Microbial Community Dynamics Within Frost Boils of Browning Peninsula, Antarctica

Microbial Community Dynamics within Frost Boils of Browning Peninsula, Antarctica

Sarita Pudasaini

A thesis submitted in fulfillment of the requirements for the degree of

Master of Philosophy (Research)

School of Biotechnology and Biomolecular Sciences

University of New South Wales

August, 2014 TABLE OF CONTENTS

TABLE OF CONTENTS ...... ii

ABSTRACT ...... vii

ACKNOWLEDGMENT ...... x

LIST OF FIGURES ...... xi

LIST OF TABLES...... xviii

ABBREVIATIONS ...... xix

1 INTRODUCTION...... 1

1.1 Background ...... 1

1.2 Microbial diversity of Antarctica ...... 2

1.2.1 Bacterial diversity of Antarctica...... 2

1.2.2 Relationship of bacterial abundance with environmental parameters in Antarctica ..... 3

1.2.3 Fungal diversity in terrestrial Antarctica ...... 4

1.2.4 Relationship of fungal abundance with environmental parameters in Antarctica ...... 5

1.3 Culture- independent approach ...... 6

1.3.1 Qualitative PCR (qPCR) ...... 6

1.3.2 Molecular tools for identification of bacteria and fungi...... 6

1.3.3 Pyrosequencing: a powerful tool of next generation sequencing(NGS) ...... 9

1.3.4 Illumina metabarcoding ...... 11

1.4 Available culture-dependent approach ...... 11

1.4.1 Novel approach to cultivation ...... 11

1.4.2 Novel approach to cultivate fungi ...... 13

1.5 Windmill Islands of Eastern Antarctica ...... 15

1.6 Browning Peninsula (BP) ...... 15

1.6.1 Frost boils formation ...... 17

1.7 Microbiology of Browning Peninsula (BP) ...... 19

1.7.1 Bacterial community of Browning Peninsula...... 19

1.7.2 Bryophyte and fungal community of Browning Peninsula ...... 20

1.8 Aim ...... 21

2 MATERIALS AND METHODS ...... 22

2.1 Site description ...... 22

2.2 Sampling design and collection ...... 22

2.3 Physical and chemical analysis of soil samples...... 24

2.4 Culture independent techniques ...... 25

2.4.1 DNA extraction from Soil ...... 25

2.4.2 Quantification of genomic DNA using Picogreen assay ...... 26

2.4.3 Multiplexed pyrosequencing targeting bacterial and fungal genes ...... 26

2.4.4 Raw data processing pipeline for pyrosequencing data ...... 27

2.4.5 Quantitative PCR (qPCR) ...... 30

2.5 Culture dependent techniques ...... 31

2.5.1 Soil substrate membrane system (SSMS) cultivation ...... 31

2.5.2 Cultivation of bacteria and fungi on artificial media ...... 38

2.6 DNA extraction from pure cultures...... 40

2.6.1 DNA extraction of pure cultures by boiling ...... 40

2.6.2 DNA extraction of pure cultures using bead beating ...... 40

2.6.3 DNA extraction of fungal cultures ...... 41 iii

2.7 Bacterial 16S rDNA and fungal internal transcribed spacer (ITS) gene PCR amplification 41

2.8 Gel electrophoresis ...... 43

2.9 Restriction fragment length polymorphism (RFLP) ...... 43

2.10 PCR product purification ...... 44

2.11 Cloning ITS region of fungi into T vector ...... 45

2.11.1 Ligation of DNA ...... 45

2.11.2 Optimisation of insert : vector molar ratios ...... 45

2.11.3 Transformation using the pGEM®-T ligation reaction ...... 45

2.12 Sanger sequencing of PCR products ...... 46

2.13 Identification of 16S and ITS sequences ...... 47

3 MICROBIAL COMMUNITY DYNAMYCS ...... 48

3.1 Bacterial and fungal community diversity ...... 48

3.2 Phylogenetic distribution of bacteria in BP...... 49

3.2.1 Distribution of bacterial diversity across individual polygons ...... 50

1.1.2 Distribution of genus level diversity across polygons ...... 52

3.3 Phylogenetic distribution of fungi in Browning Peninsula ...... 55

3.3.1 Distribution of fungi phyla level fungal diversity across individual polygons ...... 56

3.3.2 Distribution of fungi genus level diversity across individual polygons ...... 57

3.4 Bacterial community dissimilarity across Browning Peninsula ...... 58

3.5 Fungal community dissimilarity across Browning Peninsula ...... 61

3.6 Relationships between environmental variables and bacterial community structure 64 iv

3.7 Genes distribution across polygons of Browning Peninsula ...... 67

3.7.1 ...... 69

4 CULTURE BASED CHARACTERISATION OF MICROBIAL

COMMUNITY DYNAMICS ...... 70

4.1 Soil substrate membrane system cultivation diversity ...... 70

4.1.1 Bacterial phylum level diversity following SSMS enrichment ...... 71

1.1.3 Distribution of bacterial genus level diversity on SSMS enrichment ...... 72

4.1.2 Distribution of fungi phylum and genus level diversity on SSMS enrichments ...... 74

4.1.3 Bacterial and fungal community dissimilarity on SSMS enrichments ...... 75

4.2 Bacterial and fungal diversity recovered from artificial media ...... 78

4.2.1 Sub-culturing ...... 78

1.1.4 Molecular identification of bacterial isolates ...... 78

4.2.2 Sub-culturing of fungi ...... 81

1.1.5 Molecular identification of fungal isolates...... 84

4.2.3 Comparison between molecular and cultivation approaches to microbial

characterisation 87

5 DISCUSSION ...... 91

5.1 Microbial Diversity of Browning Peninsula ...... 91

5.1.1 Bacterial diversity in soils ...... 92

5.1.2 Fungal diversity in soils ...... 95

5.1.3 Pure bacterial and fungal cultures ...... 97

5.2 Environmental drivers of community composition...... 99

5.3 Microbial community structure ...... 100

5.4 A culture clash ...... 103

5.5 Conclusion ...... 105

REFERENCES ...... 106

APPENDICS ...... I

Appendix 1: Correlation value of PCO plots (bacteria and fungi) ...... I

Appendix 2: Environmental data: physical variables of BP soils ...... II

Appendix 3: Environmental data: chemical variables of BP soils ...... III

ABSTRACT

The Antarctic continent faces a range of extreme climatic conditions and constitutes one of the harshest environments on Earth. Despite many adverse environmental constraints

(extreme low temperatures, low water availability, high salinity, high UV radiation and low nutrient availability) it has been shown to support extensive microbial life. Browning

Peninsula is a barren and ice free landscape in the Windmill Island, Eastern Antarctica which has experienced very little human activity. It is located in low valley consisting of large number of frost boils approximately 2-10 m in diameter, combined with an active layer of about 30 cm. Our aim was to investigate the microbial diversity of this unique, pristine environment.

For this study, 18 soils were collected across 3 parallel transects using a spatially explicit sampling design. Soil genomic DNA was extracted and 454 pyrotag sequencing along with qPCR was performed targeting the bacterial 16S rRNA and fungal ITS/18S rRNA genes. Environmental soil metadata were also obtained including spatial, physical and chemical properties. Both the microbial and environmental data were analysed using

Primer 6 and Permanova software and the community diversity and their similarity was investigated. Our results showed the bacterial community to consist predominantly of

Actinobacteria (up to 55%), Chloroflexi (≥ 10%), Acidobacteria, Cyanobacteria,

Proteobacteria and Gemmatimonadetes. While Ascomycota (≥ 96%), Basidiomycota

(more than 1.5%) and Fungi incertae sedis (less than 1%) were the dominant fungi. Within the dominant bacterial phyla there was a high proportion of uncultured genera such as unclassified_Gaiellaceace (Actinobacteria), unc_Ellin6529 (Chloroflexi) and unc_RB41

vii

(Acidobacteria). While in Fungi Devriesia, Capronia and Aurobasidium (Ascomycota) were the most dominating genera.

In order to evaluate culturable diversity, a novel cultivation approach the Soil

Substrate Membrane System (SSMS) and artificial media (Ravan, BG11, R2A, starch casein agar for bacteria and cooke rose bengal agar, malt extract agar, potato dextrose agar for fungi) were used to recover bacterial and fungal species from this unique environment.

The SSMS recovered more than 48% of the total bacterial and more than 24% of fungal genera detected within the soil diversity profile. While as expected, artificial media cultivation recovered just 3% of the bacterial and 8% of the fungal genera in soil. For fungi only two phyla Ascomycota and Basidiomycota were recovered from both the SSMS and artificial media. However within these phyla of 31 genera were enriched on the SSMS compared to the isolation of species from of 10 fungal genera via traditional media.

A significant relationship between microbial community structures and environmental properties (extrinsic and intrinsic) was observed. For bacterial communities

- -- elevation, sand, conductivity, Cl , SO4 and total nitrogen were driving community pattern, while mud, total nitrogen, total carbon, SiO2 and sodium oxide were affecting fungal community distributions.

Bacterial community dissimilarity and environmental variables were polygon specific. Bacterial communities from samples of same polygon were more similar than the communities from different polygons and distance. While Fungal community distributions were random (soil sample specific) ie no trend was observed over the landscape or individual polygons. The ANOSIM and analysis variability via nMDS plots revealed that bacterial community distribution and environmental parameters were more significant (P <

viii

0.001) within polygon than distance while fungal community distribution was not significant (P < 1) over polygon or distance. Dispersal limitation may have affected the fungal communities with very few OTUs and somewhat random community structure across the site was observed.

In this study, a culture clash was observed between the culture dependent and independent approaches to microbial characterisation. The artificial media recovered four

(Pedobacter, Aminobacter, Dyella, Shingopyxix) bacterial genera and three (Engyodontium,

Peniopora, Phoma) fungal genera which neither present in SSMS nor in soil based 454 pyrosequencing data. In addition, the SSMS recovered 74 bacterial and 14 fungal genera that were not present in the soil pyrotag dataset.

In Browning Peninsula bacterial community structure was polygon specific and a more comprehensive picture of microbial community distribution across the soil was obtained by implementation of polyphasic approach, integrating artificial cultivation,

SSMS and 454 pyrotag sequencing. Comprehensive analysis of inter-boil variation may uncover what is happening in these boils. In the future it is required to have polygon specific sampling across different positions (edge, centre), various depths of individual polygon and use of illumina pyrotaq sequencing platform to understand the ecosystem within these polygons with greater microbial dataset coverage.

ACKNOWLEDGMENT

Firstly, I am thankful to my supervisor Dr. Belinda Ferrari for accepting me as her student for this research. Her constant inspiration, valuable suggestions and continuous guidance has always helped upgrade my skills and improve quality of working in lab and writing. Secondly, I would like to thank my co supervisor John Wilson for all his support and care that boosted my experience during this research.

I would also like to thank Australian Antarctic Division (AAD) for providing me soil samples, all environmental dataset required for this study, beautiful photographs of

Browning Peninsula (sampling site) and funding my research.

To my lab mates Josie van Dorst, Mukan Ji and Chengdong Zhang for helping me adapt with the laboratory facilities (equipment and software) required for this study. Also, their valuable suggestions had always praised me to learn more and work in team. I would like to thank technical staffs of BABS (Lili Zhang and Shamima Shirin) for providing me reagents and chemicals available when I was in need and Dr. Timothy Williams for guiding me use the quarantine containment facilities.

Additionally, I am thankful to my Master Mr. Prem Rawat for his constant inspiration in my studies. I would like to dedicate this research to my late mother Bishnu

Maya Pudasaini. I am feeling blessed for her strong belief in me. This work would not have been possible without my father’s (Kul Prasad Pudasaini) support and trust, second mother’s care and sister’s love. A special thanks to my brothers who funded me for my entire course work, tolerated me with my unlimited demands. I feel proud being their sister.

LIST OF FIGURES

Figure 1-1: Antarctic continent indicating East Antarctica (Windmills Islands in red

circle) and West Antarctica with ice free areas (black square) source: (Verleyen

et al., 2011) ...... 1

Figure 1-2: Map of ribosomal RNA genes and corresponding ITS gene regions used

for the fungal PCR strategy. Positions of forward (right-pointing arrow) and

reverse (left-pointing arrow) primers are shown on the map of ITS regions and

the surrounding ribosomal RNA genes. The ranges covered by the respective

subset databases (see text) are also indicated (Toju et al., 2012)...... 8

Figure 1-3: Overview amplicon and reads generated from sanger sequencing and 454

sequencing for bacteria. The amplicon generated by each primer is indicated in

red, orange Position and numbering based on Escherichia coli reference

sequence. Green and orange arrow indicates the sequencing direction of Sanger

and 454 sequencing respectively. Different variable region is represented by blue

colour (Group Jumpstart consortium Microbiom project Data Generation

Working group, 2012)...... 11

Figure 1-4: The Windmill Islands showing Browning Peninsula (yellow) (source:

Scientific Committee on Antarctic Research (SCAR))...... 16

Figure 1-5: Sampling site Browning Peninsula (source: Australian Antarctic Division)

...... 17

Figure 1-6: A systematic diagram showing the structure of non-sorted polygons

distributed irregularly along a site, with various broken layers of soil horizons

highlighted (Ah, Bmy, Cy and Cz). The active layer consists of stones and cracks xi

while the permafrost table consists predominantly of ice lenses (Courtesy of C.

Tarnocai)...... 19

Figure 2-1: Browning Peninsula, Eastern Antarctica with frost boils (Photograph

source: Australian Antarctic Division (AAD)) ...... 22

Figure 2-2: Spatially explicit sampling design illustrating frost boils across transects:

Each coloured circles represent polygons and P1 -P41 denotes unique frost boils

number. Three horizontal lines represent transects parallel to each other are

presented as 1, 2 and 3 with the distance of 2 m in between. The crossing points of

vertical lines indicate the variable lag distance (0, 2, 100, 102, 200 and 202) from

where samples were collected...... 23

Figure 2-3: Soil substrate membrane system (SSMS) set up. a) the soil substrate is

prepared within the tissue culture insert b) An inoculated PC membrane is

placed on top of the inverted TCI, which contain the soil slurry c) Four soil

substrate membrane system replicates were placed into 6-well plate for

incubation. Picture taken from Ferrari et al. (2008)...... 33

Figure 2-4: Filtration manifold SSMS ...... 34

Figure 2-5 Anaerobic chamber containing six well plate with SSMS and gas generator

sachet with the indicator present ...... 35

Figure 2-6: Sectioning of PC membrane: 1) used for microscopic observation under

fluorescence microscope. 2, 3, and 4) used for the DNA extraction...... 36

Figure 3-1: Rarefaction curve indicating bacterial and fungal OUT coverage across 18

soils pyrosequenced using the 'universal' bacterial 16S primer set (28F/519R) and

the universal ITS region fungal primer set (ITS1/ITS4)...... 49

xii

Figure 3-2: Phylogenetic distribution of bacteria across BP soils. Others indicate sum

of more than 1% phyla or genera present in all soils. Actinobacteria and

Chloroflexi are most dominating bacteria and unc_Gaiellaceae and

unc_Ellin6529 are most dominant bacterial genera. Unc represents unclassified.

...... 50

Figure 3-3: Phylum level bacterial diversity across polygons of BP. The abundance of

Actinobacteria, Acidobacteria and Cyanobacteria are varying across polygons.

Others represent a total sum of minor phyla like TM7, Thermi, OP9, TM6 and

WPS-2 in less than 1% relative abundance...... 52

Figure 3-4: Bacterial diversity at the genus level across polygons.The relative

abundance of genera within the 4 major phyla present in BP is presented A)

Actinobacteria B) Chloroflexi C) Acidobacteria D) Cyanobacteria. Others

represent a total sum of genera present at less than 1% relative abundance...... 54

Figure 3-5: Phylum and genus level distribution of fungi across Browning Peninsula.

Ascomycota is dominating fungi phylum and Devriesia, followed by Capronia

were dominating genera. Others indicate those phyla present at less than 1%

relative abundance across all soils...... 55

Figure 3-6: Phylum level distribution of fungi across polygons. Ascomycota dominated

all soil except BP10 of polygon P25. Sample BP5 consisted of phyla

Entomophthoromycota...... 57

Figure 3-7: Genus level distribution of fungi within individual polygons. Others

represent a total sum of less than 1% genera present in individual samples.

xiii

Others in sample BP11 included only three genera while sample BP14 included

48 different genera but were found in relatively low abundance...... 58

Figure 3-8: Nonmetric multi - dimensional scaling (nMDS) plots of bacterial

community showing relationships among soil samples to factors A) Polygons B)

Position along the transects. 1, 2 and 3 represent transect one, two and three

respectively. Clustering was performed on the basis of 20, 40 and 50%

community similarity...... 60

Figure 3-9: nMDS plot of the environmental parameter resemblance matrix within BP

soils indicating A) Polygon B) Position along the transects. 1, 2 and 3 represent

transect one, transect two and transect three respectively...... 61

Figure 3-10: nMDS plots of the fungal community dissimilarity showing the

relationship among samples to factor A) Polygons B) Position along the transects.

1, 2 and 3 represent transect one, transect two and transect three respectively. On

clustering few samples from different polygons and position exhibited 20% and

40% similarity...... 62

Figure 3-11: Correlation of PCO plot generated with the bacterial resemblance matrix

against environmental variables. Additional factor chosen was polygon number.

Con* indicate conductivity, Ele* (Elevation) and S* (Sand)...... 66

Figure 3-12: Correlation of PCO plot generated with the fungal resemblance matrix

against environmental variables. Additional factor chosen was polygon number.

TN indicates total nitrogen and TC indicates total carbon...... 67

Figure 3-13: qPCR analysis A) SSU gene copy numbers across polygons. 16S samples

denoted by green bars on the right scale and 18S gene copy numbers are denoted

xiv

by blue bars on the left scale. B) Average of fungal /bacterial (F/B) ratios within

different polygons...... 69

Figure 4-1: Bacterial microcolonies present on the SSMS following incubation at 8 °C

for 21 days stained with SYBR green II A) Microcolonies in long chain form from

BP1. B) Small microcolonies in separate clusters in BP2...... 71

Figure 4-2: Bacterial phylum distribution among SSMS growth membrane and thus 6

different polygons labelled as P2, P17, P18, P4, P25 and P28. Proteobacteria

dominated all SSMS membranes followed by Actinobacteria. Others indicate a

total sum less than 2% genera present in individual soils...... 72

Figure 4-3: Genus level distribution of bacteria following SSMS cultivation. A)

Proteobacteria B) Actinobacteria. Others represent sum of less than 1% genera

present under same the phylum. Microbacterium (Actinobacteria), Spingomonas

and Pseudomonas (Proteobacteria) were dominant genera across all SSMS...... 73

Figure 4-4: Fungal diversity enriched on SSMS after A) Aerobic B) Anaerobic

incubation at 8 °C. UNC represent the unclassified fungi and others indicate a

total sum of less than 1% genera present. Others in sample BP1 included 14

different genera composition but were present in relatively low abundance...... 75

Figure 4-5: nMDS plots indicating community similarity in SSMS enrichments across

6 soil samples A) Bacterial B) Fungal. On clustering bacteria, five samples

exhibited 40% similarity and three samples exhibited 60% similarity. ON

clustering fungi, only two samples exhibited 40% and four samples exhibited

20% similarity...... 77

Figure 4-6: Pure bacterial isolates visualised on plates and by EFM. A) Bacterial

strain SP.B20 (Hymenobacter aerophilus). B) Bacterial strain SP.B14

(Streptomyces indigoferus). C, D) microscopic observation of bacterial strain

SP.20 (Hymenobacter aerophilus) and SP.B14 (Streptomyces indigoferus)

respectively stained with SYBR II...... 81

Figure 4-7: Fungal strains growing in PDA plates after incubation at RT for 15 days.

From top left fungal strains are A) SP.F3 (Theloblolus microsporous) B) SP.F6

(Phoma herbarum) C) SP.F9 (Engyodontium album) D) SP.F10 (Cladosporium

cladosporides) E) SP.F11(Geomyces plannorum) F) SP.F13 (Thelobolus globosus)

G) SP.F14 (Cryptococcus victoriae) H) SP.F15 (Penipora lycii) I) SP.F17

(Holtermanniella watticus)...... 86

Figure 4-8: Microscopic observation of fungal isolates stained in Calcofluor white.

From top left the respective strains are A) SP.F3 (Thelobolus microsporous) B)

SP.F6 (Phoma herbarum) C) SP.F9 (Engyodontium album) D) SP.F10

(Cladosporium cladosporides) E) SP.F13 (Thelobolus globosus) F) SP.F14

(Cryptococcus victoriae) under 100X...... 87

Figure 4-9: Venn diagram representing A) recovery of bacterial phylum level in all

three techniques where whole yellow circle indicate all phylum level covered in

soil, red colour represents phylum that was recovered in SSMS and green

(culture plate). B recovery of genus level: yellow (soil only), red (SSMS only),

green (culture plate only) and blue, pink, brown and white colour represent

genera common in soil and SSMS, Soil and culture plate, SSMS and culture plate

and common in all respectively...... 89

xvi

Figure 4-10: Venn diagram representing A) recovery of fungal phylum level in all

three techniques where whole yellow circle indicate all phylum level covered in

soil, red color represents two phylum that was recovered in SSMS and culture

plate. Venn diagram B represents recovery of genus level: yellow (soil) only, red

SSMS only, green culture plate only and blue, pink, brown and white colour

represent genera common in soil and SSMS, Soil and culture plate, SSMS and

culture plate and common in all respectively...... 90

xvii

LIST OF TABLES

Table 2-1: Soil samples, frost boil number and position along transect...... 24

Table 2-2 Media used for bacteria and fungi cultivation ...... 39

Table 2-3: Domain specific primers for all molecular analysis ...... 42

Table 3-1: Calculated diversity estimates for bacteria and fungi ...... 48

Table 3-2: ANOSIM between different resemblance matrices and factors (polygons

and positions)...... 63

Table 3-3: DistLM results indicating the correlation between environmental

parameters as a predictor of the microbial community distribution...... 65

Table 4-1: Bacteria isolated from different culture media from two different soils .....79

Table 4-2: Colony morphology of fungal strains isolated in this study...... 83

Table 4-3: Fungi isolated in different artificial culture media ...... 85

xviii

ABBREVIATIONS

°C degree Celsius µl Microliter µm Micrometer AAD Australian Antarctic Division ANOSIM Analysis of similarities bp base pairs BP Browning Peninsula CRBA Crook Rose Bengal Agar CzA Czapak Agar dbRDA distance-based redundancy analysis DES Dnase Pyrogen -free water DistLM distance-based linear models dsDNA double stranded DNA EDTA Ethylenediaminetetraacetic acid EF1α elongation factor-1 alpha EFM Epi-fluorescence microscopy ETOH ethanol g gram GIS Geographical Information System GPS Global Positioning System h hour(s) ichip isolation chip ITS Internal Transcribed Spacer kb kilobase pairs km Kilometer l liter LB Luria Burteni medium LSU ribosomal large subunit m meter MEA Malt Extract Agar mg milligram min minute(s) NA Nutrient Agar National centre for Biotechnology NCBI Information nm nanometer nMDS Non metric multidimentional scaling

xix

NSG Next Generation Sequencing OC organic carbon OCws Water soluble organic carbon O/N Over night OTU Operational Taxonomic Unit PB Phosphate buffer PC Polycarbonate PCO Principal Coordinates analysis PCR polymerase chain reaction PDA Potato Dextrose Agar PPS Protein precipitate solution qPCR Qualitative Polymerase Chain Reaction RFLP Restriction fragment length polymorphism RNA Ribonucleic acid RPB RNA polymerase II subunit RT Room temperature SCA Starch Casein agar sec Second SSMS Soil substrate membrane system SSU Ribosomal small subunit TC Total carbon TCI Tissue culture insert TN Total Nitrogen u Unit Unc Unclassified UV Ultraviolet V Volt VMC Volumetric water content vol Volume(s) XRF X Ray fluorescence

Introduction

1 INTRODUCTION

1.1 Background

Antarctica is the fifth largest Continent on earth with a summer area of 14 million km2. It has the highest mean elevation (~3000 m) of any continent (Bockheim and Hall,

2002). The Antarctic continent offers a range of extreme climatic conditions and constitutes one of the harshest environments on Earth. Despite many adverse environmental constraints

(extreme low temperatures, low water, availability, high salinity, high UV radiation, and low nutrient availability) it has been shown to support extensive microbial biomass (Cary et al., 2010, Franzmann, 1996). Since most of the continent is covered with glacial ice sheets only 0.4% is ice free (Cary et al., 2010).

Figure 1-1: Antarctic continent indicating East Antarctica (Windmills Islands in red circle) and West Antarctica with ice free areas (black square) source: (Verleyen et al., 2011)

Introduction

Terrestrial ecosystem in Antarctica includes a great variety of habitat types from ice free areas of the continent to the comparatively warm sub Antarctic (Stewart et al., 2011).

The main barren ice-free areas that has been studied are Schirmacher Oasis, the islands and peninsulas in the Lützow Holm Bay region, Amery Oasis, Larsemann Hills, Rauer Islands,

Vestfold Hills, Bunger Hills, Windmill Islands and McMurdo Dry Valleys as illustrated in

Figure 1-1 (Verleyen et al., 2011).

1.2 Microbial diversity of Antarctica

Prokaryotes play central roles in the flux of matter and energy through the biosphere

(Caruso et al., 2013) and are comprise of greatest bio mass in surface (Franzmann, 1996,

Jing et al., 2013). Initially, Antarctic mineral microbiology was based on the culture based studies (Smith et al., 2006). A phylogenetic survey of Antarctic Dry valley suggests that the soil harbor pool of novel phychrotopic taxa (Smith et al., 2006).

1.2.1 Bacterial diversity of Antarctica

Different taxonomic composition of the clone library based on the number of unique operational taxonomic units (OTUs) across different sites of Antarctic Peninsula was obtained by Chong et al. (2012). The OTUs included Bacteriodetes, Acidobacteria,

Actinobacteria, α-Proteobacteria, β-Proteobacteria, γ-Proteobacteria, Cyanobacteria,

TM7, Gemmatimonadetes, Firmicutes, Chloroflexi, Verrucomicrobia, Planctomyces,

Ktedonobacteria and unclassified. In a separate study done by Pearce et al. (2013) bacterial diversity found in sub-glacial lakes sediment were Actinobacteria (23.8%), Proteobacteria

(21.6%), Planctomycetes (20.2%) and 11.6% of Chloroflexi.

Introduction

Large number of bacterial genera such as Arthrobacter, Brevibacterium,

Cellulomonas, and Corynebacterium were reported together with Gracilicutean isolates

(members of the Gram-negative Eubacteria) such as Pseudomonas and Flavobacterium

(Firmicutean bacteria), Bacillus, Micrococcus, Nocardia, Streptomyces, Flavobacterium, and Pseudomonads, Beijerinckia (less common genera), Xanthomonas (a pathogen associated with higher plants) and Planococcus (a marine genus) from Antarctic soils

(Friedmann, 1993, Cameron et al., 1972, de la Torre et al., 2003). Cyanobacteria were also well-documented inhabitants of Antarctic soil biotopes (de la Torre et al., 2003, Taton et al., 2003). Besides above bacteria Pearce et al. ( 2012) reported many other dominating bacterial taxa such as Rubrobacter, Acidobacteria, Oscillatoria, Phormidium, Deinococcus,

Sphingomonas, Bacteroides, Brevundimonas, Chloroflexus, Hymenobacter, Leptolyngbya,

Nostoc, Pseudonocardia, Psychrobacter, Rhodococcus, Synechococcus, Actinobacteria,

Anabaena, Cytophaga, Fervidobacterium, Friedmanniella, Microcoleus, Microcystis,

Nitrosospira, Sphingobacterium and Sporosarcosina.

1.2.2 Relationship of bacterial abundance with environmental parameters in Antarctica

The main water source of Antarctic desert is snow and soil humidity (Cary et al.,

2010). Relative abundance and diversity of bacteria was positively correlated with pH which may be because of narrow pH range for optimal growth of bacteria (Strickland and

Rousk, 2010). Soil organic matter, moisture, salinity, latitude and variable soil microclimate were other chemical and physical variables that changed the microbial community composition (Barrett et al., 2006). According to Smith et al. (2006)

Cyanobacterial signal was restricted to high altitude in the mineral soil of cold Antarctic desert. While, cryoturbation changed the physical and chemical factors of the soil along the

Introduction

chronosequence, thus resulting shift in the bacterial community (Barrett et al., 2006,

Zdanowski et al., 2012).

1.2.3 Fungal diversity in terrestrial Antarctica

Fungal diversity had been investigated by mycologists since mid-nineteenth on throughout Antarctica and sub-Antarctic regions (Ferrari et al., 2011, Rao et al., 2011,

Farrell et al., 2011). Compared with the large number of described fungi species in temperate regions, the understanding of fungal diversity in the Antarctic and sub-Antarctic regions is poorly understood. So far, approximately 1,000 accepted fungal species had been reported from Antarctic regions (Bridge and Newsham, 2009, Ferrari et al., 2011). Most of the reported Antarctic fungi species were cosmopolitan, while a small proportion was proposed to be indigenous (Ruisi et al., 2006).

The fungal isolates such as Chrysosporium verrucosum, Geomyces pannorum,

Phoma herbarumand Thelebolus microsporus from Ascomycota division were isolated

(Frate and Caretta, 1990, Connell et al., 2006, Godinho et al., 2013, Arenz et al., 2006,

Arenz and Blanchette, 2011). Similarly, Acremonium strictum, Cladosporium herbarum,

Mortierella Antarctica (Zygomycota), Paecilomyces farinosus (Ascomycota), Phialophora fastifiata, Scytalidium thermophile, Thermomyces lanuginosus, Verticillum sp, Mycelia sterilia and Cryptococcus albidus (Basidomycota) and Torula sporadelbrueckii

(Ascomycota) were identified (Frate and Caretta, 1990, Connell et al., 2006, Arenz et al.,

2006).

Acremonium strictum (Ascomycota) and Paecilomyces farinosus (Ascomycota) are commonly found in soil while Cladosporium herbarum (Ascomycota) and Phialophora fastigiata (Ascomycota) are rarely encountered in soil dung and bird feathers. Other

Introduction

Ascomycota like Phoma, Chrysosporium, Geomyces and Thelebolus (mostly associated with plant and animals) were frequently isolated fungi from different parts of Antarctic soils (Frate and Caretta, 1990, Connell et al., 2006, Godinho et al., 2013, Arenz et al.,

2006, Arenz and Blanchette, 2011). Yeasts were probably predominant on continental

Antarctica while other microfungi usually do so in maritime and sub Antarctica (Vishniac,

1996, Rao et al., 2011).

1.2.4 Relationship of fungal abundance with environmental parameters in Antarctica

Fungal diversity in terrestrial ecosystem of Antarctica increases with the availability of water and energy (Vishniac, 1996) and 0.6% of mold reported in Antarctica were water mold (Onofri et al., 2004). The occurrence and distribution of culturable fungi had the significant differences in species distribution pattern with respect to soil pH, moisture, distance from marine coastline, carbon-nitrogen ratio, chlorophyll a, salinity, conductivity, elevation and solar inputs (Connell et al., 2006, Arenz and Blanchette, 2011). Arenz &

Blanchette (2011) also reported that soil moisture is positively correlated with fungal abundance. Farrell et al. (2011) found that the fungal abundance was consistently correlated with the percent of carbon and nitrogen present in soil. In contrast, Farrell et al.

(2011), had reported that fungi are less consistent with soil moisture, salinity and pH but varied by location. Filamentous fungi are most closely associated with habitats having higher pH and soil moisture while non-filamentous (yeast or yeast like) fungi do not have any specific associations with these factors as they are found in broader range of habitat

(Connell et al., 2006).

Introduction

1.3 Culture- independent approach

1.3.1 Qualitative PCR (qPCR)

qPCR is a valuable molecular tool for community analysis of dominant bacteria and fungi groups found in soil (Fierer et al., 2005, Chemidlin et al., 2011). Despite being a powerful culture independent tool qPCR have some limitations such as primer specificity and the length of targeted region. Thus, it is important to satisfy its requirements and enhance the efficacy and reproducibility of the technique (Chemidlin et al., 2011). Fierer et al. (2005) had described a qPCR based approach to estimate the relative abundance of major taxonomic groups of bacteria and fungi in soil. The bacterial qPCR assay target 16S rRNA genes using Eub338 and Eub518 while 5.8S and ITS1F target for all fungi (Fierer et al., 2005). Gittel et al.(2005) used nu-SSU-0817-5' and nu-SSU1196-3' primers for fungal

SSU genes. In a study done by Chemidlin et al.(2011) , primers FR1/FF390 was found to be the best one with greater specificity, coverage and amplicon length which facilitated the quantification of the total fungi in soil with SYBEGreen® technology.

1.3.2 Molecular tools for identification of bacteria and fungi

The actual measurement of microbial and genetic diversity, population size, spatial distribution and the fraction of metabolically active members within a complex microbial community have proved to be difficult using traditional enrichment and isolation technique

(Stackebrandt and Goebel, 1994). The Polymerase chain reaction (PCR) technique can be used to amplify DNA sequence of any type. Molecular identification has been introduced into modern mycology and has subsequently increased fungal biodiversity and taxonomy investigations, as it is rapid, highly specific and reliable (Chase and Fay, 2009, Begerow et al., 2010). 6

Introduction

DNA extraction procedure was found to affect the sequencing results. An ISO standard 11063 DNA extraction procedure was used (Plassart et al., 2012) for assessing soil

Microbial abundance. Plassaet et al.(2012)had shown that DNA yield was improved with the GnS-GII and ISOm procedures, and fungal community patterns were found to be strongly dependent on the extraction method too. Similarly a modified bead beating process was implemented (Ferrari et al., 2011).

The molecular method have shown markedly different view of fungal community composition compared to that based on the previously culture dependent method (Evans and Seviour, 2012). Molecular technique 16S rRNA is most commonly used in bacterial and archea, although sequence data for 23S and 5S rRNA genes of many organisms are also available (Singleton, 2002). Several other characteristics such as essential function, ubliquity and evolutionary properties have enhanced its consistence use in microbial ecology (Case et al., 2007).

16S rRNA gene contains nine hypervariable regions from V1-V9 and a single region is not able to differentiate all bacteria (Chakravorty et al., 2007). Some hypervariable region such as V1 and V2 are with less bias, as they provide fairly consistent taxonomic alignment with wide range of genera (Guo et al., 2013). The community fingerprint generated by V1-V3 and V7-V9 primers provide results with greater similarity, thus it is important to use the primer targeting these two region for deep sequencing to obtain higher resolution (Kumar et al., 2011).

Molecular identification was primarily based on the amplification of a fungal target gene or DNA fingerprinting. Currently, accepted molecular targets include the internal transcribed spacer (ITS), ribosomal small subunit (SSU), ribosomal large subunit (LSU), α-

Introduction

and β-tubulin, actin, RNA polymerase II subunit (RPB), elongation factor-1 alpha (EF1α) and mitochondrial cytochrome C oxidase I (COI) genes (Begerow et al., 2010, Chase and

Fay, 2009, Min and Hickey, 2007). Recently, SSU, LSU, ITS, EF1α and RPB genes were used for phylogenetic analysis to reconstruct the early evolution of fungi (James et al.,

2006). Additionally, the latest higher-level phylogenetic re-classification of fungi was also based on these molecular targets (Hibbett et al., 2007).

Figure 1-2: Map of ribosomal RNA genes and corresponding ITS gene regions used for the fungal PCR strategy. Positions of forward (right-pointing arrow) and reverse (left- pointing arrow) primers are shown on the map of ITS regions and the surrounding ribosomal RNA genes. The ranges covered by the respective subset databases (see text) are also indicated (Toju et al., 2012).

Internal transcribed spacer (ITS) of nuclear DNA has been used since 15 years as target for fungal diversity and standard DNA barcoding (Bellemain et al., 2010). A high-

Introduction

coverage fungal ITS primers which facilitate investigation of the remarkable diversity of fungi was developed (Toju et al., 2012). Some of the newly designed primers enable selective amplification of fungal ITS sequences and are useful for investigating fungal diversity and community structures within mycorrhizal associations, leaf, soil, and other environmental samples that potentially contain plant tissues and detritus. There can be little doubt that DNA barcoding will dramatically enhance our knowledge of fungal diversity and communities during the next decade (Toju et al., 2012).

To further fuel use of DNA barcoding in ecological and microbiological studies, the potential bias caused by primers should be quantitatively evaluated based on next- generation sequencing (Tedersoo et al., 2010).

1.3.3 Pyrosequencing: a powerful tool of next generation sequencing(NGS)

The increasing number of large-scale DNA sequencing projects (sequence bacterial and eukaryotic genomes) in recent years has driven a search for alternative methods to reduce time, complexity and cost (Margulies et al., 2005). Pyrosequencing is one of the powerful tool of next generation sequencing, a technology that generates about 400,000 of intermediate lengths DNA reads (80-250 base pairs) through a massively parallel sequencing-by-synthesis approach with the implementation of GS FLX which has 96% accuracy within a single run (Quince et al., 2009, Margulies et al., 2005). Pyrosequencing has an ability to produce long reads up to 400 bp that are believed to allow higher taxonomic resolution (Liu et al., 2013). Bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) was originally described by Dowd et al. (2008) which has been utilized on study of different microbial diversity in different livestock species. The 16S rRNA universal eubacterial primer 28F and 519 were used (Kautz et al., 2013, Fan et al.,

Introduction

2012) to amplify variable V1-V3 region. Different 16S variable region indicate different position of the reference sequences (Figure 1-3). Kim et al.(2011) recommended that the

V1-V3 or the V1-V4 region should be targeted for the accurate phylogenetic estimation for bacterial diversity.

The degree of distribution of fungal ITS1 and ITS2 sequences was assessed, in a dataset of 454 libraries where ITS1 and ITS2 sequences were assigned to 1,660 and 1,393

Operational Taxonomic Units (OTUs; as defined by 97% sequence similarity) (Orgiazzi et al., 2013). Although, the ITS region has been accepted for barcoding marker for fungi, the intra genomic ITS variability has been observed in diverse range of taxa (Lindner et al.,

2013). For the accurate estimation of microbial diversity Margulies et al.(2005) presented an algorithm Pyronoise which is not only restricted to 16S rRNA sequences but also applied to diverse genes (eukaryotic or viral diversities). Although, pyrosequencing is a powerful alternative tool for identification of fungi community, careful selection of pipeline

(UNITE) is required (Tedersoo et al., 2010).

Introduction

Figure 1-3: Overview amplicon and reads generated from sanger sequencing and 454 sequencing for bacteria. The amplicon generated by each primer is indicated in red, orange Position and numbering based on Escherichia coli reference sequence. Green and orange arrow indicates the sequencing direction of Sanger and 454 sequencing respectively. Different variable region is represented by blue colour (Group Jumpstart consortium Microbiom project Data Generation Working group, 2012).

1.3.4 Illumina metabarcoding

Illumina metabarcoding is a method that generates shorter reads but achieves deeper sequences than 454 metabarcoding approach (Schmidt et al., 2013). Illumina has been very rapid and effective method for analysing the biodiversity assessment (Liu et al., 2013,

Schmidt et al., 2013). It is accurate large scale sequencing platform with higher throughput and could potentially reduce the cost and sequence quality as well but the sequence length would be of 150 bp (Liu et al., 2013).

1.4 Available culture-dependent approach

1.4.1 Novel approach to cultivation

One of the oldest unresolved microbiological phenomena is why only a small fraction (less than one percent) of the diverse microbiological population is culturable

(Watve et al., 2000) Since 2000, number of novel approaches of cultivating different species of bacteria and fungi has been introduced. A novel high throughput platform for parallel cultivation and isolation of previously uncultivated microbial species from a variety of environment was developed (Nichols et al., 2010) using isolation chip (ichip) composed of several hundred miniature diffusion chambers, each inoculated with a single environmental cells. The microbial recovery in the ichip exceeded many folds than afforded by standard cultivation, and the grown species are of significant phylogenetic novelty

Introduction

(Nichols et al., 2010). This new method allowed access to a large and diverse array of previously inaccessible microorganisms and was well suited for both fundamental and applied research. The ichip represented a practical device for massively parallel in situ cultivation of environmental microorganisms (Nichols et al., 2010).

Additionally, a novel microcultivation method was described by Ferrari et al.

(2005) for soil bacteria that mimic natural conditions using soil substrate membrane system (SSMS). The SSMS included soil as substrate in combination with a polycarbonate

(PC) membrane as a growth support. The combination of SSMS with fluorescent in situ hybridization, candidate division TM7 (previously uncultured division) was identified

(Ferrari et al., 2005). Also, SSMS allowed microcultivation of fastidious soil bacteria as mixed microbial communities (Ferrari and Gillings, 2009).

Watve et al. (2000) stated that ‘unculturability’ is unlikely to be absolute as microorganisms grow under natural conditions and they should grow under the right mimicked set of laboratory conditions. Thus a technique of growing oligophilic bacteria on dilute media was developed (Watve et al., 2000). The discovery of ravan media and its use in the rich form as well as the diluted (1:100) form initially resulted in the recovery of 90 different bacterial cultures (Watve et al., 2000).

Similarly, the widespread photosynthetic marine bacterial genus Prochlorococcus was being cultured in both natural and synthetic seawater in the hopes that it could be used as a model for marine microbial ecology (Moore et al., 2002). The other isolates were all dependent on heterotrophic bacteria for co-culture, and it was nearly impossible to grow these organisms from a single cell as a colony on a petriplate (Moore et al., 2002). A number of subsequent efforts to culture other bacteria also revealed plentiful examples of

Introduction

co-culture dependent isolates. The increased growth of previously uncultured isolate

Catellibacterium nectariphilum was observed by Tanaka et al. (2004) from a sewage treatment plant in the presence of spent medium from another bacterium. A chamber was developed by Lewis et al. (2010), in which some bacteria were isolated which would not grow in petriplate unless they were grown close to other bacteria from the same environment, demonstrating co-culture dependence for these isolates. The uncultured isolates from marine sediment bioﬁlm grow on a petriplate in the presence of cultured organisms from the same environment (D'Onofrio et al., 2010). The lack of growth in the laboratory for many strains from this habitat stems from an inability to autonomously produced siderophores and resulting chemical dependence on other microorganisms regulated community establishment in the environment (D'Onofrio et al., 2010).

Delavat et al. (2012) designed FD (a culture media for bacterial growth) which mimicked mineral conditions found in Acid Mine Drainage (ADMs) Carnoulès, France (the sample site), with the exception of absence of toxic compounds such as arsenic in order to decrease the selective pressure. These approaches generated 49 isolates representing 19 genera belonging to four different phyla among which three were previously uncultured.

1.4.2 Novel approach to cultivate fungi

Similar reports were limited for describing greater fungal diversity. In the only report published, Collado et al. (2007) developed a high-throughput dilution-to-extinction culturing technique to recover fungi from plant litter. The result showed a significant increase in species richness compared to a traditional nutrient rich culturing method. In this study, total 88 fungal species were recovered from five litter samples, whereas only 32 species were recovered by both methods. A total of 73 species were recovered using

Introduction

dilution-to-extinction approach and 47 species were isolated using traditional isolation approach.

Novel cultivation strategies for bacteria are widespread and well described for recovering greater diversity from the “hitherto” unculturable majority (Ferrari et al., 2011) while similar approaches have not yet been demonstrated for fungi. It was suggested that of the 1.5 million estimated species less than 5% were recovered into pure culture (Ferrari et al., 2011). High concentrations of nutrients selected predominantly different fungal species to that recovered using a low nutrient media (Ferrari et al., 2011). By combining both media approaches to the cultivation of fungi from contaminated and non-contaminated soils, 91 fungal species were recovered, including 63 unidentified species (Ferrari et al.,

2011).

Soil plate method was used by Thorn et al. (1996). A procedure with the combined advantages of soil particle washing, selective inhibitors, and an indicator substrate was developed to isolate saprophytic basidiomycetes from soil (Thorn et al., 1996). Organic particles were washed from soil and plated on a medium containing lignin, guaiacol and benomyl, which reduced mold growth and allowed detection of basidiomycetes producing laccase or peroxidase. The 64 soil samples yielded 67 basidiomycete isolates, representing

51 groups on the basis of morphology and physiology. This method facilitated investigations into the biodiversity of soil basidiomycetes (Thorn et al., 1996).

Arenz & Blanchette (2011) used exotic substrate (sterile wood and cellulose) with and without addition of nutrient buried in soil and left for two or four years for incubation in field. The results indicated that the fungal abundance on soil adhering to substrate found

Introduction

were similar to that found in non- polar soils. The genera found in this study were

Geomyces pannorum, Cadophora, Penicillium, Cladosporiums and Cryptococcus.

Rose bengal-malt extract-agar may give an underestimate of the total numbers of

Actinomycetes in soil; however, when a comparative study of a large number of soils is required, this simple medium may be very convenient if both fungi and Actinomycetes are focus (Ottow and Glathe, 1968). Additionally, Evans & Seviour (2012) used malt extract agar to culture fungal diversity from different activated plants dealing with different waste materials.

1.5 Windmill Islands of Eastern Antarctica

Several parts of ice free areas of Antarctica are undergoing scientific research

(Figure 1-1). Windmill Islands is located in Eastern Antarctica and have been in focus of human activities as well as scientific research over the last 5 decades (Palmer et al., 2010).

One of three permanent bases of Australia, Casey station is located on Bailey Peninsula

(the coastal area of Windmill Islands) (Palmer et al., 2010). Major research sites of

Windmill Islands that have little or no biotic influence are Mitchell Peninsula, Browning

Peninsula, Robinson Ridge, Beall Island, Herring Island and Snyder Rocks and site having strong biotic influence are Shirley Island, Whitney Point and Odbert Island (Azmi and

Seppelt, 1998). Browning Peninsula is one of the dry valleys of the Windmill Islands.

1.6 Browning Peninsula (BP)

The location of Browning Peninsula is 66°28’20’’S; 110°32’59’’E, approximately,

20 km from Casey Station (Chong et al., 2009). It is the ice free barren landscape with low human activity (Stewart et al., 2011). Mean annual air temperature at BP is approximately - 15

Introduction

9.3 °C with the average temperature rising above freezing (+ 0.2°C) only in January (Beyer and Bolter, 2000). Annual precipitation is approximately 176 mm and falls primarily as snow (Beyer and Bolter, 2000). The study site is located in a low valley approximately 100 m wide, which lies between two 30 m ridges (Stewart et al., 2011). Vegetation of continental Antarctica has been classified as Polar Desert Biome; however, there are no surficial bryophytes or other indications of vegetation at the study site (BP) (Stewart et al.,

2011).

Figure 1-4: The Windmill Islands showing Browning Peninsula (yellow) (source: Scientific Committee on Antarctic Research (SCAR)).

Introduction

Figure 1-5: Sampling site Browning Peninsula (source: Australian Antarctic Division)

The valley has a five percent slope with a frost boil field occupying the entire area, which is approximately 100 m by 200 m. Individual boils were approximately 2 m wide with visual sorting on the surface of the circles (Stewart et al., 2011). Maritime Eastern

Antarctica has a continuous distribution of permafrost with an active layer ranging from 60 to 150 cm and 1.5–28% of moisture content (Bockheim and Hall, 2002). But being a site of

Maritime Eastern Antarctica, BP had only 30 cm active layer in these frost boils when sampled in 2005 and soil moisture values at the site ranged from 9 - 15% volumetric water content (VWC) (Stewart et al., 2011).

1.6.1 Frost boils formation

Frost boils are barren non-sorted sparsely vegetated soil circles which are formed as the result of cryoturbation (a complex of seasonally interchanging processes of frost heave and thaw settlement) ranging from 2-3 m in diameter or more (Walker et al., 2004). Several

Introduction

aspects of frost boil formation that includes its bowl shape, the formation of an organic layer at its periphery, the elevated centre and resistance of the soil surface to vegetation colonization has been studied (Shur et al., 2005). The formation of frost boils was correlated with the thawing degree days (TDD) which is the sum of daily mean temperature more than 0 °C (Walker et al., 2004).

Accumulation of organic matter at the lower part of the active layer leads to a decrease in active-layer thickness under the boils, which in turn leads to accumulation of aggradational ice and perennial irreversible frost heave (Shur et al., 2005). This increases the difference in elevation of the surface between frost boils and inter-boil areas (Shur et al., 2005). During freezing, they form owing to differences in the expansion of water

(Walker et al., 2004). Because of the three-dimensional nature of the freezing front, frost heave in inter-boil areas forms the bowl shape (Shur et al., 2005). During sub-zero condition, the differences of temperature between upper and lower layer of ground can cause the migration of soil and water from layers above the permafrost to the surface followed by freezing results in considerable mixing of the soil during the frost-boil formation (Figure 1-6) (Biasi et al., 2005). As the result, carbon and nitrogen are unequally distributes in frost boils and are concentrated in the depressions between the frost-boil formations (Biasi et al., 2005).

The model simulating water redistribution in the active layer is postulated by

Daanen et al. (2007) . The three postulates include the reduction rate of horizontal movement of water in soil decreases with the decrease in freezing temperature, insulation outside the non-sorted circle, increase the water flow towards the centre and the increase in

Introduction

insulation during freezing temperature reduces the flow of water towards the centre

(Daanen et al., 2007).

Figure 1-6: A systematic diagram showing the structure of non-sorted polygons distributed irregularly along a site, with various broken layers of soil horizons highlighted (Ah, Bmy, Cy and Cz). The active layer consists of stones and cracks while the permafrost table consists predominantly of ice lenses (Courtesy of C. Tarnocai).

1.7 Microbiology of Browning Peninsula (BP)

1.7.1 Bacterial community of Browning Peninsula

Previously, a handful of studies have investigated the microbial diversity of

Browning Peninsula using culture independent techniques. The soil samples of BP were considered to have a low pH, low conductivity and limited organic matter along with lowest ATP concentrations and bacterial biomass compared to Bell Island, Shirley Island and Bailey Peninsula (Roser et al., 1993). Most recently Chong et al. (2009) reported

Introduction

Acinetobacter sp (97% identity), Paenibacillus daejeonensis (95% identity), Delftia tsuruhatensis strain A90 (98% identity) along with unclassified Bacteroidetes and uncultured Flavobacteria bacterium clone at BP using DGGE fingerprinting. Additionally van Dorst et al. (2014a) recovered a large number of unique bacterial OTUs using 454 tag pyrosequencing against the two genotypic fingerprinting techniques (T-RFLP and ARISA) which found the diversity of BP to be lower than Mitchell Peninsula, Casey station located in the Windmill Islands of Eastern Antarctica.

1.7.2 Bryophyte and fungal community of Browning Peninsula

In a study done by Melick et al. (1994), the paucity of vegetation frequency on

Browning Peninsula and the southern islands relative to the more northerly peninsulas were marked with only Buellia frigida lichen and to a lesser extent Candelariella hallettensis.

The crustose lichen Buellia frigida in the BP was occasional in fact it was the lichen of largest dominance observed within the survey areas while the bryophytes like

Bryumpseudotri quetrum, Caloplaca citrine, Lecanora expectans, Rhizocarponflavu and

Rhizoplaca melanophthalma were rare.

Fungal isolates such as Chrysosporium sp, Mortierellagamsii, Mycelia sterilia1,

Mycelia sterilia2, Phoma sp and Thelebolus microspores from the soil sample of BP with little or no biotic impact was reported (Azmi and Seppelt, 1998).

The organisms living in Antarctic region (phychrophiles and psychrotolerants) present adaptation in their enzymatic system in their membranes and therefore their genes that represent a great biotechnological potential (Tosi et al., 2010). Frost boil ecosystems are widely distributed in polar region and result in the formation of disturbed and undisturbed patches across landscapes (Walker et al., 2004). Repeated freeze thaw cycle, or

Introduction

cryoturbation suggests they are an ideal ecosystem to be studied in terms of understanding what process drive microbial community assembly. Moreover, very little is known about their functions or which major biogeochemical cycles occur in Antarctic frost boils ecosystems, such as carbon sequestration which is known to be a important in the Arctic. A whole host of ecosystem processes involved in self organization of patterned landscape

(Walker et al., 2004). The lack of higher plants and animals in Browning Peninsula

(Stewart et al., 2011) means that the major effects of cryoturbation during frost boil development would be on the microbial community present. Previously physical, chemical and microbial properties between the middle and edge of frost boils of Browning Peninsula revealed significant differences in the physical and chemical soil properties between frost boils (Stewart et al., 2011). This led us to question what major community and ecosystem processes may be affected in this unique ecosystem.

1.8 Aim

Since very little information is known about the prokaryotes and eukaryotes living in BP of Antarctica, this research aimed to discover the microbial community (bacteria and fungi) present in the soils (active layer of frost boils) of BP. The aim continued with evaluation of bacterial and fungal distribution in different polygons uncovering the relationship of those communities with different environmental variables available in this harshest environment. Additionally, bacterial and fungal diversity recovered from the novel cultivation method (SSMS), artificial media and 454 tag pyrosequencing were compared.

Material and Methods

2 MATERIALS AND METHODS

2.1 Site description

Soil sampling was carried out as part of large Antarctic biodiversity project in 2005.

Soil samples were collected from Browning Peninsula (66°47’21N -11°05’47”5E), Eastern

Antarctica. Browning peninsula is an Antarctic desert consisting of frost boils of 2-10 m in diameter.

Figure 2-1: Browning Peninsula, Eastern Antarctica with frost boils (Photograph source: Australian Antarctic Division (AAD))

2.2 Sampling design and collection

The spatially explicit design consisted of 3 transects (300 m each), 2 m apart from each transects at variable lag distances of 0, 0.1, 0.2, 0.5, 1, 2, 5, 10,20, 50, 100,100.1, 100.2,

100.5, 101, 102, 105, 110, 120, 150, 200, 200.1, 200.2, 200.5, 201, 202, 205, 210, 220 250, 22

Material and Methods

300 m (Banerjee and Siciliano, 2012). From the entire 93 samples (31 from each transect) obtained, we selected a subset from three transects at distances of 0, 2, 100, 102, 200, and

202 (Figure 2-2) representing 0-2 m top, 100-102 m mid and 200-202 m bottom (Table

2-1). These 3 transects were parallel to each other with the distance of 2 m. The soils taken from active layer (top 10 cm) aseptically were sieved and 50 g were transferred in 50 ml sterile tube from a number of frost boils and each of them were given an arbituary number from 1-42. The samples were then stored at -80 °C until required.

Figure 2-2: Spatially explicit sampling design illustrating frost boils across transects: Each coloured circles represent polygons and P1 -P41 denotes unique frost boils number. Three horizontal lines represent transects parallel to each other are presented as 1, 2 and 3 with the distance of 2 m in between. The crossing points of vertical lines indicate the variable lag distance (0, 2, 100, 102, 200 and 202) from where samples were collected.

Material and Methods

Table 2-1: Soil samples, frost boil number and position along transect.

Frost boil Sample Distance Transect Position (Polygon Polygon Position Name m Number along number) transect BP1 P2 0 T2 Top Edge BP2 P17 100 T1 Mid Middle BP3 P4 2 T3 Top S (no sieve) BP4 P18 100 T3 Mid Edge BP5 P25 200 T1 Bottom Edge wet BP6 P28 202 T3 Bottom Edge BP7 P1 0 T1 Top Edge BP8 P1 2 T1 Top Centre BP9 P17 102 T1 Mid Edge BP10 P25 202 T1 Bottom Centre BP11 P2 2 T2 Top M BP12 P17 100 T2 Mid Edge BP13 P17 102 T2 Mid Edge BP14 P26 200 T2 Bottom Edge BP15 P26 202 T2 Bottom Edge BP16 P18 102 T3 Mid Centre BP17 P27 200 T3 Bottom Edge BP18 P41 0 T3 Top Centre

2.3 Physical and chemical analysis of soil samples

A range of physical and chemical properties of all soils were determined by

Australian Antarctic Division (van Dorst et al., 2014a). The site parameters such as location, slope gradient, slope aspect and elevation was obtained via a global positioning system (GPS) using Geographic information system (GIS) and site digital elevation model.

Material and Methods

The chemical properties including pH and conductivity were obtained by 1 in 5 dilution of

− − − −− + soil in distilled water. Similarly, NO2 , NO3 , PO4 , SO4 , and NH4 were obtained by diluting 1 in 5 of soil in distilled water in dry matter basis (mg kg-1). The anions and cations

+ (NH4 ) were analysed separately by ion chromatography and calorimetric indophenol methods respectively. Other elements such as SiO2, TiO2, Al2O3, Fe2, MnO, MgO, CaO,

− Na2O, K2O, P2O5, SO3 and Cl were calculated using X-Ray fluorescence (XRF) analysis.

The other physical characteristics such as grain size was measured by sieving the particles larger than 2 mm (gravel) and then separating the particles based on size into silt (< 63 µm) and sand (63-2000 µm).

2.4 Culture independent techniques

2.4.1 DNA extraction from Soil

Genomic DNA extraction for all 18 soil samples was carried out using the MP

FastDNA™ SPIN Kit for soil (MP Biomedicals, NSW Australia) according to manufacturer’s instructions. For extraction, 0.25-0.3 g of soil was taken in the Lysing

Matrix E tube and 978 μl of Phosphate Buffer and 122 μl MT Buffer was added. Soil samples were then homogenised in the FastPrep® Instrument for 40 seconds at a speed setting of 6.0 and centrifuged at 14,000 x rpm for 10 min to pellet debris. Supernatant was then transferred to a 2 ml micro-centrifuge tube and 250 μl of protein precipitation solution

(PPS) was added and mixed by shaking tube. The mixture was centrifuged at 14,000 rpm for 5 min to precipitate the pellet.

The supernatant was then transferred to a clean tube and the tube was re-suspended using 1 ml of binding matrix (mixed well before use) and inverted by hand for 2 min to

Material and Methods

allow binding of DNA. The tube was allowed to settle in a rack for 3 min and 600 µl of supernatant was discarded. The binding matrix was re-suspended in the remaining amount of supernatant which were then transferred in the SPIN™ Filter and centrifuged at 14,000 rpm for 1 min. To the SPIN™ filter tube SEWS-M was added, gently re-suspended and centrifuged twice. The catch tube was then replaced and spin tube was incubated at room temperature (RT) for 5 min. Then 75 µl of DNase/ Pyrogen-Free water (DES) was used to re-suspend the binding matrix followed by incubation and centrifugation. The catch tube was stored at -20 °C until use.

2.4.2 Quantification of genomic DNA using Picogreen assay

Quanti-iT™ Picogreen ® dsDNA reagent (Invitrogen, Paisley UK) was used for quantification of genomic DNA present in DNA lysates (extracted from various processes) according to manufacturer's manual. Lambda DNA standard (100 µl/ml) was diluted to the final concentration of 1 µl/ml, 100 ng/ml, 10 ng/ml, 1 ng/ml and a blank was prepared using 1X TE with the final volume of 100 µl. Each sample (1 µl) was added to 99 µl of 1X

TE placed in each well. All analysis was done in triplicate. To these wells 100 µl of 1X

Quanti-iT™ Picogreen reagent was added and incubated for 5 min in the dark. The fluorescence was then measure using fmax fluorescence Microplate Reader (Molecular

Devices Corporations, Sunnyvale USA). Final DNA concentrations were calculated using

Microsoft office excel 2010.

2.4.3 Multiplexed pyrosequencing targeting bacterial and fungal genes

The DNA lysates were concentrated to (0.21-0.37 ng/µl) before being sent to an external research laboratory facility (MR DNA, Molecular Research Laboratory, TX USA) for barcode tag pyrosequencing utilising Roche 454 FLX titanium instruments. The 26

Material and Methods

research laboratory followed single step 30 cycles PCR using HotStartTaq Plus Master Mix

Kit (Qiagen, Valencia, CA) under the following conditions: 94 °C for 3 min, followed by

28 cycles of 94 °C for 30 sec; 53 °C for 40 sec and 72 °C for 1 min after the final elongation step at 72 °C for 5 min was performed (Dowd et al., 2008). Primers used for bacteria were 28F and 519R (Dowd et al., 2008) and for fungi were ITS1 and ITS4 (Gardes and Burns, 1993). Following PCR, all amplicon products from different samples were mixed in equal concentration and purified using Agencourt Ampure beads (Agencourt

Bioscience Corporation, MA, USA).

2.4.4 Raw data processing pipeline for pyrosequencing data

Raw 454 pyrosequenced data were received in the form of standard flowgram format (sff) file. The sff file consisted of sequence information and flowgrams which was processed according to Schloss et al. (2011) using Mothur software Version 1.32.1.

2.4.4.1 Processing amplicon pyrosequencing data (bacteria)

Initially, bacterial 16S reads were denoised by implementation of PyroNoise component ensuring all reads were 200 bp long. This step included removal of the barcode, primer sequences, sequences with homo polymers longer that 8 bp and getting back the reverse complement for each sequence (Quince et al., 2011). Then an alignment of sequences was generated by aligning the data to SILVA-compatible alignment database as reference alignment (Pruesse et al., 2007) following the removal of chimeras and other contaminants sequences (Schloss et al., 2009). The sequences were then pre-clustered at

1% to account for 454’s titanium instrument error (Huse et al., 2010). Aligned sequences were then clustered into OTUs at 4% divergence (96% similarity) for species level OTUs

Material and Methods

(Lane, 1991) for the best definition of species level OTU. The sample by OTU abundance matrix along with various diversity indices was then produced by Mothur software pipeline

(Schloss et al., 2009) and taxonomic assignment was determined against the Greengenes database provides within Mothur package (Kim et al., 2011). All representative sequences were classified using the Wang method (version May 13) (Schloss, 2009, Kim et al.,

2011). The bacterial sequences with a cut off value less than 50% were classified as unclassified sequences. These data were then further used for other multivariate analysis.

2.4.4.2 Processing amplicon pyrosequencing data (fungi)

The chimeras were removed using uchime in Mothur pipeline (Edgar et al., 2011).

The group name was then added to the sequence name using the SEED platform (Overbeek et al., 2014) and sequences of tags and barcode were removed. Then by using ITSx (a Perl based software tool), ITS1 region was extracted as ITSx tool has the capacity of extracting higher proportion of true positive and removing of non-ITS sequences from data sets

(Bengtsson-Palme et al., 2013). Thus obtained reads were then clustered together at 97% similarity to closely define species using USEARCH (a unique sequence analysis tool) and representative sequences were identified using mafft (Caporaso et al., 2010). These sequences were then compared against the UNITE fungal ITS database (Koljalg et al.,

2005). OTU abundance table was prepared using SEED package and the representative sequences were classified using BLASTn program in NCBI database (Overbeek et al.,

2014).

The OTU abundance matrix and shared taxonomic file were then imported separately and aggregated separately into PRIMER 6 version 6.1.13 and PERMANOVA

Material and Methods

version 1.0.3. The abundance of bacteria and fungi in various soil samples from phylum level to genus level was aggregated and rarefaction curve was created.

2.4.4.3 Multivariate data analysis

In addition to these, 16S rDNA and ITS genes sequence singletons were removed from the both (bacterial and fungal) data set. The relationship between bacterial communities and different polygons or distance along transects was observed using non metric multidimentional scaling (nMDS) plots. The OTU data were entered in Primer 6 version 6.1.13 and PERMANOVA version 1.0.3, standardised and (dis) similarity matrix was generated using Bray-Curtis coefficient (Clarke and Warwick 2001; Anderson et al.,

2008). nMDS maps were then plotted showing the relationship between communities in different polygons or distances. Similarly nMDS maps were analysed using different environmental data against different polygons and distances. Besides group clustering of

OTU abundances was performed showing 20, 40 and 50% community similarity in all soils. Same resemblance matrix was used for analysis of similarities (ANOSIM) too.

2.4.4.4 Principal coordinates analysis (PCO)

Principal Coordinates analysis was performed using PRIMER 6 version 6.1.13 and

PERMANOVA version 1.0.3 (Clarke and Gorley, 2006). The OTU abundance matrix and environmental data were used to do this analysis. Initially, the abundance table was transformed, transformed data were standardised and a resemblance matrix was made by implementing Bray-Curtis similarity coefficient. PCO plot was then created via

PERMANOVA + option. The created PCO plot was configured with normalized environmental data using (0.6) Pearson correlation.

Material and Methods

2.4.4.5 Distance based linear models (DISTLM)

DISTLM was used to analyse models that contain categorical and continuous predictor variables (Anderson et al., 2008) as described by resemblance matrix. The approach used by DISTLM is called a distance-based redundancy analysis (dbRDA)

(Legendre & Anderson 1999) which was utilized to determine the predicator capacity of each environmental variables analysed alone (marginal tests) ignoring all other variables and in combination with other environmental variables (sequential test). In sequential test all variables were fitted as covariates and each test were examined to check the significance of particular variable to contribute variation. Besides the step wise condition was selected which begins with the null model adding a variable that would improve the selection procedure. Adjusted R2 was used as selection criterion.

2.4.5 Quantitative PCR (qPCR)

qPCR assay was conducted in polypropylene 96 well plates (USA Scientific Inc,

Ocala USA) using Quantifast SYBR Green PCR kit (Qiagen, Hilden Germany) in 20 µl of volumes on Bio-rad CFX96 Real-Time System (supplier) combined with C1000 thermal cycler. A typical two step real-time PCR protocol was followed as described in iQ™SYBR® Green Supermix manual from Bio-rad. Each 20 µl reaction volume contained

10 µl of master mix, 1 µl of each primers at 10 µM; 16S (Eub338, Eub518) (Fierer et al.,

2005) and 18S (FR1, FF390) (Chemidlin et al., 2011), 1.25 µl of DNA template and 6.75 µl of nuclease free water. PCR conditions included 95 °C for 5 min followed by 40 cycles of

94 °C for 20 sec with the annealing and extension temperature of 60 °C for 50 sec. Final step was added for the melt curves analysis 50-95°C with increments of 0.5 °C. Each of these reactions was performed in triplicates per sample. The results of qPCR were then 30

Material and Methods

analysed using Bio-Rad CFX Manager 2.1 software after detecting no PCR inhibition in

DNA samples (Ma et al., 2008) and no amplification in the no template control (van Dorst et al., 2014b). Consistent single peaked melting point, slope (~ -3.4) and reaction efficacy

(90-110%) and R2 value 0.987 - 0.997 were checked for each samples along with the standards before performing the calculation.

2.4.5.1 Making of standards used for qPCR (bacteria and fungi)

Environmental DNA extracted from soil kit as described in 2.4.1 was taken as DNA samples to prepare 16S DNA standard for bacteria. One of the pure fungal strain's (isolated from BP soil in artificial media) DNA sample was used for 18S DNA standard. To amplify the genomic DNA of bacteria and fungi PCR protocol as explained in 2.8 were used. The primer used for bacteria was Eub 338 and Eub 518 and fungi was FR1 and FF390. The

PCR product was then purified following 2.11 and concentrated DNA was quantified using

Picogreen facility (2.4.2). Thus quantified purified DNA was then diluted to make the concentration of 108-101. These diluted DNA lysates were then used as standards for bacteria and fungi.

2.5 Culture dependent techniques

2.5.1 Soil substrate membrane system (SSMS) cultivation

SSMS setup was done according to the protocol described by Ferrari et al. (2008) with some modification.

Material and Methods

1.1.1.1 Soil substrate membrane system setup

Depending on the soil, 3-5 g was placed into a sterile Tissue Culture insert (TCI)

(Merck Millipore, MA, USA). Two to 3 drops of sterile water was pipetted onto the soil within the TCI. Using the bench top vortex mixer, a soil slurry was prepared (Figure 2-3a).

Ultrapure Milli-Q (500 µl) water was then pipetted into the middle of a well within a sterile

6-well plate to ensure the soil did not dry during the incubation period. The TCI was invert into the 6-well plate so the membrane was facing upward (Figure 2-3b).

The same soil (100 X dilution) was then used to prepare as the inoculum for the

TCIs. Each 0.1 g soil was placed into a sterile 1.5 ml tube with 1 ml of ultrapure milliQ water and the tube vigorously mixed in vortex. The large particulates were allowed to sediment by letting the sample stand for 5 min. The resulting supernatant (100 µl) was used as the inoculum as it contained the majority of microbial biomass. The inoculum was then filtered onto a polycarbonate (PC) membrane (0.02 µm pore size) (Merck Millipore, MA,

USA)

Material and Methods

Figure 2-3: Soil substrate membrane system (SSMS) set up. a) the soil substrate is prepared within the tissue culture insert b) An inoculated PC membrane is placed on top of the inverted TCI, which contain the soil slurry c) Four soil substrate membrane system replicates were placed into 6-well plate for incubation. Picture taken from Ferrari et al. (2008).

For every SSMS to be set up, a sterile 0.02 µm 25 mm PC membrane was placed on top of the pre-wet glass fibre filter in the filtration manifold (Figure 2-4). Then, a sterile stainless steel cylinder was placed on top of each of the PC membrane on the manifold. A portion (10 ml) of milliQ water was poured into each cylinder followed by 100 µl of the 1 in 10 fold inoculum. Gentle pipetting was done to mix and ensure an even distribution of cells on the PC membrane during filtration. A vacuum pump was turned on and valve opened to draw the diluted inoculum through the PC membranes (Figure 2-4). The valves were closed as soon as the diluted inoculum had passed through the PC membrane. The PC membrane was then placed on top of prepared TCI (Figure 2-3c).

Material and Methods

Figure 2-4: Filtration manifold SSMS

The PC membrane along with TCI was incubated for 21 days at 8 °C under aerobic and anaerobic conditions. For anaerobic condition the six well plate was incubated in anaerobic chamber (BD Diagnostics, Sparks Glencoe MD 21152, USA) using three BD

GasPak EZ Gas Generating Sachet with Indicator (BD Diagnostics) per chamber (Figure 2-

5). Care was taken not to let the soil dry and thus membranes as well as soils were examined in every three days and water was added in the soil slurry in every 4-5 days in soil.

Material and Methods

Figure 2-5 Anaerobic chamber containing six well plate with SSMS and gas generator sachet with the indicator present

2.5.1.1 Sectioning of PC membrane after incubation

After incubation of the SSMS for 21 days, the PC membrane was removed from the

TCI using sterile tweezers and placed on top of a drop of milliQ water in a sterile petridish.

Each PC membrane was cut into four equal halves using a sterile surgical blade (Figure

2-6). One fourth of the PC membrane was used for the microscopic confirmation under epi- fluorescence microscopy (EFM) to confirm adequate growth via presence of micro colonies

(2.5.1.3).

Material and Methods

Figure 2-6: Sectioning of PC membrane: 1) used for microscopic observation under fluorescence microscope. 2, 3, and 4) used for the DNA extraction.

2.5.1.2 Epi - fluorescence microscopy (EFM) for confirmation of microbial growth

First the PC membrane was embedded in agarose. To do this 0.1% of agarose was heated in the microwave until it began to boil. The thin layer of agarose was put in a sterile petridish and then allowed to cool to 30-40 °C. One quarter of the PC membrane was then placed on the agarose and the plate was then incubated at 30 °C until the PC membrane dried out. To remove the PC membrane from the plate 96% ethanol was pipetted onto the side of PC membrane and it was removed. For EFM, 4 µl of vectashield mounting media

(Vector laboratories, Burlingame, USA) was placed on glass slide and PC membrane was placed on top. Then with 1 µl of 100 X SYBR Green II RNA (excitation at ~ 488 nm with maximum emission at 521 nm) gel strain for bacteria and 10 µl of 2 X Calcofluor

(excitation ~ 355 nm emission at 433 nm) for fungi was added and cover slip was placed onto the PC membrane. Growth was then observed using Olympus BX61 Microscope and a

Nikon DXM 1200F digital camera (Olympus, North Ryde, Australia) with the appropriate filter set for SYBR Green II (WIGA) and Calcofluor (WIBA). 36

Material and Methods

2.5.1.3 DNA extraction using prepGEM method

The prepGEM DNA extraction protocol described by Ferrari et al., 2008, with some modification was followed. Firstly 1/4 of the PC membrane was placed in a PCR tube with

99 μl of 1X Buffer (diluted from 10X) and 1 µl of prepGEM enzyme. Samples were mixed by vortexing. The tube was then placed in a thermocycler (Bioteke corporation), following the program of 37°C for 15 min, 75°C for 15 min and 95°C for 15 min. A portion (100 µl) of DNA extract was transferred to a sterile 1.5 ml microfuge tube. The microfuge tube was then centrifuged at 14000 rpm for 3 min. PC membrane was removed with the help of sterile tweezers and 80 µl of DNA lysate was transferred into new PCR tube and store at -

20°C. The DNA concentration was then measured using Picogreen assay as described in

2.4.2. Since the concentration of DNA was too low to send for 454 sequencing, the DNA samples were then concentrated using QIA Quick Purification kit (Qiagen GmbH, Hilden

Germany).

2.5.1.4 Concentrating DNA for 454 pyrosequencing

The DNA extracted using prepGEM was further concentrated using the QIA Quick

Purification Kit (Qiagen GmbH, Hilden Germany) according to the manufacturer’s manual.

DNA lysates from the three of PC membranes were mixed and 140 µl were then transferred to new 1.5 ml microfuge tube and 700 µl of buffer PB was added. The mixture was then placed in a QIAquick spin column in a provided 2 ml collection tube. The lysate was centrifuged and the flow-through was discarded. Buffer PE (750 µl) was added to the

QIAquick column and centrifuged two times. QIAquick was placed in a clean 1.5 ml microcentrifuge tube and to elute DNA 20 µl of elution buffer was added to the column

Material and Methods

which was then incubated at RT for 5 min and centrifuged again. The column was removed and 0.2 µl of 0.5 M EDTA was added to the DNA lysate which was stored at -20 °C until sent for 454 sequencing.

Concentrated genomic DNA was quantified and 10 µl of the concentrated DNA was sent for 454 pyrosequencing. The data obtained was processed as described in 2.4.4

2.5.2 Cultivation of bacteria and fungi on artificial media

2.5.2.1 Sample preparation for culturing

Two soils were collected for cultivation based on nMDS plots. One gram of soil was mixed with 10 ml of ultrapure milliQ water and serial dilutions between 10-2-10-4 prepared. From each 10 ml suspension, 100 µl of each was transferred onto various media as listed in table 2-2, in triplicates.

To prepare BG11 initially 1% agar with 980 ml water was autoclaved and then 20 ml of 50 X BG11 was added and mixed well. A selection of media such as BG11, SCA

(Starch Casein Agar), RAVAN (Watve et al., 2000), CRBA (Crook Rose Bengal Agar) and

MEA (Malt Extract Agar) were used at 1X and 0.1 X concentrations. Inoculated plates were incubated at 8 °C or RT (20-22) °C for 1-2 months or 7- 15 days respectively.

Material and Methods

Table 2-2 Media used for bacteria and fungi cultivation

Media Media Composition 1X

Luria- Bertani (LB) 10 g Tryptone, 5 g Yeast extract, 10 g Nacl, 15 g agar and 1 l water Nutrient Agar (NA) 13 g of Nutrient Broth, 15 g agar BG11(Sigma Aldrich, 20 ml Cyanobacteria BG11 Freshwater solution and Castle Hill, Australia) 980 ml of 1% agar sterilized solution) Bacteria Starch casein Agar (US 63 g in 1 l of water Biological, Swampscott USA) RAVAN 5 g glucose, 5 g peptone, 5 g yeast extract, 5 g sodium acetate, 5 g sodium citrate 0.6335 ml Pyruvic acid (98%), 15 g agar and 1 l water R2A (Oxoid Ltd, 18.1 g of R2A in 1 l water Hampshire England ) Potato Dextrose Agar 39 g per 1 l of water (PDA) (Sigma Aldrich, Castle Hill Australia)

Crook Rose Bengal Agar glucose 100 g, Soytone 50 g, KH2P04 10 g,

Fungi (CRBA MgS04.7H20 5 g, Rose Bengal 350 mg Agar 20 g, 1 l Water Malt Extract Agar 50 g in 1 l of water (MEA) (Sigma Aldrich, Castle Hill, Australia),

Material and Methods

2.5.2.2 Obtaining pure cultures

All the bacterial isolates were sub-cultured in Nutrient Agar plates and incubated further 1-2 months (for the isolates isolated from the 8 °C) and 7 days for RT isolates. Pure colonies from the sub-cultured plates were then used for the molecular identification.

For fungi, sub cultivation was done by point inoculation (pick colony and gentle stab at centre of the culture plate) in PDA plates and incubated at RT for seven days to one month depending on the growth of fungi in the plate. Each colony was subcultured three times in order to obtain pure cultures.

2.6 DNA extraction from pure cultures

Various approaches were applied in order to extract DNA from pure cultures. Two different method of DNA extraction were performed for bacterial isolates and one applied for fungi.

2.6.1 DNA extraction of pure cultures by boiling

A loop of colony was added to 1 ml milliQ water and vortexed well. The mixture was then boiled at 99 °C for 5 min and centrifuged at 14,000 rpm for 3 min to pellet debris, which could interfere with the PCR. An aliquot (800 µl) of suspension was then transferred in a new sterile 1.5 ml microfuge tube and stored at -20 °C. This extraction procedure did not work well for majority of bacterial and fungal isolates so a bead beating process was followed.

2.6.2 DNA extraction of pure cultures using bead beating

Based on the principle of FastPrepR instrument from MP Biomedicals, a simplified

53 Fastprep bead beating method developed by Ferrari et al. (2011) was applied here. A

Material and Methods

loop full of bacterial colony or a 5 mm3 agar plug containing a fungal colony were placed into a screw cap 2 ml microfuge tube (Sarstedt AG & Co, Nuembrecht, Germany) containing 0.5 g of 0.1 mm, 0.5 mm glass beads (Mo Bio, Carlsbad, USA) and 1 ml ultrapure milliQ water. Tubes were homogenised using the FastPrepR 120 instrument (MP

Biomedicals) for 40 sec set at 6.0. The samples were incubated at 95 °C for 5 min before centrifugation at 14,000 rpm for 1 min. The DNA lysates were transferred into 500 µl tube and stored at -20 °C until required.

2.6.3 DNA extraction of fungal cultures

DNA extraction of all fungi isolates were carried out using the Fast DNA® SPIN

Kit for soil as described in 2.4.1. In filamentous, 5 mm3 agar plug containing a fungal colony was cut from agar plate using sterile surgical blade but in case of yeast a loop full of colony was taken.

2.7 Bacterial 16S rDNA and fungal internal transcribed spacer (ITS) gene PCR

amplification

All of these primers required for bacterial and fungal PCR amplification were obtained from Integrated DNA Technology (IDT) MCLeans Rigdes, Australia (Table 2-3).

The reaction components used for bacterial 16S rDNA gene PCR were 10 µl of 5X Go Taq buffer (Promega, Madison, USA), 4 µl MgCl2 (25 mM), 1 µl dNTP (10 mM), 1 µl 356F

(Forward Primer (40 mM)), 1 µl 1064R (Reverse Primer (40 mM)), 0.25ul (5 u/µl) Taq polymerase, 2 µl DNA, 5 ml BSA (0.1 µg/µl), 24.75 µl water (Nuclease free water). PCR program consisted of an initial denaturation of 95 °C for 3 min, 35 cycles of 95 °C for 35

Material and Methods

sec, annealing temperature of 60 °C for 30 sec and an extension step at 72 °C for 5 min.

Other details of primers are listed in Table 2-3.

Fungal internal transcribed spacer (ITS) region genes were amplified using ITS1 forward primer and ITS4 reverse primer (White et al., 1990) as described by Ferrari et al.,

2011 with slight modification. The PCR reaction mixture consisted of 10 µl of 5X Go Taq buffer, 4 µl Mgcl2 (25 mM) , 1 µl dNTP (10 mM), 1 µl ITS1 (Forward Primer (40 mM)), 1

µl ITS4 (Reverse Primer (40 mM)), 0.25 µl (5 u/µl) Taq polymerase, 2 µl DNA, 5 ml BSA

(0.1 µg/µl ), 24.75 µl water (Nuclease free water). PCR program used consisted of 94 °C initial denaturation step for 2 min, 30 cycles of 94 °C for 45 sec, annealing temperature of

55 °C for 30 sec and a final extension step at 72 °C for 5 min. All PCR amplicons were then visualized on 2% agarose gel.

Table 2-3: Domain specific primers for all molecular analysis

Molecular Primer Sequences Reference Approaches Name PCR 356F 5’ ACWCCTACGGGWGGCWGC 3' Winsley et Bacteria 1064R 5' AYCTCACGRCACGAGCTGAC 3’ al. (2012) PCR Fungi ITS1 5’ TCCGTAGGTGAACCTGCGG 3’ Ferrari et al. ITS4 (2011) 5’ TCCTCCGCTTATTGATATGC 3’ 454 28F 5' GAGTTTGATCNTGGCTCAG 3' Dowd et al. pyrosequencing 519R 5' GTNTTACNGCGGCKGCTG 3' (2008) Bacteria 454 ITS1 5’ CTTGGTCATTTAGAGGAAGTAA 3’ Gardes and pyrosequencing ITS4 5' TCCTCCGCTTATTGATATGC-3 Bums Fungi (1993) qPCR Bacteria Eub 5'- ACTCCT ACGGGAGGCAGAG -3 Fierer et al.

Material and Methods

338 (2005) Eub 5'ATTACCGCG GCTGCTGG 3' 518 qPCR Fungi FR1 5’ AICCATTCAATCGGTAIT 3’ Chemidlin FF390 5’ CGATAACGAACGAGACTT 3’ et al. (2011) Cloning Fungi M13F 5´d(CGCCAGGGTTTTCCCAGTCACGA Hindley & C)-3´ Phear 5´- (1981) M13R d(TCACACAGGAAACAGCTATGAC)-3´

2.8 Gel electrophoresis

To prepare the 2% agarose gel, 1 g of agarose was mixed in 50 ml of 1X Tris- acetate ethylene diamine tetraacetic acid (TAE) buffer which was dissolved by heating the mixture for 2 min in microwave oven. A portion (5 µl) of SYBR safe (Invitogen Ltd,

Paisley, UK) was added in the gel before being poured. Then 5 µl of each PCR product was loaded and gels were run at 220V for 13 min. DNA Ladder 100 (Promega, Madison, USA) was used to compare bp size of each PCR product. The gel was then visualised in

UltraPower® Visible light Transilluminator (Bioteke Corporation, Beijing, China).

2.9 Restriction fragment length polymorphism (RFLP)

Two restriction enzymes Hinf1 (Promega, Madison, USA) with cleavage site 5΄-

G↓ANT C-3΄, 3΄-C TNA↑G-5΄ and Rsal (Promega, Madison, USA) with cleavage site

5’↓GTAC-3’-3’ CATG↑ 5’ were used. Protocol and reaction components were used according to the manufacturer’s instruction. The restriction enzyme mix included 0.17 µl

Hinf1, 2 µl 10 X buffer B, 0.2 µl 100 X bovine serum albumin (BSA) and 2.63 µl of water.

Material and Methods

A portion (5 µl) of restriction enzyme mix was used in each 15 µl of PCR reaction mix and incubated O/N (overnight) at 37 °C. The digest was then loaded onto 2% agarose gel and analysed by electrophoresis. Similar method was used for Rsal but the buffer used was

Buffer C. Depending on the fragment number and size, different isolates were selected for identification.

2.10 PCR product purification

All PCR products (bacteria and fungi) were purified by using the QIAquick PCR purification kit from Qiagen following the manufacturer's instruction. For 45 µl of PCR reaction five times (225 µl) of PB was added. The mixture was then transferred in the

QIAquick spin column and allowed to bind followed by centrifuging the column at 13,000 rpm for 1 min. The flow- through was discarded and 750 µl of PE buffer was added and spin column was re-centrifuged after discarding the flow through. Then the column was placed in a clean 1.5 ml microcentrifuge tube and 25 µl of Elution Buffer (EB) was added into it which was allowed to stand for 2 min and then centrifuged again collecting the pure

DNA in the new tube. Thus obtained purified DNA was analysed on 2% agarose gel by adding 1 µl of loading dye to 5 µl of purified DNA and mixing well before placing in gel.

Subsequently, purified bacterial DNA was then quantified using NanoDrop 1000

Spectrophotometer (NanoDrop Technologies, Wilmington, USA) while fungal DNA was quantified using Picogreen (as precision of DNA concentration was required for cloning) and then stored at -20 °C until used.

Material and Methods

2.11 Cloning ITS region of fungi into T vector

2.11.1 Ligation of DNA

The purified PCR product was used as DNA for cloning. Firstly the pGEM®-T vector and control Insert DNA were briefly centrifuged to collect the contents at the bottom of the tubes and ligation reaction were setup as per the protocol provided by Promega.

The ligation component included 2X Rapid Ligation Buffer (T4 DNA Ligase) 5 µl,

PGEM®-T (50ng) 1 µl, PCR Product 0.5-2 µl depending on the concentration of DNA, T4

DNA Ligase (3 Weiss u/µl) 1 µl, and Nuclease free water to volume of 10 µl. The reaction mixture was mixed well with the help of pipettes and incubated O/N at 4 °C.

2.11.2 Optimisation of insert : vector molar ratios

The insert: vector ratio was optimized to 3:1 using the following equation

ng of vector x kb of size of insert x insert: vector molar ratio = ng of inert

kb size of vector

2.11.3 Transformation using the pGEM®-T ligation reaction

The tubes containing ligation reaction was centrifuged briefly. LB media plates with ampicillin/IPTG/X-Gal at final concentration of 100 µg/ml /0.1 mM / 2 µl/ml were prepared.

Highly efficient competent cells XL10 Gold® from Promega (≥1 x 108 cfu/µg

DNA) were used for transformations (stored in -80 °C) and were taken out in the ice bath until just thawed (for 5 min). The competent cells were then transferred into a 2 ml microfuge tube and 10 µl of ligation reaction was added and mixed well by pipetting in and

Material and Methods

out. The mixture was then incubated at ice bath for 20 min and placed at 42 °C for 50 sec.

Then 900 µl of SOC media was added in the same tube and incubated at 37 °C for 1.5 hrs at

150 rpm. 100 µl of each transformation culture was diluted 10 times and 100 µl of the each diluted ones were plated in LB/Ampicillin/IPTG/X-Gal plates which were then incubated at

37 °C for 24-48 hrs.

Cloning PCR was performed using the picked white colony. The 50 µl PCR reaction mixture included 10 µl of 5X Go taq Buffer, 6 µl of 25 mM of MgCl2, 1 µl of 10

µm dNTP, 5 µl of 1 mg/ml BSA, 1 µl of 20 µM M13 forward primer and 1 µl of 20 µM

M13 reverse primer, 0.25 µl of 5 U/µl of Taq and volume was made up to 50 µl by adding nuclease free water. Each colony (very small amount) was placed in the PCR reaction mixture. The PCR cycle consisted of an initial denaturation step of 95 °C for 2 min followed by denaturation at 95 °C for 30 sec, annealing temperature of 55 °C for 1 min and extension temperature of 72 °C for 1 min repeating the cycle for 30 times. An aliquot of 5

µl of the PCR product was then checked in 2% agarose gel and each positive PCR product was purified using the Qiagen PCR purification kit following the manufacturer’s instruction. Thus purified PCR product was used for sequencing following the sequencing protocol 2.12.

2.12 Sanger sequencing of PCR products

Sequencing of individual bacterial and fungal PCR products were performed in the

Ramaciotti Centre (University of New South Wales) for Gene Function Analysis, using the sequencing protocol for ABI 3070 Capillary sequencer. According to the protocol (The

Ramaciotti Centre for Gene Function Analysis (UNSW)) the sequencing reaction mixture

Material and Methods

included 1 µl of Big Dye terminator V3.1, 100-500 ng plasmid or 20-50 ng PCR product

(depending on the PCR product size), 0.5 µl primer (Forward primer 356F (bacteria), M13F

(Fungi)), 3.5 µl 5X buffer and nuclease free water up to 20 µl and 25 cycles of PCR amplification consisted of 96 °C for 10 Sec, 50 °C for 5 sec 60 °C for 4 min. Final holding temperature was 4 °C. PCR products (20 µl) were added to 5 µl of 125 mM

Ethylenediaminetetraacetic acid (EDTA) and 60 µl of 100% ETOH. The tube was left at

RT for 15 min to precipitate followed by spinning at 14,000 rpm for 20 min. The supernatant was aspirated and discarded and 160 µl of freshly prepared 70% ethanol was added to the tube and vortexed briefly and then centrifuged at 14,000 for 10 min and supernatant was aspirated and discarded. This step was repeated with 80 µl 70% ethanol samples were then dried (Eppendorf Thermomixture Compact) at 90 °C for 1 min and were submitted for the analysis.

2.13 Identification of 16S and ITS sequences

DNA sequences were checked for the callable peaks and trimmed when required using the finch TV version 1.4.0 (Geospiza) and analysed against the Gene Bank database within National Centre for Biotechnology Information (NCBI) using blastn for both bacteria and fungi. The morphological information of isolates was determined in combination with the 16S and ITS sequences identification from GeneBank.

Results

3 MICROBIAL COMMUNITY DYNAMYCS

3.1 Bacterial and fungal community diversity

For the bacterial tag pyrosequencing dataset, 57839 quality checked bacterial reads were obtained covering 2063 unique OTUs, with an average of 3249 (± 1343) per sample.

For the fungal dataset, 62547 quality checked reads were obtained varying between 1092 to

18637 per sample. A total of 279 unique OTUs of fungi were recovered with an average of

27 OTUs per soil sample. Two samples (BP7 and BP17) contained no reads, thus were excluded from further study of fungi. Rarefaction curves showed that the bacterial diversity recovered did not reach asymptote indicating limitations of the current sequencing effort

(Figure 3.1). In comparison, fungal diversity appeared to have reached an asymptote at approximately (279) OTUs.

A huge difference in diversity indices, within total bacterial and fungal community diversity in the active layer of Browning Peninsula soils was observed (Table 3-1). The bacterial diversity was high, with an overall Pielou’s evenness and Shannon diversity index

(H’) of 0.721 and 5.502 while fungal was just 0.526 and 3.517.

Table 3-1: Calculated diversity estimates for bacteria and fungi

Shannon Margalef's Pielou's Unique Total Diversity 1- Pyrosequenced Diversity Evenness OTUs reads index H' Simpson total soil index (M) index (J') (loge) Bacteria 2063 57839 188.04 0.72103 5.5028 0.98421 Fungi 279 62547 34.68 0.591 3.5170 0.8944

Results

2000 1800 Fungi ITS1 1600 Bacteria 16S 1400 1200 1000 800 No of OTUs 600 400 200 0 0 1400 2800 4200 5600 7000 8400 9800 11200 12600 14000 15400 16800 18200 19600 21000 22400 23800 25200 26600 28000 29400

No of sequences

Figure 3-1: Rarefaction curve indicating bacterial and fungal OUT coverage across 18 soils pyrosequenced using the 'universal' bacterial 16S primer set (28F/519R) and the universal ITS region fungal primer set (ITS1/ITS4).

3.2 Phylogenetic distribution of bacteria in BP

Thirty six different phyla were present in Browning Peninsula (Figure 3-2). The most dominant was Actinobacteria (more than 35% of total relative abundance) followed by Chloroflexi (over 19%), Acidobacteria (10%), Cyanobacteria (9%) and Proteobacteria

(9%).Others represent the sum of all the phyla present at less than one percent (Figure 3-2).

A total of 408 genera were retrieved from BP soils. Over 18% of relative abundance at genera level was attributed to unc_Gaiellaceae (Actinobacteria) followed by over 6% unc_Ellin6529 (Chloroflexi), 4% unc_RB41 (Acidobacteria), 3% unc_Solirubrobacterales

(Actinobacteria), 3% unc_Pseudanabaenaceae (Cyanobacteria), 3% unc_Gitt-GS-136

Results

(Chloroflexi), 3% unc_Nocardioidaceae (Actinobacteria). The remainder of genera made up 57% and consisted of bacterial genera present in relative abundance less than 2%.

Figure 3-2: Phylogenetic distribution of bacteria across BP soils. Others indicate sum of

more than 1% phyla or genera present in all soils. Actinobacteria and Chloroflexi are

most dominating bacteria and unc_Gaiellaceae and unc_Ellin6529 are most dominant

bacterial genera. Unc represents unclassified.

3.2.1 Distribution of bacterial diversity across individual polygons

Approximately 41 individual polygons were distributed throughout BP. At phyla level investigation of bacterial distribution within polygons revealed to vary in the proportion of the most abundant phyla present (Figure 3-3). A total of 36 bacterial phyla were retrieved from 454 tag pyrosequencing data. Each polygon exhibited a minimum of 16 and a maximum of 30 bacterial phyla. Polygons P27 and P28 exhibited 30 and 16 phyla 50

Results

while P1 and P26 contained 25 each. The relative abundance of the major phyla present in these polygons were varying. For example, P18 contained the highest relative abundance of

Actinobacteria (more than 50%) while Polygon P4 and P28 contained the lowest relative abundance (10-20%) of Actinobacteria combined with a higher relative abundance of

Cyanobacteria (more than 20%). Polygon P27 exhibited the highest relative abundance of

Chloroflexi and a low abundance of Cyanobacteria. The relative abundance of

Proteobacteria was more than 5-10% within polygons. Firmicutes was present in only

0.138%. Twenty six minor phyla including WS2, Nitrospirae, FBP, Chlorobi, OP9, TM7 and Thermi were present in less than 2.2% relative abundance. These minor phyla distribution ranged from 7-19 phyla per polygon.

Results

100%

90% Others

80% Verrucomicrobia

Armatimonadetes 70% Bacteroidetes 60% Planctomycetes

50% Gemmatimonadetes

Proteobacteria 40% Cyanobacteria 30% Acidobacteria

20% Chloroflexi

Actinobacteria 10%

0% P2 P4 P1 P17 P18 P25 P28 P26 P27 P41

Figure 3-3: Phylum level bacterial diversity across polygons of BP. The abundance of Actinobacteria, Acidobacteria and Cyanobacteria are varying across polygons. Others represent a total sum of minor phyla like TM7, Thermi, OP9, TM6 and WPS-2 in less than 1% relative abundance.

1.1.2 Distribution of genus level diversity across polygons

Large difference in genera composition within polygons was observed. An average of 208 genera ranging from 137-293 genera were distributed across each polygon. As in phyla polygon P17 exhibited largest (293) genera composition and P28 contained the least

(137). Polygons P25, P1 and P27 exhibited 227, 223 and 226 bacterial genera respectively. 52

Results

The distribution of genus diversity across polygons revealed large number of unclassified genera. Actinobacteria included unc_Gaiellaceae, unc_Solirubrobacterales, unc_Nocardioidaceae and unc_0319-7L14 as uncultured and unclassified genera while cultured were Sporichthya, Rubrobacter and Solirubrobacter (Figure 3-4A). Unclassified

Gaiellaceae covered more than 40-60% relative abundance of Actinobacteria in these polygons while unc_Solirubrobacterales covered more than 5-20%.

Unc_Ellin6529, unc_Gitt-GS-136, unc_C0119, unc_WCHBI-50, unc_SHD-14, unc_JG30-KF-CM45 were other major Chloroflexi present in these polygons (Figure

3-4B). Unc_Ellin6529 was most abundant Chloroflexi present. P4 contained lowest abundance of unc_Ellin6529 and P27 contained the highest. Similarly, P18 had highest amount of unc_Gitt-GS-136. The sum of relative abundance of minor Chloroflexi genera present in these polygons were varying in proportion ranging from 30-60%.

Unclassified RB41, unc_Ellin6075, unc_32-30, unc_mb2424, unc_iii1-15 were uncultured Acidobacteria available in all frost boils but in different proportion (Figure

3-4C). Unc_RB41 dominated the Acidobacteria phyla which ranged from 20-70% of total

Acidobacteria present.Unc_Ellin6075 was highest in P4 and P1with respect to other polygons.

Similarly different genus of Cyanobacteria in varying number was observed in frost boils. Unclassified Pseudanabaenaceae (family Cyanobacteria) dominated the majority of frost boils except P28, P26 and P27 which were dominated by Leptolyngba (P26 and P28) and others genera representing less than 1% abundance(P27) (Figure 3-4D).

Results

Figure 3-4: Bacterial diversity at the genus level across polygons.The relative abundance of genera within the 4 major phyla present in BP is presented A) Actinobacteria B) Chloroflexi C) Acidobacteria D) Cyanobacteria. Others represent a total sum of genera present at less than 1% relative abundance.

Results

3.3 Phylogenetic distribution of fungi in Browning Peninsula

Four different fungal phyla were detected in these soils. Ascomycota was the most dominating phyla exhibiting an average relative abundance of more than 96% across the site (Figure 3-5). Basidiomycota was present in very low relative abundance (more than

1.5%) and was present in only seven soils.

Overall, 105 genera representing from within the four phyla along with unclassified fungi present at Browning Peninsula were retrieved from these soils. The most dominant fungi genera was Devriesia (Asocmycota) with more than 38% relative abundance followed by more than 8%Capronia (Ascomycota), more than 4% Aureobasidium

(Ascomycota), more than 4% Buellia (Ascomycota), more than3.5%Rhinocladiella

(Ascomycota). Three Basidiomycota genera, Malassezia, Tomentella and Inocybe together accounted for 1% of total relative abundance.

Figure 3-5: Phylum and genus level distribution of fungi across Browning Peninsula. Ascomycota is dominating fungi phylum and Devriesia, followed by Capronia were 55

Results

dominating genera. Others indicate those phyla present at less than 1% relative abundance across all soils.

3.3.1 Distribution of fungi phyla level fungal diversity across individual polygons

The distribution of fungi phyla were soil sample specific. Very few samples contained all four phyla. No trend of fungal phyla distribution was observed within polygons. The limited available phyla were randomly distributed. Two samples from polygon P2 contained different composition of phyla, one sample (BP1) consisted of

Ascomycota only while BP11 consisted of Ascomycota and Fungi incertae sedis (Figure

3-6). Similar trend of difference in phyla composition within samples of same polygon were observed in P17, P18, P25 and P26. Few samples from polygon P17, P25 and P26 consisted of Basidiomycota in greater abundance. Similarly, in sample BP3 and BP15 unclassified fungi were observed. Other few samples (BP11, BP9 and BP5) consisted of

Fungi incertae sedis. Out of 16 samples tested seven samples were full of Ascomycota phylum while rest of all consisted of 2-4 phyla with varying abundance.

Results

100% Entomophthoromycota 95% Unc_Fungi 90% Basidiomycota 85% Fungi incertae sedis 80% Ascomycota 75% BP1_P2 BP3_P4 BP8_P1 BP11_P2 BP2_P17 BP9_P17 BP4_P18 BP5_P25 BP6_P28 BP12_P17 BP13_P17 BP16_P18 BP10_P25 BP14_P26 BP15_P26 BP18_P41

Figure 3-6: Phylum level distribution of fungi across polygons. Ascomycota dominated all soil except BP10 of polygon P25. Sample BP5 consisted of phyla Entomophthoromycota.

3.3.2 Distribution of fungi genus level diversity across individual polygons

The genus level distribution in Browning Peninsula soils were sample specific meaning a high level of variability was found within soils from the same polygon (Figure

3-7). Devriesia was the most dominating fungal genera. Additionally, Capronia, Buellia and Rhinocladiella were distributed in higher abundance across more than six samples.

Sample BP11 consisted of three different genera (Crocicreas, Cadopora and Mortierella) that were predominantly found at less than 1% relative abundance. Additionally, BP8 of polygon P1 was restricted with Aureobasidium and Capronia while sample BP16 from polygon P18 was restricted with Devriesia and Malassezia. Other samples from P17, P4,

P25, P26 and P41 were rich in fungal genus diversity distribution (Figure 3-7).

Results

100% Others 90% Lecidella

80% Candelaria Sthughesia 70% Atla 60% Rinodina 50% Polyblastia

40% Tetracladium Amandinea 30% Lecanora 20% Rhinocladiella 10% Buellia

0% Aureobasidium Capronia

BP1_P2 BP3_P4 BP8_P1 Devriesia BP11_P2 BP2_P17 BP9_P17 BP4_P18 BP5_P25 BP6_P28 BP12_P17 BP13_P17 BP16_P18 BP10_P25 BP14_P26 BP15_P26 BP18_P41

Figure 3-7: Genus level distribution of fungi within individual polygons. Others represent a total sum of less than 1% genera present in individual samples. Others in sample BP11 included only three genera while sample BP14 included 48 different genera but were found in relatively low abundance.

3.4 Bacterial community dissimilarity across Browning Peninsula

Six of the ten polygons studied had more than one samples taken for analysis (Table

2-1). According to nMDS plot generated using Bray Curtis dissimalarity matrix bacterial community structure within each polygon were more similar to each other than to different polygons (Figure 3-8A). Soil samples within each polygon were more similar to each other than within different polygons. Soil samples from within frost boils P2, P17, P1, P18 and

P25 were in closer proximity to each other. While,similar trend of bacterial distribution

Results

with closer proximity was observed within the samples from distance 100-102 m but other samples from 0-2 m and 200-202 m were distributed all across nMDS plot (Figure 3-8B).

Within Browning Peninsula soils, microbial community exhibited 20% similarity.

Individual polygons P1, P2, P17, P18 exhibited more than 50% community similarity while

P25 and P26 had more than 40% (Figure 3-8A).

Similar results were obtained from the environmental data. Each samples from single polygon exhibited similar environmental properties (Figure 3-9A). Individual samples from P1, P26, P17 and P18 were in closer proximity in nMDS plot (Figure 3-9A).

Additionally, there were two exceptions in environmental data in sample BP3 and BP5.

Sample BP3 was not sieved and BP5 was a wet soil (Table 2-1). These two physical factors may have been reflected in position of these two samples in nMDS plots (Figure 3-9). The environmental resemblance matrix within different positions (0-2 m, 100-200 m and 200-

202 m) across the transect were observed (Figure 3-9B) in closer proximity than the bacterial community rexemblance martixacross positions (Figure 3-8B).

Results

Figure 3-8: Nonmetric multi - dimensional scaling (nMDS) plots of bacterial community showing relationships among soil samples to factors A) Polygons B) Position along the transects. 1, 2 and 3 represent transect one, two and three respectively. Clustering was performed on the basis of 20, 40 and 50% community similarity.

Results

Figure 3-9: nMDS plot of the environmental parameter resemblance matrix within BP soils indicating A) Polygon B) Position along the transects. 1, 2 and 3 represent transect one, transect two and transect three respectively.

3.5 Fungal community dissimilarity across Browning Peninsula

According to nMDS plot generated using Bray Curtis dissimalarity matrix, fungal community structure within samples of a polygon were varying (Figure 3-10A).

Similarly,no trend of fungal diversity distribution within distance of 0-2 m, 100-102 m and 61

Results

200-202 m was observed (Figure 3-10B). This may be due to large difference in OTU composition of fungi retrieved within soils of Browning.Very few samples (BP2, BP10 and

BP16) were clustered together exhibiting 40% similarity and these samples were not related with specific polygon or distance.

Figure 3-10: nMDS plots of the fungal community dissimilarity showing the relationship among samples to factor A) Polygons B) Position along the transects. 1, 2 and 3 represent transect one, transect two and transect three respectively. On clustering few samples from different polygons and position exhibited 20% and 40% similarity.

Results

The nMDS plot obtained against bacterial distribution, environmental parameters and fungal community distribution for their significance was supported by one way

ANOSIM (Table 3-2). The higher the global R value (towards one) and lesser the P value

(less than 0.001), the greater the significance of the result obtained. In this study, polygons exhibited a global R value of 0.922 and 0.726 with P value less than 0.001 showing a significant effect on both the bacterial community and environmental parameters (Figure

3-8, Figure 3-9). Also, the bacterial community and different environmental constrains within distance of two meter were also significant but were comparatively less significant than polygon as global R values of polygon were greater than position (Table 3-2). In contrast, the hypotheis for fungi was neither observed in nMDS (Figure 3-10) nor supported by ANOSIM (P value less than one). Fungal community had no particular trend of distribution within these frost boils and distance.

Table 3-2: ANOSIM between different resemblance matrices and factors (polygons and positions).

Matrix One way test results Global R P value Environmental Polygon Number 0.726 less than 0.001 Position (0-2 m, 100-102 m, 200-202 m) 0.576 less than 0.001 Bacterial Ploygon Number 0.922 less than 0.001 community Position (0-2 m, 100-102 m, 200-202 m) 0.672 less than 0.001 Fungal Polygon Number -0.215 more than1 community Position (0-2 m, 100-102 m, 200-202 m) 0.004 more than 1 * Pless than 0.05 was considered as significant;

* Global R value positive value near to one was significant.

Results

3.6 Relationships between environmental variables and bacterial community

structure

Distance based linear models (DistLM) and Principal coordinate analysis (PCO) was performed to determine the correlation between measured environmental variables and microbial communities in Browning Peninsula soils. PCO analysis of bacteria and fungi were based on the percentage of total variation inherent in the resemblance matrix that was explained by first two axis of respective PCO plot that was 34.3% (bacteria) (Figure 3-11) and 32.4% (fungi) (Figure 3-12).

Conductivity, total nitrogen (TN), Slope, NO3, total phosphorus (TP), PO4 and total carbon (TC) were significant predictor variables with Pseudo-F less than 2.49 and P less than 0.05 when analysed with marginal test (Table 3-3). Conductivity was the most significant predicator variable with Pseudo F= 4.73, P = 0.1% in both marginal and sequential tests. This predicator variable also correlated to bacterial communities in PCO plots (Figure 3-11). Other variables such as sand, Cl−, SO4−− and elevation were not significant in sequential test (Table 3-3) but they exhibited either a positive correlation with

PCO2 and PCO1 or a negative correlation (Figure 3-11).

Results

Table 3-3: DistLM results indicating the correlation between environmental parameters as a predictor of the microbial community distribution.

Environmental Marginal tests Sequential tests Variables Pseudo- P Prop Pseudo- P Propo §Cumul F F Conductivity 4.73 < 0.228 4.731 < 0.001 0.228 0.22 uS/cm 0.001 TKN mg/kg 3.77 <0.001 0.19 4.7531 < 0.001 0.18 0.41 Slope (deg) 4.049 <0.001 0.20 3.21 < 0.008 0.10 0.52 TKP mg/kg 3.031 <0.004 o.15 3.19 < 0.003 0.093 0.61

NO3 mg/kg 1.82 <0.065 0.10 5.65 < 0.005 0.089 0.70

PO4 mg/kg 2.097 <0.044 0.11 4.22 < 0.001 0.081 0.78 Gravel % (>2 2.54 <0.011 0.13 2.49 < 0.013 0.042 0.83 mm) TC% w/w 2.79 <0.006 0.14 2.56 <0.028 0.037 0.86 Aspect (deg) 2.44 <0.023 0.132 1.70 <0.136 0.023 0.89 PH 2.06 <0.04 0.11 1.65 <0.147 0.020 0.91 Cl mg/kg 4.62 <0.001 0.22 1.2 <0.38 0.013 0.97 Sand% 63- 3.16 <0.003 0.16 1.7 <0.137 0.019 0.93 2000 µm

SO4 mg/kg 3.16 <0.001 0.22 0.734 <0.572 0.0087 0.96 * P value less than 0.001 was considered as significant. Pseudo-F, a multivariate version of fisher's F statistic, §cumul, represented the cumulative proportion of variance and deg represented degree. − Similarly, TKN (F=4.75, P less than 0.001) and PO4 (F=4.22, P less than 0.001) contributed to 18% and eight percent of total biological variation in sequential tests (Table

3-3). Also, a positive correlation for PCO1 (0.434) and PCO2 (0.423) in the plots were observed (Figure 3-11). These constrains were found to have strong positive correlations with polygons P2 and P27.

Results

Figure 3-11: Correlation of PCO plot generated with the bacterial resemblance matrix against environmental variables. Additional factor chosen was polygon number. Con* indicate conductivity, Ele* (Elevation) and S* (Sand).

Polygons P17 and P18 exhibited positive correlations with PCO2, but were negatively correlated with PCO1. Elevation had a strong positive correlation within PCO1

(0.455) but was in negative correlation with PCO2 (-0.419). While polygons P4, P41 and

P26 clustered towards elevation having strong correlation in PCO2.

A similar correlation generating a PCO plot with the fungal resemblance matrix against environmental variables resemblance matrix was carried out (Figure 3-12).

However some of environmental variables were observed to have positive correlation with fungal communities at this site.TC, TN, SiO2 and mud were environmental predictors affecting the fungal community diversity of Browning Peninsula soils. TC was negatively 66

Results

correlated with PCO1 (-0.561) and positively correlated with PCO2 (0.116). Similar correlation was obtained for TN (PCO1 (-0.463) and PCO2 (0.221)) and SiO2 (PCO1 (-

0.444) and PCO2 (0.270)). A negative correlation was observed for mud with PCO1 (-

0.283) and PCO2 (-0.459) (Appendix 3). Few samples near to SiO2, TN and TC from P41 and P18 were observed in PCO plot. Similarly, samples from P2, P28, P17 and P4 were observed nearer to mud.

Figure 3-12: Correlation of PCO plot generated with the fungal resemblance matrix against environmental variables. Additional factor chosen was polygon number. TN indicates total nitrogen and TC indicates total carbon.

3.7 Genes distribution across polygons of Browning Peninsula

Qualitative PCR was carried out to evaluate the total bacterial and fungal biomass load between soils of different polygons. The SSU rRNA gene copy numbers for bacteria and fungi ranged between8.43 X 107 to9.48 X 108 and 3.45 X 105 to 2.42 X 106 copy

Results

numbers/g soil respectively (Figure 3-13). The average 16S (bacterial) and 18S (fungal) rRNA gene copy numbers from Browning soils was 4.21 X 108 and 1.58 X 106 respectively. The 16Sand 18S gene copy numbers were varying from polygon to polygon.

Soil samples from P2 exhibited the highest 16S rRNA gene copy numbers whilepolygonP28 exhibited the least. Polygons P41and P25 exhibited highest and lowest

18S rRNA gene copy numbers (Figure 3-13A).

F/B ratios varied between polygons. Due to large difference (more than 100X) in

16S and 18S rRNA gene copy numbers the FB ratio obtained were very low ranging from

0.0011-0.014. The highest F/B ratios were obtained from two frost boils P4 and P28

(Figure 3-13B) that contained lowest bacterial 16S genes copy numbers and highest 18S genes copy numbers with respect to other samples (Figure 3-13A). In contrast, frost boils

(P2 and P27) exhibited lowest F/B ratios with higher 16S rRNA gene copy numbers and less 18S rRNA genes numbers present compared with other polygons.

Results

3. 7. 1

Figure 3-13: qPCR analysis A) SSU gene copy numbers across polygons. 16S samples denoted by green bars on the right scale and 18S gene copy numbers are denoted by blue bars on the left scale. B) Average of fungal /bacterial (F/B) ratios within different polygons.

Microbial community diversity (Figure 3-8, Figure 3-10) and abundance (Figure

3-3, Figure 3-4, Figure 3-6, Figure 3-7) was used to select six soils (BP1-BP6) from six frost boils for further analysis in culture dependent approaches (SSMS) (Table 2-1).

Result

4 CULTURE BASED CHARACTERISATION OF MICROBIAL

COMMUNITY DYNAMICS

4.1 Soil substrate membrane system cultivation diversity

Successful growth of microcolonies was present in PC membrane after 21 days incubation, both aerobically and anaerobically for all six samples studied. Observed microcolonies varied in size between 10 - >100 µm (Figure 4-1) and consisted of only 8 -15 cells in a cluster or in chain (Figure 4-1B). A few colonies were single cocci or rod shaped.

Barcode tag pyrosequencing of communities enriched on the PC membranes recovered a total of 401 unique bacterial OTUs and 86 fungal OTU along with 31797 and

110851 respective sequences. A wide range of fungal (9-34) and bacterial (58-200) OTUs were present representing approximately 20% of the total OTU recovered in soil (Table

3-1). The Margalef's diversity index (M) of bacteria and fungi in SSMS were 38.487 and

7.317 respectively. The Shannon diversity index (H') of bacteria was comparatively lower

(3.155) in SSMS than original soil (5.502). The Pielou’s evenness of bacteria (0.526) was lower than fungi (0.579) in SSMS.

Result

Figure 4-1: Bacterial microcolonies present on the SSMS following incubation at 8 °C for 21 days stained with SYBR green II A) Microcolonies in long chain form from BP1. B) Small microcolonies in separate clusters in BP2.

4.1.1 Bacterial phylum level diversity following SSMS enrichment

The SSMS covered 20 different phyla ranging from 10-14 different phyla per SSMS soil. The most dominating phylum present in the encircled PC membranes was

Proteobacteria (more than 58%), followed by Actinobacteria (more than 35%), Firmicutes

(more than 1%), Acidobacteria (more than one percent), Bacteroidetes (less than 1%) and

15 more phyla (more than 2%) (Figure 4-2). Proteobacteria was initially more than nine percent in all soil, SSMS enriched to more than 58%. Cyanobacteria one of the dominating phylum initially in all soils was not supported in SSMS. Sample BP1 and BP4 consisted of

14 different phyla while BP2 and BP5 exhibited 13 phyla. Other two samples (BP3 and

BP6) exhibited 10 phyla (Figure 4-2).

Result

100% Others 90% TM7 80% Verrucomicrobia 70% Chloroflexi 60% Planctomycetes 50% 40% Gemmatimonadetes 30% Bacteroidetes 20% Acidobacteria

10% Firmicutes 0% Actinobacteria

Proteobacteria BP1_P2 BP3_P4 BP2_P17 BP4_P18 BP5_P25 BP6_P28

Figure 4-2: Bacterial phylum distribution among SSMS growth membrane and thus 6 different polygons labelled as P2, P17, P18, P4, P25 and P28. Proteobacteria dominated all SSMS membranes followed by Actinobacteria. Others indicate a total sum less than 2% genera present in individual soils.

1.1.3 Distribution of bacterial genus level diversity on SSMS enrichment

Altogether 234, bacterial genera were recovered from the SSMS (Figure 4-3).

Sample BP1 and BP2 recovered the highest number of genera, 121 and 99 respectively.

Less than 70 genera were recovered from other 4 soils with only 41 genera present in sample BP6. Greater than 27% of total relative abundance were Microbacterium

(Actinobacteria) which was followed by more than 16% Spingomonas (Proteobacteria),

12% Pseudomonas (Proteobacteria), 6% Phaeospirillum (Proteobacteria), 5%

Mesorhizobium (Proteobacteria), 3% Blastococcus (Actinobacteria), 1.5% Arthrobacter

Result

(Actinobacteria) and more than 27% other genera. In contrast, the most dominant genera in soil were unclassified Gaiellaceae (Actinobacteria), unc_Ellin6529 (Chloroflexi) and 32-20

(Acidobacteia) but their relative abundance was less than 0.3% in the SSMS.

Figure 4-3: Genus level distribution of bacteria following SSMS cultivation. A) Proteobacteria B) Actinobacteria. Others represent sum of less than 1% genera present under same the phylum. Microbacterium (Actinobacteria), Spingomonas and Pseudomonas (Proteobacteria) were dominant genera across all SSMS.

Result

As observed in bacterial genera composition (Figure 4-3) the genus level composition of phylum Proteobacteria and Actinobacteria in three soil samples BP4, BP5 and BP6 were similar. The only difference was the abundance of particular genera. Sample

BP1 and BP2 consisted of higher relative abundance of less than one percent Proteobacteria and Actinobacteria genera (Figure 4-3). The total relative abundance of Microbacterium was ranging from 50-90% among all Actinobacteria genera present (Figure 4-3B) while

Spingomonas and Pseudomonas were the highest ranging from more than 35-60% of total relative abundance of Proteobacteria present (Figure 4-3A). In sample BP2 Blastococcus

(Actinobacteria) was in larger abundance (Figure 4-3B).

4.1.2 Distribution of fungi phylum and genus level diversity on SSMS enrichments

Overall, only two fungal phyla (Ascomycota and Basidiomycota) were enriched by the SSMS while 30 genera were present across both aerobic and anaerobic conditions

(Figure 4-4). The most dominant fungal genera were Aureobacidium (31%), 14%

Cladosporium, and 9% Malassezia in both aerobic and anaerobic condition. More than 9%

Exophiala and 1.5% Toxicocladosporium were only present in anaerobic condition while most of other genera were present either in aerobic or anaerobic conditions. Among the genera enriched Malassezia, Rhodotorula, Penipora and Heterocaete belonged to

Basidiomycota while remaining all were from Ascomycota. As in soil, fungal genera were randomly present in SSMS. As indicated in community dissimilarity plot fungi (Figure

4-2B), sample BP2 and BP6 had similar fungal genus composition in both aerobic and anaerobic condition (Figure 4-4).

Result

Figure 4-4: Fungal diversity enriched on SSMS after A) Aerobic B) Anaerobic incubation at 8 °C. UNC represent the unclassified fungi and others indicate a total sum of less than 1% genera present. Others in sample BP1 included 14 different genera composition but were present in relatively low abundance.

4.1.3 Bacterial and fungal community dissimilarity on SSMS enrichments

Since six samples for SSMS enrichments were selected from different polygons, relationships between enriched microbial (bacterial and fungal) was obtained in nMDS plot

(Figure 4-5). No stress was observed in these nMDS plots indicating the highest dimensional relationship between these samples (Figure 4-5). Sample BP4, BP5 and BP6 75

Result

clustered together (Figure 4-5A) had similar bacterial genera composition (Figure 4-3).

These samples overlapped with each other in nMDS plot (Figure 4-5A). Sample BP1, BP2 and BP3 consisted of higher community dissimilarity and were distant from each other.

But, in case of fungi community sample BP2 and BP6 were overlapping with each other

(Figure 4-5B) and remaining four samples were distributed through-out the plot(Figure

4-5B).

Clustering was performed which showed 20% of bacterial diversity were common in all SSMS, with 40% of community similarity observed in all samples except sample

BP1, with 60% community similarity observed in three samples (BP4, BP5 and BP6). In contrast, only 20% of fungal community was common in four SSMS samples (BP1, BP2,

BP4 and BP6) while other two were composed of higher dissimilar fungal communities.

Two samples BP1 and BP2 represented the best diversity at phyla (Figure 4-2) and genera level (Figure 4-3, Figure 4-4) composition of bacteria and fungi compared to other four samples (BP3, BP4, BP5 and BP6). Thus, these two soils samples were further selected for culturing using different artificial media and conditions.

Result

Figure 4-5: nMDS plots indicating community similarity in SSMS enrichments across 6 soil samples A) Bacterial B) Fungal. On clustering bacteria, five samples exhibited 40% similarity and three samples exhibited 60% similarity. ON clustering fungi, only two samples exhibited 40% and four samples exhibited 20% similarity.

Result

4.2 Bacterial and fungal diversity recovered from artificial media

4.2.1 Sub-culturing

In total, 380 bacterial isolates were sub-cultured to purity in NA and 207 were then successfully extracted and amplified for identification. In total 34 different strains were identified from a range of media and temperature (Table 4-1). Among 34 bacterial strains,

20 were cultivated at 8 °C while 14 were recovered from RT. BG11 recovered six genera including Kribbella, Mycobacterium, Paenisporosarcina, Sphingomonas, Strepromyces and

Arthrobacter. Strepromyces and Arthrobacter were distributed in all media types. In contrast R2A, LB and Ravan recovered four genera each (Table 4-1). Actinobacteria and

Proteobacteria were dominant across all media whereas Firmicutes and Bacteroidetes were selectively cultivated on NA, R2A and BG11 (Table 4-1). All the bacteria cultivated had different colony colour and morphology. For example bacterial strain SP.B20

(Hymenobacter aerophilus) consisted of red pigmentation(Figure 4-6A) while strain

SP.B14 (Streptomyces indigoferus) isolated were filamentous in nature (Figure 4-6B).

1.1.4 Molecular identification of bacterial isolates

A total of 19 Actinobacteria (99-100% identity), eight Proteobacteria (96-100%), 4

Firmicutes (96-100%) and three Bacteroidetes (99%)strains were identified (Table 4-1).

The majority, 14% of pure cultures were members of Arthrobacter (Actinobacteria) while

28% were Streptomyces. Bacterial strains SP.B22 having 99% identity with Pedobacter oryzae (Bacteriodetes), SP.B26 (Paenisporosarcina quisquiliarum: 96% identity) and

SP.B33 (Sphingopyxis baekryungensi: 96% identity) were also isolated.Proteobacteria,

Sphingomonas dokdonensis, Dyella japonica and Caulobacter segnis were also recovered from the soils. 78

Result

Table 4-1: Bacteria isolated from different culture media from two different soils

Strain Media Used Temp* Best match Identity% Accession Phylum Name SP.B26 0.1X BG11 8 °C Paenisporosarcina quisquiliarum strain SK 55 96 JF309238.1 Firmicutes SP.B19 0.1X BG11 8 °C Streptomyces fildesensis strain TX1G2 99 KF620268.1 Actinobacteria SP.B16 0.1X BG11 8 °C Streptomyces indigoferus strain 30-4 99 EU054371.1 Actinobacteria SP.B4 1X BG11 RT Arthrobacter scleromae strain PAMC 25156 99 KF528714.1 Actinobacteria SP.B6 1X BG11 RT Kribbella ginsengisoli strain TX1J4 100 KF620306.1 Actinobacteria SP.B32 1X BG11 RT Sphingomonas dokdonensis strain St15 99 HN700077.1 Proteobacteria SP.B11 1X BG11 RT Streptomyces fildesensis strain TX1G2 100 JX122145.1 Actinobacteria SP.B7 1XBG11 RT Mycobacterium fluoranthenivorans strain S32432 99 AB648999.1 Actinobacteria SP.B18 0.1X CRBA 8 °C Streptomyces fildesensis strain TX1G2 100 KF620268.1 Actinobacteria SP.B15 0.1X CRBA 8 °C Streptomyces indigoferus strain 30-4 99 EU054371.1 Actinobacteria SP.B27 LB RT Aminobacter aminovorans strain lous 2-3 99 KC767647.1 Proteobacteria SP.B31 LB RT Rhizobium qenosp. TUXTLAS-27 strain 100 JN703472.1 Proteobacteria SP.B10 LB 8 °C Rhodococcus yunnanensis strain Tibetlhz-22 100 JX827199.1 Actinobacteria SP.B25 LB 8 °C Sporosarcina aquimarina strain A1-37c-1 99 JX517273.1 Firmicutes SP.B3 1X NA 8 °C Arthrobacter psychrochitiniphilus strain IARI-R-98 99 JX429031.1 Actinobacteria SP.B5 NA 8 °C Arthrobacter scleromae strain PAMC 25156 100 KF528714.1 Actinobacteria SP.B29 NA RT Dyella japonica strain CRRI-58 99 JN592475.1 Proteobacteria SP.B20 NA 8 °C Hymenobacter aerophilus strain DSM 13606 99 EU155008.1 Bacteroidetes

Result

SP.B8 NA 8 °C Mycobacterium frederiksbergense strain MQ-200s 99 KF580851.1 Actinobacteria SP.B22 NA 8 °C Pedobacter oryzae strain NW13 99 NR_116174.1 Bacteroidetes SP.B30 NA RT Rhizobium phaseoli strain GYS7 100 JQ342895.1 Proteobacteria SP.B34 NA 8 °C Spingomonas faeni strain TP-Snow-C72 100 KC987002.1 Proteobacteria SP.B13 NA RT Streptomyces fildesensis strain TX1G2 100 KF620268.1 Actinobacteria SP.B21 1X R2A 8 °C Hymenobacter swuensis DY53 99 CP007145.1 Bacteroidetes SP.B23 1X R2A 8 °C Paenisporosarcina macmurdoenisis strain WX82 99 KC921194.1 Firmicutes SP.B24 1X R2A 8 °C Paenisporosarcina macmurdoenisis strain WX82 99 KC921194.1 Firmicutes SP.B14 1X R2A 8 °C Streptomyces indigoferus strain 30-4 99 EU054371.1 Actinobacteria SP.B2 0.1X Ravan 8 °C Arthrobacter psychrochitiniphilus strain IARI-R-98 99 JX429031.1 Actinobacteria SP.B9 0.1X Ravan 8 °C Rhodococcus yunnanensis strain tibetlhz 100 JX827199.1 Actinobacteria SP.B1 1X Ravan RT Arthrobacter phenanthrenivorans strain CIM A82 99 KF203127.1 Actinobacteria SP.B33 1X Ravan 8 °C Sphingopyxis baekryungensis 96 HF913439.1 Proteobacteria SP.B28 1X SCA 8 °C Caulobacter segnis ATCC 21756 strain ATCC 21756 99 NR074208.1 Proteobacteria SP.B12 1X SCA 8 °C Streptomyces fildesensis strain TX1G2 100 KF620268.1 Actinobacteria SP.B17 1X SCA 8 °C Streptomyces indigoferus strain 30-4 99 EU054371.1 Actinobacteria Temp* indicate incubation temperature

Result

Figure 4-6: Pure bacterial isolates visualised on plates and by EFM. A) Bacterial strain SP.B20 (Hymenobacter aerophilus). B) Bacterial strain SP.B14 (Streptomyces indigoferus). C, D) microscopic observation of bacterial strain SP.20 (Hymenobacter aerophilus) and SP.B14 (Streptomyces indigoferus) respectively stained with SYBR II.

4.2.2 Sub-culturing of fungi

In total, 80 fungal strains were obtained from different media which were sub- cultured successfully in PDA. DNA extraction was performed for all 80 isolates. After

RFLP, only 17 strains were selected for cloning and sequencing. Out of 17 strains identified

11 grew at 8 °C and 6 at RT. A total of 10 genera were identified. The majority of recovered genera grew on MEA and CRBA media (Table 4-3). MEA recovered six fungal isolates at 8 °C while five strains were successfully cultivated on CRBA.

Result

Morphology and microscopy of some of those strains are shown (Figure 4-7 and

Figure 4-8). The fungi strains ranged were from single celled to multi cellular, non- filamentous to branched-filamentous and non-spore former to spore former (Table 4-2). Out of 17 isolates, 13 strains (SP.F1-SP.F13 and SP.F15) were filamentous while three (SP.F14,

SP.F16 and SP.F17) were non filamentous. Strain SP.F14 (Cryptococcus victoriae) was pink in colour (Table 4-2). No visible spores were observed on SP.F6 (Phoma herbarum),

SP.F9 (Engyodontium album) and SP.F13 (Thelobolus globosus). Additionally, a wide range of colony colour was observed in strains SP.F9 (bright white), SP.F13 (light orange),

SP.F10 (dark green) and SP.F16 (creamy white) (Figure 4-7). Calcofluor was used to stain all fungal isolates to view them under microscope (Figure 4-8). Each strain was morphologically unique following microscopic observation. Strain SP.F14 (Cryptococcus victoriae) was unicellular while other strains SP.F3 (Thelobolus microsporous), SP.F10

(Cladosporium cladosporides) and SP.F6 (Phoma herbarum) were multi-cellular with branched hyphae under microscopy (Figure 4-8).

Result

Table 4-2: Colony morphology of fungal strains isolated in this study.

Strain Colony colour Colony Nature of Name Closest Match texture spores Structure Geomyces Light Brown at the SP.F1 pannorum isolate centre cottony white at Wet Powdery Filamentous E10 the edge Filamentous Thelobolus Accumulated SP.F2 Light orange Wet attached with microsporous at centre media Thelobolus Filamentous Accumulated SP.F3 microsporous Light orange Wet attached with at centre isolate10 BI media Light green at centre Pseudeurotium No spores, SP.F4 (reddish brown centre Wet Filamentous bakeri strain 842 spiny hyphae when matured) Cladosporium Short SP.F5 grevilleae strain Dark green Dry Powdery Filamentous CBS 114271 Phoma herbarum Light green centre with Long SP.F6 Wet No spores CBS 615.75 light brownish edge filament Thelobolous Filamentous Accumulated SP.F7 globosus isolate Light orange Wet attached with at centre ANT03-221 media Engyodontium No spores SP.F9 album strain Bright cottony white Wet Filamentous observed LVPEI.H1584 Cladosporium Short SP.F10 cladosporides Dark green Dry Powdery filaments strain DUCC5020 Geomyces Greenish centre with Short SP.F11 pannorum isolate Dry Powdery white peripheri filaments E10 SP.F12 Cladsporium Dark Green Dry Powdery Short 83

Result

oxysporum strain Filamentous CASVK1 Swarming Thelobolus Light orange with filamentous SP.F13 globosus strain Wet No spores brown small pigments across the UFMCB 6095 media Cryptococcus Non spore Non SP.F14 victoriae strain Light pink Wet former filamentous P41A001 Non spore Long SP.F15 Peniophora lycii White filamentous Wet former filamentous Holtermanniella Non spore Non SP.F16 watticus isolate Creamy white Wet former filamentous T2Hw Holtermanniella Non spore Non SP.F17 Creamy white Wet watticus former filamentous

1.1.5 Molecular identification of fungal isolates

A total of 13 Ascomycota (98-100% identity match) and 4 Basidiomycota (95-

100%) fungal strains were identified. Four strains of Thelobolous with the identity ranging from 95-99% were identified. Additionally, SP.F4 (Pseudeurotium bakeri; 95% identity),

SP.F3 (Thelobolus microsporous; 95%), SP.F14 (Cryptococcus victoriae; 98%), SP.F17

(Holtermanniella watticus; 100%) were identified (Table 4-3). Strains SP.F14

(Cryptococcus victoriae) and SP.F17 (Holtermanniella watticus) were common

Basidiomycota in polar sites and were isolated from the soils in MEA. Ascomycota genera recovered included Geomyces pannorum, Thelobolus microsporous, Cladosporium grevilleae, Pseudeurotium bakeri, Chaetomium globosum and Engyodontium album.

Result

Table 4-3: Fungi isolated in different artificial culture media

ITS Strain Media Best Match Identity Accession Phylum Name Temp % 0.1X Geomyces pannorum SP.F1 8 °C 99 HQ703417.1 Ascomycota Ravan isolate E10 1X Thelobolus SP.F2 8 °C 99 GU004196.1 Ascomycota MEA microsporous Thelobolus SP.F3 PDA 8 °C microsporous 95 GQ4836440.1 Ascomycota isolate10 BI 1X Pseudeurotium bakeri SP.F4 8 °C 95 GU934582.1 Ascomycota MEA strain 842 Cladosporium SP.F5 LB RT grevilleae strain CBS 99 JF770450.1 Ascomycota 114271 Phoma herbarum CBS SP.F6 PDA RT 99 KF251212.1 Ascomycota 615.75 Thelobolous globosus SP.F7 PDA RT 100 JX171196.1 Ascomycota isolate ANT03-221 Chaetomium globosum SP.F8 CRBA 8 °C 99 JQ964802.1 Ascomycota isolate TNAU Cq Engyodontium album SP.F9 NA RT 99 JX868630.1 Ascomycota strain LVPEI.H1584 Cladosporium SP.F10 CRBA RT cladosporides strain 99 KJ410042.1 Ascomycota DUCC5020 Geomyces pannorum SP.F11 CRBA RT 99 HQ703417.1 Ascomycota isolate E10 Cladsporium 0.1X SP.F12 8 °C oxysporum strain 99 KF974329.1 Ascomycota CRBA CASVK1

Result

Thelobolus SP.F13 MEA 8 °C globosusstrain 96 KC341719.1 Ascomycota UFMCB 6095 Cryptococcus victoriae SP.F14 MEA 8 °C 98 JX88144.1 Basidomycota strain P41A001 0.1X SP.F15 8 °C Peniophora lycii 98 HG798718.1 Basidomycota CRBA Holtermanniella SP.F16 MEA 8 °C 99 JQ857031.1 Basidomycota watticus isolate T2Hw Holtermanniella SP.F17 MEA 8 °C 100 JQ857031.1 Basidomycota watticus

Figure 4-7: Fungal strains growing in PDA plates after incubation at RT for 15 days. From top left fungal strains are A) SP.F3 (Theloblolus microsporous) B) SP.F6 (Phoma herbarum) C) SP.F9 (Engyodontium album) D) SP.F10 (Cladosporium cladosporides) E)

Result

SP.F11(Geomyces plannorum) F) SP.F13 (Thelobolus globosus) G) SP.F14 (Cryptococcus victoriae) H) SP.F15 (Penipora lycii) I) SP.F17 (Holtermanniella watticus).

Figure 4-8: Microscopic observation of fungal isolates stained in Calcofluor white. From top left the respective strains are A) SP.F3 (Thelobolus microsporous) B) SP.F6 (Phoma herbarum) C) SP.F9 (Engyodontium album) D) SP.F10 (Cladosporium cladosporides) E) SP.F13 (Thelobolus globosus) F) SP.F14 (Cryptococcus victoriae) under 100X.

4.2.3 Comparison between molecular and cultivation approaches to microbial

characterisation

Thirty six phyla were recovered from soil. In total, 11% phyla was recovered from pure culture with 52% of total phylum in soil was recovered by SSMS (Figure 4-9A). In this study, artificial culture media recovered only four different phyla; Actinobacteria,

Proteobacteria, Firmicutes and Bacteriodetes. SSMS recovered a greater diversity with

Chloroflexi, Acidobacteria, Gemmatimonadetes, Cyanobacteria, TM7 along with 10 more phyla (Figure 4-9A). Soil also included phyla minor phyla such as FBP, WS2,

Deferribacteres, OP9, WS3 and Dictyoglomi.

Result

A total of 484 genera were recovered from polyphasic approaches used. Analysis of genera showed different trend from phyla with 407 genera present all in soil, 234 in SSMS and 15 in artificial media (Figure 4-9B). SSMS recovered more than 48% of genus level retrieved and culture plate recovered three percent of the total genera recovered. There were

152 genera which were common in SSMS and soil, including unc_Gaeillaceae

(Actinobacteria), unc_Ellin 6529 (Chloroflexi), Micrococcaceae, Polaromonas, Lysobacter and unc_Micrococcaceae.

Only 8 different bacterial genera (Arthrobacter, Kribbella, Mycobacterium,

Streptomyces, Hymenobacter, Caulobacter, Rhyzobium and Spingomonas) were common in all three different techniques. Four bacterial genera (Pediobacter, Aminobacter, Dyella,

Shingopyxix) were neither present in PC membrane nor in soil. Additionally,

Paenisporosarcinia was common in artificial media and soil while Sporosarcinia and

Rhodococcus were common in SSMS and artificial media isolates.

Result

Figure 4-9: Venn diagram representing A) recovery of bacterial phylum level in all three techniques where whole yellow circle indicate all phylum level covered in soil, red colour represents phylum that was recovered in SSMS and green (culture plate). B recovery of genus level: yellow (soil only), red (SSMS only), green (culture plate only) and blue, pink, brown and white colour represent genera common in soil and SSMS, Soil and culture plate, SSMS and culture plate and common in all respectively.

A total of four fungal phyla and 124 genera were recovered from polyphasic approach used. All four (Ascomycota, Basidiomycota, Fungi incertae sedis and

Entomophthoromycota) phyla were recovered from soil. Only two phyla (Ascomycota and

Basidiomycota) were recovered in SSMS and culture medium (Figure 4-10A). In total, 104 fungal genera were present in all soil, 30 in SSMS and 10 artificial culture media.

Result

Figure 4-10: Venn diagram representing A) recovery of fungal phylum level in all three techniques where whole yellow circle indicate all phylum level covered in soil, red color represents two phylum that was recovered in SSMS and culture plate. Venn diagram B represents recovery of genus level: yellow (soil) only, red SSMS only, green culture plate only and blue, pink, brown and white colour represent genera common in soil and SSMS, Soil and culture plate, SSMS and culture plate and common in all respectively.

At the genus level, recovery rate of fungal genera in the SSMS was 24% and cultivation was eight percent. Overlapping of fungal genera obtained from three techniques was similar to that observed for bacteria genera (Figure 4-10B). Only two genera

(Cladospohorium, Pseudeurotium) were identified as common among all three techniques.

There were three genera (Engyodontium, Peniopora and Phoma) common in culture plate and PC membrane and only 11 genera (Acremonium, Beauveria, Malassezia, Penicillium and Rhinocladiella) common to PC membrane and soil. Additionally, only two common genera (Chaetomium, Cryptococcus) were recovered in culture media and soil.

Discussion

5 DISCUSSION

5.1 Microbial Diversity of Browning Peninsula

Browning Peninsula is a pristine Antarctic desert consisting of frost boils across an entire area of 100-200 m (Stewart et al., 2011). A polyphasic approach (culture dependent and culture independent techniques) of the microbial diversity across the site revealed 36 bacterial phyla and 487 genera to be present. A total of four phyla, 124 genera and several unclassified fungi were identified. This level of diversity is lower than the polar soils of

Siberian Tundra. For example diverse levels of diversity with a total of 93 bacterial classes representing 38 phyla and 19 fungal classes representing six phyla were previously obtained from permafrost of Siberian Tundra (Gittel et al., 2014).

In terms of bacterial diversity indices, Pielou's evenness, Shannon index (H') were

0.72 and 5.5, while fungi were just 0.59 and 3.5.This is comparable to the previously reported H' of 5.8 from polygons of Arctic (Frank-Fahle et al., 2014) and 4.9-5.7 from rhizosphere soils in Antarctica (Teixeira et al., 2013). In temperate soil H' are reported to be higher in grassland (5.6-7.1) (Will et al., 2010) and pampas forests and grassland soils (10-

10.6)(Suleiman et al., 2013). Shannon index of fungi was reported by Gittel et al. (2014) was six in permafrost and more than six in topsoil, subsoil and buried soil. Bacterial diversity structure along BP revealed that only 20% bacterial community similarity occurred between soil samples across the site (Figure 3-8A). In contrast 40-50% bacterial community similarity was observed in samples from the same polygon. Similar results were observed by Frank-Fahle et al. (2014) with bacterial communities from individual polygon more similar to each other than to the communities present in separate polygons. This

Discussion

heterogeneity revealed that an individual polygon appeared to act as a discrete ecosystem despite a highly similar in the physical structure (circular). Each polygon microbial community was also dependent on its environment (Frank-Fahle et al., 2014) with TC, TN,

- SiO2,Cl , SO4 and mud. The main drivers of community structure in Browning Peninsula were polygons than the geographical factor such as position.

Additionally, in a conceptual model presented by Dannen et al. (2007), different types of insulation systems include vegetation, organic matter and snow presence at the edge of each polygon or frost boil. Browning soil lacks vegetation and is poor in organic matter content (Stewart et al., 2011, Chong et al., 2009, Roser et al., 1993). During frost heave (a process of freezing and thawing) and cryoturbation (soil mixing), particles and organic matter are carried out towards the edge of the polygon due to soil movement and leaching (Walker et al., 2004). Thus, the edge of the polygons are organic content rich and may act as a barrier or insulator between the frost boils (Walker et al., 2004). Cryoturbation within each frost boil may have lead to higher community similarity within frost boils, as the insulator formed at the edge of polygons may have led to dispersal limitation across the site.

5.1.1 Bacterial diversity in soils

Actinobacteria (more than 35%) dominated soils at Browning Peninsula followed by Chloroflexi, Acidobacteria, Cyanobacteria and Proteobacteria which together represented more than 54% of total relative abundance across this site (Figure 3-2). In permafrost soils of the Siberian tundra, Actinobacteria also dominate followed by

Proteobacteria, Verrucomicrobia, Acidobacteria and Bacteriodetes together accounting for more than 84% of total relative abundance (Gittel et al., 2014).

Discussion

Greater bacterial diversity has been discovered from different surveys of Arctic and

Antarctica soils with variations in the dominant phyla and their distributions reported

(Jansson and Tas, 2014, Cowan et al., 2014, Smith et al., 2006, Rao et al., 2011).

In arctic polygons, Proteobacteria accounted for 40-50%, Bacteroidetes 20-40% and

Actinobacteria 10-15% abundance following total community analysis using pyrosequencing (Frank-Fahle et al., 2014). In Miers Valley (an Antarctic desert) permafrost soils from Southern side have been found to be dominated by Proteobacteria and

Actinobacteria while Bacteriodetes, Acidobacteria, Firmicutes and Actinobacteria were in higher abundance in the Northern side (Stomeo et al., 2012). The dominating bacterial phyla were Bacteriodetes and Proteobacteria in lake sediment of Antarctic dry valley including Firmicutes and Actinobacteria in lower abundance (Tang et al., 2013, Clocksin et al., 2007). While biological crusts in the Arctic polar desert consisted of Acidobacteria,

Actinobacteria, Bacteriodetes, Chloroflexi and Cyanobacteria and were dominant (Steven et al., 2013). Phylogenetic 16S ribosomal RNA gene sequences from the McMurdo Dry valleys indicated 23% Proteobacteria, 20.5% Actinobacteria, 15.5% Acidobacteria, 4.5%

Cyanobacteria, 8% Bacteriodetes (Cary et al., 2010). Major dominating phyla from the

McMurdo Valleys were similar to Browning Peninsula but their abundance were different.

To date, five phyla (Cyanobacteria, Proteobacteria, Chlorobi, Chloroflexi and

Firmicutes) contain chlorophototrophs (chlorophyll based phototrophs) (Chew and Bryant,

2007)and all these phyla were present at more than 38% of total relative abundance across

Browning Peninsula. The chlorophototrophs synthesise two class of pigments; Chlorophylls and bacteriochlorophylls which function in light harvesting and carotenoids which not only act as photoprotective pigments but also capable of participating in light harvesting (Chew

Discussion

and Bryant, 2007). Despite the low nutrient availability in Browning Peninsula the presence of potential chlorophototrophic and heterotrophic members suggest that sustainability of entire microbial communities may be due to the presence of these light harvesting species such as Leptolyngbya (Cyanobacteria) and unc_Pseudanabaenaceae (family Cyanobacteria)

(Sciuto et al., 2011).

Uncultured Actinobacteria in the form of (unc_Gaiellaceae) contributed to large fraction (more than 18%) of the bacterial communities present in these frost boils (Figure

3-2). In addition unc_Ellin6529 (Chloroflexi) and unc_RB41 (Acidobacteria) contributed more than 10% of total bacterial relative abundance. Different genera of Actinobacteria were well known to degrade different forms of organic carbon (Yergeau et al., 2010) and the presence of Actinobacteria in polar soil active layers allows for organic matter turnover

(Yergeau et al., 2010, Gittel et al., 2014). Additionally, Actinobacteria are successful at maintaining metabolic activity and DNA repair mechanisms in temperatures less than zero

(Jansson and Tas, 2014). Unc_Ellin6529 was previously reported to be slow growing under low substrate availability present in paddy field soil (Lopes et al., 2014) and unc_RB41 was initially retrieved from sandy ecosystem (Quaiser et al., 2003).

Unc_Pseudanabaenaceae (family Cyanobacteria) and Leptolyngbya were also present in high relative abundance contributing to more than 6% of the total bacterial community. Leptolyngbya is a well described Cyanobacteria previously isolated from

Antarctic Dry Valley soils (Smith et al., 2006, Khan et al., 2011), polygons soils of Coal

Nunatak (Maritime Antarctica) (Brinkmann et al., 2007) and soils of Victoria Land

(Aislabie et al., 2006). Although, Cyanobacteria phylotypes signals are higher in high altitude (Smith et al., 2006) and vary along latitudinal transects (Brinkmann et al., 2007),

Discussion

the spatial dispersal of the Cyanobacteria in Browning Peninsula may be because of washing out cells from soil particle (as Cyanobacteria cells are loosely attached with the soil particle) during summer due to melting of ice (Brinkmann et al., 2007).

5.1.2 Fungal diversity in soils

More than 95% of detectable fungal diversity was attributed to Ascomycota followed by Basidiomycota in Browning Peninsula soils. However, a variable number of

OTUs and species richness was observed with just 5-87 OTUs present across all 18 samples. Patterned ground features in Arctic soils are also dominated by Ascomycota and

Basidiomycota while six further phyla (Zygomycota, Chytridiomycota, Glomeromycota,

Blastocladiomycota, Neocallimastigomycota and Cryptomycota) were reported (Timling et al., 2014). Previous culture based studies have reported the recovery of 94.5% Ascomycota,

2.8% Zygomycota and 2.6% Basidiomycota in the Antarctic Peninsula (Arenz and

Blanchette, 2011). In contrast, in the McMurdo Dry Valleys (Antarctic largest ice free dry valley) approximately 53% of isolates were Ascomycota and 46.8% were Basidiomycota, with no Zygomycota were reported (Arenz and Blanchette, 2011). Although Browning

Peninsula and McMurdo Dry Valleys share similar environmental conditions and both of them were ice free dry valleys, the ratio of fungal phylum level composition in both sites were different. Similarly in Siberian Tundra permafrost, 66% of Ascomycota, more than

30% of Basidiomycota and less than four percent of other phyla (Zygomycota,

Chytridiomycota, Glomeromycota) (Gittel et al., 2014). The lack of diverse fungal phyla in

Browning Peninsula may be due to high level of heterogeneity of soil fungal communities

(Geml et al., 2012), dispersal limitation (Rao et al., 2011), their obligate symbiotic association with plants (Geml et al., 2012, Timling et al., 2012), lack of vegetation (Stewart

Discussion

et al., 2011) and substrate and nutrient availability (Azmi and Seppelt, 1998, Timling et al.,

2014).

Up to 23 different lichenised fungal genera were recovered from BP soils in this study, with 3 genera (Buellia, Leconora and Candelaria) highly abundant in six soil samples and their presence suggest they may be involved in carbon and nitrogen fixation.

Previously, the lichens Buellia frigida and Candelariella hallettensis were also reported from this site (Melick et al., 1994). The presence of lichens in this site has been suggested to be as a result of unstable substrate and severe local climate (Melick et al., 1994)..

Browning Peninsula's fungal diversity was relatively unknown with limited reports based on cultivation using three different artificial media PDA, Czapek's agar and MEA reporting Chrysosporium sp, Mortierellagamsii, Mycelia sterilia1, Mycelia sterilia2,

Phoma sp and Thelebolus microspores (Azmi and Seppelt, 1998). Very few fungal OTU

(384) were retrieved from Browning Peninsulas pyrotag data, with just 20% of a total

OTUs recorded from Arctic (Timling et al., 2014). Despite this low diversity, the rarefaction curves appeared to reach asymptote (Figure 3-1).

Comparison of the total 16S and 18S gene rDNA copy numbers (Figure 3-13A)

Browning Peninsula soils revealed a more than 10-100 fold increase in microbial abundance compared to active layer of Canadian high Arctic (Yergeau et al., 2010).The bacterial and fungal SSU gene copy number obtained by Yergeau et al. (2010) were 3.05 X

107 and 8.60 X 104 respectively. The biomass in the active layers were 10-20 times higher than that in two meter permafrost which were 1.55 X 106 and 8.80 X 103respectively

(Yergeau et al., 2010). The bacterial and fungal biomass obtained from Browning

Peninsula were 100-2000 times higher than the biomass obtained from 2 m permafrost by

Discussion

Yergeau et al. (2010). In contrast, the average bacterial and fungal SSU genes obtained from the top soil of northeast Siberia by Gittel et al. (2014) was 100 times higher than the average SSU genes obtained from Browning Peninsula. This suggests that although

Browning is a pristine site with no or little human impact has higher number of total bacterial and fungal genes than the polar Canadian Arctic. Bacterial gene copy abundance of Browning soils were 100-1000 fold greater that total fungal gene copy numbers obtained

(Figure 3-13). In contrast, the average SSU genes of fungi (6.4 X 1010) was two-fold higher than the average SSU genes of bacteria (3.5 X 1010) from topsoil samples (Gittel et al.,

2014). A similar report was obtained from permafrost soils of the same site with 0.4 X 106 fungal SSU rRNA genes and 0.1 X 106 bacterial SSU rRNA genes per gram of dry soils

(Gittel et al., 2014).

5.1.3 Pure bacterial and fungal cultures

Fifteen different bacterial genera were isolated from Browning using artificial media (Table 4-1). Among them Arthrobacter and Streptomyces, were widely distributed and have previously been isolated from ice covered lakes (Clocksin et al., 2007), Antarctic cold deserts (Smith et al., 2006), post glacier soils (King George Island) (Zdanowski et al.,

2012) and Sør Rondane Mountains soil (Peeters et al., 2011) of Antarctica. Negoita and

Gesheva (2011) reported some of the Streptomyces isolated from Haswell Island of

Antarctica produced hydrolytic enzymes (celluloses, proteases, amylases) and antibiotics.

Additionally, Arthrobacter, Rhodococcus and Streptomyces produced extracellular anti- bacterial and antifungal substances (Gesheva, 2010). Bacterial genera isolated in this study such as Kribella (Smith et al., 2006), Spingomonas (Clocksin et al., 2007, Peeters et al.,

2011, Zdanowski et al., 2012), Caulobacter (Clocksin et al., 2007), Rhodococcus

Discussion

(Zdanowski et al., 2012, Peeters et al., 2011, Gesheva, 2010), Sporosarcinia, Pedobacter

(Zdanowski et al., 2012), Hymenobacter (Aislabie et al., 2006, Peeters et al., 2011),

Mycobacterium (Aislabie et al., 2009) and Rhizobium (Hart et al., 2011) have been previously isolated, particularly from different soils of Antarctica. Sphingopyxis,

Paenisporosarcinia, Dyella and Aminobacter isolated from Browning Peninsula have not yet been reported from other Antarctic soils.

Ten different fungal genera were isolated from Browning Peninsula. Geomyces,

Thelobolus and Phoma (Frate and Caretta, 1990, Connell et al., 2006, Godinho et al.,

2013, Arenz et al., 2006, Arenz and Blanchette, 2011), Cladosporium, Cryptococcus (Frate and Caretta, 1990, Connell et al., 2006, Arenz et al., 2006) and Pseudeurotium (Arenz and

Blanchette, 2011, Ferrari et al., 2011) were previously isolated from Antarctic soils.

Among these isolates Phoma is UV radiation resistant (Hughes et al., 2003). In a study done by Tosi et al. (2010), fungal genera Cladosporium and Geomyces produce antioxidant enzymes (superoxide dimutase and catalase).

Both bacterial (particularly Streptomyces) and fungal genera isolated in this study have the potential to produce novel molecules. Further work exploring potential bioactivity may lead to the isolation of novel compounds for exploitation of medicinal use. Novel species present on SSMS can be picked using micromanipulator and culture using low concentration media.

Devriesia sp (phylum Ascomycota), was a dominant genus at Browning. Members here exhibited ITS rDNA similarity ranging from 83-95% and together they accounted for the highest proportion (more than 38%) of total fungal relative abundance (Figure 3-5).

Devriesia has not been reported in Antarctica before but was described by Seifert et al.,

Discussion

2004 as a heat resistant fungi related to Cladosporium staurophorum. This germinated after heat shock treatment at 75 °C for 30 min in MEA at the incubation temperature of 25 °C.

Onofri et al.(2011) also reported Devriesia sp in rocks, beach sand and water in Italy indicating its habitat in extreme conditions. Despite the use of MEA culture media and the rich abundance of Devriesia in Browning soils they could not be grown in culture media in this study. We propose these genera and potentially many others that were not successfully cultured here require germination prior to cultivation attempts.

5.2 Environmental drivers of community composition

Stewart et al. (2011) reported difference in physical, chemical and microbial properties of positions (edge and middle) of frost boils. But, the polygon-microbial community relationship was not investigated in great detail (Stewart et al., 2011, Walker et al., 2008). We also observed variations in soil properties with environmental parameters in

Browning appearing to the frost boil specific with soils from same frost boils exhibiting similar environmental dataset and thus closer proximity on the nMDS plots to each other while the samples from different frost boils were further apart. Here, the environmental parameters such as conductivity, nitrogen, slope, carbon, phosphate and % gravel played significant roles in the biological distribution of bacterial and fungal communities across different position along Browning (P less than 0.001) and individual polygons (P less than

0.001) (Table 3-2,Table 3-3). Similar report of distribution trend of different soil environmental properties (organic carbon, extractable nitrogen, phosporus and potassium) were observed by Michaelson et al., 2012. According to Michaelson et al., 2012, the nutrient level of all frost boils across all sites as well as across individual site were not

Discussion

consistent but were dependent on the organic carbon (OC) content (also indicated as

Carbon : Nitrogen ratio) of the respective site and individual frost boil. This difference in microenvironment of patterned ground features and between patterned ground features might be due to frost heave and cracking during seasonal change (Timling et al., 2014).

5.3 Microbial community structure

The bacterial community abundance observed was variable across position along transects. The composition of these fungal diversity were neither polygon nor distance related thus their distribution in Browning Peninsula appeared to be random. Initially, there was the hypothesis that stated the spatial variation of bacterial community is not affected by dispersal barriers and historical contingencies but is affected by local contemporary interactions among microorganisms in the habitat (Lindstrom and Langenheder, 2012).

Challenging this hypothesis, recent studies suggesting mechanism such as mass effect, dispersal limitation and neutral assembly are important drivers of the composition of beta diversity (Lindstrom & Langenheder, 2012).

Fungal community composition was heterogeneous, irrespective of whether the soil was from the same polygon (Figure 3-10). Fungi require overland routes for migration

(Geml et al., 2012). For example Leccinum sp (Basidiomycota) had obligate symbiotic association with specific host Betula (woody plant widely distributed in Svalbard) that restricted its migration (Geml et al., 2012). Some studies have also suggested that fungi are more freeze thaw tolerant than other prokaryotes (Sharma et al., 2006). In contrast, fungal diversity were reported to be more susceptible to the extreme environment (Rao et al.,

2011) and a relatively small distances were enough to act as barrier to the dispersal

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Discussion

(Brinkmann et al., 2007). In BP, the low diversity of fungi present suggests that limitations or barriers caused from frost boil development were limiting the growth and distribution of fungi. We believe the limited amount of fungal diversity in these frost boils may be due to dispersal limitation (Rao et al., 2011). Microbial size was an important factor for microbial dispersion and microbes less than 20 µm have the highest potential to dispersal while decrease in the dispersal potential has been observed in microbes ranging from 20- 40 µm

(Wilkinson et al., 2012). This indicates that smaller microorganisms, such as bacteria, were less likely to be dispersal limited than the large microorganism, such as fungi. This variation in dispersal may have led to the observation at Browning Peninsula (Schmidt et al., 2014).

The active layer microorganisms of Antarctic dry valleys and Arctic permafrost were particularly observed degrading complex soil carbon (Yergeau et al., 2010, Ball and

Virginia, 2014). Soil heterogeneity can result from variable concentrations of carbon, nitrogen and the degree of drainage in soils during cryoturbation (Walker et al., 2004,

Kaiser et al., 2005, Gittel et al., 2014). Additionally, in the soil samples obtained from patterned ground features (PGF) along a North American Arctic transects and active layer of frost boils across Arctic Alaska, difference in N cycling and C cycling were observed within different polygons which were influenced by different organic matter content, microbial community and soil moisture (Kelley et al., 2012).

In this study bacterial diversity was correlated with chloride, conductivity, sand and sulfate (Figure 3-11). In contrast, chloride content was previously reported as important factor associated with the fungal diversity (Siciliano et al., 2014), while fungi were correlated with SiO2. Michaelson et al. (2012) also reported that non acidic soil rich in

101

Discussion

inorganic carbon were effective in buffering pH changed with the organic carbon accumulation. In addition water soluble organic carbon (OCws) of non-acidic frost boils were correlated with total organic carbon of soil while OCws of acidic frost boils were correlated with TN of soil (P less than 0.01) (Michaelson et al., 2012). In this study bacterial diversity distribution were strongly correlated with TN (Figure 3-11) while fungal diversity distribution were observed to have correlation with TN as well as TC (Figure

3-12). In addition others factors such as the soil depth (Brinkmann et al., 2007, Frank-Fahle et al., 2014, Stomeo et al., 2012), potassium, calcium (Stomeo et al., 2012), temperature, salinity and moisture content (Cary et al., 2010) were suggested to drive the microbial community in different Antarctic and Arctic sites.

Here the pH of all Browning Peninsula soils ranged from 6.4 - 6.75 (Appendix 2), this was within the range 6.2 - 7 obtained by Melick et al.(1994) from same site. In the studies done by Rousk et al.(2010) ; Gittel et al. (2014) and Siciliano et al. (2014), the relative abundance and diversity of bacteria were positively correlated with pH whereas for fungi pH did not affect the abundance but the diversity was weakly related. Rousk et al.

(2010) had also specified that due to the narrow pH range for optimal growth of bacteria, pH has strong influence on the bacterial community composition. In contrast, the consistent result of pure fungi exhibiting wider pH range for optimal growth thus having weaker influence was obtained (Rousk et al., 2010). Additionally, bacterial species were reported highest in soil close to neutral pH while bacterial diversity was lowered in acidic or basic soils (Fierer and Jackson, 2006, Fierer et al., 2012).

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Discussion

5.4 A culture clash

Although, traditional cultivation methods only have capacity to recover one percent of the total microbial diversity new technique such as SSMS was able to recover 24% of total fungal genera and 48% of total bacterial genera within Browning Peninsula soils in this study. A clash exists between the retrieval of microorganisms from cultivation methods compared to culture independent techniques such as ribosomal sequence analysis via NSG

(Donachie et al., 2007, Shawkey et al., 2005, Maturrano et al., 2006).

In total, 19 phyla were recovered from SSMS including common dominating phyla

Proteobacteria, Actinobacteria, Bacteriodetes, Firmicutes and Acidobacteria (Figure 4-2).

However, understudied phyla such as Chloroflexi, Gemmatimonadetes and uncultured

Candidate Division TM7 had been enriched for using this approach (Ferrari et al., 2005).

SSMS recovered 14 different fungal genera which were neither retrieved in 454 soil diversity nor in artificial culture library (Figure 4-10B). Compared to artificial media more than 200 genera were recovered in SSMS among which 74 different genera were neither isolated in artificial culture media nor were they detected in 454 pyrosequenced data

(Figure 4-9). Also, SSMS was capable of enriching minor populations in soil

(Aureobasidium, Exophiala, Penipora, Aspergillus, and Toxicocladosporium) which may have been missed during DNA extraction of soil due to their stress tolerant nature and small sized spore forming capability (Kochkina et al., 2012). In this study, 454 pyrosequencing data was limited to 3000 reads thus it is not surprising that the bacterial rarefaction curve did not reach asymptote (Figure 3-1). However, fungal data reached an asymptote and still missed the diversity. This suggests to use recent Illumina technology which could achieve up to 10,00,000 reads with high recovery rate and better specificity.

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Discussion

Artificial culture media only recovered 4 unique genera, both of which were neither detected in the 454 pyrosequenced data nor enriched in SSMS (Figure 4-9). A similar culture clash was obtained by Donachie et al. (2007) who investigated seven different habitats. For example a total of 147 bacterial OTUs from culture dependent and 159 bacterial OTUs from culture independent approaches along with eight common OTUs were reported from birds feathers (Donachie et al., 2007, Shawkey et al., 2005). While, a total of

25 OTUs from Hawaiian lakes and 15 OTUs from hypersaline salterns were recovered from culture dependent compared to eight and 17 unique OTUs from the respective sites retrieved using culture independent approach (Donachieet al., 2007). Thus, this study supporting Donachie et al. (2007) suggest to implement polyphasic approach. This includes culturing techniques (SSMS and artificial culture media) and culture independent next generation sequencing techniques (454 or Illumina) to describe greater microbial diversity.

Surprisingly, recovery of fungal community diversity using the three techniques was similar to that observed for bacteria (Figure 4-10). Genera specific to Ascomycota,

Basidiomycota and very few unclassified fungi were enriched for using the SSMS while only two fungal genera (Cladospohorium and Pseudeurotium) were common to all three techniques implemented. This result was similar to Kochkina et al.(2012) who recovered similar fungi (Penicillium chrysogenum and P. olsonii) using both techniques. The clash between approaches may be due to biases during DNA extraction (Plassart et al., 2012),

PCR amplification (Schloss et al., 2011), sequencing region (Chakravorty et al., 2007,

Kumar et al., 2011). Kochkina et al.(2012) also isolated Penicillium and Cladosporium genera using cultivation in artificial media but were unable to identify these genera following molecular cloning amplicon library development. In this case low fungal

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Discussion

diversity was retrieved using this molecular approach as approximately 50% of the samples did not detect Penicillium and Cladosporium (Kochkina et al., 2012).

5.5 Conclusion

Bacterial community diversity at Browning Peninsula was related to the polygons the soils came from rather than position along transects. This is contrary to most studies where communities become more dissimilar as they move further apart (Martiny et al.,

2006). In contrast, the fungal diversity was neither polygon specific nor sites specific but were appeared to be soil sample specific due to low substrate and nutrient availability, lack of vegetation for symbiosis and dispersal limitation. The soil environmental parameters were also polygon specific with soils from same polygon more similar to each other than position along the 300 m transect.

Application of a polyphasic approach here showed that none of the techniques applied here was capable of recovering every genera present in soil. Here, 3000 bacterial reads were not enough to reach asymptote. In contrast fungal diversity did reach asymptote indicating the relatively limited abundance and coverage of fungi diversity. Thus there is still value in using cultivation dependent techniques too, as molecular methods currently fail to detect some genera which can be recovered in following cultivation (Kochkina et al.,

2012, Donachie et al., 2007). Illumina technology is a new NSG platform currently being used to sequence up to 10,00,000 bacterial reads (Gittel et al., 2014). This technique may be new option to recover bigger picture in biodiversity studies and reduce the culture clash.

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APPENDICS

Appendix 1: Correlation value of PCO plots (bacteria and fungi)

Bacteria Fungi Environmental PCO1 PCO2 PCO1 PCO2 Parameters (24.7% (11.1% (20.9% (11.5% of total of total of total of total variation) variation) variation) variation) Elevation 0.456 -0.419 0.004 0.091 Aspect 0.627 0.083 -0.312 0.163 Slope -0.192 0.371 -0.093 -0.058 Conductivity -0.793 0.224 0.151 -0.175 pH 0.358 0.314 0.077 -0.099 Cl -0.816 0.196 0.145 -0.175 NO2 0.274 -0.472 -0.123 0.186 NO3 0.037 0.037 -0.151 0.231 SO4 -0.529 0.367 -0.040 -0.038 NH4 0.000 0.000 0.000 0.000 PO4 0.521 0.421 -0.229 -0.190 Mud 0.544 -0.042 -0.283 -0.460 Sand -0.599 0.216 0.069 0.150 Gravel 0.307 -0.170 0.131 0.125 SiO2 0.251 0.107 -0.444 0.271 TiO2 0.059 0.066 -0.171 0.346 MnO 0.232 -0.173 -0.272 0.069 MgO 0.358 0.188 -0.381 0.069 CaO 0.147 -0.100 -0.113 0.206 Na2O -0.499 -0.287 0.283 -0.395 K2O -0.065 0.234 -0.101 0.172 P2O5 0.476 0.057 -0.272 0.043 SO3 -0.091 0.295 -0.357 0.109 TC 0.242 0.337 -0.561 0.116 TN 0.435 0.423 -0.463 0.221 TP 0.299 0.163 -0.022 0.146

Appendix 2: Environmental data: physical variables of BP soils

Physical parameters % Soil % Sand % Mud Grave Sample Log(Elevation Aspect Log(Slop Log(conductivit 63- 63um^0.2 l s ) (deg) e (deg)) y (uS/cm)) 2000u 5 >2m m m BP1 3.70 293.85 1.43 3.38 1.49 63.66 31.44 BP2 3.69 80.14 1.16 3.88 1.41 69.77 26.26 BP3 3.70 293.36 1.40 3.60 1.96 70.25 15.10 BP4 3.69 90.41 1.08 3.92 1.47 82.84 12.47 BP5 3.77 127.34 0.28 3.43 1.27 68.09 29.34 BP6 3.77 143.87 0.28 2.79 1.99 58.06 26.24 BP7 3.70 295.74 1.38 3.61 1.37 67.86 28.57 BP8 3.70 297.32 1.34 3.72 1.48 78.70 16.46 BP9 3.69 83.61 1.13 3.88 1.37 70.67 25.84 BP10 3.77 123.98 0.33 3.46 1.64 73.09 19.61 BP11 3.70 294.88 1.38 3.48 1.57 72.71 21.23 BP12 3.69 85.40 1.11 4.31 1.31 76.76 20.31 BP13 3.69 88.90 1.09 3.92 1.46 78.06 17.44 BP14 3.77 140.30 0.30 2.54 1.41 66.01 30.04 BP15 3.77 136.53 0.30 2.98 1.53 69.96 24.55 BP16 3.69 93.93 1.06 4.03 1.47 75.12 20.27 BP17 3.77 147.55 0.31 2.77 1.52 71.70 22.94 BP18 3.70 292.62 1.44 3.30 1.53 65.40 29.10

Appendix 3: Environmental data: chemical variables of BP soils

Chemical Samples Parameters BP1 BP2 BP3 BP4 BP5 BP6 BP7 BP8 BP9 BP10 BP11 BP12 BP13 BP14 BP15 BP16 BP17 BP18 pH 6.66 6.7 6.64 6.58 6.4 6.6 6.75 6.64 6.46 6.52 6.7 6.53 6.38 6.54 6.46 6.52 6.51 6.59 Log(Cl (mg/kg 3.26 4.01 3.6 4.08 3.4 2.61 3.61 3.68 4.06 3.11 3.15 4.37 4.09 2.26 2.63 4.18 2.18 3.05 DMB)) Log(NO2 ------2.59 -2.59 -2.59 -2.59 -2.59 -0.48 -0.49 -2.59 -2.59 -2.59 (mg/kg, DMB)) 2.59 2.59 2.59 2.59 2.59 2.59 2.59 2.59 Log(NO3 - - 0.98 1.02 0.33 0.09 1.48 1.04 0.91 -0.14 0.59 0.75 0.24 -0.1 0.8 -0.11 0.77 0.93 (mg.kg, DMB)) 0.05 0.02 Log(SO4 2.05 2.32 2.25 2.47 1.98 1.5 2.4 2.36 2.4 1.43 2.17 2.41 2.29 0.97 1.71 2.47 1.45 2.14 (mg/kg DMB)) Log(NH4 - - - - 0.59 0.23 0.09 0.16 0.24 -0.98 0.19 0.51 -0.06 0.23 1.03 0.12 0.6 0.27 (mg/kg DMB)) 0.98 0.08 0.12 0.98 Log(PO4 2.89 2.25 2.25 2.25 2.25 3.23 2.25 2.25 2.25 2.25 2.76 2.25 2.25 2.25 2.25 2.25 2.25 2.25 (mg/kg DMB)) TiO2 (%) 0.85 0.76 0.6 0.99 0.98 0.86 1 1.15 0.69 0.86 0.92 1.11 0.94 0.87 0.97 1 1.08 1.15 Log(MgO (%)) 0.34 0.18 0.15 0.35 0.29 0.42 0.3 0.42 0.12 0.22 0.31 0.36 0.35 0.28 0.42 0.41 0.44 0.45 Log(CaO (%)) 1.18 1.18 1.09 1.2 1.19 1.24 1.24 1.21 1.13 1.2 1.18 1.22 1.21 1.24 1.19 1.17 1.2 1.23 Na2O (%) 2.72 2.94 2.88 2.79 2.78 2.85 2.79 2.74 2.94 2.89 2.73 2.86 2.89 2.86 2.78 2.79 2.77 2.67 K2O (%) 3.5 3.1 2.79 3.22 3.06 2.88 3.17 3.22 3.06 3.03 2.99 3.04 3.08 2.95 3.05 2.93 2.96 3.38 ------Log(P2O5 (%)) -1.56 -1.59 -1.59 -1.6 -1.62 -1.59 -1.6 -1.59 -1.58 -1.48 1.82 1.82 1.61 1.65 1.38 1.55 1.52 1.84 Log(TC (% ------1.2 -2.41 -1.56 -1.43 -1.39 -1.83 -0.92 -1.56 -1.27 -2.04 w/w)) 2.12 2.12 1.43 1.35 1.61 1.83 1.77 2.21 Log(TKN 6.06 5.19 4.7 5.14 5.39 5.01 5.14 5.25 4.5 4.5 5.67 5.19 5.25 4.94 5.52 4.79 5.35 5.6 (mg/kg DMB)) Log(TKP 6.8 6.57 6.41 6.49 6.7 6.82 7 6.81 6.48 6.75 6.87 6.68 6.58 6.49 6.38 6.52 6.59 6.91 (mg/kg DMB))

III