNA extraction and Ion Tag sequencing

DNA was extracted from both rhizosphere soil and roots at the second harvest (at 6

weeks) and used for subsequent sequencing for rhizosphere bacteria and root colonising

bacteria. DNA was extracted from 0.5 g of rhizosphere soil taken form each soil

amendments using the MoBio Powersoil DNA isolation kit (Geneworks, Australia),

utilising beat beating and column purification, according to the manufacturer's


134

guidelines. All the extractions were done in triplicates. Extracted DNA was quantified

and checked for purity at A260/280 nm (Nanodrop,Thermo Fisher Scientific, USA)

prior to storage at -20 °C.

Root DNA was extracted in triplicates using 50-100 mg of roots collected randomly

from well-mixed root fragments stored at -80 oC for the molecular analysis. Total DNA

was extracted from roots using the PowerPlant® pro DNA isolation kit (MO BIO, USA)

according to the manufacturer's guidelines. Extracted DNA was quantified and checked

for purity at A260/280 nm (Nanodrop,Thermo Fisher Scientific, USA) prior to storage

at −20 °C. Both rhizosphere soil and roots DNA samples were further diluted to 1ng/uL

for PCR amplification.

For the sequencing of both rhizosphere and root colonised bacteria, PCR was performed

on the 16S rRNA genes using V4/5 Domain specific primers (Appendix 3). Each

individual DNA sample had a unique Golay barcode added to the primer. In brief,

primers were labelled according to whether they have sequencing adaptors or not. For

example 806R_BACT is the untagged reverse bacterial V4/5 primer, whilst

806R_BACT_P1 is the tagged version. 515F-BACT is the forward untagged Bacterial

V4/5 primer. Whilst, 515_BACT_A_xx (barcode) is the forward tagged primer. For the

untagged and the reverse primers, a mixture was made (universal primer mix). The

universal primer mix was made in low Tris-EDTA (TE) buffer to obtain the desired

primer concentrations as 806R_BACT_P1 (final concentration: 4uM), 515F_BACT

(final concentration: 0.44 uM), 806R_BACT (final concentration: 0.44 uM), and there

was no barcoded forward tagged primer added in this mixture. The PCR was set up in

total volume of 20 μL. In brief, 18 uL of master mix per reaction [(H2O; 8.56 μL, BSA-

non-acetylated (50 ug uL-1); 0.24 μL, universal primer mix (4 uM); 1.20 μL, 5PRIME

HOT MasterMix (2.5x); 8 μL] was added with 1uL barcoded forward primer (5 uM)

and 1 uL DNA template (1.0 ng uL-1) to each reaction. One uL H2O was used for NTC

(no template control) and any barcode. The reaction conditions were 94 °C for 2 min

(initial denaturation) followed by 25 cycles of 94 oC, 45 S at (denaturation); 50 °C, 60 S

(annealing); 65 °C, 90 min (extension) and another 2 cycles of 94 oC, 45 S

(denaturation); 65 °C, 90 S (annealing/ extension) and a final extension step at 65°C for

10 min using a thermocycler (Techgene, Techne Inc, New Jersey, USA). All PCR

products (5 uL) were checked for size and specificity by electrophoresis on a 2%

agarose gel with a HyperLadder 1. Concentration of 1 uL of each reaction was


135

measured using Qubit including the NTC. Baseline acceptable concentration was

established using the NTC, to determine pass/failure performance metrics (e.g. set

minimum concentration to 2x higher concentration than the NTC). The highest

concentration in the batch of amplicons to be pooled was determined. One uL of that

sample was used for pooling (e.g. 1 uL of 10.5 ng uL-1 reaction), and an equivalent

amount (in ng) of all the other reactions (e.g. 2uL of a 5.25ng uL-1 reaction).

Purification was done (LSBFG uses Ampure XP reagent, at a ratio of 1.2x volume), to

remove excess primers; elute in Low TE (10mM Tris, 0.1mM EDTA) or 10mM Tris-

HCl. The pool was quantified using Qubit to get an approximate concentration, which

were used to dilute the pool to the appropriate concentration range for the Agilent

Bioanalyzer. The sequencing was performed using 400 base-pair chemistry in

accordance with the manufactures protocol using the OneTouch Ion sphere particle

(ISP) emulsion and recovery. The samples were then washed and enriched prior to been

loaded onto the Semi-conductor chip. The enriched ISP was then sequenced using the

Ion Torrent Personal Genome Machine. The results were split into fasta and qual files,

and analysed using the QIIME pipeline (Caporaso et al. 2010). Assigning the

multiplexed reads to samples was done using default parameters (minimum quality

score = 25, minimum/maximum length = 130/220, no ambiguous base calls, remove

reverse primers and no mismatches allowed in the forward and reverse primer

sequences). The rest of the analysis was done as described in the Chapter 3 (section

3.2.4).

6.2.6 ANOVA and multivariate statistical analysis

All environmental variables were analysed by two-way ANOVA using the R statistics

package (V 2.13.0 © 2011 The R Foundation for Statistical Computing). Separation of

means was done using the least significant difference (LSD) method. Canonical

Correspondence Analysis (CCA) was used to model the changes in the community

profile of the different treatments to the measured variables (Jongman et al. 1995) to

explore which of these parameters best explained the differences in bacterial

communities between treatments. Triplicated samples for each treatment from the

second harvest (at 6 weeks) were used to generate a sequencing data matrix of relative

taxon abundances for both of the soil and root colonised bacteria. A corresponding

matrix of the plant variables (Table 6.5) and soil variables (Table 6.6) for each treatment

was also prepared in triplicates. Canonical correspondence analysis (CCA) performed


136

using the software package Canoco v4.55 (Plant Research International © 2006). The

results were analysed to ascertain which covariates best explained the changes in

bacterial community profiles (Jenkins et al. 2009).

6.3. Results

6.3.1. Effect of soil amendments on plant growth, P uptake and AM colonization

The soil amendments showed significant differences in measured plant properties (Table

6.6). The shoot dry weight (DW) for all the soil amendments increased significantly

(P<0.001) with time and the highest shoot DW was observed in Balance50/Agras50 for

all the 3 harvests (Figure 6.1a; Table 6.6). The unamended soil (control) had the least

shoot DW for all the 3 harvests. Shoot and root DW following application of Agras100

was significantly lower at weeks 4 and 6 (Table 6.6) and had increased significantly

(P<0.001) at the third harvest (8 weeks). Root DW showed similar trends as shoot DW

except that application of Agras100 had the highest root dry weight at 8 weeks

compared to the other treatments (Figure 6.1b; Table 6.6). The total root length (m per

pot) for all the soil amendments increased significantly (P<0.001) over time and

significantly greater (P<0.001) total root length was observed for Balance50/Agras50 in

comparison with other soil amendments (Figure 6.1c). Adding pelletised compost in

combination with synthetic fertiliser at a low rate (50 kg ha-1) resulted in better plant

shoot and root growth compared to their single application at higher rates (100 kg ha-1).

There was better plant establishment and faster plant growth with Balance50/Agras50

compared to other treatments at each harvest. In contrast, poor plant establishment was

observed for the soil amended with Agras100, Balance100, and the control until the

second harvest (at 6 weeks). Thereafter, application of Agras100 increased tillering and

panicle formation compared to application of Balance100 and the control. At the last

harvest (8 weeks), the greatest plant growth was observed for the treatments with

application of Balance50/Agras50 followed by Agras100 (Appendix 4).

Cha

pter

6: A

pplic

atio

n of

pig

gery

was

te c

ompo

st to

soil

137

Tab

le 6

.6 E

ffec

t of d

iffer

ent s

oil a

men

dmen

ts o

n m

easu

red

plan

t pro

perti

es (s

hoot

and

root

dry

wei

ght,

tota

l roo

t len

gth,

sho

ot P

con

cent

ratio

n, A

M

colo

nise

d ro

ot le

ngth

, and

AM

col

onis

atio

n (%

)) a

fter e

ach

harv

estin

g tim

e. V

alue

s pre

sent

ed a

re m

eans

± st

anda

rd e

rror

of t

he m

ean,

n =

3.

Trea

tmen

ts

Har

vest

Sh

oot d

ry

wei

ght

Roo

t dry

w

eigh

t To

tal r

oot

leng

th

Plan

t P

Plan

t P/p

ot

Plan

t P

AM

co

loni

satio

n A

M C

olon

ised

ro

ot le

ngth

(g /p

lant

) (g

/ pla

nt)

(m/p

ot)

(g/k

g)

(mg/

pot)

(%)

(%)

(m/p

ot)

Agr

as

4 w

eeks

0.

10 ±

0.0

1g 0.

11 ±

0.0

2fg

5±0.

1f 14

54 ±

49.

2d 0.

13±0

.010

ef

1.45

±0.0

5d 23

±1.2

d 1±

0.1d

6

wee

ks

0.74

± 0

.14ef

0.

49 ±

0.0

4de

73±8

.8de

31

44 ±

62.

4bc

2.34

±0.4

9c

3.14

±0.0

6bc

16±3

.1e

11±1

.9d

8

wee

ks

3.44

± 0

.08b

1.98

± 0

.20a

182±

13.8

ab

2761

± 2

09.0

c 9.

50±0

.82a

2.76

±0.2

1c 2.

0±0.

1g 4±

0.3d

Bal

ance

4

wee

ks

0.11

± 0

.01g

0.17

± 0

.01fg

10

±0.7

f 13

42±

187.

1d 0.

15±0

.02ef

1.

34±0

.19d

50±4

.5c

5±0.

3d

6

wee

ks

0.34

± 0

.01fg

0.

32 ±

0.0

1ef

61±4

.1e

1560

± 1

75.4

d 0.

52±0

.05ef

1.

56±0

.18d

69±0

.4b

42±2

.8c

8

wee

ks

1.17

± 0

.15cd

0.

80 ±

0.0

6c 14

1±9.

2bc

1473

± 2

18.8

d 1.

76±0

.38cd

1.

47±0

.22d

73±3

.0b

103±

9.9a

Bal

ance

+Agr

as

4 w

eeks

0.

28 ±

0.0

2g 0.

29 ±

0.0

1ef

7.3±

0.7a

3954

± 3

70.9

a 1.

11±0

.10de

f 3.

96±0

.37a

11±2

.0ef

1±0.

5d

6

wee

ks

1.57

± 0

.04c

1.35

± 0

.04b

184±

11.8

a 35

95±

167.

0ab

5.66

±0.4

1b 3.

59±0

.17ab

4±

0.3fg

8±

0.2d

8

wee

ks

4.75

± 0

.41a

1.41

± 0

.17b

193±

35.7

f 18

91 ±

209

.3d

8.90

±0.8

2a 1.

89±0

.21d

6±

0.7g

12±2

.9d

Con

trol

4 w

eeks

0.

1 0

± 0.

01g

0.18

± 0

.01fg

8±

0.5cd

70

5 ±

48.8

e 0.

07±0

.01f

0.71

±0.0

5e 51

±2.7

c 4±

0.4d

6

wee

ks

0.48

± 0

.05fg

0.

56 ±

0.1

7cde

110±

26.7

de

1568

± 2

33.7

d 0.

74±0

.04de

f 1.

57±0

.23d

53

±2.3

c 57

±11.

1b

8

wee

ks

0.91

± 0

.17de

0.

60 ±

0.0

4cd

86±3

.7f

1363

± 1

23.3

d 1.

27±0

.30cd

e 1.

36±0

.12d

81

±1.3

a 69

±1.9

b

P va

lue

Tre

atm

ent

<

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1

Har

vest

< 0.

001

< 0.

001

< 0.

001

< 0.

001

< 0.

001

< 0.

001

<0.0

1 <

0.00

1

Trea

tmen

t x H

arve

st

<

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 <

0.00

1 LS

D

T

reat

men

t

0.24

2 0.

157

24.4

00

324.

550

0.68

8 0.

325

3.76

8 7.

66

H

arve

st

0.

210

0.13

6 21

.130

28

1.06

9 0.

596

0.28

2 3.

263

6.63

0

T

reat

men

t x H

arve

st

0.

420

0.27

2 42

.260

56

2.13

8 1.

192

0.56

4 6.

527

13.2

7 M

eans

in th

e sa

me

colu

mn

follo

wed

by

the

sam

e le

tter a

re n

ot si

gnifi

cant

ly d

iffer

ent (

P= 0

.05)

.


138

Figure 6.1 Effect of treatments on (a) shoot dry weight, (b) root dry weight, and (c) root length from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.


139

The highest total P uptake (mg per pot) and P concentration (%) was observed for

Balance50/Agras50 (P<0.001) (Figure 6.2a and Figure 6.2b respectively). The total P

uptake increased over time for all the soil amendments (Figure 6.2a; Table 6.6), the

shoot P concentration (%) declined over time for the Balance50/Agras50 treatment

(Figure 6.2 b; Table 6.6).

AM colonised root length (m per pot) increased over the time and was highest for the

Balance100 treatment (P<0.001) followed by the control (Figure 6.3a). Conversely, AM

colonised root length was significantly lower with the application of either

Balance50/Agras50 or Agras100 (P<0.001) treatments, while they showed a slightly

increasing trend (during the 3 harvests). The AM colonisation % was higher in soil

amended with Balance100 (P<0.001) and in the unamended soil, while it was

considerably lower in soil amended with Balance50/Agras50, and Agras100 (Figure

6.3b; Table 6.6). Pelletised piggery waste alone enhanced AM colonisation % but

reduced it in combination with synthetic fertiliser or when synthetic fertiliser was

applied alone. Photos of AM colonization of roots of wheat under the different soil

amendments at the first harvest (4 weeks) are shown in Appendix 5.

6.3.2 Effect of different soil amendments on soil properties

Soil amendments showed significant differences in soil nutrients (Table 6.7). Soil

available P (Colwell P) was significantly higher (P<0.001) with application of

Balance50/Agras50 and decreased over time compared to the other soil amendments

(Table 6.7). Available P in the soil amended with Agras100 increased progressively

over time. On the other hand, available P in the soil amended with Balance100, and the

control were significantly lower compared to the other treatments. Furthermore, soil

available P (mg kg-1) was positively correlated with plant P uptake (mg kg-1) (R2 =0.83)

and negatively correlated with AM colonization (%) (R2 =0.59) (Figure 6.4a and Figure

6.4b respectively) and soil available P had a positive effect on plant P uptake and

negative effect on AM colonisation (%).


140

Figure 6.2 Effect of treatments on (a) P uptake (mg/pot), and (b) P concentration (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.


141

Figure 6.3 Effect of treatments on (a) arbuscular mycorrhizal fungi colonised root length (m/pot), and their colonisation (%) from 3 harvests (4, 6 and 8weeks) in soil amended with (1) Agras100 (2) Balance100 (3) Balance50/Agras50 (4) control. All treatments were done in triplicate and error bars indicate the standard error where n=3.

Cha

pter

6: A

pplic

atio

n of

pig

gery

was

te c

ompo

st to

soil

142

Tab

le 6

.7 E

ffec

t of d

iffer

ent s

oil a

men

dmen

ts o

n so

il ph

ysic

o-ch

emic

al p

aram

eter

s af

ter e

ach

harv

estin

g tim

e (4

, 6, a

nd 8

wee

ks).

Val

ues

pres

ente

d ar

e m

eans

± st

anda

rd e

rror

of t

he m

ean,

n =

3.

Trea

tmen

ts

Har

vest

So

il P

Soil

TC

Soil

TN

Nitr

ate-

N

Am

mon

ium

-N

pH

EC

(mg

/ kg)

(%

) (%

) (µ

g/ g

soil)

(µ

g/ g

soil)

(C

aCl 2)

(µ

S/cm

)

Agr

as10

0 4

wee

ks

37.4

9 ±

1.33

e 4.

17±

0.13

ab

0.25

± 0

.01ab

cd

54.4

1 ±

3.00

b 10

4.64

± 1

1.24

a 4.

86±0

.03cd

39

0.33

±21.

79b

6

wee

ks

47.7

8 ±

1.13

c 3.

92 ±

0.0

5b 0.

26 ±

0.0

1abc

72.1

6 ±

2.19

a 11

0.71

± 4

.22a

4.77

±0.0

1de

434.

67±1

1.68

a

8

wee

ks

55.1

2 ±

2.02

b 3.

90 ±

0.0

8b 0.

26 ±

0.0

1ab

48.6

8 ±

0.72

c 63

.87

± 1.

60c

4.71

±0.0

1e 39

2.67

± 3.

93b

Bal

ance

100

4 w

eeks

34

.60

± 0.

48ef

4.03

± 0

.13b

0.24

± 0

.01bc

d 13

.63

± 1.

15e

1.58

± 0

.29d

4.96

±0.0

2bc

75.1

0±2.

34ef

6

wee

ks

37.7

6 ±

0.57

e 3.

91 ±

0.0

5b 0.

23 ±

0.0

1d 3.

82 ±

0.3

9f 0.

94 ±

0.1

9d 5.

06±0

.02a

99.1

0±2.

28e

8

wee

ks

35.5

4 ±

0.88

ef

4.10

± 0

.06ab

0.

25 ±

0.0

1abcd

0.

79 ±

0.0

5f 2.

59 ±

0.2

5d 5.

00±0

.01a

b 13

5.57

±15.

40d

Bal

ance

50/A

gras

50

4 w

eeks

64

.59

± 0.

76a

4.03

± 0

.11b

0.26

± 0

.01ab

c 47

.17

± 1.

93c

77.6

6 ±

1.79

b 4.

81±0

.01de

24

2.07

±6.7

8ef

6

wee

ks

51.3

7 ±

1.11

c 3.

98 ±

0.1

4b 0.

24 ±

0.0

1bcd

27.5

2 ±

2.42

d 8.

11 ±

1.2

3d 4.

83±0

.08d

248.

47±3

.18e

8

wee

ks

48.3

9 ±

0.84

c 4.

00 ±

0.1

1b 0.

24 ±

0.0

1bcd

12.2

1 ±

1.42

f 3.

71 ±

0.6

2d 4.

80±0

.08de

23

1.40

±10.

54d

Con

trol

4 w

eeks

42

.02

± 1.

15d

3.96

± 0

.04b

0.24

± 0

.01cd

14

.03

± 0.

51e

1.54

± 0

.25d

4.94

±0.0

1bc

67.9

3±2.

44f

6

wee

ks

37.1

2 ±

1.72

e 4.

33 ±

0.1

4a 0.

27 ±

0.0

1a 1.

62 ±

0.1

8f 1.

20 ±

0.1

2d 4.

99±0

.02ab

10

0.20

±3.3

8e

8

wee

ks

33.2

3 ±

1.84

f 4.

14 ±

0.0

7ab

0.26

± 0

.01ab

c 1.

23 ±

0.2

6f 0.

63 ±

0.1

4d 4.

93±0

.03bc

93

.17±

1.60

ef

P

valu

e T

reat

men

t

< 0.

001

0.24

5*

0.11

6*

< 0.

001

< 0.

001

< 0.

001

< 0.

001

Har

vest

< 0.

05

0.97

8*

0.28

8*

< 0.

001

< 0.

001

0.07

7*

< 0.

01

T

reat

men

t x H

arve

st

<

0.00

1 0.

073*

0.

099*

<

0.00

1 <

0.00

1 0.

127*

<

0.01

LSD

T

reat

men

t

2.10

9 0.

168

0.01

3 2.

574

5.10

0 0.

004

15.8

30

Har

vest

1.82

7 0.

145

0.01

1 2.

229

5.19

4 0.

051

13.7

20

T

reat

men

t x H

arve

st

3.

653

0.29

1 0.

022

4.45

9 10

.390

0.

102

27.4

30

Mea

ns in

the

sam

e co

lum

n fo

llow

ed b

y th

e sa

me

lette

r are

not

sign

ifica

ntly

diff

eren

t (P=

0.0

5).


143

Figure 6.4 Relationship between (a) soil available P (mg/kg) and plant P uptake (mg/kg), and (b) soil available P (mg/kg) and AM fungal colonization (%). All treatments were done in triplicate and error bars indicate the standard error where n=3.

There was no significant difference in soil TC and soil TN content among the different

fertilizer amendments and harvesting time (Table 6.7). Both NO3--N and NH4

+-N (µg/g

soil) differed among soil amendments and harvesting time. NO3--N and NH4

+-N levels

in soil after each harvest were significantly higher in soil amended with Agras100

(P<0.001) and tended to increase up to 6 weeks and thereafter decreased. The second

highest NO3--N and NH4

+-N levels in soil after each harvest were observed with


144

application of Balance50/Agras50 and generally decreased over time with plant

maturity. Compared to Agras100 and Balance50/Agras50, lower NO3--N and NH4

+-N

levels were observed for both Balance100, and control for each harvest. There was a

significant difference (P<0.001) between soil amendments on soil pH but no significant

difference with harvesting time. Further, addition of Balance100 slightly increased soil

pH compared to the Agras100 treatment. Soil amended with Agras100 had the highest

impact on EC. The second highest EC level for each harvest was observed with

application of Balance50/Agras50. The significant difference between soil amendments

for EC was more pronounced (P<0.001) than harvesting time (P<0.01).

6.3.3 Effect of soil amendments on rhizosphere and root colonising bacterial

population dynamics

The rhizosphere and root colonising bacterial population dynamics were determined for

the second harvest (6 weeks from sowing) for each soil amendment. Alpha rarefaction

(the distribution of number of sequences per sample) was performed using the observed

species metrics for both rhizosphere bacteria and plant root colonising bacteria (Figures

6.5a and 6.5b respectively). All the samples were normalised to a sequence number of

5000 where the samples were getting parallel with the x axis, revealing that the overall

bacterial diversity was well represented for both rhizosphere and root colonised bacteria

for all samples. However, at a sequence number of 5000, overall bacterial diversity of

rhizosphere was higher (OTUs 1044-1064) than for root colonising bacteria (OTUs 324-

415) for all soil amendments.

Bacterial diversity indicated by phylogenetic diversity chao1 richness and Shannon’s

index for both rhizosphere and root colonising bacteria is shown in Table 6.8. Overall,

species richness and diversity of the bacterial population were higher in the unamended

soil than for the amended soils. Furthermore, addition of pelletised piggery compost

alone (Balance100) altered the species richness and diversity for both rhizosphere and

root colonising bacteria considerably more than for the synthetic fertiliser alone

amendment. However, pelletised compost applied in combined with synthetic fertiliser

caused a loss in bacterial diversity for both rhizosphere bacteria and root colonising

bacteria but not as much as when soils received only synthetic fertiliser.

Relative abundance of bacterial community composition in rhizosphere bacteria and

root colonising bacteria for different soil amendments is shown in Figure 6.6.


145

Figure 6.5 Alpha diversity rarefaction plots of phylogenetic diversity for (a) rhizosphere soil bacteria, and (b) root colonising bacteria. Value represents the mean of triplicate determinations. Relative mean abundance between different soil amendments for rhizosphere bacteria

was fairly stable (Figure 6.6a) and dominated by phyla Proteobacteria (26±0.3%),

Acidobacteria (17±1.8%), Actinobacteria (14±1.0%), Gemmatimonadetes (5±0.4%),

Verrucomicrobia (6±1.0%), Bacteroidetes (5±0.4%), Chloroflexi (3±0.5%), and

Firmicutes (3±0.5%). The observed slight differences between the soil amendments

were mainly associated with the phyla Acidobacteria (by 1.8%) and Actinobacteria (by

1.0%) in soil amended with Balance100 implying that that adding pelletised compost

alone (Balance100) influenced in a slight increase in the abundance of Acidobacteria

and Actinobacteria in the rhizosphere but there was little change associated with the

other soil amendments for the rhizosphere bacteria.


146

Table 6.8 Bacterial diversity of rhizosphere soil bacteria and plant roots colonising bacteria indicated by phylogenetic diversity, Chao1 richness, and Shannon’s index. (Calculation of richness and diversity estimators was based on OTU tables rarefied to the same sequencing depth; the lowest one of total sequencing reads: 5000).

Treatments Phylogenetic diversity Chao richness Shannon’s

index Rhizosphere soil bacteria Balance100 74.93±0.55 2104.71±198.92 8.51±0.03

Agras100 72.64±1.51 1966.14±126.61 8.48±0.08

Balance50/Agras50 70.92±1.01 1781.40±75.30 8.41±0.04

Control 78.75±3.12 2156.04±30.31 8.64±0.12 Root colonised bacteria Balance100 26.34±5.39 633.09±182.88 5.34±0.41

Agras100 20.01±3.99 511.94±117.24 4.41±0.49

Balance50/Agras50 21.66±1.28 529.95±17.36 4.54±0.27

Control 27.37±1.00 758.64±71.48 5.56±0.36

In contrast to the rhizosphere bacteria, the relative mean abundance between different

soil amendments for root colonising bacteria was altered markedly (Figure 6.6a) by soil

amendments and was dominated by the phyla Proteobacteria (29±4.5%),

Cyanobacteria/Chloroplast (12±4.6%), Bacteroidetes (11±5.9%), Actinobacteria

(9±3.0%), and Acidobacteria (1±0.1%) (Figure 6.6b). The increase in Proteobacteria

(5%), Bacteroidetes (6%), and Actinobacteria (3.0%), and decrease of

Cyanobacteria/Chloroplast (5%) were mainly associated with Balance100.

A higher abundance of Cyanobacteria/Chloroplast (16%) was observed for Agras100

and Balance50/Agras50 compared to both Balance100 (7%) and control (9%).

Actinobacteria and Bacteroidetes were relatively less abundant in Agras100 (8% and

3% respectively) and Balance50/Agras50 (5% and 11%) with respect to Balance100

(12% and 17%) and control (11% and 13%). Adding synthetic fertiliser alone or in

combination with pelletised compost increased the abundance of

Cyanobacteria/Chloroplast and decreased the abundance of Actinobacteria and

Bacteroidetes on roots. Comparison of Figures 6.6a and 6.6b shows that the bacterial

community composition and diversity in the rhizosphere soil bacteria and root

colonising bacteria appeared to differ significantly within and between the soil

amendments.


147

Figure 6.6 Relative abundance of (a) rhizosphere bacteria and, (b) root colonised bacteria at phylum level by different soil amendments. Value represents the mean of triplicate determinations. (Relative abundance <1% is ignored)


148

Relative changes in the bacterial abundance and diversity in both rhizosphere soil and

root colonising bacteria up to genus level are shown in Table 6.9. According to the

mean abundance of bacteria between soil amendments for the rhizosphere soil bacteria,

the bacterial communities between different treatments were very similar and the most

dominant groups (>5%) across all soil amendments were Acidobacteria (21±1.6%),

Acidimicrobiales (13±0.9%), Gemmatimonas (5±0.5%), and Sphingomonadaceae

(5±0.2%). In contrast, root colonising bacteria showed a considerable fluctuation

between soil amendments mainly by Cyanobacteria/Chloroplast (5%), Pseudomonas

(4%), Sphingobacteriales (5%). Cyanobacteria/Chloroplast was the most dominant root

colonising bacteria in both Agras100 and Balance50/Agras50 compared to other soil

amendments. Pseudomonas was the second most dominant class of root colonised

bacteria among soil amendments and their relative abundance was higher in

Balance50/Agras50 (15±3%). Pseudonocardiacea and Sphingobacteria were markedly

lower in soil amended with both Agras100 and Balance50/Agras50 compared to other

soil amendments. Overall, the effect of soil amendments on root colonising bacterial

population dynamics was more prominent than on rhizosphere bacteria.

6.3.3.1 Changes in the rhizosphere bacterial community profile of the different

treatments to the measured plant and soil variables

Apart from the changes in the major taxa, there were some changes in the abundance of

minor taxa up to genus level associated with the changes in the environmental factors

among different soil amendments. To explore this further, CCA analysis was used to

examine the relationship between plant and soil variables and species composition of

both rhizosphere bacteria and root colonising bacteria (Figure 6.7 and Figure 6.8).

Bacterial community composition for each treatment, or individual taxa distribution for

soil or plant variables are given in Figures 6.7a, 6.8a and Figures 6.7b, 6.8b

respectively. Differences in bacterial composition of rhizosphere bacteria between soil

amendments by differences in plant and soil variables are shown in Figure 6.7a. The

first 2 axes of the CCA analysis explained 46 % of the total variance. Triplicate samples

of Balance100 and control are grouped closer to each other and positioned along a

vector associated with positive correlations with pH, AM colonisation % and the

colonised root length (Figure 6.7a).

Cha

pter

6: A

pplic

atio

n of

pig

gery

was

te c

ompo

st to

soil

149

Tab

le 6

.9 R

elat

ive

abun

danc

e of

(a) r

hizo

sphe

re b

acte

ria a

nd, (

b) ro

ot c

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isin

g ba

cter

ia u

p to

gen

us le

vel b

y di

ffer

ent s

oil a

men

dmen

ts.

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ue re

pres

ents

the

mea

n of

trip

licat

e de

term

inat

ions

. (R

elat

ive

abun

danc

e <1

% is

igno

red)

Rhi

zosp

here

soi

l bat

eria

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as10

0*B

alan

ce10

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alan

ce50

+Agr

as50

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ontr

ol*

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n**

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eria;

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2222

1921

211.

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cter

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ctin

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teria

;Act

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1412

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130.

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cter

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emm

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e;G

emm

atim

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hapr

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eria;

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ngom

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ngom

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e5

55

55

0.2

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eria;

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lpha

prot

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ther

43

34

30.

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cter

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erru

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icro

bia

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phin

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eria;

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prot

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13

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eria;

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dom

onas

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cter

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oteo

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eria;

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map

rote

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teria

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t con

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ol*

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n**

SD**

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912

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156

124

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cido

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eria

41

11

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cter

ia;G

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ria;X

anth

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cter

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eria;

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prot

eoba

cter

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rkho

lder

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34

36

42

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eria;

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dete

s;Sp

hing

obac

teria

;Sph

ingo

bact

erial

es2

134

107

5Ba

cter

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ctin

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teria

;Act

inob

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ctin

omyc

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s;St

rept

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etac

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23

21

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eria;

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lpha

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hing

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s;Sp

hing

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ngom

onas

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12

21

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eria;

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inob

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ria;A

ctin

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teria

;Act

inom

ycet

ales;

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dono

card

iacea

e1

71

53

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cter

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oteo

bact

eria;

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hapr

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eria;

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zobi

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Rhi

zobi

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e;R

hizo

bium

14

35

32

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eria;

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eoba

cter

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lpha

prot

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cter

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hizo

biale

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adyr

hizo

biac

eae;

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yrhi

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um0

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cter

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cter

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tes;

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obac

teria

;Flav

obac

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les;F

lavob

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ae;C

hrys

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ium

03

62

33

*ab

unda

nce

of e

ach

soil

amen

dmen

t rep

rese

nts

mea

n va

lue

of tr

iplic

ate

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rmin

atio

ns**

mea

ns a

nd s

tand

ard

devi

atio

n be

twee

n th

e di

ffere

nt s

oil a

men

dmen

ts

Cha

pter

6: A

pplic

atio

n of

pig

gery

was

te c

ompo

st to

soil

150

Fi

gure

6.7

Can

onic

al c

orre

spon

denc

e an

alys

is (C

CA

) bip

lot s

how

ing

the

rela

tions

hip

betw

een

(a) d

iffer

ent s

oil a

men

dmen

t and

mea

sure

d pl

ant

and

soil

varia

bles

b)

indi

vidu

al t

axa

dist

ribut

ions

with

mea

sure

d pl

ant

and

soil

varia

bles

(b)

for

rhi

zosp

here

soi

l ta

ken

from

pot

ex

perim

ent u

nder

diff

eren

t fer

tilis

er tr

eatm

ents

(

) at

6 w

eeks

. A

rrow

s re

pres

ent t

he m

easu

red

varia

bles

[pH

, NH

3, C

olw

ell P

, Pla

nt P

up

take

, ele

ctric

al c

ondu

ctiv

ity (E

C),

Shoo

t and

root

DW

, AM

col

onis

ed ro

ot le

ngth

(RL)

, and

AM

col

onis

atio

n %

]. Tr

iang

les

(▲) o

n th

e gr

aph

(b) r

epre

sent

indi

vidu

al b

acte

rial t

axa.

Tax

onom

ic id

entit

ies f

or th

e ba

cter

ial s

eque

nces

are

giv

en in

Tab

le 6

.10.


151

Balance50/Agras50 was positively associated with shoot DW, root DW, soil available

P, and plant P uptake and negatively associated with pH, AM colonisation %, and AM

colonised root length. Also it showed that treatment of Agras100 was positively

correlated with soil nitrate-N, ammonium-N, and EC. A second biplot (Figure 6.7b) was

constructed using the individual taxa scores to assess the contribution of individual taxa

to the scatter seen in Figure 6.7a (phylogenetic identities of the taxa are shown in Table

6.10). There were marked changes in the relative abundance of some minor bacterial

taxa between soil amendments whose distributions and responses were particularly

closely correlated with the plant and soil variables (Figure 6.7b, Table 6.10). The minor

taxa belonging to Edaphobacter (#1), Georgenia (#4), Arthrobacter (#9), Actinoplanes

(#10), Mycobacterium (#12), Actinomycetales (#15), Streptomycetaceae (#17),

Rhizobium (#38), Oxalobacteraceae (#47), and Nitrosospira (#48) responded to the

addition of Balance50/Agras50 whilst Janthinobacterium (#46), Burkholderia (#44),

Xanthomonadaceae (#56), Rhizomicrobium (#33), were more influenced by Agras100.

Moreover, Nocardioides (#14), Skermanella (#40), Steroidobacter (#55), Myxococcales

(#51), Acidimicrobiales (#3) had responded to the treatments of both Balance100 and

the control.

6.3.3.2 Changes in the root colonising bacterial community profile of the different

treatments to the measured plant and soil variables

The CCA biplot for plant root colonising bacteria (Figure 6.8a) showed an opposite

trend to the CCA biplot of soil rhizosphere bacteria (Figure 6.7b). The first 2 axes of the

CCA analysis explained 67 % of the total variance. More clear treatment separation was

observed for root colonising bacterial taxa between soil amendments by differences in

plant and soil variables (Figure 6.8a). In addition to the effect of soil amendments on the

relative abundance of major phyla (Figure 6b) for root colonising bacteria, considerable

influence was observed on some minor bacterial taxa (Figure 6.8b, Table 6.11). For

example Edaphobacter (#1), Pseudomonas (#41), Cyanobacteria/Chloroplast (#24),

Xanthomonadaceae (#45) responded by the addition of Balance50/Agras50.

Conversely, Acidobacteria group 2 (#3), Microbacteriaceae (#6), Bacillales (#25),

Burkholderia (#37), Acidobacteria group 1 (#2), Dyella (#43), Rhodanobacter (#46)

responded by the addition of Agras100.


152

Table 6.10 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for rhizosphere bacteria Code Taxa Code Taxa

1 Acidobacteria; Edaphobacter 26 Gemmatimonadetes; Gemmatimonas

2 Acidobacteria; group 1 27 Bacteria;Other

3 Acidobacteria;group 2 28 Alphaproteobacteria;Caulobacteraceae

4 Acidobacteria; Group 3 29 Alphaproteobacteria; Bosea

5 Actinobacteria;Catenulispora 30 Alphaproteobacteria; Bradyrhizobium

6 Actinobacteria;Microbacteriaceae 31 Proteobacteria; Devosia

7 Actinobacteria;Micromonosporaceae 32 Alphaproteobacteria; Rhizobiales

8 Actinobacteria;Mycobacterium 33 Alphaproteobacteria; Phyllobacteriaceae

9 Actinobacteria;Kribbella 34 Alphaproteobacteria; Rhizobium

10 Actinobacteria;Amycolatopsis 35 Alphaproteobacteria; Inquilinus

11 Actinobacteria;Kutzneria 36 Alphaproteobacteria; Sphingomonadaceae

12 Actinobacteria;Pseudonocardiaceae 37 Betaproteobacteria; Burkholderia

13 Actinobacteria; Pseudonocardia 38 Betaproteobacteria; Ralstonia

14 Actinobacteria;Saccharothrix 39 Betaproteobacteria; Comamonadaceae

15 Actinobacteria;Streptomyces 40 Betaproteobacteria; Oxalobacteraceae

16 Actinobacteria; Nonomuraea 41 Gammaproteobacteria; Pseudomonas

17 Actinobacteria; Thermomonosporaceae 42 Gammaproteobacteria; Dokdonella

18 Actinobacteria; Solirubrobacterales 43 Gammaproteobacteria; Dyella

19 Bacteroidetes; Chryseobacterium 44 Gammaproteobacteria; Luteibacter

20 Bacteroidetes; Chitinophagaceae 45 Gammaproteobacteria; Xanthomonadaceae

21 Bacteroidetes; Dyadobacter 46 Gammaproteobacteria; Rhodanobacter

22 Bacteroidetes; Sphingobacteriaceae 47 Gammaproteobacteria; Stenotrophomonas

23 Chloroflexi; Ktedonobacter 48 Bacteria; TM7

24 Cyanobacteria/Chloroplast 49 Verrucomicrobia

25 Firmicutes; Bacillales

The distribution of Kribbella (#9), Saccharothrix (#14), Bradyrhizobium (#30),

Comamonadaceae (#39), Bosea (#29), Phyllobacteriaceae (#33), Rhizobium (#34) was

influenced by the addition of Balance100. Overall, CCA analyses of both rhizosphere

bacteria and root colonising bacteria indicated that addition of soil amendment has

changed the abundance and community composition of bacterial taxa which in turn has

caused to change plant and soil conditions.

Cha

pter

6: A

pplic

atio

n of

pig

gery

was

te c

ompo

st to

soil

153

Fi

gure

6.8

Can

onic

al c

orre

spon

denc

e an

alys

is (C

CA

) bip

lot s

how

ing

the

rela

tions

hip

betw

een

(a) d

iffer

ent s

oil a

men

dmen

t and

mea

sure

d pl

ant a

nd s

oil v

aria

bles

b) i

ndiv

idua

l tax

a di

strib

utio

ns w

ith m

easu

red

plan

t and

soi

l var

iabl

es (b

) for

root

col

onis

ing

bact

eria

in s

oil t

aken

fr

om p

ot e

xper

imen

t und

er d

iffer

ent f

ertil

iser

trea

tmen

ts (

) at 6

wee

ks.

Arr

ows

repr

esen

t the

mea

sure

d va

riabl

es [p

H, N

H3,

Col

wel

l P,

Plan

t P u

ptak

e, e

lect

rical

con

duct

ivity

(EC

), Sh

oot a

nd ro

ot D

W, A

M c

olon

ised

root

leng

th (R

L), a

nd A

M c

olon

isat

ion

%].

Tria

ngle

s (▲

) on

the

grap

h (b

) rep

rese

nt in

divi

dual

bac

teria

l tax

a. T

axon

omic

iden

titie

s for

the

bact

eria

l seq

uenc

es a

re g

iven

in T

able

6.1

1.


154

Table 6.11 Taxonomic identities for the CCA biplot showing the relationship between measured variables and individual taxa distributions for root colonising bacteria

Codes Taxa Codes Taxa 1 Acidobacteria;Edaphobacter 31 Bacteria;Other 2 Acidobacteria 32 Planctomycetes;Zavarzinella 3 Actinobacteria;Acidimicrobiales 33 Alphaproteobacteria;Rhizomicrobium 4 Actinobacteria;Georgenia 34 Alphaproteobacteria;Phenylobacterium 5 Actinobacteria;Catenulispora 35 Alphaproteobacteria;Other 6 Actinobacteria;Fodinicola 36 Alphaproteobacteria;Rhizobiales 7 Actinobacteria;Geodermatophilaceae 37 Alphaproteobacteria;Mesorhizobium 8 Actinobacteria;Microbacteriaceae 38 Alphaproteobacteria;Rhizobium 9 Actinobacteria;Arthrobacter 39 Alphaproteobacteria;Rhodospirillales 10 Actinobacteria;Actinoplanes 40 Alphaproteobacteria;Skermanella 11 Actinobacteria;Micromonosporaceae 41 Alphaproteobacteria;Porphyrobacter 12 Actinobacteria;Mycobacterium 42 Alphaproteobacteria;Novosphingobium 13 Actinobacteria;Kribbella 43 Alphaproteobacteria;Sphingomonadaceae 14 Actinobacteria;Nocardioides 44 Betaproteobacteria;Burkholderia 15 Actinobacteria;Actinomycetales 45 Betaproteobacteria;Burkholderiales 16 Actinobacteria;Pseudonocardiaceae 46 Betaproteobacteria;Janthinobacterium 17 Actinobacteria;Streptomycetaceae 47 Betaproteobacteria;Oxalobacteraceae 18 Actinobacteria;Streptomyces 48 Betaproteobacteria;Nitrosospira 19 Actinobacteria;Thermomonosporaceae 49 Betaproteobacteria;Other 20 Actinobacteria;Actinobacteria other 50 Betaproteobacteria;Cystobacteraceae 21 Actinobacteria;Solirubrobacterales 51 Betaproteobacteria;Myxococcales 22 Armatimonadetes;Armatimonadetes 52 Deltaproteobacteria;Other 23 Bacteroidetes 53 Gammaproteobacteria;Other 24 Chloroflexi 54 Gammaproteobacteria;Pseudomonas 25 Bacteria;Cyanobacteria/Chloroplast 55 Gammaproteobacteria;Steroidobacter 26 Firmicutes;Bacilli 56 Gammaproteobacteria;Xanthomonadaceae 27 Firmicutes;Clostridium 57 Proteobacteria;Other 28 Firmicutes;Turicibacter 58 TM7 29 Gemmatimonadetes;Gemmatimonas 59 Verrucomicrobia 30 Nitrospira;Nitrospira


155

6.4. Discussion

6.4.1 Effects of soil amendments on plant growth and soil fertility

Application of pelletised piggery compost (Balance®), in combination with synthetic P

fertiliser (Agras®) at a lower than commercially recommended rate (50 kg ha-1 each) on

plant growth and soil nutrient improvement was evaluated in comparison to their single

application at the commercial application rate (100 kg ha-1). Balance50/Agras50 was the

most effective soil amendment on wheat growth and soil fertility compared to their

single applications (100 kg ha-1). Adding Balance50/Agras50 resulted in significant P

uptake in plants and P availability in soils and showed the best plant growth in terms of

shoot and root dry weight.

The reason for achieving a better growth with Balance50/Agras50, in comparison with

Agras100 alone, could be associated with the increased early uptake of nutrients and

presence of adequate nutrient supply over time in the soil amended with

Balance50/Agras50. Whereas, Agras100 showed poor seeding establishment and low

growth until 6 weeks and thereafter a rapid growth was observed. Agras® contains

relatively high concentrations of ammonium nitrogen (http://www.csbp-

fertilisers.com.au) and here it was observed higher amounts of NH4+-N after adding

Agras at a higher rate (100 kg ha-1). It has been shown that root growth is sensitive to

excess NH4+-N and its toxic effects are common in higher plants (Krupa 2003; Li et al.

2010). Hence, slow growth at early stages of the Agras100 could be attributed to the

excess supply of NH4+-N, which might be toxic for immature plant roots due to the

close proximity of fertilizer to the seed. However in this study the effect was diluted

over time with the plant maturity and tended to perform well after 6 weeks, leading

Agras100 as the second best soil amendment.

Wide range of studies investigated combined nutrient management by recycling animal

manures or crop residues as the organic portions of the organic-inorganic blend

(MacDonald et al. 2011; Sommer et al. 2013). The application of an organic-inorganic

compound fertilizer can slowly release nutrients into soil, promote plant growth and

increase crop yield (Shi et al. 2003; Zhao et al. 2014). The Balance50/Agras50 seemed

to be act as a slow releasing fertiliser in which the nutrients are gradually make

available for the growing plants. The differences in biomass yield between the different

soil amendments in this study are most likely to correspond to the differences in nutrient

release from the applied soil amendments. Therefore, the blend of the reduced rate of


156

synthetic P fertiliser and pelletised piggery compost (50 kg ha-1 each) in close proximity

to seeds during planting would be a more effective soil amendment than applying the

synthetic P fertiliser alone or pelletised piggery compost alone at 100 kg ha-1.

6.4.2 Effect of soil amendments on beneficial bacteria associated with rhizosphere

soil and root surface

Favourable alterations in the bacterial community diversity and abundance associated

with rhizosphere soil and root surface by the Balance50/Agras50 is an attractive

outcome of the use of pelletised piggery compost in combination with reduced rate of

synthetic P fertilisers. Few studies have investigated bacterial colonisation of the root

surface of wheat influenced by different soil/plant or environmental factors (Germida et

al. 1998; Rengel et al. 1998; Turnbull et al. 2001; Meyer et al. 2013). Here, it was

observed that the bacterial community composition of both root colonising bacteria and

rhizosphere bacteria was associated with soil amendment influences on the soil

available P, plant P uptake, and shoot and root dry weights. Soil available P is an

important covariate in determining the distribution of bacterial taxa (Figure 6.7a and

6.8a). The bacterial community composition under the Balance50/Agras50 could have

involved in increasing the P nutrient in soil and improving their accessibility to plant

uptake. Pseudomonas, a known plant growth-promoting bacteria and one of the best

root colonizers (Lugtenberg et al. 2001) were found to be highly abundant on the roots

of wheat plant treated with Balance50/Agras50 comparted to the rhizosphere soil of the

same treatment. It is possible that Pseudomonas could play an important role in

increasing soil available P (via solubilising and mineralising P), and facilitating plant P

uptake (via root colonisation) in this soil amendment. P-solubilising and P-mineralising

capacity of Pseudomonas were also reported previously (Richardson and Hadobas 1997;

Jorquera et al. 2008b; Tao et al. 2008). Apart from the major taxa, some minor taxa

identified in this study (Arthrobacter, Rhizobium, Streptomycetaceae, Actinoplanes)

responded to the addition of Balance50/Agras50. These groups have previously been

shown to be involved in P transformation. For example, Arthrobacter (Rodrı́guez and

Fraga 1999; Chen et al. 2006), Rhizobium (Rodrı́guez and Fraga 1999; Chen et al. 2006;

Harvey et al. 2009), Streptomycetaceae (Ragot et al. 2013), Actinoplanes (El-Tarabily et

al. 2008) are known P-solubilisers. Compost amendment was also enhanced by

phosphate solubilizing bacteria Arthrobacter (Wickramatilake et al. 2011). This implies

that there is potential for P mineralisation and solubilisation in soils ameneded with

pelletised piggery compost in combination with low rate of synthetic P fertilisers and


157

therefore more P available to plants which may account for the increased shoot growth.

Thus, bacteria associated with rhizosphere soil and colonising roots under the

Balance50/Agras50 appear to be involved in increasing P availability in soil, and plant P

uptake, thereby enhancing plant growth, which is consistent with the first hypothesis.

The bacterial community composition of rhizosphere soil bacteria only slightly shifted

compared to root colonising bacteria by the soil amendments. Some differences in

composition and diversity were also observed between the rhizosphere bacteria and root

colonising bacteria within and between the soil amendments. The relationship between

plant and soil variables and species composition for different soil amendments showed

that the bacterial community composition was positively linked to the root growth,

indicating an expected link between bacterial populations and root exudates. The most

dominant classes (>5%) of the root associated bacteria, across all soil amendments were

Cyanobacteria/Chloroplast, Gammaproteobacteria, Actinobacteria, and

Alphaproteobacteria. The reason for the higher abundance of

Cyanobacteria/Chloroplast and Gammaproteobacteria, associated with roots of both

Agras100 and Balance50/Agras50 could be related to the fast growing response of those

groups (Copiotrophic bacteria) in soils with high nutrients availability in rhizosphere.

Apart from the changes in these major classes, there were some changes in the

abundance of minor taxa on the genus level due to the variations on soil amendments.

This indicates that minor taxa may have more sensitive responses to different soil

amendments due to their own characteristics (Zhao et al. 2014).

Slightly higher pH was observed when soil was amended with pelletised pig compost

(Balance100) or in unamended soil compared to chemical fertiliser alone (Agras100) or

in combination with the pelletised pig compost (Balance50/Agras50). Some chemical

fertilizers are already known to acidify the soil by accumulating hydrogen cations

(Barak et al. 1997; van Diepeningen et al. 2006). Changes in soil pH could alter the

bacterial community composition (Lauber et al. 2009) and percentage of AM

colonisation (Coughlan et al. 2000). The difference between soil amendments in terms

of rhizosphere soil bacteria was mainly observed for Balance100, which was mainly

associated with the slight increase of Acidobacteria (by 1.8 %), and Verrucomicrobia

(by 1.0 %), and decrease of Actinobacteria (1.0%). In contrast, marked increase of

Proteobacteria (5 %), Bacteroidetes (6 %), Actinobacteria (3.0 %), and decrease of

Cyanobacteria/Chloroplast (5 %) in root associated bacteria were observed for soil

amended with Balance100 compare to the other soil amendments. These findings


158

roughly correspond with previous studies demonstrating that the bacterial community

composition significantly correlated with differences in soil pH and largely driven by

changes in the relative abundances of Acidobacteria, Actinobacteria, and Bacteroidetes

across the range of soil pHs (Lauber et al. 2009). In addition, it was found that

Proteobacteria and Actinobacteria were more sensitive to pH variation (Li et al. 2012)

and were generally predominant in organic farming systems (Fließbach et al. 2007; Li et

al. 2012). It has been previously reported that some members of Actinobacteria such as

Streptomyces, Acidimicrobium, Actinospica, Arthrobacter, Norcardia, Micrococcus and

Mycobacterium were particularly associated with manures and organic compost added

soils (Atagana 2004; Jenkins et al. 2009). Inorganic fertiliser had a major impact on the

Actinobacteria community structure, since relative amount of some Actinobacteria

groups were significantly reduced in soils amended with inorganic N (Jenkins et al.

2009). Similar results were observed on the significant reduction of Actinobacteria with

the addition of synthetic fertiliser, as the abundance of Actinobacteria was

comparatively low in Balance50/Agras50 and Agras100 amendments compared to

Balance100 and control.

6.4.3 Effect of soil amendments on AM fungal colonisation

Although the addition of Balance50/Agras50 linked to the increases in the relative

abundance of some major and minor taxa that are previously recognised as P

mineralisers and P solubilisers, it reduced the colonisation percentage of AM fungi as a

consequence. This could be due to the high availability of P in this treatment which

leads to reduce the AM colonisation. Mycorrhizal fungi prefer low- nutrient soils (P and

N) or in soils receiving slow release fertilisers (e.g. pelletised compost). AM

colonisation (%) was shown to be decreased by both increasing N and P (Gazey et al.

2004). There was a strong negative relationship to soil available P level and AM

colonisation. The trend of low AM fungal colonisation (%) under sufficient P level for

plant growth in soil was consistent with previous studies (Graham and Abbott 2000;

Raiesi and Ghollarata 2006). Relationship between plant and soil variables and species

compositions between soil amendments (Figure 6.7a and 6.8a) revealed that the

presence of adequate P and N in soil causes a reduction in AM colonisation (%).

Therefore, the hypothesis that statement on the increase of P in soil by fertilisers that

would lead to a decrease in the percentage AM colonisation (%) was accepted. On the

other hand, the corresponding hypothesis statement on increasing the length of root

colonised by AM fungi was rejected.


159

6.5. Conclusions

Application of pelletised piggery compost in combination with inorganic P fertiliser at

low rates enhanced wheat growth and soil fertility compared to their conventional

application rates. The bacterial community composition for this soil amendment was

associated with an increase in soil available P, plant P uptake, and shoot and root dry

weights. These responses were most likely to be the reactions of plant and bacterial

communities to the changes in soil nutrient levels by effective blending of pelletised

piggery compost with inorganic fertilisers.

Banding pelletised piggery compost with chemical fertiliser at a low rate with no yield

reduction is an attracting outcome and an effective strategy for sustainable nutrient

management in agriculture. Improved understanding of these interactions can be also

use to optimise P-use efficiency by identifying suitable blends of other animal waste

products for soil amendment. The reduced volumes and associated transport and

spreading costs should provide budget savings. Therefore, application of low rates of

fertilisers would be financially and technically viable for the farmers. The potential for

using piggery waste as a component of P fertiliser could also provide an additional

income for pig farmers while reducing the amount of on-farm waste accumulation. As

for a future direction, field trials are necessary to verify the data for other conditions,

develop strategies for the efficient and practical management of nutrient resources, and

expand on the interpretation that causes the observed effects.

Chapter 7: General Discussion

160

CHAPTER 7

General Discussion

7.1 Summary of the work performed

7.1.1 Overview

This thesis sought to explore bacteria involved in P transformation in a model piggery

waste treatment process (a detail treatment process is found in Appendix 1). The main

objective was to characterise taxa involved in P transformation pathways (P

mineralisation, P solubilisation, and polyP accumulation) and their specific functions in

piggery waste by-products, and in association with mycorrhizal fungi in soils amended

with piggery waste. Knowledge of the diversity, abundance, and activity of

microorganisms involved in P transformation in the piggery waste treatment process is

critical but it has been constrained by the methods used to date. Therefore, particular

emphasis was placed on applying methodologies for characterising microorganisms

involved in P transformation in the piggery waste treatment process. The emphasis was

on applying an integrated approach using epi-fluorescence microscopy, flow cytometry,

cell sorting, and next generation sequencing.

7.1.2 Specific objectives

1. To characterise the piggery waste treatment process in terms of physico-

chemical properties, bacterial community composition, and P cycling potential

(Chapter 3).

2. To quantify the abundance, and diversity of P mineralising bacteria (the fraction

of cells that expressed phosphatase activity) during the pig waste treatment

process by developing an integrated approach using the enzyme labelled

fluorescence technique coupled with epi-fluorescence microscopy, cell sorting,

and next generation sequencing (Chapter 4).

3. To identify key microbes involved in polyP accumulation and its enhancement

under acidic conditions for assessing the efficacy of enhanced biological P

removal technology applied in high P loaded waste remediation (Chapter 5).


161

4. To demonstrate the impact of application of pelletised piggery compost to soil

on plant growth, soil nutrient improvement, and changes in bacterial and fungal

community composition when banded with a reduced rate of synthetic fertiliser

(Chapter 6).

7.2. Key factors driving the P cycling bacterial diversity and activity in

the piggery waste treatment process The main contributions of this thesis in relation to the P transformation in the model

piggery waste treatment process are summarised in Table 7.1. Understanding how P

cycling bacterial community diversity and activity changes in response to

environmental factors is essential for understanding P cycling pathways in wastewater.

Here, it was observed that community composition of P cycling microorganisms

fluctuated across sequential stages in the waste treatment process as a response to

prevailing environmental conditions (pH, total organic C and P, total solids and volatile

solids, C:N ratio) (Chapter 3). The proportion of P mineralising bacteria present at

stages of the piggery waste treatment process fluctuated, and this could be due to the

differences in organic P level (Chapter 3 and Chapter 4). It was found that pH was a

major factor influencing the community diversity and species richness for polyP

accumulating microorganisms (Chapter 5).

Previous studies have shown that alkaline PO4ase synthesis and activity (i.e. P

mineralisation) for aquatic bacteria appear to be controlled by organic-P, temperature,

ionic strength, pH, and the presence of metal ions (Güngör and Karthikeyan, 2008),

internal N:P ratio, P demand of the cell (Espeland and Wetzel, 2001), composition of

wastewater (Li and Chróst, 2006). Starvation, salinity, presence of primary substrate,

pH, and volatile fatty acids (VFAs) had different expressions of total PO4ase activity of

anaerobic sludge (Anupama et al. 2008).

Cha

pter

7: G

ener

al D

iscu

ssio

n

162

Tab

le 7

.1. T

he sp

ecifi

c co

ntrib

utio

ns o

f thi

s the

sis i

n re

latio

n to

the

P tra

nsfo

rmat

ion

in th

e m

odel

pig

gery

was

te tr

eatm

ent p

roce

ss.

Cha

pter

s A

im

Spec

ific

cont

ribu

tions

of t

his t

hesi

s in

rela

tion

to th

e P

tran

sfor

mat

ion

in

pigg

erie

s and

soil

amen

ded

with

pig

gery

was

te

Cha

pter

3

To c

hara

cter

ise

the

pigg

ery

was

te t

reat

men

t pr

oces

s in

te

rms

of

phys

ico-

chem

ical

pr

oper

ties,

bact

eria

l co

mm

unity

com

posi

tion,

an

d P

cycl

ing

pote

ntia

l

1)

Pi

gger

y w

aste

was

hig

h in

bot

h or

gani

c an

d so

lubl

e P

and

its d

istri

butio

n va

ries

amon

g th

e di

ffer

ent s

tage

s of

the

was

te tr

eatm

ent p

roce

ss. A

lso

the

cove

red

anae

robi

c po

nd b

otto

m sl

udge

was

app

aren

tly h

igh

in m

iner

al P

. 2)

Th

e hi

gher

leve

l of s

olub

le P

in th

e en

d pr

oduc

t of t

he w

aste

wat

er e

fflu

ent

impl

ies

the

requ

irem

ent o

f rem

oval

of s

olub

le P

up

to a

low

er le

vel b

efor

e be

ing

used

in a

gric

ultu

re o

r dis

posa

l bac

k to

env

ironm

ent.

3)

Occ

urre

nce

of P

min

eral

isat

ion

was

hig

her

in a

naer

obic

pon

ds a

nd p

olyP

ac

cum

ulat

ion

was

gra

ter

in t

he t

reat

ed w

aste

wat

er a

t ev

apor

atio

n po

nd/

aero

bic

pond

. 4)

O

rgan

ic P

ava

ilabi

lity

is o

ne o

f th

e ke

y dr

iver

s fo

r th

e P

min

eral

isin

g ca

paci

ty in

the

pigg

ery

was

te tr

eatm

ent p

roce

ss.

Cha

pter

4

To q

uant

ify th

e ab

unda

nce,

and

div

ersi

ty o

f P

min

eral

isin

g ba

cter

ia (

the

frac

tion

of c

ells

that

ex

pres

sed

phos

phat

ase

activ

ity)

durin

g th

e pi

g w

aste

tre

atm

ent

proc

ess

by

deve

lopi

ng

an

inte

grat

ed a

ppro

ach

usin

g th

e en

zym

e la

belle

d flu

ores

cenc

e te

chni

que

coup

led

with

ep

i-flu

ores

cenc

e m

icro

scop

y, c

ell s

ortin

g, a

nd n

ext

gene

ratio

n se

quen

cing

1)

An

inte

grat

ed a

ppro

ach

was

dev

elop

ed fo

r ide

ntify

ing

func

tiona

lly a

ctiv

e fr

actio

n of

P m

iner

alis

ing

bact

eria

in w

aste

wat

er.

2)

P m

iner

alis

atio

n w

as c

ompa

rativ

ely

high

er in

ana

erob

ic p

onds

com

pare

d to

the

aero

bic

pond

. 3)

Ba

cter

oida

les,

Clo

stri

dial

es, C

ampy

loba

cter

ales

, and

Syn

ergi

stal

es w

ere

the

mos

t dom

inan

t gro

ups

of P

min

eral

isin

g ba

cter

ia in

eac

h st

age

of th

e w

aste

trea

tmen

t pro

cess

. 4)

Th

e id

entif

ied

P m

iner

alis

ing

bact

eria

co

uld

empl

oy

as

pote

ntia

l in

ocul

um/‘s

eeds

”(es

tabl

ishe

d m

icro

bial

com

mun

ity)

for

enha

ncin

g th

e P

min

eral

isat

ion

in th

e ea

rly st

ages

of p

igge

ry w

aste

trea

tmen

t pro

cess

Cha

pter

7: G

ener

al D

iscu

ssio

n

163

Tab

le 7

.1.

The

spec

ific

cont

ribut

ions

of

this

the

sis

in r

elat

ion

to t

he P

tra

nsfo

rmat

ion

in t

he m

odel

pig

gery

was

te t

reat

men

t pr

oces

s

(con

tinue

d….)

Cha

pter

s A

im

Spec

ific

cont

ribu

tions

of t

his t

hesi

s in

rela

tion

to th

e P

tran

sfor

mat

ion

in

pigg

erie

s and

soil

amen

ded

with

pig

gery

was

te

Cha

pter

5

To i

dent

ify k

ey m

icro

bes

invo

lved

in

poly

P ac

cum

ulat

ion

and

its e

nhan

cem

ent u

nder

aci

dic

cond

ition

s fo

r as

sess

ing

the

effic

acy

of

enha

nced

bi

olog

ical

P

rem

oval

te

chno

logy

ap

plie

d in

hig

h P

load

ed w

aste

rem

edia

tion

1)

A s

igni

fican

t hig

her p

olyp

hosp

hate

acc

umul

atio

n w

as o

bser

ved

at p

H 5

.5

com

pare

d to

pH

8.5

, with

sig

nific

ant e

nric

hmen

t of p

olyp

hosp

hate

kin

ase

and

exop

olyp

hosp

hata

se g

enes

at p

H 5

.5.

2)

Func

tiona

lly a

ctiv

e PA

O a

ccum

ulat

ors

wer

e id

entif

ied

as A

erom

onas

hy

drop

hila

, Ae

rom

onas

sa

lmon

icid

a,

Acin

etob

acte

r ba

uman

nii,

Bord

etel

la p

ertu

ssis

, Citr

obac

ter

kose

ri,

Esch

eric

hia

coli,

Ent

erob

acte

r sp

. K

lebs

iella

, Ps

eudo

mon

as

aeru

gino

sa,

Salm

onel

la

ente

rica

, an

d Sh

igel

la fl

exne

ri.

3)

Ther

efor

e, th

ose

spec

ific

bact

eria

l gro

ups

can

be m

anip

ulat

ed u

nder

pH

5.

5 fo

r im

prov

ing

the

EBPR

was

te t

reat

men

t pr

oces

s an

d de

velo

p hi

gh

valu

e an

d lo

w e

nviro

nmen

tal r

isk

liqui

d fe

rtilis

ers.

Cha

pter

6

To d

emon

stra

te t

he i

mpa

ct o

f ap

plic

atio

n of

pe

lletis

ed p

igge

ry c

ompo

st t

o so

il on

pla

nt

grow

th, s

oil n

utrie

nt im

prov

emen

t, an

d ch

ange

s in

bac

teria

l and

fung

al c

omm

unity

com

posi

tion

whe

n ba

nded

with

a r

educ

ed r

ate

of s

ynth

etic

fe

rtilis

er

1)

Ban

ding

of

pelle

tised

pig

gery

com

post

in

com

bina

tion

with

syn

thet

ic

ferti

liser

at a

low

er r

ate

to s

oil i

ncre

ased

the

avai

labl

e P

in s

oil,

plan

t P

upta

ke, a

nd sh

oot a

nd ro

ot d

ry w

eigh

t.

2)

Abo

ve re

spon

ses

are

mos

t lik

ely

to re

flect

pla

nt a

nd b

acte

rial c

omm

unity

re

spon

ses

to c

hang

es i

n so

il nu

trien

t le

vels

due

to

the

appr

opria

te

blen

ding

of b

oth

pelle

tised

pig

gery

com

post

and

inor

gani

c P

ferti

liser

s 3)

D

ata

indi

cate

that

ban

ding

of

pelle

tised

pig

gery

com

post

in c

ombi

natio

n w

ith s

ynth

etic

ferti

liser

at a

low

er ra

te w

ould

be

an a

ltern

ativ

e P

ferti

liser

fo

r agr

icul

ture


164

For enhanced biological P removal by PAOs, environmental parameters such as pH,

COD, availability of ions (magnesium, calcium, and potassium), sludge retention time,

temperature, excessive aeration, redox potential, and light intensity influenced the

efficiency of P removal from the wastewater.

Although a major focus of this thesis was on identification of P cycling bacteria in a

piggery waste treatment process, it was also found that both abiotic factors (physico-

chemical variables) and biotic factors (community diversity and its interaction) had a

role in shaping P cycling bacterial communities in association with different stages of

waste treatment process.

Based on the finding of this thesis and previous findings, the key factors driving the P

cycling microbial diversity and activity in piggery waste treatment process can be

categorised as abiotic (e.g. pH, C:N ratio, P availability, temperature, P availability,

volatile fatty acids), biotic (microbial community diversity and its interaction with

bacterial P functional groups), and management (e.g. loading rate, storage conditions,

hydraulic retention, feed type etc.). Figure 7.1 shows the key factors influencing P

cycling bacterial diversity and activity in wastewater treatment plants.

7.2.1. Abiotic factors

Among different abiotic factors effecting P cycling microorganisms, pH and availability

of P in wastewater were most influential in the piggery waste treatment process.

The effect of pH

The pH of the piggery waste treatment system is a major driver of community structure

and activity of P cycling bacteria (e.g. polyP accumulating organisms, P mineralising

bacteria). CCA analysis showed that pH was higher in the Evaporation pond compared

to early stages in piggery waste treatment process (Chapter 3) and the community

composition of Evaporation pond was different from the other waste treatment ponds.

Hence, pH was a major factor influencing community diversity and spatial distribution

of microorganisms present within the piggery waste treatment process. This was

illustrated as an effect on the percentage removal of P from the wastewater (Chapter 5,

Figure 5.4 a) and also on the relative abundance and diversity of PAOs community

under changed pH conditions (Chapter 5, Figure 5.7). The effect of acidic pH in

enhancing P removal in EBPR has previously been observed (McGrath et al. 2001;

Mullan et al. 2002; Moriarty et al. 2006b). It is clear that pH drives community diversity

and activity of P mineralising microorganisms in the piggery waste treatment process.


165

Hydrolysis of Pi from organic or other complex P compounds (i.e. P mineralisation) is

mediated by phosphomonoesterase and phosphodiesterase activity (Anupama et al.

2008). The activity of phosphomonoesterases is dependent on pH and they are classified

as either alkaline (pH>7; EC 3.1.3.1) or acid (pH<6; EC 3.1.3.2) phosphatases

depending upon their pH optimum level (Geesey 1999 and Anupama et al. 2008;

Kloeke and). The observed P mineralisation capacity in the piggery waste treatment

process was assumed to be related to the alkaline phosphatases (pH>7; EC 3.1.3.1)

activity as the pH of the system was above 7 (Chapter 3, Table 3.1).

Figure 7.1 General diagram showing some of the factors influence of the P cycling microbial diversity and activity in wastewater treatment plants.

P cycling bacterial diversity and activity

in wastewater

Abiotic factors

pH, C:N ratio, P availability, temperature, P availability, volatile fatty acids

Biotic factors

Interactions of bacterial P cycling groups with other microbial

communities such as microalgae, fungi, and phytoplankton

Management practices in animal husbandry

Waste loading rate, storage conditions, hydraulic retention, feed type etc.


166

P availability

Organic P seemed to be an intrinsic factor for the microbial community composition

(Chapter 3, Figure 3.4) as it was found in the earlier stages of the waste treatment

process. This indicated that P mineralisers play an important role in degrading organic P

at the early stages of the treatment process. Extracellular phosphatases (such as alkaline

PO4ase) catalyse the liberation of Pi from various organic P compounds. Alkaline

PO4ase synthesis and activity in aquatic bacteria appear to be controlled by the levels of

specific forms of external organic-P, type, and by the concentration of the substrate and

the enzyme (Güngör and Karthikeyan, 2008).

Inorganic P concentration in the environment either directly or indirectly influences the

P mineralisation. A negative correlation between PO4ase activity and inorganic P has

been reported widely, indicating algal and bacterial PO4ase activity is inhibited by

elevated Pi concentrations (Dignum et al., 2004). However, few other studies have

reported that alkaline PO4ase synthesis in many bacteria is not inhibited by elevated Pi

(MH and HJ, 1961; Chrost et al., 1986; Kloeke and Geesey, 1999). A small fraction of

total bacterial cells in the waste samples displayed PO4ase activity (0.3 %- 5.5 %),

(Chapter 4, Figure 4.7), which could be due to potential inhibition of this enzyme at

high Pi levels (10.8 - 26.3 mg/L). However, there was no direct relationship between

ELF activity and Pi level among the samples as was found in some other studies.

Therefore, there might be species-specific differences in the relationship between Pi and

PO4ase activity. Meseck et al. (2009) explained that expression of PO4ase activity at a

high soluble reactive P concentration could be attributed to the ratio of DNA to protein

or more of an individual response, rather than a population response. Concerning these

observations, the level of PO4ase activity among different stages of waste treatment

could be a cumulative effect of both biological and physicochemical dynamics of the

each waste treatment stage.

7.2.2. Biotic factors

Theinfluence of biotic factors on polyP accumulating microorganisms was illustrated in

Chapter 5. Microscopy observations of polyP granules in both microalgae and bacteria

(Chapter 5, Figure 5.5) show the likelihood of competition for soluble P between the

two groups. It was further confirmed that the microbial community composition in both

unfiltered and unfiltered samples maintained at pH 5.5 greatly affected the abundance of

polyP accumulating bacteria (Chapter 5; Figure 5.7). For example, a marked difference


167

in relative abundance of Alteromonadales and Aeromonadaceae in wastewater

maintained at pH 5.5 was observed between the filtered (Alteromonadales 59 %;

Aeromonadaceae 26 %) and unfiltered (Alteromonadales 4 %; Aeromonadaceae 73 %)

samples. The accumulation of polyP in both bacteria and microalgae was previously

observed under pH 5.5 (McGrath et al., 2001). It has also been observed that two

species of microalgae (Chlorella vulgaris and Scenedesmus dimorphus) were capable of

removing up to 55% of the phosphates from an agroindustrial wastewater system

associated with dairy and pig farming wastewaters (Gonzalez et al. 1997). Apart from

microalgae, some other primary producers, such as phytoplankton have the ability to

assimilate both organic and inorganic P fractions (Withers and Jarvie 2008) and thereby

influence bacterially mediated P cycling in wastewater. Heterotrophic bacteria and

biofilms (mixtures of microbes, algae, and particulate matter within a polysaccharide

matrix) are recognised as the dominant sites for accumulation of microbial biomass,

rapid P cycling and grazing activity in natural water bodies (Withers and Jarvie 2008)

indicating the effect of biotic factors on the P cycling bacterial diversity and abundance.

7.2.3 Management practices

Management practices such as loading rates, hydraulic retention time, and composition

of pig feeds influence methane yield of digestate (Menardo et al. 2011). Thus,

management practices lead to changes in microbial community composition in the early

stages of the waste treatment process, and hence on organic P mineralisers. Diversity

and abundance of P mineralising bacteria at early stages in waste treatment and CAP

digesters seem to be controlled by organic P, total solids (TS), volatile solids (VS)

(Chapter 3, Figure 3.4). Loading rates of piggery waste cause changes in total solids

(TS) and volatile solids (VS) in waste. The composition of pig feeds will also affect the

amount of energy and nutrients (e.g. carbon, nitrogen, phosphorus, potassium, organic

nutrients) available for microbial growth. Furthermore, phytate in pig feed is a substrate

for P mineralising bacteria (Lim et al. 2007; Baxter et al. 2003) and therefore the

amount of phytate in pig feed directly influences P mineralising bacteria in waste.

7.3 Methodological Considerations This research has shown that bacterial communities and their functional P components

(P mineralisation, polyP accumulation, and P solubilisation) in the piggery waste

treatment system investigated are spatially separated, as was confirmed by two


168

independent approaches (i.e.16S rRNA Ion Tag sequencing, and community

metagenomic analysis) (Chapters 3 to 5). Overall, the dual approach was advantageous

when studying wastewater bacterial P communities using molecular methods targeting

the 16S rRNA gene and community metagenomic analysis, because a variety of

community descriptors, including abundance, composition, species richness, alpha and

beta diversity indicators, were able to be investigated simultaneously. According to the

findings of this study (Chapters 3 to 5) these molecular techniques were generally

compatible. Therefore, the methods applied should be applicable to detecting taxonomic

and functional diversity of P cycling bacteria in similar studies. Chapters 4 and 5

reported the application of epi-fluorescence microscopy, flow cytometry, cell sorting,

and 16S rRNA Ion Tag sequencing approach which linked microbial identity with

activity of P cycling microorganisms. Epi-fluorescence microscopy and flow cytometry

approaches further assisted in co-locating and quantifying P mineralising bacteria

(Chapter 4), and polyP accumulating microorganisms (Chapter 5). One of major

obstacles in detecting P microbial communities is the lack of primers targeting P cycling

microorganisms (see Chapter 2). This was addressed by using a combined approach in

this study.

7.3.1 Sampling strategy

In order to reduce sampling biases, samples from each pond were collected into several

sampling bottles and corresponding samples were mixed to make a composite sample

per each stage of waste treatment process. Additionally, technical replicates were also

used for the majority of analyses, except for next generation sequencing approaches due

to the high cost of sequencing. Nevertheless, the sequencing data were reliable and

reproducible for identifying P mineralisers (Chapter 4), and polyP accumulators

(Chapter 5) were representative members of the community in this piggery waste

treatment process (Chapter 3) as the three studies were done independently to each

other. The bacterial populations in the covered anaerobic digester were similar to the

taxonomic identity of the bacterial community in the same CAP digester studied

previously (Whiteley et al. 2012). Samples from the piggery wastewater treatment

process were collected at one time during the year to maintain consistency but seasonal

variation (which was not assessed) could follow changes in environmental conditions in

the waste ponds over time. Nevertheless, it has been previously shown that this CAP

digester has a relatively stable community composition over a period of more than 10

months (Whiteley et al. 2012).


169

7.3.2. Methodological considerations in fluorescence staining, flow cytometry, and

cell sorting

Single-cell-based methods such as fluorescence microscopy and flow cytometry are

appropriate for selective analysis of specific microorganisms (such as PAOs) within

complex communities (Günther et al. 2009). However, there are some limitations which

need to be addressed when applying these fluorescence techniques to complex

environmental samples comprising high microbial diversity and debris. Auto

fluorescence derived from debris can interfere with fluorescence signals from stained

cells which are present in different sizes, granularity and shapes making it difficult to

assign accurate gating strategy for discrimination of cells from auto fluorescence.

Piggery wastewater effluent and slurry also comprises complex microbial communities

and large amounts of debris (e.g. organic matter and clay minerals). Therefore,

optimisation of the staining protocol for ELF-stained cells (Chapter 3), and polyP

granules (Chapter 4) was a challenging task.

In Chapter 3, the selection of a fluorescent dye for counter staining of ELF-stained cell

was done carefully. Excitation and emission wavelength of the counterstaining dye

should be within the range of the excitation and emission wave length of the ELF97®

(345-530 nm) with minimum spectral spillover between each fluorescence channels. In

order to capture the maximum fluorescence from each channel, appropriate filters were

selected according to the type of the flow cytometer (as explained in Figure 4.2). In

brief, to obtain a clear separation between ELF+ cells (cells expressed phosphatase

activity) and ELF- cells (other nucleated cells) in flow cytometric analysis, three

potentially suitable fluorescent dyes, DAPI (358-461 nm), SYTO9 (485-498 nm), and

PI (535-616 nm) were evaluated. Based on the spectral set-up in the flow cytometry

used, a significant spectral spillover of DAPI and PI into the ELF emission detector was

initially nobserved. Spectral spillover of DNA binding dyes is caused by difficulty in

separating ELF+ cells from other nucleated cells (i.e ELF- cells) and noise. In contrast,

SYTO9 was the best fluorescent dye in separating ELFA-labelled cells from both ELF-

non-labelled cells (other nucleated cells) and background (auto-fluorescence) (Chapter

4, Figure 4.5b).

In chapter 5, optimisation of DAPI was done to assess the best concentration of DAPI to

stain the accumulated polyP granules in the piggery waste microbial communities. In

general, DAPI at higher concentration (5-50 µg/ mL) is recommended for staining

polyP granules (Günther et al. 2009, Klauth et al. 2006) and low concentrations of


170

DAPI fluorescence (0.24-5 µM) is related to bacterial DNA. However, an optimum

concentration is needed for staining polyP granule and this appears to vary with the type

of environmental samples, complexity of microbial community, and size of the polyP

granules. The other problem associated with DAPI is unspecific fluorescence derived

from other cellular constituents (such as lipids) when DAPI is applied at high

concentrations (180 μM) (Streichan et al. 1990). Based on this study (Chapter 5, Figure

5.2), a 15 µg/ mL DAPI concentration was sufficient for staining polyP granules in the

tested piggery wastewater samples. Another study showed that dual staining of

fluorescence antibiotic tetracycline (TC) and DAPI can also be used for reliable and

accurate detection and quantification of PAOs (Günther et al. 2009). This dual staining

has the added advantage that it is a quantitative method for PAO detection and DNA

content analysis (such as bacterial growth rate) (Günther et al. 2009). Apart from

quantification of polyP granules and information on bacterial growth rates, the activity

states of PAOs would be useful for the bioengineering aspect of EBPR. As for further

methodological improvement, application of a dual staining protocol for TC and DAPI

for detection and quantification of PAOs in piggery wastewater is proposed.

Cell sorting coupled to flow cytometry was an efficient and accurate way to separate P

mineralising bacteria in a complex diverse environment such as piggery waste treatment

ponds. Nevertheless, the DNA concentration recovered from sorted cells (from 106

cells) was low and not sufficient for downstream next generation sequencing. In

particular, it was not sufficient for PCR independent metagenomic analysis. The reason

for the low recovery of DNA from the sorted cell could be mainly due to the

stabilisation of sorted cells by cross-links formed due to paraformaldehyde fixation and

this stabilization hampered the release of DNA from the fixed cells. Piggery wastes

harbour pathogenic microorganisms and therefore fixation with an appropriate cell

fixative is a compulsory step prior to handling and also to ensure no contamination

occurs in the flow cytometry instrument. On the other hand, fixation is important to

maintain the cell structure for microscopy and flow cytometric analysis. The need for

purity of cell sorting from complex environmental samples like piggery waste means it

is very time consuming to retrieve an adequate amount of cells (>109) for DNA

extraction, especially when handling a large number of samples. Even if a large number

of cells are sorted, most of DNA remains inside the fixed cells. This could bias the PCR

amplification because of inefficiency of DNA extraction and could lead to deceptive

microbial composition for a given environment. Negative effects of formaldehyde


171

fixation on the efficacy and fidelity of PCR amplification of DNA have been described

previously (Wallner et al. 1997).

In order to address these issues, sorted ELF+ve cells were pre-treated prior to DNA

extraction to facilitate the DNA extraction from the PFA fixed cells (Chapter 4). The

sorted cells were concentrated by centrifuging at 16,000 g for 20 min and the

supernatant was carefully removed. The remaining cells in the Eppendorf tubes were

pre-incubated with freshly prepared 10 % SDS (70 µL) plus 20 mg mL-1 proteinase K

(10 µL) for 1 hr at 65 oC followed by DNA extraction was done. The treated cells were

used for DNA extraction using the MoBio UltraClean® Microbial DNA Isolation Kit

(Geneworks, Australia), utilising beat beating and column purification, according to the

manufacturer’s guidelines. The DNA recovered in this way and the subsequent nested

PCR yielded an adequate concentration of DNA with a good purity for 16S rRNA Ion

Tag library preparation. On the other hand, other fixation methods could be tested

which were not evaluated in this study. For example, 10% sodium azide, as a fixative,

has been applied successfully to determine the abundance and identity of polyP

accumulating microorganisms in a wastewater treatment plant using fluorescence

labelling of polyP, cell sorting, and denaturing gradient gel electrophoresis (DGGE)

(Mehlig et al. 2013).

7.3.3 Methodological considerations to 16S rRNA Ion Tag sequencing

The Ion Torrent Personal Genome platform has proven to be an effective tool to assess

microbial community structure, temporal stability and key taxa in the same CAP

digester with appreciable levels of sequence outputs at low cost (Whiteley et al. 2012).

The protocol explained by Whiteley et al. (2012) was used with Golay barcoded Ion

Tags for multiplex analyses of microbial communities to determine the microbial

community structure in the piggery waste treatment process (Chapter 3), P mineralising

bacteria (Chapter 4), and PAOs (Chapter 5). This was based on amplification of a

standard 200 b.p. V3 region of bacterial 16S rRNA using Golay barcode and Ion

Torrent adapter modified core primers 341F and 518R (Muyzer et al. 1993).

However, there are some limitations that have to be considered when estimating the

relative abundance of microbial community structure derived from PCR amplification

using the 200 b.p. V3 region. For example, the relative abundance derived from the 200

bp could be relatively low and bias can occur when assigning taxonomic identity based

on a short sequence length of 16S rRNA. Ion Torrent sequencing has some inherent


172

biases such as low quality scores and chimera sequences. Nonetheless, efforts were

made to reduce the effects of low quality sequences retrieved from the Ion Torrent

platform and also the possibility of miss-assigning taxa to the wrong group using the

QIIME software package (Caporaso et al. 2010). For example, quantity filtering was

done by defining appropriate sequence selection criteria (Chapter 3-6). Possibility of

miss-assigning taxa to wrong group was further avoided by removing chimeras and

singletons followed by picking OTUs at 97% similarity cut off.

7.3.4. Discrepancy of degree of P mineralisation as revealed by ELF coupled to

flow cytometry and metagenomics

Having seen that the number of gene encoding for alkaline phosphatase was the highest

in the CAP-Bottom (Chapter 3), it was assumed that the highest abundance of ELF+

cells could be found in CAP-Bottom when the ELF was coupled to flow cytometry.

However, there a lower % of ELF+ bacteria in the CAP-Bottom was revealed by ELF

coupled to flow cytometry. The observed discrepancy might be due to a number of

alkaline phosphatase reads found in CAP-Bottom being associated with the activity of

Methanosarcina spp., anaerobic methanogen which were not accounted for by ELF and

bacterial 16S rRNA tag sequencing. The discrepancy in abundance of alkaline

phosphatase gene revealed by metagenomic analysis, and the % of ELF + bacteria

revealed by ELF, also indicated that archea might be playing an important role in

PO4ase activity in the waste treatment process in addition to activity of bacteria.

Anupama et al. (2008) showed that both archaea and bacteria played equal roles in

PO4ase activity in anaerobic bioreactors. Therefore, further studies are required to

understanding the contribution of archaea in the mineralisation of organic P in this

piggery waste treatment process, especially as this environment is significantly favoured

by anaerobic microorganisms.

The second reason for the discrepancy of degree of P mineralisation revealed by ELF

coupled to flow cytometry and metagenomics could be associated with variable cell

extraction from the environmental samples during the sample preparation for ELF

staining. For example, the majority of bacterial cells in CAP-bottom can be attached to

the clay/silt and organic particulates and fewer cells can be expected when compared to

extraction from the evaporation pond effluent which was very low in clay and organic

particulate. When the number of cells and purity is low, interferences caused by

impurities lead for underestimation of total bacteria/ELF + cells in a sample. This

happens when the numbers of ELF + cells present as a percentage of the total bacteria


173

cells. This is one of the disadvantages of quantification of ELF +cells in environmental

samples using flow cytometric analyses. In this study, fluorescence interferences were

minimised by assigning a proper gating strategy (Chapter 4, Figure 4.6). As for further

optimisation to the current ELF protocol, different extraction methods can be tested. For

example, interference of clay minerals and organic matter while recovering higher

number of cells from sludge samples or lake sediments can be achieved by using

advance cell recovering procedures such as Nycodenz gradient centrifugation (Poté et

al. 2010).

7.3.5 Limitations in the pot trial

Effect of pelletised piggery compost (alone or in combination with inorganic fertiliser)

on plant growth, soil nutrients, and soil bacterial and AM fungal community was

assessed up to 8 weeks at 3 independent harvests (at 4, 6, and 8 weeks) to investigate

the effect of temporal variation on the above parameters. However, the discontinuation

of the pot experiment (at 8 weeks) before obtaining the yield limits conclusions about

the best-performed soil amendment. Differences in responses to soil amendments

observed over time. For example, some fertiliser amendments (e.g. inorganic fertiliser

alone) started to perform well only after 6 weeks (inorganic P fertilisers alone).

In addition to PCR-based sequencing approaches used in Chapter 6, PCR independent

metagenomic sequencing is a more powerful approach for identifying the P cycling

microbial community structure together with their putative metabolic potential based on

genes and pathways. Therefore, the integration of these methods could provide a greater

insight into the microbial diversity and functional activities in soil amended with both

pelletised piggery compost and inorganic P fertilizers and proposed for future studies.

7.4 Underlying mechanisms in P cycling and proposed pathways for

the piggery waste system

More complete understanding of the underlying mechanisms of the P transformation in

the piggery waste treatment process would aid both reduction of environmental loading

of inorganic P and recovery of valuable by-products. According to this study (Table 7.1)

and related literature (De-Bashan and Bashan 2004, Kulakovskaya et al. 2012, Yoon et

al. 2004), the probable mechanism of P transformations in the model piggery waste

treatment process is illustrated in Figure 7.2a.


174

In the CAP digester of the piggery waste treatment system, P transformation can occur

via three main pathways (P mineralisation, polyP degradation, and crystallisation). The

anaerobic environment is electron acceptor deficient and carbon rich. Therefore, it is

proposed that PolyP is degraded to Pi (orthophosphate) from microbial sludge for

energy. The energy is used for uptake of acetate and form microbial biopolymers such

as polyhydroxyalkanoate (PHA) and consequently orthophosphates is excreted from

cells to the digestate (Figure 7.2a). On the other hand, organic P mineralisation (the

process of hydrolysis of Pi) also releases orthophosphate into the digestate. The higher

abundance of genes encode for alkaline phosphatases (Chapter 3, Figure 3.6a), in the

CAP digester confirmed that P mineralisation is primarily occurring at this stage. Other

P transformation occurring in the CAP digester is chemical precipitation of P. With the

availability of some cations, the orthophosphate in digestate becomes progressively less

soluble with the formation of crystallised P forms (Stuvite;MgNH4PO4.6H2O,

Ca3(PO4)2, Mg3(PO4)2, Fe3(PO4)2). Chemical precipitation of orthophosphate as

crystallised P forms has been documented in anaerobic digestion of piggery waste

(Mehta and Batstone 2013). Solubilisation of precipitated forms of P is governed by P

solubilising microorganisms. Although there was no direct evidence of in situ P

solubilisation activity in the CAP-Bottom sludge, P solubilisation could possibly occur

in the CAP-Bottom where precipitated forms of P are high.

The end product of anaerobic digestion (digestate) is generally rich in orthophosphate

compared to the starting wastewater (De-Bashan and Bashan 2004). The same trend was

observed in this study (Chapter 3, Table 1). For example, the concentration of

orthophosphate in the CAP-inlet (i.e. holding tank: 25.1 Pi-mg/L) was higher than the

CAP-outlet (20.5 Pi-mg/L). Orthophosphate concentration in the anaerobic digesters is

a result of the net effect of P mineralisation, polyP degradation, and P crystallisation.

Thus, orthophosphate concentration varies among different anaerobic digesters, and

clearly, microbial communities in CAP digesters play an important role in P

transformation.


175

Figure 7.2 Probable mechanisms of P transformations in the CAP digester and Evaporation Pond under its natural states (a). A proposed method for improving the current wastewater treatment process (b).

The anaerobically treated wastewater is collected into an evaporation pond which is in

natural aerobic state. There are two main P transformations taking place in this last

stage of piggery waste treatment process (polyP accumulation and P solubilisation).

This environment is generally electron acceptor rich but carbon deficient. It has been

proposed that PHA is degraded and PolyP is synthesized from the ATP generated from

the PHA metabolism. The removal of P in EBPR as polyP is well documented and an

anaerobic condition followed by aerobic condition is a prerequisite for the activity of

PAOs. Therefore, conditions in the aerobic pond are more favourable for polyP

accumulation (Chapter 3 Figure 3b) and less favourable for P mineralisation (Chapter 3

Figure 3a). Therefore, polyP accumulation under EBPR could potentially be an

important mechanism for P removal in the piggery waste treatment systems where high

P in wastewater is a problem. As more orthophosphate is taken up during the aerobic

phase than released during the anaerobic phase, reduction of orthophosphate


176

concentration in aerobic pond is expected. There was a lower concentration of

orthophosphates in the aerobic pond (12.2 mg/L) compared to the outlet of the

anaerobic pond (i.e. CAP-outlet; 20.5 Pi-mg/L). However, the reduction in

orthophosphate in the evaporation pond/aerobic pond up to 12.2 mg/L under its natural

state is not at the required standard for recycling the treated wastewater as the irrigation

water. In particular, irrigation of sandy soils with the recycled water leads to phosphate

leaching and subsequent pollution of waste bodies (Obaja et al. 2003). Therefore,

further improvement is necessary to reduce the soluble P (i.e. orthophosphate) in the

aerobic pond before they are used as liquid fertilisers on sandy soils.

A proposed method for improving the current waste treatment process is illustrated in

Figure 7.2b. P removal in EBPR can be enhanced by providing intermittent anaerobic

and aerobic conditions (De-Bashan and Bashan 2004). Therefore, circulation of

wastewater between the evaporation/aerobic pond and the anaerobic pond is proposed.

Furthermore, the knowledge gained about how to manipulate and exploit polyphosphate

accumulating organisms to enhance P uptake by altering the pH (Chapter 5) provides

the basis of a novel strategy for improving the piggery waste treatment process and

developing high value liquid fertilisers for land application. Subsequent investigations

should therefore focus on assessing the economic feasibility of incorporating EBPR

systems into existing piggery waste treatment systems by lowering the pH of the aerobic

pond. Acidification of the aerobic pond using acids in its current state (larger volume)

would not be economically feasible and could result pond failure due to the breakdown

of other microbial pathways resulting in unexpected consequences. Therefore, the

introduction of another two ponds for acidification of wastewater (i.e. acidification

pond) and purification of water (a sedimentation tank) is advisable. The size of the

secondary acidification pond, loading rate and frequency can be decided based on the

daily requirement of the irrigation water for the farm land. Inclusion of a separate

acidification pond is an added advantage for producing only the required volume of

wastewater for subsequent on farm irrigation and also for easy maintenance. Efficiency

can be further improved by enriching the acidification pond with acid loving PAOs

(identified in the Chapter 5). The acid treated wastewater can then be passed through a

membrane bioreactor which is made of polyethylene fibre where the PAO bacteria and

other microbes attach forming a biofilms. Therefore, the membrane bioreactor facilitates

the recovery of phosphate-depleted cleaner effluent (Yoon et al. 2004) which can be

used for irrigation or other farm activities. The sediment accumulated in the bottom of


177

the sedimentation tank over time can be further processed as compost/pelletised forms

which are considered to be high in P form (biomass P, mineral P, and organic P).

CAP-bottom sludge can be high in mineral P sources and can be recycled in slow

releasing P fertilisers (Figure 7.2b). P in these products is expected to be present mainly

in crystallised and organic P forms and appeared to be an effective slow releasing P

fertiliser for plants with a low risk for eutrophication. Gradual release of plant available

P can be expected in the root zone of plant with the activities P-solubilising and P-

mineralising bacteria, which are generally assumed to be the main contributor of P

turnover in soils.

7.5 Research Perspectives

This research will be beneficial to different stakeholders (including the scientific

community, pork industry, environmentalist, public, farmers). This section highlights

some of implications for these groups.

7.5.1 Relevance to scientific community

This thesis involves the development of single-cell-based methods (i.e. fluorescence

microscopy and flow cytometry) coupled to next generations sequencing approach for

characterising P mineralising bacteria and PAOs in a piggery wastewater treatment

system. The findings help to track P transformations in piggeries through the microbial

community to provide greater insight into P cycling in the piggery waste management.

These techniques could be modified and adapted for different systems in natural,

agricultural, and engineered environments for understanding P cycling pathways.

Knowledge of the taxa mediating these P transformations pathways means possibility of

developing molecular biomarkers (i.e. probes/primers) for monitoring P cycling bacteria

in different environmental settings. Knowledge of P mineralising microorganisms and

the factors affecting their activities would provide the opportunity to optimise organic P

degradation during the anaerobic digestion processes to yield high biogas production.

Efficiency and reliability of EBPR can be achieved by discovering highly efficient

PAOs (e.g. bioengineering).


178

7.5.2 Relevance to small scale and large scale pig farmers

Economic Benefits

Adoption of CAPs and sustainable re-use of animal by-products will benefit both small

scale and large scale commercial farmers by reducing the cost of production through on

farm energy production (e.g. biogas generation), increasing profitability of crop

production (e.g. low cost P fertilisers), and recycling of water (e.g. treated wastewater

for irrigation).

On the other hand, pelletised piggery compost would be economically more favourable

because it can be applied through an air seeder and its application beneath the soil

surface allows the rate of application to be significantly reduced, while simultaneously

reducing dependence on inorganic fertilisers. The reduced volumes would facilitate

transport and spreading costs leading to greater financial and technical viability for the

farmer. This would also be beneficial to pig farmers since it increases marketability of

piggery manures outside their farms.

Environmental Benefits

From an environmental perspective, recycling or re-use of piggery waste will reduce the

waste accumulation on piggery farms which in turn leads to reduced cost of waste

management and land required for waste disposal. Reduction of P in wastewater

through EBPR reduces the P leaching and subsequent eutrophication.

Social Benefits

Dissemination of the findings of this study through on-farm demonstrations and training

will encourage farmers to adapt new sustainable farming practices for reducing on farm

waste accumulation, efficient biogas generation and recovery of P for re-use as

fertilizer. Adoption of CAPs and sustainable re-use of by-product will reduce odour

emissions, pathogenicity, and GHG emission leads for improving the wellbeing of pig

farmers and neighbouring communities.

7.6. Future research directions

As summarized above, the research study generated important fundamental knowledge

relating to the P transformation in the piggery waste treatment process. This research

study also identified a number of issues that require further investigation to enhance the

practical application of the findings to the real environment as summarised below.


179

7.6.1 Research directions for methodological development in tracking P cycling

in environments

Primer design of sequencing retrieved

- Retrieved sequences from the identified taxa in this study and other related work can

be exploited in design of highly specific oligonucleotide primers for screening P

cycling bacteria. As discussed in Chapter 2, primers and probes targeting P mediating

microbes are limited, non-specific or poorly developed. Also, the application of

molecular methods to P transformation is limited (Wasaki and Maruyama 2011) by the

availability of sequences in the current databases for genes involved in P mineralization

/solubilisation / polyP accumulation. With the advancement of novel molecular

techniques which provides functional details of a community (such as

metatranscriptomics, metagenomic, proteomics, metabolomics), a considerable number

of sequences encoding for P cycling genes (Chapter 2. Table 2.4) are now available and

these can be used to design molecular monitoring tools such as primers and probes for

exposing the role of P mediating microbes in a given environment. Therefore, sequence

retrieved from this study and available P cycling gene sequences in current databases

would be a basis for design of new primers.

Stable isotope approaches linking diversity with function of P cycling

pathways

- The combined molecular microscopy approaches in Chapters 4 and 5 were successful

when applied to the P mineralising and polyp accumulating microorganisms. However,

there are a number of alternative strategies that could be used in situ, which may offer

new opportunities for detecting functional gene sequences at a more detailed level,

linking functional capacity with the diversity of those P cycling microorganisms. One

promising way is use of stable isotope techniques, SIP-CHIP approaches and

NanoSIMs (Read and Whiteley 2010; Wasaki and Maruyama 2011). These novel

methods can be used to track P partitioning into different P pools and to identify the key

microorganisms involved in P transformations (P immobilisation, P mineralisation, and

P solubilisation) in a given environment. Development of stable isotope probing

techniques for determining the impact of piggery waste by-products (such as pelletised

piggery compost or Eco-shelter manure) on microbial P cycling in soil would assist in

unravelling the partitioning of P into different soil P fractions in soils receiving organic

and inorganic P inputs. This can be achieved by tracing the fate of 18O-labelled organic


180

P and 18O-labelled inorganic P inputs into the soil microbial community. By identifying

the taxa involved in the decomposition and/or transformations of the organic and

inorganic P the relative importance of the bacteria and fungi in P-mineralisation and P-

immobilisation can be determined.

Metagenomics / metatranscriptomics approaches for linking diversity with

function of P cycling pathways

- It is recommended that metagenomic analysis be used as a part of a risk and benefit

analysis when a new manure management practice or land application method is being

introduced. Therefore, study of P transformation genes in the rhizosphere and root

colonised bacteria and fungi using metagenomics / metatranscriptomics (Bastida et al.

2009) which in turn provide reliable validation for the effect of P mediating

microorganisms in soils amended with piggery waste by-products with a proper control

is proposed.

- Further research should be focus on understanding P solubilising activities in piggery

waste treatment process using both culture dependant and independent approaches. As

noted in Chapter 2, finding suitable P solubilising microorganisms (bacteria, fungi, or

archaea) which can be employed for solubilisation of precipitated forms of P (e.g.

struvite and hydroxyapatite) in situ under strictly anoxic environment like anaerobic

digesters would be a highly beneficial in P removal from effluents and sludge.

- Optimisation of the cell fixation protocol and cell sorting to maximise DNA yield and

improve the success of PCR amplifications would maximise the quality and reliability

of sequencing data.

7.6.2 Research directions for improving the current piggery waste treatment

process

- Investigation of the role of microalgae in P cycling in piggery waste treatment

process. As microalgae play an important role in polyP accumulation in the piggery

waste treatment process, further research is proposed to understand their taxonomic

identity, function and factors that affect their activity. Understanding the interaction

between polyP accumulating bacteria and microalgae would help improve P removal in

these systems and would enable improvements in engineering for current piggery waste

treatment processes.


181

- Detection of changes of P transformation in piggeries with respect to the variability of

environment, diet composition, and management practices.

- Evaluation of enhanced P uptake by altering the pH under on-farm conditions.

7.6.3 Research directions for enhancing the low rate application of pelletised

piggery compost

- Larger and longer-term field trials combined with laboratory assessment are necessary

to verify positive effects of low rate application of organic and inorganic soil

amendment (Balance50/Agras50) under field conditions.

- Identifying other compatible organic or inorganic fertilisers to blend with pelletised

piggery compost to increase profitability and technical viability for the farmers.

- Canonical correspondence analysis (CCA) was used to explore the relationship

between environmental variability and individual bacteria taxa (Chapter 3 and Chapter

5). These data can also be analysed using Structural Equation Modelling (SEM), a new

modelling approaches used to test hypothesised pathways, links, and then identify the

most reliable model that explains the observed data. In this way, SEM can be used to

evaluate the impact of different P-fertilisers on P cycling by allowing P partitioning and

transformation to be modelled and predicted.

Appendices

182

Appendix 1

Appendix 1. (a) Location of the study site. (b) The piggery waste treatment process at Medina Research Station, Department of Agriculture and Food, Western Australia (DAFWA) for treating piggery effluent waste and capture bioenergy.

Appendices

183

Sampling site description

The piggery wastewater treatment process at Medina Research Station can be described

as several stages as shown above. Medina Research Station has a pig shed which can

accommodate maximum 400 pigs at a time. The pig shed has four storage pits (1.2 m

deep x 1.8 m) under the slatted areas of the pens that are discharged every two weeks.

Bore water is used to flush piggery waste from the pens into the pits. The waste

treatment process is separated into 5 stages: pits in the pig shed; solid separation

screens, holding tank, the covered anaerobic pond (CAP) and finally a secondary

evaporation pond. Effluent from the pig pens is collected in the pits and held there until

pits are ¾ full and then released into a 100,000 L underground tank from where it is

pumped over a static run-down screen (solid separator) that removes about 10-15% the

total solids (TS). The remaining wastewater is transferred to the holding tank prior to

being pumped into the covered anaerobic pond (CAP) (ca. 25m x25m x5m) digester on

a weekly basis (75,000 L/wk). Treated effluent is then transferred to the secondary

pond (ca. 50m x50m x5m) in aerobic state, where the treated waste water evaporates.

The biogas produced from the CAP is removed through a perforated pipe system placed

around the perimeter of the pond. A small centrifugal fan draws the gas off and the flow

of biogas is measured using a domestic gas meter. The gas is currently ignited using a

biogas flare and converted to carbon dioxide (CO2), a less potent Greenhouse gas

(GHG).

Appendices

184

Appendix 2

Appendix 2. Source hit distribution of CAP-Bottom metagenome that were annotated by the different databases. Bars represent annotated reads, which are colored according to their e-value range.

Appendices

185

Appendix 3

Primer sequences

A1 different for each sample

Universal Primer Mix:

Vol (uL) Stock Conc Final Conc

515F_BACT_A_xx (barcode) 0 5uM - 806R_BACT_P1 10 100uM 4uM

515F_BACT 1.1 100uM 0.44uM

806R_BACT 1.1 100uM 0.44uM

Low TE 237.8 0 0

TOTAL: 250

PCR reactions

BACT v4/5 - Reaction Setup Component Vol (1x) MM (n) Final H2O 8.56 239.68 BSA (50ug/uL) 0.24 6.72 0-600ng/uL Univ. primer pool (4uM) 1.20 33.60 0.2uM Barcoded Fwd primer (5uM) 1.00 - 0.2uM DNA Template (0.25ng/uL) 1.00 - 5PRIME HOT MM (2.5x) 8 224.00 20uL 504.00 Aliquot 18uL MM per rxn; Added 1uL Barcoded Forward primer to each sample; Added 1uL DNA template to each reaction and Used 1uL H2O for NTC (no template control) and any barcode

Oligo Name Length

515F_BACT GTGCCAGCMGCCGCGGTAA 19

806R_BACT GGACTACHVGGGTWTCTAAT 20

806R_BACT_P1 CCTCTCTATGGGCAGTCGGTGATCCGGACTACHVGGGTWTCTAAT 45

515F_BACT_A_1 CCATCTCATCCCTGCGTGTCTCCGACTCAGTCCCTTGTCTCCGTGTGCCAGCMGCCGCGGTAA 63

Sequence (5' to 3')

Appendices

186

PCR Conditions:

BACT v4/5 - Amplification Conditions Stage Temp Time

Denature 94° 2m Denature 94° 45s

x 25 cycles Anneal 50° 60s Extend 65° 90s

Denature 94° 45s x 2 cycles

Anneal/Extend 65° 90s Final Extension 65° 10m

Hold 4° ∞

Appendices

187

Appendix 4

Appendix 4 (a) Wheat plant growth in soils receiving different soil amendments from left to right: Balance50/Agras50, Control, and Agras100 at 4 weeks after sowing.

Appendix 4. Plant growth performances at 8 weeks. (a) Agras100 (b) Balance100 (c) Balance50/Agras50 (d) control (nothing added).

Appendices

188

Appendix 5

Appendix 5. AM colonization of roots of wheat at the first harvest (4 weeks). High colonisation % was observed for Balance® 100 kg ha-1 and control and low colonisation % was observed for Agras® 100 kg ha-1 and Balance® 50 kg ha-1+Agras® 50 kg ha-1).

References

189

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Microbial Phosphorus Transformation Pathways in Piggery ...€¦ · covered anaerobic piggery wastewater treatment systems. This thesis sought to characterise taxa involved in P transformation