THE FORENSIC CHARACTERISATION OF THE SOIL MICROBIAL … · THE FORENSIC CHARACTERISATION OF THE SOIL MICROBIAL COMMUNITY IN RESPONSE TO CADAVER DECOMPOSITION Kerith-Rae Dias (BSc,

THE FORENSIC CHARACTERISATION OF THE

SOIL MICROBIAL COMMUNITY IN RESPONSE TO

CADAVER DECOMPOSITION

Kerith-Rae Dias (BSc, GDipForSci)

Centre for Forensic Science

University of Western Australia

This thesis is presented in partial fulfilment of the requirements for the

Master of Forensic Science

2011

i

ABSTRACT

The cadaver undergoes a complex and dynamic breakdown process after

death, known as decomposition. Taphonomy is the study of these processes;

their mechanisms, agents and interactions with the environment. As

decomposition progresses, nutrient-rich products are released from the

cadaver into the surrounding area that may include soil. Soil is a complex

medium, within which, its diverse community of microbiota is significantly

influenced by edaphic and environmental factors. The soil microbial

community is known to be affected by changes to its immediate environment.

The concept of resource-driven succession, as applied in entomology using the

succession of colonising insects, could theoretically also be applied to the

microbes involved in decomposition. The advent of molecular technology has

revolutionized the field of microbial ecology by providing culture-independent

methods of examining the diversity of a soil microbial community in any

ecosystem.

The primary aim of this research was to investigate if the soil microbial

community changed in response to the presence of a decomposing cadaver.

The objective was to determine that if these changes did occur, could they be

detected by the selected methodologies. Phospholipid fatty acid analysis and

fungal terminal restriction fragment length polymorphism community profiling

were used to provide a qualitative and quantitative analysis of these

transformations in soil microbial populations.

Soils were analysed from two previous experiments. A controlled laboratory

experiment was conducted, where replicate juvenile rat cadavers were

interred for incubation, in microcosms containing two types of tropical

savanna soils from Queensland, Australia. The cadavers were interred as

complete cadavers, incised and sown-up cadavers or eviscerated cadavers and

compared with control soils to determine how these treatments would alter

the soil microbial communities. A second experiment consisted of analysing

ii

soil that had been sampled periodically beneath two human cadavers during

the process of decomposition and associated control sites at the Forensic

Anthropology Center at the University of Tennessee, Knoxville.

The structure of the soil bacterial and fungal communities was affected

significantly by the presence of the decomposing rat and human cadavers.

Both PLFA and fungal T-RFLP were able to detect the alteration of the soil

microbial community, with respect to the different treatments of the rat

cadaver and along a temporal axis of the human decomposition period.

Furthermore, potential patterns of fungal succession were observed with the

human cadaver experiment.

The current study has demonstrated that the introduction of a cadaver into

the soil ecosystem has a significant effect on the surrounding soil microbial

community. The process is affected by environmental variables, the soil in

which the cadaver is placed and the characteristics of the cadaver. The

preliminary evidence demonstrated by this research holds potential for the

development of a novel tool for the estimation of post-mortem and post-

burial intervals based on soil microbial community succession. An accurate

estimation of time since death is an important aim of every medico-legal

investigation and its determination can direct an entire forensic case.

iii

ACKNOWLEDGEMENTS

I would like to thank my brilliant supervisors, Mark Tibbett, Jacqui Horswell

and David Carter for the opportunity to undertake a project I was passionate

about, lending their technical expertise whenever I needed it and providing

me with encouragement and guidance throughout the project. A sincere

thank you to Dr Ian Dadour for enabling me to undertake the Forensics

program, his troubleshooting skills and coordinating all administrative

responsibilities.

I am deeply indebted to Dr Richard Cookson who was extremely patient and

had a great sense of humour during my early PCR days at UWA, Dr Catriona

MacDonald for the strong work ethic she instilled in me at ESR, Dr Paul

Greenwood who‟s door was always open to me for technical advice and

counsel in general and Dr Natasha Banning who never failed to help me with

the most pedantic of my questions. A further thank you to Mark Tibbett for

implementing the multivariate statistics and to Bob Clarke for his guidance

with it.

I wish to express gratitude to a number of people who were involved in the

successful completion of this project: Dr Susan Barker, Dr Suman George, Dr

Kevin Murray, Dr Krystyna Haq and the fabulous ESR team in New Zealand. A

huge thank you to Rachel Parkinson without whom this project would not have

been possible or fun for that matter, for giving me something to aspire to and

being so generous with her soils, time and advice.

Endless thanks to the Centre of Forensic Science, Centre of Land

Rehabilitation and the Graduate Research School at UWA and The Institute of

Environmental and Science Research in New Zealand for generously covering

the costs of my project and for giving me the opportunity to attend and

present at my first conference.

iv

A big thanks to the “Decomp Divas”, Kathryn, Taryn and Natascha for all the

laughs, the encouragement, commiserating all through equipment failure,

experiment flops and writer‟s block as only a fellow student knows how and

for especially making my time in Perth memorable.

Finally, I would like to thank my family, who gave me everything.

v

DECLARATION

I declare that the research presented in this 36 point thesis, as part of the 96

point Master degree in Forensic Science, at the University of Western

Australia, is my own work. The results of the work have not been submitted

for assessment, in full or part, within any other tertiary institute, except

where due acknowledgement has been made in the text.

…………………………………………………

Kerith-Rae Dias

vi

Table of Contents

Abstract ................................................................................ i

Acknowledgements ................................................................... iii

Declaration .............................................................................. v

List of Figures ........................................................................... x

List of Tables ........................................................................ xviii

Chapter 1: INTRODUCTION ............................................. 1

1.1 Cadaver decomposition .......................................... 1

1.2 Soil Microbial Communities ..................................... 1

1.3 Post Mortem Interval ............................................. 2

1.4 Purpose of the current research ............................... 2

1.5 Studies in decomposition and microbiology .................. 3

1.6 Aims of the research ............................................. 5

1.7 Research approach ............................................... 6

Chapter 2: REVIEW OF THE LITERATURE ............................ 7

2.1 Decomposition .................................................... 7

2.1.1 The process of decomposition ....................................... 7

2.1.2 Factors affecting decomposition .................................... 9

2.1.3 The microbiology of decomposition .............................. 10

2.2 Soil ................................................................ 12

2.2.1 The microbiology of soil ............................................ 13

2.2.1.1 Bacteria ...............................................................13

2.2.1.2 Fungi ..................................................................14

2.2.1.3 Other inhabitants ....................................................15

2.2.2 Properties affecting the soil microbial community ............ 16

2.2.2.1 Physical and chemical properties .................................16

2.2.2.2 Nutrient availability.................................................18

2.2.2.3 Soil depth .............................................................18

2.2.2.4 Human activity .......................................................19

vii

2.3 Post-Mortem Interval Estimation ............................. 19

2.3.1 Pathology and Anthropology ....................................... 20

2.3.2 Entomology ............................................................ 21

2.4 Microbial Community Analysis ................................ 23

2.4.1 Phospholipid Fatty Analysis ........................................ 24

2.4.1.1 Structure and function of PLFAs .................................. 24

2.4.1.2 Significance of PLFAs............................................... 25

2.4.1.3 PLFA method ........................................................ 26

2.4.1.4 PLFA Studies ......................................................... 26

2.4.2 Terminal Restriction Fragment Length Polymorphism Analysis

........................................................................... 27

2.4.2.1 T-RFLP method ...................................................... 27

2.4.2.2 Target genes ........................................................ 28

2.4.2.3 T-RFLP Studies ...................................................... 31

2.5 Data handling and statistical analysis ....................... 32

Chapter 3: RAT CADAVER EXPERIMENT ............................ 35

3.1 Introduction ..................................................... 35

3.2 Aims and Objectives ............................................ 35

3.3 Experimental Background ..................................... 35

3.4 Materials/Methods and Results ............................... 36


3.4.1.1 Extraction ............................................................ 36

3.4.1.2 Fractionation ........................................................ 37

3.4.1.3 FAME Derivitisation ................................................. 37

3.4.1.4 Gas Chromatography/Mass Spectrometry ....................... 38

3.4.1.5 Statistics ............................................................. 39


........................................................................... 41

3.4.2.1 DNA Extraction ...................................................... 41

3.4.2.2 DNA Quantification ................................................. 42

3.4.2.3 Polymerase Chain Reaction ....................................... 42

viii

3.4.2.4 PCR Product Clean-up ..............................................48

3.4.2.5 Restriction Enzyme Digestion ......................................48

3.4.2.6 T-RF Analysis .........................................................49

3.5 Data Handling and Statistical Analysis ....................... 56

3.5.1 PLFA Datasets ......................................................... 56

3.5.2 T-RFLP Datasets ...................................................... 58

3.6 Discussion ........................................................ 64

3.6.1 PLFA Results .......................................................... 64

3.6.2 T-RFLP Profiling Results ............................................ 65

3.6.2.1 Controls ...............................................................65

3.6.2.2 Bacterial T-RFLP Profiling Results ................................66

3.6.2.3 Fungal T-RFLP Profiling Results ...................................68

3.6.3 Other Considerations ................................................ 69

Chapter 4: HUMAN CADAVER EXPERIMENT ........................ 71

4.1 Introduction ..................................................... 71

4.2 Aims and Objectives ............................................ 71

4.3 Experimental Background ..................................... 72

4.4 Materials/Methods and Results ............................... 76

4.4.1 Accumulated degree-days .......................................... 76


4.4.3 Fungal Terminal Restriction Fragment Length Polymorphism .

........................................................................... 77

4.4.3.1 DNA Extraction .......................................................77

4.4.3.2 DNA Quantification ..................................................77

4.4.3.3 Polymerase Chain Reaction ........................................77

4.4.3.4 PCR Product Clean-up ..............................................82

4.4.3.5 Restriction Enzyme Digestion ......................................82

4.4.3.6 Fungal Community Profile Generation ...........................83

4.4.3.7 Fungal ITS-TRF Detection ........................................ 102

4.5 Data Handling and Statistical Analysis ...................... 103

4.5.1 PLFA Dataset ......................................................... 103

4.5.2 Fungal Community Dataset ........................................ 107

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4.6 Discussion ....................................................... 109

4.6.1 Method Comparison ................................................ 109

4.6.2 PLFA Results ......................................................... 110

4.6.3 Fungal T-RFLP Profiling Results ................................. 111

4.6.3.1 Controls ............................................................. 111

4.6.3.2 Cadavers ............................................................ 111

Chapter 5: CONCLUSION .............................................. 119

References ............................................................... 127

Appendices ............................................................... 140

I PowerSoil™ DNA Isolation Kit .......................................... 140

II FastDNA SPIN kit for Soil Protocol with added Plant DNAzol Protocol

.............................................................................. 141

III DNA Visualisation Protocol with Sybr SAFE ......................... 142

IV Pico Green Assay ......................................................... 143

V QIAquick PCR Purification Kit Protocol .............................. 143

VI Bacterial Digestion Protocol ........................................... 144

VII Fungal Digestion Protocol .............................................. 144

VIII Source of Materials ...................................................... 145

IX Temperature data and ADD calculation for cadaver P and R .... 146

X Phospholipid fatty acid peak area data of Pallarenda and

Wambiana soil microbial communities………………………………………..148

XI Phospholipid fatty acid peak area data of control O and cadaver P

…………………………………………………………………………………………………………149

XII Phospholipid fatty acid peak area data of control Q and cadaver

R………………………………………………………………………………………………………150

x

List of Figures

Figure 2.1: Common microbial species that colonise a human body during life

(Jawetz, Melnick and Melnick, 1982). ......................................... 10

Figure 2.2: The soil textural triangle. The basic soil textural classes

consisting of percentages of clay silt and sand (Murray and Tedrow,

1992). ............................................................................... 13

Figure 2.3: Arrangement of phospholipids in the membrane of a living cell

(Kaur 2005). ....................................................................... 25

Figure 2.4: Overview of the T-RFLP method (Applied Biosystems, 2005). ..... 28

Figure 2.5: The rRNA Operon. It consists of three rRNA molecules: 16S, 23S

and 5S, which are separated by internal transcribed spacer (ITS) regions

(Flechtner et al., 2002). ......................................................... 29

Figure 2.6: The 16S rRNA secondary structure. Primary sequence with near

universal conservation (thick lines), intermediate conservation (normal

lines) and hypervariability (dashed lines) is shown (Ward et al., 1992).

Arrows and black lines indicate the region of the gene amplified by PCR.

The grey regions at the 3‟ and 5‟ ends are not amplified. ................. 30

Figure 2.7: Internal transcribed spacer (ITS) region map. The ITS regions

exist in two segments, the ITS1 and ITS2, which bracket the 5.8S rDNA. 31

Figure 3.2: Optimising the effect of soil weight on DNA yield. Three weights

tested: 0.2 g, 0.4 g, 0.6 g. 1 = Pallarenda soil (A) control (C) 1 (0.2g), 2 =

AC 2 (0.4g), 3 = AC 3 (0.6g), 4 = Wambiana soil (B) incised (IN) 1 (0.2g), 5

= BIN 2 (0.4g), 6 = BIN 3 (0.6g), L = 200 bp ladder. .......................... 41

Figure 3.3: Optimising the DNA concentration used for the polymerase chain

reaction protocol. Three concentrations were tested: 1/5 dilution, 1 µL

of pure DNA extract and 2 µL of pure DNA extract. 1 = AC1 - Pallarenda

soil (A) control (C) 1 (1/5 dilution), 2 = AC2 (1/5 dilution), 3 = AC3 (1/5

dilution), 4 = AC1 (1 µL), 5 = AC2 (1 µL), 6 = AC3 (1 µL), 7 = AC1 (2 µL), 8

= AC2 (2 µL), 9 = AC3 (2 µL), 10 = Wambiana soil (B) incised (IN) (1/5

dilution), 11 = BIN (1/5 dilution), 12 = BIN (1/5 dilution), 13 = BIN (1 µL),

14 = BIN (1 µL), 15 = BIN (1 µL), 16 = BIN (2 µL), 17 = BIN (2 µL), 18 = BIN

(2 µL), P = positive control (E. coli gDNA), N = negative control (reagent),

L = 200 bp ladder. ................................................................ 43

xi

Figure 3.4: Polymerase chain reaction product of bacterial DNA from

Pallarenda soil (soil A) samples using 30 cycles. 1 = control (C) 1, 2 = C1

(duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete cadaver (CC) 1, 6 =

CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate),

12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, P =

positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp

ladder. Duplicate samples are labelled with an asterisk. ................... 44


Pallarenda soil (soil A) samples using 25 cycles. 1 = control (C) 1, 2 = C1

(duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete cadaver (CC) 1, 6 =

CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate),

12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, P =

positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp

ladder. .............................................................................. 45

Figure 3.6: Polymerase chain reaction product of fungal DNA from Pallarenda

soil (soil A) samples. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2

(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 =

incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated

(EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, L = 200 bp ladder. ................... 45


Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2

(duplicate), 4 = C3, 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 =

CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV)

1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (E. coli

gDNA), N = negative control (reagent), L = 200 bp ladder. ................. 46

Figure 3.8: Polymerase chain reaction product of fungal DNA from Wambiana

soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3,

5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN)

1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV2

(duplicate), 16 = EV3, P = positive control (C. albicans DNA), N = negative

control (reagent), L = 200 bp ladder. .......................................... 47

Figure 3.9: Bacterial terminal restriction fragment (T-RF) profile of control

sample 1 (top) and 2 (bottom) of Pallarenda soil. The blue peaks

represent the FAM-labelled T-RFs from the 5‟ end, the green peaks are

xii

the HEX-labelled T-RFs from 3‟ end, and the orange peaks represent the

LIZ500 size standard. ............................................................. 51

Figure 3.10: Fungal terminal restriction fragment profile of control sample 1

(top) and 2 bottom of Pallarenda soil. FAM = blue, LIZ500 size standard =

orange. ............................................................................. 51

Figure 3.11: Bacterial terminal restriction fragment profile of complete

cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver

1 (bottom) of Pallarenda soil. FAM-labelled 5‟ end = blue, HEX-labelled

3‟ end = green, LIZ500 size standard = orange. .............................. 52

Figure 3.12: Fungal terminal restriction fragment profile of complete cadaver

sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1

(bottom) of Pallarenda soil. FAM = blue, LIZ500 size standard = orange. 53

Figure 3.13: Bacterial terminal restriction fragment profile of complete

cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver

1 (bottom) of Wambiana soil. FAM-labelled 5‟ end = blue, HEX-labelled 3‟

end = green, LIZ500 size standard = orange. ................................. 54

Figure 3.14: Fungal terminal restriction fragment profile of complete cadaver

sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1

(bottom) of Wambiana soil. FAM = blue, LIZ500 size standard = orange. 55

Figure 3.15: Multi-dimensional scaling plot of phospholipid fatty acid profiles

of soil microbial communities of Pallarenda (

Rat Cadaver Experiment - PLFAResemblance: S17 Bray Curtis similarity

TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) and Wambiana (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) soils

containing control and treatments soils. Profiles that share at least 60%

similarity are circled in green. PR = Pallarenda, WB = Wambiana. ....... 57

Figure 3.16: Multi-dimensional scaling plot comparing the bacterial 3' end

terminal restriction fragment abundances, labelled with the fluorescent

dye HEX, for both soils. Soil A=Pallarenda, soil B=Wambiana. BH1, 3, 17,

18, 20 = control, BH5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, BH9,

10, 12, 25, 26, 2, 28 = incised cadaver, BH13, 14, 15, 16, 29, 30, 32 =

eviscerated cadaver. ............................................................. 58

Figure 3.17: Multi-dimensional scaling plot comparing the bacterial 5' end

terminal restriction fragment abundances, labelled with the fluorescent

dye FAM, for both soils. Soil A=Pallarenda, soil B=Wambiana. C =

control, CC = complete cadaver, IC = incised cadaver, EC = eviscerated

cadaver. ............................................................................ 60

xiii

Figure 3.18: Multi-dimensional scaling plot comparing the fungal restriction

fragment abundances for Pallarenda and Wambiana soils. Soil

A=Pallarenda, soil B=Wambiana. F1, 3, 17, 18, 20 = control, F5, 6, 7, 8,

21, 22, 23, 24 = complete cadaver, F9, 10, 12, 25, 26, 27, 28 = incised

cadaver, F13, 14, 15, 16, 29, 30, 32 = eviscerated cadaver. ............... 61

Figure 3.19: Multi-dimensional scaling plot of fungal terminal restriction

fragments of the Pallarenda and Wambiana soil containing C = control

soils (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), CC = complete cadaver (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), IC = incised cadaver (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), and EC =

eviscerated cadaver (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) samples. ........................................... 63

Figure 4.1: Cadaver P at ADD 106 (day 3) of decomposition. Sloughing of the

skin and some maggots visible. Orange plastic mesh is used to assist in

collection of soil samples from under the cadaver and to preserve its

integrity. ........................................................................... 73

Figure 4.2: Cadaver P at ADD 1092 (day 52) of decomposition. Cadaver is in

the skeletonised stage. .......................................................... 74

Figure 4.3: Cadaver R at ADD 23 (day 0) of decomposition on the day of

placement. ......................................................................... 74

Figure 4.4: Cadaver R at ADD 684 (day 38) of decomposition. Cadaver is in

the „bloat‟ stage. ................................................................. 75

Figure 4.5: Polymerase chain reaction product of fungal amplification from

control O soil samples. 1 = Control O (O) sampled on day 0, 2 = O3, 3 =

O6, 4 = O8, 5 = O10, 6 = O14, 7 = O16, 8 = O20, 9 = O23, 10 = O27, 11

O29= , 12 = O31, 13 = O35, 14 = O38, 15 = O42, 16 = O45, 17 = O49, 18 =

O52, 19 = O58, 20 = O62, 21 = O69, N = negative control (reagent), P =

positive control (C. albicans DNA), L = 100 bp DNA ladder. ................ 79

Figure 4.6: Polymerase chain reaction product of fungal amplification from cadaver P

samples. 1 = Cadaver P (P) sampled at ADD 27, 2 = P106, 3 = P238, 4 = P286, 5 = P376, 6

= P420, 7 = P512, 8 = P573, 9 = P660, 10 = P695, 11 = P730, 12 = P808, 13 = P854, 14 =

P927, 15 = P985, 16 = P1053, 17 = P1092, N = negative control (reagent), P = positive

control (C. albicans DNA), L = 100 bp DNA ladder. .................................... 80


re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 185, 2

= P1172, 3 = P1212, 4 = P1285, N = negative control (reagent), P =

positive control (C. albicans DNA), L = 100 bp DNA ladder. ................ 80

xiv


re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 106, 2

= P238, 3 = P420, 4 = P1212, N = negative control (reagent), P = positive

control (C. albicans DNA), L = 100 bp DNA ladder. .......................... 81


control Q samples (top) and cadaver R samples (bottom). Top: 1 =

Control Q (Q) sampled on day 0, 2 = Q3, 3 = Q7, 4 = Q9, 5 = Q11, 6 = Q15,

7 = Q18, 8 = Q22, 9 = Q25, 10 = Q29, 11 = Q32, 12 = Q38, 13 = Q42, 14 =

Q49. Bottom: 1 = Cadaver R (R) sampled at ADD 23, 2 = R85, 3 = R171, 4

= R207, 5 = R242, 6 = R319, 7 = R366, 8 = R438, 9 = R497, 10 = R564, 11 =

R603, 12 = R684, 13 = R724, 14 = R797, N = negative control (reagent), P

= positive control (C. albicans DNA), L = 100 bp DNA ladder. .............. 81


re-extracted cadaver R samples. 1 = Cadaver R (R) sampled at ADD 85, 2

= R366, 3 = R497, 4 = R684, N = negative control (reagent), P = positive

control (C. albicans DNA), L = 100 bp DNA ladder. .......................... 82

Figure 4.11: Soil fungal profiles from control O sampled on days 0, 6, 8 and 10. The grey bars represent the regions

(120–170 bp and 320-400 bp) where the dominant peaks occur in the control O profiles. Fluorescence intensity

is expressed in relative fluorescence units (RFU) to account for intra-instrument variation. ............. 84

Figure 4.12: Soil fungal profiles from control O sampled on days 14, 16, 20

and 23. Fluorescence intensity is expressed in relative fluorescence units

(RFU) to account for intra-instrument variation. ............................ 85










Figure 4.16: Soil fungal profiles from cadaver P sampled at ADD 27, 106, 376

and 512 (days 0, 3, 14 and 20 respectively). Fluorescence intensity is

expressed in relative fluorescence units (RFU) to account for intra-

instrument variation. ............................................................. 90

xv

Figure 4.17: Soil fungal profiles from cadaver P sampled at ADD 730, 927, 985

and 1053 (days 31, 42, 45 and 49 respectively). Fluorescence intensity is



Figure 4.18: Soil fungal profiles from cadaver P at ADD 1092, 1172, 1212 and

1285 (days 52, 58, 62 and 69 respectively). Fluorescence intensity is



Figure 4.19: Soil fungal profiles from control Q sampled on days 0, 3, 7 and 9. The grey bars represent the regions

(130–170 bp and 320-370 bp) where the dominant peaks occur in the control Q profiles. Fluorescence intensity

is expressed in relative fluorescence units (RFU) to account for intra-instrument variation. ............. 94

Figure 4.20: Soil fungal profiles from control Q sampled on days 11, 15, 18



Figure 4.21: Soil fungal profiles from control Q sampled on days 25, 29, 32



Figure 4.22: Soil fungal profiles from control Q sampled on days 42 and 49.

Fluorescence intensity is expressed in relative fluorescence units (RFU) to

account for intra-instrument variation. ....................................... 98

Figure 4.23: Soil fungal profiles from cadaver R at ADD 23 and 85 (days 0 and

3 respectively). The grey bars represent a peak that appears at ADD23

but disappears at ADD 85. ....................................................... 98

Figure 4.24: Soil fungal profiles from cadaver R sampled at ADD 171, 207, 242

and 366 (days 7, 9, 11 and 18 respectively). The grey bars represent a

reduction in peak height of the same peak from ADD 242 to ADD366..... 99

Figure 4.25: Soil fungal profiles from cadaver R at ADD 438, 497, 564 and 603

(days 22, 25, 29 and 32 respectively). Fluorescence intensity is expressed

in relative fluorescence units (RFU) to account for intra-instrument

variation. ......................................................................... 100

Figure 4.26: Soil fungal profiles from cadaver R sampled at ADD 684, 724, and

797 (days 38, 42 and 49 respectively). Fluorescence intensity is expressed

in relative fluorescence units (RFU) to account for intra-instrument

variation. ......................................................................... 101

xvi


for cadaver P (●) and control O (▪). Accumulated degree-days denote the

stage of decomposition when the sample was collected. 0 = day of

placement/first day of sampling. The boundaries of the ellipses are

defined by the samples within having profiles at least 80% similar to each

other. .............................................................................. 104


for cadaver R (●) and control Q (▪). Accumulated degree-days denote the

stage of decomposition when the sample was collected. 0 = day of

placement/first day of sampling. The boundaries of the ellipses with

dotted lines are defined by the samples within having profiles at least

65% similar to each other. The boundaries of the ellipses with smooth

lines are defined by the samples within having profiles at least 85%

similar to each other. ........................................................... 105

Figure 4.29: Abundance of fungal marker from phospholipid fatty acid

analysis for control O and cadaver P. A temporal profile of the fungal

marker, C18:26 (peak 27) is calculated as a percentage of total

phospholipids. 0 = day of placement/first day of sampling............... 106

Figure 4.30: Abundance of fungal marker from phospholipid fatty acid analysis

for control Q and cadaver R. A temporal profile of the fungal marker,

C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0

= day of placement/first day of sampling. ................................... 106

Figure 4.31: Multi-dimensional scaling plot of fungal internal transcribed

spacer-terminal restriction fragment profiles for cadaver P ( ) and

control O (●). Accumulated degree-days are used to denote the stage of

decomposition when the sample was collected. 0 = day of

placement/first day of sampling. The cadaver samples have been circled

to show their separation from the control samples. ....................... 107

Figure 4.32: Multi-dimensional scaling plot of fungal internal transcribed

spacer-terminal restriction fragment profiles for cadaver P. Accumulated

degree-days denote the stage of decomposition when the sample was

collected. Grey line separates early, mid and late phase fungi. 0 = day of

placement. The black arrow shows potential temporal trend. .......... 108

Figure 4.33: Multi-dimensional scaling plot of internal transcribed spacer-

terminal restriction fragment profiles for cadaver R and control Q.

xvii

Accumulated degree-days denote the stage of decomposition when the

sample was collected. The black arrow shows possible fungal pattern of

succession. 0 = day of placement/first day of sampling. The cadaver

samples have been circled to show their separation from the control

samples. .......................................................................... 109

xviii

List of Tables

Table 3.1: Polymerase chain reaction mastermix used for bacterial

amplification of soil microbial communities of the Pallarenda and

Wambiana soils. Amplification was conducted in a 50 µL reaction volume.

...................................................................................... 43

Table 3.2: Polymerase chain reaction mastermix used for fungal amplification

of soil microbial communities of the Pallarenda and Wambiana soils.

Amplification was conducted in a 50 µL reaction volume. ................. 46

Table 3.3: Amount of bacterial DNA (ng/L) present in Pallarenda and

Wambiana soil samples after polymerase chain reaction amplification. A

= Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver,

IN = incised cadaver, EV = eviscerated cadaver, 1A/1B, 2A/2B = duplicate

samples. ............................................................................ 47

Table 3.4: Amount of fungal DNA (ng/L) present in Pallarenda and

Wambiana soil samples after polymerase chain reaction amplification. A

= Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver,

IN = incised cadaver, EV = eviscerated cadaver, 1A/1B, 2A/2B = duplicate

samples. ............................................................................ 48

Table 3.5: Definition of significance levels using p-values. ...................... 56

Table 3.6: Significance results of pairwise test conducted on phospholipid

fatty acid profiles between all treatment groups and across both soil

types. ............................................................................... 57

Table 3.7: Significance results of pairwise test conducted on 3‟ end of

bacterial terminal restriction fragments between all treatment groups

and across both soil types. ...................................................... 59

Table 3.8: Significance results of pairwise test conducted on 5‟ end of

bacterial terminal restriction fragments between all treatment groups

and across both soil types. ...................................................... 60

Table 3.9: Significance results of pairwise test conducted on fungal terminal

restriction fragments between all treatment groups and across both soil

types. ............................................................................... 62

Table 3.10: Summary of the significance results of pairwise tests conducted

between all treatment groups, across both soil types and over all

xix

methods. HS = highly significant, S = significant, MS = marginally

significant, NS = not significant ................................................. 63

Table 4.1: Details of two human cadavers used in experiment. ................ 73

1

Chapter 1 : INTRODUCTION

1.1 Cadaver decomposition

A cadaver can be considered of as a source of sequestered nutrients, energy

and a microbial inoculum, which can be released into an ecosystem via the

process of decomposition. The unhindered decomposition of a cadaver

involves the consecutive processes of autolysis, putrefaction and decay (Vass,

2001). These processes initiate the breakdown of the cadavers‟ constituents

into the basic building blocks of organic matter, by the action of cellular

enzymes, microorganisms and a range of environmental variables (Evans,

1963; Clark, Worrell and Pless, 1997). The rate of decomposition is

influenced by the interplay of the intrinsic factors of the cadaver (sex, age,

physique, cause of death) and extrinsic factors (temperature, pH, moisture,

substrate type, feeding) of the environment (Mann, Bass and Meadows, 1990).

The breakdown of this high-quality resource (narrow carbon:nitrogen ratio,

high water content) introduces a concentrated and localised pulse of water,

carbon, nitrogen and other nutrients into the surrounding environment

(Carter, Yellowlees and Tibbett, 2007).

1.2 Soil Microbial Communities

Cadavers can be found in a range of enclosed (dwellings, cars) and exposed

environments (aquatic, terrestrial). They are often discovered in or on soil

and the processes of decomposition will invariably have an effect on the soil

ecosystem (Carter, Yellowlees and Tibbett, 2007). This ecosystem is

composed of a complex community of plants, animals, and microbes, which

include fungi, bacteria, actinomycetes and protozoa. Soil microbial

communities are the main decomposers of organic matter in soil (Coleman,

Crossley and Hendrix, 2004). These communities can respond to the addition

of nutrients or a modification in the environment, like the introduction of

cadaver to the soil environment, by changing in abundance and diversity

(Hopkins, Wiltshire and Turner, 2000). The different components of the

cadaver decompose at different stages, depending on their constitution,

2

metabolic activity during life, enzyme concentration or their exposure to the

environment (Megyesi, Nawrocki and Haskell, 2005). Decomposition may

begin with the intestines, stomach and heart due to their high rates of energy

transformation and end with the autolysis of skeletal muscle and connective

tissue (Vass et al., 2002). The exposed remains, therefore, present an

ephemeral and progressively changing habitat and food source to microbes.

These nutrients may be utilised by a succession of microbes, each wave

colonizing and altering the microhabitat and making way for the next

population of microbes. The dynamics of soil microbial communities in

response to cadaver decomposition might be associated with a microbial

pattern of succession, much like what is seen with the succession of insects on

a cadaver (Payne, 1965; Anderson, 2001).

1.3 Post Mortem Interval

An accurate estimation of time since death or the „post-mortem interval‟

(PMI) is one aim of every medico-legal investigation, equal in importance to

victim identification and cause of death. Determination of the PMI can direct

or re-orient an entire investigation by serving to validate or reject a suspect‟s

alibi or elucidate the victim‟s peri-mortem activities. Rarely, in a forensic

investigation, is a post-mortem estimate based on a single variable or

method. Consequently, new PMI estimation tools are constantly being sought,

as it is appreciated that the use of an array of methods will lead to a more

accurate estimation of the PMI.

1.4 Purpose of the current research

The soil microbial community is recognized as „the eye of the needle‟ through

which all organic matter must eventually pass (Jenkinson, 1977). One gram of

soil may contain up to 10 billion microorganisms of possibly thousands of

species and it is widely accepted that less than 1% of soil microorganisms have

been cultivated and characterized (Torsvik and Ovreas, 2002). Culture-

dependent methods have therefore restricted the accurate examination of the

soil microbial community. Furthermore, the study of human cadaver

decomposition is understandably restricted by the difficulty of obtaining

3

cadavers to experiment with, lack of suitable areas for the placement and

study of these processes, negative public opinion and ethical impositions

(Mann, Bass and Meadows, 1990). As a result, there is very little

understanding of the relationships and interactions between that of a

decomposing cadaver upon soil and its associated microbiology. The current

research project investigates the potential of two culture-independent

methods for soil microbial characterisation in order to study the dynamics of

soil microbial communities associated with cadaver decomposition upon soil;

deoxyribonucleic acid- (DNA) based terminal restriction fragment length

polymorphism (T-RFLP) analysis and lipid-based phospholipid fatty acid (PLFA)

analysis. These methods have revolutionized the study of microbial diversity

and community analysis by eliminating the need for culturing the soil

microbes.

1.5 Studies in decomposition and microbiology

Previous decomposition studies have provided some insight into the

relationships between decomposition, microbiology and the burial

environment, and this information has been gathered from primarily empirical

observations and anecdotal summaries. Many studies have acknowledged the

role (Child, 1995; Campobasso, Di Vella and Introna, 2001; Tibbett and Carter,

2003; Okoth, 2004) and contribution (Micozzi, 1986; Hopkins, Wiltshire and

Turner, 2000; Tibbett et al., 2004; Carter, 2006; Franicevic, 2006) of

microbes to the cadaver decomposition process but little research has focused

on advancing the understanding of how decomposition affects microbes in the

burial environment.

A handful of studies have commented on the effect of cadaver/tissue

decomposition on soil microbiota. Soil microbial biomass has been estimated

in relation to soft tissue decomposition by using substrate-induced respiration

(Hopkins, Wiltshire and Turner, 2000; Fiedler, Schneckenberger and Graw,

2004; Tibbett et al., 2004; Carter, 2006). Soil microbial activity has been

measured in response to soft tissue decomposition using CO2 respiration

(Putman, 1978a; Putman, 1978b; Child, 1995; Tibbett et al., 2004; Petkovic,

Simic and Vujic, 2005; Carter, 2006; Rapp et al., 2006; Carter, Yellowlees and

4

Tibbett, 2007; Wilson et al., 2007;). The extent of current knowledge is

limited to the understanding that cadaver decomposition can prompt the

growth and activity of the soil microbial biomass. The use of DNA-based

methods can add to this knowledge by providing insights into microbial

composition, abundance and diversity. Phospholipid fatty acids are found in

all living cells and rapidly metabolise after cell death (Frostegard, Tunlid and

Baath, 1993). The distribution of PLFAs, may represent a phenotype of extant

soil microbial communities based on the variability of fatty acids of various

microbial organisms (Zelles, 1992).

The microbial decomposition and degradation of various elements of the

cadaver have been studied. Research has demonstrated fungal tunneling and

bacterial alteration of bone and teeth in human skeletal samples (Yoshino et

al., 1991; DeGaetano, Kempton and Rowe, 1992; Child, 1995; Bell, Skinner

and Jones, 1996) and the microbial degradation of adipocere (Pfeiffer, Milne

and Stevenson, 1998), hair (Griffin, 1960; Collier, 2005; Edwards et al., 2007)

and skin (Micozzi, 1986). The bacteria and fungi known as decomposers,

breakdown organic nitrogen which is a constituent of protein, to produce

ammonium ions. Nitrifying bacteria utilize energy sources derived from the

chemical conversion of these ammonium ions to nitrite (ammonia oxidizers) or

nitrite to nitrate (nitrite oxidizers) (Dent, Forbes and Stuart, 2004). Similarly,

the transformation of unsaturated into saturated fatty acids by bacterial

enzymes (Fiedler and Graw, 2003) has been documented. Many bacterial

species are ethanol producers, therefore post-mortem microbial fermentation

can result in the production of ethanol that can affect the accurate

interpretation of blood alcohol concentration in toxicological analysis in a

forensic case (Ziavrou, Boumba and Vougiouklakis, 2005). The utilization of

microbial growth and succession in relation to the cadaver decomposition

process has been suggested as tools for estimating post-mortem interval

(Vass, 2001; Tibbett and Carter, 2003; Parkinson, 2004; Carter, Yellowlees

and Tibbett, 2007). The migration of enteric microbiota through the wall of

the small intestine in mice was evaluated as a model for assessing time of

death (Melvin et al., 1984). Janaway (1996) described the succession from

predominantly aerobic to anaerobic microbes of the enteric community during

autolysis of the cadaver. The relationship between fungal growth and

5

changes in proximal end morphology of human head hair has been evaluated

towards estimating a PMI (Collier, 2005). Griffin (1960) describes an order of

fungal succession on hair in contact with soil, from highly saprotrophic fungi

to fungi with less saprotrophic ability and finally to keratinophilic fungi. More

recently, the potential of using the successive colonization of fungi on human

cadavers for estimating the PMI, was explored (Tibbett and Carter, 2008),

using case studies and culture-dependent methods (Hitosugi et al., 2006; Ishii

et al., 2006; Sagara, Yamanaka and Tibbett, 2008).

1.6 Aims of the research

The broad aim of this work is a preliminary evaluation of the potential for

using soil microbial communities as a tool to estimate post mortem interval.

This will be achieved by determining if the exposure of a soil to a cadaver

alters soil microbial community structure, and furthermore, if this can be

developed to provide a novel line of evidence for post mortem (or burial)

interval. The specific research questions of the project were to address the

following:

(i) Does the structure of the soil microbial community change in the

presence of a decomposing cadaver?

(ii) Does this change show any predictable pattern or temporal

trend?

Subsidiary to these questions, the project was also designed to compare which

one of the two leading methodologies of determining microbial communities

(TRFLP and PLFA) give the best resolution to changes in the soil community

structure. To accomplish this I aimed to detect changes in the patterns of the

chemically diverse phosphilipid fatty acids found in membrane components of

all organisms, and the molecular profiles generated by the fluorescently-

labeled terminal restriction fragments of the 16S ribosomal RNA gene that

probes the eubacterial population and the internal transcribed region of the

fungal ribosomal RNA gene to detect the fungal population in soils from the

detritusphere of decomposing human and mouse cadavers.

6

1.7 Research approach

In this study, PLFA analysis and T-RFLP community profiling was used to

provide a qualitative and quantitative assessment, respectively, of soil

microbial communities at different stages of cadaver decomposition. These

analyses were conducted on existing experimental materials from two unique

experiments. The first experiment involved the decomposition of rat (Rattus

rattus) cadavers in two types of tropical savanna soils of Queensland,

Australia. The cadavers were treated to allow quantification of the influence

of the cadaver-derived enteric microflora on the changes observed in the soil

microbial community. The second experiment investigated soil that has been

sampled periodically from under human cadavers during the process of

decomposition, and associated control sites at the Forensic Anthropological

Centre at the University of Tennessee.

The concepts and techniques of microbiology, soil science, molecular

microbial ecology and forensic taphonomy were combined to advance the

understanding of the microbiology in a decomposition environment. With

further research, a significant application could be the development of a

model for estimation of the post-mortem and/or post-burial intervals based

on soil microbial community succession.

7

Chapter 2 : REVIEW OF THE LITERATURE

2.1 Decomposition

The human body undergoes a complex and dynamic, but successive breakdown

process that is influenced by a range of factors, after death occurs. Many of

these factors are uncontrollable mechanisms, which are difficult to simulate

in a laboratory environment and as a result there is little understanding of the

interactions that take place between soft tissue decomposition and its

surrounding environment. The study of human decomposition is

understandably restricted by ethical impositions. Nonetheless, a few

controlled experiments have been undertaken at the Forensic Anthropology

Centre in Knoxville, Tennessee. There, crime scenes are recreated in the

field, where bodies can decompose naturally and the actions of insects, soil

microorganisms, chemical interactions and various environmental conditions

are studied.

Taphonomy, (Greek taphos: grave, nomos: law) (Efremov, 1940; Aturaliya and

Lukasewycz, 1999) originally a branch of palaeontology, has recently been

associated with forensic science as a way to understand processes associated

with cadaver decomposition; its mechanisms, agents and interactions with the

surrounding burial environment. Experimental taphonomy involves exposing

the soft tissue of an organism to variables or processes that might alter the

decomposition process and then examining the effects of this exposure, to

better understand the progression of decomposition. The applications of

these studies impact a forensic investigation in numerous ways which include

development of systems for estimation of the post-mortem and/or post-burial

interval, the determination of the cause and manner of death and assistance

in the location of clandestine graves (Haglund and Sorg, 1997).

2.1.1 The process of decomposition

Classically, the process of decomposition has been divided into five stages:

fresh, bloat, decay, dry and skeletonisation which do not necessarily imply a

8

sequence of events but instead represent the overall condition of the cadaver

(Goff, 2000). However, stages may not always be observed or may even be

absent depending on the taphonomy and the environment of the cadaver

(Vass, 2001).

Human decomposition begins approximately four minutes after death (Vass,

2001). As the tissues are deprived of oxygen, carbon dioxide levels increase,

pH decreases and wastes start to accumulate in the cells. Simultaneously,

cellular enzymes start to digest cells, causing them to rupture and release

nutrient-rich fluids through a process called autolysis. This process begins in

tissues with high enzyme activity such as the brain and liver and proceeds to

the rest of the body‟s tissues. The appearance of fluid-filled blisters and the

slippage of the skin is the external indication of this stage (Vass, 2001). The

body settles to the ambient temperature (algor mortis), the blood settles in

the body due to gravity (livor mortis) and cellular cytoplasm solidifies due to

increased acidity, causing the stiffening of muscles (rigor mortis) (Vass, 2001).

The process of putrefaction follows, with microbially mediated catabolism of

the soft tissues into gases, liquids and simple molecules. A visible sign of

putrefaction, is a greenish discolouration of the skin attributed to the

formation of sulfhaemoglobin in the blood (Vass, 2001). Following this, a

distension of tissues associated with the formation of various gases, results in

the characteristic bloating of the cadaver. They are the by-products of

anaerobic activity primarily located in the gut. The gases and accumulated

fluid purge from the body‟s natural openings and also burst the skin, releasing

the products of decomposition into the external environment (Vass, 2001).

When the gases have been eliminated, active decay begins where protein is

broken down into amino acids and fats to glycerols. These degradation

products are in turn broken down by bacteria (Vass, 2001). The alternatives

to putrefaction is adipocere formation or mummification (Campobasso, Di

Vella and Introna, 2001). In warm, moist environments, the formation of

adipocere, a yellowish-white wax-like substance, develops as a result of fat

hydrolysis (Forbes et al., 2004). At the end of the active decay, the

dehydration and desiccation of tissue leaves behind parchment-like skin and

renders it unavailable to microbes as a source of nutrition. Finally bone

decomposes via the process of diagenesis, where collagen, hydroxyapatite,

9

magnesium and calcium are exchanged, deposited adsorbed and leached into

the environment (Vass, 2001).

2.1.2 Factors affecting decomposition

The process of decomposition is affected by numerous variables. Research

using case studies have identified that cadaver-specific characteristics (sex,

age, physique, cause of death, integrity of the cadaver), environmental

variables (temperature, oxygen availability, humidity, pH, soil properties) and

other variables (clothing, predator effects) can impact the progression of

decomposition (Mann, Bass and Meadows, 1990). Ambient temperature may

be the most important variable affecting the rate of decomposition. The

reduction of a cadaver to its skeletal elements has been observed in one fifth

of the time in summer compared to the time taken in winter conditions

(Galloway et al., 1989). The availability of oxygen is another important factor

affecting the decomposition process. An oxygenated environment will speed

up the decomposition process (Dent, Forbes and Stuart, 2004). Obese

cadavers decompose more rapidly due to the greater amount of liquid in the

tissues which favours the development and dissemination of bacteria

(Campobasso, Di Vella and Introna, 2001). The location of a cadaver

determines its exposure to insects, carnivores and moisture. Exposure in a

desert environment results in rapid bloating, dehydration and finally

mummification of the cadaver whereas, the confinement of the cadaver to a

closed structure such as a house or a trailer, results in a slower onset of

decomposition which is later accelerated due to the retention of moisture

(Galloway et al., 1989). The effects of clothing, position of cadaver and soil

interment on decomposition rates were studied using rat cadavers (Aturaliya

and Lukasewycz, 1999). It was found that body water loss was enhanced by

clothing or a horizontal versus a vertical position and that desiccation was

equally effective by soil interment as by air exposure. Carnivore activity can

accelerate cadaver decomposition. In southern Arizona, it has been shown

that, coyotes, bear, javelinas and packrats scavenge and consume portions of

the cadaver, thereby increasing the rate of decomposition (Galloway et al.,

1989). This study also found seasonal differences, elevation, latitude,

clothing and wounds to have an effect on decomposition.

10

2.1.3 The microbiology of decomposition

From the moment delivery commences, the newborn child leaves the sterile

environment of the womb and enters a world in which microbes abound.

These microbes mainly inhabit its skin, mucous membranes and

gastrointestinal tract (see Fig 2.1). A delicate and complex relationship

ensues between man and microbe throughout his life. However, the role of

microbes continues well after the cessation of life.

Figure 2.1: Common microbial species that colonise a human body during life (Jawetz,

Melnick and Melnick, 1982).

There are literally hundreds of microbial species involved in the

decomposition process and decomposition would not progress without them

(Vass, 2001). The following species are major colonisers of cadavers:

Clostridium perfringens and other Clostridium spp., enterobacteria

(particularly, Escherichia coli and Proteus spp.), micrococcaeae (mainly

Staphylococcus aureus), streptococci and Bacillus spp. (Vass, 2001). Fungi

Nose

Staphylococcus sp.

Branhamella catarrhalis

Haemophilus influenzae

Streptococcus

pneumoniae

Corynebacteria sp.

Mouth

Streptococcus sp.

Veillonella sp.

Fusobacterium sp.

Actinomyces sp.

Leptotrichia sp.

Stomach

Lacticos

Leveduras

Helicobacter pylori

Small Intestine

Candida albicans

Lactobacillus

Enterococcus

Bacteroides sp.

Throat

Streptococcus sp.

Staphylococcus sp.

Branhamella catarrhalis

Corynebacterium sp.

Neisseria sp.

Mycoplasma sp.

Large Intestine

Bacteroides sp. Klebsiella sp.

Enterobacter Candida albicans

Escherichia coli Proteus sp.

Lactobacillus sp. Fusobacterium sp.

Streptococcus sp. P. aeruginosa

Clostridium sp.

Skin

Staphylococcus sp.

Propionebacterium sp.

Micrococcus sp.

Acinetobacter sp.

Bacillus sp.

Urethra

Streptococcus sp.

Mycobacterium sp.

Bacteroides sp.

11

such as C. albicans and other Candida spp. and Saccharomyces cerevisiae and

Saccharomyces spp. may be found on cadavers too. Vass (2001) isolated many

microbial species in the very early stages of decomposition such as

Staphylococcus, Candida, Malasseria, Bacillus and Streptococcus species, but

indicated many more were involved in cadaver decomposition. As

decomposition progressed, he observed that putrefactive bacteria such as

Escherichia coli were introduced, and followed by anaerobic bacteria such as

the Clostridium spp. and also micrococci, coliforms, diptheroids and

clostridial species. He also noticed the presence of Serratia, Klebsiella,

Proteus, Salmonella, Cytophaga, pseudomonads and flavobacteria species.

Many of the organisms that were isolated originated from the bowel and

respiratory tract of the cadaver. When a cadaver is associated with soil, the

cadaver-derived microflora, mostly habitual saprophytic hosts of the intestine

(Campobasso, Di Vella and Introna, 2001), can intermingle with thousands of

soil microorganisms such as Agrobacterium, amoebae and fungi (Vass, 2001),

as well as airborne aerobic bacteria (Campobasso, Di Vella and Introna, 2001).

It is also suspected that microbes are deposited on the corpse by the visiting

insects and arthropods.

The role of microbes in decomposition begins with the putrefaction of the

cadaver. During putrefaction, the aggressive intervention of exogenous and

endogenous microbial factors can add to the lesser effects of autolysis

(Campobasso, Di Vella and Introna, 2001). Temperatures ranging between

25°C and 35°C are optimal for the development of bacteria (Campobasso, Di

Vella and Introna, 2001), therefore very high or low temperatures inhibit

bacterial proliferation and slows down decomposition. Dry and windy

conditions which dehydrate the cadaver will impair bacterial growth. It has

been demonstrated that decomposition of buried cadavers by bacteria can

result in a temperature differential with cadavers buried at a depth of 30cms

up to 10°C higher than the adjacent soil (Rodriguez and Bass, 1985). The

microbial species associated with decomposition may be present for months or

even years after death, depending on local conditions. A DNA-based study

was able to identify intestinal microbiota of a 12,000 year-old mastadon, an

extinct mammal, from excavated remains in ancient sediments in Ohio and

Michigan (Rhodes, 1998). Recently, the analysis of DNA was able to identify

12

intestinal microbiota from two glacier mummies from the Alps, where there

was possible colonisation of the cadaver by microbes from the outer

environment (Rollo, 2007).

2.2 Soil

Soil is a physically, chemically and biologically complex medium consisting of

minerals, organic matter, animals, plants, and a diverse community of

microbiota (bacteria, fungi, algae, yeast) including its residues, that are

distributed between liquid and gas phases (Andrasco, 1981; Stotzky, 1997). It

is formed and forever changes, due to five major factors: the parent material,

time, climate, the living organisms and topography. The specific composition

of soil varies widely due to the varying proportions of these components

present at different geographical locations (see Fig 2.2) (Liesack et al., 1997).

This milieu can also differ spatially due to the influence of vegetation and

human activity (Prosser, 2002). Inorganic mineral particles in soil can range

in a continuum from 1% to 99% across the planet. The smallest of these

mineral particles are defined as clays (< 0.002 mm), the intermediate size as

silt (0.002-0.05 mm) and the coarser particles, as sand (0.05 - 2 mm) and

stones (> 2 mm) (Murray and Tedrow, 1992). The organic component of soil is

relatively small and rarely exceeds 5% by volume (Andrasco, 1981). It consists

of: plant root systems; animal, plant and microbial residues in various stages

of decay; humus, a heterogeneous complex of organic residues; and the active

microbiota.

13

Figure 2.2: The soil textural triangle. The basic soil textural classes consisting of percentages of clay silt and sand (Murray and Tedrow, 1992).

2.2.1 The microbiology of soil

Like a cadaver, the soil is inhabited by a diverse array of organisms ranging

from microfauna (fungi, bacteria and viruses) but also includes mesofauna

(acari and collembola) and the macrofauna (arthropods, nematodes,

earthworms, millipedes and amoeba). These soil inhabitants form complex

relationships with each other and plant systems.

2.2.1.1 Bacteria

Bacteria represent the largest biological component of soil. It has been

estimated that there may be as many as 109 bacterial cells per gram of soil

(Harris, 1994) and that bacteria in the top two to three centimetres of the soil

represents half of the total biomass on earth (Thornton, 1986). Soil bacteria

have adapted morphologically and physiologically to utilise the complexity of

the soil habitat effectively. One example is a thick mucilaginous capsule

surrounding the bacterial cell which is not found in laboratory grown isolates

of the same species (Coleman, Crossley and Hendrix, 2004). This capsule

protects the bacterium from desiccation, toxic compounds and may affect

adhesion to soil particles (Riley et al., 2001). Many soil bacteria have the

ability to slow their metabolism in response to low levels of available

nutrients and then increase it when nutrient levels rise (Wood, 1995). This

14

adaptation allows the persistence of microbial species over time in

nutritionally poor soil (Stotzky, 1997). Bacteria have numerous functions

within soil. The most common members of the bacterial community are

heterotrophic bacteria, or those that derive energy from organic carbon.

They carry out the decomposition of animal, plant and microbial residues

(Coleman, Crossley and Hendrix, 2004). Heterotrophs also perform nitrogen

fixation, a role mainly exhibited by the Rhizobium species, which inhabits

legume root nodules. The chemoautotrophic bacteria in soil, or those that

derive energy by oxidation of inorganic substances, are largely nitrifiers and

sulphur oxidisers.

Winogradsky (1949) divided soil microbial populations into two distinct classes

based on their response to nutrients and described the classes as

autochthonous and zymogenous microbial populations. Autochthonous

populations are the indigenous soil organisms, which persist actively in the

soil for long periods of time and at relatively constant levels. They are the

most competitive at low substrate concentrations and use the soil carbon

sources that are more resistant to degradation, such as humus. The genus

Arthrobacter is an example of autochthonous soil microbes that can maintain

their presence in soil even when available carbon is limited. The zymogenous

soil microbial populations proliferate when substrates such as plant or animal

residues are introduced into the soil. They have the ability to multiply rapidly

and can form resistant spore structures once the substrate is consumed. It is

likely that these general groups of microbial populations exist within the soil,

even though they may not be as distinct as suggested by Winogradsky

(Killham, 1994). Competition between organisms is likely to lead to

specialisation in terms of rate of growth and substrate utilization.

2.2.1.2 Fungi

Fungi are significant contributors to the biomass of soil microbiota and are the

primary decomposers in all terrestrial ecosystems (Bruns, White and Taylor,

1991). Differing soil conditions can influence the diversity of fungi, however

an average population has been estimated at 10-20 million individual colony

forming units per gram of soil (Griffin, 1960). Soil fungi hold important roles

related to water dynamics, nutrient cycling and disease suppression however,

15

their most important role is the decomposition or organic matter, ranging

from simple sugars to the most resistant polymers such as lignin and complex

humic acids. Soil fungi can be grouped into three general functional groups

based on how they derive their energy. Saphrotrophic fungi convert dead

organic material into fungal biomass, carbon dioxide and small molecules such

as organic acids (Griffin, 1972). These fungi are important for immobilising

nutrients in the soil and they also help increase the accumulation of humic-

acid rich organic matter in the soil. The widely studied mycorrhizal fungi

colonise the roots of plants and form a symbiotic relationship with them

(Sagara, Yamanaka and Tibbett, 2008). The majority of plants have these

associations, in which the fungi provide nutrients and protection from drought

stress and plant pathogens (Coleman, Crossley and Hendrix, 2004). The third

group of fungi are pathogenic or parasitic fungi, which cause reduced

production or death when they colonise roots and other organisms. Fungi

such as Verticillium and Rhizoctonia cause major damage to agriculture each

year, whereas nematode-trapping fungi parasitise disease-causing nematodes

and may be useful as biocontrol agents (Paul, 2007).

Higher fungi usually grow as hyphae, and these filaments confer advantages

over bacteria in some environments. In dry conditions, hyphae help bridge

gaps between pockets of moisture, and so the fungi can persist where

bacteria cannot (Paul, 2007). Soil fungi are also able to use nitrogen from the

soil that allows them to decompose surface residue low in nitrogen (Paul,

2007). They are more tolerant of acidic soils than bacteria and are

consequently predominant in decomposition processes under these conditions

(Thorn, 1997). Fungi are aerobic organisms and the anaerobic conditions

encountered in waterlogged soil and compacted soil generally loses its fungal

component (Paul, 2007).

2.2.1.3 Other inhabitants

Other components of the soil microbial community include actinomycetes,

algae and protozoa. Actinomycetes are bacteria but have a mycelial

morphology that resembles fungi. They degrade an array of carbonaceous

substrates such as chitin, celluloses and hemicelluloses (Wood, 1995). Algae

are photoautotrophic, meaning they use sunlight as an energy source. They

16

are often primary colonisers as they synthesise their own carbon compounds

using photosynthesis and are involved in soil formation and maintaining the

structural stability of degraded soils. Soils commonly support between 103

and 104 algae per gram of soil, although as many as 108 algae per gram have

been documented (Metting 1981). Protozoa are predators of the soil

microbial population and can be found in the order of ten million per gram of

soil (Bardgett and Griffiths, 1997). The larger soil organisms comprise

oligochaetes (earthworms), nematodes, arthropods (millipedes, centipedes

and mites) and molluscs (slugs and snails). Their main ecological role is the

processing and vertical and horizontal mixing of the soil through their

burrowing activity as well as contributing to organic material decomposition

(Wood, 1995; Griffiths and Bardgett, 1997).

2.2.2 Properties affecting the soil microbial community

Microbial communities vary considerably between soils (Liesack et al., 1997;

Zhou, 2003). Soil microbial diversity has been demonstrated not only

between sampling sites but also within sites due to localized variations in the

soil environment that can influence the population at this level (Coleman,

Crossley and Hendrix, 2004). Numerous properties of the soil can influence

the microbial diversity within this environment.

2.2.2.1 Physical and chemical properties

The water/atmosphere content, clay content, pH and temperature contribute

strongly to the heterogeneity of microbial communities in soil (Liesack et al.,

1997). Water makes up about 20-30% of the average soil volume and is

essential to all life within the soil (Wood, 1995). The soil atmosphere is the

gaseous component of the soil. The water content/atmosphere ratio

fluctuates in response to rainfall and temperature and the physical

composition of the soil also affects the amount of available water. Clays and

organic matter tend to retain water, whereas silt and sand allow rapid

drainage to the local water table. In situations where water content is low,

microbial life is reduced whereas if the water content is too high, oxygen is

reduced and only anaerobic life can flourish. The rate of decomposition is

greatly reduced in anaerobic soils, resulting in high organic content (Stotzky,

1997). Additionally, most bacteria are not motile and so their dispersion

17

primarily depends on water movement as well as root growth and the activity

of other organisms (Lavelle and Spain, 2001).

The proportion and type of clay minerals present can greatly influence

microbial activity, by modifying physiochemical characteristics of

microhabitats such as pH, nutritional status, the activity of toxic substances

and water availability (Killham, 1994; Stotzky, 1997). Clay minerals retain

water and are therefore essential for microbial life (Stotzky, 1997). Their

negative charge attracts nutrient cations such as NH4+, Ca2+, Mg2+ and K+.

However, some organic molecules such as amino acids and toxic compounds

can bind to clay minerals reducing their bioavailability (Stotzky, 1997).

Additionally, clay minerals can form aggregates which retain water, causing

the formation of microhabitats which can exclude predators due to small pore

sizes thereby protecting the bacteria within (Killham, 1994; Griffiths and

Bardgett, 1997).

The pH of a soil can significantly affect microbial community structure. Most

soil bacteria and fungi prefer a neutral pH, but their responses to alkali (pH

7.5-8.5) and acidic conditions (pH 4 – 6.5) vary noticeably. Fungi generally

predominate in acidic soils, although postputrefaction fungi are known to

proliferate at the high pH values found around cadavers (Sagara, Yamanaka

and Tibbett, 2008). Whereas bacteria and actinomycetes dominate in near-

neutral or moderately alkaline soils (Stotzky, 1997). Bacteria are efficient

competitors at mid-high pH values, most are intolerant to low pH values, with

the exception of species such as acidophiles (Wood, 1995). The pH of soil also

affects the solubility, availability and toxicity of mineral nutrients that can

significantly affect microbial populations (Coleman, Crossley and Hendrix,

2004).

The average soil temperature greatly affects the composition of the microbial

population. Psychrophilic microbes, with a low temperature preference,

inhabit sub-freezing polar and mountain soils, whereas thermophilic microbes,

with a high temperature preference, flourish in geothermal areas. Mesophilic

microbes, which prefer moderate temperatures predominate the majority of

soils worldwide (Stotzky, 1997). In well established soils, microbial

18

populations reach an equilibrium that is generally resistant seasonal

temperature fluctuations (Wood, 1995). A study presenting a continental–

scale description of the soil bacterial communities from 98 sites across North

and South America showed that microbial biogeography is influenced primarily

by edaphic variables, for example soil moisture and carbon availability, rather

than site temperature, latitude etc that typically predict plant and animal

diversity (Fierer and Jackson 2005). Interestingly, it showed that the degree

of similarity between soil bacterial communities was largely unrelated to

geographical distance.

2.2.2.2 Nutrient availability

The decomposition of vegetation and animals results in an uneven distribution

of substrates on the surface of the soil. This produces localised zones of

nutrients thereby creating niches within the soil where microbes are

concentrated (Stotzky, 1997). Within the rhizosphere, rhizobacteria

proliferate in response to stimulation by root exudates. However, the high

microbial numbers associated with the rhizosphere do not correlate with high

microbial diversity. This is because a selective pressure only allows species

that can utilize carbon most efficiently to proliferate (Coleman, Crossley and

Hendrix, 2004). Similarly, spatial variability in vegetation directly affects

spatial distribution of soil microbes.

2.2.2.3 Soil depth

The number of microbes found in soil is known to decrease with depth (Hurt

et al., 2001) which is mainly due to the reduction of the quality and quantity

of nutrients with depth. Surface soils have a more heterogenous microbial

population, whereas deeper soils show a dominance of a few microbial

groups. Gram negative bacteria, fungi and protozoa are higher in the upper

levels of soil, whereas Gram positive bacteria predominate at depth (Fierer et

al., 2005). Two theories suggest a cause for the differences seen in

biodiversity. The theory of spatial isolation suggests that if microbial groups

are separated, diversity is maintained, but if microbes are allowed to

interact, competition occurs and the fittest dominate. The second theory is

based on carbon availability, where variation in the types of carbon and their

levels, prevent competition and maintain high diversity. Therefore under low

19

carbon condition, fewer microbial species exist and biomass and diversity

decrease (Coleman, Crossley and Hendrix, 2004).

2.2.2.4 Human activity

Human activity such as agriculture, building development, waste disposal and

mining all has profound effects on soil microbial populations. The burning of

fossil fuels has led to the acidification of soils worldwide (Galloway, 2001).

High salinity due to poor irrigation techniques, high concentrations of heavy

metals, fertilizers, pesticides and radioactivity have complex effects on soil

microbial communities that are not fully understood. Many studies show that

these occurrences reduce total biomass and species diversity and impact

heavily on microbial processes (Frostegard, Tunlid and Baath, 1993). This

upsets the balance of the biological community and causes shifts in microbial

community structure. Exotic microbes can be introduced into soil by direct

inoculation to help promote crop growth or for the purpose of biological

control and when impacted by microbially hosted material such as sewage

sludge. More recently, the use of genetically engineered organisms in

agriculture, and horizontal gene transfer between transgenic plants and soil

microbes have potential implications for soil microbial communities. These

implications include disruptive effects, such as elevated or reduced biomass,

activity and diversity of soil microbial communities. Transgenic plants might

express antimicrobial compounds which could confer antibiotic resistance due

to microbial adaptation (Lukow, Dunfield and Liesack, 2000).

2.3 Post-Mortem Interval Estimation

An accurate estimation of time since death or the postmortem interval (PMI)

is one key aim of every medicolegal investigation, equal in importance to

victim identification and cause of death. Determination of the PMI can direct

or re-orient an entire investigation by serving to validate or reject a suspect‟s

alibi or elucidate the victim‟s perimortem activities. Pathology, anthropology

and entomology have developed criteria to fine-tune the estimation of PMI.

20

2.3.1 Pathology and Anthropology

Forensic anthropologists and pathologists have traditionally relied on

observations of the decay of soft tissues to estimate PMI. Cadaver events are

divided into abiotic phenomena and transformative phenomena (Campobasso,

Di Vella and Introna, 2001). Abiotic phenomena can be instant, like loss of

consciousness and absence of breathing or consequential like body cooling and

acidification. These consequential phenomena can be used in the estimation

of time since death. Transformative phenomena can be destructive like the

processes of autolysis and putrefaction (Campobasso, Di Vella and Introna,

2001). The four classical stages of putrefaction: discolouration, bloating,

liquefaction and active decay or skeletonisation follow a timeline and

established diagnostic markers can be used to indicate time elapsed since

death (Campobasso, Di Vella and Introna, 2001). These stages should not be

regarded as clearly defined events, but rather a sequence of overlapping

events until the organic matter is completely destroyed. Putrefactive

changes can only be used for estimating PMI when they are integrated with

environmental and circumstantial elements. Decomposition varies from

cadaver to cadaver, environment to environment and one part of the same

cadaver to another (Campobasso, Di Vella and Introna, 2001).

During the first 24 hours postmortem, the cooling of the cadaver or algor

mortis is the most useful indicator of the time of death. The assessment is

made on the basis of body core temperature, requiring a direct measurement

of the rectal or intra-abdominal temperature. However it is imperative to

consider all the possible variables which influence the rate of cadaver heat

loss. Several additional innovative techniques have been developed to

estimate PMI. Exploiting the degradation of a protein to estimate the PMI has

been investigated using cardiac troponin I (Sabucedo, 2003) and various

proteins found in cerebrospinal fluid (Madea et al., 1994). The levels of

creatinine and serum uric acid have been used to estimate PMI (Zhu et al.,

2002). Creatinine is a by-product of active muscles and is produced at a fairly

constant rate by the body. The relationship between the potassium

concentration in the vitreous humour and the postmortem interval has been

studied by several authors (James, Hoadley and Sampson, 1997; Munoz et al.,

2001). Volatile fatty acids (VFA) and various anions and cations from human

21

decomposition have been used for PMI estimations (Vass et al., 1992). This

study found a direct correlation between the decomposition stages and the

VFA production.

2.3.2 Entomology

The most successful estimation of PMI relates to the developmental biology of

insects such as the blowfly by the field of entomology. Insects arrive at a

cadaver in a pattern that is characteristic and identifiable. The concept of

arthropod succession allows the association of a species or group to a well-

established decomposition stage, thus, estimating the post mortem interval.

Arthropods are classified by the feeding habits of its members and are divided

into five distinct ecological groups – necrophages, necrophiles, omnivores,

opportunists and accidentals. The former three are considered most

important for forensic purposes. The necrophages are useful for establishing

the time of death, as they arrive in a predictable sequence. Necrophagous

arthropods have highly specialized sense organs specifically stimulated by

organic putrefaction odours and gases, which help them locate the cadaver

soon after death (Campobasso, Di Vella and Introna, 2001). Omnivores that

appear practically at the same time as necrophiles and remain through all the

decomposition stages can provide information about the cadaver itself, any

manipulation of it and the arthropod community (Arnaldos et al., 2005).

The decomposition of a cadaver is a sequential but continuous process.

Entomology has replaced the use of broad, qualitative categories to define

decomposition, with an analytical technique known as accumulated degree-

days (ADD). It describes decomposition more precisely as a continuous

variable, by assigning point values to express decomposition. This results in

an increased statistical power of hypothesis testing and could provide more

information about the relationship between decomposition and the PMI

(Megyesi, Nawrocki and Haskell, 2005). Accumulated degree-days represent

heat energy units, known as degree-days (D), available to drive a biological

process such as bacterial or fly larvae growth. Upper and lower

developmental thresholds are the temperatures at which development stops

and needs to be separately determined for each organism being studied.

22

Most entomological evidence has been collected from research done on the

decomposition of various animals. An early study of the entomofauna of 43

dog carcasses was conducted in Knoxville, Tennessee. It reported on the

occurrence and abundance of 240 species, attracted to the carcasses over a

period of a year (Reed Jr, 1958). Arthropod colonisation on 39 small mammal

carcasses in Illinois, Indiana were found to have a “fairly regular successional

pattern”, which depended upon the season of the year (Johnson, 1975). Pig

carcasses are popular analogues for the human cadaver. A five month study

of three pigs buried in Essendon, United Kingdom showed the impact of soil

characteristics, such as pH, tannin levels and the clay component of the soil,

on the decomposition process and how they may confound results when using

blowfly larvae to estimate PMI (Turner and Wiltshire, 1999). A study

conducted in Russia observed the effects of 13 biotypes (environments) on the

entomofauna involved in the decomposition of 211 animals (Marchenko,

2001). It identified the Diptera families as the leading species involved in the

decomposition of a cadaver, followed by Coleptera families.

However, few researchers have had the advantage of testing the validity of

human cadaver entomological evidence for estimating PMI. A two-year study

was conducted on buried human cadavers with respect to a handful of

variables, one being arthropod activity, to contribute to a more accurate

estimation of PMI. It found the burial of a cadaver restricts the access of

many arthropods to the cadaver, thereby slowing down decomposition rates.

Entomological evidence from literature and experimental studies were

applied to seven real forensic cases in the Iberian Peninsula. The importance

of constructing regional specific databases based on different geographical

situations and different habitats is observed, in order to be useful to a

forensic case (Arnaldos et al., 2005).

Theoretically, this concept of succession could also apply to the microbes

involved in decomposition. A cadaver has come to be viewed as a source of

sequestered nutrients and energy that is returned to the wider ecosystem

upon decomposition (Carter, Yellowlees and Tibbett, 2007). When a cadaver

is introduced into an environment and decomposition begins, the

environmental equilibrium is disrupted due to modifications in the soil,

23

addition of nutrients etc. Cadaver-derived enteric microflora may be

introduced and their metabolic processes may modify the environment.

These modifications may result in conditions that are not ideal for the

introduced species, but rather may favour a secondary species, which may

then become dominant. This occurs in continuous succession and as nutrients

are consumed, species are enabled or competitively excluded. This concept

of „resource selects community‟ (Beijerinck, 1913; Connell and Slatyer, 1977)

provides the underlying principles for a microbial PMI estimation model. An

early study into postmortem change in field conditions used traditional

culturing techniques to show a „microbial succession sequence‟, which was

observed from enteric to soil organisms over a period of 6 days of

decomposition (Micozzi, 1986). The microbiological interactions associated

with decomposition may be extremely useful in the development of post-

mortem interval estimation tools and may also be relevant to victim

identification and locating human remains or clandestine graves.

2.4 Microbial Community Analysis

The living component of the soil was first recognized as being useful in

forensic science by Thornton and McLaren (1975). They suggested that the

biochemical properties arising from the metabolic processes of microbes in

the soil could impart uniqueness to a soil. They tested soils from different

sites in close proximity and proved they could be successfully distinguished by

their enzyme activity patterns. However, it has since been shown that drying

and storage can influence enzyme activity (Lorenz et al., 2006). Another

attempt to compare soils based on microbial functional diversity was made,

using a multi-substrate testing method (Omelyanyuk, Alekseev and Somova,

1999). The method was based on functional characterization of microbial

communities, using eleven sources of organic carbon, while observing the

change of colour of tetrazolium salt from yellow to purple. This method was

applied effectively to a real forensic case study.

The advent of molecular technology has revolutionized the field of microbial

ecology by providing nucleic acid-based techniques of examining the diversity

of a soil microbial community in any ecosystem. However, our understanding

24

of soil microbial communities has been limited by the use of culture-based

methods and morphological techniques. It is well known that less than 10% of

soil microbes can be cultured using existing techniques (Dierksen et al.,

2002). Due to these restrictions, available data from culture related methods

provide a selective and biased look at microbial diversity. A number of

approaches that do no rely on culturing and isolating, such as lipid

biomarkers, have been developed which can contribute to microbial

community characterisation. The chemical diversity and cellular abundance

of nucleic acids and lipids make them potentially useful chemical targets for

investigating microbial communities. A strong data correlation was seen

between polymerase chain reaction (PCR)-based methods and lipid-based

methods for comparison of microbial diversity (Ritchie et al., 2000). These

analytical tools are sufficiently sensitive and robust to be used for forensic

sample analysis and as evidence in a court of law.

2.4.1 Phospholipid Fatty Analysis

2.4.1.1 Structure and function of PLFAs

Phospholipid fatty acids (PLFA) are essential membrane components of all

living cells. They consist of a single molecule of glycerol. Two OH groups of

the glycerol are bound to a fatty acid chain (hydrophobic tail) and one OH

group is bonded to a phosphate group (hydrophilic head). The asymmetric

lipids form a bilayer in membranes, with hydrophilic ends towards the outer

surface of the membrane and hydrophobic towards each other (see Fig 2.3)

(Kaur et al., 2005).

25

Figure 2.3: Arrangement of phospholipids in the membrane of a living cell (Kaur 2005).

These compounds degrade rapidly upon cell death and are not found in

storage products and are therefore good indicators of the living microbial

community (Drenovsky et al., 2004). Additionally they are chemically diverse

and abundant in soil and the relative abundance of certain PLFAs can identify

specific groups of soil microorganisms (Zelles 1999, Drenovsky et al 2004).

2.4.1.2 Significance of PLFAs

PLFAs make up a relatively small but constant proportion of the biomass of

organisms (Zelles, 1999). Studies have indicated that rapid changes in

microbial community structure can be detected by changes in PLFA patterns

(Bossio, 1998; Kaur et al., 2005). Certain PLFAs can be used as biomarkers for

specific populations, taxonomic or functional groups. For example, bacteria

contain the unique β–OH, cyclopropane and branched-chain fatty acids which

are not common to other organisms (Zelles, 1999). However, there is limited

indication that individual lipids can serve as unique biomarkers for a specific

microbial species. This is due to overlap in the PLFA composition of different

microbes, where an individual species can have numerous fatty acids, some of

which may occur in many other organisms (Bossio, 1998). Furthermore, the

determination of signature PLFAs for specific microbes requires their isolation

in pure culture and PLFA patterns for individual populations can vary in

response to environmental stimuli (Ramsey et al., 2006). For these reasons,

26

PLFAs are more reliable as reflections of the composition and dynamics of the

microbial community and are not used to calculate microbial diversity

(Lechevalier, 1977; Bossio, 1998).

2.4.1.3 PLFA method

PLFA analysis of microbial communities, originally developed by Bligh and

Dyer (1959) and later modified by White et al. (1979) and Zelles (1992), is a

biochemical method, which provides a culture-independent and broad-scale

analyses of the abundance and change in microbial community structure. It is

the quantitative measurement of ester-linked fatty acids in phospholipids that

has been known to be the most sensitive and reliable measure of microbial

biomass and community structure (Zelles, 1992; Drenovsky et al., 2004). The

PLFA profile of a sample is derived from the whole viable microbial

community and each species contributes to the profile in proportion to its

biomass (White et al., 1979; Zelles, 1999).

The fatty acid nomenclature used is as follows; total number of carbon

atoms:number of double bonds, followed by the position of the double bond

from the methyl end of the molecule. Cis and trans geometry are indicated

by the suffixes c and t. The prefixes a and i refer to anteiso- and iso-

branching (Bossio, 1998). In general, fatty acids most commonly used to

indicate bacteria are 15:0 and 17:0, whereas a good indicator of fungi is

18:26 (Kaur et al., 2005). Due to a limited number of fungal-specific

markers, PLFA signatures only serve to provide an estimate of total fungal

biomass in soil (Anderson, 2004). Generally, odd-number and branched-chain

fatty acids are produced by Gram-positive bacteria, while even number

straight-chain and cyclopropyl fatty acids are from Gram-negative bacteria

(Zelles, 1992).

2.4.1.4 PLFA Studies

PLFAs were measured in soils with differing farming systems in terms of the

source of fertilizer and the presence of a winter cover crop. The importance

of environmental variables on PLFA profiles was determined as following: soil

type > time > spatial variation (Bossio, 1998). Fatty acids observed in a study

of human putrefactive fluids (Cabirol et al., 1998) in a liquid and gelled form,

27

showed solidification of putrefactive fluid may decrease the rate of

decomposition due to declining substrate availability (Cabirol et al., 1998). In

addition to community profiling, PLFAs have been used as indicators of:

environmental stress, high temperature, organic compound toxicity and

osmotic stress, starvation, low pH, and the presence of heavy metals (Kaur et

al., 2005). Differences in the metabolic status and microbial composition of

estuarine microbial mats were monitored by PLFA analysis to determine

changes in the physiological status, biomass and microbial composition. The

study revealed an increase in biomass in the morning hours and a decrease in

growth rate in the deeper layers of the mat (Villanueva et al., 2004).


The polymerase chain reaction (PCR) heralded the molecular era by enabling

researchers to amplify the large amounts of DNA required by many molecular

techniques. One such technique is terminal restriction fragment length

polymorphism (T-RFLP) analysis (Avaniss-Aghajani et al., 1994), which is an

automated profiling method used to study complex microbial communities.

2.4.2.1 T-RFLP method

In the T-RFLP method (see Fig 2.4), the target gene is amplified with the

polymerase chain reaction. A fluorescent-labelled primer is used to allow

detection of this fragment. A restriction enzyme recognises and cleaves the

PCR product at a particular sequence. In the variable regions, the restriction

sites occur at different places resulting in different length fragments. The

more diverse the microbial community is the greater the range of fragments.

In principle, each fragment represents a unique operational taxonomic unit

(OTU) of the sample (Liu et al., 1997; Marsh, 1999; Osborn, Moore and

Timmis, 2000; Blackwood et al., 2003). The relative quantitative distribution

within a profile can be determined, since the fluorescence intensity of each

peak is proportional to the amount of genomic DNA present for each OTU in

the sample. This results in distinct profiles, known as electropherograms,

dependent on the species composition of the communities of the samples.

The profiles can be visually compared and the semi-quantitative data

statistically compared and used for generating information of the relative

abundance of operational taxonomic units (Liu et al., 1997).

28

Figure 2.4: Overview of the T-RFLP method (Applied Biosystems, 2005).

Despite the advantages of T-RFLP analysis, it is subject to problems such as

the systematic biases caused by PCR and restriction enzyme digestion

efficiency. Preferential annealing to particular primer pairs can cause the

amplification of particular sequences and an increase in PCR cycles may lead

to an increase in the incidence of chimeric PCR products (Egert, 2003;

Lueders, 2003). Enzymatic lysis favour the recovery of DNA from Gram-

negative bacteria, whereas mechanical lysis is considered to give a more

representative sample (Ward et al., 1992). The extraction of DNA, use of

replicates, pooling of samples, dilution effects, choice of polymerase and

reaction annealing temperature may all effect the final profile of the

microbial community (Osborn, Moore and Timmis, 2000). Additionally, operon

heterogeneity can cause the copy number of the 16S gene to vary between 1

and 14 in different bacterial species with some variation between them,

artificially increasing the diversity seen in a profile (Crosby and Criddle,

2003).

2.4.2.2 Target genes

Any gene of interest that has both conserved and diverse regions of genetic

information can be used in T-RFLP analysis. The most widely used genes are

those that code for the RNA component of the small subunit of the cellular

29

ribosomal machinery (see Fig 2.6): the 16S ribosomal RNA (rRNA) gene for

prokaryotes (see Fig 2.5) and the 18S rRNA for eukaryotes (see Fig 2.7).

16S rRNA gene

The 16S rRNA gene encodes for the ribosomal RNA small subunit, which makes

up part of the bacterial ribosome and has the largest representation of any

gene in the public databases (Liesack et al., 1997). Ribosomes are the protein

synthesizing machines of the cell and are made up of two subunits, which are

composed of large protein complexes and rRNAs. Bacteria have three types of

rRNA molecules: the 16S rRNA is part of the small subunit and the 5S and 23S

rRNAs are part of the large subunit.

Figure 2.5: The rRNA Operon. It consists of three rRNA molecules: 16S, 23S and 5S, which are separated by internal transcribed spacer (ITS) regions (Flechtner et al., 2002).

The essential function of the 16S rRNA molecule means that it is evolutionarily

conserved across all known bacterial species (Ward et al., 1992). The gene

has nine regions of high variability interspersed with regions of less variability.

Less conserved regions have changed through evolution without negatively

impacting the organism. Different bacterial species and even strains of the

same species can have very dissimilar 16S rRNA sequences, however if the

species have a close phenotypic relationship, their 16S rRNA sequences can be

similar (Ward et al., 1992). This variability can be used to assess the diversity

of a soil bacterial community.

30

Figure 2.6: The 16S rRNA secondary structure. Primary sequence with near universal conservation (thick lines), intermediate conservation (normal lines) and hypervariability (dashed lines) is shown (Ward et al., 1992). Arrows and black lines indicate the region of

the gene amplified by PCR. The grey regions at the 3’ and 5’ ends are not amplified.

Internal transcribed spacer region

The internal transcribed spacer (ITS) regions of fungal ribosomal RNA genes

are suitable targets for molecular analysis of fungal communities (Buchan et

al., 2002). The ITS regions are stretches of DNA between the 18S, 5.8S and

28S rRNA genes with intervening sequence (IS) regions that do not encode

structural products.

31

Figure 2.7: Internal transcribed spacer (ITS) region map. The ITS regions exist in two segments, the ITS1 and ITS2, which bracket the 5.8S rDNA.

The ITS regions encode structural genes for tRNA, but the IS regions are where

the sequence divergence exists. Their high sequence variability relative to

the flanking rRNA genes makes them valuable for genus- and species-level

identification. In fungi, the rRNA operons, are often found as tandem repeats

of up to 100 copies, hence the possibility exists of significant interspecies

differences in ITS copy number (Buchan et al., 2002).

2.4.2.3 T-RFLP Studies

The T-RFLP method has been used to characterise bacterial (Liu et al., 1997)

and fungal populations (Jones and Bessemer, 2004) in natural habitats and has

been identified as a reproducible and accurate tool for community profiling

(Egert, 2003). Four soil communities were able to be differentiated based on

their 16S rRNA terminal restriction fragment (TRF) profiles, where T-RFLP

analysis was very effective at showing similarity relationships with good

detection sensitivity, but not at comparing community richness and evenness

(Dunbar, Ticknor and Kuske, 2000). The T-RFLP method has been

preliminarily tested to produce soil bacterial community profiles for

comparative forensic purposes (Horswell et al., 2002). Mock crime scenes

were set up, soil evidence collected and the bacterial community profiled.

Soil samples from the same site produced similar profiles, whereas profiles

from different sites produced significantly different profiles. Persistence of

the soil bacterial structure was exhibited with a similar profile to the original

observed at one site after an eight-month period. The presence and

quantification of fungi taken from organic and non-organic vineyard soils was

32

compared using the T-RFLP method and the ITS region of rDNA. Non-organic

soil showed a greater and more diverse amount of fungal rDNA. However DNA

samples were harder to retrieve from the organic soil and only two vineyards

were sampled (Jones and Bessemer, 2004). Similarly, four diverse bacterial

communities from termite hind-gut, aquifer sediment and two activated

sludge samples were distinguished by Liu et al. (Liu et al., 1997). The

selection of a restriction endonuclease is very important and the enzyme HhaI

is shown to provide a greater insight into estimates of biodiversity when

compared with the enzyme CviJI (Marsh, 1999). This was reinforced when

HhaI was compared with eight other restriction enzymes (Osborn, Moore and

Timmis, 2000). A multiplex T-RFLP technique was developed by Singh et al.

(2006) where up to four microbial taxa were analysed simultaneously and

differentiated from rhizosphere bacterial, fungal and rhizobial/agrobacterial

communities in three environments.

2.5 Data handling and statistical analysis

The most simplistic approach in comparing the PLFA and T-RF profiles is to

visually compare traces for the presence or absence of different peaks.

However, more information can be gleaned from quantitative analyses of

these data sets. In particular, multivariate statistical methods have been

extremely valuable in analyzing complex data sets, which can comprise a

variety of variables and need not be limited to species lists (Rees et al.,

2004).

The analysis of the PLFA data began with the generation of chromatograms by

the GC-MS. The integration and handling of peak data was performed by the

GC-MS software, ChemStation (Agilent Technologies, Paolo Alto, CA). The

chromatograms were tentatively identified by comparison of retention times

and mass spectra of fatty acid standards run under the same conditions.

Peaks were manually integrated and peak area data quantified using standard

calibration curves. The quantification data was then imported to an

electronic spreadsheet for statistical analysis.

33

The first step in analyzing the T-RF data is to use an appropriate method for

aligning peaks. This is particularly important since peak size discrimination is

to 1 base pair (Fierer et al., 2005). The position and height of individual

peaks in the microbial community profile indicate the presence and relative

abundance of different ribotypes. Fragment lengths usually range from 0 to

1500 bp, but in reality, the number of base pairs belongs to a discrete series

(eg 1 bp, 2bp). However, automatic sequencers produce values on a

continuous scale to two decimal places and so have to be rounded to the

nearest integer and assigned to the ribotype associated with that integer

(Scallan et al., 2008). The RiboSort program eliminates the need to manually

sort and manipulate the data into the desired format for statistical analyses,

which can be tedious and error-prone (Scallan et al., 2008). The RiboSort

program was used to generate information on ribotype abundances, ribotype

proportions and sequencer detections for the current research.

The next step is to analyse the PLFA and T-RF data sets using statistical tools.

The current research used the multivariate software package, Primer V6

(Primer-E Ltd, Plymouth, UK). Cluster analysis is the method of choice when

relationships between objects are expected to be discontinuous and where

defined groups of objects are expected (Ramette, 2007). The basic aim of

ordination and cluster analysis is to represent the (dis)similarity between

objects, so that similar objects are depicted near to each other and dissimilar

objects are found further apart (Ramette, 2007). A similarity matrix is

calculated using the Bray-Curtis coefficient. The Bray-Curtis coefficient is an

ideal coefficient for the construction of similarity matrices because, it has the

ability to deal with data sets containing multiple blocks of zeros in a

meaningful manner (Rees et al., 2004). Non-metric multidimensional scaling

(MDS) is used to ordinate the similarity data and prepare visual interpretations

of the microbial community as represented by T-RFLP and PLFA data. MDS

uses an algorithm that takes the multidimensional data of a similarity matrix

and presents it in typically two dimensions, although three dimensional plots

can be employed to visualise group differences (Clarke and Ainsworth, 1993).

The result of an MDS ordination is a map where the position of each sample is

determined by its distance from all other points in the analysis. The units on

axes are usually not included, as they would only serve the purpose of

34

indicating the relative positions of the objects, but not have any real

meaning. An important component of an MDS plot is a measure of „goodness

of fit‟ termed the „stress‟ of the plot. A stress value greater than 0.2

indicates that the plot is close to random and a stress value less that 0.2

indicates a useful two dimensional picture (Clarke and Ainsworth, 1993).

An Analysis of Similarity (ANOSIM) routine is used to examine statistical

significance between samples (Clarke and Ainsworth, 1993). ANOSIM tests the

null hypothesis that the average rank similarity between objects within a

group is the same as the average rank similarity between objects between

groups. It produces a test statistic (R) which can range from -1 to 1, which is

a relative measure of separation of the a priori-defined groups. Objects that

are more dissimilar between groups than within groups will be indicated by an

R statistic greater that 0. An R value of 0 indicates that the null hypothesis is

true. The significance level statistic is produced as a percentage which can

be converted into the p-value. It is a measure of how much evidence there is

against the null hypothesis and has a probability ranging from zero to one. A

small p-value (<0.05) is evidence against the null hypothesis while a large p-

value (>0.05) means little or no evidence against the null hypothesis.

35

Chapter 3 : RAT CADAVER EXPERIMENT

3.1 Introduction

Animal models have long been used to study the process of decomposition

(Reed Jr, 1958; Payne, 1965; Wilson et al., 2007). The current research

involved the use of soils from the decomposition of juvenile rat cadavers in

Pallarenda and Wambiana tropical savanna soils from Queensland, Australia

(Stokes et al., 2005). The cadavers were originally used to determine if the

evisceration of a cadaver would decrease the rate of decomposition (Carter,

2005). Putrefaction is predominantly driven by the enteric microflora (Vass,

2001) and therefore the removal of it and the internal organs may slow down

the rate of decomposition. To address this issue, Carter (2005) used four

treatments that included a complete cadaver, an eviscerated cadaver, a

cadaver with a sown up incision only and a control (soil without cadaver) were

investigated. The current research analysed the gravesoils using lipid-based

phospholipid fatty acid analysis and nucleic acid-based terminal restriction

fragment polymorphism analysis to characterise the differences between the

soil microbial communities of two soil types and four treatments.

3.2 Aims

The aim of this experiment was to determine whether there are changes in

the soil microbial communities associated with decomposition in the two

distinct soils types and four cadaver treatments, and if this could be

distinguished with the aforementioned analyses.

3.3 Experimental Background

Existing experimental soils from an earlier research project (Carter, 2005),

were used to pursue the aims of this experiment. Soils from the Pallarenda

and Wambiana sites in tropical savanna ecosystems in Queensland were

collected in September 2003. They were calibrated to a matric potential of -

0.05 megapascals (MPa) and equilibrated for seven days at 22ºC. The

36

experiment used four treatments of juvenile rat cadavers aged 8-10 days

(Rattus rattus), which included a complete cadaver, an eviscerated cadaver,

an incised and sown cadaver and a control (soil without cadaver). The rats

were killed with carbon dioxide. Incisions, where needed, were made from

the anus to the sternum and the internal organs of the lower respiratory tract

and accessory digestive organs were removed for the eviscerated cadavers.

Following evisceration, the abdominal and thoracic cavities were rinsed with

sterile distilled water and sown up whereas the incised cadavers were incised

and stitched without evisceration. The cadavers were buried in soil (500 g dry

weight) and placed into soil microcosms (Tibbett et al., 2004). The

experiment was replicated four times with four sequential harvest events on

days 7, 14, 21 and 28. At each harvest event cadavers were exhumed, along

with the soil adhered to them, and the soil directly surrounding the cadaver.

Harvested soils samples were weighed into sterile culture tubes and

immediately stored at -20C. Subsequently, only the replicates from day 14

were shipped to Western Australia in an icebox and stored in a freezer at -

20C with no freeze-thaw cycle, therefore analysis could only be carried out

for this harvest.

3.4 Materials/Methods and Results


The PLFA analysis of the gravesoils followed the protocol of White et al.

(1979) and Zelles et al. (1992) with a few modifications. It consists of the

three main preparation steps of extraction, fractionation and derivitisation

followed by gas chromatography-mass spectrometric analysis.

3.4.1.1 Extraction

The following replicate soil samples were available for testing: Pallarenda

samples (2 control soils, 4 complete cadaver soils, 3 incised cadaver soils and

4 eviscerated cadaver soils) and Wambiana samples (3 control soils, 4

complete cadaver soils, 4 incised cadaver soils and 3 eviscerated cadaver

soils). Frozen soils were left to thaw for 40 mins to an hour. Lipid extraction

was performed using 2 g (±0.1 g) of thawed moist soil. A solution of

37

phosphate buffer (8.7g K2HPO4/L, neutralised with 1 N HCl to pH 7.4)

methanol (99%) and chloroform (99%; v/v/v 2 ml, 5 ml, 2.5 ml) was added to

the sample. The mixture was sonicated for 15 minutes in an ultrasonic bath

and then centrifuged for 5 minutes at 3500 rpm. The supernatant was

decanted and chloroform and deionised (Milli Q) water were added (3 ml

each). After standing for 30 minutes on ice, the chloroform phase (bottom

phase) was isolated and dried down under a stream of nitrogen gas (N2).

3.4.1.2 Fractionation

The total lipid extract (TLE) was remobilised in chloroform (2 ml) and was

separated into polarity-based fractions by successive solvent elutions through

silica-bonded columns (Supelclean LC-Si-SPE, Sigma-Aldrich (Supelco), Poole,

UK). The silica powder columns were first conditioned with 2 ml of methanol

and 2 ml of chloroform pulled through with a vacuum followed by an

additional 1 ml of chloroform allowed to drip through without the vacuum.

The TLE was then added to the column and successively eluted with

chloroform (2 ml) to remove the neutral fatty acids, acetone (2 ml) to remove

the free glycol fatty acids and methanol (2 ml) to remove the phospholipid

fatty acids.

3.4.1.3 FAME Derivitisation

The phospholipid fatty acid fraction was resuspended in a solution of

methanol and toluene (1:1, v/v, 0.2 ml) and vortexed. A mixture of

potassium hydroxide and methanol (0.2 M, 0.5 ml) was added and heated on a

hot block to 75ºC for 10 minutes with intermittent agitation. On cooling to

room temperature, the fraction was neutralised with acetic acid (0.2 M, 0.5

ml). Chloroform and deionised water (1 ml each) was added, the sample was

allowed to stand and the bottom chloroform phase was removed. The

remaining top aqueous phase was re-extracted and the two chloroform phases

were combined and concentrated under a stream of nitrogen gas. The

remaining sample was aliquoted into a glass capillary placed inside an amber

GC-MS vial along with a methylnonadecanate or C19:0 fatty acid (20 µL, 10 ng

µL-1, Sigma-Aldrich) internal standard for quantitative GCMS purposes and

stored at -20ºC. Analytical grade solvents and chemicals were used and

38

stored at 4ºC. All glassware was acid washed prior to use to remove possible

contaminants.

3.4.1.4 Gas Chromatography/Mass Spectrometry

The fatty acid methyl ester (FAME) fractions were analysed by an Agilent

6890/5973 Gas Chromatography-Mass Spectrometer. A 60 m x 0.25 mm a 0.25

µm ZB-5 (Phenomenex) capillary column was used. The GC was used in

pulsed-splitless mode and the oven was programmed from an initial

temperature of 70ºC held isothermal for 1 minute and then increased at a rate

of 10ºC min-1 to 150ºC, followed by 3ºC min-1 to 300ºC and held isothermal for

a final 20 minutes. Helium carrier gas was maintained at a constant flow of

1.1 mL min-1. Full scan (50-550 Da) data were acquired. Other standard mass

spectral conditions were applied including an electron energy of 70 eV; source

temperature of 230ºC. Product identifications were based on comparison to

library mass spectra. The concentration of each fatty acid was determined

relative to the C19:0 internal standard and was calculated as:

µg individual fatty acid g-1 soil = (PFAME x µg Std) / (PISTD x W)

where PFAME stands for the peak area of the fatty acid samples and PISTD

stands for the peak area of the internal standards; µg Std is the concentration

of the internal standard (µg uL-1 solvent); and W is the dry weight cm-3.

The GCMS data was processed using ChemStation software (Agilent

Technologies, Palo Alto, CA). PLFA peak assignments were based on retention

time and mass spectral correlation (see Fig. 3.1). Identification was aided by

corresponding GCMS analysis of a standard mixture of authentic PLFA

products. The peak area of selected PLFAs in either the total or m/z 74 ion

chromatograms were calculated by peak integration, quantified by

comparison to the measured area of the C19:0 internal standard (see Table

3.1) and statistically processed.

39

Peak Nomenclature Full Compund Name

4 i-15:0 Me.13-methyltetradecanoate

5 a-15:0 Me.12-methyltetradecanoate

6 15-0 Me.pentadecanoate

8 i-16:0 Me.14-methylpentadecanoate

10 16:1(w7c) Me.cis-?-hexadecenoate

11 16:1(9) Me.cis-9-hexadecenoate

12 16-0 Me.hexadecanoate

13 ?-17:0 Me.?-methylhexadecanoate

14 ?-17:0 Me.?-methylhexadecanoate

15 i-17:0 Me.15-methylhexadecanoate

16 a17:0 Me.?-methylhexadecanoate

17 17-0(D) Me.cis-9,10-methylenehexadecanoate

18 17-0 Me.heptadecanoate

21 18:2(9,12) Me.cis-9,10-octadecadienoate

22 18:1(9) cis Me.cis-9-octadecenoate

23 18:1(9) trans Me.trans-9-octadecenoate

24 18-0 Me.octadecanoate

25 i19:0 Me.?-methyloctadecanoate

26 19-0(D) Me.cis-9,10-methyleneoctadecanoate

27 19-0 Me.nonadecanoate

Figure 3.1: An example of a typical phospholipid fatty acid distribution with the assignment of major peaks. Peaks that have been further identified are shown in the

table above.

40

3.4.1.5 Statistics

The Primer 6 software package (Primer-E Ltd., Plymouth, United Kingdom)

was used to generate multi-dimensional scaling (MDS) plots and analysis of

similarity (ANOSIM) calculations.

41


3.4.2.1 DNA Extraction

A trial was conducted with the UltraClean™ Soil DNA Isolation Kit (Mo Bio

Laboratories, Inc.), but it did not remove contaminating humic acids

effectively (yellow to brown extractions) and the resulting DNA product was

very poor. The Powersoil™ DNA Isolation Kit (Mo Bio Laboratories, Inc.)

trialled next, resulted in clear extractions and consistent, good quality DNA

product, and therefore was used to extract the DNA from the rat cadaver soil

samples. Throughout the protocol, some samples were duplicated to measure

the reproducibility of the method. The manufacturer‟s protocol was followed

with slight modifications to enhance DNA yield and decrease co-extraction of

humic substances (Appendix I). Three weights of soil (0.2 g, 0.4 g and 0.6 g)

were tested, in selected samples from each soil suite, in an attempt to

increase DNA yield (see Fig 3.2). A soil weight of 0.4 g gave a consistent and

good quality yield of DNA.

Figure 3.2: Optimising the effect of soil weight on DNA yield. Three weights tested: 0.2 g, 0.4 g, 0.6 g. 1 = Pallarenda soil (A) control (C) 1 (0.2g), 2 = AC 2 (0.4g), 3 = AC 3

(0.6g), 4 = Wambiana soil (B) incised (IN) 1 (0.2g), 5 = BIN 2 (0.4g), 6 = BIN 3 (0.6g), L = 200 bp ladder.

The extraction protocol used 0.4 g of soil added to a solution (C1) that

dispersed the soil particles, began dissolving humic acids and broke down the

lipids that are associated with cell membranes. Mechanical lysis of the

microbial cells was performed with a bead beater used for 2 minutes at

2500 rpm. The resulting supernatant was separated from the pellet of cell

debris, soil, beads and humic acids. Consecutive solutions (C2) that

1 2 3 4 5 L L 6

10000 bp

1500 bp

42

precipitated non-DNA organic and inorganic material including humic acid,

cell debris and proteins, (C3) precipitated additional humic acids and other

DNA inhibitors, and (C4) bound the DNA to the silica membrane were added to

the supernatant. An ethanol-based wash solution (C5) was used to further

clean the DNA bound to the silica filter by removing salt, humic acid and

other contaminants. A final sterile elution buffer (C6) released the DNA from

the membrane and eluted it into the tube ready for further application. All

centrifugation steps were carried out at 10,000 x g. The DNA from all samples

was successfully extracted. The DNA was stored between -20ºC to -30ºC until

ready to use.

3.4.2.2 DNA Quantification

The success of each extraction was determined by visualisation on a 2%

agarose gel stained with ethidium bromide. The gels were run with the

molecular weight marker Hyperladder I (Bioline, NSW, Australia), a

quantitative DNA marker ranging from 200 to 10,000 bp.

3.4.2.3 Polymerase Chain Reaction

PCR reagents were defrosted on ice prior to use and reactions were put

together in a laminar flow cabinet using filter tips and pre-labelled tubes.

Positive (DNA) and negative (reagent) controls were included in every PCR

reaction suite. Separate PCR reactions were performed to amplify the

bacterial and fungal communities in the soil. A fluorescent-labelled forward

primer FAM63F (5‟-CAG GCC TAA CAC ATG CAA GTC-3‟) (Marchesi et al.,

1998) and a fluorescent-labelled reverse primer HEX1087R (5‟-CTC GTT GCG

GGA CTT ACC CC-3‟) (Singh et al., 2006), which generates a 1,064-base

product of the 16SrRNA gene, were used to amplify conserved eubacterial 16S

ribosomal RNA gene sequences present in the soil extracts. Bacterial

amplification was conducted in 50 µL reaction volumes (see Table 3.2) that

contained PCR buffer (Bioline, Alexandria, NSW, Australia), MgCl2 (Bioline,

Alexandria, NSW, Australia), dNTPs (Bioline, Alexandria, NSW, Australia),

BIOTAQ™ DNA polymerase (Bioline, Alexandria, NSW, Australia) and primers

(GeneWorks, Hindmarsh, SA, Australia).

43

Table 3.1: Polymerase chain reaction mastermix used for bacterial amplification of soil microbial communities of the Pallarenda and Wambiana soils. Amplification was

conducted in a 50 µL reaction volume.

Reagent Concentration Per reaction (L)

Buffer 10 X 5

MgCl2 50 mM 3

dNTPs 25 mM each 1

Taq 5 U/L 0.25

H2O - 37.75

DNA Template 1:1 1

FAM63F 10 M 1

HEX1087R 10 M 1

Three concentrations of bacterial DNA (1/5 dilution, 1 µL of pure extract and

2 µL of pure extract) were tested for each soil type, in an attempt to optimise

the PCR protocol (see Fig 3.3). A concentration of 1 µL of pure extracted DNA

was used for subsequent PCR reactions.

Figure 3.3: Optimising the DNA concentration used for the polymerase chain reaction protocol. Three concentrations were tested: 1/5 dilution, 1 µL of pure DNA extract and 2 µL of pure DNA extract. 1 = AC1 - Pallarenda soil (A) control (C) 1 (1/5 dilution), 2 = AC2 (1/5 dilution), 3 = AC3 (1/5 dilution), 4 = AC1 (1 µL), 5 = AC2 (1 µL), 6 = AC3 (1 µL), 7 =

AC1 (2 µL), 8 = AC2 (2 µL), 9 = AC3 (2 µL), 10 = Wambiana soil (B) incised (IN) (1/5 dilution), 11 = BIN (1/5 dilution), 12 = BIN (1/5 dilution), 13 = BIN (1 µL), 14 = BIN (1 µL), 15 = BIN (1 µL), 16 = BIN (2 µL), 17 = BIN (2 µL), 18 = BIN (2 µL), P = positive control (E.

coli gDNA), N = negative control (reagent), L = 200 bp ladder.

L 1 2 3 4 5 6 7 8 9 P N L

L 10 11 12 13 14 15 16 17 18 P N L

10000 bp

1500 bp

1/5 1µL 2 µL

1/5 1µL 2 µL

44

The thermal cycling protocol was modified from Osborn et al. (2000). A trial

of 30 cycles produced some smearing, probably due to the amplification of

non-specific products (see Fig 3.4) and so 25 cycles were used (see Fig 3.5). A

two minute denaturation step at 94ºC was followed by 25 cycles of 94ºC for

one minute, 55ºC for one minute and 72ºC for two minutes. A final extension

step was conducted at 72ºC for ten minutes. The amplification of bacterial

DNA was successful for all samples (see Fig 3.5, 3.7). Three replicate PCR

reactions were performed under the same conditions and the products were

pooled.

Figure 3.4: Polymerase chain reaction product of bacterial DNA from Pallarenda soil (soil A) samples using 30 cycles. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2

(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 =

EV4, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder. Duplicate samples are labelled with an asterisk.

L 1 2* 3 4* 5 6 7 8 P N L

9 10 11* 12 13 14 15 16

Control Complete cadaver

Eviscerated

cadaver

Incised cadaver

45

Figure 3.5: Polymerase chain reaction product of bacterial DNA from Pallarenda soil (soil A) samples using 25 cycles. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2

(duplicate), 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 =

EV4, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder.

Figure 3.6: Polymerase chain reaction product of fungal DNA from Pallarenda soil (soil A) samples. 1 = control (C) 1, 2 = C1 (duplicate), 3 = C2, 4 = C2 (duplicate), 5 = complete

cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN2 (duplicate), 12 = IN3, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV3, 16 = EV4, L = 200 bp

ladder.

A fluorescent-labelled forward primer FAM ITS-1F (5‟-CTT GGT CAT TTA GAG

GAA GTAA-3‟) (Gardes and Bruns, 1993) and an unlabelled reverse primer

ITS4R (5‟-TCC TCC GCT TAT TGA TAT GC-3‟) (White et al, 1990) were used to

amplify the highly variable internal transcribed spacer region of fungal

ribosomal RNA gene. Fungal amplification was conducted in 50 µL reaction

volumes (see Table 3.3) that contained PCR buffer (Bioline, Alexandria, NSW,

Australia), MgCl2 (Bioline, Alexandria, NSW, Australia), dNTPs (Bioline,

L 1 2 3 4 5 6 7 8

9 10 11 12 13 14 15 16

P N L

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 L

Control Complete cadaver

Incised cadaver Eviscerated cadaver

46

Alexandria, NSW, Australia), BIOTAQ™ DNA polymerase (Bioline, Alexandria,

NSW, Australia) and 2 L of each primer (GeneWorks, Hindmarsh, SA,

Australia).

Table 3.2: Polymerase chain reaction mastermix used for fungal amplification of soil microbial communities of the Pallarenda and Wambiana soils. Amplification was

conducted in a 50 µL reaction volume.


Buffer 10 X 5

MgCl2 50 mM 3

dNTPs 25 mM each 1

Taq 5 U/L 0.25

H2O - 35.75

DNA Template 1:1 1

ITS4R 10 M 2

ITS-1F (FAM) 10 M 2

The Wambiana soil (soil B) showed a lower concentration of DNA overall in

comparison with the Pallarenda soil (soil A). Therefore, to achieve a higher

concentration of DNA, 30 cycles instead of 25 cycles were used. A 5 minute

denaturation step at 95ºC was followed by 30 cycles of 94ºC for 45 seconds,

55ºC for 45 seconds and 72ºC for one minute. A final extension step was

conducted at 72ºC for 20 minutes. The amplification of fungal DNA was

successful for all samples (see Fig 3.6, 3.8). Three replicate PCR reactions

were performed under the same conditions and the products were pooled.

Figure 3.7: Polymerase chain reaction product of bacterial DNA from Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3, 5 = complete cadaver (CC) 1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 =

eviscerated (EV) 1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (E. coli gDNA), N = negative control (reagent), L = 200 bp ladder.

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P N L

47

Figure 3.8: Polymerase chain reaction product of fungal DNA from Wambiana soil (soil B) samples. 1 = control (C) 1, 2 = C2, 3 = C2 (duplicate), 4 = C3, 5 = complete cadaver (CC)

1, 6 = CC2, 7 = CC3, 8 = CC4, 9 = incised (IN) 1, 10 = IN2, 11 = IN3, 12 = IN4, 13 = eviscerated (EV) 1, 14 = EV2, 15 = EV2 (duplicate), 16 = EV3, P = positive control (C.

albicans DNA), N = negative control (reagent), L = 200 bp ladder.

Both bacterial and fungal DNA was successfully amplified for all samples.

Amplified DNA was imaged and quantified (see Tables 3.4 and 3.5) using

TotalLab software, v. 1.10 (Nonlinear Dynamics, Durham, NC). The amount of

DNA after amplification was variable between the samples and their

replicates.

Table 3.3: Amount of bacterial DNA (ng/L) present in Pallarenda and Wambiana soil samples after polymerase chain reaction amplification. A = Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver,

1A/1B, 2A/2B = duplicate samples.

Pallarenda Samples

DNA amount (ng/L) Wambiana Samples

DNA amount (ng/L)

AC1A 8.3 BC1 11.7

AC1B 11.3 BC2A 8.2

AC2A 10.2 BC2B 3.8

AC2B 7.0 BC3 3.9

ACC1 8.7 BCC1 2.2

ACC2 12.5 BCC2 5.8

ACC3 10.6 BCC3 18.6

ACC4 10.7 BCC4 21.3

AIN1 15.1 BIN1 12.8

AIN2A 20.7 BIN2 70.7

AIN2B 11.0 BIN3 8.2

AIN3 76.7 BIN4 5.8

AEV1 18.2 BEV1 3.4

AEV2 7.5 BEV2A 44.9

AEV3 22.8 BEV2B 75.5

AEV4 50.2 BEV3 13.8

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 P N L

48

Table 3.4: Amount of fungal DNA (ng/L) present in Pallarenda and Wambiana soil samples after polymerase chain reaction amplification. A = Pallarenda soil, B = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver,

1A/1B, 2A/2B = duplicate samples.

Pallarenda Samples

DNA amount (ng/L) Wambiana Samples

DNA amount (ng/L)

AC1A 7.4 BC1 2.2

AC1B 5.9 BC2A 3.8

AC2A 5.7 BC2B 5.2

AC2B 5.9 BC3 3.8

ACC1 20.7 BCC1 1.9

ACC2 25.4 BCC2 4.4

ACC3 4.5 BCC3 95.6

ACC4 15.0 BCC4 110.4

AIN1 22.4 BIN1 82.6

AIN2A 54.6 BIN2 111.6

AIN2B 57.1 BIN3 41.1

AIN3 90.9 BIN4 15.0

AEV1 7.0 BEV1 6.1

AEV2 9.0 BEV2A 108.2

AEV3 57.6 BEV2B 204.0

AEV4 103.1 BEV3 30.8

3.4.2.4 PCR Product Clean-up

All PCR products were purified from primers, nucleotides, polymerases and

salts with the QIAquick PCR Purification Kit (Qiagen) using QIAquick silica-

gel membrane spin columns in a microcentrifuge using the manufacturer‟s

directions (Appendix V).

3.4.2.5 Restriction Enzyme Digestion

The amplified and cleaned bacterial DNA was digested with MspI enzyme (Liu

et al., 1997) (Sigma-Aldrich, St Louis, MO) (Appendix VI) and the fungal DNA

was digested with HhaI enzyme (Osborn, Moore and Timmis, 2000) (Promega,

Madison, WI) (Appendix VII). Digest (without DNA) and enzyme (without

enzyme) blanks were included with every digestion reaction suite. The

reactions were digested at 37ºC for 3 hours followed by 65ºC for 20 minutes.

49

3.4.2.6 T-RF Analysis

The digested products were sent to the Australian Genome Research Facility

in Adelaide, Australia for T-RFLP analysis, where the fluorescently labeled

terminal restriction fragments were separated using the automated

sequencing platform AB3730xl. The output data was imported into Excel for

further statistical analysis. The output of the T-RFLP analysis is generated in

two forms. An electropherogram shows the profile of the microbial

community as a series of coloured peaks of varying heights. The second

output is numerical and consists of a table, which includes the fragment size

measured in base pairs, and the area and height of each peak, measured in

fluorescence units. Peaks generated by fragments less than 50 bp and more

than 500 bp were omitted in order to avoid the T-RFs caused by primer-dimers

and to obtain fragments within the linear range of the internal size standard

(LIZ500). The RiboSort package (Scallan et al., 2008) for the statistical

software R automatically assigned the fragments and their respective peak

heights to appropriate ribotypes. Three separate spreadsheets were produced

containing data from the fungal profiles, the bacterial profiles from the 3‟

fragment end and the bacterial profiles from the 5‟ fragment end. These data

were then inputed into the Primer 6 software package (Clarke and Ainsworth,

1993) for generating MDS plots and ANOSIM calculations.

The selection of T-RF profiles generated has been shown below. Five dyes

have been detected and are shown as different coloured peaks: FAM dye in

blue, HEX dye in green, PET dye in red, the size standard in orange and NED in

yellow. The FAM and HEX dyes indicate the peaks of importance, whereas the

PET, NED and size standard peaks are ignored for the visual comparison of the

profiles. The bacterial profiles from two duplicate controls of the Pallarenda

soil show a high degree of similarity to each other, with a similar number of

peaks and only a slight difference in peak heights (see Fig 3.9). The fungal

profiles of the same duplicate controls of the Pallarenda soils show a slight

difference in the number and height of peaks, but overall show some degree

of similarity (see Fig 3.10). The bacterial T-RF profiles of the complete,

incised and eviscerated cadavers of the Pallarenda soils have been illustrated

for comparison (see Fig 3.11). The complete cadaver samples show more

peaks and higher peak heights than the incised and eviscerated cadaver

samples. The incised and eviscerated profiles seem more similar to each

50

other than the cadaver samples in terms of low peak number and height. The

fungal profiles of the same samples (see Fig 3.12) are very different to the

bacterial profiles, with a higher number of peaks and peak heights overall.

Once again the incised and eviscerated cadaver samples tend to be more

similar to each other than when compared to the complete cadaver.

However, the incised cadaver sample seems to have the most number of

peaks and higher peak heights overall. A dominant peak of approximately 600

bp in size is present in the complete cadaver sample, but disappears in the

incised and eviscerated cadaver samples. However, this fragment is outside

the parameters for T-RF analysis. The bacterial T-RF profiles of the

complete, incised and eviscerated cadavers of the Wambiana soils have been

shown for reference (see Fig 3.13). These profiles look very different to those

of the Pallarenda soils as they seem to have a higher number of peaks with

higher peak heights. The profiles also differ in number of peaks, fragment

lengths and the heights of the peaks. A significant observation is an increase

in the number of HEX-labelled fragments for the incised cadaver sample

compared to the rest of the samples. The fungal profiles of the same samples

have been included (see Fig 3.14). The profiles are once again different to

each other in terms of peak numbers, fragment sizes and peak heights. In

general, FAM-labelled fragments have a higher frequency compared to the

HEX-labelled fragments.

51

Figure 3.9: Bacterial terminal restriction fragment (T-RF) profile of control sample 1 (top) and 2 (bottom) of Pallarenda soil. The blue peaks represent the FAM-labelled T-RFs from the 5’ end, the green peaks are the HEX-labelled T-RFs from 3’ end, and the orange

peaks represent the LIZ500 size standard.

Figure 3.10: Fungal terminal restriction fragment profile of control sample 1 (top) and 2 bottom of Pallarenda soil. FAM = blue, LIZ500 size standard = orange.

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

52

Figure 3.11: Bacterial terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Pallarenda soil.

FAM-labelled 5’ end = blue, HEX-labelled 3’ end = green, LIZ500 size standard = orange.

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

53

Figure 3.12: Fungal terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Pallarenda soil. FAM

= blue, LIZ500 size standard = orange.

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

54

Figure 3.13: Bacterial terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Wambiana soil. FAM-

labelled 5’ end = blue, HEX-labelled 3’ end = green, LIZ500 size standard = orange.

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

55

Figure 3.14: Fungal terminal restriction fragment profile of complete cadaver sample 1 (top), incised cadaver 1 (mid) and eviscerated cadaver 1 (bottom) of Wambiana soil. FAM

= blue, LIZ500 size standard = orange.

Rela

tive f

luore

scence u

nit

s (r

fu)

Fragment size (bp)

56

3.5 Data Handling and Statistical Analysis

The two-way crossed analysis of similarities produces a „significance level of

sample statistic‟. For ease of interpretation of the data in this chapter this

statistic will be converted to the p-value using the following formula:

Significance level of sample statistic/100 = p-value

The significance levels will be interpreted as defined in Table 3.5.

Table 3.5: Definition of significance levels using p-values.

p-value Significance Level

<0.01 Highly significant

<0.05 Significant

<0.1 Marginally significant

>0.1 Not significant

3.5.1 PLFA Datasets

The PLFA peak area data was used to construct a similarity matrix using the

Bray-Curtis coefficient and generate Multi Dimensional Scaling (MDS) plots.

The MDS plot (see Fig 3.15) shows a general separation of phospholipid fatty

acid profiles between soil microbial communities of the Pallarenda and

Wambiana soils, with overlap of a small number Pallarenda with Wambiana

soil profiles at 60% similarity. The profiles that share at least 60% similarity

are circled in green. A two-way crossed analysis of similarities (ANOSIM)

conducted on the differences between the two soil types and across the four

treatment groups resulted in a p-value of 0.0006. This indicates a highly

significant difference between the microbial communities of the two soils.

57


SoilPR

WB

Similarity60

2D Stress: 0.02

Figure 3.15: Multi-dimensional scaling plot of phospholipid fatty acid profiles of soil

microbial communities of Pallarenda (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) and Wambiana (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) soils containing control and treatments soils. Profiles that share at least 60% similarity are circled in green. PR =

Pallarenda, WB = Wambiana.

A two-way crossed analysis testing for differences between the treatment

groups across both soil types resulted in a p-value of 0.018, which indicates a

significant difference between the microbial communities between all the

treatment groups. A further pairwise test (see Table 3.6) was carried out and

resulted in the following statistics:

Table 3.6: Significance results of pairwise test conducted on phospholipid fatty acid profiles between all treatment groups and across both soil types.

Pairwise Test p-value Significance

Control, Complete 0.093 Marginally significant

Control , Incised 0.066 Marginally Significant

Control, Eviscerated 0.093 Marginally significant

Complete, Incised 0.019 Significant

Complete, Eviscerated 0.322 Not significant

Incised, Eviscerated 0.451 Not significant

58

3.5.2 T-RFLP Datasets

The bacterial dataset was separated into the two sets of terminal restriction

fragments (T-RFs) of the digested product, that is, one from the 5‟ end

labelled with FAM and the other from the 3‟ end labelled with HEX. A

similarity matrix was constructed using the Bray-Curtis coefficient. The data

was transformed using the square root function for the 3‟ end fragment data

and fourth root function for the 5‟ end fragment data.

An MDS plot has compared the 3‟ fragments labelled with HEX (see Fig 3.16),

of both soils. Based on this plot, soil A and soil B have formed separate

clusters from each other with no overlap. This suggests the soil bacterial

community of the Pallarenda soil is considerably different to the Wambiana

soil. Carter Bacterial (HEX) T-RFLP AbundancesTransform: Square root

Resemblance: S17 Bray Curtis similarity

soilA

B

BH1

BH3

BH5

BH6

BH7

BH8

BH9

BH10

BH12

BH13BH14

BH15

BH16BH17

BH18

BH20

BH21

BH22

BH23

BH24

BH25

BH26

BH27

BH28

BH29

BH30

BH32

2D Stress: 0.16

Figure 3.16: Multi-dimensional scaling plot comparing the bacterial 3' end terminal restriction fragment abundances, labelled with the fluorescent dye HEX, for both soils. Soil A=Pallarenda, soil B=Wambiana. BH1, 3, 17, 18, 20 = control, BH5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, BH9, 10, 12, 25, 26, 2, 28 = incised cadaver, BH13, 14, 15,

16, 29, 30, 32 = eviscerated cadaver.

59

A two-way crossed ANOSIM was conducted on the T-RF data from the 3‟ end to

test for differences between the two soil types and across all treatment

groups. This resulted in a p-value of 0.0001 which indicates a highly

significant difference between the soil bacterial communities of the two soils.


test for differences between the treatment groups and across both soil types.

This resulted in a p-value of 0.009 and indicates a highly significant difference

between the soil bacterial communities of the treatment groups. A further

pairwise test (see Table 3.7) revealed the following statistics:

Table 3.7: Significance results of pairwise test conducted on 3’ end of bacterial terminal restriction fragments between all treatment groups and across both soil types.


Control, Complete 0.048 Significant

Control , Incised 0.003 Highly Significant

Control, Eviscerated 0.207 Not Significant

Complete, Incised 0.081 Marginally Significant


Incised, Eviscerated 0.082 Marginally Significant

The second set of T-RFs from the 5‟ end, labelled with the fluorescent dye

FAM was compared between the soil types. There is a distinct separation of

bacterial communities based on these fragments between the Pallarenda and

Wambiana soil types (see Fig 3.17).

60

Carter Bacterial (FAM) T-RFLP AbundancesTransform: Fourth root


SoilA

B

C

C

CC

CC

CC

CC

IC

IC

IC

EC

EC

EC

EC

C

C

C

CC

CC

CCCC

ICIC

ICIC

EC

EC

EC

2D Stress: 0.15

Figure 3.17: Multi-dimensional scaling plot comparing the bacterial 5' end terminal restriction fragment abundances, labelled with the fluorescent dye FAM, for both soils. Soil A=Pallarenda, soil B=Wambiana. C = control, CC = complete cadaver, IC = incised

cadaver, EC = eviscerated cadaver.


test for differences between the two soil types and across all treatment

groups. This resulted in a p-value of 0.0001 which indicates a highly

significant difference between the soil bacterial communities of the

Pallarenda and Wambiana soils. A second two-way crossed ANOSIM was

conducted on the T-RF data from the 5‟ end to test for differences between

the treatment groups and across both soil types. This resulted in a p-value of

0.013 and indicates a significant difference between the soil bacterial

communities of the treatment groups. A pairwise test (see Table 3.8) shows

the following statistics:

Table 3.8: Significance results of pairwise test conducted on 5’ end of bacterial terminal restriction fragments between all treatment groups and across both soil types.


Control, Complete 0.004 Highly Significant


Control, Eviscerated 0.053 Marginally Significant

61

Complete, Incised 0.04 Significant



The fungal T-RF data was transformed using the log(X+1) function. A MDS has

compared the fungal T-RF profiles for the Pallarenda and Wambiana soil

treatments (see Fig 3.18). It showed a separation of Pallarenda samples from

the Wambiana samples. There is almost no overlap seen between the two soil

sample suites and this suggests that the fungal populations are different

between the Pallarenda and Wambiana soils. Carter Fungal T-RFLP AbundancesTransform: Log(X+1)


SoilA

B

F1

F3

F5F6

F7

F8

F9F10

F12F13

F14

F15

F16

F17

F18

F20

F21

F22

F23F24

F25

F26F27

F28

F29

F30

F32

2D Stress: 0.16

Figure 3.18: Multi-dimensional scaling plot comparing the fungal restriction fragment abundances for Pallarenda and Wambiana soils. Soil A=Pallarenda, soil B=Wambiana. F1, 3, 17, 18, 20 = control, F5, 6, 7, 8, 21, 22, 23, 24 = complete cadaver, F9, 10, 12, 25,

26, 27, 28 = incised cadaver, F13, 14, 15, 16, 29, 30, 32 = eviscerated cadaver.

A two-way crossed ANOSIM was conducted on the fungal T-RF data to test for

differences between the two soil types and across all treatment groups. This

resulted in a p-value of 0.0001 which indicates a highly significant difference

between the soil fungal communities of the Pallarenda and Wambiana soils. A

62

second two-way crossed ANOSIM was conducted on the fungal T-RF data to

test for differences between the treatment groups and across both soil types.

This resulted in a p-value of 0.0001 and indicates a highly significant

difference between the soil bacterial communities of the treatment groups. A

pairwise test (see Table 3.9) shows the following statistics:

Table 3.9: Significance results of pairwise test conducted on fungal terminal restriction

fragments between all treatment groups and across both soil types.


Control, Complete 0.002 Highly Significant


Control, Eviscerated 0.007 Highly Significant

Complete, Incised 0.003 Highly Significant

Complete, Eviscerated 0.005 Highly Significant


A MDS was used to compare the fungal T-RFs between all treatment groups of

both soil types (see Fig 3.19). It showed a separation of the control samples

away from the three cadaver treatments. The complete cadaver samples

separated away from the incised and eviscerated cadaver treatments. The

incised and eviscerated samples mostly separated out with some overlap seen

between them. This suggests that the fungal populations are different

between the control soils and the soils that contained the cadaver. It also

shows that the fungal population of the complete cadaver is different to the

two other cadaver treatments.

63

Carter Fungal T-RFLP AbundancesTransform: Log(X+1)


TreatmentC

CC

IC

EC

3D Stress: 0.11

Figure 3.19: Multi-dimensional scaling plot of fungal terminal restriction fragments of the

Pallarenda and Wambiana soil containing C = control soils (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), CC = complete cadaver

(


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), IC = incised cadaver (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

), and EC = eviscerated cadaver (


TreatmentCont

Comp

Incis

Evis

3D Stress: 0.01

) samples.

A summary of the significance results produced cross the four methods used is

shown in the table below (see Table 3.10). It showed that the T-RFLP method

using the fungal population of the soil community results in the greater

proportion of significant results, followed by bacterial T-RFLP using FAM

fragments, HEX fragments and PLFA.

Table 3.10: Summary of the significance results of pairwise tests conducted between all treatment groups, across both soil types and over all methods. HS = highly significant, S =

significant, MS = marginally significant, NS = not significant

Pairwise

tests of

treatments

METHODS

PLFA Bacterial T-RFLP

(HEX fragments)

Bacterial T-RFLP

(FAM fragments) Fungal T-RFLP

Control,

Complete MS S HS HS

Control,

Incised MS HS HS HS

Control,

Eviscerated MS NS MS HS

Complete,

Incised S MS S HS

64

Complete,

Eviscerated NS NS NS HS

Incised,

Eviscerated NS MS NS NS

3.6 Discussion

A soil microbial community responds much faster to disturbances and

perturbations in the soil than physiochemical attributes such as pH and

organic carbon measures (Atlas, 1984). Therefore, the dynamics of a soil

microbial community can closely mirror ecosystem dynamics associated with

environmental change. The current study investigates the effects of

decomposition with and without the internal cadaver microflora on the soil

bacterial and fungal communities in a sandy (Pallarenda) and a clayey

(Wambiana) soil.

3.6.1 PLFA Results

In the current research, PLFA analysis was not used to identify functional or

taxonomic groups of microbes, nor provide a measurement of diversity. The

primary aim of this research was to determine if the presence of a

decomposing rat cadaver in soil would change the dynamics of the soil‟s

indigenous microbial community. The PLFA analysis successfully provided

profiles of the soil microbial communities, which allowed comparisons to be

made between the two soils types and the four cadaver treatments within

each soil type. Quantitative assessment of a subset of common PLFAs was

used to detect differences in the overall community structure of the soil

samples studied. Therefore, the differences discussed here relate to

microbial structural change rather than diversity.

There were highly significant differences (p = 0.0006) between the PLFA

abundances of soil microbial communities between the Pallarenda and

Wambiana soils. The differences in the soil microbial communities were more

significant between the two soil types and across the treatment groups than

between the treatment groups and across the two soil types using the PLFA

and bacterial T-RFLP methods. Physical and chemical properties of soil

65

contribute strongly to the heterogeneity of microbial communities in soil

(Liesack et al., 1997). The Pallarenda soil was classified as a Rudosol with a

sand texture, which can be described as a well-drained moist seasonal

savanna soil with a pH of 5.0. The Wambiana soil was classified as a Grey

Vertosol with a medium clay texture (>30% clay) with a pH of 6.1 (Carter,

2005). It is known that clays tend to retain water whereas sand allows rapid

water drainage and in situations where water content is low, microbial life is

reduced (Stotzky, 1997).

The slight scatter in the replicated data of the control samples could be

attributed to soil heterogeneity, due to localised variations in the soil

environment that influence microbial populations (Coleman, Crossley and

Hendrix, 2004). Microbial biomass was calculated, using substrate-induced

respiration, for the soils in the dry season (when they were collected) in the

original experiment. It was calculated at 839 g g-1 soil for the Pallarenda

soil and 766 g g-1 soil for the Wambiana soil (Carter, 2005). It is unknown if

this difference in microbial biomass contributed to the variation seen in the

PLFA abundances of the Wambiana cadaver treatments.

3.6.2 T-RFLP Profiling Results

The bacterial and fungal T-RFLP analysis successfully produced profiles that

could be compared between the two soil types and their respective cadaver

treatments. These profiles were analysed based on the number of fragments

produced in each sample due to their differences in fragment lengths and not

their abundance indicated by peak height. Therefore, these profiles could be

used to compare microbial diversity.

3.6.2.1 Controls

The Pallarenda soils were more diverse and have a larger microbial population

than the Wambiana soils which coincides with the higher Pallarenda soil

biomass estimates (Carter, 2005). A greater cumulative CO2-C, an indicator of

microbial activity, was observed in the cadaver samples when compared with

the control samples (Carter, 2005). This signified a larger microbial biomass

in the cadaver samples than the control samples. This was reflected in the

66

cadaver profiles by a greater diversity and higher abundance overall. The

bacterial profiles of the Pallarenda replicate control soils showed a high

similarity in terms of the number of FAM- and HEX-labelled fragments and

their abundances. However, the replicate control profiles of the Wambiana

soils were very different to each other, with many differences in species type,

number and abundance. The fungal profiles showed an overall similarity

between replicate control samples for the Pallarenda soil, however there

were slight differences in terms of the appearance and disappearance of

species and species abundance. The differences between replicates of the

control Wambiana soils were considerable. This may indicate that the soil

sample was not representative of the total fungal population or exhibit the

effects of spatial variation (Prosser, 2002) and soil heterogeneity (Coleman,

Crossley and Hendrix, 2004). Other factors that may contribute to variations

of soil microbial communities seen in control or replicate soil samples are

floristic and soil physicochemical composition (Kennedy et al., 2005), meio- or

macrofaunal abundance at the centimetre scale, topography at the metre

scale and temporal variation in terms of temperature and seasons (Scala and

Kerkhof, 2000).

3.6.2.2 Bacterial T-RFLP Profiling Results

PCR-induced artefacts resulting from PCR errors and PCR bias can contribute

to an inaccurate estimation of microbial diversity. PCR errors occur due to

the formation of chimeric molecules, heteroduplex molecules and Taq DNA

polymerase error (Acinas et al., 2005). PCR bias occurs due to intrinsic

differences in the amplification efficiency of templates or to the inhibition of

amplification by the self-annealing of the most abundant templates (Acinas et

al., 2005). To minimise these artefacts, it is recommended that several

replicate PCR amplifications should be combined and to minimise chimeras

and Taq DNA polymerase errors, the smallest possible number of PCR

amplification cycles should be carried out (Acinas et al., 2005). In view of

this, three replicate PCR reactions that amplified the bacterial and fungal

DNA were carried out and pooled and 30 cycles of PCR amplification were

reduced to 25 cycles.

67

The replicate samples of the cadaver treatment profiles for the Pallarenda

and Wambiana soil exhibited variability. In most of the profiles the FAM-

labelled fragments dominated the HEX-labelled fragments in number and

abundance. The complete cadaver profiles had more diversity overall than

the control and eviscerated soils, and a common dominant peak (111 bp) were

seen in all of its replicates in the Pallarenda soil. The dominant FAM-labelled

peak (111 bp) was seen in three replicate incised cadaver profiles and the

same peak was seen in all replicate eviscerated cadaver profiles of the

Pallarenda soil. It was present in the control soils at much lower abundance

and could represent a bacterial species that could be associated with the

decomposition process. The HEX-labelled fragments seemed to dominate the

FAM-labelled fragments in number in the Wambiana complete and incised

cadaver treatment profiles. The Wambiana eviscerated samples had two

dominant FAM-labelled fragments (111 and 124 bp) and one dominant HEX-

labelled fragment (421 bp) in all four replicates.

The bacterial analysis utilising the FAM-labelled 3‟ end of the restriction

fragment resulted in a significant difference between the bacterial

community of the Pallarenda and Wambiana soil types, as did the HEX-

labelled 5‟ end of the restriction fragment. However, the separation of the

two clusters was greater in the 5‟ end analysis, as demonstrated by the MDS

plots. It is likely that the greater discrimination observed, results from a

greater number of FAM-labelled T-RFs and is a consequence of the length

heterogeneities at the 5‟ end of the gene, within the V1, V2 and V3 regions

(Osborn, Moore and Timmis, 2000).

The differences in soil bacterial communities between the incised and

eviscerated cadaver soils were mostly not significant. An incision in the

ventral region of the cadaver would have introduced oxygen into the

abdominal cavity and may have disturbed the otherwise anaerobic

environment of the cadaver‟s internal microbiota. This may have led to the

destruction of the cadaver‟s internal microbiota, in much the same way as

evisceration.

68

3.6.2.3 Fungal T-RFLP Profiling Results

Overall, the fungal profiles from the replicate samples were more similar to

each other than the bacterial profiles. The complete cadaver profiles from

the Pallarenda soils exhibited one dominant peak (331 bp) in all four

replicates, the incised profiles had two dominant peaks (318 and 330 bp) in all

the replicates and the eviscerated profiles had one dominant peak (330 bp)

found in all the replicates. The peak at 330 bp was present in the control

profiles but at very low abundances in comparison. This peak could represent

a fungal species that is present in the soil but whose growth is stimulated and

proliferates in the presence of the decomposing cadaver. A dominant peak

(330 bp) was present in high abundance in all four replicates of the complete

cadaver profiles, in three of the incised cadaver replicate profiles and all four

replicates of the eviscerated cadaver profiles. This peak was common to both

soil types and its growth seems to be stimulated by the presence of the

decomposing cadaver.

The fungal T-RFLP analysis resulted in a significant difference between the

fungal community of the Pallarenda and Wambiana soil types, although the

separation seems less distinct than between the soil bacterial communities.

This might be due to the higher diversity seen in the bacterial community.

The fungal analysis showed highly significant differences (p = 0.0001) between

the fungal populations of the Pallarenda and Wambiana soils. It also showed

highly significant differences (p = 0.0001) between the fungal communities of

the cadaver treatments in both soil types. There was a clustering of control

profiles and separation away from the treatment profiles which was the

expected outcome. The separation of the complete cadaver profiles from the

incised and eviscerated profiles confirmed that the cadaver‟s internal

microbiota contribute to the modification of the indigenous soil fungal

population. Some overlap was seen with the incised and eviscerated profiles

which indicated their effects are similar on the soil fungal community.

Furthermore, the fungal T-RFLP method was the only method that detected a

highly significant difference between the complete cadaver and eviscerated

profiles. A significant difference between these profiles would be expected

as the complete cadaver would release the cadaver‟s internal microbiota into

the soil and modify it, whereas the eviscerated cadaver would not.

69

Consequently, the fungal T-RFLP analysis appeared to be more effective at

separating out the different sample suites than the bacterial T-RFLP analysis.

These results may suggest that the T-RFLP method has greater discrimination

than the PLFA method in this experiment.

3.6.3 Other Considerations

It is important to note that the endogenous cadaveric microbial population of

the juvenile rats used in the original experiment, might not have developed

into the complex community associated with adults. As putrefaction is

initiated by the cadaver‟s internal microbiota, this could affect the rate of

decomposition. Additionally, when the products of decomposition are

released into the environment, it can be assumed that this includes a purge of

internal microbiota as well. If this microbiota was not completely developed

in the juvenile rats, this could account for lesser differences seen between

the microbial communities of the control and cadaver soils within the soil

incubation chambers.

The samples were all harvested on day 14 of the decomposition. The cadaver

decomposition period was divided into three stages and day 14 was

categorised as the mid phase of the decomposition. By this stage, a drop in

microbial activity was observed by measuring the CO2-C evolution and enzyme

activity of the eviscerated cadavers in both soils (Carter, 2005) when

compared with the other samples. This reduction of microbial activity seen in

the eviscerated samples was corroborated by the bacterial and fungal T-RF

profiles, by the decrease in the number of peaks observed. This was most

likely due to the fact that the eviscerated samples did not have its internal

microflora to contribute to the soil microbial community diversity.

Furthermore, the removal of the internal organs would reduce the amount of

organic material available to the soil microbial community for growth and

proliferation. The complete cadavers exhibited an overall increased diversity

compared to the other profiles. This was because, by day 14 the soil would

have contained the cadaver‟s internal microflora released through the purging

of decomposition fluids as well as the indigenous soil microbial community.

The incised and sown up cadavers would have allowed a gradual release

70

rather than a burst of decomposition fluids and internal microflora into the

soil. The incision may have also allowed the introduction of oxygen inside the

abdominal cavity, which might have altered the succession of the endogenous

microbes that takes place after death (Vass, 2001).

The biomass estimates increased in complete and incised cadaver samples by

day 14 in the Pallarenda soil (Carter, 2005). However, an increase in biomass

of the eviscerated cadavers only increased after day 14 (Carter, 2005). This

was reiterated, upon observing a low microbial diversity in the T-RF profiles

of the eviscerated samples. This may be explained by the slower rate of

decomposition due to the removal of the internal microflora. Consequently,

there would be a delay in the proliferation of the soil microbial community. A

variety of hydrolytic enzymes and microbes are associated with the organs

that are removed during evisceration. The absence of these and the nutrients

the organs present, may have contributed to the variation seen in the

eviscerated profiles. The greatest biomass increase occurred in the

Wambiana soil. This may reflect the low biomass that the Wambiana soil had

to start with. The soil microbial biomass may play a more important role in

cadaver decomposition in clay soils while enteric microflora may play a more

prominent role in sandy soil (Carter, 2005). This could be due to many

reasons such as dominance of desiccation, removal of viscera as a source of

enteric microbes and as an organic resource for the soil microbial

communities. In general, fungal diversity was lower than bacterial diversity

on visual inspection of the profiles. If fungal activity was predominant in the

later stages of decomposition due to competition with the overwhelming

bacterial population during the early stage of decomposition, this would be

seen later than day 14 of decomposition.

71

Chapter 4 : HUMAN CADAVER EXPERIMENT

4.1 Introduction

Human decomposition is a complex process involving large numbers and great

diversity of species of microbes (Vass, 2001). Previous research (Parkinson,

2004) has investigated the soil bacterial community associated with human

decomposition using bacterial molecular biological techniques. This was

achieved by sampling soil from under two human cadavers that were laid on

the soil surface to decompose at the Forensic Anthropology Center in

Knoxville, Tennessee. The current research has analysed these gravesoils

using lipid-based phospholipid fatty acid analysis and fungal terminal

restriction fragment polymorphism analysis, to characterise the differences in

the soil microbial community as decomposition progresses. The underlying

concept is that as decomposition proceeds, a sequence of microbial

succession will occur in response to the succession of cadaver-derived

nutrients released from the cadaver into the underlying soil. Also

contributing to the dynamics of the soil microbial community, is the cadaver‟s

internal microflora that will be released into the soil when the cadaver fluids

begin to be purged. Decomposition has been defined in this research, using

accumulated degree-days (ADD) (section 4.4.1) instead of the classical stages

that are based on the visual cues of decomposition. If recurring shifts in the

microbial community can be correlated with the ADD measurements, the

microbial succession may present evidence to estimate the post mortem

interval (PMI). Understanding decomposition-associated microbiology may

eventually lead to the development of a new technique to estimate PMI.

4.2 Aims

The aim of this research tests the hypothesis that the presence of a

decomposing human cadaver upon a soil substrate changes the dynamics of

the surrounding indigenous soil microbial community. It investigates if these

changes can be visualised and compared as decomposition proceeds by

utilising the microbial PLFA and fungal T-RFLP methodologies. Furthermore,

72

it evaluates the potential of developing these methods as tools to estimate

PMI using microbial succession.

4.3 Experimental Background

The ethical impositions of using human cadavers in decomposition

experiments have limited the acquisition of data in this unique environment.

The Forensic Anthropology Centre at the University of Tennessee-Knoxville,

Tennessee, USA provides a unique opportunity to investigate the human

decomposition processes in a natural setting. An opportunity to investigate

the objectives of this research was presented when soils from a previous

decomposition experiment conducted by Rachel Parkinson, were made

available. A caveat of carrying out this type of research is that cadavers are

accepted as they are donated and therefore replication of the experiment, in

effect, of the cadavers is impossible. In this instance, the soils from two

cadavers chosen at random out of six cadavers were analysed. The two

cadavers, arbitrarily named P and R were placed at separate sites on the

facility within 20 days of each other (see Table 4.1). These sites had not

previously been used for decomposition studies and were chosen for similarity

based on soil content and vegetation. Respective control sites were selected

a few metres away from the decomposition site and soil samples were

collected at the same time as for the cadaver soil samples. Both cadavers

were in the fresh stage of decomposition and presented with no visual signs of

decay. The cadavers differed in size immensely with cadaver P weighing 159

kg (see Fig 4.1) and cadaver R (see Fig 4.3) only 44 kg. An area, of

approximately 1 m x 60 cm x 10 cm of soil at the sites, was prepared by

removing stones, plant, leaf and root material and loosening and

homogenising by raking and mixing. Each cadaver was placed on a plastic

mesh sheet to allow for the cadaver to be rolled to the side during sample

collection, ensuring minimal damage to the cadaver and minimum disruption

to the decomposition process. Approximately 50 g of soil was collected from

the surface by dragging the lip of a plastic centrifuge tube in an S-shape

pattern. Soil is known to be heterogenous across very small distances,

therefore different soil strata were not sampled over time, and a grid-like

sampling pattern was not used. Insect larvae were removed from samples

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before storing at 4°C. Soil samples were transported at -20°C to ESR in New

Zealand for further analysis.

Table 4.1: Details of two human cadavers used in experiment.

Body P Body R

Official ID UT61-06 UT66-06

Study ID Body P Body R

Date of Birth 1936 13-09-1943

Date of Death 18-08-2006 09-09-2006

Cause of Death Natural Renal Failure

Age 69 62

Sex Male Female

Race Caucasian Caucasian

Height (cm) 184 162.5

Weight (kg) 159 44

Autopsied No No

Placement Date 22-08-2006 11-09-2006

Substrate Soil Soil

Figure 4.1: Cadaver P at ADD 106 (day 3) of decomposition. Sloughing of the skin and some maggots visible. Orange plastic mesh is used to assist in collection of soil samples from

under the cadaver and to preserve its integrity.

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Figure 4.2: Cadaver P at ADD 1092 (day 52) of decomposition. Cadaver is in the skeletonised stage.

Figure 4.3: Cadaver R at ADD 23 (day 0) of decomposition on the day of placement.

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Figure 4.4: Cadaver R at ADD 684 (day 38) of decomposition. Cadaver is in the „bloat‟ stage.

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4.4 Materials/Methods and Results

4.4.1 Accumulated degree-days

The decomposition of a human cadaver is a sequential but continuous

variable. It is generally described in broad, qualitative stages relying on the

gross observations of the decay of soft tissues. These stages are often used to

provide a rough estimate of PMI depending on environmental conditions.

However, for this research, the application of a quantitative approach using

assigned values to express decomposition seemed more reasonable. A greater

number of quantified stages would increase the statistical power of

hypothesis testing and could provide more information about the relationship

between decomposition and the PMI (Megyesi 2005). Ambient temperature

(minimum and maximum) and precipitation data were collected at all four

sites on every day for the duration of the two decompositions. A daily

average was calculated by averaging the maximum and minimum

temperatures for that day. Accumulated degree-days represent heat energy

units needed to drive a biological process such as bacterial or fly larvae

growth (Megyesi 2005). The ADDs were calculated by adding together all the

average daily temperatures from placement of the cadaver until the end of

the experiment (Appendix IX). In order to differentiate samples in this study,

cadaver data points will be labelled with ADDs and control data points will be

labelled with the day of decomposition the samples were collected.


The PLFA analysis of the human cadaver soils follows the method described in

Chapter 3. The extractions were carried out at ESR and transported to UWA

for the subsequent steps of fractionation, derivitisation and GCMS analysis.

PLFA profiles were successfully detected for 18 of 21 control O samples, 19 of

21 cadaver P samples and all 14 Q and R samples. The PLFA peak area data

has been shown for control O and cadaver P (see Appendix XI) and control Q

and cadaver R (see Appendix XII). An internal standard, methylnonadecanate,

represented by peak 35, was used for quantification.

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4.4.3 Fungal Terminal Restriction Fragment Length Polymorphism

4.4.3.1 DNA Extraction

The FastDNA® SPIN kit for Soil (QBiogene, CA) protocol (Martin-Laurent et al.,

2001) was used to extract DNA from the human cadaver soil samples. The

manufacturer‟s protocol was followed with slight modifications to enhance

DNA yield and decrease co-extraction of humic substances (Appendix II). This

kit involves DNA extraction via mechanical lysis of fungal cells, followed by

DNA purification using a silica matrix. The frozen samples were homogenized

by thorough mixing before sub-sampling. A DNAzol (Invitrogen) wash step

was incorporated into the DNA extraction procedure to remove any remaining

contaminants. DNAzol is a guanidine-detergent based lysing reagent

commonly used for selective isolation of genomic DNA from various sample

types. The extraction protocol used 300 mg of soil instead of the 500 mg

recommended as this gave a better yield of DNA. The tubes were processed

in the FastPrep instrument for 90 seconds at a speed of 5.5. The Plant DNAzol

step was added after the first centrifugation step. The FastDNA SPIN kit

protocol is then adhered to through to the final step where the DNA is eluted

into the catch tube and stored at -20°C. All centrifugation steps were carried

out at 14,000 x g (13,200 rpm) unless otherwise indicated.

4.4.3.2 DNA Quantification

The extractions were run on a 2% Seakem® LE agarose (Cambrex, Rockland,

ME, USA) gel stained with Sybr SAFE™ (Invitrogen Molecular Probes) (Appendix

III). The fungal DNA was quantified using the Pico Green Assay (Appendix IV)

and read at a wavelength of 485 nm. Standard solutions were made up and

used to calculate a calibration curve. The sample values were then

correlated to calculate the amount of DNA present in each sample using the

FluroStar Galaxy Fluorescence Instrument and software. The amounts of DNA

were variable and measured in the range of ~2-200 ng.

4.4.3.3 Polymerase Chain Reaction

PCR reagents were defrosted on ice prior to use and reactions were put

together in a laminar flow cabinet using filter tips and pre-labelled tubes.

78

Positive and negative controls were included in every PCR reaction suite. The

analysis of the bacterial component of these samples was carried out as part

of another research project (Parkinson, 2004) and was therefore not

duplicated here.

PCR Optimization

The PCR product was low for most samples and so the concentration of DNA,

concentration of MgCl2 and the annealing temperatures were optimised. The

following concentrations of DNA were tested: 1 µL of neat DNA extract, 1/10

dilution, 1/20 dilution, 1/50 dilution, 1/100 dilution and 1/200 dilution. A

concentration of 1 µL of neat DNA extract gave the best yield for both

cadaver P and R and their control samples. The following volumes of MgCl2

were tested: 0 µL, 0.5 µL, 1 µL, 2 µL, 3 µL, and 4 µL. A volume of 2 µL was

adequate at producing a good quality DNA yield. The following annealing

temperatures were tested using a PCR gradient program on the thermalcycler:

50.0°C, 51.0°C, 51.9°C, 52.9°C, 53.8°C, 54.6°C, 55.4°C, 56.3°C, 57.1°C,

58.1°C, 59.0°C and 60.0°C. An annealing temperature of 57°C produced the

best DNA yield.

Fungal amplification was performed using a fluorescently labelled forward

primer FAM ITS-1F (5‟-CTT GGT CAT TTA GAG GAA GTA A-3‟) (Gardes and

Bruns, 1993) and an unlabelled reverse primer ITS4R (5‟TCC TCC GCT TAT TGA

TAT GC 3‟) (White et al, 1990). A 50 µL reaction volume was used containing

5 ng – 20 ng of extracted DNA, PCR buffer (Qiagen), MgCl2 (Qiagen), dNTPs

(Qiagen), Taq polymerase (Qiagen) and 2 µL of each primer. The

thermalcycling protocol (Osborn, Moore and Timmis, 2000) included a 5

minute denaturation step at 95ºC was followed by 30 cycles of 94ºC for 45

seconds, 57ºC for 45 seconds and 72ºC for 1 minute. A final extension step

was conducted at 72ºC for 20 minutes. If DNA was not amplified using the

original mastermix (see Table 4.2), the concentration was either increased

(more template DNA) or decreased (removal of PCR inhibitors) until DNA was

amplified. If amplification was still unsuccessful, DNA was re-extracted from

the soil samples. Eventually, the amplification of fungal DNA was successful

for all control O samples (see Fig 4.5), and sample O8 was successfully

amplified on the second attempt, none of cadaver P samples, with the

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exceptions of soil samples at ADD 27, 376, 512, 730, 927, 985, 1053, 1092 (see

Figs 4.6, 4.7 and 4.8), all control Q samples (see Fig 4.9), all R cadaver

samples except for sample R15 (see Fig 4.9 and 4.10). The unsuccessful

amplification could be either due to an insufficient amount of DNA being

extracted from the soil, or due to the presence of PCR inhibitors co-extracted

along with the DNA.

Table 4.2: Polymerase chain reaction mastermix used in fungal amplification of soil DNA.

Reagent Concentration Per reaction L (final conc.)

Buffer 10 X 5 (1X)

MgCl2 25 mM 2 (1mM)

dNTPs 10 mM each 1 (0.2 M)

BSA 50 µg/µL 1 (1 µg/µL)

Taq 5 U/L 0.25 (0.025 U/L)

H2O - 35.75

DNA Template 2-20 ng/L 1

ITS4R 10 M 2 (0.4 M)

ITS-1F (FAM) 10 M 2 (0.4 M)

Figure 4.5: Polymerase chain reaction product of fungal amplification from control O soil samples. 1 = Control O (O) sampled on day 0, 2 = O3, 3 = O6, 4 = O8, 5 = O10, 6 = O14, 7 =

O16, 8 = O20, 9 = O23, 10 = O27, 11 O29= , 12 = O31, 13 = O35, 14 = O38, 15 = O42, 16 = O45, 17 = O49, 18 = O52, 19 = O58, 20 = O62, 21 = O69, N = negative control (reagent), P = positive

control (C. albicans DNA), L = 100 bp DNA ladder.

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 N P L

300bp

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Figure 4.7: Polymerase chain reaction product of fungal amplification from re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 185, 2 = P1172, 3 = P1212, 4 = P1285, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.

Figure 4.6: Polymerase chain reaction product of fungal amplification from cadaver P samples. 1 = Cadaver P (P) sampled at ADD 27, 2 = P106, 3 = P238, 4 = P286, 5 = P376, 6 = P420, 7 = P512, 8 = P573, 9 = P660, 10 = P695, 11 = P730, 12 = P808, 13 =

P854, 14 = P927, 15 = P985, 16 = P1053, 17 = P1092, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 N P L

L 1 2 3 4 N P L 300bp

200bp 100bp

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 N P L

100bp

1000bp

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Figure 4.8: Polymerase chain reaction product of fungal amplification from re-extracted cadaver P samples. 1 = Cadaver P (P) sampled at ADD 106, 2 = P238, 3 = P420, 4 = P1212, N = negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.

Figure 4.9: Polymerase chain reaction product of fungal amplification from control Q samples (top) and cadaver R samples (bottom). Top: 1 = Control Q (Q) sampled on day 0, 2 = Q3, 3 = Q7, 4 = Q9, 5 = Q11, 6 = Q15, 7 = Q18, 8 = Q22, 9 = Q25, 10 = Q29, 11 = Q32, 12 =

Q38, 13 = Q42, 14 = Q49. Bottom: 1 = Cadaver R (R) sampled at ADD 23, 2 = R85, 3 = R171, 4 = R207, 5 = R242, 6 = R319, 7 = R366, 8 = R438, 9 = R497, 10 = R564, 11 = R603, 12 = R684, 13 = R724, 14 = R797, N = negative control (reagent), P = positive control (C. albicans DNA), L =

100 bp DNA ladder.

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 L

L 1 2 3 4 5 6 7 8 9 10 11 12 13 14 N P L

L 1 2 3 4 N P L

200bp

100bp

400bp

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Figure 4.10: Polymerase chain reaction product of fungal amplification from re-extracted cadaver R samples. 1 = Cadaver R (R) sampled at ADD 85, 2 = R366, 3 = R497, 4 = R684, N =

negative control (reagent), P = positive control (C. albicans DNA), L = 100 bp DNA ladder.

The amplified DNA was covered with foil to prevent degradation of labelled

primers. Once PCR was completed, the products were spun down briefly and

stored at –20C until ready for further use.

4.4.3.4 PCR Product Clean-up

All PCR products were purified from primers, nucleotides, polymerases and

salts with the QIAquick PCR Purification Kit (Qiagen) using QIAquick silica-

gel membrane spin columns in a microcentrifuge using the manufacturer‟s

directions (Appendix V). To further concentrate the DNA, the elution buffer

was warmed on a 70°C heating block prior to use.

4.4.3.5 Restriction Enzyme Digestion

Restriction enzyme digestion was trialled with Taq polymerase but produced

few or no fragments. The amplified and cleaned fungal DNA was digested

with HhaI enzyme (Promega, Madison, WI, USA) (Appendix VII). Digest

(without DNA) and enzyme (without enzyme) blanks were included with every

digestion reaction suite. The reactions were digested at 37ºC for 3 hours

followed by 65ºC for 20 minutes, to heat-inactivate the restriction enzyme, as

per manufacturers instructions. The fungal digestions were quantified with

the Pico Green Assay to ensure at least 5ng/µL of template DNA was available

for T-RFLP analysis.

L 1 2 3 4 N P L

400bp

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4.4.3.6 Fungal Community Profile Generation

The digested products were sent to the Allan Wilson Centre at Massey

University in Palmerston North, New Zealand for the fungal T-RFLP analysis.

Fragment detection was performed using an automated capillary ABI PRISM

310 Genetic Analyser and analysed using the Genescan 3.1 Analysis Software

(Applied Biosystems, Australia). The Genescan software evaluates the raw

data generated by the Genetic Analyser and resolves the size of the

fluorescent-labelled DNA fragments by comparing them to fragments

contained in a size standard. It also quantifies each fragment by measuring

the amount of fluorescence emitted. Fungal profiles (examples seen in Figs

4.7, 4.11, 4.12, 4.13) were produced using the Genetic Analyser and Genescan

software. As the products were amplified from the ITS region of the fungal

DNA, the resulting fragments will be known as internal transcribed spacer-

terminal restriction fragments (ITS-TRFs). The output was imported into

Microsoft Excel for further statistical analysis.

Fungal ITS-TRF profiles were obtained from 20 of 21 samples from control O,

12 of 21 samples from under cadaver P, all control Q samples (14) and 13 of

14 samples from under cadaver R. Of the successful amplifications, profiles

were not generated for the following samples and so they were removed from

further analysis: control O day 3, cadaver P ADD 573 and cadaver R ADD 319.

Cadaver P gave a progression of profiles from ADD 27 to 1302 and the

decomposition to skeletonisation lasted 70 days. Cadaver R gave a

progression of profiles from ADD 23 to 813 and the last sample was collected

49 days into the decomposition, however complete decomposition was not

reached in this case.

The fungal community under both cadavers changed markedly as

decomposition progressed. Shifts in dominant peaks, the appearance and

disappearance of peaks (see Fig 4.11) and the varying peak areas over time

were observed for both cadavers. Determining the stages of decomposition

based on visual observations of the cadaver can be difficult and therefore ADD

measurements were used to semi-quantify decomposition. The ADD value

could be correlated with approximate times when major changes in the

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nutrient release from the body may have occurred and aid in the

interpretation of the profiles.

Figure 4.11: Soil fungal profiles from control O sampled on days 0, 6, 8 and 10. The grey bars represent the regions (120–170 bp and 320-400 bp) where the dominant peaks occur in the control O profiles. Fluorescence

intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument variation.

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Figure 4.12: Soil fungal profiles from control O sampled on days 14, 16, 20 and 23. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for

intra-instrument variation.

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Fragment size (bp)

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Fragment size (bp)



Fragment size (bp)

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Control O

The profiles of control O are shown in Figures 4.11 to 4.15. Although the

fungal profiles obtained from control O soils varied in species abundance by

observing the relative fluorescence units of the peaks, a number of dominant

peaks are found in almost all of the profiles between the fragment size ranges

of approximately 120 – 170 bp and 320 – 400 bp (see Fig 4.11). Profiles

generated from samples O23 and O29 exhibit very low fluorescence units,

however the smaller peaks still dominate in the aforementioned ranges.

There is an absence of all peaks except for one in the 120 – 170 bp range for

the profile generated from sample O35. The profile from sample O49

illustrates a bigger spread of peaks over the entire profile and the dominant

peaks are not contained within the above ranges.

90

Figure 4.16: Soil fungal profiles from cadaver P sampled at ADD 27, 106, 376 and 512 (days 0, 3, 14 and 20 respectively). Fluorescence intensity is expressed in relative

fluorescence units (RFU) to account for intra-instrument variation.

131

131

131

410

410

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Figure 4.17: Soil fungal profiles from cadaver P sampled at ADD 730, 927, 985 and 1053 (days 31, 42, 45 and 49 respectively). Fluorescence intensity is expressed in relative


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Figure 4.18: Soil fungal profiles from cadaver P at ADD 1092, 1172, 1212 and 1285 (days 52, 58, 62 and 69 respectively). Fluorescence intensity is expressed in relative


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133

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Cadaver P

The profiles for cadaver P are shown in Figures 4.16 to 4.18. Fewer profiles,

than the controls, were generated from samples collected under cadaver P

and for this reason the points where changes in the profiles may have first

occurred are not discernible. There are no common peaks observed in any

profiles over the entire decomposition period of cadaver P. The first sample

collected from under cadaver P at ADD 27 (see Fig 4.16) displayed dominant

peaks in the same regions as seen in the associated control O profile, which is

approximately in the ranges of 120 – 170 bp and 320 – 400 bp, although, there

was a decrease in the diversity and abundance in comparison to the control O

soil. This might be due to the effect of handling and movement of the

cadaver during placement or a diminished aerobic population due to levels of

oxygen being restricted by the overlying cadaver. The profile at ADD 106 has

already changed noticeably, in comparison with the control collected at the

same time (O3) and ADD 27, with dominant peaks appearing between the 300

and 400 bp fragment size region that were not present before. The overall

fungal diversity of the profiles (ie number of peaks) decrease suddenly at ADD

376 and this reduced diversity is maintained through to ADD 927. The fungal

diversity increases again from ADD 985 onwards. The peak at fragment size

410 bp is present in 7 of 12 samples. The peaks at fragment sizes 44, 123,

124, 132, 134, 172, 305, 356, 410 and 414 bp are only present from ADD 1053

to ADD 1212. These peaks are not however present in the control samples at

the associated ADDs. The peak with a fragment size of 415 bp occurs in

profiles at ADD 1092 - 1212. This peak is unique to cadaver P and is not seen

in any other profiles generated for the experiment.

94

Figure 4.19: Soil fungal profiles from control Q sampled on days 0, 3, 7 and 9. The grey bars represent the regions (130–170 bp and 320-370 bp) where the dominant peaks occur in the control Q profiles. Fluorescence

intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument variation.

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Figure 4.20: Soil fungal profiles from control Q sampled on days 11, 15, 18 and 22. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for


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Figure 4.21: Soil fungal profiles from control Q sampled on days 25, 29, 32 and 38. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for


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Control Q

The fungal profiles for control Q soils are shown in Figures 4.19 to 4.22.

Similar to the sample collected on the first day at the control O site, QO

exhibits a higher diversity than on any other control collection day. The

dominant peaks for most of the profiles lie in the fragment size ranges of

approximately 130 – 170 and 320 – 360 bp (see Fig 4.19). The profiles

generated from the sample Q7 assumes a reduced diversity and abundance

compared to the earlier profiles. However, these attributes resume in the

next profile from soil sample Q9. Only one peak within the selected relative

florescence unit parameters is detected for the profiles generated from the

sample Q29. A spread of peaks over the entire profile is seen for the profile

from sample Q32, instead of being dominant in the regions mentioned above.

Very low diversity is observed for the profile from sample Q42, where only

two peaks are present at 102 and 328 bp.

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Figure 4.22: Soil fungal profiles from control Q sampled on days 42 and 49. Fluorescence intensity is expressed in relative fluorescence units (RFU) to account for intra-instrument

variation.

Figure 4.23: Soil fungal profiles from cadaver R at ADD 23 and 85 (days 0 and 3 respectively). The grey bars represent a peak that appears at ADD23 but disappears at

ADD 85.

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298

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Figure 4.24: Soil fungal profiles from cadaver R sampled at ADD 171, 207, 242 and 366 (days 7, 9, 11 and 18 respectively). The grey bars represent a reduction in peak height

of the same peak from ADD 242 to ADD366.

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298

298

298

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Figure 4.25: Soil fungal profiles from cadaver R at ADD 438, 497, 564 and 603 (days 22, 25, 29 and 32 respectively). Fluorescence intensity is expressed in relative fluorescence

units (RFU) to account for intra-instrument variation.

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298

298

298

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Figure 4.26: Soil fungal profiles from cadaver R sampled at ADD 684, 724, and 797 (days 38, 42 and 49 respectively). Fluorescence intensity is expressed in relative fluorescence

units (RFU) to account for intra-instrument variation.

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Cadaver R

The fungal profiles from cadaver R are shown in Figures 4.23 to 4.26. The

sample collected on the first day of decomposition of cadaver R is visually

similar to its associated control sample Q0 in terms of diversity, abundance

and regions where dominant peaks occur. The profile at ADD 85 of

decomposition shows some reduction in diversity with the disappearance of

some peaks in the region of 130 – 180 bp and 340 – 360 bp. In the next four

profiles (ADD 171 - 319), peaks appear and disappear, however, the dominant

peaks mainly occur in the fragment size regions of approximately 130 – 180

and 320 – 360 bp. For the following ADDs: 438, 603, 684 and 724, almost all

the peaks in the fragment size region of 130 – 180 bp have disappeared,

except for the peak of fragment size 174 bp which occurs in differing

abundances. The peak of fragment size 298 bp occurs in all R profiles but

only in Q0 and Q22. The peak at 328 bp occurs in all R profiles except for ADD

724 and this peak is shared in most of the control profiles as well. The peaks

that some cadaver R profiles have in common with some cadaver P profiles

are at fragment sizes 131, 356 and 410 bp.

4.4.3.7 Fungal ITS-TRF Detection

Only fragments within a size range of 30-500 bp were included in the analysis

because this is the range in which fragments can be accurately sized using the

size calling method available with the Genescan software. Additionally, peaks

outside these parameters were omitted in order to avoid the T-RFs caused by

primer-dimers. The minimum fluorescence unit or peak height cut-off was

arbitrarily set at 50 relative fluorescence units (rfu) to eliminate noise

interference. The RiboSort package for the statistical software R

automatically assigned the fragments and their respective peak heights to

appropriate ribotypes (Scallan et al., 2008). Two separate spreadsheets were

produced containing data from the fungal profiles of O and P samples and Q

and R samples. This data was then used by the Primer 6 package to generate

multi-dimensional scaling (MDS) plots and ANOSIM calculations (Clarke and

Ainsworth, 1993).

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4.5 Data Handling and Statistical Analysis

4.5.1 PLFA Dataset

An MDS plot shows the PLFA profiles obtained from control O and cadaver P

samples (see Fig 4.27). A clear separation is seen between the soil microbial

communities from the control O and cadaver P samples. Upon closer

inspection, the profile of the first day of sampling cadaver P (ADD 27) rests

within the cluster of control O samples and within close proximity to the

control sample collected at the same time. A greater spread of soil microbial

community profiles is seen with the cadaver P samples, relative to the control

O samples. Control O and cadaver P samples that are at least 80% similar with

respect to their phospholipid fatty acids have been separately circled. All the

control O samples were at least 80% similar to each other. Cadaver P samples

at ADD 573, ADD 695 and ADD 1212 were less than 80% similar to the other

cadaver P samples.

An ANOSIM test was conducted on the PLFA profiles of the control O and

cadaver P samples. A one-way analysis resulted in a significance level statistic

of 0.1% or a p-value of 0.001, confirming the significant difference between

the PLFA profiles of control O and cadaver P samples at the 5% level.

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Figure 4.27: Multi-dimensional scaling plot of phospholipid fatty acid profiles for cadaver P (●) and control O (▪). Accumulated degree-days denote the stage of decomposition when the sample was collected. 0 = day of placement/first day of sampling. The boundaries of the ellipses are defined by the samples within having profiles at least 80% similar to each other.

An MDS plot showing the PLFA profiles obtained from control Q and cadaver R

can be seen in Fig 4.28. There is tight clustering of the control Q samples

additionally magnified outside the main plot. The PLFA profile from the first

day of sampling cadaver R (ADD 23) rests within the control Q cluster in close

proximity to the control sample of the same time. The cadaver samples

collected at ADD 85 and ADD 242 clustered with the control samples but

plotted further away from them. A cluster of seven cadaver R samples

grouped together, while the two cadaver R samples that plotted further away

led to a larger spread than the control samples.

An ANOSIM test was conducted on the PLFA profiles of the control Q and

cadaver R samples. A one-way analysis resulted in a significant level statistic

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of 0.1% or a p-value of 0.001, emphasizing very distinct soil microbial

communities at the 5% level.

Figure 4.28: Multi-dimensional scaling plot of phospholipid fatty acid profiles for cadaver R (●) and control Q (▪). Accumulated degree-days denote the stage of decomposition when the sample was collected. 0 = day of placement/first day of sampling. The boundaries of the

ellipses with dotted lines are defined by the samples within having profiles at least 65% similar to each other. The boundaries of the ellipses with smooth lines are defined by the

samples within having profiles at least 85% similar to each other.

65

85

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Figure 4.29: Abundance of fungal marker from phospholipid fatty acid analysis for control

O and cadaver P. A temporal profile of the fungal marker, C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0 = day of placement/first day of sampling.

Figure 4.30: Abundance of fungal marker from phospholipid fatty acid analysis for control

Q and cadaver R. A temporal profile of the fungal marker, C18:26 (peak 27) is calculated as a percentage of total phospholipids. 0 = day of placement/first day of sampling.

A known fungal marker, C18:26 (Stahl and Klug, 1996) (peak 27 in

Appendices XI and XII) was calculated as a percentage of total phospholipids

in order to assess the growth dynamics of fungi for both cadaver (P and R) and

control samples (O and Q; see Figs 4.29 and 4.30). The temporal profile of

the fungal marker showed no obvious trend with either control or cadaver

samples, although the cadavers do suggest some stimulation, albeit quite

sporadic.

Accumulated degree-days

Accumulated degree-days

Abundance (

%)

Abundance (

%)

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4.5.2 Fungal Community Dataset

An MDS plot visually compares the fungal T-RF profiles obtained from control

O and cadaver P (see Fig 4.31). It shows a separation of profiles detected

from the cadaver and the control samples over the decomposition/sampling

process. A natural variation in the control O samples over time is seen, but

they cluster a lot more closely than the cadaver P samples. This indicates

control O samples are more similar to each other than cadaver P samples are

to each other.

Figure 4.31: Multi-dimensional scaling plot of fungal internal transcribed spacer-terminal

restriction fragment profiles for cadaver P ( ) and control O (●). Accumulated degree-days are used to denote the stage of decomposition when the sample was collected. 0 = day

of placement/first day of sampling. The cadaver samples have been circled to show their separation from the control samples.

A snapshot has been taken of the cadaver P samples in isolation, to depict the

separation of the profiles in another view (see Fig 4.32). It shows a possible

grouping of fungal species in relation to time elapsed during decomposition,

where an arrow has been drawn to indicate a potential temporal trend. The

species of fungi that may be colonising in the early phase (ADD 27 (0), 106,

376 and 512) of decomposition are different to the species that may be

colonising in the mid (730, 927 and 985) and late phase of decomposition (ADD

1053 to 1285).

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Figure 4.32: Multi-dimensional scaling plot of fungal internal transcribed spacer-terminal restriction fragment profiles for cadaver P. Accumulated degree-days denote the stage of decomposition when the sample was collected. Grey line separates early, mid and late phase

fungi. 0 = day of placement. The black arrow shows potential temporal trend.

An MDS plot depicts the fungal T-RFLP profiles obtained from control Q and

cadaver R samples (see Fig 4.33). The majority of control Q samples cluster

together, but four of the controls (ADD 171, 319, 497 and 724) plot further

away from this cluster. The T-RFLP profile sampled from cadaver R on the

first day of decomposition (ADD 0) lies in close proximity to the control Q

sample collected at the same time. An arrow has been drawn to indicate a

potential temporal trend from early (ADD 0, 85,171 and 207) to mid (ADD 366,

438, 497, 603) to late phase fungi (ADD 564, 684, 724, and 797).

Mid

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Figure 4.33: Multi-dimensional scaling plot of internal transcribed spacer-terminal restriction fragment profiles for cadaver R and control Q. Accumulated degree-days

denote the stage of decomposition when the sample was collected. The black arrow shows possible fungal pattern of succession. 0 = day of placement/first day of sampling. The cadaver samples have been circled to show their separation from the control samples.

4.6 Discussion

4.6.1 Method Comparison

The total PLFA distribution reflects the whole soil microbial community and

identifies trends on a large scale, whereas fungal T-RFLP analysis evaluates

solely the fungal component of the soil microbial community. The PLFA

profile comprises of 37 main peaks, which are used to generate a fingerprint

of the soil microbial community for comparative purposes. PLFAs are

biomarkers of bacteria and fungi, but with few exceptions (eg, C18:26)

cannot be used to indicate a single species (Zelles, 1999). The identity of

many PLFA compounds can be identified on the basis of their GC or MS data

(White et al., 1979). However, many of the peaks of this correlation study

are not unequivocally identified. Nevertheless, quantitative differences in

the abundances of the detected PLFAs are used to detect structural changes

in the soil microbial community. While the PLFA method is time-consuming

and not automated except for the GC/MS step, more samples are successfully

profiled than T-RFLP which carries a significantly higher financial expense.

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After fungal T-RFLP parameters are set to exclude instrument noise and

amplification irregularities, all peaks are assumed to be representative of the

total fungal community. T-RFLP data therefore gives a quantitative (in terms

of relative signal intensities) and qualitative (presence or absence of distinct

populations) interpretation of the data.

Although most of the method is automated, time is spent on optimising PCR

reactions and trouble-shooting challenging samples. The PCR reactions, which

failed, are mainly cadaver samples. A cadaver is a complex biological sample,

which contains inhibitory substances that may reduce the amplification

efficiency by obstructing the cell lysis step, inactivation of the thermostable

DNA polymerase and or interfering with nucleic acids (Al-Soud and Radstrom,

2000). Some known PCR inhibitors are bile salts, complex polysaccharides in

faeces, haeme in blood and urea in urine (Al-Soud and Radstrom, 1998). In

addition, Taq (thermus aquaticus) DNA polymerase can be degraded by

proteinases (Rossen et al., 1992), denatured by phenol and detergents and

inhibited by blocking of the active site by the inhibitor (Al-Soud and

Radstrom, 1998). Additional PCR inhibitors such as bilirubin, fulvic acids,

humic acids, tannic acids, NaCl, SDS, TritonX-100 and EDTA have also been

indentified (Kreader, 1996). Furthermore, DNA seems more difficult to

extract than lipids, as fewer samples are successfully profiled. A limitation of

this method is that the estimated abundances may not equal true percentages

in the soil samples. This may be due to DNA extraction bias which can alter

the estimated abundances of certain groups, heterogeneity in ribosomal

operon number and the inability of the primers at amplifying rRNA genes

belonging to all members of the population (Fierer et al., 2005). Like all PCR-

based techniques, T-RFLP is subject to PCR biases, such as preferential

amplification of certain templates and template reannealing with increasing

PCR cycle numbers (Lueders, 2003).

4.6.2 PLFA Results

The PLFA profiles demonstrate a significant separation of soil microbial

communities between the cadavers and control soils. As expected, the soil

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microbial community profiles of cadaver and control soils on the first day of

sampling are similar. Greater temporal variation of the PLFA profiles is

evident for cadaver samples. This is a strong indication that the equilibrium

of the microbial communities is being significantly altered by the presence of

the cadaver as decomposition proceeds. The large fat content of cadaver P

may have released a large amount of fatty acids and nutrients into the soil

upon decomposition, which might stimulate the soil microbial communities.

In the case of cadaver R, a slower rate of decomposition, based on the visual

progression of decomposition, is observed. This might explain why the

profiles from the first few sampling days of cadaver R rest within the control

sample cluster. The delayed release of organic products from cadaver R may

have led to little change in the soil microbial community in the early days of

decomposition.

4.6.3 Fungal T-RFLP Profiling Results

4.6.3.1 Controls

The purpose of the control soils is to provide a „baseline‟ of the indigenous

soil fungal populations, so that any changes in the cadaver soil samples can be

distinguished. Visual inspection of the relative profiles showed all were

relatively similar throughout, however the control soils, especially for control

O, showed higher fungal diversity (more peaks) and concentrations (high

fluorescence units) than the cadaver profiles. Natural variation in the control

soil fungal communities over time, was confirmed by the MDS plots which

show a scatter of both control samples. This is likely due to the effects of

temperature change over time where temperatures decreased from 28ºC in

August to 5ºC in October, spatial variability (Prosser, 2002) and soil

heterogeneity (Coleman, Crossley and Hendrix, 2004). However the changes

are small compared to those associated with the decomposing cadavers.

Therefore, it is likely that decomposition is a major cause of the community

changes observed, although environmental effects may also contribute.

4.6.3.2 Cadavers

The soil fungal community profiles obtained from both cadavers clearly

separated from their associated control samples in the MDS plots. The

cadaver profiles demonstrate that changes in the soil fungal population were

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occurring as cadaver decomposition progressed. Most of the samples

generated profiles, however, some were of low quality, with few peaks

present and/or low total fluorescence. The samples collected from under

cadaver P proved to be especially difficult to amplify resulting in a less than

complete set of samples. This may have been due to contaminants, such as

humic acids, that might have been co-extracted along with the DNA, not

enough template DNA extracted and/or impurities remaining in the digested

DNA that can affect the uptake of DNA by the Genetic Analyser (LaMontagne

et al., 2002).

It is evident that the fungal community was always changing with sporadic

appearance of species throughout the period of decomposition. No major

peaks were consistently detected from cadaver P, and only one recurrent

peak (298 bp) was consistently detected in the cadaver R samples, although at

varying abundances. The same major peaks were detected in the first cadaver

P and R samples and most of the associated control profiles. Few

decomposition products would have yet reached the soil microbial

community. However, these initial cadaver samples do show a decrease in

peaks compared to the control samples. This could be due to the physical

presence of the cadaver on the soil and the microhabitat changes associated

with it. When a cadaver is placed upon the soil, it changes the physical soil

environment. Sunlight is blocked from reaching the soil under the body,

which causes vegetation to die. This leads to an increased nutrient source for

the microbial community, as well as a cessation of rhizobial deposits from

root structures, all altering the surrounding microbial communities. These

effects are likely to be small on the soil microbial community, but due to the

delicate balance of the microhabitats in which they live, it might contribute

to some change in the profiles seen very early on in the decomposition.

A rapid response of the soil fungal community to the presence of the cadaver

is observed even before decomposition products are released. The profile at

ADD 106 for cadaver P sees the appearance of dominant peaks between 300

and 400 bp that were not present before and not seen in the control profiles.

The overall fungal diversity of the cadaver P profiles drops suddenly at ADD

376 and may be maintained through to ADD 927. However, due to the missing

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profiles in the middle of the cadaver P decomposition this cannot be said

conclusively. The same reduced diversity is observed for cadaver R at ADD

85, but an increase in diversity is quickly re-established at ADD 171. This

initial reduced diversity is more likely to be in response to changes in the

fungal physical microhabitat, like soil compaction or disruption of rhizobial

interactions due to the presence of the cadaver, than the release of organic

compounds (Rodriguez and Bass, 1985). Organic decomposition products are

first released in the late „fresh‟ and early „bloat‟ stages, when internal

microflora begin to decompose soft tissues and decomposition products begin

to purge from cadaver orifices (Vass, 2001). Therefore, it may be possible to

correlate ADD 376 with the initiation of this release from cadaver P. This

cannot be elucidated for cadaver R due to the short interval of time before

the fungal diversity is increased again. There is a substantial microbial

population explosion in the early stages of decomposition, mainly consisting of

the cadaver‟s intestinal microflora (Haglund and Sorg, 1997). The drop in

fungal diversity could be explained by the overwhelming presence of the

bacterial community due to this flush of the intestinal microbiota into the soil

as well as the response of the soil bacteria to the release of cadaver-derived

nutrients into the soil. The bacterial population may be out-competing the

fungal community during this early period of decomposition. The increase in

fungal diversity of cadaver P from ADD 985 onwards may be explained by

competition, by which the species that most effectively utilises the nutrients

will proliferate and dominate (Coleman, Crossley and Hendrix, 2004). This

seems to be happening to the fungal population in the later stages of

decomposition of cadaver P (ADD 985 - 1212). This increase in fungal diversity

may also be in response to the release of antibiotics by the maggots present

on the cadaver at this stage. As part of their waste products, maggots

produce ammonia (Nigam et al., 2006). Ammonia increases pH creating

alkaline conditions, which are unfavourable for many bacterial species.

Additionally, larvae of Phaenicia sericata carry the commensal Proteus

mirabilis in their midgut (Nigam et al., 2006). These commensals produce

phenylacetic acid and phenylacetaldehyde acid, which are known

antibacterial agents (Nigam et al., 2006). A more likely explanation is that

the maggots ingest the bacteria, which are killed as they pass through the

maggot‟s digestive tract.

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The results from the cadaver P MDS plots have indicated that there may be

certain species of fungi that colonise at different stages (early, mid and late)

of decomposition (see Fig 4.32). This correlates with the observation from

cadaver R MDS plots, of a potential temporal trend of fungal colonisation with

respect to the progression of decomposition. The eight peaks that proliferate

only between ADD 1053 and 1212 for cadaver P could indicate fungi that

colonise the cadavers‟ components like skin and skeletal remains, in the later

stages of decomposition. The isolation of several species of soil fungi found

on the skin and bones of human cadavers is consistent with this finding (Ishii

et al., 2006). The dominant species was identified as Eurotium repens in the

telomorphic and anamorphic stages, amidst Eurotium rubrum, Eurotium

chevalieri and Gliocladium species.

The appearance and disappearance of major and minor peaks in both cadaver

profiles reflect the proliferation of species that most effectively utilise the

cadaver-derived nutrients and the demise of species that are competitively

excluded from the community. Approximately 12 peaks detected in cadaver P

profiles were not present in the control O profiles and 41 peaks detected in

cadaver R profiles were not present in the control Q profiles. This suggests

that these fungal species may be specifically connected to the decomposition

process and stimulated by the presence of the cadavers. Of these new peaks,

peaks at 141, 237, 251 and 296 bp were common to both cadavers. These

peaks could represent fungal species that were cadaver-derived, previously

dormant until the release of cadaver-specific nutrients or introduced to the

gravesoils by other means. One peak at 415 bp (ADD 1092 - 1212) was found

to be unique to cadaver P and may be specific to cadaver P as it was not

detected in any of the other experimental soils. Due to the gaps in profile

succession for cadaver P and the overall slow rate of decomposition for

cadaver R, it is not possible to determine whether community changes

occurred at around the same ADDs for both cadavers. Therefore, it is difficult

to comment on peaks that might indicate a particular ADD or peaks that are

common to both profiles at a particular ADD and then disappear.

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It is apparent from comparing the profiles from cadavers P and R, that

individual cadavers will produce different profiles even when they are at the

same stage of decomposition. Both cadavers were placed on the same type of

soil and within a short distance of each other, so it is obvious that some other

variables influenced the decomposition process. Environmental conditions,

like exposure to differing amounts of sunlight, shade or rain able may

influence a microbial community (Wood, 1995). Another critical variable is

the cadaver itself. The cadaver‟s internal microbiota are a complex

community of several hundred species and sub species, therefore this

combination is unique to an individual (Jawetz, Melnick and Melnick, 1982). It

is likely that the changes seen in the fungal populations as decomposition

progresses, is a contribution of the indigenous soil microbes and the unique

insect and cadaver-derived microflora to the overall population at different

times and at different abundances. Although, this combination might be

unique to an individual, 30 – 40 common species predominate, such as

Clostridium, Bacteroides, Fusobacterium and Bifidobacterium, due to the

significant roles they play in the intestinal microfloral community. It is not

known whether the internal microflora of the cadaver, mostly consisting of

obligate anaerobes, would survive long in the soil environment. However, the

presence of the body and its decomposition fluid on the soil may temporarily

make conditions in the soil anoxic, thus allowing these species to persist for a

while. Conversely, bacteria originating from the skin of the cadaver may have

higher chances of survival in the soil environment. Recently, research showed

that many bacterial species previously known to inhabit the soil also prefer

human skin (Pennisi, 2008). Almost 60% of the human dermal population is

made up of the Gram-negative bacteria Pseudomonas, which flourishes in soil,

water and decomposing organic debris, followed by Janthinobacterium,

another common Gram-negative soil and water bacteria making up 20% of the

population. Of 113 dermal bacteria identified, just 10 species accounted for

90% of the population indicating the volunteers shared a common core set of

dermal microbes. Every human body also differs in biochemical composition,

proportions of body components and body mass. Fat and muscle proportions

differ in males and females, suggesting the gender of the cadaver may

eventually affect the microbial changes during decomposition. An obese

cadaver would release more breakdown products from lipid than a cadaver

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with little body fat. This would expectedly affect the chemical composition

of the soil environment directly below the cadaver and therefore influence

the surrounding microbial community.

It is noted that the rate of decomposition for cadaver R was a lot slower than

that observed for cadaver P. Cadaver R did not reach skeletonisation and at

the collection of the last sample (ADD 797) cadaver R was still in the „bloat‟

stage (see Fig 4.4). It is known that cadaver R was treated with extensive

medication for illness preceding her death. The slow rate of decomposition

may also be accounted for by a decreased population of internal microflora

due to the effects of the medication. Obese cadavers decompose more

rapidly due to the greater amount of liquid in the tissues which favours the

development and dissemination of bacteria (Campobasso, Di Vella and

Introna, 2001). It may also be possible that a greater body mass may bring

about a greater retention of heat thus resulting in a faster rate of

decomposition. Cadaver P was an obese individual with a body mass index

(BMI) of 47.0 and his high fat content would have contributed to the unusually

rapid rate of decomposition observed (see Fig 4.2). In comparison, cadaver R

was underweight (BMI 15.2) and therefore had a significantly lower fat

content. This would result in a slower onset and rate of decomposition. If

the release of cadaver fluids is slow, species utilizing those nutrients within

the soil may proliferate but perhaps not out-compete other members in the

community.

One of the most important variables in decomposition in Tennessee is ambient

temperature. The placement date for cadaver P was 22nd August 2006 and

cadaver R was placed 20 days later on 11th September 2006. This is the

autumn season in Tennessee and temperatures can get quite low.

Temperatures were in the mid twenties (ºC) at the start of cadaver P

decomposition whereas they were significantly colder for the duration of the

decomposition of cadaver R. This may have also contributed to the slow rate

of decomposition observed for cadaver R. Cadaver microflora prefer

temperatures around 37ºC and when exposed to lower temperatures may

result in their metabolic activity being slowed. It has been documented that

soil temperature under a cadaver rises due to maggot masses and other insect

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activity (Mann, Bass and Meadows, 1990). This could underestimate ADD

values and result in an increased rate of decomposition.

Soil pH is known to become more alkaline in the presence of a decomposing

cadaver on the surface of the soil (Rodriguez and Bass, 1985). Soil pH

measurements were consistent with this finding (Rachel Parkinson, personal

communication). The pH values significantly increased in alkalinity between

ADD 27 to 420 for cadaver P, followed by a drop in and eventual stabilising of

pH between ADD 512 to 1285 for cadaver P. Cadaver R exhibited the initial

increase of pH, however the pH did not drop later but fluctuated at this level.

Fungi are known to predominate in acidic soils while bacterial and

actinomycete populations dominate in near-neutral or moderately alkaline

soils (Stotzky, 1997; Thorn, 1997). This coincides with the observations of the

fungal profiles especially for cadaver P, where fungal diversity decreases

when the pH is high.

Sampling of soil beneath cadaver P stopped soon after skeletonisation and

cadaver R did not reach this stage. However, it can be expected that once

there is no longer active nutrient release from the cadaver, the soil microbial

community would begin to revert to its original structure. When the cadaver-

derived nutrients are all utilised and the soil environment becomes

oligotrophic, the zymogenous species would die off and in turn become a

nutrient source (Winogradsky, 1949). Cadaver-derived microflora would be

superseded by soil microbes. However, many compounds released from the

body may take time to degrade and so the microbial community may take a

considerable amount of time to return to their original structures. The same

can be said for the physical and chemical characteristics of the soil. This

persistence of change in the soil microbial community structure might provide

information towards estimating PMI long after skeletonisation has occurred.

Similarly, if a cadaver is moved from the site of decomposition, the soil

microbial community is likely to be affected by the decomposition compounds

already in the soil and this could provide information about decomposition in

the absence of the cadaver.

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It is important to note that the microbial succession data have been gathered

from only two cadavers and therefore it is imprudent to draw universal

conclusions about the soil microbial changes observed as decomposition

progresses. Further research using more cadavers would be required to

establish the processes occurring and the variables that contribute to the

changing soil microbial communities.

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Chapter 5 : CONCLUSION

The decomposition of a cadaver is a continuous process, which breaks down

the cadaver‟s constituents into their basic elements. The destruction of the

tissues releases a variety of cadaver-derived nutrients and microflora into the

immediate environment as decomposition proceeds. When this occurs upon or

within a soil medium, it can create considerable modifications in soil

chemistry and biology. A soil microbial community responds to alterations in

its ecological niche by changes in abundance and diversity. Nucleic acid-

based and other culture-independent technology have revolutionised our

capability to detect these dynamics in the soil microbial community

accurately and rapidly. There has been little research into the microbiology

associated with decomposition and even less on the succession of microbial

community changes in response to cadaver decomposition.

The objective of this thesis was to investigate if changes occurred in the soil

microbial community in response to the decomposition of a cadaver on a soil

substrate. Based on this hypothesis, the specific aims were to investigate the

ability of two methods, PLFA and T-RFLP community profiling, to characterise

these dynamics in the soil microbial community and compare and contrast

these methodologies. Furthermore, a preliminary evaluation of the potential

of using soil microbial communities as a tool to estimate post mortem interval

would be conducted. The thesis preliminarily investigated this concept, by

testing the hypothesis that a cadaver would release different nutrients into

the soil at different stages of the decomposition, promoting a succession

pattern of changes in the soil microbial community. These changes would

then be captured in the successive profiles generated by the two

methodologies.

A high variability was seen between PLFA and T-RFLP profiles generated from

replicate extractions of the same soil sample in the rat cadaver experiment.

This could be explained by the fact that soils contain a complex system of

plants and microbes in a heterogeneous solid medium in which chemical and

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physical conditions fluctuate at the level of the molecule and the cell.

However, it also raises the question of the soil sample size required to

comprise a representative sample. A more thorough method of homogenising

the soil may be critical for analytical reproducibility. The rat cadaver

experiment showed that two different soils, differing in characteristics such

as pH and composition, had an effect on the diversity and abundance of the

soil microbial community. This in turn can affect the progression of

decomposition because the bacterial and fungal communities of the soil play a

significant role in the decomposition of a cadaver. This could mean that a soil

with a greater microbial content could lead to a faster onset and rate of

decomposition. This would contrast the findings of Mant (1950), which

suggested soil type would probably have little effect on cadaver

decomposition. It is known that the cadaver‟s internal microflora plays a

significant role in the decomposition process (Vass, 2001). The present

research has demonstrated that upon the disruption of cadaveric tissues, the

introduction of the cadaver‟s internal microflora into the soil alters the

indigenous soil microbial community notably.

The human cadaver experiment led to some fascinating preliminary data on

the important relationship that exists between soil microbiology and the

decomposition of human cadavers. The profiles generated from both methods

showed significant changes in abundance and diversity of the microbial

populations throughout the duration of decomposition. It demonstrated that

considerable alterations occurred in the soil microbial community with

decomposition. In addition, the experiment has presented partial and

potential evidence for a successive colonisation of certain fungal species

during the early, intermediate and late phases of decomposition. This

indication suggests that there may be a temporal pattern of change in the soil

fungal community in association with cadaver decomposition. It is highly

probable that the dynamics seen in the soil fungal communities are in

response to competition for the variety of cadaver-derived products released

into the soil at different phases in the decomposition.

There is significant experimental evidence presented in this thesis that soil

microbial communities are strongly influenced by the decomposition of a

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cadaver. Changes in the community continue throughout the progression of

decomposition and alteration of the community may continue even after

skeletonisation is reached. The implication of these results demonstrates the

potential of profiling soil microbial communities as a tool for post mortem

interval (PMI) estimation. A useful technique will detect changes at genera,

species and sub-species level. The profiling of the soil microbial communities

using the PLFA and T-RFLP methods holds immense potential to be developed

into a robust and reproducible technique that can be used for PMI estimation.

The PLFA results from the rat and human cadaver experiments demonstrated

decomposition of the cadaver led to significant changes in the soil microbial

communities. The PLFA method was successful in detecting and characterizing

changes in the microbial communities between: soils exposed to rat cadavers

and control soils, rat cadaver treatments, and soils adjacent to decomposing

cadavers and control soils. Phospholipids are important biomarkers of the

living microbial biomass. They degrade rapidly after cell death, are not found

in storage lipids or in anthropogenic contaminants and have a high natural

turnover rate. However, few PLFAs have been associated with specific

taxonomic or functional groups. Individual species comprise many different

fatty acids and many fatty acids occur in many different microbes (Bossio,

1998). Environmental factors such as temperature, pressure, pH, water

activity, nutrients, ions and chemicals and enzyme action can influence

microbial lipid composition. Therefore, any significant changes in these

factors must be considered when interpreting phospholipid fatty acid profiles.

Phospholipid fatty acid analysis is a reliable method to detect changes in the

structure of soil microbial communities. Relative to molecular methods it is

more cost- and time-effective without the loss of high precision or quality in

the data obtained.

The T-RFLP technique differentiates microbes according to the patterns

derived from the cleavage of their DNA, and produces a specific fingerprint of

the community based on the polymorphism of the target gene. It is a high-

throughput, reproducible method that allows the semi-quantitative analysis of

the diversity of a particular gene in a community. The T-RFLP results from the

rat and human cadaver experiments identified soil bacterial and fungal

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community dynamics and demonstrated that significant changes occurred in

the diversity and abundance of soil microbial communities in response to the

decomposition of the cadaver. However, due to the fact that the same T-RFs

were not observed at similar stages of decomposition for the two cadavers,

„markers‟ of a specific decomposition stage could not be picked out. This

could be because the sensitivity of the T-FRLP technique distinguishes profiles

at the sub-species level. To overcome this, genera-specific primers could be

used in a multiplex PCR reaction.

A challenging aspect of successfully profiling the soil microbial community

using the T-RFLP method was the amplification of the DNA using PCR. Soil

contains macromolecules with complex structures that are derived directly

from the alteration of the soil organic matter or formed de novo in the soil by

factors that make up the humic fraction in the soil, such as humic acids

(Lueders, 2003). These molecules are co-extracted along with the DNA and

can operate as contaminants and inhibitors, which block the PCR reaction. It

is unknown whether the loss in overall diversity of the human cadaver

exposed soil samples was due to an increase in these contaminants, which

have yet to be characterised. The PCR reaction had to be additionally

optimised to increase the yield of DNA, which even though time-consuming, is

a necessary step in DNA amplification. This was required especially in the

human cadaver experiment and was accomplished by optimising the dilution

of DNA used, the annealing temperature and the MgCl2 amount, which all have

significant effects on the yield of DNA. Solutions to overcoming the difficulty

of amplifying DNA from complex biological samples such a cadaver, could be

to use alternative thermostable DNA polymerases which may be more

resistant to inhibitors such as Hot Tub, Pwo, rTth and Tfl DNA polymerases

(Al-Soud and Radstrom, 1998) or using amplification facilitators such as bovine

serum albumin which binds haeme, betaine which increases the thermal

unfolding transition temperatures of proteins, single-stranded DNA binding T4

gene 32 protein which protects single-stranded DNA from nuclease digestion,

organic solvents and proteinase inhibitors (Al-Soud and Radstrom, 2000).

The extent to which T-RFLP analysis can be effective is dependent on the

specificity of primer pairs, which should be complementary to all known

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sequences. However, none of the known universal primers for 16S rDNA have

been shown to hybridize or amplify all sequences from the eukaryal, bacterial

and archaeal domains (Liu et al., 1997). Therefore, the universal primers that

were used in these experiments represent only a portion of the total species

diversity of the microbial world. The use and interpretation of the TRF

profiles is limited by the biases inherent to any DNA and PCR-based

investigation of environmental samples. The templates with good primer

homologies will be preferentially amplified and some templates may not

compete well for primers and therefore be underrepresented or missing from

the profile (Egert, 2003). The estimates of amplicon abundance may be

biased as a result of the heterogeneity of the gene copy number (Crosby and

Criddle, 2003). The range of the rRNA gene copy number in eubacteria is

from 1 to 13 with an average of 3.8 copies per genome (Kitts, 2001). This

operon heterogeneity and the different terminal restriction sites seen

between the gene copies can artificially increase the diversity seen in a

profile. Other PCR-based artefacts such as the formation of chimeric

amplicons, primer-dimers and incomplete digestion of PCR products can result

in additional restriction fragments in the TRF profiles (Lukow, Dunfield and

Liesack, 2000). In this study, the number of PCR cycles was kept within

accepted ranges to minimise some of these effects and three replicate PCR

reactions from a single sample were pooled to ensure random PCR artefacts

were minimised.

The research presented in this thesis clearly demonstrates the potential of

soil microbial community analysis as a promising technique in the post-

mortem interval estimation of a human cadaver. However, there is a need for

extensive research, standardisation of procedures and comprehensive

validation studies that will expand the method development and data analysis

and interpretation of both methodologies. Further research could enable us

to ascertain the particular microbial species or populations that are

stimulated at a particular „stage‟ of decomposition, as well as the effect of

environmental variables that may influence their development.

Standardisation would establish parameters for the method, apparatus and

statistical evaluation of the data that forensic laboratories could adhere to

worldwide. Validation studies would be essential to prove the reproducibility

124

and reliability of the techniques for application to forensic casework. These

qualities are critical to facilitate the evidence to withstand the strict scrutiny

it will encounter in a court of law.

Future research would benefit from experimenting with a larger number of

cadavers in a range of different environments. This would enable researchers

to observe more statistically sound trends in the dynamics of the soil

microbial community in response to decomposition and simultaneously

observe any differences originating from the unique nature of the individual

cadaver. Furthermore, the identification of decomposition stage-specific

„markers‟ would be easier if the microbial succession was of specific genera,

at a specific time and in response to a specific compound. Primers that

target specific groups of microbes such as Archaeabacteria, Eubacteria,

Basidoymycetes and Oomycetes may be useful for this purpose. The soil

microbial community profiling techniques depend strongly on the spatial

variability of the soil microbial communities. If soil varies significantly over

short distances, the sampling strategy employed must be representative of

the soil. Further development of the PLFA and T-RFLP methods will enhance

the amount of data gleaned from the soil microbial community.

In addition to viable biomass and microbial community structure data, PLFA

can help reveal the nutritional and physiological status of the microbial

community. The fatty acids extracted from soil can be used to classify

distinct microbial groups: microeukaryotes (polyunsaturated fatty acids),

aerobic prokaryotes (monounsaturated fatty acids), gram-positive and other

anaerobic bacteria (saturated and branched fatty acids in the range from C14

to C16). The extraction of this data could identify microbial groups involved

at different phases of cadaver decomposition.

Although the primers and restriction enzymes employed in the T-RFLP

technique facilitated successful profiles, the potential to improve this result

can be further investigated. Experimenting with the choice of primer pairs

for the PCR reaction and restriction enzyme for the digestion could determine

combinations that produced the best quality and most discriminatory profiles.

A primer pair that generates shorter PCR products may increase the number

125

of terminal restriction fragments generated from a soil microbial community

and provide greater resolution of a profile. Similarly, the combination of

more than one restriction enzyme will produce a more discriminatory profile.

The T-RFLP technique can be used to characterize functional diversity in soil

microbial communities. Primers with homology to broadly conserved

sequences in functional genes can be used to produce TRF profiles that can

illustrate the measure of diversity in functional genotypes. Specific genes

could be targeted to investigate specific functionality groups such as N2-

fixation (nifH), nitrification (amoA), denitrification (nosZ) and mercury

resistance (merR) (Kitts, 2001). Moreover, this would help elucidate the

microbial processes occurring in response to the decomposition of a cadaver.

In this study, the bacterial and fungal populations were targeted by specific

primers, however by using primers that target other members of the soil

microflora, additional community structure information may be expounded

and contribute further to decomposition microbiology applications.

The identification of microbial species could indicate if dominating species

originate from the indigenous soil community or from the cadaver-derived

intestinal microflora and help recognise key microbial players at certain times

during decomposition. Identification of the key microbes could then be used

to develop a system for PMI estimation, where the presence, absence or

combination of these indicator microbial species in a soil sample could predict

the stage of decomposition. The data produced by TRF patterns can be used

to search databases for matching sequences that might identify individual

microbes in the community profile. However, this must be done with caution,

as database matching of TRF sizes can be imprecise and may not produce

species- or even genera-specific identification. In addition, if an existing

sequence database is incomplete, some TRF peaks in a pattern might not be

represented in the database.

Advances in technology could offer alternatives to the PLFA and T-RFLP

techniques in the future. The recent merging of biotechnology with

nanotechnology has introduced ultra-sensitive and multiplexed technologies

that allow rapid detection of microbes and measurement of genes, proteins

and cells. Microarray technology has the ability to rapidly identify the

126

presence or absence of hundreds of key microbial species in a soil sample.

„Lab-on-a-chip‟ devices perform multiple processes such as microbial sample

preparation, reaction and detection that are required for discovering targeted

microbes at a single-cell level and can be made portable for on-site use.

„Lab-on-a-bead‟ devices achieve biochemical reactions on a bead surface that

contains short strands of DNA. The bead is embedded with quantum dots,

which are tiny light-emitting crystals that recognise certain DNA molecules of

interest and tag them by giving off a particular colour and intensity of light,

when certain genes or proteins are detected. When key microbial species

associated with certain phases of decomposition have been identified, these

technologies could be utilised for their rapid, on-site detection in soil

samples. There is a high possibility that additional species, which are

supported by cadaver substrates and not detected by present techniques,

might be evident utilizing these new technologies.

This preliminary investigation into the soil microbial changes associated with

cadaver decomposition unlocks many possibilities for future research. In

addition to a powerful tool for corroborating PMI, soil microbial community

profiling could provide additional valuable information of forensic relevance.

By using the techniques described in this thesis, samples of soil from suspect

locations could be compared with adjacent or alternative sites to identify

where significant changes or disruptions to the indigenous soil microbial

community might have occurred. This information could then be used to

indicate the location of a cadaver in the absence of any cadaveric material,

either through the removal of the cadaver to another location or the

complete decomposition of the cadaver. Similarly, this technology could be

used to identify the location of clandestine graves. Once fully developed, soil

microbial community profiling is an affordable method, using equipment

already in use in most forensic laboratories and thus places another powerful

tool in the hands of forensic scientists worldwide.

127

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140

Appendices

I PowerSoil™ DNA Isolation Kit

1. To the PowerBead Tubes provided, add 0.4 g of soil sample.

2. Gently vortex to mix.

3. Add 60 l of Solution C1 and vortex briefly.

4. Secure PowerBead Tubes horizontally and homogenize the sample in a

bead beater for 2 minutes at 2500 rpm.

5. Transfer the supernatant to a clean 2 ml Collection Tube (provided).

6. Add 250 l of Solution C2 and vortex for 5 seconds. Incubate at 4C for

5 minutes.

7. Centrifuge the tubes at room temperature for 1 minute at 10,000 x g.

8. Avoiding the pellet, transfer up to, but no more than, 600 l of

supernatant to a clean 2 ml Collection Tube (provided).

9. Add 200 l of Solution C3 and vortex briefly. Incubate at 4C for 5

minutes.

10. Centrifuge the tubes at room temperature for 1 minute at 10,000 x g.

11. Avoiding the pellet, transfer up to, but no more than, 750 l of

supernatant into a clean 2 ml Collection Tube.

12. Add 1200 l of Solution C4 to the supernatant and vortex for 5 seconds.

13. Load approximately 675 l onto a Spin Filter and centrifuge at

10,000 x g for 1 minute at room temperature. Discard the flow through

and add an additional 675 l of supernatant to the Spin Filter and

centrifuge at 10,000 x g for 1 minute at room temperature. Load the

remaining supernatant onto the Spin Filter and centrifuge at 10,000 x g

for 1 minute at room temperature.

14. Add 500 l of Solution C5 and centrifuge at room temperature for 30

seconds at 10,000 x g.

15. Discard the flow through.

16. Centrifuge again at room temperature for 1 minute at 10,000 x g.

141

17. Carefully place Spin Filter in a clean 2 ml Collection Tube. Avoid

splashing any Solution C5 onto the Spin Filter.

18. Add 100 l of Solution C6 to the centre of the white filter membrane

and leave for 5 minutes.

19. Centrifuge at room temperature for 30 seconds at 10,000 x g.

20. Discard the Spin Filter. The DNA in the tube is now ready for any

downstream application.

II FastDNA SPIN kit for Soil Protocol with added Plant DNAzol

Protocol

1. Add 0.3 g of soil to the Tissue Matrix E tube.

2. Add 978 L Sodium Phosphate Buffer and 122 L MT Buffer.

3. Secure tubes in FastPrep Instrument and process for 90 seconds at

speed 5.5.

4. Centrifuge Tissue Matrix tubes at 14,000 x g for 1 minute.

5. Follow Plant DNAzol Protocol below:

6. After centrifugation, transfer the supernatant to a clean tube and add

500 L of Plant DNAzol. Mix gently a few times.

7. Add 400 L of 100% ethanol and invert the mixture 6-8 times. Leave at

room temperature for 5 minutes.

8. Centrifuge at 4,000 x g for 4 minutes – repeat if necessary.

9. Tip off the supernatant, air-dry the DNA/soil pellet for ~10 minutes and

re-suspend in 200 L Sodium Phosphate buffer.

10. Continue with FastDNA SPIN kit protocol: Add 250 L PPS reagent and

mix by shaking the tube by hand 10 times.

11. Centrifuge at 14,000 x g for 5 minutes to precipitate the pellet.

12. Transfer the supernatant to a clean 1.5 mL tube. Add 1 ml Binding

Matrix Suspension to the supernatant.

13. Invert by hand for 2 minutes to allow binding of DNA to matrix. Place

tube in a rack for 3 minutes to allow settling of silica matrix.

14. Remove 500 L of supernatant being careful to avoid settled Binding

Matrix. Discard supernatant. Re-suspend the Binding Matrix in the

remaining amount of supernatant.

142

15. Transfer ~600 L of the mixture to a Spin Filter and centrifuge at

14,000 x g for 1 minute. Empty the catch-tube and add the remaining

supernatant to the Spin Filter and spin again.

16. Add 500 L SEWS-M to the Spin Filter and centrifuge at 14,000 x g for 1

minute. Discard flow through and replace Spin Filter in catch-tube.

Centrifuge at 14,000 x g for 2 minutes to „dry‟ the matrix of residual

SEWS-M wash solution.

17. Remove Spin Filter and place in fresh kit-supplied catch-tube. Air-dry

the Spin Filter for 5 minutes at room temperature.

18. Add 50 L DES and gently stir matrix on filter membrane with a pipette

tip to re-suspend the silica for efficient elution of the DNA.

19. Centrifuge at 14,000 x g for 1 minute to transfer eluted DNA to catch-

tube.

20. Discard the Spin Filter. DNA is now application ready.

III DNA Visualisation Protocol with Sybr SAFE

1. Make a 2% agarose gel with 2 g agarose and 100 mls TE Buffer. Heat in

microwave until all particles have dissolved and leave to cool.

2. Add 3 L of Sybr SAFE and mix well

3. Pour agarose into gel holder with teeth in place, making sure there are

no bubbles and let the gel set for 20 minutes

4. Place gel holder and gel into tank filled with TE buffer, ensuring the

buffer covers the gel completely.

5. Load 2 L of DNA ladder to first and last wells

6. Load 5 L of loading dye per sample onto parafilm

7. Add 5 L of amplified DNA to dye and mix both in the tip

8. Add 10 L of the mixture into the well carefully

9. When loading is complete, close lid firmly

10. Set the powerpack to 90 volts and run for 40 minutes.

143

IV Pico Green Assay

Use Nunc back-well, flat bottom plates only

1. Prepare 1 x TE buffer by diluting from 20 x TE provided in kit.

1/20 dilution in DNase/RNase free water.

2. Standard solutions are prepared in 1st row of plate using DNA standard

stock (100 g/mL) and 1 x TE buffer:

To make 1 mL of each standard:

Add 100 L DNA from kit to 4900 L 1 x TE buffer = 2 g/mL

Add 100 L of the above 2 g/mL solution to 900 L 1 x TE buffer

= 0.2 g/mL

200 ng = 1000 L of 2 g/mL solution

100 ng = 500 L of 2 g/mL solution + 500 L 1 x TE



12.5 ng = 62.5 L of 2 g/mL solution + 973.5 L 1 x TE

2.5 ng = 125 L of 0.2 g/mL solution + 875 L 1 x TE

1 ng = 50 L of 0.2 g/mL solution + 950 L 1 x TE

0.5 ng = 25 L of 0.2 g/mL solution + 975 L 1 x TE

3. Samples are diluted 2:100 in TE by adding 2 L of sample to 98 L in a

well (add buffer to well first).

4. Dilute PicoGreen quantitation reagent 1:200 in DNase/RNase free water

(100 L needed for each well, including standards and a blank).

5. Add 100 L of diluted PicoGreen to each well. Cover plate with foil

and mix gently. Leave to stand at room temperature for 5 minutes.

6. Read plate at an excitation wavelength of 485 nm and an emission of

538 nm.

7. Using the standards construct a standard curve.

8. Correlate the sample values with the curve after subtraction of a blank

to calculate the amount of DNA present in each sample.

V QIAquick PCR Purification Kit Protocol

1. Add 5 volumes of Buffer PB to 1 volume of the PCR sample and mix.

2. Place a QIAquick spin column in a provided 2ml collection tube.

144

3. To bind DNA, apply the sample to the QIAquick column and centrifuge

for 60 seconds.

4. Discard flow-through. Place the QIAquick column back into the same

tube.

5. To wash, add 0.75 ml Buffer PE to the QIAquick column and centrifuge

for 60 seconds.

6. Discard flow-through and place the QIAquick column back in the same

tube. Centrifuge the column for an additional 1 minute.

7. Place QIAquick column in a clean 1.5 ml microcentrifuge tube.

8. To elute an increased concentration of DNA, add 30 l Buffer EB

(10 mM Tris-Cl, pH 8.5) to the centre of the QIAquick membrane, let

the column stand for 1 minute and centrifuge the column for 1 minute.

VI Bacterial Digestion Protocol

Digest PCR products using the following mastermix:


MspI Enzyme 10 U/L 2

Buffer A 10 X 3

DNA 1:1 15

Total - 20

VII Fungal Digestion Protocol

Digest PCR products using the following mastermix:


HhaI Enzyme 10 U/L 2

Buffer 10 X 1.5

BSA 10 mg/ml 0.2

H2O - 0.3

DNA 1/20 or 1:1 16

Total - 20

145

VIII Source of Materials

Material Company

Phosphate Ajax Finechem (Labchem), Auburn, NSW,

Australia

Methanol:

Anhydrous, HPLC grade

Biolab (Aust) Ltd. (NZ protocol)

Sigma-Aldrich, Australia (Australia

protocol)

Chloroform:

Alcohol-free, HPLC grade

Mallinckrodt Baker Inc., Phillipsburg, NJ,

USA (NZ protocol)

Sigma-Aldrich, Australia (Australia

protocol)

Hydrochloric acid Sigma-Aldrich, Australia

Hexane:

HPLC grade

Sigma-Aldrich, Australia

Nitrogen gas:

G-size, high purity

BOC, Australia

Acetone:

99.9% acs reagent, HPLC grade

Sigma-Aldrich, Australia

Toluene Sigma-Aldrich, Australia

Potassium hydroxide Ajax Finechem (Labchem), Auburn, NSW,

Australia

Acetic acid Sigma-Aldrich, Australia

GC-MS vial Grace Davison, Deerfield, IL, USA

Glass capillary tube Grace Davison, Deerfield, IL, USA

Pasteur pipettes Crown Scientific

Extraction glass vials Alltech (Adelab Scientific), Australia

Glass vial caps Alltech (Adelab Scientific), Australia

146

IX Temperature data and ADD calculation for cadaver P and R

(collected by Rachel Parkinson at the Forensic Anthropology Centre,

University of Tennessee)

Project

DATE

Min

(F)

Max

(F)

Ave

Ambient

temp

(C)

Day Body P Day Body R

01/08/2006 95 71 28.3

02/08/2006 93 75 28.9

03/08/2006 94 74 28.9

04/08/2006 93 75 28.9

05/08/2006 92 71 27.8

06/08/2006 94 71 28.3

07/08/2006 95 73 28.9

08/08/2006 94 73 28.9

09/08/2006 95 70 28.3

10/08/2006 97 71 28.9

11/08/2006 80 71 24.4

12/08/2006 87 72 26.7

13/08/2006 86 67 25.0

14/08/2006 89 70 26.7

15/08/2006 88 72 26.7

16/08/2006 91 73 27.8

17/08/2006 91 71 27.2

18/08/2006 90 69 26.7

19/08/2006 91 72 27.8

20/08/2006 83 71 25.0

21/08/2006 87 70 26.1

22/08/2006 87 73 26.7 0.0 26.7

147

Project

DATE

Min

(F)

Max

(F)

Average

Ambient

temp

(C)


23/08/2006 87 68 25.6 52.3

24/08/2006 90 68 26.1 78.4

25/08/2006 91 70 27.2 3.0 105.6

26/08/2006 90 68 26.1 131.7

27/08/2006 93 67 26.7 158.4

28/08/2006 89 70 26.7 6.0 185.0

29/08/2006 89 71 26.7 211.7

30/08/2006 86 72 26.1 8.0 237.8

31/08/2006 78 71 23.9 261.7

01/09/2006 82 69 24.4 10.0 286.1

02/09/2006 81 68 23.9 310.0

03/09/2006 82 63 22.8 332.8

04/09/2006 79 67 22.8 355.6

05/09/2006 73 64 20.6 14.0 376.1

06/09/2006 80 63 22.2 398.4

07/09/2006 83 59 21.7 16.0 420.0

08/09/2006 83 63 22.8 442.8

09/09/2006 82 62 22.2 465.0

10/09/2006 84 64 23.3 488.4

11/09/2006 84 64 23.3 20.0 511.7 0.0 23.3

12/09/2006 77 63 21.1 532.8 44.4

13/09/2006 75 64 21.1 553.9 65.5

14/09/2006 76 57 19.4 23.0 573.4 3.0 85.0

15/09/2006 80 57 20.6 593.9 105.5

16/09/2006 83 59 21.7 615.6 127.2

17/09/2006 82 57 21.1 636.7 148.3

148

Project

DATE

Min

(F)

Max

(F)

Average

Ambient

temp

(C)


18/09/2006 86 60 22.8 27.0 659.5 7.0 171.1

19/09/2006 76 60 20.0 679.5 191.1

20/09/2006 69 50 15.6 29.0 695.0 9.0 206.6

21/09/2006 73 46 15.6 710.6 222.2

22/09/2006 75 58 19.4 31.0 730.0 11.0 241.6

23/09/2006 77 66 22.2 752.3 263.9

24/09/2006 75 62 20.6 772.8 284.4

25/09/2006 71 56 17.8 790.6 302.2

26/09/2006 72 53 17.2 35.0 807.8 15.0 319.4

27/09/2006 76 48 16.7 824.5 336.1

28/09/2006 74 50 16.7 841.1 352.7

29/09/2006 65 44 12.8 38.0 853.9 18.0 365.5

30/09/2006 74 49 16.7 870.6 382.2

01/10/2006 78 56 19.4 890.0 401.6

02/10/2006 78 50 17.8 907.8 419.4

03/10/2006 79 53 18.9 42.0 926.7 22.0 438.3

04/10/2006 82 56 20.6 947.3 458.9

05/10/2006 82 59 21.7 968.9 480.5

06/10/2006 69 52 16.1 45.0 985.0 25.0 496.6

07/10/2006 66 48 13.9 998.9 510.5

08/10/2006 73 47 15.6 1014.5 526.1

09/10/2006 79 54 19.4 1033.9 545.5

10/10/2006 78 54 18.9 49.0 1052.8 29.0 564.4

11/10/2006 77 61 20.6 1073.4 585.0

12/10/2006 63 40 11.1 1084.5 596.1

149

Project

DATE

Min

(F)

Max

(F)

Average

Ambient

temp

(C)


13/10/2006 56 34 7.2 52.0 1091.7 32.0 603.3

14/10/2006 61 32 8.3 1100.0 611.6

15/10/2006 65 33 9.4 1109.5 621.1

16/10/2006 55 48 11.1 1120.6 632.2

17/10/2006 67 52 15.6 1136.1 647.7

18/10/2006 76 59 20.0 1156.1 667.7

19/10/2006 65 57 16.1 58.0 1172.3 38.0 683.9

20/10/2006 62 42 11.1 1183.4 695.0

21/10/2006 65 37 10.6 1193.9 705.5

22/10/2006 66 44 12.8 1206.7 718.3

23/10/2006 46 37 5.6 62.0 1212.3 42.0 723.9

24/10/2006 49 32 5.0 1217.3 728.9

25/10/2006 58 30 6.7 1223.9 735.5

26/10/2006 61 45 11.7 1235.6 747.2

27/10/2006 58 51 12.8 1248.4 760.0

28/10/2006 59 41 10.0 1258.4 770.0

29/10/2006 68 40 12.2 1270.6 782.2

30/10/2006 73 43 14.4 69.0 1285.0 49.0 796.6

31/10/2006 72 49 16.1 1301.1 812.7

150

X Phospholipid fatty acid peak area data (%) relative to the internal standard (C19:0) consisting of both soil types with their respective

cadaver treatments. PR = Pallarenda soil, WB = Wambiana soil, C = control, CC = complete cadaver, IN = incised cadaver, EV = eviscerated cadaver. The peaks

have been named according to tentative identification based on PLFA database comparison.

-P[m14:1a] -P[m14:1b] -P[F] -P[15:0] -P[m15:0] -P[16:1a] -P[16:1b] -P[16:0] -P[M] -P[m16:0a] -P[m16:0b] -P[m16:0c] -P[m16:1] -P[17:0] -P[m17:0] -P[u] -P[18:1a] -P[18:1b] -P[18:0] -P[m18:0] -P[m18:0] -P[19:1] -P[19:0]

PR C 56.5 27.6 5.1 0.7 40.9 0.6 6.1 98.1 1.2 9.0 15.5 16.7 2.9 5.0 3.6 0.5 2.3 22.7 37.9 1.0 2.0 10.7 100.0

PR C 17.5 4.6 0.3 2.7 14.4 0.6 0.3 66.6 1.4 5.5 5.3 4.7 0.9 1.9 0.8 0.3 2.9 1.9 32.1 0.8 0.8 2.6 100.0

PR CC 28.3 13.7 3.5 1.5 29.1 2.1 3.7 120.3 0.7 4.2 0.5 10.6 0.8 4.5 2.8 0.4 9.7 10.2 37.2 5.7 1.4 0.5 100.0

PR CC 51.9 25.6 7.5 1.0 47.0 0.3 2.8 167.6 0.8 12.8 17.3 17.4 1.7 0.6 0.8 0.9 10.8 28.0 46.6 8.6 5.1 17.5 100.0

PR CC 41.6 18.0 3.7 0.3 28.7 1.2 0.6 81.9 0.8 7.6 11.4 11.2 1.7 3.5 1.9 0.8 9.3 16.0 25.9 3.5 2.0 8.6 100.0

PR CC 56.5 27.6 5.1 0.7 40.9 0.6 6.1 98.1 1.2 9.0 15.5 16.7 2.9 5.0 3.6 0.5 2.3 22.7 37.9 1.0 2.0 10.7 100.0

PR IN 126.0 51.0 13.5 2.4 101.2 4.0 0.0 360.0 0.0 29.6 40.7 37.8 2.9 9.8 1.2 1.3 20.8 49.8 102.0 14.3 7.7 8.1 100.0

PR IN 166.9 71.2 20.4 0.0 144.9 7.3 0.0 470.3 4.8 31.5 39.4 46.3 7.9 8.0 10.1 2.3 40.4 58.5 113.5 20.3 5.8 34.1 100.0

PR IN 181.0 92.0 19.3 0.0 140.4 5.3 0.0 508.0 2.0 27.8 36.7 49.0 12.4 17.9 8.0 20.6 47.4 61.3 115.3 26.8 13.3 25.4 100.0

PR EV 105.2 37.4 13.9 0.0 101.4 4.8 0.0 356.3 4.9 30.6 31.6 36.6 4.9 10.2 9.5 2.0 28.7 46.3 119.9 17.6 8.8 28.1 100.0

PR EV 93.1 37.5 9.0 7.1 113.1 4.3 3.6 426.3 9.0 30.3 31.2 34.6 5.0 4.4 2.4 1.9 33.7 48.1 111.1 6.8 12.3 12.0 100.0

PR EV 25.6 9.0 1.0 0.0 21.7 1.2 0.9 97.5 1.4 5.5 7.1 6.7 0.9 0.9 0.0 9.5 9.3 12.5 28.0 1.4 1.8 8.4 100.0

PR EV 32.5 10.3 5.2 0.7 43.6 1.6 1.6 166.7 0.7 11.0 16.5 15.0 1.9 5.1 3.5 0.8 10.0 11.5 52.9 5.3 3.5 7.9 100.0

WB C 228.9 57.9 23.1 1.0 86.2 29.1 12.6 303.0 3.4 56.4 58.8 40.7 20.6 22.7 10.8 1.2 39.5 1.8 20.7 25.2 2.2 42.8 100.0

WB C 331.6 78.3 29.5 10.6 114.4 1.6 22.2 44.5 2.3 87.1 78.4 59.6 32.2 30.0 10.4 1.8 76.1 88.2 90.6 43.2 5.5 103.1 100.0

WB C 164.8 39.3 14.0 4.6 58.7 1.3 7.4 208.2 1.1 24.0 37.4 29.6 15.1 13.1 5.2 1.9 27.0 36.3 38.2 10.0 3.4 31.5 100.0

WB CC 285.8 77.2 28.1 6.6 119.2 27.5 0.0 458.5 1.2 56.5 70.9 59.5 26.9 27.5 26.1 1.4 69.1 103.8 94.0 43.9 7.2 115.6 100.0

WB CC 257.0 61.1 22.7 9.1 108.6 1.4 19.0 375.0 2.0 66.0 37.7 47.2 22.3 22.4 28.4 5.6 60.0 98.7 88.9 42.2 2.7 78.3 100.0

WB CC 321.7 78.4 28.7 10.8 138.1 22.3 0.0 506.5 2.4 86.4 78.3 66.5 27.1 33.2 32.0 2.2 39.8 117.7 108.3 58.0 11.9 100.6 100.0

WB CC 467.8 119.8 45.0 16.0 205.8 36.5 0.0 753.2 7.4 87.6 114.3 98.1 40.2 45.0 50.0 4.3 124.0 189.3 158.9 85.1 17.0 149.2 100.0

WB IN 105.2 37.4 13.9 0.0 101.4 4.8 0.0 356.3 4.9 30.6 31.6 36.6 4.9 10.2 9.5 2.0 28.7 46.3 119.9 17.6 8.8 28.1 100.0

WB IN 126.0 51.0 13.5 2.4 101.2 4.0 0.0 360.0 0.0 29.6 40.7 37.8 2.9 9.8 1.2 1.3 20.8 49.8 102.0 14.4 7.7 8.1 100.0

WB IN 461.0 119.3 41.7 11.1 201.2 28.5 0.0 675.3 4.9 73.3 114.5 92.8 42.1 44.0 20.4 10.6 117.9 152.0 146.4 72.0 8.4 120.1 100.0

WB IN 590.9 150.9 58.1 12.7 263.5 2.8 25.8 900.2 5.8 107.6 141.5 113.7 26.4 47.4 32.5 8.3 127.6 166.1 204.8 91.0 19.7 141.9 100.0

WB EV 279.8 92.9 41.5 21.8 151.3 38.4 18.0 416.6 4.4 31.9 110.0 74.6 29.4 31.6 38.4 1.7 74.2 69.1 144.3 62.6 62.6 69.8 100.0

WB EV 399.0 100.1 38.3 9.0 150.9 2.7 22.8 562.9 3.4 24.2 101.3 75.9 21.2 38.4 44.7 3.7 86.8 119.2 123.4 56.1 17.9 90.8 100.0

WB EV 468.9 115.5 42.9 20.5 193.0 36.4 2.6 699.3 4.1 91.6 117.4 88.5 40.7 20.8 51.5 8.0 115.9 155.3 157.3 72.2 11.6 134.2 100.0

SOIL T/MENT

PEAK AREA

151

XI Phospholipid fatty acid peak area data relative to the internal standard (peak 35) of control O and cadaver P.

Sample Peak 1 Peak 2 Peak 3 Peak 4 Peak 5 Peak 6 Peak 7 Peak 8 Peak 9 Peak 10 Peak 11 Peak 12 Peak 13 Peak 14 Peak 15 Peak 16 Peak 17 Peak 18 Peak 19 Peak 20 Peak 21 Peak 22 Peak 23 Peak 24 Peak 25 Peak 26 Peak 27 Peak 28 Peak 29 Peak 30 Peak 31 Peak 32 Peak 33 Peak 34 Peak 35 Peak 36 Peak 37 Peak 38

O0 206.4 64.0 27.0 23.8 358.6 272.4 30.8 22.3 19.7 169.1 94.0 322.3 28.3 238.3 759.6 71.4 149.6 697.4 34.7 122.1 125.1 149.4 86.0 70.7 24.4 14.3 62.6 533.5 1064.5 128.9 185.6 112.7 142.4 528.8 100.0 17.8 3.2 16.9

O03 103.1 29.9 8.3 10.1 208.7 171.4 12.6 9.3 12.3 92.1 46.0 166.9 17.2 125.6 393.4 40.0 85.3 427.6 16.5 71.2 72.8 86.2 56.9 23.3 11.1 6.1 35.8 304.9 534.4 71.6 96.7 59.7 93.0 323.0 100.0 6.9 4.3 5.9

O06 105.5 33.8 13.4 11.2 204.6 159.6 14.3 9.9 11.9 90.1 44.2 174.0 14.8 114.6 400.4 39.3 82.8 381.0 16.2 66.4 64.7 85.0 56.5 24.2 11.9 6.0 35.6 276.3 509.8 61.3 94.8 53.1 81.5 252.7 100.0 7.7 3.8 7.3

O10 172.3 48.5 23.0 19.5 323.6 253.7 21.8 18.2 17.3 135.9 81.9 304.5 19.5 203.8 629.2 65.3 151.2 627.4 27.0 101.6 101.7 144.5 85.0 37.7 17.2 10.7 51.0 457.7 893.5 78.4 137.0 96.6 117.9 444.1 100.0 12.1 2.2 10.2

O14 160.6 53.4 23.4 17.7 315.4 239.8 21.6 15.0 17.2 135.8 74.1 323.1 20.1 192.0 652.4 64.9 126.1 621.9 26.3 104.6 100.6 135.6 80.0 35.0 15.9 7.4 47.7 439.0 881.9 78.4 145.3 86.4 119.4 431.3 100.0 11.0 1.8 9.7

O16 211.3 65.3 21.8 23.7 415.3 328.8 28.3 24.0 22.5 184.3 104.9 362.6 32.5 220.8 794.1 84.6 171.6 797.0 35.8 147.2 143.8 157.8 128.9 54.2 28.3 11.9 64.4 602.8 1076.0 104.2 202.3 133.7 177.5 606.2 100.0 18.6 2.2 23.7

O20 121.4 39.9 17.0 14.4 249.4 196.8 16.3 14.9 13.8 108.3 58.6 211.2 18.3 139.7 469.0 47.3 99.2 450.2 19.8 83.3 78.7 103.6 64.6 26.2 14.9 8.5 43.5 328.6 620.0 66.3 108.5 67.2 105.1 324.7 100.0 10.8 1.8 11.2

O23 87.8 33.6 16.2 12.0 185.0 139.1 12.2 9.5 10.7 68.1 43.7 170.7 11.7 117.5 339.5 33.1 83.5 350.4 12.8 52.4 51.5 82.6 34.5 20.5 7.5 10.2 19.7 236.1 460.9 44.9 73.6 50.7 59.2 210.6 100.0 5.4 1.5 4.8

O27 119.0 32.7 13.0 12.7 229.8 172.8 12.7 9.9 13.0 92.2 51.3 217.3 14.8 150.5 434.7 50.7 101.9 488.5 19.4 75.4 71.4 99.8 60.6 26.8 10.8 5.8 26.9 324.9 638.7 60.0 101.3 74.5 83.0 316.4 100.0 7.5 0.9 6.7

O29 181.3 45.4 29.6 20.7 348.5 274.7 20.6 21.1 17.3 148.5 79.0 303.0 21.4 212.7 615.9 88.7 172.0 708.8 28.4 120.7 118.6 153.1 105.7 44.5 21.5 9.2 40.2 467.5 880.1 90.5 152.4 100.9 150.0 481.9 100.0 13.2 1.8 13.8

O31 156.0 52.0 20.7 18.9 324.4 246.9 21.2 17.3 17.3 133.0 75.2 266.8 22.1 189.2 562.0 62.8 134.6 584.2 26.1 104.5 103.5 128.1 87.2 35.3 19.0 12.0 39.8 405.4 740.8 91.6 138.2 82.8 99.6 361.4 100.0 13.5 2.1 14.4

O35 165.0 56.5 28.9 19.8 364.6 281.3 22.1 21.9 19.8 153.3 81.0 316.8 25.3 198.8 597.7 75.4 144.8 655.8 30.9 118.0 114.3 146.8 104.4 40.7 21.7 11.0 57.8 447.4 801.5 90.0 145.3 91.6 162.4 412.4 100.0 13.3 2.5 13.3

O42 196.2 58.2 33.9 26.8 422.6 327.3 27.5 30.8 23.4 184.4 101.5 339.7 24.0 236.1 692.6 75.9 160.0 745.5 35.3 131.6 134.0 167.3 113.9 60.9 26.1 19.1 72.9 513.2 912.8 108.2 154.4 106.4 163.6 462.5 100.0 14.5 6.1 11.7

O45 209.6 69.9 32.0 29.6 461.5 335.0 31.4 26.9 34.0 196.2 121.3 386.7 30.5 294.7 813.4 85.8 200.5 894.7 41.9 144.5 148.2 187.4 124.9 69.9 28.4 20.5 94.7 644.1 1148.5 133.0 183.7 122.3 132.6 535.1 100.0 17.7 5.3 18.9

O52 162.4 54.1 33.0 22.2 367.5 285.7 24.5 24.6 21.2 149.1 92.5 292.1 25.3 223.0 617.7 67.3 143.4 626.3 32.8 117.5 119.7 150.3 98.2 53.1 18.0 11.4 74.6 476.6 823.0 115.0 140.2 93.4 139.5 410.9 100.0 16.3 5.0 18.6

O58 242.7 76.1 38.2 31.1 524.2 405.5 32.7 32.8 28.5 213.1 124.5 471.0 31.2 288.5 906.6 109.5 201.4 1032.4 47.4 165.7 174.6 186.0 131.7 66.0 28.4 15.2 108.5 633.8 1253.1 165.3 200.9 126.7 197.3 650.1 100.0 15.1 6.4 11.4

O62 216.7 77.7 37.5 32.9 451.7 353.3 35.9 31.5 28.6 184.9 126.7 578.9 36.7 301.2 832.8 83.0 172.8 829.7 39.0 130.8 138.7 175.3 99.8 59.3 23.5 18.5 71.2 593.6 1191.2 132.3 170.9 118.4 149.2 457.2 100.0 16.6 3.8 18.3

O69 163.4 52.4 33.3 20.0 325.4 237.8 23.4 17.9 14.2 131.0 82.7 343.9 18.8 242.0 593.9 40.5 113.9 538.4 26.8 93.1 99.8 117.7 63.2 30.9 13.6 12.8 57.5 415.5 879.1 108.3 131.3 72.3 107.8 331.9 100.0 9.1 1.4 6.5

P0 138.9 73.8 23.6 20.0 428.7 266.7 36.8 21.5 21.8 165.1 107.0 398.0 24.1 218.1 752.1 70.4 150.7 649.8 32.9 133.2 117.8 135.5 89.1 38.5 23.0 15.1 66.0 458.5 929.3 98.0 179.5 105.5 137.3 576.6 100.0 19.0 7.4 21.3

P03 18.0 42.7 2.7 1.5 153.7 40.9 14.2 2.2 6.1 23.9 32.3 28.2 3.4 9.5 253.9 4.2 12.4 28.1 4.8 19.5 14.4 6.8 20.1 9.2 3.3 1.7 109.4 304.8 86.6 8.0 50.3 17.7 17.6 34.1 100.0 6.3 0.7 3.9

P06 15.1 67.6 6.8 2.8 146.1 48.5 10.0 2.0 3.4 23.6 10.8 47.9 4.3 7.1 387.9 3.3 13.7 16.7 13.3 15.6 12.6 3.8 19.9 3.5 2.3 1.6 169.9 550.0 123.1 9.3 103.5 3.9 11.3 26.5 100.0 34.2 4.8 27.6

P08 11.0 75.4 8.6 2.7 84.3 41.6 12.4 1.0 4.5 17.0 25.7 73.3 5.0 5.5 582.1 4.1 2.0 19.0 15.0 10.7 11.4 3.7 14.4 13.2 1.2 7.7 384.4 952.0 224.8 54.5 396.7 4.5 12.3 33.1 100.0 68.0 6.7 54.3

P10 20.7 133.8 2.6 37.9 137.8 83.9 26.3 2.7 0.9 23.8 63.4 101.0 18.0 6.3 957.6 80.8 6.9 33.6 1.0 14.1 16.9 6.4 24.4 19.4 1.4 5.2 406.1 1199.5 314.1 133.1 301.3 2.5 9.2 41.2 100.0 122.9 17.8 93.9

P14 12.5 108.6 1.6 1.6 116.7 54.5 20.8 0.8 9.0 32.7 33.9 81.5 7.3 7.3 737.4 3.2 41.5 41.9 67.3 2.9 17.4 5.2 24.6 10.0 5.5 6.6 326.3 895.3 229.9 2.9 245.1 2.8 2256.9 23.7 100.0 49.6 2.1 39.5

P16 5.0 80.0 1.3 0.7 63.7 23.4 15.0 0.5 0.6 15.5 6.7 20.1 1.0 1.7 654.2 1.7 6.0 7.7 7.2 17.3 8.6 0.8 3.3 0.3 0.5 0.4 15.6 184.0 45.3 0.7 215.2 3.5 58.8 5.6 100.0 65.6 1.3 51.0

P20 17.7 251.9 1.3 1.7 197.4 74.6 48.6 0.5 1.0 31.6 8.8 32.4 7.7 6.8 1527.7 11.8 1.2 4.5 1.2 15.6 18.8 1.8 18.4 3.3 0.3 0.8 15.6 324.5 66.5 2.4 250.9 2.1 7.7 0.7 100.0 105.1 3.3 94.2

P23 9.5 30.9 8.4 9.7 9.0 23.6 5.3 0.4 2.3 5.1 6.6 13.3 5.3 4.0 230.2 2.5 6.5 12.7 16.0 12.9 24.0 13.8 18.7 9.8 4.0 1.1 0.8 54.3 217.5 39.4 2.2 57.3 2.1 25.3 100.0 3.1 0.8 0.5

P27 30.1 259.0 6.8 7.1 368.8 187.9 51.8 6.7 7.1 67.2 32.0 143.8 98.3 32.5 2323.1 1.0 7.5 13.8 7.5 36.5 40.5 9.3 6.4 2.0 2.1 6.7 409.9 2118.8 504.8 347.5 367.6 3.8 310.2 45.7 100.0 161.2 12.5 165.9

P29 7.2 79.0 1.0 0.9 61.8 31.9 13.1 2.9 3.0 10.4 4.3 10.2 3.7 3.0 512.7 1.8 0.4 2.0 0.7 4.7 0.8 0.9 3.1 1.9 1.4 0.3 0.3 7.7 120.3 18.0 89.4 89.6 1.2 4.4 100.0 20.9 0.7 21.2

P35 5.9 39.2 1.5 0.2 37.3 39.6 5.5 3.2 3.2 6.4 6.0 14.3 19.9 9.0 445.6 2.3 1.7 2.5 4.6 11.8 3.5 0.8 4.3 3.5 0.2 0.7 25.3 286.8 59.0 64.6 36.5 0.5 44.5 1.9 100.0 8.1 0.2 7.1

P42 7.8 57.8 0.3 1.2 36.4 39.5 7.5 0.6 6.8 8.1 27.0 22.7 24.3 18.7 319.6 1.7 6.5 6.3 2.7 4.5 4.3 2.9 15.2 15.2 1.2 4.2 56.3 297.7 122.7 10.5 54.3 4.0 20.9 15.7 100.0 11.6 0.9 11.2

P45 26.3 97.9 6.2 2.2 86.2 136.9 11.4 1.7 15.7 17.1 45.5 40.3 20.4 11.2 494.4 2.2 5.4 8.5 4.2 56.1 10.3 5.4 5.7 8.7 1.4 6.5 60.5 490.6 211.6 94.9 94.4 4.0 29.1 40.8 100.0 12.9 3.8 18.0

P49 94.9 393.4 2.7 3.6 329.7 522.8 40.3 2.2 22.8 55.5 94.3 684.9 282.2 22.5 2261.6 9.3 23.0 15.6 8.8 19.2 33.1 34.4 40.0 2.6 3.9 8.0 156.2 2768.8 1013.6 30.7 288.5 12.1 107.9 213.1 100.0 8.7 6.2 11.6

P52 34.8 134.8 2.1 1.4 104.4 188.9 14.5 2.4 11.2 22.4 24.4 123.9 52.3 8.3 720.4 3.8 11.7 9.4 3.3 29.3 12.8 11.6 9.1 7.2 1.2 6.4 61.0 767.5 320.7 10.2 104.7 4.1 31.2 75.2 100.0 7.2 0.7 7.0

P58 45.1 141.1 4.4 4.8 129.2 222.7 20.1 2.1 5.9 23.4 43.2 127.3 59.1 9.2 727.6 1.2 11.0 8.0 7.4 10.8 22.0 18.7 19.8 5.4 4.1 2.9 80.4 839.0 346.5 12.8 93.3 11.7 66.3 84.4 100.0 10.0 5.2 7.0

P62 3.9 10.5 0.4 0.1 19.5 27.8 3.4 1.9 1.9 2.8 0.1 4.7 2.8 0.5 41.2 0.7 1.5 0.4 0.2 0.8 0.7 0.2 1.4 1.9 0.2 0.7 2.3 44.3 20.3 0.2 5.7 1.4 1.4 1.3 100.0 0.2 0.3 0.2

P69 69.0 213.2 1.6 2.1 218.9 291.8 20.3 3.1 10.5 31.2 21.0 140.9 145.2 15.7 843.5 4.0 18.0 8.7 3.5 6.5 16.1 10.8 14.8 19.0 4.8 8.2 73.3 999.4 483.1 13.0 107.6 3.8 43.1 80.5 100.0 6.6 3.8 5.5

152

XII Phospholipid fatty acid peak area data relative to the internal standard (peak 35) of control Q and cadaver R.

Sample Peak 1 Peak 2 Peak 3 Peak 4 Peak 5 Peak 6 Peak 7 Peak 8 Peak 9 Peak 10 Peak 11 Peak 12 Peak 13 Peak 14 Peak 15 Peak 16 Peak 17 Peak 18 Peak 19 Peak 20 Peak 21 Peak 22 Peak 23 Peak 24 Peak 25 Peak 26 Peak 27 Peak 28 Peak 29 Peak 30 Peak 31 Peak 32 Peak 33 Peak 34 Peak 35 Peak 36 Peak 37 Peak 38

Q0 70.0 27.2 15.1 13.3 220.9 163.6 13.6 12.2 11.1 83.1 46.2 149.7 8.2 99.1 336.3 40.4 87.4 368.7 19.7 78.7 80.6 89.5 65.7 25.7 18.7 14.6 32.4 240.1 434.5 57.5 98.4 57.3 62.4 306.1 100.0 17.2 1.7 12.3

Q03 57.7 25.7 15.5 10.0 199.6 137.6 9.9 6.6 8.9 66.7 30.5 145.6 12.4 82.5 343.5 32.6 69.2 325.3 12.8 56.3 52.0 73.9 39.7 15.9 12.5 8.7 25.5 234.3 484.1 38.8 81.0 57.7 95.2 286.8 100.0 9.1 2.7 9.5

Q07 66.1 24.7 10.7 9.0 229.7 166.9 11.4 9.1 11.2 81.7 37.6 136.0 12.6 89.6 325.5 40.6 70.7 387.5 17.3 67.2 63.2 71.9 48.4 20.3 9.7 4.1 21.2 205.9 415.3 46.0 70.4 51.3 80.8 277.9 100.0 4.1 0.1 3.0

Q09 96.5 50.4 19.4 16.0 325.5 234.1 19.4 17.2 20.6 120.4 67.3 217.3 16.0 146.7 509.3 52.5 117.2 573.8 25.2 102.7 93.1 116.7 73.4 46.6 16.6 9.5 35.8 346.4 667.9 68.1 125.2 81.8 86.0 417.6 100.0 10.0 5.2 8.5

Q11 65.7 38.4 13.8 11.4 270.4 179.6 13.7 10.9 13.2 98.6 46.9 183.9 10.6 124.5 416.2 46.7 93.4 430.9 18.9 80.7 72.4 94.1 51.6 22.6 12.8 7.6 22.5 283.5 586.5 59.0 96.2 68.3 96.9 349.2 100.0 6.8 0.5 6.6

Q15 125.0 64.0 32.8 21.1 455.5 302.9 28.2 23.1 21.6 170.4 94.5 323.4 29.1 208.8 734.2 74.6 160.6 688.9 35.1 138.0 124.6 172.9 102.5 55.8 22.4 16.6 82.7 552.6 1003.1 99.1 163.8 116.4 166.2 586.0 100.0 14.0 8.1 11.5

Q18 61.2 27.8 8.3 8.4 243.2 160.1 12.3 7.1 8.7 84.1 36.3 170.5 12.7 97.0 392.3 40.6 72.0 401.2 13.9 68.9 61.8 82.5 48.3 19.8 8.9 4.6 17.7 233.0 528.1 43.5 81.0 50.5 77.6 299.4 100.0 4.3 0.2 3.2

Q22 102.2 47.4 23.3 16.7 367.2 251.9 18.7 16.7 19.2 125.5 72.0 258.2 22.6 162.8 576.9 70.7 129.7 642.1 30.8 110.6 100.5 135.2 75.2 47.2 16.0 14.4 35.1 397.6 804.7 91.0 130.6 86.0 132.4 494.6 100.0 8.7 0.3 7.4

Q25 117.0 59.3 22.4 19.8 398.7 296.8 25.6 21.7 26.5 145.7 93.6 302.0 27.4 168.2 616.8 67.2 139.9 613.4 31.6 118.9 115.2 150.8 89.2 53.6 20.6 31.3 67.2 445.1 755.0 87.0 137.6 88.1 156.5 455.3 100.0 13.7 7.2 13.3

Q29 127.0 92.2 25.3 17.7 427.8 302.6 30.3 22.8 24.4 150.7 92.1 304.4 27.3 199.4 786.1 66.3 150.2 653.5 36.6 126.3 120.3 178.7 83.7 46.0 19.5 12.5 72.4 500.0 1013.0 124.0 170.8 105.5 116.8 637.2 100.0 13.7 3.1 12.9

Q32 139.7 66.8 41.8 22.4 514.0 342.8 32.6 27.5 25.0 196.1 105.9 374.7 27.6 271.7 872.3 95.4 182.7 824.0 41.5 163.0 152.0 199.7 116.2 63.4 26.9 38.0 180.4 609.8 1200.0 140.8 191.5 131.8 134.1 708.6 100.0 16.4 3.5 15.2

Q38 53.2 31.8 13.5 7.5 190.7 129.8 13.4 9.5 8.1 67.4 36.4 235.7 11.8 84.6 362.8 26.7 59.5 277.6 12.2 46.5 46.7 59.9 30.5 13.0 6.9 5.4 44.3 199.0 480.7 45.1 69.1 35.9 55.7 218.2 100.0 4.8 2.2 5.3

Q42 82.9 36.6 18.3 12.1 311.3 212.4 15.9 14.2 13.7 116.8 59.1 288.6 16.0 137.5 528.9 56.5 98.8 519.6 22.9 91.6 87.4 100.4 66.8 25.0 12.6 7.0 44.2 329.5 739.8 77.9 106.6 73.3 98.2 405.5 100.0 7.0 0.7 4.9

Q49 89.1 46.6 20.3 12.7 304.9 208.3 16.7 16.5 15.8 107.6 66.3 265.7 17.3 172.7 522.0 50.4 117.7 506.7 25.3 90.3 88.2 103.6 58.6 27.3 13.0 10.9 46.7 347.4 724.1 97.9 116.2 71.9 70.7 405.2 100.0 9.5 1.6 11.8

R0 103.2 49.8 18.3 15.0 326.1 196.9 22.1 16.0 15.3 122.5 75.0 315.8 15.8 178.4 595.5 54.5 131.8 554.5 24.4 105.0 88.0 119.4 67.7 29.5 16.7 14.5 50.0 363.2 858.7 87.5 144.6 94.9 105.7 512.6 100.0 14.2 0.1 13.0

R03 46.5 18.1 9.5 7.3 145.2 91.9 8.0 6.5 8.3 55.4 30.2 116.9 6.4 72.8 222.0 26.0 58.5 253.5 9.7 42.8 39.3 50.9 33.7 14.9 7.7 5.2 12.4 146.7 320.4 29.8 52.7 36.3 47.8 186.1 100.0 3.9 0.2 4.6

R07 5.5 12.2 0.9 0.8 20.1 13.3 0.9 0.4 2.8 5.6 3.4 15.0 0.8 6.5 31.2 2.4 25.3 0.5 0.5 4.1 3.7 8.9 2.8 0.8 0.5 0.3 1.1 15.0 41.8 2.1 5.2 3.5 4.6 24.5 100.0 0.5 0.1 0.4

R09 0.0 0.1 0.2 0.0 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.1 0.2 0.6 0.4 0.0 0.3 100.0 0.1 0.1 0.0

R11 49.3 41.5 7.1 5.7 168.3 142.4 15.4 5.2 6.2 64.4 69.4 241.7 21.3 51.2 551.7 18.6 51.2 207.3 8.1 41.2 43.0 102.3 32.2 10.4 9.5 8.8 113.8 680.8 557.5 31.8 92.5 43.8 87.6 278.0 100.0 6.8 2.3 6.4

R15 3.8 5.0 0.2 0.3 18.3 14.4 1.3 0.2 3.5 2.4 2.4 6.7 0.7 1.2 33.1 0.1 3.6 5.4 0.2 1.3 1.3 0.9 1.0 0.9 0.3 0.4 4.2 27.3 16.1 0.9 8.3 0.8 0.9 4.3 100.0 0.4 0.1 0.2

R18 0.1 1.3 0.1 0.0 0.8 0.6 0.2 0.0 0.1 0.2 0.3 0.6 0.3 0.2 7.1 0.0 0.1 0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.2 2.1 8.8 1.1 0.3 1.7 0.2 0.4 0.3 100.0 0.5 0.1 0.5

R22 8.8 16.1 2.1 0.9 44.2 35.4 3.1 0.6 2.1 7.8 8.7 40.3 9.6 4.3 90.2 0.3 3.8 12.1 0.2 3.6 4.1 2.6 2.5 1.7 0.4 1.6 15.9 127.7 63.7 7.3 18.4 2.1 2.8 9.5 100.0 0.7 0.1 0.7

R25 9.1 13.0 1.5 1.7 50.5 35.1 3.1 0.3 1.5 6.2 3.3 15.8 4.8 2.0 60.4 1.3 1.6 8.4 0.3 3.4 3.1 1.7 3.0 1.9 0.7 1.4 7.6 49.1 51.0 4.9 15.4 1.5 3.1 10.8 100.0 1.0 0.5 0.9

R29 4.3 7.9 1.1 0.1 31.3 28.0 3.0 0.3 1.0 2.9 2.3 21.7 5.8 1.8 50.4 0.2 1.6 4.7 0.1 2.1 2.0 1.0 0.4 1.5 0.5 0.7 3.6 50.9 70.2 3.2 7.7 1.4 0.9 6.0 100.0 0.2 0.1 0.2

R32 6.2 10.3 0.7 0.3 33.8 33.6 2.7 0.1 2.1 4.1 2.7 15.1 5.0 1.4 51.6 0.7 2.6 5.1 0.4 2.4 2.1 1.2 1.6 1.1 0.2 0.9 8.3 47.0 44.5 4.0 9.9 0.5 3.9 5.4 100.0 0.2 0.0 0.2

R38 38.2 51.1 3.3 1.6 252.8 203.0 13.3 0.8 2.3 26.9 15.0 143.4 34.8 8.5 318.9 1.0 10.3 30.3 1.8 15.2 11.8 11.3 6.1 1.9 1.0 1.9 31.4 287.8 292.8 2.5 36.0 4.1 20.7 36.4 100.0 1.0 0.5 1.3

R42 31.8 26.9 5.3 3.1 112.5 77.5 10.8 3.1 4.7 33.1 18.7 140.6 33.6 31.0 244.7 10.5 27.8 114.6 4.4 23.6 20.0 31.2 17.9 5.3 2.9 2.8 61.4 207.1 330.2 21.5 43.9 20.5 33.1 118.5 100.0 4.0 1.9 4.4

R49 12.3 23.1 2.1 1.2 81.6 82.4 5.9 0.3 2.4 6.6 2.9 49.8 22.2 2.5 100.7 0.3 5.3 8.1 0.5 4.6 3.2 2.6 3.5 2.3 0.2 1.7 3.0 72.3 152.2 10.5 17.0 1.0 1.9 0.5 100.0 0.3 0.2 0.5

153

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THE FORENSIC CHARACTERISATION OF THE SOIL MICROBIAL … · THE FORENSIC CHARACTERISATION OF THE SOIL MICROBIAL COMMUNITY IN RESPONSE TO CADAVER DECOMPOSITION Kerith-Rae Dias (BSc,