Microbial Diversity and Ecology in the Interfaces of the Deep-Sea Anoxic

Microbial Diversity and Ecology in the Interfaces of the Deep-sea

Anoxic Brine Pools in the Red Sea

Dissertation by

Tyas Ikhsan Hikmawan

In Partial Fulfillment of the Requirements

For the Degree of

Doctor of Philosophy

King Abdullah University of Science and Technology

Thuwal, Kingdom of Saudi Arabia

Tyas Ikhsan Hikmawan

EXAMINATION COMMITTEE APPROVALS FORM

The dissertation of Tyas Ikhsan Hikmawan is approved by the examination committee.

Committee Chairperson: Ulrich Stingl

Committee Member: Xosé Anxelu G Morán

Committee Member: Arnab Pain

Committee Member: Mohamed Jebbar

ABSTRACT

Microbial Diversity and Ecology in the Interfaces of the Deep-sea

Anoxic Brine Pools in the Red Sea

Tyas Ikhsan Hikmawan

Deep-sea anoxic brine pools are one of the most extreme ecosystems on Earth, which are characterized by drastic changes in salinity, temperature, and oxygen concentration. The interface between the brine and overlaying seawater represents a boundary of oxic-anoxic layer and a steep gradient of redox potential that would initiate favorable conditions for divergent metabolic activities, mainly methanogenesis and sulfate reduction. This study aimed to investigate the diversity of Bacteria, particularly sulfate-reducing communities, and their ecological roles in the interfaces of five geochemically distinct brine pools in the Red Sea. Performing a comprehensive study would enable us to understand the significant role of the microbial groups in local geochemical cycles. Therefore, we combined culture-dependent approach and molecular methods, such as 454 pyrosequencing of 16S rRNA gene, phylogenetic analysis of functional marker gene encoding for the alpha subunits of dissimilatory sulfite reductase (dsrA), and single-cell genomic analysis to address these issues. Community analysis based on 16S rRNA gene sequences demonstrated high bacterial diversity and domination of Bacteria over Archaea in most locations. In the hot and multilayered Atlantis II Deep, the bacterial communities were stratified and hardly overlapped. Meanwhile in the colder brine pools, sulfate- reducing Deltaproteobacteria were the most prominent bacterial groups inhabiting the 4 interfaces. Corresponding to the bacterial community profile, the analysis of dsrA gene sequences revealed collectively high diversity of sulfate-reducing communities.

Desulfatiglans-like dsrA was the prevalent group and conserved across the Red Sea brine pools. In addition to the molecular studies, more than thirty bacterial strains were successfully isolated and remarkably were found to be cytotoxic against the cancer cell lines. However, none of them were sulfate reducers. Thus, a single-cell genomic analysis was used to study the metabolism of uncultured phyla without having them in culture.

We analysed ten single-cell amplified genomes (SAGs) of the uncultivated euryarchaeal

Marine Benthic Group E (MBGE), which contain a key enzyme for sulfate reduction.

The results showed the possibility of MBGE to grow autotrophically only with carbon dioxide and hydrogen. In the absence of adenosine 5’-phosphosulfate reductase, we hypothesized that MBGE perform sulfite reduction rather than sulfate reduction to conserve energy.

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to my committee chair, Dr. Ulrich Stingl for his supervision, guidance, and support throughout my PhD dissertation. I also want to extend my gratitude to the committee members, Dr. Xosé Anxelu G Morán, Dr. Arnab

Pain, Dr. Mohamed Jebbar for their valuable advice and comments.

I am very thankful to Dr. Andre Antunes, Dr, David Ngugi, Dr. Sunil Sagar, and Dr. Rita

Bartossek for inspiring discussions and great collaborations. My appreciation also goes to colleagues at Stingl Lab. and my KAUSTINA friends for making my time at King

Abdullah University of Science and Technology a great experience. I would like to express my gratitude to KAUST for the Discovery Fellowship and to SEDCO for the research funding.

Finally, my heartfelt gratitude is extended to my parents for their encouragement, to my lovely wife Difla Yustisia Qurani for her endless patience and support, and to my beloved son Fatih Muhammad Himmaty.

TABLE OF CONTENTS

Page

EXAMINATION COMMITTEE APPROVALS FORM ...... 2 ABSTRACT ...... 3 ACKNOWLEDGEMENTS ...... 5 TABLE OF CONTENTS ...... 6 LIST OF ABBREVIATIONS ...... 10 LIST OF ILLUSTRATIONS ...... 12 LIST OF TABLES ...... 16 INTRODUCTION ...... 17 Deep-Sea Hypersaline Anoxic Basins ...... 17 Characteristics of the Brine-seawater Interface ...... 21 Microbial Diversity in the Red Sea brine pools ...... 22 Sulfate-reducing Communities in the Red Sea brine pools ...... 27 Adaptation to Osmotic Stress in the brine pools ...... 30 Isolation of Novel Microbes from Red Sea brine pools ...... 31 Bioactivity of the bacterial isolates from the Red Sea brine pools ...... 33 Single-cell Genomics ...... 33 Marine Benthic Group-E ...... 34 References ...... 35 OBJECTIVES ...... 43 Chapter 1: Phylogenetic diversity of bacterial communities in the interfaces of five deep-sea anoxic brine pools in the Red Sea ...... 45 1.1. Abstract ...... 46 1.2. Introduction ...... 47 1.3. Methods ...... 48 1.3.1. Sample collection ...... 48 1.3.2. DNA extraction ...... 49 1.3.3. Pyrosequencing of 16S rRNA gene amplicons ...... 50 1.3.4. Bacterial clone libraries based on Sanger sequencing of the 16S rRNA gene 51 1.3.5. Diversity and phylogenetic analysis of bacterial clone libraries ...... 52 1.3.6. Quantification of gene copy numbers using real-time PCR ...... 53 1.3.7. Correlations of the bacterial communities and the environmental factors ...... 53 1.3.8. Accession numbers of nucleotide sequences ...... 53 1.4. Results ...... 54 1.4.1. Physicochemical characteristics of the sampling locations ...... 54 1.4.2. Bacteria/Archaea ratio ...... 55 7

1.4.3. Bacterial community structure revealed by 454 pyrosequencing ...... 56 1.4.4. Bacterial community analysis based on clone libraries ...... 58 1.4.5. Relationship between bacterial communities and the environmental factors .. 64 1.5. Discussion ...... 66 1.6. Conclusions ...... 72 1.7. References ...... 72 Chapter 2: Diversity of sulfate-reducing Bacteria in the interfaces of five deep hypersaline anoxic basins of the Red Sea ...... 77 2.1. Abstract ...... 78 2.2. Introduction ...... 78 2.3. Methods ...... 79 2.3.1. Sample collection ...... 79 2.3.2. DNA extraction ...... 80 2.3.3. Functional gene analysis ...... 81 2.3.4. Accession numbers of nucleotide sequences ...... 83 2.4. Results and discussion ...... 83 2.5. Conclusions ...... 89 2.6. References ...... 89 Chapter 3: Cytotoxic and apoptotic evaluations of marine bacteria isolated from brine-seawater interface of the Red Sea ...... 93 3.1. Abstract ...... 94 3.2. Introduction ...... 94 3.3. Methods ...... 97 3.3.1. Field sampling ...... 97 3.3.2. Source of bacterial isolates ...... 97 3.3.3. PCR amplification ...... 98 3.3.4. Bacterial biomass ...... 98 3.3.5. Extract preparation ...... 99 3.3.6. Cell culture ...... 99 3.3.7. MTT assay ...... 100 3.3.8. APOPercentage assay ...... 100 3.3.9. Statistical analysis ...... 101 3.4. Results ...... 101 3.4.1. Microbial isolates ...... 101 3.4.2. Cytotoxic activities of bacterial extracts ...... 102 3.4.3. Apoptotic evaluations ...... 106 3.5. Discussion ...... 106 3.6. Conclusions ...... 109 3.7. References ...... 110 8

Chapter 4: Induction of apoptosis in cancer cell lines by the Red Sea brine pool bacterial extracts ...... 114 4.1. Abstract ...... 115 4.2. Introduction ...... 116 4.3. Methods ...... 118 4.3.1. Field sampling ...... 118 4.3.2. Source of bacterial isolates ...... 119 4.3.3. PCR amplification ...... 119 4.3.4. Bacterial biomass ...... 120 4.3.5. Extract preparation ...... 121 4.3.6. Cell culture ...... 121 4.3.7. MTT assay ...... 121 4.3.8. APOPercentage assay ...... 122 4.3.9. Microscopy ...... 122 4.3.10. MMP assay ...... 122 4.3.11. Caspase assay ...... 123 4.3.12. Western blotting ...... 123 4.3.13. Z-factor ...... 124 4.4. Results ...... 124 4.4.1. Microbial isolates from the Red Sea ...... 125 4.4.2. Antiproliferative activities of marine bacterial extracts ...... 125 4.4.3. Apoptotic cell death in HeLa cells ...... 126 4.4.4. Effects of extracts on MMP ...... 128 4.4.5. PARP-1 cleavage through caspases ...... 130 4.5. Discussion ...... 132 4.6. Conclusions ...... 136 4.7. References ...... 137 Chapter 5: Insights into the metabolism of uncultivated euryarchaeal Marine Benthic Group-E (MBGE) from the interface of the Atlantis II Deep brine pool .. 144 5.1. Abstract ...... 145 5.2. Introduction ...... 145 5.3. Materials and methods ...... 147 5.3.1. Sampling and cell sorting ...... 147 5.3.2. Assembly and quality check ...... 148 5.3.3. Sequence annotation ...... 150 5.3.4. Phylogenetic analysis ...... 150 5.3.5. Phylogenomic profiling ...... 151 5.4. Results and Discussion ...... 152 5.4.1. Single-cell genome features ...... 152 5.4.2. Phylogenetic analysis of the MBGE ...... 153 9

5.4.3. Central carbon metabolism ...... 156 5.4.4. Autotrophic carbon metabolism ...... 160 5.4.5. Sulfur metabolism ...... 164 5.4.6. Transporters ...... 166 5.4.7. Osmoadaptation mechanisms ...... 168 5.5. Conclusions ...... 171 5.6. Supplementary Data ...... 171 5.7. References ...... 180 CONCLUSIONS ...... 189 Author's contribution ...... 194

LIST OF ABBREVIATIONS

ACDS Acetyl Co-A Decarbonylase/Synthase

ADP Adenosine DiPhosphate

AMP Adenosine MonoPhosphate apr adenosine-5'-phosphosulfate reductases

ATP Adenosine TriPhosphate

BLAST Basic Local Alignment Search Tool

BSI Brine-Seawater Interface

CCA Canonical Correspondence Analysis

CDD Conserved Domain Database

COG Cluster of Orthologous Group

CTD Conductivity Temperature Depth

DHAB Deep Hypersaline Anoxic Basin

DMEM Dulbecco’s Modified Eagle’s Medium

DNA DeoxyriboNucleic Acid

DO Dissolved Oxygen dsr dissimilatory sulfite reductases

EMP Embden-Meyerhof-Parnas

GB Glycine-Betaine

Hdr Heterodisulfide reductase

HTFC High Throughput Flow Cytometer

INDIGO INtegrated Datawarehouse of mIcrobial GenOmes 11

KEGG Kyoto Encyclopedia of Genes and Genomes

LCL Lower Convective Layer

MBGE Marine Benthic Group E

MDA Multiple-Displacement-Amplification

MMP Mitochondrial Membrane Potential mRNA messenger RiboNucleic Acid

NADPH Nicotinamide Adenine Dinucleotide Phosphate (reduced form)

ORF Open Reading Frame

OTU Operational Taxonomic Unit

PCR Polymerase Chain Reaction

PS PhosphatidylSerine qPCR quantitative Polymerase Chain Reaction

RubisCO Ribulose-1,5- bisphosphate Carboxylase/Oxygenase rRNA ribosomal RiboNucleic Acid

SAG Single-cell Amplified Genome

SRB Sulfate Reducing Bacteria

TCA TriCarboxylic Acid

TFF Tangential Flow Filtration

TMA TriMethylAmine

TNF TetraNucleotide Frequency

UCL Upper Convective Layer

WGA Whole Genome Amplification

LIST OF ILLUSTRATIONS

Figure 1. Geographical location of the brine pools in the Red Sea. The axial zone of the Red Sea can be divided into four regions. From the north to south: Northern Region, Transitional Region, Multi-Deeps Region, and Rift Valley Region ...... 18 Figure 2. Chemical profile of the brine pools in the Red Sea [11] ...... 19 Figure 3. Bathymetric map of the Atlantis II and Discovery Deep [39] ...... 20 Figure 4. Microbiological and geochemical profiling of the BSI of the Bannock Deep, Mediterranean [26] ...... 21 Figure 5. Phylogenetic diversity of Archaea in the brine pools based on 16S rRNA. The representative sequences from the Red Sea brine pools, in red (SD, Shaban Deep; KD, Kebrit Deep; DD, Discovery Deep), the Mediterranean Sea, in blue (LAB, L’Atalante Basin; UB, Urania Basin; DB, Discovery Basin; BB, Bannock Basin), and those that occur in both brines, in purple [11] ...... 23 Figure 6. Taxonomic classification of the bacterial and archaeal reads using the RDP classifier at 80% confidence level [29] ...... 24 Figure 7. KEGG maps showing significant differences in completeness between the ABP and DBP [40] ...... 25 Figure 8. 16S rRNA gene-based phylogenetic tree of the bacterial domain, including the 16S rRNA sequences of the KB1 group and KT-2, the new halophilic isolates from Kebrit Deep [33] ...... 26 Figure 9. The sequential pattern of microbial degradation of complex organic matter in anoxic environments in the presence (a) and absence of sulfate (b) [48] ...... 27 Figure 10. Phylogenetic analysis of bacterial 16S rRNA sequences and dsrAB gene transcripts retrieved from the interface of the hypersaline Lake Kryos [43] ...... 28 Figure 11. Microbial profile in the interface of the Discovery Deep, Mediterranean Sea, showing cDNA clone sequences of 16S rRNA (yellow) and cDNA dsrAB mRNA (red). D’bc indicates clones of Desulfobacteraceae and D’hb indicates clones of Desulfohalobiaceae [44] ...... 29 Figure 12. SEM images of H. contractile show complex morphology and peculiar contractile protrusions [36] ...... 32 Figure 1.1. Sampling locations of five deep-sea brines in the Red Sea ...... 54 Figure 1.2. Taxonomic classification of the 454 pyrosequencing reads based on ARB- SILVA 16S rRNA SSU 115 database ...... 57 Figure 1.3. Taxonomic classification, relative abundances, and hierarchical cluster analysis (Jaccard similarity index) of bacterial communities based on Sanger sequencing in the interfaces of the Red Sea brine pools ...... 59 13

Figure 1.4. 16S rRNA gene-based phylogenetic tree of Bacteria (excluding Proteobacteria phyla), harboring representative sequences from the interfaces of the Red Sea brine pools. The tree was constructed by maximum likelihood analysis using ARB. Taxonomy is based on SSURef_115_SILVA (http://www.arb-silva.de)...... 61 Figure 1.5. 16S rRNA gene-based phylogenetic tree showing the affiliation of Proteobacterial lineage detected from the interfaces of the Red Sea brine pools. The tree was constructed by maximum likelihood analysis using ARB. Taxonomy is based on SSURef_115_SILVA (http://www.arb-silva.de). The scale bar represents 0.10 fixed mutation per nucleotide position. Bootstrap values above 50% are shown...... 62 Figure 1.6. 16S rRNA gene-based phylogenetic tree of the Deltaproteobacteria, including the representative sequences from the Atlantis II, Discovery, Erba, Kebrit, and Nereus Deep. The topology of the tree is based on maximum-likelihood method with 1000 bootstraps. The scale bar represents 0.10 fixed mutation per nucleotide position. Bootstrap values above 50% are shown...... 64 Figure 1.7. Redundancy Analysis (RDA) ordination plots showing relationship between environmental factors and bacterial communities. Red arrows indicate the bacterial taxa, Deltaproteobacteria (1), Gammaproteobacteria (2), Deferribacteres (3), Alphaproteobacteria (4), Planctomycetes (5), Chloroflexi (6), Candidate division OP8 (7), Candidate division OP1 (8), Candidate Division OD1 (9), Firmicutes (10), Nitrospirae (11), Actinobacteria (12), Bacteroidetes (13), Candidate division KB1 (14), Candidate division OP3 (15), Spirochaetes (16), Nitrospinae (17), Other groups (18). Blue arrows indicate environmental parameters measured in the sampling sites...... 65 Figure 1.8. 16S rRNA gene-based phylogenetic tree of the candidate division KB1 group, including the representative sequences from the previous studies in the Red Sea brine pools, the Mediterranean Deep Hypersaline Anoxic Basins (DHABs), and the Atlantis II, Discovery, Kebrit Deep BSIs in this current study. The topology of the tree is based on maximum-likelihood method with 1000 bootstraps. The scale bar represents 0.03 fixed mutation per nucleotide position. Bootstrap values above 50% are shown...... 69 Figure 2.1. Phylogenetic tree based on deduced amino acid sequences of the dsrA clones from the BSIs of Red Sea brine pools, including sequences from Mediterranean DHABs. The topology of the tree is based maximum-likelihood method using 1000 bootstrap replicates. The scale bar represents 0.10-fixed mutation per nucleotide position and bootstrap values above 50% are shown...... 86 Figure 3.1. Percent growth inhibition of MCF-7 cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk (*p<0.05 and **p<0.01) are significantly different from untreated control (Un). +ve represents positive control (H2O2)...... 102 Figure 3.2. Percent growth inhibition of HeLa cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk 14

(*p<0.05 and **p<0.01) are significantly different from untreated control (Un). +ve represents positive control (H2O2)...... 103 Figure 3.3. Percent growth inhibition of DU145 cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk (*p<0.05 and **p<0.01) are significantly different from untreated control (Un). +ve represents positive control (H2O2)...... 104 Figure 3.4. Percent apoptosis in HeLa, DU145 and MCF-7 cells after treatment with 1 mg/mL marine bacterial extracts. The treatment of extracts was done for 48 hr in duplicates. Data were normalized to untreated controls...... 109 Figure 4.1. Apoptosis induction in HeLa cells after treatment with marine bacterial extracts. Detection of externalization of PS from cell membrane was done using APOPercentage assay. Cells were stained with dye and fluorescence was measured using flow cytometry. The photographs were taken by using inverted microscope. Apoptosis is shown as the percentage of cells that uptake APOPercentage dye relative to untreated cells. A) Cells were treated with H2O2 or extracts at a final concentration of 500 µg/mL for 48 h, and B) for 24 h. The Z- factor was calculated to be 0.68 and 0.77 for 48 h and 24 h experiments, respectively. C) The morphology of cancer cells after 24 h of treatment with chosen extracts...... 127 Figure 4.2. MMP changes in HeLa cells after treatment with marine bacterial extracts. Cells were treated with 500 µg/mL of each extract for 12 and 16 h and stained with 50 µM JC-1 and analyzed by flow cytometry. Figure presents the 2D plots of FL-2H vs. FL- 1H indicating percentage of cells (± S.D) with intact (red) or permeable (green) mitochondrial membranes. Untx represent untreated sample and 100 mM H2O2 was used as a positive control. The Z- factor was calculated to be 0.79 and 0.80 for 12 h and 16 h time points, respectively...... 129 Figure 4.3. Caspase-3/7 activity in HeLa cells after treatment with marine bacterial extracts. Cells were treated with 500 µg/mL extracts for 16 h and caspase-3/7 activity was estimated by measuring luminescence using ApoTox-Glo kit (Promega). The caspase-3/7 activity is represented as fold-change in activity when compared to untreated cells. .... 130 Figure 4.4. Western blot analysis of PARP-1 cleavage, phosphorylated γH2AX, Caspase- 8 and −9 in HeLa cells after treatment with marine bacterial extracts. Protein lysates of HeLa cells treated with 500 µg/mL of each extract for 12 and 24 h were subjected to western blotting probing for cleaved PARP-1 (cl-PARP-1), procaspase-9, cleaved caspase-8 and γH2Ax. Cells treated with 400 nM docetaxel for 24 h were used as a positive (+ve) control, Untx represent untreated control and β-tubulin was used as a loading control...... 131 Figure 5.1. Predicted metabolic functions and relative percentage based on Cluster of Orthologous Group (COG) analysis ...... 153 Figure 5.2. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) inferred using complete/nearly complete 16S rRNA genes from the SAGs and comparative 15 sequences from the SILVA SSU 115 database. The scale bar represents 0.10-fixed mutation per nucleotide position and bootstrap values above 50% are shown...... 154 Figure 5.3. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on the amino-acid matrix of 18 concatenated archaeal proteins present in 3 of the MBGE SAGs and other archaeal genomes. The same set of proteins from Korarchaeota was included as an out-group. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown...... 155 Figure 5.4. An overview of the proposed central cellular metabolism of MBGE...... 157 Figure 5.5. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on the amino acid of concatenated acetyl-CoA decarbonylase synthase (ACDS; cdhCDE) proteins from the SAGs and the references sequences, including methanogens, sulfate reducers, and acetogens. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown...... 159 Figure 5.6. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on deduced amino acid sequences of the dsrAB gene from the SAGs and the references sequences, including reductive and oxidative bacterial/archaeal type of dsrAB gene. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown...... 163 Figure 5.7. Predicted ABC transporters present in the SAGs ...... 167 Figure 5.8. Box plots showing the distribution in the isoelectric point of predicted proteomes of MBGE and other cultivated microorganims. The four panels show data for (a) the overall predicted proteomes, (b) protein-coding genes without any transmembrane domains (TMDs), (c), with a single TMDs, (d), and those with two or more TMDs...... 170 Figure S5.1. Visualization of Canonical Correspondence Analysis (CCA) using genomic features such as phylogenetic affiliation, tetranucleotide frequency (TNF), GC content, and size of the contigs ...... 178 Figure S5.2. Cluster of Orthologous Groups (COG) analysis reveals metabolic functions and the relative abundance that are present in the SAGs of MBGE ...... 179

LIST OF TABLES

Table 1.1. Geographic coordinates, physical and geochemical parameters, and Bacteria/Archaea ratio in the interfaces of five Red Sea brine pools ...... 55 Table 1.2. Number of amplicons, observed OTUs, and alpha diversity indices ...... 56 Table 1.3. Bacterial communities comparison using LIBSHUFF ...... 60 Table 1.4. Shared OTUs among the sampling locations and the relative abundance of bacterial 16S rRNA genes ...... 67 Table 2.1. Physical and geochemical parameters of the sampling locations ...... 80 Table 2.2. Number of clones, observed OTUs, and alpha diversity indices ...... 83 Table 2.3. OTU classification and the relative abundance of dsrA genes in each sampling locations ...... 88 Table 3.1. Taxonomic identification, location of collection and source of origin for 12 microbial strains ...... 101 Table 3.2. The comparison of percentage growth inhibition of HeLa, MCF-7 and DU145 cells after treatment with 1 mg/mL marine bacterial extracts in triplicate for 24 and 48 hr ...... 105 Table 4.1. Taxonomic identification and collection location for 24 microbial strains ... 124 Table 4.2. Evaluation of antiproliferative effects of marine bacterial extracts on various cancer cell lines ...... 126 Table 4.3. Summary of the results of PS exposure apoptosis assay, Caspase-3/7 activity, MMP changes, PARP-1 and Caspase-8 cleavage, full-length Caspase-9 and γH2Ax protein expression in HeLa cells treated with 500 µg/mL concentration of extracts ..... 134 Table 5.1. Assembly statistics of MBGE SAGs described in this study ...... 152 Table 5.2. List of gene involves in osmoadaptation ...... 169 Table S5.1. Single-cell Amplified Genomes (SAGs) ID, sampling location, and taxonomy classification of 16S rRNA genes of MBGE based on SILVA database ...... 171 Table S5.2. Geographic coordinates and physicochemical parameters of the sampling location ...... 172 Table S5.3. Number of contigs and total length of MBGE SAGs before and after the cleaning process ...... 172 Table S5.4. List of essential genes and the number of copies identified in the MBGE genomes ...... 173 Table S5.5. Best BLASTp hits for deduced amino acid sequences of the dsrAB gene against the UniProt database ...... 176 Table S5.6. List of predicted hydrogenases in the SAGs of MBGE ...... 177 17

INTRODUCTION

Deep-Sea Hypersaline Anoxic Basins

Deep-sea hypersaline environments are one of the most extreme ecosystems on Earth [1].

In the Red Sea, a series of hypersaline deeps associated with metalliferous sediments has been discovered at a depth of 1,400-2,850 meters below sea level [2]. These hypersaline deeps are formed due to the dissolution of a Miocene evaporates deposit that was exposed after the divergent movement of the Arabian and African continental plates [3, 4]. The plate movement formed several isolated topographical depressions within the Red Sea basin, and this process was followed by the migration and accumulation of salt-enriched waters on the seafloor [4].

Approximately 25 deep-sea anoxic brine pools occupy the axial section of the Red Sea

(Fig.1) [2, 5]. Based on morphotectonic characteristics, the axial zone of the Red Sea can be divided into the Northern Region, the Transitional Region, and the Multi-deeps

Region [4]. The Kebrit Deep is located in the Northern Region, where hydrogen sulfide is present in enormous quantities [5]. Several brine pools were discovered in the

Transitional Region (i.e., the Thetis, Nereus, Bannock, and Vema Deeps). In the Multi- deeps Region, some brine pools are currently thermally active, such as the Atlantis II and

Discovery Deeps; however, some brine pools in this region are inactive, such as the Erba

Deep [4].

The Red Sea brine pools are characterized by drastic changes in physicochemical conditions in comparison to the overlaying seawater, including increases in salinity (from 18

4% to up to 26%) and temperature (up to 69°C), altered concentrations of heavy metals, and decreases in pH and oxygen levels [6]. Many investigations have studied the geological and geochemical characteristics of the Red Sea brine pools [3, 6-9]. These surveys reported remarkable features, such as steep gradients in the brine-seawater interfaces (BSIs), stratification within the multilayered Atlantis II Deep, and the presence of hydrocarbon gasses in different brine pools [6, 10].

Each deep-sea brine in the Red Sea has unique and extreme physical features and chemical compositions (Fig. 2) [11]. Among the brine pools, the Kebrit Deep contains 19 vast amounts of carbon dioxide and hydrogen sulfide [5]. The basin of this anaerobic deep is filled by an 84-m thick and slightly acidic brine with a salinity level of up to 26%

[12].

Figure 2. Chemical profile of the brine pools in the Red Sea [11]

The Atlantis II Deep, which is the largest deep in the Red Sea, remains hydrothermally active, with a temperature that reaches 69°C [12]. Temperature increases in the Atlantis II

Deep occur as a result of the flux of hot brine from the bottom of the basin [13]. Due to this flux, multiple layers that differ in salinity and temperature were created [14]. The structure of the Atlantis II Deep consists of four convective layers (Fig. 3) with steep temperature and salinity gradients, including the Upper Convective Layers (UCLs) UCL1

(i.e., the BSI), UCL2, and UCL3 and the brine body, which is recognized as the Lower

Convective Layer (LCL) [14]. A 2008 study reported that an additional upper convective layer is developing below the BSI [15]. Heavy metals, such as zinc, copper, and 20 manganese, were found in notably high concentration in the Atlantis II Deep; these concentrations were 1,000 to 60,000 times higher than the estimated normal levels in seawater [8]. The iron concentration in the brine body of the Atlantis II Deep is approximately 100 times greater than the optimum range for the growth of sulfate- reducing prokaryotes [16].

To the southwest of the Atlantis II Deep, a brine pool called the Discovery Deep is separated from the Atlantis II Deep by a roughly structured bottom morphology (Fig. 4)

[12]. This brine pool extends to a depth of more than 2,200 m [17]. Although the

Discovery Deep is adjacent to the Atlantis II Deep, the physicochemical conditions of these two deeps are different. Discovery Deep possesses a lower temperature (44.7 °C in the basin) [12] and a four-fold lower abiogenic methane content [10].

Figure 3. Bathymetric map of the Atlantis II and Discovery Deep [39] 21

In addition to these reports, geochemical investigations have been conducted in the

Suakin, Nereus, and Conrad Deeps [5, 18]. Two basins were reported in the western and eastern portions of the Nereus Deep that differ markedly with respect to physicochemical parameters [19]. The western basin of the Nereus Deep is smaller, less saline, and may be present due to seepage or spillage from the main pool in the eastern basin [5]. The lower salt and metal contents observed in the western basin might be caused by mixing with the normal Red Sea water [20]. Calcium and magnesium were present in high concentrations in this brine pool [21].

Figure 4. Microbiological and geochemical profiling of the BSI of the Bannock Deep, Mediterranean [26]

Characteristics of the Brine-seawater Interface

The brine-seawater interfaces (BSIs) of the Red Sea brine pools are characterized by a steep gradient of temperature and salinity, which leads to stratified conditions in these 22 environments [22]. The high density of the BSI entraps the organic nutrients and minerals falling from the overlaying seawater [23, 24]. In addition, the BSIs represent an interface between the oxic-anoxic layers and a steep gradient of redox potential that can generate favorable conditions for divergent metabolic activities and environmental niches within the microbial community (Fig. 5) [25, 26].

The BSI represents a microbial hotspot that consists of diverse and metabolically active microbial communities [27-29]. Various biogeochemical processes actively occur in these environments, such as the in situ reduction of sulfate, heterotrophic, methanotrophic, and methanogenic activity [25]. The sulfur cycle and methanogenesis are recognized as two primary metabolic pathways that shape microbial colonization in the BSIs of most Deep Hypersaline Anoxic Basins (DHABs) in the Mediterranean Sea

[30]. In the Red Sea, methane oxidation was detected in the BSI of the Atlantis II Deep and was assumed to occur via an aerobic process performed by resident microorganisms

[10, 22].

Microbial Diversity in the Red Sea brine pools

In early studies, the brine pools of the Red Sea were initially regarded as being sterile

[31]. However, investigations of microbial communities in the last decade revealed the potential of the Red Sea brine pools as a haven for extremophile microbes [32-37]. To date, microbiological studies have been performed in the BSIs, the brine bodies, and the sediments of four brine pools (i.e., the Atlantis II, Discovery, Kebrit, and Shaban Deeps)

[11]. 23

Figure 5. Phylogenetic diversity of Archaea in the brine pools based on 16S rRNA. The representative sequences from the Red Sea brine pools, in red (SD, Shaban Deep; KD, Kebrit Deep; DD, Discovery Deep), the Mediterranean Sea, in blue (LAB, L’Atalante Basin; UB, Urania Basin; DB, Discovery Basin; BB, Bannock Basin), and those that occur in both brines, in purple [11] 24

Initial studies of microbial communities in the BSI were primarily carried out in the northern brine pools, such as the Kebrit [33] and Shaban Deeps [24], and recently focused on the hot brine pools, such as the Atlantis II and Discovery Deeps [29, 38].

Phylogenetic analysis based on 16S rRNA gene sequences in the BSIs of the Shaban and

Kebrit Deeps uncovered an unexpectedly high microbial diversity (Fig. 6) [24]. Reports indicated that novel lineages of Archaea and Bacteria thrived in both brines [33]. Most of the 16S rRNA sequences detected in both brines were very different from previously isolated prokaryotes or environmental sequences [24, 32]. The study also revealed two novel archaeal groups (i.e., the SA1 and SA2 groups) that were distantly related to

Methanomicrobiaceae and Halobacteriaceae (Fig. 6) [24].

Figure 6. Taxonomic classification of the bacterial and archaeal reads using the RDP classifier at 80% confidence level [29]

Pyrosequencing of the 16S rRNA genes revealed a distinctive microbial community structure in the stratified interface layers of the Atlantis II Deep (Fig. 7) [29]. Compared with the Atlantis II Deep, the diversity and abundance of microbes varied substantially in the BSI of the Discovery Deep, despite the presence of a similar microbial composition in 25 the overlaying deep-sea waters [29]. Consistent with previous studies that predicted biological methane oxidation [10, 22], phylogenetic analysis of pmoA demonstrated the existence of diverse communities of aerobic methanotrophs in the BSI of the Atlantis II

Deep [38].

Figure 7. KEGG maps showing significant differences in completeness between the ABP and DBP [40]

Pathways for methane oxidation were also detected in the brine body of the Discovery

Deep, as demonstrated by a recent metagenomic study [39]. In contrast, in the Atlantis II 26

Deep, the brine body was enriched with heterotrophic microbes that can degrade aromatic compounds (Fig. 8) [39]. Divergent bacterial compositions were present in both brine pools, and the majority of Bacteria were affiliated with the genus Cupriavidus [40]. The genus Cupriavidus is able to utilize aromatic compounds and is known as a metal- resistant bacterium due to its resistance to copper and other metals [41].

Figure 8. 16S rRNA gene-based phylogenetic tree of the bacterial domain, including the 16S rRNA sequences of the KB1 group and KT-2, the new halophilic isolates from Kebrit Deep [33] 27

Unique microbial consortia were found thriving in the sediments of the Atlantis II and

Discovery Deeps [42]. Many of these consortia are hypothesized to play a dominant role in the sulfur cycle [42]. In the Kebrit Deep, a novel phylogenetic lineage that branches between the Aquificales and the Thermotogales (Fig. 9; classified as candidate division

KB1) was recovered from the brine sediment [32]. Interestingly, candidate division KB1 was found to be the prominent group in the lower and saltier interface of the hypersaline

Lake Kryos and Discovery Deeps of the Mediterranean Sea, suggesting the preference of these organisms for high-salinity environments [43, 44].

Sulfate-reducing Communities in the Red Sea brine pools

Figure 9. The sequential pattern of microbial degradation of complex organic matter in anoxic environments in the presence (a) and absence of sulfate (b) [48]

Based on the molecular data that have accumulated in recent years, hypersaline environments harbor a more complex microbial community structure than previously assumed [45]. Metabolic diversity, such as anaerobic respiration, fermentation, and homoacetogenic, methanogenic, and methanotrophic activity, occur in these ecosystems

[46]. Sulfate reduction is one of the primary metabolic pathways that drive microbial colonization in most DHABs in the Mediterranean Sea [30].

Figure 10. Phylogenetic analysis of bacterial 16S rRNA sequences and dsrAB gene transcripts retrieved from the interface of the hypersaline Lake Kryos [43]

Sulfate-reducing prokaryotes are a phylogenetically diverse anaerobic group with a wide ecological distribution [47]. To yield energy, this group oxidizes hydrogen or organic compounds and reduces sulfate to sulfide (Fig. 11) [47]. Sulfate-reducing prokaryotes compete with other anaerobes, including homoacetogens and methanogens, for the available common substrates in anaerobic conditions with a low redox potential [48]. In classic terms, sulfate-reducing prokaryotes are defined as obligate anaerobes; nevertheless, pure cultures of sulfate-reducing prokaryotes can tolerate the presence of a certain amount of oxygen [49]. Some of these prokaryotes have several properties, such as catalase and superoxide dismutase activity, that allow them to cope with the presence of oxygen [50].

Figure 11. Microbial profile in the interface of the Discovery Deep, Mediterranean Sea, showing cDNA clone sequences of 16S rRNA (yellow) and cDNA dsrAB mRNA (red). D’bc indicates clones of Desulfobacteraceae and D’hb indicates clones of Desulfohalobiaceae [44]

Based on 16S rRNA sequences, the isolated sulfate-reducing prokaryotes can be classified into five bacterial lineages and two archaeal lineages [48]. The diversity of sulfate-reducing communities can be studied using functional genes that encode dissimilatory sulfate reductase (dsr), which is a key enzyme in the reduction of sulfite to hydrogen sulfide [51]. A study based on dsr gene sequences in the L’Atalante and Urania

Deeps in the Mediterranean Sea demonstrated that the sulfate-reducing communities were abundant, unique, and highly diverse [52]. The presence of active sulfate-reducing communities was detected in the hypersaline Lake Kryos (Fig. 12) and Discovery Deeps in the Mediterranean Sea [43, 44]. A phylogenetic analysis of dsrAB mRNA in the

MgCl2-rich Discovery Deep of the Mediterranean Sea revealed that Desulfohalobiaceae dominated in the saltier interface layer (from 1.60 up to 2.23 M MgCl2) (Fig. 13) [44].

Adaptation to Osmotic Stress in the brine pools

Microorganisms in the brine pools face multiple stresses, such as the absence of oxygen, hypersaline conditions, high temperatures, and heavy metals stress [11]. Adaptation to this stressful environment is essential for the survival and proliferation of these organisms

[53]. To survive, these organisms develop adaptive responses to cope with osmotic stress

[54]. Two different strategies are involved in the osmoadaptation mechanism in hypersaline environments [55]. First, the salt-in strategy involves inorganic salt accumulation and the generation of a saline cytoplasm to maintain osmotic balance [56].

The second strategy is based on the synthesis or accumulation of organic compatible solutes [57]. Osmoprotection is achieved via either the transport of betaine and choline or the synthesis of ectoine and hydroxyectoine [58]. In either case, high salinity induces the 31 upregulation of the glycine betaine/l-proline ABC transporter, leading to the accumulation of the osmoprotectant glycine betaine [47].

Isolation of Novel Microbes from Red Sea brine pools

Although the phylogenetic analysis of the Red Sea brine pools revealed unexpectedly high microbial diversity, few microorganisms could be isolated [11]. In the early 1960s, ten strains of Desulfovibrio were successfully isolated from the BSI of the Atlantis II

Deep [16]. However, these Desulfovibrio strains were not characterized. The first fully described novel bacterium isolated from the Red Sea brine pools was Flexistipes sinusarabici strain MAS 10 [59]. This moderately thermophilic bacterium belongs to the family Deferribacteraceae and can grow in the presence of up to 18% salinity [60]. In the

Shaban Deep, two novel species of Gammaproteobacteria (i.e., Salinisphaera shabanensis [34] and Marinobacter salsuginis [35]), were isolated from the BSI. In addition, in the sediment layer of this brine pool, the new bacterium Haloplasma contractile [36] and the extremely halophilic euryarchaeon Halorhabdus tiamatea were successfully isolated [37].

Genome analysis of these isolated microbes revealed that each microbe possesses unique features. For example, S. shabanensis has the physiological flexibility to survive in a broad range of stress conditions [34]. With respect to adaptation to extreme environments, the S. shabanensis genome contains multiple genes involved in heavy metal removal and iron uptake [61]. An analysis of H. contractile genomes revealed a complex morphology with contractile protrusions (Fig. 10) [62]. In addition, H. contractile was reported to have the highest copy number of MreB/Mbl homologs 32 involved in cytoskeleton synthesis [62].

Figure 12. SEM images of H. contractile show complex morphology and peculiar contractile protrusions [36]

The adaptive ability to survive in these hypersaline environments makes the inhabiting

Bacteria promising candidates for biotechnological applications and the search for novel bioactive molecules [11]. Cytotoxic and apoptotic evaluations of bacterial isolates from the Red Sea brine pools generated promising results and the possibility of further exploration [63]. Additionally, halophilic microorganisms produce stable enzymes that are capable of functioning under extreme conditions and can be used in industrial applications [64].

Bioactivity of the bacterial isolates from the Red Sea brine pools

The deep-sea ecosystems have been recognized as one of the species-rich habitats in marine environment [65, 66]. The deep-sea becomes an emerging isolation source of the bioactive compounds for anticancer therapy [67, 68]. These environments are a treasure trove of bioactive molecules produced by microorganisms, with thousands of compounds have been isolated and many of them have been tested in clinical trials [69, 70]. With the vast improvements in isolation and cultivation techniques, the discovery of unexplored chemical diversity from the deep-sea environments increased significantly [71].

Bisucaberin, Loihichelins, and phenazine derivative are the few examples of many secondary metabolites extracted from deep-sea microbes [67, 72-74]. Loihichelins A-F were extracted from Halomonas sp. LOB-5 [73]. Many strains of Halomonas have been known to produce exoenzymes, which are potentially useful in biotechnology applications [75]. For instance, heteropolysaccharides isolated from Halomonas stenophila strain B100 have displayed selective pro apoptotic effect on T leukemia cells

[76]. The novel compound 3-(4′- hydroxyphenyl)-4-phenylpyrrole-2,5-dicarboxylic acid

(HPPD-1) from Halomonas sp. strain GWS- BW-H8hM showed a promising result to inhibit tumor activities [77].

Single-cell Genomics

Deep-sea hypersaline anoxic basins (DHABs) of the Red Sea harbor many uncultivated microbial groups, which are widely known as candidate phyla [11]. Compared to other extreme environments and low-energy ecosystems, the candidate phyla inhabiting the deep-sea hypersaline anoxic brines are still largely unexplored [78, 79]. Extensive 34 microbial diversity and phylogenetic studies using next-generation sequencing technologies were not sufficient explain the ecological significance of the candidate phyla [78]. Recent single-cell genomics approaches by combining cell sorting and multiple-displacement-amplification (MDA) have improved our understanding on many candidate phyla in different extreme ecosystems [80-82].

Marine Benthic Group-E

Phylogenetic studies in Atlantis II Deep revealed the presence of many uncultured candidate phyla, such as Candidate division MSBL1 (Mediterranean Sea Brine Lakes-1), the deep-sea euryarchaeotic group (DSEG), deep-sea hydrothermal vent group 6, and

Marine Benthic Group E (MBGE) [29, 42]. In the sediments of the S-rich Atlantis II

Deep, Candidate division MBGE dominated with half of the archaeal sequences being assigned to this uncultivated group [42]. MBGE were deeply branching and not specifically associated with any known archaeal isolates [83]. They have been detected in various deep-sea environments from the sediments of Atlantic Ocean to the deep-sea hydrothermal vent in Western Pacific Ocean [84-86]. MBGE were found to be most abundant taxa in surficial layers in subseafloor of the Canterbury Basin, inactive sulfide chimney structure, and microbial mat at deep-sea hydrothermal field [87-89]. Some studies speculated MBGE might be associated with weathering of the metal-rich sulfide deposits based on their presence in iron-rich habitat [87, 90, 91]. Others suggested their metabolism is related to methanogenesis because their phylogenetic position between members of the Methanobacteriales and the Methanosarcinales [83, 92].

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OBJECTIVES

This study aimed to investigate the diversity of Bacteria, particularly sulfate-reducing communities, and their ecological roles in the interfaces of the Red Sea brine pools by combining molecular-based approaches and culture-dependent methods. Samples were collected from the interfaces of five Red Sea brine pools, which possess distinctive geological and physicochemical characteristics. Thus, performing a comprehensive study of bacterial diversity in these locations enabled us to understand the community structure and the potentially significant role of different bacterial groups in local geochemical cycles.

This research project consists of five chapters. Chapter 1 aimed to study the bacterial community structure in the interfaces of the Red Sea brine pools using 454 pyrosequencing and a Sanger sequencing-based clone library of 16S rRNA gene sequences. The study focused on the community compositions, predominant groups, and novel phylogenetic lineages that inhabit these extreme environments.

Sulfate reduction is regarded as one of the major energy-generating processes at the interfaces of deep-sea brines; nonetheless, the microbial communities involved in this process are largely unknown in the Red Sea brine pools. Therefore, in Chapter 2, a specific molecular marker that encodes the alpha subunit of dissimilatory sulfite reductase (dsrA) was used to study the diversity of the sulfate-reducing communities.

In addition to that molecular-based approach, Chapters 3 and 4 were dedicated to the isolation of microbes from the interfaces of the Red Sea brine pools. A culture-dependent 44 approach is essential for understanding the physiology and potential applications of isolated Bacteria. The adaptive ability to survive in these extreme environments makes the inhabiting Bacteria promising candidates in the search for novel bioactive molecules.

Hence, in these two chapters, extracts from the isolated Bacteria were tested for their anticancer potential against three human cancer cell lines.

Despite remarkable progress in isolation techniques and many isolation attempts, the majority of microorganisms from the Red Sea brine pools remain resistant to cultivation.

Consequently, the metabolism and ecological significance of the uncultured groups remain unknown. In Chapter 5, we screened more than one hundred single-cell genomes. We analyzed ten single-cell genomes from uncultured euryarchaeal Marine

Benthic Group-E (MBGE) that contain dissimilatory sulfite reductase (dsrAB), a key enzyme for sulfate reduction. This section aimed to broaden our understanding of the potential metabolism and adaptive strategies of this uncultured group in hypersaline environments.

Chapter 1: Phylogenetic diversity of bacterial communities in the

interfaces of five deep-sea anoxic brine pools in the Red Sea

1 1 2 1 1 Tyas Hikmawan , Yue Guan , Andre Antunes , David K Ngugi , and Ulrich Stingl

1 Red Sea Research Center King Abdullah University of Science and Technology

(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

2Computational Bioscience Research Centre, King Abdullah University of Science and

Technology (KAUST), Building 3, 23955-6900, Thuwal, Saudi Arabia

1.1. Abstract

Deep-sea anoxic brine pools are characterized by drastic physicochemical changes in the transition from the overlaying seawater to the brine body, including increases in salinity, temperature, and heavy metal concentrations and a shift from oxic to anoxic conditions.

The interface between the brine and the overlaying seawater (i.e., the brine-seawater interface (BSI)) represents an extremely peculiar environment that harbors a high microbial diversity and biomass. We conducted microbial community profiling in the interfaces of five Red Sea brine pools by combining 454 pyrosequencing and Sanger sequencing of the 16S rRNA gene, qPCR, and physicochemical analyses. This study aimed to uncover the composition of the bacterial communities, the predominant groups, and the significant role of these Bacteria in local geochemical cycles. Relative to archaeal

16S rRNA genes, Bacteria dominated over Archaea in most locations. Phylogenetic analysis revealed a high bacterial diversity in the interfaces. In the hot and multilayered

Atlantis II Deep, the bacterial communities were stratified, hardly overlapped, and shifted from Nitrospinae-dominated communities in the BSI to candidate division KB1- dominated communities in the lower interfaces. Meanwhile, in the colder brine pools,

Deltaproteobacteria were the most prominent bacterial groups associated with the BSIs of the Kebrit, Nereus, and Erba Deeps. The presence of niche-specific bacterial groups suggested that some groups were specially adapted to certain conditions in these extreme environments.

1.2. Introduction

Hypersaline bodies of water at the ocean bottom (i.e., brine pools) can be formed by the re-dissolution of evaporite layers [1]. Evaporite-based brine pools are present in the

Mediterranean Sea, the Gulf of Mexico, and the Red Sea [2]. In the Red Sea, there are approximately 25 deep-sea anoxic brines pools, which occupy the axial section of the

Red Sea and are associated with hypersaline brines and metalliferous sediments [2-4].

These environments are extremely saline (most of them are completely salt-saturated), anoxic, and characterized by drastic changes in physicochemical conditions when compared to the overlaying seawater [5-7]. The presence of short- and long-chained hydrocarbons was also reported in some of these brine pools [8-10].

The interface between the brine and the overlaying seawater (i.e., the brine-seawater interface (BSI)) represents an extremely peculiar environment; in all brine pools investigated to date, the BSI harbors a high microbial diversity and biomass [11-13]. The increase in microbial biomass can be explained by the drastic changes in density, which act as an in-situ particle trap for debris sinking through the water column and thus increase the concentrations of available nutrients [13-15]. In addition, the BSI also represents an oxic-anoxic boundary layer, where sharp changes in redox potential occur, providing a broad variety of environmental niches for the resident microbial communities

[16, 17].

In the last decade, an increasing number of microbiological studies revealed that the brine pools of the Red Sea are metabolically active and represent a source of novel extremophilic microbes [2, 18, 19]. Unique microbial consortia were found to thrive in the sediments of the Atlantis II and Discovery Deeps; many of these consortia are 48 hypothesized to play a dominant role in the sulfur cycle [20]. Recent metagenomic studies of the brine bodies of both deeps detected pathways for the degradation of aromatic compounds and methanogenesis [10, 21]. Despite these extensive studies, most of the Red Sea brine pools remain underexplored, particularly in the BSI. Initial studies of the microbial communities in the BSI were carried out primarily in the northern region brine pools, such as the Kebrit [22] and Shaban Deeps [13]. Recent studies focused on the hot brine pools (i.e., the Atlantis II and Discovery Deeps) [12, 23]. To broaden our understanding of the diversity of Bacteria and the significant role of bacterial groups in local geochemical cycles, we performed microbial community profiling in five geochemically distinct brine pools in the Red Sea, including the Erba and Nereus Deeps, which have never been studied previously. The objectives of this study were to understand the composition of the bacterial communities and the prominent groups that inhabit the interface of each brine pool. Considering the physicochemical characteristics of the interfaces in the Red Sea brine pools, it was reasonable to hypothesize that a high diversity of Bacteria and unique community structures occur in each sampling location.

To test our hypothesis, we performed 454 pyrotag and Sanger sequencing of the 16S rRNA gene, qPCR, and physicochemical analyses.

1.3. Methods

1.3.1. Sample collection

Water samples from the BSIs and the upper convective layers of the deep-sea brines were collected during the 3rd KAUST Red Sea Expedition on board the R/V Aegaeo in

November 2011. The samples were collected using a rosette sampler equipped with 10-l 49

Niskin bottles and a CTD (Conductivity, Temperature, and Depth) unit (Sea-Bird

Electronic, Bellevue, Washington, USA) for monitoring salinity, temperature, transmission, and pressure. Large volumes (ca. 200 l) of water were collected from the

Atlantis II Deep BSI (Ai1); the second, third, and fourth upper-convective layers of the

Atlantis II Deep (labeled Ai2, Ai3, and Ai4, respectively); the Discovery Deep BSI (Di); the Erba Deep BSI (Ei); the Kebrit Deep BSI (Ki); and the Nereus Deep BSI (Ni) in 20- liter carboys. Samples were sparged with N2 during filtration for approximately 45 minutes, pre-filtered with a five µm filter, and concentrated using a Tangential Flow

Filtration (TFF) system equipped with a 0.1 µm cassette filter. 200 ml of the concentrated sample were collected in 50 ml Falcon tubes, mixed with glycerol to a final concentration of 10%, and immediately stored at –20 °C. Cells in the retentate were captured on 0.1-µm filters (Durapore VVPP, Millipore) for DNA extraction.

1.3.2. DNA extraction

Nucleic acids were extracted from the 0.1 µm membrane filters after three cycles of freeze (in liquid nitrogen) and thaw (in a 65°C water bath) in 50-ml Falcon tubes. The filters were subsequently treated with 5 ml of lysis buffer (0.1 M Tris- EDTA, pH 8, 0.1

M Na2H2PO4, and 1.5 M NaCl), lysozyme (100 mg/ml TE), proteinase K (20 mg/ml),

RNAase, and 20% SDS, and nucleic acids were extracted with 6 ml of phenol- chloroform-isoamyl alcohol (24:24:1, by vol.). The samples were centrifuged at 2,500 × g for 5 minutes at room temperature to separate the phases. Approximately 90% of the upper, aqueous layer was removed and carefully transferred into a sterile Eppendorf tube.

At this stage, the aqueous phase was extracted a second time with an equal volume of 1:1 chloroform-isoamyl alcohol (24:1). The DNA extracts were transferred into Amicon 50

Ultra filter units (Amicon, Germany) pre-conditioned with 1 ml of 10 mM TE (pH 8) and centrifuged at 3,500 × g for 25 minutes to concentrate the DNA. Purification was performed using ethanol precipitation steps with 0.1 vol. of 3 M sodium acetate and 2 vol. of 96% ethanol and a final wash with 70% ethanol before air-drying the DNA extracts.

1.3.3. Pyrosequencing of 16S rRNA gene amplicons

PCR amplification of the bacterial 16S rRNA gene for pyrosequencing was performed using primers for the V3–V6 region (B343F/B1099rc) [24]. The primers consisted of the

FLX Titanium adaptors (Lib-L), a domain-specific primer fused with a two-nucleotide linker (to reduce unspecific binding) and a sample-specific 8-bp barcode for multiplexing. HPLC-purified primers were obtained from Eurofins MWG Operon

(Ebersberg, Germany). PCR was performed in 50-µl reactions that contained (final concentrations): 25 µl of the PfuUltra II Hot-start PCR master mix (Strategene, La Jolla,

CA, USA), 0.2 µM of each primer (Eurofins MWG, Ebersberg, Germany), and 10 ng of template DNA. The PCRs (in triplicate) were run with the following conditions: initial denaturation at 94°C for 2 min; 30 cycles at 94°C for 20 s, 48°C for 20 s, and 72°C for 2 min; and a final extension at 72°C for 10 min. The barcoded 16S rRNA gene amplicons

(850 bp) from the different samples were pooled together in equimolar amounts after purification with MiniElute Spin Columns (Qiagen, Hilden, Germany) and quantification with Quanti-iT PicoGreen (Molecular Probes Invitrogen, Darmstadt, Germany). The pooled amplicons library was then sequenced on a 454 GS FLX+ Sequencer (454 Life

Sciences, Branford, CT, USA) with one-fourth of a pico-titer plate each at our Genomics 51

Core Lab facility in KAUST (Thuwal, Saudi Arabia) as per the manufacture’s instructions. Pre-processing, operational taxonomic units (OTUs) clustering (3% distance threshold), and taxonomic assignment of sequence data was done using the MOTHUR software package v1.31 [25] as previously described [26]. The total number of post- processed bacterial reads were (405 ± 105 bp): 31,809 (Atlantis II Deep), 35, 825

(Discovery Deep), 3,592 (Erba Deep), 101,477 (Kebrit Deep), and 19,311 (Nereus Deep).

The bacterial reads were aligned into SILVA SSU 115 database embedded in MOTHUR.

The OTUs generated from the clustering were then used for calculation of alpha diversity indices such as Shannon and Chao1 index.

1.3.4. Bacterial clone libraries based on Sanger sequencing of the 16S rRNA gene

Partial 16S rRNA genes were amplified via PCR using combinations of the Bacteria- specific primer 27 F (5′-AGAGTTTGATCMTGGCTCAG-3′) paired with the universal primer 1492R (5′-GGTTACCTTGTTACGACTT-3′) [27]. The PCR reaction mixture contained, in a total volume of 25 µl, Taq DNA polymerase (New England Biolabs, UK),

10x ThermoPol reaction Buffer (New England Biolabs, UK), 2mM deoxynucleoside triphosphates, 10 µM forward and reverse primer (Sigma-Aldrich, Germany), and nuclease-free water. DNA from the sample was added as the template. The conditions for

PCR were 3 min at 94°C, 35 cycles of 1 min at 94°C, 1 min at 53°C, and 1.5 min at 72°C, and a final step for 7 min at 72°C. The quality and yield of the PCR products were examined using electrophoresis with agarose gels stained with SYBR Safe (Invitrogen,

California, USA). PCR products were purified by MinElute PCR Purification Kit

(Qiagen, Hilden, Germany) and were cloned into PCR®2.1 TOPO vectors (Invitrogen,

California, USA) according to the manufacturer’s instructions to construct bacterial 16S 52 rRNA clone libraries. Positive clones, from each library (807 bacterial clones) were randomly selected for plasmid extraction and bidirectionally sequenced using M13 primers with an ABI 3730 × l Capillary Sequencer at the Biosciences Core Laboratory at

KAUST. Raw 16S rRNA sequences were quality checked, trimmed and assembled using

Sequencher v.4.9 (Gene Codes Corporation, Ann Arbor, MI, USA).

1.3.5. Diversity and phylogenetic analysis of bacterial clone libraries

The assembled bacterial 16S rRNA sequences were aligned and analyzed using

MOTHUR v.1.31, yielding operational taxonomic units (OTUs) that were grouped at

97% stringency [25]. Potential chimeric sequences were removed using the Uchime 4.2 package [28], as implemented in MOTHUR 1.31. The diversity of bacterial OTUs and the community overlap were examined using rarefaction analysis, the Shannon index,

LIBSHUFF, and Venn diagrams in MOTHUR. LIBSHUFF statistical analysis [29] was performed to determine the signiﬁcance of differences in community composition. The

Bonferroni correction for multiple comparisons was applied in this analysis. If the P value generated by LIBSHUFF is below or equal to the Bonferroni correction P values

(0.0012 for the bacterial libraries), then we consider the community composition of two libraries to differ signiﬁcantly at 95% conﬁdence. Sequence alignment of the reference sequences and the bacterial OTUs and the recovery of closely related sequences from

GenBank using BLASTn [30] were performed using the automatic SINA aligner

(http://www.arb-silva.de/aligner/) and the SILVA SSU 115 database [31]. The SILVA- aligned sequences were then used to construct phylogenetic trees with the maximum likelihood algorithm as implemented in the ARB software v.5.3 [32]. Bootstrap analyses were performed with 1,000 repetitions. 53

1.3.6. Quantification of gene copy numbers using real-time PCR

The copy numbers of total bacterial and archaeal 16S rRNA genes from each sample location were determined via quantitative real-time PCR (qPCR) using the EXPRESS qPCR SuperMixes (Invitrogen) and a two-step qPCR cycling program on an ABI

7900HT Fast Real-Time PCR System (Applied Biosystem). The Bacteria-specific primers Bac518F and Bac786R were used for qPCR [33]. The qPCR protocols were performed as described previously [34]. The standards were generated from plasmids containing inserts of bacterial 16S rRNA gene sequences.

1.3.7. Correlations of the bacterial communities and the environmental factors

Multivariate analysis using an R software package vegan (http://cran.r- project.org/web/packages/vegan/vegan.pdf) was performed to examine the correlations of the microbial communities and the environmental influences. The percentages of relative abundance data of bacterial groups were used as the species input and the environmental parameters were functioned as the environmental input. The unconstrained Redundancy

Analysis (RDA) was chosen as the ordination methods along with vector fitting procedure to alleviate the interpretation.

1.3.8. Accession numbers of nucleotide sequences

The 16S rRNA gene sequences of Bacteria from the present study were deposited in

Genbank under accession numbers KM018335–KM019141 and KP083299– KP083370. 54

1.4. Results

1.4.1. Physicochemical characteristics of the sampling locations

Samples were collected from the interfaces of five different brine pools at the bottom of the Red Sea (Fig. 1.1). The physicochemical features of these samples reflected the intermediate nature of the BSI, with values between those of deep-seawater and brine

(Table 1.1). A noteworthy feature is the wide range of salinities (from 5.6% to 15.2%) and temperatures (from 31.9°C to 65°C) that were observed in samples from the Atlantis

II Deep, which result from the stratified nature of the BSI of this deep. Additionally, the methane concentration in the Kebrit Deep BSI was remarkably high, with values 30 times greater than those observed in the other locations (Table 1.1).

Figure 1.1. Sampling locations of five deep-sea brines in the Red Sea 55

1.4.2. Bacteria/Archaea ratio

The ratio of the copy numbers of bacterial and archaeal 16S rRNA genes was used as a

proxy for the abundances of those organisms in each sample, as estimated using qPCR.

The 16S rRNA gene copy number ratios of Bacteria to Archaea in the BSIs ranged from

0.15 to 148.51 (Table 1.1). Bacteria dominated over Archaea in the majority of the

sampling locations, except for the Kebrit and Atlantis II Deep BSIs.

Table 1.1. Geographic coordinates, physical and geochemical parameters, and Bacteria/Archaea ratio in the interfaces of five Red Sea brine pools

Atlantis II Discovery Erba Kebrit Nereus Sampling site Ai1 Ai2 Ai3 Ai4 Di Ei Ki Ni Latitude (N)1 21° 20.76' 21° 20.76' 21° 20.76' 21° 20.76' 21° 16.98' 20° 43.80' 24° 43.41' 23° 11.53' Longitude (E)1 38° 4.68' 38° 4.68' 38° 4.68' 38° 4.68' 38° 3.18' 38° 10.98' 36° 16.63' 37° 25.09' Depth (m)1 1998 2006 2025 2048 2038 2381 1467 2432 Salinity (%)1 5.6 8.6 11.2 15.4 13.8 9.8 18.2 15.4 Temperature (°C)1 31.8 46 52.1 65 35.8 23.1 21.9 27.9 pH1 6.5 5.82 5.71 5.18 6.63 7.7 5.67 7.65 Oxygen (µmol/l)1 2.2 0.5 0.5 0.5 0.3 0.3 4.6 0.3 Sulfate (mM)1 32.46 30.18 28.65 25.17 26.68 38.58 31.07 22.52 Nitrate (µM)1 55.3 37.9 46.1 21.9 30.1 29.0 28.4 24.2 Ammonia (µM)1 2.0 107.0 174.0 378.0 289.0 190.0 2017.0 650.0 Fe (µM)1 0.5 0.8 24.5 70.5 0.6 <0.1 40.4 0.5 Chloride (M)1 0.8 1.5 2.0 3.0 2.5 1.6 3.4 2.9 Na (M)1 0.7 1.4 1.8 2.9 2.3 1.5 3.2 2.5 1 H2S (µmol/l) b.d.l. b.d.l. b.d.l. b.d.l. b.d.l. b.d.l. 149.8 b.d.l.

CH4 (ppmV) b.d.l. 85.5 330 977 53 15.5 28955.5 23

CO2 (ppmV) 7878 18953 19288 12226 791.5 <400 103731 <400 Bacteria/Archaea ratio 0.62 17.89 3.63 13.96 8.35 19.92 0.15 148.51 16S rRNA gene Bac. 197 467 5.48 2.54 2380 241 343 79.90 4 copy number x10 Arc. 319 2.61 1.51 0.18 285 12.1 2220 0.53 b.d.l. below detection limit (~1 µM). 1 Physicochemical data refer to Ngugi et al. 2014. Abbreviations: Atlantis II Deep BSI (Ai1); second upper-convective layer (Ai2); third upper-convective layer (Ai3); the forth upper-convective layer (Ai4); Discovery Deep BSI (Di); Erba Deep BSI (Ei); Kebrit Deep BSI (Ki); Nereus Deep BSI (Ni) 56

1.4.3. Bacterial community structure revealed by 454 pyrosequencing

More than 190,000 post-processed reads were obtained from the BSIs of the Red Sea brine pools via 454 pyrosequencing targeting the hypervariable regions V3-V6 of the 16S rRNA gene. The numbers of qualified reads with an average read length of 405 ± 105 bp were 31,809, 35,825, 3,592, 101,477, and 19,311 for Ai, Di, Ei, Ki, and Ni, respectively

(Table 1.2). The Good’s coverage based on OTUs at 3% dissimilarity showed very high values (92-99%) in most of the bacterial libraries (Table 1.2). This result indicated that our sequencing efforts were sufficient to characterize the bacterial communities accurately. In general, the Shannon index was higher in the Erba, Kebrit, and Nereus

Deep BSIs. These values indicated that a greater diversity of Bacteria thrived in the BSI layer of the colder brines (Table 1.2).

Table 1.2. Number of amplicons, observed OTUs, and alpha diversity indices No of Observed Chao1 Shannon Sample ID qualified Coverage OTUs index index reads/clones 454 pyrosequencing Ai 31809 488 919.72 3.12 0.99 Di 35825 320 941.30 1.60 0.99 Ei 3592 280 542.86 3.35 0.94 Ki 101477 1906 4928.17 3.60 0.98 Ni 19311 1667 5400.04 4.67 0.92 Sanger sequencing-based clone library Ai1 92 18 27 1.84 0.85 Ai2 177 59 116.3 3.43 0.80 Ai4 23 13 26 2.24 0.65 Di 115 53 116 3.30 0.70 Ei 143 91 213 4.03 0.68 Ki 153 73 320.5 3.03 0.86 Ni 104 53 93.62 3.65 0.68 Bacterial library of Ai3 is not generated due to insufficient sample 57

Most of the sequences in the BSIs of the Red Sea brine pools could be assigned to a known phylum using the SILVA SSU 115 database with 3% stringency at a confidence threshold of 80% (Fig. 1.2). Nitrospina-related sequences were a prominent component of the bacterial communities in the Atlantis II Deep BSI (40%), followed by

Alphaproteobacteria (19%), Zetaprotobacteria (15%), Gammaproteobacteria (10%), and

Epsilonproteobacteria (3%) Meanwhile, Gammaproteobacteria predominated in the

Discovery Deep BSI, constituting more than 80% of the total sequences in this location

(Fig. 1.2). OTUs related to Deltaproteobacteria were barely detected in the BSIs of the hot brine Atlantis II and Discovery Deeps. In contrast, the majority of the sequences in the Erba, Kebrit, and Nereus Deep BSIs were associated with Deltaproteobacteria

(relative abundance values of 52, 48, and 53%, respectively) (Fig. 1.2). Various bacterial phyla were detected at low abundance in the BSIs of the Red Sea brine pools, such as

Deferribacteres, Bacteroidetes, Choloflexi, candidate division OP8, and Spirochaetes.

Deltaproteobacteria 2%

19% Alphaproteobacteria

Gammaproteobacteria 82%

Proteobacteria

Proteobacteria 15% Zetaproteobacteria

Atlantis II Deep Discovery Deep Nitrospinae

10% Gammaproteobacteri Actinobacteria

Nitrospinia 40% 5% Deferribacteres 3% Epsilonproteobacteria a Bacteroidetes

Fusobacteriia1% Acidimicrobida2%

17% Flavobacteria

e Figure 1.2. Taxonomic classification of the 454 pyrosequencing reads based on ARB- SILVA 16S rRNA SSU 115 database

Cont. Figure 1.2

3% Alphaproteobacteri 11% Alphaproteobacteria

21% Gammaproteobacteria 9% Gammaproteobacteria

Proteobacteria Proteobacteria

Erba Deep Deltaproteobacteria 53% Deltaproteobacteria 52% Nereus Deep

9% Deferribacteres 2% Deferribacteres 1% Acidimicrobiia

4% Candidate division OP8

Chloroflexi 3 more 3% Anaerolineae 2% Sphingobacteriia

2% Dehalococcoidia 2% Candidate division OP8 Bacteroidetes 3% Clostridia 1% Cytophagia 1% BHI80-139 2% Sphingobacteriia

Alphaproteobacteria 6%

8% Gammaproteobacteria

Proteobacteria

27% Epsilonproteobacteria

Kebrit Deep

Deltaproteobacteria 42% 2% Deferribacteres

6% Spirochaetes 2% BD1-5

1.4.4. Bacterial community analysis based on clone libraries

Phylogenetic analysis based on 16S rRNA gene sequences from Sanger sequencing

revealed a high microbial diversity in the brine interfaces of geochemically distinct brine

pools of the Red Sea (Fig. 1.3). A total of 807 bacterial sequences (>1,400 bp length)

were obtained from the BSIs of the five brines (i.e., Ai, Di, Ei, Ki, Ni) and in the upper

convective layers of the Atlantis II Deep (i.e., Ai2 and Ai4). These non-chimeric 16S 59 rRNA bacterial gene sequences were clustered into 281 OTUs (at a 97% sequence identity level).

Ni (15.4%)

Ei (9.8%)

Di (13.8%)

Ai2 (8.6%)

Ki (18.2%)

Ai1 (5.6%)

Ai4 (15.4%)

0.05 50% 100%

Deltaproteobacteria Gammaproteobacteria Deferribacteres Alphaproteobacteria Plactomycetes Chloroflexi Candidate Div. OP8 Candidate Div. OP11 Candidate Div. OD1 Firmicutes Nitrospirae Actinobacteria Bacteroidetes Candidate Div. KB1 Candidate Div. OP3 Spirochaetes Unclassified Nitrospinae

Figure 1.3. Taxonomic classification, relative abundances, and hierarchical cluster analysis (Jaccard similarity index) of bacterial communities based on Sanger sequencing in the interfaces of the Red Sea brine pools

Good's coverage, Shannon index, and LIBSHUFF analysis were performed to estimate and evaluate the coverage and diversity of the microbial communities (Table 1.2). The coverage values for all bacterial clone libraries were lower than the corresponding values for 454 pyrosequencing. The lower value of the coverage for bacterial libraries may reveal the need for deeper sequencing to avoid underestimating the bacterial diversity in these sampling locations (Table 1.2). The Shannon index values revealed a relatively higher bacterial diversity (ranged from 3.03 to 4.04) in the BSIs of the colder brine pools, such as the Erba, Kebrit, and Nereus Deeps (Table 1.2). In contrast, the BSI of the

Atlantis II Deep (Ai1) contained a less diverse community than the BSIs of the other locations (1.84).

Hierarchical cluster analysis based on the Jaccard similarity index revealed no apparent clustering, although the Erba and Nereus Deeps were more similar, implying that in general, the bacterial communities were distinct (Fig. 1.3). This result is consistent with the LIBSHUFF analysis, which demonstrated that the bacterial communities in all of the locations were significantly different, except for the communities in the Erba and Nereus

Deeps (Table 1.3). Interestingly, despite being separated by only a few meters, the BSI and the adjacent convective layers of the Atlantis II Deep differed significantly (Table 1.1 and 1.3).

Table 1.3. Bacterial communities comparison using LIBSHUFF

Bacteria Ai1 Ai2 Ai4 Di Ni Ki Ei Ai1 - <0.0001* <0.0001* <0.0001* <0.0001* <0.0001* <0.0001* Ai2 <0.0001* - 0.0258 <0.0001* <0.0001* <0.0001* <0.0001* Ai4 <0.0001* <0.0001* - 0.0134 <0.0001* <0.0001* 0.0819 Di <0.0001* <0.0001* <0.0001* - <0.0001* <0.0001* <0.0001* Ni <0.0001* <0.0001* <0.0001* 0.0001* - <0.0001* 0.3421 Ki <0.0001* <0.0001* <0.0001* <0.0001* <0.0001* - <0.0001* Ei <0.0001* <0.0001* <0.0001* <0.0001* 0.0016 <0.0001* - *Significant different Bonferroni correction P value= 0.0012 -Not compared

A wide diversity of Bacteria was observed at all sampling locations. The majority of bacterial 16S rRNA sequences were affiliated with Proteobacteria (relative abundance ranged from 37.5–89.4%), Bacteroidetes (0.8–13.2%), Deferribacteres (0.8–13.9%), and

Chloroflexi (1.1–4.2%) (Fig. 1.3). The remaining bacterial sequences belong to uncultured bacterial groups with an unknown physiology, such as candidate division

KB1, MSBL 2, and ST12-K34 (Fig. 1.4).

96 Clone Ei 100 Clone Ki 97 Clone Ai2 67 Candidatus Scalindua brodae (EU142948) 100 Clone Ai2 79 Clone Di 96 Clone Ki 100 Candidatus Kuenenia stuttgartiensis (CT573071) Planctomycetes 100 Clone Ai2 100 87 Urania Group 100 Clone Di 52 uncultured Planctomycetales from low temperature hydrotherml oxides (JN860337) Clone Di Clone Ai2 100 Lenthisphaeria 100 Clone Ai2 uncultured bacterium from Kazan Mud Volcano, East Mediterranean Sea (FJ712442) 100 89 Candidate Division OP8 100 Clone Ei 91 uncultured Acidobacteria from inactive hydrothermal field (FJ205223) Acidobacteria 54 Clone Ai2 99 Clone Di 100 Clone Ei 55 Clone Ai2 100 Clone Ai1 Nitrospinae 72 Nitrospina gracilis (L35504) 100 Clone Ki uncultured Nitrospinaceae (AB177223) 100 Candidate Division TM6 99 Marine Group A 100 Clone Ai1 100 Clone Di 94 Halobacteroides halobius (U32595) 97 Halanaerobium praevalens (CP002175) Firmicutes 100 Clone Di 100 Clone Ei 95 Clostridium caminithermale (AF45877) Haloplasma contractile (EF999972) 100 Spirochaetes MSBL2 Group 97 Anaerophaga thermohalophila (AJ418048) Cytophaga fermentans (D12661) 77 Clone Ei 65 Clone Ki 100 Di 100 Zunongwangia profunda (CP001650) Bacteroidetes 94 Flavobacterium limicola (AB075230) 100 Ei 67 Saprospira grandis (CP002831) 100 Clone Ei 100 Clone Ni 67 Clone Ei uncultured SB1 group from Shaban Deep (AJ347765) 73 Clone Ai2 Caldithrix abyssi (AJ430587) Deferribacteres 89 Clone Ei Clone Ni 98 Clone Ei 100 Clone Ki 100 Clone Ai2 100 Clone Ai1 Actinobacteria Candidatus Microthrix parvicella (X89774) 100 Ferrimicrobium acidiphilum (AF251436) 64 Aciditerrimonas ferrireducens (AB517669) 92 Thermoleophilum minutum (AJ458464) Clone Ai2 97 Clone Ai2 53 Clone Ei Gemmatimonadetes Gemmatimonas aurantiaca (AP009153) Candidate Division OP11

69 Candidate Division OD1 99 BD1−5 Group 100 Candidate Division OP3 100 Clone Ki 89 uncultured 'KTK 41' from Kebrit Deep (AJ133619) 98 metagenome from hypersaline Lake Thetis (AGBK01000802) 100 Clone Ki Candidate 84 uncultured 'KTK 14' from Kebrit Deep (AJ133615) 100 Clone Ai2 Division KB1 66 Clone Di 100 Clone Ai4 uncultured KB1 from iron oxide chimney (FJ905663) 100 Clone Di 74 uncultured Chloroflexi from seafloor sediments (AY542195) 100 Anaerolinea thermophila (AB046413) 89 Clone Ai1 84 Clone Di 98 Clone Ei Chloroflexi 56 Caldilinea aerophila (AB067647) uncultured Chloroflexi from saline-alkaline soil (JN037919) Clone Ai2 100 Clone Ai4 Clone Ei 100 Clone Di 100 uncultured Nitrospiraceae from hydrothermal oxide (JN860329) 66 Clone Ai2 100 Clone Ai2 97 Thermodesulfovibrio hydrogeniphilus (EF081294) Nitrospirae 100 Clone Ei 57 Nitrospira moscoviensis (X82558) 89 Clone Di Leptospirillum ferrooxidans (X86776) Thermodesulfobacterium thermophilum (AF334601)

0.10 Atlantis II Deep BSI Atlantis II Deep Ai4 Discovery Deep BSI Nereus Deep BSI Atlantis II Deep Ai2 Kebrit Deep BSI Erba Deep BSI Mediterranean DHABs

80 Clone Di 50 Marinobacter hydrocarbonoclast (X67022) 90 Marinobacter salsuginis (EF028328) 100 Clone Ai2 100 Marinobacter vinifirmus (DQ235263) 50 Clone Di Clone Di 100 Pseudomonas mendocina (Z76664) 70 Clone Ei 80 Clone Ai1 100 Clone Di 70 Chromohalobacter salexigens (CP000285) 90 Clone Ai4 100 Salinicola halophilus (AJ427626) 90 Clone Di 100 Halomonas axialensis (AF212206) Clone Ai2 Gammaproteobacteria 80 Clone Ai2 60 Alteromonas simiduii (DQ836766) 100 Alteromonas macleodii ATCC 271 (CP003841) Clone Di 100 Clone Di 80 Idiomarina baltica (AJ440214) Clone Ei 100 Clone Ai1 uncultured SUP05 cluster (HQ163057) Fangia hongkongensis (AB176554) 60 Clone Ai1 50 Clone Ni 100 Clone Di Legionella gresilensis (AF122883) 100 Clone Ki 50 Thiomicrospira halophila (DQ390450) Clone Ki Methylomonas scandinavica (AJ131369) Clone Ai2 100 Thiohalospira halophila (DQ469576) 80 Clone Ei 100 Clone Ai4 Betaproteobacteria 90 Cupriavidus metallidurans CH34 (CP000352) 100 Clone Ki 60 Methylophaga thiooxydans DMS01 (DQ660915) 60 Clone Ai1 70 Red Sea bacterium KT−2K34 (AJ309526) Zetaproteobacteria 100 Clone Ai2 50 Mariprofundus ferrooxydans (EF493243) 80 Clone Ai2 Constrictibacter antarcticus (AB510913) Clone Ei 50 Clone Di 90 Pelagibius litoralis (DQ401091) Clone Ei 100 Clone Ai1 Clone Ei 100 Clone Di Alphaproteobacteria 90 Sulfitobacter pontiacus (DQ915637) 100 Rhodobacter veldkampii (D16421) 100 Clone Ai1 100 Clone Ai4 90 Clone Di 50 Clone Ki 100 Candidatus Pelagibacter ubique (AF510192) 80 Clone Ei 50 Clone Ki 100 Clone Ki 100 uncultured Magnetococcus sp. (GU979423) 60 Magnetococcus marinus MC−1 (CP000471) 100 Deltaproteobacteria

50 Clone Ki 70 Sulfurovum lithotrophicum (AB091292) 100 uncultured Epsilonproteobacteria (AJ441620) 100 Clone Ki Epsilonproteobacteria 100 Sulfurimonas denitrificans (L40808) Thioreductor micantisoli (AB175498) Fusobacterium nucleatum (AB540989)

0.10

Atlantis II Deep BSI Atlantis II Deep UCL4 Discovery Deep BSI Nereus Deep BSI

Atlantis II Deep UCL2 Kebrit Deep BSI Erba Deep BSI

Figure 1.5. 16S rRNA gene-based phylogenetic tree showing the affiliation of Proteobacterial lineage detected from the interfaces of the Red Sea brine pools. The tree was constructed by maximum likelihood analysis using ARB. Taxonomy is based on SSURef_115_SILVA (http://www.arb-silva.de). The scale bar represents 0.10 fixed mutation per nucleotide position. Bootstrap values above 50% are shown.

The bacterial communities in the Atlantis II Deep shifted from Nitrospinae-dominated communities (~80%) in the upper interfaces to candidate division KB1-dominated communities in the deepest convective layer (Fig. 1.3). Candidate division KB1 is a group with no cultured representatives that branches between Aquificales and

Thermotogales (Fig. 1.4). Gammaproteobacteria, especially Halomonodaceae-related sequences, dominated the bacterial communities (~50%) in the BSI of the Discovery

Deep (Fig. 1.3 and 1.5). Other groups, such as Epsilonproteobacteria, were only detected in the Kebrit Deep BSI. The closest relatives of these sequences were Sulfurimonas sp. and Sulforovum sp. (Fig. 1.5). In colder brines, such as the Kebrit, Erba, and Nereus

Deeps, sequences affiliated with Deltaproteobacteria were dominated the BSIs (Fig. 1.3).

In contrast, Deltaproteobacteria were less abundant in the BSIs of the hot brines in the

Atlantis II and Discovery Deeps.

As shown in Fig. 1.6, most of the OTUs in the class Deltaproteobacteria were classified into four orders: Desulfobacterales, Desulfurellales, Desulfovibrionales, and

Syntrophobacterales. The typical activities of the members of Deltaproteobacteria that were found in these BSIs suggest that these organisms may be responsible for sulfate reduction. The remaining OTUs were assigned into four lineages with no cultured representatives, such as 10bav-F6, DTB120, Candidatus Entotheonella, and SAR 324

(Fig. 1.6). A large fraction of the Deltaproteobacteria (~33.3% of the total

Deltaproteobacteria sequences) in Ai2 could not be classified to any known family based on the ARB-SILVA SSU 115 database and presumably constituted novel lineages of

Deltaproteobacteria. 64

95 Desulfobacula phenolica (AJ237606) 72 OTU 182, Di_RS21(KM018627) OTU 14, Ai2_RS90 (KM018505) 97 OTU 213, Ei_RS249 (KM018932) 98 98 OTU 6, Ki_RS16 (KM018988) 81 Guerrero Negro Hypersaline Mat (JN509209) Desulfobacterium autotrophicum HRM2 (CP001087) 61 Desulfocella halophila (AF022936) 72 Desulfonema limicola (U45990) 100 OTU 202, Di_RS53 (KM018645) 98 Desulfosalsimonas propionicica (DQ067422) Desulfobacterales 82 OTU 12, Ni_RS4 (KM019133) OTU 214, Ki_RS60 (KM019030) 86 65 low temperature hydrothermal oxides (JN860325) 55 OTU 3, Ni_RS1 (KM019131) 59 OTU 65, Ei_RS47 (KM018777) 99 100 OTU 165, Ei_RS46 (KM018776) Desulfatiglans anilini (AJ237601) OTU 135, Ki_RS67 (KM019034) 100 OTU 132, Ei_RS181 (KM018883) 100 subseafloor sediment of the South China Sea (EU385910) 10bav-F6 marine sediment (EU181479) 100 Desulfurella acetivorans (X72768) 100 Desulfurella propionica (Y16942) 88 Hippea maritima DSM 10411 (CP002606) Desulfurellales 100 OTU 38, Ai2_RS61 (KM018482) saturated zone of the Hanford Site (HM187256) 91 80 Desulfohalobium utahense (DQ067421) 92 OTU 153, Ki_RS6 (KM018979) 100 Desulfohalobium retbaense (U48244) Desulfovibrionales 71 Desulfothermus okinawensis (AB264617) 87 Desulfonauticus autotrophicus (FJ194951) 89 iron oxide chimney−like structure, Tonga Arc (FJ905689) 68 100 subseafloor massive sulfide deposit (AB722091) DTB120 OTU 169, Ai2_RS120 (KM018524) 100 Thermodesulforhabdus norvegica (U25627) 90 Desulfacinum infernum (L27426) Syntrophobacterales 100 OTU 80, Ai2_RS68 (KM018488) active hydrothermal field sediments (FJ205259) 51 100 uncultured Candidatus Entotheonella sp.(AY897120) Lake Medee deep-sea hypersaline anoxic basin, Mediterranean Sea (JF809701) 99 OTU 27, Ai2_RS7 (KM018431) 100 active hydrothermal field sediments (FJ205265) 50 OTU 178, Di_RS27 (KM018630) 95 OTU 167, Ai2_RS112 (KM018519) 86 100 OTU 18, Ai2_RS102 (KM018514) Candidatus Entotheonella Manantial del Toro hypersaline groundwater (JF747662) 58 59 OTU 75, Ai2_RS73 (KM018491) 92 100 OTU 36, Di_RS62 (KM018652) subseafloor sediment of the South China Sea (EU386067) 99 seafloor lavas of Loi'hi Seamount Pisces Peak (EU491182) 100 OTU 201, Di_RS54 (KM018646) Lake Medee deep-sea hypersaline anoxic basin, Mediterranean Sea (JF809757) SAR324 cluster bacterium JCVI−SC AAA005 (AGAU01000387) 100 100 OTU 97, Ki_RS241 (KM019101) 100 OTU 235, Ai4_RS23 (KM018619) SAR 324 71 uncultured SAR324 cluster bacterium (HM798884) OTU 29, Ni_RS13 (KM019141) 88 Desulfuromonas chloroethenica (U49748) 93 Desulfuromonas thiophila (Y11560) Desulfuromusa ferrireducens (AY835392) 100 Pelobacter propionicus DSM 2379 (CP000482) Geobacter chapelleii (U41561) Mariprofundus ferrooxydans (EF493243)

0.10 Atlantis II Deep BSI Atlantis II Deep Ai4 Discovery Deep BSI Nereus Deep BSI Atlantis II Deep Ai2 Kebrit Deep BSI Erba Deep BSI Mediterranean DHABs

Figure 1.6. 16S rRNA gene-based phylogenetic tree of the Deltaproteobacteria, including the representative sequences from the Atlantis II, Discovery, Erba, Kebrit, and Nereus Deep. The topology of the tree is based on maximum-likelihood method with 1000 bootstraps. The scale bar represents 0.10 fixed mutation per nucleotide position. Bootstrap values above 50% are shown.

1.4.5. Relationship between bacterial communities and the environmental factors

Redundancy analysis (RDA) was performed to determine the influences of the

environmental parameters on the bacterial communities as shown in Figure 1.7. The

arrow points to the direction of most rapid change in the environmental variable. The

length of the arrow is proportional to the correlation between ordination and

environmental variable. Temperature is the only statistically significant factor affecting 65 the bacterial communities in the interfaces of Red Sea brine pools. Deltaproteobacteria as an abundant group in the colder brines and bacterial groups such as candidate division

OP3 and candidate division OP8 (rarely present in the Atlantis II and Discovery Deep) showed a negative correlation with the temperature. On the contrary, the presence of candidate division KB1 and OP1 were correlated positively with the temperature. Both groups dominated the bacterial communities in the Atlantis II Deep interface layer Ai4. 1.5

sit4Di

Ai2sit2 1.0

2 11 3 5

13 0.5

Temperature Salinity

PC2 6 9 sit6Ki 0.0 15 Nitrate 1 7 sit7Ni 17 4 Oxygen12 8 14 pH 16

-0.5 Sulfate Ai4sit3

Ai1sit1

sit5Ei

18 -1.0

-1.5 -1.0 -0.5 0.0 0.5 1.0 PC1 Figure 1.7. Redundancy Analysis (RDA) ordination plots showing relationship between environmental factors and bacterial communities. Red arrows indicate the bacterial taxa, Deltaproteobacteria (1), Gammaproteobacteria (2), Deferribacteres (3), Alphaproteobacteria (4), Planctomycetes (5), Chloroflexi (6), Candidate division OP8 (7), Candidate division OP1 (8), Candidate Division OD1 (9), Firmicutes (10), Nitrospirae (11), Actinobacteria (12), Bacteroidetes (13), Candidate division KB1 (14), Candidate division OP3 (15), Spirochaetes (16), Nitrospinae (17), Other groups (18). Blue arrows indicate environmental parameters measured in the sampling sites. 66

Principal component analysis of the bacterial communities (marked with the box in Fig.

1.7) displayed no apparent clustering of bacterial communities among different sampling locations. The pattern is in good agreement with the hierarchical cluster analysis based on the Jaccard similarity index (Fig. 1.3). Distinct community profile occurred in the different layers of the Atlantis II Deep.

1.5. Discussion

The predominance of Bacteria over Archaea appears to be a general trend in the BSIs

(and brine bodies) of brine pools from the Red Sea ([12, 21]; this study) and the

Mediterranean Sea [16, 35-37]; the exceptions to this trend are the BSIs of the Kebrit and

Atlantis II Deeps (this study) and the brine layer of the Urania Deep in the Mediterranean

Sea [35]. The probable explanations for these discrepancies include the fact that these brine pools are highly sulfidic (in the Kebrit and Urania Deeps, ~150 and 10 mM H2S, respectively; [17]; Table 1); in addition, in the case of the Atlantis II Deep, the in situ temperature reaches up to 52°C (Table 1). The BSIs of the Kebrit and Atlantis II Deeps also have dissolved oxygen (DO2) concentrations that are 4–9 times higher than the other locations, where DO2 is close to depletion (Table 1.1).

A detailed analysis of 16S rRNA gene sequences showed that highly diverse bacterial communities colonized the BSI in all locations (Fig. 1.4 and 1.5). This result is consistent with previous studies of the Red Sea brine pools [12, 13, 22]. The high diversity of

Bacteria in the BSIs firmly supported the argument that the accumulation of various nutrients in these chemoclines may enhance microbial diversity and activity and biogeochemical cycling [16, 22]. 67

Table 1.4. Shared OTUs among the sampling locations and the relative abundance of bacterial 16S rRNA genes

Acc. no of a Shared Phylogenetic Relative abundance (%) Domain representative OTU Affiliation sequence Ki Ei Ni Di Ai1 Ai2 Ai4 Bacteria OTU3 KM018757 Desulfobacteraceae - 0.2 1 2.6 - - - OTU5 KM018689 Halomonadaceae - - - 24.3 1.1 - - OTU7 KM019134 SAR406 - 0.1 1 0.9 - 0.6 - OTU12 KM018842 Desulfobacteraceae - 0.1 1 1.7 - - - OTU13 KM018808 Candidatus Scalindua 0.7 0.1 - - - 3.4 - OTU14 KM018800 Desulfobacteraceae 3.9 0.1 - - - 1.1 - Gammaproteobacteria OTU17 KM018890 E01-9C-26 group 2 0.1 - - - 1.1 - OTU19 KM018941 Acidimicrobiaceae 2 0.1 - - 2.2 0.6 - OTU21 KM018785 CD. OP3 - 0.1 1.9 - - - - OTU23 KM018927 CD. OP8 - 0.1 1 - - 0.6 - OTU24 KM018592 Nitrospinaceae - - - - 1.1 3.4 - OTU28 KM018812 CD. OP3 - 0.1 - 0.9 - - - OTU29 KM018950 SAR324 - 0.1 1 0.9 - - - OTU32 KM018372 Mariprofundaceae - - - - 5.4 0.6 - OTU33 KM018974 SAR11 0.7 0.1 - 2.6 - - - OTU34 KM018857 SAR406 - 0.1 - 0.9 - - - OTU35 KM018734 Alteromonadaceae - - - 3.5 - 0.6 - OTU36 KM018506 Nitrospinaceae - - - 0.9 - 2.3 - OTU54 KM018721 Nitrospinaceae 0.7 0.1 - 0.9 - - - OTU56 KM018968 Phycisphaeraceae - 0.1 - 0.9 - - - OTU57 KM018934 Sphingobacteriaceae - 0.1 1 - - - - OTU77 KM018692 Halomonadaceae - - - 0.9 - 1.1 - OTU87 KM018815 SAR406 - 0.1 - 0.9 - - - OTU97 KM019101 SAR324 0.7 0.1 - - - - - aThe calculation of relative abundace is based on the number of clones in each OTU divided by total clones per location Abbreviation: CD (candidate division)

The composition of the bacterial communities in the BSIs was analyzed using 454

pyrosequencing and a Sanger sequencing-based clone library. The results revealed a

different bacterial composition in each brine pool (Fig. 1.2 and 1.3). The percentages of

the dominant bacterial groups in the 454 pyrosequencing libraries were broadly

consistent with the Sanger sequencing-based clone libraries, even though some minor 68 genera were not detected in the Sanger sequencing-based clone libraries. We observed stratification of the bacterial communities across the multiple interfaces of the Atlantis II

Deep (Table 1.4), which is consistent with previous studies [12, 38]. Temperature and salinity were identified as the major environmental factors affecting the microbial community in the multilayer Atlantis II Deep [12]. Redundancy analysis (RDA) using our datasets also demonstrated that the temperature influenced the bacterial communities in the interfaces of the Red Sea brine pools (Fig. 1.7).

The physicochemical gradients associated with the stratified interfaces of the Atlantis II

Deep offer a variety of ecological niches for different groups of Bacteria [2, 17]. Based on the 454 pyrosequencing data (Fig. 1.2), sequences belonging to a putative nitrite- oxidizing Nitrospina-like cluster comprised approximately half of the 16S rRNA gene sequences recovered from the Atlantis II Deep BSI; however, this cluster was only marginal in the other BSIs. Nitrospina are predicted to play a fundamental role in oceanic nitrite oxidation [39]. In a previous study, the local archaeal community in the Atlantis II

Deep BSI was composed primarily of a single, highly abundant Nitrosopumilus-like phylotype [38]. The co-occurrence of Nitrospina and archaeal ammonia-oxidizing

Nitrosopumilus suggested that Nitrospina might be natural nitrite-oxidizing partners of ammonia-oxidizing Archaea [38]. This situation was also reported in other marine environments [39, 40]. We hypothesized that both groups might play a substantial role in the local nitrogen cycle of this location.

from Kebrit Deep Red Sea

from Kebrit Deep Red Sea 4

4 i2i2

Anaerobaculum thermoternum

Atlantis II Deep Ai2 Discovery Deep BSI Mediterranean DHABs Atlantis II Deep Ai4 Kebrit Deep BSI Red Sea DHABs previous studies

Figure 1.8. 16S rRNA gene-based phylogenetic tree of the candidate division KB1 group, including the representative sequences from the previous studies in the Red Sea brine pools, the Mediterranean Deep Hypersaline Anoxic Basins (DHABs), and the Atlantis II, Discovery, Kebrit Deep BSIs in this current study. The topology of the tree is based on maximum-likelihood method with 1000 bootstraps. The scale bar represents 0.03 fixed mutation per nucleotide position. Bootstrap values above 50% are shown.

Certain bacterial groups appeared to be particularly adapted to high salinity. For instance, in the lowest convective layer of the Atlantis II Deep (i.e., Ai4, salinity 15.4%), the bacterial communities were dominated by candidate division KB1, which is a group with 70 no cultured representatives (Fig. 1.3). This group branches between the Aquificales and the Thermotogales and is restricted to hypersaline conditions [41]. These organisms were initially retrieved from sediments in the Kebrit Deep [22] and were later obtained in enrichments of BSIs with high salinity [42]. In this study, candidate division KB1 was also detected in other interfaces with high salinity, forming divergent phylogenetic clades

(Fig. 1.8). The metabolic preferences of candidate division KB1 remain unknown.

However, according to the scarce information obtained from a cultivation study in Lake

Medee in the Mediterranean Sea, these organisms partially rely on the reductive cleavage of the osmoprotectant glycine betaine, resulting in the formation of trimethylamine

(TMA) and acetate [42].

Even though the Discovery Deep is located close to the Atlantis II Deep, the bacterial communities in these locations differed significantly. The Discovery Deep is situated approximately 3 km southwest of the Atlantis II Deep and is separated from the latter deep by a roughly structured bottom morphology [4, 43]. In the Discovery Deep BSI, sequences affiliated with Gammaproteobacteria were abundant. Despite the low oxygen concentration, aerobic Gammaproteobacteria, such as Chromohalobacter salexigens,

Marinobacter bryozyum, and several Halomonas strains, have been successfully isolated from this location [18, 19]. An analysis of these isolates confirmed the presence of active microbial populations and the co-existence of aerobic and anaerobic Bacteria in the BSI.

Our study represents the first report on the prokaryotic communities in the Erba and

Nereus Deeps. Surprisingly, despite their salinity differences (9.8% vs. 15.4%), these deeps harbor similar microbial communities (Fig. 1.3). A community comparison based on LIBSHUFF analysis revealed no significant differences between these deeps. The 71 primary bacterial taxon in both locations was Deltaproteobacteria, which accounted for

35.5% (Erba Deep BSI) and 27.9% (Nereus Deep BSI) of the total sequences. In addition, six bacterial OTUs that accounted for ~8% of all sequences were present in both brine pools. These OTUs are affiliated primarily with sulfate-reducing or sulfur-oxidizing taxa

(e.g., Desulfobacteraceae and SAR324; Table 1.4).

The similarities and dissimilarities of microbial community compositions between different brine pools raise a question about the origin of microorganisms in these environments. Some scientists speculated that such microbes originated from the overlaying seawater [2]. Considering many typical brine pools groups such as KB1 and

SA1 have not been detected in the water column, this theory seems rather unlikely. As an alternative explanation, the microbial colonization in these environments was achieved by the revival of similar microbes trapped in the evaporite layers beneath the brine pools

[2].

The abundance of Deltaproteobacteria in the Erba, Kebrit, and Nereus Deep BSIs is higher than those of the Atlantis and Discovery Deeps, implying that sulfate-reducing communities contribute more to the geochemical cycle in the colder brine pools. The high abundance of sulfate-reducing Deltaproteobacteria in these locations is consistent with previous reports from Deep Hypersaline Anoxic Basins (DHAB) in the Mediterranean

Sea [11, 37]. The Deltaproteobacteria were found to be one of the most prominent metabolically active microbial groups thriving in the chemocline of the MgCl2-rich

Discovery Deep [44], the hypersaline Lake Kryos [36], and the hydrothermal mud fluids of the Urania Deep in the Mediterranean Sea [45]. This finding confirms the hypothesis 72 that sulfate reduction is one of the primary metabolic processes that occurs in the chemoclines of deep-sea brines [11, 35].

1.6. Conclusions

In summary, the bacterial communities were very diverse and dominated over Archaea in the majority of the studied locations. Distinct microbial community composition occurred in each brine pool. The bacterial communities were highly stratified throughout the interfaces of the hot and multi-layered Atlantis II Deep. In the BSI of the Atlantis II and

Discovery Deep, Deltaproteobacteria were less abundant. In contrast,

Deltaproteobacteria were the most common microbial groups associated with the chemoclines of the colder brine pools such as Erba, Kebrit and Nereus Deep. Our findings suggest the presence of bacterial groups that are specially adapted to certain conditions in these extreme environments. This study may contribute to the understanding of the community structure and the unexplored bacterial groups in the interfaces of the Red Sea brine pools.

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Chapter 2: Diversity of sulfate-reducing Bacteria in the interfaces of

five deep hypersaline anoxic basins of the Red Sea

1 1 2 1 1 Tyas Hikmawan , Yue Guan , Andre Antunes , David K Ngugi , and Ulrich Stingl

1 Red Sea Research Center King Abdullah University of Science and Technology

(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

2Computational Bioscience Research Centre, King Abdullah University of Science and

Technology (KAUST), Building 3, 23955-6900, Thuwal, Saudi Arabia

2.1. Abstract

Brine-seawater interfaces (BSIs) have been shown to support methanogenesis and sulfate-reducing activities in several deep-sea hypersaline anoxic basins (DHABs) across the Mediterranean Sea, yet no systematic studies have been conducted to address the potential functional diversity of sulfate-reducing communities in Red Sea brine pools.

Here, we evaluated the diversity and abundance of sulfate-reducing communities in the interfaces of five different brine pools using functional marker genes that encode the alpha subunit of dissimilatory sulfite reductase (dsrA). Collectively, our study revealed a high diversity and unique community composition for the sulfate reducers in all of the studied locations. The prevalent group Desulfatiglans-like dsrA is conserved across the

Red Sea brine pools.

2.2. Introduction

The brine–seawater interfaces (BSIs) of the deep-sea anoxic brine pools in the Red Sea are characterized by strong physicochemical gradients, including gradients in salinity, temperature, and oxygen concentration, that cause stratified conditions in the brines [1,

2]. The BSIs represent highly peculiar environments that harbor diverse microbial communities, as suggested by previous studies [3-5]. The accumulation of organic and inorganic materials from the seawater enhances microbial activity and provides an environmental niche for microbial assemblages [6].

Microbial community studies in the Mediterranean Deep Hypersaline Anoxic Basins

(DHABs) revealed that diverse biogeochemical processes apparently co-occur in the BSI

[7]. Other surveys pointed to the importance of methanogenesis, sulfate reduction, and 79 sulfur cycling [8, 9]. These reports are also corroborated by recent metagenomic studies, in which pathways for sulfate reduction were detected in the DHABs of the

Mediterranean Sea [10]. In the Red Sea, sulfate-reducing Desulfovibrio strains have been successfully isolated from the BSI of the Atlantis II Deep [11]. A microbial community study based on 16S rRNA pyrotag showed that many microbial groups that reside in the sediments of the Atlantis II and Discovery Deeps are likely to play essential roles in the sulfur cycle [12]. According to these studies, sulfate reduction could thus be considered an important biogeochemical process in deep-sea brines [9, 13]. However, the microbial communities involved in this process are largely unknown in the Red Sea brine pools.

Considering the extreme conditions in these environments and the unique combination of physicochemical features in each brine pool [14], we postulated the existence of novel, niche-adapted groups of sulfate reducers in the BSIs. Moreover, despite the micro-oxic conditions [15], members of this group are capable of tolerating minute amounts of oxygen [16, 17]. Therefore, we analyzed the sulfate-reducing communities in the BSIs of geochemically distinct brine pools of the Red Sea using functional marker genes that encode the alpha subunit of dissimilatory sulfite reductase (dsrA) to uncover the diversity of these communities and identify the prominent sulfate-reducing Bacteria (SRB) groups.

2.3. Methods

2.3.1. Sample collection

Water samples from the brine-seawater interfaces (BSI) and the upper convective layer

(UCL) of the Red Sea brine pools were collected during the R/V Aegaeo during the 3rd

KAUST Red Sea Expedition in November 2011 using a rosette sampler equipped with 80

10-l Niskin bottles and a CTD unit (Sea-Bird Electronic, Bellevue, Washington) for

monitoring salinity, temperature, transmission, and pressure. Large volumes (ca. 200 l) of

sample were collected from Atlantis II Deep BSI (Ai1); second, third, fourth upper-

convective layer of Atlantis II Deep (labeled Ai2, Ai3, Ai4, respectively); Discovery

Deep BSI (Di); Erba Deep BSI (Ei), Kebrit Deep BSI (Ki); Nereus Deep BSI (Ni) (Table

2.1). The samples then were filter-concentrated using a Tangential Flow Filtration (TFF)

as described elsewhere [15]. The chemical analyses in the BSI were determined using the

commercial service provided by GEOMAR Helmholtz Centre for Ocean Research, Kiel

(Germany).

Table 2.1. Physical and geochemical parameters of the sampling locations

Atlantis II Discovery Erba Kebrit Nereus Sampling site Ai1 Ai2 Ai3 Ai4 Di Ei Ki Ni Latitude (N)1 21° 20.76' 21° 20.76' 21° 20.76' 21° 20.76' 21° 16.98' 20° 43.80' 24° 43.41' 23° 11.53' Longitude (E)1 38° 4.68' 38° 4.68' 38° 4.68' 38° 4.68' 38° 3.18' 38° 10.98' 36° 16.63' 37° 25.09' Depth (m)1 1998 2006 2025 2048 2038 2381 1467 2432 Salinity (%)1 5.6 8.6 11.2 15.4 13.8 9.8 18.2 15.4 Temperature (°C)1 31.8 46 52.1 65 35.8 23.1 21.9 27.9 pH1 6.5 5.82 5.71 5.18 6.63 7.7 5.67 7.65 Oxygen (µmol/l)1 2.2 0.5 0.5 0.5 0.3 0.3 4.6 0.3 Sulfate (mM)1 32.46 30.18 28.65 25.17 26.68 38.58 31.07 22.52 1 H2S (µmol/l) b.d.l. b.d.l. b.d.l. b.d.l. b.d.l. b.d.l. 149.8 b.d.l.

CH4 (ppmV) b.d.l. 85.5 330 977 53 15.5 28955.5 23

CO2 (ppmV) 7878 18953 19288 12226 791.5 <400 103731 <400 b.d.l. below detection limit (~1 µM) 1 Physicochemical data refer to Ngugi et al. 2014 Abbreviations: Atlantis II Deep BSI (Ai1); second upper-convective layer (Ai2); third upper-convective layer (Ai3); the forth upper-convective layer (Ai4); Discovery Deep BSI (Di); Erba Deep BSI (Ei); Kebrit Deep BSI (Ki); Nereus Deep BSI (Ni)

2.3.2. DNA extraction

Nucleic acids were extracted from the 0.1 µm membrane filters after three cycles of

freeze (in liquid nitrogen) and thaw (in a 65 °C water bath) in 50-ml Falcon tubes. 81

Subsequently, the filters were treated with a 5-ml lysis buffer (0.1 M Tris- EDTA, pH 8,

0.1 M Na2H2PO4, 1.5 M NaCl), lysozyme (100 mg/ml TE), proteinase K (20 mg/ml),

RNAase, and 20% SDS, and nucleic acids were extracted with six ml phenol-chloroform- isoamyl alcohol. The samples were centrifuged at 2500 × g for five minutes at room temperature. About 90% of the upper, aqueous layer was removed and carefully transferred into a sterile Eppendorf tube. The aqueous phase from this stage was re- extracted with an equal volume of chloroform-isoamyl alcohol (24:1). The DNA extracts were transferred into Amicon Ultra filter units (Amicon, Germany) pre-conditioned with

1 ml of 10 mM TE (pH 8) and centrifuged at 3,500 × g for 25 minutes to concentrate the

DNA. Purification was done using ethanol precipitation steps with 2 vol. of 96% ethanol and 0.1 vol. of 3 M sodium acetate and final wash with 70% ethanol before air-drying of the DNA extracts.

2.3.3. Functional gene analysis

The dsrA gene primer sets and analysis were performed as described previously [18]. The mix primer sets were prepared in equimolar ratios. For amplification, a touchdown PCR protocol was used. The PCR reaction mixture contained, in a total volume of 25 µl, Taq

DNA polymerase (New England Biolabs, UK), 10x ThermoPol reaction Buffer (New

England Biolabs, UK), 2mM deoxynucleoside triphosphates, 10 µM forward and reverse primer, and nuclease-free water. DNA from the sample was added as the template. Taq

DNA polymerase was added after the initial 3-min denaturation step (95°C). The PCRs were carried out in a Mastercycler (Eppendorf, Germany) with the following conditions: a pre-denaturation temperature for 3 min at 95°C, followed by 10 cycles of 30 s at 95°C, 82 primer annealing at 58–48°C (decreasing by 1°C per cycle for 30 s), and an elongation step at 72°C for 1.5 min, which was followed by a standard PCR program with 25 cycles at an annealing temperature of 48°C and a final 10-min extension at 72°C. The correct sizes of the PCR products were examined on a 1% (wt/vol) agarose gel stained with 5 µl of SYBR Safe. The PCR amplicons were then purified with a Gel Extraction Kit (Qiagen,

Hilden, Germany).

The PCR products were purified using the MinElute PCR Purification Kit (Qiagen,

Hilden, Germany) and cloned into PCR®2.1 TOPO vectors (Invitrogen) based on the kit’s manual to construct dsrA clone libraries. Positive clones were randomly selected for plasmid extraction and were bidirectionally sequenced using M13 primers with an ABI

3730 × l Capillary Sequencer at the Biosciences Core Laboratory at KAUST. Raw 16S rRNA sequences were quality checked, trimmed and assembled using Sequencher v.4.9

(Gene Codes Corporation, Ann Arbor, MI, USA).

Chimera check, OTU classification at a 3% distance cutoff, and rarefaction analysis of the dsrA genes were performed using the online functional gene pipeline & repository

FunGene (http://fungene.cme.msu.edu/) [19]. The outputs of this process were clean sequences, clustering based on the distance cut off, rarefaction curve, and alpha diversity indices. The deduced amino acid sequences of the dsrA genes were aligned with the references using ClustalW, as implemented in Mega 5.2 [20]. The references of the dsrA gene sequences were obtained from NCBI database and DOME database

(http://www.microbial-ecology.net/download.asp). The alignments of the dsrA genes were used for phylogenetic analyses, with the maximum-likelihood method (amino acid 83 substitution model: LG+G) and 1,000 bootstraps, as implemented in the Mega 5.2 software package.

2.3.4. Accession numbers of nucleotide sequences

The dsrA gene sequences analyzed in this study were deposited in Genbank under accession numbers KM241874–KM242055.

2.4. Results and discussion

Sulfate-reducing prokaryotes are a phylogenetically diverse group of anaerobes, and the majority of these organisms belong to the Deltaproteobacteria [21]. To yield energy, this group oxidizes hydrogen or organic compounds and reduces sulfate to sulfide. The dissimilatory sulfite reductase (dsr), which contains two subunits (i.e., dsrA and dsrB), is the key enzyme in this process and is widely used as a molecular marker to study the diversity of sulfate-reducing communities [22].

Table 2.2. Number of clones, observed OTUs, and alpha diversity indices Observed Chao1 Shannon Sample ID No of clones OTUs index index Ai1 3 1 2 0.63651 Ai2 10 3 5 1.16828 Di 16 3 3 0.60192 Ei 91 13 15.5 1.27592 Ki 37 8 9.5 1.61433 Ni 25 7 9 0.92565 dsrA gene in of Ai3 and Ai4 could not be amplified

In the present study, a clone library of the dsrA gene that consisted of 182 sequences was constructed from the interfaces of five different brine pools in the Red Sea (Table 2.1).

The dsrA gene sequences obtained from all locations were clustered into 27 OTUs with a

0.03 distance cutoff (Table 2.2). This study represents the first investigation of the 84 diversity of sulfate-reducing communities in the Red Sea brine pools. Phylogenetic analysis based on the deduced amino acid sequences of the dsrA gene revealed a high diversity of sulfate-reducing communities throughout the BSIs. Compared with the other brines in the Mediterranean Sea [23, 24], the Red Sea brine pools possess a relatively higher SRB diversity.

The majority of the dsrA gene sequences in the Erba Deep, Kebrit Deep, and Nereus

Deep were affiliated with Desulfohalobiaceae and members of Desulfobacteraceae, such as Desulfatiglans, Desulfosalsimonas, Desulfobacterium, and Desulfobacula (Fig. 2.1,

Table 2.3). Meanwhile, in the hot brines of the Atlantis II and Discovery Deeps, most of the OTUs formed distinct phylogenetic lineages that clustered together with environmental sequences (Fig. 2.1). This result indicated the existence of a different composition of sulfate-reducing communities in each geochemically distinct brine pool.

In addition, the diversity of the sulfate-reducing community that was obtained based on the dsrA genes is consistent with the presence of sequences related to members of the

Desulfobacteraceae and Desulfohalobiaceae in the 16S rRNA data (Chapter 1).

As shown in Figure 2.1, six different phylogenetic clades of dsrA gene sequences were found in the interfaces of the Red Sea brine pools. In Clade BSI I, two OTUs from the

Kebrit Deep BSI were affiliated with the genus Desulfobacula. Those OTUs formed clusters with the dsrA clones from the interface of L’Atalante Deep and Lake Kryos in the Mediterranean Sea, suggesting that the sulfate-reducing groups in these environments are specifically adapted to the conditions in the BSIs [8]. Meanwhile, in the Clade BSI II, one OTU from the Erba Deep BSI and one OTU from the Kebrit Deep BSI were affiliated with the Desulfobacteraceae member Desulfosalsimonas propionicica. The 85 sequences from the Kebrit Deep in this clade were closely related to the clone obtained from the lower BSI of Kryos Lake [23]. The predominance of Desulfobacteraceae in the

BSIs of different brine pools might be explained by the wide range of nutritional diversity, oxygen tolerance, and metabolic plasticity among these organisms [25].

Clade BSI III included two OTUs recovered from the Kebrit Deep BSI (18.2% salinity) that were distantly related to two isolated halophilic SRB species: Desulfohalobium retbaense [26] and Desulfohalobium utahense (both growing at salinities of up to 24%)

[27]. These OTUs were only present in the Kebrit Deep, suggesting that these organisms prefer higher salinity environments. This finding is consistent with a previous analysis of dsrAB mRNA in the MgCl2-rich Discovery Deep of the Mediterranean Sea, which revealed that Desulfohalobiaceae dominated in the saltier interface layer (from 1.60 up to

2.23 M MgCl2) [28].

The OTUs in Clade BSI IV were widely distributed in the Red Sea brine pools (Fig. 2.1,

Table 2.3), especially in the ecosystems with higher sulfate concentrations (Table 2.1).

These OTUs were related to Desulfatiglans anilini, which is a sulfate-reducing phenol- degrading bacterium that is capable of degrading a variety of other aromatic compounds

[29]. A recent metagenomics study demonstrated that the microbial community in the

Atlantis II Deep possesses the capacity to degrade aromatic compounds [30]. Thus, it is possible that the Desulfatiglans-related microorganism in this brine could be involved in the consumption of various aromatic compounds. Remarkably, sequences related to this group are also found in the Kebrit Deep (salinity 18%) and form a cluster with a clone from the brine of L’Atalante Deep (salinity 27%), which exceeds the maximum limit predicted for complete oxidizers (approximately 13%) [31]. Interestingly, nearly all 86

halophilic and halotolerant strains of SRB isolated to date are incomplete oxidizers,

which oxidize substrates to acetate as an intermediate product [32].

94 Desulfobacterium autotrophicum (AF418182) Desulfobacula phenolica (AAQ05965) 99 93 OTU 16, Kidsr RS17A03 (KM241997) 70 OTU 18, Kidsr RS37E05 (KM242007) Desulfobacula-related clade interface of L’atalante Deep, Mediterranean Sea clone (AY590522) (Clade BSI I) Kryos Lake AWW, Mediterranean Sea clone (AY590528) 95 97 OTU 25, Kidsr RS6F01 (KM242021) 57 New England salt marsh clone (AAV68650) Desulfosalsimonas propionicica (ABD46860) 53 interface of Urania Deep, Mediterranean Sea clone (AY590524) 88 Desulfosalsimonas-related clade OTU 24, Kidsr RS1A01 (KM241999) 97 95 (Clade BSI II) 55 Kryos Lake AWW Mediterranean Sea clone (KJ922628) OTU 5, Eidsr RS56H07 (KM241949) Desulfohalobium retbaense DSM 5692 (AF418190) 80 Desulfohalobium utahense (ABD46858)

99 Kryos Lake AWW, Mediterranean Sea clone (KJ922627) 61 Kryos Lake AWW, Mediterranean Sea clone (KJ922630) Desulfohalobium-related clade (Clade BSI III) 95 OTU 15, Kidsr RS19C03 (KM241998) 88 OTU 19, Kidsr RS34B05 (KM242006) interface of L’atalante Deep, Mediterranean Sea clone (AY590541) OTU 17, Kidsr RS86F11 (KM242026) Desulfatiglans anilini (AAQ05939)

57 marine sediment, Aarhus Bay clone (AM408824)

OTU 21, Ai2dsr RS45E11 (KM241878) OTU 27, Kidsr RS90B12 (KM242027) brine layer of L’atalante Deep, Mediterranean Sea clone (AY590509)

99 OTU 9, Eidsr RS4 (KM241933) interface of Urania Deep, Mediterranean Sea clone (AY590507) Desulfatiglans-related clade OTU 23, Eidsr RS79G102 (KM241973) (Clade BSI IV) OTU 7, Eidsr RS2B09 (KM241921)

66 OTU 13, Eidsr RS6F012 (KM241964) OTU 14, Eidsr RS8H01 (KM241983) OTU 6, Eidsr RS18B11 (KM241905) OTU 20, Nidsr RS06F01 (KM242032) 99 92 OTU 3, Eidsr RS37E052 (KM241929) OTU 4, Eidsr RS38F05 (KM241930) OTU 10, Eidsr RS89A122 (KM241982) OTU 12, Nidsr RS26B04 (KM242036) Desulfotomaculum geothermicum (AF273029) 99 Desulfotomaculum sp. For-1 (AFI98563) 56 Desulfotomaculum thermosapovorans (AF271769)

92 Desulfotomaculum kuznetsovii DSM 6115 (YP 004518774) 95 Desulfotomaculum thermoacetoxidans (AAK83691) OTU 11, Eidsr RS89A12 (KM241981) Thermodesulfatator-related clade Thermodesulfatator atlanticus DSM 21156 (CAX51964) (Clade BSI V) 65 98 Desulfacinum infernum (AF418194) Thermodesulforhabdus norvegica (AF334597) OTU 22, Didsr RS10H06 (KM241882)

50 100 depth gradient in acidic fen clone (CAJ47279) depth gradient in acidic fen clone (CAJ47303)

89 OTU 2, Didsr RS13C07 (KM241883)

95 OTU 26, Nidsr RS04D01 (KM242031) Deep-branching clade (Clade BSI VI) 97 deep-sea sediment of Eastern Mediterranean Sea clone (AY590556) OTU8, Nidsr RS07G01 (KM242033) OTU 1, Didsr RS30D09 (KM241885) 94 97 deep-sea sediment of Eastern Mediterranean Sea clone (AY590554) deep-sea sediment of Eastern Mediterranean Sea clone (AY590555) Archaeoglobus profundus DSM 5631 (AAC78309)

0.1 Atlantis II Deep BSI Discovery Deep BSI Nereus Deep BSI Mediterranean DHABs Atlantis II Deep Ai2 Kebrit Deep BSI Erba Deep BSI

Figure 2.1. Phylogenetic tree based on deduced amino acid sequences of the dsrA clones from the BSIs of Red Sea brine pools, including sequences from Mediterranean DHABs. The topology of the tree is based maximum-likelihood method using 1000 bootstrap replicates. The scale bar represents 0.10-fixed mutation per nucleotide position and bootstrap values above 50% are shown. 87

Ei Ki

Ni Di

Ai2

Figure 2.2. Rarefaction analysis of dsrA clone libraries (Atlantis II Deep second upper- convective layer (Ai2); Discovery Deep BSI (Di); Erba Deep BSI (Ei); Kebrit Deep BSI (Ki); Nereus Deep BSI (Ni)) 88

Table 2.3. OTU classification and the relative abundance of dsrA genes in each sampling locations

b Acc. Relative abundance (%) Cluster Phylotype a OTUs number Ki Ei Ni Di Ai1 Ai2 Clade BSI I Desulfobacula phenolica OTU16 KM241997 8.1 - - - - - OTU18 KM242007 27 - - - - -

OTU25 KM242021 2.7 - - - - -

Clade BSI II Desulfosalsimonas propionicica OTU05 KM241949 - 2.2 - - - - OTU24 KM241999 2.7 - - - - -

Clade BSI III Desulfohalobium utahense OTU15 KM241998 10.8 - - - - - OTU19 KM242006 5.4 - - - - -

Clade BSI IV Desulfatiglans anilini OTU03 KM241929 - 15.4 - - - - OTU04 KM241930 - 61.5 68 - - -

OTU06 KM241905 - 1.1 - - - -

OTU07 KM241921 - 3.3 - - - -

OTU09 KM241933 - 2.2 - - - -

OTU10 KM241982 - 3.3 - - - -

OTU12 KM242036 - 2.2 8 - - -

OTU13 KM241964 - 1.1 4 - - -

OTU14 KM241983 - 1.1 - - - -

OTU17 KM242026 40.6 - - - - -

OTU20 KM242032 - - 4 - - -

OTU21 KM241878 - - - - - 10

OTU23 KM241973 - 1.1 - - - -

OTU27 KM242027 2.7 - - - - -

Clade BSI V Thermodesulfatator atlanticus OTU11 KM241981 - 3.3 4 - - - Clade BSI VI Deep-branching lineage OTU01 KM241885 - - - 81.3 100 80 OTU02 KM241883 - - - 12.5 - 10 OTU08 KM242033 - 2.2 8 - - - OTU22 KM241882 - - - 6.2 - - OTU26 KM242031 - - 4 - - - Atlantis II Deep BSI (Ai1); second upper-convective layer (Ai2; Discovery Deep BSI (Di); Erba Deep BSI (Ei); Kebrit Deep BSI (Ki); Nereus Deep BSI (Ni)

In Clade BSI V (Fig. 2.1), the dsrA gene sequences from the Erba and Nereus Deeps

were distantly related to Thermodesulfatator atlanticus, which is a chemolithoautotrophic

SRB species within the family Thermodesulfobacteriaceae that was isolated from a

hydrothermal vent [33]. Some of the OTUs in the hot brines of the Atlantis II and 89

Discovery Deeps formed three deeply branching evolutionary lineages (Clade BSI VI) that were different from any isolated sulfate-reducing Bacteria (Fig. 2.1). As there are no cultivated representatives of this clade, the metabolism and physiology of this clade remain obscure. However, the phylogenetic analysis indicates that these organisms are related to organisms retrieved from similar environments in the Mediterranean Sea [8], implying that these phylotypes are specially adapted to the anoxic brine pools.

Considering the high abundance of deeply branching sequences in the Clade BSI VI

(Table 2.3), we presume that the interfaces of the Atlantis II and Discovery Deeps harbor distinct sulfate-reducing communities that are quite different from known SRB.

2.5. Conclusions

In conclusion, the sulfate-reducing communities investigated in this study were collectively diverse, with the majority of the OTUs in the Erba, Kebrit, and Nereus Deeps being affiliated with genus Desulfatiglans. The hot Atlantis II and Discovery Deeps harbor a deeply branched lineage of dsrA gene sequences, which suggest that novel lineages of SRB reside in these environments. In a broader sense, these findings may provide more insights into the ubiquity of sulfate reducers in hypersaline habitats and should increase the impetus for future cultivation attempts of novel halophilic microorganisms.

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[10] La Cono V, Smedile F, Bortoluzzi G, Arcadi E, Maimone G, Messina E, et al. Unveiling microbial life in new deep-sea hypersaline Lake Thetis. Part I: Prokaryotes and environmental settings. Environ Microbiol 2011;13:2250-68.

[11] Truper HG. Bacterial sulfate reduction in the Red Sea hot brines. Springer-Verlag, New York, 1969.

[12] Siam R, Mustafa GA, Sharaf H, Moustafa A. Unique prokaryotic consortia in geochemically distinct sediments from Red Sea Atlantis II and Discovery Deep brine pools. Plos One 2012;7:e42872.

[13] van der Wielen PWJJ, Bolhuis H, Borin S, Daffonchio D, Corselli C, Giuliano L, et al. The enigma of prokaryotic life in deep hypersaline anoxic basins. Science 2005;307:121-3. 91

[14] Antunes A, Ngugi DK, Stingl U. Microbiology of the Red Sea (and other) deep‐sea anoxic brine lakes. Environ Microbiol Rep 2011;3:416-33.

[15] Ngugi DK, Blom J, Alam I, Rashid M, Ba-Alawi W, Zhang G, et al. Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea. The ISME journal 2014;9:396-411.

[16] Brune A, Frenzel P, Cypionka H. Life at the oxic-anoxic interface: microbial activities and adaptations. Fems Microbiol Rev 2000;24:691-710.

[17] Marschall C, Frenzel P, Cypionka H. Influence of oxygen on sulfate reduction and growth of sulfate-reducing bacteria. Arch. Microbiol. 1993;159:168-73.

[18] Pester M, Bittner N, Deevong P, Wagner M, Loy A. A ‘rare biosphere’ microorganism contributes to sulfate reduction in a peatland. The ISME journal 2010;4:1591-602.

[19] Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front. Microbio. 2013;4:291.

[20] Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 2011;28:2731-9.

[21] Muyzer G, Stams AJ. The ecology and biotechnology of sulphate-reducing bacteria. Nat Rev Microbiol 2008;6:441-54.

[22] Wagner M, Loy A, Klein M, Lee N, Ramsing NB, Stahl DA, et al. Functional marker genes for identification of sulfate‐reducing prokaryotes. in: JR Leadbetter (Ed.), Methods in Enzymology, Academic Press, 2005, pp. 469-89.

[23] Yakimov MM, La Cono V, Spada GL, Bortoluzzi G, Messina E, Smedile F, et al. Microbial community of the deep-sea brine Lake Kryos seawater-brine interface is active below the chaotropicity limit of life as revealed by recovery of mRNA. Environ Microbiol 2014.

[24] Hallsworth JE, Yakimov MM, Golyshin PN, Gillion JL, D'Auria G, de Lima Alves F, et al. Limits of life in MgCl2-containing environments: chaotropicity defines the window. Environ Microbiol 2007;9:801-13.

[25] Rabus R, Hansen TA, Widdel F. Dissimilatory sulfate- and sulfur-reducing prokaryotes. in: M Dworkin, S Falkow, E Rosenberg, KH Schleifer and E Stackebrandt (Eds.), Prokaryotes Springer New York, Singapore, 2006, pp. 659-768.

[26] Ollivier B, Hatchikian CE, Prensier G, Guezennec J, Garcia JL. Desulfohalobium retbaense gen. nov., sp. nov., a halophilic sulfate-reducing bacterium from sediments of a hypersaline lake in Senegal. Int J Syst Bacteriol 1991;41:74-81. 92

[27] Jakobsen TF, Kjeldsen KU, Ingvorsen K. Desulfohalobium utahense sp. nov., a moderately halophilic, sulfate-reducing bacterium isolated from Great Salt Lake. Int J Syst Evol Microbiol 2006;56:2063-9.

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[29] Ahn YB, Chae JC, Zylstra GJ, Haggblom MM. Degradation of phenol via phenylphosphate and carboxylation to 4-hydroxybenzoate by a newly isolated strain of the sulfate-reducing bacterium Desulfobacterium anilini. Appl Environ Microb 2009;75:4248-53.

[30] Wang Y, Yang J, Lee OO, Dash S, Lau SCK, Al-Suwailem A, et al. Hydrothermally generated aromatic compounds are consumed by bacteria colonizing in Atlantis II Deep of the Red Sea. The ISME journal 2011;5:1652-9.

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[33] Alain K, Postec A, Grinsard E, Lesongeur F, Prieur D, Godfroy A. Thermodesulfatator atlanticus sp nov., a thermophilic, chemolithoautotrophic, sulfate- reducing bacterium isolated from a Mid-Atlantic Ridge hydrothermal vent. Int J Syst Evol Micr 2010;60:33-8.

Chapter 3: Cytotoxic and apoptotic evaluations of marine bacteria

isolated from brine-seawater interface of the Red Sea

1 1 2 2 2 Sunil Sagar , Luke Esau , Tyas Hikmawan , Andre Antunes , Karie Holtermann , Ulrich

2 1 1 Stingl , Vladimir B Bajic and Mandeep Kaur

1 Computational Bioscience Research Centre, King Abdullah University of Science and

Technology (KAUST), Building 3, 23955-6900, Thuwal, Saudi Arabia

2 Red Sea Research Center King Abdullah University of Science and Technology

(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

3.1. Abstract

High salinity and temperature combined with presence of heavy metals and low oxygen renders deep-sea anoxic brines of the Red Sea as one of the most extreme environments on Earth. The ability to adapt and survive in these extreme environments makes inhabiting Bacteria interesting candidates for the search of novel bioactive molecules.

Total 20 i.e. lipophilic (chloroform) and hydrophilic (70% ethanol) extracts of marine

Bacteria isolated from brine-seawater interface of the Red Sea were tested for cytotoxic and apoptotic activity against three human cancer cell lines, i.e. HeLa (cervical carcinoma), MCF-7 (Breast Adenocarcinoma) and DU145 (Prostate carcinoma). Among these, twelve extracts were found to be very active after 24 hours of treatment, which were further evaluated for their cytotoxic and apoptotic effects at 48 hr. The extracts from the isolates P1-37B and P3-37A (Halomonas) and P1-17B (Sulfitobacter) have been found to be the most potent against tested cancer cell lines. Overall, bacterial isolates from the Red Sea displayed promising results and can be explored further to find novel drug-like molecules. The cell line specific activity of the extracts may be attributed to the presence of different polarity compounds or the cancer type i.e. biological differences in cell lines and different mechanisms of action of programmed cell death prevalent in different cancer cell lines.

3.2. Introduction

The marine environment is a rich source of unique bioactive molecules with unlimited 95 chemical and functional diversity. Approximately 30,000 natural products have been isolated from marine organisms (http://dmnp.chemnetbase.com/intro/index.jsp) and many of the drug candidates are currently in clinical trials [1, 2]. In the recent reports, both, deep-seawater bodies and seafloors have been shown to be some of the most biodiverse and species-rich habitats on the planet [3, 4]. The continuous search for new chemically diverse molecules and the development of new methods for culturing deep-sea microorganisms have placed the deep-sea environment as a new frontier in the drug discovery process. The general opinion is that the survival of deep-sea organisms in the extreme conditions of pressure, absence of light, low levels of oxygen, high temperature and salt concentration may have affect their primary and secondary metabolite profiles

[5, 6] which, in turn, can be suitable candidates for discovery of structurally unique molecules.

Siderophore Bisucaberin [7] was the first cytotoxic compound isolated from deep-sea marine Bacteria Alteromonas haloplanktis, collected at 3000 m depth off the coast of

Aomori Prefecture, Japan. Lately, Homann et al. reported the isolation of peptidic siderophores Loihichelins A-F [8] from the cultures of heterotrophic bacterium

Halomonas LOB-5 collected from the southern rift zone of Loihi Seamount east of

Hawaii. Novel cytotoxic phenazine derivatives [9] with unique ring system have been described from pacific sediment Bacillus sp. (5059 m depth). Several other compounds with significant cytotoxic activity which were isolated from deep-sea Bacillus and 96

Streptomyces sp. and actinomycete have been described by Danielle Skropeta in a recent review [10]. In our effort to exploit the biodiversity of these environments, several strains of marine Bacteria were isolated from Kebrit Deep, Nereus Deep, Discovery Deep and

Erba Deep of the Red Sea [11] and evaluated for their cytotoxic and apoptotic potential.

The Red Sea harbors approximately 25 deep-sea anoxic brine pools, which were formed by a process of re-dissolution of evaporitic deposits [12-14]. The deep-sea anoxic brines of the Red Sea are considered to be one of the most extreme environments on Earth, which are characterized by drastic changes in physicochemical conditions when compared to overlaying seawater. This includes the increases in salinity (from 4% up to

26% salinity), temperature (up to 70°C), concentrations of heavy metals, and decrease in oxygen [15, 16].

The present study describes the assessment of anticancer activity of marine microorganisms from the brine-seawater interface of the Red Sea. The cytotoxic activities of lipophilic, i.e. chloroform and hydrophilic, i.e. 70% ethanol extracts of marine Bacteria isolated from deep-sea brine pools of Red Sea, against HeLa (cervical carcinoma), MCF-

7 (breast adenocarcinoma) and DU145 (prostate carcinoma) cell lines, were measured at

24 hr time point. Some of the highly active extracts were further assessed for cytotoxicity and apoptotic activity at 48 hr. 97

3.3. Methods

3.3.1. Field sampling

Water samples from the brine-seawater interface were collected using a rosette sampler equipped with 10 l Niskin bottles and a conductivity-temperature-depths (CTD) unit for monitoring salinity, temperature, transmission, and pressure. At each sampling site, 180 l of samples were collected and pre-filtered through a 5.0 µm SMWP membrane (diameter

290 mm; Millipore, Ireland) to remove suspended particles, then filter-concentrated with

Tangential Flow Filtration (TFF) system through Durapore 0.1 µm PVDF filter (Pellicon

2 Cassette Filter, Screen type C, size 0.5 m2, Millipore Corporation, MA, USA). After filtration, one liter of each concentrated sample was stored in the dark at 4°C and was used as inoculum.

3.3.2. Source of bacterial isolates

Twelve strains of Bacteria investigated in this study were isolated from brine-seawater interface of deep-sea brines in the Red Sea. Brine-seawater samples for bacterial isolation were collected from four different deep brines named Discovery Deep (2050 m), Kebrit

Deep (1549 m), Nereus Deep (2300 m), and Erba Deep (2300 m). Each of the deep-sea brine has its unique physicochemical composition [11]. In the Discovery deep, brine– seawater boundaries are characterized by strong temperature and salinity gradients [14].

All of the bacterial strains isolated in this study were obtained by the streak plate method 98 described elsewhere [17]. All of the strains grew in 10% NaCl with the exception of one strain isolated from Discovery Deep. This strain was successfully isolated using 20%

NaCl.

3.3.3. PCR amplification

PCR amplifications of the extracted DNA of the cultures were performed in a 25 µl reaction having 12.5 µl Promega PCR Master Mix (Promega, USA), 0.5 µl (final concentration 0.5 µM) of primer 27bF (5’-AGAGTTTGATCMTGG CTCAG-3’) and

1492uR (5’-TACCTTGTTACGACTT-3’). PCR reaction were carried out in

Mastercycler (Eppendorf, Germany) under the following conditions: a pre-denaturation temperature of 95°C for 5 min; 30 cycles of 95°C for 60s, 55°C for 60s, 72°C for 30s; followed by final extension at 72°C for 5 min. The yield and quality of the PCR products were examined on 1% (wt/vol) agarose gel (SeaKem GTG, Lonza, USA) stained with

SYBR Safe (Invitrogen, USA), then purified with Illustra Exostar 1-step (GE, Healthcare,

UK) according to the manufacturer’s protocol. The 16S rRNA genes were sequenced with an ABI 3730xl capillary DNA sequencer (PE Applied Biosystems), at Core Lab.

KAUST, Saudi Arabia.

3.3.4. Bacterial biomass

Strains of halophilic Bacteria were isolated into pure culture by streaking the samples onto three different solid media types, marine agar 2216 (Difco), plate count agar

(Teknova), and R2A agar (Oxoid). These media were supplemented with either 10% or 99

20% NaCl to adjust salinity. All plates were incubated at 30°C and inspected daily for two weeks. Colonies with different morphologies were picked and pure cultures of these colonies were obtained after three transfers. Taxonomic identifications were based on

16S rRNA gene sequencing. DNA extraction of the pure cultures, 16S rRNA gene amplification, and sequencing were performed according to [18]. A BLASTN search was used to identify strains to their closest relatives in GenBank. To prepare extracts, Bacteria were grown in 1 L of Marine Broth (Difco) supplemented with NaCl. Cultures were incubated at 30°C in a shaking incubator and harvested after two weeks by centrifugation. Cell pellets were frozen at −80°C until used for extract preparation.

3.3.5. Extract preparation

Lipophilic, i.e. chloroform and hydrophilic, i.e. 70% ethanol, extracts of 12 strains of marine Bacteria were prepared at a concentration of 100 mg mL-1 (speedvac dried material/mL of solvent). Solutions were sonicated with ultrasound probe (Biologics Inc.,

Model 150 V/T) for 3 × 2 minutes on ice. The solutions were centrifuged at 10000 g for

15 minutes; the supernatants were recovered and stored at −20°C.

3.3.6. Cell culture

MCF-7 (Breast Adenocarcinoma), DU145 (Prostate carcinoma) and HeLa (Cervical carcinoma) were obtained from the American Type Cell Culture Collection (ATCC,

Manassas, VA). All cell lines were cultured in DMEM (Dulbecco’s Modified Eagle’s

Medium), supplemented with 10% FCS (Fetal calf serum), penicillin (100U/ml) and 100 streptomycin (100 µg/ml) at 5% CO2 in a 37°C incubator.

3.3.7. MTT assay

The cytotoxicity of marine bacterial extracts was estimated by MTT (3-(4, 5-

Dimethylthiazol-2-yl)-2, 5- diphenyltetrazolium bromide) assay. Cells were seeded at a density of 2.5 × 103 cells per well in a 384-well culture plates and treated with 1 mg/mL marine bacterial extract for 24 and 48 hr (hours). Cells were treated with 5 mM H2O2 as a positive control. Following incubation with extracts, 5 µl of sterile MTT (5 mg/mL) dissolved in PBS was added to each well and incubated with cells for 4 hr followed by the addition of 30 µl of solubilisation solution (10% SDS, 10 mM HCl) which was incubated with cells for 16 hr at 37°C. The OD (optical density) of each well was measured at 595 nm using a microtiter plate reader (BMG Labtech PHERAstar FS,

Germany) and results were analyzed using Microsoft Office Excel©.

3.3.8. APOPercentage assay

The cells were seeded in 96 well plates at a density of 5 × 103 cells per well in duplicate in 90 µl of media. After 24 hr, cells were treated with marine bacterial extracts diluted in complete DMEM to a final concentration of 1 mg/mL and incubated at 37°C for 48 hr.

Cells were treated with 2.5 mM H2O2 for 30 minutes as a positive control. The cells were stained with APOPercentage ye as per manufacturer’s instructions (Biocolor, UK).

Duplicates were pooled, the OD was measured at 550 nm absorbance using a microtiter plate reader (BMG Labtech PHERAstar FS, Germany) and percentage staining was 101 calculated.

3.3.9. Statistical analysis

Student’s t-test was used to compare the samples (treated vs. Untreated) and were noted to be statistically significantly different where p<0.05. All statistics including mean and

SD calculations were performed using Microsoft Office Excel©.

3.4. Results

Table 3.1. Taxonomic identification, location of collection and source of origin for 12 microbial strains

Chloroform Ethanol Simi- GenBank Isolate Source Closest relative Ref extract extract larity access no. Kebrit Sulfitobacter - 1E P1-17B 99% NR_026418.1 [34] interface pontiacus ChLG-10 Nereus Halomonas 2C - P1-37B 100% AF212217.2 [35] interface meridiana Slthf1 Halomonas 3C 3E P3-82 Erba interface 100% AF212217.2 [35] meridiana Slthf1 Kebrit Halomonas 4C 4E P5-16A 100% NR_027219.1 [36] interface axialensis Althf1 Kebrit Halomonas 5C 5E P5-16B 100% NR_027219.1 [36] interface axialensis Althf1 Nereus Halomonas 6C 6E P5-37A 99% AF212217.2 [35] interface meridiana Slthf1 Idiomarina baltica 7C 7E P5-82 Erba interface 99% AJ440215.1 [37] OS146 Nereus Marinobacter 8C - P1-37A 98% NR_027192.1 [38] interface bryozoorum 50-11 Halomonas 9C 9E P1-82 Erba interface 100% NR_042066.1 [39] meridiana DSM 5425 Kebrit Vibrio sinaloensis 10C 10E P1-16B 98% NR_043858.1 [40] interface CAIM 797 Nereus Halomonas 11C 11E P3-37A 100% HQ188659.1 [41] interface aquamarina N126 Discovery Chromohalobacter 12C - P6-86 99% EU868854.1 [42] interface salexigens C3-1

3.4.1. Microbial isolates

Twelve strains of marine Bacteria were isolated from the Kebrit Deep, Nereus Deep, 102

Discovery Deep and Erba Deep of the Red Sea (Table 3.1). The isolation of Bacteria and their culturing were done by Red Sea Research Center at King Abdullah University of

Science and Technology. Total 20 extracts i.e. lipophilic (chloroform) and hydrophilic

(70% ethanol) have been investigated for their anticancer potential against above mentioned three human cancer cell lines.

3.4.2. Cytotoxic activities of bacterial extracts

Figure 3.1. Percent growth inhibition of MCF-7 cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk (*p<0.05 and **p<0.01) are significantly different from untreated control

(Un). +ve represents positive control (H2O2).

103

Figure 3.2. Percent growth inhibition of HeLa cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk (*p<0.05 and **p<0.01) are significantly different from untreated control (Un). +ve represents positive control (H2O2).

Cytotoxic activities for the extracts were assessed by MTT assay. The growth inhibitions were measured after treating the cell lines with microbial extracts for 24 hr (Fig. 3.1, 3.2, and 3.3). Out of total 20 bacterial extracts, 13 extracts i.e. 2C, 5C, 7C, 8C, 11C, 1E, 4E,

5E, 6E, 7E, 9E, 10E and 11E displayed maximum growth inhibitory effects on different cancer cell lines. The specific activity of different extracts for particular cell lines may be attributed to the presence of different polarity compounds. Extracts 2C, 5C, 11C, 4E, 5E,

7E and 11E (Fig. 3.1) showed 40-60% of growth inhibition in MCF-7 cells. 104

Figure 3.3. Percent growth inhibition of DU145 cells after treatment with 1 mg/mL marine bacterial extracts for 24 hr. Data were normalized to DMSO treatment controls, and error bars represent the standard deviation for triplicate experiments. Values marked as asterisk (*p<0.05 and **p<0.01) are significantly different from untreated control

(Un). +ve represents positive control (H2O2).

Bacterial extracts 5C, 1E, 5E and 7E (Fig. 3.2) demonstrated 50-70% growth inhibition against HeLa cells. Extracts 1E and 5E were found to be highly active against HeLa with growth inhibition of 70% and 60% respectively. Similarly, extracts 1E, 5E, 7E and 9E inhibited the growth of DU145 cells by 40% or more after 24 hr of treatment (Fig. 3.3).

Extract 1E was the most active fraction against DU145 with about 70% growth inhibition. The comparison of data (Fig. 3.1, 3.2 and 3.3) revealed that extract 11E is most active in MCF-7 but comparatively has low cytotoxic activity in HeLa and DU145 105 cells. Also, extracts 4E and 6E are only active against HeLa cells (20% and 30% growth inhibition respectively) and do not affect MCF-7 and DU145 cells. Overall, the results showed that marine bacterial extracts have cell line specific cytotoxic activity.

Table 3.2. The comparison of percentage growth inhibition of HeLa, MCF-7 and DU145 cells after treatment with 1 mg/mL marine bacterial extracts in triplicate for 24 and 48 hr

Marine bacterial HeLa MCF-7 DU145 extract 24 hr 48 hr 24 hr 48 hr 24 hr 48 hr 2C 44.906 58.003 61.307 78.608 30.233 86.682 5C 50 41.931 47.173 7.654 33.194 50.26 7C 41.094 38.25 21.555 −17.160 16.597 15.263 8C 10.151 23.494 −3.180 −25.303 −9.779 16.262 11C 39.736 50.176 57.244 36.271 −8.074 54.699 1E 69.434 61.787 44.7 13.56 66.836 59.324 4E 20.604 34.164 −3.710 −10.350 2.661 37.471 5E 59.472 41.556 40.901 24.234 51.077 47.287 6E 27.547 15.256 1.502 −23.946 −7.775 21.345 7E 51.434 42.027 40.283 20.021 46.202 47.725 10E 21.925 9.215 20.936 8.992 10.616 15.956 11E 22.453 29.769 59.806 16.475 15.371 45.672

To observe the long term effects of the bacterial extracts on the growth of cancer cell lines, the lead extracts (2C, 5C, 7C, 8C, 11C, 1E, 4E, 5E, 6E, 7E, 10E and 11E) were further evaluated for cytotoxicity at 48 hr time point. These extracts were selected based on their activities and availability. Cell growth inhibition induced by some of the extracts was increased with time (Table 3.2) while for others the growth inhibition decreases slightly which may indicate the recovery of the cells, especially in the case of MCF-7 cells. 106

3.4.3. Apoptotic evaluations

To further confirm that the mode of cell death is via apoptosis, the lead marine bacterial extracts (2C, 5C, 11C, 1E, 4E, 5E, 7E, 10E and 11E) were evaluated by APOPercentage assay. The results for this assay showed that most of the lead extracts induced apoptosis in HeLa cells (Fig. 3.4), while in DU145 and MCF-7 cells, no significant apoptotic effect was observed at 48 hr time point, however, the bacterial extract 2C showed high apoptotic activity in MCF-7 cells (Fig. 3.4).

3.5. Discussion

The results showed that most of the microbial extracts from the brine pools of the Red

Sea have displayed significant cytotoxic and apoptotic activity. Cytotoxicity which was measured by MTT assay (3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide), targets the activity of succinate dehydrogenase in mitochondria which in turn reduces the tetrazolium salt into formazan crystals [19]. The intensity of the color of formazan dye correlates to the number of viable cells. The isolates P1-37B (2C) and P3-

37A (11C and 11E) which has 100% sequence similarity to Halomonas (Table 3.1) showed about 60% growth inhibition (Fig. 3.1) in MCF-7 cell line. Not much work has been done on the anticancer activities of Halomonas sp. and very few studies have been reported so far. Marine bacterial strain GWS-BW-H8hM was reported to inhibit growth of HM02 (gastric adenocarcinoma), HepG2 (hepatocellular carcinoma) and MCF7 cell lines and induce apoptosis via cell cycle arrest [20]. Highly heterogeneous polymers i.e. 107 exopolysaccharides (EPSs) and sulphated EPSs isolated from H. stenophila sp. inhabiting hypersaline environment have also been reported for their pro-apoptotic effects on T- leukemia cells [21]. A range of amphiphilic siderophores, loihichelins A-F, consisting of an octapeptide comprised of D-threo-β- hydroxyaspartic acid, D-serine, L-glutamine, L- serine, L-N(δ)-acetyl-N(δ)-hydroxyornithine, dehydroamino-2- butyric acid, D-serine, and cyclic N(δ)-hydroxy-D-ornithine, appended by one of a series of fatty acids ranging from decanoic acid to tetradecanoic acid have been isolated from cultures of the marine bacterium Halomonas sp. LOB-5 [8]. The isolation of cytotoxic hydroxyphenylpyrrole- dicarboxylic acids i.e. 3-(4_-hydroxyphenyl)-4-phenylpyrrole- 2,5-dicarboxylic acid

(HPPD-1), 3,4-di-(4_-hydroxy-phenyl) pyrrole-2,5- dicarboxylic acid (HPPD-2) and the literature-known indole derivatives 3-(hydroxyacetyl)-indole, indole-3- carboxylic acid, indole-3-carboxaldehyde, and indole-3-acetic acid have also been reported from a marine

Halomonas sp. [22]. Both HPPD-1 and HPPD-2 have shown potent antitumor-promoting activities. Two most active extracts were obtained from the isolates P1- 17B (1E) and P5-

16B (5E), which inhibited the cell growth by 50-70% (Fig. 3.2 and 3.3) in HeLa and

DU145 cells and showed sequence similarity of 100% to Sulfitobacter and Halomonas respectively (Table 3.1). The cytotoxic activity observed in isolates P1-37B and P3-37A

(Halomonas) could be due to the above mentioned cytotoxic compounds from

Halomonas sp.. No published records were found in PubMed regarding the cytotoxic activities and isolation of bioactive molecules from Sulfitobacter. The most prominent 108 effect was found for extract 1E (Isolate P1-17B- Sulfitobacter) for HeLa and DU145 cell lines which makes it a suitable candidate for future isolation of bioactive molecules.

Cytotoxic effects for the most active extracts were also observed at 48 hr time point and activity for some of these extracts were found to be lower than that at 24 hr (Table 3.2, see non-bold numbers). It may be due to the recovery of the cells from the effects of these extracts. In general the hydrophilic extracts were found to be more active than lipophilic extracts, which may indicate the presence of significant amount of cytotoxic compounds in the polar fractions as compared to the non-polar fractions.

To further confirm the cell death via apoptotic mode, APOPercentage assay was performed on the most active extracts. Apoptosis is a form of a programmed cell death where cell undergo specific hallmark changes during progression towards death [23]. It has been established that apoptosis plays a key role in death of cancer as well as normal cells [24]. The APOPercentage assay (http://www.biocolor.co.uk/) captures apoptotic cells representing a hallmark change on the surface of plasma membranes i.e. phosphatidylserine (PS) externalization. PS is found in the inner leaflet and is exposed to outer leaflet during apoptosis and this externalized PS acts as a recognition tag for multiple processes including macrophages and phagocytosis [25, 26]. The maximum apoptotic activity was displayed by isolate P1-37B (2C) in both HeLa as well as MCF-7 cells (Fig. 3.4). Similar studies targeting bacterial extracts from other marine habitats have been published previously, mainly targeting Cyanobacteria [27] and Bacteria 109 associated with other marine organisms such as seaweeds and invertebrates [28] and sponges [29, 30].

Figure 3.4. Percent apoptosis in HeLa, DU145 and MCF-7 cells after treatment with 1 mg/mL marine bacterial extracts. The treatment of extracts was done for 48 hr in duplicates. Data were normalized to untreated controls.

The cell line specific activity of the extracts may be attributed to the different polarity compounds. Previous studies on different cancer cell lines have shown that the variable response of cell lines towards apoptosis inducers may also be attributed to the cancer type i.e. biological differences in cell lines and different mechanisms of action of programmed cell death prevalent in different cancer cell lines [31]. Even the timeline of progression through apoptosis process can vary among cell lines [32, 33].

3.6. Conclusions

In conclusion, present work showed that extracts of most of the marine bacterial strains 110 collected from interface of brine pools and sea water of the Red Sea have displayed significant growth inhibitory and apoptotic effect on the various cancer cell lines. The extracts from the isolates P1-37B and P3-37A (Halomonas) and P1-17B (Sulfitobacter) have been found to be the most potent against tested cancer cell lines and could be the lead candidates for the future work of isolation and structure elucidation of bioactive molecules.

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Chapter 4: Induction of apoptosis in cancer cell lines by the Red Sea

brine pool bacterial extracts

1 1 2 2 2 Sunil Sagar , Luke Esau , Karie Holtermann , Tyas Hikmawan , Guishan Zhang , Ulrich

2 1 1 Stingl , Vladimir B Bajic and Mandeep Kaur

1 Computational Bioscience Research Centre, King Abdullah University of Science and

Technology (KAUST), Building 3, 23955-6900, Thuwal, Saudi Arabia

2 Red Sea Research Center King Abdullah University of Science and Technology

(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

115

4.1. Abstract

Marine microorganisms are considered to be an important source of bioactive molecules against various diseases and have great potential to increase the number of lead molecules in clinical trials. Progress in novel microbial culturing techniques as well as greater accessibility to unique oceanic habitats has placed the marine environment as a new frontier in the field of natural product drug discovery. A total of 24 microbial extracts from deep-sea brine pools in the Red Sea have been evaluated for their anticancer potential against three human cancer cell lines. Downstream analysis of these six most potent extracts was done using various biological assays, such as Caspase-3/7 activity, mitochondrial membrane potential (MMP), PARP-1 cleavage and expression of

γH2Ax, Caspase-8 and -9 using western blotting. In general, most of the microbial extracts were found to be cytotoxic against one or more cancer cell lines with cell line specific activities. Out of the 13 most active microbial extracts, six extracts were able to induce significantly higher apoptosis (>70%) in cancer cells. Mechanism level studies revealed that extracts from Chromohalobacter salexigens (P3-86A and P3-86B(2)) followed the sequence of events of apoptotic pathway involving MMP disruption, caspase-3/7 activity, caspase-8 cleavage, PARP-1 cleavage and Phosphatidylserine (PS) exposure, whereas another Chromohalobacter salexigens extract (K30) induced caspase-

9 mediated apoptosis. The extracts from Halomonas meridiana (P3-37B),

Chromohalobacter israelensis (K18) and Idiomarina loihiensis (P3-37C) were unable to induce any change in MMP in HeLa cancer cells, and thus suggested mitochondria- independent apoptosis induction. However, further detection of a PARP-1 cleavage product, and the observed changes in caspase-8 and -9 suggested the involvement of 116 caspase-mediated apoptotic pathways. Altogether, the study offers novel findings regarding the anticancer potential of several halophilic bacterial species inhabiting the

Red Sea (at the depth of 1500–2500 m), which constitute valuable candidates for further isolation and characterization of bioactive molecules.

4.2. Introduction

Over the last four decades, natural products have played an important role in drug discovery against cancer, one of the deadliest diseases in the world and the second most common cause of death in developed countries. Almost 47% of the anticancer drugs approved in the last 50 years were either natural products or synthetic molecules inspired by natural products [1]. However, due to high toxicity and undesirable side effects associated with cancer drugs and, in particular, due to the development of resistance to chemotherapeutic drugs, there is a continuous need for novel drugs with greater therapeutic efficiency and/or with fewer side effects [2].

Marine microorganisms are considered to be an important source of bioactive molecules against various diseases and have great potential to increase the number of lead molecules in clinical trials. Approximately 3000 natural products have been isolated from marine microbial/algal sources and are described in Antibase [3]. Several of these microbial natural products have been evaluated in clinical trials for the treatment of various cancers [4, 5]. Two cyanobacteria-derived antimicrotubule agents, i.e. dolastatin

A and curacin A have been clinically evaluated against cancer and served as a lead structure for the synthesis of number of synthetic analogs/derivatives [6]. Another compound, salinosporamide A, isolated from a marine-derived actinomycete, a highly 117 potent irreversible inhibitor of 20S proteasome, was also used in clinical trials as an anticancer agent [7]. Additionally, there is circumstantial evidence that several lead molecules in the clinical development pipeline, thought to originate from higher marine organisms, may actually be produced by marine microbes [8].

In the last decade, the deep-sea has emerged as a new frontier in the isolation and screening of natural products, especially for cancer research [9, 10]. With advancements in technology leading to greater accessibility as well as improvements in techniques used to culture microorganisms, deep-sea environments are becoming ‘hot spots’ for new and unexplored chemical diversity for drug discovery [11]. Approximately 30,000 natural products have been isolated from marine organisms, yet less than 2% of those derive from deep-water marine organisms (http://dmnp. chemnetbase.com/intro/index.jsp). Of these, several cytotoxic secondary metabolites isolated from deep-sea microorganisms have been described in the literature [9, 12-14]. In our own efforts in this direction, cytotoxic and apoptotic potentials of marine halophilic Bacteria isolated from the deep- sea brine pools of the Red Sea, a largely unexplored resource in the area of natural product research, have been described previously [15], including the first report of cytotoxic activity of ‘Sulfitobacter’.

In the present study, ethyl acetate extracts of 24 marine bacterial strains, isolated from the deep-sea brine pools of the Red Sea, have been evaluated for their anticancer potential against HeLa (cervical carcinoma), DU145 (prostate carcinoma), and MCF-7 (breast adenocarcinoma) cell lines. The rationale behind choosing the cell lines lie in the severity and/or prevalence of various cancers in Saudi Arabia as well as around the world. The 118 prevalence of breast cancer in the Kingdom of Saudi Arabia has increased from 10.2% in

2000 to 47.2% in 2007 [16]. Similarly, a screening program has demonstrated higher prevalence of prostate cancer [17] in the Kingdom. Another study anticipated a significant increase in proportion of cervical cancer [18] cases in the Kingdom. These cells lines are the also among the most robust cell line models used for in vitro drug screening. The evaluation of proapoptotic potential of highly cytotoxic extracts further revealed six highly potent extracts that were subjected to more detailed assays to infer the pathways involved in apoptotic mode of cell death in cancer cells.

4.3. Methods

4.3.1. Field sampling

The samples were retrieved from brine-seawater interfaces, brine layers, and sediments of deep-sea brine pools during KAUST Red Sea Expedition 2011. Water samples were collected using a rosette sampler equipped with 20 Niskin bottles (10 liter each) and a conductivity-temperature-depth (CTD) unit for monitoring salinity, temperature, transmission, and pressure (Idronout S.r.l, Italy). At each sampling site, approximately

180 litres of sample were collected and pre-filtered through a 5.0 µm SMWP membrane

(diameter 290 mm; Millipore, Ireland) to remove suspended particles. A tangential flow filtration (TFF) system (Pellicon 2 Filter Acrylic Holder, Millipore, US) was used in order to filter-concentrate the samples. One liter of each concentrated sample was obtained after retention through a Durapore 0.1 µm PVDF filter (Pellicon 2 Cassette

Filter, Screen type C, size 0.5 m2, Millipore Corporation, MA, USA). These concentrated samples were stored in a dark bottle at 4°C, and used as inoculum for microbial 119 isolations. Sediment collection was performed by deploying a multicore sampling device into the bottom of the brine pools. The top layer of sediment (approximately 10 cm) was cut, kept in anoxic containers in the dark, and were later used as inoculum for microbial isolations.

4.3.2. Source of bacterial isolates

A total of 24 bacterial strains were successfully isolated from deep-sea brine pools of the

Red Sea. Nineteen of them were isolated from brine-seawater interfaces, one strain from brine, and four strains from sediments (Table 4.1). The inocula for bacterial isolation were collected from five different brine pools named Atlantis II (2194 m), Discovery

Deep (2224 m), Kebrit Deep (1573 m), Nereus Deep (2458 m), and Erba Deep (2395 m)

[19]. Each of the deep-sea brine pools has its unique physicochemical composition, with salinity up to 26%, including notably high temperature, as well as high concentrations of heavy metals [20]. The Atlantis II Deep and the Discovery deep are considered as hot brines, with maximum temperatures 67.8°C and 44.8°C, respectively. Brine–seawater boundaries in these brine pools are characterized by strong temperature and salinity gradients [21].

All of the bacterial strains isolated in this study were obtained by the streak plate method described elsewhere [22]. Eighteen strains grew in salinities of 10% NaCl and the rest of the isolates grew well in salinities of 20% NaCl.

4.3.3. PCR amplification

Nucleic acids were extracted with Qiagen kit (DNeasy blood & tissue kit, Qiagen,

Germany) according to the instruction manual. PCR amplifications of the extracted DNA 120 were performed in a 25 µl reaction, each mixture containing 12.5 µl Promega PCR

Master Mix 2x (Promega, USA), 1 µl (final concentration 0.5 µM) of primer 27bF (5′-

AGAGTTTGATCMTGGCTCAG-3′) and 1492uR (5′-TACCTTGTTACGACTT-3′), 8.5

µl RNAase&DNAase free H2O (Teknova), and DNA template. PCR was carried out in

Mastercycler (Eppendorf, Germany) under following conditions: 94°C for 3 min; 35 cycles of 94°C for 60 s, 53°C for 90 s, 72°C for 90 s. A final extension was done for 7 min at 72°C. The yield and quality of the PCR products were examined on 1% (wt/vol) agarose gel (SeaKem GTG, Lonza, USA) stained with SYBR Safe (Invitrogen, USA). All sequencing reactions were purified with Illustra Exostar 1-step (GE, Healthcare, UK) according to the manufacturer’s protocol. The 16S rRNA sequences were determined using an ABI 3730xl capillary DNA sequencer (PE Applied Biosystems), at Core

Laboratory KAUST, Saudi Arabia.

4.3.4. Bacterial biomass

The concentrated samples were inoculated onto three different agar media, plate count agar (Teknova), marine agar 2216 (Difco), and R2A agar (Oxoid), which were supplemented with either 10% or 20% NaCl (w/v) to adjust salinity. The plates were incubated at 30°C for up to three weeks and inspected daily. Colonies from various agar plates were picked based on difference in colony morphology. Pure isolates of these colonies were obtained after three successive transfers to the fresh agar media.

Taxonomic identifications of the isolates were based on 16S rRNA gene sequencing. 16S rRNA gene amplification and sequencing steps were performed according to [23].

Sequence similarity was analyzed using BLASTN search program to identify the strains to their closest relatives in GenBank database (http://www.ncbi.nlm.nih.gov/BLAST). 121

Bacteria were inoculated in 1 liter of Marine Broth (Difco) supplemented with NaCl to collect the biomass, and then were incubated at 30°C in a shaking incubator. After two weeks of incubation, bacterial cultures were harvested by centrifugation at ambient temperature for an hour (h). The centrifugation step was repeated by adding sterile water at the same salinity to wash the pellets. Cell pellets were stored at −80°C until used for extract preparation.

4.3.5. Extract preparation

Ethyl acetate extracts of 24 strains of marine Bacteria were prepared at a concentration of

100 mg/mL (speedvac dried material/mL of solvent). Solutions were sonicated with ultrasound probe (Biologics Inc., Model 150 V/T) for 5 × 2 minutes on ice. The solutions were centrifuged at 10000 g for 15 minutes; the supernatants were recovered and stored at −20°C.

4.3.6. Cell culture

MCF-7 (Breast Adenocarcinoma), HeLa (Cervical carcinoma), and DU145 (Prostate carcinoma) were obtained from the American Type Cell Culture Collection (ATCC,

Manassas, VA). All cell lines were cultured in DMEM (Dulbecco’s Modified Eagle’s

Medium), supplemented with 10% FCS (Fetal calf serum), penicillin (100 U/mL) and streptomycin (100 µg/mL) at 5% CO2 in a 37°C incubator.

4.3.7. MTT assay

The cytotoxicity of marine bacterial extracts was estimated by MTT (3-(4, 5-

Dimethylthiazol-2-yl)-2, 5- diphenyltetrazolium bromide) assay. Cells were seeded at a density of 2.5 × 103 cells per well in a 384-well culture plates and treated with 200 and 122

500 µg/mL marine bacterial extracts for 48 h. Following incubation with extracts, 5 µL of sterile MTT (5 mg/mL) dissolved in PBS was added to each well and incubated with cells for 4 h followed by the addition of 30 µL of solubilization solution (10% SDS, 10 mM

HCl), which was further incubated with cells for 16 h at 37°C. The OD (optical density) of each well was measured at 595 nm using a microtiter plate reader (BMG Labtech

PHERAstar FS, Germany) and results were analyzed using Microsoft Office Excel©.

4.3.8. APOPercentage assay

HeLa cells were seeded in 96-well plates at a density of 5 × 103 cells per well in quadruplicate in 90 µL of media. After 24 h, cells were treated with marine bacterial extracts diluted in complete DMEM to a final concentration of 500 µg/mL and incubated at 37°C for 24 and 48 h. Cells were treated with 10 mM H2O2 for 30 minutes as a positive control. The cells were lifted and stained with APOPercentage dye (Biocolor,

UK). Percentage of cells stained positive for apoptosis was determined with a high throughput flow cytometer (HTFC) Screening System (IntelliCyt Corporation,

Albuquerque, NM). Cells were gated for FSC-H, SSC-H and in the FL-2H channel recording a minimum of 1000 events per well.

4.3.9. Microscopy

The morphological evaluation and photography of cells after treatment with extracts was done in 96-well plates using Primo Vert inverted microscope (Carl Zeiss, Inc.)

4.3.10. MMP assay

HeLa cells were seeded in 96-well plates at a density of 5 × 103 cells per well in quadruplicate in 90 µL of media and allowed to settle overnight. Next day, cells were 123 treated with 500 µg/mL marine bacterial extracts for 12 and 16 h and stained with 50 µM cyanine dye JC-1 (5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimi- dazolyl-carbocyanine iodide, Life Technologies, UK) for 1 h. Cells were analyzed by HTFC system by plotting

FL2-H vs. FL-1H and applying a quadrant gate to determine JC-1 aggregates (red) and monomers (green).

4.3.11. Caspase assay

HeLa cells were seeded at a density of 2.5 × 103 cells per well in 20 µL of media in 384- well plates. After 24 h, 5 µL of marine bacterial extract (500 µg/mL) was added and incubated for a further 16 h. Caspase-3/7 activity was estimated using ApoTox-Glo kit

(Promega) following the manufacturer’s instructions. Luminescence was measured using a luminescence plate reader (BMG Labtech PHERAstar FS, Germany). The results were normalized to cell viability (measured using MTT assay).

4.3.12. Western blotting

HeLa cells were seeded at a density of 3 × 105 cells per well in 6-well plates and left overnight to settle. Cells were treated with 500 µg/mL of marine bacterial extracts for 12 and 24 h. Protein was harvested with RIPA lysis buffer (150 mM NaCl, 1% Triton X 100,

0.1% SDS, 10 mM Tris pH 7.5, 1% sodium deoxycholate) and quantitated with a BCA protein determination kit (Pierce Thermo Scientific). 10–20 µg of protein lysate was subjected to electrophoresis on 12% SDS page gels, transferred to nitrocellulose membrane and probed with Caspase-8 (Sigma), Caspase-9 (Thermo Scientific), PARP-1

(Trevigen) and pH2Aγ (Enzo Life Technologies) antibodies. β-Tubulin (Santa Cruz) was used as a loading control. 124

4.3.13. Z-factor

Z-factor was determined for each assay and a Z-factor score of ≥0.6 was recorded

indicating good to excellent robustness for assays [24].

4.4. Results

Table 4.1. Taxonomic identification and collection location for 24 microbial strains Similarity (16S Accession Isolates Source Closest relative Ref. rRNA genes) number P1-16A Kebrit interface Halomonas meridian Slthf1 100% AF212217 [72] P1-16C Kebrit interface Idiomarina loihiensis L2TR 98% AE017340 [39] P1-26A Nereus brine Virgibacillus salaries SA-Vb1 98% NR_041270 [73] Sediminimonas qiaohouensis P1-5 Kebrit interface 99% NR_044577 [74] YIM B024 Chromohalobacter salexigens P2-13A Kebrit sediment 99% CP000285 [75] DSM 3043 Halobacillus halophilus DSM P2-13B Kebrit sediment 99% HE717023 [76] 2266 P2-16A Kebrit interface Halomonas meridiana Slthf1 99% AF212217 [72] P3-16A Kebrit interface Halomonas meridiana Slthf1 99% AF212217 [72] P3-16B Kebrit interface Halomonas meridiana Slthf1 99% AF212217 [72] P3-37A Nereus interface Halomonas meridiana Slthf1 99% AF212217 [72] P3-37B Nereus interface Halomonas meridiana Slthf1 99% AF212217 [72] P3-37C Nereus interface Idiomarina loihiensis L2TR 91% AE017340 [39] Chromohalobacter salexigens P3-86A Discovery interface 100% CP000285 [75] DSM 3043 Chromohalobacter salexigens P4-13A Kebrit sediment 99% CP000285 [75] DSM 3043 Staphylococcus sp. U1371- P4-13B Kebrit sediment 100% JQ082193 [77] 101227-XH136 P5-86A Discovery interface Idiomarina baltica OS145 99% NR_027560 [78] Chromohalobacter salexigens P5-86B Discovery interface 99% CP000285 [75] DSM 3043 Chromohalobacter salexigens P6-86 Discovery interface 99% CP000285 [75] DSM 3043 Zunongwangia profunda SMA- K-2 Atlantis II interface 100% NR_074656 [79] 87 K-18 Atlantis II interface Chromohalobacter israelensis 2 100% AM945672 [80] Chromohalobacter salexigens K-30 Atlantis II interface 99% CP000285 [75] DSM 3043 H102 Erba brine interface Marinobacter adhaerens HP15 100% NR_074765 [81] H105 Erba brine interface Idiomarina zobellii KMM 231 99% NR_024892 [82] P3-86B Chromohalobacter salexigens Discovery interface 96% CP000285 [75] (2) DSM 3043

125

4.4.1. Microbial isolates from the Red Sea

Twenty-four strains of marine Bacteria were isolated from the samples collected from brine-seawater interfaces, brine layers, and sediments of five deep-sea brine pools of the

Red Sea. Taxonomic classification and location of collection for these microbial strains is presented in Table 4.1. The samples were extracted by using ethyl acetate and evaluated for their anticancer potential through various biological assays.

4.4.2. Antiproliferative activities of marine bacterial extracts

The antiproliferative effect of 24 marine bacterial extracts was evaluated in vitro by MTT assay against three human cancer cell lines, i.e. DU145 (prostate cancer), MCF-7 (breast cancer) and HeLa (cervical cancer). The cancer cells were exposed to marine extracts for

48 h (Table 4.2), at the concentrations of 200 and 500 µg/mL. In general, most of the microbial extracts were able to induce growth inhibition in one or more cancer cell line/s; however, extracts P1-5, P2-13B, P3-37B, H-102, P3-86B and P3-86A displayed up to

60% growth inhibition in DU145 cell line at 500 µg/mL. Similarly in MCF-7 cells, several microbial extracts were found to be cytotoxic at the same concentration. HeLa emerged as the most sensitive cell line as 13 microbial extracts inhibited 30% or more cell growth at 500 µg/mL concentration (Table 4.2). Extracts from Halomonas meridiana

(P3-37B) and Chromohalobacter salexigens (K-30 and P3- 86B(2)) displayed the highest growth inhibition, i.e. > 85%. Microbial extracts with more than 30% growth inhibition were chosen for further apoptotic analysis. HeLa was chosen for the downstream analysis of selected microbial extracts due to its higher sensitivity to most of the extracts.

126

Table 4.2. Evaluation of antiproliferative effects of marine bacterial extracts on various cancer cell lines

HeLa MCF-7 DU145 Strain 200 µg/mL 500 µg/mL 200 µg/mL 500 µg/mL 200 µg/mL 500 µg/mL P1-16A 8 16.2 0 0 3.2 1 P3-16A 12.9 32.8 0 0 4.9 6.1 P5-86B 2.8 2.8 0 0 0.8 8.3 H102 54.6 37 21.7 18 3 2 P3-16B 0 0 0 0 11.7 14.4 P6-86 0 0 0 0 2 6.8 P2-16A 0 0 0 0 7.3 6 P1-26A 0 0 0 0 3.9 2.7 P5-86A 0 0 0 0 1.4 10.2 P3-86B(2) 0 95.2 0 0 4.5 7.2 P3-86A 31.9 39.8 32.1 35.3 2.3 3 P1-5 27.2 56.3 33.4 54 52.1 59.9 P1-16C 30.7 35.3 33.5 51.1 41.4 47 H105 17.1 19 16.3 23.4 7.4 7.5 P3-37A 1.1 13.6 16.4 28.5 24.3 28.8 K2 1.4 15.5 13.2 12.4 14.6 11.9 P4-13A 0 6.9 4.2 3.8 0 12.3 P3-37C 0 4.9 22.1 34.8 21.7 1.5 K18 16 36.6 11.6 37 24.1 45.3 P3-37B 12.3 43.6 32.5 89.8 41.8 34.6 K30 24.7 28.4 27.9 88.2 18.6 30.4 P2-13B 35.3 50.5 39.5 51.4 62.5 60.7 P4-13B 8.2 8.4 29 37.7 44.7 39.8 The growth of cells was inhibited in a dose-dependent manner at concentrations of 200 and 500 µg/mL after 48 h. MTT assay was used to assess the growth inhibitory potential of extracts and is presented as percent growth inhibition relative to untreated cells. The Z- factor for this experiment was calculated to be 0.76. The extracts selected for apoptosis analysis are presented in bold letters.

4.4.3. Apoptotic cell death in HeLa cells

Since anticancer agents are known to induce apoptosis in cancer cells and apoptosis biomarkers are being increasingly used in clinical trials [25], a total of 13 extracts showing significant cytotoxicity were tested for their proapoptotic potential in HeLa cells by using APOPercentage assay. Seven extracts were found to induce apoptosis at 500 127

µg/mL concentration after 48 h (Fig. 4.1.A).

Figure 4.1. Apoptosis induction in HeLa cells after treatment with marine bacterial extracts. Detection of externalization of PS from cell membrane was done using APOPercentage assay. Cells were stained with dye and fluorescence was measured using flow cytometry. The photographs were taken by using inverted microscope. Apoptosis is shown as the percentage of cells that uptake APOPercentage dye relative to untreated cells. A) Cells were treated with H2O2 or extracts at a final concentration of 500 µg/mL for 48 h, and B) for 24 h. The Z- factor was calculated to be 0.68 and 0.77 for 48 h and 24 h experiments, respectively. C) The morphology of cancer cells after 24 h of treatment with chosen extracts. 128

Extracts from Chromohalobacter salexigens (P3-86A, K30, P3-86B(2)),

Chromohalobacter israelensis (K18), Halomonas meridiana (P3-37B) and Idiomarina loihiensis (P3-37C) induced more than 70% apoptosis in HeLa cells. Therefore, six most potent extracts (P3-86A, K30, P3-37B, K18, 2 and P3- 37C) were also evaluated for apoptosis at 24 h (Fig. 4.1.C), and chosen for further investigation to confirm the pathway of induced apoptotic cell death in HeLa cells. The cells were also evaluated for their morphological features of apoptosis using microscopy. Visual inspection showed that the morphological changes were visible within few hours after treatment of certain extracts

(Fig. 4.1.C).

4.4.4. Effects of extracts on MMP

The changes in MMP were used to evaluate its role in initiating apoptosis. In the present study, MMP was assessed using JC-1 dye. The JC-1 is a membrane permeable dye that has a unique characteristic of attraction to negative charge potential. The electron transport chain in energized mitochondria (normal MMP) attracts JC-1 dye into mitochondria where it accumulates to form J-aggregates (fluoresces red at 595 nm), while mitochondria with disrupted membrane potential (apoptotic cells) cannot accumulate JC-

1, thus leaving the dye in the monomeric form (fluoresces green at 530 nm). Extracts from Chromohalobacter salexigens (P3-86A and P3-86B(2)) were only able to induce changes in MMP by 45% and 29% respectively (Fig. 4.2) confirming their role in mitochondrial-mediated apoptosis.

Activation of caspases in response to treatment with extracts To gain insights into the potential mechanisms of apoptosis involved, caspase-3/7 activity as well as protein 129 expression of caspase-8 and −9 were measured for the six most potent extracts in HeLa cells after 16 h of treatment. All six extracts were able to activate caspase-3/7 and can be grouped further into two categories of ‘active’ and ‘highly active’ depending on the fold increase in observed caspase-3/7 activity as compared to untreated cells (Fig. 4.3).

Figure 4.2. MMP changes in HeLa cells after treatment with marine bacterial extracts. Cells were treated with 500 µg/mL of each extract for 12 and 16 h and stained with 50 µM JC-1 and analyzed by flow cytometry. Figure presents the 2D plots of FL-2H vs. FL- 1H indicating percentage of cells (± S.D) with intact (red) or permeable (green) mitochondrial membranes. Untx represent untreated sample and 100 mM H2O2 was used as a positive control. The Z- factor was calculated to be 0.79 and 0.80 for 12 h and 16 h time points, respectively.

Microbial extracts from P3-86A, P3-37B and K18 showed <10 fold increase in caspase-

3/7 activity and were termed as active (in the range of 1.7-6.0 folds) while extracts from 130

Chromohalobacter salexigens (K30, P3-86B(2)) and Idiomarina loihiensis (P3-37C) were considered highly active due to their remarkably high caspase-3/7 activity (≥50 fold increase) as compared to untreated cells. All extracts except Chromohalobacter salexigens (P3-86A) showed significant reduction in full-length caspase-9. Similarly, cleavage of caspase-8 (cl-Caspase-8) was observed in cancer cells treated with all other extracts except Chromohalobacter salexigens extract (K30).

Figure 4.3. Caspase-3/7 activity in HeLa cells after treatment with marine bacterial extracts. Cells were treated with 500 µg/mL extracts for 16 h and caspase-3/7 activity was estimated by measuring luminescence using ApoTox-Glo kit (Promega). The caspase-3/7 activity is represented as fold-change in activity when compared to untreated cells.

4.4.5. PARP-1 cleavage through caspases

The concerted action of caspases-3 and -7 lead to PARP-1 cleavage in response to DNA damaging agents [26, 27] and is considered as a hallmark of apoptosis [28, 29]. To 131 further explore that induced apoptosis in HeLa cells was via PARP-1 cleavage, western blotting was performed. Figure 4.4 shows an elevation in the cleaved fragment of PARP-

1 (85 kDa) in a time dependent manner for the extracts from Chromohalobacter salexigens (P3-86B(2)), Chromohalobacter israelensis (K18), Halomonas meridiana

(P3-37B) and Idiomarina loihiensis (P3-37C). The PARP-1 cleavage is quite significant after 12 h of treatment; however only a cleaved fragment was noticeable for these extracts (except for P3-37B) at 24 h. These observations confirmed the involvement of caspases mediated PARP-1 cleavage in response to the treatment with these four marine extracts in HeLa cells.

Figure 4.4. Western blot analysis of PARP-1 cleavage, phosphorylated γH2AX, Caspase- 8 and −9 in HeLa cells after treatment with marine bacterial extracts. Protein lysates of HeLa cells treated with 500 µg/mL of each extract for 12 and 24 h were subjected to western blotting probing for cleaved PARP-1 (cl-PARP-1), procaspase-9, cleaved caspase-8 and γH2Ax. Cells treated with 400 nM docetaxel for 24 h were used as a positive (+ve) control, Untx represent untreated control and β-tubulin was used as a loading control.

Activation of γH2Ax, a DNA damage marker γH2Ax is a variant of H2A histone and is phosphorylated at serine 139 in the presence of DNA double-stand breaks caused by 132

DNA damage [30] and DNA fragmentation during apoptosis [31]. Substantial DNA damage was measured in HeLa cancer cells within 12 h of treatment with extracts P3-

37B, P3-37C, P3-86B(2) and K18 (Fig. 4.4), confirming their role as DNA damaging agents.

4.5. Discussion

In the present study, 24 extracts of marine Bacteria isolated from the deep-sea brine pools of the Red Sea were evaluated for their cytotoxic effects against three human cancer cell lines. Out of all, 13 extracts were found to be significantly active against one or more cancer cell lines indicating their cell line specific behavior. The cell line specific activity of the extracts may be due to the presence of particular secondary metabolites and/or the different mechanisms of action of programmed cell death prevalent in different cancer cell lines.

Apoptosis or programmed cell death in multicellular organisms maintain the homeostasis by eliminating unwanted or defective cells [32]. It is well known that inefficient apoptosis contribute to several human malignancies [33, 34]; therefore, the identification of anticancer agents that induce cell death via apoptosis is one of the attractive strategies for chemotherapy [35]. The extracts from Chromohalobacter salexigens (P3-86A, K-30,

P3-86B(2)), Halomonas meridiana (P3-37B), Idiomarina loihiensis (P3-37C) and

Chromohalobacter israelensis (K-18) were found to be most actively inducing apoptosis in HeLa cells (Fig. 4.1). These extracts induced either one or more apoptosis related molecular changes such as cell shrinkage, PS exposure by membrane flipping, caspase-

3/7, -8 or -9 activation, PARP-1 cleavage (representing DNA fragmentation) and increase 133 in phosphorylation of γH2Ax (indicating DNA damage). Not much work has been published on the isolation of cytotoxic compounds from these microbial species. Our group [15] and others [36-38] have shown previously that Halomonas species produce metabolites that have the potential to kill cancer cells. Idiomarina loihiensis is an aerobic heterotrophic bacterium capable of catabolizing amino acids as a primary energy source

[39]. In PubMed, there are only 10 articles on Idiomarina loihiensis and most of these focus on describing its isolation and characterization [40], metabolism [39], and biofilm forming capabilities [41]. No study to date has focused on evaluating the bioactive potential of this species.

In the present study, extract from Idiomarina loihiensis (P3-37C) displayed caspase- dependent apoptosis in HeLa cells where a strong (229 fold) increase in caspase-3/7 activity was observed. Extract from K-18 also induced caspase-dependent apoptosis in our study, which showed 100% similarity to Chromohalobacter israelensis.

Chromohalobacter israelensis is a euryhaline halophile shown to change its concentration of unsaturated fatty acids in response to change in salt concentration, thus providing a mechanism for halophiles to tolerate environmental stresses [42]. Nothing has been reported so far regarding cytotoxic potential of this strain. Isolates P3-86A, K-

30 and P3-86B(2) were found to have high 16 s similarity with Chromohalobacter salexigens. This is one of the most investigated strain (out of the four we are reporting here) as a PubMed search on 15th July 2013 displayed 33 articles on Chromohalobacter salexigens. The Work to date has focused broadly on compatible solutes [43-46] and metabolism [47-50]. To the best of our knowledge, no attempt has been made to assess the cytotoxicity potential of these Bacteria. 134

Table 4.3. Summary of the results of PS exposure apoptosis assay, Caspase-3/7 activity, MMP changes, PARP-1 and Caspase-8 cleavage, full-length Caspase-9 and γH2Ax protein expression in HeLa cells treated with 500 µg/mL concentration of extracts

PS exposure Caspase- (percentage 3/7 activity PARP-1 Caspase-9 Caspase-8 Extract MMP γH2Ax of stained (fold cleavage reduction cleavage cells) change) P3-86A 0.7395 1.7 + + - - + K30 0.8674 50 - - + + - P3-37B 0.8489 4.2 - + + + + K18 0.8585 5.9 - + + + + P3-86B(2) 0.8001 282.1 + + + + + P3-37C 0.8477 229.7 - + + + + ‘+’ represents a positive observation, whereas ‘-’represents a negative observation.

The key objectives of the present study were to estimate the proapoptotic potential of novel halophytes isolated from the brine pools of the Red Sea and to shed light on the mechanism of apoptosis induction in cancer cells. We investigated the mode of induction of apoptosis by marine bacterial extracts by targeting the intrinsic and extrinsic pathways in human cervical cancer cell line (HeLa). Broadly, apoptosis is known to work through two pathways, i.e., mitochondria-mediated intrinsic pathway and death receptors mediated extrinsic pathway [51]. Intrinsic pathway is activated by either permeabilization of the outer membrane of mitochondria leading to disrupted MMP, or via DNA damage.

Both routes activate caspase-9 and consequently lead to activation of caspase-3 [52].

Extrinsic pathway involves interaction of ligands (Fas and TNF-alpha) to their transmembrane receptors [53], thus activating caspase-8, which further activates caspase-

3 directly [54] or by first activating intrinsic pathway followed by activation of caspase-3

[55]. Intrinsic- and extrinsic- pathways merge at caspase-3, which further cleaves PARP-

1 and results in apoptosis [56]. The results of pathway level investigations of the marine bacterial extracts are summarized in Table 4.3. 135

We reveal here that extracts from Chromohalobacter salexigens (P3-86A and P3-86B(2)) induced MMP disruption, caspase-3/7 activation, PARP-1 cleavage and PS exposure. PS externalization represents an early event during execution phase of apoptosis occurring between caspases activity and nuclear condensation [57]. Further investigation into the expression of caspase-8 and −9 determined the cleavage of caspase-8 after treatment with extract P3-86A, while no change in expression of full-length caspase-9 was observed,

(Fig. 4.4). This confirms that P3-86A induces apoptosis through extrinsic pathway.

Extract P3-86B(2) was found to reduce expression of both full-length caspase-8 and −9

(Fig. 4.4), thus suggesting that both extrinsic and extrinsic pathways of apoptosis are involved in its mechanism of action. The extracts from Halomonas meridiana (P3-37B),

Chromohalobacter israelensis (K18) and Idiomarina loihiensis (P3- 37C) were unable to induce any change in MMP in HeLa cancer cells and thus suggest the mitochondrial- independent apoptotic induction. The expression of both full-length caspase-8 and −9 was significantly reduced thus confirming the involvement of these initiator caspases in apoptosis induction. DNA damage was also observed in cancer cells which is known to activate Caspase-9 [58, 59] leading to intrinsic apoptosis in the absence of mitochondrial- mediated pathway. In case of Chromohalobacter salexigens (K30) extract, in spite of a

50-fold increase in caspase-3/7 activity and PS externalization, K-30 neither caused any change in MMP nor cleavage of PARP-1, although a slight increase in γH2Ax was observed indicating DNA damage. It is well documented that PARP activity is induced in response to DNA strand breaks in cells that have been exposed to DNA- damaging agents

[60]. Although it is widely accepted that PARP is specifically cleaved during apoptosis

[61] by caspase-3 [62] and caspase-7 [63], but studies have also shown that PARP 136 activity [64], activation of PARP- cleaving enzymes [65] and cleavage of PARP-1 [66] are not essential for induction of apoptosis. In another study, uncleavable PARP has been shown to accelerate apoptosis and necrosis [67] with possible explanation that uncleavable PARP may lead to imbalanced energy pool by depleting NAD + and ATP pools, which further disrupts MMP, thus releasing proapototic factors from mitochondria.

In our study, K30 did not disrupt MMP and hence the above mentioned explanation does not clarify the mechanism of apoptosis induction by K30. Caspase- 9 was significantly reduced at 24 h after K30 induction. This suggests that the K30 induces apoptosis in cancer cells through intrinsic pathway where DNA damage leads to activation of caspase-

9 that further contributes to the observed activities of caspase-3/7 and PS exposure.

In the last decade, phosphorylated gamma-H2AX (γH2Ax) has emerged as a marker of

DNA damage [68] and drug response in cancer patients [69, 70]. The chemicals/drugs that lead to DNA damage in cells are known as ‘genotoxic drugs’. Several genotoxic compounds such as cisplatin, carboplatin, oxaliplatin, methotrexate, doxorubicin, daunorubicin etc., are currently being used in the treatment of various types of cancers

[71]. The extracts tested in the present study also showed strong DNA damage as measured using γH2Ax, which shows that these extracts may contain compounds that could find potential therapeutic use in cancer patients. This study opens up avenues for identifying new DNA damaging compounds from deep-sea Bacteria.

4.6. Conclusions

This study reports for the first time the cytotoxic activities of several halophilic bacterial species isolated from deep-sea brine pools of the Red Sea and provides in-depth insights 137 into the possible mechanisms of apoptosis induced by the extracts in various human cancer cell lines. Overall, six extracts from Chromohalobacter salexigens (P3-86A, K-30,

P3-86B(2)), Halomonas meridiana (P3-37B), Idiomarina loihiensis (P3-37C), and

Chromohalobacter israelensis (K-18) have displayed significant anticancer activities and can be further explored for isolation and characterization of bioactive molecules. This study also provides conclusive evidence that brine pools of the Red sea harbor several species of Bacteria producing anticancer secondary metabolites.

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[81] Gardes A, Kaeppel E, Shehzad A, Seebah S, Teeling H, Yarza P, et al. Complete genome sequence of Marinobacter adhaerens type strain (HP15), a diatom-interacting marine microorganism. Stand Genomic Sci 2010;3:97 - 107.

[82] Ivanova E, Romanenko L, Chun J, Matte M, Matte G, Mikhailov V, et al. Idiomarina gen. nov., comprising novel indigenous deep-sea bacteria from the Pacific Ocean, including descriptions of two species, Idiomarina abyssalis sp. nov. and Idiomarina zobellii sp. nov. Int J Syst Evol Microbiol 2000;50:901 - 7.

144

Chapter 5: Insights into the metabolism of uncultivated euryarchaeal

Marine Benthic Group-E (MBGE) from the interface of the Atlantis II

Deep brine pool

1 1 1 1 Tyas Hikmawan , Rita Bartossek , Mamoon Rashid , David K Ngugi , Manikandan

1 2 2 1 Vinu , Wail Baalawi , Intikhab Alam , and Ulrich Stingl

1 Red Sea Research Center King Abdullah University of Science and Technology

(KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

2Computational Bioscience Research Centre, King Abdullah University of Science and

Technology (KAUST), Building 3, 23955-6900, Thuwal, Saudi Arabia

145

5.1. Abstract

The deep-sea anoxic brine pools of the Red Sea have been reported to harbor many novel microbial lineages. However, the majority of the novel microbial groups in these hypersaline environments remain resistant to cultivation. By using a single-cell genomic approach we are able to improve our understanding about many uncultured phyla without having to culture them. We analyzed 10 single-amplified genomes (SAGs) of the uncultured euryarchaeal Marine Benthic Group E (MBGE) retrieved from the hottest brine known, the Atlantis II Deep. Our goal was to understand the potential metabolic capabilities and properties that allow these organisms to thrive in the Red Sea brine pools. Phylogeny based on 16S rRNA and conserved single-copy genes confirmed a deep branching phylogenetic position of MBGE between methanogens and sulfate-reducing archaea. The SAGs of the MBGE encode for all the genes responsible for CO2 fixation according to the Wood-Ljungdahl pathway, which enables them to grow autotrophically only on CO2 and H2. The presence of several genes implicated in sulfur metabolism, including the key enzyme for the dissimilatory sulfate reduction (DsrAB), suggests that

MBGE might be able to use sulfur species for energy conservation. In the absence of adenosine 5’-phosphosulfate reductase gene, we hypothesized that MBGE perform sulfite reduction rather than sulfate reduction to conserve energy. In response to osmotic stress,

MBGE are potentially accumulating glycine betaine and importing K+ into the cell.

5.2. Introduction

The majority of microorganisms remain resistant to cultivation, despite many cultivation attempts and remarkable progress in terms of isolation techniques [1]. In fact, microorganisms that have been successfully isolated are rarely numerically dominant in 146 their respective communities [2]. These yet-uncultivated microbial groups comprise more than half of approximately 60 major lineages within the bacterial and archaeal domains that are widely known as candidate phyla [3, 4]. Extensive microbial diversity and phylogenetic studies using next-generation sequencing technologies have not been successful at explaining the ecological significance of the candidate phyla [5]. Recent single-cell genomics approaches enable us to unravel the metabolisms, energy conservation pathways, molecular adaptation to stress, and ecological significance of yet- uncultured candidate phyla, focusing on the most abundant groups in the ecosystems [6,

7]. Combinations of cell sorting and multiple-displacement-amplification (MDA) approaches have improved our understanding of many candidate phyla in different ecosystems, including extreme environments [8-10]. Compared with other extreme environments and low-energy ecosystems, the candidate phyla inhabiting deep-sea hypersaline anoxic brines still remain largely unexplored [11].

The deep-sea anoxic brine pools of the Red Sea represent an extremely peculiar environment characterized by a high microbial diversity and biomass [12-14]. The brine pools of the Red Sea represent one of the most extreme ecosystems that are extremely saline (most of them are completely salt-saturated), anoxic, and characterized by drastic physicochemical changes compared with the overlaying seawater [11]. The largest brine,

Atlantis II Deep, remains hydrothermally active and possesses multiple layers with increasing salinities and temperatures up to 68ºC due to double-diffusive convection [15,

16]. Phylogenetic studies in Atlantis II Deep have revealed the presence of many uncultured candidate phyla, such as candidate division Mediterranean Sea Brine Lakes-1

(MSBL1), the deep-sea euryarchaeotic group (DSEG), deep-sea hydrothermal vent group 147

6 (DHVEG-6), and Marine Benthic Group E (MBGE) [17, 18]. In the sediments of the sulfur-rich Atlantis II Deep, candidate division MBGE dominated with half of the archaeal sequences being assigned to this uncultivated group [18].

MBGE were deeply branching and not specifically associated with any known archaeal isolates [19]. They have been detected in various deep-sea environments from the sediments of Atlantic Ocean to the deep-sea hydrothermal vents in the Western Pacific

Ocean [20-22]. MBGE were found to be the most abundant taxa in surficial layers in the subseafloor of the Canterbury Basin, inactive sulfide chimney structures, and microbial mats in deep-sea hydrothermal fields [23-25]. Some studies have speculated MBGE might be associated with the weathering of metal-rich sulfide deposits based on their presence in iron-rich habitats [23, 26, 27]. Other authors have suggested that their metabolism is related to methanogenesis due to their phylogenetic position between the members of Methanobacteriales and Methanosarcinales [19, 28]. However, their physiology and ecological role remain enigmatic. In this study, we analyzed 10 single- cell amplified genomes (SAGs) of the MBGE group. Our goal is to describe the main genomic features of single-cell partial genomes of candidate division MBGE, particularly in terms of metabolic capabilities and other properties that allow the organisms to thrive in such a hot and hypersaline environment.

5.3. Materials and methods

5.3.1. Sampling and cell sorting

Water samples were collected from the interface between Upper Convective Layer 4 and

Lower Convective Layer (brine body) of the Atlantis II Deep (Table S5.1, S5.2), in the 148

Red Sea during the 3rd KAUST Red Sea Expedition on board of the R/V Aegaeo in

November 2011. The samples were sent for single-cell sorting and whole-genome amplification [6] to the Single Cell Genomics Center, Bigelow Laboratory for Ocean

Sciences (Maine, USA). Cell sorting, whole genome amplification (WGA), PCR-based screening of 16S rRNA positive cells, and quality check was performed using protocols described on their website (https://scgc.bigelow.org/; [29]). The genomic DNA resulting from the multiple displacement amplification (MDA)-step was then diluted 50-fold in TE buffer for PCR screening of candidate division MBGE from a total of 48 cells. Ten MDA libraries of candidate division MBGE were then sequenced using the Illumina GAIIx platform (San Diego, CA, USA) at KAUST.

5.3.2. Assembly and quality check

Assemblies of the single-cell amplified genomes (SAGs) were generated using a pipeline that employs a choice of assemblers designed for single-cell sequencing data including

IDBA-UD [30], VelvetSC [31], and Spades [32], along with several pre and post assembly data quality checks using Trimmomatic [33]. SAG sequencing using MDA suffers from lesser genome coverage and an uneven amplification of the genomic regions. Moreover, the chances of contamination are also very high. As a result, we developed SAG quality control steps to remove any contamination from SAG assembly.

To decontaminate the SAG draft assemblies, we used genomic features such as the GC content, size, phylogenetic affiliation, and tetranucleotide frequency (TNF) of the contigs

(Fig. S5.1). The filter for all of these characteristics were kept independent and only a contig that passed all filters was directed into the clean bin (Table S5.3). Conversely, if a 149 contig/scaffold failed in any of these filters, it as sorted into the contamination bin.

We calculated the mean GC ratio of the SAGs by taking all contigs of a preliminary assembly of AAA261A19 into account with a mean of 0.5006; a set cut-off of 10% was determined. The contigs of all 10 SAGs beyond this range were discarded into the contaminated bin. The size threshold of the contigs was set to 2000 bp. Any contig below

2000 bp was sorted into the contaminated bin; nonetheless, these contaminated-labeled contigs were considered during the manual curation of the annotation of the SAGs.

The INtegrated Datawarehouse of MIcrobial GenOmes (INDIGO) annotation pipeline

[34], which is used as well for the manual curation of the annotation, produces a file with the phylogenetic affiliation of every gene on every contig at the genus/species level. If the majority (50% or more) of the genes in a scaffold/contig hit to a phylum other than the source phylum, that contig was labeled as contaminated.

Once the binning was complete, the clean and total bins were subjected to Canonical

Correspondence Analysis (CCA) using the TNF of the sequences and the contigs that were visualized on the plots (Fig. S5.1). The CCA analysis and plotting was accomplished in R using custom scripts. The plot showing all of the contigs of the assembled genome provided a clear idea of the level of contamination in terms of the phylogenetic and GC content profiles of the contigs (Fig. S5.1).

To check the distribution of conserved cluster of orthologous groups (COGs) in the genome, different COG sets for Bacteria and Archaea (adopted from the Human

Microbiome Project) were applied. A COG is present in the genome if one or more genes in the genome hit to that COG with at least 50% amino acid identity and 50% query 150 coverage (Figs. 5.1, S5.2). The number of multiple single-copy conserved genes in a genome is a very important indicator of the contamination; this number may also represent spurious assembly. We observed in our single-cell genome data that multiple copies of conserved genes belong to multiple contigs. The smaller contig of the identical copy was discarded into the contaminations bin.

5.3.3. Sequence annotation

We annotated clean contigs from each of our SAGs using the Automatic Annotation of

Microbial Genomes (AAMG) pipeline implemented in INDIGO [34]. Briefly, AAMG predicted the rRNA and tRNA genes first and masked the DNA to avoid predicting the protein Open Reading Frame (ORF) in the RNA regions [35]. ORFs were annotated using BLAST [36] by comparing with public databases such as UniProt [37], NR,

Conserved Domain Database (CDD) [38], and KEGG [39]. Furthermore, all ORFs were compared to protein functional signature domain databases using InterProscan [40]. Gene ontologies related to Enzyme Classification (EC) numbers were obtained from the

InterProscan results. KEGG orthologs and further EC numbers were obtained from the

KEGG BLAST results. To ensure easy look-up of the annotations and features, we produced a data warehouse containing the total sequences and annotation features of all the SAGs [34].

5.3.4. Phylogenetic analysis

Nearly full-length 16S rRNA genes were amplified by PCR from the genomic DNA resulting from the MDA step using combinations of the archaeal-specific primers 4F (5′-

TCCGGTTGATCCTGCCRG-3′) [41] paired with the universal primer 1492R (5′- 151

GGTTACCTTGTTACGACTT-3′) [42]. The PCR conditions were performed with an initial denaturation of 5 min at 94°C, 30 cycles of 1 min at 94°C, 1 min at 55°C, 1.5 min at 72°C, and then a final extension step of 7 min at 72°C using the Taq polymerase kit

(New England Biolab, UK). The yield and quality of the PCR products were examined on

1% (wt/vol) agarose gel (SeaKem GTG, Lonza, USA) stained with SYBR Safe

(Invitrogen, USA), then purified with Illustra Exostar 1-step (GE, Healthcare, UK) according to the manufacturer’s protocol. The PCR products with the correct insert size were sequenced into an ABI 3730×l DNA Analyzer in our Genomics Core Lab Facility at

KAUST and then were quality checked, trimmed, and assembled using Sequencher v.4.9

(Gene Codes Corporation, Ann Arbor, MI, USA). The assembled archaeal 16S rRNA sequences were aligned with the automatic SINA aligner (http://www.arb- silva.de/aligner/) by comparing iwth the SILVA SSU 115 database [43]. The SILVA- aligned sequences were then used to construct phylogenetic trees with the maximum likelihood algorithm as implemented in ARB software v.5.3 [44]. Bootstrap analyses were performed with 1,000 repetitions.

5.3.5. Phylogenomic profiling

To infer the evolutionary profiling of the MBGE SAGs with the selected reference genomes we performed phylotyping of archaeal housekeeping marker genes, which are present in all MBGE SAGs and reference genomes. We identified the marker genes using the phylogenomic inference tool AMPHORA2 [45]. After we identified the common marker genes, the gene sequences were individually concatenated in identical order. In the case of multiple sequences for a marker gene in the same genome, the longest protein was used for the analysis. The concatenated sequences were aligned using Muscle with 152 default settings [46]. To eliminate any alignment-related issues, the highly divergent and ambiguous regions were removed using the Gblocks tool with default settings [47].

5.4. Results and Discussion

Table 5.1. Assembly statistics of MBGE SAGs described in this study

A07 A19 A23 G11 G15 I04 I13 K19 L22 N04

Total Contigs1 73 127 126 116 135 66 125 60 113 48 Total Length (kb) 516.47 104.62 759.70 853.79 762.89 405.14 758.94 389.59 751.33 313.25 Min.Length (kb) 2.01 2.06 2.04 2.03 2.03 2.06 2.00 2.00 2.00 2.01 Avg. Length (kb) 7.07 8.24 6.03 7.36 5.65 6.14 6.07 6.49 6.65 6.53 Max Length 28.94 45.46 24.38 35.57 24.66 21.92 33.02 24.79 28.37 18.49 GC % 52.28 51.94 51.39 51.55 51.36 50.45 51.66 50.03 52.24 49.87 N50 16 28 35 29 35 17 29 15 30 13 No. of Gene 684 1296 1000 1052 1004 526 1012 483 935 382 Detected tRNA 17 20 19 12 15 10 19 6 16 3 CDS 667 1276 981 1040 989 516 993 477 919 379 Coverage (%)2 41.35 46.15 39.42 42.31 35.58 14.42 38.46 35.58 39.42 17.31 1 Only contigs with the length 2000 base pairs or longer were considered 2 Based on 104 determined conserved single-copy gene common to all archaeal genomes

5.4.1. Single-cell genome features

A total of 10 SAGs of MBGE described in this study were retrieved from the hottest brine so far publicly described, Atlantis II Deep. Statistics of the single-cell genome assemblies, such as the number of contigs, the size, and the number of genes per genome, are listed in Table 5.1. An estimation of genome coverage using 104 conserved single- copy genes of Archaea showed that the genome completeness ranged between 14.4% and

46.2% with the highest genome completeness in SAG AAA261A19 (Table 5.1).

The incomplete recovery of genomes is an obvious limitation of the single-cell genomics approach [6, 48]. These estimated genome recoveries were a bit lower than those of previous single-cell studies [49-51]. Nonetheless, many valuable insights regarding the 153 potential metabolisms, carbon fixation, sulfur metabolism, and osmoadaptation mechanisms of MBGE may be drawn from these SAGs despite the relatively low coverage.

N04

L22

K19

I13

I04

G15

G11

A23

A19

A07

0% 20% 40% 60% 80% 100%

CELLULAR PROCESSES AND SIGNALING INFORMATION STORAGE AND PROCESSING METABOLISM POORLY CHARACTERIZED

Figure 5.1. Predicted metabolic functions and relative percentage based on Cluster of Orthologous Group (COG) analysis

5.4.2. Phylogenetic analysis of the MBGE

Since the SAGs in this study have only a limited coverage (maximum: 46%) and furthermore a strong resemblance to one other and 100% identical 16S rRNA gene sequences, all 10 SAGs were treated in the following annotation as one potentially near- complete “pangenome” [52]. The 16S rRNA gene sequences of the SAGs were classified within the class Thermoplasmata and related to the MBGE sequences from the 154 hydrothermal environments (Table S5.1), with identity scores between 84.44% and

84.94% based on the SILVA database [43].

MBGE_AC261A19 MBGE_AC261G11 MBGE_AC261L22 MBGE_AC261I04 MBGE_AC261I13 MBGE_AC261A23 MBGE_AC261K19 Marine Benthic Group E 100 MBGE_AC261N04 91 MBGE_AC261A07 MBGE_AC261G15 100 Uncl. MBGE from Black rust formation on a borehole observatory (AB260057) 99 Uncl. MBGE from subseafloor sediment at the Deep Basin (JQ815967) Uncl. MBGE from hydrothermal fluid (AB213069) Uncl. MSBL1 from the brine of Discovery, Mediterranean Sea (AY226367) 87 75 100 Uncl. MSBL1 from the brine of L'Atalante, Mediterranean Sea (AY226347) 100 Uncl. MSBL1 from the brine of Bannock, Mediterranean Sea (AY226366) Candidate Division MSBL1 Uncl. MSBL1 from the brine of Urania, Mediterranean Sea (AY226377) 100 44 Uncl. MSBL1 from the brine of Discovery Deep, Red Sea (HQ530525) 97 Uncl. archaeon SAGMA−E (AB050209) 100 Uncl. archaeon SAGMA−R (AB050223) South African Gold Mine Clade Uncl. archaeon SAGMA−I (AB050213) 85 Methanotorris igneus (CP002737) 100 Methanococcus aeolicus (U39016) Methanococci Methanocaldococcus fervens (AF056938) 100 Methanosarcina acetivorans (M59137) 71 100 Methanosarcina barkeri (AF028692) Methanohalophilus mahii (M59133) 85 76 Methanomicrobia 82 Methanohalobium evestigatum (FR733675) Methanocella conradii (JN048683) Methanospirillum hungatei (M60880) 99 100 Halorhabdus tiamatea (AB576122) 75 Halorhabdus utahensis (AF071880) 84 Halobacteria 100 Natrialba magadii (X72495) 88 Haloferax mediterranei (D11107) 100 Natronomonas pharaonis (D87971) 83 Archaeoglobus profundus (AJ299219) 46 Ferroglobus placidus (AF220166) 100 Archaeoglobus veneficus (Y10011) Archaeoglobi Archaeoglobus fulgidus (X05567) 69 99 Methanothermus fervidus (CP002278) Methanothermobacter thermautotrophicus (X15364) Methanobacteria Methanopyrus kandleri (M59932) Methanopyri 91 Desulfurococcus mucosus (CP002363) 51 89 Staphylothermus marinus (X99560) Fervidicoccus fontis (EF552404) Thermoprotei (Crenarchaeota) 100 Sulfolobus acidocaldarius (D14053) 100 Caldivirga maquilingensis (AB013926) Pyrobaculum arsenaticum (AJ277124) Nitrosopumilus maritimus (DQ085097) Candidatus Korarchaeum cryptofilum OPF8 (CP000968)

0.10

Figure 5.2. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) inferred using complete/nearly complete 16S rRNA genes from the SAGs and comparative sequences from the SILVA SSU 115 database. The scale bar represents 0.10-fixed mutation per nucleotide position and bootstrap values above 50% are shown.

A maximum-likelihood phylogenetic tree of the nearly full-length 16S rRNA gene demonstrated a deep branching phylogenetic position of MBGE (Fig. 5.2). MBGE were distantly related to the environmental sequences candidate division MSBL1. The candidate division MSBL1 was first reported to inhabit the hypersaline anoxic basins of the Mediterranean Sea [53]. 155

100 Methanocaldococcus fervens (CP001696) 100 Methanotorris igneus (CP002737) Methanococci 99 Methanococcus aeolicus (CP000743) Methanopyrus kandleri (AE009439) Methanopyri Methanothermobacter thermautotrophicus (AE000666) 82 69 100 Methanobacteria Methanothermus fervidus (CP002278) 100 AAA261A07 100 AAA261A23 Marine Benthic Group E AAA261I13 100 66 Archaeoglobus veneficus (CP002588) 84 Archaeoglobus fulgidus (AE000782) Archaeoglobi 100 Archaeoglobus profundus DSM 5631 (CP001857) Ferroglobus placidus (CP001899) 100 Methanospirillum hungatei (CP000254) 100 Methanosarcina barkeri (CP000099) 100 Methanosarcina acetivorans (AE010299) 99 Methanomicrobia 100 Methanohalophilus mahii (CP001994) 52 99 Methanohalobium evestigatum (CP002069) Methanocella conradii (CP003243) 91 Natrialba magadii (CP001932) 100 Haloferax mediterranei (CP001868) Natronomonas pharaonis (CR936257) Halobacteria Halorhabdus utahensis (CP001687) 96 100 Halorhabdus tiamatea (HF571520) Thermofilum pendens (CP000505) 100 100 Caldivirga maquilingensis (CP000852) Pyrobaculum arsenaticum (CP000660) 68 Sulfolobus acidocaldarius (CP000077) 100 Fervidicoccus fontis (CP003423) Thermoprotei 90 Hyperthermus butylicus (CP000493) 99 100 Desulfurococcus mucosus (CP002363) Staphylothermus marinus (CP000575) Nitrosopumilus maritimus (CP000866) Candidatus Korarchaeum cryptofilum (CP000968)

0.2

Figure 5.3. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on the amino-acid matrix of 18 concatenated archaeal proteins present in 3 of the MBGE SAGs and other archaeal genomes. The same set of proteins from Korarchaeota was included as an out-group. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown.

In the Red Sea brine pools, MBGE constituted a significant fraction of the archaeal diversity [11]. The closest isolated Archaea to MBGE are the members of

Archaeaoglobales and Methanococcales (Fig. 5.2). Archaeaoglobales are hyperthermophilic sulfur-metabolizing anaerobic Archaea that are inhabitants of hot springs, oil fields, and hydrothermal vents [54, 55]. On the other hand, the members of

Methanococcales are all hydrogenotrophic methanogens and are isolated from marine habitats [55]. The topology of the tree is in good agreement with the phylogeny based on 156 conserved single-copy genes (Fig. 5.3), confirming the placement of MBGE as a distinct archaeal lineage between methanogens and sulfate-reducing archaea.

5.4.3. Central carbon metabolism

For the Embden-Meyerhof-Parnas (EMP) pathway (glycolysis), all enzymes apart from one were encoded in the combined genomes of the MBGE SAGs (Table S5.4). The only enzyme missing in the glycolysis was pyruvate kinase (Pk). Pk is the final enzyme that is responsible for the energy-generating step, the conversion of phosphoenolpyruvate (PEP) to pyruvate with a gain of 2 adenosine triphosphate (ATP) [56]. Not including the last reaction, the glycolysis is energetically neutral since a prior investment of 2 ATP has to be made in the first part of the EMP for the phosphorylation of glucose and fructose-6- phosphate [56, 57]. These 2 ATP are only regained in the conversion of two 1,3 bisphosphoglycerate to 3-phosphoglycerate, which causes this EMP pathway to become energetically neutral. Such an EMP would only occur in the interconversion of sugars to provide intermediates for reducing equivalents and other pathways and cellular building blocks [56, 58].

It is unclear whether the last enzyme (PK) is indeed not encoded in the MBGE genomes or is just lacking in the dataset due to the incompleteness of the SAGs and thus is functionally present in the repertoire of the MBGE. Since Archaea often use a modified version of well-studied bacterial metabolic counterparts, the conversion is potentially performed by another enzyme or an unidentified non-homologous enzyme [57].

157

2- SO4

ABC-type CO2 + MFR 2H + Fd (red) Fwd H2O + Fd (ox) CHO-MFR

H4MPT Ftr

2 2- SO MFR 4 sulfate reduction CHO-H4MPT MQH H+ Sat ATP Mch PPi H O 2- 2 S2O3 CH=H MPT APS 4 F H Mtd 420 2 F +H+ AprAB 420 CH =H MPT AMP 2 4 F H ? 420 2 HS- + SO 2- 2- Mer 3 SO3 F420

sulfite reduction CH3-H4MPT CO2 + CO H +Fd(red) DsrAB CO DsrMKOP 2 CoDH ACS + ] H O+Fd H4MPT 2 (ox) DsrC [2e SH Por SH pyruvate acetyl-CoA biomass SH Fd(ox) + SH Fd(red) 4H DsrMK S S S2- S S PEP ADP acetate ATP

MvH:Hdr 2H2 acetogenesis ?

MQ MQ

MQH2 MQH2 HdrDE [H+] hydrogenases H + 2 2H

Figure 5.4. An overview of the proposed central cellular metabolism of MBGE.

Enzyme abbreviations catalyzing reactions are italized. Fd, ferredoxin; THF, tetrahydrofolate; H4TPT, tetrahydropterin); MFR, methanofuran; F420, coenzyme F420. Enzymes: Fwd, formyl-MFR dehydrogenase; Ftr, formyl-MFR:tetrahydromethanopterin formyl-transferase; Mch, methenyl-tetrahydromethanopterin cyclohydrolase; Mtd, methylene-tetrahydromethanopterin dehydrogenase; Mer, methylene- tetrahydromethanopterin reductase; CoDH/ACS, CO dehydrogenase/acetyl-CoA synthase. Dsr, Dissimilatory sulfite reductase; Hdr, heterodisulfide reductase; Sat, ATP sulfurylase; Apr, adenosine 5’-phosphosulfate reductases. Red color indicates the enzymes were not detected in the SAGs.

PEP Synthetase (PEPS) and Pyruvate:Phosphate Dikinase (PPDK) are known to be functional in the reverse reaction of glycolysis (gluconeogenesis) [59]. The two enzymes are responsible for the energy-consuming conversion of pyruvate into PEP. Both of these 158 enzymes are capable of the reverse reaction, i.e., producing pyruvate from PEP and gaining ATP under certain conditions [59, 60]. While the PPDK is not encoded several different copies of PEPS are encoded in the SAGs of the MBGE. Whether they can use

PEPS for the production of pyruvate and ATP remains to be elucidated experimentally.

The majority of enzymes in the Entner-Doudoroff pathway are not present in the SAGs of the MBGE (Table S5.4). This pathway is an alternative glycolytic pathway for the catabolizing of glucose to pyruvate with a net energy yield of 1 ATP [56]. Therefore, it can be concluded that it is not functional in MBGE as a method for interconversion or energy yield.

None of the SAGs encode for genes of the oxidative part of the pentose phosphate pathway, which is consistent with other findings so far for Archaea [56]. The oxidative formation of NADPH to produce reductive equivalents has to be performed in other reactions independently of the pentose phosphate pathway [56]. Genes for the non- oxidative pentose phosphate pathway are partially present, and these genes can be used for the generation of pentose sugars according to a few previously described Archaea [61,

62]. Homologous of key genes for the alternative ribulose monophosphate pathway [61,

63] could not be detected in the SAGS.

The oxidative tricarboxylic acid (TCA) cycle is most likely not functional in the MGBE considering that most of the necessary genes are not encoded in the SAGs. Furthermore, the replacement of the genes by novel non-homologous genes is unlikely. The partial set of the reductive TCA (rTCA) cycle, as described for some methanogens, is present in the

SAGs (for details, see the next chapter) [64, 65]. 159

Methanogens Methanosarcina ac Methanosarcina

Methanolob

Methan

Methanosae Meth ei ococcoides b

u s psychro a

Sulfate-reducing Archaea mazei nosarcina

ta thermophila e tivoran Archaeoglobus fulgi lum hungat

urtonii p

hilus

s s a arvoryzae

ethanospiril nocell dus 100 M tha 100 Me AA 100 A261L2 Methanoregula boonei 100 MBGE SAGs AAA261G15 100 0 2 0 100 1 sulfobacterium commune 11 AAA261G 77 se 100 Thermode 4 00 7 8 1 9 Thermodesulfobacterium hveragerden rus kandleri Methanopy 100 100

100 93 1 94 100 0 ii 0 i ccus jannasch ocunicul anocaldoco 100 100 0.2 rgula therm Meth ens 100 Desulfovi rv 9 100 6 Desulfoto cus fe Desulfotoma mac Sulfate-reducing Bacteria ulum vannielii anocaldococ thermocisternum

Meth Candidatus

Ac Moorella t culu

e m a cus maripaludis tonem lcoholivorax ethanocaldococcus vulcaniusMethanococcus M hermoacetica Scalindua brodae

a longum nobacterium formicicum nobacterium

Methanococ Metha

Methanogens Methanothermobacter thermautotrophicus Methanothermobacter Acetogens

Figure 5.5. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on the amino acid of concatenated acetyl-CoA decarbonylase synthase (ACDS; cdhCDE) proteins from the SAGs and the references sequences, including methanogens, sulfate reducers, and acetogens. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown.

The SAGs of MBGE encode for all of the genes responsible for CO2 fixation according to the Wood-Ljungdahl pathway (Fig. 5.4), which enable autotrophic growth only on CO2 and H2 (for details, see the next chapter). The phylogenetic analysis based on the amino acid of concatenated acetyl-CoA decarbonylase synthase (ACDS; cdhCDE) proteins showed that MBGE were branching between sulfate reducer Archaeoglobus fulgidus and hydrogenotrophic methanogens Methanococci (Fig. 5.5). Interestingly, A. fulgidus is able to yield energy as acetogen with CO as a source of carbon. This species can survive and 160 grow without the presence of sulfate, thiosulfate, or other sulfur compounds as an electron acceptor [66].

5.4.4. Autotrophic carbon metabolism

Carbon dioxide (CO2) assimilation into organic material represents the most essential biosynthetic process in nature [58]. To date, six pathways for CO2 fixation have been elucidated. These six pathways are the Calvin-Benson reductive pentose phosphate

(CBB) cycle, the rTCA cycle, the reductive acetyl coenzyme A (acetyl-CoA) pathway

(Wood-Ljungdahl pathway), the 3-hydroxypropionate bicycle (3-HP), the 3-hydroxypropionate/4-hydroxybutyrate (3-HP/4-HB) cycle, and the dicarboxylate/4-hydroxybutyrate cycle [67, 68].

The SAGs in this study expectedly lacked the majority of the genes for the three latter

CO2-fixation pathways (Table S5.4). Up until now, the pathway for 3-HP has only been detected in members of the Chloroflexaceae family [67]. The 3-HP/4-HB cycle and the dicarboxylate/4-hydroxybutyrate cycle are only present in Crenarchaeota [67, 68].

The SAGs contain genes coding for the large subunit of ribulose-1,5- bisphosphate carboxylase/oxygenase (RubisCO). RubisCO is one of the key enzymes in the CBB cycle responsible for CO2 fixation in most photosynthetic organisms [69]. Four types of

RubisCO have been described; type III is solely present in Euryarchaeota and harbors the residues necessary for ribulose 1,5-bisphosphate carboxylase oxygenase activities [69].

Nonetheless, a functional CBB pathway has so far not been identified in Archaea [68].

Furthermore, many other enzymes of the CBB cycle are missing in the SAGs including phosphoribulokinase, a key enzyme in this cycle [68, 70]. This enzyme is also absent in many other archaeal genomes bearing a RubisCO type III. RubisCO type III is postulated 161 to be involved in adenosine monophosphate (AMP) metabolism in Archaea together with two additional newly described enzymes: AMP phosphorylase and a ribose 1,5- bisphosphate isomerase [69, 71]. The RubisCO type III is responsible for the generation of two molecules of 3PG from Ru1, 5BP, CO2, and H2O in Thermococcus kodakarensis

[66, 67]. The AMP phosphorylase releases the Adenine of AMP and replaces it with a phosphate group to generate ribose 1,5-bisphosphate. Then, the ribose 1,5-bisphosphate isomerase converts it further into ribulose 1,5-bisphosphate [69, 71].

Homologous for all three enzymes are present in the SAGs of this study, indicating that the MBGEs can potentially produce 3PG, an intermediate of the central sugar metabolism, from AMP via this pathway instead of using the RubisCO for CO2 fixation

[69]. The SAGs also encode genes for a reductive citric acid cycle. This rTCA has been proposed to be operational in certain Archaea [56, 67]. However, due to recent findings this proposal has been vitiated [70]. Nonetheless, the partial rTCA cycle has been described in many methanogens as not being responsible for CO2 fixation but rather for the biosynthesis of intermediates [72, 73]. In methanogens, the incomplete TCA cycle generally starts from oxaloacetate and leads to oxoglutarate [67]. The cycle can proceed either via the reductive or the oxidative path, depending on the class of methanogen [64].

Members of the order Methanosarcinales adopt the oxidative direction with iso-citrate as an intermediate [65]. Obligately hydrogenotrophic methanogens use the reductive pathway via malate, fumarate, and succinate [65].

The genes encoded in the SAGs cover almost the complete set of partial rTCA needed for the biosynthesis of 2-Oxoglutarate. The only enzyme that the SAGs are lacking is malate dehydrogenase protein. The absence of the protein is either due to the incompleteness of 162 the SAGs or a non-homologous novel malate dehydrogenase. Since these predictions are genome-based, only experimental investigations can reveal if partial or even a full cycle of rTCA can be performed using alternative enzymes [74].

The SAGs additionally contain the complete set of genes for the reductive acetyl-CoA pathway (Fig. 5.4). All genes encoded are the most similar to homologous genes in methanogens. The reductive acetyl-CoA (Wood-Ljungdahl) pathway is a noncyclic pathway that occurs in a variant that is predominantly found in acetogens using tetrahydrofolate and in methanogens using tetrahydromethanopterin or tetrahydrosarcinopterin as a C1 carrier [75]. While the methanogens can withdraw methyl tetrahydromethanopterin from the pathway and reduce it via methyl coenzyme M to methane during the process of CO2 fixation for energy conversation, the process is not possible in Archaeoglobus since they lack the methyl-CoM reductase (McrA) [75, 76].

The production of small amounts of methane was shown for Archaeoglobus infectus, A. venificus, and A. fulgidus [77], which might be merely the result of the reduction of N5- methyl tetrahydromethanopterin to methane and tetrahydromethanopterin by carbon monoxide (CO) dehydrogenase [76].

Since we lack the key enzyme of methanogenesis (Table S5.4), the McrA, the same could hold true for the group of MBGE. Apart from the key enzyme of methanogenesis, the

MBGE SAGs encode for the full methanogenic pathway. Only experimental procedures can demonstrate if MBGE are able to perform methanogenesis. However, this process is highly unlikely since we lack all five genes for the subunits of the enzyme. 163

100 Desulfovibrio africanus (AB061535) 98 Desulfovibrio desulfuricans (AF273034) 99 Desulfovibrio vulgaris (NC002937) 71 Desulfovibrio aerotolerans (AY749039) Desulfovibrio alkalitolerans (AY864856) 99 100 Desulfobacula phenolica (AF551758) Desulfotignum balticum (AF420286) 68 55 Desulfohalobium retbaense (AF418190) 100 Desulfohalobium utahense (DQ386236) Desulfomonile tiedjei (AF334595)

94 Desulforhopalus vacuolatus (AF334594) Desulfotalea arctica (AY626032) 100 Desulfobulbus marinus (AF337902) 100 74 Desulfobulbus rhabdoformis (AJ250473) Desulfobulbus elongatus (AJ310430) 100 Reductive bacterial type dsrAB 100 Desulfobulbus propionicus (AF218452) Desulfatiglans anilini (AF482455)

100 97 Desulfotomaculum acetoxidans (AF271768) Desulfotomaculum thermosapovorans (AF271769) 75 100 Desulfotomaculum kuznetsovii (AF273031) Desulfotomaculum thermocisternum (AF074396)

87 Desulforhabdus amnigena (AF337901) 100 Desulfovirga adipica (AF334591) 100 Thermodesulforhabdus norvegica (AJ277293)

100 Thermodesulfatator atlanticus (FN186055)

100 Thermodesulfatator indicus (CP002683) 87 100 Thermodesulfobacterium commune (AF334596) Thermodesulfobacterium thermophilum (AF334598) Desulfoarculus baarsii (AF334600) Desulfobacca acetoxidans (AY167463)

86 Archaeoglobus fulgidus (NC 000917) 100 Archaeoglobus veneficus (AF482452) Reductive archaeal type dsrAB Archaeoglobus profundus (AF071499) AAA261G15 00050_51 AAA261L22 00023_24 100 AAA261G11 00786_85 86 AAA261A23 00519_20 100 AAA261A19 00092_91 dsrAB Marine Benthic Group E Clade AAA261A19 00747_48

100 AAA261G15 00701_72 99 AAA261I13 00827_28 AAA261L22 00434_33 Magnetococcus marinus (CP000471) 100 Chlorobium phaeovibrioides (CP000607)

100 99 Chlorobium luteoum (NC007512) Prosthecochloris vibrioformis (NZ_AAJD01000006) 100 Chlorobium chlorochromatii (NC 007514) Oxidative bacterial type dsrAB Pelodctyon phaeoclathratiforme (NZ_AAIK01000042)

59 Chlorobium tepidum (NC002932) Chlorobium phaeobacteroides (CP000492) 50 64 Chlorobium limicola (CP001097) Prosthecochloris aestuarii (CP001108) Thermodesulfovibrio yellowstonii (U58122)

0.2

Figure 5.6. Maximum likelihood phylogenetic tree (1000 bootstrap replicates) based on deduced amino acid sequences of the dsrAB gene from the SAGs and the references sequences, including reductive and oxidative bacterial/archaeal type of dsrAB gene. The scale bar represents 0.20-fixed mutation per nucleotide position and bootstrap values above 50% are shown. 164

5.4.5. Sulfur metabolism

The presence of several genes implicated in sulfur metabolism, such as ATP sulfurylase and dissimilatory sulfite reductases subunit alpha and beta (dsrAB), suggests that they may be able to use sulfur species for energy conservation (Table S5.4). The BLAST results of dsrAB gene sequences in our dataset compared with the UniProt database showed 48–52% similarity to the dsrAB gene of A. fulgidus (Table S5.5). Meanwhile, the phylogenetic analysis based on the deduced amino acid sequences of the dsrAB genes demonstrated that they were deeply branching, positioned between Archaeoglobus and sulfur-oxidizing bacteria (Fig. 5.6).

The DsrAB is a key enzyme in both the reductive and oxidative steps of sulfate [78-80].

This gene catalyzes the last part in the dissimilatory sulfate reduction pathway [79].

Based on canonical dissimilatory sulfate reduction pathway (the Rees model), sulfate is activated to adenosine 5’-phosphosulfate (APS) by the enzymes ATP sulfurylase (Sat) and subsequently reduced to sulfite by adenosine 5’-phosphosulfate reductase (Apr) [81,

82]. The terminal step is a direct six-electron reduction of sulfite to sulfide by dissimilatory sulfite reductase (DsrAB) [83].

The apparent absence of the genes encoding adenosine 5’-phosphosulfate reductase (apr) in the SAGs raises intriguing questions about energy conservation of MBGE. Without the apr gene, adenosine 5’-phosphosulfate (APS) cannot be reduced to sulfite. Therefore, we hypothesized that MBGE performs sulfite reduction to conserve energy instead of reducing sulfate, under the assumption that the apr gene is truly absent in the MBGE genome. Sulfur oxidation is highly unlikely to be performed by MBGE considering that only one key enzyme is present (sulphite oxidoreductase). 165

From a bioenergetic viewpoint, sulfate is an unfavourable electron acceptor for microorganisms [84]. Activation of sulfate requires 2 ATP because AMP need to be converted to adenosine diphosphate by adenylate kinase [81]. On the contrary, sulfite is more reactive and energetically more efficient. Metabolism of sulfite does not require further activation by ATP [81, 83].

Whether there is enough sulfite in the environment to sustain that hypothesis and the potential lifestyle of the MBGE can only be speculated. Sulfites form naturally during the decomposition of reduced sulfur compounds such as thiosulfate, polythionates, and sulfonates [85]. Sulfites are able to persist in the environment in the absence of oxygen

[86, 87]. Thiosulfate and sulfite (as well as hydrogen and CO2) are commonly found in hydrothermal fluids where MBGE thrive [88]. Free sulfite is very reactive and, within cells, can lead to irreversible damage to DNA. An efficient mechanism for removing sulfite from cells is either detoxification or simply the exportation of the material [89].

In order to support our hypothesis, sulfite should be present and other essential enzymes involved in the process should be encoded in the MBGE genome. In this case, energy conservation via sulfite reduction is likely facilitated via menaquinone to multiple membrane-bound heterodisulfide reductase (Hdr) complexes and the DsrC protein [90].

It has been proposed that the Hdr complex transfers electrons to DsrC in a process that may result in energy conservation [91]. A previous study has suggested that DsrC likely acts as a shuttle carrying oxidizing capacity to the membrane [92]. The dsrC genes were identified in our SAGs and subsequently positioned with the dsrAB gene. Additionally, the genes encoding for HdrA, a flavin-containing subunit of the soluble heterodisulfide 166 reductases, were present in MBGE SAGs. Considering the presence of hydrogenase in the SAGs (Table S5.6), hydrogen is a potentially favorable electron donor for MBGE.

The dsrAB genes are conserved in all isolated sulfate-reducing prokaryotes and distributed in two archaeal phyla, the genus Archaeoglobus in the Euryarchaeota and the genera Thermocladium and Caldirvirga in the Crenarchaeota [93]. These genes are present in some microorganisms that cannot utilize sulfate as a terminal electron acceptor, including sulfite-reducing microorganisms such as Desulfitobacterium and

Pyrobaculum [94, 95]. Remarkably, there are two Archaeoglobus species that are unable to reduce sulfate although both of them possess the sat and aprA genes. A. veneficus grows chemolithoautotrophically by sulfite reduction using molecular hydrogen as an electron donor and cannot reduce sulfate, nitrate, or nitrite [55]. Meanwhile, A. infectus oxidizes molecular hydrogen and utilizes thiosulfate and sulfite as electron acceptors

[77]. Other compounds such as sulfate, elemental sulfur, nitrate, and nitrite are not used as electron acceptors by this species [77].

5.4.6. Transporters

A detailed investigation of the MBGE genomes revealed genes that potentially encode for a number of transporter for the uptake of a suite of nutrients (Fig. 5.7). The encoded genes are involved in the uptake of sulfate (sulfate permease and related transporters), phosphate (PstSCAB), iron (FhuC), sulfur (sulfate permease, CysA, and related transporters), thiamine (TbpA), tungstate (TupAB), alkanesulfonate (SsuAB), and cobalt/nickel (CbiMNOQ) and have been identified in the SAGs (Fig. 5.7). Sulfate permease actively transports sulfate anion into the cell [96]. Four branched-chain amino acid transporter genes were identified in the MBGE genome (LivFGHKM). In addition, 167 genes for ABC transporters that are responsible for glycine betaine/proline transport system substrate-binding protein (ProVWX) were present in the SAGs (Table 5.2). The key gene regulators arsR that encode a specific transporter to pump out the arsenite from the cell were found in MBGE SAGs (Fig. 5.7) [97].

Figure 5.7. Predicted ABC transporters present in the SAGs 168

5.4.7. Osmoadaptation mechanisms

Living in the Atlantis II Deep, MBGE are confronted with multiple stress factors, of which osmotic pressure is likely the most challenging. An efficient strategy to adapt to this stressful condition is essential for their survival [98]. In order to cope with osmotic stress, microorganisms developed adaptive responses known as osmoadaptation [99].

Two different strategies are involved in the osmoadaptation mechanism in hypersaline environments [100]. The “salt-in-strategy” is performed by accumulating molar concentrations of inorganic salts (mainly potassium chloride) to generate a saline cytoplasm to maintain osmotic balance [101]. The second strategy is based on the synthesis and/or accumulation of organic compatible solutes [102].

In the second strategy, osmotic balance is achieved by the accumulation of compatible solutes either by the transport of betaine and choline into the cell or via the synthesis of ectoine and hydroxyectoine [103]. Synthesizing osmolites is expensive. Furthermore, in such a harsh environment, it is highly likely that an organism with a strict energy regime will rather take up glycine betaine from the environment instead of producing it. The majority of Archaea (e.g., Methanohalophilus [104] and Methanosarcina [105]) lack the ability to produce glycine betaine de novo and import it from the environment instead

[106].

The full set of subunits essential for a glycine-betaine transporter is encoded in multiple copies in the SAGs of the MBGEs (Table 5.2, Fig. 5.7). This ABC transport system for the osmolites is encoded by ProV (ATP-binding protein), ProW (system permease protein), and ProX (glycine betaine-binding periplasmic protein) [107]. A high salt concentration induces the upregulation of the glycine betaine/l-proline ABC transporter, 169 which leads to the intracellular accumulation of osmoprotectant glycine betaine [108].

Glycine betaine increases the cytoplasmic volume and free water content of the cells at high osmolality [109]. Additionally, the SAGs of the MBGEs also encode homologous for the ABC transport systems OpuA (Table 5.2), which is known to be the main glycine betaine uptake system of Bacillus subtilis [110].

Table 5.2. List of gene involves in osmoadaptation

Function Gene product Gene Lokus Potassium transport Trk system potassium TrkA A23_00178 A23_00845 G15_00288 I04_00360 uptake protein Trk system potassium TrkH G15_00374 G15_01104 uptake protein Glycine-betaine Glycine betaine/proline transport transport ATP-binding proV L22_00047

protein Glycine betaine/proline transport system proW L22_00049

permease protein Glycine betaine-binding proX L22_00049 periplasmic protein Glycine betaine/proline transport ATP-binding opuAA L22_00049

protein Biosensors for Mechanosensitive msc A19_00348 G11_00634 I13_01047 osmoregulation channels protein

Interestingly, transporters for K+ encoded by TrkA and TrkH were detected (Table 5.2,

Fig. 5.7), implying the potential ability to perform the “salt-in” strategy by transferring

K+ ions into the cell for osmotic balance. The osmoadaptation mechanism by combining glycine betaine accumulation and K+ import has been reported in Methanosarcina spp. in response to osmotic stress [105]. However, the slightly acidic proteome signature is associated with organisms employing the “salt-out” strategy in contrast to the extreme halophiles that have a highly acidic proteome and use the “salt-in” strategy (Fig. 5.8).

170

Figure 5.8. Box plots showing the distribution in the isoelectric point of predicted proteomes of MBGE and other cultivated microorganims. The four panels show data for (a) the overall predicted proteomes, (b) protein-coding genes without any transmembrane domains (TMDs), (c), with a single TMDs, (d), and those with two or more TMDs. 171

5.5. Conclusions

The single-cell genomic approach has enabled us to gain valuable insights regarding potential metabolisms, carbon fixation, sulfur metabolism, and osmoadaptation mechanisms. The SAGs of MBGE encode for all the genes responsible for CO2 fixation according to the Wood-Ljungdahl pathway, which makes autotrophic growth possible with only CO2 and H2. The presence of several genes implicated in sulfur metabolism suggests that they may be able to use sulfur species for energy conservation. We hypothesize that MBGE performs sulfite reduction to conserve energy instead of reducing sulfate, under the assumption that MBGE lack the apr gene. Regarding the osmoadaptation mechanism in a hypersaline environment, MBGE may perform glycine betaine accumulation and potassium ion transport into the cell.

5.6. Supplementary Data

Table S5.1. Single-cell Amplified Genomes (SAGs) ID, sampling location, and taxonomy classification of 16S rRNA genes of MBGE based on SILVA database

No SAG ID Taxonomy (SILVA on ARB)* Identity (%) Sampling Site 1 SAG-AAA261-A07 Unclassified MBGE Archaeon 84.99 Atlantis II UCL4 - LCL 2 SAG-AAA261-A19 Unclassified MBGE Archaeon 84.60 Atlantis II UCL4 - LCL 3 SAG-AAA261-A23 Unclassified MBGE Archaeon 84.44 Atlantis II UCL4 - LCL 4 SAG-AAA261-G11 Unclassified MBGE Archaeon 84.91 Atlantis II UCL4 - LCL 5 SAG-AAA261-G15 Unclassified MBGE Archaeon 84.75 Atlantis II UCL4 - LCL 6 SAG-AAA261-I04 Unclassified MBGE Archaeon 84.78 Atlantis II UCL4 - LCL 7 SAG-AAA261-I13 Unclassified MBGE Archaeon 84.78 Atlantis II UCL4 - LCL 8 SAG-AAA261-K19 Unclassified MBGE Archaeon 84.94 Atlantis II UCL4 - LCL 9 SAG-AAA261-L22 Unclassified MBGE Archaeon 84.94 Atlantis II UCL4 - LCL 10 SAG-AAA261-N04 Unclassified MBGE Archaeon 84.94 Atlantis II UCL4 - LCL *SILVA database (http://www.arb-silva.de/) Abbreviation: UCL4: Upper convective layer (lowest interface) LCL: Lower convective layer (brine body) 172

Table S5.2. Geographic coordinates and physicochemical parameters of the sampling location

Parameter Sampling site Atlantis II UCL4 - LCL Lat./Long. 21 20.76’/38 04.68’ Sample type Brine-interface Temperature (C) 57 - 63 Salinity (%) 15.1 - 16.8 Depth (m) 2036 pH 5.2 Oxygen (mM) 0.5 Sulfate (mM) 25.2 Nitrate (mM)b 21.9 Ammonia (mM) 378 Urea (mM) 1.0 Mn (mM) 1.8 Fe (mM) 70.5 Chloride (M) 3.0 Bromide (mM) 1.3 Ca (mM) 81.6 UCL4: Upper convective layer (lowest interface) LCL: Lower convective layer (brine body)

Table S5.3. Number of contigs and total length of MBGE SAGs before and after the cleaning process

No. of Contigs Total Length SAG ID % size after cleaning Raw Clean Raw Clean AAA261A07 448 73 786479 516467 65.67 AAA261A19 545 127 1289824 1046215 81.11 AAA261A23 661 126 1093742 759700 69.46 AAA261G11 678 116 1217992 853799 70.10 AAA261G15 767 135 1208919 762895 63.11 AAA261I04 451 66 688343 405135 58.86 AAA261I13 573 125 1061179 758944 71.52 AAA261K19 483 60 653961 389591 59.57 AAA261L22 572 113 1062342 751325 70.72 AAA261N04 422 48 557243 313253 56.21

173

Table S5.4. List of essential genes and the number of copies identified in the MBGE genomes

EnzymeID Description A07 A19 A23 G11 G15 I04 I13 K19 L22 N04 Carbon fixation pathways in prokaryotes

Isocitrate dehydrogenase 1.1.1.42 1 1 - - - 1 - - 1 - (NADP(+)). 1.2.7.1 Pyruvate synthase. - 2 4 4 4 - - 1 4 1 1.2.7.3 2-oxoglutarate synthase. - 3 2 7 1 - 3 - 6 - Carbon-monoxide 1.2.99.2 1 1 - 1 1 - 1 - 1 - dehydrogenase (acceptor). Methylenetetrahydrofolate 1.5.1.20 ------2 - reductase (NAD(P)H). 5- 2.1.1.245 methyltetrahydrosarcinapte - 1 2 2 2 - - - 2 2 rin Acetyl-CoA C- 2.3.1.9 - - 1 ------acetyltransferase. 2.7.9.2 Pyruvate, water dikinase. - - - 1 1 - - 2 - - Phosphoenolpyruvate 4.1.1.31 - - 1 1 2 - 1 - 1 - carboxylase. 4.2.1.2 Fumarate hydratase. 2 - 2 3 ------6.2.1.1 Acetate--CoA ligase. 1 - 1 1 1 - 1 - 1 - Succinate--CoA ligase 6.2.1.5 - - 2 ------(ADP-forming). 6.4.1.1 Pyruvate carboxylase. - 1 1 1 1 - 1 - 3 - Citrate cycle (TCA cycle)

Isocitrate dehydrogenase 1.1.1.42 1 1 - - - 1 - - 1 - (NADP(+)). 1.2.7.1 Pyruvate synthase. - 2 4 4 4 - - 1 4 1 1.2.7.3 2-oxoglutarate synthase. - 3 2 7 1 - 3 - 6 - 4.2.1.2 Fumarate hydratase. 2 - 2 3 ------Succinate--CoA ligase 6.2.1.5 - - 2 ------(ADP-forming). 6.4.1.1 Pyruvate carboxylase. - 1 1 1 1 - 1 - 3 - Oxidative phosphorylation

1.10.3.- NA - - 2 ------NADH:ubiquinone 1.6.5.3 reductase (H(+)- 11 1 2 2 - - - 1 2 3 translocating). 1.6.99.3 NADH dehydrogenase. - 1 - 1 1 - - - 1 - 1.9.3.1 Cytochrome-c oxidase. 1 - - - - - 1 - - - 3.6.1.1 Inorganic diphosphatase. - - - 1 - - 1 2 2 - H(+)-transporting two- 3.6.3.14 1 12 12 2 10 6 9 12 12 2 sector ATPase. Pentose phosphate pathway

Quinoprotein glucose 1.1.5.2 dehydrogenase (PQQ, 1 1 - 1 - - 1 - - - quinone). Aldehyde ferredoxin 1.2.7.5 - 1 - 1 1 - - - - - oxidoreductase. 2.7.1.12 Gluconokinase. - 1 - 1 1 - 1 - - 1 ADP-specific 2.7.1.146 - 1 - 1 ------phosphofructokinase. 2.7.1.15 Ribokinase. - 1 1 ------Ribose-phosphate 2.7.6.1 - 1 - 1 ------diphosphokinase. Fructose-bisphosphate 4.1.2.13 - 2 1 - 1 - - - 1 - aldolase. 3-hexulose-6-phosphate 4.1.2.43 - - - 1 1 - 1 - - - synthase. 4.3.-.- NA - - 2 - - - - - 2 - 5.3.1.6 Ribose-5-phosphate - 2 - 2 1 - - - - 1

174

isomerase. 5.4.2.8 Phosphomannomutase. - 1 ------Glycolysis / Gluconeogenesis

1.1.1.1 Alcohol dehydrogenase. - 1 - - - 1 - - 1 - Glyceraldehyde-3- 1.2.1.12 phosphate dehydrogenase - 2 1 - - - - - 1 - (phosphorylating). 1.2.7.1 Pyruvate synthase. - 2 4 4 4 - - 1 4 1 Aldehyde ferredoxin 1.2.7.5 - 1 - 1 1 - - - - - oxidoreductase. 2.7.1.1 Hexokinase. - - - 1 - - 1 - - - ADP-specific 2.7.1.146 - 1 - 1 ------phosphofructokinase. 2.7.2.3 Phosphoglycerate kinase. - 1 - 1 - - 2 1 - - Fructose-bisphosphate 4.1.2.13 - 2 1 - 1 - - - 1 - aldolase. Phosphopyruvate 4.2.1.11 1 1 - - - - 1 1 - 1 hydratase. Triose-phosphate 5.3.1.1 - - 1 ------isomerase. Phosphoglycerate mutase 5.4.2.11 (2,3-diphosphoglycerate- - 1 1 - 1 1 1 - 1 1 dependent). Phosphoglycerate mutase 5.4.2.12 (2,3-diphosphoglycerate- 1 2 1 2 1 1 2 - 2 - independent). 5.4.2.8 Phosphomannomutase. - 1 ------6.2.1.1 Acetate--CoA ligase. 1 - 1 1 1 - 1 - 1 - Acetate--CoA ligase 6.2.1.13 - - 1 ------(ADP-forming). Sulfur metabolism -

Ferredoxin--NAD(+) 1.18.1.3 1 - 1 - 1 - - - - - reductase. Sulfite reductase 1.8.1.2 ------1 (NADPH). 1.8.3.1 Sulfite oxidase. 1 ------3 Phosphoadenylyl-sulfate 1.8.4.8 2 4 3 1 2 1 3 1 2 - reductase (thioredoxin). Sulfite reductase 1.8.7.1 - - - - - 1 - - - - (ferredoxin). 1.8.99.2 Adenylyl-sulfate reductase. - 1 1 - - - 1 1 - - Dissimilatory sulfite 1.8.99.3 - 4 2 2 4 - 2 3 4 - reductase 2.3.1.30 Serine O-acetyltransferase. 1 2 - 1 - - - 1 - - Homoserine O- 2.3.1.31 - - - - 1 - - - - - acetyltransferase. 2.5.1.47 Cysteine synthase. ------1 2.7.1.25 Adenylyl-sulfate kinase. - 1 - 1 1 - 1 - - - Sulfate 2.7.7.4 - - - - 1 - - - - - adenylyltransferase. Thiosulfate 2.8.1.1 1 2 - 1 1 1 1 - - - sulfurtransferase. Methane metabolism

Phosphoglycerate 1.1.1.95 2 1 1 1 1 - - 1 1 1 dehydrogenase. 1.12.7.2 Ferredoxin hydrogenase. 1 2 1 - - - 1 1 1 - Coenzyme F420 1.12.98.1 - 1 1 2 1 - - - 2 - hydrogenase. 1.2.1.2 Formate dehydrogenase. - 1 ------1.2.7.1 Pyruvate synthase. - 2 4 4 4 - - 1 4 1 Carbon-monoxide 1.2.7.4 dehydrogenase - - 1 ------(ferredoxin). Carbon-monoxide 1.2.99.2 1 1 - 1 1 - 1 - 1 - dehydrogenase (acceptor).

175

Formylmethanofuran 1.2.99.5 4 6 4 6 - 3 - 3 - 2 dehydrogenase. 1.5.99.11 Transferred entry: 1.5.98.2. 2 - - 2 - - - - 2 - 1.5.99.9 Transferred entry: 1.5.98.1. - 1 ------2 - CoB--CoM heterodisulfide 1.8.98.1 1 7 1 5 4 4 2 1 4 1 reductase. 5- 2.1.1.245 methyltetrahydrosarcinapte - 1 2 2 2 - - - 2 2 rin [Methyl-Co(III) methanol- 2.1.1.246 ------specific corrinoid protein] [Trimethylamine-- 2.1.1.250 corrinoid protein] Co- 2 - 2 - 2 1 - - 1 1 methyltransferase. Tetrahydromethanopterin 2.1.1.86 - - - - - 1 - - 1 - S-methyltransferase. Glycine 2.1.2.1 1 1 1 1 - 1 - 1 - 1 hydroxymethyltransferase. 2.3.1.- NA - - - 1 1 - - - 1 - 2.3.1.101 Formylmethanofuran - 1 1 1 1 - - 3 1 - Phosphate 2.3.1.8 ------acetyltransferase. 7,8-didemethyl-8-hydroxy- 2.5.1.77 2 - 2 2 - - 2 - - - 5-deazariboflavin synthase. 2-phospho-L-lactate 2.7.7.68 - - - 1 - - 1 - - - guanylyltransferase. 2-phospho-L-lactate 2.7.8.28 2 1 1 - - - - - 1 - transferase. 2.7.9.2 Pyruvate, water dikinase. - - - 1 1 - - 2 - - Phosphoserine 3.1.3.3 - - - 1 - - 1 - 1 - phosphatase. 2-phosphosulfolactate 3.1.3.71 - 1 - 1 - - 1 - - - phosphatase. Methenyltetrahydromethan 3.5.4.27 1 - 1 1 1 - - - - - opterin cyclohydrolase. Phosphoenolpyruvate 4.1.1.31 - - 1 1 2 - 1 - 1 - carboxylase. Sulfopyruvate 4.1.1.79 - - - 1 - - 1 - - - decarboxylase. Fructose-bisphosphate 4.1.2.13 - 2 1 - 1 - - - 1 - aldolase. 3-hexulose-6-phosphate 4.1.2.43 - - - 1 1 - 1 - - - synthase. Phosphopyruvate 4.2.1.11 1 1 - - - - 1 1 - 1 hydratase. 4.3.-.- NA - - 2 - - - - - 2 - Phosphosulfolactate 4.4.1.19 - 1 - 1 - - 1 - - - synthase. Phosphoglycerate mutase 5.4.2.11 (2,3-diphosphoglycerate- - 1 1 - 1 1 1 - 1 1 dependent). Phosphoglycerate mutase 5.4.2.12 (2,3-diphosphoglycerate- 1 2 1 2 1 1 2 - 2 - independent). 6.2.1.1 Acetate--CoA ligase. 1 - 1 1 1 - 1 - 1 - Nitrogen metabolism

1.18.6.1 Nitrogenase. 3 2 3 3 3 - - - - - Glutamate synthase 1.4.1.13 - 3 2 3 2 - 3 - - - (NADPH). Glutamate dehydrogenase 1.4.1.3 1 1 ------1 - (NAD(P)(+)). NADH:ubiquinone 1.6.5.3 reductase (H(+)- 10 1 2 2 - - - 1 2 3 translocating). Nitrite reductase 1.7.1.4 1 1 1 1 1 - - - - - (NAD(P)H). 1.7.2.6 Hydroxylamine - 1 1 1 1 - - 1 1 -

176

dehydrogenase. 1.7.99.4 Nitrate reductase. - 1 2 - - 1 1 - 1 1 1.9.3.1 Cytochrome-c oxidase. 1 - - - - - 1 - - - Glutamate--ammonia 6.3.1.2 - 1 ------1 - ligase. Asparagine synthase 6.3.5.4 1 3 2 2 - - 2 - 2 - (glutamine-hydrolyzing).

Table S5.5. Best BLASTp hits for deduced amino acid sequences of the dsrAB gene against the UniProt database

Protein Blast result against Uniprot Identity. % AC261A19_00749 dsr alpha Archaeoglobus fulgidus 52 AC261I13_00793 dsr alpha Archaeoglobus fulgidus 52 AC261K19_00135 dsr alpha Archaeoglobus fulgidus 52 AC261L22_00456 dsr alpha Archaeoglobus fulgidus 52 AC261K19_00713 dsr beta Archaeoglobus fulgidus 46 AC261A19_00087 dsr beta Archaeoglobus fulgidus 49 AC261A23_00517 dsr beta Archaeoglobus fulgidus 48 AC261G11_00811 dsr beta Archaeoglobus fulgidus 47 AC261G15_00052 dsr beta Archaeoglobus fulgidus 48 AC261I13_00794 dsr beta Archaeoglobus fulgidus 49 AC261L22_00026 dsr beta Archaeoglobus fulgidus 48 AC261A19_00750 dsr beta Archaeoglobus fulgidus 49 AC261G15_00719 dsr beta Archaeoglobus fulgidus 49 AC261K19_00134 dsr beta Archaeoglobus fulgidus 49 AC261L22_00455 dsr beta Archaeoglobus fulgidus 49

177

Table S5.6. List of predicted hydrogenases in the SAGs of MBGE

Gene Description A07 A19 A23 G11 G15 I04 I13 K19 L22 cooH Carbon monoxide-induced hydrogenase protein +

ehaR energy-converting hydrogenase A subunit R EhaR + + +

frhB Coenzyme hydrogenase subunit beta protein + + + + +

frhD Coenzyme hydrogenase subunit delta protein + +

hupA Uptake hydrogenase small subunit protein

hupB Uptake hydrogenase large subunit protein +

hyaB Hydrogenase-1 large chain protein

hyaB Hydrogenase-1 large chain protein +

hynB2 Periplasmic NiFe hydrogenase small subunit 2 protein +

hypA Probable hydrogenase nickel incorporation HypA + + +

hypB Hydrogenase nickel incorporation protein HypB + + + + +

hypC Hydrogenase expression-formation protein HypC + + +

hypE hydrogenase expression-formation protein +

hypF Probable carbamoyltransferase HypF protein + + +

MJ0734 ferredoxin hydrogenase protein + +

mvhD Coenzyme F420-reducing hydrogenase protein +

nrfC Protein NrfC + +

rbr Rubrerythrin protein + +

ttrB Tetrathionate reductase subunit B protein + + + + +

ynfG Probable anaerobic dimethyl sulfoxide reductase + + + + +

. ferredoxin hydrogenase protein + +

. Hydrogenase formation hypA protein + + +

. AIR synthase related protein + + +

. coenzyme hydrogenase beta subunit protein +

178

Figure S5.1. Visualization of Canonical Correspondence Analysis (CCA) using genomic features such as phylogenetic affiliation, tetranucleotide frequency (TNF), GC content, and size of the contigs

Taxonomy binning

GC content

Size

179

Figure S5.2. Cluster of Orthologous Groups (COG) analysis reveals metabolic functions and the relative abundance that are present in the SAGs of MBGE

Secondary metabolite biosyn., transport (Q)

Inorganic ion transport & metabolism (P)

Lipid transport & metabolism (I)

Coenzyme transport & metabolism (H)

Carbohydrate transport & metabolism (G) METABOLISM METABOLISM Nucleotide transport & metabolism (F)

Amino acid transport & metabolism (E)

Energy production & conversion (C)

DNA replication, recombination & repair (L)

Transcrition (K)

Translation, ribosomal structure & biogenesis (J) PROCESSING Chromatin structure and dynamics (B)

INFORMATION STORAGE AND STORAGE INFORMATION RNA processing and modification (A)

AAA261N04 AAA261L22 Defense mechanisms (V) AAA261K19 AAA261I13 Intracellular traffiking, secretion (U) AAA261I04 AAA261G15 Signal transduction and mechanisms (T) AAA261G11 AAA261A23 Posttranslational modifi., turnover, chaperones (O) AAA261A19 AAA261A07 Cell motility (N)

Cell wall/membrane/envelope biogenesis (M) CELLULAR PROCESSES AND SIGNALING CELLULARPROCESSES Cell division & chromosome partitioning (D)

0 5 10 15 20 25 Abundance (%) 180

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189

CONCLUSIONS

The interface between the brine and overlaying seawater of the deep-sea anoxic brines was reported to be a hotspot of microbial diversity, which supported divergent metabolic activities, mainly methanogenesis and sulfate reduction. However, the microbial communities that are responsible for the sulfate reduction in the Red Sea brine pools remain largely unexplored. The goals of this study were to investigate the diversity of

Bacteria, particularly sulfate-reducing communities, and their ecological roles in the interfaces of the deep-sea anoxic brine pools in the Red Sea.

In order to understand the significant role of the microbial groups in local geochemical cycles, especially sulfate-reducing communities, we performed an integrated study in five geochemically distinct brine pools in the Red Sea by combining culture-dependent approach and molecular methods. The profiling of bacterial communities was performed using 454 pyrosequencing and Sanger sequencing of 16S rRNA gene, qPCR, and physicochemical analysis as described in Chapter 1. In Chapter 2, the analysis based on functional marker gene was applied for identification of sulfate-reducing prokaryotes.

Initially, we attempted to isolate novel sulfate reducers from these environments. Instead of obtaining pure culture of sulfate-reducing prokaryotes, we succeeded to cultivate more than thirty aerobic bacteria, which were evaluated for their cytotoxic and apoptotic effects in Chapter 3 and 4. Since no culture of sulfate reducer was obtained, thus in

Chapter 5, a single-cell genomic analysis was used to study the metabolism of uncultured phyla without having them in pure isolate. 190

Based on the analysis of the 16S rRNA gene sequences, a high diversity of Bacteria was detected in the interface of the Red Sea brine pools. Up to the moment, the microbiological studies of the brine pools in the Red Sea have been restricted in the

Kebrit, Shaban, Atlantis II, and Discovery Deep. In this dissertation, we extended the study into the two brine pools that have never been studied previously (i.e. Nereus and

Erba Deep). Our finding supports the hypothesis that the interfaces of the Red Sea brine pools harbor a high microbial diversity and biomass, and indeed offer a variety of ecological niches for different groups of Bacteria. In the colder brine pools (the Erba,

Kebrit, and Nereus Deep), sulfate-reducing Deltaproteobacteria was found to be the most prominent Bacteria inhabiting the interfaces.

Correspond with the previous report, the bacterial communities were stratified and hardly overlapped across the multiple interfaces of the Atlantis II Deep. Additionally, the dissertation provides evidence of the presence of bacterial groups that specially adapted to the certain conditions in these extreme environments, for example Nitrospina and candidate division KB1. The of typical bacterial groups that generally inhabit the brine pools such as KB1 in the Red Sea brine pools and Mediterranean DHABs raises questions about their origin and dispersal because none of them were detected in the overlaying seawater. One hypothesis stated that the microbial colonization in these environments was achieved by revival of similar microbes trapped in the evaporite layers beneath the brine pools. Further investigation needs to be performed to prove this hypothesis.

The high abundant of Desulfobacteraceae in the interfaces of some brine pools was a good indication of the presence of sulfate-reducing communities. Indeed, the analysis of 191 the alpha subunit of dissimilatory sulfite reductase (dsrA) gene sequences revealed collectively high diversity of sulfate-reducing communities, especially in the Kebrit and

Erba Deep. We suggested that sulfate-reducing communities contribute more to the geochemical cycle in the colder brine pools than in the Atlantis II and Discovery Deep.

Remarkably, the hot Atlantis II and Discovery Deep harbor deeply branched lineage of dsrA gene sequences, implying that novel lineages of sulfate reducers might reside in these environments. Overall, the divergent sulfate-reducing communities confirm the hypothesis that sulfate reduction plays an important role in metabolic processes occurring in the chemoclines of the deep-sea brine pools.

In addition to the molecular studies, more than thirty bacterial strains were successfully isolated. This study reports, for the first time, the cytotoxic activities of several halophilic bacterial species isolated from deep-sea brine pools of the Red Sea and provides in-depth insights into the possible mechanisms of apoptosis induced by the extracts in various human cancer cell lines. This dissertation also provides conclusive evidence that brine pools of the Red sea harbor several species of bacteria producing anticancer secondary metabolites. Furthermore, the results of Chapter 3 and 4 verified the co-occurrence of aerobic and anaerobic microorganisms in the interfaces.

Since the pure isolate of sulfate reducer was not available, in Chapter 4, we analyzed 10 single-cell amplified genomes (SAGs) of the uncultivated euryarchaeal Marine Benthic

Group E (MBGE), which contain a key enzyme for sulfate reduction. Using this approach, we successfully gained many valuable insights regarding the potential metabolisms, carbon fixation, sulfur metabolism, and osmoadaptation mechanisms. The results demonstrated the possibility of MBGE as acetogen that grow autotrophically only 192 with carbon dioxide and hydrogen. In the absence of adenosine 5’-phosphosulfate reductase, we hypothesized that MBGE perform sulfite reduction rather than sulfate reduction to conserve energy.

This dissertation contributes to the understanding of the unexplored bacterial communities in the interfaces of the Red Sea brine pools. In the broader sense, these findings provide more insights on the ubiquity of sulfate-reducing communities in hypersaline habitats. Moreover, our findings should increase the impetus for future cultivation-attempts of the novel halophilic microorganisms, especially sulfate reducers.

This dissertation also contributes significantly to answer the enigmatic metabolisms of

MBGE.

The studies in Chapter 1 and 2 were based on DNA extraction instead of RNA. As a consequence of these methods, the study encountered a number of limitations to conclude whether the microbial groups are metabolically active in the environments. We realized about the long-term stability of the DNA and rRNA in the deep-sea hypersaline brine pool. Further research might well be conducted in order to get an overview of the active microbial communities in the interfaces of the Red Sea brine pools by designing sampling technique that enable us to obtain the sample and fix the RNA in situ. Chapter 3 and 4 were initial steps for screening bioactive compound. The next step would be extraction and characterization of the bioactive compounds, which were responsible for the apoptotic activities. Regarding the metabolisms of MBGE, only experimental investigations are able to reveal if different metabolic capabilities can be performed in reality since these predictions are the genome-based analysis. Nonetheless, our finding was a major leap to reveal metabolisms of uncultured MBGE. Despite all these 193 limitations, the study has provided a comprehensive perspective on bacterial community structure; particularly sulfate reducers in the interfaces of geochemically distinct Red Sea brine pools.

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AUTHOR'S CONTRIBUTION

Chapter 1

Tyas Hikmawan collected samples, planned the study, performed experiments, and wrote manuscript. Yue Guan performed DNA extraction and qPCR. David Ngugi performed 454 pyrosequencing and helped in manuscript writing. Andre Antunes was responsible for the sample collection and helped in manuscript writing. Ulrich Stingl was responsible for the planning of the expedition, provided general coordination of the study, and helped in manuscript writing.

Chapter 2

Tyas Hikmawan collected samples, planned the study, performed experiments, and wrote manuscript. Yue Guan performed DNA extraction. David Ngugi helped in manuscript writing. Andre Antunes was responsible for the sample collection and helped in manuscript writing. Ulrich Stingl was responsible for the planning of the expedition, provided general coordination of the study, and helped in manuscript writing.

Chapter 3

Holtermann was responsible for growth of the strains in large batches. Ulrich Stingl was 195 responsible for the planning of the expedition and the cultivation experiments, provided general coordination of the study and helped in manuscript writing. Vladimir Bajic provided general coordination of the study and helped in manuscript writing.

Chapter 4

Sunil Sagar and Mandeep Kaur planned the study, wrote manuscript and performed experiments along with Luke Esau. Tyas Hikmawan collected samples, isolated the strains, provided taxonomic classification of the bacterial strains, and helped in manuscript writing. Guishan Zhang isolated the strains. Karie Holtermann was responsible for growth of the strains in large batches. Ulrich Stingl was responsible for the planning of the expedition and the cultivation experiments, provided general coordination of the study and helped in manuscript writing. Vladimir Bajic provided general coordination of the study.

Chapter 5

Tyas Hikmawan collected samples, planned the study, performed experiments, and wrote manuscript. Rita Bartossek performed experiments and helped in manuscript writing. Mamoon Rashid performed quality check of the singe-cell genomes. David

Ngugi and Manikandan Vinu performed bioinformatics analyses. Wail Baalawi performed genomes assembly. Intikhab Alam developed annotation pipeline. Ulrich

Stingl was responsible for the planning of the expedition, provided general coordination of the study, and helped in manuscript writing.