SYSTEMATICS, ECOLOGY AND

PLANT ASSOCIATIONS OF

AUSTRALIAN SPECIES OF THE

GENUS METARHIZIUM

Shah Mohammad Naimul Islam

MS in Biotechnology

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Earth, Environmental and Biological Sciences

Science and Engineering Faculty

Queensland University of Technology

2018

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Keywords

Australia, ecology, endophyte, entomopathogen, haplotype, Metarhizium, MzIGS3,

new species, plant associations, Queensland, rhizosphere, strigolactone, systematics,

5’-TEF.

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Abstract

Fungi of the genus Metarhizium (Family: Clavicipitaceae, Order: Hypocreales) are

pleomorphic entomopathogens that are widely used as biocontrol agents. As with

many Hypocrealean fungi, they are saprophytes in soil and colonisers of the

rhizosphere in a mutualistic relationship with plants that can result in improved plant

growth, nutrient uptake, protection from invertebrate herbivores and suppression of

plant pathogens. Inoculation of the rhizosphere of crops with Hypocrealean fungi may

have significant benefits to agriculture, but establishment of fungal inoculum in the

rhizosphere has been inconsistent. Establishment might be improved by analysis of

the biological and ecological factors in the plant/fungus relationship, and the

identification of strains or species with improved rhizospheric competence in crops.

This study collected and described Metarhizium isolates from agricultural fields,

grasslands and forests soils in three locations in Queensland, Australia. Multi-locus

analysis of concatenated sequence data sets of MzIGS3 and 5’-TEF sequences was

used to determine the systematic relationships of 164 Metarhizium isolates. Five

clades with strong support values were identified: three known species (M. robertsii,

M. pingshaense and M. anisopliae) and two previously unidentified ‘indeterminate’

clades (provisionally annotated as M. indet. 1 and M. indet 2). Systematic and

population genetic analyses indicated strongly that the two indeterminate clades

represent two new Metarhizium species. Moderate genetic differences and gene flow

were observed between populations from different locations and ecotypes.

Factors that might affect the diversity of Metarhizium species in soils of different

ecotypes, crops and locations were analysed using multivariate and univariate

analyses. The distribution of Metarhizium species varied with location, ecotype and

crop. The reconstructed phylogenetic relationships, population and ecological

analyses of Metarhizium isolates from soil in legume and maize fields identified

associations between specific clades with legume and maize crops. M. anisopliae and

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M. robertsii isolates were significantly associated with soil from legume crops and

forests, and with higher nitrogen and carbon content in soil. Isolates of the M. indet. 1

clade were associated with maize and grassland soils and with low nitrogen and

carbon content. The isolates of M. robertsii and M. indet. 1 were more abundant in

iron rich soil. There was no relationship between phosphate and occurrence of any

species.

The relationship of isolates with legumes and maize plants was investigated. M.

robertsii, M. anisopliae, M. pingshaense were found to be abundant in soils of legume

crops, whereas the 2 new taxonomic clades M. indet. 1 and M. indet. 2 were only

isolated only from soils in which maize had been recently grown. Colonisation of pea

and maize roots was quantified for six fungal isolates from legume crops and six

isolates from maize. Colonisation of pea roots was significantly higher than those of

maize for all isolates, but isolates differed significantly in their colonisation of roots

of the two plant types. The results support the proposal that the host plant influences

both the Metarhizium species found in soil and colonisation of the plant roots.

Finally, the effect of the plant hormone strigolactone on early stages in root

colonisation by M. anisopliae was examined indirectly, through exposure to selected

pea mutants with differing levels of strigolactone expression. Conidium germination

in root exudates, and fungal colony forming units on the roots, were compared in wild

type pea (Pisum sativum L. cv Torsdag) and two pea mutants, rms 5-3 ‘strigolactone

deficient’, and rms 4-1 ‘strigolactone overproducing’ mutants. Plant varieties with

known higher production of strigolactone resulted in increase in both conidium

germination and root colonisation by Metarhizium.

These research findings suggest that selection of Metarhizium species or strains may

enhance the establishment of beneficial inocula in the rhizosphere of crops.

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Table of Contents

KEYWORDS.................................................................................................................................. I

ABSTRACT................................................................................................................................... II

TABLE OF CONTENTS ........................................................................................................... IV

LIST OF FIGURES .................................................................................................................... VI

LIST OF TABLES ...................................................................................................................... IX

LIST OF ABBREVIATIONS .................................................................................................... XI

STATEMENT OF ORIGINAL AUTHORSHIP .................................................................. XII

ACKNOWLEDGEMENTS .................................................................................................... XIII

CHAPTER 1 : GENERAL INTRODUCTION ....................................................................... 1 1.1 INTRODUCTION AND BACKGROUND ............................................................................................................. 2 1.2 AIMS AND OUTLINE OF THIS THESIS ............................................................................................................. 4

CHAPTER 2 : LITERATURE REVIEW ................................................................................ 6 2.1 THE ENTOMOPATHOGENIC FUNGI OF THE GENUS METARHIZIUM. ....................................................... 7 2.2 TAXONOMY OF METARHIZIUM ....................................................................................................................... 9 2.3 METARHIZIUM ABUNDANCE ........................................................................................................................ 15 2.4 SOIL AND ENVIRONMENTAL FACTORS AFFECTING METARHIZIUM ABUNDANCE ........................... 15 2.5 METARHIZIUM, A RHIZOSPHERE ASSOCIATE ........................................................................................... 16 2.7 SIGNALLING DURING COLONISATION OF THE RHIZOSPHERE .............................................................. 19 2.8 SUMMARY ........................................................................................................................................................ 20

CHAPTER 3 : SYSTEMATICS OF AUSTRALIAN METARHIZIUM ISOLATES ...... 21 ABSTRACT ............................................................................................................................................................... 22 3.1 INTRODUCTION ............................................................................................................................................... 23 3.2 MATERIALS AND METHODS ........................................................................................................................ 27

3.2.1 Collection of soil samples .............................................................................................................. 27 3.2.2 Isolation of fungal isolates ........................................................................................................... 27 3.2.3 Genomic DNA extraction, PCR amplification, and sequencing .................................... 28 3.2.4 Phylogenetic tree construction .................................................................................................. 30 3.2.6 Haplotype distribution analysis ................................................................................................. 33 3.2.7 Statistical analysis ........................................................................................................................... 34

3.3 RESULTS ........................................................................................................................................................... 35 3.3.1 Occurrence of Metarhizium isolates ........................................................................................ 35 3.3.2 ITS phylogeny ..................................................................................................................................... 39 3.3.3 Phylogenetic analysis...................................................................................................................... 39 3.3.4 DNA divergence, genetic differentiation and gene flow ................................................. 46 3.3.5. Haplotype analysis .......................................................................................................................... 52

3.4 DISCUSSION ..................................................................................................................................................... 59 3.7 SUPPLEMENTARY DATA ............................................................................................................................... 65

CHAPTER 4 : FACTORS AFFECTING THE OCCURRENCE AND DIVERSITY OF

METARHIZIUM SPECIES IN AGRICULTURAL, GRASSLAND AND FOREST

SOILS ........................................................................................................................................... 81 ABSTRACT ............................................................................................................................................................... 82 4.1 INTRODUCTION ............................................................................................................................................... 83 4.2 MATERIALS AND METHODS ......................................................................................................................... 85

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4.2.1 Soil collection, isolation and description ............................................................................... 85 4.2.2 Soil analysis ......................................................................................................................................... 85 4.2.3 Colony forming unit (CFU)............................................................................................................ 86 4.2.5 Statistical analysis ............................................................................................................................ 87

4.3 RESULTS ........................................................................................................................................................... 88 4.3.1 Fungal species distribution in soil samples........................................................................... 88 4.3.2 Ecological association .................................................................................................................... 94 4.3.3 Soil factors............................................................................................................................................ 96

4.4 DISCUSSION................................................................................................................................................... 101

CHAPTER 5 : ASSOCIATION OF METARHIZIUM SPECIES WITH

AGRICULTURAL CROPS AND EVALUATION OF COLONISATION OF

SELECTED ISOLATES IN PEA AND MAIZE. ............................................................... 105 ABSTRACT ............................................................................................................................................................. 106 5.1 INTRODUCTION ............................................................................................................................................ 107 5.2 MATERIALS AND METHODS ...................................................................................................................... 109

5.2.1 Collection of soil samples, fungal isolation, Genomic DNA extraction, PCR amplification, and sequencing ............................................................................................................ 109 5.2.3 Phylogenetic tree construction ................................................................................................ 109 5.2.4 Genetic differentiation and gene flow studies .................................................................. 109 5.2.5 Metarhizium colonisation in pea and maize ..................................................................... 110 5.2.6 Data analysis .................................................................................................................................... 112

5.3 RESULTS ........................................................................................................................................................ 113 5.3.1 Metarhizium abundance in agricultural soils .................................................................. 113 5.3.2 Multilocus phylogenetic analysis ............................................................................................ 113 5.3.3 Genetic differentiation studies ................................................................................................. 116 5.3.4 Ecotype association study .......................................................................................................... 120 5.3.5 Plant colonisation .......................................................................................................................... 121

5.4 DISCUSSION................................................................................................................................................... 126 5.8 SUPPLEMENTARY DATA ............................................................................................................................. 129

CHAPTER 6 : THE EFFECT OF STRIGOLACTONE ON CONIDIUM

GERMINATION AND ROOT COLONISATION BY METARHIZIUM ANISOPLIAE

..................................................................................................................................................... 131 ABSTRACT ............................................................................................................................................................. 132 6.1 INTRODUCTION ............................................................................................................................................ 133 6.2 MATERIALS AND METHODS...................................................................................................................... 136

6.2.1 Fungus material ............................................................................................................................. 136 6.2.2 Plant material ................................................................................................................................. 136 6.2.3 In vitro germination experiment ............................................................................................ 137 6.2.4 Root colonisation experiment .................................................................................................. 137 6.2.5 Data analysis .................................................................................................................................... 138

6.3 RESULTS ........................................................................................................................................................ 139 6.3.1 M. anisopliae conidium germination .................................................................................... 139 6.3.2 Root colonisation ........................................................................................................................... 141

6.4 DISCUSSION................................................................................................................................................... 142 6.5 SUPPLEMENTARY DATA ............................................................................................................................. 144

CHAPTER 7 : CONCLUSIONS ........................................................................................... 146 7.1 KEY FINDINGS............................................................................................................................................... 147 7.2 FUTURE DIRECTIONS .................................................................................................................................. 152

REFERENCES ............................................................................................................................ 154

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List of Figures

Figure 2.1 Cadaver of green vegetable bug, Nezzara viridula, infected by

Metarhizium anisopliae and covered in green conidia produced following death of the

host (Photo: K. Knight & C. Hauxwell) .......................................................................................... 7 Figure 3.1 Mean percentage of occurrence of Metarhizium in soil samples collected

from different locations (above) and ecotypes (below) of Queensland. Bar plot (mean

percentageSEM) with different lowercase letter indicates statistical differences on

Tukey’s post hoc test (GLM, p < 0.05). ........................................................................................ 37 Figure 3.2 Mean percentage of occurrence of Metarhizium in soil samples collected

from different disturbed and natural habitats (above) and disturbed and natural

habitats of three locations of Queensland (below). Bar plot (mean percentageSEM)

with different lowercase letter indicates statistical differences on Tukey’s post hoc test

(GLM, p < 0.05). .................................................................................................................................... 38 Figure 3.3 Phylogenetic tree of concatenated data set of 5’TEF and MzIGS3 of 164

isolates of the genus Metarhizium with reference strains. Clades containing multiple

sequences were collapsed and are presented as triangles marked by the clade name

with number of isolates retained by each clade in parenthesis. Clade supporting value

obtained from maximum likelihood (above) and Bayesian (below) analyses were

presented in the node. ........................................................................................................................... 43 Figure 3.4 Maximum likelihood tree of concatenated data set of MzIGS3 and 5’-TEF

of 164 Australian Metarhizium isolates with reference strains ............................................ 45 Figure 3.5 Haplotype networks for the Metarhizium populations from five groups of

isolates based on combined sequence data. Different ‘pie’ colours in the circles

indicate the location of haplotype origin, with size of the circles indicating number of

specimens; pie-segmentation is proportional to haplotype frequency. Perpendicular

tick marks on the lines represent the mutations between the linked haplotypes. Clades

within the ‘M. pingshaense’ complex (M. indet. 1, M. indet. 2 and M. pingshaense)

shared haplotypes, and clades within the ‘M. robertsii/M. anisopliae’ complex shared

1 haplotype, but there were no shared haplotypes between the two complexes. ........... 56 Figure 3.6 Haplotype networks for the Metarhizium populations from three locations

based on combined sequence data. Different ‘pie’ colours in the circles indicate the

location of haplotype origin, with size of the circles indicating number of specimens;

pie-segmentation is proportional to haplotype frequency. Perpendicular tick marks on

the lines represent the mutations between the linked haplotypes. ....................................... 57 Figure 3.7 Haplotype networks for the Metarhizium populations from four ecotypes

based on combined sequence data. Different ‘pie’ colours in the circles indicate the

location of haplotype origin, with size of the circles indicating number of specimens;

pie-segmentation is proportional to haplotype frequency. Perpendicular tick marks on

the lines represent the mutations between the linked haplotypes. None of the 11

grassland haplotypes (purple) were shared with any other ecotype. .................................. 58 Figure 3.8 Maximum likelihood tree of ITS sequence data. Three sequences from

genus Pochonia that are listed on MycoBank under the synonyms Metacordyceps

chlamydosporia UCR3, Metacordyceps chlamydosporia IAM14700, and Cordyceps

chlamydosporia (Syn Metacordyceps chlamydosporia) NBRC9249 were included in

genus/clade Pochonia. The six isolates that aligned with this clade were excluded

from subsequent analysis. ................................................................................................................... 66 Figure 3.9 Bayesian tree of concatenated data set of 5'-TEF and MzIGS3 for

Metarhizium isolates ............................................................................................................................. 68 Figure 3.10 Maximum likelihood tree of MzIGS3 data of Metarhizium isolates ......... 70

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Figure 3.11 Maximum likelihood of 5'-TEF data of Metarhizium isolates ..................... 72 Figure 3.12 Bayesian tree of MzIGS3 data of Metarhizium isolates ................................. 74 Figure 3.13 Bayesian tree of 5’-TEF data of Metarhizium isolates .................................... 76 Figure 3.14 Maximum Likelihood tree of combined data set of MzIGS3, 5’-TEF and

ITS for Metarhizium isolates ............................................................................................................. 78 Figure 3.15 Bayesian tree of combined data set of MzIGS3, 5’-TEF and ITS for

Metarhizium isolates ............................................................................................................................ 80 Figure 4.1: Occurrence of each clade as a proportion of the soil samples in which at

least one colony forming units was identified. The number of soil samples in which

each clade was identified is in brackets. ....................................................................................... 90 Figure 4.2 The occurrence of Metarhizium species as a proportion of soil samples at

different locations (above) and ecotypes (below). No statistical significant difference

was observed. .......................................................................................................................................... 92 Figure 4.3 Geographical representation of soil samples of different locations of

Queensland in which different species of Metarhizium were detected. Pie charts

showing species level distribution of Metarhizium relative abundance (%) in the soil

samples in which Metarhizium detected in Bundaberg, Kingaroy and Gatton. ............. 93 Figure 4.4 Heatmap of Metarhizium species associated with locations and ecotypes

based on Czekanowski dissimilarity in DICE analysis of presence-absence data,

R=0.403, significance =0.001. .......................................................................................................... 95 Figure 4.5 Scree plot: percentages of variables explained by dimension of principal

component analysis ............................................................................................................................... 97 Figure 4.6 Separation of Metarhizium species identified in soil samples using

principal component 1 and 2. Separation of species along Dimension 1 axis is due to

variation in Cu, Fe, Mn, Ti, S, Si and Al. Soil elements Zn, Ba, Ca, K, Zr, Mg and Na

contribute in Dimension 2 2 axis. .................................................................................................... 99 Figure 4.7 Densities (CFU/g dry soil) of Metarhizium observed in the numbers of soil

samples ................................................................................................................................................... 101 Figure 5.1 Phylogenetic tree obtained from maximum likelihood analysis of

concatenated data of MzIGS3 and 5'-TEF.Taxon label colour indicates the source of

the isolates, red for maize and blue for legume field. Taxon within blue box was used

for colonisation experiment. ........................................................................................................... 114 Figure 5.2 Phylogenetic tree obtained from Bayesian inference analysis of

concatenated data of MzIGS3 and 5'-TEF.Taxon label colour indicates the source of

the isolates, red for maize and blue for legume field. ........................................................... 115 Figure 5.3 Haplotype networks for the Metarhizium populations from five clades

based phylogenetic analysis of combined sequence data. Different pie colours in the

circles indicate the location of haplotype origin with size of the circles and pie-

segment is proportional to haplotype frequency. Perpendicular tick marks on the lines

represent the mutations between the linked haplotypes. ...................................................... 118 Figure 5.4 Haplotype networks for the Metarhizium populations from two ecotypes.

Different pie colours in the circles indicate the location of haplotype origin with size

of the circles and pie-segment is proportional to haplotype frequency. Perpendicular

tick marks on the lines represent the mutations between the linked haplotypes. Only

one haplotype was shared between isolates from the two crop types. ............................ 119 Figure 5.5 Heatmap of Metarhizium species associated with maize and legume

ecotypes based on Czekanowski dissimilarity in DICE analysis of presence-absence

data, R=0.5339, significance =0.001. .......................................................................................... 120 Figure 5.6 Colonization of legume (pea) and maize roots by Metarhizium isolates

from maize fields (colonisation experiment 1). Bar plot (meanSEM) with different

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lowercase letters indicates statistical differences on Tukey’s post hoc test, p < 0.05.

.................................................................................................................................................................... 122 Figure 5.7 Colonization of legume (pea) and maize roots by 3 Metarhizium clades

isolated from maize fields (colonisation experiment 1). Bar plot (meanSEM) with

different lowercase letters indicates statistical differences on Tukey’s post hoc test,

p < 0.05. BMA2 is the only isolate of M. robertsii from maize. BMD20 is clade M.

indet 1. All other isolates are clade M. indet 2. ....................................................................... 122 Figure 5.8 Effects of plant types and Metarhizium legume field isolates on root

colonisation (colonisation experiment 2). Bar plot (meanSEM) with lowercase letter

indicates statistical differences using Tukey’s post hoc test, p < 0.05. Isolates

KLA15.2 and KLD 17 are M. robertsii, KLA17.1 and KLA17.2 are M. pingshaense,

KLB7 and KLD 5.1 are M. anisopliae. ...................................................................................... 124 Figure 5.9 Effects of plant types and Metarhizium clades from legume field isolates

on root colonisation (colonisation experiment 2). Bar plot (meanSEM) with different

lowercase letter indicates statistical differences on Tukey’s post hoc test, p < 0.05. 124 Figure 5.10 Effects of plant types on root colonisation : A: Mean cfu of Metarhizium

isolates from maize fields, B: Mean cfu of Metarhizium isolates from legume fields

(Bar plot (meanSEM) with asterisk mark indicates statistical differences on Tukey’s

post hoc test, p < 0.05. ....................................................................................................................... 125 Figure 5.11 Maximum likelihood tree of ITS data sets of isolated entomopathogens

from agricultural fields ..................................................................................................................... 130 Figure 6.1 Effects of pea plant varieties and root exudate concentrations on M.

anisopliae conidium germination after 24 hours. Bar plot (mean±SEM) with different

lowercase letters indicates statistical differences on Tukey’s post hoc test (p < 0.05).

.................................................................................................................................................................... 140 Figure 6.2 Spore germination of M. anisopliae as a function of concentration of RE of

wild, rms4-1 and rms5-3 pea plants. Spore germination was assessed after 24 hours.

.................................................................................................................................................................... 140 Figure 6.3 Effect of pea plant varieties (wild, rms4-1 and rms5-3) on mean cfu of M.

anisopliae at 3 days and 7 days post inoculation. Bar plot (mean±SEM) with different

lowercase letter indicates statistical differences on Tukey’s post hoc test (p < 0.05).

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

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List of Tables

Table 2:1 Current Metarhizium species and their synonyms (Kepler et al., 2014,

http://www.naro.affrc.go.jp/org/fruit/epfdb/syn/esyn-de.htm) ............................................. 11 Table 3:1 Description of primers used in polymerase chain reactions (PCR)................ 31 Table 3:2 The number of soil samples with Metarhizium isolates in different locations

and ecotypes. ........................................................................................................................................... 36 Table 3:3 Summary tables of phylogenetic analyses showed the number of isolates

included in the clade with its support values (in parenthesis). NR denotes clade not

resolved in the analysis. Table 3.3A shows the value for three major clades: M.

robertsii, M. pingshaense plus Metarhizium indet. spp. and M. anisopliae. Table 3.3B

shows the values for M. pingshaense and Metarhizium indet. clades. Table 3.3C

shows the values for M. pingshaense and Metarhizium indet. 1 and Metarhizium indet.

2 clades. ..................................................................................................................................................... 41 Table 3:4 DNA divergence data between the clades of Metarhizium in the two

analysed loci and combined data set............................................................................................... 47 Table 3:5 DNA divergence data between the populations of Metarhizium in different

locations for combined MzIGS3 and 5’-TEF data set ............................................................. 48 Table 3:6 DNA divergence data between the populations of Metarhizium in different

ecotypes for combined MzIGS3 and 5’-TEF data set .............................................................. 48 Table 3:7 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between different Metarhizium clades. ............................................................................. 49 Table 3:8 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between Metarhizium populations in different locations ........................................... 50 Table 3:9 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between Metarhizium populations in different ecotypes. Isolates from forest and

legume showed no low differentiation. ......................................................................................... 51 Table 3:10 Distribution of haplotypes within Metarhizium clades and isolates ............ 53 Table 3:11 Number of shared haplotypes of Metarhizium populations between the

groups of clades (A), locations (B) and ecotypes (C) .............................................................. 55 Table 4:1 Metarhizium species and number of colony observed in a plate in 58 soil

samples from different ecotypes and locations by transect. .................................................. 88 Table 4:2 Binomial logistic regression analysis shows the relationship between the

species of Metarhizium in soils as a function of location and ecotype ............................. 96 Table 4:3 Eigenvalue and variance extracted from PCA analysis for soil factors ........ 97 Table 4:4 Loading value by principal components showing significant differences in

soil components across 11 sites & ecotypes. Mauve coloured cells show a significant

positive relationship and orange coloured cells show a significant negative

relationship. .............................................................................................................................................. 98 Table 4:5 Point biserial correlation between the presence of Metarhizium species and

the soil elements. Only statistically significant relationships (both positive and

negative) are presented. ** and * indicate that the correlation is significant at the 0.01

and 0.05 level (respectively). ......................................................................................................... 100 Table 5:1 Metarhizium isolates selected for root colonisation experiments ................ 111 Table 5:2 DNA divergence data between the populations of Metarhizium in

phylogenetic clades and ecotypes ................................................................................................. 117 Table 5:3 Genetic differentiation and gene flow estimates between the populations of

Metarhizium in phylogenetic clades and ecotypes ................................................................. 117 Table 5:4 Generalised linear model for experiment 1 showing the effects of plant,

fungus and their interaction on colonisation............................................................................. 129

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Table 5:5 Generalised linear model for experiment 2 showing the effects of plant,

fungus and their interaction on colonisation............................................................................. 129 Table 6:1 Logistic regression analysis for plant variety and root exudate (RE)

concentration on conidium germination of Metarhizium .................................................... 144 Table 6:2 Regression analysis of percent conidium germination as a function of

concentration of root exudates of wild, rms4-1, rms5-3 pea plants. ............................... 144 Table 6:3 Generalised liner model showing effect of plant variety on colonisation of

M. anisopliae after 3 days of conidium inoculation .............................................................. 145 Table 6:4 Generalised liner model showing effect of plant variety on colonisation of

M. anisopliae after 7 days of conidium inoculation .............................................................. 145

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List of Abbreviations

SM= Selective Medium

SDAY= Sorbitol Dextrose Agar Yeast medium

ML= Maximum Likelihood

MSN= Minimum Spanning Network

RE= Root Exudate

GLM= Generalised Linear Model

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

QUT Verified Signature

Signature: Shah Mohammad Naimul Islam

Date: April 2018

xiii

Acknowledgements

First, I would like to thank my principal supervisor, A/Professor Caroline Hauxwell,

for her great support to initiate and complete my higher studies in QUT. Thank you

very much for your invaluable advice, encouragement and patience throughout this

research. I also appreciate the excellent support, helpful comments, guidance and

advice given by my associate supervisor, Dr. Tanya Scharaschkin.

I gratefully acknowledge financial support from the QUT Postgraduate Research

Award (QUTPRA) scholarship. I would like to acknowledge the research facilities by

QUT. Thanks to Centre for Analytical Research Facility (CARF) for their

experimental facilities.

I thank Neil V. Halpin, John D. Duff, and Hugh Brier of the Department of

Agriculture and Fisheries (DAF), Queensland, for facilitating the collection of soil

samples from, respectively, the Bundaberg, Gatton and Kingaroy research stations.

Some of the data reported in this paper was generated in the Central Analytical

Research Facility (CARF) operated by the Institute for Future Environments (QUT).

Access to CARF was supported by generous funding from the Science and

Engineering Faculty (QUT).

Special thanks to my best friend and wife, Dr. Razia Sultana, for bearing with me all

during this study and sharing all the stresses of my PhD life. Thanks to our boys

Tawseef Tahmeed Aaryan (two years) and Tayif Tareem Adrian (one month) who are

colouring our life and inspiring me always. I would also like to express my love and

gratitude to my beloved family members, for their understanding and endless love. In

particular, my mother for her patience for his encouragement to completion of this

thesis although being constantly sick since I left Bangladesh. I am really proud of my

younger brother, Rakibul Islam, for taking care of the parents during this time.

Furthermore, I would like to thank all my colleagues and friends for motivation,

advice and help rendered: special thanks to Anne-Marie McKinnon, Vincent Chand,

Robert Spence, Andrew Dickson, Shane Russell, for their continued support during

my candidature especially with experiments. Thanks a lot, to Sarie Gould, Courtney

Innes and Tiziana LaMendola (SEF) for their very friendly helpful supports in

financial and administrative issues. I would like to thank Dr. Paul Melloy, Christopher

Noune, Boyd Tarlinton, Nathaniel Crane, Lille Gill, Purnika Ranasinghe, Tharanga

Niroshini, Dr. Melodina Fabillo, Karma Wangchuck, Dr. Joshua Comrade Buru, Thita

Soonthornvipat, for their fruitful suggestion and help at different stages of my PhD

journey. Special thanks to Dr. Mostafizur Rahman and Dr. Md. Farhad Hossain for

helping me in soil sample collection from remote places. Many thanks to QUT-

Bangladeshi Association.

Not but the least, I am thankful to my motherland Bangladesh and beautiful Australia.

1

Chapter 1 : General introduction

2

1.1 Introduction and background

Global food production is facing increasing challenges, including crop losses from

insect pests. Many insecticides have been deregistered due to health and

environmental concerns (Hawke, 2010), and many insect pests have developed

resistance to them (Chandler et al., 2011). New technologies such as more selective

chemistry and genetic modification of plants have been used to reduce crop losses

(Casida and Quistad, 1998). However, these also face challenges from resistance as

the number of control options is reduced, and the costs of developing and registering

new controls increases (“APVMA” 2014).

Entomopathogenic fungi of the order Hypocreales (phylum Ascomycota) frequently

cause disease in insects. These entomopathogens have been used as biological control

agents (biopesticides) for management of insect pests (Zimmermann, 2007) and as an

alternative to chemical insecticides in crop protection (Lacey et al 2015, Lacey and

Goettel, 1995). Hypocrealean fungi of the genus Metarhizium Metschnikoff Sorokin

are widely distributed in soil, and cause ‘green muscardine disease’ in a wide range of

insects (Zimmermann, 2007). The genus has become a model for basic research on

fungal entomopathogens (St. Leger, 2008).

Fungi of the genus Metarhizium are, like many Hypocrealean fungi, saprophytes in

soil and colonisers of the rhizosphere in a mutualistic relationship with plants that can

result in improved plant growth and nutrient uptake, protection from invertebrate

herbivores, and induction of plant systemic resistance leading to reduced

susceptibility to plant diseases (Jaber and Ownley, 2017, Behie et al., 2012, Wyrebek

et al., 2011, Harman, 2006, Hu and St. Leger, 2002). Inoculation of the rhizosphere of

crops with Hypocrealean fungi may have significant benefits to agriculture including

the potential to control insect pests if inoculation of the plants can be successfully

3

achieved under field conditions (Jaber & Ownley 2017, Sasan, 2012, Parsa et al.,

2013).

The experimental inoculation of plants with entomopathogenic fungi has produced

inconsistent results, with the fungus failing to colonise the majority of plants (Parsa et

al., 2013). This is a significant limit to their potential use as rhizospheric inocula.

Research on the evolutionary and ecological association of Metarhizium with plants

may identify strains that increase the consistency and efficacy of the inoculation of

Metarhizium spp. in crops.

Colonisation of the plant may be affected by a number of factors, including plant

nutrition, plant and fungal signalling, host specificity and ecological adaptation.

Phosphate availability and secretion of growth hormones as signalling molecules

(strigolactones) by plants play an important role in the colonisation of plant roots by

symbiotic fungi (Harrison, 2005). However, the plant and fungal signals involved in

successful colonisation of plant roots by Metarhizium are not known.

Metarhizium species exhibit ecological and plant-specific association. For example,

different species of Metarhizium are associated with different habitats such as the

rhizospheres of grasses, shrubs and trees (Wyrebek et al., 2011, Bidochka et al.,

2001). These species or strain-specific associations probably arise from the long-term

associations of the fungi with plant species in certain ecotypes (Bruck, 2010). These

associations could be used as criteria to select Metarhizium species or strains for use

in biological control (Bidochka, 2001). They may also have significant impacts on the

success of colonisation of particular crop types.

4

1.2 Aims and outline of this thesis

The aims of this research were to determine:

1. The natural occurrence, diversity and population genetics of Metarhizium in

Queensland’s soils, particularly in agricultural crops, grassland and forest

ecotypes.

2. Factors affecting the distribution and abundance of Metarhizium species in

different ecotypes and soils.

3. The association of Metarhizium species with agricultural crops and their

colonisation potential in different crops, specifically monocots (maize) and

Eudicots (bean) systems.

4. The possible role of the plant hormone strigolactone on the germination and

colonisation by Metarhizium in plant roots.

Following the General Introduction of Chapter 1, Chapter 2 provides a literature

review, followed by four research Chapters (Chapter 3-Chapter 6) and a final

conclusion, Chapter 7. Materials and methods are discussed in their respective

research Chapters.

Chapter 3 focuses on the diversity and systematics of Australian Metarhizium isolates.

This study includes the isolation of Metarhizium isolates from soils in Queensland.

Multilocus phylogenetic analyses were used to identify the taxonomic position of

Metarhizium isolates. A population genetic study also describes the structure of the

Metarhizium population and gene flow in the ecotypes and locations studied.

Chapter 4 analyses the soil factors affecting the abundance and diversity Metarhizium

species in agricultural, grassland and forest soils using multivariate and univariate

statistical analyses.

5

In Chapter 5, the germination and rhizospheric colonisation of selected Metarhizium

species and strains in pea and maize plants was evaluated in laboratory.

Chapter 6 evaluates the role of the plant hormone, strigolactone, on the germination of

Metarhizium and colonisation by Metarhizium in plant roots using strigolactone pea

mutants.

Finally, Chapter 7 provides a general conclusion to the thesis, arguments for its

significance, and offers future directions for research.

6

Chapter 2 : Literature review

7

2.1 The entomopathogenic fungi of the genus Metarhizium.

Entomopathogenic fungi of the order Hypocreales (Phylum Ascomycota) frequently

cause disease a wide range of host insects, including major pests of agriculture such

as coleoptera and lepidoptera, thrips and aphids (Lacey et al 2015).

Entomopathogenic fungi have attracted interest as bio-control agents worldwide due

to their minimal impacts on non-target organisms and increasing use in insecticide

resistance management strategies (Lacey et al 2015, Strasser et al., 2010,

Zimmermann 2007).

Among the members of the order Hypocreales (Phylum: Ascomycota), the

entomopathogenic fungi belonging to genus Metarhizium of Family Clavicipitaceae

are under intense research for their potential traits favouring their use as microbial

control agents against invertebrate pests (Strasser et al., 2010, Bruck, 2010).

Metarhizium spp. infect insects by their conidia (asexual spores) and cause fatal

‘green muscardine’ disease (Figure 2.1).

Figure 2.1 Cadaver of green vegetable bug, Nezzara viridula, infected by

Metarhizium anisopliae and covered in green conidia produced following death of the

host (Photo: K. Knight & C. Hauxwell)

8

The process of infection in a suitable host by Metarhizium spp. follows several well-

documented steps (for example, Thomas and Read 2007). Firstly, fungal conidia

adhere to the host cuticle using adhesion protein and hydrophobic interaction. Conidia

germinate on the host cuticle and form an appressorium, followed by penetration of

cuticle by mechanical force and cuticle degrading enzymes including, chitinases,

proteases and lipases. Within the host, transmission of infection proceeds via

production of blastospores or hyphal growth in the haemocoel and production of

secondary metabolites. Following death of the host and under favourable conditions

of humidity, hyphae emerge from the cadaver and conidia are produced (Fig 2.1)

Metarhizium spp. infect a wide range of insects, including (amongst others)

Coleoptera, Lepidoptera, Hemiptera, Orthoptera and Isoptera (Lacey et al 2015,

Goettel et al., 1990). Their potential is enhanced by direct infection of the host by

germination on and penetration of the insect cuticle, which removes the requirement

for the host to ingest the pathogen (as is required with, for example, the use of

Bacillus thuringiensis or baculoviruses as biopesticides), and their saprophytic growth

on semiartificial media enables relatively straight forward industrial production

(Lacey et al 2015, Dorta et al., 1996). Commercial formulations have been registered

and used in a number of countries including Australia, which includes the registration

and use of M. acridium (Driver and Milner) J.F. Bisch, Rehner & Humber (2009) in

the product ‘Green Guard’ to control the Australian plague locust (Chortoicetes

terminifera Walker) (Hauxwell et al 2010, Faria and Wraight, 2007).

9

2.2 Taxonomy of Metarhizium

Elias Metschnikoff first described Entomophthora anisopliae as a pathogen of the

scarab beetle, Anisoplia austriaca, in 1879 (Zimmermann et al., 1995). A year later,

he renamed the fungus Isaria destructor. In 1883, Metschnikoff placed the fungus

under a new genus, Metarhizium, and called the fungus M. anisopliae (Zimmermann

et al., 1995).

Tulloch (1976) carried out a major revision of Metarhizium taxonomy based on its

morphological characteristics, accepting only two species, M. anisopliae

(Metschnikoff) Sorokin and M. flavoviride (W. Gams and Rozspal), and two varieties:

a short-spored form of M. anisopliae (Metschnikoff) Sorokin var. anisopliae and a

long-spored form, M. anisopliae (Metschnikoff) Sorokin var. major (Johnston)

(Tulloch, 1976). However, the taxonomic classification based on the morphology

faced challenges, as many of the identifying characters (phialide and conidia) varied

within the same fungal isolates and on different culture media (Glare et al., 1996,

Driver et al., 2000, Kamat et al., 1952).

The first molecular taxonomic revision of Metarhizium used the internal transcribed

spacer (ITS) sequence and identified ten distinct phylogenetic clades and several

Metarhizium varieties, but the taxonomic relationship among the taxa was poorly

resolved due to the limited resolution of the sequence analysis (Kepler et al., 2014,

Driver et al 2000,). Subsequently, the first multilocus analysis of the genus was

conducted using a number of other molecular markers (EF-1α, RPB1, RPB2 and β-

tubulin markers), which promoted several varieties to species status, and described

some new species of Metarhizium (Bischoff et al. 2009). The taxonomy was

subsequently revised using a multigene approach to clarify Metarhizium systematics

10

(Kepler et al. 2014). The revised taxonomy of genus Metarhizium recognizes 39

species and includes several (though not all) species formerly classified as

Metacordyceps, and species formerly classified in the genus Chameleomyces, the

green conidium-producing members of genus Nomuraea, and some other species

formerly of the genus Paecilomyces. Table 2.2 lists current Metarhizium species and

their synonyms.

11

Table 2:1 Current Metarhizium species and their synonyms (Kepler et al., 2014,

http://www.naro.affrc.go.jp/org/fruit/epfdb/syn/esyn-de.htm)

Species name, authorities and year of

description

Synonym(s) Voucher collection

1 Metarhizium anisopliae

(Metsch.) Sorok., (1883)

Isaria destructor Metsch. (1880)

Entomophthora anisopliae Metch. (1883) Oospora destructor (Metsch.) (1893)

Isaria anisopliae (Metsch.) Pettit, (1895)

Isaria anisopliae var. americana Pettit, (1895) Penicillium anisopliae (Metsch.) Vuill. (1904)

Penicillium cicadinum Hohn. (1909)

Metarhizium cicadinum (Hohn.) Petch, (1931)

Sporotrichum paranense Marchionatto, (1933) Beauveria paranesis (Marchion) Gosswald (1939)

Paecilomyces paranensis (Marchion) Gunth. Muller (1965)

ARSEF 7487

2 Metarhizium brunneum

Petch, (1935)

ARSEF 2107

3 Metarhizium

guizhouense Q.T. Chen

& H.L. Guo, (1986)

CBS 258.90

4 Metarhizium

pingshaense Q.T. Chen

& H.L. Guo,

(1986).

CBS 257.90

5 Metarhizium acridum

(Driver & Milner) J.F.

Bisch., Rehner & Humber, (2009)

Metarhizium anisopliae var. acridum Driver & Milner,

(2000)

ARSEF 7486

6 Metarhizium robertsii

J.F. Bisch., Rehner &

Humber (2009)

ARSEF 7501

7 Metarhizium globosum

J.F. Bisch., Rehner &

Humber (2009)

ARSEF 2596

8 Metarhizium lepidiotae (Driver & Milner) J.F.

Bisch., Rehner &

Humber (2009)

Metarhizium anisopliae var. lepidiotae Driver & Milner (2000)

ARSEF 7488

9 Metarhizium majus (J.R. Johnst.) J.F.

Bisch., Rehner &

Humber (2009)

Metarhizium anisopliae f. major J.R. Johnst. (1915) Metarhizium anisopliae f. oryctophagum Friederichs,

(1990)

Metarhizium anisopliae var. major (J.R. Johnst.) M.C. Tulloch, (1976)

ARSEF 1914

10 Metarhizium frigidum J.

Bisch. & S. A. Rehner,

(2006)

ARSEF 4124

11 Metarhizium

flavoviridae Gams &

Rozsypal, (1973)

CBS 218.56

12

Species name,

authorities and year of

description

Synonym(s) Voucher collection

12 Metarhizium atrovirens

(Kobayasi & Shimizu) Kepler,

S.A. Rehner & Humber,

(2014)

Cordyceps atrovirens Kobayasi & Shimizu, (1978)

Metacordyceps atrovirens (Kobayasi & Shimizu) Kepler, G.H. Sung & Spatafora, (2012)

NBRC

103797

13 Metarhizium brittlebankisoides (Zuo

Y. Liu, Z.Q.

Liang, Whalley, Y.J. Yao & A.Y. Liu) Kepler,

S.A.

Rehner & Humber,

(2014)

Cordyceps brittlebankisoides Zuo Y. Liu, Z.Q. Liang, Whalley, Y.J. Yao & A.Y. Liu, (2001)

Metacordyceps brittlebankisoides (Zuo Y. Liu, Z.Q.

Liang, Whalley, Y.J. Yao & A.Y. Liu) G.H. Sung, J.M. Sung, Hywel-Jones & Spatafora, (2007)

ARSEF 3145

14 Metarhizium brasiliense

Kepler, S.A. Rehner &

Humber, (2014)

ARSEF 2948

15 Metarhizium

campsosterni (W.M. Zhang & T.H. Li)

Kepler, S.A. Rehner &

Humber, (2014)

Cordyceps campsosterni W.M. Zhang & T.H. Li, (2004)

Metacordyceps campsosterni (W.M. Zhang & T.H. Li) G.H. Sung, J.M. Sung, Hywel-Jones & Spatafora,

(2007)

HMIGD

200885

16 Metarhizium carneum (Duche´ & R. Heim)

Kepler,

S.A. Rehner & Humber, (2014)

Spicaria carnea Duche´ & R. Heim (1931) Paecilomyces carneus (Duche ́& R. Heim) A.H.S. Br. &

G. Sm., (1957)

Penicillium nopporoense Y. Sasaki & Nakane, (1943)

Penicillium nopporoensum Y. Sasaki & Nakane, (1943)

Spicaria carnosa J.H. Mill., Giddens & A.A. Foster,

(1957) Spicaria decumbens Oudem., (1902)

CBS 239.32

17 Metarhizium

cylindrosporum Q.T. Chen & H.L. Guo,

1986

Nomuraea cylindrospora (Q.T. Chen & H.L. Guo)

Tzean, L.S. Hsieh, J.L. Chen & W.J. Wu, (1993)

CBS 256.90

18 Metarhizium

granulomatis (Sigler) Kepler, S.A. Rehner

& Humber, (2014)

Chamaeleomyces granulomatis Sigler, (2010) UAMH 11176

19 Metarhizium

guniujiangense (C.R. Li, B. Huang, M.Z.

Fan & Z.Z. Li) Kepler,

S.A. Rehner & Humber, (2014)

Metacordyceps guniujiangensis C.R. Li, B. Huang, M.Z.

Fan & Z.Z. Li, (2010)

RCEF 2001

20 Metarhizium indigoticum

(Kobayasi & Shimizu)

Kepler,

S.A. Rehner & Humber,

(2014)

Cordyceps indigotica Kobayasi & Shimizu, (1978)

Metacordyceps indigotica (Kobayasi & Shimizu) Kepler,

G.H. Sung & Spatafora, (2012)

NBRC 100684

Table 2.2 (Continued) Current Metarhizium species and their synonyms

13

Species name, authorities and year of description

Synonym(s) Voucher collection

21 Metarhizium khaoyaiense

(Hywel-Jones) Kepler, S.A.

Rehner & Humber, (2014)

Cordyceps khaoyaiensis Hywel-Jones, (1994)

Metacordyceps khaoyaiensis (Hywel-Jones) Kepler,

G.H. Sung & Spatafora, (2012)

BCC 14290

22 Metarhizium koreanum

Kepler, S.A. Rehner &

Humber, (2014)

ARSEF 2038

23 Metarhizium kusanagiense (Kobayasi & Shimizu)

Kepler, S.A. Rehner &

Humber, (2014)

Cordyceps kusanagiensis Kobayasi & Shimizu, (1983)

Metacordyceps kusanagiensis (Kobayasi & Shimizu)

Kepler, G.H. Sung & Spatafora, (2012)

NBRC109322

24 Metarhizium marquandii

(Massee) Kepler, S.A.

Rehner & Humber

Verticillium marquandii Massee, (1898)

Paecilomyces marquandii (Massee) S. Hughes,

(1951)

Spicaria violacea E.V. Abbott, (1926)

CBS 182.27

25 Metarhizium martiale (Speg.)

Kepler, S.A. Rehner &

Humber, (2014)

Cordyceps martialis Speg., (1889)

Metacordyceps martialis (Speg.) Kepler, G.H. Sung

& Spatafora, (2012)

MB#806087

26 Metarhizium minus (Rombach, Humber & D.W.

Roberts) Kepler, S.A. Rehner

& Humber, (2014)

Metarhizium flavoviride var. minus Rombach, Humber & D.W. Roberts, (1986)

ARSEF 2037

27 Metarhizium

novozealandicum Kepler,

S.A. Rehner &

Humber, (2014)

Metarhizium flavoviride var. novozealandicum Driver

& R.J. Milner, (2000)

ARSEF 4674

28 Metarhizium owariense

(Kobayasi) Kepler, S.A.

Rehner & Humber, (2014)

Cordyceps owariensis Kobayasi, (1939)

Ophiocordyceps owariensis (Kobayasi) G.H. Sung,

J.M. Sung, Hywel-Jones & Spatafora, (2007)

NBRC

33258

29 Metarhizium owariense f.

viridescens (Uchiy. &

Udagawa) Kepler, S.A. Rehner & Humber, (2014)

Cordyceps owariensis f. viridescens Uchiy. &

Udagawa, (2002)

Ophiocordyceps owariensis f. viridescens (Uchiy. & Udagawa) G.H. Sung, J.M. Sung, Hywel-Jones &

Spatafora, (2007)

Metacordyceps owariensis f. viridescens (Uchiy. & Udagawa) Kepler, G.H. Sung & Spatafora, (2012)

Nomuraea owariensis Uchiy. & Udagawa (2002)

NBRC 33258

30 Metarhizium pemphigi

(Driver & R.J. Milner) Kepler,

S.A. Rehner & Humber,

(2014)

Metarhizium flavoviride var. pemphigi Driver & R.J.

Milner, (2000)

CABI 177416

31 Metarhizium pseudoatrovirens (Kobayasi

& Shimizu)

Kepler, S.A. Rehner & Humber, (2014)

Cordyceps pseudoatrovirens Kobayasi & Shimizu, (1982)

Metacordyceps pseudoatrovirens (Kobayasi &

Shimizu) Kepler, G.H. Sung & Spatafora, (2012)

NBRC 103797

14

Species name, authorities and year of

description

Synonym(s) Voucher collection

32 Metarhizium rileyi

(Farl.) Kepler, S.A. Rehner &

Humber, (2014)

Botrytis rileyi Farl., (1883)

Spicaria rileyi (Farl.) Charles, (1936) Beauveria rileyi (Farl.) Gösswald, (1939)

Nomuraea rileyi (Farl.) Samson, (1974)

Nomuraea prasina Maubl., (1903)

ARSEF 1972

33 Metarhizium taii Z.Q. Liang & A.Y. Liu, Acta

Mycologica Sinica,

10:260, 1991

Cordyceps taii Z.Q. Liang & A.Y. Liu, (1991) Metacordyceps taii (Z.Q. Liang & A.Y. Liu) G.H.

Sung, J.M. Sung, Hywel-Jones & Spatafora, (2007)

RCEF0772

34 Metarhizium yongmunense (G.H.

Sung, J.M. Sung &

Spatafora) Kepler, S.A.

Rehner & Humber, (2014)

Metacordyceps yongmunensis G.H. Sung, J.M. Sung & Spatafora, (2007)

EFCC 2131

35 Metarhizium viridulum

(Tzean, L.S. Hsieh, J.L. Chen

& W.J. Wu) B. Huang

& Z.Z. Li,

(2004)

Nomuraea viridula Tzean, L.S. Hsieh, J.L. Chen &

W.J. Wu, (1992)

ARSEF 6927

36 Metarhizium viride

(Segretain, Fromentin,

Destombes, Brygoo & Dodin ex

Samson) Kepler, S.A.

Rehner & Humber,

(2014)

Paecilomyces viridis Segretain, Fromentin, Destombes,

Brygoo & Dodin ex Samson, (1974)

Chamaeleomyces viridis (Segretain, Fromentin, Destombes, Brygoo & Dodin ex Samson) Sigler,

(2010)

ARSEF 2456

37 Metarhizium alvesii

Lopes, Faria, Montalva

& Humber sp. nov. (2017)

ARSEF 13308

38 Metarhizium

bibionidarum O. Nishi, H. Sato, sp. nov. (2017)

NBRC

112661

39 Metarhizium

purpureogenum O.

Nishi, S. Shimizu, H.

Sato, sp. nov. (2017)

ARSEF 12571

Abbreviations for collections: ARSEF, USDA-ARS Collection of Entomopathogenic Fungal Cultures, Ithaca, New York, USA;

BCC, Biotech Culture Collection, KlongLuang, Thailand; CABI, CAB International Bioscience, Oxfordshire, UK; CBS,

Centraalbureau voor Schimmelcultures, Utrecht, the Netherlands; EFCC, Entomopathogenic Fungal Culture Collection,

Chuncheon, Korea; NBRC, Biological Resource Centre, National Institute of Technology and Evaluation, Japan; RCEF,

Research Centre for Entomopathogenous Fungi, Anhui Agricultural University, Hefei, Anhui, China; UAMH, University of

Alberta Microfungus Collection and Herbarium, Alberta, Canada.

Table 2.2 (continued) Current Metarhizium species and their synonyms

15

2.3 Metarhizium abundance

The natural occurrence and abundance of Metarhizium have been studied by several

researchers in different countries. The presence of Metarhizium in soil samples was

reported in 28% of samples in Tasmania, Australia (Rath et al., 1992), 17.5% in the

UK (Chandler et. al., 1998), 44.6% in Finland (Vanninen et al., 1989), 91% in Canada

(Bidochka et al., 1998), 71.7% in Spain (Quesada-Moraga et al., 2007) and 19.3% in

Switzerland (Schneider et al., 2011). However, these recovery percentages are not

comparable, as the fungal isolation techniques for Metarhizium varied across studies.

For instance, the Tasmanian study used an agar plate isolation technique from soils,

whereas the studies in Spain, Canada and UK used insect bait methods.

2.4 Soil and environmental factors affecting Metarhizium abundance

Understanding the factors determining the species distribution in Metarhizium may

provide useful agronomical and ecological information into the successful application

of Metarhizium in agricultural settings and help to identify species or strains with a

high likelihood of success.

Soil is the main source of isolates of Metarhizium spp. (Meyling et al., 2011). The

distribution of Metarhizium spp. in the soil environment, however, is not random, and

is associated with habitat and environmental factors (Bidochka et al., 2001, Wyrebek

et al., 2011), although specific associations of the different species have not been

fully determined. Soil type and annual rainfall were reported to influence the

distribution of Metarhizium strains in Tasmania but based only morphological

identification of strains (Rath et al., 1992). Several soil factors, including higher

organic matter, soil acidity, and texture, were reported to be associated with the

presence of Metarhizium spp. in cultivated habitats in Spain (Quesada-Moraga et al.,

16

2007). In contrast, geographic location but not soil factors was reported to determine

the occurrence of Metarhizium in Finland (Vanninen, 1996).

The abundance of Metarhizium spp. can be higher in agricultural (disturbed habitat)

or meadows (semi-disturbed habitat) soils compared to other entomopathogens

(Vanninen, 1996, Quesada-Moraga et al., 2007). Agricultural practices such as tillage

might increase the dispersion of Metarhizium propagules, thus lead to higher

occurrence of some Metarhizium species that tolerate disturbance in agricultural soils

(Kepler et. al., 2015). The effect of soil tillage and disturbance may affect species

differently, though the associations have not been conclusively established. In one

study M. robertsii was the dominant species in agricultural fields but M. brunneum

was mostly isolated species in an undisturbed forest habitat in Canada, but in

Denmark M. brunneum was the most frequent species isolated from agricultural soil

(Bidochka et al., 2001; Wyrebek et al., 2011, Steinwender et al., 2014). Bidochka et

al. (2001) describe “cold active” and “heat active” Metarhizium species that were

abundant in (respectively) forest and agricultural fields in Canada.

These results indicate that ecological factors influence the occurrence and diversity of

Metarhizium species in local habitats and agro-ecosystems may have important

effects on Metarhizium species distribution (Quesada-Moraga et al., 2007, Bidochka

et al., 1998, Steenberg, 1995).

2.5 Metarhizium, a rhizosphere associate

The plant rhizosphere is a narrow region of soil influenced by plant roots, where root

exudates control the activities of microbial populations (St. Leger, 2008).

Metarhizium spp. are found in a wide range of ecosystems and are abundant in the

rhizosphere (Hu and St. Leger, 2002). The rhizospheric competence of Metarhizium

spp. was first identified following released a genetically modified M. anisopliae

17

expressing the green fluorescent protein (GFP) reporter gene (Hu and St. Leger 2002).

The transgenic isolate was released in a cabbage field and the distribution in the soil

monitored over a period of time using the reporter gene to identify the released strain,

during which it was observed that that the fungus was more frequently found

colonizing the cabbage root rather than in bulk soil. In another study, the persistence

and ecology of M. anisopliae was tracked in pots growing spruce (P. abies) for a year

and a higher population of M. anisopliae was again observed in the rhizosphere than

in the bulk soil (Bruck, 2005).

The rhizospheric competence of different Metarhizium species is dependent on the

host plant, with a significant degree of plant-specific association (Bruck, 2010). In

several studies M. guizhouense was reported to be exclusively associated with tree

species (Fisher et al, 2011, Wyrebek et al., 2011). However, the data on host plant

associations is not yet conclusive: in one study M. brunneum and M. robertsii were

found associated only with, respectively, shrubs and grasses (Wyrebek et al., 2011),

but in another, M. robertsii was significantly associated with ‘Christmas’ trees. M.

brunneum was found to be the dominant species in grassland in Switzerland

(Steinwender et al., 2014), but also in strawberry and blueberry crops in the USA

(Fisher et al. 2011). This plant-specific association is further supported by research

reporting that Metarhizium spp. evolved from plant-associated fungi (Gao et al.,

2011).

Colonisation of the rhizosphere by Metarhizium spp. is beneficial to plant growth.

The colonisation of roots of switchgrass (Panicum vigratum L.) and haricot beans

(Phaseolus vulgaris L.) resulted in increased formation of higher number of root hairs

on the plant (Sasan and Bidochka, 2012). In tomato (Solanum lycopersicum) plant

height, root length, shoot and root dry weight increased when colonised by M.

18

anisopliae (Elena et al., 2001). M. robertsii has been shown to play an important role

in nutrient uptake, promoting plant growth by translocation of nitrogen directly from

dead insects to the plant (Behie et al., 2012).

Colonisation of the rhizosphere by Metarhizium spp. has significant additional

benefits, protecting the plant from both insect pests and pathogens. Endophytic

Metarhizium retains pathogenicity against insect pests including mealworm (Tenebrio

molitor L.) in wheat (Keyser et al., 2014) and black vine weevil (Otiorhynchus

sulcatus F.) in Norway spruce (Picea abies) (Bruck, 2005). Application of the

Metarhizium brunneum in strawberry protects plants from spider mite damage (Dara,

2015). Sweet potato weevil, Cylas formicarius (Fab.) were repelled by plants when a

virulent isolate of Metarhizium anisopliae was present in roots (Dotaona et al., 2017).

Colonisation of the rhizosphere by Metarhizium spp may have other benefits.

Colonization of soybean plants with M. anisopliae-increased isoflavonoid

phytoalexins with antifungal, antibacterial, and antiviral properties that beneficial to

soybean productivity and also elevated proline and reduced superoxide dismutase and

malondialdehyde contents in soybean plants showed mitigation of salt stress

compared with control plants. (Khan et al., 2012). Bean plants colonised by M.

robertsii exhibited protection against root rot disease caused by Fusarium solani and

showed healthier plant growth (Sasan and Bidochka, 2013). Treatment of wheat seeds

with Metarhizium and Clonostachys rosea, which is antagonistic to other fungi, was

found to be highly effective at controlling the wheat pathogen Fusarium culmorum

(Keyser et al., 2014). M. brunneum produced antifungal compounds that reduce the

number of the pathogen propagules in the soil and the severity of Verticillium wilt

(Lozano-Tovar, et al., 2017). These research findings suggest that inoculation of the

roots with Metarhizium spp. may provide multiple benefits for agriculture.

19

2.7 Signalling during colonisation of the rhizosphere

Plant-fungal symbioses can be antagonistic, mutualistic or neutral. In a mutualistic

interaction, fungi translocate minerals and water to the plant (Walton et al., 1994).

Plants produce root exudates into the rhizosphere that include carbohydrates, amino

acids, organic acids, phenolic compounds, proteins, and mucilage (Bais et al., 2001,

Walker et al., 2004). The exudates also contain plant hormones and signalling

molecules that are involved in regulation of plant-plant, plant-insect or plant-microbe

interactions in the rhizosphere (Davies et al., 2010), and initiate interaction between

plants and soil microorganisms (Walker et al., 2004).

Arbuscular mycorrhizal (AM) fungi, the typical examples of plant-fungus mutualism,

form a highly branched structure in the cortex of plant roots (the arbuscule) for

nutrient exchange (Selosse and Rousset, 2011). Plant hormones and soil nutrient

levels are associated with mycorrhizal formation in plants. Phosphate deficient

conditions in plant, regulates strigolactone, a signalling molecule by which the AM

fungi detect the host plant (Oldroyd, 2013, Kohlen et al., 2011, Akiyama et al., 2005,

Harrison, 2005). Species of Trichoderma Pers. (Order: Hypocreales, Family:

Hypocreaceae), a genus closely related to Metarhizium, are known as facultative

symbionts able to colonise the plant root, and have been used as antagonists to reduce

plant pathogenic infections (Viterbo and Horwitz, 2010, Hermosa et al., 2012). It was

reported that T. harzianum Rifai could detect and metabolise strigolactone, thus

enhancing the establishment of the fungus on the plant (Vurro, pers. comm. March

2014).

M. anisopliae uses adhesin (a cell-surface protein) to adhere to roots during the

colonisation process in plant (Wang and St. Leger, 2007). The fungus hydrolyses the

root epidermal cell wall by secreting hydrolytic enzymes (Prade et al., 1999) and

20

enters into the epidermis and further enters into the outer cortex of roots (Liao et al.

2013). A recent phylogenetic study of two different genes for Metarhizium adhesin-

like proteins, MAD1 and MAD2, showed that the plant host is the main factor in

Metarhizium species divergence rather than the insect host (Wyrebek and Bidochka,

2013). However, the signalling molecules and potential role of plant hormones

involved in colonisation of the rhizosphere by Metarhizium, are still unknown.

Further understanding of these processes may lead to greater success in the use of the

fungus as a beneficial inoculant for crops.

2.8 Summary

Fungi of the genus Metarhizium are well-known insect pathogens, and have been

shown to form a symbiotic relationship with plants via the rhizosphere, supporting

plant growth by transporting nutrients from insect cadavers and providing protection

from insect herbivores and plant pathogens. Ecological factors are an important factor

in the distribution of Metarhizium spp. in different ecotypes and host plants. Plant

signals may play an important role in establishing Metarhizium in plant systems.

Studies of Metarhizium ecology, host-associations and plant factors that regulate the

Metarhizium colonisation may lead to identification of strains or species that enhance

the establishment of Metarhizium spp. in crop plants and thus maximise multiple

benefits in nutrient uptake, drought tolerance, and protection from insects and plant

pathogens in agriculture.

21

Chapter 3 : Systematics of Australian

Metarhizium isolates

22

Systematics of Australian Metarhizium isolates

Abstract

Metarhizium acridium (Driver & Milner) J. F. Bisch., Rehner & Humber (2009) has

been registered and used as a biopesticide, and M. anisopliae isolates have been

identified, but little is known about the occurrence and diversity of Metarhizium

species in Australia. Legislative restrictions on the introduction of new species of

microorganism into Australia is a significant hurdle to the registration and use of new

biopesticides and inocula. Identification of species already present can facilitate

import and registration.

This study isolated Metarhizium strains from agricultural fields, grassland and forest

soils at three locations in Queensland, Australia. A total of 164 isolates were

identified as Metarhizium spp. based on their appearance on selective agar and ITS

sequence results. A multi-locus analysis of concatenated data sets of MzIGS3 and 5’-

TEF inferred the taxonomic position of these isolates in five well supported clades.

Three of the clades were assigned to known species: M. robertsii, M. pingshaense and

M. anisopliae. Two other indeterminate clades represent probable new Metarhizium

species. Population genetic analysis showed that the Metarhizium clades were

strongly genetically differentiated. Moderate genetic differences and gene flow were

observed between the Metarhizium populations from different locations and ecotypes.

This research established a baseline on species present in Australia for future

introduction of Metarhizium species in biocontrol programs.

Keywords: Entomopathogen, Metarhizium, markers, phylogenetic analysis, genetic

differentiation, gene flow.

23

3.1 Introduction

Members of the genus Metarhizium (Family: Clavicipitaceae, Order: Hypocreales),

are natural enemies of insects and have been historically used as a biopesticide

(Zimmermann, 1993, Zimmermann et al., 1995, Meyling and Eilenberg, 2007, Sasan,

2012). These insect pathogens have a broad host range including many species of

Coleoptera, Hemiptera, Orthoptera etc. (Goettel et al., 1990, Veen, 1968, Zimmerman,

2007, Hauxwell et al., 2010). Several commercial biopesticides based on Metarhizium

have been registered and used in many countries including Australia, Africa, USA,

Brazil, the EU and India (Hauxwell et al., 2010, Wraight et al., 2001, Copping, 2004,

Zimmermann, 2007, Kabaluk and Gazdik, 2005). In Australia, research on the

application of Metarhizium as a biopesticide began in the early 1990s by the

Commonwealth Scientific and Industrial Research Organisation (CSIRO) and a

commercial formulation of Metarhizium acridum Bisch., Rehner & Humber (syn.

Metarhizium anisopliae var. acridum) was developed to control the Australian plague

locust (Chortoicetes terminifera Walker). The fungus resulted in a high percentage

(80%) reduction of the locust population and led to the aerial application of

Metarhizium in Australia against plague locusts (reviewed in Hauxwell et al., 2010).

M. anisopliae is has also been described and tested as a potential biopesticide

(Hauxwell et al., 2010).

Despite the recognized potential of Metarhizium species for biological control and as

a root inoculant for agricultural crops, the import and release of exotic species and

strains of microorganisms in Australia is very strictly regulated under biosecurity

legislation (Biosecurity act 2015, https://www.legislation.gov.au/). The identification

of species already present in Australia can facilitate the import and release of new

isolates of the same species from overseas, but the identity and distribution of

24

Australian Metarhizium species and especially the molecular characterization of

cryptic species, is still to be investigated (Hauxwell et al 2010).

The species of Metarhizium exhibit a complex pleomorphic life cycle and show

cryptic diversity in their lineage. Metarhizium spp. may have different morphology in

different life stages (telomorph, anamorph), their morphology may change in response

to environmental factors, and many of the identifying features overlap among the

species or even within the other members of the order Hypocreales (Glare et al.,

1996, Driver et al., 2000). Early taxonomic classification of Metarhizium spp. based

on morphology has proved inconsistent because of variation in the appearance of

features such as phialides and conidia within the same isolate and in response to

culture on different media (Kamat et al., 1952, Glare et al., 1996, Driver et al., 2000).

Recently, the International Code of Nomenclature (ICN) for algae, fungi, and plants

has revised the regulations for nomenclature of pleomorphic fungus (article 59)

stating that one name is applicable for one fungus, regardless of its sexual and asexual

life stages (McNeil et al., 2012). Molecular approaches are now extensively used in

the classification and taxonomy of fungi, especially for the cryptic fungal species

(Douhan et al., 2008). Accurate identification of the Metarhizium species requires

diagnostic molecular data (Bischoff et al. 2009, Steinwender et al. 2014).

The taxonomy of Metarhizium spp. was initially revised using internal transcribed

spacer (ITS) sequence and identified ten distinct phylogenetic clades and several

Metarhizium varieties (Driver et al. 2000). However, the taxonomic relationship

between the taxa was poorly resolved due to the limited resolution of the ITS

sequence analysis (Kepler et al., 2014).

25

Bischoff et al. (2009) identified nine species of Metarhizium by multilocus

phylogenetic analysis using five gene regions (5’-TEF, RPB1, RPB2, β-tubulin and

ribosomal intergenic spacer region (IGS). Among the markers, Bischoff et al. (2009)

reported that 5’ end of elongation factor (5’-TEF) could be used as a single reliable

marker for Metarhizium species discrimination. However, this region did not produce

sufficient resolution to resolve Metarhizium phylogeny at the species level

(Steinwender et al., 2014).

In a recent study, new phylogenetically-informative nuclear intergenic sequence

markers have been developed to describe the species of the genus Metarhizium, with

MzIGS3 recommended as a reliable single marker to describe previously unknown

Metarhizium species in the complex (Kepler and Rehner 2013). These intergenic

sequence markers were compared with the 5’-Transcription elongation factor (5’-

TEF) marker was used for taxonomic identification and diversity analysis of Brazilian

Metarhizium species (Rezende et al. 2015), on the basis of which it was suggested

that MzIGS3 was not suitable as a stand-alone marker, but that 5’-TEF may be used

alone or in combination with MzIGS3 to resolve species identification.

The molecular typing tool ‘single sequence repeat (SSR) markers’ have been used for

analysis of population structure of seven Metarhizium species: M. anisopliae, M.

brunneum, M. guizhouense, M. lepidiotae, M. majus, M. pingshaense and M. robertsii.

However, this approach is not sufficiently powerful to differentiate new species or to

establish phylogenetic relationships (Mayerhofer et al., 2015; Castro et al., 2016).

In recent year, matrix assisted laser desorption ionization-time of flight mass

spectrometry (MALDI-TOF MS), a molecular based taxonomic tool, has been used to

identify and discriminate Metarhizium species rapidly and accurately (Lopes et al.

2014). However, the MALDI-TOF MS has limitation to identify new isolates as it

26

requires the spectral database contains peptide mass fingerprints (PMF) of the type

strains of specific species/strains (Singhal et al. 2015).

Species of Metarhizium are frequently isolated from the soil and are widely

distributed all over the world. As reviewed in Chapter 1, their distribution may be

associated with environmental and ecological factors, including plant hosts and soil

disturbance. The few studies conducted so far are not always consistent in describing

these factors. In Canada, a greater abundance of Metarhizium isolates were found in

cultivated sites (disturbed habitat) than in permanent forests (Bidochka et al. 1998),

while another study in Switzerland found that permanent grassland and field margins

contained a higher density of Metarhizium species than either forest or cultivated

arable land (Schneider et al. 2012). These differences may be due to differences in

methodology and analysis, but also suggest that different species or strains of

Metarhizium may adapt to ecotypes of different local habitats or regions under

different management conditions.

In this study, multilocus phylogenetic and population genetics analyses were used to

investigate the occurrence, diversity, systematics and gene flow of Metarhizium

species in three different habitats (arable crops, grassland and forests) at three

different locations of Queensland, Australia. The aim was to identify the species

found using DNA sequencing and to determine any apparent associations with habitat.

27

3.2 Materials and Methods

3.2.1 Collection of soil samples

Soil samples were collected from three of the Department of Agriculture and

Fisheries (DAF) research sites in Bundaberg (24.8471 S, 152.405 N), Gatton

(27.54445 N, 152.33157 E) and Kingaroy (26.57888 N, 151.83638 E) in Queensland,

Australia.

Three ecotypes were sampled at Bundaberg sites: maize field, grassland and forest.

Four ecotypes were sampled at both Gatton and Kingaroy: maize field, legume field,

grassland and forest.

All sampling sites in each location were located within 500 m of each other to

minimise factors other than ecotype. At each sampling site, four parallel transects

were made 10 m apart, and 20 samples per transect were collected at a separation of 5

m. For each sample, approximately 250-300 g of soil was collected with a soil trowel

to a depth of 10-15 cm. Eighty soil samples from each of the ecotype were collected

from each of the sites, a total of 880 samples. Soil samples were kept in individual zip

lock bags, labelled and stored at 4°C in a cold room until analysed.

3.2.2 Isolation of fungal isolates

Fungus isolation from soil samples was conducted as per the method described by

Kepler et al. (2014) with some modification. Soil samples were sieved through a 5-

mm mesh and 5 g of soil was suspended in 50 mL of sterile 0.1% Tween 80, in a 50-

mL screw cap plastic tube, and incubated at room temperature for 3 hours. The tubes

were inverted five times every 30 minutes. Following incubation, the tubes were kept

for sedimentation for 20 seconds. A volume of 100 µL of supernatant from each tube

was plated on a Petri plate with selective medium (SM) containing peptone 10g/L,

28

glucose 20 g/L, agar 18 g/L, cycloheximide 50 mg/L, streptomycin 100 mg/L,

tetracycline 50 mg/L and dodine 100 mg/L (Strasser et al., 1996).

Plates were incubated at 22° C for two weeks and examined for characteristic

Metarhizium growth) every 2-3 days. Isolates likely to be Metarhizium sp. were

identified by a characteristic amorphous, parallel, and compact mycelium with dark

green to yellow spores. The soil samples with characteristic Metarhizium colonies

were counted.

Typically, one colony was selected from each plate. However, if the morphology of

probable Metarhizium colonies were different (for example: spore colour), then

representative colonies of each variant were selected from each plate. The

characteristic fungal colonies were then sub-cultured to a fresh SDA plate without

antibiotics and given an identification number. The first letter of the sample identifier

indicates the location of sampling (B=Bundaberg, G=Gatton, K=Kingaroy); second

letter denotes the ecotypes (M= maize field, L= legume field, G=grassland, F= Forest),

the third letter denotes the transect letter (A, B, C, D) and the numbers are the

isolate’s serial number.

3.2.3 Genomic DNA extraction, PCR amplification, and sequencing

Fungal sub cultures were grown on Sabouraud dextrose agar with yeast (SDAY)

medium without antibiotics and further screened by ITS sequence analysis. DNA was

extracted as follows: approximately 200 mg of fungal mycelium was harvested off the

plate into a microcentrifuge tube, homogenised under liquid nitrogen with a pestle,

resuspended in 1 mL lysis buffer (400 mM Tris-HCl, pH 8.0, 60 mM EDTA, 150 mM

NaCl and 1% SDS) and incubated at 50°C for 1 hour in a water bath. A volume of

150 μl of precipitation buffer (5 M potassium acetate 60.0 mL, glacial acetic acid 11.5

mL, distilled water 28.5 mL) was added and vortexed and incubated on ice for 5

29

minutes. The aqueous layer was separated from the precipitated organic phase by

centrifugation at 10000 rcf for 1 minute. Following the centrifugation, 500 μl of

supernatant was transferred to a new microcentrifuge tube and an equal volume of

isopropanol was added to precipitate DNA. The DNA pellet was collected after

centrifugation at 18,000 rcf for 20 minutes and washed with 1 mL of 70% ethanol.

The DNA pellet was air dried for 10 minutes and dissolved in 100 μl of Tris-EDTA

(TE) buffer. The quality of the DNA was checked by electrophoresis through a 1.5%

agarose gel in 1x Tris-Borate-EDTA (TBE) buffer with gel red dye and 1kb plus

hyper-ladder, for 40 minutes at 90 volts.

The Internal transcribed spacer (ITS) region, intergenic sequence (MzIGS3) and 5’-

Transcription elongation factor (5’-TEF) regions of the fungal isolates were amplified

by polymerase chain reaction (PCR) and sequenced. The primer information and their

thermal PCR profiles are summarised in Table 3.1.

PCR products were subjected to electrophoresis in 1% agarose in 1x TBE buffer at

120 volts with GelRed nucleic acid stain and visualised under UV light using a

‘Molecular Imager® (Gel DocTM). The PCR products were purified using an ‘Isolate

DNA Kit’ (Bioline) following the standard manufacturer protocol. The purified PCR

products were prepared for sequencing using Big Bye Terminator V3.1 kit (ABI) with

forward sequencing primers ITS_1F, MzIGS3_2F and EF1T (Table 3.1). The samples

were cleaned using the ethanol/EDTA precipitation process as per the kit’s instruction.

Sequencing was conducted in the Central Analytical Research Facility (CARF),

Queensland University of Technology (QUT), Australia and Macrogen Corporation,

South Korea.

30

3.2.4 Phylogenetic tree construction

The Sanger sequence results were edited in Geneious v9.0.4 and compared to the

National Centre for Biotechnology Information (NCBI) database using Basic Local

Alignment Search Tool (BLAST) (BLAST result not shown) (Altschup et al., 1990,

Tatusova et al., 2014). The available reference nucleotide sequences for the three loci

were retrieved from NCBI. Multiple alignments of the nucleotide sequences were

done separately with their respective reference strains using a multiple sequence

alignment program MAFFT v7.2.8 in the Geneious v9.0.4 Plugin (Katoh and

Standley, 2013).

31

Table 3:1 Description of primers used in polymerase chain reactions (PCR).

Mark

er

Pri

mers

Nu

cle

oti

de s

eq

uen

ce

Init

ial

den

atu

rati

on

Den

atu

rati

on

An

nealin

g

Ext

en

sio

nT

ota

l

Cy

cle

Fin

al

Ext

en

sio

nR

efe

ren

ce

ITS

_1F

5’-

CT

TG

GT

CA

TT

TA

GA

GG

AA

GT

AA

ITS

_4R

5’-

TC

CT

CC

GC

TT

AT

TG

AT

AT

GC

MzI

GS

3_

2F

5’-

GT

GG

CT

CC

TG

AC

CA

TG

GT

TG

C

MZ

IGS

3_

3R

5’-

CG

GG

GG

AG

CC

GA

CT

TG

GA

TT

T

EF

1T

5’-

AT

GG

GT

AA

GG

AR

GA

CA

AG

AC

EF

2T

5’-

GG

AA

GT

AC

CA

GT

GA

TC

AT

GT

T72°C

, 10 m

Bis

ch

off

et

al.

(2009)

TE

F95°C

, 15 m

94°C

, 40 s

65°C

, 40 s

72 °

C, 2 m

45

72°C

, 15 m

Tu

inin

ga e

t al.

(2010)

MzI

GS

3 9

0°C

, 2 m

95°C

, 30 s

58°C

, 30 s

72 °

C, 1 m

40

72°C

, 15 m

Kep

ler

an

d

Reh

ner

(2013)

ITS

90°C

, 2 m

95°C

, 30 s

58°C

, 30 s

72 °

C, 1 m

40

Tab

le 3

.1 D

escr

ipti

on o

f pri

mer

s use

d i

n p

oly

mer

ase

chai

n r

eact

ions

(PC

R).

32

ITS phylogeny was conducted using the maximum likelihood (ML) analysis with the

available reference taxa: M. pingshaense ARSEF1009, M. guizhouense ARSEF977,

M. brunneum ARSEF820, M. anisopliae isolate 68, M. anisopliae isolate 69, M.

anisopliae isolate 251, M. anisopliae XL5001, M. robertsii ARSEF2575, Beauveria

bassiana 1040, Beauveria bassiana KSUBK54276, Isaria farinosa HNE3,

Lecanicillium sp. ID05 F0200 and Trichoderma viride ATCC28038. Three sequences

from genus Pochonia listed on MycoBank under the synonyms Metacordyceps

chlamydosporia UCR3, Metacordyceps chlamydosporia IAM14700, Cordyceps

chlamydosporia (Syn Metacordyceps chlamydosporia) NBRC9249 were included.

ML analyses were conducted for ITS data set using the software RAxML (Stamatakis,

2006) to search best-scoring tree under the GTRGAMMA model with 1000 replicates.

The individual data set of MzIGS3and 5’-TEF were aligned separately and trimmed

to include the first 729 bp (Kepler et al., 2014) and 549 bp, respectively. Phylogenetic

trees for single and concatenated data sets of MzIGS3 and 5’-TEF were developed

using the maximum likelihood (ML) and Bayesian inference analyses with the

currently recognized reference strains (M. globosum ARSEF 2596, M. acridum

ARSEF 7486, M. majus ARSEF 1914, M. lepidiotae ARSEF 7488, M. guizhouense

CBS 258.90, M. anisopliae ARSEF 7487, M. anisopliae 68, M. anisopliae 69, M.

anisopliae 251, M. pingshaense CBS 257.90 and M. robertsii ARSEF 7501). The

support values on the best-scoring ML tree, for each individual data set, was drawn in

RAxML with a rapid bootstrap (1000 replicates) under GTRGAMMA substitution

model. The multigene analysis combined the MzIGS3 and 5’-TEF data in a matrix

and ML analysis was done using the above parameters with each locus run in a

different partition. The software Mr. Bayes (Huelsenbeck and Ronquist, 2001,

Ronquist and Huelsenbeck, 2003) was used to determine posterior probabilities under

the GTR substitution model for single and combined data. The posterior output from

33

Mr. Bayes was obtained by running 4 MCMC heated chains with 1,000,000 generations,

sampling at every 1,000 after discarding the first 25% (burn-in value). The

phylogenetic trees were visualised and edited by TreeGraph 2 (Stover and Muller,

2010) and ‘ape’ package in R (Paradis et al., 2004).

3.2.5 DNA divergence, genetic differentiation and gene flow analysis

Nucleotide polymorphism, fixed, shared mutations, nucleotide diversity between the

M. anisopliae (Mani), M. robertsii (Mrob), M. pingshaense (Mpin), Metarhizium

indet. 1 (Mindet1) and Metarhizium indet. 2 (Mindet2) clades identified in multilocus

(MzIGS3 and 5’-TEF) phylogenetic analysis were calculated using DNAsp 5.10.01

(Rozas et al., 2003). In order to calculate the genetic differentiation within the

Metarhizium linages, haplotype-based statistics such as Hs and Hst (Hudson et al.,

1992a and 1992b) and nucleotide sequence-based statistics such as Ks, Kst and Snn

(Hudson’s statistic of genetic differentiation, Hudson et al., 2000) were estimated

with permutation tests using 1000 randomization to get the statistical significance in

DNAsp. The software also used to calculate the gene flow estimates, Nst and Fst

value, the indirect measurement of effective number of migrants (Nm) (Hudson et al.,

1992b) among the populations. DNA sequences from three locations and four

ecotypes were also used for genetic differentiation and gene flow studies.

3.2.6 Haplotype distribution analysis

Nucleotide sequence alignment of combined data set of MzIGS3 and 5’-TEF of

Metarhizium isolates was imported into DnaSP 5.10.01 for haplotypes analysis and

generation of haplotype data file in nexus file format. The nexus file was edited to add

clade, location and ecotype information in a character matrix and a minimum

spanning network (MSN) was constructed in PopART (http://popart.otago.ac.nz) to

determine the relationships among haplotypes for Metarhizium population in different

34

clades, locations and ecotypes. The MSN is based on a minimum spanning tree where

a set of sequence types connects all given types without creating any cycles where the

total length (sum of distance between linked sequences) is minimum (Bandelt et al.,

1999).

3.2.7 Statistical analysis

The mean percent presence of Metarhizium in soil samples data were compared using

a generalised linear model (GLM) with the logit link function in ‘R’ (R core team,

2016). Tukey’s post-hoc test was conducted using ‘lsmeans’ package to determine

statistical significant differences among locations and ecotypes

35

3.3 Results

3.3.1 Occurrence of Metarhizium isolates

Metarhizium isolates were recovered from all 3 locations, and from all ecotypes:

agricultural crops, grasslands and forest soils. A total of 313 isolates were obtained

from 880 soil samples using the selective medium and preliminary isolation based on

morphology. BLAST analysis of the ITS data showed that out of these 313 isolates,

164 were from the genus Metarhizium, 29 from the genus Beauveria and Isaria, 2

from genus Lecanicillium, and 6 from genus Pochonia. The rest of the isolates were

members of the genera Penicillium, Purpureocillium and Spiromastix.

As discussed above, several species of the Metacordyceps have been recognised as

members of Metarhizium genus; for example, Metarhizium atrovirens, Metarhizium

brittlebankisoides etc. (Kepler et al. 2014). The species Pochonia chlamydosporia,

(synonymously Cordyceps chlamydosporia, and Metacordyceps chlamydosporia), is

not within the boundaries of Metarhizium and remain classified in genus Pochonia

(Kepler et al. 2014). The 6 isolates identified as genus Pochonia (Syn Metacordyceps,

Cordyceps) were excluded from subsequent analysis (fig 3.8, supplementary data).

Table 3.2 shows the presence of the Metarhizium isolates in the number of soil

samples in sampling locations and sites.

36

Table 3:2 The number of soil samples with Metarhizium isolates in different

locations and ecotypes.

Location Ecotypes Number of

soil samples

collected

Number of soil samples in which Metarhizium

colony observed

Bundaberg

Maize field 80 10

Grassland 80 1

Forest 80 12

Gatton

Maize field 80 0

Legume field 80 1

Grassland 80 3

Forest 80 5

Kingaroy

Maize field 80 13

Legume field 80 13

Grassland 80 0

Forest 80 0

Metarhizium was recovered from just 6.60% of soil samples from 880 total soil

samples. The highest percentage of soil samples within a location that contained

Metarhizium were found in Bundaberg (9.58%) and Kingaroy (8.13%) (Figure 3.1).

Metarhizium was recovered from a significantly smaller number of soil samples in

Gatton (2.81%).

When ecotypes were compared, there was no significant difference between the mean

percentage of soil samples containing Metarhizium in agricultural soils or forests, but

a significantly lower occurrence in grassland (p < 0.05). The agricultural and forest

soils had the higher percentages of samples containing Metarhizium: 9.58% of soil

samples in maize, 8.75% of soil samples from legume fields and 7.10% of samples

from forest. Grassland had a significantly lower proportion of soil samples (1.67%)

containing Metarhizium isolates than either crops or forests. (Figure 3.1).

37

Figure 3.1 Mean percentage of occurrence of Metarhizium in soil samples

collected from different locations (above) and ecotypes (below) of Queensland.

Bar plot (mean percentageSEM) with different lowercase letter indicates

statistical differences on Tukey’s post hoc test (GLM, p < 0.05).

Overall, a higher percentage of occurrence of Metarhizium was observed in samples

from disturbed (crop) habitat (9.25%) than from natural (grassland, forest) habitats

(4.38%) (Figure 3.2). The result was not consistent across locations: higher

percentages of isolate occurrence were observed in soil samples from disturbed

habitats in Kingaroy and Bundaberg, and no isolates were recovered from undisturbed

habitats in Kingaroy. In contrast, occurrence of Metarhizium in samples from Gatton

and Bundaberg samples was not significantly different in samples natural habitat from

38

those in the disturbed (crop), and only a very small proportion of samples from crops

at Gatton contained any isolates.

Figure 3.2 Mean percentage of occurrence of Metarhizium in soil samples

collected from different disturbed and natural habitats (above) and disturbed and

natural habitats of three locations of Queensland (below). Bar plot (mean

percentageSEM) with different lowercase letter indicates statistical differences

on Tukey’s post hoc test (GLM, p < 0.05).

39

3.3.2 ITS phylogeny

A maximum likelihood phylogenetic analysis was conducted using the ITS sequence

information of 164 field isolates with entomopathogenic reference strains of the

genera Metarhizium, Beauveria, Isaria, and Lecanicillium, with Trichoderma viride

as an outgroup. Reference sequences of the genus Pochonia, which are published on

Mycobank as ‘Cordyceps chlamydosporia’ and its synonym ‘Metacordyceps

chlamydosporia’ that have been reclassified as Pochonia chlamydosporia, are

identified by the Mycobank species labels but the cluster is labelled genus ‘Pochonia’

(supplementary data Figure 3.8). The result shows four major clades with very high

bootstrap support values (supplementary data Figure 3.8). The Metarhizium clade

includes 164 fungal isolates, whereas the other three clades (including genus

Pochonia) are distinct from the Metarhizium clade with high support values.

3.3.3 Phylogenetic analysis

A total of 164 Metarhizium isolates from different locations and sites were selected

for the single locus and multilocus phylogenetic analyses based on the ITS sequence.

Phylogenetic analyses using MzIGS3 and 5’-TEF were not congruent and showed a

number of phylogenetic groups with less number of isolates and polytomies in single

gene analyses. Analysis of the 5’-TEF locus differentiated 1 clade (M. anisopliae)

using ML analysis and 2 clades (M. anisopliae and M. pingshaense) using Bayesian

analysis. The MzIGS3 marker differentiated more clades than 5’-TEF: 3 clades

(Metarhizium sp. indet. 1, Metarhizium sp. indet. 2 and M. pingshaense) using ML

analysis and 5 clades (M. robertsii, Metarhizium sp. indet. 1, Metarhizium sp. indet. 2,

M. pingshaense and M. anisopliae) using Bayesian analysis.

40

The combined two-gene (MzIGS3 and 5’-TEF) dataset totalled 1278 bp and included

MzIGS3 (729bp) and 5’-TEF (549bp) partition. Maximum likelihood and Bayesian

trees of combined analysis of two-gene (MzIGS3 and 5’-TEF) resolved the taxonomic

relationships of all isolates and showed similar topology (Figure 3.3). Both the

analyses identified five major clades of Metarhizium species, three of which grouped

with reference M. robertsii, M. anisopliae and M. pingshaense and two taxonomically

unassigned clades named as Metarhizium indet. 1 (indeterminate 1) and Metarhizium

indet. 2 (indeterminate 2) with high support values (>70%). In both cases, M.

robertsii clade includes the highest number of isolates (n=83), followed by

Metarhizium sp. indet. 1 clade (n=48) and Metarhizium sp. indet. 2 clade (n=11). M.

pingshaense and M. anisopliae clades included 6 and 16 isolates respectively. Figure

3.3 shows the maximum likelihood tree of the combined data set of two genes. The

Bayesian tree is in supplementary data (Figure 3.9).

The combined data of three-gene (MzIGS3, 5’-TEF and ITS) contained a total of

1905 bp and included MzIGS3 (729bp), 5’-TEF (549bp) and ITS (627bp) partition.

The three-gene phylogenetic tree produced similar topology of two-gene tree only in

ML analysis and but fewer clades in Bayesian analysis (Supplementary data Figure

3.14 and 3.15). However, both ML and Bayesian trees that included ITS resulted in

lower statistical supports compared to two-gene trees. The ITS locus and its

combination with other two loci were excluded from further population genetics

analysis.

The summary information retrieved from the single gene and multi-gene analyses of

the Queensland Metarhizium isolates is given in Table 3.3. Phylogenetic trees for

MzIGS3 and 5’-TEF data sets and three- gene-combined data sets (ITS, MzIGS3 and

5’-TEF) are provided as supplementary data (Figure 3.10: ML tree of MzIGS3 data,

41

Figure 3.11: ML tree of 5’-TEF data, Figure 3.12: Bayesian tree of MzIGS3 data,

Figure 3.13: Bayesian tree of 5’-TEF data, Figure 3.14: ML tree of three-gene

combined data, Figure 3.15: Bayesian tree of three-gene combined data).

Table 3:3 Summary tables of phylogenetic analyses showed the number of isolates

included in the clade with its support values (in parenthesis). NR denotes clade not

resolved in the analysis. Table 3.3A shows the value for three major clades: M.

robertsii, M. pingshaense plus Metarhizium indet. spp. and M. anisopliae. Table

3.3B shows the values for M. pingshaense and Metarhizium indet. clades. Table

3.3C shows the values for M. pingshaense and Metarhizium indet. 1 and

Metarhizium indet. 2 clades.

A.

Marker Clade Maximum

likelihood Bayesian inference

5’-TEF

M. robertsii NR NR

M. pingshaense &

Metarhizium indet. NR 28 (0.71)

M. anisopliae 17 (81%) 17 (0.70)

MzIGS3

M. robertsii NR 38 (0.99)

M. pingshaense &

Metarhizium indet. spp. 65 (98%) 65 (1.00)

M. anisopliae NR 16 (0.89)

Combined

(MzIGS3 & 5’-

TEF)

M. robertsii 83 (75%) 83 (0.72)

M. pingshaense &

Metarhizium indet. spp. 65 (100%) 65 (1.00)

M. anisopliae 16 (93%) 16 (0.76)

Combined

(MzIGS3, 5’-TEF

and ITS)

M. robertsii 83 (71%) NR

M. pingshaense &

Metarhizium indet. spp. 65 (100%) 65 (1.00)

M. anisopliae 16 (87%) 16 (0.76)

(Continued)

42

Table 3.3 (Continued)

B.

Marker Clade Maximum

likelihood Bayesian inference

5’-TEF Metarhizium indet. spp. NR NR

M. pingshaense NR 28 (0.71)

MzIGS3 Metarhizium indet. spp. 59 (92%) 59 (1.00)

M. pingshaense 6 (100%) 6 (1.00)

Combined

(MzIGS3 & 5’-

TEF)

Metarhizium indet. spp. 59 (82%) 59 (1.00)

M. pingshaense 6 (84%) 6 (1.00)

Combined

(MzIGS3, 5’-TEF

and ITS)

Metarhizium indet. spp. 59 (85%) 59 (0.78)

M. pingshaense 6 (84%) 6 (1.00)

C.

Marker Clade Maximum likelihood Bayesian inference

5’-TEF

Metarhizium indet. 1 NR NR

Metarhizium indet. 2 NR NR

M. pingshaense NR 28 (0.71)

MzIGS3

Metarhizium indet. 1 48 (96%) 48 (1.00)

Metarhizium indet. 2 11 (98%) 11 (1.00)

M. pingshaense 6 (100%) 6 (1.00)

Combined

(MzIGS3 & 5’-

TEF)

Metarhizium indet. 1 48 (91%) 48 (1.00)

Metarhizium indet. 2 11 (84%) 11 (1.00)

M. pingshaense 6 (84%) 6 (1.00)

Combined

(MzIGS3, 5’-TEF

and ITS)

Metarhizium indet. 1 48 (87%) 48 (0.79)

Metarhizium indet. 2 11 (83%) 11 (1.00)

M. pingshaense 6 (84%) 6 (1.00)

43

(83)

(48)

(11)

(16)

((6)M. pingshaense Clade (6)

Figure 3.3 Phylogenetic tree of concatenated data set of 5’TEF and MzIGS3 of 164

isolates of the genus Metarhizium with reference strains. Clades containing multiple

sequences were collapsed and are presented as triangles marked by the clade name

with number of isolates retained by each clade in parenthesis. Clade supporting

value obtained from maximum likelihood (above) and Bayesian (below) analyses

were presented in the node.

44

Figure 3.4 (continued)

45

M. pingshaense CBS257.90

M. pingshaense

M. anisopliae isolate 68M. anisopliae isolate 69M. anisopliae isolate 251

Metarhizium indet. 1

Metarhizium indet. 2

Figure 3.4 Maximum likelihood tree of concatenated data set of MzIGS3 and 5’-

TEF of 164 Australian Metarhizium isolates with reference strains

46

3.3.4 DNA divergence, genetic differentiation and gene flow

The summary statistics of DNA divergence in the comparison between the

Metarhizium clades for MzIGS3, 5’-TEF and combined MzIGS3 and 5’-TEF loci are

presented in Table 3.4. A greater number of polymorphic sites and nucleotide

differences were observed in the combined data set in data for an individual locus. In

combined gene analysis, the lowest value of the average number of nucleotide

differences (k) value, 6.59, was observed for M. robertsii-M. indet. 2 pair, while the

highest value, 30.44, was observed for M. anisopliae-M. indet. 2 pair. The highest

value of nucleotide diversity (π), 0.043, was found in M. anisopliae- M. indet. 2

comparisons and the lowest, 0.012, was found in M. pingshaense- M. indet. 1

comparison in combined locus analysis.

In the comparisons between the Bundaberg, Gatton and Kingaroy populations, the

number of polymorphic sites, nucleotide differences (k) and nucleotide diversity (π)

were higher in Bundaberg-Kingaroy comparison, and lower in the Gatton-Kingaroy

and Bundaberg-Gatton comparisons (Table 3.5).

The DNA divergence results in comparisons between the ecotypes populations were

presented in Table 3.6. The highest number of polymorphic site, nucleotide

differences (k) and nucleotide diversity (π) were observed between Legume-Forest

populations and the lowest value observed between the Grassland-Forest comparison.

47

Locu

sC

lad

es

No. of

poly

morp

hic

site

No. of

fixed

dif

fere

nce

s

No. of

site

s

poly

morp

hic

vs

mon

om

orp

hic

*

No. of

site

s

poly

morp

hic

vs

mon

om

orop

hic

**

Sh

ared

mu

tati

on

s

Avera

ge n

o. of

nu

celo

tid

e

sub

stit

uti

on

(Dxy)

Avera

ge

nu

mb

er o

f

nu

celo

tid

e

dif

fere

nce

s (k

)

Nu

cleo

tid

e

div

ers

ity

(π)

M. anis

opli

ae-

M. ro

ber

tsii

00

00

00

00

M. anis

opli

ae-

M. pin

gsh

aen

se175

20

163

10

0.0

5613

24.7

23

0.0

4176

M. anis

opli

ae-

M.

indet.

1142

19

66

66

50.0

7523

12.9

18

0.0

3618

M. anis

opli

ae

-M. in

det.

2140

16

130

00

0.0

9281

19.6

07

0.0

6557

M. ro

bert

sii-

M. pin

gsh

aen

se0

00

00

00

0

M. ro

bert

sii-

M.

indet.

10

00

00

00

0

M. ro

bert

sii-

M.

indet.

20

00

00

00

0

M. pin

gsh

aense

-M.

indet.

185

12

077

00.0

3968

6.5

79

0.0

1649

M. pin

gsh

aense

-M.

indet.

211

11

00

00.0

3056

5.3

38

0.0

1483

M.

indet.

1-M

. in

det.

230

327

00

0.0

1528

2.0

97

0.0

0859

M. anis

opli

ae-

M. ro

ber

tsii

192

01

214

80.0

2994

8.6

17

0.0

2329

M. anis

opli

ae-

M. pin

gsh

aen

se25

910

35

0.0

2521

7.0

87

0.0

1392

M. anis

opli

ae-

M.

indet.

166

77

51

60.0

2447

7.3

45

0.0

1457

M. anis

opli

ae

-M. in

det.

279

37

69

80.0

3558

10.8

32

0.0

2655

M. ro

bert

sii-

M. pin

gsh

aen

se194

2217

05

0.0

2845

8.2

24

0.0

2223

M. ro

bert

sii-

M.

indet.

1197

1199

823

0.0

2835

8.4

44

0.0

2282

M. ro

bert

sii-

M.

indet.

2153

0131

26

20

0.0

6464

6.5

86

0.0

2217

M. pin

gsh

aense

-M.

indet.

158

01

51

70.0

0794

4.4

39

0.0

0876

M. pin

gsh

aense

-M.

indet.

273

00

69

80.0

32

13.3

68

0.0

326

M.

indet.

1-M

. in

det.

281

014

48

26

0.0

3091

6.8

73

0.0

1697

M. anis

opli

ae-

M. ro

ber

tsii

192

01

214

80.0

2994

8.6

17

0.0

2329

M. anis

opli

ae-

M. pin

gsh

aen

se200

29

173

45

0.0

4184

31.8

10.0

2889

M. anis

opli

ae-

M.

indet.

1208

26

73

117

11

0.0

4552

20.2

63

0.0

2353

M. anis

opli

ae

-M. in

det.

2219

19

137

69

80.0

5978

30.4

39

0.0

4305

M. ro

bert

sii-

M. pin

gsh

aen

se194

2217

05

0.0

2845

8.2

24

0.0

2223

M. ro

bert

sii-

M.

indet.

1197

1199

823

0.0

2835

8.4

44

0.0

2282

M. ro

bert

sii-

M.

indet.

2153

0131

26

20

0.0

3464

6.5

86

0.0

2217

M. pin

gsh

aense

-M.

indet.

1143

12

1128

70.0

2192

11.0

18

0.0

1216

M. pin

gsh

aense

-M.

indet.

284

11

069

80.0

3133

18.7

06

0.0

2429

M.

indet.

1-M

. in

det.

2111

341

48

26

0.0

2503

8.9

70.0

1382

MzI

GS

3

5'-

TE

F

Com

bin

ed

MzI

GS

3

and 5'-

TE

F

Table 3:4 DNA divergence data between the clades of Metarhizium in the two analysed

loci and combined data set

48

Table 3:5 DNA divergence data between the populations of Metarhizium in different

locations for combined MzIGS3 and 5’-TEF data set

Table 3:6 DNA divergence data between the populations of Metarhizium in different

ecotypes for combined MzIGS3 and 5’-TEF data set

49

Table 3.7 shows the genetic differentiation and gene flow estimates for the

comparisons between the Metarhizium clades. The genetic differentiation was

evaluated by calculating both haplotype indices (Hs and Hst) and nucleotide indices

(Ks, Kst and Snn). The Kst value close to zero indicates no differentiation and Snn

value close to one indicates differentiation (Hudson, 2000). The statistical significant

values for Ks, and Kst were observed for all comparisons between the Metarhizium

clades revealed the presence of strong genetic differentiation. The highly significant

(P<0.001) Snn value ranged from 0.977 to 1.000, indicating high genetic

differentiation between the clades. The higher Fst values between M. anisopliae-M.

robertsii (0.58), M. anisopliae –M. pingshaense (0.70), M. anisopliae –M. indet. 1

(0.75), M. anisopliae –M. indet. 2 (0.58), M. robertsii - M. pingshaense (0.51), M.

pingshaense - M. indet. 1 (0.68) and M. pingshaense – M. indet. 2 (0.59) indicate

restricted gene flow between the populations. In contrast, the lower Fst values

between M. robertsii - M. indet. 1 (0.49), M. robertsii - M. indet. 2 (0.27) and M.

indet. 1 - M. indet. 2 (0.36) indicates moderate gene flow between the populations.

Table 3:7 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between different Metarhizium clades.

Clades Genetic differentiation estimate

Gene flow

estimate

Hs Hst Ks Kst Snn Nst Fst

M. anisopliae-M. robertsii 0.85*** 0.067*** 6.85*** 0.20*** 0.97*** 0.54 0.58

M. anisopliae-M. pingshaense 0.88* 0.048* 18.46*** 0.42*** 1.00*** 0.69 0.70

M. anisopliae-M. indet. 1 0.87*** 0.067*** 9.01*** 0.56*** 1.00*** 0.75 0.75

M. anisopliae-M. indet. 2 0.90** 0.051** 18.24*** 0.04*** 1.00*** 0.57 0.58

M. robertsii-M. pingshaense 0.89** 0.016** 7.55*** 0.08*** 0.99*** 0.47 0.51

M. robertsii-M. indet. 1 0.85*** 0.083*** 6.05*** 0.28*** 0.99*** 0.46 0.49

M. robertsii-M. indet. 2 0.89*** 0.026*** 6.01*** 0.09*** 0.98*** 0.25 0.27

M. pingshaense-M. indet. 1 0.89** 0.025** 8.28*** 0.25*** 1.00*** 0.69 0.68

M. pingshaense-M. indet. 2 0.92* 0.044* 11.85*** 0.37*** 1.00*** 0.59 0.59

M. indet. 1-M. indet. 2 0.90** 0.027** 7.18*** 0.20*** 0.98*** 0.36 0.36

Probability obtained by the permutation test with 1000 replicates:

*=0.01<P<0.05; **= 0.001<P<0.01; ***= P<0.001

50

The Ks, Kst and Snn values observed in the comparisons between the Metarhizium

populations from different locations indicate low genetic differentiations between the

populations (Table 3.8). This is supported by the low Fst values observed in all

comparison, which indicates high gene flow between the locations.

Table 3:8 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between Metarhizium populations in different locations

Location Genetic differentiation estimate Gene flow estimate

Hs Hst Ks Kst Snn Nst Fst

Bundaberg-Gatton 0.92** 0.018** 4.86*** 0.072*** 0.82*** 0.15 0.17

Bundaberg-Kingaroy 0.94*** 0.019*** 18.62* 0.025* 0.83*** 0.06 0.06

Gatton-Kingaroy 0.88*** 0.051*** 7.45*** 0.149*** 0.81*** 0.23 0.26

Probability obtained by the permutation test with 1000 replicates

*, 0.01<P<0.05; **, 0.001<P<0.01; ***, P<0.001

The genetic differentiation and gene flow estimates between the Metarhizium

populations in the ecotypes were also obtained (Table 3.9). The strong genetic

differentiation and moderate gene flow were observed between Maize-Grassland,

Maize-Forest, Legume-Grassland and Grassland-Forest populations supported by

significant Ks, Kst and Snn indices and Fst values. Low genetic differences and

moderate gene flow observed in Maize-Legume comparison. The insignificant Kst

value (0.075, close to zero) and low Fst (0.01957) observed between isolates from

Legume-Forest denote the low genetic differences and high gene flow between the

populations.

51

Table 3:9 Genetic differentiation and gene flow of combined MzIGS3 and 5’-TEF

genes between Metarhizium populations in different ecotypes. Isolates from forest

and legume showed no low differentiation.

Probability obtained by the permutation test with 1000 replicates:

*= 0.01<P<0.05; **= 0.001<P<0.01; ***= P<0.001; ns= not significant

Ecotypes Genetic differentiation estimate Gene flow estimate

Hs Hst Ks Kst Snn Nst Fst

Maize-Legume 0.91*** 0.038*** 14.02*** 0.030*** 0.97*** 0.42 0.44

Maize-Grassland 0.81*** 0.104*** 4.78*** 0.390*** 0.95*** 0.55 0.56

Maize-Forest 0.89*** 0.057*** 9.33*** 0.344*** 0.92*** 0.50 0.50

Legume-Grassland 0.87*** 0.060*** 12.38*** 0.129*** 0.97*** 0.19 0.21

Legume-Forest 0.92ns 0.007ns 25.39ns 0.075ns 0.60* 0.02 0.02

Grassland-Forest 0.84*** 0.089*** 4.59*** 0.092*** 0.95*** 0.17 0.17

52

3.3.5. Haplotype analysis

The combined nucleotide sequences identified 50 haplotypes in 164 Metarhizium

isolates from Queensland. Table 3.10 is showing the clades and isolates from which

haplotypes were detected. The clade-haplotype analysis found one haplotype (number

9) shared by three clades: M. pingshaense, M. indet. 1 and M. indet. 2. Haplotype

numbers 8 and 9 were common in M. pingshaense and M. indet. 1 populations. M.

anisopliae and M. robertsii shared one haplotype (number 1). Clades within the ‘M.

pingshaense’ complex (M. indet. 1, M. indet. 2 and M. pingshaense) shared

haplotypes, and clades within the ‘M. robertsii/M. anisopliae’ complex shared 1

haplotype, but there were no shared haplotypes between the two complexes. The

haplotype network presented in Figure 3.4 showed the haplotype distribution in

among the Metarhizium populations of different Metarhizium taxonomic groups. The

number of shared haplotypes in comparisons of the populations of different groups of

phylogenetic clades, locations and ecotypes are presented in Table 3.11.

The location-haplotype analysis found that all the locations, Bundaberg, Gatton and

Kingaroy shared three common haplotypes (haplotype numbers 4, 7 and 17).

Haplotype numbers 12 and 16 were common in Bundaberg and Kingaroy populations,

whereas two haplotypes (number 18 and 27) were common in Gatton and Kingaroy

populations. The haplotype network presented in Figure 3.5 showed the haplotype

distribution in among the Metarhizium populations of different locations.

The ecotype-haplotype relationship analysis found two common haplotypes (numbers

1 and 8) among the Metarhizium populations of maize, legume and forest. Two

haplotypes (numbers 2 and 9) was shared between maize and legume populations; and

53

Table 3:10 Distribution of haplotypes within Metarhizium clades and isolates

Haplotype

number

Species Isolate

Haplotype 1 M. anisopliae GFA1.1, GFA1.2, GFA1.3, GFA1.4, GFA1.5, GFA6.1,

KLB11, KLC3.1, KLC3.2, KLA17.5

M. robertsii KLC3.3

Haplotype 2 M. anisopliae KLB7

Haplotype 3 M. anisopliae KLB16

Haplotype 4 M. anisopliae KLD5.1

Haplotype 5 M. anisopliae KLA17.4

Haplotype 6 M. anisopliae KLA17.6

Haplotype 7 M. anisopliae KLA17.8

Haplotype 8 M. pingshaense KLA17.1

M. indet. 1 KMA12.5, KMC9.6

Haplotype 9 M. pingshaense KLA17.2, KLD5.2

M. indet. 2 BMB5

M. indet. 1 KMA4.4, KMA12.3, KMA13.2, KMC7.4, KMC9.7, KMC9.8,

KMD5.1, KMD5.2, KMD11.3, KMD17.2, KMD17.3,

KMD17.4

Haplotype 10 M. pingshaense KLA17.3, GFB1.2

M. indet. 1 BMD20, KMA4.1, KMA4.2, KMA4.3, KMA12.1, KMA12.2,

KMA12.4, KMA17, KMC1.1, KMC1.2, KMC5.2, KMC7.1,

KMC7.2, KMC7.3, KMC9.1, KMC9.3, KMC9.4, KMC9.5,

KMD17.1

Haplotype 11 M. pingshaense GFB1.1

Haplotype 12 M. indet. 2 BMB10

Haplotype 13 M. indet. 2 BMC1

Haplotype 14 M. indet. 2 BMC10, BMD1, BMD2.1, BFA12.1

Haplotype 15 M. indet. 2 BFA11

Haplotype 16 M. indet. 2 BFA20.1

Haplotype 17 M. indet. 2 BFB10.1

Haplotype 18 M. indet. 2 BFB13

Haplotype 19 M. indet. 1 GGB19.1

Haplotype 20 M. indet. 1 GGB19.2, GGB19.3

Haplotype 21 M. indet. 1 GGB19.4

Haplotype 22 M. indet. 1 BFC12

Haplotype 23 M. indet. 1 BMD5

Haplotype 24 M. indet. 1 BMD9, KMC1.3, KMD9, KMD11.2

Haplotype 25 M. indet. 1 KMA13.1

Haplotype 26 M. indet. 1 KMC5.1

Haplotype 27 M. indet. 1 KMC9.2

Haplotype 28 M. indet. 1 KMC17, KMD11.1

(Continued)

54

Table 3.10 (Continued) Distribution of haplotypes within Metarhizium clades and

isolates

Haplotype number

Species Isolate

Haplotype 29 M. robertsii BFB4, BFD6, BMA2, GFC19.2, GFC19.4, GFC19.7,

GFC19.8, GFC19.22, GFC19.31, KLA15.2, KLA15.4,

KLA16.1, KLA17.9, KLA18, KLA20, KLD3.2

Haplotype 30 M. robertsii BFC9, BFD4, GFC19.5, GFC19.10, GFC19.11, GFC19.15,

GFC19.18, GFC19.20, GFC19.24, GFC19.30, GFC20.7, KLD3.1

Haplotype 31 M. robertsii BFD7.1

Haplotype 32 M. robertsii BFD8

Haplotype 33 M. robertsii BGD12.1

Haplotype 34 M. robertsii GFC19.3

Haplotype 35 M. robertsii GFC19.13

Haplotype 36 M. robertsii GFC19.14

Haplotype 37 M. robertsii GFC19.19

Haplotype 38 M. robertsii GFC19.27

Haplotype 39 M. robertsii GFC20.3

Haplotype 40 M. robertsii GFC20.5

Haplotype 41 M. robertsii GFC20.6, GLD4.5, KLA16.2, KLB1.1

Haplotype 42 M. robertsii GGB13.1, GGB13.4, GGB13.8, GGB13.11, GGB13.13,

GGB13.24, GGB13.26, GGB13.27, GGB13.28, GGB14.3,

GGB14.11, GGB14.12, GGB14.15, GGB14.17, GGB14.23

Haplotype 43 M. robertsii GGB13.2, GGB13.16, GGB14.4, GGB14.10, GGB14.19

Haplotype 44 M. robertsii GGB13.6, GGB13.14, GGB13.15, GGB13.17, GGB13.18,

GGB13.19, GGB13.20, GGB13.25, GGB13.30, GGB13.32,

GGB14.8, GGB14.22, GGB14.5

Haplotype 45 M. robertsii GGB13.9, GGB14.13

Haplotype 46 M. robertsii GGB13.12

Haplotype 47 M. robertsii GGB14.1

Haplotype 48 M. robertsii GGB14.21

Haplotype 49 M. robertsii KLA15.1

Haplotype 50 M. robertsii KLD17

55

haplotype number 5 was common in maize and forest populations. Three haplotypes

(numbers 14, 17 and 22) were shared between in legume and forest. There was no

shared haplotype between the grassland haplotypes and haplotypes from other

ecotypes. Figure 3.6 illustrated the haplotype distribution among the Metarhizium

populations from different ecotypes.

Table 3:11 Number of shared haplotypes of Metarhizium populations between the

groups of clades (A), locations (B) and ecotypes (C)

A.

Clades Shared haplotype

M. anisopliae-M. robertsii 1

M. anisopliae-M. pingshaense 0

M. anisopliae-M. indet. 1 0

M. anisopliae-M. indet. 2 0

M. robertsii-M. pingshaense 0

M. robertsii-M. indet. 1 0

M. robertsii-M. indet. 2 0

M. pingshaense-M. indet. 1 3

M. pingshaense-M. indet. 2 1

M. indet. 1-M. indet. 2 1

B.

Locations Shared haplotype

Bundaberg-Gatton 3

Bundaberg-Kingaroy 5

Gatton-Kingaroy 3

C.

Ecotypes Shared haplotype

Maize-Legume 3

Maize-Grassland 0

Maize-Forest 3

Legume-Grassland 0

Legume-Forest 5

Grassland-Forest 0

56

M. pingshaense

M. anisopliae

M. robertsii

M. indet. 1

M. indet. 2

Figure 3.5 Haplotype networks for the Metarhizium populations from five groups of

isolates based on combined sequence data. Different ‘pie’ colours in the circles

indicate the location of haplotype origin, with size of the circles indicating number of

specimens; pie-segmentation is proportional to haplotype frequency. Perpendicular

tick marks on the lines represent the mutations between the linked haplotypes. Clades

within the ‘M. pingshaense’ complex (M. indet. 1, M. indet. 2 and M. pingshaense)

shared haplotypes, and clades within the ‘M. robertsii/M. anisopliae’ complex shared

1 haplotype, but there were no shared haplotypes between the two complexes.

57

Figure 3.6 Haplotype networks for the Metarhizium populations from three locations

based on combined sequence data. Different ‘pie’ colours in the circles indicate the

location of haplotype origin, with size of the circles indicating number of specimens;

pie-segmentation is proportional to haplotype frequency. Perpendicular tick marks on

the lines represent the mutations between the linked haplotypes.

58

Figure 3.7 Haplotype networks for the Metarhizium populations from four ecotypes

based on combined sequence data. Different ‘pie’ colours in the circles indicate the

location of haplotype origin, with size of the circles indicating number of specimens;

pie-segmentation is proportional to haplotype frequency. Perpendicular tick marks on

the lines represent the mutations between the linked haplotypes. None of the 11

grassland haplotypes (purple) were shared with any other ecotype.

59

3.4 Discussion This study has six major findings.

1. Metarhizium species were widely distributed in the Queensland’s soils and

recovery of isolates from soil samples from disturbed (crop) soils were overall

significantly higher than in samples from undisturbed (forest, grassland),

though the results were not consistent across sites.

2. Multilocus phylogenetic analysis with 5’-TEF and MzIGS3 nuclear regions

were found to be suitable for taxonomic identification and analysis of cryptic

species amongst the field isolates of Metarhizium.

3. Two new Metarhizium indeterminate species (provisionally named M. indet. 1

and M. indet. 2) were identified in a complex with M. pingshaense, and their

species status was strongly supported by the genetic differentiation.

4. Clades within the ‘M. pingshaense’ complex (M. indet. 1, M. indet. 2 and M.

pingshaense) shared haplotypes, and clades within the ‘M. robertsii/M.

anisopliae’ complex shared 1 haplotype, but there were no shared haplotypes

between the two groups.

5. Moderate genetic differences were observed between the Metarhizium

population from different location, with moderate gene flow between

Metarhizium populations across geographical location.

6. There was significant genetic differentiation between isolates from grassland

and those from other ecotypes, and none of the 11 haplotypes from grassland

were shared with any other ecotype. In contrast, there was very low genetic

differentiation between isolates from forest and legume crops, with 5 shared

haplotypes.

Metarhizium isolates were recovered from all 3 locations, and from all ecotypes:

agricultural crops, grasslands and forest soils. However, Metarhizium was recovered

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from just 6.70% of soil samples from 880 total soil samples which is a lower rate of

recovery than the occurrence of Metarhizium reported elsewhere. The recovery rate

was reported as 28% in Tasmania, Australia (Rath et al., 1992), and outside of

Australia at 17.5% in the UK (Chandler et. al., 1998), 19.3% in Switzerland

(Schneider et al., 2011), 44.6% in Finland (Vanninen et al., 1989), 71.7% in Spain

(Quesada-Moraga et al., 2007), and 91% in Canada (Bidochka et al., 1998). However,

these recovery percentages are not comparable, as the fungal isolation techniques for

entomopathogens were different in different studies. For instance, the Tasmanian soil

study used the agar plate isolation technique, whereas the studies in Spain, Canada

and UK used the insect bait method.

The proportion of samples containing Metarhizium was significantly higher in

agricultural soils than from grassland, but there was no significant difference between

forest and agricultural soils. Overall there was a significantly higher recovery of

isolates from soils from disturbed habitats (crops) than from undisturbed (grassland,

forest), but these results were not consistent across locations. Results are also

inconsistent globally: Metarhizium spp. were more frequently isolated from

agricultural soils than from undisturbed or semi-disturbed habitats in Canada, Finland,

the Netherlands and China (Bidochka et al., 1998, Vanninen, 1996, Steenberg, 1995,

Meyling and Eilenberg, 2007). However, a study in Switzerland found that permanent

grassland and field margins contained a higher density of Metarhizium species than

either forest or cultivated arable land (Schneider et al. 2012).

The reasons for the higher recovery of Metarhizium species in agricultural and forest

soil are still not clear. Long term persistence of Metarhizium spores in agricultural

soils (Latch and Fallon, 1976) and wide dispersion of Metarhizium propagules by

agricultural tools (Kepler et al., 2015) might be the causes of higher occurrence of

61

Metarhizium species in agricultural soils. The very low percentage of Metarhizium

observed in grassland soil samples in Queensland might result from movement of

heavy agricultural machineries and greater compaction of soils than in forests or tilled

cropping. It may be that there is a more consistent association with other factors (such

as plant types, e.g. legumes) than with disturbance, or that there may be species

specific associations by different fungal clades, which are discussed below.

The internal transcribed region (ITS) marker was found to be a useful tool in

preliminary screening of isolates. However, ITS sequences were used only in an

initial screen to characterise a large number of isolates to the genus level. Later, those

identified as Metarhizium spp. were characterised by more appropriate markers

analysis to determine identity at the species level.

Single locus phylogenetic analysis with MzIGS3 and 5’-TEF gene were conducted

here with different phylogenetic inference tools, maximum likelihood and Bayesian

inference. The resultant trees for both individual markers produced polytomies and

could not fully resolve the taxonomic position of our isolates. MzIGS3 was found to

be more informative than 5’-TEF in resolving the phylogeny of the Australian isolates,

but neither were found to be suitable as a single tool for taxonomic validation of the

Metarhizium species. In this thesis, the MzIGS3 marker was sufficient to resolve the

three clades in the M. pingshaense complex (M. pingshaense and the two new

indeterminate clades) but not to resolve M. robertsii and M. anisopliae, which could

only be resolved using a combined analysis of the MzIGS3 and 5’-TEF markers.

Intergenic sequence markers were compared with the 5’-Transcription elongation

factor (5’-TEF) marker in the taxonomic identification of Brazilian Metarhizium

species (Rezende et al. 2015), on the basis of which it was suggested that MzIGS3

was not suitable as a stand-alone marker, but that 5’-TEF may be used alone or in

combination with MzIGS3 to resolve species identification. From our results, we

62

conclude that single locus phylogenetic analysis with current markers cannot be used

to identify Metarhizium species and that a multilocus analysis should be used.

The study identified two new, phylogenetically indeterminate clades that are sister

groups of M. pingshaense. The multilocus phylogenetic analysis differentiates the two

indeterminate clades from M. pingshaense with high support values (Table 3), values

that are higher than for the differentiation of M. robertsii from M. anisopliae.

The multi-locus analysis (ML) of concatenated data sets of MzIGS3 and 5’-TEF

identified five principal clades of Metarhizium with very high bootstrap supports for

the Queensland’s isolates. Among the members of the “PARB” clade, which includes

M. pingshaense, M. anisopliae, M. robertsii, and M. brunneum, described by Bischoff

et al. (2009), this study identified five species: M. pingshaense, M. anisopliae and M.

robertsii, with two additional new and previously undescribed ‘indeterminate’

Metarhizium clades. All the clades and the reference M. brunneum (ARSEF 2107)

formed the inner core of the phylogenetic tree, produced a similar topology to the

phylogenetic tree using the classified Metarhizium species by Bischoff et al. (2009)

and Kepler et al. (2015). The phylogenetic position of the authenticated reference taxa

presented in our two-gene (MzIGS3 and 5’-TEF) analysis is also supported by the

three-gene (MzIGS3, MzFG543igs and 5’-TEF) analysis of Rezende et al. (2015).

Overall, the results of this study demonstrated the usefulness of multi-locus analysis

of the nuclear markers MzIGS3 and 5’-TEF for identification Metarhizium species.

This study used statistical methods to investigate the genetic differentiation of the five

clades identified in phylogenetic analysis. The genetic differentiation statistical tests,

Ks and Kst were performed with a null hypothesis: “that there is no genetic difference

between the Metarhizium clades”. All the tests rejected the null hypothesis (P<0.001)

to conclude that the clades are genetically different from each other. In addition, the

nearest-neighbour statistics Snn (Hudson, 2000) test showed the value for all the

63

comparisons were close to one (1). More importantly the Snn values in the

comparison between M. pingshaense-Metarhizium indet. 1, M. pingshaense-

Metarhizium indet. 2 and Metarhizium indet. 1- Metarhizium indet. 2 were 1.000,

1.000 and 0.98305, again indicates that the clades are strongly genetically different.

Moreover, commonly used fixation estimates Nst and Fst between M. pingshaense-

Metarhizium indet. 1 and M. pingshaense-Metarhizium indet. 2 were 0.68 and 0.59

respectively; indicate high degree of genetic differentiations. In contrast, the Nst and

Fst values between Metarhizium indet. 1- Metarhizium indet. 2 was comparatively

low (0.35805) denotes the moderate genetic differentiation.

The possible reason of low genetic differentiation observed between Metarhizium

indet. 1- Metarhizium indet. 2 in compare to other comparisons is that both the

isolates were recovered from similar ecological niches (fields of maize, a

monocotyledon). Clades within the ‘M. pingshaense’ complex (M. indet. 1, M. indet.

2 and M. pingshaense) shared haplotypes, and clades within the ‘M. robertsii/M.

anisopliae’ complexes shared one (1) haplotype, but there were no shared haplotypes

between the two complexes. Overall, the phylogenetic, genetic differentiation and

haplotype evidence presented in this paper, suggests that the two new clades are new

Metarhizium species within a complex of sister species including M. pingshaense.

These two-new species will be fully described, deposited in reference collections and

the results published subsequent to the submission of this thesis.

This study showed that the haplotypes of Metarhizium populations not significantly

associated with location, which suggests that gene flow among Metarhizium

populations is not reduced by location. There was significant genetic differentiation

between isolates from grassland and those from other ecotypes, and none of the 11

haplotypes from grassland were shared with any other ecotype. In contrast, there was

very low genetic differentiation between isolates from forest and legume crops, with 5

64

shared haplotypes. The high genetic similarity between the legume crop and forest

populations may be as a result of the rhizospheric association of the Metarhizium

strains with broad-leaf Eudicots, and specifically with legumes, since Australian

forests contain a high proportion of Acacia spp. (Fam. Fabacea, Sub Fam.

Mimosoideae).

Though this study did not explain the possible mechanism (or limits to) gene flow

between locations and ecotypes, this information is valuable in the application of

commercial Metarhizium isolates in an ecosystem to evaluate the risk of gene flow

from released biocontrol agents with the indigenous populations (Wang et al., 2011).

Kepler et al. (2015) suggested that there is a low chance of gene flow between

Metarhizium populations. However, the high levels of gene flow in Australian isolates

suggests that this requires further study.

The influence of plant habitat for the evolution and distribution of Metarhizium

population also reported (Wyrebek and Bidochka, 2013, Bidochka et al., 1998). The

species in the genus Metarhizium show evidence of ecological speciation (Douhan et

al., 2008). Bidochka et al. (2001) found “cold active’ M. robertsii and “heat active”

M. brunneum in two different ecotypes: forest and agricultural fields, respectively, in

Canada. This association of species with plant and ecotype will be explored in more

detail in Chapters 4 and 5.

65

Figure 3.8 (continued)

Metarhiz ium anisopliae isolate 69

Metarhizium anisopliae isolate 251

Metarhizium anisopliae isolate 68

3.7 Supplementary data

66

Metarhizium pingshaense ARSEF1009

Pochonia ( Syn. Metacordyceps)

Figure 3.8 Maximum likelihood tree of ITS sequence data. Three sequences from genus

Pochonia that are listed on MycoBank under the synonyms Metacordyceps chlamydosporia

UCR3, Metacordyceps chlamydosporia IAM14700, and Cordyceps chlamydosporia (Syn

Metacordyceps chlamydosporia) NBRC9249 were included in genus/clade Pochonia. The

six isolates that aligned with this clade were excluded from subsequent analysis. .

67

Figure 3.9 (continued)

68

M. anisopliae isolate 68

M. pingshaense CBS257.90

M. pingshaense

M. anisopliae isolate 69

M. anisopliae isolate 251

Figure 3.9 Bayesian tree of concatenated data set of 5'-TEF and MzIGS3 for

Metarhizium isolates

69

Figure 3.10 (continued)

Metarhizium indet. 1

M. anisopliae isolate 69

M. anisopliae isolate 251

M. anisopliae isolate 68

Metarhizium indet. 2

M. pingshaense CBS257.90

70

Figure 3.10 Maximum likelihood tree of MzIGS3 data of Metarhizium isolates

71

Figure 3.11 (continued)

M. anisopliae isolate 69

M. anisopliae isolate 68

M. pingshaense CBS257.90

M. anisopliae isolate 251

72

Figure 3.11 Maximum likelihood of 5'-TEF data of Metarhizium isolates

73

Figure 3.12 (continued)

Metarhizium indet. 1

M. anisopliae isolate 69

M. anisopliae isolate 251

M. anisopliae isolate 68

Metarhizium indet. 2

M. pingshaense CBS257.90

M. pingshaense

74

Figure 3.12 Bayesian tree of MzIGS3 data of Metarhizium isolates

75

Figure 3.13 (continued)

76

M. anisopliae isolate 69

M. anisopliae isolate 251

M. anisopliae isolate 68

M. pingshaense CBS257.90

M. pingshaense

Figure 3.13 Bayesian tree of 5’-TEF data of Metarhizium isolates

77

Metarhizium indet. 1

M. anisopliae isolate 69

M. anisopliae isolate 251

M. anisopliae isolate 68

Metarhizium indet. 2

M. pingshaense CBS257.90

M. pingshaense

Figure 3.14 (continued)

78

Figure 3.14 Maximum Likelihood tree of combined data set of MzIGS3, 5’-TEF and

ITS for Metarhizium isolates

79

Metarhizium indet. 1

M. anisopliae isolate 69

M. anisopliae isolate 251

M. anisopliae isolate 68

Metarhizium indet. 2

M. pingshaense CBS257.90

M. pingshaense

Figure 3.15 (contined)

80

Figure 3.15 Bayesian tree of combined data set of MzIGS3, 5’-TEF and ITS for

Metarhizium isolates

81

Chapter 4 : Factors affecting the

occurrence and diversity of Metarhizium

species in agricultural, grassland and forest

soils

82

Factors affecting the occurrence and diversity of Metarhizium species in

agricultural, grassland and forest soils

Abstract The fungi of the genus Metarhizium are both soil saprophytes and insect pathogens, and have

recently been identified associated with the rhizosphere. The factors influencing

Metarhizium species occurrence and distribution in ecotypes, however, are poorly

understood. To identify factors that might determine Metarhizium species diversity in

agriculture, grassland and forest, soil samples were characterised and their relationships with

previously identified Metarhizium clades analysed using multivariate and univariate

analyses. Regression analysis showed that the ecotype associated with crop or plant

rhizosphere predicted the occurrence of M. anisopliae and M. robertsii and location

predicted the occurrence of M. indet. 1 in soil samples. M. anisopliae and M. robertsii were

predominantly recovered from legume and forest soils and associated with high nitrogen and

carbon contents in soil. Isolates of the M. indet. 1 clade were predominantly found in maize

and grassland and associated with low nitrogen and carbon. M. robertsii and M. indet. 1

were more abundant in Fe rich soil. A minor soil element zirconium was positively

associated with M. anisopliae, M. robertsii and M. indet. 1 isolates. These results suggest

that the use of Metarhizium as rhizospheric inocula may be enhanced by selection species or

strains for use in specific conditions.

Keywords: Metarhizium, occurrence, diversity, soil factors, multivariate, univariate.

83

4.1 Introduction

Entomopathogenic fungi of the genus Metarhizium (Order Hypocreales) are common

saprophytes found in soil and play an important role as entomopathogens in regulating the

insect population in terrestrial ecosystems (Keller and Zimmerman, 1989). Several

commercial formulations have been made from the species of Metarhizium and used as

biocontrol agent against soil-dwelling insects worldwide (Faria and Wraight, 2007).

Knowledge of naturally occurring species of biological control agents and their distribution

in an ecosystem is required for their application in conservation biological control strategies

(Steinwender et al., 2014).

Recent studies showed that Metarhizium species are associated with plants in the rhizosphere

(Hu and St. Leger, 2002, Sasan and Bidochka, 2012) and their distribution is more clearly

determined by the specific soils and plant hosts than by their insect host (Bidochka et al.,

1998). However, understanding the factors governing the distribution of species of

Metarhizium in local habitats is important to find the best suitable species for each specific

environment to conserve and increase the benefits of biological control agents (Quesada-

Moraga et al., 2007).

A few studies have been conducted to identify the effect of ecological and soil factors such

as disturbance, annual rainfall, temperature, elevation and soil properties on the distribution

of entomopathogenic Hypocrealean fungi including Metarhizium (Sun and Liu, 2008.

Quesada-Moraga et al., 2007, Meyling and Eilenberg, 2006. Bidochka, et al., 1998, Chandler

et al., 1997, Vanninen, 1996, Rath et al., 1992). These studies had limitations, as they

identified the species of entomopathogens based on morphology or using the internal

transcribed sequence (ITS) marker, which have been subsequently found to be inadequate for

differentiating pleomorphic Metarhizium species (Steinwender et al., 2014). Moreover, the

studies only took into consideration the effects of only a few factors on the occurrence of the

84

fungi considering only genus, for example, a comparison between the genera Beauveria and

Metarhizium. The taxonomy of the genus Metarhizium has been recently revised and nine

species were accepted based on the multilocus phylogenetic analysis on nuclear markers

(Bischoff et al., 2009). The revised taxonomy will give new insights to understand the

ecological factors affecting the Metarhizium species isolation, distribution and application in

a specific habitat as an effective biological control agent.

Chapter 3 described the isolation of Metarhizium spp. from four ecotypes (maize field,

legume field, grassland and forest) in three locations (Bundaberg, Gatton and Kingaroy) in

Queensland, Australia, and used multilocus analysis to identify five clades of Metarhizium

species based on the recent revision. Haplotypes within the Metarhizium populations were

not significantly associated with location, which suggests that gene flow among Metarhizium

populations is not reduced by location. However, there was significant genetic differentiation

between isolates from grassland and those from other ecotypes, and none of the 11

haplotypes from grassland were shared with any other ecotype. In contrast, there was overall

no statistically significant genetic differentiation between isolates from forest and legume

crops, with 5 shared haplotypes between the two habitats.

Chapter 4 investigates the relationships between the five Australian species with location,

ecotype, soil properties and soil elements using multivariate and univariate analyses. To our

knowledge, this study reports for the first time the relationship between the species of

Metarhizium and ecological and soil characteristics, which may be useful for selection of

Metarhizium species to improve their potential use as rhizospheric inocula in agriculture.

85

4.2 Materials and methods

4.2.1 Soil collection, isolation and description

Soil samples were collected from 4 ecotypes and three locations in Queensland, Australia

(Bundaberg, Gatton and Kingaroy) as described in Chapter 3. The species of Metarhizium

were isolated and identified using multilocus phylogenetic analysis reported in Chapter 3.

4.2.2 Soil analysis

In general, 1 composite soil sample represents properties of 2.5 acres of land (Peters and

Laboski, 2013). Each sampling site in this research covered 0.74 acres (3000sqm) of land.

Two composite soil samples were made by mixing soil samples from transects A and B, and

C and D from each ecotype to give a total of 22 composite soil samples for soil properties

and elemental analysis. Composite soil samples were sieved by 5mm sieve to remove plant

debris, air-dried and finely ground using soil mill before analysing their properties and

elements.

Soil moisture

The soil moisture (dry basis) was determined by following the standard procedure described

by Standards Association of Australia (1990). Samples were weighed, then dried in an oven

at 105°C overnight and weighed again.

pH

The pH of the soil samples was determined in 1:5 soil: water suspension using a pH meter

(Rayment and Higginson, 1992). After mechanically shaking the soil suspension for 1 hour

at 15 rpm, three pH-readings were recorded from each sample and the mean value of pH was

calculated.

86

Soil elements

Carbon (C) and Nitrogen (N) were measured in LECO TruMac Elemental Analyser. A mass

of 0.1 g of soil was placed into a large ceramic boat, loaded into a ceramic high temperature

furnace (1300C), and combusted in a pure oxygen environment to ensure complete

oxidation. Combustion gases were directed through several columns to remove moisture. A

small aliquot of sample was taken and passed through an infrared detector for carbon and

nitrogen determination.

Major and trace elements in soil samples were determined by X-ray Fluorescence (XRF)

technique by preparing 40 mm diameter fused glass discs from soil samples. The XRF discs

were prepared using standard disc preparation procedure, and are reported briefly here.

Approximately 1.15 g of soil sample was fused with approximately 8.85 g of a 50:50 mix of

lithium metaborate and lithium tetraborate flux containing 0.5% LiBr (by mass) as a wetting

agent in an Ox automated fusion machine for 25 minutes at 1050°C.

The elements were measured on a 40mm PANAlytical Axios wavelength dispersive (WD)

X-ray Fluorescence (XRF) spectrometer equipped with a 1kW Rh tube was used for all XRF

analyses. The elements were acquired using PANalytical’s WROXI application and

calibration standard procedures. Loss-On-Ignition data derived from a separate analysis

procedure were incorporated for each sample for matrix correction purposes. XRF major

element data are reported as oxide%.

4.2.3 Colony forming unit (CFU)

The number of colonies per plate of each Metarhizium isolate on selective agar medium was

counted for each soil sample where fungus was observed (Chapter 3). The CFU per g of dry

soil was calculated after the soil moisture determination for each sampling site.

87

4.2.5 Statistical analysis

The presence-absence data of Metarhizium species in the 880 soil samples from different

ecotypes and locations were arranged in a matrix and analysed using binomial logistic

regression to identify the association of Metarhizium species occurrence in soil samples with

location and ecotypes. Analysis of similarity (ANOSIM) with a permutation test for

significance was also conducted using “Vegan” package in Rv3.3.1 (R Core team, 2016).

A complete data set was developed for the 58 soil samples, representing 5 Metarhizium

species from 164 Metarhizium isolates. Principal component analysis (PCA), a multivariate

analysis, was used with the aim was to reduce the large number of variables to a small set

containing most of the information. Biplot analysis was used to separate the Metarhizium

species using principal components. Generalised Linear Model (GLM) with logit link was

conducted to identify the effect of the significant soil factors identified in PCA on the

presence of Metarhizium species in all soil samples. The statistical software programs

‘R’v3.3.1 (R core team, 2016) were used for the analyses.

88

4.3 Results

4.3.1 Fungal species distribution in soil samples

A total of 164 Metarhizium isolates were recovered in this study, and phylogenetic analysis

reported in Chapter 3 showed that the isolates represented five groups or clades of species.

Three clades were identified as M. robertsii, M. anisopliae, M. pingshaense and 2 new clades

were identified: Metarhizium clades indeterminate 1 (indet. 1) and indeterminate 2 (indet. 2).

As reported in Chapter 3 (table 3.2), Metarhizium species were identified in 58 of the 880

soil samples (6.59%). Table 4.1 lists occurrence of clades by soil transect and sample

number.

Table 4:1 Metarhizium species and number of colony observed in a plate in 58 soil samples

from different ecotypes and locations by transect.

Location Ecotype Transect Species Number of

colony/plate

Bundaberg Maize A2 M. robertsii 1

Bundaberg Maize B5 Metarhizium indet. 2 1

Bundaberg Maize B10 Metarhizium indet. 2 1

Bundaberg Maize C1 Metarhizium indet. 2 1

Bundaberg Maize C10 Metarhizium indet. 2 1

Bundaberg Maize D1 Metarhizium indet. 2 1

Bundaberg Maize D2 Metarhizium indet. 2 1

Bundaberg Maize D5 Metarhizium indet. 1 1

Bundaberg Maize D9 Metarhizium indet. 1 1

Bundaberg Maize D20 Metarhizium indet. 1 1

Bundaberg Grassland D12 M. robertsii 1

Bundaberg Forest A11 Metarhizium indet. 2 1

Bundaberg Forest A12 Metarhizium indet. 2 1

Bundaberg Forest A20 Metarhizium indet. 2 1

Bundaberg Forest B4 M. robertsii 1

Bundaberg Forest B10 Metarhizium indet. 2 1

Bundaberg Forest B13 Metarhizium indet. 2 1

Bundaberg Forest C9 M. robertsii 1

Bundaberg Forest C12 Metarhizium indet. 1 1

Bundaberg Forest D4 M. robertsii 1

89

Bundaberg Forest D6 M. robertsii 1

Bundaberg Forest D7 M. robertsii 1

Bundaberg Forest D8 M. robertsii 1

Gatton Grassland B13 M. robertsii 23

Gatton Grassland B14 M. robertsii 15

Gatton Grassland B19 Metarhizium indet. 1 4

Gatton Forest A1 M. anisopliae 5

Gatton Forest A6 M. anisopliae 1

Gatton Forest B1 M. pingshaense 2

Gatton Forest C19 M. robertsii 19

Gatton Forest C20 M. robertsii 4

Gatton Legume D4 M. robertsii 1

Kingaroy Maize A4 Metarhizium indet. 1 4

Kingaroy Maize A12 Metarhizium indet. 1 5

Kingaroy Maize A13 Metarhizium indet. 1 2

Kingaroy Maize A17 Metarhizium indet. 1 1

Kingaroy Maize C1 Metarhizium indet. 1 3

Kingaroy Maize C5 Metarhizium indet. 1 2

Kingaroy Maize C7 Metarhizium indet. 1 4

Kingaroy Maize C9 Metarhizium indet. 1 8

Kingaroy Maize C17 Metarhizium indet. 1 1

Kingaroy Maize D5 Metarhizium indet. 1 2

Kingaroy Maize D9 Metarhizium indet. 1 1

Kingaroy Maize D11 Metarhizium indet. 1 3

Kingaroy Maize D17 Metarhizium indet. 1 4

Kingaroy Legume A15 M. robertsii 3

Kingaroy Legume A16 M. robertsii 2

Kingaroy Legume A17 M. anisopliae (4), M. pingshaense (3), M. robertsii (1) 8

Kingaroy Legume A18 M. robertsii 1

Kingaroy Legume A20 M. robertsii 1

Kingaroy Legume B1 M. robertsii 1

Kingaroy Legume B7 M. anisopliae 1

Kingaroy Legume B11 M. anisopliae 1

Kingaroy Legume B16 M. anisopliae 1

Kingaroy Legume C3 M. anisopliae (2), M. robertsii (1) 3

Kingaroy Legume D3 M. robertsii 2

Kingaroy Legume D5 M. anisopliae (1), M. pingshaense (1) 2

Kingaroy Legume D17 M. robertsii 1

90

M. pingshaense (3)

Typically, only one Metarhizium clade was identified in each of the 55 of the 58 positive

samples. Only 3 soils samples, all from legume crop in Kingaroy, had more than one

clade/species present (Table 4.1). There was no significant difference between the mean

occurrence of each species as a percentage of total (880) soil samples, but there were

differences in the occurrence of the 5 clades. M. robertsii was found in 22 of the 880 soil

samples (2.5%), followed by Metarhizium indet. 1 in 18 samples (1.3%). Metarhizium indet.

2, M. anisopliae and M. pingshaense occurred at low percentages, in 1.3 (18%), 8 (0.9%) and

3 (0.3%) of the soil samples respectively. M. robertsii was found in the highest proportion

(35%) of soil samples that contained at least 1 colony of Metarhizium, followed by

Metarhizium indet. 1 (29%), Metarhizium indet. 2 (18%), M. anisopliae (13%) and M.

pingshaense (5%) (Figure 4.1).

Figure 4.1: Occurrence of each clade as a proportion of the soil samples in which at

least one colony forming units was identified. The number of soil samples in which

each clade was identified is in brackets.

91

Figure 4.2 shows the occurrence of the Metarhizium species in different locations and

ecotypes by percentage of soil samples. In Bundaberg, Metarhizium indet. 2 was found in

4.58% of soil samples, M. robertsii in 3.33% and Metarhizium indet. 1 in 1.67%. In Gatton

soils, M. robertsii was found in 1.25% of samples, M. anisopliae in 0.56%, Metarhizium

indet. 1 and M. pingshaense in just 1 sample (0.28%). At Kingaroy, Metarhizium indet. 1

was found in 3.61% of samples, followed by M. robertsii (2.78%), M. anisopliae (1.67%),

and M. pingshaense in 2 samples (0.56%). Metarhizium indet. 1 was found at all three

locations, while Metarhizium indet 2 was only found at Bundaberg. Figure 4.3 is showing

geographical representation of soil samples of different locations in which different species

of Metarhizium were detected. In Bundaberg, M. indet. 2 was more frequently isolated

followed by M. robertsii and M. indet. 1 from the soil samples. M. indet. 1 was the dominant

clade in Kingaroy soil followed by M. robertsii, M. anisopliae and M. pingshaense. In

Gatton soil, M. robertsii were more frequent than M. anisopliae and M. pingshaense.

Metarhizium indet. 1 was the most common isolate found in maize soil (6.67% of samples),

followed by Metarhizium indet. 2 (2.5%) and M. robertsii in just 1 sample (0.42%). The

highest percentage of soil samples in legumes contained M. robertsii (6.25%), followed by

M. anisopliae (3.75%) and M. pingshaense (1.25%). The two new indeterminate clades were

not found in legume crops at any location. Only two species were isolated from grassland:

M. robertsii (1.25% of soil samples) and one isolate of Metarhizium indet. 1 from Gatton

(0.42% of soil samples). In contrast, all the isolates were found in forest soils, with higher

percentages of soil samples containing M. robertsii (3.33%), followed by Metarhizium indet.

2 (2.08%), M. anisopliae (0.83%), Metarhizium indet. 1 (0.42%) and M. pingshaense

(0.42%).

92

Figure 4.2 The occurrence of Metarhizium species as a proportion of soil samples at

different locations (above) and ecotypes (below). No statistical significant difference was

observed.

93

Figure 4.3 Geographical representation of soil samples of different locations of Queensland

in which different species of Metarhizium were detected. Pie charts showing species level

distribution of Metarhizium relative abundance (%) in the soil samples in which Metarhizium

detected in Bundaberg, Kingaroy and Gatton.

Bundaberg

Gatton

Kingaroy

94

4.3.2 Ecological association

The analysis of similarity (ANOSIM) showed a significant (R=0.403, significance =0.001)

pattern of association of the species of Metarhizium with the location and ecotype. M.

robertsii and M. indet. 1 species were found at all three locations. M. robertsii was found at 6

of the 11 sites (location × ecotype), predominantly in legume and forest but also in grassland

and maize. A trend of habitat preference was observed in M. indet. 1 species, which was

strongly associated with maize at Kingaroy and Bundaberg, and M. indet. 2 species, which

was associated with maize and forest at Bundaberg (the only location at which M. indet. 2

was found) (Figure 4.3). Neither M. indet. 1 or M. indet. 2 were found in legume crop at any

location. The other two species, M. anisopliae and M. pingshaense, were associated with the

Gatton-forest and the Kingaroy-legume ecotypes respectively.

95

M.a

nis

op

liae

M.p

ing

ha

en

se

M.ro

berts

ii

M.in

de

t1

M.in

de

t2

Gatton_ForestKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeGatton_ForestGatton_ForestKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeKingaroy_MaizeGatton_GrasslandBundaberg_ForestBundaberg_MaizeBundaberg_MaizeBundaberg_MaizeBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_MaizeBundaberg_MaizeBundaberg_MaizeBundaberg_MaizeBundaberg_MaizeBundaberg_MaizeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeKingaroy_LegumeGatton_LegumeGatton_ForestGatton_ForestGatton_GrasslandGatton_GrasslandBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_ForestBundaberg_MaizeBundaberg_GrasslandKingaroy_LegumeKingaroy_Legume

0

0.2

0.4

0.6

0.8

1

M. a

nis

op

liae

Me

tarh

iziu

m in

de

t. 1

Me

tarh

iziu

m in

de

t. 2

M. ro

berts

ii

M. p

ing

sh

ae

ns

e

Figure 4.4 Heatmap of Metarhizium species associated with locations and ecotypes based on

Czekanowski dissimilarity in DICE analysis of presence-absence data, R=0.403, significance

=0.001.

96

Binomial logistic regression analysis showed only the main effects of the predicted variables

‘ecotype’ and ‘location’ on the occurrence of Metarhizium species in soil samples. Ecotype

was the significant main factor for the occurrence of M. robertsii and M. anisopliae, found

predominantly in legume crops, and with M. indet. 1, found predominantly in maize. The

occurrence of M. indet. 1 could be also predicted by location as this was predominantly

found in Kingaroy. Again, regression analysis could not estimate the significance

relationship with M. indet. 2 and M. pingshaense with the ecotype and location. M. indet. 2

was only recovered from a small number (11 out of 880 soil samples) and only in

Bundaberg-Maize which was too low to obtain sufficient statistical support. The occurrence

of M. pingshaense (3 of 880 soil samples) was also too low for statistical analysis. Table 4.2

shows the Wald chi-square values and significance levels of the logistic regression analysis.

Table 4:2 Binomial logistic regression analysis shows the relationship between the species

of Metarhizium in soils as a function of location and ecotype

Metarhizium species Wald chi-square value

Location Ecotype

M. robertsii 1.299 14.156***

Metarhizium indet. 1 6.383* 13.091***

M. anisopliae 2.606 6.244*

Asterisk marks show significance level, *= p<0.05, ***= p<0.001

4.3.3 Soil factors

Principal component analysis of the soil factors showed that first two principal components

had higher eigenvalues and both together retained more than 70% variance (Table 4.3,

Figure 4.4). Correlation between the soil components and principal components (loading

value) is presented in Table 4.4. The first principal component (Dimension 1) was strongly

positively correlated with titanium (Ti), copper (Cu), iron (Fe), sulphur (S), manganese

(Mn), aluminium (Al) and chromium (Cr) and strongly negatively correlated with potassium

97

46.7%

26.9%

14.3%

5.9%

3.7%

1.8%0.7%

0% 0% 0%0

10

20

30

40

1 2 3 4 5 6 7 8 9 10

Dimensions

Perc

enta

ge o

f expla

ined v

arian

ces

Scree plot

(K) and silicon (Si). The second principal component (Dimension 2) was strongly positively

correlated with barium (Ba), calcium (Ca), magnesium (Mg) and sodium (Na) and strongly

negatively correlated with zirconium (Zr). The third principal component (Dimension 3) was

positively correlated with nitrogen (N) and carbon (C) and negatively correlated with soil

pH. Two major soil components phosphorus (P) and moisture with some minor soil elements

vanadium (V), nickel (Ni), zinc (Zn) and strontium (Sr) were not significantly vary across

the soils of 11 sites. The separation of the Metarhizium species across the first two principal

components is presented graphically in Figure 4.5.

Table 4:3 Eigenvalue and variance extracted from PCA analysis for soil factors

Dimension Eigenvalue Variance percent Cumulative variance

percent

Dim.1 10.74 46.68 46.68

Dim.2 6.18 26.88 73.56

Dim.3 3.29 14.31 87.87

Dim.4 1.37 5.94 93.81

Dim.5 0.86 3.74 97.55

Dim.6 0.41 1.76 99.31

Dim.7 0.16 0.69 100.00

Figure 4.5 Scree plot: percentages of variables explained by dimension of principal

component analysis

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Table 4:4 Loading value by principal components showing significant differences in soil

components across 11 sites & ecotypes. Mauve coloured cells show a significant positive

relationship and orange coloured cells show a significant negative relationship.

Variable Dimension

1 2 3

Ti 0.92 0.35 -0.04

Cu 0.89 0.23 0.08

Fe 0.89 -0.26 0.25

S 0.84 0.28 0.33

Mn 0.82 0.31 0.11

Al 0.82 0.17 -0.25

Cr 0.71 -0.36 -0.53

V 0.69 -0.37 0.51

Ni 0.68 -0.08 -0.55

P 0.57 0.59 0.47

Ba 0.50 0.84 -0.06

Moisture 0.48 0.56 0.37

Zn 0.44 0.83 0.05

Zr 0.20 -0.93 0.16

pH -0.04 0.41 -0.81

Sr -0.51 -0.48 -0.15

N -0.60 0.12 0.73

Ca -0.62 0.77 0.00

Mg -0.64 0.72 -0.18

Na -0.64 0.75 -0.14

C -0.68 0.06 0.72

K -0.77 0.63 -0.07

Si -0.96 -0.24 -0.09

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Metarhizium indet. 1

M. pingshaense

Metarhizium indet. 2

Figure 4.6 Separation of Metarhizium species identified in soil samples using principal

component 1 and 2. Separation of species along Dimension 1 axis is due to variation in

Cu, Fe, Mn, Ti, S, Si and Al. Soil elements Zn, Ba, Ca, K, Zr, Mg and Na contribute in

Dimension 2 2 axis.

Correlation studies were conducted after reduction the soil factors by PCA. The correlation

of the presence of M. anisopliae, M. robertsii and M. indet. 1 with the significant soil

elements are summarized in Table 4.5. The presence of M. robertsii and M. anisopliae were

significantly associated with higher soil N and C contents. In contrast, M. indet. 1 species

was associated with low soil N and C contents. A minor soil element Zr was positively

correlated with presence of all the three species.

100

The elements Cu, Fe and Mn showed positive association with the occurrence of M.

robertsii. A negative association was observed between M. anisopliae and Ti. The

occurrence of M. indet. 1 had positive correlation with Cr and weakly significant correlation

with Fe, and significant negative correlation with K, Mg and Na.

M. indet. 2 was only recovered from only one location and excluded from the univariate

analysis. Only three soil samples contained M. pingshaense and the occurrence was again too

low to determine a relationship with the soil factors.

Table 4:5 Point biserial correlation between the presence of Metarhizium species and the

soil elements. Only statistically significant relationships (both positive and negative) are

presented. ** and * indicate that the correlation is significant at the 0.01 and 0.05 level

(respectively).

Element M. robertsii M. anisopliae Metarhizium indet. 1

N 0.07* 0.07* -0.11**

C 0.07* 0.10** -0.12**

Cu 0.07*

Fe 0.10** 0.07*

Mn 0.07*

Cr 0.19**

Ti -0.08*

K -0.1

Zr 0.09* 0.09** 0.12**

Mg -0.09

Na -0.09

101

The density of the Metarhizium species in soil samples was found to be 1126 to 30003

CFU per gram of dry soil and the major portion (86%) of the isolation from the soil

samples was as recorded below 6000 CFU/g of dry soil (Figure 4.10).

Figure 4.7 Densities (CFU/g dry soil) of Metarhizium observed in the numbers of

soil samples

4.4 Discussion

This study found that the occurrence of widely distributed Metarhizium species in the

Queensland soil was influenced by the ecotypes and locations. The recovery of the

species was also associated with soil elements.

M. robertsii was found to be the dominant species by proportion of soil samples in

legume field, grassland and forest, with a single isolate found in maize, whereas M.

indet. 1 was the dominant species in maize fields. The majority of samples recovered

from grassland were M. robertsii, with only 1 other isolate (M. indet. 1) recovered in a

single grassland sample. Wyrebek et al. (2011) similarly found a strong association

between M. robertsii and grassland in a comparison of three Metarhizium species (M.

brunneum Petch, M. robertsii and M. guizhouense Q.T. Chen & H.L. Guo) with plant

102

species categorised as grasses, wildflowers, shrubs and trees respectively. M. robertsii

was the only species associated with grasses. While M. robertsii was also associated

with other ecotypes in this thesis, the strong genetic differentiation of haplotypes

associated with grassland from those found in other ecotypes (as described in Chapter

3) suggests that further work on association of haplotypes and species with grassland

compared to other ecotypes should be conducted.

Very few studies have been conducted to identify the naturally occurring species in

agricultural fields, grasslands and forest ecotypes of a locality. Bidochka et al. (2001)

found “cold active” and “heat active” Metarhizium strains in Canada, which were

abundant in forest and agricultural fields respectively. The strains were later identified

as M. brunneum, and M. robertsii and respectively (Bischoff et al., 2009). Strane

(2014) attempted to isolate Metarhizium species from the forest, grassland and

agricultural fields and only isolated two species form forest, M. pemphigi and M.

guizhouense, due to adverse local weather conditions during the sampling time. In

other studies, M. brunneum was the dominant species in grasslands in Switzerland

(Steinwender et al., 2014), and in agricultural fields in the USA (Fisher et al., 2011).

In Japan, M. pingshaense was frequently isolated from agricultural and forest habitat,

but M. brunneum and M. pemphigi were comparatively abundant in forest habitat

(Nishi et al., 2013). On the other hand, in Denmark, M. brunneum was found

dominant species in agricultural soils (Steinwender et al., 2014). The variation of the

recovery of the Metarhizium species in different soil habitats indicates that the local

soil environment and potentially host plant composition strongly influences the

occurrence of the Metarhizium species.

The binomial regression analysis showed that ecotype dominated by crop or plant

species and location both predicted the occurrence of some of the species. M. robertsii

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and M. anisopliae were predicted by ecotypes, Metarhizium indet. 1 by both ecotype

and location. Quesada-Moraga et al. (2007) also reported that the occurrence of

Metarhizium spp. was predicted by the field crop habitat and geographical location

(latitude and longitude), but their study broadly reported the occurrence of genus

Metarhizium, and did not study the association of the species of Metarhizium with

location and ecotype. To our knowledge, this is the first study to show the relationship

between the species of Metarhizium and location and ecotype.

The adaptation of M. anisopliae to slightly acidic condition has been reported

elsewhere (Issaly et al., 2005, Quesada-Moraga et al., 2007). Metarhizium spp. were

associated with higher soil pH in Tasmania (Rath et al, 1992). The soil pH of the

studied samples ranged from slightly acidic to slightly alkaline (6.03 to 7.42), but the

presence of Metarhizium species in soil samples showed no relationship with soil pH.

This study did not find any correlation between occurrences of Metarhizium species

with soil moisture. Possibly the soil moisture retained in the soil samples did not limit

germination of Metarhizium conidia.

This study highlighted for the first time the relationship between soil elements and the

presence of Metarhizium species in soil. The occurrences of M. anisopliae and M.

robertsii were positively correlated with the soil elements N, C and Zr, and M.

anisopliae was negatively correlated with Ti. In addition, Cu, Fe, and Mn were

positively correlated with the isolation of M. robertsii. The isolation of Metarhizium

indet. 1 increased with increased soil Fe, Cr and Zr and decreased with greater C, N,

and K. Importantly, M. anisopliae and M. robertsii were isolated mostly from legume

and forest soils that were rich in high N and C. In contrast, M. indet. 1 was

predominantly isolated from maize and (in the case of one isolate) from grassland

soils, with low nitrogen and carbon content. This may be a significant benefit to the

plant, since Metarhizium species in the rhizosphere have been shown to supply insect-

104

derived nitrogen to the plant in exchange of plant-derived carbohydrates (Behie et al.,

2017, Behie et al., 2012).

This study also revealed that M. robertsii and Metarhizium indet. 1 were more

abundant in Fe rich soil. Recent findings showed that Metarhizium

brunneum solubilised Fe from complex form to available form for plant uptake in

calcareous and sandy soils (Raya–Diaz, et al. 2017). The abundance of M. robertsii

and Metarhizium indet. 1 in Fe-rich soil suggests that the relative mobilisation of iron

by these strains should be further investigated.

The results demonstrated that Metarhizium species have the capability to adapt to a

wide range of soils, but also that occurrence of Metarhizium species to different soil

environments may be heavily influenced by dominant plant species, and by crop type,

even though crop type will change from year to year as different crops are rotated.

Forest and grassland ecotypes contain a wide variety of species, including both grass

and legume species, including woody legumes such as Acacia spp. in Australian

forests. Analysis of the composition of plant taxa as well as soil composition and

ecotype or land use should be conducted in future studies to develop a deeper

understanding of Metarhizium ecology and selection and application of Metarhizium

species in the future biological pest management strategies in specific local habitats.

Limitation of this study

Due to time constraints in the PhD timeline, this study did not include any

biogeography and habitat modelling (King et al., 2010) to compare the occurrence of

Metarhizium clades by ecotype and location. Similarly, an ultrahigh-variable selection

algorithm to select co-variates that predict the distribution of Metarhizium populations

(Fitzpatrick et al. 2016). Both these analyses need advance statistical knowledge that

were beyond the timeframe of this PhD. We will complete those analyses before

submitting the manuscript for publication.

105

Chapter 5 : Association of Metarhizium species

with agricultural crops and evaluation of

colonisation of selected isolates in pea and

maize.

106

Association of Metarhizium species with agricultural crops and

evaluation of colonisation of selected isolates in pea and maize.

Abstract

Entomopathogenic fungi of the genus Metarhizium are used as microbial insecticides

and also have a mutually beneficial relationship with the rhizosphere of plants. The

factors that determine the natural occurrence and diversity of Metarhizium species in

agricultural fields and their colonisation of different crops are, however, still unclear

but may have a significant role in selection of Metarhizium species for use in

agriculture. This study used 78 Metarhizium isolates from maize and legume crops

(Chapter 3) and analysed their association with different crops. M. robertsii, M.

anisopliae, M. pingshaense were found to be abundant in soils of legume crops,

whereas the 2 new taxonomic clades M. indet. 1 and M. indet. 2 were only isolated

only from soils in which maize had been recently grown.

Colonisation of the rhizosphere in pea and maize by six isolates isolated from legume

fields and six isolates from maize field were evaluated by quantification of colony

forming units (cfus) in the laboratory. The results showed plant type and fungal

isolates significantly affected root colonisation, with higher cfus in pea than maize

irrespective of crop from which the strain was isolated. The results indicate that the

host plant influences the diversity and colonisation of Metarhizium.

Keywords: Metarhizium, multilocus phylogenetic analysis, agriculture, rhizosphere,

pea, maize.

107

5.1 Introduction

The species of the entomopathogenic fungus genus Metarhizium (Family:

Clavicipitaceae, Order: Hypocreales) are widely distributed in soils and cause ‘green

muscardine disease’ in a wide range of insects (Zimmermann, 2007). Historically

Metarhizium species have been used as a biological control agent (Liao et al., 2014,

Lomer et. al., 2001). Faria et al., (2009) listed 59 commercial products of

Metarhizium that are either registered or used to control insect pest in crops or pasture

in countries including the USA, UK, Australia, Brazil, and India. In addition to this

entomopathogenic life history, Metarhizium species are able to colonise the plant

rhizosphere, and to increase plant growth and development (Wyrebek et al., 2011,

Harman, 2006, Hu and St. Leger, 2002).

Root colonisation by Metarhizium was first observed in a cabbage field by Hu & St.

Leger (2002and was subsequently investigated in many plant systems (Liao et al.,

2014). The association of Metarhizium robertsii J.F. Bisch., Rehner & Humber with

switchgrass (Panicum vigratum L.) and haricot beans (Phaseolus vulgaris L.) resulted

in greater proliferation and formation of a high density of root hairs on the plant roots

(Sasan and Bidochka, 2012). Moreover, Elena et al. (2011) found increased plant

height, root length, shoot and root dry weight in tomatoes following colonisation by

M. anisopliae (Metch.) Sorokin. Furthermore, Metarhizium in the plant rhizosphere

has demonstrated pathogenicity against insect pests, for example, mealworm beetle

(Tenebrio molitor L.) in wheat (Keyser et al., 2014) and black vine weevil

(Otiorhynchus sulcatus F.) in pine (Picea abies) (Bruck, 2005). The beneficial

associations of Metarhizium with the rhizosphere and their entomopathogenicity

makes them worthy growth promoters and bio-pesticides.

108

The distribution and evolutionary divergence of Metarhizium species is closely

associated with certain soils and plant types (Bidochka and Hajek, 1998, Wyrebek

and Bidochka 2013). Wyrebek et al. (2011) showed that the associations of three

Metarhizium species (M. brunneum Petch, M. robertsii and M. guizhouense Q.T.

Chen & H.L. Guo) were not random, but that each was associated with plant species

categorised as grasses, wildflowers, shrubs and trees respectively. M. robertsii was

the only species associated with grasses, while M. guizhouense was found to be

closely associated with trees of the genus Acer. These species’ strain-specific

associations probably arise from long associations of the fungi with plant species in

certain ecotypes (Bruck, 2010) and may have important impacts on the successful

colonisation of particular plant types.

In Chapter 3, Metarhizium isolates were recovered from three different ecotypes:

agricultural fields (maize and legume), grassland and forest from three locations

(Bundaberg, Gatton and Kingaroy). In this Chapter, phylogenetic relationship of

Metarhizium isolates were reconstructed by using MzIGS3 and 5’-TEF sequence data

of isolates recovered only from crops. The colonisation of the rhizosphere of two crop

types, maize and pea, were then compared. The combined ecological and biological

knowledge can be used for efficient Metarhizium species selection for pest control

and plant growth in crop fields in Australia.

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5.2 Materials and methods

5.2.1 Collection of soil samples, fungal isolation, Genomic DNA extraction, PCR

amplification, and sequencing

A total of 400 soils from 5 agricultural fields (3 maize and 2 legume fields, 80

sample/field) were collected as described in Chapter 3.

Chapter 3 also described the fungal isolation, DNA extraction and PCR amplification

and sequencing of ITS, MzIGS3 and 5'-TEF regions.

5.2.3 Phylogenetic tree construction

Phylogenetic trees for concatenated data sets of MzIGS3 and 5'-TEF were re-

developed using maximum likelihood (ML) and Bayesian inference with the currently

recognised reference strains: M. globosum ARSEF 2596, M. acridum ARSEF 7486,

M. majus ARSEF 1914, M. lepidiotae ARSEF 7488, M. guizhouense CBS 258.90, M.

anisopliae ARSEF 7487, M. anisopliae isolate 68, M. anisopliae isolate 69, M.

anisopliae isolate 251, M. pingshaense CBS 257.90 and M. robertsii ARSEF 7501.

Support Values on best-scoring ML tree for each individual data set was drawn in

RAxML with a rapid bootstrap (1000 replicates) under GTRGAMMA substitution

model. For multigene analysis, the MzIGS3 and 5’-TEF data were combined in a

matrix and ML analysis was done using the above parameters with each locus run in

the different partition.

5.2.4 Genetic differentiation and gene flow studies

Nucleotide polymorphism, fixed, shared mutations, nucleotide diversity between the

M. anisopliae (Mani), M. robertsii (Mrob), M. pingshaense (Mpin), Metarhizium

indet. 1 (Mindet1) and Metarhizium indet. 2 (Mindet2) clades identified in multilocus

(MzIGS3 and 5’-TEF) phylogenetic analysis were calculated using DNAsp 5.10.01

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(Rozas et al. 2003). In order to calculate the genetic differentiation within the

Metarhizium linages, haplotype based statistics such as Hs and Hst (Hudson et al.

1992a and 1992b) and nucleotide sequence based statistics such as Ks, Kst and Snn

(Hudson’s statistic of genetic differentiation, Hudson et al. 2000) were estimated with

permutation tests using 1000 randomization to get the statistical significance in

DNAsp. The software also used to calculate the gene flow estimates, Nst and Fst

value, the indirect measurement of effective number of migrants (Nm) (Hudson et al.

1992b) among the populations. DNA sequences from three locations and four

ecotypes were also used for genetic differentiation and gene flow studies.

Nucleotide sequence alignment (nexus file) of combined data set of MzIGS3 and 5’-

TEF of Metarhizium isolates was imported into DnaSP 5.10.01 for haplotypes

analysis and generation of haplotype data file in nexus file format. The nexus file was

edited to add location and ecotypes information in a character matrix and a minimum

spanning network (MSN) was constructed in PopART (http://popart.otago.ac.nz) to

determine the relationships among haplotypes for Metarhizium population in different

locations and ecotypes. The MSN is based on a minimum spanning tree where a set of

sequence types connects all given types without creating any cycles where the total

length (sum of distance between linked sequences) is minimum (Bandelt et al. 1999).

5.2.5 Metarhizium colonisation in pea and maize

5.2.5.1 Fungus material

Six isolates of each of legume and maize fields were selected for the plant

colonisation experiments based on their different phylogenetic position in the

cladogram (Table 5.1). One set of isolates, BMA2, BMB5, BMB10, BMC1, BMC10

and BMD20 from maize field and another set of isolates, KLA15.2, KLD17,

KLA17.1, KLA17.2, KLB7 and KLD5.1 from legume field were selected.

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Table 5:1 Metarhizium isolates selected for root colonisation experiments

Isolates Source Clades

BMA2 Maize M. robertsii

BMB5 Maize Metarhizium indet. 2

BMB10 Maize Metarhizium indet. 2

BMC1 Maize Metarhizium indet. 2

BMC10 Maize Metarhizium indet. 2

BMD20 Maize Metarhizium indet. 1

KLA15.2 Legume M. robertsii

KLD17 Legume M. robertsii

KLA17.1 Legume M. pingshaense

KLA17.2 Legume M. pingshaense

KLB7 Legume M. anisopliae

KLD5.1 Legume M. anisopliae

Spores of selected Metarhizium strains were produced by culture on Sabouraud

Dextrose Agar Yeast (SDAY) medium (peptone 10 g/L, yeast extract 10 g/L, dextrose

40 g/L and agar 15 g/L) at 25°C in an incubator for 3 weeks. Spores were collected by

tapping of the plate and suspended in autoclaved milliQ water in a 500-mL glass

bottle and vigorously shaken by hand for 2 minutes to produce a homogenous

suspension, and then filtered using sterilized cheese-cloth to remove hyphae and other

debris. Spore concentration was estimated using a haemocytometer and a final

concentration of 1×106 conidium/mL in autoclaved milliQ water was made.

5.2.5.2 Plant material

Colonisation experiments in pea and maize system were conducted separately,

experiment: 1 for maize field isolates and experiment: 2 for legume field isolates in

the laboratory. There were five replications for each type of fungal isolates. In each

experiment, a total of 35 pea and 35 maize seeds were sterilised by immersing for 2

minutes in 70% ethanol and 5 minutes in 30% bleach, then rinsed three times with

sterilised water and grown in an autoclaved vermiculite and sand mixture (1:1, v: v).

The plants were rinsed three times a week with modified LANS medium with 50 μM

NaH2PO4 (Foo 2013).

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5.2.5.3 Root colonisation

Two weeks after planting, plants were inoculated at the base of the stem with 10ml of

conidium suspension (1×106 conidium/ml in autoclaved milliQ water) of selected

strains. After inoculation, plants were returned to the growth room and arranged in a

randomised block layout in trays. At 2 weeks after inoculation, the plants were

carefully uprooted and gently washed in running deionised water. A mass of 1 g of

root from each plant was taken from the middle of the tap root and surface sterilised

by immersing for 2 minutes in 70% ethanol and 5 minutes in 30% bleach, then rinsed

three times with sterilised water and homogenised in 1 ml of 0.05% Tween 80. The

root suspension was diluted ten-fold (100 µl in 900 ml of 0.05% Tween 80) and 100

µl of root suspension was spread in selective medium containing peptone 10g/L,

glucose 20 g/L, agar 18 g/L, cycloheximide 50 mg/L, streptomycin 100 mg/L,

tetracycline 50 mg/L and dodine 100 mg/L) (Strasser et al. 1996) and incubated in the

dark at 22° C. The agar plates were observed after 7 days and the number of colony

forming units (CFU) was recorded.

5.2.6 Data analysis

The presence of Metarhizium species in soils samples of maize and legume fields was

analysed with analysis of similarity (ANOSIM) with a permutation test for

significance using “Vegan” package in Rv3.3.1 (R Core team, 2016).

The colonisation experiments were conducted in completely randomised design with

five replications. Statistical analyses were performed in ‘R’v3.3.1 (R core team,

2016). Generalised linear model for the poisson data family was used to analyse the

effects of the plant and fungus in colonisation and mean values were compared by

Tukey’s post hoc test using ‘lsmeans’ package.

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5.3 Results

5.3.1 Metarhizium abundance in agricultural soils

A total of 114 fungal isolates from 400 soil samples of agricultural fields were

identified as members of insect pathogenic fungal groups using the ITS sequence

highest similarity search in NCBI database. The maximum likelihood (ML)

phylogenetic tree for ITS data set showed among the 114 entomopathogens, 28

isolates were from the genera Isaria and Beauveria, 2 isolates from the genus

Lecanicillium, 6 isolates from the genus Pochonia (syn. Metacordyceps) and 78

isolates from the genus Metarhizium with high bootstrap support (>70%)

(Supplementary data, Figure 5.8). The presence of identified 78 Metarhizium isolates

was observed in 37 of 400 soil samples. The total percent occurrence of Metarhizium

was calculated 9.25% in agricultural soil, with 9.58% in maize soil and 8.75% in

legume soils.

5.3.2 Multilocus phylogenetic analysis

The MzIGS3 and 5’-TEF nuclear sequences from 78 field isolates were aligned with

nine recognised and three in-house Metarhizium isolates, yielding 1278 aligned

nucleotide positions. The resulting maximum phylogenetic tree (Figure 5.1) and

Bayesian tree (Figure 5.2) identified five major clades of Metarhizium species with

broad supports (>70%). Among the five clades, three clades were currently defined

taxa, M. robertsii, M. anisopliae and M. pingshaense, and two clades of taxonomic

indeterminate Metarhizium species. The M. robertsii clade included a total 15

isolates; 14 from legume fields and 1 from maize field, whereas, M. anisopliae and M.

pingshaense included 10 and 4 isolates respectively and all were recovered from

legume fields. The indeterminate species were divided into two groups, Metarhizium

sp. indet. 1 and Metarhizium sp. indet. 2 and included 43, and 6 isolates respectively.

All the indeterminate Metarhizium isolates were obtained from maize fields only.

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M. pingshaense

M. anisopliae isolate 68

M. pingshaense CBS257.90

M. anisopliae isolate 69M. anisopliae isolate 251M. anisopliae ARSEF7487

Metarhizium indet. 1

Metarhizium indet. 2

Figure 5.1 Phylogenetic tree obtained from maximum likelihood analysis of

concatenated data of MzIGS3 and 5'-TEF.Taxon label colour indicates the source of

the isolates, red for maize and blue for legume field. Taxon within blue box was used

for colonisation experiment.

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M. pingshaense

M. anisopliae isolate 68

M. pingshaense CBS257.90

M. anisopliae isolate 69M. anisopliae isolate 251M. anisopliae ARSEF7487

Metarhizium indet. 1

Metarhizium indet. 2

Figure 5.2 Phylogenetic tree obtained from Bayesian inference analysis of

concatenated data of MzIGS3 and 5'-TEF.Taxon label colour indicates the source

of the isolates, red for maize and blue for legume field.

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5.3.3 Genetic differentiation studies

The summary statistics of DNA divergence in the comparison between the

Metarhizium clades and ecotypes were presented in Table 5.2. The number of

polymorphic sites and average nucleotide differences (k) were varied from 33- 316

and 6.213 - 34.762 respectively. Table 5.2 shows the genetic differentiation and gene

flow estimates for the comparisons between the Metarhizium clades and ecotypes.

The genetic differentiation was evaluated by calculating both haplotype indices (Hs

and Hst) and nucleotide indices (Ks, Kst and Snn). The Kst value close to zero

indicates no differentiation and Snn value close to one indicates differentiation

(Hudson, 2000). The statistical significant values for Ks, and Kst were observed for

all comparisons between the Metarhizium clades revealed the presence of strong

genetic differentiation. The significant (P<0.001-0.05) Snn value ranged from

0.96667 to 1.00000 also reported the higher genetic differentiation between the

clades. In addition, the higher Fst values in the comparisons between the clades were

varied from 0.46 to 0.86 indicating moderate to strong genetic differentiations among

the clades. The genetic differentiation estimates between the crop types ‘maize’ and

‘legume’ were highly significantly different (P <0.001) (Table 5.3).

The combined nucleotide sequences identified 42 haplotypes in the Metarhizium

clades. The clade-haplotype analysis found no shared haplotype between the

Metarhizium populations in different clades. The haplotype network presented in

Figure 5.3, shows the haplotype distribution in among the Metarhizium populations in

different clades.

Among the 42 haplotypes compared in the ecotype-haplotype analysis, only one

shared haplotype (number 1) was shared between Metarhizium populations from both

maize and legume crops. Figure 5.4 shows the relationship between maize and

legume ecotypes through minimum spanning network.

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Table 5:2 DNA divergence data between the populations of Metarhizium in

phylogenetic clades and ecotypes

Table 5:3 Genetic differentiation and gene flow estimates between the

populations of Metarhizium in phylogenetic clades and ecotypes

* the number of sites that are polymorphic in the 1st species and monomorphic in 2nd species in the comparison ** the number of sites that are polymorphic in the 2nd species and monomorphic in 1st species in the comparison

Probability obtained by the permutation test with 1000 replicates

*, 0.01<P<0.05; **, 0.001<P<0.01; ***, P<0.001

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Figure 5.3 Haplotype networks for the Metarhizium populations from five clades

based phylogenetic analysis of combined sequence data. Different pie colours in

the circles indicate the location of haplotype origin with size of the circles and pie-

segment is proportional to haplotype frequency. Perpendicular tick marks on the

lines represent the mutations between the linked haplotypes.

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Figure 5.4 Haplotype networks for the Metarhizium populations from two

ecotypes. Different pie colours in the circles indicate the location of haplotype

origin with size of the circles and pie-segment is proportional to haplotype

frequency. Perpendicular tick marks on the lines represent the mutations between

the linked haplotypes. Only one haplotype was shared between isolates from the

two crop types.

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5.3.4 Ecotype association study

The analysis of similarity (ANOSIM) showed a significant (R=0.5339, significance

=0.001) pattern of association of the species of Metarhizium with the crop types

‘maize’ and ‘legume’. Two indeterminate species (Metarhizium indet. 1 and 2) were

associated with maize crops, whereas, M. robertsii, M. anisopliae and M. pingshaense

were associated with legume crops (Figure 5.5).

Figure 5.5 Heatmap of Metarhizium species associated with maize and legume

ecotypes based on Czekanowski dissimilarity in DICE analysis of presence-absence

data, R=0.5339, significance =0.001.

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5.3.5 Plant colonisation

5.3.5.1 Experiment 1: Colonisation of maize field isolates in pea and maize systems

Six Metarhizium isolates from maize field were tested for their colonisation ability in

pea and maize systems in first plant colonisation experiment. Both the main factors,

plant type and fungal strains and their interaction had significant (p<0.001) effect on

root colonisation by fungal isolates of maize fields (Figure 5.6, Supplementary data,

Table 5-4). The colonisation of maize field isolates ranged from a mean of 10410-

19830 CFU per g of fresh root. Four Metarhizium isolates BMC1, BMA2, BMC10

and BMB10 showed higher colonisation in pea than maize. In contrast, two isolates

BMD20 and BMB5 showed higher colonisation in maize plant than pea. Complaining

the root colonisation by different clades of Metarhizium species, all clades except M.

indet. 1 showed higher colonisation in pea than maize. M. indet. 1 colonised higher in

maize (11960±679.41CFU per g fresh root) than pea (1171±1646.4 per g fresh root)

(Figure 5.7). The highest amount of root colonisation was observed in pea by M.

robertsii (16813±2264 CFU per g fresh root).

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Figure 5.6 Colonization of legume (pea) and maize roots by Metarhizium isolates

from maize fields (colonisation experiment 1). Bar plot (meanSEM) with

different lowercase letters indicates statistical differences on Tukey’s post hoc

test, p < 0.05.

Figure 5.7 Colonization of legume (pea) and maize roots by 3 Metarhizium clades

isolated from maize fields (colonisation experiment 1). Bar plot (meanSEM)

with different lowercase letters indicates statistical differences on Tukey’s post

hoc test, p < 0.05. BMA2 is the only isolate of M. robertsii from maize. BMD20 is

clade M. indet 1. All other isolates are clade M. indet 2.

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5.3.5.2 Experiment 2: Colonisation of legume field isolates in pea and maize systems

The colonization of maize and legume (pea) plants by six Metarhizium isolates from

legume fields was quantified. The effects of plant type and fungal isolates and their

interaction showed significant differences in colonisation (Figure 5.8, Supplementary

data, Table 5.5). All the Metarhizium isolates from legume field had a higher count of

CFUs on pea than on maize.

When analysed by clade, (Figure 5.9), all three clades had significantly higher cfu

counts per g of fresh root on pea than on maize roots (P<0.05). The highest cfu count

was observed by isolates of the clade M. anisopliae (18450± 1610.5 CFU per g of

fresh root) in pea root.

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Figure 5.8 Effects of plant types and Metarhizium legume field isolates on root

colonisation (colonisation experiment 2). Bar plot (meanSEM) with lowercase

letter indicates statistical differences using Tukey’s post hoc test, p < 0.05.

Isolates KLA15.2 and KLD 17 are M. robertsii, KLA17.1 and KLA17.2 are M.

pingshaense, KLB7 and KLD 5.1 are M. anisopliae.

Figure 5.9 Effects of plant types and Metarhizium clades from legume field

isolates on root colonisation (colonisation experiment 2). Bar plot (meanSEM)

with different lowercase letter indicates statistical differences on Tukey’s post

hoc test, p < 0.05.

125

Overall, the colonization of pea plant roots was significantly higher than on maize in

both experiments (Figure 5.10). However, the difference between the mean cfu on pea

and the mean cfu on maize was greater in isolates from the soil of legume crops than

in isolates from the soil of maize crops.

Figure 5.10 Effects of plant types on root colonisation : A: Mean cfu of

Metarhizium isolates from maize fields, B: Mean cfu of Metarhizium isolates

from legume fields (Bar plot (meanSEM) with asterisk mark indicates

statistical differences on Tukey’s post hoc test, p < 0.05.

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5.4 Discussion

This research has three principal findings.

1: Species of the genus Metarhizium were abundant and co-occurred in soil from

agricultural crops.

2: Metarhizium species were diverse in both legume and maize crops but showed a

clear genetic differentiation in the two crops.

3: While colonisation of legume (pea) roots was typically higher than in maize by

most clades and isolates, there was a significant difference in ability to colonise maize

by different fungal taxa.

The agricultural soils of Queensland, Australia are a source of diverse

entomopathogenic fungi. Fungi of the genus Metarhizium occurred with higher

frequency than those of other genera in agricultural soils. This higher abundance of

Metarhizium species in Australian agricultural soils is similar with global patterns.

Among the entomopathogens, Metarhizium was more frequently recovered from

agricultural soils than from forest or grassland habitats in Canada, Finland, the

Netherlands and China (Bidochka et al. 1998, Vanninen 1996, Steenberg 1995,

Meyling and Eilenberg, 2007). The persistence of Metarhizium in agricultural habitats

was also observed by Hu and Leger (2002), Lingg and Donaldson (1989) and Clerk

(1969). However, the factors influencing the agricultural habitat preference of

Metarhizium have not been identified. Kepler et al. (2015) suggested that tillage

operations might increase the dispersion of Metarhizium propagules resulted in higher

recovery of Metarhizium in agricultural field. The long-term viability of Metarhizium

conidia in soils (Latch and Fallon 1976) may be another cause of higher persistence of

Metarhizium species in agricultural soils.

127

This study identified several co-occurring taxonomic groups of Metarhizium in

agricultural fields. This trend was also observed by Steinwender et al. (2014), who

observed several Metarhizium species in a single arable field. In contrast, only a

single clade of Beauveria bassiana was identified by Meyling et al. (2009) in

agricultural soils in Denmark. The results suggest that Metarhizium species are

tolerant of soil disturbance caused by agricultural practices.

This study observed five clades within the genus Metarhizium in the agricultural

fields, which were significantly differentiated by crop. The clades of Metarhizium

isolates showed the crop specific association: Metarhizium robertsii, M. anisopliae,

M. pingshaense were associated with legume crops, while the new taxonomically

indeterminate groups M. indet. 1 and M. indet. 2 were associated with maize. Crop-

specific association of Metarhizium species has also been reported in the USA, where

M. brunneum was found strongly associated with blueberry and strawberry (Fisher et

al. 2011). Little is known about the factors governing local adaptation of

Metarhizium, though rhizosphere associations are reported to stabilize Metarhizium

population through beneficial mutations (Wang et. al. 2011). However, this is the first

study to report crop-specific associations between monocot and Eudicot species, and

particularly between two crops of global significance (maize and legume).

Association of the different Metarhizium taxa with the rhizosphere of legumes and

maize showed significant differences between taxa. Although the fungal isolates were

isolated from different crops, colonisation (as measured by colony forming units per

gram weight fresh root) was overall significantly higher in pea than in maize roots in

the majority of Metarhizium taxa. However, the difference between mean cfu on pea

and mean cfu on maize was greater in isolates from legume crops than those from

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maize crops. In particular, isolate of the new taxon M. indet. 1, which were isolated

from maize crops, showed a significantly higher colonisation of maize roots than pea

roots.

An understanding of the factors governing association of Metarhizium species with

specific crops may be important in selection of isolates as rhizospheric inocula. Maize

and pea belong the monocot and Eudicots, with significant differences in root

anatomy and in the type and quantity of hormones secreted by roots. Plant hormones

are thought to play an important role in the establishment of symbiotic relationships in

the rhizosphere, and this will be explored in greater detail in Chapter 6.

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5.8 Supplementary data

Table 5:4 Generalised linear model for experiment 1 showing the effects of plant, fungus

and their interaction on colonisation

Coefficients:

Estimate Std. Error z value Pr (>|z|)

(Intercept) 9.724812 0.008169 1190.445 <2e-16 ***

Plant -0.133327 0.005214 -25.570 <2e-16 ***

Fungus 0.017122 0.002076 8.248 <2e-16 ***

Plant:Fungus -0.016629 0.001336 -12.445 <2e-16 ***

---

Significance values: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05.

(Dispersion parameter for poisson distribution taken to be 1)

Null deviance: 78178 on 58 degrees of freedom

Residual deviance: 70714 on 55 degrees of freedom

AIC: 71388

Number of Fisher Scoring iterations: 4

Table 5:5 Generalised linear model for experiment 2 showing the effects of plant, fungus

and their interaction on colonisation

Coefficients:

Estimate Std. Error z value Pr (>|z|)

(Intercept) 10.165216 0.007197 1412.475 < 2e-16 ***

Plant -0.344890 0.004792 -71.974 < 2e-16 ***

Fungus -0.016849 0.001857 -9.072 < 2e-16 ***

Plant:Fungus 0.007465 0.001235 6.046 1.49e-09 ***

---

Significance values: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05.

(Dispersion parameter for poisson distribution taken to be 1)

Null deviance: 70078 on 59 degrees of freedom

Residual deviance: 46772 on 56 degrees of freedom

AIC: 47466

Number of Fisher Scoring iterations: 4

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Metarhizium anisopliae isolate 68

Metarhizium pingshaense ARSEF1009

Metarhizium anisopliae isolate 69

Metarhizium anisopliae isolate 251

Pochonia ( Syn. Metacordyceps)

Figure 5.11 Maximum likelihood tree of ITS data sets of isolated entomopathogens from agricultural fields

131

Chapter 6 : The effect of strigolactone on

conidium germination and root colonisation

by Metarhizium anisopliae

132

The effect of strigolactone on germination of conidia and root colonisation

by Metarhizium anisopliae

Abstract

Hypocrealean fungi of the genus Metarhizium are known rhizospheric endophytes that support

increased plant growth and development. However, the role of plant: fungal signals and

specifically of plant hormones during colonisation of plant roots by Metarhizium is unknown.

The plant hormone strigolactone is a plant growth hormone known to play an important role in

signalling during rhizospheric colonisation by arbuscular mycorrhizae. Germination of conidia

and colonisation of the root by M. anisopliae were estimated on wild variety pea (Pisum

sativum L. cv Torsdag) and strigolactone pea mutants, rms 5-3: strigolactone deficient and rms

4-1: strigolactone overproducing mutants. Condiospore germination was significantly lower in

root exudates from the strigolactone-deficient pea (rms 5-3) than in exudates from wild and

strigolactone -overproducing (rms 4-1) mutant plants. Colonisation of root segments was

higher in wild and strigolactone -overproducing (rms 4-1) mutant plants than the

strigolactone-deficient (rms 5-3) plants. This is the first report indicating that plant

strigolactone ingresses both conidium germination and colonisation of the root by

Metarhizium.

Keywords: Strigolactone, Metarhizium, symbiosis, rhizosphere, colonisation.

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6.1 Introduction

The rhizosphere is a narrow zone of soil directly influenced by plant roots and associated

microorganisms into which plants secrete a number of chemical compounds (Bais et al., 2001,

Walker et al., 2004). These root exudates include carbohydrates, amino acids, organic acids,

phenolic compounds, proteins, and mucilage, and a number of plant hormones and signalling

molecules that regulate plant-plant, plant-insect or plant-microbe interactions in the

rhizosphere (Davies et al., 2010). These symbiotic associations may be commensal, mutualist

or parasitic: for example, the associations of some fungi or rhizobia with the rhizosphere

benefit plant hosts through increased nutrient supply, but infection by plant pathogens is

harmful to the plant host. Some chemicals in plant root exudates act as a signalling molecules

to initiate interaction between plants and soil microorganisms (Walker et al., 2004).

Plant hormones and soil nutrient levels influence mycorrhizal formation in plants. The plant

hormone auxin is secreted by fungi and contributes to symbiotic associations, changing root

anatomy and promoting the development of lateral roots that are the site of establishment of

most fungi (Ludwig-Muller and Guther, 2007). Trichoderma virens Persoon produces auxin-

related compounds that enhance lateral root and plant growth through an auxin dependent

mechanism (Contreras-Cornejo et al., 2009). Some fungi produce cytokinins during infection

that induce the root cortical cells to enlarge during symbiotic infection (Kraigher et al., 1991;

Jameson, 2000 and NG et. al. 1982). However, it should be noted that Foo et al. (2013)

concluded that there is no relationship between cytokinins synthesis and mycorrhizal

association in plants.

The plant hormones ‘strigolactones’ are also involved as signalling molecules in the

rhizosphere (Gomez-Roldan et al., 2008, Xie et al., 2010). These carotenoid-derived plant

compounds are synthesized in the lower parts of the stem and roots and are involved in many

aspects of plant developments, such as inhibition of shoot branching, root architecture,

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adventitious root formation, cambium activity stimulation and elongation of stem (Gomez-

Roldan et al., 2008, Boyer et. al., 2013). Strigolactones also act as signalling molecules for

arbuscular mycorrhizal symbiosis (Gomez-Roldan et al., 2008, Foo et. al., 2013), stimulating

the hyphal branching during spore germination (Besserer et al., 2016, Akiyama et al., 2005,

Akiyama and Hayashi 2006, Harrison, 2005), which increases the probability of root

colonisation by arbuscular mycorrhizae.

Fungi other than arbuscular mycorrhizae can colonise the rhizosphere as endophytes and have

beneficial effects on plant growth and development. Species of Trichoderma (Order:

Hypocreales, Family: Hypocreaceae) are facultative symbionts able to colonise the plant root,

and have been used as antagonists to reduce plant pathogenic infections (Viterbo and Horwitz,

2010, Hermosa et al., 2012). Plant-derived monosaccharides, disaccharides and

polysaccharides excreted by roots facilitate the growth of Trichoderma in the rhizosphere and

enhance root colonisation by the fungus (Vargas et al., 2009). Surprisingly, strigolactone does

not activate response in Trichoderma suggesting that interactions with strigolactone might be

specific to colonisation by arbuscular mycorrhizal fungi (Steinkellner et al., 2007, de-Brujn,

2013, Lopez-Raez and Pozo, 2013). However, Vurro (pers. comm. March, 2014) reported that

Trichoderma harzianum Rifai can detect and metabolize strigolactone, which could enhance

the establishment of the plant/fungus interaction.

The members of the Hypocrealean fungus of genus Metarhizium are known as both pathogens

of invertebrates and also establish as an endophyte in the rhizosphere of plants (Wyrebek et

al., 2011, Harman, 2006, Hu and St. Leger 2002, Behie et al., 2012). This endophytism starts

a symbiotic association between plant and Metarhizium, in where host plant is providing

photosynthates to the fungus, in exchange of insect-derived nitrogen (Behie et al., 2017, Behie

et al., 2012). These symbiotic association of Metarhizium in the plant rhizosphere result in

increased plant growth and development (Keyser et al., 2014), and reducing infection by plant

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pathogens (Ownley et al., 2010). M. robertsii, stimulated root proliferation and high-density

root hair formation in switchgrass (Panicum vigratum L.) and haricot beans (Phaseolus

vulgaris L.) (Sasan and Bidochka, 2012). Metarhizium systematically colonised the stem of

tomato Lycopersicon esculentum Mill.) after application of fungal mycelium to roots (Parsa et

al., 2018) and increased plant height, root length, shoot and root dry weight (Elena et al.,

2011). M. brunneum systemically colonised different parts of faba bean (Vicia faba L.) and

improvedM plant growth when applied as a seed treatment (Jaber and Enkerli, 2016).

However, the role of plant hormones during establishment of the plant-Metarhizium

association remains little studied.

In this study, the role of strigolactones as plant signals on conidium germination and

colonisation of the rhizosphere in pea (Pisum sativum L.) roots by M. anisopliae was

examined. Pea plants were selected as the model in this study because of the availability of

characterised wild variety (cv. Torsdag) and strigolactones biosynthesis mutants,

strigolactones deficient mutant rms5-3: and strigolactone overproducing mutant rms4-1: (Foo

et al., 2013). Understanding the role of strigolactones has potential impact on further

understanding of the role of rhizospheric signalling in establishment of beneficial

Hypocrealean fungi in plant roots.

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6.2 Materials and Methods

6.2.1 Fungus material

M. anisopliae isolate 69 was selected for this study. The Metarhizium anisopliae isolate EFD

69 was regularly cultured in our laboratory for bioassay experiment and its genome sequence

was published by Pattemore et al. (2014). The strain was isolated from Kingaroy region,

Queensland and obtained Department of Agriculture and Forestry (DAF). Spores of M.

anisopliae isolate 69 were produced by culture on Sabouraud Dextrose Agar Yeast (SDAY)

medium (peptone 10 g/L, yeast extract 10 g/L, dextrose 40 g/L and agar 15 g/L) at 25°C in an

incubator for 3 weeks. Spores were collected by tapping of the plate and suspended in

autoclaved milliQ water in a 500-mL glass bottle and vigorously shaken by hand for 2 minutes

to produce a homogenous suspension, and then filtered using sterilized cheese-cloth to remove

hyphae and other debris. Spore concentration was estimated using a haemocytometer and a

final concentration of 1×106 conidium/mL in autoclaved milliQ water was made.

6.2.2 Plant material

Seeds of three varieties of pea plants, wild-variety (Torsdag), and the strigolactone-

overproducing (rms4-1, and strigolactone-deficient (rms 5-3) mutants were obtained from

Professor Christine Beveridge, The University of Queensland.

Pea seeds were surface sterilized by immersing for 2 minutes in 70% ethanol and 5 minutes in

30% bleach. After rinsing 3 times with sterile distilled water, the sterilized pea seeds were

grown in a sterile vermiculite and sand mixture (1:1 v: v) and watered twice in a week with a

modified low-phosphate Long Ashton nutrient solution (LANS, Hewitt 1966) for 4 weeks

under 16 h light-8 h dark photoperiod at 24°C (Foo 2013). Two sets of plants were grown, one

for the conidium germination in root exudate experiment and another for the fungal

colonisation in root experiment.

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6.2.3 In vitro germination experiment

6.2.3.1 Preparation of pea root exudates (RE)

After four weeks, five plants of each strain were carefully uprooted and washed lightly with

water. Roots of each plant were submerged in 100 mL phosphate-free LANS liquid medium

(Foo 2013, Gomez-Roldan et al. 2008) separately. After 24 hours, the roots were separated

from the stem and weighed to determine root fresh weight of each plant. The liquid medium

in which the plants were grown was collected and filtered using Whatman number 1 filter

paper. Partial purification of the root exudates (RE) was conducted by fractionation with ethyl

acetate and water (1/1 v/v) and freeze-dried following the method described by Gomez-

Roldan et al. (2008). Water was then added to the exudate concentrate to a final volume of

1mL of root exudate per gram of fresh root to get stock RE. A dilution series were made at

100%, 75%, 50%, 25% and 10% (v/v) of the stock RE with sterile distilled water.

6.2.3.2 Metarhizium conidium germination in root exudate

For the in vitro conidium germination test, 100 μl of conidium suspension (1×106

conidium/mL) were added to a 1.5 mL-Eppendorf tube containing 900 μl of root exudates of

each100%, 75%, 50%, 25% and 10% of RE of each plant. Spores were also added to 5 tubes

containing 900 μl 0.1% yeast extract to estimate conidium viability. After 24 hours, 50 μl of

each conidium suspension in exudate was placed on a glass microscope slide with a drop of

lactophenol cotton blue stain and spores were counted by a light microscope using x20

objective. The percentage of conidium germination was determined by recording the

percentage of spores that had formed a germ tube.

6.2.4 Root colonisation experiment

This experiment used the method described by Parsa et al. (2013). Metarhizium colonisation

in pea roots was assessed in two time points, 3 and 7 days after fungus inoculation. Two sets

of plants were grown for assay at each of two time points: for the 3-day time point, 25 plants

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from each pea variety and for 7-day time point, 15 plants from each pea variety were used.

Additional five plants of each variety of each of time points were grown as control. Sterilised

seeds were grown in an autoclaved vermiculite and sand mixture (1:1 v: v). The plants were

watered three times in a week with modified low phosphate LANS medium (Foo 2013). After

4 weeks of planting, plants were inoculated at the base of the stem with 10 mL of conidium

suspension (1×106 conidium/mL in autoclaved milliQ water). The control plants were

inoculated with 10 mL of milliQ water only. Plants were watered to soil capacity 24 hours

before inoculation. After inoculation, plants were kept in growth room and arranged in a

randomised block layout in trays.

At 3 and 7 days after inoculation the plants were carefully uprooted and gently washed in

running deionised water. Two main roots were cut from each plant and a 3cm section was cut

from the middle of the root and surface sterilized by immersing for 2 minutes in 70% ethanol

and 5 minutes in 30% bleach, then rinsed three times with sterilized water and placed on an

autoclaved paper towel for up to 1 minute to remove excess water in a laminar air flow

cabinet. Each 3cm root section was trimmed into six sections of 0.5 cm each and placed on a

Petri dish with selective medium (peptone 10g/L, glucose 20 g/L, agar 18 g/L, cycloheximide

50 mg/L, streptomycin 100 mg/L, tetracycline 50 mg/L and dodine 100 mg/L) (Strasser et al.

1996) and incubated in the dark at 25° C. The plates were observed every 2-3 days for 20 days

and the number of root segments on which characteristics fungal growth was observed was

recorded. The control plants were also uprooted and roots were washed, sterilized trimmed

and placed on selective medium. No fungal growth on control plant section ensured ‘no

contamination’ or ‘no growth of other endophyte’ in plant sections.

6.2.5 Data analysis

The conidium germination (%) and colonisation (%) data were analysed using generalised

linear model and binomial errors with logit link function in statistical software ‘R’ (R core

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team, 2016). Tukey’s post-hoc test was done to determine statistical significant differences

among the plant varieties (wild variety, rms5-3, rms4-1) for conidium germination and root

colonisation by the fungus using ‘lsmeans’ package. Regression analysis was conducted to

compare the relationship between percent conidium germination and concentration of root

exudates of plant varieties.

6.3 Results

6.3.1 M. anisopliae conidium germination

The conidium viability test showed that 94.81% spores germinated in yeast extract, which

confirmed the conidium viability. No spores germinated in water after 24 hours. Logistic

analysis using a generalised linear model showed that both the main factors, plant variety and

root exudate concentration, had significant effect (p<0.05) on conidium germination of M.

anisopliae (Supplementary data, Table 6.1).

The mean Metarhizium conidium germinations varied from 3.17% to 30.67%. The highest

percentage of conidium germinated (30.61%) in 100% root exudate of wild variety of plant

and lowest number of spores germinated (3.17%) in 10% root exudate of strigolactone

deficient (rms5-3) pea plant after 24 hours (Figure 6.1). In all root exudate concentrations

(100% to 10%), the highest percentage of conidium germination was observed in wild variety

pea followed by strigolactone overproducing mutant (rms4-1). The lowest conidium

germination was observed in strigolactone deficient plant (rms5-3) in all root concentrations.

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Figure 6.1 Effects of pea plant varieties and root exudate concentrations on M.

anisopliae conidium germination after 24 hours. Bar plot (mean±SEM) with different

lowercase letters indicates statistical differences on Tukey’s post hoc test (p < 0.05).

The linear regression showed a significant positive association between germination of M.

anisopliae spores and concentration of root exudates (RE) for all three varieties of pea plant

(Figure 6.2, supplementary data 6.2). Spore germination increased linearly with the increase

of root exudate concentration of each plant variety. This data also indicated that the chemical

compounds presented in the root exudates were active in very low concentration of 10% RE of

all three types of pea strains.

Figure 6.2 Spore germination of M. anisopliae as a function of concentration of RE of

wild, rms4-1 and rms5-3 pea plants. Spore germination was assessed after 24 hours.

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6.3.2 Root colonisation

Analysis using a generalised linear model showed that the effect of pea plant variety on mean

colonisation of M. anisopliae in root was significant (p<0.05) at both 3 days and 7 days after

conidium inoculation in root rhizosphere (Supplementary data Table 6.3 and 6.4). The highest

proportion of fungal colonisation was found in the wild variety pea root (17.39% at 3 days and

32.74% at 7 days), followed by the strigolactone overproducing plant (rms4-1) (15.58% at 3

days and 25% at 7 days) (Figure 6.3). The lowest percentages of colonisations (11.46% at 3

days and 20.83% at 7 days) were found in the strigolactone-deficient plants (rms5-3) (Figure

6.3). There was no fungal growth observed in root section in selective medium.

Figure 6.3 Effect of pea plant varieties (wild, rms4-1 and rms5-3) on mean cfu of M.

anisopliae at 3 days and 7 days post inoculation. Bar plot (mean±SEM) with different

lowercase letter indicates statistical differences on Tukey’s post hoc test (p < 0.05).

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6.4 Discussion

This study found a significant effect of the plant strigolactone mutants on conidium

germination of M. anisopliae and root colonisation by M. anisopliae using wild-variety

(Torsdag) and the strigolactone pea mutants rms4-1 and rms5-3. The result showed that

germination of M. anisopliae conidia was significantly reduced in root exudates from the

strigolactone-deficient mutant rms5-3 compared with germination in root exudates from wild

pea variety (Torsdag) and the strigolactone-overproducing mutant rms4-1. Our result also

demonstrated that root exudates at lower concentrations also increased conidium germination

in M. anisopliae compared to incubation in water.

This is the first report of the effect of strigolactone on conidium germination in Metarhizium

spp. Strigolactones have also been reported to stimulate conidium germination in arbuscular

mycorrhizal fungal species Glomus intraradices and Glomus claroideum by increasing

mitochondrial density and respiration (Besserer et al. 2006). Similarly, strigolactone

deficiency also reduced the germination of seeds of the parasitic plant Orobanche in wild and

strigolactone deficient mutants (ccd8, rms5-3) exudates (Gomez -Roldan et al. 2008). Plants

release strigolactones in very low concentration, and these were shown to be active at

dilutions as low as 10-3 M on G. rosea hyphae (Besserer et al. 2006).

The plant root colonisation experiment showed that colonisation by M. anisopliae was

significantly reduced in the strigolactone-deficient pea mutant rms5-3 when compared to both

the wild and rms4-1 varieties. This result was similar to observed mycorrhizal association in

pea roots (Gomez -Roldan et al. 2008), where the extent of root colonisation by the arbuscular

mycorrhizal fungus G. intraradices in ccd8 mutant was significantly lower than in wild peas,

and that the colonisation capacity of ccd8 mutant was restored by the supplementation of a

chemical analogue of strigolactone GR24.

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Both the conidium germination and colonisation results presented here showed similar trends:

the wild variety supported both greater conidium germination and colonisation followed by

reduced germination and colonisation in rms4-1 and least in rms5-3. These trends were

supported by the strigolactone quantification data in wild variety, strigolactone overproducing

(rms4) and strigolactone deficient mutant ccd7 (rms5-3) determined by Foo et al. (2013), who

reported that the highest amounts of strigolactones were detected in both root exudates and

root tissues of wild variety of pea (12 and 10.2 ng/gFW respectively), followed by

strigolactone overproducing (rms4) pea (10.4 ng/gFW in root exudates and 9.5 ng/gFW in root

tissue). They found no strigolactone found in the deficient mutant rms5-3.

Foo et al. (2013) described three natural strigolactones, viz., orobanchol, fabacyl acetate and

orobanchyl acetate in the root tissue and root exudates of these wild variety and mutant-

varieties. They noted that the strigolactone ‘overproducing’ mutant rms4 is unable to perceive

strigolactones. They reported that colonisation of the pea roots by the arbuscular mycorrhizal

fungus species G. intraradices was reduced in the rms4 mutant compared to the wild- variety

pea, and suggested that this indicated that strigolactones must also be perceived by the plant in

order for root colonisation to proceed. These observations mirror those observed in this study,

and suggest that the responses to strigolactone in colonisation of the root by the Hypocrealean

fungus M. anisopliae may mirror those observed in G. intaradices.

This is the first study to demonstrate that strigolactones play a role in conidium germination

and early colonisation by Hypocrealean fungi in plant roots. Further study will be required to

test the cellular changes that occurred in Metarhizium in the presence of strigolactones to

understand the fungal response during the symbiotic process. This knowledge will contribute

to understanding the role of plant hormones in establishment of symbiotic fungi in the

rhizosphere and may provide pathways to enhance the use and establishment of Metarhizium

as a beneficial symbiont in crops.

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6.5 Supplementary data

Table 6:1 Logistic regression analysis for plant variety and root exudate (RE)

concentration on conidium germination of Metarhizium

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 0.22313 0.19150 1.165 0.244

Plant -0.54476 0.09763 -5.580 2.41e-08 ***

RE Conc. -0.52196 0.07126 -7.325 2.39e-13 ***

Plant: RE Conc. 0.02877 0.03663 0.785 0.432

---

Significance values: ‘***’ <0.001, ‘**’< 0.01, ‘*’ <0.05.

(Dispersion parameter for binomial distribution taken to be 1)

Null deviance: 446.12 on 74 degrees of freedom, Residual deviance: 46.59 on 71 degrees

of freedom, AIC: 349.07

Number of Fisher Scoring iterations: 4

Table 6:2 Regression analysis of percent conidium germination as a function of

concentration of root exudates of wild, rms4-1, rms5-3 pea plants.

Wild rms4-1 rms5-3

Best-fit values

Slope 0.2169 ± 0.008207 0.1671 ± 0.01148 0.1347 ± 0.01310

Y-intercept when X=0.0 11.69 ± 0.5039 9.560 ± 0.7048 7.973 ± 0.8046

X-intercept when Y=0.0 -53.91 -57.22 -59.17

1/slope 4.611 5.985 7.422

95% Confidence Intervals

Slope 0.1908 to 0.2430 0.1306 to 0.2036 0.09304 to 0.1764

Y-intercept when X=0.0 10.09 to 13.30 7.318 to 11.80 5.412 to 10.53

X-intercept when Y=0.0 -69.00 to -41.94 -88.82 to -36.58 -110.5 to -31.44

Goodness of Fit

R square 0.9957 0.986 0.9724

Sy.x 0.5992 0.838 0.9567

Is slope significantly non-zero?

F 698.2 211.9 105.7

DFn, DFd 1.000, 3.000 1.000, 3.000 1.000, 3.000

P value 0.0001 0.0007 0.002

Deviation from zero? Significant Significant Significant

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Table 6:3 Generalised liner model showing effect of plant variety on colonisation

of M. anisopliae after 3 days of conidium inoculation

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -1.28804 0.08585 -15.003 < 2e-16 ***

Plant -0.23765 0.04149 -5.728 1.02e-08 ***

---

Significance values: ‘***’ <0.001, ‘**’< 0.01, ‘*’ <0.05.

(Dispersion parameter for binomial distribution taken to be 1)

Null deviance: 337.72 on 69 degrees of freedom, Residual deviance: 304.60 on 68

degrees of freedom, AIC: 606.27

Number of Fisher Scoring iterations: 4

Table 6:4 Generalised liner model showing effect of plant variety on colonisation

of M. anisopliae after 7 days of conidium inoculation

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) -0.42465 0.09179 -4.626 3.72e-06 ***

Plant -0.31222 0.04546 -6.868 6.51e-12 ***

---

Significance values: ‘***’ <0.001, ‘**’< 0.01, ‘*’ <0.05.

(Dispersion parameter for binomial distribution taken to be 1)

Null deviance: 285.07 on 36 degrees of freedom

Residual deviance: 236.97 on 35 degrees of freedom

AIC: 415.55

Number of Fisher Scoring iterations: 4

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Chapter 7 : Conclusions

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7.1 Key findings

The species of anamorphic fungal genus Metarhizium have been used as bio-

pesticides against insect pests and replicate as saprophytes in soil and as components

of the rhizospheric microbiome. Establishment of Metarhizium in a symbiotic

association with plants can in increased nutrient uptake and thus greater plant growth,

resistance to infection and protection from plant herbivores (insect pests). However,

inconsistent and variable results were observed in field trial limits the application of

Metarhizium in the large-scale application as biological control agent.

Recent studies showed that associations with plants are as important role in the

evolution and divergence of these species. The application of Metarhizium as

rhizospheric inocula may be improved through increased understanding of the

evolution and functional ecology of Metarhizium species with plant hosts

The aim of this thesis is to improve the understanding the cryptic diversity and

population structure of naturally occurring species of Metarhizium in Australian’s

ecosystems, including soil factors that may affect the distribution of the species,

association with agricultural practices and crops, the role of plant signals in

establishment in the rhizosphere. Several studies were conducted to fulfil the research

aims. The results of these studies are presented in Chapter 3, Chapter 4, Chapter 5 and

Chapter 6.

Chapter 3

Strains of Metarhizium were isolated from soil samples from different crops (maize,

pulses) and ecotypes (forest, grassland and agricultural crops) at three locations of

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Queensland. Metarhizium species were found to be widely distributed in Queensland

soils, and more frequently isolated from soil in field crops, which are regularly

disturbed by ploughing, than semi-disturbed grassland or undisturbed natural forest, a

pattern similar to that observed also observed in Canada, Finland, the Netherlands and

China (Bidochka et al. 1998, Vanninen 1996, Steenberg, 1995, Meyling and

Eilenberg, 2007). Agricultural practice such as tillage operation might increase the

dispersion of Metarhizium propagules, which favour higher occurrence of

Metarhizium populations in agricultural soils (Kepler et. al. 2015), or from other

factors associated with agricultural practice such as use of fertilisers or crop rotations.

Multilocus phylogenetic analysis using nuclear markers 5’-TEF and MzIGS3 on the

Australian isolates was found to give good resolution of clades within the genus. Two

new clades, provisionally named M. indet.1 and M. indet.2 were identified in a cluster

of clades related to M. pingshaense. The internationally-recognised species M.

robertsii, M. pingshaense and M. anisopliae were also found in the Australian

samples. Species recognition using a phylogenetic approach has been increasingly

used to differentiate cryptic fungi with unknown sexual reproductive stages (Taylor et

al., 2000, Douhan et al., 2008).

This study also observed strong genetic differentiation and gene flow between the

species of Metarhizium, but the gene flow was not restricted by the geographical

separation. This is particularly important to assessing risk of unwanted genetic

contamination of indigenous populations by commercial Metarhizium isolates in an

ecosystem.

These results suggest that a number of species of fungi not found elsewhere in the

world may be present in Australia. It also facilitates the import of Metarhizium for use

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in biocontrol programs in Australia by confirming the presence of M. robertsii and M.

anisopliae, which have been developed as commercial biopesticides overseas

(Hauxwell et al 2010).

The association of Metarhizium spp. with plant and ecotype is discussed in Chapter 4.

Chapter 4

The effect of ecological location, ecotypes, soil properties and soil components on

occurrence of Metarhizium species was investigated. M. robertsii was to be present in

all locations, predominantly in legume and forest but also in grassland and maize

fields. M. anisopliae and M. pingshaense were also observed in legume and forest

soils. In contrast, M. indet.1 was predominantly found in maize fields.

Habitat preference of Metarhizium species has been observed in multiple studies.

“Cold active’ M. brunneum and “heat active” M. robertsii found to associate with

different ecotypes: forest and agricultural fields, respectively (Bidochka et al., 2001,

Nishi et al., 2013). Similar ecological associations of M. brunneum with forest and M.

robertsii with agricultural fields were also observed in Russia (Kryukov et al., 2017).

In Japan, M. pingshaense was frequently isolated from both agricultural and forest

habitat, but M. brunneum and M. pemphigi were more abundant in forest habitat

(Nishi et al., 2013). In contrast, M. brunneum was found to be the dominant species in

agricultural soils in Denmark (Steinwender et al., 2014). Thus, some species appear to

have no strong association with a particular ecotype, but some species of Metarhizium

show some associations, particularly between forest (M. brunneum) and cropped soils

(M. robertsii), though this may vary in different studies.

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The soil analysis and correlation studies showed that M. anisopliae and M. robertsii

are associated with higher nitrogen and carbon contents in soil of legume field and

forest. In contrast, M. indet.1 was associated with lower nitrogen and carbon contents

in soil of maize and grassland. Both nitrogen and carbon are important for

Metarhizium nutrition and may influence establishment of symbiotic relationship with

plants, where Metarhizium supplies insect-derived nitrogen to the plant in exchange

for plant-derived carbohydrates (Behie et al., 2017, Behie et al., 2012). Further study

on rhizospheric competence and capacity of the fungal species to persist in different

soils, particularly in association with crops such as maize, might help to identify more

detailed factors to enhance the use of Hypocrealean fungi as root inoculants.

Our study also demonstrated that M. robertsii and Metarhizium indet. 1 were more

abundant in iron-rich soils. M. brunneum has been found to solubilise iron and

increased its availability for plant uptake in iron-poor calcareous and sandy soils

(Raya–Diaz, et al., 2017). The impact of iron availability on persistence and relative

solubilisation of iron by M. robertsii and Metarhizium indet. 1 requires further study.

In summary, M. indet.1 was found associated with disturbed soils with low N and C

contents, and with high iron content. This suggests that there is potential to select

fungal species more able to persist and colonise the rhizosphere in soils that may be

less-favourable to persistence of other species of the fungus.

Chapter 5

This chapter evaluated the association of the Australian isolates of Metarhizium with

two different of agricultural crops: legumes (a Eudicot) and maize (a Monocot). The

results showed that M. robertsii, M. pingshaense and M. anisopliae were abundant in

soil from legume crops, whereas M. indet.1 and M. indet.2 were only observed in soil

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from maize crops. Minimum gene flow was also observed between isolates from the

two crop types. Crop-specific association of Metarhizium species has also been

reported in the USA, where M. brunneum was found strongly associated with

blueberry and strawberry (Fisher et al. 2011).

Laboratory assays with selected isolates in maize and pea showed that root

colonisation was significantly higher in pea than in legume for most species of the

fungus, irrespective of their crop soil from which they were isolation (maize or

legume fields). The exception was isolates of M. indet. 1 which were isolated from

maize field and showed significantly higher colonisation in maize roots.

Little is known about the factors governing local adaptation of Metarhizium, though

rhizosphere associations are reported to stabilize Metarhizium population through

beneficial mutations (Wang et. al. 2011). However, this is the first study to report

crop-specific associations of species of Metarhizium with monocot and Eudicot crops,

and particularly with two crops of global significance (maize and legume).

Chapter 6

Plant hormones play a significant role in establishment of fungi in symbiotic

relationships with plants (Walker et al., 2004).

The role of plant hormone strigolactones on conidium germination and root

colonisation by M. anisopliae in pea (Pisum sativum L.) roots was examined using an

indirect assay using wild type and strigolactone-expression mutants of the pea. The

results suggest that strigolactone enhances conidium germination and early

colonisation of pea roots by Metarhizium. This finding was consistent with observed

mycorrhizal association in pea roots (Gomez -Roldan et al. 2008), where the extent of

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root colonisation by the arbuscular mycorrhizal fungus G. intraradices in

strigolactone deficient mutant ccd8 pea plants was significantly lower than in wild

peas. This is the first study to suggest that strigolactones may play a role in both

conidium germination and early colonisation of plant roots by Hypocrealean fungi.

This knowledge suggests additional screens to select isolates or species of

Hypocrealean fungi to enhance beneficial colonisation of crops with different levels

of strigolactone expression.

7.2 Future directions

This research leads to the following suggestions:

1. The gene flow estimate (Fst value) showed gene flow between the Metarhizium

populations of different locations and ecotype. This study did not explain the possible

cause of gene flow. As sexual reproduction is not yet observed in Metarhizium

elsewhere, further examination of the reproduction and gene flow in Australian

Metarhizium isolates is necessary.

2. It has been reported that plant attachment factor Metarhizium adhesion-like protein-

2 (MAD2) is a diverse region in Metarhizium genome responsible for plant

attachment (Wyrebek and Bidochka, 2013). Phylogenetic and sequence-diversity

analysis of MAD2 gene of the studied isolates might give more clear insight in the

mechanisms of association of Metarhizium species with different crops, particularly

legumes and cereals.

3. Habitat-association model should be tested to examine for biogeographical patterns

for Metarhizium isolates in a wide range of habitats and further refine analysis of the

soil and environmental factors responsible for Metarhizium species distribution in

Australian soils.

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4. Growth hormones produced by the endophytic fungus is an important factor for

plant root colonisation by symbiotic fungi. Further study should be conducted to

identify any secretion of growth hormone by Metarhizium during the root colonisation

process.

Finally, the research studies presented in thesis generate important knowledge in

Metarhizium research, advancing the knowledge of naturally-occurring Metarhizium

species in Australia, particularly in agricultural crops, increase the understanding of

ecological factors governing Metarhizium species distribution, and suggesting further

avenues to investigate the role of plant-fungal signals in rhizosphere colonisation.

154

References

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