BACTERIAL NATURAL PRODUCT GENE
BIOMINING IN POLAR DESERT SOILS
A DISSERTATION SUBMITTED BY
NICOLE BENAUD
IN FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF
DOCTOR OF PHILOSOPHY
SCHOOL OF BIOTECHNOLOGY AND BIOMOLECULAR SCIENCES
UNIVERSITY OF NEW SOUTH WALES, SYDNEY AUSTRALIA
SUPERVISOR: ASSOCIATE PROFESSOR BELINDA C. FERRARI
CO- SUPERVISOR: DR JOHN A. KALAITZIS
June, 2019
THESIS ABSTRACT
New antimicrobial agents are urgently required to address a global antibiotic resistance crisis.
Natural products, biosynthesised through secondary metabolite pathways, remain at the forefront of drug discovery. Extreme environments are attractive targets for microbial biomining, due to their potential as reservoirs for novel metabolites. In polar regions, environmental conditions are some of Earth's most severe, and microbes dominate the biosphere. Moreover, arid polar soils comprise high relative abundances of Actinobacteria and Proteobacteria, prolific producers of natural products. This research had three main objectives: to identify polar soil bacterial communities with novel biosynthetic potential; to establish a culture collection of Antarctic isolates with demonstrated bioactive capabilities; and to perform whole genome sequencing (WGS) on biotechnologically promising isolates for biosynthetic gene cluster (BGC) mining. Third generation long-read PacBio sequencing was employed to survey > 200 Antarctic and high Arctic soils for non-ribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) domain amplicons. Significant negative relationships were observed between natural product genes and soil fertility factors carbon, nitrogen and moisture. Sequences primarily aligned to domains encoding antifungal, antitumour and antimicrobial/surfactant compounds, but with low sequence similarity
(< 70%) to known genes. Using novel culturing approaches, 19 bacterial genera across 4 phyla were isolated from Antarctic soils, including 32 Actinomycetales species. Extended oligotrophic incubation times were related to the recovery of novel and rare strains. In in situ antimicrobial assays, Streptomyces was the only genus to produce measurable activity. WGS was performed for 17 Antarctic isolates using PacBio technology. Genomes predominantly returned high-quality assemblies, and BGC analysis revealed an abundance of terpene,
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NRPS, PKS, bacteriocin and siderophore clusters, with minimal gene similarity (< 70%) to known BGC. In accordance with amplicon sequencing results, many NRPS and PKS domains aligned most closely to antifungal, antitumour and antimicrobial/surfactant- encoding genes. These findings indicate that Antarctic desert soils are excellent candidates for novel natural product bioprospecting and gives further insight into the functional and ecological relevance of natural products in terms of competition between microbiota for scarce resources.
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PUBLICATIONS
Peer reviewed journal article
Benaud, N., Zhang, E., van Dorst, J., Brown, M.V., Kalaitzis, J.A., Neilan, B.A., Ferrari, B.C. (2019). Harnessing Long-Read Amplicon Sequencing to Uncover NRPS and Type I PKS Gene Sequence Diversity in Polar Desert Soils. FEMS Microbiology Ecology, doi.org/10.1093/femsec/fiz031
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TABLE OF CONTENTS
ABSTRACT...... i
PUBLICATIONS...... iii
TABLE OF CONTENTS...... iv
ACKNOWLEDGEMENTS...... x
LIST OF FIGURES...... xi
LIST OF TABLES...... xvi
ABBREVIATIONS...... xix
CHAPTER 1 1 INTRODUCTION...... …………...... 1 1.1 Antibiotic resistance drives the need for novel bioactive compounds...... 1 1.2 Microbial natural products...... 3 1.3 Natural product biosynthesis...... 6 1.3.1 Polyketide synthases (PKS)...... 8 1.3.1.1 Type I PKS...... 9 1.3.1.2 Type II PKS...... 13 1.3.1.3 Type III PKS...... 13 1.3.2 Non-ribosomal peptide synthetases (NRPS)...... 15 1.4 Dominant natural product-producing bacterial phyla...... 16 1.4.1 The Actinobacteria...... 17 1.4.2 The Proteobacteria...... 19 1.4.3 The Cyanobacteria...... 20 1.4.4 The Firmicutes...... 20 1.5 Microbial natural product diversity...... 21 1.6 Cold-adapted bacteria as a source of novel natural products...... 23 1.7 Polar terrestrial environments and their microbial diversity...... 29 1.8 Molecular technologies for natural products discovery...... 34 1.9 Thesis scope and aims……………...... 37
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CHAPTER 2 2 HARNESSING LONG-READ AMPLICON SEQUENCING TO UNCOVER NRPS AND TYPE I PKS GENE SEQUENCE DIVERSITY IN POLAR DESERT SOILS...... 40 2.1 INTRODUCTION…………………………...... 40 2.1.1 The polar deserts of East Antarctica and the High Arctic...... 40 2.1.2 Surveying polar desert soils for natural product genes...... 44 2.2 MATERIALS AND METHODS………………...... 45 2.2.1 Polar locations and soil collection...... 45 2.2.2 DNA extraction and 16S rDNA gene sequencing...... 47 2.2.3 Soil physical and chemical properties...... 48 2.2.4 PKS PCR amplification, gel extraction and barcoding...... 48 2.2.5 NRPS PCR amplification and barcoding...... 51 2.2.6 Natural product amplicon library preparation for SMRT sequencing…………………………………………………. 52 2.2.7 Processing PacBio SMRT sequencing data...... 52 2.2.8 Taxonomic classification of sequences using the BLAST database.. 53 2.2.9 Multivariate data analysis...... 53 2.2.10 Statistical analysis...... 54 2.2.11 Construction of phylogenetic trees...... 55 2.3 RESULTS………………………………...... 56 2.3.1 PKS and NRPS gene sequences compared across polar soils...... 56 2.3.2 PKS and NRPS biosynthetic diversity in polar soils...... 57 2.3.3 Classification and distribution of natural product gene cluster families…………………………………………………………….. 58 2.3.4 Phylogenetic analysis of NP domain sequences………………...... 64 2.3.5 Bacterial and Actinobacterial diversity of polar soils...... 67 2.3.6 Relationships between polar natural product genes, microbiomes and soil fertility parameters……………………………………….. 69 2.3.7 NP domain sequence novelty...... 75 2.4 DISCUSSION…………………...... 76
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CHAPTER 3 3 CULTURING COLD ADAPTED BACTERIA FROM MAJOR NATURAL PRODUCT PRODUCING PHYLA USING NOVEL APPROACHES...... 80 3.1 INTRODUCTION…………………………...... 80 3.2 MATERIALS AND METHODS………………………...... 84 3.2.1 Site description and soil characteristics...... 84 3.2.1.1 Herring Island ...... 85 3.2.1.2 Mitchell Peninsula...... 86 3.2.1.3 Rookery Lake...... 87 3.2.1.4 Wilkes Tip...... 87 3.2.2 Direct soil culturing methods...... 88 3.2.2.1 Herring Island and Mitchell Peninsula DSC...... 88 3.2.2.2 Rookery Lake and Wilkes Tip DSC...... 90 3.2.2.3 Isolation and purification of bacteria from DSC...... 91 3.2.3 SSMS culturing at cold temperatures...... 91 3.2.3.1 Assessing microcolony growth and bacterial viability on the SSMS...... 93 3.2.3.2 Secondary cultivation of SSMS microcolonies using artificial media...... 96 3.2.3.3 Isolation and purification of bacteria from SSMS cultures...... 97 3.2.4 Gram and lactophenol cotton blue stain differentiation...... 97 3.2.5 Isolate DNA extraction and purification...... 98 3.2.6 PCR amplification and Sanger sequencing of isolate 16S rDNA genes...... 98 3.2.7 Cryopreservation of strains...... 100 3.2.8 Type I PKS and NRPS domain screening by PCR...... 100 3.2.9 In situ antimicrobial testing by cross-streak method...... 101 3.2.10 Type I PKS and NRPS domain screening and antimicrobial assays for strains isolated in previous studies...... 102
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3.2.11 Bacterial 16S rDNA gene analysis for pristine soils...... 103 3.2.12 Venn diagram visualisation of species shared between sites...... 104 3.2.13 Biotechnological and biosynthetic potential of isolates...... 104 3.3 RESULTS………………………………...... 104 3.3.1 Direct soil culturing...... 104 3.3.2 Cold-temperature SSMS cultures...... 112 3.3.3 Summary of bacterial isolates cultured by DSC and SSMS...... 116 3.3.3.1 Total bacteria cultured by all methods across four sites...... 116 3.3.3.2 Bacterial colony pigmentation...... 118 3.3.4 Natural product domain amplification and in situ antimicrobial activity for selected isolates...... 118 3.3.4.1 Strains isolated in this study...... 118 3.3.4.2 Strains isolated from previous studies...... 122 3.3.5 Selection of isolates for whole genome sequencing...... 124 3.4 DISCUSSION…………………...... 126
CHAPTER 4 4 ANTARCTIC BACTERIAL GENOMES HARBOUR A WEALTH OF UNCHARACTERISED BIOSYNTHETIC GENE CLUSTERS...... 131 4.1 INTRODUCTION…………………………...... 131 4.2 MATERIALS AND METHODS………………………...... 132 4.2.1 High molecular weight genomic DNA extractions………………... 132 4.2.1.1 Spore harvesting for Streptomyces and Kribbella isolates... 133 4.2.1.2 Modified Kirby method for Streptomyces and Kribbella genomic DNA extraction...... 134 4.2.1.3 Phenol-chloroform genomic DNA extraction for other genera...... 136 4.2.1.4 Quantification and quality assessment of genomic DNA...... 137 4.2.2 Multi-genome DNA library preparation and sequencing...... 138 4.2.3 De novo genome assembly from multi-genome libraries...... 139 4.2.3.1 Genome annotation, functional prediction and assessment of genome quality...... 140 vii
4.2.3.2 Phylogenetic analysis of genome-retrieved 16S rDNA genes...... 141 4.2.4 Secondary metabolite gene cluster analysis...... 141 4.2.4.1 AntiSMASH analysis for all Antarctic genomes...... 141 4.2.4.2 BLASTp and NaPDoS analysis of detected BGC domain sequences...... 142 4.3 RESULTS………………………………...... 143 4.3.1 Sequencing output and assembly of multi-genome libararies...... 143 4.3.2 Individual genome assemblies, annotation and quality assessment...144 4.3.3 Annotation and functional distribution of genes...... 146 4.3.4 Phylogenetic analysis based on 16S rDNA genes...... 150 4.3.4.1 Actinobacteria: Streptomyces group...... 150 4.3.4.2 Actinobacteria: non-Streptomyces group...... 152 4.3.4.3 Alphaproteobacteria group...... 152 4.3.4.4 Bacteroidetes: Hymenobacter...... 155 4.3.5 Biosynthetic gene clusters detected in Antarctic genomes...... 155 4.3.6 Biosynthetic gene cluster verification for Streptomyces, Kribbella and Azospirillum isolates...... 158 4.3.6.1 Streptomyces INR7 BGCs...... 158 4.3.6.2 Streptomyces NBH77 BGCs...... 162 4.3.6.3 Streptomyces NBSH44 BGCs...... 167 4.3.6.4 Kribbella SPB151 BGCs...... 170 4.3.6.5 Azospirillum INR13 BGCs...... 171 4.3.6.6 PKS and NRPS gene amino acid sequence similarity to known genomic regions...... 171 4.3.7 NaPDoS analysis of condensation and ketosynthase domains...... 174 4.4 DISCUSSION…………………...... 179
CHAPTER 5 5 DISCUSSION AND CONCLUSIONS...... 185 5.1 RESEARCH MOTIVATIONS AND OBJECTIVES...... 185 5.2 KEY FINDINGS...... 187 viii
5.2.1 Soil fertility is associated with natural product gene presence and diversity in polar desert soils...... 187 5.2.2 Bacterial adaptation to the Antarctic environment includes desiccation-, starvation- and radiation- resistance...... 188 5.2.3 Biosynthetic gene clusters in Antarctic bacteria highlight survival strategies...... 191 5.2.3.1 Carotenoids, siderophores and biosurfactants...... 192 5.2.3.2 Long-chain polyunsaturated fatty acids...... 194 5.2.4 Antarctic soil bacteria contain an abundance of uncharacterised biosynthetic domains...... 195 5.2.5 Eukaryotic cells are targeted by many of the predicted biosynthetic pathways...... 196 5.2.6 Long-read sequencing for natural product domain amplicon and genomic BGC analysis...... 197 5.3 FUTURE DIRECTIONS...... 198
REFERENCES……………………………………...... 202
APPENDICES...... 240
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ACKNOWLEDGMENTS
Firstly, I would like to thank my Supervisor, Belinda Ferrari, who devotes an incredible amount of time to her students, providing motivation, inspiration and support, as well as steering us in challenging directions. I couldn't have asked for a better supervisor. Other special thankyou's go to John Kalaitzis, Josie van Dorst, Mukan Ji, Mark Brown and Brett
Neilan, who have all shared their valuable time and expertise, and to Eden Zhang, who provided the NRPS domain sequencing and processing data for Chapter 2 and worked tirelessly and patiently alongside me to co-author the publication stemming from that research. For genome assembly and annotation, and a great deal of bioinformatics support for genome analysis in Chapter 4, I would like to thank Dr Richard Edwards and Timothy
Amos, UNSW. Also Brigid Betz-Stablein and Mark Tanaka from UNSW Sydney’s Stats
Central for statistical advice in Chapter 2. To Ferrari Lab team not already mentioned, thankyou to the most friendly, helpful and enthusiastic people; Sarita Pudasaini, Sally Crane,
Kate Montgomery, Angelique Ray, Sin Yin Wong, Carolina Gutiérrez-Chávez, Lauren
Williams, Jieyu Liu, Lucien Alperstein, Iskra Nicetic, Chengdong Zhang, and members of other labs who have helped me and shared their knowledge; Tim Williams and James
Charlesworth. I would very much like to thank the AAD’s expedition teams in 2005 and 2012 for sample collection, and Bioplatforms Australia who provided Vestfold Hills biodiversity data. For sequencing I would like to thank Tonia Russell, Dr Carolina Correa Ospina and Dr
Jackie Chan from The Ramaciotti Centre, UNSW. Finally, and most of all, I would like to thank my partner Tony for being so supportive, always encouraging me even when this journey has been extremely challenging for both of us at times, my friends, and my parents who are always there for me, providing love and financial support which has enabled this to be possible at all. Thankyou all.
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LIST OF FIGURES
CHAPTER 1
Figure 1.1 Structural diversity within microbial natural product antibiotics...... 4
Figure 1.2 Examples of bacterial secondary metabolites...... 6
Figure 1.3 Biosynthesis of erythromycin A by the Type I PKS system,
DEBS...... 11
Figure 1.4 Schematic of Module 1 from the trans-AT PKS responsible for
virginiamycin biosynthesis...... 12
Figure 1.5 Schematic of actinorhodin biosynthesis by Type II PKS...... 14
Figure 1.6 Biosynthesis of vancomycin...... 16
Figure 1.7 Morphological examples of major antimicrobial-producing
bacterial phyla...... 17
Figure 1.8 Characteristic developmental cycle of Streptomyces species...... 18
Figure 1.9 Novel in situ cultivation techniques supply microbes with diffusible
nutrients from their natural environment...... 22
Figure 1.10 Under-explored and extreme environments are targets for novel
natural products discovery...... 24
Figure 1.11 Cold-adapted bacteria lower the melting temperature of their
membrane phospholipids, through modification of fatty acid
(FA) components...... 27
Figure 1.12 Map of Antarctica...... 29
Figure 1.13 The Arctic climate is moderately less extreme than the Antarctic and
supports more animal and vascular plant life...... 31
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Figure 1.14 Bacterial phylogenetic diversity of McMurdo Dry Valleys,
eastern Antarctica...... 32
CHAPTER 2
Figure 2.1 Maps of eastern Antarctica highlighting Windmill Islands and Vestfold
Hills regions...... 42
Figure 2.2 Map of the high Arctic, focussing on Ellesmere Island and
Svalbard...... 43
Figure 2.3 Geospatial transect sampling design...... 46
Figure 2.4 Capture of natural product diversity in polar soils...... 58
Figure 2.5 PKS domain sequence taxonomy by genera and phyla, assigned
through BLASTx analysis...... 60
Figure 2.6 NRPS domain sequence taxonomy by genera and phyla, assigned
through BLASTx analysis...... 63
Figure 2.7 Phylogenetic relationship of PKS protein sequences with reference
bacteria based on BLASTx output...... 65
Figure 2.8 Phylogenetic relationship of NRPS protein sequences against
reference bacteria based on BLASTx output...... 66
Figure 2.9 Soil bacterial diversity observed from 16S amplicon sequencing of
soil from each of the 12 sites analysed...... 67
Figure 2.10 Actinobacterial diversity by Order and Family, observed from 16S
amplicon sequencing...... 68
Figure 2.11 Natural product gene amplification revealed significant relationships
with soil carbon, and dry matter fraction...... 70
Figure 2.12 Natural product gene association with soil fertility factors...... 72
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Figure 2.13 Natural product gene nMDS analysis...... 73
Figure 2.14 Bacterial community 16S rDNA gene analysis and measured soil
parameters show clustering similarities...... 74
Figure 2.15 Natural product domain sequence novelty when compared to known
secondary metabolite protein sequences for NRPS and PKS...... 75
CHAPTER 3
Figure 3.1 Under starvation conditions Myxococcales form conspicuous,
macroscopic fruiting bodies...... 81
Figure 3.2 Antarctic soils used for culturing were selected from three
pristine polar deserts and one human-impacted site...... 84
Figure 3.3 Bacterial 16S rDNA diversity for the three pristine samples cultured;
HI, MP and RL...... 86
Figure 3.4 Direct soil culturing using both E. coli lawn and cellulose baiting
methods on WCX agar plates...... 89
Figure 3.5 Direct soil culturing using the E. coli lawn method with the addition
of rabbit dung pellets...... 90
Figure 3.6 Principles of the soil substrate membrane system (SSMS)...... 92
Figure 3.7 Flowchart for bacterial cultivation using cold-incubated SSMS...... 94
Figure 3.8 Pattern of inoculation for cross-streak agar assay...... 102
Figure 3.9 Substrate mycelium-like filaments were observed by microscopy of
direct soil cultures...... 105
Figure 3.10 Visible colonies were directly picked from soil cultures using a
sterile toothpick and stereomicroscopy...... 106
Figure 3.11 Colony morphology for six different Streptomyces isolates...... 110
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Figure 3.12 DSC isolates with ≤ 98% 16S rDNA gene sequence similarity to
known species...... 111
Figure 3.13 Cold-incubated SSMS microcolonies visualised using epi-fluorescence
microscopy...... 113
Figure 3.14 Bacteria cultured from HI by the cold-incubated SSMS...... 115
Figure 3.15 Cultured bacterial species recovered across four Antarctic soils by DSC
and the SSMS...... 117
Figure 3.16 Carotenoid-like pigmentation was observed in half of all cultured
isolates...... 118
Figure 3.17 Cross-streak antimicrobial assay for bacterial isolates...... 119
CHAPTER 4
Figure 4.1 Functional classification of protein-coding genes in Antarctic
bacterial genomes by abundance of Clusters of Orthologous
Groups (COGs)...... 148
Figure 4.2 Maximum likelihood phylogenetic tree of 16S rDNA gene for
Streptomyces Antarctic isolates...... 151
Figure 4.3 Maximum likelihood phylogenetic tree of 16S rDNA gene for
Antarctic isolates belonging to the Actinobacteria phylum
(excepting Streptomyces)...... 153
Figure 4.4 Maximum likelihood phylogenetic tree of 16S rDNA gene for
Antarctic isolates belonging to the Proteobacteria phylum...... 154
Figure 4.5 Maximum likelihood phylogenetic tree of 16S rDNA gene for
Bacteroidetes Antarctic isolate Hymenobacter NBH84...... 155
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Figure 4.6 Biosynthetic gene clusters detected by AntiSMASH in Antarctic
bacterial genomes...... 157
Figure 4.7 Circular representation of the Streptomyces isolate INR7
genome...... 160
Figure 4.8 The Streptomyces INR7 genome contains an NRPS BGC, Region 25,
with 100% gene similarity to the tambromycin BGC...... 162
Figure 4.9 Circular representation of the Streptomyces isolate NBH77
genome...... 164
Figure 4.10 The Streptomyces isolate NBH77 NRPS-Type I PKS BGC,
Region 3...... 166
Figure 4.11 Circular representation of the Streptomyces isolate NBSH44
genome...... 168
Figure 4.12 The putative plasmid, contig 2, carried by Streptomyces
NBSH44...... 170
Figure 4.13 Circular representation of the Kribbella isolate SPB151 genome…. 172
Figure 4.14 The Azospirillum isolate INR13 harbours a potential polyunsaturated
fatty acid (PUFA) synthase cluster...... 174
Figure 4.15 Phylogenetic analysis of ketosynthase domains by maximum
likelihood method against NaPDoS database domains...... 177
Figure 4.16 Phylogenetic analysis of condensation domains by maximum
likelihood method against NaPDoS database domains...... 178
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LIST OF TABLES
CHAPTER 2
Table 2.1 Mean annual weather statistics for regions within eastern Antarctica
and the high Arctic...... 44
Table 2.2 PCR primers and conditions for amplification of PKS ketosynthase/
acyl transferase domains, and NRPS adenylation domains...... 50
Table 2.3 Analyses of relationship between natural product gene presence and
total carbon (TC), total nitrogen (TN) and dry matter
fraction (DMF)...... 71
CHAPTER 3
Table 3.1 Location and soil characteristics for selected Antarctic soils...... 85
Table 3.2 Primer sets employed for PCR targeting 16S bacterial rDNA, PKS and
NRPS domain fragments...... 99
Table 3.3 Strains isolated in previous studies which were screened for PKS and
NRPS domains and antimicrobial activity...... 103
Table 3.4 Phylogenetic distribution of bacterial species cultured from all sites
by DSC...... 108
Table 3.5 Phylogenetic distribution of isolates cultured from Herring Island by
cold-temperature SSMS...... 114
Table 3.6 SSMS followed by liquid media enrichment conditions for
recovered species...... 116
Table 3.7 Natural product domain amplification and in situ antimicrobial activity
for strains isolated in this study...... 120
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Table 3.8 Natural product domain amplification and in-situ antimicrobial activity
for isolates from previous studies...... 123
Table 3.9 Characteristics used to select eighteen strains for whole
genome sequencing...... 125
CHAPTER 4
Table 4.1 Genomic DNA extraction methods for Antarctic bacteria...... 133
Table 4.2 Distribution of isolates within all three multi-genome DNA
libraries...... 139
Table 4.3 Sequencing output and assembly summaries for three multi-
genome libraries...... 144
Table 4.4 Antarctic bacterial genome assembly and quality assessments...... 145
Table 4.5 Predicted protein-coding sequences and CDS assigned to COGs...... 147
Table 4.6 Proportion of Antarctic bacterial genomes dedicated to secondary
metabolite biosynthetic clusters...... 156
APPENDICES...... 240
APPENDIX ONE (CHAPTER 2)...... 240
Appendix Table A1.1 Taxonomic classification of PKS KS/AT
domain sequences when analysed using both nucleotide BLASTn and
translated protein sequence BLASTx algorithms...... 240
Appendix Table A1.2 Taxonomic classification of NRPS AD
domain sequences when analysed using both nucleotide BLASTn and
translated protein sequence BLASTx algorithms...... 247
APPENDIX TWO (CHAPTER 3)...... 257 xvii
A2.1 STOCK SOLUTIONS...... 257
A2.2 MEDIA...... 258
APPENDIX THREE (CHAPTER 4)...... 260
A3.1 MEDIA...... 260
Appendix Table A3.1 Representative reference genomes chosen for
mapping to Antarctic bacterial libraries, with corresponding quality
measures determined by CheckM...... 261
Appendix Table A3.2 Biosynthetic gene clusters detected in
Antarctic bacterial genomes by antiSMASH...... 262
Appendix Table A3.3 NRPS gene BLASTp and NaPDos analysis
of condensation domains...... 268
Appendix Table A3.4 Type I PKS gene BLASTp and NaPDos
analysis of ketosynthase domains...... 273
Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and
NaPDos analysis of condensation and ketosynthase domains...... 275
Appendix Table A3.6 Type II PKS gene BLASTp and NaPDos
analysis of ketosynthase domains...... 280
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ABBREVIATIONS
AAD Australian Antarctic Division
AAP Aerobic anoxygenic phototroph
ACP Acyl carrier protein domain
AD Adenylation domain
AF Adams Flat
AFH Alexandra Fjord Highland
AMR Antimicrobial Resistance
AT Acyltransferase domain
A+T Adenine and thymine
ATCC American type culture collection
BGC Biosynthetic gene cluster
BP Browning Peninsula bp Base pairs
C Condensation domain
CHS Chalcone synthase
CRE Carbapenem resistant Enterobacteriaceae
CS Casey station d Day(s)
DEBS 6-deoxyerythronolide B synthase
DH Dehydratase domain
DMF Dry matter fraction
DMSO Dimethyl sulfoxide
DNA 2’deoxyribonucleic acid
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dNTP Deoxynucleotide triphosphate
E Epimerisation domain
EB Elution buffer
ER Enoylreductase domain
FA Fatty Acid
FAS Fatty acid synthase g G-Force g Gram
Gb Giga base pairs
G+C Guanine and cytosine h Hour(s)
HI Herring Island
HTS High-throughput sequencing
HV Heidemann Valley
ISP4 Inorganic salts starch agar kb Kilobase pairs km Kilometre
KR Ketoreductase domain
KS Ketosynthase domain
L Litre
LC-MS Liquid chromatography-mass spectrometry
LC-PUFA Long chain polyunsaturated acid
M Methylation domain
Mb Mega base pairs
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min Minute(s) mL Millilitre mm Millimetres
MP Mitchell Peninsula
MRSA Methicillin resistant Staphylococcus aureus
NA Nutrient agar ng Nanogram
NMR Nuclear magnetic resonance spectroscopy
NP Natural product
NRP Non-ribosomal peptide
NRPS Non-ribosomal peptide synthetase
OW Old Wallow
PBS Phosphate buffered saline
PCP Peptide carrier protein domain
PCR Polymerase chain reaction pmol Picomole
PK Polyketide
PKS Polyketide synthase
PUFA Polyunsaturated fatty acid
R Reduction domain rDNA Ribosomal deoxyribonucleic acid
RL Rookery Lake
RNA Ribonucleic acid
RR Robinson Ridge
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RT Room temperature (~21°C) s Second(s)
SGS Second-generation sequencing
SM Secondary metabolite
SMRT Single Molecule Real-Time sp. Species spp. Species (plural)
TAE Tris-acetate-ethylenediaminetetraacetic acid
TC Total carbon
TE Thioesterase domain
TE buffer Tris-ethylenediaminetetraacetic acid
TGS Third-generation sequencing
TN Total nitrogen
µL Microlitre
µm Micrometre
µM Micromolar v/v volume/volume
WGS Whole genome sequencing
WT Wilkes Tip w/v weight/volume
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CHAPTER ONE
1 INTRODUCTION
1 1.1 Antibiotic resistance drives the need for novel bioactive compounds
2 Antimicrobial resistance (AMR) has been described as one of the primary threats currently
3 facing human health. Predictions of a post-antibiotic era emerged following the discovery, in
4 2008, of a carbapenem-resistant Enterobacteriaceae (CRE) isolate, resistant to all clinically
5 useful drugs (Kumarasamy et al. 2010, WHO, 2015a). The dissemination of CRE holds
6 particular concerns because the Gram-negative Enterobacteria are a common cause of
7 infection, affecting approximately 140,000 people per year in the United States (US) alone.
8 Furthermore, carbapenem drugs are considered the last resort treatment (Ventola 2015, CDC,
9 2013). The mortality rate for those infected by CRE currently stands at 26-44% (Falagas et
10 al. 2014). In addition to CRE, other primary AMR concerns include: multi-drug resistant
11 tuberculosis, which accounts for an estimated 240,000 deaths per year worldwide (WHO,
12 2017); methicillin-resistant Staphylococcus aureus (MRSA), a common cause of nosocomial
13 and community-acquired infections, responsible for over 11,000 deaths per year in the US
14 (CDC, 2013); and the sexually transmitted disease Gonorrhoea, which annually affects over
15 78 million people worldwide, and whose multi-resistance now includes the last-line
16 cephalosporin’s (WHO, 2015b). Incidences of multi-drug resistant fungal infections such as
17 Candida are also on the increase, with limited therapeutic options (Arendrup & Patterson
18 2017). Overall, deaths resulting from resistant infections are double that for non-resistant
19 strains (WHO, 2014).
20
1
21 The development of pan-resistance has prompted the clinical return of drugs discontinued
22 due to poor safety profiling. For example, one of only two treatment options now available
23 for CRE infection is colistin, a polymyxin drug which was removed from human use due to
24 considerable nephrotoxicity (Li et al. 2006, MacNair et al. 2018). Ironically, throughout its
25 human treatment hiatus, colistin has remained in agricultural use, and colistin resistance has
26 already been detected, sourced to pig production (Liu et al. 2016, MacNair et al. 2018). This
27 development highlights the problematic relationship between medicine and agriculture in
28 terms of antibiotic stewardship.
29
30 Antibacterial resistance genes have been present in the environment long before the clinical
31 use of antibiotics (Abraham & Chain 1940, D’costa et al. 2011, Aminov & Mackie 2007),
32 with genes conferring resistance to modern clinical antimicrobials amplified from 30,000-
33 year-old permafrost sediment ice cores (D’costa et al. 2011). Resistance mechanisms are
34 thought to have evolved due to complex competitive relationships between microorganisms
35 and the environment (Aminov & Mackie 2007). However, while the development of AMR
36 is a naturally-occurring evolutionary phenomenon, selection pressures brought on by the
37 overuse and misuse of antibiotics both in health care and agriculture, have accelerated the
38 evolution of resistance (Kumarasamy et al. 2010, Ventola 2015). Additionally, the genes
39 conveying resistance may be easily shared amongst bacteria through horizontal gene transfer
40 (HGT), due to carriage on gene cassettes, integrons and plasmids (Liu et al. 2016, Aminov
41 & Mackie 2007, Baker et al. 2018).
42
43 The discovery of CRE's not only heralded a post-antibiotic era, but highlighted the
44 inadequacies in current antibiotic discovery efforts, particularly against Gram-negative
2
45 pathogens (CDC, 2013, Wyres & Holt 2018). For over a decade there has been a steady
46 decline in antibiotic research and development by pharmaceutical companies, due primarily
47 to economic factors (Baltz 2007, Bérdy 2005, Gilbert 2010). The number of companies
48 pursuing antibiotic research has fallen from 18 in 1990, to just 4 in 2013 (Butler et al. 2013).
49 Antibiotics are time-consuming and expensive to develop; taking 10-15 years, and costing
50 between $800 million and $1.7 billion US dollars to produce. What's more, in comparison
51 with drugs for chronic illness, the profit margin is low, due to short term treatment regimens
52 (Lobanovska & Pilla 2017, IDSA, 2004). This downsizing has had a noticeable impact on
53 the number of new drug approvals, with only two novel drugs approved in the five-year
54 period between 2008-2012, compared with 16 between 1983 and 1987 (CDC, 2013, IDSA,
55 2004). Recent government and health organisation incentives have resulted in a number of
56 fast-tracked approvals, leading to several new antibiotics approved by the FDA in 2014 and
57 2015 (FDA, 2014, FDA, 2015). Unfortunately, none of these new antibiotics address the
58 problem of CRE.
59
60 1.2 Microbial natural products
61 Most antibiotics share a natural product (NP) origin. They are small (< 3000 gmol-1),
62 bioactive molecules produced by microbes (bacteria, fungi, yeasts and slime moulds), plants,
63 and some animals (Bérdy 2005, Harvey et al. 2015, Donadio et al. 2007). NPs have a vast
64 range of known chemical structures, and their bioactivities are similarly broad, ranging from
65 antiviral, antibacterial, antifungal, insecticidal, antitumour, anticholesterol to antiparasitic
66 activities (Newman & Cragg 2012, Shen 2015).
3
Figure 1.1 Structural diversity within microbial natural product antibiotics. β- lactams (e.g. penicillin, cefotaxime, meropenem) contain 4-membered lactam rings.
Aminoglycosides (e.g. gentamicin) contain amino sugar moieties. Streptogramins (e.g. virginiamycin) and macrolides (e.g. erythromycin) both contain macrocyclic lactone rings. Tetracyclines contain four hydrocarbon rings. Ansamycins (e.g. rifamycin) are cyclic structures formed from aromatic moiety and aliphatic chain. Glycopeptides (e.g. vancomycin) and lipopeptides (e.g. daptomycin) are peptides with attached sugar or lipid moieties, respectively. Source: ChemACX database (2018). 4
67 Antimicrobial NP structural groups (Fig. 1.1) include the β-lactams (e.g. penicillin,
68 cephalosporins, carbapenems), glycopeptides (e.g. vancomycin, bleomycin), lipopeptides
69 (e.g. daptomycin), macrolides (e.g. erythromycin, pimaricin), ansamycins (e.g. rifamycin,
70 geldanamycin), tetracyclines (e.g. doxycycline, tetracycline), aminoglycosides (e.g.
71 streptomycin, gentamicin), and streptogramins (e.g. etamycin, virginiamycin) (Harvey et al.
72 2015, Bérdy 2005).
73
74 Despite advances in chemical synthesis methods, microbially-derived NPs remain at the
75 forefront of antibiotic drug discovery (Shen 2015, Harvey et al. 2015, Pye et al. 2017).
76 Combinatorial chemical synthesis, while initially showing enormous potential for
77 development of novel structures, has instead emphasised the major challenge of successful
78 drug design; bioactivity. Of the staggering > 4 million new organic chemicals designed, only
79 0.001% have gone on to become clinically useful drugs, compared to ~0.3% from NP sources
80 (Bérdy 2005, Newman & Cragg 2012). Unlike their synthetic counterparts, NPs have evolved
81 their biological activity. Perhaps it is therefore not surprising that they tend to show better
82 human bioavailability, attributable to similarities between microbial and mammalian
83 metabolites (Bérdy 2005, Harvey et al. 2015).
84
85 Over 23,000 bioactive microbial compounds have been uncovered thus far, with filamentous
86 bacteria and fungi amongst the most prolific producers of those used in medicine and industry
87 (Katz & Baltz 2016, Bérdy 2005). Together, they account for approximately 90% of all
88 clinical antibiotics. Bacteria from the Actinomycetales order (phylum Actinobacteria) are
89 particularly prolific, being responsible for around 65% of antibiotics currently on the market
90 (Bérdy 2005, Newman & Cragg 2012).
5
91 1.3 Natural product biosynthesis
Figure 1.2 Examples of bacterial secondary metabolites. Astaxanthin is a carotenoid
produced by many red-orange pigmented bacteria such as Paracoccus spp. (Tsubokura et
al. 1999). Desferrioxamine, a siderophore, is produced by a diverse range of bacteria
including all Streptomyces spp. (Barona-Gómez et al. 2004). The cyanobacterial toxin
cyanopeptolin contributes to water contamination during blooms of Cyanobacteria such
as Mycrocystis spp. (Faltermann et al. 2014). The lipopeptide biosurfactant and toxin,
syringomycin, is commonly produced by soil and rhizosphere bacteria such as
Pseudomonas spp. (Raaijmakers et al. 2010).
92 6
93
94 Microorganisms produce NPs through secondary metabolism pathways. While not essential
95 for the organism's viability, secondary metabolites are involved in a variety of chemical
96 processes and are hypothesised to confer an evolutionary advantage (Karlovsky 2008,
97 Maplestone et al. 1992). The full extent of their ecological role remains to be determined, but
98 NPs contribute to complex competitive and symbiotic interactions with other organisms
99 (Karlovsky 2008). NPs include toxins such as cyanopeptolin (Fig. 1.2); photoprotective
100 pigments, such as carotenoids and melanins; siderophores, such as desferrioxamines, which
101 facilitate the extraction of iron and other essential metals; and biosurfactants, such as
102 syringomycin (Fig. 1.2) (Fechtner et al. 2011, Raaijmakers et al. 2010).
103
104 Correlations have been reported between the length of microbial genomes and the carriage
105 of NP genes. In genomes under 2 Mb, NP genes appear rare or absent (Donadio et al. 2007,
106 Wang et al. 2014, Jenke-Kodama et al. 2005). Genome size is not the sole factor, however,
107 as they are also absent from certain large bacterial genomes (> 8 Mb). NP-encoding regions
108 are large, and their encoded products are energetically expensive to produce. Their
109 maintenance thus comes with high metabolic cost, suggesting an equally strong selective
110 pressure for their upkeep (Wang et al. 2014, Fischbach et al. 2008, Pickens et al. 2011). Genes
111 encoding NPs are typically arranged in clusters, and in prokaryotes, the genes’ transcription
112 is usually controlled by one operon (Pfeifer & Khosla 2001, Zhu et al. 2014). Genome
113 sequencing has shown that individual Actinomycetales may carry over 30 different
114 biosynthetic gene clusters (BGC). However, the majority are not expressed under general
115 laboratory conditions (Zaburannyi et al. 2014). For example, Streptomyces coelicolor is
116 known to produce five antibiotics, yet its genome reveals 29 predicted BGCs (Liu et al. 2013).
7
117
118 NPs are produced in bacteria through a variety of enzyme-catalysed pathways. The majority
119 are biosynthesised by two mega-enzyme systems: polyketide synthase (PKS), and non-
120 ribosomal peptide synthetase (NRPS) gene families, and hybrids of these systems (Donadio
121 et al. 2007, Newman et al. (2017)). Others include terpene, aminocoumarin, aminoglycoside,
122 nucleoside, alkaloid and ribosomal-peptide pathways (Medema et al. 2011).
123
124 1.3.1 Polyketide Synthases (PKS)
125 PKSs catalyse consecutive condensation reactions between small carboxylic acid derivatives,
126 in a process similar to fatty acid (FA) biosynthesis (Hertweck 2009, Weissman 2015b).
127 Acetyl-CoA commonly forms the starter unit, while extender units are usually malonyl-CoA
128 or methylmalonyl-CoA (Donadio et al. 2007). Although PKS clusters are generally similar
129 in basic structure, and display high genetic conservation of domains, their polyketide (PK)
130 products are a highly diverse group of chemicals, with a broad range of activities (Banik &
131 Brady 2010, Pfeifer & Khosla 2001). They include antibiotics, such as erythromycin;
132 immunosuppressants such as Sirolimus (rapamycin); anti-cholesterol drugs lovastatins; and
133 potent anticancer compounds, epothilones (Pfeifer & Khosla 2001, Katz & Baltz 2016).
134 Diversity is achieved through small variations on the basic building theme, plus additional
135 post-synthesis modifications (Moffitt & Neilan 2003, Hertweck 2009). Several types of PKS
136 are characterised, classified according to their resemblance to the type of fatty acid synthase
137 (FAS), from which they most certainly evolved (Jenke-Kodama et al. 2005). They are
138 designated Types I, II or III; but many also display hybrid functionality (Hertweck 2009,
139 Weissman 2015b).
140
8
141 1.3.1.1 Type I PKS
142 Type I PKS resemble Type I FAS, found in animals and fungi. They are the most versatile
143 and complex of the PKSs (Keatinge-clay 2012). Often, Type I PKS systems construct
144 complex macrolide ring molecules, comprising of 8 to 62 carbons, such as erythromycin (Fig.
145 1.3). The typical Type I PKS, also referred to as cis-AT, modular, or non-iterative PKS, are
146 constructed with multiple catalytic sites arranged along numerous modules, under the same
147 open reading frame (ORF) (Fig. 1.3) (Cuadrat et al. 2018, Davison et al. 2014). Entire BGCs
148 can be upwards of 150 kb in length and are usually high (> 70%) in guanine-cytosine (G+C)
149 content (Laureti et al. 2011, Peiru et al. 2009, Pfeifer & Khosla 2001). Each module is
150 responsible for one extension step during formation of the PK chain. The product is then
151 passed to the next module, in a process analogous to assembly line manufacture (Fischbach
152 & Walsh 2006). Modular, non-iterative Type I PKS are exemplified by the enzyme 6-
153 deoxyerythronolide B synthase (DEBS); which synthesises the macrolide antibiotic
154 erythromycin A (Fig. 1.3), originally isolated from Saccharopolyspora erythraea (Staunton
155 & Wilkinson 1997).
156
157 Since its discovery in the early 1990s, DEBS has been extensively studied (Pfeifer & Khosla
158 2001, Davison et al. 2014). Three core domains, an acyltransferase (AT), an acyl carrier
159 protein (ACP) and a ketosynthase (KS), exemplify a minimal module of Type I PKS (Fig.
160 1.3). The AT domain binds the chosen extender unit, transferring it to the ACP, forming a
161 thioester bond. The KS-domain then catalyses a decarboxylative condensation between this
162 extender unit and the growing intermediate PK bound to the ACP-domain of the previous
163 module (Fig. 1.3) (Cane 2010, Staunton & Wilkinson 1997).
9
164 The loading module of the DEBS PKS contains only AT and ACP sites, and the terminal
165 module contains only a thioesterase (TE) domain, responsible for the completion and release
166 of the PK (Cane 2010). Additional PKS domains can include β-ketoreductase (KR),
167 dehydratase (DH), enoylreductase (ER), and in some cases methylation (M) domains
168 (Donadio et al. 2007).
169
170 Recently, a variant of the Type I PKS system has been described which is thought to have
171 evolved independently; the trans-AT PKS (Helfrich & Piel 2016, Davison et al. 2014, Cheng
172 et al. 2003). While generally resembling cis-AT PKS modularity, trans-AT PKSs feature
173 important distinctions, primarily that the AT domains are discrete, free-standing proteins that
174 act iteratively (Fig. 1.4). Trans-AT PKSs may also exhibit unusual domain type and order,
175 duplicated and functionless domains, and modules split between two proteins. They are also
176 commonly found as hybrid systems incorporating NRPS (Helfrich & Piel 2016, Davison et
177 al. 2014, Wang et al. 2014).
10
Figure 1.3 Biosynthesis of erythromycin A by the Type I PKS system DEBS. The starter unit is propionyl-CoA and the extender units are six methylmalonyl-CoA. Modules involved in the production of 6-deoxyerythronolide include a loading module, six modules for polyketide extension, and a terminal TE which cyclises and releases the product. The loading module contains an AT and ACP, while chain elongation modules 1-6 minimally contain AT, ACP and KS domains. Additional domains are KR, DH and ER, and the
DEBS system comprises three large proteins (DEBS 1, 2 and 3). The final product, erythromycin A, is formed following several post-PKS modifications; two hydroxylations, two glycosylations and a methylation. Adapted from Davison et al.
(2014). 11
178
Figure 1.4 Schematic of Module 1 from
the trans-AT PKS responsible for
virginiamycin biosynthesis, which
includes > nine chain extension modules.
This system exhibits several typical
characteristics of the trans-AT PKS,
including a discrete, iteratively acting AT
domain and duplicated domains (e.g. ACP).
Adapted from Davison et al. (2014).
179
180 General predictions can be made about the chemical structure of unknown PKS products,
181 based on genes within the BGC and their arrangement. As trans-AT PKS lack the co-linearity
182 of cis-AT PKS, structural predictions are more difficult (Davison et al. 2014). For cis-AT
183 PKS, the length of the PKs can be estimated by the number of enzyme modules, and domains
184 within each module can predict the types of reactions the PKs have undergone (Donadio et
185 al. 2007). Remarkably, fragments of KS domain sequences alone can accurately predict BGC
186 end-products. This was demonstrated by Gontang et al. (2010), who PCR-amplified KS
187 domain sequences (~700 bp) with a high level of amino acid sequence similarity (≥ 85%) to
188 previously characterised tetronomycin BGCs, and subsequently confirmed tetronomycin
189 production in harbouring isolates (Gontang et al. 2010).
190
12
191 1.3.1.2 Type II PKS
192 Type II PKS are found almost exclusively in Actinobacteria, but resemble Type II FAS found
193 in plants and bacteria (Hertweck 2009, Das & Khosla 2009). They differ to Type I PKS in
194 that they are collections of separate enzymes, working iteratively with minimal domain
195 functions. Although the enzymes are discrete entities, in vivo it is thought they form
196 complexes similar to the Type I PKS enzymes (Hertweck 2009, Austin & Noel 2003).
197 Additionally, despite having acyl-transferase abilities, their AT-domain is absent. Uniquely
198 among the PKS enzymes, Type II PKS contain two coupled β-ketosynthase subunits: KS and
199 chain length factor (CLF), which form a heterodimer (Hertweck 2009, Kim & Yi 2012). Type
200 II PKS typically produce aromatic, polycyclic structures, such as the tetracyclines (Fig. 1.1).
201 Biosynthesis is exemplified by the production of the isochromanequinone antibiotic
202 actinorhodin by Streptomyces coelicolor, from eight malonyl precursors (Fig. 1.5) (Pfeifer &
203 Khosla 2001, Keatinge-Clay et al. 2004). Tailoring enzymes can include oxygenases and
204 glycosyl, amino- and methyl-transferases. Several subclasses are known, yet many
205 complexities of Type II PKS production remain to be determined (Kim & Yi 2012).
206
207 1.3.1.3 Type III PKS
208 The third type of PKS are often referred to as CHS-type PKS, due to their similarity to the
209 first and most well-known Type III PKS, chalcone synthase (CHS) discovered in plants
210 (Austin & Noel 2003, Shimizu et al. 2017). Type III PKS are not well investigated, with the
211 first bacterial Type III PKS not characterised until 1999. They have a simple architecture,
212 usually consisting of a self-contained homodimer of identical KS domains. Starter building
213 blocks may be ring or chain-type acyl-CoA units, such as benzoyl‐CoA or malonyl-CoA, and
13
Figure 1.5 Schematic of actinorhodin biosynthesis by Type II PKS. PK initiation and
elongation is catalysed by the coupled KS-CLF domains, which perform repetitive
decarboxylative condensations of malonyl precursors, delivered to them as thioesters by
ACP. Malonyl precursors are attached to the ACP by malonyl-CoA:ACP transacylase
(MCAT). Following elongation, the ACP delivers the chain to tailoring enzymes,
including KR, responsible for carbonyl group reduction; aromatases (ARO) and cyclases
(CYC), which catalyse regiospecific cyclisations of the chain. Adapted from Kim & Yi
(2012); and Das and Khosla (2009).
214
215 extender units are usually malonyl CoA (Shimizu et al. 2017). A single active site within
216 each KS domain catalyses PK synthesis through iterative priming, extension, and cyclisation
217 reactions. Other accessory tailoring enzymes perform downstream modifications such as
218 hydroxylation, acetylation, oxidation and methylation. Type III PKs often produce precursor
14
219 molecules for antibiotics, UV protection pigments, antimicrobial resistance enzymes and
220 alternative electron carriers (Austin & Noel 2003, Shimizu et al. 2017).
221
222 1.3.2 Non-ribosomal peptide synthetases (NRPS)
223 NRPS produce many medically useful bioactive compounds, including antibiotics such as
224 penicillin and vancomycin, immunosuppressants like cyclosporine, and anti-tumour drugs,
225 such as bleomycin (Roongsawang et al. 2005, Weissman 2015a). NRPS possess many
226 similarities to Type I PKS, and indeed they may form hybrid mega-enzymes which utilise
227 both peptide and ketide extension units (Weissman 2015a). Like cis-AT PKS, NRPS are
228 large, multi-functional, modular enzymes, which synthesise complex molecules via
229 oligomerisation of smaller building blocks. Here however, the precursors are amino acids or
230 hydroxy acids (Donadio et al. 2007, Weissman 2015a). Three core domains usually make up
231 the minimal NRPS module. In NRPS these are an adenylation (AD) domain, a peptide carrier
232 protein (PCP) and a condensation (C) domain (Fig. 1.6) (Donadio et al. 2007, Weissman
233 2015a). The AD-domain selects and activates the appropriate amino acid. The resultant
234 amino acyl adenylate is then transferred and bound to the PCP via a thioester bond. The C-
235 domain catalyses peptide bond formation between PCP bound adenylates, and the peptidyl
236 intermediate bound to the preceding module’s PCP. In NRPS there is similarly a loading
237 module, containing only the AD- and PCP-domains, and a cyclising and terminating TE
238 domain. Additional domains can include epimerization (E), which convert L-amino acids
239 into the required D-amino acids; reduction (R) and methylation (M) domains (Donadio et al.
240 2007, Roongsawang et al. 2005). A typical NRPS cluster is exemplified by the vancomycin
241 glycopeptide antibiotic pathway, first isolated from Amycolatopsis orientalis (Fig. 1.6).
242 15
Figure 1.6 Biosynthesis of vancomycin. While some mechanisms of the pathway are still
unclear, components of the vancomycin backbone are assembled by an NRPS with three
subunits: VpsA, VpsB and VpsC. The seven modules contain domains with AD, PCP, C,
E and TE functions. Module 7 contains a domain X, for which function remains unknown.
Other tailoring steps include crosslinking by OxyB, OxyA and OxyC P450-like enzymes,
chlorine substitutions and glycosylation. Adapted from Shmartz et al. (2014).
243
244 1.4 Dominant natural product-producing bacterial phyla
245 Within the bacterial kingdom, NPs are disproportionately biosynthesised by four phyla:
246 Actinobacteria, Proteobacteria, Cyanobacteria and Firmicutes (Fig. 1.7) (Donadio et al. 2007,
247 Wang et al. 2014).
248
16
Figure 1.7 Morphological examples of major antimicrobial-producing bacterial
phyla. (A) Actinobacteria: Streptomyces sp. isolated from Antarctic soil. (B)
Proteobacteria: Myxococcus xanthus. Source Velicer & Yu (2003). (C) Cyanobacteria:
Nostoc flagelliforme. Source Feng et al. (2012). (D) Firmicutes: Bacillus sp. isolated from
Antarctic soil.
249
250 1.4.1 The Actinobacteria
251 Actinobacteria form a large proportion of soil microbial biomass, typically measuring 106 to
252 109 cells per gram of soil, and along with fungi are the primary decomposers of organic matter
253 (Barka et al. 2016, Babalola et al. 2009, Sun et al. 2017). The Streptomyces species, order
254 Actinomycetales (Fig. 1.7A), are the most abundant producers of antibiotics (Bérdy 2005,
255 Watve et al. 2001). However, the Actinobacteria phylum is large, and producers are also 17
256 found amongst Mycobacterium, Arthrobacter, Rhodococcus, and Nocardia spp., as well as
257 ‘rare’ Actinobacterial genera, defined as non-Streptomyces that are infrequently brought into
258 culture (Tiwari & Gupta 2013, Subramani & Aalbersberg 2013). While not necessarily scarce
259 in the environment, the rarer taxa are more fastidious, and include Saccharopolyspora,
260 Micromonospora, Streptosporangium, Actinomadura, Streptoverticillium, Kribbella and
261 Actinoplanes (Bérdy 2005, Subramani & Aalbersberg 2013, Lazzarini et al. 2000).
262
Figure 1.8 Characteristic developmental cycle of Streptomyces species. On
germination, spores swell and give rise to a germ tube, which develops into substrate and
aerial mycelium, comprised of multiple threadlike hyphae. Pre-spore compartments
differentiate into spores at the ends of the aerial hypha. The majority of NP biosynthesis
coincides with aerial hyphae growth and spore germination developmental stages.
Adapted from Flärdh & Buttner (2009).
18
263 Many of the Actinomycetales, such as Streptomyces spp., have morphological similarities to
264 fungi, including substrate and aerial mycelium formed from networks of hyphae; and spores,
265 which are resilient to environmental stressors (Flärdh & Buttner 2009, Olano et al. 2014).
266 The Streptomyces lifecycle is initiated by spore germination. Chains of cells branch out,
267 forming substrate mycelium, followed by aerial mycelium. From the aerial hyphae, spores
268 differentiate from apical compartments (Fig. 1.8) (Flärdh & Buttner 2009, Olano et al. 2014).
269 Research shows that the majority of NP expression coincides with the development of aerial
270 hyphae and the stationary phase of growth, in response to nutrient deficiency (Liu et al. 2013,
271 Čihák et al. 2017). NP are also produced during germination, where they are hypothesised to
272 function as signalling molecules, and suppressors of competitor microorganisms (Čihák et
273 al. 2017).
274
275 1.4.2 The Proteobacteria
276 The large and diverse Gram-negative phylum Proteobacteria, is divided into five classes:
277 Alpha- (α), Beta- (β), Gamma- (γ), Delta- (δ) and Epsilon- (ε) proteobacteria. Many genera
278 are well known pathogens, including Escherichia, Pseudomonas, Bordetella,
279 Campylobacter, Neisseria, Legionella, Salmonella and Yersinia genera (Kersters et al. 2006,
280 Gupta 2000).
281
282 Antibiotic producers are found in a diversity of Proteobacterial classes, including
283 Gammaproteobacterial genera such as Pseudomonas and Lysobacter, and family
284 Vibrionaceae (Bérdy 2005, Xie et al. 2012, Mansson et al. 2011). Family Myxobacteria
285 (Deltaproteobacteria) are prolific NP-producers, and have been the source of some unusual
19
286 compounds, such as antitumour drugs epothilones from Sorangium cellulosum, and
287 saframycin from Myxococcus xanthus (Fig. 1.7B) (Wenzel & Müller 2007).
288
289 1.4.3 The Cyanobacteria
290 Cyanobacteria are phototrophic prokaryotes which can produce energy and oxygen through
291 photosynthesis. Morphologically, they are a diverse group, ranging from single-cell forms to
292 large multicellular filaments (Calteau et al. 2014). Cyanobacteria produce a range of
293 bioactive NPs, including antimicrobial and antitumour compounds, and are notorious for
294 producing dangerous toxins such as microcystin when proliferating in water habitats (Calteau
295 et al. 2014, Faltermann et al. 2014). Important NP producing genera within the Cyanobacteria
296 phylum include Anabaena and Nostoc spp. (Fig. 1.7C) (Burja et al. 2001).
297
298 1.4.4 The Firmicutes
299 All Gram-positive bacteria were once grouped under phylum Firmicutes, including the
300 Actinobacteria. The groupings have since been re-designated, whereby the high G+C
301 Actinobacteria and the low G+C Firmicutes form distinct phyla. Firmicutes members
302 comprise endospore-forming genera such as Bacillus (Fig. 1.7D) and Clostridium, notable
303 human pathogens like Streptococcus and Staphylococcus, and the pleomorphic genus
304 Mycoplasma (Galperin 2013). Bacillus and Paenibacillus spp. produce a range of NPs, and
305 are common producers of antibiotic biosurfactant lipopeptides (e.g. iturin, fengycin and
306 surfactin), produced through NRPS pathways (Sansinenea & Ortiz 2011, Bérdy 2005, Sumi
307 et al. 2015).
308
20
309 1.5 Microbial natural product diversity
310 Despite over 70 years of NP research, the diversity of natural chemistry is far from exhausted.
311 Modelling has estimated < 3% of all Streptomyces antibiotics have been uncovered thus far
312 (Bérdy 2005, Watve et al. 2001, Harvey et al. 2015, Clardy et al. 2006). Technological
313 limitations continue to impede discovery of novel compounds: the majority of
314 microorganisms are recalcitrant to cultivation, many isolates do not express antibiotic
315 compounds under standard laboratory conditions, continual re-discovery of known
316 compounds wastes time and resources, and the screening process remains slow and labour
317 intensive, despite attempts at creating more high-throughput technologies (Harvey et al.
318 2015, Palazzolo et al. 2017).
319
320 Conventional methods of cultivation are believed to recover less than 1% of soil
321 microorganisms (Ferrari et al. 2008, Lewis 2013). Fastidious and rare organisms, particularly
322 from the chemically rich Actinobacteria, are expected to harbour a vast untapped source of
323 novel compounds, as NPs recovered from rare Actinobacteria have included unique
324 molecules, such as the medically important aminoglycoside, gentamicin (Fig. 1.1), produced
325 by Micromonospora spp. (Bérdy 2005, Lewis 2013). Thus, new culturing approaches are
326 warranted.
327
328 Over the last decade, novel in situ culturing approaches have improved the recovery of
329 recalcitrant microorganisms, including candidate divisions (Ferrari et al. 2005, Kaeberlein et
330 al. 2002, Nichols et al. 2010a). The strategies similarly exploit extended incubation
331 timeframes, combined with oligotrophic substrates supplying microbes with diffusible
332 substances direct from their natural environment. For example, the soil substrate membrane 21
333 system (SSMS), developed by Ferrari et al. (2005), cultivates microorganisms on the surface
334 of a thin polycarbonate membrane, separated from the originating soil sample by a semi-
335 permeable membrane (Fig. 1.9A).
336
Figure 1.9 Novel in situ cultivation techniques supply microbes with diffusible
nutrients from their natural environment. Examples include (A) SSMS (Ferrari et al.
2008), (B) the iChip (Source: https://news.northeastern.edu/2017/03/21/researcher-
develops-technology-to-advance-antibiotic-discovery/, and (C) the diffusion chamber.
(Source: http://blogs.jcvi.org/2014/08/trapping-microbes-750-miles-north-of-the-arctic-
circle/).
337
338 Nichols et al. (2010a) devised the isolation chip (iChip), a plastic plate containing multiple
339 through-holes, which can be dipped in a microbial/agar suspension. The compartmentalised
340 design provides a high-throughput culturing option as it allows for the isolation of single
341 bacterial cells, and thus pure cultivation in a single step. Following inoculation, semi-
342 permeable membranes are fixed to either side of the agar with external plates, and the iChip
343 is incubated in the natural environment (Fig. 1.9B). Another earlier diffusion chamber
344 method, designed by Kaeberlein et al. (2002) (Fig. 1.9C), was designed for incubation in
22
345 aquatic environments and sediments. Like the iChip, microbes in the diffusion chamber are
346 suspended in a layer of agar and sandwiched between semipermeable membranes.
347
348 In terms of antibiotic development, novel culturing techniques have borne fruit, with the
349 recent discovery of a novel antibiotic from a newly isolated Betaproteobacterial species,
350 Eleftheria terrae, captured using the iChip (Ling et al. 2015). The antibiotic, teixobactin, is
351 a new class of cell wall biosynthesis inhibitor, with activity against Gram-positive pathogens
352 including multi-drug resistant S. aureus. Importantly its mode of action appears to defy the
353 development of resistance over time (Ling et al. 2015). Unfortunately, while the iChip is
354 high-throughput, it is still labour intensive, as indicated by the pathway to teixobactin
355 discovery, which required the screening of fermentation extracts from ~10,000 iChip
356 cultivated bacteria (Nichols et al. 2010a, Ling et al. 2015). Interestingly, the iChip method
357 did not recover Actinobacteria such as Streptomyces spp., as successfully as Proteobacteria
358 and Firmicutes (Nichols et al. 2010a). Nevertheless, in situ cultivation techniques such as the
359 iChip have allowed for increased recovery of bacterial species, with up to 50% of inoculated
360 bacteria forming colonies. With domestication onto standard agar plates enhanced through
361 several rounds of cultivation, the iChip has expanded the success of antibiotic recovery from
362 novel species (Nichols et al. 2010a).
363
364 1.6 Cold-adapted bacteria as a source of novel natural products
365 In the search for new bacterial species and novel NP compounds, the focus is increasingly
366 turning to under-explored and extreme environments, including hydrothermal vents, caves,
367 rainforests, deserts, oceans and polar regions (Fig. 1.10) (Dhakal et al. 2017, Lazzarini et al.
368 2000, Bérdy 2005). These environments are under-explored due to limited accessibility and 23
369 pose unique survival challenges for resident microbiota. As a result, they are known to
370 harbour unique
371
Figure 1.10 Under-explored and extreme environments are targets for novel natural
products discovery. They include (A) caves, (B) polar regions, (C) hyperthermal vents,
(D) oceans, (E) deserts and (F) rainforests.
(Image sources: (A) J. Spies, http://yourshot.nationalgeographic.com | (B) C. Anthony,
NSF, https://photolibrary.usap.gov | (C) NSF/NOAA, https://www.pmel.noaa.gov/ | (D)
J. Reed http://dorsrv1.fau.edu/ | (E) J-C. Latombe, http://ai.stanford.edu/ | (F) F.
Fakhrurrazi https://www.nationalgeographic.com).
372
373 microorganisms, whose metabolic pathways yield valuable new enzymes and metabolites (Ji
374 et al. 2017, Terpe 2013, de Pascale et al. 2012). For example, biotechnologically vital 24
375 polymerases, such as thermostable high-fidelity pfu, have been isolated from
376 hyperthermophiles such as Pyrococcus furiosus, which grows optimally at 100°C (Lundberg
377 et al. 1991), while cold-adapted organisms are a source of industrially useful antifreeze
378 proteins, such as Afp1, isolated from the psychrophilic yeast Glaciozyma antarctica (Hashim
379 et al. 2013).
380
381 The Earth is primarily classified as a cold environment. Collectively, polar and alpine areas,
382 deep oceans, subterranean caves, and the upper atmosphere represent an estimated 85% of
383 the biosphere and maintain a temperature of 5°C or less. About 90% of Earth’s oceans, and
384 26% of land regions are consistently ≤ 5°C (Margesin & Miteva 2011). A large proportion
385 of Earth's microbial diversity is therefore cold-adapted, classified as either psychrophiles or
386 psychrotrophs according to optimal and maximum growth temperatures when in culture
387 (Morita 1975). The accepted definition of a psychrophile is a bacterium or archaeum with an
388 optimal growth temperature of 15°C or less, and a maximum growth temperature of 20°C.
389 Conversely, psychrotrophs are those which grow at low temperatures but whose optimal
390 growth temperature is > 15°C, with a maximum of 30°C (Chintalapati 2004, Blanc et al.
391 2012, Morita 1975). Thus far, true psychrophiles have been cultured from environments
392 which remain consistently at or below 4°C, such as oceans, sea ice, and sediments (Bowman
393 et al. 2005). Permafrosts and polar soils typically harbour psychrotrophic rather than
394 psychrophilic microbial isolates (Morita 1975, De Maayer et al. 2014), though questions
395 remain regarding the appropriateness of sample collection, storage and culturing techniques
396 for the successful capture of psychrophiles from these environments (Morita 1975, De
397 Maayer et al. 2014, Soina et al. 2004).
398
25
399 Unicellular microorganisms lack the physiology to regulate their temperature, therefore cold-
400 adapted microorganisms have evolved various structural and functional mechanisms to
401 enable survival and metabolism at sub-zero temperatures (Casanueva et al. 2010). Cold–
402 adapted bacteria possess modified proteins, amino acids, and cell wall and membrane
403 components (Margesin & Miteva 2011, Nikrad et al. 2016). For example, as well as
404 antifreeze proteins, microbes adapted to survive in extremely cold conditions produce unique
405 lipids and biosurfactants (Janek et al. 2010, Gentile et al. 2003). Dormancy is also a survival
406 strategy, achieved through formation of spores, and cyst-like resting cells which display
407 capsularised, thickened cell walls resistant to a range of environmental stressors (Soina et al.
408 2004). These resting forms exhibit strongly reduced respiration and metabolism
409 (Blagodatskaya & Kuzyakov 2013). Further, it has been suggested that energy sourced
410 through the scavenging of atmospheric trace hydrogen plays a significant role in their
411 persistence (Blagodatskaya & Kuzyakov 2013, Greening et al. 2015). Previous researchers
412 have examined whether microorganisms in sub-zero environments are metabolically active.
413 The lowest recorded temperature of activity thus far has been by Panikov and colleagues,
414 who measured metabolic respiration occurring in an Arctic soil community at a remarkable
415 −39°C (Panikov et al. 2006).
416
417 Arguably the most significant adaptation in cold-adapted microorganisms are changes to the
418 cell membrane. Referred to as homeoviscous adaptation, the process involves maintenance
419 of the membrane bilayer and viscosity via alteration of FAs incorporated into the membrane
26
Figure 1.11 Cold-adapted bacteria lower the melting temperature of their membrane
phospholipids, through modification of fatty acid (FA) components. Changes include
a decrease in saturated FAs such as hexadecanoic acid, and increases in methyl-branched
and unsaturated FAs, such as anteiso-heptadecanoic acid, and cis-9-hexadecanoic acid. A
small group of microorganisms synthesise and insert long chain polyunsaturated FA
(PUFA), such as docosahexaenoic acid (DHA) into their membrane (Kralova 2017,
Chintalapati 2004).
420
27
421 (Ernst et al. 2016, Kralova 2017). Changes typically include a proportional increase in
422 methyl-branched and unsaturated FA, with increased cis-configuration. Incorporation of
423 these types of FAs decreases the melting temperature of membrane phospholipids,
424 maintaining membrane fluidity and nutrient transport at cold temperatures (Fig. 1.11)
425 (Okuyama et al. 2007, Bianchi et al. 2014, Nichols et al. 1993, Kralova 2017). Some
426 psychrophilic bacteria incorporate long-chain polyunsaturated fatty acids (LC-PUFA), such
427 as docosahexaenoic acid (DHA) (Fig. 1.11).
428
429 The synthesis of unusual FAs in cold-adapted bacteria bears relevance to antibiotic
430 bioprospecting in polar regions. The evolutionary relatedness of PKS and FAS, and their
431 sharing of precursors, suggests that cold-adapted microorganisms harbouring atypical FAs
432 may likewise have evolved unique PKs.
28
433 1.7 Polar terrestrial environments and their microbial diversity
434
Figure 1.12 Map of Antarctica. Only 0.36% of the Antarctic continent is ice-free, and
coastal ice-free areas support the majority of occupied research stations (a selection of
USA, UK and Australian stations are shown in red). East and West Antarctica are divided
by the Transantarctic mountains. Adapted from USGS (2008).
435
29
436 Antarctica is one of Earth’s harshest environments. Of its 13,661,000 km², only 0.36% of the
437 continent is ice-free (Fig. 1.12) (Babalola et al. 2009, Ji et al. 2017). The Antarctic continent
438 is described as the “highest, driest, windiest and coldest” place on Earth (Yergeau et al. 2012),
439 with temperatures ranging from a record minimum of -89°C, to a summer maximum of
440 +10°C, and a yearly average of -10°C in coastal areas to -60°C inland (Scambos et al. 2018,
441 Cary et al. 2010). In comparison to Antarctica, the regional Arctic climate is less extreme,
442 and as such supports a greater diversity of animal and vascular plant life (Fig. 1.13) (Williams
443 et al. 2017). For example, in the high Arctic archipelago of Svalbard, Norway, a minimum
444 record temperature of -46°C, and maximum of +21°C have been experienced, with a yearly
445 average of around −5°C (Piskozub 2017, NMI, 2017).
446
447 Polar soils comprise an upper 'active' layer, covering a deeper layer of permafrost; ground
448 which retains a temperature ≤ 0°C for a minimum of two years consecutively (Janet &
449 Neslihan 2014, Stewart et al. 2012, Makhalanyane et al. 2015b). In both the Arctic and
450 Antarctica, active layer soils are subjected to alternating long seasonal periods of darkness
451 and high UV radiation, and regular freeze-thaw cycles (Obbels et al. 2016, Cary et al. 2010,
452 Stomeo et al. 2012).
453
454 Molecular surveys have revealed a much greater microbial richness in Arctic desert soils
455 when compared with Antarctica. For example, Ferrari et al. (2015) found up to 6-fold higher
456 microbial richness in high Arctic desert regions compared with eastern Antarctica. In eastern
457 Antarctic soils, bacteria dominate, with eukaryotic and archaeal richness an estimated 17-
458 fold and 40-fold lower respectively (Zhang et al. 2019, Ji et al. 2017, Ferrari et al. 2015).
459 Furthermore, fungal groups make up only 10% of eukaryotic diversity (Zhang et al. 2019),
30
460 compared with ~48% in Arctic tundra (Shi et al. 2015). These differences have been
461 attributed to increased Arctic soil fertility, and co-presence of vegetation, insect and animal
462 life (Ferrari et al. 2015, Siciliano et al. 2014). Regardless, despite being markedly low in
463 moisture and nutrients, Antarctic soils harbour surprisingly diverse bacterial communities,
464 spanning over 60 phyla (Cary et al. 2010, Ferrari et al. 2015, Ganzert et al. 2011, Zhang et
465 al. 2019).
466
Figure 1.13 The Arctic climate is moderately less extreme than the Antarctic and
supports more animal and vascular plant life. (A) The barren landscape of Adams Flat,
in the Vestfold Hills region of eastern Antarctica. (B) Vestpynten, Svalbard in the high
Arctic, showing the presence of vegetation. Photographs courtesy of AAD.
467
468 Permafrosts and polar desert soils have consistently revealed a high proportion of
469 Actinobacteria and Proteobacteria, while other dominant phyla include Acidobacteria,
470 Bacteroidetes, Chloroflexi, Gemmatimonadetes Deinococcus-Thermus and Cyanobacteria
31
471 (Ferrari et al. 2015, Ji et al. 2016, Cary et al. 2010, Jansson & Taş 2014, Yergeau et al. 2007,
472 Aislabie et al. 2008). For example, in the McMurdo Dry Valleys, Proteobacteria dominated
473 molecular studies with 23% abundance, followed by Actinobacteria (20%) (Fig. 1.14) (Cary
474 et al. 2010).
475
Figure 1.14 Bacterial phylogenetic diversity of McMurdo Dry Valleys, eastern
Antarctica. Screening of 16S rDNA gene bacterial diversity shows that terrestrial
Antarctic is typically comprised of a high abundance of Actinobacteria and Proteobacteria,
with similar phylogenetic profiles to those seen here. Adapted from Cary et al. (2010).
476
477 While Actinobacteria and Proteobacteria are environmentally ubiquitous, they appear well-
478 adapted to life at Earth’s poles. The high relative abundance of these prolific NP-producers 32
479 in polar regions increases their attractiveness as targets for bioprospecting (Núñez-Pons &
480 Avila 2015). Furthermore, phylogenetic studies have confirmed that the majority of
481 Actinobacteria uncovered by Antarctic molecular studies remain to be cultured (Babalola et
482 al. 2009).
483
484 Previously, microbial research efforts in Antarctica have focused mainly on ice-free coastal
485 areas in proximity to occupied research stations, such as Victoria land near McMurdo station,
486 and the maritime region of the Antarctic Peninsula to the west of the continent (Fig. 1.12)
487 (Pulschen et al. 2017, Chong et al. 2012). Consequently, aquatic samples and marine
488 sediments are the focus of most studies, while desert soil microbial studies remain rare,
489 particularly for eastern Antarctica (Wilkins et al. 2013, Zhu et al. 2015, Nichols et al. 1999).
490 Bacteria cultured from terrestrial Antarctica thus far show a dominance of Actinobacteria,
491 Proteobacteria, Firmicutes and Bacteroidetes phyla (Smith et al. 2006, Cary et al. 2010,
492 Zdanowski et al. 2013, Pudasaini et al. 2017, Chong et al. 2015).
493
494 In terms of NPs discovery, gene screening and bioactivity surveys on Antarctic isolates have
495 been modest in number and scale. However, bacteria isolated from sediments, soils, penguin
496 rookeries, permafrosts and glacial waters have been screened for PKS and NRPS gene
497 amplicons and/or antimicrobial activity (Shekh et al. 2011, Zhao et al. 2011, Zhao et al. 2008,
498 Encheva-Malinova et al. 2014, Yi Pan et al. 2013, Gesheva 2010, Silva et al. 2018, Lee et al.
499 2012). For example, Zhao et al. (2011, 2008) used molecular techniques to analyse Antarctic
500 coastal sediments for Type I PKS and NRPS genes. Analysis showed genes with closest
501 homology primarily to members of Cyanobacteria, Firmicutes and Proteobacteria. The
502 sequences exhibited low sequence similarity (~50-80%) to known gene sequences (Zhao, et
33
503 al., 2008; Zhao, et al., 2011). In antimicrobial assays, cultured Antarctic bacteria have
504 displayed activity predominantly against Gram-positive genera (e.g. Staphylococcus and
505 Bacillus) and fungi (e.g. Candida), including some multi-resistant strains (Gesheva 2010,
506 Shekh et al. 2011, Lee et al. 2012).
507
508 1.8 Molecular technologies for natural products discovery
509 An unsolved question remains whether harsh polar landscapes are worthy targets for NP
510 biomining compared with mesophilic soils. Perhaps microorganisms in polar deserts, at much
511 lower numbers than their temperate soil counterparts, have not been required to evolve the
512 same chemical competitive advantages, but instead gain greater fitness through physiological
513 changes. Conversely, with so few resources available to share, competitive advantages
514 provided through secondary metabolism may be a key to survival.
515
516 Molecular technologies such as high-throughput sequencing (HTS) offer a means to answer
517 this question, via whole genome and metagenome mining for BGC clusters, as well as
518 amplicon screening of NP genes from environmental DNA. HTS technologies, while not
519 without limitations, have vastly improved our understanding of microbial ecology, and
520 provide a more accurate estimation of diversity by taking into account the uncultured
521 majority (Caporaso et al. 2011, Hugenholtz et al. 2016, van Dijk et al. 2018). For example,
522 in several recent studies, Charlop-Powers et al. (2014, 2015, 2016), used HTS platforms
523 Roche 454 and Illumina to assess PKS and NRPS amplicons from geographically and
524 chemically diverse soils throughout the USA, Asia, Africa, Hawaii, Australia and the
525 Dominican Republic. Soil types included temperate and alpine forests, rainforests, hot
526 deserts, coastal sediments and urban parkland. Their results suggest arid soils present the 34
527 greatest biosynthetic potential, and that a population bias toward NP-rich phyla such as
528 Actinobacteria in these soil types contributes to greater PKS and NRPS richness (Charlop-
529 Powers et al. 2014, Charlop-Powers et al. 2015). More recently, molecular techniques were
530 used to survey PKS and NRPS genes in soil bacterial communities from diverse locations
531 including the Antarctic Peninsula. The authors found that Antarctic soils harboured endemic
532 NP sequences with low similarity to known compound sequences (Borsetto et al. 2019).
533
534 HTS platforms can be broadly characterised into short- or long-read technologies, and the
535 selection of a specific platform involves inevitable trade-offs between cost, coverage,
536 accuracy, and resolution (Goodwin et al. 2016). Short-read, second generation sequencing
537 (SGS) technologies (~50-300 bp), are exemplified by Illumina, who dominate the field by
538 providing instruments of the highest throughput and accuracy at the lowest cost (e.g. MiSeq,
539 HiSeq) (Goodwin et al. 2016, Levy & Myers 2016, Sedlazeck et al. 2018). For Illumina
540 instruments, limitations include an under-representation of A+T-rich and G+C-rich regions,
541 and difficulty in resolving long repetitive regions and structural variations, which become
542 particularly apparent in de novo genome and metagenome assembly (Goodwin et al. 2016,
543 van Dijk et al. 2018, Chen et al. 2013). Repetitive elements can comprise up to 10% of a
544 bacterial genome, spanning lengths far greater than that achievable by short-read
545 technologies. This inevitably leads to fragmentation, misassembles and genome gaps which
546 are difficult or impossible to resolve (Goodwin et al. 2016, Levy & Myers 2016, Miller et al.
547 2017). Importantly, essential and functional genes have been missed, such as ribosomal RNA
548 operons and transposons, discovered through direct comparison with long-read assemblies
549 (Driscoll et al. 2017, Hoefler et al. 2013).
550
35
551 These challenges are similarly relevant to NP discovery efforts. BGCs, including those of
552 PKS and NRPS, regularly span long (~20 kb) contiguous genomic regions, are rich in G+C
553 content, and, due to the highly-conserved and modular nature of the clusters, are repetitious
554 (Miller et al. 2017, Laureti et al. 2011, Gomez-Escribano et al. 2016, Nakano et al. 2017).
555 Fragmentation can be deleterious to the accurate annotation of these biotechnologically
556 important gene clusters (Goldstein et al. 2019, Hoefler et al. 2013). BGCs are examined by
557 NP chemists to make predictions about substrate selection, chemical structure,
558 stereochemistry, mechanisms of action and binding targets (Miller et al. 2017, Donadio et al.
559 2007). For cryptic pathways, where heterologous expression may present the most attractive
560 approach to large-scale compound production, accurate resolution of the complete BGC
561 sequences is fundamental (Miller et al. 2017).
562
563 Third-generation sequencing (TGS) long-read platforms, such as Pacific Biosciences
564 (PacBio) single-molecule real-time (SMRT) (e.g. RS II, Sequel) and Oxford Nanopore
565 Technologies (ONT) (e.g. MinION) instruments are capable of resolving regions unable to
566 be determined by short-read sequencing by producing 8 kb to > 1 Mb reads, at the expense
567 of lower throughput, higher error rate and cost (Goodwin et al. 2016, Levy & Myers 2016,
568 Sedlazeck et al. 2018, Payne et al. 2018). For long-read technologies, the high error rate,
569 occurring most commonly as random indels in raw data at a frequency as high as 15% for
570 SMRT, and 30% for nanopore, is the primary weakness (van Dijk et al. 2018, Goodwin et al.
571 2016). Fortunately, the stochastic nature of the errors enables correction through repeated
572 sequencing of single molecules, which is a feature of SMRT, but not currently for nanopore
573 (van Dijk et al. 2018), or repeated coverage of the same genomic region which can then
574 undergo consensus polishing. For SMRT sequencing, a high consensus accuracy of
36
575 ~99.999% can be achieved with coverage ~30 x (Goodwin et al. 2016, Levy & Myers 2016,
576 Nakano et al. 2017).
577
578 1.9 Thesis scope and aims
579 Terrestrial Antarctica is one of the most extreme habitats on Earth and remains under-
580 explored in terms of microbial and chemical diversity. Antarctic soils are dominated by
581 Actinobacteria and Proteobacteria, phyla proven to be rich sources of bioactive metabolites.
582 Cold-adaption in microorganisms is facilitated by modifications with relevance to secondary
583 metabolism, such as the biosynthesis of unique FAs, which are evolutionarily related to
584 secondary metabolites, PKs. A handful of small-scale studies have demonstrated
585 antimicrobial potential in polar bacterial isolates, but little is known regarding the
586 biosynthetic potential of east Antarctic desert soil communities. Recently, an analysis of
587 antimicrobial-associated genes across a range of soil types has suggested that arid soils offer
588 the greatest biosynthetic potential. We therefore hypothesised that the extremely limiting
589 environmental conditions of polar deserts may provide a rich source of unique NP genes and
590 compounds, with bioactivities that may contribute to the success and survival of the dominant
591 phyla in these regions, the Actinobacteria and Proteobacteria.
592
593 We aimed to determine the novel NP capacity of cold-adapted bacteria from under-
594 investigated regions of eastern Antarctica and the high Arctic, using both culture-independent
595 methods harnessing the latest in HTS technology, as well as novel culture-dependent
596 approaches. The primary objectives of this research were: (1) to identify polar soil
597 communities with novel biosynthetic potential by conducting a first-of-its-kind, large scale
598 investigation into NP-encoding genes in polar desert soils, targeting bacterial PKS and 37
599 NRPS-encoding genes, (2) to establish a culture collection of Antarctic isolates with
600 demonstrated bioactive capabilities, cultured using novel approaches targeting the
601 Actinobacteria and Proteobacteria phyla from soils exhibiting diverse and novel NP genes,
602 (3) to perform whole genome sequencing (WGS) on a number of the most promising isolates
603 with antimicrobial activity, NP genes, or other biotechnological value such as pigmentation,
604 and to conduct a deep investigation to uncover their novel natural product potential through
605 biosynthetic gene cluster (BGC) mining.
38
39
CHAPTER TWO
2 HARNESSING LONG-READ AMPLICON SEQUENCING
TO UNCOVER NRPS AND TYPE I PKS GENE
SEQUENCE DIVERSITY IN POLAR DESERT SOILS
This Chapter has been published as:
Benaud, N., Zhang, E., van Dorst, J., Brown, M.V., Kalaitzis, J.A., Neilan, B.A., Ferrari,
B.C. (2019). Harnessing Long-Read Amplicon Sequencing to Uncover NRPS and Type I
PKS Gene Sequence Diversity in Polar Desert Soils. FEMS Microbiology Ecology.
doi.org/10.1093/femsec/fiz031.
1 2.1 INTRODUCTION
2 2.1.1 The polar deserts of East Antarctica and the High Arctic
3 The majority of ice-free regions in Antarctica (Fig. 2.1A) and the high Arctic (Fig. 2.2A) are
4 classified as polar deserts, collectively spanning approximately 5 million km2 of terrestrial
5 Earth (Barry & Hall-McKim 2018, Goryachkin et al. 1999). Annual precipitation in these
6 systems compares to that of dry deserts, such as the Sahara and Gobi (Campbell & Claridge
7 1987b). The bioavailability of water is further restricted as precipitation falls mainly as snow
8 (Genthon et al. 2018, Campbell & Claridge 1987b, Lesins et al. 2010). In addition, very low
9 seasonal atmospheric humidity contributes to remarkably low surface soil water content, with
10 water availability reported as one of the most important variables influencing the activity and 40
11 distribution of polar desert biota, along with low soil nutrients (Stomeo et al. 2012, Obbels
12 et al. 2016, Campbell & Claridge 1987a). Polar deserts are largely devoid of vegetation,
13 which is driven by sub-zero temperatures and high aridity (Fig 1.13). In turn, carbon, nitrogen
14 and phosphorous are strongly limited, leading to extremely reduced soil biodiversity,
15 particularly in Antarctica (Siciliano et al. 2014, Cary et al. 2010, Campbell & Claridge 1987a,
16 Maestre et al. 2015).
17
18 Eastern Antarctica is home to several permanently occupied Australian research facilities
19 including Casey and Davis stations (Fig. 2.1). Casey station is situated in the Windmill
20 Islands region of Wilkes Land (Fig. 2.1B), an ice-free oasis of low lying (< 110 m) islands
21 and five major peninsulas (Clark, Bailey, Mitchell, Robinson Ridge and Browning
22 Peninsulas) (Goodwin 1993, Melick et al. 1994). Davis station is situated 1400 km from
23 Casey, in the Vestfold Hills, Princess Elizabeth Land (Fig. 2.1C). The low lying (< 160m)
24 Vestfold Hills region features three main peninsulas (Mule, Broad and Long Peninsulas), and
25 numerous sea-inlets and lakes (Kiernan & McConnell 2001, Verleyen et al. 2011, Seppelt et
26 al. 1988). Regional weather conditions in the Vestfold Hills are slightly milder on average
27 than those of the Windmill Islands (Table 2.1), although both experience low annual
28 precipitation (< 200 mm) (Campbell & Claridge 1987b, Seppelt et al. 1988, Melick et al.
29 1994).
41
Figure 2.1 Maps of eastern Antarctica highlighting Windmill Islands and Vestfold
Hills regions. (A) Antarctic continent showing location of Vestfold Hills and Windmill
Islands regions. (B) Windmill Islands region showing the location of Casey Station (CS),
Mitchell Peninsula (MP), Robinson Ridge (RR), Herring Island (HI) and Browning
Peninsula (BP) (C) The Vestfold Hills, depicting Davis station, Adam’s Flat (AF),
Heidemann Valley (HV), Old Wallow (OW) and Rookery Lake (RL). Maps adapted from
AADC (2017), photographs of MP and AF sampling sites courtesy of AAD and Tom
Mooney.
30
42
Figure 2.2 Map of the high Arctic, focussing on Ellesmere Island and Svalbard. Polar deserts are typically located > 75° N. (A) The Arctic circle, highlighting Canada,
Greenland and Svalbard. (B) Alexandra Fjord Highlands (AFH), Ellesmere Island,
Canada. (C) Spitsbergen, Svalbard, Norway, showing locations of Skjæringa (SS) and
Vestpynten (SV). Maps adapted from England et al. (2000); NPI (2016) and UT Libraries
(2009). Photograph of Alexandra Fjord by Katriina O’Kane http://arcticjournal.ca/featured/alexandra-fiord-a-high-arctic-oasis/, photograph of SV sampling sites courtesy of AAD.
43
31 Table 2.1 Mean annual weather statistics for regions within eastern Antarctica and the
32 high Arctic.
EAST ANTARCTICA HIGH ARCTIC Mean annual weather statistics Windmill Is. Vestfold Hills Ellesmere Is. Svalbard Precipitation (mm) < 175 < 200 < 200 < 270 Temperature range (°C) -41 to +9 -40 to +13 -41 to +9 -24 to +21 Wind speed (km/h) > 54 20 11 20 (Sources: Melick et al. 1994, Campbell & Claridge 1987b, Lévesque 1997, Lesins et al. 2010, Stewart et al. 2011, Rayback 2006, Isaksen et al. 2016, Hansen et al. 2014, NMI, 2017, Seppelt et al. 1988).
33
34 In the high Arctic (Fig. 2.2), desert soils are typically found above 75° N, in parts of Canada,
35 Norway, Alaska, Greenland and Russia (Goryachkin et al. 1999, Tedrow 2004, Barry & Hall-
36 McKim 2018). Two such polar deserts include Alexandra Fjord highlands, situated on Johan
37 Peninsula, Ellesmere Island, Nunavut, Canada (Fig. 2.2B), and Longyearbyen, on the
38 Norwegian archipelago of Svalbard (Fig. 2.2C), the latter of which boasts the world’s
39 northernmost human-populated township (Isaksen et al. 2016, Hansen et al. 2014, Stewart et
40 al. 2011). Climate change is impacting the Arctic at a greater rate than the world average,
41 with Svalbard experiencing an increase in annual mean winter temperature of 4.6°C since the
42 1990s. Summer temperatures in Longyearbyen can reach upwards of +21°C (Table 2.1)
43 (Isaksen et al. 2016, Hansen et al. 2014).
44
45 2.1.2 Surveying polar desert soils for natural product genes
46 In this chapter, we aimed to elucidate the diversity of NP-encoding genes present in an
47 extensive collection of desert soils from Antarctica and the high Arctic, for which the NP
48 diversity is unknown. We hypothesised that the unique environmental challenges faced by
44
49 the microbiota in these regions could select for novel NP genes and compounds, and may
50 contribute to the success and survival of the dominant phyla, the Actinobacteria and
51 Proteobacteria. Thus, we were particularly interested in the level of sequence novelty
52 compared with known NP genes, and the bioactivities of predicted compounds encoded by
53 NP genes harboured by resident bacteria. Further, to assist future bioprospecting efforts, we
54 aimed to identify specific polar soils with the greatest novel NP discovery potential.
55
56 We chose to employ the power of the third generation, long-read SMRT sequencing platform
57 PacBio RS II for this analysis. Short read (200-400 bp) amplicon sequencing technologies
58 have been employed to profile biosynthetic genes in temperate, hot desert and high altitude
59 soil biomes, as well as sponge microbiomes (Charlop-Powers et al. 2014, Charlop-Powers et
60 al. 2016, Aleti et al. 2017, Woodhouse et al. 2013, Borchert et al. 2016). We proposed that
61 longer read lengths would enable capture of entire gene amplicons produced by commonly
62 employed degenerate primer sets, which target long regions of conserved NRPS AD-domain
63 (~700 bp) (Fig 1.6), and PKS KS/AT-domain sequences (~1200-1400 bp) (Fig. 1.3) (Ayuso-
64 Sacido & Genilloud 2005, Owen et al. 2013, Peng et al. 2018). Thus, taxonomic resolution
65 would be enhanced, particularly at the protein level, to assist in functional predictions.
66
67 2.2 MATERIALS AND METHODS
68 2.2.1 Polar locations and soil collection
69 Soils were sampled from twelve polar locations, encompassing nine eastern Antarctic and
70 three high Arctic sites (Figs. 2.1 & 2.2). Five Antarctic sites were from the Windmill Islands
71 region; Mitchell Peninsula (MP) (66°19’S, 110°32’E), Robinson Ridge (RR) (66°22’S,
72 110°35’E), Browning Peninsula (BP) (66°28’S, 110°33’E), Herring Island (HI) (66°25’S, 45
73 110°39’E) and Casey station (CS) (66°17’S 110°32’E) (Fig. 2.1B), and four from the
74 Vestfold Hills, near Davis station; Adams Flat (AF) (68°33'S, 78°1'E), Heidemann Valley
75 (HV) (68°35'S, 78°0'E), Rookery Lake (RL) (68°30'S, 78°7'E) and Old Wallow (OW)
76 (68°36'S, 77°57'E) (Fig. 2.1C). High Arctic sites comprised Alexandra Fjord Highland
77 (Canada) (AFH) (78°52’N, 75°54’W) (Fig. 2.2B), and two from Svalbard (Norway);
78 Spitsbergen Longyearbyen Skjæringa (SS) (78°14’N, 15°30’W), and Spitsbergen
79 Longyearbyen Vestpynten (SV) (78°14’N, 15°20W) (Fig. 2.2C).
80
Figure 2.3 Geospatial transect sampling design (not to scale). Soils were sampled along
three 300m parallel transects, and analysed at distance points 0, 2, 100, 102, 200 and 202
m from all 12 polar locations, excepting CS (0, 2, 100, 102, 105, 110 m). Photograph of
BP courtesy of AAD, figure adapted from Zhang (2016).
81
82 Soils were sampled by the Australian Antarctic Division (AAD) during the summer months
83 2005 and 2012, using a spatially explicit design (Fig. 2.3) (Siciliano et al. 2014, van Dorst et
84 al. 2014, Ferrari et al. 2015). At each site, samples were taken from the top 10 cm of soil
85 along three parallel transects, situated 2 m apart and 300 m in length (Fig. 2.3). Here, 18
46
86 samples were selected per site, collected at distance points 0, 2, 100, 102, 200 and 202 m
87 along each of the three transects, except for CS which were taken at 0, 2, 100, 102, 105, 110
88 m distances, totalling 216 samples.
89
90 2.2.2 DNA extraction and 16S rDNA gene sequencing
91 DNA extraction and 16S rDNA sequencing and analysis were performed previously, as part
92 of larger biodiversity studies, described in detail in van Dorst et al (2014), Siciliano et al.
93 (2014), Bissett et al. (2016) and Ferrari et al. (2015). Briefly, DNA was extracted from 300
94 mg each soil sample, in triplicate using the FastDNA SPIN kit for soil (MP Biomedicals,
95 NSW, Australia) (Siciliano et al. 2014, van Dorst et al. 2014, Ferrari et al. 2015). DNA was
96 quantified with the Quant-iT Picogreen dsDNA Assay kit (Life Technologies, VIC,
97 Australia) and stored at -80oC until further use. For the Windmill Island and high Arctic sites,
98 bacterial 16S rDNA gene fragments were amplified using the primer set 27F and 519R and
99 sequenced using the 454 FLX titanium platform (Siciliano et al. 2014, van Dorst et al. 2014).
100 For the Vestfold Hills sites sequencing was performed on the lllumina MiSeq platform
101 (Bissett et al. 2016). Operational taxonomic units (OTUs) were clustered using ≥ 97 %
102 similarity and taxonomy assigned using the Green Genes database (Bissett et al. 2016, Ferrari
103 et al. 2015).
104
105 Here, analysis of 16S bacterial relative abundance at phyla level, as well as Actinobacteria at
106 order and family levels were performed, and visualised as stacked barcharts in R 3.4.0 using
107 the ggplot2 package v2.2-1 (Wickham 2011).
108
47
109 2.2.3 Soil physical and chemical properties
110 Fifty physical and chemical parameters were collected by standard methods, and are
111 described in detail in Siciliano et al. (2014) and Bissett et al. (2016). Properties included
112 slope, aspect and elevation, pH, conductivity, dry matter fraction (DMF), soil particle size,
- 113 total phosphorous (TP), total carbon (TC), total nitrogen (TN); water extractable ions NO2 ,
- - 3- 2- + 114 Br , NO3 , PO4 , SO4 , and NH4 ; and major elemental concentrations such as SiO2, TiO2,
115 Al2O3, Fe2O3, MnO, MgO, CaO, Na2O, K2O, P2O5, SO3 and Cl (Siciliano et al. 2014, Bissett
116 et al. 2016).
117
118 Soil physical and chemical data obtained for all sites were transformed and normalised for
119 further analysis. Skewed variables such as TN, TC, S, Na, Zn, Ca, Mg were log transformed,
120 while CaO, MgO, Fe2O3 and TP were square root transformed. Three missing TC values
121 were estimated by the EM algorithm (Clarke & Gorley 2015).
122
123 2.2.4 PKS PCR amplification, gel extraction and barcoding
124 To attach adaptors and barcodes, and increase yield, three rounds of PCR were employed for
125 PKS tag sequencing. As our soils contained high relative abundances of Actinobacteria, we
126 selected the published primers K1F and M6R (Table 2.2), previously designed and reported
127 for Actinomycetales by Ayuso-Sacido and Genilloud (2005). First-round PCR employed the
128 primers K1F/M6R and was performed under a touchdown thermocycler program to optimise
129 primer specificity (Table 2.2). Optimised reaction mixtures comprised 10 µL 5X Q5 Buffer
130 (NEB, Massachusetts), 10 µL 5x Q5 High G+C enhancer (NEB), 1.5 mM MgCl2, 0.2 mM
131 each dNTP, 16.25 µL water, 0.5 µM each primer (K1F, M6R), 1 unit of Q5 Hotstart High
132 Fidelity DNA Polymerase (NEB), and 5 µL of 1:10 dilution of DNA template (~5–10 ng/µL). 48
133 PCR products were visualised on 2% (w/v) agarose gel, and target amplicons (~1200-1400
134 bp length) extracted using the Zymoclean™ Gel DNA Recovery kit (Zymo Research,
135 California). Target amplicons were quantified using the NanoDrop 1000 Spectrophotometer
136 (Thermo Scientific, NSW, Australia), then used as templates in second-round PCR, to attach
137 adaptors. Primers K1F/M6R were modified to include a 5’ block and SMRT universal primer
138 (UP) adaptors (UPF-K1F/ UPR-M6R) (Table 2.2). Optimised reactions contained 10 µL 5X
139 Q5 Buffer, 10 µL 5X Q5 High G+C enhancer, 1.5 mM MgCl2, 0.2 mM each dNTP, 18.875
140 µL water, 0.5 µM each primer (UPF-K1F, UPR-M6R), 1 unit of Q5 Hotstart High Fidelity
141 DNA Polymerase, and approximately 10 ng of product as template, under optimal
142 thermocycler conditions (Table 2.2). Gel extracted target amplicons were quantified using
143 Quant-iT Picogreen dsDNA Assay kit (Life Technologies, VIC, Australia).
49
Table 2.2 PCR primers and conditions for amplification of PKS ketosynthase/ acyl transferase domains, and NRPS adenylation domains.
PCR Primer Primer Primer Sequence (5’-3’) Thermocycler Conditions Round Name Ref PKS Touchdown: 98°C 1 min, 10 cycles [98°C 1 K1F TSAAGTCSAACATCGGBCA Ayuso- 30 s, 60°C - 50°C 30 sec, decreasing by Sacido & 1°C each cycle, 72°C 40 s], 20 cycles Genilloud M6R CGCAGGTTSCSGTACCAGTA [98°C 30 s, 50°C 30 s, 72°C 40 s], 72°C 2 (2005) min UPF- /5AmMC6/-GCAGTCGAACATG Ayuso- 2 K1F TAGCTGACTCAGGTCAC-K1F Sacido & 98°C 1 min, 25 cycles [98°C 30 s, 65°C 30 Genilloud
UPR- /5AmMC6/-TGGATCACTTGTG s, 72°C 50 s], 72°C 2 min (2005),
M6R CAAGCATCACATCGTAG-M6R PacBio (2015)
3 B-UPF lbc#-UPF 98°C 30 s, 20 cycles [98°C for 10 s, 60°C PacBio B-UPR lbc#-UPR 30 s, 72°C 50 s], 72°C 2 min (2015) NRPS /5AmMC6/- Ayuso- UPF- 1 GCAGTCGAACATGTAGCTGACTCAGG Sacido & A3F TCACGCSTACSYSATSTACACSTCSGG 98°C 3 min, 35 cycles [98°C 20 s, 65°C 30 Genilloud /5AmMC6/- s, 72°C 30 s], 72°C 3 min (2005), UPR- TGGATCACTTGTGCAAGCATC PacBio A7R ACATCGTAGSASGTCVCCSGTSCGGTAS (2015)
2 B-UPF lbc#-UPF 98°C 3 min, 25 cycles [98°C 20 s, 71.3°C PacBio 30 s, 72°C 30 s], 72°C 3 min (2015) B-UPR lbc#-UPR 144 A3F/ A7R primer sequences indicated in bold
50
145 Third-round barcoding PCR was performed using PacBio supplied 96-well plates containing
146 unique barcoded (B) SMRT universal primer (UP) sets (B-UP F/R), under optimal
147 thermocycler conditions (Table 2.2). PKS samples were run in duplicate. Reactions
148 comprised 5 µL 5X Q5 Buffer, 5 µL 5X Q5 High G+C enhancer, 1.5 mM MgCl2, 0.2 mM
149 each dNTP, 2 µM B-UP F/R Primers, 0.5 units of Q5 Hotstart High Fidelity DNA
150 Polymerase, 10 ng of Picogreen-quantified, gel-purified product as template, and water to 25
151 µL.
152
153 2.2.5 NRPS PCR amplification and barcoding
154 NRPS-encoding gene amplifications and NRPS data processing was performed by E. Zhang
155 (2016). Two rounds of PCR were performed prior to tag sequencing for NRPS domains
156 (Zhang 2016). We selected the primer set A3F and A7R (Table 2.2), again previously
157 reported for Actinomycetales by Ayuso-Sacido and Genilloud (2005), and shown to amplify
158 AD sequences from a range of bacterial phyla (Owen et al. 2013, Charlop-Powers et al.
159 2014). First-round PCR employed degenerate primers A3F/A7R with an additional 5’ block
160 and universal SMRT primer (UP) adaptor (UPF-A3F, UPR-A7R) (Table 2.2) (Ayuso-Sacido
161 & Genilloud 2005, Pacific Biosciences 2015). Optimised thermocycler conditions were used
162 (Table 2.2), with mixtures comprising 10 µL 5x Q5 Buffer, 10 µL 5x Q5 High G+C
163 Enhancer, 1.5 mM MgCl2, 0.2 mM each dNTP, 16.25 µL water, 0.5 µM each Primer (UPF-
164 A3F, UPR-A7R), 1 unit Q5 Hotstart Hi-Fidelity DNA Polymerase, and 5 µL of 1:10 dilution
165 DNA (~5-10 ng/µL). PCR products were visualised on 2% (w/v) agarose gel, and target
166 amplicons (~700 bp length) extracted and purified using the Zymoclean Gel DNA Recovery
167 kit. Prior to barcoding, NRPS PCR products were NanoDrop quantified and pooled in
51
168 equimolar amounts representing the start (0, 2 m), middle (100, 102 m) and end (200, 202
169 m) of each transect (Fig. 2.2) for all positive sites.
170
171 Gel-purified first-round PCR products were used as templates for the second-round
172 barcoding PCR, as described for PKS, under thermocycler conditions outlined in Table 2.2.
173 Second-round NRPS PCR reaction mixtures contained 5 µL 5X Q5 Buffer, 5 µL 5X Q5 High
174 G+C Enhancer, 1.5 mM MgCl2, 0.2 mM each dNTP, 2 µM B-UP F/R primers, 0.25 units Q5
175 Hotstart Hi-Fidelity DNA Polymerase and ~ 2-8 ng of gel-purified PCR product, and water
176 up to 25µL.
177
178 2.2.6 Natural product amplicon library preparation for SMRT sequencing
179 PKS and NRPS barcode-tagged PCR products were gel-extracted, Picogreen-quantified, and
180 pooled into two libraries. Libraries were submitted to The Ramaciotti Centre for Genomics
181 (UNSW Sydney, NSW, Australia) for SMRTbell library preparation and multiplexed SMRT
182 sequencing on the PacBio RS II (P4/C2) platform, employing one SMRT cell per library
183 (Pacific Biosciences).
184
185 2.2.7 Processing PacBio SMRT sequencing data
186 Demultiplexed SMRT sequencing output was assessed for read quality using FastQC
187 (Andrews 2010). Processing was performed using the QIIME (v 1.9.1) UPARSE pipeline
188 (Caporaso et al. 2010, Edgar 2013). Barcode labels were assigned, and individual reads
189 concatenated. Sequences were quality processed, dereplicated, and chimeras removed, to
190 generate a unique set of sequences, which were clustered at 95% similarity for generation of
191 amplified sequence variants (ASV) tables. 52
192
193 2.2.8 Taxonomic classification of sequences using the BLAST database
194 PKS and NRPS ASVs were analysed using both the BLASTn and BLASTx algorithms
195 (Altschul et al. 1990). Identical BLAST results for multiple ASVs were manually combined
196 to generate a new set of ASVs for each dataset (Appendix Tables A1.1, A1.2). A
197 representative nucleotide sequence was selected based on longest read length match. Relative
198 abundances at genera level for all PKS and NRPS positive sites were calculated by total and
199 visualized as bubbleplots using the ggplot2 package in R 3.4.0 (Wickham 2011) (Figs. 2.5,
200 2.6).
201
202 2.2.9 Multivariate data analysis
203 Sites containing > 1 positive sample were included in multivariate analyses, which were
204 performed using PRIMER v7 with the PERMANOVA+ add on feature (Clarke & Gorley
205 2015). NP genes and bacterial 16S abundance datasets (van Dorst et al. 2014, Bissett et al.
206 2016) were square-root transformed and standardised by total to generate Bray-Curtis
207 dissimilarity matrices. Non-metric multidimensional scaling (nMDS) plots were generated
208 for NP genes and visualised in 3D space (Fig. 2.13). Principal component ordination (PCO)
209 plots were created for bacterial 16S data (Fig. 2.14A). Transformed, normalised soil physical
210 and chemical parameters were used to create a Euclidean distance resemblance matrix for
211 PCO analysis (Fig. 2.14B) (Clarke & Gorley 2015).
212
213 For rarefaction curve analysis, subsampling of ASVs was performed without replacement to
214 the lowest number of sequence reads per site (4000 PKS and 800 NRPS reads), at a step size
215 of five, using a loop script of the rarefy function in the vegan package v2.4-3 in R 3.3.0 (Work 53
216 et al. 2010, Oksanen et al. 2017). Rarefaction curves including standard error were visualised
217 using the ggplot2 package v2.2-1 in R 3.3.0 (Fig. 2.4) (Wickham 2009).
218
219 Mantel tests were performed between the PKS/NRPS ASV and corresponding bacterial 16S
220 Bray-Curtis resemblance matrices using the RELATE function in PRIMER v7 with 999
221 permutations (Clarke & Gorley 2015).
222
223 2.2.10 Statistical analysis
224 To calculate the effect of soil fertility parameters on presence/absence of PKS and NRPS
225 gene amplification, a generalised linear mixed model (GLMM) was selected to account for
226 the binary nature of our data using the ‘lme4’ package in R 3.3.0 (Bates et al. 2015). P-values
227 were calculated for log TC, log TN and DMF effects using the bootstrap option (n = 1000),
228 with an expected significance level of P < 0.05. The significance of each soil fertility
229 parameter was tested both as separate, and paired models (Table 2.3). The relationship
230 between amplification of NP genes, log TC and DMF (%) for all samples was visualised as
231 a combined barchart and dotplot in R 3.3.0 using the ggplot2 package v2.2-1 (Fig. 2.11)
232 (Wickham 2009).
233
234 Chao1 estimates for PKS and NRPS gene richness were calculated in R 3.3.0, using the vegan
235 package v2.4-3 estimateR function (Oksanen et al. 2017). Statistical analyses of the
236 relationships between estimated Chao1 richness and selected soil parameters (TC, TN and
237 soil moisture (1-DMF)) for PKS and NRPS genes were carried out using the lm() function of
238 the ggplot2 package v 3.0-0 in R 3.51 (Wickham 2009). These were visualised as scatterplots
54
239 with linear regression lines, adjusted R squared values and significance level with a 0.05 p-
240 value cutoff (Fig. 2.12).
241
242 2.2.11 Construction of phylogenetic trees
243 Maximum likelihood trees with 1,000 bootstrap replications were constructed using PHYML
244 (Guindon et al. 2010), as part of the Phylogeny.fr pipeline (Dereeper et al. 2008).
245 Representative protein sequences were retrieved from GenBank (Benson et al. 2011).
246 Multiple sequence alignment was performed using MUSCLE (Edgar 2004) in full processing
247 mode, passed through PHYML and visualised in iTOL (Figs. 2.7 & 2.8) (Letunic & Bork
248 2016). Predictions about the type of compounds produced by our ASVs were made by
249 uploading phylogenetic tree representative sequences to the Natural Product Domain Seeker
250 database (NaPDoS) (Ziemert et al. 2012).
251
55
252 2.3 RESULTS
253 2.3.1 PKS and NRPS gene sequences compared across polar soils
254 Of the 216 polar soils analysed, 59 produced PKS PCR amplicons. Four Antarctic sites; BP,
255 HV, OW and CS, did not produce PCR amplicons under the optimised conditions used. PKS
256 sequences were recovered from multiple soil samples for three Antarctic sites in the Windmill
257 Island region MP, RR and HI, and single soil samples from all three high Arctic sites (AFH,
258 SS and SV). Sequences were not recovered from the four sites in the Vestfold Hills region;
259 AF, RL, HV and OW. In total, 23,240 circular consensus sequence (CCS) sequences were
260 retrieved, with an average predicted sequencing accuracy of 97%, length of 1383 bp, and
261 G+C content of 69%. Sequence processing resulted in 292 KS/AT domain ASVs, including
262 singletons. Subsequent BLAST analysis, manual ASV combination and removal of
263 singletons resulted in 82 KS/AT domain ASVs (Appendix Table A1.1).
264
265 For the NRPS genes, PCR amplicons were produced for 137 of the 216 samples examined.
266 NRPS sequences were recovered for all nine Antarctic sites but only one positive sample for
267 the human-impacted CS. The three high Arctic sites, AFH, SS and SV, did not yield any
268 NRPS sequences. A total of 19,596 CCS reads were obtained, with a mean predicted
269 sequencing accuracy of 97%, length of 805 bp, and G+C content of 69%. Sequence
270 processing resulted in 1,669 NRPS AD domain ASVs including singletons. Following
271 BLAST analysis, manual ASV combination and the removal of singletons, 144 unique AD
272 domain ASVs remained (Appendix Table A1.2).
273
56
274 2.3.2 PKS and NRPS biosynthetic diversity in polar soils
275 PKS diversity ranged between 2-35 ASVs per site, being particularly low in the single-
276 sample high Arctic sites (2-7 ASVs). These sites were consequently excluded from
277 multivariate analysis. For the three PKS positive Antarctic sites, MP, HI and RR, diversity
278 was comparable, with 30, 32 and 35 ASVs respectively. Sequencing depth did not reach
279 asymptote (Fig. 2.4A), indicating greater sequencing depth would capture further Type I PKS
280 diversity at these locations.
281
282 For NRPS, between 6 and 56 NRPS ASVs were recovered per site. Rarefaction curves neared
283 asymptote, indicating the sampling strategy provided adequate coverage of diversity (Fig.
284 2.4B). The human-impacted CS exhibited the lowest NRPS diversity with only 6 ASVs and
285 was subsequently removed from multivariate analysis. The greatest diversity was observed
286 at BP, while all four Vestfold Hills sites; AF, HV, OW and RL, displayed relatively high
287 NRPS diversity.
57
Figure 2.4 Capture of natural product diversity in polar soils. (A) For PKS,
sequencing depth did not quite capture total diversity. (B) For NRPS, rarefaction curves
are nearing or reaching horizontal asymptote, indicating sufficient sequencing depth.
288
289 2.3.3 Classification and distribution of natural product gene cluster families
290 Retrieved PKS sequences were assigned to nine phyla, including Actinobacteria (84%),
291 Proteobacteria (4%), Cyanobacteria (3%), and Bacteroidetes (1%). KS/AT primers also
292 amplified genes from Deinococcus-Thermus (1%), Chloroflexi (< 0.1%), Nitrospirae (<
293 0.1%), and Gemmatimonadetes (< 0.1%). Furthermore, 7% of all sequences were assigned
294 to dehydratases within several Euryarchaeota genera (Fig. 2.5). Overall, 66% of the
295 sequenced reads corresponded to KS/AT domains, the remainder were characterised as
58
296 phosphatases (25%), dehydratases (7%), a transposase (< 1%), a primase/polymerase (< 1%),
297 epoxide hydrolase (< 0.1%), oxidoreductase (< 0.1%) and uncharacterised proteins (< 0.1%)
298 (Appendix Table A1.1). As many BGCs contain these domains they were retained in the
299 analysis (Donadio et al. 2007, Li et al. 2008, Migita et al. 2009, Aparicio 2003). PKS gene
300 sequences matched > 25 known NP biosynthesis pathways (Appendix Table A1.1), primarily
301 antifungals (pimaricin, heronamide, antifungal L-155,175, ambruticin), and to a lesser extent
302 antibiotics (simocyclinone, quartromycin, thuggacin and rubradirin) and antiparasitics
303 (lobosamide and indanomycin) (Appendix Table A1.1) (Aparicio 2003, Schulze et al. 2015).
304
59
305
60
Figure 2.5 PKS domain sequence taxonomy by genera and phyla, assigned through BLASTx analysis. Bubble size represents relative abundance of total reads. KS/AT domains were sequenced from 22 samples, from six sites in total. The highest relative abundance and diversity was found in Antarctic sites MP, RR and HI. Some non-KS/AT amplicons were also identified, including DH domains from Euryarchaeota, which were amplified from all HI samples, and one MP sample. Interestingly, DHs are regularly found within PKS BGCs.
61
306 After protein sequence analysis, two known gene clusters remained; one with 64% similarity
307 to simocyclinone, an antibiotic with antitumour activity (Trefzer et al. 2002, Flatman et al.
308 2005), and the other exhibiting 35% similarity to quartromycin, a spirotetronate with activity
309 against human immunodeficiency virus (Wu et al. 2014) (Appendix Table A1.1).
310
311 NRPS gene sequences were assigned to nine bacterial phyla (Fig. 2.6), with the majority
312 belonging to the Actinobacteria (40%). Other established NRP-producers were also
313 represented, including Proteobacteria (22%), Cyanobacteria (19%) and Firmicutes (17%).
314 ASVs were also assigned to five phyla that are less commonly associated with NRP
315 production: Nitrospinae/Tectomicrobia (2%), Planctomycetes (1%), Chloroflexi (< 0.1%),
316 Armatimonadetes (< 0.1%) and Defferibacteres (< 0.1%). A high proportion (90%) of
317 recovered NRPS sequences corresponded to known AD domains (Appendix Table A1.2), as
318 well as hypothetical proteins (10%), hybrid NRPS-PKS (6%) and ATP-dependent acyl-CoA
319 ligases (< 0.1%). Nucleotide analysis of NRPS sequences revealed > 20 matches to known
320 bioactive compound gene clusters (Appendix Table A1.2). These included antitumour agents
321 (quinocarcin, collismycin, nannocystin), antibacterials (gramicidin, clorobiocin, teixobactin,
322 bacitracin), and antifungals (myxochromide, microsclerodermin) (Kawatani et al. 2016,
323 Raaijmakers et al. 2010, Schäberle et al. 2014). At the protein level, only one known bioactive
324 compound sequence match remained that was 49% similar to the antifungal surfactant, iturin
325 (Appendix Table A1.2).
62
Figure 2.6 NRPS domain sequence taxonomy by genera and phyla, assigned through
BLASTx analysis. Bubble size represents relative abundance of total reads. Domains were sequenced from nine sites. Many ASV communities were shared across Antarctic regions, except the human-impacted CS. Actinobacteria and Proteobacteria were the most dominant phyla, in accordance with 16S bacterial biodiversity (Fig. 2.9).
63
326 2.3.4 Phylogenetic analysis of NP domain sequences
327 Phylogenetic analysis confirmed the novelty of PKS gene families recovered, which were
328 distributed among three main branches (Fig. 2.7). Similarities to known bioactive compounds
329 included nystatin, an antifungal belonging to the same family of polyene macrolides as
330 pimaricin (Aparicio 2003), as well as the well-known anthelmintic, avermectin (Burg 1979);
331 and tetronomycin, a polyether tetronate antibiotic active against Gram-positive bacteria
332 (Keller-Juslén et al. 1982) (Fig. 2.7). One branch containing five sequences consisted entirely
333 of homologues to compounds with potent antitumour properties; calicheamicin and
334 epothilone (Nicolaou & Dai 1991).
335
336 Phylogenetic analysis confirmed a high level of novelty in NRPS sequences (Fig. 2.8). Of
337 particular note, 19 ASVs formed a monophyletic branch that contained only a single
338 characterised representative, the syringomycin biosynthetic cluster in the
339 Gammaproteobacterial genus Lysobacter. Interestingly, several other branches contained
340 syringomycin biosynthetic cluster sequences, albeit from a variety of genera (Fig. 2.8).
341 Syringomycin, gramicidin and iturin are peptides which exhibit both biosurfactant and
342 antibiotic properties (Raaijmakers et al. 2010). Other branches were comprised of matches
343 most similar to antibiotics (tyrocidine and bacitracin), cyanobacterial toxins (microcystin and
344 cyanopeptolin), and antitumour agents (actinomycin, bleomycin and epothilone) (Fig. 2.8)
345 (Ageitos et al. 2017, Nicolaou & Dai 1991, Faltermann et al. 2014).
346
64
Figure 2.7 Phylogenetic relationship of PKS protein sequences with reference bacteria based on BLASTx output. Evolutionary relationships were determined using the maximum likelihood method using MUSCLE and the
PHYML algorithm to perform ~1400 bp multiple sequence alignment and visualised in iTOL. Polar soil ASVs are indicated in bold. Bootstrap values < 0.5 have been collapsed. Branches of the tree generally group according to type of encoded biosynthetic compound, such as those with antitumour activity (yellow).
65
Figure 2.8 Phylogenetic relationship of
NRPS protein sequences against reference bacteria based on BLASTx output.
Evolutionary relationships were determined using the maximum likelihood method using
MUSCLE and the PHYML algorithm to perform ~700 bp multiple sequence alignment and visualised in iTOL. Polar soil
ASV sequences are indicated in bold.
Bootstrap values < 0.5 have been collapsed.
Biosynthetic genes encoding compounds with surfactant properties (syringomycin and gramicidin) are present on all branches of the tree (purple).
66
347 2.3.5 Bacterial and Actinobacterial diversity of polar soils
348 As observed previously, the polar soils examined comprised a high proportion of phyla
349 associated with NP biosynthesis, particularly Actinobacteria (16.6-42.8%), and
350 Proteobacteria (8.8-41.6%) (Fig. 2.9) (van Dorst et al. 2014, Ferrari et al. 2015). In terms of
351 overall bacterial diversity, the Windmill Islands sites MP and RR were most similar, while
352 CS, which is human-impacted, showed the lowest similarity to any other soil sample.
353 Interestingly, BP communities departed from the regional patterns observed, and were more
354 similar to the high Arctic sites.
355
Figure 2.9 Soil bacterial diversity observed from 16S amplicon sequencing of soil
from each of the 12 sites analysed. Phyla level diversity revealed soils at all sites to be
dominated by NP-producing phyla, in particular the Actinobacteria and Proteobacteria.
356
67
Figure 2.10 Actinobacterial diversity by Order and Family, observed from 16S
amplicon sequencing. (A) Actinomycetales were the dominant order at CS,
Solirubrobacterales dominated MP and RR, and MC47 was prominent within BP and the
high Arctic sites (SS, SV, AFH). In contrast, the Vestfold Hills soils (AF, HV, OW, RL)
were dominated by Acidomicrobiales and Rubrobacterales. (B) At Family level,
Actinobacteria communities were comprised of a large unclassified proportion,
particularly for MP, BP, RR and the high Arctic sites.
357 68
358
359 Further analysis of Actinobacteria showed that, at order level, Actinomycetales dominated at
360 CS (Fig. 2.10A). However, this did not correspond to an increase in NRPS or PKS richness
361 (Fig. 2.6). Furthermore, no trend was observed between NP gene richness and relative
362 abundance of Actinobacteria or Actinomycetales in these soils. At the family level, a large
363 proportion of Actinobacteria were unclassified (Fig. 2.10B), particularly those from the
364 Antarctic sites MP, BP and RR, and the three high Arctic sites.
365
366 2.3.6 Relationships between polar natural product genes, microbiomes and soil
367 fertility parameters
368 NP gene amplicons were not detected in 25% of our samples, most notably the more fertile
369 high Arctic sites (Fig. 2.11). Drier, lower carbon soils were more likely to result in
370 amplification of the PKS and NRPS-coding genes targeted with the primer sets employed
371 here (Table 2.3, Fig. 2.11). For PKS, a significant (P < 0.05) correlation was observed with
372 DMF (P < 0.001) (Table 2.3), while significant correlations were observed between NRPS
373 genes and TC (P < 0.001), TN (P < 0.001), and DMF (P < 0.001), with carbon being the
374 most important factor associated with a lack of NRPS gene recovery (Table 2.3). Soils
375 exhibiting < 75% DMF were negative for PKS amplicons, while those < 80.8% DMF were
376 negative for NRPS (Fig. 2.11). Soils > 36,410 ppm TC were negative for the recovery of PKS
377 amplicons while those comprising > 18,490 ppm TC were NRPS negative (Fig. 2.11).
378
69
Figure 2.11 Natural product gene amplification revealed significant relationships with soil carbon (A), and dry matter fraction (DMF) (B).
Drier, lower carbon soils were more likely to be positive for PKS and NRPS-coding genes. Carbon was most statistically significant for NRPS
(P < 0.001), while only DMF was significant for PKS (P < 0.001).
379
70
Table 2.3 Analyses of relationship between natural product gene presence and total
carbon (TC), total nitrogen (TN) and dry matter fraction (DMF).
PKS NRPS Separate Model Log TC 0.090 < 0.001 Log TN 0.733 < 0.001 DMF < 0.001 < 0.001 Paired Model Log TC 0.027 < 0.001 DMF 0.001 0.088 Log TC - 0.001 Log TN - 0.923 Log TN - < 0.001 DMF - 1 Numbers in bold are significant 380
381
382 For the sequenced NP communities, PKS Chao1 richness was significantly negatively
383 correlated with soil moisture (1-DMF) (Fig. 2.12A), while NRPS Chao1 gene richness
384 estimates displayed significant negative correlation with soil fertility factors carbon and
385 nitrogen (Fig. 2.12B & C).
386
71
Figure 2.12 Natural product gene association with soil fertility factors revealed significant (P < 0.05) negative correlations. (A) PKS gene richness as a function of soil moisture. (B & C) NRPS gene richness (Chao1) as a function of carbon (B) and nitrogen
(C).
72
387
Figure 2.13 Natural product gene nMDS analysis. (A) PKS and (B) NRPS. ASV
communities have clustered according to their geographic region. Windmill Islands sites
BP and HI form individual clusters, while MP and RR group together. Vestfold Hills sites
(AF, HV, OW & RL) form a grouped cluster.
388
389 Non-metric multidimensional scaling (nMDS) ordination plots showed that the NP gene
390 sequence communities obtained were more similar within, rather than between sites (Fig.
391 2.13). In both PKS and NRPS analyses, the Windmill Islands sites MP and RR clustered
392 together, while BP and HI formed individual clusters (Fig. 2.13A & B). For NRPS, regional
393 clustering was observed, with distinct groups of assemblages forming for Windmill Island
394 and Vestfold Hills sites (Fig. 2.13B). Similar relationships were observed in both the 16S
395 rDNA gene bacterial communities (Fig. 2.14A), and environmental parameters (Fig. 2.14B).
396 Indeed, significant correlations (P = 0.001) were found between NP gene diversity and
397 bacterial diversity (Mantel r = 0.615 (PKS), 0.81 (NRPS)).
73
398
Figure 2.14 Bacterial community 16S rDNA gene analysis and measured soil
parameters show clustering similarities. Principle Coordinates analysis (PCO) reveal
geographically distinct groupings for both 16S Bacterial diversity (A) and environmental
parameters (B)
399
74
400 2.3.7 NP domain sequence novelty
401 For all sites, except CS, the majority of NRPS gene families recovered were novel, sharing
402 low homology (< 70%) to genes that synthesise known NPs (Fig. 2.15A). In particular, the
403 Vestfold Hill sites HV and AF contained the greatest number of novel sequences (92% and
404 84%, respectively). For PKS (Fig. 2.15B), excluding the low diversity sites (AFH, SS & SV),
405 the highest number of novel (< 70% similarity) PKS sequences were retrieved from Windmill
406 Island site, HI (91%).
407
Figure 2.15 Natural product domain sequence novelty when compared to known
secondary metabolite protein sequences for NRPS (A) and PKS (B). The majority of
NP gene sequences that were recovered exhibited < 70% sequence identity to known
NRPS or PKS protein sequences, indicating a high potential for novel compound
production by bacteria in these polar soils.
408
75
409 2.4 DISCUSSION
410 Exploration of NP-encoding gene sequences using long-read technology revealed intriguing
411 functional groupings for both PKS and NRPS ASVs in polar desert soils. Antarctic NRPS
412 AD domain sequences predominantly clustered with biosurfactant-like lipopeptide and
413 decapeptide BGCs in phylogenetic analysis, specifically syringomycin and gramicidin (Fig.
414 2.8). Biosurfactant peptides are versatile metabolites, with roles in cell motility, cation
415 chelation, soil-water distribution, biofilm formation, sporulation, and degradation of
416 hydrocarbons, in addition to antibiotic activities (Raaijmakers et al. 2010, Fechtner et al.
417 2011). Biosurfactant production is common in cold-adapted microorganisms, particularly in
418 Bacillus, Burkholderia, Pseudomonas, Rhodococcus and Sphingomonas (Perfumo et al.
419 2018), genera which have been previously recovered from eastern Antarctic soils (Pudasaini
420 et al. 2017, Nicetic 2016, Wong 2018). While further work is required to confirm
421 biosurfactant secretion from bacterial isolates, the widespread occurrence of biosurfactant
422 genes in polar soil bacteria would suggest they provide a competitive advantage through
423 enhancement of water and nutrient bioavailability.
424
425 For PKS KS/AT, many ASVs demonstrated closest homology to a variety of polyene
426 macrolide antifungal agents, such as pimaricin and nystatin (Appendix Table A1.1, Fig. 2.7),
427 compounds which are hypothesised to provide Actinobacteria with a competitive edge over
428 fungi in soil environments (Aparicio 2003). Macrolides interact with the major cell
429 membrane sterol in fungi; ergosterol, affecting membrane integrity and inhibiting transport
430 of amino acids and glucose across the membrane (te Welscher et al. 2012, Sant et al. 2016,
431 Aparicio et al. 2016).
432
76
433 Biosynthetic gene richness has been previously associated with low carbon and low soil
434 moisture content across a range of soil biomes (Charlop-Powers et al. 2014). Here we found
435 carbon, nitrogen and moisture content to be correlated with both the detection and diversity
436 of our targeted NP-encoding gene sequences (Figs. 2.11 & 2.12, Table 2.3), with drier, more
437 nutrient-starved soils more likely to yield greater amplification and diversity of PKS and
438 NRPS gene sequences across both poles, using the degenerate primer sets employed in this
439 study (Tables 2.1 & 2.2). Interestingly, NP-encoding genes were either not successfully
440 recovered or exhibited the lowest diversity in polar soils with the greatest anthropogenic
441 influence, including high Arctic Svalbard sites (SS and SV), and eastern Antarctic site CS.
442 This is contrary to the relatively high abundances of Actinomycetales, the leading NP-
443 producing bacterial order, reported at these sites (Fig. 2.10A) (Bérdy 2005). The correlation
444 of NP genes with low-nutrient soils supports their ecological relevance and functional
445 usefulness regarding competition between microbes for limited resources (de Pascale et al.
446 2012).
447
448 While the threshold for determining functional gene novelty is disputable, some studies have
449 stated that a sequence identity < 70% is considered novel for secondary metabolite genes
450 (Busti 2006, Komaki et al. 2008). The majority (79.6%) of retrieved NP-encoding sequences
451 were under this threshold (Appendix Tables A1.1 & A1.2; Fig. 2.15), indicating value for
452 novel metabolite bioprospecting in eastern Antarctic soils. Additionally, our results revealed
453 potential for NP in rare, and previously unknown PK and NRP-producing phyla including
454 Nitrospirae, Armatimonadetes, Deinococcus-Thermus, Gemmatimonadetes and the
455 Euryarchaeota (Figs. 2.5 & 2.6, Appendix Tables A1.1 & A1.2) (Wang et al. 2014).
456
77
457 Here, we successfully employed long-read amplicon sequencing technology to capture large
458 PCR domain fragments (PKS ~1400 bp and NRPS ~700 bp), allowing translation into amino
459 acid sequences and enabling functional taxonomic predictions (Fig. 2.7 and 2.8). Through
460 our screening efforts we established a number of sites for future novel natural product
461 bioprospecting, with particularly exciting targets being arid soils of the Windmill Islands
462 region (HI, MP and RR) of eastern Antarctica and hyper-arid soils from the Vestfold Hills
463 (AF, RL and HV), which contained the highest diversity of potentially novel natural products.
464 We conclude that our sequencing approach is an advance for screening analyses of large gene
465 fragments such as PKS and NRPS.
78
79
CHAPTER THREE
3 CULTURING COLD ADAPTED BACTERIA FROM
MAJOR NATURAL PRODUCT PRODUCING PHYLA
USING NOVEL APPROACHES
1 3.4 INTRODUCTION
2 Culture-dependent approaches are known to vastly underestimate soil microbial diversity
3 (Amann et al. 1995, Cary et al. 2010, Ferrari et al. 2008, Lewis 2013). However, for NP
4 discovery microbial isolation remains critical to downstream analysis (Milshteyn et al.
5 2014, Katz & Baltz 2016). Rarely-cultured members of the dominant NP phyla (the
6 Actinobacteria, Proteobacteria, Firmicutes and Cyanobacteria) are thought to represent
7 the greatest potential for novel bioactives, along with other rare and as-yet-uncultured
8 divisions, estimated to contain a wealth of hidden chemical diversity (Lewis 2013, Müller
9 et al. 2015). Traditional culturing methods which rely on serial liquid dilutions and plating
10 to nutrient-rich artificial media (Zengler 2009) do not usually prove successful for
11 capturing novel taxa, even within the well characterised Actinobacteria and
12 Proteobacteria phyla (Jensen & Mafnas 2006, Nichols et al. 2010a, Janssen et al. 2002).
13
14 The Actinomycetales genus Streptomyces has historically provided the richest source of
15 bacterial NPs (Bérdy 2005, Baltz 2007). In recent years, however, attention has turned to
16 another prolific but under-studied order; the Gram-negative Myxococcales (Masschelein
17 et al. 2017). These fascinating microorganisms belong to the Deltaproteobacteria, and are
18 ubiquitous
80
Figure 3.1 Under starvation conditions Myxococcales form conspicuous,
macroscopic fruiting bodies. (A) Myxococcus fulvus on soil crumbs. (B) Stigmatella
aurantiaca on wood particles. (C) M. stipitatus on wood particles. (D) M. virescens,
on rabbit dung. Magnification bar = 500 µm. Adapted from: Dawid (2000).
19
20 in soil, but to date, descriptions of cold-adapted members are rare (Wenzel & Müller
21 2009, Herrmann et al. 2017, Dawid et al. 1988). Myxococcales show predatory, co-
22 operative social behaviour, and swarm toward food sources using slime secretion and
23 gliding motility, analogous to snail movement (Wenzel & Müller 2009). Prey comprise
24 organic macromolecules, which includes other microorganisms (Wenzel & Müller 2009,
25 Dawid 2000). Under starvation conditions, colonies develop into conspicuous fungi-like
26 fruiting bodies 50-500 µm in size (Fig. 3.1) (Wenzel & Müller 2009, Shimkets et al. 2006,
81
27 Dawid 2000). Traditional culturing techniques often over-look the Myxococcales, which
28 are outcompeted by more abundant, faster-growing species (Shimkets et al. 2006).
29 Myxococcales are usually isolated directly from environmental samples such as soil,
30 wood and animal dung, exploiting the taxa's unique features; such as the formation of
31 fruiting bodies, which are easily visualised and give rise to predatory cells which swarm
32 toward a bait source, typically comprising bacteria, yeast or cellulose (Shimkets et al.
33 2006, Karwowski et al. 1996, Dawid et al. 1988, Gaspari et al. 2005).
34
35 The results from PKS and NRPS domain sequencing in Chapter 2 indicated that the most
36 exciting novel NP biomining targets were pristine eastern Antarctic soils with low soil
37 fertility factors (Figs. 2.5, 2.6, 2.12, 2.15). Three of those pristine soils were selected here
38 for culturing based on PKS/NRPS gene findings: HI, MP and RL. Selected sites were
39 particularly low in carbon and moisture, they displayed a high level of novelty and
40 diversity of biosynthetic domain sequences and had high relative abundances of
41 Actinobacteria and Proteobacteria. Additionally, regional clustering had been observed
42 in multivariate analyses in Chapter 2; thus, soils were selected that represented the three
43 main clusters (Fig. 2.13, 2.14). Specifically, HI was selected because it displayed the
44 lowest average carbon content of all sites (703 ppm) (Fig. 2.12B), was one of the driest
45 (av. 96% DMF) (Fig. 2.12A), and displayed a diversity of both PKS and NRPS domains
46 (Figs. 2.5, 2.6). Importantly, 91% of all PKS domain sequences from HI were deemed
47 novel (< 70% similarity) (Fig. 2.15). Of the pristine sites, HI exhibited the highest relative
48 abundance of Actinomycetales (Fig. 2.10A) and formed a unique cluster in all
49 multivariate analyses (Figs. 2.13, 2.14). The MP site contained a diversity of PKS and
50 NRPS biosynthetic domains, with around 80% of sequences novel (Figs. 2.5, 2.6, 2.15)
51 and formed a cluster with RR in all multivariate analyses (Figs. 2.13, 2.14). Out of MP
52 and RR, MP displayed the lowest average soil carbon (4050 ppm) and moisture (97%
82
53 DMF), and highest average proportions of Actinobacteria (30%) and Proteobacteria
54 (11%) (Figs. 2.9, 2.12). RL was selected to represent the Vestfold Hills regional cluster
55 (Figs. 2.13, 2.14), and it measured the lowest average carbon content of this region (1415
56 ppm) (Fig. 2.12B). On average, RL contained a high relative abundance of Proteobacteria
57 (19%) (Fig. 2.9). Culturing studies have not been previously reported for any of the
58 chosen sites. As a comparison to the three pristine sites, a fourth site, Wilkes Tip (WT),
59 situated close to CS (Fig. 2.1A), was selected to represent a human-impacted Antarctic
60 soil.
61
62 In this chapter, two non-traditional oligotrophic culturing methods were employed with
63 the aim to target rare and cold-adapted NP-producing bacteria, specifically the
64 Myxococcales and Actinomycetales (Bérdy 2005, Masschelein et al. 2017). The first
65 method was adapted from Myxobacterial cultivating methods and was named here direct
66 soil culturing (DSC). Soil was directly incubated on low nutrient media with additional
67 bait sources (Shimkets et al. 2006). The second method was the soil substrate membrane
68 system (SSMS) (Fig. 1.9A), a novel culturing approach which has previously enabled
69 recovery of new species of Proteobacteria, Actinobacteria and Bacteroidetes (van Dorst
70 et al. 2016, Ferrari et al. 2005). The SSMS has been found to enrich rarely-isolated taxa
71 such as Saccharibacteria (previously known as candidate division TM7), as well as rare
72 phyla shown to harbour biosynthetic gene clusters, including Gemmatimonadetes,
73 Chloroflexi, Chlorobi and Verrucomicrobia (Ferrari et al. 2005, van Dorst et al. 2016,
74 Wang et al. 2014). Here, the SSMS was employed under psychrophilic incubation
75 temperatures for the first time.
83
76 3.5 MATERIALS AND METHODS
77 3.5.1 Site description and soil characteristics
Figure 3.2 Antarctic soils used for culturing were selected from three pristine polar
deserts and one human-impacted site. (A) Herring Island (HI), HI/T2/200, (B) Mitchell
Peninsula (MP), MP/T2/200, and (C) Rookery Lake (RL), RL/T2/200, East Antarctica.
The fourth sample, (D) Wilkes Tip (WT), was collected from a site contaminated with a
variety of waste including fuel and domestic rubbish (Fryirs et al. 2013). Photographs
courtesy of AAD. 84
78 3.5.1.1 Herring Island
79 HI is an ice-free island, devoid of vascular plant life (Fig. 3.2A) and is composed primarily
80 of garnet-bearing granite gneiss rock (Paul et al. 1995, Bailey et al. 2016). The island is
81 remote from human activity, situated approximately 15 km south of Casey station (Fig. 2.1A),
82 and is frequented by weddell seals and a variety of petrel seabird species (AADC, 2018, Paul
83 et al. 1995, Bailey et al. 2016). The selected HI sample (HI/T2/200), was low in moisture,
84 carbon and nitrogen, combined with a near-neutral pH (Table 3.1), and in sequenced bacterial
85 diversity, showed remarkably high relative abundance of Actinobacteria (67%), followed by
86 Chloroflexi (14%) and Acidobacteria (5%) (Fig. 3.3).
87
Table 3.1 Location and soil characteristics for selected Antarctic soils.
HI MP RL WT AAD Barcode 36815 36809 120310 124573 Transect/Distance T2/ 200m T2/ 200m T2/ 200m Bulk soil Antarctic Region Windmill Is. Windmill Is. Vestfold Hills Windmill Is. Moisture (%) 3.2 4.6 0.06 11 * Total Carbon (ppm) 600 2042 1114 < 5000 * Total Nitrogen (ppm) 130 210 130 < 5000 * pH 6.6 5.2 7.4 5.3 * 66° 24' 41”S, 66° 18' 46”S, 68° 29' 34”S, 66° 15’ 35”S; Latitude/Longitude 110° 39' 30”E 110° 32' 4”E 78° 6' 47”E 110° 32’ 22”E Garnet- Garnet- Mossel gneiss Geological bearing bearing (orthopyroxene- Unknown composition granite gneiss granite gneiss quartz-feldspar) * estimates based on Chong et al. 2009
85
Figure 3.3 Bacterial 16S rDNA diversity for the three pristine samples cultured; HI,
MP and RL. All three soils have high relative abundance of Actinobacteria and
Chloroflexi phyla. Proteobacteria also make up a large proportion in the MP and RL
samples. The fourth site, WT, was not characterised by 16S gene sequencing.
88
89 3.5.1.2 Mitchell Peninsula
90 MP lies approximately 5 km south of Casey station (Fig. 2.1A) (AADC, 2018) and, like HI,
91 is an ice-free desert, formed from garnet-bearing granite gneiss (Fig. 3.2B) (Paul et al. 1995,
92 Bailey et al. 2016). Fauna have not been recorded at MP (Ji et al. 2016, Chong et al. 2009).
93 However, some vegetation in the form of a low diversity of lichens and bryophytes have been
94 described by Melick et al. (1994). The MP sample (MP/T2/200), was higher in carbon and
95 moisture content than HI, and was more acidic (Table 3.1). The sample's bacterial community
96 showed greater diversity than HI, including high proportion of candidate divisions (WPS-2
97 and AD3) (9% and 6% respectively), and was dominated by Actinobacteria (26%),
98 Chloroflexi (17%) and Proteobacteria (14%) (Fig. 3.3). 86
99
100 3.5.1.3 Rookery Lake
101 The RL sampling site (Fig. 3.2C) is located 1.7 km north-east from rookery lake; a circular
102 body of water situated on Long Peninsula, Vestfold Hills, approximately 9.3 km north of
103 Davis station (Fig. 2.1B). The lake supports several Adélie penguin colonies (AADC, 2018).
104 Long Peninsula is formed primarily from Mossel gneiss rock (Sheraton 1983). The selected
105 RL sample, (RL/T2/200), was hyper-arid, and exhibited a slightly alkaline pH (Table 3.1).
106 Sequenced bacterial 16S rDNA diversity revealed high relative abundances of Actinobacteria
107 (33%), Proteobacteria (18%), Chloroflexi (16%), and Gemmatimonadetes phyla (13%) (Fig.
108 3.3).
109
110 3.5.1.4 Wilkes Tip
111 The fourth soil, from WT, was collected as a single bulk sample in 2005 from a contaminated
112 site undergoing evaluation for bioremediation (Table 3.1, Fig. 3.2D), and is situated on Clark
113 Peninsula, approximately 3 km north from Casey station (Fig. 2.1A). WT was a former
114 rubbish disposal site for Wilkes station, a facility abandoned in 1969 (Fryirs et al. 2013). WT
115 is contaminated with a diversity of legacy waste including general domestic rubbish, fuel
116 drums, gas cylinders, batteries and mechanical items. The site is almost permanently covered
117 by snow and ice, except in years of extreme melt, which occur every 4-5 years (Fryirs et al.
118 2013, AAD, 2002). Unlike samples HI, MP and RL; the WT sample has not been analysed
119 for soil physical and chemical properties, nor bacterial 16S diversity. Estimations of WT soil
120 chemical properties were made here based on data from Chong et al. (2009), who analysed
121 soil from the same site (Table 3.1). Additionally, they reported bacterial diversity by
122 denaturing gradient gel electrophoresis (DGGE) fingerprinting of amplified 16S rDNA gene
87
123 fragments. Their results were dominated by Cytophaga–Flexibacter–Bacteroides phylum,
124 followed by Proteobacteria (Chong et al. 2009).
125
126 3.5.2 Direct soil culturing methods
127 3.5.2.1 Herring Island and Mitchell Peninsula DSC
128 For HI and MP DSC, two baiting methods were employed: Escherichia coli lawn, and
129 cellulose bait (Fig. 3.4). Two preparations were used for culturing soils, designated 'untreated'
130 and 'pretreated'. The untreated soils (1 g) were removed from -80°C storage and defrosted at
131 4°C, suspended in 500 µL of sterile Milli-Q water and briefly vortexed before use, while the
132 pretreated soils (0.5 g) were defrosted at RT (~21°C), air dried in covered petri plates at 37°C
133 for 30 min, then suspended in 3.5 mL sterile Milli-Q water. The soil-water suspension was
134 placed in an ultrasonicator (XUBA1, Grant, UK) 44 kHz for 1 min, then incubated in a water
135 bath at 56°C for 10 min (Karwowski et al. 1996). This pretreatment was hypothesised to
136 select for Myxococcales, whose spores are resistant to mild heat and sonication. Other spore-
137 forming bacteria such as Streptomyces spp. should also be similarly selected (Karwowski et
138 al. 1996, Daza et al. 1989).
139
140 For the pretreated soils, water agar plates (WCX) were prepared with 25 µg/ mL
141 cycloheximide (R&D Systems, Minneapolis) (Appendix A2.2), to suppress fungal growth
142 (Shimkets et al. 2006). For the untreated soil WCX plates, cycloheximide concentration was
143 doubled to 50 µg/ mL (Appendix A2.2). Four plates were prepared for each soil as follows:
144 a dense suspension of live E. coli ATCC 25922 was applied as either cross streak (Fig. 3.4A),
145 or circular lawns to WCX agar plates (Fig. 3.4C), and dried at RT. For the cellulose bait
146
88
147
Figure 3.4 Direct soil culturing using both E. coli lawn (A & C) and cellulose (B & D)
baiting methods on WCX agar plates. The soil preparations were either pretreated with
mild heat and sonication (A & B), or untreated (C & D).
148
149 methods, sterile 10 mm diameter Whatman® grade 1 filter paper discs were applied to WCX
150 agar either singularly (Fig. 3.4B) or in groups (Fig. 3.4D) (Dawid et al. 1988, Shimkets et al.
151 2006). Pea-sized portions (~10 mm diameter) of either the pretreated (Fig. 3.4A & B) or
152 untreated soil (Fig. 3.4C & D), were then applied to the surface of each bait, using a sterile
153 spatula. Plates were wrapped in parafilm and incubated at RT in the dark, for up to 8 months,
154 with small amounts of sterile water added periodically to maintain moisture.
155
89
156 3.5.2.2 Rookery Lake and Wilkes Tip DSC
157 RL and WT soils were cultured using a third Myxococcales culturing method, consisting of
158 E. coli baiting with the addition of rabbit dung pellets (Fig. 3.5). Herbivore dung has been
159 demonstrated as a favoured substrate for Myxococcales, with rabbit dung the most commonly
160 used in isolation studies (Shimkets et al. 2006). Prior to use, the dung pellets, collected from
161 an Australian property on the NSW/QLD border, were autoclaved at 121°C for 45 min,
162 cooled, soaked for 1 hr in cycloheximide solution (30 µg/mL) to inhibit fungal growth, and
163 aseptically dried. Duplicate WCX plates with 50 µg/mL cycloheximide were prepared with
164 circular E. coli lawns and portions of untreated soil preparation, as previously described (Fig.
165 3.4C). Rabbit dung pellets were moistened with liquid from the untreated soil preparation,
166 then embedded into soil portions on the WCX E. coli plate (Fig. 3.5) (Gaspari et al. 2005,
167 Shimkets et al. 2006). Plates were wrapped in parafilm and incubated at RT in the dark for
168 up to 7 months, with small amounts of sterile water added periodically to maintain moisture.
169
170
Figure 3.5 Direct soil culturing using the E. coli lawn method with the addition of
rabbit dung pellets. Culturing was performed using untreated soils on WCX agar plates.
90
171 3.5.2.3 Isolation and purification of bacteria from DSC
172 Following incubation, all DSC WCX plates were observed every 1-3 d under a
173 stereomicroscope (40x magnification), and light microscope (100x magnification), for
174 visualisation of Myxococcale-like fruiting bodies, or other visible colony formation. Visible
175 colonies were picked directly using microscopy and a sterile toothpick and sub-cultured onto
176 a variety of media: 0.75x Nutrient Agar (NA) (Oxoid, Thermo Scientific, Massachusetts),
177 soil extract with gellan gum (SEGG) (Appendix A2.1 & A2.2), and WCX agar with E. coli
178 or cellulose bait (Appendix A2.2). Purified isolates were maintained on 0.75x NA.
179
180 3.5.3 SSMS culturing at cold temperatures
181 In addition to DSC, the HI soil was selected for culturing via the SSMS, adapted from Ferrari
182 et al. (2008) (Fig. 3.6) with the aim of selecting for cold-adapted microorganisms. All
183 equipment and reagents were equilibrated to 4°C prior to use, and low temperatures (< 10°C)
184 were maintained throughout the entire experiments.
185
186 HI soil (16.5 g) was removed from -80°C storage and defrosted at 4°C. SSMS cultures were
187 prepared in triplicate. Tissue culture inserts (TCI) (Millicell®, 30 mm, polycarbonate, 0.4
188 µm, Millipore, Australia), were used to provide the soil substrate for bacterial growth. Each
189 TCI was prepared by gently vortexing 4.5 g HI soil with ~300 µL of 0.9% NaCl to form a
190 homogenous soil slurry which evenly covered the underside of the filter membrane (Fig.
191 3.6A). The slurry was then secured against the membrane by filling the remaining TCI space
192 with gellan gum (5 g/ L) (Gelzan™ Gelrite®, Sigma-Aldrich), and the TCI inverted into the
193 6-well culture plate and placed at 4°C while the inoculum was prepared; 3 g of HI soil was
194 added to 27 mL 0.9% NaCl and vigorously vortexed for 10 s. Large particles were allowed
91
195
196
Figure 3.6 Principles of the soil substrate membrane system (SSMS). (A) Tissue
culture inserts (TCI) are prepared with a soil slurry of the soil sample of interest. (B) A
polycarbonate membrane is inoculated with microbial suspension and applied to the outer
TCI filter. Nutrients for growth diffuse through the filters from the soil. (C) The TCI
replicates are incubated within a 6-well culture plate, with sterile water added to outer
wells to prevent drying. Source: Ferrari et al. (2008).
197
198 to sediment at 4°C for 1 min. A 1:100 dilution was then prepared by adding 100 µL of the
199 1:10 dilution to 900 µL 0.9% NaCl. For each triplicate culture, a 25 mm diameter, 0.22 µm
200 pore size, hydrophilic polycarbonate membrane (PCM) (Isopore®, Millipore) was placed
92
201 onto a moistened 25 mm diameter glass fibre filter (Whatman) on a sample filtration manifold
202 (Carbon 14 Centralen, Denmark) fitted with Millivac-Mini vacuum pump (Millipore). A 20
203 mL sterile stainless-steel cylinder was then secured and filled with 10 mL 0.9% NaCl and 50
204 µL of 1:100 innoculum and filtered onto PCM replicates. Each PCM was then applied to an
205 inverted TCI membrane, ensuring complete contact (Fig. 3.6B), and the TCIs inserted into
206 the 6-well plate. Sterile water was added to the plates outer wells to maintain hydration of
207 cultures (Fig. 3.6C). The plate was sealed with parafilm and incubated at 4°C, for a total of
208 162 d.
209
210 3.5.3.1 Assessing microcolony growth and bacterial viability on the SSMS
211 Microcolony growth and viability was assessed at 50, 78 and 162 d of incubation (Fig. 3.7A).
212 On each occasion, ¼ PCM from one TCI replicate was abstracted using a sterile razor blade
213 and secured to a microscope slide with 0.1% agarose. The PCM portion was treated with 1
214 drop (~25µL) of Vectashield mounting medium (Vector Laboratories, California), containing
215 a 1:1 ratio of Ultrapure™ water and the LIVE/DEAD® BacLight™ Bacterial Viability stain
216 (Invitrogen), and incubated at 4°C in the dark for 30 min. PCM portions were then observed
217 via epi-fluorescent microscopy using an Olympus BX51 microscope with DP74 camera
218 (Olympus, North Ryde, Australia), and filters appropriate for excitation/emission maxima of
219 480/500 nm for SYTO 9 and 490/635 nm for propidium iodide (PI). When stained with the
220 SYTO 9 and PI nucleic acid stains, live intact cells fluoresce green, while dead cells fluoresce
221 red.
93
94
Figure 3.7 Flowchart for bacterial cultivation using cold-incubated SSMS. (A) Portion of the PCM was removed and microcolony growth and viability assessed by epi-fluorescent microscopy using a live/dead stain. (B) Portion of the PCM was vortexed with saline to dislodge and suspend cells. (C) PCM was removed from saline and rubbed over surface of RAVAN media. (D) The cell suspension was serial diluted and spread-plated. (E) The cell suspension was passed into two rounds of enrichment in RAVAN liquid media and spread-plated. (F) Resulting colonies were sub-cultured to RAVAN media for purification, then nutrient media for maintenance.
95
222 3.5.3.2 Secondary cultivation of SSMS microcolonies using artificial media
223 In addition to microscopy at 50, 78 and 162 d incubation, ¼ size portions of PCM’s from
224 TCI replicates were placed into 1.5 mL tubes containing 1 mL 0.9 % NaCl and vortexed for
225 1 min to dislodge cells (Fig. 3.7B). PCMs were removed from the cell suspension and applied
226 directly over the surface of 8°C equilibrated RAVAN/ TSV/ GG plates (Appendix A2.1 &
227 A2.2) and wrapped in parafilm (Fig. 3.7C). The low-concentration culturing medium,
228 RAVAN, is designed to select for oligophilic bacteria (Watve 2000), and was adapted here
229 to target Actinomycetales and novel bacteria. RAVAN was prepared at 0.05x concentration,
230 modified with additional trace salt and vitamin solutions and gellan gum was used as the
231 solidifying agent (Appendix A2.1 & A2.2). Gellan gum was used as it may improve capture
232 of environmental bacteria, including novel phyla such as Gemmatimonadetes (Tanaka et al.
233 2014). Trace salts were added to provide electrolytes and minerals commonly used for
234 recovery of Actinomycetales and have been found to significantly promote sporulation in
235 Streptomyces (Shirling & Gottlieb 1966, Karandikar et al. 1996). The trace vitamin solution
236 was included because B-vitamins have been found to improve recovery of Actinomycetales
237 from environmental samples (Hayakawa & Nonomura 1987, Zotchev et al. 2008, Wolin et
238 al. 1963).
239 For the cell suspension (Fig. 3.7B), serial dilutions were made by addition of 100 µL cell
240 suspension to 900 µL 0.9% NaCl (Fig. 3.7D). Each of the dilutions, as well as the undiluted
241 cell suspension, were spread onto RAVAN/ TSV/ GG plates. Additional liquid culture
242 enrichments were made (Fig. 3.7E), whereby 10 µL of each cell suspension was added to 0.2
243 mL tubes containing 190 µL RAVAN/ TSV broth (Appendix A2.2). Following incubation at
244 8°C for 15-20 d, 100 µL aliquots of enrichments were plated onto RAVAN/ TSV/ GG. This
245 process was repeated for a total of two enrichments (Fig. 3.7E).
246 96
247 3.5.3.3 Isolation and purification of bacteria from SSMS cultures
248 Spread-plated cultures (Fig. 3.7) were regularly observed for growth, with incubation ranging
249 between 27-347 d at 8°C. Visible colonies were extracted using a 1µl sterile loop and sub-
250 cultured onto RAVAN/ TSV/ GG plates until pure colonies were obtained (Fig. 3.7F). Once
251 established in pure culture, isolates were tested for the ability to grow on 0.75x NA at 8°C,
252 followed by RAVAN/ TSV/GG and 0.75x NA at RT.
253
254 3.5.4 Gram and lactophenol cotton blue stain differentiation
255 Gram-staining was performed on all cultured isolates; for initial characterisation, to
256 determine purity, and to aid elimination of fungal isolates from further analysis (Beveridge
257 2001). A small portion of a single colony was removed with a sterile loop and emulsified
258 with 1 drop of sterile water on a glass slide and air dried. The smear was heat-fixed, and
259 flooded with Gram's crystal violet solution (Sigma-Aldrich) for 1 min. Slides were rinsed
260 with water, and Gram's iodine solution (Sigma-Aldrich) applied for 1 min. Smear was de-
261 colourised with 95% EtOH, then water, and flooded with dilute carbol fuchsin (Pro-Lab
262 Diagnostics) for 30 s. Smears were air dried, then visualised with oil-immersion light
263 microscopy.
264
265 Lactophenol cotton blue mounts were additionally performed on suspected fungal colonies
266 (Leck 1999). One drop of Lactophenol Cotton Blue stain (Sigma-Aldrich) was applied to a
267 glass slide. With minimal disruption, colonies were removed with a sterile loop, combined
268 with the stain and a coverslip applied. The wet mount was visualised by light microscopy.
269
97
270 3.5.5 Isolate DNA extraction and purification
271 Genomic DNA was extracted from isolates using a bead-beating approach followed by
272 ethanol precipitation. A single large bacterial colony was transferred to a 2 mL screw-top
273 microcentrifuge tube (Sarstedt AG and Co., Germany), containing 1 mL autoclaved Milli-Q®
274 water (Merck Millipore, Massachusetts), and 0.5 g of an equal proportion 0.1 mm and 0.5 mm
275 diameter glass beads (Mo Bio, Carlsbad). The mixture was homogenized using the FastPrep®-
276 120 homogenisation instrument (MP Biomedicals, California) for 40 s, on speed setting 6.0, and
277 incubated for 5 min at 95°C. Samples were centrifuged at 20,800 x g for 3 min, and DNA lysates
278 removed.
279
280 For ethanol precipitation, 1/10 volume of 3M sodium acetate (CH3COONa, pH 5.2) was added
281 to DNA lysates, followed by two volumes of ice-cold 100% EtOH. DNA was precipitated at 8°C
282 for 20 min, then centrifuged at 17,900 x g for 20 min, and supernatants discarded. Pellets were
283 re-suspended in 1 mL fresh 70% EtOH, and centrifuged at 17,900 x g for 5 min. Following
284 removal of supernatants, pellets were dried on a heat block for 15 min at 55°C, re-suspended in
285 150 μL TE buffer (10 mM Tris-HCl (pH 8.0), 0.1 mM EDTA). Genomic DNA lysates were
286 quantified using Nanodrop and stored at -20°C until further use.
287
288 3.5.6 PCR amplification and Sanger sequencing of isolate 16S rDNA genes
289 Taxonomic identification of strains was performed based on Sanger sequencing of the 16S
290 rDNA gene for selected strains. Near full-length 16S bacterial rDNA genes were PCR
291 amplified from gDNA using the primer set 27F/1492R (Table 3.2) (Integrated DNA
292 technologies, Singapore) (Lane et al. 1985). Reaction mixtures contained 5 µL 5x Green
293 Gotaq® Flexi Buffer (Promega, Wisconsin), 2.5 mM MgCl2, 0.2 mM each dNTP, 10% v/v
294 Dimethylsulfoxide (DMSO) (Sigma-Aldrich), 0.4 µM each primer, 0.625 units of GoTaq®
98
295 Hotstart DNA Polymerase (Promega), 3 µL of purified DNA template, and Ultrapure™ water
296 to 25 µL (Invitrogen). Amplification was performed in an MJ Mini™ Thermal Cycler (Bio-
297 Rad, Australia). Thermocycler conditions were as follows: 94°C for 2 min, 30 cycles of 94°C
298 for 30 s, 60°C for 30 s, 72°C for 90 s, final extension at 72°C for 5 min. PCR amplification
299 was confirmed via gel electrophoresis, with 10 µL PCR product loaded onto 2% (w/v) agarose
300 gel in Tris-acetate-ethylenediaminetetraacetic acid buffer (1 x TAE), with addition of 0.01%
301 SYBR safe DNA stain (Invitrogen). Gels were visualised using the Safe Imager™ 2.0 Blue Light
302 Transilluminator (Invitrogen).
303
Table 3.2 Primer sets employed for PCR targeting 16S bacterial rDNA, PKS and
NRPS domain fragments.
Primer Length Target Primer Sequence (5'-3') Reference Name (bp) 16S 27F AGAGTTTGATCMTGGCTCAG 1500 Lane 1985 rDNA 1492R TACGGYTACCTTGTTACGACTT K1F TSAAGTCSAACATCGGBCA 1200- PKS Ayuso- M6R CGCAGGTTSCSGTACCAGTA 1400 Sacido & A3F GCSTACSYSATSTACACSTCSGG Genilloud NRPS 700 A7R SASGTCVCCSGTSCGGTAS 2004 304
305
306 PCR products were submitted directly to The Ramaciotti Centre for Gene Function Analysis,
307 at UNSW Sydney (NSW, Australia), for purification and preparation for single-end
308 sequencing, using primer 1492R, on the Sanger ABI 3730 Capillary Sequencer (Applied
309 Biosystems, Australia).
310
311 Resulting FASTA sequences (~1200 bp) were visualised with FinchTV v1.4.0 trace viewer
312 (Geospiza, Washington, USA), quality trimmed to ~1000 bp, and compared with known gene
99
313 sequences in GenBank, using the BLAST search tool (Altschul et al. 1990). For isolates with
314 identical 16S gene sequences, two representative strains were chosen for further analysis.
315
316 3.5.7 Cryopreservation of strains
317 Two strains from each species from each site/method (HI, MP and RL DSC and HI SSMS)
318 were selected for cryopreservation in triplicate using the Microbank™ cryovial bacterial
319 storage system (Pro-Lab Diagnostics, Canada). Pure cultures in exponential growth phase on
320 solid media were aseptically transferred to cryovials and the tubes inverted for ~30 s to allow
321 binding of bacterial cells to supplied beads. Excess liquid was removed, and tubes were
322 stored at -80°C until further use.
323
324 3.5.8 Type I PKS and NRPS domain screening by PCR
325 Isolate genomic DNA was screened for presence of Type I PKS and NRPS genes, targeting the
326 conserved KS/AT and AD domains. Each 50 μL reaction comprised 10 μL 5X Green GoTaq®
327 Flexi Buffer (Promega), 0.2 mM each dNTP, 2.5 mM MgCl2, 10% v/v DMSO, 0.8 μM each
328 primer (PKS: K1F/M6R or NRPS: A3F/A7R) (Table 3.3), 1.25 units of GoTaq® Flexi DNA
329 Polymerase (Promega), 18.75 μL Ultrapure™ water, and 5 µL purified gDNA.
330
331 Thermocycler conditions for Type I PKS comprised 94°C for 2 min, 30 cycles of 94°C for 30 s,
332 55°C for 30 s, 72°C for 2 min, and final extension 72°C for 5 min; for NRPS; 95°C for 5 min, 30
333 cycles of 95°C for 30 s, 59°C for 30 s, 72°C for 4 min, and final extension at 72°C for 10 min.
334 The positive control was purified genomic DNA from the Type I PKS and NRPS positive
335 Streptomyces strain, CZ24 (van Dorst et al. 2017).
336
100
337 3.5.9 In situ antimicrobial testing by cross-streak method
338 Strains were screened for antimicrobial activity using the cross-streak agar method (Carvajal
339 1947, Hopwood 2007, Kamat & Velho-Pereira 2011). This is a relatively rapid screening
340 assay to establish antimicrobial activity from the isolate in situ and provides semi-
341 quantitative results (Kamat & Velho-Pereira 2011). Strains were inoculated in triplicate onto
342 NA as a central streak using a sterile 1 µL loop (Fig. 3.8). Plates were incubated at RT for 1-
343 7 d depending on genus, to allow sufficient growth and production of active compounds.
344
345 Test pathogens comprised five opportunistic human pathogen strains commonly utilized in
346 antibiotic sensitivity testing (ATCC 2014). They included a selection of Gram-positive
347 pathogens: Staphylococcus aureus ATCC 25923 and Bacillus subtilis ATCC 11774; Gram-
348 negative pathogens: E. coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853; and
349 one fungal pathogen, Candida albicans ATCC 10231. Test pathogens were streaked from
350 the edge of the plate to the polar isolates in perpendicular lines using a 1 µL sterile loop (Fig.
351 3.8). Plates were incubated for a further 1-4 d at RT, and the zone of inhibition measured.
352 Negative controls consisted of pathogens streaked in an identical way with no isolate,
353 positive controls were by disc diffusion method (Bondi et al. 1947), whereby a small portion
354 of test pathogen colony was inoculated into 1 mL phosphate-buffered saline (PBS), spread-
355 plated onto NA, and allowed to dry. Discs infused with tobramycin (30 µg/ mL) (Bio-Rad,
356 California) were applied to the bacterial lawns, while amphotericin B discs (10 µg/ mL)
357 (Sigma-Aldrich, Missouri) were applied to C. albicans lawns, and the plates incubated at RT
358 for 48 h before measurement of zones of clearing.
359
101
Figure 3.8 Pattern of inoculation for cross-streak agar assay. Test pathogens were
inoculated perpendicular to each polar isolate analysed.
360
361 3.5.10 Type I PKS and NRPS domain screening and antimicrobial assays for strains
362 isolated in previous studies
363 An additional 20 strains, which had been previously isolated from eastern Antarctic sites
364 Browning Peninsula (BP), Robinson Ridge (RR) and Wilkes Tip (WT) (Fig 2.1A) by
365 colleagues (Pudasaini et al. 2017, Nicetic 2016), were selected here to undergo Type I PKS
366 and NRPS domain screening and cross-streak antimicrobial assay. These strains comprised
367 14 Actinobacteria, four Proteobacteria and two Firmicutes (Table 3.3).
368
102
Table 3.3 Strains isolated in previous studies which were screened for PKS and
NRPS domains and antimicrobial activity.
Site Strain Closest cultured representative Phylum ID (%) INR13 Azospirillum zeae α-Proteobacteria 100 INR4 Bacillus aryabhattai Firmicutes 100 INR6 Burkholderia jiangsuensis β-Proteobacteria 99 RR INR15 Frondihabitans australicus Actinobacteria 99 INR9 Leifsonia shinshuensis Actinobacteria 99 INR17 Mesorhizobium qingshengii α-Proteobacteria 100 INR7 Streptomyces spororaveus Actinobacteria 99 INWT7 Cryobacterium mesophilum Actinobacteria 99 INWT5 Methylobacterium brachiatum α-Proteobacteria 99 WT INWT6 Quadrisphaera granulorum Actinobacteria 99 INWT3 Rhodococcus aerolatus Actinobacteria 99 SPB151 Kribbella sandramycini Actinobacteria 99 SPB164 Mycobacterium fluoranthenivorans Actinobacteria 99 SPB16 Paenisporosarcina macmurdoensis Firmicutes 99 SPB1 Rhodococcus yunnanensis Actinobacteria 100 BP SPB167 Streptomyces abikoensis Actinobacteria 99 SPB35 Streptomyces beijiangensis Actinobacteria 99 SPB162 Streptomyces fildesensis Actinobacteria 99 SPB13 Streptomyces indigoferus Actinobacteria 99 SPB4 Streptomyces lavendulae Actinobacteria 99 RR: Robinson Ridge, WT: Wilkes Tip, BP: Browning Peninsula 369
370
371 3.5.11 Bacterial 16S rDNA gene analysis for pristine soils
372 Bacterial 16S rDNA sequencing data previously described in Chapter 2 (Section 2.2.2), was
373 analysed for the three pristine soil samples used in this chapter, and visualised as relative
374 abundance by phyla (Fig. 3.3) in R 3.4.0 using the ggplot2 package 2.2.1 (Wickham 2009).
375
103
376 3.5.12 Venn diagram visualisation of species shared between sites
377 Cultured bacterial species shared across sites by DSC and SSMS methods were calculated
378 and visualised as a Venn diagram in R 3.4.0 with the VennDiagram package 1.6.17 (Fig.
379 3.15) (Chen & Boutros 2011).
380
381 3.5.13 Biotechnological and biosynthetic potential of isolates
382 Based on overall results, a number of isolates were chosen to undergo whole genome
383 sequencing in Chapter 4. Isolates were prioritised based on the following criteria:
384 • Antimicrobial activity,
385 • Presence of NP domains,
386 • 16S rDNA gene sequence identity to known species < 99%,
387 • Rarely-cultured Actinobacteria and Proteobacteria groups,
388 • High quality genome assembly for the species absent from the genome taxonomy
389 database (GTDB) (http://gtdb.ecogenomic.org/),
390 • Relevance to other ongoing Antarctic microbial research, including known
391 hydrocarbon degrading genera with potential for bioremediation, and pigmented
392 strains, which are commonly associated with NPs and are valuable to diverse
393 industries.
394
395 3.6 RESULTS
396 3.6.1 Direct soil culturing
397 At 8 d incubation, mycelium-like microcolonies were observed extending out from soil
398 particles and into the WCX agar (Fig. 3.9). Further sub-culturing and analysis revealed these
399 to be Streptomyces species. During the ~8 month incubation period, visible colonies were
104
400 sub-cultured from the surfaces of agar (Fig. 3.10A), soil particles (Fig. 3.10B) and dung
401 pellets (Fig. 3.10D).
402
Figure 3.9 Substrate mycelium-like filaments were observed by microscopy of direct
soil cultures. (A) Mycelium extending from soil particles, and (B) spreading throughout
the agar. The mycelium were sub-cultured using a sterile toothpick, and gave rise to
various Streptomyces spp.
403
404 From 15 d incubation, blue-pigmented fruiting forms began developing on soil particles (Fig
405 3.10C). These were determined to be fungal following sub-culturing and microscopy with
406 Gram and lactophenol cotton blue staining. Over the culturing period, several filamentous
407 fungal morphotypes grew prevalently on WCX plates, particularly pretreated soils from MP,
408 RL and WT. This was despite the application of increased concentrations of cycloheximide
409 (Section 3.1.2.1). Visibility of bacterial microcolonies was thus reduced, leading to lower
410 numbers of colonies picked for sub-culturing.
411
105
Figure 3.10 Visible colonies were directly picked from soil cultures using a sterile toothpick and stereomicroscopy. (A) HI E. coli baiting plate with untreated soil; yellow pigmented colonies were Rhodococcus spp. (B) MP cellulose baiting plate with untreated soil; Streptomyces sp. M1 was recovered from the microcolonies seen here growing on the soil crumb surface. (C) HI cellulose baiting with pretreated soil. Several filamentous fungi grew on the WCX plates despite the addition of cycloheximide. Here, blue- pigmented conidia were visible on the surface of a soil crumb. (D) White sporulating colonies on the surface of rabbit dung pellets. Similar sub-cultured colonies from RL were
Streptomyces sp. 106
412 In total > 100 bacteria were isolated by DSC from all four sites, with 43 isolates determined
413 to be different at species level (Table 3.4). HI yielded the greatest number of species (28),
414 compared to MP (7), RL (4) and WT (4) (Fig. 3.15). The majority of isolates belonged to
415 Actinobacteria (32), the remaining were Alphaproteobacteria (6), Betaproteobacteria (3), and
416 one representative each from Bacteroidetes and Firmicutes phyla (Table 3.4).
417
418 Myxococcales fruiting bodies were not detected over the 8-month observation period, nor
419 were any Deltaproteobacteria recovered, which may reflect their low abundance in these soils
420 (0.2-0.7% rDNA gene relative abundance). Interestingly, Streptomyces was the most
421 abundant genus recovered by DSC, comprising 40% of all isolates (Fig. 3.11, Table 3.4).
422 Streptomyces were particularly abundant in HI, which was known to harbour high
423 Actinobacterial relative abundance via culture-independent methods (Fig. 3.3). Streptomyces
424 colony morphology was varied (Fig. 3.11), and sporulation was predominantly olive green
425 (Fig. 3.11B and F), white (Fig. 3.11A and C) or brown (Fig. 3.11D). One species, S.
426 lienomycini NBH81, produced a striking red colony pigmentation (Fig. 3.11E). Diffused
427 melanin-like pigments ranged from very dark brown, as in S. lavendulae NBH20 and S.
428 gougerotti NBH77 (Fig. 3.11A and D), to tan-coloured, for example S. flavogriseus NBH21
429 and S. parvus NBM1 (Fig. 3.11B and F). Species that produced no diffused melanin-like
430 pigments included S. atroolivaceus NBH70 and S. lienomycini NBH81 (Fig. 3.11C and E).
107
Table 3.4 Phylogenetic distribution of bacterial species cultured from all sites by DSC.
Strain Closest species match Phylum Sim. (%) Accession Bait Soil Trmt Days † No. NBM4 Arthrobacter koreensis Actinobacteria 99 KP715106.1 E P 41 1 NBM25 Burkholderia cepacia β-Proteobacteria 99 KT906686.1 E U 154 1 NBM12 Burkholderia sordidicola β-Proteobacteria 99 KJ606828.1 E, C U, P 96 4 NBH87 Frigoribacterium faeni Actinobacteria 99 KX809655.1 E U 134 1 NBWT11 Geodermatophilus soli Actinobacteria 98 NR_109440.1 ED U 146 1 NBWT1 Geodermatophilus terrae Actinobacteria 99 NR_109441.1 ED U 42 1 NBH84 Hymenobacter xinjiangensis Bacteroidetes 97 JF496493.1 E U 124 1 NBH82 Janibacter melonis Actinobacteria 99 KT720303.1 E U 124 1 NBRL9 Massilia timonae β-Proteobacteria 99 EU221406.1 ED U 42 1 NBH50 Methylobacterium populi α-Proteobacteria 100 KY882116.1 E P 39 2 NBRL2 Microbacterium aerolatum Actinobacteria 100 LN774527.1 ED U 27 1 NBWT6 Microbacterium foliorum Actinobacteria 100 KY405917.1 ED U 50 1 NBH49 Microbacterium schleiferi Actinobacteria 98 KY681786.1 E P 36 1 NBH85 Microbacterium testaceum Actinobacteria 100 KX809655.1 E P 151 1 NBH64 Micrococcus yunnanensis Actinobacteria 99 MH790299.1 E, C U, P 32 >5 NBM3 Micrococcus yunnanensis Actinobacteria 100 KX082873.1 E U 46 4 NBRL5 Micrococcus yunnanensis Actinobacteria 99 KT719527.1 ED U 27 1 NBM11 Novosphingobium subterraneum α-Proteobacteria 99 JF459977.1 E P 35 1 NBH48 Paracoccus carotinifaciens α-Proteobacteria 99 NR_024658.1 E U 29 1 NBM5 Planococcus plakortidis Firmicutes 99 LT160774.1 E P 46 1 NBH57 Pseudarthrobacter sulfonivorans Actinobacteria 99 KX056505.1 E U 25 1 NBH51 Rhodococcus fascians Actinobacteria 99 LN999546.1 E P 85 >2 NBH73 Rhodococcus luteus Actinobacteria 99 AJ576249.1 E U, P 52 >6 NBH83 Sphingomonas aerolata α-Proteobacteria 99 LN774415.1 E U 116 1 NBH67 Sphingomonas endophytica α-Proteobacteria 99 NR_117869.1 E P 59 1 NBWT7 Sphingomonas mucosissima α-Proteobacteria 99 JF496278.1 ED U 50 1 H: Herring Island, M: Mitchell Peninsula, RL: Rookery Lake, WT: Wilkes Tip, E: E.coli, C: cellulose, ED: E.coli & dung, P: pretreated, U: untreated, †: days from initial DSC set up to colony picking, No.: number of strains cultured.
108
431
Table 3.4 Phylogenetic distribution of bacterial species cultured from all sites by DSC cont.
Strain Closest species match Phylum Sim. (%) Accession Bait Soil Trmt Days† No. NBH70 Streptomyces atroolivaceus Actinobacteria 100 KX527679.1 E, C U, P 25 2 NBH41 Streptomyces badius Actinobacteria 100 KY007184.1 E, C U, P 38 >6 NBH53 Streptomyces californicus Actinobacteria 99 FJ481076.1 C P 36 1 NBH13 Streptomyces coelicoflavus Actinobacteria 100 KT758401.2 C P 32 2 NBH1 Streptomyces cyaneofuscatus Actinobacteria 99 KY514161.1 C P 14 1 NBH86 Streptomyces daghestanicus Actinobacteria 99 KX775313.1 E U 134 1 NBH21 Streptomyces flavogriseus Actinobacteria 99 KU324455.1 E P 36 1 NBH65 Streptomyces globosus Actinobacteria 100 KU324456.1 E U 42 1 NBH77 Streptomyces gougerotii Actinobacteria 99 KT758400.1 C P 112 1 NBH78 Streptomyces griseus Actinobacteria 99 FN298358.1 E P 112 1 NBH20 Streptomyces lavendulae Actinobacteria 99 KX698040.1 E P 34 >4 NBH81 Streptomyces lienomycini Actinobacteria 99 KY753328.1 C P 155 2 NBH61 Streptomyces parvus Actinobacteria 100 MF359745.1 E U 24 >2 NBM1 Streptomyces parvus Actinobacteria 100 MF359745.1 E U 35 1 NBH42 Streptomyces praecox Actinobacteria 100 KX507060.1 E, C U, P 61 >5 NBH12 Streptomyces pratensis Actinobacteria 99 KU973960.1 C U, P 23 >7 NBRL4 Streptomyces pratensis Actinobacteria 100 KU973960.1 ED U 27 2 H: Herring Island, M: Mitchell Peninsula, RL: Rookery Lake, WT: Wilkes Tip, E: E.coli, C: cellulose, ED: E.coli & dung, P: pretreated, U: untreated, †: days from initial DSC set up to colony picking, No.: number of strains cultured.
432
109
433
Figure 3.11 Colony morphology for six different Streptomyces isolates. (A) S.
lavendulae NBH20 formed white sporulation, with a dark melanin-like pigmentation
which diffused into surrounding agar. (B) S. flavogriseus NBH21 produced olive
green/white sporulation with a tan-coloured diffused pigment. (C) For S. atroolivaceus
NBH70, sporulation was white and diffused pigments were absent. (D) S. gougerotii
NBH77 formed ringed brown/tan sporulating colonies with a dark melanin-like pigment.
(E) S. lienomycini NBH81 produced red colony pigmentation and no diffused pigments.
(F) S. parvus NBM1 were olive green/white sporulating colonies with a tan melanin-like
pigment.
434
110
435 For HI and MP DSC, the most successful baiting method was E. coli, which yielded the
436 greatest diversity (15 genera) and number (24) of isolates, compared to only three genera
437 from six isolates via cellulose baiting. Five isolates were recovered via both methods (Table
438 3.4). The pretreatment of soil with heat and sonication resulted in slightly more isolates (16)
439 than untreated soil (12), while seven isolates grew from both treatments. The diversity of
440 genera retrieved from each soil treatment was the same (10 genera each) (Table 3.4).
441
Figure 3.12 DSC isolates with ≤ 98% 16S rDNA gene sequence similarity to known
species. (A) NBWT11 shared 98% similarity to Geodermatophilus soli. (B) NBH49 was
98% similar to Microbacterium schleiferi and (C) NBH84 exhibited 97% sequence
similarity to Hymenobacter xinjiangensis.
442
443 Isolates were predominantly recovered from sub-cultures on 0.75x NA, rather than SEGG or
444 2nd round WCX bait media (Section 3.2.2.3). Exceptions were Janibacter melonis NBH82,
445 Sphingomonas aerolata NBH83, Hymenobacter xinjiangensis NBH84, Frigoribacterium
446 faeni NBH87 and S. parvus NBM1, which were retrieved through sub-culturing to 2nd round
447 WCX and E. coli. The median time taken from initial DSC set-up to visible colony formation
448 for the four soils ranged from 27-50 d (Table 3.4). Isolates which were particularly slow 111
449 growing or had lengthy lag-phases took > 100 d for adequate growth to appear, including two
450 potentially novel species: Geodermatophilus soli NBWT11 and H. xinjiangensis NBH84.
451 Additionally, several species recovered from the 2nd round WCX and E. coli media were also
452 slow growing: F. faeni NBH87, J. melonis NBH82 and Sphingomonas aerolata NBH83, and
453 several of the morphologically striking Streptomyces such as S. gougerotii NBH77 and S.
454 lienomycini NBH81 (Fig. 3.11, Table 3.4).
455
456 Many of the DSC isolates showed high 16S rDNA gene sequence identity to known bacterial
457 species (99-100%). Three exhibited sequence identities of 97-98%, indicating they may be
458 novel species. These were G. soli NBWT11, Microbacterium schleiferi NBH49 and H.
459 xinjiangensis NBH84 (Fig. 3.12).
460
461 3.6.2 Cold-temperature SSMS cultures
462 At 50 and 78 d incubation at 4°C, epi-fluorescent microscopy revealed small microcolonies
463 comprised of three or more small cocci or short rod-shaped cells < 1µM (Fig. 3.13A). After
464 162 d of incubation, larger microcolonies were observed, predominantly small cocci and
465 short rod-shaped cells (Fig. 3.13B). Energy limited cells are known to decrease their cell size,
466 and alter cell shape to coccoid morphology (Lever et al. 2015). A small number of larger rod-
467 shaped cells (6-8 µm) were also present at 162 d.
468
469 A total of 90 isolates were recovered from cold-temperature SSMS methods, with 10 of these
470 determined to be different at species level (Table 3.5). Isolates belonged to both
471 Actinobacteria and Proteobacteria phyla. Dominant genera were Rhodococcus,
472 Pseudoarthrobacter and Arthrobacter (Fig. 3.14), followed by Streptomyces (Table 3.5, Fig.
473
112
Figure 3.13 Cold-incubated SSMS microcolonies visualised using epi-fluorescence
microscopy. When stained with SYTO 9 and propidium iodide, live cells with
uncompromised cell membranes fluoresce green, while dead/damaged cells fluoresce red.
(A) At 50 d incubation only a few small microcolonies were observed. Cells were cocci
and short rods < 1µm in size. (B) Numerous live microcolonies were observed at 162 d
incubation. Small cocci and short rod-shaped cells < 1µm in size predominated.
474
475 3.14D). One isolate (NBSH29) was a potentially novel strain, sharing low 16S
476 rDNAsequence similarity to the closest known species Mesorhizobium olivaresii (98%)
477 (Table 3.5, Fig. 3.14F).
478
479 Enrichment in liquid RAVAN media (Fig. 3.7) led to the recovery of three species which
480 were present in low abundance. These were R. erythropolis NBSH38, M. olivaresii NBSH29,
481 and S. clavifer NBSH56 (Table 3.5), all of which were only recovered through one
482 enrichment round (Table 3.6). Two other low abundance species were only recovered without
483 enrichment: S. lucensis NBSH23 and Simplicispira psychrophila NBSH78 (Table 3.6).
484 113
485
Table 3.5 Phylogenetic distribution of isolates cultured from Herring Island by cold-
temperature SSMS
Herring cold-inc. Id Days Strain SSMS Phylum (%) Blast Acc. † No. NBSH28 Arthrobacter Actinobacteria 100 KR140255.1 277 >10 alpinus NBSH29 Mesorhizobium α-Proteobacteria 98 LN681548.1 294 3 olivaresii NBSH8 Pseudarthrobacter Actinobacteria 100 KX056505.1 231 >20 sulfonivorans NBSH38 Rhodococcus Actinobacteria 99 KU904404.1 254 1 erythropolis NBSH10 Rhodococcus Actinobacteria 99 LN999546.1 191 >14 yunnanensis NBSH90 Rhodococcus Actinobacteria 100 AJ576249.1 191 >20 luteus NBSH78 Simplicispira β-Proteobacteria 99 NR_113622.1 288 1 psychrophila NBSH56 Streptomyces Actinobacteria 99 KU324446.1 280 >1 clavifer NBSH44 Streptomyces Actinobacteria 100 KP718539.1 229 >5 finlayi NBSH23 Streptomyces Actinobacteria 99 KJ571105.1 147 >1 lucensis †: mean days from SSMS set-up to colony picking, No.: number of strains cultured.
486
114
Figure 3.14 Bacteria cultured from HI by the cold-incubated SSMS. (A) The RAVAN media spread-plated communities were dominated by three main morphotypes; large yellow, large white, and smaller yellow-orange colonies. (B) Large white colonies were
Pseudarthrobacter sulfonivorans (e.g. SH8). (C) Small yellow-orange colonies were
Rhodococcus spp. such as R. luteus (e.g. SH90). (D) Several Streptomyces spp. were recovered, the most abundant was S. finlayi (e.g. SH44). (E) Large yellow colonies were
Arthrobacter alpinus (e.g. SH28). (F) Mesorhizobium olivaresii SH29 exhibited 98% similarity to known species.
115
Table 3.6 SSMS followed by liquid media enrichment conditions for recovered
species
Liquid media
enrichment Strain Herring cold inc. SSMS No. 0 x1 x2 NBSH28 Arthrobacter alpinus >10 NBSH29 Mesorhizobium olivaresii 3 NBSH8 Pseudarthrobacter sulfonivorans >20 NBSH38 Rhodococcus erythropolis 1 NBSH10 Rhodococcus yunnanensis >14 NBSH90 Rhodococcus luteus >20 NBSH78 Simplicispira psychrophila 1 NBSH56 Streptomyces clavifer >1 NBSH44 Streptomyces finlayi >5 NBSH23 Streptomyces lucensis >1 * *: Growth on RAVAN TSV only as co-culture
487
488 Cold-incubated SSMS isolates were slow to grow, taking a median of 280 d from initial
489 SSMS set-up to visible colony formation (Table 3.5). Interestingly, the S. lucensis NBSH23
490 isolate grew on RAVAN/ TSV/ GG only in co-culture with other microorganisms (Table
491 3.6), suggesting helper strains were supplying nutritional requirements not provided by the
492 low-nutrient media alone. Pure colony isolation of this strain was only achieved through sub-
493 culture onto nutrient-rich 0.75x NA. All cold-adapted species cultured by the SSMS at 8°C
494 were also capable of growth at RT.
495
496 3.6.3 Summary of bacterial isolates cultured by DSC and SSMS
497 3.6.3.1 Total bacteria cultured by all methods across four sites
498 Overall, culturing from all sites resulted in a final library of 53 isolates, spanning 47 different
499 species. Actinobacteria were the dominant phylum, with 34 species, of which 32 belonged to
116
500 order Actinomycetales. This was followed by Alphaproteobacteria (7 spp.),
501 Betaproteobacteria (4 spp.), and one isolate each from Bacteroidetes and Firmicutes phyla.
502 Three species were recovered across multiple sites, suggesting they are endemic in these
503 regions. These were Micrococcus yunnanensis, found at all pristine sites (HI, RL and MP)
504 (Fig. 3.15), S. pratensis, recovered from HI and RL, and S. parvus, found at HI and MP. For
505 HI, only two species were recovered by both DSC and SSMS methods: Pseudarthrobacter
506 sulfonivorans and Rhodococcus luteus. The contaminated site, WT, did not share species
507 with any other sites (Fig. 3.15).
508
Figure 3.15 Cultured bacterial species recovered across four Antarctic soils by DSC
and the SSMS. In total, 47 species were cultured. Herring Island (HI) isolates recovered
via DSC and SSMS shared only 2 species: Pseudarthrobacter sulfonivorans and
Rhodococcus luteus. Pristine sites HI, RL and MP shared Micrococcus yunnanensis. HI
and RL DSC also shared Streptomyces pratensis, and HI and MP DSC also shared S.
parvus. The contaminated site, WT, did not share any species with the pristine sites.
117
509 3.6.3.2 Bacterial colony pigmentation
510 Approximately half (23) of all bacterial isolates recovered from eastern Antarctica displayed
511 carotenoid-like pigmentation (Fig. 3.16), which varied from pale yellow through orange and
512 red. Pigmented bacteria spanned all 4 phyla.
513
Figure 3.16 Carotenoid-like pigmentation was observed in half of all cultured
isolates. Here, a selection of species is shown, displaying a range of pigmentation.
514
515 3.6.4 Natural product domain amplification and in situ antimicrobial activity for
516 selected isolates
517 3.6.4.1 Strains isolated in this study
518 All 53 isolates cultured in this study were analysed for NP domains and bioactivity against
519 five pathogens. Eighteen were positive for Type I PKS KS/AT domains, and 23 were positive
520 for NRPS AD domains (Table 3.7). Of these, 10 Actinobacteria and one Betaproteobacterium 118
521 were positive for both NP domains (Table 3.7). In the cross-streak antimicrobial assay, 15
522 strains showed measurable activity against the Gram-positive pathogens S. aureus and B.
523 subtilis, three against the Gram-negative pathogens E. coli and P. aeruginosa, and four
524 against the yeast C. albicans (Table 3.7). Streptomyces was the only genus that displayed
525 measurable activity, although some inhibition of pathogen growth was evident for
526 Paracoccus, Pseudarthrobacter, Rhodococcus, Novosphingobium and Sphingomonas
527 species. The pathogen most commonly inhibited was B. subtilis (21 isolates) (Table 3.7, Fig.
528 3.17), followed by S. aureus (18 isolates), then C. albicans (11 isolates). S. lavendulae isolate
529 NBH20 displayed the greatest activity, via inhibition of all five pathogens.
530
531
Figure 3.17 Cross-streak antimicrobial assay for bacterial isolates. Cold-incubated
SSMS grown isolate NBSH44 showed measurable activity against Gram-positive
pathogens, Bacillus subtilis and Staphylococcus aureus, and some inhibition of Gram-
negative E. coli.
119
Table 3.7 Natural product domain amplification and in situ antimicrobial activity for strains isolated in this study.
PCR Mean Antimicrobial Cross-Streak Activity (mm) Strain Closest cultured representative Phylum PKS NRPS S. aur. B.subt. E.coli P.aerug. C.albic. NBSH28 Arthrobacter alpinus Actinobacteria - - 0 0 0 0 0 NBM4 Arthrobacter koreensis Actinobacteria - - 0 0 0 0 0 NBM25 Burkholderia cepacia β-Proteobacteria + + 0 0 0 0 0 NBM12 Burkholderia sordidicola β-Proteobacteria - + 0 0 0 0 0 NBH87 Frigoribacterium faeni Actinobacteria - + 0 0 0 0 0 NBWT11 Geodermatophilus soli Actinobacteria - - 0 0 0 0 0 NBWT1 Geodermatophilus terrae Actinobacteria - - 0 0 0 0 0 NBH84 Hymenobacter xinjiangensis Bacteroidetes - - 0 0 0 0 0 NBH82 Janibacter melonis Actinobacteria - - 0 0 0 0 0 NBRL9 Massilia timonae β-Proteobacteria - - 0 0 0 0 0 NBSH29 Mesorhizobium olivaresii α-Proteobacteria - - 0 0 0 0 0 NBH50 Methylobacterium populi α-Proteobacteria - - 0 0 0 0 0 NBRL2 Microbacterium aerolatum Actinobacteria - - 0 0 0 0 0 NBWT6 Microbacterium foliorum Actinobacteria - - 0 0 0 0 0 NBH49 Microbacterium schleiferi Actinobacteria - - 0 0 0 0 0 NBH85 Microbacterium testaceum Actinobacteria + - 0 0 0 0 0 NBH64 Micrococcus yunnanensis Actinobacteria - - 0 0 0 0 0 NBM3 Micrococcus yunnanensis Actinobacteria - - 0 0 0 0 0 NBRL5 Micrococcus yunnanensis Actinobacteria - - 0 0 0 0 0 NBM11 Novosphingobium subterraneum α-Proteobacteria - - † † 0 0 † NBH48 Paracoccus carotinifaciens α-Proteobacteria - - † 0 0 0 0 NBM5 Planococcus plakortidis Firmicutes - - 0 0 0 0 0 NBH57 Pseudarthrobacter sulfonivorans Actinobacteria - - 0 0 0 0 0 NBSH8 Pseudarthrobacter sulfonivorans Actinobacteria - - † 0 0 0 † NBSH38 Rhodococcus erythropolis Actinobacteria - + 0 0 0 0 0 NBH51 Rhodococcus fascians Actinobacteria + + 0 0 0 0 0 NBSH10 Rhodococcus yunnanensis Actinobacteria + + 0 0 0 0 0 †: Some inhibition of pathogen growth 532
120
Table 3.7 Natural product domain amplification and in situ antimicrobial activity for strains isolated in this study cont.
PCR Mean Antimicrobial Cross-Streak Activity (mm) Strain Closest cultured representative Phylum PKS NRPS S. aur. B.subt. E.coli P.aerug. C.albic. NBH73 Rhodococcus luteus Actinobacteria + + 0 0 0 0 † NBSH90 Rhodococcus luteus Actinobacteria + + 0 0 0 0 0 NBSH78 Simplicispira psychrophila β-Proteobacteria + - 0 0 0 0 0 NBH83 Sphingomonas aerolata α-Proteobacteria + - 0 0 0 0 0 NBH67 Sphingomonas endophytica α-Proteobacteria - - 0 0 0 0 0 NBWT7 Sphingomonas mucosissima α-Proteobacteria - - 0 † 0 0 † NBH70 Streptomyces atroolivaceus Actinobacteria - + 5 3 † 0 † NBH41 Streptomyces badius Actinobacteria - + † † 0 0 0 NBH53 Streptomyces californicus Actinobacteria - + † 8 † 0 † NBSH56 Streptomyces clavifer Actinobacteria + - † † 0 0 0 NBH13 Streptomyces coelicoflavus Actinobacteria - + 3 1 0 1 0 NBH1 Streptomyces cyaneofuscatus Actinobacteria - + 8 8 † † 9 NBH86 Streptomyces daghestanicus Actinobacteria + + 1 3 0 0 0 NBSH44 Streptomyces finlayi Actinobacteria + + 1 8 † 0 0 NBH21 Streptomyces flavogriseus Actinobacteria - - 0 4 0 0 0 NBH65 Streptomyces globosus Actinobacteria - + 0 3 0 0 0 NBH77 Streptomyces gougerotii Actinobacteria + - 5 3 0 0 2 NBH78 Streptomyces griseus Actinobacteria + + 0 † 0 0 0 NBH20 Streptomyces lavendulae Actinobacteria - + 11 12 2 † 6 NBH81 Streptomyces lienomycini Actinobacteria + + 8 12 0 0 0 NBSH23 Streptomyces lucensis Actinobacteria - + 2 5 0 † 0 NBH61 Streptomyces parvus Actinobacteria + - 0 † 0 0 0 NBM1 Streptomyces parvus Actinobacteria + - 1 1 3 0 2 NBH42 Streptomyces praecox Actinobacteria - + 8 5 † † † NBH12 Streptomyces pratensis Actinobacteria + + 2 0 0 0 0 NBRL4 Streptomyces pratensis Actinobacteria + + 0 † 0 0 0 †: Some inhibition of pathogen growth 533
121
534 3.6.4.2 Strains isolated from previous studies
535 Of the 20 strains which had been previously isolated by colleagues, six were positive for
536 Type I PKS domains, and 15 were positive for NRPS domains (Table 3.8). Of these, six
537 Actinobacteria were positive for both of the NP domains (Table 3.8). In the cross-streak
538 antimicrobial assay, three strains showed measurable activity against Gram-positive
539 pathogens, two had activity against Gram-negative pathogens, and two against the yeast
540 C. albicans (Table 3.8). Here, two non-Streptomyces spp., Frondihabitans australicus
541 INR15 and Mesorhizobium qingshengii INR17, displayed measurable activity. Overall,
542 S. spororaveus INR7 was the most exciting Antarctic bacterium in terms of antimicrobial
543 activity, displaying considerable inhibition of Gram-negative pathogens E. coli and P.
544 aeruginosa, as well as S. aureus and C. albicans (Table 3.8).
122
Table 3.8 Natural product domain amplification and in-situ antimicrobial activity for isolates from previous studies.
PCR Mean Antimicrobial Cross-Streak Activity (mm) Strain Closest cultured representative Phylum PKS NRPS S. aur. B.subt. E.coli P.aerug. C.albic. INR13 Azospirillum zeae α-Proteobacteria - + 0 0 0 0 0 INR4 Bacillus aryabhattai Firmicutes - - 0 0 0 0 † INR6 Burkholderia jiangsuensis β-Proteobacteria - - 0 0 0 0 0 INWT7 Cryobacterium mesophilum Actinobacteria - + 0 0 0 0 0 INR15 Frondihabitans australicus Actinobacteria - + 6 3 † 0 0 SPB151 Kribbella sandramycini Actinobacteria - + 0 0 0 0 0 INR9 Leifsonia shinshuensis Actinobacteria + + 0 0 0 0 0 INR17 Mesorhizobium qingshengii α-Proteobacteria - + 0 1 0 0 0 INWT5 Methylobacterium brachiatum α-Proteobacteria - + 0 0 0 0 0 SPB164 Mycobacterium fluoranthenivorans Actinobacteria + + 0 0 0 0 0 SPB16 Paenisporosarcina macmurdoensis Firmicutes - - 0 0 0 0 0 INWT6 Quadrisphaera granulorum Actinobacteria - + 0 0 0 0 0 INWT3 Rhodococcus aerolatus Actinobacteria - - 0 0 0 0 0 SPB1 Rhodococcus yunnanensis Actinobacteria - + 0 0 0 nt 0 SPB167 Streptomyces abikoensis Actinobacteria - - † † 0 nt 0 SPB35 Streptomyces beijiangensis Actinobacteria + + † † † nt † SPB162 Streptomyces fildesensis Actinobacteria + + † † 0 nt † SPB13 Streptomyces indigoferus Actinobacteria - + 0 0 0 nt 0 SPB4 Streptomyces lavendulae Actinobacteria + + 0 † 1 nt 12 INR7 Streptomyces spororaveus Actinobacteria + + 13 † 16 5 15 †: Some inhibition of pathogen growth, nt: not tested 545
123
546 3.6.5 Selection of isolates for whole genome sequencing
547 Using the selection criteria outlined in Section 3.2.13, eighteen isolates were chosen to
548 undergo WGS. The primary reasons for the selection of each isolate were highlighted in
549 Table 3.9. Three Streptomyces species were prioritised due to the highest measurable
550 antimicrobial activity, in addition to this genus's well-established value in NP discovery.
551 Selected Streptomyces isolates were the S. spororaveus INR7, which showed the greatest
552 antimicrobial activity overall, including against Gram-negative pathogens (Table 3.9); S.
553 finlayi NBSH44, which was uniquely cultured through cold-incubated SSMS methods
554 and displayed activity against Gram-positive pathogens; and S. gougerotii NBH77, which
555 inhibited Gram-positive bacteria and the yeast C. albicans (Table 3.9). The S. finlayi and
556 S. gougerotii strains have no high-quality genomes in the GTDB database. As
557 Streptomyces are known to be difficult to differentiate by 16S gene sequence similarity
558 alone (Cheng et al. 2016, Labeda et al. 2017), the selected Streptomyces spp. were
559 morphologically different, and known to belong to phylogenetically distinct clades to
560 avoid sequencing two closely-related strains (Cheng et al. 2016, Labeda et al. 2017). Of
561 the non-Streptomyces species selected, 12 were Actinobacteria: Kribbella sandramycini
562 SPB151, Cryobacterium mesophilum INWT7, Frigoribacterium faeni NBH87,
563 Frondihabitans australicus INR15, Geodermatophilus NBWT11, Leifsonia shinshuensis
564 INR9, Pseudarthrobacter sulfonivorans NBSH8, Quadrisphaera granulorum INWT6
565 and Rhodococcus luteus NBSH90. Five were Alphaproteobacteria: Mesorhizobium sp.
566 nov NBSH29, Azospirillum zeae INR13, Novosphingobium subterraneum NBM11,
567 Paracoccus carotinifaciens NBH48, Sphingomonas mucosissima NBWT7; and one was
568 a Bacteroidetes, Hymenobacter sp. nov NBH84 (Table 3.9). Nine of these genera were of
569 additional biotechnological interest, including Rhodococcus, Sphingomonas,
570 Novosphingobium, Paracoccus, Azospirillum, Geodermatophilus and Pseudarthrobacter
124
Table 3.9 Characteristics used to select eighteen strains for whole genome sequencing.
PKS/NRPS
AB activity
GTDB
Other
Strain Closest cultured representative Site Phylum ID (%) INR13 Azospirillum zeae RR α-Proteobacteria 100 - - -+ + INWT7 Cryobacterium mesophilum WT Actinobacteria 99 - - -+ NBH87 Frigoribacterium faeni HI Actinobacteria 99 - - -+ INR15 Frondihabitans australicus RR Actinobacteria 99 - ++ -+ NBWT11 Geodermatophilus soli WT Actinobacteria 98 - - -- + NBH84 Hymenobacter xinjiangensis HI Bacteroidetes 97 - - -- + SPB151 Kribbella sandramycini BP Actinobacteria 99 - - -+ INR9 Leifsonia shinshuensis RR Actinobacteria 99 - - ++ NBSH29 Mesorhizobium olivaresii HI α-Proteobacteria 98 - - -- + NBM11 Novosphingobium subterraneum MP α-Proteobacteria 99 + † -- + NBH48 Paracoccus carotinifaciens HI α-Proteobacteria 99 - † -- + NBSH8 Pseudarthrobacter sulfonivorans HI Actinobacteria 100 + † -- + INWT6 Quadrisphaera granulorum WT Actinobacteria 99 - - -+ NBSH90 Rhodococcus luteus HI Actinobacteria 100 + - ++ + NBWT7 Sphingomonas mucosissima WT α-Proteobacteria 99 - † -- + NBSH44 Streptomyces finlayi HI Actinobacteria 100 - ++ ++ NBH77 Streptomyces gougerotii HI Actinobacteria 99 - ++ +- INR7 Streptomyces spororaveus RR Actinobacteria 99 + ++++ ++ Primary reasons for selection for each isolate are highlighted in red. †: Some inhibition of pathogens. 571
125
572 which are known hydrocarbon degraders of interest in bioremediation (Brooijmans et al.
573 2009).
574
575 3.7 DISCUSSION
576 Antarctic desert soil Actinomycetales and Myxococcales were targeted here using two novel
577 culturing techniques, DSC and SSMS. While no Myxococcales were recovered, the methods
578 were successful in capturing a total library of 47 Antarctic bacterial species (Tables 3.4 &
579 3.5), spanning 19 genera across four phyla, and included 32 different Actinomycetales
580 species. The goal to target Myxococcales was optimistic, as they are known to be
581 predominantly mesophilic, with only four psychrophilic Myxococcales previously recorded
582 (Ruckert 1985, Shimkets et al. 2006, Brockman & Boyd 1963, Dawid et al. 1988), and
583 molecular studies indicated low relative abundance of Deltaproteobacteria in these soils
584 (< 0.7%). Nevertheless, DSC was an effective, unconventional culturing technique for the
585 capture of other Antarctic genera, particularly Streptomyces. This was most evident for HI,
586 which exhibited high 16S rDNA gene relative abundance of Actinobacteria (67%) compared
587 with the other DSC soils (< 33%) (Fig. 3.3). Sporulating Streptomyces microcolonies were
588 visualised atop soil crumbs and picked by stereomicroscopy (Figs. 3.9, 3.10). To my
589 knowledge, this is the first report of DSC for recovery of Streptomyces. Fungal overgrowth
590 was problematic during DSC, with several filamentous fungi unaffected by the antifungal
591 cycloheximide. For future DSC, a combination of antifungals, such as cycloheximide plus
592 nystatin would be advisable (Karwowski et al. 1996).
593
594 The soil substrate membrane system (SSMS) was employed here for the first time under
595 psychrophilic incubation conditions. Lower diversity of bacteria was obtained from SSMS 126
596 in comparison to DSC for the same HI soil (Tables 3.4, 3.5). However, the majority of species
597 isolated from SSMS were not recovered by DSC (Fig. 3.14, Tables 3.4, 3.5). The exceptions,
598 Pseudarthrobacter and Rhodococcus, were highly abundant in SSMS and were well-adapted
599 to growth at ≤ 8°C and 21°C. All isolates retrieved by cold-incubated SSMS were capable of
600 growth at RT. Previous studies have similarly noted a tendency toward psychrotrophy rather
601 than psychrophily in terrestrial Antarctic microorganisms, which has been attributed to their
602 need to endure regular freeze-thaw cycles (Morita 1975, De Maayer et al. 2014, Soina et al.
603 2004). Temperatures up to +18°C have been recorded in southern Antarctic surface soils,
604 with large daily fluctuations (~10°C) during summer, correlated with the proportion of
605 incoming solar radiation (Aislabie et al. 2004, Balks et al. 2002).
606
607 Extended incubation times assist in recovery of rare, oligotrophic taxa, especially those from
608 nutrient poor environments, and soils where the communities may be largely dormant, such
609 as in Antarctica (Pulschen et al. 2017, Alain & Querellou 2009, Davis et al. 2005). Here,
610 extended culture times (> 100 d) led to the recovery of several strains likely to be novel. For
611 example, Hymenobacter NBH84 (Table 3.4), Geodermatophilus NBWT11 and
612 Mesorhizobium NBSH29 (97-98% identity to known species) (Tables 3.4, 3.5). Additionally,
613 morphologically distinct Streptomyces; S. gougerotii, S. lienomycini (Fig. 3.11), were also
614 recovered after extended incubation times (Table 3.4). The expression of pigments in bacteria
615 commonly coincides with nutrient deprivation (Couso et al. 2012, Liu et al. 2013); thus,
616 lengthy culturing times may have assisted in visible differentiation of some of these isolates.
617 Other slow-growers included rarely-cultured Actinomycetales genera, Frigoribacterium and
618 Janibacter (Tiwari & Gupta 2013) (Tables 3.4). Only four species of Frigoribacterium have
619 been previously reported, with the first being a psychrotroph, isolated from airborne dust
127
620 (Kampfer et al. 2000, Kong 2016). Members of the Janibacter genus are also rare, with only
621 10 species described thus far (Maaloum et al. 2019).
622
623 Carotenoid-like pigmentation has been reported to be widespread in cold-adapted
624 microorganisms, and this was similarly found here with 23 yellow to red pigmented strains
625 (Fig. 3.16) (Baraúna et al., 2017; De Maayer et al., 2014; Koblížek & Brussaard, 2015;
626 Peeters et al., 2011). Carotenoids are most commonly associated with protection from UV
627 radiation via the scavenging of free radicals such as singlet oxygen (Walter & Strack 2011,
628 Maresca et al. 2008), but they are also hypothesised to assist with homeoviscous adaptation,
629 playing a regulatory role in membrane fluidity (Chattopadhyay & Jagannadham 2001, Walter
630 & Strack 2011). Furthermore, carotenoids function as accessory light-harvesting pigments in
631 aerobic anoxygenic phototrophs (AAP), assisting in bacteriochlorophyll-mediated
632 photosynthesis (Tahon & Willems 2017, Imhoff et al. 2018, Koblížek & Brussaard 2015).
633 AAP comprise certain members of Alpha- Beta- and Gammaproteobacteria, and include a
634 number of genera which are commonly recovered from polar soils, and which were also
635 isolated in this chapter; the Methylobacterium and Sphingomonas (Makhalanyane et al.
636 2015a, Tahon & Willems 2017, Walter & Strack 2011, Imhoff et al. 2018). Phototrophy may
637 thus be an important survival strategy for AAP Proteobacterial members in Antarctic desert
638 soils.
639
640 The contaminated site, Wilkes Tip, was the only soil not to share species with other samples
641 (Fig. 3.15). Of only four isolates recovered from Wilkes Tip, two were Geodermatophilus
642 species, one of which is novel (Table 3.4). Family Geodermatophilaceae are predominantly
643 associated with soil and rock surfaces in desert and polar regions (Normand 2006).
128
644 Geodermatophilus have a known tolerance for harsh environmental conditions such as UV
645 and ionizing radiation, desiccation, high salinity; and of particular interest, petroleum and
646 heavy metals contamination (Sghaier et al. 2015, Wang et al. 2017, Montero-Calasanz et al.
647 2013). It is likely that soil microbial diversity has been affected by contamination at the WT
648 site (Fig. 3.2D). Significant reductions in species richness and diversity have been previously
649 reported in soils of increasing fuel contamination, along with a corresponding enrichment of
650 hydrocarbon degrading taxa (van Dorst et al. 2016, Aislabie et al. 2004). All of the species
651 isolated from WT in this study were known hydrocarbon degraders, and therefore may prove
652 useful for bioremediation in low temperature environments (Haritash & Kaushik 2009,
653 Andreoni et al. 2004, Hassanshahian et al. 2012, Brooijmans et al. 2009).
654
655 Here, the greatest antimicrobial potential was found in Streptomyces spp., which displayed
656 the strongest inhibition of pathogen growth (Table 3.7 & 3.8). Two other genera,
657 Frondihabitans and Mesorhizobium, were interesting as they produced measurable activity
658 against Gram-positive pathogens (Table 3.8). Previously, a marine Mesorhizobium sp. has
659 been reported to produce a homoserine lactone compound with antibacterial activity against
660 B. subtilis, in addition to cytotoxic activity against tumour cell lines (Krick et al. 2007).
661 Antimicrobial activity has not been previously described for Frondihabitans.
662
663 To conclude, the culturing outcomes from this chapter have led to the selection of eighteen
664 isolates, including three Streptomyces spp., for whole genome sequencing in Chapter 4. The
665 chosen isolates spanned 16 genera and originated from four pristine Antarctic desert soils;
666 Herring Island (HI), Mitchell Peninsula (MP), Robinson Ridge (RR) and Browning Peninsula
667 (BP); as well as the contaminated site, Wilkes Tip (WT) (Table 3.9). Of the Streptomyces
129
668 isolates, the most exciting strain was S. spororaveus INR7, which displayed activity against
669 the Gram-negative pathogens E. coli and P. aeruginosa which are of particular concern in
670 terms of AMR (WHO, 2014) (Section 1.1).
130
CHAPTER FOUR
4 ANTARCTIC BACTERIAL GENOMES HARBOUR A
WEALTH OF UNCHARACTERISED BIOSYNTHETIC
GENE CLUSTERS
1 4.1 INTRODUCTION
2 More than twenty years have passed since the first completely sequenced bacterial genome
3 (Fleischmann et al. 1995). In that time, rapid advances in HTS and bioinformatics
4 technologies have led to spectacular growth in the number of available genome assemblies
5 (Schmid et al. 2018, Levy & Myers 2016). Presently, over 190,000 bacterial genomes reside
6 in the NCBI Genome database. Around 5,600 of these are awarded with reference and
7 representative genome status, curated to indicate high-quality assemblies (NCBI, 2019).
8 However, the majority of publicly available genomes remain in a draft state of varying
9 contiguity, completion and correctness (Schmid et al. 2018, Koren et al. 2013, Studholme
10 2016). This has implications for the accuracy of downstream analyses and our understanding
11 of microbial processes. For example, Daniel-Ivad et al. (2017) recently reported the complete
12 sequence of a cryptic BGC, despite the source genome assembly (Streptomyces
13 GCA_001974775) containing an estimated 82% contamination from DNA from a different
14 microorganism (http://gtdb.ecogenomic.org/genomes?gid=GCA_001974775.1).
15
16 In this chapter we aimed to achieve high-quality genome assemblies for the 18 bacterial
17 isolates selected for genome sequencing in Chapter 3 (Table 3.9). Thus far, very few
131
18 complete genomes have been reported for bacterial isolates from eastern Antarctica. They
19 include a Firmicutes genus, Carnobacterium, recovered from seawater (Zhu et al. 2016); a
20 Proteobacterial genus, Glaciecola, isolated from sea ice (Qin 2014, Bowman et al. 1998);
21 and one terrestrial Actinobacterial genus, Nesterenkonia, from the McMurdo Dry Valleys
22 (Aliyu et al. 2016). Primarily, the goal of this chapter was in the characterisation of BGCs
23 harboured by each isolate, including evaluation of BGC novelty, and, where possible,
24 prediction of encoded compounds. We selected the long-read platform PacBio RS II in order
25 to optimise capture of BGCs, which usually span long, repetitive, high G+C regions that are
26 difficult to resolve with SGS platforms (Section 1.8) (Nakano et al. 2017, Gomez-Escribano
27 et al. 2016). It was hypothesised that the resolution provided by long reads would allow for
28 the sequencing of multiple genomes from one sequencing library. We proposed that
29 differences in isolates at genus level would be distinct enough to allow for adequate
30 separation of species during genome assembly.
31
32 4.2 MATERIALS AND METHODS
33 4.2.1 High molecular weight genomic DNA extractions
34 The PacBio RS II sequencing platform requires large quantity input of high quality, high
35 molecular weight genomic DNA (Ramaciotti Centre for Genomics 2015). Thus, steps were
36 taken throughout extraction procedures to minimise DNA damage and fragmentation. Vortex
37 mixing, heating, and freeze/thaw cycles were avoided, and large bore pipette tips were
38 employed throughout. DNA extraction methods varied slightly depending on isolate genera
39 (Table 4.1).
40
132
Table 4.1 Genomic DNA extraction methods for Antarctic bacteria.
Isolate DNA extraction method Reference Streptomyces NBSH44 Streptomyces INR7 Kirby method Keiser 2000 Streptomyces NBH77 Kribbella SPB151 Mesorhizobium NBSH29 Hymenobacter NBH84 Geodermatophilaceae NBWT11 Sphingomonas NBWT7 Quadrisphaera INWT6 Pseudarthrobacter NBSH8 Frigoribacterium NBH87 Phenol-chloroform Rusch et al., 2007; Cryobacterium INWT7 method Yau et al., 2013 Rhodococcus NBSH90 Frondihabitans INR15 Paracoccus NBH48 Leifsonia INR9 Novosphingobium NBM11 Azospirillum INR13 Isolates in bold required an additional chloroform extraction 41
42 4.2.1.1 Spore harvesting for Streptomyces and Kribbella isolates
43 Triplicate spore stocks were harvested for Streptomyces strains NBSH44, INR7 and NBH77,
44 and the Kribbella isolate SPB151 using methods described by Kieser et al. (2000) and
45 Shephard et al. (2010). From pure, sporulating 7-day old cultures, grown on ISP4 agar
46 (Appendix A3.1), spores from a single colony were extracted using a 1 µL sterile loop and
47 spread in a cross-hatch format to cover an entire fresh ISP4 agar plate. Plates were incubated
48 at RT until a sporulating lawn was well-developed (~14 days). Sterile water (3 mL) was
49 added to the lawn and the hydrophobic spores dislodged and suspended using a sterile
50 spreader. Once suspended, water and spores were aseptically transferred to a 50 mL screw
133
51 top falcon tube (Corning) and water was added up to 30 mL. Spore chains were disrupted by
52 vigorous vortexing until a homogenous mixture was obtained (~10 min). To remove debris,
53 samples were filtered through a 10 mL syringe plugged with sterile cotton wool. Spore
54 suspensions were centrifuged 2,000 x g for 10 min, supernatants were immediately removed,
55 and spores re-suspended in 20% glycerol (1 mL) and stored at -80°C until further use.
56
57 4.2.1.2 Modified Kirby method for Streptomyces and Kribbella genomic DNA extraction
58 For Streptomyces spp., and the morphologically similar Kribbella isolate, an adapted Kirby
59 mix method, reported to retrieve genomic DNA ~40 kb in length (Kieser et al. 2000), was
60 used for genomic DNA extraction (Table 4.1). Triplicate 25 mL cultures, comprising nutrient
61 broth (Oxoid) inoculated with 100 uL spore stock, were incubated at RT in 250 mL baffled
62 Erlenmeyer flasks (Corning, Victoria, Australia) using an orbital shaker (Ratek, Victoria,
63 Australia) (200 rpm), and harvested at late-exponential phase. To confirm purity of cultures
64 at the time of extraction, subcultures were prepared onto NA plates and examined after two
65 days incubation at RT. Harvested cultures were centrifuged in 50 mL falcon tubes at 500 x g
66 for 10 min. Supernatants were removed and mycelium washed with 10% sucrose solution.
67 Centrifugation was repeated, supernatants removed, and the wet mycelium re-suspended in
68 3 mL TE25S buffer (25 mM Tris-HCl pH 8, 25 mM EDTA pH 8, 0.3 M sucrose).
69
70 For DNA extraction, a 100 µL aliquot of lysozyme (60 mg/mL) (Sigma-Aldrich) was added
71 to enable digestion of the Gram-positive cell walls. Tubes were incubated at 37°C for 10 min.
72 This was followed by the addition of 4 mL of 2 x Kirby Mix (2 g sodium dodecyl sulfate
73 (SDS), 12 g sodium 4-aminosalicilate, 5 mL 2M Tris-HCl pH 8, 6 mL buffered phenol pH 8,
74 made up to 100 mL with sterile water) with gentle agitation at RT for 3 min, followed by the 134
75 addition of 8 mL of phenol:chloroform:isoamyl alcohol mixture (PCI) (25:24:1) at pH 8
76 (Sigma-Aldrich), with gentle agitation for 15 s. Emulsions were centrifuged for 10 min at
77 1,500 x g to allow separation of the organic and aqueous phases. The upper aqueous phase,
78 which contained the polar nucleic acids, was transferred to a clean 50 mL falcon tube. The
79 extraction was repeated with the addition of 3 mL of PCI and 600 µL of 3 M unbuffered
80 sodium acetate (Sigma-Aldrich), and gently agitated 15 s. Phases were separated with
81 centrifugation for 10 min at 1,500 x g, and the aqueous phase transferred to a clean 50 mL
82 falcon tube. DNA was precipitated by addition of 0.6 volume of isopropanol (Ajax
83 FineChem), followed by centrifugation at 6,842 x g for 30 min. Supernatants were discarded,
84 and the DNA pellet re-suspended in minimal isopropanol and transferred to 2 mL
85 microcentrifuge tubes for centrifugation at 18,506 x g for 10 min. Supernatants were removed
86 and the DNA pellet was washed with 600 µL of 70% ethanol, centrifugation was repeated
87 and supernatants removed, and the pellet allowed to dry at RT. To hydrolyse RNA, pellets
88 were re-dissolved in 50 µL of Tris-EDTA (TE buffer) at pH 8 (Sigma-Aldrich), and 2 µL of
89 pre-boiled RNase A solution (4mg/mL) (Life Technologies, Australia) added for incubation
90 at 37°C for 10 min. DNA was re-extracted from the sample with an equal volume of PCI and
91 centrifuged at 18,506 x g for 5 min. The aqueous phase was mixed with 1/10 volume of 3M
92 sodium acetate pH 8, and 0.6 volume of isopropanol to precipitate the DNA, and centrifuged
93 at 18,506 x g for 5 min. DNA pellets were washed with 100 µL of 70% ethanol, re-
94 centrifuged, dried at RT and re-suspended in 100 µL elution buffer (EB) (Qiagen) and stored
95 at 4°C.
96
135
97 4.2.1.3 Phenol-chloroform genomic DNA extraction for other genera
98 For all other genera (Table 4.1), a modified phenol-chloroform DNA extraction method was
99 used (Rusch et al. 2007, Yau et al. 2013). Nutrient broth was prepared (25 mL) in 250 mL
100 baffled flasks and single colonies inoculated for incubation overnight at RT. Triplicate 250
101 mL Erlenmeyer flasks with 25 mL nutrient broth were inoculated with overnight cultures to
102 an OD600 of 0.005. Cultures were incubated at RT using an orbital shaker (200 rpm), and
103 cells were harvested at late-exponential phase. Purity of cultures at the time of extraction was
104 confirmed as previously described (Section 4.2.1.2). Harvested cultures were centrifuged at
105 500 x g for 10 min, supernatants discarded, and pellets resuspended in 10 mL sterile water.
106 To digest proteins, a 1/20 volume of TE buffer (pH 8), and 100 µL Proteinase K (20 mg/ mL,
107 Sigma-Aldrich) were added to samples with inversion, followed by 1 mL of 10% SDS to
108 enable cell lysis. Samples were incubated at 55°C for 2 hrs in a water bath with shaking at
109 175 rpm. To separate nucleic acids from protein and lipid cellular components, an equal
110 volume of buffered phenol (pH 8, Sigma-Aldrich), was added with inversion to mix.
111 Emulsions were centrifuged 1,622 x g 15 min 25°C to separate the phases, and the top
112 aqueous layer which contained the DNA was retained.
113
114 At this point, several DNA extracts (isolates NBM11, INR15, NBH48, INR13 and INR9)
115 proved difficult to separate from the organic phase (Table 4.1), possibly due to higher
116 concentrations of hydrophobic polymers such as lipids, carbohydrates or excess proteins. To
117 allow further separation of the phases these extracts were subjected to an additional
118 chloroform extraction (Psifidi et al. 2015), due to chloroform's higher density. An equal
119 volume of chloroform (AnalaR, Merck) was added with gentle mixing. Samples were
120 centrifuged at 1,622 x g for 15 min, and the aqueous layer retained. For all DNA extracts, an 136
121 equal volume of isopropanol was then added and mixed by inversion. DNA was precipitated
122 overnight at 4°C, then centrifuged at 6,842 x g for 30 min at 20°C, and the isopropanol
123 removed. The resulting pellet was re-suspended in minimal isopropanol and transferred to a
124 2 mL microcentrifuge tube, centrifuged at 18,506 x g RT for 10 min, and isopropanol
125 discarded. The resulting pellet was air dried at RT, then re-suspended in 50 µL TE buffer pH
126 8 and incubated at 4°C for 1 hr.
127
128 To remove RNA from DNA extracts, an aliquot (2 µL) of RNase A (4 mg/ mL) was added
129 and incubated at 37°C for 10 min. The volume was then increased to 700 µL using TE buffer
130 pH 8. DNA extraction was repeated by addition of an equal amount of PCI, and the phases
131 mixed with inversion. Phases were separated by centrifugation at 18,506 x g RT for 5 min.
132 The upper aqueous phase was retained, and DNA precipitated using a 1/10 volume of 3 M
133 sodium acetate (pH 8), and an equal volume of isopropanol. DNA was pelleted by
134 centrifugation at 18,506 x g at RT for 30 min, and supernatants removed. Pellet was washed
135 with 100 µL 70% ethanol, and re-centrifuged for 10 min. Ethanol was removed and the DNA
136 pellet air dried at RT, followed by resuspension in 100 µL EB. Purified DNA was stored at
137 4°C until quantified and pooled for sequence library preparation.
138
139 4.2.1.4 Quantification and quality assessment of genomic DNA
140 Genomic DNA was quantified using the Quant-iT Picogreen dsDNA Assay kit (Life
141 Technologies). Following quantification, the Rhodococcus isolate NBSH90 was excluded
142 from further sequencing preparation, due to insufficient DNA retrieval resulting from poor
143 growth in liquid culture. For all other isolate DNA extractions, the presence of majority high-
137
144 molecular weight genomic DNA > 40 kb was verified via 1% agarose gel electrophoresis
145 (Section 3.2.6).
146
147 4.2.2 Multi-genome DNA library preparation and sequencing
148 In preparation for sequencing, three multi-genome libraries (A1, A2 and A3) were created
149 from pooled isolate DNA (Table 4.2). For each library, bacteria to be combined were
150 different at genus level based on prior 16S rDNA gene sequencing (Section 3.2.6, Table 3.9).
151 Final DNA submission conformed to PacBio RS II sequencing guidelines of > 20 µg DNA
152 per pooled library. Libraries A1 and A2 each comprised DNA from six isolates, while library
153 A3 contained DNA from five isolates, totalling 17 individual isolates (Table 4.2). To
154 normalise coverage for each individual genome during genome sequencing, an equimolar
155 ratio of DNA was calculated, with correction based on the estimated genome size for each
156 genus.
157
158 Multi-genome libraries were submitted to The Ramaciotti Centre for Gene Function
159 Analysis, at UNSW Sydney (NSW, Australia), for Agencourt AMPure XP (Beckman
160 Coulter, Brea, CA, USA) magnetic bead clean up, SMRT-bell library preparation with 10-
161 20kb Blue Pippin size selection, and PacBio RS II sequencing with P6/C4 chemistry,
162 employing three SMRT cells per library.
163
138
Table 4.2. Distribution of isolates within all three multi-genome DNA libraries.
Est. DNA genome Library
conc. size input Isolate (ng/μL) (Mb) vol. (μL) 1 Streptomyces NBSH44 67.4 8 99 2 Mesorhizobium NBSH29 22.0 7 265 Library 3 Hymenobacter NBH84 44.4 5 94 A1 4 Frondihabitans IN R15 102.5 5 41 5 Paracoccus NBH48 75.5 4 44 6 Leifsonia INR9 140.5 3 18 1 Streptomyces INR7 95.4 8 70 2 Kribbella SPB151 51.9 7 112 Library 3 Geodermatophilaceae NBWT11 134.6 5 31 A2 4 Sphingomonas NBWT7 83.0 5 50 5 Quadrisphaera INWT6 31.4 4 106 6 Novosphingobium NBM11 24.4 3 103 1 Streptomyces NBH77 60.0 8 133 2 Azospirillum INR13 35.6 7 197 Library 3 Pseudarthrobacter NBSH8 41.3 5 121 A3 4 Frigoribacterium NBH87 50.5 4 79 5 Cryobacterium INWT7 50.1 4 80 164
165 4.2.3 De novo genome assembly from multi-genome libraries
166 For the three multi-genome libraries (A1, A2 and A3), de novo assemblies were performed
167 using FALCON v 1.8.6 (Chin et al. 2013). Preassembly seed read cutoffs, corresponding to
168 30X coverage and based on an approximate combined genome size of 30 Mb per library,
169 were predicted using in-house software, SMRTSCAPE (SMRT Subread Coverage &
170 Assembly Parameter Estimator; http://rest.slimsuite.unsw.edu.au/smrtscape). Preassembly
171 length cutoffs for seed reads were 18,289 bp (A1), 14,431 bp (A2) and 14,662 bp (A3),
172 followed by 7,000 bp for each assembly. Circularisation and joining was performed with
173 Circlator v1.4.0 (Hunt et al. 2015), with dependencies prodigal v2.6.3 (Hyatt et al. 2010),
139
174 samtools v1.7 (Li et al. 2009), spades v3.7.0 (Nurk et al. 2013), bwa v0.7.17 (Li 2013),
175 mummer v3.23 (Kurtz et al. 2004) and canu v1.7 (Koren et al. 2017). Assemblies were
176 subjected to two rounds of consensus polishing, using the GenomicConsensus package tool
177 variantCaller v2.2.1, applying the Arrow algorithm (Chin et al. 2013, PacBio, 2019).
178
179 4.2.3.1 Genome annotation, functional prediction and assessment of genome quality
180 PacBio subreads were aligned to assemblies using pbalign v0.3.1
181 (https://github.com/PacificBiosciences/pbalign) and the resulting contigs assigned to
182 individual species using PAGSAT v2.5.1 (Edwards & Palopoli 2015, Edwards et al. 2018),
183 with reference to representative genomes downloaded from the NCBI Refseq database
184 (Appendix Table A3.1). Binned contigs were annotated using Prokka v1.13 (Seemann 2014),
185 with dependencies hmmer v3.1b2 (Eddy 2011), prodigal v2.6.3, tbl2asn v25.6
186 (https://www.ncbi.nlm.nih.gov/genbank/tbl2asn2/), rnammer v1.2 (Lagesen et al. 2007),
187 parallel v20180622 (Tange 2018) and BLAST+/2.7.1 (Camacho et al. 2009). RJE_GFF
188 v0.1.0 was employed to investigate incorrectly split annotations (R.J. Edwards, pers. comm).
189 Prokka-annotated proteins were then aligned to those of uniprot using MULTIHAQ v1.4.1
190 (Edwards et al. 2007), with dependencies BLAST+ v2.7.1, R v3.5.1, mafft v7.310 (Katoh &
191 Standley 2013), clustalw v2.1 (Larkin et al. 2007) and clustalo v1.2.2 (Sievers et al. 2011).
192 Annotated protein sequences were phylogenetically classified into clusters of orthologous
193 groups (COGs), assigned via the COG functional annotator module of WebMGA
194 (http://weizhong-lab.ucsd.edu/webMGA/) (Niu et al. 2011, Tatusov 2000), with an E-value
195 cut-off of 0.001. Relative abundances of COG categories for each Antarctic genome were
196 visualised as a heatmap with R 3.4.0 using the ggplot2 v2.2.1 package. Genome assemblies
197 were quality assessed for completeness and contamination using the CheckM v1.0.7 140
198 command lineage_wf (Parks et al. 2015), with the dependencies prodigal v2.6.3, hmmer
199 v3.1b2 and pplacer v1.1.alpha16 (Matsen et al. 2010).
200
201 4.2.3.2 Phylogenetic analysis of genome-retrieved 16S rDNA genes
202 Antarctic bacterial 16S rDNA gene sequences retrieved from respective genomes were
203 compared against the NCBI BLASTn database (https://blast.ncbi.nlm.nih.gov/Blast.cgi)
204 (Altschul et al. 1990). The sequences of the five most closely related species were exported
205 for construction of phylogenetic trees using the phylogeny.fr tool
206 (http://www.phylogeny.fr/alacarte.cgi) (Dereeper et al. 2008). Sequences were aligned with
207 MUSCLE 3.8.31 (Edgar 2004) in full processing mode and curated with GBlocks 0.91b
208 (Castresana 2000) using default settings. Phylogeny was inferred by maximum-likelihood
209 method with PHYML 3.0 (Guindon et al. 2010), using 100 bootstrap iterations. Resulting
210 newick files were imported to iTOL 4.3.3 (https://itol.embl.de/) (Letunic & Bork 2016) for
211 tree visualisation. The Streptomyces, Hymenobacter, Proteobacterial and non-Streptomyces
212 Actinobacterial clades were visualised separately, via truncation at the corresponding clade
213 branch. Bootstrap values > 50% were displayed.
214
215 4.2.4 Secondary metabolite gene cluster analysis
216 4.2.4.1 AntiSMASH analysis for all Antarctic genomes
217 Un-annotated nucleotide sequences for all Antarctic genome contigs were analysed for BGCs
218 using antiSMASH v5.0.0 (antibiotics and secondary metabolite analysis shell;
219 https://antismash.secondarymetabolites.org/ ) (Medema et al. 2011, Blin et al. 2019), with all
220 optional features enabled including ClusterBLAST, SubclusterBLAST,
141
221 KnownClusterBLAST (Medema et al. 2015, Medema et al. 2011), Pfam analysis (Finn et al.
222 2016) and active site finder (Weber et al. 2015). Resulting clusters were verified manually
223 through visual inspection. This included examination of each BGC for completeness and
224 contiguity, and examination of individual genes and domains within BGC for order and
225 similarity to known sequences. AntiSMASH cluster results were compiled as a table in
226 Appendix Table A3.2, and a summary of BGC categories found in each genome visualised
227 as a heatmap in R 3.4.0 using the ggplot2 v2.2.1 package. For Streptomyces and Kribbella
228 isolates, BGCs and their corresponding similarity to known clusters were mapped to contigs
229 as circular plots, generated using Circa 1.2.1 (http://omgenomics.com/circa/). Predicted
230 chemical structures were created using ChemDraw Prime 15.0 (Perkinelmer, Victoria,
231 Australia).
232
233 4.2.4.2 BLASTp and NaPDoS analysis of detected BGC domain sequences
234 Further analysis was conducted on the five genomes found to contain Type I PKS, NRPS,
235 and Type II PKS clusters, namely the Streptomyces spp. NBSH44, INR7 and NBH77,
236 Kribbella SPB151 and Azospirillum INR13. Amino acid sequences for PKS and NRPS genes
237 were analysed by BLASTp (https://blast.ncbi.nlm.nih.gov/Blast.cgi) against the entire NCBI
238 reference database with default settings (Warren & David 1993). Additionally, amino acid
239 sequences corresponding to PKS ketosynthase domains (KS), and NRPS condensation
240 domains (C) were compiled and analysed using NaPDoS
241 (http://napdos.ucsd.edu/napdos_home.html) (Ziemert et al. 2012), retrieving three matches
242 per sequence. Phylogenetic trees were constructed for KS and C domains in NaPDoS, using
243 the maximum likelihood method, employing MUSCLE alignment of sequences alongside
244 BLAST matches against the curated NaPDoS database, and FastTree (Guindon & Gascuel 142
245 2003) for phylogenetic analysis. The resulting newick files for both KS and C domain
246 analyses were imported into iTOL 4.3.3 for tree visualisation (Letunic & Bork 2016). Trees
247 were pruned to remove multiple trimmings of identical domain sequence regions produced
248 by NaPDoS. Leaves were coloured to indicate the primary bioactivities of the compounds
249 produced by the corresponding homologous pathways. Complete NaPDoS results were
250 compiled alongside BLASTp results in Appendix Tables A3.3 to A3.6.
251
252 4.3 RESULTS
253 4.3.1 Sequencing output and assembly of multi-genome libararies
254 PacBio sequencing for the three multi-genome libraries (A1, A2 and A3) yielded 174,282,
255 132,856 and 138,043 subreads per library, with N50 values of 17,552 bp, 16,476 bp and
256 16,755 bp respectively (Table 4.3). Restricting data to the longest subread per ZMW yielded
257 127,482 subreads (1.80 Gb) for A1; 93,547 (1.27 Gb) for A2; and 96,491 (1.30 Gb) for A3
258 (Table 4.3). FALCON assembly of unique reads for A1, A2 and A3 resulted in 55, 39 and 95
259 contigs respectively, with total lengths of 32.7 Mb, 34.5 Mb and 23.8 Mb per library, with
260 N50 values of 4.4 Mb (A1), 4.1 Mb (A2) and 3.3 Mb (A3) (Table 4.3).
261
143
Table 4.3 Sequencing output and assembly summaries for three multi-genome
libraries.
Combined Unique FALCON Library Sequences subreads sequences assembled Total number 174,282 127,482 55 Total length 2,070,377,334 1,803,951,037 32,727,291 Min. length (bp) 35 36 19,929 A1 Max. length (bp) 46,582 46,582 6,659,843 Mean length (bp) 11,879.47 14,150.63 595,041.65 Median length (bp) 11,065 14,843 63,488 N50 length (bp) 17,552 18,401 4,432,358
Total number 132,856 93,547 39 Total length 1,522,567,248 1,267,874,915 34,533,235 Min. length (bp) 35 37 17,238 A2 Max. length (bp) 45,106 45,106 8,306,415 Mean length (bp) 11,460.28 13,553.35 885,467.56 Median length (bp) 11,070 14,236 33,683 N50 length (bp) 16,476 17,548 4,142,770
Total number 138,043 96,491 95 Total length 1,554,898,583 1,297,410,554 23,820,564 Min. length (bp) 35 38 14,223 A3 Max. length (bp) 42,612 42,612 6,196,405 Mean length (bp) 11,263.87 13,445.92 250,742.78 Median length (bp) 10,725 14,028 39,656 N50 length (bp) 16,755 17,769 3,319,087
262
263 4.3.2 Individual genome assemblies, annotation and quality assessment
264 Of the 17 bacterial genomes sequenced, eight returned very high-quality assemblies,
265 displaying high contiguity (L50=1; N50=3.2-8.3 Mb), uniform coverage (> 40x), and
266 completeness (> 99% complete) (Table 4.4). These were the Streptomyces spp. NBSH44,
267 INR7 and NBH77, Leifsonia INR9, Kribbella SPB151, Sphingomonas NBWT7,
268 Pseudarthrobacter NBSH8 and Cryobacterium INWT7 isolates.
144
Table 4.4 Antarctic bacterial genome assembly and quality assessments.
Assembled Mean G + C Complete Contam Largest Strain Contigs L50 N50 (bp) size (bp) Coverage (%) (%) (%) contig
Streptomyces NBSH44 7,678,790 3 1 7,474,454 94 70 99.7 0.7 Linear Mesorhizobiums x2 INR15-NBSH29 11,524,875 10 1 6,667,022 73 60.9 100.0 100.0 Circular Hymenobacter NBH84 5,388,077 6 1 4,779,090 37 56.7 99.4 0.6 Circular Paracoccus NBH48 2,970,638 12 2 707,228 22 67 82.6 0.4 Linear Leifsonia INR9 4,608,984 3 1 4,438,093 76 70.4 99.5 2.5 Circular Streptomyces INR7 8,320,846 1 1 8,320,846 46 72.4 99.6 0.8 Linear Kribbella SPB151 8,156,807 1 1 8,156,807 58 67.4 99.1 3.2 Circular Geodermatophilaceae NBWT11 4,594,226 1 1 4,594,226 33 74 97.7 0.8 Circular Novosphingobium NBM11 5,274,127 4 1 4,151,563 26 65.6 99.1 1.9 Linear Sphingomonas NBWT7 3,386,925 2 1 3,259,464 45 67.2 99.6 0.7 Circular Quadrisphaera INWT6 4,201,516 8 2 1,065,298 22 75.3 90.1 0.5 Linear Streptomyces NBH77 7,021,282 3 1 6,848,830 62 73.3 99.9 0.4 Linear Azospirillum INR13 4,695,673 50 10 134,676 18 67.4 64.5 4.3 Linear Pseudarthrobacter NBSH8 4,060,736 1 1 4,060,736 64 64.9 99.7 0.2 Circular Frigoribacterium NBH87 3,407,805 2 1 3,328,375 37 73.1 98.5 0.0 Circular Cryobacterium INWT7 3,477,829 2 1 3,328,990 91 66.1 99.5 0.0 Circular Shading indicates very high-quality assemblies; Numbers in bold indicate lower quality markers
269
145
270 A further four genomes, Hymenobacter NBH84, Geodermatophilaceae NBWT11,
271 Novosphingobium NBM11 and Frigoribacterium NBH87, produced assemblies which were
272 within high-quality ranges determined by CheckM (> 95% complete, < 5% contaminated)
273 (Parks et al. 2015), but exhibited slightly lower coverage and uniformity (26-37x),
274 completeness (97-99%) and/or contiguity (L50 < 2; N50=3.3-4.8 Mb) (Table 4.4).
275
276 Three Antarctic bacterial genomes; Paracoccus NBH48, Quadrisphaera INWT6 and
277 Azospirillum INR13 diverged from high-quality indicator range for completeness (83%, 90%
278 and 65% complete respectively), and exhibited fragmentation (8-50 contigs) and low
279 coverage (18-22x) (Table 4.4). Additionally, two isolates formed a dual assembly;
280 Mesorhizobium NBSH29 and isolate INR15, signalling the original misidentification of
281 INR15 as a Frondihabitans species, which instead belongs to Mesorhizobium genus. This
282 was confirmed by CheckM analysis which showed 100% contamination (Table 4.4).
283
284 4.3.3 Annotation and functional distribution of genes
285 Prokka annotation yielded an average of 930 protein-coding sequences per 1 Mb of genome
286 (Table 4.5). Between 70% and 84% of coding sequences (CDS) for each genome were
287 assigned function in COGs analysis (Table 4.5). Of the COG-characterised proteins,
288 categories related to metabolism (C, G, E, F, H, I, P and Q) (Fig. 4.1) comprised the largest
289 overall proportion of COGs, averaging 42%, with amino acid (E) and carbohydrate transport
290 and metabolism (G) groups accounting for the greatest abundance. Group E proteins were
291 particularly abundant in combined Mesorhizobium genomes INR15-NBSH29,
292 Pseudarthrobacter NBSH8 and Paracoccus NBH48, while group G was most abundant in
293 Leifsonia INR9, Frigoribacterium NBH87 and Quadrisphaera INWT6 isolates (Fig. 4.1).
146
Table 4.5 Predicted protein-coding sequences and CDS assigned to COGs.
No. of CDS Genome Isolate CDS tRNA tmRNA rDNA assigned to COG/ (Mb) (% of all CDS) Streptomyces NBSH44 7.7 7021 82 1 18 4962 (70.6) Mesorhizobiums x2 INR15-NBSH29 11.5 11455 102 0 9 9653 (84.3) Hymenobacter NBH84 5.4 4576 49 1 9 3296 (72.0) Paracoccus NBH48 3.0 3053 47 0 9 2576 (84.4) Leifsonia INR9 4.6 4444 53 1 3 3541 (79.7) Streptomyces INR7 8.3 7425 93 1 21 5669 (76.4) Kribbella SPB151 8.2 7851 73 1 9 5785 (73.7) Geodermatophilaceae NBWT11 5.0 4441 53 1 9 3595 (81.0) Novosphingobium NBM11 5.3 4996 60 0 6 3909 (78.2) Sphingomonas NBWT7 3.4 3231 53 0 6 2673 (82.7) Quadrisphaera INWT6 4.2 3922 59 1 9 3077 (78.5) Streptomyces NBH77 7.0 5860 93 1 21 4540 (77.5) Azospirillum INR13 4.7 4447 65 0 24 3573 (80.3) Pseudarthrobacter NBSH8 4.1 3745 53 1 12 3085 (82.4) Frigoribacterium NBH87 3.4 3146 54 1 6 2431 (77.3) Cryobacterium INWT7 3.5 3350 47 1 3 2676 (79.9)
147
294
148
Figure 4.1 Functional classification of protein-coding genes in Antarctic bacterial genomes by abundance of Clusters of Orthologous
Groups (COGs). Isolates are arranged by phyla, followed by genome size, left (largest) to right (smallest). Predicted functions, carbohydrate and amino acid metabolism, transcription and signal transduction were among the most abundant COGs classes.
149
295 COG groups related to information storage and processing (J, A, K, L and B) accounted for
296 approximately 19% of the characterised proteins, with transcription proteins being the most
297 abundant category presented (Fig. 4.1), particularly for Kribbella SPB151, Leifsonia INR9
298 and the three Streptomyces isolates, accounting for approximately 11-13% relative
299 abundance. Proteins involved in cellular processes and signalling COG classes (D, Y, V, T,
300 M, N, Z, W, U and O), accounted for an average of 19% of characterised COGs. Here, signal
301 transduction mechanisms (T) and cell wall/membrane/envelope biogenesis (M) were the
302 most abundant, with group T particularly abundant in Quadrisphaera INWT6, Azospirillum
303 INR13 and Streptomyces INR7 isolates, and group M most abundant in Bacteroidetes isolate
304 Hymenobacter NBH84 (Fig. 4.1). Approximately 20% of proteins were assigned to poorly
305 characterised COG groups (R and S), representing predicted proteins and those of unknown
306 function (Fig. 4.1).
307
308 4.3.4 Phylogenetic analysis based on 16S rDNA genes
309 4.3.4.1 Actinobacteria: Streptomyces group
310 In 16S rDNA gene sequence analysis, Streptomyces isolate INR7 revealed closest homology
311 to S. virginiae (99% sequence identity), a species known to produce the streptogramin
312 antibiotic virginiamycin (Fig. 1.1) (Kingston & Kolpak 1980). In phylogenetic tree analysis
313 by maximum likelihood method (Fig. 4.2), INR7 formed a closely related clade which
314 included S. virginiae S. lavendulae and S. cirratus. Analysis using 16S rDNA gene sequence
315 for Streptomyces spp. is known to produce trees with low support for delineated clades, as
316 seen here (Fig. 4.2) (Nouioui et al. 2018).
317
150
318
Figure 4.2 Maximum likelihood phylogenetic tree of 16S rDNA gene for Streptomyces
Antarctic isolates. The Antarctic Streptomyces spp. belonged to three distinct clades
comprised of closely related species. Numbers at the branches correspond to confidence
values based on 100 bootstrap replications, with only those > 50% shown.
319
320 Streptomyces NBSH44 was most similar to S. finlayi (99%), a species originally isolated
321 from an Egyptian soil rhizosphere (Szabo 1978). In phylogenetic analysis, NBSH44
322 additionally formed a clade with S. clavifer (Fig. 4.2). Streptomyces NBH77 shared an
323 identical 16S rDNA sequence with S. rutgersensis (100%), a species previously reported to
324 produce a bacteriolytic enzyme SR1, active against Gram-positive bacteria (Shimonishi et al.
325 1999). NBH77 was distributed in a closely related clade alongside S. gougerotti and S.
326 intermedius.
327
151
328 4.3.4.2 Actinobacteria: non-Streptomyces group
329 For the non-Streptomyces Actinobacterial isolates Kribbella SPB151, Pseudarthrobacter
330 NBSH8, Frigoribacterium NBH87 and Leifsonia INR9, 99% sequence similarity was
331 observed for the related species K. qitaiheensis, P. phenanthrenivorans, F. endophyticum and
332 L. shinshuensis respectively (Fig. 4.3). Phylogenetic tree analysis confirmed that isolates
333 Geodermatophilaceae NBWT11, Cryobacterium INWT7 and Quadrasphaera INWT6 were
334 potentially novel species, with nearest identities reported to Klenkia marina (97%), F.
335 endophyticum (97%) and Q. granulorum (98%) (Fig. 4.3).
336
337 4.3.4.3 Alphaproteobacteria group
338 Paracoccus NBH48, Mesorhizobium spp. INR15 and NBSH29 and Sphingomonas NBWT7
339 all shared 99% sequence similarity to related species; P. carotinifaciens M. australicum, M.
340 chacoense and Sphingomonas jeddahensis (Fig 4.4). Isolates Azospirillum INR13 and
341 Novosphingobium NBM11 showed lower homology (98%) to related species A. zeae and N.
342 stygium respectively (Fig. 4.4).
343
344
152
345
Figure 4.3 Maximum likelihood phylogenetic tree of 16S rDNA gene for Antarctic
isolates belonging to the Actinobacteria phylum (excepting Streptomyces spp.) Branch
numbers correspond to confidence values based on 100 bootstrap replications, with only
those > 50% shown. The Cryobacterium isolate INWT7, and the Geodermatophilaceae
isolate NBWT11 are likely to be novel, both sharing 97% similarity to known species.
The Quadrasphaera isolate INWT6 shares 98% similarity to known species.
153
346
Figure 4.4 Maximum likelihood phylogenetic tree of 16S rDNA gene for Antarctic
isolates belonging to the Proteobacteria phylum. Numbers at the branches correspond
to confidence values based on 100 bootstrap replications, with only those > 50% shown.
The Azospirillum isolate INR13 and the Novosphingobium isolate NBM11 both 98%
similar to known species.
347
154
348 4.3.4.4 Bacteroidetes: Hymenobacter
349 Prior to genome sequencing, the Hymenobacter isolate NBH84, showed closest similarity
350 (97%) to known species H. xinjiangensis. Following genome sequencing and NCBI database
351 updates, the isolate now shares 99% identity to a newly discovered species, H. defluvii (Fig.
352 4.5).
353
354
Figure 4.5 Maximum likelihood phylogenetic tree of 16S rDNA gene for
Bacteroidetes Antarctic isolate Hymenobacter sp. NBH84. Prior to genome sequencing
the isolate shared 97% homology to known species. Following updates to the NCBI
database, the strain now shows 99% similarity to newly identified species H. defluvii.
Numbers at the branches correspond to confidence values based on 100 bootstrap
replications, with only those > 50% shown.
355
356 4.3.5 Biosynthetic gene clusters detected in Antarctic genomes
357 Across the genomes of all 17 Antarctic bacteria, a total of 147 BGCs were detected using
358 antiSMASH. The greatest number of BGCs were found in Streptomyces isolates (Table 4.6,
359 Fig. 4.6). Strain INR7 carried the most, totalling 31 clusters, spanning 1.4 Mb and
360 representing 17% of the isolate's genome size (Table 4.6, Fig. 4.6). Strain NBSH44 carried
361 26 clusters, which spanned 0.7 Mb in total, comprising 10% of its genome, and isolate 155
362 NBH77 dedicated 14% of its genome to 22 BGCs spanning 1.0 Mb. Kribbella SPB151, had
363 the greatest number of BGCs of the non-Streptomyces isolates, with 10 detected clusters,
364 representing 5.9 % of its genome and covering nearly 0.5 Mb (Fig. 4.6, Table 4.6). Overall,
365 BGC similarity (percent of genes which showed similarity to known clusters) was low, with
366 111 BGCs (75%) displaying < 70% similarity (Table 4.6). Fifty BGCs (34%) shared no
367 similarity to any known clusters (Appendix Table A3.2).
368
Table 4.6 Proportion of Antarctic bacterial genomes dedicated to secondary
metabolite biosynthetic clusters.
Proportion of BGC Total % of <70% Genome BGCs (Mb) genome similar Streptomyces INR7 1.43 17.2 21/31 Streptomyces NBH77 1.01 14.4 14/22 Streptomyces NBSH44 0.79 10.3 19/26 Kribbella SPB151 0.48 5.9 9/10 Cryobacterium INWT7 0.15 4.4 4/5 Pseudarthrobacter NBSH8 0.16 4.0 4/5 Leifsonia INR9 0.18 3.9 4/5 Frigoribacterium NBH87 0.13 3.8 5/5 Paracoccus NBH48 0.10 3.2 2/4 Novosphingobium NBM11 0.16 3.1 5/6 Mesorhizobium x2 INR15_SH29 0.33 2.8 13/13 Geodermatophilaceae NBWT11 0.10 2.3 4/4 Quadrisphaera INWT6 0.08 2.0 1/3 Sphingomonas NBWT7 0.07 1.9 1/2 Azospirillum INR13 0.09 1.9 2/2 Hymenobacter NBH84 0.09 1.7 3/4 369
156
370 Figure 4.6 Biosynthetic gene
371 clusters detected by AntiSMASH in
372 Antarctic bacterial genomes. The
373 most abundant BGCs were terpene
374 and NRPS-containing clusters. The
375 three Streptomyces isolates
376 harboured the greatest number of
377 BGCs, followed by the Kribbella.
378 Isolates are arranged left to right by
379 highest number of BGCs, and clusters
380 are arranged top to bottom, by
381 greatest number of BGC type.
157
382 Terpenes were the most abundant BGC class identified, with 30 clusters predicted. This was
383 followed by NRPSs (15), Type III PKSs (14), bacteriocins (9) and siderophores (9) (Fig 4.6).
384
385 A quarter of all BGCs were comprised of several classes of biosynthetic machinery, including
386 chemical hybrids (Fig. 4.6, Appendix Table A3.2). Overall, 44% of clusters contained NRPS
387 and/or PKS genes. Combined, the number of clusters containing NRPS genes were
388 comparable with the number of terpenes (30). Type I PKS-containing clusters totalled 19, the
389 majority of which were hybrid NRPS/ Type I PKS clusters (12). Only two Type II PKS
390 clusters were detected.
391
392 4.3.6 Biosynthetic gene cluster verification for Streptomyces, Kribbella and
393 Azospirillum isolates
394 4.3.6.1 Streptomyces INR7 BGCs
395 Manual inspection of the 31 BGCs detected in Streptomyces isolate INR7 revealed 10 clusters
396 with high homology (> 70%) to known BGCs, indicating encoding of the same or similar
397 products (Fig. 4.7, Appendix Table A3.2). These included two different ribosomally
398 synthesised post-translationally modified peptides (RiPPs): SapB, which acts as a surfactant
399 during formation of aerial hyphae (100% sim, Region 29); and venezuelin, a class IV
400 lanthipeptide of unknown activity (100% sim, Region 31) (Straight et al. 2006, van der Donk
401 & Nair 2014). Others included siderophores, coelichelin (100% sim, Region 7) and
402 desferrioxamine B (83% sim, Region 22) (Figs. 1.2 and 4.7); three terpenes: 2-
403 methylisoborneol, geosmin and avermitilol (100% sim, Regions 6, 14 & 26); a Type III PKS
404 encoding an phenolic lipid antimicrobial compound also involved in cyst formation,
158
405 alkylresorcinol (100% sim, Region 2) (Funa et al. 2006); and two NRPSs, one encoding a
406 tetrapeptide antitumour compound, tambromycin (100% sim, Region 25) (Fig 4.7, Appendix
407 Table A3.2), and one similar to that encoding broad-spectrum antibiotic streptothricin (87%
408 sim, Region 28) (Yu et al. 2018). For the tambromycin BGC (Region 25), gene placement
409 was identical to the known compound cluster (Fig. 4.8), with individual genes showing 91-
410 99% identity. This indicates the same compound is likely to be produced by INR7.
411
412 For the remaining 21 clusters detected in Streptomyces INR7, inspection revealed lower
413 homology to known BGCs (< 70% genes showed similarity), suggesting the encoding of
414 different end products. For three of these regions, a large proportion of genes were similar to
415 known BGCs, but gene order differed, and several genes were absent. They most closely
416 matched BGCs encoding hopene, a sterol-like membrane lipid which affects membrane
417 fluidity (Seipke & Loria 2009, Nett et al. 2009) (61% sim, Region 11), a curamycin-like Type
418 II PK spore pigment (63% sim, Region 27), and an isorenieratene-like carotenoid terpene
419 (66% sim, Region 30). Fourteen BGCs displayed < 50% similarity to known BGCs, with
420 closest matches encoding for kedarcidin, A54145, istamycin, monensin, chloramphenicol,
421 echosides, herboxidiene, svaricin, elloramycin, friulimicin, jerangolid, RK-682, kinamycin
422 and himastatin. Four BGCs were not similar to any known cluster (Appendix Table A3.2).
423 Interestingly, although S. virginiae is typically known as a producer of the antibiotic
424 virginiamycin (Pulsawat et al. 2009), this cluster was not detected in the INR7 strain.
425
426
159
160
Figure 4.7 Circular representation of the Streptomyces INR7 genome. The position and type of BGCs detected by antiSMASH are depicted as coloured bars. For each BGC, the percentage of genes which showed similarity to known BGCs are displayed in the inner ring. Clusters likely to produce the same or similar compound as the closest BGC match have the compound name in the outer ring. The INR7 genome was a single, linear contig 8.3 Mb in length, with approximately 17% of the genome dedicated to BGC.
161
427 .
Figure 4.8 The Streptomyces INR7 genome contains an NRPS BGC, Region 25, with
100% gene similarity to the tambromycin BGC. Tambromycin is an antitumour
compound containing an unusual amino acid, tambroline (shaded). The region is
contiguous and individual genes show high protein sequence similarity (91-99%),
indicating the same compound is likely produced. Tambromycin structure adapted from
Goering et al. (2016).
428
429 4.3.6.2 Streptomyces NBH77 BGCs
430 Of the 22 clusters detected in Streptomyces NBH77, six showed potential for biosynthesis of
431 the same or similar compounds, exhibiting 100% gene cluster similarity (Fig. 4.9). These
432 included ectoine, a compound which assists microorganisms to cope with osmotic stress
433 (Vicente et al. 2018) (Region 6), siderophore desferrioxamine B (Region 7), the odiferous
434 compound geosmin (Region 14), the antibiotic terpene albaflavenone (Region 13), a
435 polycyclic tetramate macrolactam, SGR PTMs (Region 19), and a large NRPS-Type I PKS
436 cluster encoding two compounds; the antibiotic antimycin, and the antifungal compound,
162
437 candicidin (Region 3) (Fig. 4.9, Appendix Table A3.2). The Region 3 BGC spanned a total
438 of 209 kb, with the NRPS-Type I PKS hybrid region matching the antimycin BGC, which
439 includes a gramicidin synthetase, with 82-97% individual gene sequence identity (Fig. 4.10).
440 The neighbouring Type I PKS cluster matched both candicidin and FR-008 clusters, but with
441 an additional transposase gene (Fig. 4.10). Individual genes within the Type I PKS show high
442 homology to both candicidin and FR-008 BGC genes, with 84-95% identity.
443
444 Two Streptomyces NBH77 clusters, while showing high similarity to known terpene clusters,
445 encoding isorenieratene (85% sim, Region 5) and hopene (76% sim, Region 18), displayed
446 different gene placements but may produce similar compounds. Eight BGCs showed low
447 homology (7-40% genes similar) to known clusters, encoding mannopeptimycin,
448 daptomycin, chloramphenicol, herboxidiene, leinamycin, pellasoren and scabichelin. Six
449 clusters displayed no similarity to known BGCs (Appendix Table A3.2).
450
163
164
Figure 4.9 Circular representation of the Streptomyces NBH77 genome. The position and type of BGCs detected by antiSMASH are depicted
as coloured bars. For each BGC, the percentage of genes which showed similarity to known BGCs are displayed in the inner ring. Clusters likely
to produce the same or similar compound as the closest BGC match have the compound name in the outer ring. The NBH77 genome was
comprised of one large linear contig, 6.8 Mb in length, and two smaller contigs, 147 kb and 25 kb in length which are putative plasmids. No
BGCs were detected in the second or third contigs. BGCs spanned over 14% of the NBH77 genome.
451
165
452
Figure 4.10 The Streptomyces isolate NBH77 NRPS-Type I PKS BGC, Region 3. The hybrid NRPS-Type I PKS region closely resembles the
antimycin cluster, an antibiotic, with 100% of genes showing similarity. The neighbouring Type I PKS region closely resembles clusters encoding
antifungal polyene macrolides, candicidin and FR-008, with individual gene identity of 84-95%, except that an extra transposase gene was present
between FscT11 and FscC. The antimycin/ candicidin BGCs are known to cluster together in other harbouring species.
166
453 4.3.6.3 Streptomyces NBSH44 BGCs
454 Of the 26 BGCs detected in Streptomyces NBSH44, five were highly similar (100% of genes
455 showed similarity) to clusters encoding for isorenieratene (Region 1), melanin (Region 5),
456 ectoine (Region 19), AmfS RiPP (Region 22), and geosmin (Region 23) (Fig 4.11, Appendix
457 Table A3.2), suggesting the production of the same or similar products. Two BGCs were
458 highly similar but displayed alternate gene placement; desferrioxamine B (80% sim, Region
459 15) and hopene (84% sim, Region 2). The remaining 19 NBSH44 BGCs showed low
460 homology (< 51% sim) to known clusters, which included β-lactam carbapenem MM 4550,
461 lactonamycin, lysolipin, mannopeptimycin, enduracidin, clorobiocin, goadsporin,
462 bacillibactin, steffimycin, maduropeptin, clavulanic acid and thiolutin gene clusters.
463
464 One region showed 42% gene similarity to antitumour compound C-1027 (Contig 2, Region
465 2) (Fig. 4.11, Appendix Table A3.2). On further examination, the cluster revealed high
466 similarity (100% sim) to a C-1027 sub-cluster, indicating likely production of a C-1027-like
467 enediyne (Fig. 4.12). Individual genes showed high similarity (85-96%). However, additional
468 nuclease and transcriptional regulator domains were present, suggesting that the NBSH44
469 sequence may be more complete. Six NBSH44 clusters shared no association with any known
470 BGCs (Fig 4.11, Appendix Table A3.2).
471
167
168
Figure 4.11 Circular representation of the Streptomyces NBSH44 genome. The position and type of BGCs detected using antiSMASH are depicted as coloured bars. For each BGC, the percentage of genes which were similar to known BGCs are displayed in the inner ring. Clusters likely to produce the same or similar compound as the closest BGC match have the compound name in the outer ring. The NBSH44 genome was comprised of one large linear contig, 7.4 Mb in length, and two smaller contigs, 180 kb and 23 kb in length which are most likely plasmids. The second contig contained three BGCs. In total, BGCs spanned approximately 10% of the NBSH44 genome.
169
472
Figure 4.12 The putative plasmid, contig 2, carried by Streptomyces isolate NBSH44,
contains a Type 1 PKS (Region 2) with 42% of genes similar to the antitumour compound
C-1027 BGC. The sub-cluster, encoding an enediyne, shared 100% gene similarity,
suggesting capacity for production of a C-1027-like enediyne chromophore. Several
additional genes were present, which may indicate the NBSH44 sequence is more
complete than the matching sub-cluster. C-1027 enediyne structure adapted from Liu et
al. (2002).
473
474 4.3.6.4 Kribbella SPB151 BGCs
475 Of the ten BGCs uncovered in Kribbella isolate SPB151, only one showed high gene
476 similarity (100%) to a known cluster; alkylresorcinol (Region 8) (Fig 4.13). Four BGCs
477 exhibited low gene similarity (< 35%) to asukamycin, avilamycin, albachelin and
478 thiocoraline gene clusters, and five BGCs had no known similar homologs in the database
479 (Fig. 4.13, Appendix Table A3.2).
480
170
481 4.3.6.5 Azospirillum INR13 BGCs
482 The fragmented, low coverage genome of Azospirillum isolate INR13 contained two BGCs
483 which exhibited low identity to those known. Closest matches were to BGCs encoding for
484 fengycin and anthracimycin respectively (~20% of genes showed similarity). Interestingly,
485 the anthracimycin-like cluster (Contig 10, Region 1), showed high sequence similarity (100%
486 sim) to the polyunsaturated fatty acid (PUFA) biosynthetic gene cluster from the genome of
487 the terrestrial myxobacteria genus, Aetherobacter (Fig. 4.14). The genes appear fragmented
488 in comparison to the genome of similar Azospirillum species and revealed only 54-57%
489 individual gene identity with the Aetherobacter sp. genes, Pfa1, Pfa2 and Pfa3. However,
490 there is potential that the Azospirillum species may have the capacity to produce
491 biotechnologically important PUFA compounds (Fig 4.14).
492
493 4.3.6.6 PKS and NRPS gene amino acid sequence similarity to known genomic regions
494 When compared with amino acid sequences in the Genbank database, PKS and NRPS-
495 containing genes for Streptomyces isolates INR7 and NBH77 revealed close matches (84-
496 100%, av. 99%) to previously sequenced genome regions (Appendix Table A3.3-A3.6). On
497 average, lower similarity matches were observed for both Streptomyces NBSH44 (40-91%,
498 av. 83%), and Kribbella SPB151 isolates (49-92%, av. 76%) (Appendix Table A3.3-A3.6).
499 The PKS genes in Azospirillum INR13 shared 93-95% identity to known genome regions
500 (Appendix Table A3.3-A3.6).
171
172
Figure 4.13 Circular representation of the Kribbella SPB151 genome. The position and type of BGCs detected in by antiSMASH are depicted as coloured bars. For each BGC, the percentage of genes which showed similarity to known BGCs are displayed in the inner ring. The Type III
PKS cluster encoding synthesis of alkylresorcinol was the only gene cluster likely to produce the same or similar compound as the closest BGC match. The SPB151 genome was comprised of one large circular contig, 8.1 Mb in length, with BGCs spanning approximately 6% of the genome.
173
501
Figure 4.14 The Azospirillum isolate INR13 harbours a potential polyunsaturated
fatty acid (PUFA) synthase cluster. 100% of genes showed similarity to an
Aetherobacter sp. cluster known to produce PUFA. The genes are fragmented in the
INR13 strain, in comparison to the same region detected in similar species Azospirillum
lipoferum.
502
503 4.3.7 NaPDoS analysis of ketosynthase and condensation domains
504 Across all BGCs, a total of 64 PKS ketosynthase domains (KS) and 100 NRPS condensation
505 domains (C) were identified. These were exclusively found in Streptomyces strains INR7,
506 NBSH44 and NBH77, and Kribbella SPB151 and the Azospirillum INR13. Overall, these
507 domains exhibited low protein sequence identity to NaPDoS database pathway domain
508 sequences, averaging 63% for KS and 44% for C domains. This indicated that the encoded
509 products are likely to differ from those of the pathways curated in the database (Appendix
510 Table A3.3-A3.6). For the NaPDoS database, a domain similarity threshold of > 85% is
511 suggested to encode the same or similar compound (Ziemert et al. 2012). Here, only a single
174
512 domain identity passed this threshold; the Streptomyces NBSH44 contig 2, Region 2 KS
513 domain, which aligned with C-1027 enediyne with 92% similarity, a result which
514 complements the BGC antiSMASH match.
515
516 Interestingly, for the Streptomyces NBH77 candicidin-like cluster (Region 3), KS domains
517 aligned most closely with another polyene macrolide antifungal pathway; nystatin (68-82%
518 identity), rather than candicidin (Fig 4.12). Overall, 33% of Antarctic KS domains aligned
519 most closely with NaPDoS-curated pathways encoding polyene macrolide antifungal
520 compounds. A further 36% of KS domains showed similarity to antitumour compounds, such
521 as epothilone, leinamycin and alnumycin, with sequence similarities ranging from 42-72%.
522
523 Four KS domains aligned most closely with PUFA type domains, and these relationships
524 were confirmed by phylogenetic tree analysis (Fig. 4.15). Two of these were from the
525 Azospirillum INR13 genome, confirming the antiSMASH result. Interestingly, the remaining
526 two PUFA-like KS domains were detected in Streptomyces INR7, within two separate Type
527 I PKS BGCs (Regions 9 and 24) (Appendix Table A3.2). Respectively, these INR7 BGCs
528 exhibited 0% and 3% similarity to known BGCs, but both show high identity to previously
529 sequenced genome regions, suggesting as-yet-uncharacterised biosynthetic pathways.
530
531 A large proportion (28%) of the bacterial isolate genome C domains showed closest similarity
532 to lipopeptide pathways encoding biosurfactants, such as syringomycin and iturin, albeit with
533 low sequence similarity (av. 39%), indicating encoding of different lipopeptides. These
534 relationships were confirmed following phylogenetic analysis, where domains from multiple
535 isolates formed clades but did not align to any known database pathways (Fig 4.16). Other
175
536 domains formed closer relationships to pathway domains encoding antitumour compounds
537 actinomycin, thiocoraline and bleomycin, and antimicrobial pathways such as calcium-
538 dependent antibiotic (CDA) and pristinamycin (Fig 4.16).
176
Figure 4.15 Phylogenetic analysis of ketosynthase domains by maximum likelihood method against
NaPDoS database domains.
The majority of KS domains showed closest similarity to pathways encoding antitumour and antifungal compounds.
PUFA-like domains were found in both the Azospirillum INR13 and Streptomyces INR7 strains.
177
Figure 4.16 Phylogenetic analysis of condensation domains by maximum likelihood method against
NaPDoS database domains.
Predominantly, C domains aligned most closely to pathways encoding antitumour, antimicrobial and surfactant compounds.
178
539 4.4 DISCUSSION
540 Here, we report high-quality genome assemblies for twelve Antarctic bacteria, produced
541 through long-read PacBio sequencing of multi-genome libraries. The high-quality assemblies
542 were classed here as being 97.7-99.9% complete, with low contamination (0-4.3%) and high
543 contiguity (N50 > 3.2 Mb) (Table 4.4) (Parks et al. 2015, Sedlazeck et al. 2018, Koren et al.
544 2013). The genomes of Streptomyces, Kribbella, Sphingomonas, Novosphingobium,
545 Leifsonia, Geodermatophilaceae, Pseudarthrobacter, Hymenobacter, Cryobacterium and
546 Frigoribacterium are some of the first complete bacterial genomes thus far described from
547 the arid desert soils of eastern Antarctica. Multi-genome sequencing methods were unable to
548 resolve complete assemblies for five additional isolates, four of which displayed
549 fragmentation and low coverage, most likely due to either complications during DNA
550 extraction, or preferential sequencing of dominant strains due to insufficient DNA input
551 (Table 4.4). Additionally, two strains could not be distinguished, due to original taxon
552 misidentification, resulting in closely related species being combined in the same multi-
553 genome library.
554
555 The greatest NP capacity was demonstrated by Actinomycetales with genomes > 7 Mb in
556 size, which carried 10-31 BGCs each, specifically the Streptomyces and Kribbella isolates.
557 This is consistent with previous reports which show correlation between BGC carriage and
558 genome size, with Actinomycetales known to be particularly prolific in terms of NP
559 biosynthesis (Baltz 2017, Wang et al. 2014, Donadio et al. 2007). Here, Streptomyces spp.
560 had a mean genome size of 7.7 Mb, harbouring an average of 26 BGCs spanning 1.1 Mb, or
561 ~14% of the genome. These results are fractionally lower than those reported by Baltz (2017),
562 who found an average Streptomyces genome size of 9.3 Mb, with ~35 BGC, covering 1.5 179
563 Mb, and representing 16% of total genome size (Baltz 2017). In contrast, the average
564 prokaryote genome dedicates ~4% of their genome to BGCs (Cimermancic et al. 2014).
565
566 Clusters with similarity to those encoding desferrioxamine, geosmin and hopene were found
567 in all three Antarctic Streptomyces genomes, which along with those encoding melanin,
568 ectoine, and isorenieratene are known to be highly conserved in Streptomyces (Kim et al.
569 2015, Vicente et al. 2018, Komaki et al. 2018) (Appendix Table A3.2). An additional set of
570 BGCs were common across a variety of the Antarctic bacterial genomes, namely
571 alkylresorcinol (5 spp.), and a diversity of carotenoid clusters (12 spp.). Together, these
572 groups accounted for 26 of the 36 BGCs which showed high similarity (> 70%) to known
573 clusters, and emphasise the ecological roles for secondary metabolites in the natural
574 environment; iron chelation, defense, signalling, and protection against environmental
575 stressors (Kim et al. 2015, Vicente et al. 2018, Komaki et al. 2018, Adamek et al. 2018).
576
577 Three quarters of all BGCs detected in Antarctic bacterial strains showed < 70% gene cluster
578 similarity to known BGCs (Appendix Table A3.2). Compared with known clusters they
579 exhibited alternate gene order, absent or additional genes, matches only to accessory genes,
580 and/or low individual gene identity (< 60%) to those known. As genetic variance correlates
581 with compound structural variance, clusters with moderate similarity to known clusters may
582 encode molecules within the same class, but which structurally diverge from the known
583 pathway product (Crits-Christoph et al. 2018, Blin et al. 2017a). Furthermore, a third of all
584 BGCs across Antarctic genomes shared no similarity to any known cluster, implying new
585 chemical entities, or compounds for which the BGC remains uncharacterised (Blin et al.
586 2017a, Baldim et al. 2017, Challis 2008).
180
587
588 At least three known NP compounds; tambromycin, antimycin and candicidin, are likely
589 biosynthesised by PKS and NRPS pathways in the Antarctic Streptomyces isolates INR7 and
590 NBH77, inferred from highly analogous gene clusters (Figs. 4.8 & 4.12). Tambromycin is an
591 unusual antitumour agent incorporating a novel pyrrolidine‐containing amino acid,
592 tambroline. Although the compound has only recently been uncovered, the tambromycin
593 cluster appears widely distributed amongst the S. virginiae clade, to which INR7 belongs
594 (Goering et al. 2016, Zhang et al. 2018) (Figs. 4.8 and 4.2). The combined antimycin and
595 candicidin cluster (NBH77, Fig. 4.10) is also widely disseminated amongst various
596 Streptomyces spp. (Caffrey et al. 2016, Jorgensen et al. 2009). Antimycin is a depsipeptide
597 which exhibits diverse bioactivities including antifungal, insecticidal, nematocidal and
598 antitumour properties, while the candicidin polyene macrolide is an antifungal (Joynt &
599 Seipke 2018, Caffrey et al. 2016) (Figs. 4.10). Across the Antarctic genomes, analysis of all
600 PKS ketosynthase and NRPS condensation domains revealed closest similarity to pathway
601 domain sequences encoding for antitumour, antifungal, antimicrobial and biosurfactant
602 compounds. Here, protein sequence identities were < 85%, indicating product structural
603 divergence (Figs. 4.15, & 4.16, Appendix Table A3.3-A3.6).
604
605 The cold-adapted Streptomyces sp. (NBSH44), which was recovered by novel SSMS
606 methods (Section 3.2.3, Fig. 3.14D), and the Kribbella, a rarely-cultured Actinobacterial
607 genus, both exhibited lower BGC gene similarity to those of known genome regions (av. 76-
608 83%), when compared with the Streptomyces strains INR7 and NBH77 (av. 99%) (Appendix
609 Tables A3.3-A3.6). This suggests that rarer Actinobacteria with large genomes are
610 particularly promising targets for novel NP discovery.
181
611
612 Antarctic bacteria are established sources of unusual fatty acids such as LC-PUFA, whose
613 primary functions involve maintenance of membrane fluidity and nutrient transport at cold
614 temperatures (Gemperlein et al. 2014, Bianchi et al. 2014, Nichols et al. 1993). Additionally,
615 psychrophilic bacteria, Serratia, have been found to incorporate PUFA-synthase like
616 machinery in the formation of unusual zeamine antibiotics (Masschelein et al. 2015). Here,
617 two isolates were found to contain BGCs with PUFA-domain sequence homology;
618 Streptomyces INR7 and Azospirillum INR13 (Fig. 4.15; Appendix Table A3.4). The
619 Azospirillum PUFA-like region is particularly exciting, exhibiting high similarity to the
620 PUFA synthase of Aetherobacter species. In Aetherobacter, the PUFA cluster encodes for
621 production of the omega-6 PUFA, Arachidonic acid (AA); and two omega-3 PUFAs,
622 eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (Fig. 4.14). PUFA-producing
623 bacteria are almost exclusively slow-growing psychrophiles, giving limitation to their
624 industrial value. In contrast, Azospirillum INR13 grows abundantly at RT. If it indeed
625 produces PUFA, the strain may be highly desirous for commercial purposes as a sustainable
626 source of nutritional supplements for human health industries and aquaculture (Gemperlein
627 et al. 2014). Thus, this isolate represents a worthy target for future fatty acid profiling work.
628
629 In conclusion, generation of high-quality bacterial genomes from eastern Antarctica here
630 highlighted the presence of unusual BGCs which represent exciting targets for medically
631 related NPs discovery, such as potential antifungal, antitumour and antibacterial compounds;
632 as well as industrially important clusters putatively encoding lipids, pigments, biosurfactants
633 and siderophores, which provide insights into cold-adapted microbial ecology. The genomes
634 revealed a high proportion of uncharacterised BGCs, confirming that Antarctic bacteria,
182
635 especially those from rarely isolated and psychrotrophic Actinomycetales groups, show great
636 promise regarding novel NPs.
183
184
CHAPTER FIVE
5 DISCUSSION AND CONCLUSIONS
1 5.1 RESEARCH MOTIVATIONS AND OBJECTIVES
2 Polar regions represent some of the most extreme habitats on Earth. Through adaptation,
3 microorganisms survive and thrive in these inhospitable environments, enduring freezing
4 temperatures, extreme nutrient and water limitation, high UV radiation, long periods of
5 darkness and frequent freeze-thaw cycles (Yergeau et al. 2012, Obbels et al. 2016, Aislabie
6 et al. 2004). Polar soils are exciting targets for NP biomining owing to an abundance of
7 Actinobacteria, Proteobacteria and uncharacterised taxa (Ji et al. 2017), coupled with an
8 elevated novel metabolite potential, predicted as a response to the unique environment (de
9 Pascale et al. 2012). The biosynthetic capacity of polar microbiomes, however, has remained
10 largely unknown. This thesis has examined the NP capacity of cold-adapted bacteria residing
11 in desert soils of eastern Antarctica and the high Arctic, revealing a wealth of novel NP gene
12 diversity.
13
14 The cultivation of microbes remains critical to NPs discovery. The microbial dark-matter,
15 which are resistant to traditional culturing approaches, still vastly outnumber characterised
16 taxa (Lloyd et al. 2018, Rappé & Giovannoni 2003). A major impediment to progress in the
17 NPs field has been the continual re-discovery of identical metabolites from similar microbial
18 species (Bérdy 2005, Masschelein et al. 2017, Baltz 2007, Harvey et al. 2015). Therefore,
19 the discovery of new metabolites demands the capture of novel or rarely-isolated groups.
20 Here, two novel oligotrophic culturing methods resulted in the capture of 47 Antarctic 185
21 bacterial species, predominantly from two of the most prolific phyla associated with bioactive
22 compound production; the Actinobacteria and Proteobacteria, and included rarely-cultured
23 Actinomycetales genera such as Frigoribacterium and Janibacter, as well as 18 members of
24 the biosynthetically rich Streptomyces genus.
25
26 The field of NPs discovery has been revived by contemporary advancements in sequencing
27 and bioinformatics (Milshteyn et al., 2014). In this study, the 3rd generation long-read
28 sequencing platform PacBio RS II was employed to retrieve long DNA reads for biosynthetic
29 gene analysis; from soil directly, and to obtain whole genomes from newly isolated Antarctic
30 bacteria. First, amplicon sequencing was used to survey > 200 polar soils for PKS
31 ketosynthase/acyltransferase and NRPS adenylation domains. Sequences predominantly
32 showed low protein sequence similarity (< 70%) to known genes, aligning most closely to
33 domain sequences encoding antifungal, antitumour and antimicrobial/surfactant compounds.
34 Further, arid Antarctic soils showed the greatest biosynthetic potential. Secondly, long-read
35 sequencing was used to obtain whole genomes from 17 of the isolated Antarctic bacterial
36 species. High-quality assemblies were obtained, revealing 147 BGCs in total, of which the
37 majority displayed < 70% similarity to known BGCs. In accordance with amplicon
38 sequencing results, many PKS and NRPS domains aligned most closely to antifungal,
39 antitumour and antimicrobial/surfactant-encoding genes.
40
186
41 5.2 KEY FINDINGS
42 5.2.1 Soil fertility is associated with natural product gene presence and diversity in
43 polar desert soils
44 Limitations of certain nutrients; carbon, nitrogen, phosphorous and iron, are known to have
45 regulatory roles in microbial secondary metabolite biosynthesis in laboratory settings (van
46 der Heul et al. 2018). Associations between low soil carbon and moisture, and biosynthetic
47 gene richness across diverse soil biomes have also been identified (Charlop-Powers et al.
48 2014), with arid soils predicted to be promising targets for NP bioprospecting, due to their
49 high abundance of Actinomycetales (Charlop-Powers et al. 2014). In Chapter 2, correlations
50 were indeed observed between biosynthetic genes and soil fertility factors (Fig. 2.12, Table
51 2.3), with significant negative associations observed between soil carbon, nitrogen and
52 moisture, and the detection (P < 0.001) and richness (P < 0.05) of the targeted biosynthetic
53 domains (Section 2.3.6). In polar soils no trend was observed between natural product
54 domain richness and the relative abundance of Actinobacteria or Actinomycetales, but
55 significant correlations (P = 0.001) were found between total bacterial diversity and NP gene
56 diversity (Section 2.3.6). We found that NP gene communities displayed closest similarities
57 at the regional and local level (Fig. 2.13), with relatedness patterns highly similar to both
58 phylogenetic diversity, and soil environmental parameters (Fig. 2.14). This indicates that the
59 microbial communities at these sites are significantly influenced by abiotic soil conditions
60 (Dumbrell et al. 2010, Ferrari et al. 2015).
61
62 The increased presence and diversity of BGCs in more nutrient-limited polar soils is
63 intriguing, and supports the ecological relevance of secondary metabolism in terms of
64 survival and competition between microbiota for scarce resources (de Pascale et al. 2012). 187
65 BGCs commonly span long genetic regions, and biosynthesis of their encoded compounds
66 comes at a high metabolic cost (Pickens et al. 2011, Bruns et al. 2018, Fischbach et al. 2008).
67 For the microbes that carry them, the consequences of BGC maintenance include increased
68 genome length, and thus, replication burden (Bruns et al. 2018). Strong selection pressure
69 towards metabolic efficiency is known to lead to expulsion of functionally superfluous DNA
70 (Bruns et al. 2018, Ofria et al. 2003, Lynch 2006). This suggests that the BGCs harboured
71 here by Antarctic bacteria, even if silent under laboratory conditions, remain functional in
72 environmental settings (Bruns et al. 2018).
73
74 5.2.2 Bacterial adaptation to the Antarctic environment includes desiccation-,
75 starvation- and radiation- resistance
76 In Chapter 3, culturing resulted in the recovery of 34 Actinobacteria, 11 Proteobacteria, and
77 one each of Bacteroidetes and Firmicutes phyla (Tables 3.4 & 3.5). These four phyla typically
78 dominate environmental culturing efforts, including those from Antarctic soils (Smith et al.
79 2006, Cary et al. 2010, Zdanowski et al. 2013, Pudasaini et al. 2017, van Dorst et al. 2016).
80 In molecular surveys too, the polar desert sites contained an abundance of Actinobacteria (av.
81 17-43%) and Proteobacteria (av. 9-42%) (Fig. 2.9). While ubiquitous in all soils,
82 Actinobacteria and Proteobacteria have adapted well to both hot and cold desert systems
83 (Battistuzzi & Hedges 2009, Makhalanyane et al. 2015a, Cary et al. 2010). For
84 Actinobacteria, this is generally attributed to an increased tolerance to desiccation and
85 starvation (Delgado-Baquerizo et al. 2018), provided by the Gram-positive cell wall, in
86 addition to strategies which exploit dormancy during unfavourable conditions, which
87 includes the development of spores, and for the non-spore formers, cyst-like resting cells
88 (Soina et al. 2004). These dormant forms are highly resistant to environmental challenges,
188
89 and resource limitation has been shown to regulate dormancy in natural microbial
90 populations (Lennon & Jones 2011, Battistuzzi & Hedges 2009, Makhalanyane et al. 2015a).
91 Atmospheric trace gas scavenging has been implicated as an important survival strategy
92 during dormancy (Greening et al. 2015), including for Actinobacteria in Antarctic soils (Ji et
93 al. 2017). For the Proteobacteria, of which the Gamma and Alpha groups typically dominate
94 in deserts (Makhalanyane et al. 2015a), aerobic anoxygenic phototrophy may be an important
95 survival strategy for certain genera (Section 3.4) (Makhalanyane et al. 2015a, Tahon &
96 Willems 2017). Indeed, several known AAP species were recovered in Chapter 3, the
97 Sphingomonas and Methylobacterium genera (Table 3.4).
98
99 A high proportion (~80%) of isolates cultivated in Chapter 3 (Table 3.4 & 3.5) are members
100 of genera shown to have high desiccation and radiation tolerance; Geodermatophilus,
101 Rhodococcus, Arthrobacter, Sphingomonas, Methylobacterium, Hymenobacter,
102 Streptomyces, Microbacterium, Micrococcus and Planococcus (Narvaez-Reinaldo et al.
103 2010, McBride et al. 2014, Marizcurrena et al. 2019, Barnard et al. 2013, Rainey et al. 2005).
104 Co-occurrence of desiccation and radiation resistance is common, as both stressors result in
105 accumulation of free radicals, leading to analogous damage to DNA. The resistance to
106 radiation is therefore believed to be a secondary adaptation to desiccation resistance
107 (Musilova et al. 2015). The prevalence of the above genera in culturing studies from
108 Antarctic soils (Nicetic 2016, Pudasaini et al. 2017, Peeters et al. 2011, Tahon & Willems
109 2017) suggests endemism, and supports phylogenetic surveys suggesting that desiccation and
110 radiation-resistant groups show a higher prevalence in Antarctica (Cowan et al. 2014,
111 Musilova et al. 2015). But it also demonstrates their versatility for adaptation to artificial
112 cultivation, and to more copiotrophic growth conditions (Fierer et al. 2007), compared with
189
113 more fastidious taxa such as the rarely-cultured phylum Chloroflexi (Hanada 2014), which
114 remain uncultured in our studies despite high abundance at some sites (Fig. 2.9).
115
116 In Chapter 3, lengthy incubation times (> 100 days) (Table 3.4 & 3.5), resulted in the recovery
117 of Geodermatophilus, Mesorhizobium and Hymenobacter isolates predicted to be novel at
118 species level (97-98% identity), as well as rarely cultured Actinomycetales, Frigoribacterium
119 and Janibacter (Tiwari & Gupta 2013). The novel culturing approaches (DSC and SSMS)
120 used here were successful in capturing a diversity of Actinobacteria (Table 3.4 & 3.5),
121 particularly the Streptomyces spp. (18 species), most of which were recovered by DSC (Table
122 3.4). Results here suggest that three main Streptomyces clades are endemic in eastern
123 Antarctic soils. These are:
124
125 • The S. lavendulae / S. spororaveus / S. virginiae clade (Fig 4.2) (Labeda et al. 2017,
126 Cheng et al. 2016); of which members have been isolated from Herring Island (Table
127 3.4), Adams Flat (Wong 2018), Robinson Ridge (Table 3.3) (Nicetic 2016) and
128 Browning Peninsula soils (Table 3.3) (Pudasaini et al. 2017). Importantly, this clade
129 displayed the greatest measurable antimicrobial activity overall (Table 3.7 & 3.8),
130 with broad-spectrum activity, spanning Gram-positive, Gram-negative and fungal
131 pathogens.
132
133 • The S. badius / S. clavifer / S. finlayi / S. griseus / S. parvus/ S. pratensis clade (Fig
134 4.2) (Labeda et al. 2017, Cheng et al. 2016); which have been recovered from Herring
135 Island, Mitchell Peninsula, Rookery Lake (Tables 3.4, 3.5) and Adams Flat (Wong
190
136 2018). For this clade, antimicrobial activity was primarily confined to Gram-positive
137 pathogens (Tables 3.7 & 3.8).
138
139 • The S. fildesensis / S. beijiangensis clade (Labeda et al. 2017, Cheng et al. 2016);
140 which have been previously recovered from Browning Peninsula (Table 3.3)
141 (Pudasaini et al. 2017), Mitchell Peninsula and Robinson Ridge (Nicetic 2016). These
142 species showed little antimicrobial activity against the pathogens tested here (Table
143 3.8).
144
145 5.2.3 Biosynthetic gene clusters in Antarctic bacteria highlight survival strategies
146
147 "Everything is everywhere: but the environment selects"
148 - L. Becking (O'Malley 2007).
149
150 Overall, BGCs detected in the Antarctic bacterial genomes emphasise the diverse ecological
151 roles of secondary metabolites, most prominently those related to survival and nutrient
152 acquisition (Appendix Table A3.2). Specifically, putative BGCs encoded compounds
153 involved in osmotic stress reduction (e.g. ectoine) (Vicente et al. 2018), membrane fluidity
154 (e.g. sterols, hopene, carotenoids, PUFAs) (Nett et al. 2009, Seipke & Loria 2009), protection
155 from UV radiation (e.g. carotenoids, melanins) (Walter & Strack 2011, Plonka 2006), aerial
156 mycelia and cyst development (e.g. AmfS, SapB, alkylresorcinol) (Funa et al. 2006), and iron
157 acquisition (e.g. siderophores) (Barona-Gómez et al. 2004) (Section 4.4). While these BGC
158 families are not unique to bacteria from arid polar soils, the secondary metabolites they
191
159 encode may play a vital role in increasing fitness for producing species in this hostile
160 environment.
161
162 5.2.3.1 Carotenoids, siderophores and biosurfactants
163 Carotenoids and siderophores are two of the most important families of secondary
164 metabolites produced by microbes, both are also taxonomically and geographically
165 widespread (Cimermancic et al. 2014, Walter & Strack 2011). Carotenoids serve a multitude
166 of functions; in photoprotection, as well as assisting in membrane fluidity and as accessory
167 pigments in phototrophy (Section 3.4). They have also been suggested to occur frequently in
168 cold-adapted bacteria (Peeters et al. 2011, Baraúna et al. 2017, De Maayer et al. 2014,
169 Koblížek & Brussaard 2015, Dieser et al. 2010). Here, 50% of all cultured isolates displayed
170 carotenoid-like pigmentation (Fig. 3.16), and 12 of the 17 genomes contained at least one
171 known carotenoid BGC (Appendix Table A3.2). These included clusters encoding for
172 isorenieratene, found in the Streptomyces spp.; astaxanthin dideoxyglycoside detected in the
173 Sphingomonas and Novosphingobium strains; and sioxanthin in the Quadrisphaera isolate.
174 Carotenoids are produced through terpene pathways (Walter & Strack 2011, Eisenreich et al.
175 2004), and terpenes were the largest group of BGCs found in the Antarctic bacterial genomes
176 (Fig. 4.6). Furthermore, seven terpene BGCs remained uncharacterised, indicating potential
177 for novel compounds (Appendix Table A3.2).
178
179 Overall, seven of the seventeen bacterial genomes carried at least one siderophore BGC
180 (Appendix Table A3.2). Without exception, Streptomyces are known to harbour
181 siderophores, such as desferrioxamine (van der Heul et al. 2018). This was also found here,
182 with all three Antarctic Streptomyces genomes harbouring a highly similar cluster (Appendix 192
183 Table A3.2). Additionally, in amplicon sequencing in Chapter 2, four siderophore pathways
184 were revealed in NRPS analysis (Appendix Table A1.2). Little is currently known regarding
185 siderophore production by Antarctic soil bacteria (De Serrano et al. 2016), however in studies
186 examining hot deserts and other soil microbiomes, siderophores have been implicated in rock
187 weathering (Liermann et al. 2000, Adams et al. 1992, Ahmed & Holmström 2015). Microbes
188 including Streptomyces are known to form attachments to mineral surfaces in the
189 environment, and siderophore production has been found to increase mineral dissolution
190 rates, providing a source of essential metals for uptake by the microbial community (Ahmed
191 & Holmström 2015, Liermann et al. 2000, Choe et al. 2018). In Chapter 3, microbial
192 attachment to mineral surfaces was observed during DSC methods (Figs. 3.9, 3.10).
193 Previously, it has been proposed that siderophore biosynthesis provides a competitive edge
194 in iron-limited soils (Galet et al. 2015), and other species, such as Pseudomonas, have
195 evolved the capability to pirate siderophores produced by others (Galet et al. 2015).
196
197 Of interest, a connectedness has been reported between the secretion of biosurfactants and
198 siderophore nutrient acquisition during mineral weathering, whereby microbial communities
199 attach to mineral surfaces via biofilm formation (Ahmed & Holmström 2015), in a process
200 known to involve both biosurfactants and siderophores (Paraszkiewicz et al. 2017, Yang et
201 al. 2012). In NRPS domain analyses in both Chapters 2 and 4, a high proportion of pathways
202 showed similarity to those encoding biosurfactant peptides such as syringomycin and
203 gramicidin (Fig. 2.8, Appendix Table A3.3). Biosurfactant metabolites exhibit highly
204 versatile ecological roles (Section 2.4), and are common in cold-adapted microorganisms
205 (Perfumo et al. 2018). Metal harvesting strategies from mineral surfaces may be of increased
206 importance in the extremely resource-limited Antarctic environment, leading to an
193
207 abundance of biosurfactant-like molecule pathways. Biosurfactant production in these
208 isolates remains to be determined, for which a number of rapid screening methods could be
209 employed (Sarwar et al. 2018). To further investigate the role of biosurfactants in mineral
210 weathering, bacterial strains could be examined for their ability to mobilise metals from
211 crushed mineral into solution (Becerra-Castro et al. 2013), and re-tested following disruption
212 to biosurfactant synthesis genes.
213
214 5.2.3.2 Long-chain polyunsaturated fatty acids
215 In bacteria, LC-PUFAs have been found almost exclusively in Gram-negative psychrophilic
216 marine Gammaproteobacteria, such as Shewanella and Colwellia spp. (Shulse & Allen 2011,
217 Bianchi et al. 2014). Here, isolate Azospirillum INR13 was found to harbour a BGC highly
218 similar to the LC-PUFA synthase of another terrestrial bacterium, Aetherobacter (Fig. 4.14)
219 (Gemperlein et al. 2014). This is an exciting prospect, as LC-PUFA have not been previously
220 reported from Azospirillum. These unusual secondary lipids are synthesised through Type I
221 PKS-like systems, and are primarily sourced from microalgae, cold climate fish and
222 invertebrates. They have substantial biotechnological value as nutritional supplements
223 because many organisms must obtain PUFA through diet (Bianchi et al. 2014, Nichols et al.
224 2010b, Sprague et al. 2016). Omega-3 PUFA, such as EPA and DHA (Fig.4.14) are given to
225 farmed salmon as a feed additive, and the enormous global demand for salmon has led to a
226 shortage of lipid supply. Consequently, over the course of a decade, a halving of EPA and
227 DHA levels in farmed fish has been reported (Sprague et al. 2016). Interestingly, genomic
228 analyses of diverse bacterial lineages have found a range of PUFA-like genes widespread
229 amongst bacteria not known to produce PUFA, suggesting that HGT events may have
230 contributed to their dissemination (Shulse & Allen 2011). They include some Actinobacterial 194
231 genera: Rhodococcus, Frankia and Streptomyces; and here too, PUFA-like domains were
232 identified in Streptomyces INR7 (Appendix Table A3.4), situated within uncharacterised
233 PKS BGCs.
234
235 5.2.4 Antarctic soil bacteria contain an abundance of uncharacterised biosynthetic
236 domains
237 The high level of novelty in gene sequences and clusters revealed in Antarctic bacteria here
238 indicate that eastern Antarctic desert soils are exciting targets for novel NP bioprospecting.
239 Overall, ~89% of all PKS and NRPS domain sequences from Chapters 2 and 4 were novel
240 (Sections 2.3.7 & 4.3.7). While this prevented final compound predictions, the inferred
241 functional subtypes predominantly encoded for polyene macrolides (e.g. nystatin),
242 macrocyclic lactones (e.g. avermectin, epothilone), lipopeptides (e.g. syringomycin) and
243 enediynes (e.g. C-1027) (Figs. 2.7, 2.8, 4.15, 4.16) (Ziemert et al. 2012, Rausch et al. 2007).
244 Prediction of biosynthetic pathway end-products is facilitated by a number of available
245 bioinformatics tools, such as antiSMASH and NaPDoS (Blin et al. 2017b, Ziemert et al.
246 2012). For the well-studied mega-synthases such as PKS and NRPS, the task is aided by
247 cluster characteristics including high conservation, modularity, and the tendency of domains
248 to cluster phylogenetically by functional subtype (Rausch et al. 2007, Ziemert et al. 2012,
249 Roongsawang et al. 2011, Medema et al. 2014). The NaPDoS database, while not all-
250 encompassing, is curated to contain representatives from all major classes of Type I and II
251 PKS KS domains and NRPS C domains (Ziemert et al. 2012).
252
195
253 5.2.5 Eukaryotic cells are targeted by many of the predicted biosynthetic pathways
254 In the eastern Antarctic desert soils analysed here, prokaryotes vastly outnumber eukaryotes,
255 with fungal diversity in particular being surprisingly low (Section 1.7) (Zhang et al. 2019, Ji
256 et al. 2017, Ferrari et al. 2015). Intriguingly, in both amplicon sequencing and BGC analysis
257 (Chapters 2 and 4), the majority of predicted biosynthetic pathways encoded chemical classes
258 with activity against eukaryotic cells; namely antifungals (candicidin, antimycin, nystatin),
259 antitumour compounds (tambromycin, C-1027, epothilone, bleomycin, actinomycin),
260 antiparasitics (avermectin, cyclomarin) and biosurfactants (syringomycin, iturin, SapB,
261 surfactin) (Figs. 2.7, 2.8, 4.15, 4.16, Appendix Table A3.2) (Ziemert et al. 2012, Rausch et
262 al. 2007). In Chapter 2 these pathways comprised 63% of the domain sequences matching
263 known BGCs (Appendix Table A1.1 & A1.2), and in Chapter 4, 64% of BGCs with similarity
264 to known clusters (Appendix Table A3.2). Furthermore, in phylogenetic analysis of C and
265 KS domains in Chapter 4, 84% of domains aligned with eukaryotic-acting compound
266 pathways (Appendix Table A3.3-A3.6).
267
268 In bioactivity assays, antifungal activity against Candida albicans was confirmed for
269 Streptomyces isolates NBH77, which was predicted to harbour the candicidin BGC (Fig.
270 4.10); and also in Streptomyces INR7 (Fig. 4.7), which carried a BGC similar to that for
271 streptothricin, whose derivatives such as streptothricin E, have demonstrated activity against
272 Candida (Gan et al. 2011). Further work is required to confirm the isolate's biosynthesis of
273 these compounds.
274
275 Interactions between resident microbiota and environmental factors are unquestionably
276 complex. Fungi, despite showing high tolerance to low temperatures and dryness (Sun et al.
196
277 2017), are less successful in arid polar deserts (Zhang et al. 2019, Ji et al. 2017, Ferrari et al.
278 2015). This has primarily been attributed to differences in carbon cycling between the
279 kingdoms, where fungi are suggested to be the dominant decomposers of organic litter and
280 more commonly form symbiotic relationships with plants than bacteria do (Sun et al. 2017,
281 Bahram et al. 2018). As plants are virtually non-existent in eastern Antarctica, and organic
282 carbon is extremely low, edaphic factors may explain the fungal minority. Why then, would
283 bacteria need to harbour such a capacity for fungi-targeted chemical warfare? The results of
284 this research suggest that the production of a variety of eukaryotic-acting secondary
285 metabolites may contribute to their abilities to out-compete fungi and other micro-eukarya
286 present in these soils. Investigation of this hypothesis could begin with bioactivity assays
287 determining the susceptibility of indigenous fungi to compounds produced by these bacterial
288 isolates. Further, soil microcosms could be used to assess the effects of competition within
289 the Antarctic soil communities, via complete removal of bacteria and measurement of
290 changes to eukaryotic abundance and diversity (Hicks et al. 2019).
291
292 5.2.6 Long-read sequencing for natural product domain amplicon and genomic BGC
293 analysis
294 Long-read sequencing technology was employed here for the first time to survey soil
295 microbiomes for NP domain tag-sequences (Chapter 2). The approach successfully captured
296 full-length amplicons of PKS and NRPS domain fragments targeted with the primer sets
297 employed here (~700 and ~1200 bp). These lengths are currently unachievable with Illumina
298 MiSeq (Goodwin et al. 2016). Amplicon sequencing has previously been employed to survey
299 natural product genes across diverse microbiomes using SGS approaches (Woodhouse et al.
300 2013, Charlop-Powers et al. 2014, Charlop-Powers et al. 2016, Katz et al. 2016, Lemetre et
197
301 al. 2017). As with all techniques, amplicon sequencing is not without bias (Krehenwinkel et
302 al. 2017). Minimisation strategies can be incorporated into study design, and here we
303 included the use of degenerate primers and high-fidelity polymerase (Stasik et al. 2018,
304 Krehenwinkel et al. 2017). Importantly, validation was given to the phylogenetic and
305 functional inferences made through amplicon analysis in Chapter 2, through genomic BGC
306 domain analysis in Chapter 4. Specifically, similar levels of novelty were revealed, in
307 addition to the abundance of particular functional subtypes.
308
309 PCR-independent long-read metagenomic approaches are becoming increasingly accessible,
310 and continual improvements to the quality of ultra-long-reads produced by platforms such as
311 ONT (> 1Mb) will undoubtably lead to major advances in microbial diversity analyses in the
312 future. We conclude that our sequencing approach was an advance for the analysis of large
313 gene fragments such as PKS and NRPS, enabling direct comparison of nucleotide and
314 translated protein sequences (Payne et al. 2018).
315
316 5.3 FUTURE DIRECTIONS
317 Antarctic bacteria harbour a wealth of poorly characterised biosynthetic pathways, with
318 potential for production of medically and industrially valuable novel compounds related to
319 antifungal macrolides, antitumour agents, biosurfactant peptides, siderophores, carotenoids
320 and PUFAs. Multiple paths of further investigation can stem from these findings.
321
322 The first priority could be given to the proverbial 'lowest hanging fruit', such as the putative
323 PUFA synthase in Azospirillum. Here, fatty acid profiling could be performed by GC–MS
324 analysis of the fatty acid methyl esters (FAMEs) to confirm production of EPA and DHA 198
325 (Gemperlein et al. 2014). If demonstrated, this isolate would represent the first known fast-
326 growing, sustainable bacterial sources of these in-demand lipids.
327
328 For bioactive NPs with medical value, four Actinomycetales isolates showed particular
329 promise for future investigation: Streptomyces spp. INR7, NBH77 and NBSH44, and
330 Kribbella SPB151, all of which were revealed to harbour uncharacterised or low-similarity
331 Type I PKS- and NRPS-containing BGCs (Appendix Table A3.2). The Kribbella and
332 Streptomyces NBSH44 isolates contained a greater proportion of uncharacterised and low
333 gene similarity clusters in comparison to the other Streptomyces: INR7 and NBH77 (Section
334 4.3.6.6); therefore these isolates should be prioritised for further work. Additionally, the
335 Gram-negative antibacterial activity displayed by Streptomyces isolate INR7 is important in
336 regards to the global antimicrobial resistance crisis (WHO, 2014), and encourages further
337 activity assays incorporating resistant strains. Here, the next steps would be fermentation,
338 solvent extraction and compound characterisation using LC-MS and NMR, followed by
339 activity assays using both crude and purified extracts (Ling et al. 2015). Notably, this research
340 has revealed gene similarities to > 20 different antitumour-encoding clusters in bacteria from
341 Antarctica (Appendix Tables A1.1, A1.2, A3.2). An exciting opportunity for further work
342 therefore lies in bioassays against various tumour cell lines, to inform on the cytotoxic
343 capabilities of the isolates (Olano et al. 2009). Further, because many BGCs may remain
344 silent under laboratory conditions, and cryptic pathways may prove the most worthy targets
345 for novel bioactive production, heterologous expression may be an attractive approach for
346 awakening and targeting specific uncharacterised BGCs (Nah et al. 2017).
347
199
348 Overall, the findings of this research indicate that the desert soils of eastern Antarctica are
349 indeed excellent candidates for novel natural product bioprospecting, and offer insight into
350 the functional and ecological relevance of secondary metabolites regarding both competition
351 between microbiota, as well as strategies for survival in soils where resources are highly
352 limited.
200
353
201
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APPENDICES
APPENDIX 1 (CHAPTER 2)
Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms.
PKS Closest sequence match Sequ. Sequ. ASV# %G+C Accession (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 1 68 CP003219.1 Streptomyces cattleya_type I PKS 68 SDI33674.1 Alloactinosynnema album_PKS 62 2 66 CP003219.1 Streptomyces cattleya_type I PKS 73 SDI33633.1 Alloactinosynnema album_PKS 31 3 66 JF970188.1 Amycolatopsis 68 AFI57005.1 Amycolatopsis 35 orientalis_quartromicin_PKS orientalis_quartromycin PKS 4 68 CP014060.1 Achromobacter 84 SEO68750.1 Amycolatopsis 65 xylosoxidans_hypothetical protein saalfeldensis_oxidoreductase 5 73 AJ278573.1 Streptomyces 70 SEO77265.1 Amycolatopsis saalfeldensis_polyene 50 natalensis_pimaricin_PKS macrolide PKS 6 67 DQ897667.1 Polyangium cellulosum_ambruticin 69 KJC40904.1 Bradyrhizobium sp._PKS 78 AmbD_PKS 7 69 CP002830.1 Myxococcus fulvus_uncharacterised 66 CUS36179.1 Candidatus Nitrospira nitrosa_PKS 51
8 60 CP012851.1 Persicobacter sp._uncharacterised 68 SDM25909.1 Catalinimonas alkaloidigena_PKS 66 9 66 CP001700.1 Catenulispora acidiphila_PKS 69 WP_015795562.1 Catenulispora acidiphila_PKS 63 10 68 CP006850.1 Nocardia nova_nitroreductase 79 KRT62639.1 Chloroflexi_malate synthase 58 11 68 CP006850.1 Nocardia nova_nitroreductase 79 OGO54799.1 Chloroflexi_malate synthase 58 12 69 CP003364.1 Singulisphaera acidiphila_PKS 65 WP_016872856.1 Chlorogloeopsis fritschii_PKS 53
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Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. Closest sequence match sim. sim. ASV# %G+C Accession (nucleotide) (%) Accession Closest sequence match (protein) (%) 13 67 AP010968.1 Kitasatospora setae_PKS 68 WP_052387098.1 Dactylosporangium aurantiacum_PKS 53
14 62 CP003382.1 Deinococcus 64 WP_029477642.1 Deinococcus 49 peraridilitoris_phosphodiesterase- frigens_metallophosphatase/nucleotidase like hydrolase 15 65 CP002047.1 Streptomyces 70 WP_007513999.1 Frankia sp._phosphatase 35 bingchenggensis_phosphatase 16 67 CP007128.1 Gemmatirosa 70 ODT02251.1 Gemmatimonadetes_uncharacterised 62 kalamazoonesis_hypothetical protein
17 68 AY596297.1 Haloarcula marismortui_dTDP- 67 WP_070364766.1 Haloarchaeon_GDP-mannose 4,6 DH 35 glucose-4,6-DH 18 68 CP011564.1 Halanaeroarchaeum 70 WP_011223374.1 Haloarcula marismortui_GDP-mannose 53 sulfurireducens_dTDP-glucose-4,6- 4,6 DH DH 19 67 CP011564.1 Halanaeroarchaeum 70 WP_066414393.1 Halorubrum sp._GDP-mannose 4,6 DH 43 sulfurireducens_dTDP-glucose-4,6- DH 20 68 CP011564.1 Halanaeroarchaeum 71 WP_049982662.1 Halorubrum sp._GDP-mannose 4,6 DH 62 sulfurireducens_dTDP-glucose-4,6- DH 21 67 CP010849.1 Streptomyces 74 WP_062432670.1 Herbidospora daliensis_PKS 51 cyaneogriseus_hypothetical protein 22 75 KP742963.1 Streptomyces sp._heronamide PKS 69 WP_062342606.1 Herbidospora sakaeratensis_PKS 50 241
Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. Closest sequence match sim. sim. ASV# %G+C Accession (nucleotide) (%) Accession Closest sequence match (protein) (%) 23 72 CP003219.1 Streptomyces cattleya_type I 67 WP_062352166.1 Herbidospora yilanensis_PKS 62 PKS 24 73 AJ278573.1 Streptomyces 70 SES41714.1 Lentzea albida_PKS 37 natalensis_pimaricin PKS 25 69 CP012590.1 Actinomyces sp_glycosyl 79 WP_052464447.1 Methyloceanibacter caenitepidi_DNA 42 transferase primase/polymerase 26 74 CP000850.1 Salinispora arenicola_beta- 73 SCG19052.1 Micromonospora echinofusca_6- 67 ketoacyl synthase methylsalicylic acid synthase 27 64 CP002047.1 Streptomyces 70 WP_014740742.1 Modestobacter marinus_phosphatase 45 bingchenggensis_phosphatase 28 68 CP002830.1 Myxococcus fulvus_type I PKS 66 WP_070392794.1 Moorea producens_PKS 52 29 68 CP002830.1 Myxococcus fulvus_type I PKS 66 SDE49951.1 Myxococcus virescens_PKS 52 30 64 CP003219.1 Streptomyces 70 SDJ25484.1 Nonomuraea 41 cattleya_phosphatase maritima_endo/exonuclease/phosphatase
31 61 AB568601.1 Streptomyces sp._reveromycin 73 WP_012409595.1 Nostoc punctiforme_PKS 47 PKS 32 66 CP002047.1 Streptomyces 71 SES38665.1 Phycicoccus cremeus_phosphatase 70 bingchenggensis_phosphatase 33 70 CP011868.1 Pseudonocardia 76 WP_060714094.1 Pseudonocardia sp._transposase 69 sp._hypothetical protein 34 75 KP742963.1 Streptomyces sp._heronamide 71 WP_037075676.1 Pseudonocardia spinosispora_PKS 69 PKS 35 74 KP742963.1 Streptomyces sp._heronamide 70 WP_051341809.1 Pseudonocardia spinosispora_PKS 53 PKS 242
Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 36 73 AP010968.1 Kitasatospora setae_modular PKS 70 WP_033442118.1 Saccharothrix sp._PKS 60 37 68 CP003219.1 Streptomyces cattleya_type I PKS 70 WP_051772302.1 Saccharothrix sp._PKS 39 38 67 CP001700.1 Catenulispora acidiphila_acyl 70 WP_033438928.1 Saccharothrix sp._PKS 45 transferase 39 73 CP000850.1 Salinispora arenicola_beta-ketoacyl 73 WP_029537616.1 Salinispora arenicola_PKS 67 synthase 40 72 CP001804.1 Haliangium ochraceum_6- 71 BAG69052.1 Sorangium cellulosum_PKS 57 deoxyerythronolide-B synthase 41 67 CP003969.1 Sorangium cellulosum_hypothetical 69 WP_013376005.1 Stigmatella aurantiaca_PKS 52 protein 42 73 AJ871581.1 Streptomyces achromogenes_rubradirin 81 CAI94682.1 Streptomyces achromogenes_PKS 73 gene cluster
43 72 GQ981380.1 Sorangium cellulosum _thuggacin PKS 71 WP_040255771.1 Streptomyces albus_PKS 49
44 74 AF324838.2 Streptomyces 72 AEU17899.1 Streptomyces 64 antibioticus_simocyclinone PKS antibioticus_simocyclinone PKS 45 67 CP002047.1 Streptomyces 69 WP_033355444.1 Streptomyces 40 bingchenggensi_phosphatase aureofaciens_phosphatase 46 72 CP006871.1 Streptomyces albulus_PKS 70 WP_014174583.1 Streptomyces 58 bingchenggensis_PKS 47 73 CP003987.1 Streptomyces sp._Erythronolide 69 WP_053927100.1 Streptomyces 38 synthase chattanoogensis_PKS 48 64 CP005080.1 Streptomyces fulvissimus_epoxide 80 WP_060893441.1 Streptomyces 82 hydrolase europaeiscabiei_epoxide hydrolase
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Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Closest sequence match Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession (protein) sim. (%) 49 75 AP010968.1 Kitasatospora setae_PKS 71 WP_051727297.1 Streptomyces griseus_PKS 98 50 65 CP002047.1 Streptomyces 71 WP_009716296.1 Streptomyces 41 bingchenggensis_phosphatase himastatinicus_phosphatase 51 70 LN997842.1 Streptomyces 68 AAQ20787.1 Streptomyces hygroscopicus_PKS 54 reticuli_phenolphthiocerol PKS 52 73 CP006567.1 Streptomyces 91 AAQ20780.1 Streptomyces hygroscopicus_PKS 59 rapamycinicus_hypothetical protein 53 66 KT209587.1 Micromonospora sp._lobosamide PKS 69 WP_060954383.1 Streptomyces hygroscopicus_PKS 58
54 73 AP010968.1 Kitasatospora setae_modular PKS 70 CDR03059.1 Streptomyces iranensis_PKS 57 55 65 CP003219.1 Streptomyces cattleya_phosphatase 70 WP_046927220.1 Streptomyces 78 lydicus_phosphatase 56 69 CP010519.1 Streptomyces albus_modular PKS 71 SED16442.1 Streptomyces 39 melanosporofaciens_PKS 57 74 AJ278573.1 Streptomyces natalensis_pimaricin 71 WP_067359169.1 Streptomyces noursei_PKS 62 PKS 58 69 FJ545274.1 Streptomyces 71 WP_069777516.1 Streptomyces puniciscabiei_PKS 59 antibioticus_indanomycin PKS 59 69 JX504844.1 Streptomyces sp._hygrocin PKS 69 WP_069776473.1 Streptomyces puniciscabiei_PKS 42 60 72 CP006567.1 Streptomyces 94 AGP59275.1 Streptomyces 94 rapamycinicus_hypothetical protein rapamycinicus_PKS 61 72 CP006567.1 Streptomyces 94 AGP59291.1 Streptomyces 78 rapamycinicus_hypothetical protein rapamycinicus_PKS 62 70 CP003987.1 Streptomyces sp._modular PKS 71 KJS52374.1 Streptomyces 65 rubellomurinus_PKS
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Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. sim. Closest sequence match Sequ. sim. ASV# %G+C Accession Closest sequence match (nucleotide) (%) Accession (protein) (%) 63 74 AJ278573.1 Streptomyces natalensis_pimaricin PKS 73 WP_051746472.1 Streptomyces 72 scopuliridis_PKS 64 73 AB469193.1 Streptomyces graminofaciens_FD-891 69 SCK25970.1 Streptomyces sp._PKS 59 PKS 65 72 CP011799.1 Streptomyces sp._hypothetical protein 71 KJY24546.1 Streptomyces sp._PKS 61
66 74 AF324838.2 Streptomyces antibioticus_simocyclinone 71 KOV34270.1 Streptomyces sp._PKS 41 PKS 67 73 AJ278573.1 Streptomyces natalensis_pimaricin PKS 71 WP_064273739.1 Streptomyces sp._PKS 39
68 72 LK022848.1 Streptomyces iranensis_type I PKS 72 ACL97724.1 Streptomyces sp._PKS 61 69 66 KT209587.1 Micromonospora sp._lobosamide PKS 69 WP_064455734.1 Streptomyces sp._PKS 58
70 73 HE648167.1 Streptomyces hygroscopicus_antifungal L- 70 WP_051906038.1 Streptomyces sp._PKS 61 155,175 PKS
71 74 AF324838.2 Streptomyces antibioticus_simocyclinone 71 WP_039633289.1 Streptomyces sp._PKS 36 PKS 72 69 CP010519.1 Streptomyces albus_modular PKS 70 WP_007269166.1 Streptomyces sp._PKS 35 73 72 HE648167.1 Streptomyces hygroscopicus_antifungal L- 72 WP_052744222.1 Streptomyces sp._PKS 62 155,175 PKS
74 68 CP010519.1 Streptomyces albus_modular PKS 71 WP_069860838.1 Streptomyces sp._PKS 36 75 69 CP010519.1 Streptomyces albus_modular PKS 71 WP_018840958.1 Streptomyces sp._PKS 55
245
Appendix Table A1.1 Taxonomic classification of PKS KS/AT domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. sim. Closest sequence match Sequ. sim. ASV# %G+C Accession Closest sequence match (nucleotide) (%) Accession (protein) (%) 76 75 CP011799.1 Streptomyces sp._hypothetical protein 70 WP_037770990.1 Streptomyces sp._PKS 57
77 71 AB284188.1 Streptomyces lasaliensis_modular PKS 73 WP_062204312.1 Streptomyces sp._PKS 79
78 71 KT209587.1 Micromonospora sp._lobosamide PKS 70 WP_046501326.1 Streptomyces sp._PKS 53
79 66 CP002047.1 Streptomyces bingchenggensis_type I 69 WP_051807727.1 Streptomyces sp._PKS 64 PKS 80 73 HE648167.1 Streptomyces hygroscopicus_antifungal 71 KJK33802.1 Streptomyces 61 L-155,175 PKS variegatus_PKS
81 70 CP001700.1 Catenulispora acidiphila_acyl transferase 74 AEM86155.1 Streptomyces 62 violaceusniger_PKS 82 67 AP010968.1 Kitasatospora setae_modular PKS 68 WP_051787920.1 Streptomyces 56 wedmorensis_PKS
246
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms.
NRPS Closest sequence match Sequ. Sequ. ASV# %G+C Accession (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 1 68 LN831029.1 Achromobacter xylosoxidans_NRPS 74 EFV85595.1 Achromobacter xylosoxidans_NRPS 66 2 69 LN831029.1 Achromobacter xylosoxidans_NRPS 75 EFV85595.1 Achromobacter xylosoxidans_NRPS 65 3 70 KR062371.1 Streptomyces sp._haoxinamide 68 KHD09577.1 Actinokineospora inagensis_NRPS 64 NRPS 4 72 CP001630.1 Actinosynnema mirum_NRPS 79 WP_006929441.1 Actinosynnema mirum_NRPS 76 5 70 LT629701.1 Allokutzneria albata_NRPS 74 OEU80475.1 Allokutzneria albata_NRPS 53 6 70 CP011799.1 Streptomyces sp._siderophore NRPS 73 CDG85848.1 Amycolatopsis halophila_NRPS 66 7 68 CP002600.1 Burkholderia gladioli_NRPS 70 WP_045823724.1 Amycolatopsis mediterranei_NRPS 41 8 72 CP020039.1 Streptomyces sp._NRPS 71 SEU13784.1 Amycolatopsis taiwanensis_NRPS 75 9 63 CP004370.1 Streptomyces albus_PKS/NRPS 71 SDS85241.1 Anabaena sp._NRPS 60 10 73 KF170355.1 Streptomyces 67 ABA23702.1 Anabaena variabilis_NRPS 54 ansochromogenes_nikkomycin NRPS 11 71 CP013220.1 Streptomyces hygroscopicus_acyl- 72 KRT78094.1 Armatimonadetes sp._NRPS 64 CoA synthetase 12 68 CP003347.1 Mycobacterium sp._gramicidin 96 WP_052659492.1 Bacillus alveayuensis_ATP- 34 NRPS dependent acyl-CoA ligase 13 69 CP002994.1 Streptomyces violaceusniger_NRPS 70 AEB21504.1 Bacillus amyloliquefaciens_iturin 49 NRPS 14 74 CP012109.1 Myxococcus hansupus_siderophore 76 EEK70326.1 Bacillus cereus_hypothetical NRPS 58 NRPS 15 72 CP001804.1 Haliangium ochraceum_NRPS 77 KOA72794.1 Bacillus 53 stratosphericus_hypothetical NRPS
247
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Closest sequence match Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession (protein) sim. (%) 16 72 AP010968.1 Kitasatospora setae_hybrid NRPS/PKS 69 WP_008971883.1 Bradyrhizobium sp._hybrid 59 NRPS/PKS 17 69 KX707969.1 Streptomyces sp._NRPS 68 WP_051188157.1 Brevibacillus thermoruber_NRPS 51 18 72 CP011509.1 Archangium gephyra_NRPS 70 WP_051188157.1 Brevibacillus thermoruber_NRPS 55 19 68 CP006003.1 Myxococcus fulvus_hypothetical protein 69 WP_071803044.1 Brevibacillus thermoruber_NRPS 54 20 70 LT607753.1 Micromonospora coxensis_NRPS 66 WP_051188157.1 Brevibacillus thermoruber_NRPS 50 21 68 CP002399.1 Micromonospora sp._NRPS 73 KIX33763.1 Burkholderia 62 pseudomallei_NRPS 22 74 CP002162.1 Micromonospora aurantiaca_NRPS 71 KIX33763.1 Burkholderia 63 pseudomallei_NRPS 23 71 CP010415.1 Azotobacter chroococcum_NRPS 71 KIX33763.1 Burkholderia 60 pseudomallei_NRPS 24 70 LT594324.1 Micromonospora 70 WP_052485744.1 Burkholderia sp._NRPS 56 narathiwatensis_NRPS 25 74 CP009322.1 Burkholderia gladioloi_D-alanine-poly 70 WP_060122821.1 Burkholderia 52 (phosphoribitol) ligase vietnamiensis_hybrid NRPS/PKS 26 75 FP885907.1 Ralstonia solanacearum_Glutamate 70 WP_073619435.1 Caldithrix abyssi_NRPS 47 racemase 27 65 CP015098.1 Streptomyces sp._hypothetical protein 71 WP_052754440.1 Calothrix sp._NRPS 77 28 72 CP013446.1 Burkholderia ubonensis_NRPS 75 ETX02901.1 Calothrix sp._NRPS 64 29 74 LT607411.1 Micromonospora viridifaciens_NRPS 68 WP_030164825.1 Candidatus Entotheonella 58 sp._hypothetical NRPS 30 66 CP003969.1 Sorangium cellulosum_hypothetical 75 OAD22016.1 Candidatus Thiomargarita 61 protein nelsonii_hypothetical NRPS 31 74 KP006601.1 Eleftheria terrae_Teixobactin gene 69 OAD22016.1 Candidatus Thiomargarita 53 cluster nelsonii_hypothetical NRPS
248
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Closest sequence match Sequ. Sequ. ASV# %G+C Accession (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 32 67 LT559118.1 Nonomuraea sp._NRPS/PKS 74 OAD22016.1 Candidatus Thiomargarita 57 nelsonii_NRPS 33 71 CP020567.1 Streptomyces aureofaciens_NRPS 70 WP_050433124.1 Candidatus Thiomargarita 54 nelsonii_NRPS 34 64 CP006871.1 Streptomyces albulus_thioester 70 WP_050433124.1 Chondromyces crocatus_hybrid 59 reductase NRPS/PKS 35 72 CP007130.1 Gemmatirosa kalamazoonesis_NRPS 70 WP_050433036.1 Chondromyces crocatus_hybrid 61 NRPS/PKS 36 69 CP019779.1 Streptomyces sp._hypothetical protein 71 WP_055409225.1 Chondromyces crocatus_NRPS 78 37 66 CP003389.1 Corallococcus coralloides_hybrid 73 WP_051188157.1 Corallococcus coralloides_hybrid 67 NRPS/PKS NRPS/PKS 38 70 AM420293.1 Saccharopolyspora erythraea_NRPS 80 WP_015204113.1 Couchioplanes caeruleus_NRPS 79 39 66 LN831790.1 Streptomyces 73 AHV79174.1 Crinalium epipsammum_NRPS 51 leeuwenhoekii_gramacidin NRPS 40 74 CP016793.1 Lentzea guizhouensis_hypothetical 72 AIW82284.1 Cyanobacteria sp._hypothetical 60 protein NRPS 41 71 KF170355.1 Streptomyces 67 WP_071904628.1 Cylindrospermum 38 ansochromogenes_nikkomycin NRPS alatosporum_NRPS 42 68 CP002047.1 Streptomyces binchenggensis_NRPS 71 OEU84093.1 Cystobacter ferrugineus_hypothetical 62 hybrid NRPS/PKS 43 66 CP001700.1 Catenulispora acidiphila_NRPS 69 OEU84093.1 Desulfobacterales sp._hypothetical 65 NRPS 44 71 HE971709.1 Streptomyces davawensis_tyrocidine 76 OEU84093.1 Desulfobacterales sp._hypothetical 54 NRPS NRPS 249
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 45 70 KT362217.1 Streptomyces calvus_WS9326 NRPS 73 OEU84093.1 Desulfobacterales sp._hypothetical 53 NRPS 46 72 KP756960.1 Streptomyces canus_telomycin NRPS 73 OEU84093.1 Desulfobacterales sp._hypothetical 50 NRPS 47 69 AB698636.1 Streptomyces turgidscabies_hypothetical 73 OEU84093.1 Desulfobacterales sp._hypothetical 59 PKS/NRPS NRPS 48 69 HE575208.1 Streptomyces sp._collismycin A NRPS 85 OEU84093.1 Desulfobacterales sp._hypothetical 53 NRPS 49 72 KF264564.1 Catenulispora acidiphila_NRPS 68 WP_030430619.1 Desulfobacterales sp._hypothetical 55 NRPS 50 71 CP011340.1 Streptomyces 75 OEU84093.1 Desulfobacterales sp._hypothetical 72 pristinaespiralis_pristinamycin NRPS NRPS 51 66 LT607750.1 Micromonospora echinofusca_NRPS 75 SDJ90284.1 Desulfobacterales sp._hypothetical 49 NRPS 52 68 CP010849.1 Streptomyces 70 WP_017308482.1 Dyella jiangningensis_NRPS 39 cyanoeogriseus_hypothetical protein 53 71 KF264564.1 Streptomyces purpeofuscus_NRPS 68 WP_026723921.1 Fischerella sp._NRPS 44 54 67 KC876490.1 Streptomyces sp._NRPS 67 WP_005549942.1 Fischerella sp._NRPS 55 55 71 CP011667.1 Streptomyces sp._hypothetical protein 78 WP_053458069.1 Fischerella sp._NRPS 52 56 67 LT629775.1 Streptomyces sp._NRPS 78 WP_062656581.1 Frankia sp._NRPS 84 57 69 CP004025.1 Myxococcus stipitatus_NRPS 74 ABX04518.1 Hapalosiphon sp._NRPS 71 58 65 JN596952.1 Lysobacter enzymogenes_WAPS NRPS 71 ABX04517.1 Herpetosiphon aurantiacus_NRPS 58 59 71 CP011522.1 Streptomyces sp._thioester reductase 84 WP_052750824.1 Herpetosiphon aurantiacus_NRPS 46
250
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Closest sequence match Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession (protein) sim. (%) 60 67 CP006996.1 Rhodococcus 73 WP_051399675.1 Hyphomicrobium sp._NRPS 65 pyridinivorans_hypothetical protein 61 61 CP016559.1 Streptomyces clavuligerus_hypothetical 71 WP_054291157.1 Janthinobacterium 64 protein agaricidamnosum_NRPS 62 73 LN877229.1 Kibdelosporangium sp._siderophore 80 WP_052478487.1 Kibdelosporangium 82 NRPS phytohabitans_NRPS 63 75 CP012752.1 Kibdelosporangium 82 WP_020387122.1 Kibdelosporangium sp._NRPS 84 phytohabitans_NRPS 64 73 KX708190.1 Streptomyces sp._NRPS 78 WP_020384980.1 Kribbella 74 catacumbae_hypothetical NRPS 65 66 AB432565.1 Streptomyces abikoensis_NRPS 74 WP_020384980.1 Kribbella catacumbae_NRPS 89 66 70 KF170330.1 Streptomyces 73 AHH97300.1 Kribbella catacumbae_NRPS 88 ansochromogenes_nikkomycin NRPS 67 67 CP007155.1 Kutzneria albida_NRPS 71 AKU98435.1 Kutzneria albida_NRPS 63 68 71 CP012333.1 Labilithrix luteola_NRPS/PKS 82 WP_068295934.1 Labilithrix luteola_hybrid 85 NRPS/PKS 69 60 CP011664.1 Streptomyces sp._hypothetical protein 67 SDJ42953.1 Labrys sp._hypothetical NRPS 52 70 74 LN850107.1 Alloactinosynnema sp._siderophore 75 WP_013226636.1 Lentzea violacea_NRPS 78 NRPS 71 73 CP013141.1 Lysobacter antibioticus_NRPS 72 WP_057917623.1 Lysobacter antibioticus_NRPS 57 72 69 FP885896.1 Ralstonia solanacearum_NRPS/PKS 72 WP_057917623.1 Lysobacter antibioticus_NRPS 58 73 71 AL646053.1 Ralstonia solanacearum_NRPS 71 WP_057917627.1 Lysobacter antibioticus_NRPS 66 74 68 CP014517.1 Variovorax sp._thioester reductase 71 WP_052103331.1 Lysobacter concretionis_hybrid 79 NRPS/PKS
251
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 75 73 CP008884.1 Dyella japonica_thioester reductase 70 WP_052103331.1 Lysobacter concretionis_hybrid 67 NRPS/PKS 76 70 CP006259.1 Streptomyces collinus_NRPS 72 WP_055900042.1 Lysobacter sp._hybrid NRPS/PKS 70 77 71 CP011371.1 Polyangium 82 WP_052197850.1 Methylibium sp._NRPS/PKS 85 brachysporum_NRPS/PKS 78 64 CP020567.1 Streptomyces aureofaciens_NRPS 74 WP_024968867.1 Microcystis aeruginosa_NRPS 39 79 71 CP007219.1 Amycolatopsis lurida_NRPS 71 ELP56284.1 Microcystis aeruginosa_NRPS 64 80 69 KX707867.1 Streptomyces sp._NRPS 73 SCL14475.1 Micromonospora 51 chaiyaphumensis_NRPS 81 69 KX708344.1 Streptomyces lunaelactis_NRPS 75 WP_027943904.1 Micromonospora nigra_NRPS 62 82 67 AB701616.1 Nocardia brasiliensis_NRPS 75 WP_051385598.1 Multispecies_NRPS 78 83 66 CP012150.1 Mycobacterium goodii_thioester 82 WP_014397014.1 Mycobacterium sp._NRPS 86 reductase 84 72 LT629775.1 Streptomyces sp._NRPS 77 WP_011554413.1 Myxococcus fulvus_NRPS 37 85 70 CP000113.1 Myxococcus xanthus_NRPS 72 ALD82526.1 Myxococcus xanthus_NRPS 63 86 68 KT067736.1 Nannocystis sp._nannocystin NRPS 83 BAT54534.1 Nannocystis sp._NRPS 78 87 69 KX707969.1 Streptomyces sp._NRPS 77 AGQ47107.1 Nostoc sp._NRPS 49 88 73 CP002830 Myxococcus fulvus_NRPS 77 AGQ47107.1 Nostoc sp._NRPS 65 89 73 CP020350.1 Pectobacterium carotovorum_NRPS 77 AGQ47121.1 Nostoc sp._NRPS 72 90 67 CP002271.1 Stigmatella aurantiaca_bacitracin 68 WP_007954891.1 Nostoc sp._NRPS 59 NRPS 91 68 JX827856.1 Myxococcus xanthus_myxochromide 70 AAW55337.1 Pelosinus fermentans_NRPS 56 A NRPS 92 70 CP012159.1 Chondromyces crocatus_hypothetical 74 WP_019506242.1 Pleurocapsa sp._NRPS 66 protein 93 69 CP011341.1 Rhodococcus aetherivorans_NRPS 73 OBQ03914.1 Pleurocapsa sp._NRPS 63 252
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Closest sequence match Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession (protein) sim. (%) 94 65 LT827010.1 Actinoplanes sp._NRPS 72 WP_063613608.1 Pseudomonas chlororaphis_NRPS 60 95 74 CP013380.1 Burkholderia sp._NRPS 75 WP_025130130.1 Pseudomonas marginalis_NRPS 75 96 66 CP009434.1 Burkholderia glumae_D-alanine- 69 ELP95414.1 Pseudomonas sp._NRPS 59 poly(phosphoribitol) ligase 97 71 CP011319.1 Janthinobacterium sp._hypothetical 72 CUV18095.1 Pseudomonas syringae_NRPS 65 protein 98 73 CP013140.1 Lysobacter enzymogenes_gramicidin 75 WP_068427237.1 Ralstonia 67 NRPS solanacearum_hypothetical NRPS 99 70 CP020567.1 Streptomyces aureofaciens_NRPS 71 WP_037252570.1 Rhodococcus 61 kyotonensis_hypothetical NRPS 100 70 HE804045.1 Saccharothrix espanaensis_NRPS 77 WP_072805399.1 Rhodococcus rhodnii_NRPS 79 101 66 CP012150.1 Mycobacterium goodii_thioester 79 WP_054188088.1 Rhodococcus sp.ADH_NRPS 67 reductase 102 73 CP015726.1 Streptomyces sp._NRPS 69 EHK80199.1 Rhodococcus 64 yunnanensis_hypothetical NRPS 103 66 CP013358 Burkholderia oklahomensis_NRPS 67 WP_073629376.1 Saccharomonospora 50 azurea_NRPS 104 68 CP003720.1 Streptomyces hygroscopicus_NRPS 77 WP_015803773.1 Pseudomonas syringae pv. 68 Atrofaciens_NRPS 105 73 JX021290.1 Streptomyces 67 WP_015250181.1 Singulisphaera acidiphila_hybrid 60 melaovinaceus_quinocarcin NRPS NRPS/PKS 106 71 LT607733.1 Micromonospora echinofusca_NRPS 67 WP_020466555.1 Singulisphaera acidiphila_NRPS 63 107 72 CP012382.1 Streptomyces ambofaciens_NRPS 77 WP_061609229.1 Sorangium cellulosum_NRPS 58 108 66 CP014168.1 Sphingomonas panacis_hypothetical 67 WP_066825801.1 Sphingomonas mali_hypothetical 57 protein NRPS 109 69 CP011131.1 Lysobacter gummosus_NRPS 74 WP_052069845.1 Streptacidiphilus albus_NRPS 37 253
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 110 71 CP020039.1 Streptomyces sp._NRPS 70 WP_052853332.1 Streptomyces celluloflavus_NRPS 63 111 71 CP001814.1 Streptosporangium roseum_NRPS 77 WP_053924717.1 Streptomyces 74 chattanoogensis_NRPS 112 72 CP003987.1 Streptomyces sp._NRPS 79 WP_053924774.1 Streptomyces 77 chattanoogensis_NRPS 113 72 AF512431.1 Saccharothrix mutabilis_NRPS 76 SED56229.1 Streptomyces 65 melanosporofaciens_NRPS 114 65 CP012687.1 Ralstonia solanacearum_NRPS 78 SFL32914.1 Streptomyces pini_NRPS 47 115 77 AB432412.1 Streptomyces lilaceus_NRPS 76 SER97618.1 Streptomyces qinglanensis_NRPS 70 116 70 AF329398.1 Streptomyces 83 WP_031224174.1 Streptomyces 77 roseochromogenes_clorobiocin NRPS roseochromogenus_NRPS 117 68 CP0171316.1 Streptomyces 81 WP_048480441.1 Streptomyces roseus_NRPS 82 rubrolavendulae_tyrocidine NRPS 118 66 AJ865878.1 Catenulispora sp._NRPS 72 SCK04959.1 Streptomyces sp. NRPS 71 119 66 CP004025.1 Myxococcus stipitatus_NRPS 68 WP_047471668.1 Streptomyces sp._hybrid 100 NRPS/PKS 120 69 LK022848.1 Streptomyces iranensis_NRPS 71 WP_073815332.1 Streptomyces sp._hypothetical 64 hybrid NRPS/PKS 121 68 AB672910.1 Actinoplanes sp._NRPS 75 WP_069737028.1 Streptomyces sp._hypothetical 65 NRPS 122 71 CP019724.1 Streptomyces pactum_hypothetical 92 KJY48006.1 Streptomyces sp._hypothetical 63 protein NRPS 123 68 KX708474.1 Streptomyces sp._NRPS 79 EYU65217.1 Streptomyces sp._hypothetical 94 NRPS 124 67 LC177441.1 Micromonospora sp._NRPS 76 APD71954.1 Streptomyces sp._NRPS 57
254
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Closest sequence match Sequ. Sequ. ASV# %G+C Accession (nucleotide) sim. (%) Accession Closest sequence match (protein) sim. (%) 125 70 CP003777.1 Amycolatopsis 74 WP_052391239.1 Streptomyces sp._NRPS 67 mediterranei_NRPS 126 69 AM746676.1 Sorangium cellulosum_NRPS 71 APD71723.1 Streptomyces sp._NRPS 55 127 68 CP006272.1 Actinoplanes friuliensis_NRPS 76 APD72147.1 Streptomyces sp._NRPS 77 128 72 KC876465.1 Streptomyces sp._NRPS 76 WP_073751644.1 Streptomyces sp._NRPS 80 129 68 CP016001.1 Burkholderia sp._hypothetical 69 SCF97699.1 Streptomyces sp._NRPS 67 protein 130 67 CP016001.1 Burkholderia sp._hypothetical 69 SCG01944.1 Streptomyces sp._NRPS 68 protein 131 73 KX708474.1 Streptomyces sp._NRPS 76 APD72146.1 Streptomyces sp._NRPS 75 132 64 CP019798.1 Streptomyces sp._hypothetical 71 WP_051806912.1 Streptomyces sp._NRPS 70 protein 133 70 AP017900.1 Norcardia seriolae_NRPS 73 WP_031040675.1 Streptomyces sp._NRPS 46 134 73 CP002593.1 Pseudonocardia 70 WP_030750732.1 Streptomyces sp._NRPS 70 dioxanivorans_NRPS 135 71 CP011499.1 Streptomyces incarnatus 79 WP_030160178.1 Streptomyces sp._NRPS 77 strain_hypothetical protein 136 68 CP002299.1 Frankia sp._NRPS 78 WP_018381666.1 Streptomyces vitaminophilus_NRPS 74 137 71 KX622587.1 Cystibacterineae 68 WP_052888712.1 Thermogemmatispora 61 sp._myxochromide D NRPS carboxidivorans_hypothetical NRPS 138 69 CP001032.1 Opitutus terrae_NRPS 73 WP_050045819.1 Tolypothrix bouteillei_NRPS 60 139 65 CP020567.1 Streptomyces 69 WP_038074856.1 Tolypothrix bouteillei_NRPS 53 aureofaciens_NRPS 140 71 LT607803.1 Variovorax sp._NRPS 72 SCK42146.1 Variovorax sp._NRPS 66
255
Appendix Table A1.2 Taxonomic classification of NRPS AD domain sequences when analysed using both nucleotide BLASTn and translated protein sequence BLASTx algorithms cont.
Sequ. Closest sequence match Sequ. ASV# %G+C Accession Closest sequence match (nucleotide) sim. (%) Accession (protein) sim. (%) 141 68 LT629735.1 Opitutus sp._NRPS 71 SCF02615.1 Williamsia sp._NRPS 76 142 72 CP017311.1 Hydrogenophaga sp._4-hydroxyphenylpyruvate 69 ABD96132.1 Wollea ambigua_NRPS 56 dioxygenase 143 68 KF657738.1 Sorangium cellulosum_microsclerodermin NRPS 71 ABD96132.1 Wollea ambigua_NRPS 56
144 66 AB432571.1 Streptomyces niger_NRPS 78 ABD96132.1 Wollea ambigua_NRPS 47
256
APPENDIX 2 (CHAPTER 3)
A2.1 STOCK SOLUTIONS
RAVAN STOCK SOLUTION WITH TRACE SALTS (1X) Per L Reference
Glucose 5 g Peptone 5 g Yeast extract 5 g Watve 2000; Sodium Acetate 5 g Shirling & Tri-sodium citrate 5 g Gottlieb 1966 Pyruvic acid 2 g Trace salts solution 1 mL
Dissolved in sterile Milli-Q water, 0.22µm filter sterilised. Used at 0.05x concentration for culturing.
SOIL EXTRACT STOCK SOLUTION Per L Reference
Antarctic bulk soil (Casey station 500 g Norman 1958 Test Plot 123567)
Soil combined with dH2O. Autoclaved 121°C for 45 min. Filtered through sterile cotton wool. Centrifuged 1753 x g, 20 min. Filtered through Whatman No. 1 filter paper, autoclaved 121°C for 15 min, sealed and stored 4°C.
TRACE SALTS SOLUTION Per L Reference
FeSO4·7H2O 1 g Shirling & MnCl2·4H20 1 g Gottlieb ZnSO4·7H2O 1 g 1966
Dissolved in 100 mL dH2O. Autoclaved 121°C for 15 min. Added 1 mL/L to RAVAN stock solution (1x).
257
WOLFE'S VITAMIN SOLUTION (10X) Per L Reference
Biotin 20mg Wolin et al. Folic acid 20mg 1963; Pyridoxine hydrochloride 100mg Bakermans Riboflavin 50mg et al. 2014 Thiamine 50mg Nicotinic acid 50mg Pantothenic acid 50mg Vitamin B12 1mg p-aminobenzoic acid 50mg Thioctic acid 50mg
0.22µm filter sterilised. Added to media at 50uL/L after autoclaving and cooling.
A2.2 MEDIA
NUTRIENT AGAR (0.75x) (NA) Per L Reference
Nutrient Broth 9.75 g Lapage et (Oxoid) al. 1970 Agar 15 g
Dissolved in dH2O, pH 7, autoclaved 121°C for 15 min.
RAVAN MEDIA (0.05x) Per L Reference (RAVAN/ TSV/ GG)
Gellan gum 7.2 g Watve 2000; MgCl ·7H 0 1 g 2 2 Tanaka et al. Wolfe's Vitamin solution (10x) 50 μL 2014; Wolin et RAVAN stock solution with trace 50 mL al 1963 salts (1x)
Vigorously mixed gellan gum, dH2O water and MgCl2, pH 7. Autoclaved 121°C for 15 min. Added sterile RAVAN stock and vitamin solution once cooled to ~50°C.
258
RAVAN/ TSV BROTH (0.05x) Per L Reference
RAVAN stock solution with trace Watve 50 mL salts (1x) 2000; Wolin et al Wolfe's Vitamin solution (10x) 50 μL 1963
Combined sterile Milli-Q water, RAVAN stock and vitamin solutions.
SOIL EXTRACT GELLAN GUM (SEGG) Per L Reference
Soil extract stock solution 500 mL Norman Gellan gum 7.2 g 1958; Suzuki CaCl ·2H O 1 g 2 2 2001
Vigorously mixed gellan gum, dH2O and CaCl2. Added soil extract, pH 7, adjusted volume to 1 L, autoclaved 121°C for 15 min.
WATER AGAR WITH CYCLOHEXIMIDE (WCX) Per L Reference
CaCl2·2H2O 1 g Agar 15 g Shimkets Cycloheximide* (0.25 200 et al. 2006 mg/mL) mL
Dissolved CaCl2 and agar in dH2O, pH 7, autoclaved 121°C for 15 min. Cooled to ~50°C, added sterile cycloheximide. *toxic- prepared in fume hood.
259
APPENDIX 3 (CHAPTER 4)
A3.1 MEDIA
ISP4 (Inorganic Salts-Starch Agar) Per L Reference
Soluble Starch 10g Shirling & K2HPO4 1g Gottlieb MgSO4·7H2O 1g 1966 NaCl 1g (NH4)2SO4 2g CaCO3 2g Agar 20g Trace salts solution 1mL
All ingredients suspended in dH2O, pH 7, heated to boiling and mixed vigorously. Autoclaved 121°C for 15 min. Gently agitated constantly while pouring plates to maintain uniformity.
260
Appendix Table A3.1 Representative reference genomes chosen for mapping to Antarctic bacterial libraries, with corresponding quality measures determined by CheckM.
REFERENCE GENOME QUALITY Total length Cntg Comp. Contam ANTARCTIC ISOLATE Name and Genbank assembly number (Mb) Cntgs L50 (%) (%) Streptomyces NBSH44 Streptomyces bottropensis GCA_000383595.1 9 4 1 100 0.9 Mesorhizobium NBSH29 Mesorhizobium loti GCA_000384055.1 7.5 5 2 99.9 1.2 Hymenobacter NBH84 Hymenobacter swuensis GCA_000576555.1 5.3 4 1 99.9 0.3 Paracoccus NBH48 Paracoccus zeaxanthinifaciens GCA_000420145.1 3 35 5 98.0 0.0 Leifsonia INR9 Leifsonia xyli GCA_000470775.1 2.7 1 1 98.7 0.5 Streptomyces INR7 Streptomyces lavendulae GCA_000715625.1 6.2 2,008 291 97.6 2.0 Kribbella SPB151 Kribbella flavida GCF_000024345.1 7.6 1 1 100 2.9 Geodermatophilaceae NBWT11 Modestobacter marinus GCA_000306785.1 5.6 1 1 100 1.7 Novosphingobium NBM11 Novosphingobium stygium GCA_900102455.1 4.2 27 4 99.8 0.0 Sphingomonas NBWT7 Sphingomonas mucosissima GCA_002197665.1 3.6 16 2 99.2 0.8 Quadrisphaera INWT6 Quadrisphaera sp. GCA_900101335.1 3.2 4 2 99.4 1.1 Streptomyces NBH77 Streptomyces hygroscopicus GCA_001553435.1 10.1 183 28 100 3.5 Azospirillum INR13 Azospirillum lipoferum GCA_000283655.1 6.8 7 2 100 3.1 Pseudarthrobacter NBSH8 Pseudarthrobacter sulfonivorans GCA_001484605.1 5.1 2 1 99.7 0.9 Frigoribacterium NBH87 Frigoribacterium sp. GCA_000878135.1 3.4 136 16 99.0 0.4 Cryobacterium INWT7 Cryobacterium psychrotolerans GCA_900101115.1 3.2 50 9 99.5 0.0
261
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH.
Isolate Cntg Rgn BGC Type From To Most sim cluster Natural Product % of genes sim MIBiG BGC-ID Streptomyces 1 1 NRPS fragment- 286868 348993 Elloramycin polyketide 12 BGC0000219 INR7 Other 2 T3PKS 370550 411608 Alkylresorcinol polyketide 100 BGC0000282 3 Siderophore 447338 459543 Kedarcidin polyketide 1 BGC0000081 4 Melanin 553133 580860 Istamycin saccharide 4 BGC0000700 5 Terpene 584230 604346 Monensin polyketide 5 BGC0000100 6 Terpene 661269 680721 2-methylisoborneol terpene 100 BGC0000658 7 NRPS 728655 778958 Coelichelin NRPS 100 BGC0000325 8 Terpene-Thiopeptide 782973 833447 - - - - 9 Otherks-T1PKS 1099877 1150566 - - - - 10 NRPS 1162303 1228965 Friulimicin NRPS 18 BGC0000354 11 Terpene 1277535 1304219 Hopene terpene 61 BGC0000663 12 T1PKS 1538950 1585195 Herboxidiene polyketide 11 BGC0001065 13 Butyrolactone- 1593272 1639156 RK-682 polyketide 36 BGC0000140 T1PKS 14 Terpene 1646997 1667094 Geosmin other 100 BGC0001181 15 Bacteriocin 1755478 1764508 - - - - 16 NRPS-Other-T1PKS 1873823 1977840 Himastatin NRPS 48 BGC0001117 17 Siderophore-T2PKS 2056759 2127469 Kinamycin polyketide 40 BGC0000236 18 Arylpolyene 2999561 3040700 Svaricin T1PKS-NRPS 12 BGC0001382 19 Bacteriocin-NRPS 3584707 3627541 - - - - 20 Butyrolactone-NRPS 3944934 3985968 Echosides NRPS 11 BGC0000340 fragment 21 Butyrolactone- 4550092 4635880 Jerangolid polyketide 19 BGC0000080 Ladderane- Phosphonate-T1PKS 22 Siderophore 5063109 5074890 Desferrioxamine B other 83 BGC0000940
262
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH cont.
Natural % of genes MIBiG BGC- Isolate Cntg Rgn BGC Type From To Most sim cluster Product sim ID Streptomyces 1 23 Betalactone-NRPS- 6651979 6724583 Chloramphenicol other 11 BGC0000893 INR7 T1PKS 24 T1PKS 6758511 6803061 A54145 NRPS 3 BGC0000291 25 NRPS 6922334 6989329 Tambromycin NRPS 100 BGC0001368 26 Terpene 7117956 7136860 Avermitilol terpene 100 BGC0000683 27 T2PKS 7309518 7382060 Spore pigment polyketide 66 BGC0000271 28 NRPS 7610016 7697431 Streptothricin NRPS 87 BGC0000432 29 Lanthipeptide 7791151 7813450 SapB RiPP 100 BGC0000551 30 NRPS-Terpene 8039841 8120566 Carotenoid terpene 63 BGC0000633 31 Lanthipeptide 8192303 8214990 Venezuelin RiPP 100 BGC0000563 Streptomyces 1 1 Terpene 207550 231831 Isorenieratene terpene 100 BGC0000664 NBSH44 2 Terpene 528348 554307 Hopene terpene 84 BGC0000663 3 Bacteriocin 1153838 1165166 - - - - 4 NRPS-T1PKS 1194207 1238983 Lysolipin polyketide 4 BGC0000242 5 Melanin 1294356 1304841 Melanin other 100 BGC0000911 6 Siderophore 1564457 1577722 - - - - 7 Terpene 2101686 2121630 - - - - 8 Blactam 2236666 2256050 Carbapenem MM other 51 BGC0000842 4550 9 NRPS-T1PKS 2605002 2657055 Enduracidin NRPS 8 BGC0000341 10 Terpene 3301371 3321625 - - - - 11 Betalactone-NRPS 3348642 3401369 Bacillibactin NRPS 15 BGC0000309 12 Butyrolactone 3998044 4008937 Lactonamycin polyketide 3 BGC0000238 13 Blactam 4532324 4555881 Clavulanic acid other 20 BGC0000845 14 NRPS fragment 4623851 4664473 - - - - 15 Siderophore 5061124 5072899 Desferrioxamine B other 80 BGC0000941 16 NRPS-T1PKS 5249900 5297396 Goadsporin RiPP 12 BGC0000565 17 NRPS 5552402 5605402 Mannopeptimycin NRPS 7 BGC0000388
263
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH cont.
Natural % of genes MIBiG BGC- Isolate Cntg Rgn BGC Type From To Most sim cluster Product sim ID Streptomyces 1 18 NRPS-Otherks 5633232 5710664 Clorobiocin other 10 BGC0000832 NBSH44 19 Ectoine 6322570 6332968 Ectoine other 100 BGC0000853 20 Terpene 6771801 6791559 Steffimycin polyketide 16 BGC0000273 21 Lanthipeptide 7111642 7133324 - - - - 22 Lanthipeptide 7218059 7240725 AmfS RiPP 100 BGC0000496 23 Terpene 7297327 7319549 Geosmin other 100 BGC0001181 2 1 NRPS 3 36985 Thiolutin NRPS 36 BGC0001193 2 Butyrolactone-T1PKS 57233 111937 C-1027 hybrid 42 BGC0000965 3 NRPS 132497 177003 Maduropeptin hybrid 16 BGC0001008 Streptomyces 1 1 Terpene 93417 113757 - - - - NBH77 2 NRPS 200003 258132 Pellasoren hybrid 25 BGC0001034 3 NRPS-NRPS fragment- 271098 480551 Candicidin polyketide 100 BGC0000034 T1PKS 4 T3PKS 481279 522376 Herboxidiene polyketide 12 BGC0001065 5 NRPS-T1PKS-Terpene 578919 639733 Isorenieratene terpene 85 BGC0000664 6 Ectoine 1316160 1326558 Ectoine other 100 BGC0000853 7 Siderophore 2186550 2197079 Desferrioxamine other 100 BGC0000941 B 8 NRPS 2847125 2952068 Mannopeptimycin NRPS 7 BGC0000388 9 NRPS 3171323 3232111 Mannopeptimycin NRPS 14 BGC0000388 10 NRPS 3255692 3303369 Scabichelin NRPS 40 BGC0000423 11 Thiopeptide 4261495 4293979 - - - - 12 NRPS 4315553 4359151 Chloramphenicol other 11 BGC0000893 13 Terpene 4779878 4799990 Albaflavenone terpene 100 BGC0000660 14 Terpene 5108889 5129563 Geosmin other 100 BGC0001181 15 Siderophore 5371274 5386444 - - - - 16 Bacteriocin 5711807 5720919 - - - - 17 Bacteriocin 6082308 6092523 - - - -
264
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH cont.
Natural % of genes MIBiG BGC- Isolate Cntg Rgn BGC Type From To Most sim cluster Product sim ID Streptomyces 1 18 Terpene 6152296 6178960 Hopene terpene 76 BGC0000663 NBH77 19 NRPS-T1PKS 6230946 6279565 SGR PTMs hybrid 100 BGC0001043 20 NRPS fragment-T1PKS 6313255 6360133 Daptomycin NRPS 7 BGC0000336 21 NRPS-Otherks- 6540628 6629986 Leinamycin hybrid 16 BGC0001101 TransatPKS 22 Lanthipeptide 6638025 6661426 - - - - Kribbella 1 1 NRPS 572163 689774 Thiocoraline NRPS 34 BGC0000445 SPB151 2 Lanthipeptide 2383021 2406061 - - - - 3 NRPS 2936473 2985624 - - - - 4 Arylpolyene 3263913 3304977 Avilamycin A polyketide 5 BGC0000026 5 Lanthipeptide 3742846 3765809 - - - - 6 NRPS fragment 5299839 5342542 - - - - 7 NRPS 6462521 6542738 Albachelin NRPS 20 BGC0001211 8 T3PKS 6890371 6931417 Alkylresorcinol polyketide 100 BGC0000282 9 NRPS-T1PKS 6952251 7002793 Asukamycin polyketide 3 BGC0000187 10 Siderophore 7727856 7743032 - - - - Mesorhizobium 1 1 Lassopeptide 259537 281875 Salecan saccharide 12 BGC0001380 x2 INR15_SH29 2 T3PKS 2207871 2248941 - - - - 3 Hserlactone 2750054 2768944 - - - - 4 Bacteriocin 3114119 3125006 - - - - 5 NRPS fragment 3219848 3263945 - - - - 6 Terpene 3951461 3972312 - - - - 7 Hserlactone 6604816 6625454 - - - - 2 1 Betalactone 2290826 2323439 - - - - 2 Bacteriocin 2330151 2341071 - - - - 3 Terpene 2592971 2613795 Surfactin NRPS 8 BGC0000433
265
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH cont.
Natural % of genes MIBiG BGC- Isolate Cntg Rgn BGC Type From To Most sim cluster Product sim ID Mesorhizobium x2 3 1 Hserlactone 241308 261973 - - - - INR15_SH29 4 1 Hserlactone 93080 113709 - - - - 2 T3PKS 312831 353910 - - - - Novosphingobium 1 1 Hserlactone- 708438 741477 - - - - NBM11 Lassopeptide 2 Betalactone 800482 826019 - - - - 3 Terpene 893078 916017 Astaxanthin hybrid 75 BGC0001086 dideoxyglycoside 4 NRPS fragment 2406638 2450738 - - - - 5 Terpene 3501987 3526848 Malleobactin NRPS 14 BGC0000386 2 1 Bacteriocin 861044 871880 - - - - Leifsonia INR9 1 1 Betalactone- 224830 266944 Carotenoid terpene 28 BGC0000636 Terpene 2 NRPS fragment 696644 740603 Meilingmycin polyketide 4 BGC0000093 3 Phosphonate 2316328 2357185 Dehydrophos other 11 BGC0000897 4 T3PKS 2902968 2944068 Alkylresorcinol polyketide 100 BGC0000282 5 Bacteriocin 3864567 3874851 - - - - Pseudarthrobacter 1 1 Siderophore 363535 375409 Desferrioxamine other 80 BGC0000941 B NBSH8 2 NRPS fragment 1452793 1495696 Streptomycin saccharide 16 BGC0000717 3 Betalactone 1527654 1552945 - - - - 4 T3PKS 2108289 2149470 - - - - 5 NRPS fragment 3245338 3287863 Antimycin hybrid 20 BGC0000958 Cryobacterium 1 1 T3PKS 38230 79297 Alkylresorcinol polyketide 100 BGC0000282 INWT7 2 Betalactone 1333898 1361237 - - - - 3 NRPS fragment 2169601 2212558 - - - - 4 Terpene 2298568 2317888 - - - - 5 Terpene 3219678 3240619 Carotenoid terpene 50 BGC0000644
266
Appendix Table A3.2 Biosynthetic gene clusters detected in Antarctic bacterial genomes by antiSMASH cont.
Natural % of MIBiG BGC- Isolate Cntg Rgn BGC Type From To Most sim cluster Product genes sim ID Frigoribacterium 1 1 NRPS fragment 834160 878242 - - - - NBH87 2 Bacteriocin 2069121 2079582 - - - - 3 Terpene 2153844 2174758 Carotenoid terpene 50 BGC0000644 4 Siderophore 2672929 2684257 Desferrioxamine B other 60 BGC0000941 5 T3PKS 2848106 2889359 Tetronasin polyketide 3 BGC0000163 Geodermatophilaceae 1 1 T3PKS 1506594 1547622 Alkyl-O- hybrid 28 BGC0001077 NBWT11 Dihydrogeranyl- Methoxyhydroquinone 2 Terpene 2350784 2370872 Herboxidiene polyketide 2 BGC0001065 3 Terpene 3743987 3764919 Carotenoid terpene 18 BGC0000633 4 Lassopeptide 3776256 3797850 Sioxanthin hybrid 37 BGC0001087 Paracoccus NBH48 1 1 T3PKS 191330 232388 - - - - 2 Terpene 243803 267376 Carotenoid terpene 100 BGC0000635 2 1 Ectoine 451315 461704 Ectoine other 100 BGC0000860 3 1 Hserlactone 585 21214 - - - - Hymenobacter 1 1 Terpene 614672 634914 Carotenoid terpene 71 BGC0000650 NBH84 2 Bacteriocin 1836800 1847672 - - - - 3 T3PKS 3878793 3919905 - - - - 4 Terpene 4381820 4402965 - - - - Quadrisphaera 1 1 T3PKS 64448 104837 Alkylresorcinol polyketide 100 BGC0000282 INWT6 2 Terpene 725933 746898 Carotenoid terpene 18 BGC0000633 2 1 Terpene 425686 446978 Sioxanthin hybrid 100 BGC0001087 Azospirillum INR13 9 1 Betalactone- 186571 230689 Fengycin hybrid 20 BGC0001095 NRPS fragment 10 1 Otherks 101961 147780 Anthracimycin T1PKS 22 BGC0001301 Sphingomonas 1 1 Terpene 2182684 2207019 Astaxanthin hybrid 75 BGC0001086 NBWT7 dideoxyglycoside 2 T3PKS 2696542 2737591 - - - -
267
Appendix Table A3.3 NRPS gene BLASTp and NaPDos analysis of condensation domains.
antiSMASH BGC BLASTp (NRPS gene) NaPDoS (Condensation domain) Gene Id Dom locus Pathway Rgn Description/ Accession Dom Closest Id (%) e-value tag (%) (aa) product Streptomyces NRPS_Streptomyces sp. 7 718 99 C 1192-1476 cdaps3_C1_DCL 41 3E-44 CDA INR7 H036 [WP_053673504.1] C 2625-2914 act3_C1_DCL 35 7E-31 actinomycin NRPS_Streptomyces sp. 10 1093 99 C 1-308 act3_C2_LCL 58 3E-69 actinomycin XY593 [WP_078963433.1] C 1060-1353 act3_C2_LCL 59 6E-67 actinomycin C 2111-2412 act3_C3_LCL 60 5E-84 actinomycin C 3168-3469 act3_C3_LCL 60 8E-88 actinomycin NRPS_multispecies 1099 99 C 27-321 syrin1_C7_LCL 44 3E-59 syringomycin [WP_078942128.1] micro1_C1_ C 1079-1373 39 7E-50 microcystin modAA NRPS_multispecies 1100 100 C 607-897 syrin1_C7_LCL 40 3E-46 syringomycin [WP_053626857.1] NRPS_multispecies 19 3216 99 C 31-331 syrin1_C6_LCL 42 7E-43 syringomycin [WP_053613903.1] NRPS_Streptomyces sp. 25 6188 99 C 47-341 act3_C2_LCL 43 5E-57 actinomycin XY533 [WP_053612853.1] NRPS_multispecies 6189 99 C 46-342 syrin1_C9_LCL 37 3E-31 syringomycin [WP_053612852.1] Hypothetical_multispecies 6190 99 C 44-330 syrin1_C5_LCL 34 1E-22 syringomycin [WP_030657526.1] NRPS_Streptomyces sp. 28 6844 99 C 1-297 act3_C2_LCL 62 8E-89 actinomycin XY593 [WP_078963375.1] C 1061-1361 act2_C2_LCL 59 9E-85 actinomycin
268
Appendix Table A3.3 NRPS gene BLASTp and NaPDos analysis of condensation domains cont.
antiSMASH BGC BLASTp (NRPS gene) NaPDoS (Condensation domain) Gene Id Dom locus Pathway Rgn Description/ Accession Dom Closest Id (%) e-value tag (%) (aa) product Streptomyces NRPS_Streptomyces 28 6845 96 C 8-303 cdaps3_C1_DCL 48 1E-47 CDA INR7 virginiae [WP_030899157.1] C 1036-1325 cyclom1C4_LCL 56 3E-69 cyclomarin C 20752375 act3_C2_LCL 56 4E-83 actinomycin NRPS_multispecies 30 7218 100 C 23-307 ituri1_C3_LCL 28 2E-09 iturin [WP_053626909.1] NRPS_Streptomyces sp. 7219 99 C 1-292 micro5_C1_H 29 2E-23 microcystin XY533 [WP_053611980.1] NDED_Streptomyces sp. 7222 99 C 58-351 tioS_C2_LCL 37 5E-35 thiocoraline XY593 [WP_063787970.1] Streptomyces NRPS_Streptomyces sp. 11 3135 91 C 618-922 cdaps1_C6_LCL 56 2E-78 CDA NBSH44 SM13 [WP_103509423.1] C 2355-2646 act3_C1_DCL 45 5E-61 actinomycin NRPS_Couchioplanes 17 5121 82 C 817-1119 micro2_C1_DCL 39 3E-52 microcystin caeruleus [WP_071803074.1] NRPS_multispecies 5122 81 C 46-346 bleom9_C1_LCL 45 1E-59 bleomycin [WP_078588596.1] NRPS_multispecies 17 5122 81 C 1574-1876 micro2_C1_DCL 41 2E-57 microcystin [WP_078588596.1] aaADP_Streptomyces sp. 18 5183 96 C 1595-1890 act3_C1_DCL 51 8E-75 actinomycin ADI98-10 [WP_124265733.1] NRPS_Streptomyces anulatus 5189 94 C 9-304 act2_C1_start 42 3E-49 actinomycin [WP_033895289.1] Chr2_ hypothetical protein Streptomyces 136 85 C 33-306 Stro2721_1 44 7E-64 sporolide 3 sp. CB02058 [WP_073753584.1]
269
Appendix Table A3.3 NRPS gene BLASTp and NaPDos analysis of condensation domains cont.
antiSMASH BGC BLASTp (NRPS gene) NaPDoS (Condensation domain) Gene Id Dom locus Pathway Rgn Description/ Accession Dom Closest Id (%) e-value tag (%) (aa) product Streptomyces NRPS_Streptomyces griseus 8 2411 99 C 600-891 cdaps1_C7_LCL 44 6E-48 CDA NBH77 [SUP57408.1] C 2099-2399 bleom8_C1_DCL 42 3E-37 bleomycin C 3154-3452 bleom9_C1_LCL 47 3E-52 bleomycin C 4664-4963 ituri2_C5_DCL 39 2E-56 iturin aaADP_Streptomyces griseus 2412 99 C 639-943 cdaps1_C2_LCL 44 7E-47 CDA [WP_115068788.1] C 2165-2469 micro2_C1_DCL 35 5E-48 microcystin C 3215-3503 syrin1_C9_LCL 43 2E-56 syringomycin aaADP_Streptomyces griseus 2413 99 C 45-363 syrin1_C6_LCL 42 1E-40 syringomycin [WP_115068787.1] C 1141-1437 syrin1_C9_LCL 45 4E-56 syringomycin C 2657-2962 micro2_C1_DCL 39 2E-56 microcystin C 3705-3999 syrin1_C9_LCL 42 2E-61 syringomycin C 5203-5505 micro2_C1_DCL 36 1E-51 microcystin C 6252-6552 cdaps3_C2_LCL 44 6E-55 CDA C 1082-1379 cdaps3_C2_LCL 46 2E-46 CDA aaADP_Streptomyces griseus 2414 99 C 45-340 cyclom1C5_LCL 46 2E-51 cyclomarin [WP_115068789.1] C 1082-1379 cdaps3_C2_LCL 46 2E-46 CDA C 2618-2917 ituri3_C2_DCL 37 6E-54 iturin NRPS_Streptomyces sp. 9 2647 99 C 627-936 syrin1_C6_LCL 38 3E-32 syringomycin TSRI0384-2 [WP_100455272.1] C 2158-2460 ituri3_C2_DCL 37 8E-55 iturin
270
Appendix Table A3.3 NRPS gene BLASTp and NaPDos analysis of condensation domains cont.
antiSMASH BGC BLASTp (NRPS gene) NaPDoS (Condensation domain) Gene Id Dom locus Pathway Rgn Description/ Accession Dom Closest Id (%) e-value tag (%) (aa) product Streptomyces aaADP_Streptomyces sp. 9 2648 99 C 45-344 cdaps1_C7_LCL 45 1E-55 CDA NBH77 ADI98-12 [WP_124287396.1] C 1588-1889 ituri3_C2_DCL 40 6E-49 iturin C 2653-2953 syrin1_C7_LCL 42 2E-57 syringomycin NRPS_Streptomyces griseus 10 2703 99 C 11-307 act2_C1_start 38 9E-28 actinomycin [WP_115068909.1] C 1052-1347 prist2_C3_LCL 55 3E-56 pristinamycin C 2113-2414 act3_C3_LCL 58 7E-73 actinomycin aaADP_Streptomyces sp. 12 3655 99 C 604-904 syrin1_C9_LCL 35 9E-30 syringomycin ADI98-12 [WP_124287620.1] Kribbella NRPS_Saccharothrix 1 549 57 C 614-921 syrin1_C7_LCL 41 8E-51 syringomycin SPB151 carnea [PSL53320.1] C 2110-2404 micro2_C1_DCL 39 8E-59 microcystin C 3578-3879 micro2_C1_DCL 38 5E-52 microcystin NRPS_Saccharothrix 550 57 C 41-333 cdaps3_C2_LCL 47 4E-59 CDA carnea [WP_106618241.1] C 1566-1867 micro2_C1_DCL 39 5E-64 microcystin C 2634-2854 syrin1_C9_LCL 49 6E-40 syringomycin NRPS_Amycolatopsis 551 57 C 1037-1340 micro2_C1_DCL 38 7E-54 microcystin orientalis [WP_044850574.1] C 2104-2401 syrin1_C9_LCL 45 7E-55 syringomycin NRPS_Streptomyces 578 49 C 4-315 tioR_C1_start 45 1E-36 thiocoraline lunaelactis [WP_108155207.1] C 1564-1860 tioR_C3_DCL 51 2E-72 thiocoraline C 2609-2911 tioS_C1_LCL 56 4E-90 thiocoraline
271
Appendix Table A3.3 NRPS gene Blastp and NaPDos analysis of condensation domains cont.
antiSMASH BGC BLASTp (NRPS gene) NaPDoS (Condensation domain) Gene Id Dom locus Rgn Description/ Accession Dom Closest Id (%) e-value Pathway product tag (%) (aa) Kribbella NRPS_Streptomyces sp. 73 1 579 63 C 1-301 tioS_C1_LCL 56 2E-92 thiocoraline SPB151 [WP_101418611.1] C 1423-1724 tioS_C2_LCL 65 1E-104 thiocoraline aaADP_Kribbella sp. 3 2794 80 C 428-711 tyroc2_C3_LCL 34 3E-34 tyrocidine NEAU-SW521 [WP_112242521.1] aaADP_Kribbella sp. 2795 75 C 35-313 cyclom1C2_LCL 43 6E-46 cyclomarin NEAU-SW521 [WP_112242523.1] C 1148-1434 syrin1_C9_LCL 44 6E-46 syringomycin aaADP_Kribbella sp. 7 6340 93 C 269-554 syrin1_C6_LCL 41 2E-52 syringomycin NEAU-SW521 [WP_112239288.1] aaADP_Kribbella sp. 6341 91 C 32-327 syrin1_C9_LCL 42 3E-58 syringomycin NEAU-SW521 [WP_112239146.1] micro1_C1_ C 1086-1384 36 1E-35 microcystin modAA micro1_C1_ C 2171-2470 33 1E-29 microcystin modAA aaADP_Kribbella sp. 6350 89 C 10-308 bacil2_C1_start 40 6E-56 bacillibactin NEAU-SW521 [WP_112248827.1] C 1359-1658 cdaps1_C6_LCL 57 3E-91 CDA C 2934-3230 cdaps3_C1_DCL 51 4E-80 CDA aaADP: amino acid adenylation domain-containing protein NDED: NAD-dependent epimerase/dehydratase family protein CDA: Calcium-dependent antibiotic
272
Appendix Table A3.4 Type I PKS gene BLASTp and NaPDos analysis of ketosynthase domains.
antiSMASH BGC BLASTp (Type 1 PKS gene) NaPDoS (Ketosynthase domain) Gene Id Dom locus Id Regn Description/Accession Dom Closest e-value Pathway product tag (%) (aa) (%) Streptomyces T1PKS_Streptomyces sp. ArsA_Azotobacter_ 9 1049 99 KS 670-1110 53 7E-111 alkylresorcinol INR7 H036 [WP_107092636.1] PUFA T1PKS_multispecies PfaA_Shewanella_ 1050 99 KS 15-478 38 1E-76 PUFA [WP_051734545.1] PUFA T1PKS_Streptomyces sp. 12 1417 99 KS 101-517 EpoD_Q9L8C7_4mod 54 3E-105 epothilone XY593 [WP_053685078.1] T1PKS_multispecies 13 1458 100 KS 3-414 EpoD_Q9L8C7_4mod 49 6E-90 epothilone [WP_053625151.1] T1PKS_Streptomyces sp. 21 4100 99 KS 20-445 TetA_BAE93722_KS1 58 9E-120 tetronomycin XY593 [WP_053683944.1] AveA3_Q9S0R4_ KS 1046-1471 70 1E-170 avermectin 2mod T1PKS_multispecies 4103 99 KS 30-455 EpoE_Q9L8C6_1mod 44 1E-77 epothilone [WP_051734506.1] T1PKS_Streptomyces sp. 4104 100 KS 36-459 EpoD_Q9L8C7_4mod 51 1E-115 epothilone XY593 [WP_078963465.1] PKS_Streptomyces sp. 4111 100 KS 39-449 EpoE_Q9L8C6_1mod 55 8E-112 epothilone XY511 [WP_078943949.1] T1PKS_Streptomyces sp. 4112 99 KS 8-428 EpoD_Q9L8C7_4mod 47 2E-99 epothilone XY511 [WP_107087159.1] b-ketoacyl-ACP synthase_ 4114 multispecies 100 KS 140-400 FabF_Bacillus_FAS 41 4E-55 FAS [WP_030649161.1] hypothetical_multispecies 4119 99 KS 74-394 FabF_Bacillus_FAS 35 4E-33 FAS [WP_053625404.1] T1PKS_multispecies PfaA_Shewanella_ 24 6046 99 KS 3-458 38 1E-81 PUFA [WP_030744309.1] PUFA
273
Appendix Table A3.4 Type I PKS gene BLASTp and NaPDos analysis of ketosynthase domains cont.
antiSMASH BGC BLASTp (Type 1 PKS gene) NaPDoS (Ketosynthase domain) Gene Id Dom locus Id Regn Description/Accession Dom Closest e-value Pathway product tag (%) (aa) (%) Streptomyces Chr2_ T1PKS_Streptomyces sp. C1027_AAL06699_ 66 91 KS 3-461 92 0 C-1027 enediyne NBSH44 2 CB02058 [WP_073753628.1] ene9 SDROR_ Streptomyces AveA2_Q9S0R7_ 20 5282 Streptomyces sp. ADI98-12 99 KS 1-368 73 2E-145 avermectin NBH77 2mod [WP_124288063.1] T1PKS_Azospirillum Azospirillum PfaA_Shewanella_ 1 108 lipoferum 93 KS 2-227 42 8E-42 PUFA INR13 PUFA [WP_014188491.1] T1PKS_Azospirillum PfaA_Shewanella_ 111 lipoferum 95 KS 12-418 56 2E-119 PUFA PUFA [WP_014188492.1] SDROR: SDR family oxidoreductase
274
Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and NaPDos analysis of condensation and ketosynthase domains.
antiSMASH BGC BLASTp (Hybrid NRPS/Type 1 PKS gene) NaPDoS (Condensation/Ketosynthase domain) Gene Id Dom locus Id Rgn tag Description/ Accession (%) Dom (aa) Closest (%) e-value Pathway product Streptomyces T1PKS_Streptomyces sp. AveA3_Q9S0R4_ 16 1692 84 KS 800-1225 74 1E-175 avermectin INR7 fd1-xmd [WP_078095288.1] 3mod NysC_Q9L4W3_ KS 2576-3002 77 0 nystatin 2mod AveA3_Q9S0R4_ KS 4336-4762 74 3E-178 avermectin 3mod T1PKS_multispecies AveA2_Q9S0R7_ 1693 100 KS 37-464 74 6E-178 avermectin [WP_053626004.1] 1mod NRPS_Streptomyces sp. 1697 100 C 30-323 bleom9_C1_LCL 42 5E-28 bleomycin XY533 [WP_053612455.1] NRPS_Streptomyces sp. 1701 99 C 1-301 cyclom1C2_LCL 57 2E-78 cyclomarin XY511 [WP_078944041.1] C 1571-1869 cdaps3_C1_DCL 48 9E-67 CDA C 3017-3319 act3_C2_LCL 57 9E-82 actinomycin C 4577-4875 tioR_C3_DCL 48 6E-62 thiocoraline T1PKS_Streptomyces sp. 23 5979 99 KS 20-450 EpoC_Q9L8C8_H 52 5E-116 epothilone XY511 [WP_053627656.1] NRPS_Streptomyces sp. 5980 99 C 632-925 micro3_C1_LCL 29 8E-30 microcystin XY511 [WP_053627657.1] b-ketoacyl-ACP synthase_ FabF_Bacillus_ 5997 100 KS 54-389 46 5E-63 FAS multispecies [WP_030660568.1] FAS Streptomyces aaADP_Streptomyces sp. KirAI_CAN89631 4 1134 75 KS 668-1101 43 2E-82 kirromycin NBSH44 ADI97-07 [WP_124280375.1] _2T C 2124-2419 tioS_C2_LCL 38 3E-12 thiocoraline T1PKS_Streptomyces sp. 9 2373 82 KS 5-436 EpoC_Q9L8C8_H 48 1E-100 epothilone [APD71787.1]
275
Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and NaPDos analysis of condensation and ketosynthase domains cont.
antiSMASH BGC BLASTp (Hybrid NRPS/Type 1 PKS gene) NaPDoS (Condensation/Ketosynthase domain) Gene Id Dom locus Id e- Regn Description/Accession Dom Closest Pathway product tag (%) (aa) (%) value Streptomyces AmTc3PP_Streptomyces sp. 9 2374 78 C 905-1195 syrin1_C6_LCL 34 4E-36 syringomycin NBSH44 NRRL S-1824 [WP_052189335.1] C 1413-1716 cyclom1C3_LCL 34 3E-30 cyclomarin T1PKS_Rhizobium sp. EpoC_Q9L8C8_ 16 4880 40 KS 624-1040 47 3E-96 epothilone R339 [WP_088678735.1] H C 2078-2339 grami2_C4_LCL 25 2E-11 gramicidin Streptomyces NRPS_Streptomyces sp. 2 198 99 C 3-305 act2_C1_start 36 6E-27 actinomycin NBH77 ADI98-12 [RPK82802.1] C 1070-1371 act3_C3_LCL 50 9E-65 actinomycin C 2104-2399 act3_C1_DCL 45 2E-32 actinomycin C 3129-3429 cdaps2_C3_LCL 42 3E-34 CDA aaADP_Streptomyces griseus KirAIV_CAN896 199 99 KS 29-439 41 2E-61 kirromycin [WP_115069650.1] 34_8T C 812-1108 prist2_C3_LCL 38 1E-42 pristinamycin C 1882-2165 act3_C1_DCL 46 5E-45 actinomycin protein kinase_Streptomyces sp. 3 252 99 C 22-250 bleom9_C1_LCL 28 2E-06 bleomycin ADI98-12 [WP_124288328.1] aaADP_Streptomyces griseus 253 99 C 7-301 bacil2_C1_start 43 7E-69 bacillibactin [WP_115068275.1] C 1069-1369 cyclom1C2_LCL 51 6E-52 cyclomarin ATDP_Streptomyces sp. EpoC_Q9L8C8_ 7E- 254 100 KS 14-443 53 epothilone ADI98-12 [WP_124288326.1] H 108 T1PKS_Streptomyces sp. NysC_Q9L4W3_ 5E- 285 99 KS 666-1093 69 nystatin ADI98-12 [RPK82844.1] 5mod 168 SDROR_Streptomyces NysC_Q9L4W3_ 2E- 289 99 KS 36-460 72 nystatin griseus [WP_115068282.1] 2mod 149
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Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and NaPDos analysis of condensation and ketosynthase domains cont.
antiSMASH BGC BLASTp (Hybrid NRPS/Type 1 PKS gene) NaPDoS (Condensation/Ketosynthase domain) Gene Id Dom locus Id e- Regn Description/Accession Dom Closest Pathway product tag (%) (aa) (%) value Streptomyces SDROR_Streptomyces NysC_Q9L4W3_ 3 289 99 KS 1795-2218 80 0 nystatin NBH77 griseus [WP_115068282.1] 5mod NysC_Q9L4W3_ KS 3539-3962 81 0 nystatin 5mod NysC_Q9L4W3_ KS 5264-5687 79 0 nystatin 5mod NysC_Q9L4W3_ 3E- KS 6975-7390 73 nystatin 5mod 177 NysC_Q9L4W3_ 5E- KS 8842-9254 73 nystatin 5mod 178 SDROR_Streptomyces NysC_Q9L4W3_ 290 99 KS 35-461 74 0E+00 nystatin griseus [WP_115068283.1] 3mod NysI_Q9L4X3_3 2E- KS 1600-2027 68 nystatin mod 176 AveA3_Q9S0R4_ 2E- KS 3705-4132 72 avermectin 3mod 167 SDROR_Streptomyces NysJ_Q9L4X2_3 7E- 291 100 KS 35-457 74 nystatin griseus [WP_115068284.1] mod 178 T1PKS_Streptomyces sp. NysJ_Q9L4X2_1 3E- 292 99 KS 36-461 79 nystatin TSRI0384-2 [WP_100455788.1] mod 178 NysJ_Q9L4X2_2 KS 2074-2499 73 0 nystatin mod NysJ_Q9L4X2_2 8E- KS 4118-4544 72 nystatin mod 175 NysK_Q9L4X1_1 KS 5646-6072 76 0 nystatin mod
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Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and NaPDos analysis of condensation and ketosynthase domains cont.
antiSMASH BGC BLASTp (Hybrid NRPS/Type 1 PKS gene) NaPDoS (Condensation/Ketosynthase domain) Gene Id Dom locus Id e- Regn Description/Accession Dom Closest Pathway product tag (%) (aa) (%) value Streptomyces SDROR_Streptomyces NysI_Q9L4X3_1 3 293 99 KS 35-451 75 0 nystatin NBH77 griseus [WP_115068285.1] mod NysC_Q9L4W3_ KS 1782-2204 82 0 nystatin 5mod NysI_Q9L4X3_3 KS 3342-3764 78 0 nystatin mod NysI_Q9L4X3_4 KS 4873-5298 79 0 nystatin mod NysI_Q9L4X3_5 KS 6429-6848 77 0 nystatin mod NysI_Q9L4X3_6 7E- KS 7947-8368 74 nystatin mod 173 aaADP_Streptomyces sp. micro1_C1_mod 5 427 99 C 433-736 33 2E-32 microcystin ADI98-12 [WP_124288291.1] AA ATDP_Streptomyces sp. 428 99 C 117-408 syrin1_C9_LCL 38 4E-37 syringomycin ADI98-12 [WP_124288289.1] ATDP_Streptomyces sp. EpoC_Q9L8C8_ 4E- 432 99 KS 13-433 50 epothilone ADI98-12 [WP_124288289.1] H 106 aaADP_Streptomyces sp. 433 99 C 1138-1411 mycos1_C3_LCL 27 1E-14 mycosubtilin ADI98-12 [WP_124288288.1] hybrid NRPS/T1PKS_ HSAF_ABL8639 19 5219 Streptomyces sp. TSRI0384-2 99 KS 12-438 78 0 HSAF 1_i [WP_100453753.1] 4E- C 1857-2153 Sare2407_1 70 putative HSAF 113 aaADP_Streptomyces sp. LnmI_AF484556 2E- 21 5487 99 KS 1849-2259 58 leinamycin ADI98-12 [WP_124287764.1] _1T 104
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Appendix Table A3.5 Hybrid NRPS/Type I PKS gene BLASTp and NaPDos analysis of condensation and ketosynthase domains cont.
antiSMASH BGC BLASTp (Hybrid NRPS/Type 1 PKS gene) NaPDoS (Condensation/Ketosynthase domain) Gene Id Dom locus Id e- Regn Description/Accession Dom Closest Pathway product tag (%) (aa) (%) value Streptomyces aaADP_Streptomyces sp. LnmI_AF484556 9E- 21 5487 99 KS 2510-2924 65 leinamycin NBH77 ADI98-12 [WP_124287764.1] _2T 127 LnmI_AF484556 6E- KS 3717-4133 72 leinamycin _3T 130 SDROR_Streptomyces sp. LnmJ_AF484556 2E- 5488 TSRI0384-2 99 KS 898-1318 69 leinamycin _1T 171 [WP_100454159.1] LnmJ_AF484556 3E- KS 2083-2508 64 leinamycin _2T 157 LnmJ_AF484556 3E- KS 3271-3707 67 leinamycin _3T 178 LnmJ_AF484556 1E- KS 4570-4998 66 leinamycin _4T 147 PK b-ketoacyl ACP synthase_ JamG_AAS98778 5493 99 KS 93-421 42 2E-44 jamaicamide multispecies [WP_100454163.1] _mod aaADP_Kribbella sp. Kribbella EpoC_Q9L8C8_ 9 6779 NEAU-SW521 92 KS 1447-1837 46 4E-89 epothilone SPB151 H [WP_112236566.1] aaADP: amino acid adenylation domain-containing protein ATDP: acyltransferase domain-containing protein CDA: Calcium-dependent antibiotic HSAF: heat stable antifungal factor AmTc3PP: Aminotransferase class III-fold pyridoxal phosphate-dependent enzyme SDROR: SDR family oxidoreductase
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Appendix Table A3.6 Type II PKS gene BLASTp and NaPDos analysis of ketosynthase domains.
antiSMASH BGC BLASTp (Type II PKS gene) NaPDoS (Ketosynthase domain) Gene Id Id e- Rgn Description/ Accession Dom Dom locus (aa) Closest Pathway product tag (%) (%) value Streptomyces β-ketoacyl-ACP synthase_ 2090689 - actinorh_NP_629237_ 3.00E- 17 817 99 KS 76 actinorhodin INR7 multispecies [WP_030650395.1] 2091954 KSa 151 KS CLF_multispecies 2090689 - AlnM_ACI88862_ 2.00E- 818 100 KS 61 alnumycin [WP_053628071.1] 2091954 KSb 109 β-ketoacyl-ACP synthase_ 7344518 - AlnL_ACI88861_ 9.00E- 27 6551 100 KS 64 alnumycin multispecies [WP_051734969.1] 7345825 KSa 117 PK β-ketoacyl synthase_ 7345822 - SaqB_ACP19354_ 6.00E- 6552 99 KS 54 saquayamycin multispecies [WP_030654459.1] 7347060 KSb 72
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