Living to the High Extreme: Unraveling the Composition, Structure

bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1 Living to the high extreme: unraveling the composition, structure,

2 and functional insights of bacterial communities thriving in the

3 arsenic-rich Salar de Huasco – Altiplanic ecosystem.

4 5 6 Castro-Severyn, J1,±; Pardo-Esté, C1,2,±; Mendez, KN3; Fortt, J1; Marquez, S3; Molina, F4; 7 Castro-Nallar, E3; Remonsellez, F1,5 * and Saavedra, CP2 *. 8 9 1 Laboratorio de Microbiología Aplicada y Extremófilos, Facultad de Ingeniería y Ciencias 10 Geológicas, Universidad Católica del Norte, Antofagasta, Chile. 11 2 Laboratorio de Microbiología Molecular, Facultad de Ciencias de la Vida, Universidad 12 Andres Bello, Santiago, Chile. 13 3 Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, 14 Universidad Andres Bello, Santiago, Chile. 15 4 Sys2Diag, UMR9005 CNRS ALCEDIAG, Montpellier, France 16 5 Centro de Investigación Tecnológica del Agua en el Desierto-CEITSAZA, Universidad 17 Católica del Norte, Antofagasta, Chile. 18 19 ± Juan Castro-Severyn and Coral Pardo-Esté have contributed equally to this work. Author 20 order was determined both by contribution and in order of increasing seniority. 21 22 *Correspondence: Francisco Remonsellez, [email protected] and Claudia P. Saavedra, 23 [email protected]. 24 25 26 Running title: Altiplanic communities living under arsenic stress. 27 28

1 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

29 ABSTRACT 30 Microbial communities inhabiting extreme environments like Salar de Huasco (SH) are 31 adapted to thrive while exposed to several abiotic pressures and the presence of toxic 32 elements like arsenic (As). Hence, we aimed to uncover the role of arsenic in shaping 33 bacterial composition, structure, and functional potential in five different sites in this 34 Altiplanic wetland using a shotgun metagenomic approach. The sites exhibit wide gradients 35 of arsenic (9 to 321 mg/kg), and our results showed highly diverse communities and a clear 36 dominance exerted by the Proteobacteria and Bacteroidetes phyla. Functional potential 37 analyses showed broadly convergent patterns, contrasting with their great taxonomic 38 variability. Arsenic-related metabolism is different among the five communities, as well as

39 other functional categories like those related to the CH4 and S cycles. Particularly, we found 40 that the distribution and abundance of As-related genes increase, following along the As 41 concentration gradient. Approximately 75% of the detected genes for As-metabolism 42 belong to expulsion mechanisms, being arsJ and arsP pumps related to sites with higher As 43 concentrations and present almost exclusively in Proteobacteria. Furthermore, taxonomic 44 diversity and functional potential are reflected in the 12 reconstructed high-quality MAGs 45 (Metagenome Assembled Genomes) belonging to the Bacteroidetes (5), Proteobacteria (5), 46 Cyanobacteria (1) and Gemmatimonadota (1) phyla. We conclude that SH microbial 47 communities are diverse and possess a broad genetic repertoire to thrive under extreme 48 conditions, including increasing concentrations of the highly toxic As. Finally, this 49 environment represents a reservoir of unknown and undescribed microorganisms, with a 50 great metabolic versatility, which needs further study. 51 52 KEY WORDS: Extremophiles, Metagenomics, Arsenic, MAGs, Altiplano. 53 54 55 IMPORTANCE 56 Microbial communities inhabiting extreme environments are fundamental for maintaining 57 the ecosystems; however, little is known about their potential functions and interactions

2 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

58 among them. We sampled the microbial communities in Salar de Huasco (SH) in the Chilean 59 Altiplano, a fragile and complex environment that comprises several stresses. We found 60 that microbes in SH are taxonomically diverse; nonetheless, their functional potential seems 61 to have an important convergence degree, suggesting high adaptation levels. Particularly, 62 arsenic metabolism showed differences associated with increasing concentrations of the 63 metalloid throughout the area, and it is effectively exerting a clear and significant pressure 64 over these organisms. Thus, this research´s significance is that we described highly 65 specialized communities thriving in little-explored environments under several pressures, 66 considered analogous of early Earth and other planets, and can have the potential for 67 unraveling technologies to face climate change repercussions in many areas of interest. 68 69 70 INTRODUCTION 71 Extreme environments like high-altitude wetlands select for adaptations in bacterial 72 communities that enable them to thrive. This particular and fragile environment resemble 73 life before the oxygenation of Earth and could serve as models for studying life in other 74 planets. Moreover, microbial communities are critical for maintaining biogeochemical 75 cycles, particularly in extreme environments where there is little presence of other life 76 forms. Therefore, microorganisms are crucial for ecosystems’ health and functioning (1). 77 Salar de Huasco (SH), a high-altitude wetland located in the Chilean Altiplano (20°18’18’’S; 78 68°50’22’’W, Chile) at 3,800 m.a.s.l. is a Ramsar protected site, considered a hotspot for 79 microbial life (2,3). This area is labeled as extreme due to very particular confluence of 80 physicochemical and environmental conditions, like negative water balance, high daily 81 temperature variation, very arid conditions, high salinity, low atmospheric pressure, high 82 solar radiation, presence of arsenic, among other stressors (4, 5, 6, 7, 8, 9). 83 84 In high altitude wetlands, arsenic concentration, moisture availability, and salt 85 concentrations model communities at a small scale (10, 11, 12, 13). The presence of toxic 86 metal(oids), such as arsenic, is one of the main drivers of microbial communities’

3 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

87 composition (14), as an important selection pressure originated from natural geochemical 88 processes or human activities. Also, previous studies have determined that the presence of 89 metal(oids) can influence biogeochemical cycles, namely C, N and S, by promoting specific 90 chemical reactions and the enrichment of chemoautotrophs, for example As(III)-oxidizing 91 bacteria that can couple this process with nitrate reduction (15, 16, 17); thus, making 92 relevant to assess the metabolic potential of indigenous microbes and communities. In the 93 North of Chile, the relationship between volcanism activities and the presence of arsenic is 94 a known feature, this geological process has been attributed to hydrothermal conditions 95 such as geysers and fumaroles in the pre-range and high plateau (5). Besides, bacteria that 96 thrive in these environments exhibit arsenic-related genes, including genes associated with 97 methylation (ArsM), oxidation (AioAB, ArxAB), and dissimilatory reduction (ArrAB) (18). The 98 most common resistance mechanism is based on As(III) extrusion from the cell by efflux 99 pumps, which is commonly coupled with As(V) reduction (19). Thus, making the genes from 100 the ars operon the most abundant arsenic resistance markers; the basic requirements 101 comprises the arsenite efflux (ArsB, Acr3) and arsenate reductase (ArsC) (20). 102 103 Arsenic is a crucial element for the microbial community composition and 104 metagenomic approaches have helped to shed some light on this subject. For instance, in 105 Socompa’s stromatolites arsenic resistance is achieved mainly through reduction and 106 expulsion of As(V) via Acr3 efflux pumps reductases (21, 22). Additionally, others have found 107 that the presence of arsenic influenced the microbial communities, and the Rhodococcus 108 genus was significantly enriched in elevated levels of the metalloid in groundwater (14). 109 Furthermore, arsenic mobilization is distributed across a broad phylogenetic lineage in 110 similar ecosystems, as arrA was detected in Betaproteobacteria, Deltaproteobacteria, and 111 Nitrospirae MAGs (23). Also, although arsenic-related genes are widespread, they are not 112 universal, in particular As metabolism genes such as aioA, arrA, arsM, arxA are less common 113 in the environment compared with acr3, arsB, arsC and arsD (24). 114

4 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

115 In this study, we tested different established sites in the SH regarding taxonomic and 116 functional heterogeneity. We focused on As-related genetic elements as well as other 117 relevant metabolic functions. Evermore, such analyses enable the identification of relatively 118 small subsets of markers associated with a particular ecologically important function. 119 Besides, identifying those and the changes of bacterial communities are potentially useful 120 bio-indicator for monitoring ecosystem health (25, 26). Therefore, considering the 121 aforementioned we set up to describe and characterize the composition, structure and 122 functional potential of bacterial communities from the sediments of five different Salar de 123 Huasco sites along an arsenic gradient, among other environmental pressures. 124 125 126 RESULTS 127 The Salar de Huasco comprises an important level of variation (27, 28), evidenced 128 within a relatively small area (sampled area spans a distance of 5.9 km); including daily 129 oscillations in a wide range of environmental parameters (temperature and humidity) and 130 other which vary in spatial gradients, i.e., arsenic and salinity increasing from north to south 131 (Figure 1) (9). In this context, shotgun metagenomic sequencing of the sediment samples 132 representing the five SH sites yielded an average of 87.2 million reads (150 bp length) per 133 sample, with a quality score ≥ 30 presented by ~95% of those obtained reads. 134

5 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

H1 Venezuela Colombia

H3 Ecuador

H4 Perú Brasil Salar de Huasco

Bolivia

SH Paraguay H5

Chile Argentina Uruguay

3 Km

135 136 Figure 1. Salar de Huasco study area. Top panel: a map showing the five sampling sites (H0: 137 20º15'48.8"S - 68º52'28.4"W; H1: 20º16'27.7"S - 68º53'3"W; H3: 20º16'59.2"S - 138 68º53'16.7"W; H4: 20º17'40.9"S - 68º53'17.3"W and H5: 20º18'37"S - 68º52'42"W) 139 investigated in this study. SH is located between the 68º47' - 68º54' W and 20º15' - 20º20' 140 S in the Tarapacá region Northern Chile (Source: Google Earth). Bottom panel: illustration 141 of the SH landscape seen from the H0 site to the South-West; the signs show some 142 particular characteristics of each site (according to those reported in Castro-Severyn et al., 143 2020 (9)). The painting is the work of the illustrator Florence Gutzwiller @spideryscrawl.

6 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

144 145 The bacterial communities of SH are highly heterogeneous and rich in unknown taxa 146 The contrasting differences presented among the SH sampling sites are reflected by 147 the taxonomic composition of bacterial communities, which showed contrasting patterns 148 and variation between sites (Figure 2). Overall, our results indicate that the Proteobacteria 149 and Bacteroidetes are the most prevalent phyla in the sampled sediments, which are 150 particularly enriched in the H3, H4 and H5 communities, accounting together for >60% of 151 all observed taxa (Figure 2A). In turn, these phyla represent ~50% in the H0 and H1 152 communities. Interestingly, the H0 site is dominated by the Cyanobacteria phylum with a 153 34% of the total community; also, this community has the highest abundance of Firmicutes, 154 Patescibacteria and Spirochaetes phyla as well as significantly lower proportion of 155 Actinobacteria. Moreover, H1 community profile presents the highest abundance of 156 Chloroflexi, Actinobacteria, Verrucomicrobia, Planctomycetes and Acidobacteria phyla. On 157 the other hand, H3, H4 and H5 communities are more similar to each other, despite the vast 158 differences in low-abundance taxa; many of which are exclusively present in only one 159 community (Supplementary Table S1). 160 161 The most abundant bacteria at the lowest (available) taxonomic rank reflect the 162 same pattern, with a great abundance of Proteobacteria genera Roseovarius and 163 Desulfotignum in the H3, H4 and H5 communities (Supplementary Figure S1). Moreover, 164 Halomonas, Thiobacillus, Luteolibacter and Truepera genera are more widespread, while 165 Rhodohalobacter, Marinobacter, Psychroflexus and Brumimicrobium are also concentrated 166 in H3, H4 and H5. Contrary, H0 and H1 are enriched in Cyanobacteria like Arthrospira and 167 three members of the Chloroplast order, as well as genera with various metabolism types 168 such as Methylibium, Hydrogenophaga and Desulfomicrobium. In addition, it is worth 169 mentioning that from the 3,801 bacterial ASVs detected, 6.5% and 58.3% could not be 170 classified within any known phylum nor genus respectively. Particularly, Halomonas and 171 Marinobacter are culturable bacteria and seem to be recurrent in the SH, as we reported 172 before (29). Nonetheless, H4 community is by far the most diverse one, according to the

7 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

173 Observed, Shannon, Chao and Simpson diversity indices, which were quite heterogeneous 174 between samples (Supplementary Figure S2). Specifically, the phylogenetic diversity 175 showed that the H0 community has a higher number of distant taxa regarding the other 176 communities under study. 177 178 As a whole, the beta diversity analysis shows that the dispersion patterns among the 179 communities correlates with what was observed in terms of composition. Hence, the 180 taxonomic composition of H3, H4 and H5 metagenomes are more similar between each 181 other and distinctive from H0 and H1, producing three very well-defined groups (Figure 2B). 182 This is contrasting to the obtained alpha diversity results, suggesting that their members 183 could differ in abundance. Thus, confirming the great level of structure among the 184 communities, considering the total bacterial abundance and taxa diversity. Moreover, the 185 distribution of most abundant taxa among the five communities could explain this 186 segregation. Furthermore, we investigated to what extent the microbial community 187 structure was explained by the environmental factors and found that many variables exert 188 a significant influence over the structure and distribution/grouping, among which arsenic 189 appears to be a main driving force shaping these communities. Besides, salinity along with 190 arsenic separate the H3, H4 and H5 communities, while H0 and H1 segregation is driven by 191 altitude and pH respectively. Nonetheless, for the temperature we have to consider that 192 the observed variation may be the result of the normal daily cycle with respect to the time 193 when samples were taken. 194 195 The co-occurrence analysis allowed us to infer possible interactions among complex 196 microbiomes. The network was composed by 112 ASVs having at least one significant 197 correlation, graphically represented as a link between taxa. Total correlations were 155, 198 being 115 positive and 40 negatives. Furthermore, the co-occurrence analyses showed 199 three distinct separated networks within these microbial assembly. However, there is a big 200 main group that comprises most taxa. As expected, the dominant phyla, Proteobacteria and 201 Bacteroidetes, are among the nodes with a highest degree of interactions (Figure 2C). It is

8 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

202 worth mentioning that most of the relevant (highly connected) taxa identified in the 203 networks are little described or studied. Overall, there is the prevalence of direct or indirect 204 relationships among taxa, and this is suggestive of complementing functions in order to 205 maintain the ecosystem. Also, there are few negative correlations that would suggest little 206 niche overlap or competition among taxa.

9 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

A 100 B Principal Coordinates Analysis 0.75 Altitude Phylum Chloroflexi Omnitrophicaeota 75 Cyanobacteria 0.50 Spirochaetes H0 Gemmatimonadetes H0 Epsilonbacteraeota Sample Halanaerobiaeota 0.25 Bacteroidetes H0 Firmicutes H1

undance (%) undance 50 Nanoarchaeaeota H3 b Deinococcus−Thermus H4 H5 Patescibacteria H3 H5 T ºC H5 PCo2 [23.4%] 0.00 Salinity Actinobacteria H3 H4 Relative A Acidobacteria H4 [As] Tenericutes Verrucomicrobia

25 Kiritimatiellaeota −0.25 Planctomycetes Proteobacteria H1 Euryarchaeota H1 −0.50 pH 0 −0.5 0.0 0.5 H0 H1 H3 H4 H5 PCo1 [54.9%] Distance measure: Bray-Curtis

C G: Seohaeicola G: Anoxynatronum Phylum Interaction degree Acidobacteria 1 2 Actinobacteria G: Woeseia P: Gemmatimonadetes 3 Bacteroidetes F: Ruminococcaceae 4 Chloroflexi F: Rhodothermaceae 5

G: Desulfomicrobium Cyanobacteria G: Desulfatiglans 6 G: Thiobacillus Deinococcus−Thermus Epsilonbacteraeota Interaction type Euryarchaeota Positive Firmicutes Negative Gemmatimonadetes G: Thiocapsa Halanaerobiaeota C: Woesearchaeia

G: Prolixibacteraceae Kiritimatiellaeota

F: Geminicoccaceae Nanoarchaeaeota

G: Geobacter G: Desulfatirhabdium G: Anaerolinea Nitrospirae

F: Hydrogenophilaceae Omnitrophicaeota O: Aminicenantales F: Rhodothermaceae F: Anaerolineaceae Patescibacteria C: Thermodesulfovibrionia

F: Desulfobacteraceae Proteobacteria

G: Rhodohalobacter Spirochaetes Tenericutes Verrucomicrobia G: Brumimicrobium Unclassified 207 208 Figure 2. Salar de Huasco bacterial communities. A) Taxonomic composition and relative 209 abundance of SH microbial communities in the five studied sites; Stacked-bars show the 210 top20 most abundant bacteria at the phylum taxonomic level. B) Beta diversity by Principal 211 Component Analysis (PCoA) on Hellinger transformed amplicon sequence variant (ASV) 212 relative abundances. Each point corresponds to a community from the different sites 213 (represented colors), and its relative distance indicates the level of similarity to all other

10 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

214 samples. The arrows indicate the explanatory power of the statistically significant 215 environmental parameters with regards to the observed variation in community 216 composition. C) Co-occurrence network analysis of the SH bacterial communities, the size 217 of each node (representing ASVs) is proportional to the number of connections (degrees), 218 the color of the edges connecting nodes represent the interaction type, the node color 219 indicates the taxonomic affiliation at phylum level and node labels are at the lowest 220 available taxonomic classification. 221 222 Functional approach of SH communities reveals metabolic specialization 223 The five SH metagenomes we analyzed represent the surface sediment of 5 different 224 sites with a great variation in arsenic content (from 9 to 321 mg/Kg). The 337.5 million 225 quality-controlled reads were co-assembled and yielded a total of 994,545 contigs (≥ 1,000 226 bp) with 2.38 millions genes, which were used to generate abundance/distributions and 227 functional profiles of the 5 studied communities (Figure 3; Supplementary Table S2). Of 228 those contigs, 148,394 (≥ 2,500 bp) were hierarchically clustered to then be profiled by 229 read-recruitment of the data from the five communities (Figure 3A). The resulting patters 230 are highly variable, considering the contigs detection in each metagenome. In particular, 231 metagenomes from H1 and H5 are the most contrasting ones, as they respectively have 232 18.78% and 78.40% presence of all available contigs in the SH. Moreover, the rest of the 233 samples also have diverse percentages of representation (H0: 25.00%; H3: 72.08% and H4: 234 48.33%), and their clustering by the read recruitment profiles correlates with the taxonomic 235 profiles presented previously; being H3, H4 and H5 more similar to each other, and H0 and 236 H1 more distanced from each other and to the others as well. Contrary, the functional 237 profiles seem to have a higher level of convergence between samples in a broad way (Figure 238 3B). Furthermore, the more enriched categories (SEED Subsystems 1) are associated with 239 metabolism (amino acid derivatives, carbohydrates, protein metabolism, DNA metabolism, 240 cofactor, vitamins, prosthetic groups and pigments) which could be evidence of the needed 241 versatility and adaptability for bacteria to thrive in these harsh environments. Also, the 242 stress response, membrane transporter, cell wall and capsule categories, which are related 243 to the ability to thriving capabilities as well. 244

11 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

245 Nonetheless, statistical testing showed that the differences presented by the 246 metagenomes in most categories were significant (Supplementary Table S3). Particularly, 247 the five SH communities showed particular differences in some categories such as arsenic 248 resistance (SEED Subsystems 3), which follows the same tendency previously mentioned; 249 where H0/H1 metagenomes are more similar regarding H3/H4/H5, with 0.21 - 0.22% and 250 0.26 - 0.27% of reads recruited by this category, respectively. Other significant categories 251 among the communities were carbon, nitrogen, and phosphate metabolism; Zinc, Nickel, 252 Cobalt, Iron and Manganese transport; osmotic stress; circadian clock in Cyanobacteria and 253 the Calvin Benson cycle. Whereas, when assessing the ability of these bacterial communities 254 to carry out necessary reactions to sustain some biogeochemical cycles, we found that S, N

255 and CH4 showed significant scores (MEBS analysis) for all five metagenomes; implying that 256 the necessary metabolic pathways and machinery is present and with proper completeness

257 (Supplementary Figure S3). Nevertheless, a wide difference is present in the CH4 cycle, 258 which is much more enriched in the H1 and H0 communities; especially in the Methane 259 oxidation, Methanol, Methanogenesis and mcrABC (markers) pathways. Also, a small 260 decrease in H1 sulfur is also observed. 261

12 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

A H5 H3 H4 H0 H1

GC-content Length Clustering by read recruitment profile

96,174,371 Total 148,394 mapped Contigs reads 0

B Functional Categories Site: H0 H1 H3 H4 H5 Virulence, Disease and Defense Virulence Transcriptional regulation Sulfur Metabolism Stress Response Secondary Metabolism RNA Metabolism Respiration Regulation and Cell signaling Protein Metabolism Predictions based on plant−prokaryote comparative analysis Potassium metabolism Plant cell walls and outer surfaces Photosynthesis Phosphorus Metabolism Phages, Prophages, Transposable elements, Plasmids Phages, Prophages, Transposable elements Nucleosides and Nucleotides Nitrogen Metabolism Motility and Chemotaxis Miscellaneous Metabolism of Aromatic Compounds Membrane Transport Iron acquisition and metabolism Fatty Acids, Lipids, and Isoprenoids Dormancy and Sporulation DNA Metabolism Cofactors, Vitamins, Prosthetic Groups, Pigments Clustering−based subsystems Central metabolism Cell Wall and Capsule Cell Division and Cell Cycle Carbohydrates Arabinose Sensor and transport module Amino Acids and Derivatives 0 2.5 5 7.5 10 12.5 Percentage (%) 262

13 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

263 Figure 3. Salar de Huasco metagenomes. A) Circular map representing the universe of 264 contigs detected in the five SH sites: the central tree represents the contigs organization on 265 Ward’s linkage with Euclidean distances, the seven circle layers (from bottom-up) 266 represent, for the corresponding contig: its length, GC-content, and presence on the five 267 metagenomes. The top-right bars represent the number of mapped reads for the 268 corresponding metagenome and the dendrogram their clustering by read-recruitment 269 profile. B) Patterns of functional potential for each metagenome, according to the presence 270 and abundance of the SEED database of metabolic pathways and functions: subsystems at 271 level 1. The circles represent the percentage value for the corresponding category in each 272 metagenome (defined by colors). 273 274 Arsenic expulsion is the main mechanism to thrive in the SH 275 To gain a broader view on the communities’ functional potential related to arsenic 276 metabolism or resistance/tolerance we determined the abundance of the genes belonging 277 to the known mechanisms (Figure 4). Again, the abundance pattern of these genes 278 correlates with the observed tendency, being more related those H3, H4 and H5 sites, 279 distant from H0 and H1. Moreover, although marker genes of arsenic methylation, 280 reduction, oxidation, respiration, and expulsion mechanisms are present in all sites, most 281 are significantly more abundant in the H3, H4 and H5 sites. This may be due to the much 282 higher arsenic concentration in the sediments of these sites. The arsR regulator, the arsC 283 reductase and the acr3 pump were the most abundant ones, which would indicate that the 284 As(V) reduction and subsequent As(III) expulsion would be the most common strategy used 285 by the SH inhabitant bacteria. Moreover, arsM was also among que most abundant genes, 286 particularly enriched in the H3, H4 and H5 metagenomes. Whereas, genes related to 287 oxidation and respiration mechanisms are less abundant and present an interesting 288 distribution; being those related to oxidation (aoxA, aoxB, aioB) are enriched in H3, H4 and 289 H5, contrary to those related to respiration (arrA, arxR) are enriched in H0 and H1. 290

14 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

Arsenic Metabolism Genes arsR 6142.15 4214.04 12356.33 18499.32 13750.63

1327.42 1188.22 2117.04 2345.06 2057.75 Normalized reads arsC 20000

1099.45 698.09 2375.33 2003.96 2183.94 acr3 15000 arsM 427.13 495.38 1491.54 2215.36 1827.64 10000 arsB 367.57 200.46 583.55 542.77 487.89 5000 1000 259.69 57.08 534.59 682.07 600.08 500 arsD 100 0 arsJ 202.04 75.23 531.96 717.95 480.64 arsA 601.44 93.67 337.78 438.65 338.12 Mechanism aoxB 113.32 0.89 519.98 613.91 485.76 Expulsion arsP 2.14 0.60 178.54 564.49 341.63 Reduction arsH 265.30 92.76 217.13 253.17 237.18 Respiration Oxidation aioB 46.54 50.63 153.38 171.91 134.68 Methylation arxB 65.25 2.50 205.48 105.97 147.89 aoxA 0.00 0.00 156.08 200.40 165.10 aioA 110.20 86.16 106.81 114.45 84.01 arxA 177.41 2.07 158.71 35.78 92.44 arxR 240.74 0.00 0.00 0.00 0.00 arrA 8.58 60.09 0.00 0.00 0.00 H0 H1 H3 H4 H5 291 292 Figure 4. Distribution and abundance of As metabolism genes in the Salar de Huasco. 293 Heatmap shows the (normalized) number of reads that aligned against the corresponding 294 protein identified in each sample, according to the color scale. Genes are grouped by colors, 295 representing the 5 As response mechanisms. 296 297 Overall, the proportional distribution pattern of the mechanisms in the 5 298 metagenomes had some similarity and is quite constant (Figure 5). Particularly, the 299 expulsion mechanism as a whole was the most abundant one for all the sites covering 300 around 75% of all the sequences (Figure 5A); observing the greatest differences in oxidation 301 and respiration mechanisms, as we stated before. Additionally, respiration genes are more 302 present in H0 and reduction in H1. Interestingly, the pattern between the five metagenomes

15 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

303 seems to be independent of the amount of detected sequences and the arsenic 304 concentration of the site. Furthermore, comparing the abundance of particular arsenic 305 efflux pumps (from the expulsion mechanism) we can observe that, indeed, most of the 306 sequences correspond to acr3 (Figure 5B); followed by arsB and arsJ. Notably, arsP which is 307 an efflux permease that confers resistance to organic arsenics (roxarsone and 308 methylarsenite) was more abundant in the sites with higher arsenic concentration. 309 Moreover, the higher acr3 variants and abundance could be due this pump is present in a 310 wider number of bacteria phyla, covering most of the found diversity (Figure 5C). Also, this 311 efflux pump seems to be more ancestral, regarding arsP and arsJ which are only present in 312 Proteobacteria and in more recent branches. 313

16 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

A Arsenic Metabolism B Arsenic Efflux Pumps

100 100

300

75 75

200 [A r senic mg/Kg] 50 50 undance (%) undance undance (%) b b Relative A Relative A

100 25 25

0 0 0

H0 H1 H3 H4 H5 Mechanism H0 H1 H3 H4 H5 Respiration Oxidation Methylation Reduction Expulsion As Pumps: arsP arsJ arsB acr3

Prolixibacteraceae

Cyclobacterium Salinimicrobium Prolixibacter Caldisalinibacter

T T Tangfeifania Clostridiisalibacter

a Mongoliibacter a Pontibacter Mongoliibacter Pontibacter Pontibacter

n Clostridiisalibacter Cyclobacterium n

Clostridiisalibacter g g

f C Rhodohalobacter Psychroflexus f Rhodohalobacter Psychroflexus Aquiflexum

e e Gillisia Gillisia

Gillisia i Crocinitomix i

f f

a a Proteobacteria (189)

n n Genus Fervidicola

i Geoalkalibacter i Geoalkalibacter

a Clostridiisalibacter Chrysiogenes a Chrysiogenes ClostridiisalibacterGeoalkalibacter

Chrysiogenes Bacteroidetes (41) Catabacter Blautia Phylum Tenericutes CaldisalinibacterTenericutes Methanocorpusculum Bacteroides Cytophaga Firmicutes (24)

Cytophaga Clostridium As Pump Halomonas NitrincolaHalomonas Actinobacteria (17) Halomonas AlkalilimnicolaHalomonas HalorhodospiraHalomonas Halomonas Halorhodospira Euryarchaeota (9) ThioalkalivibrioMarinobacter Counts Marinobacter

Caldicoprobacter Egicoccus Egicoccus Salinibacter Others (26) Salinibacter Salinibacter Pseudomonas Pseudomonas Pusillimonas Pusillimonas OceanibaculumPorticoccus OceanibaculumPontibaca acr3 (172) OceanibaculumPontibaca Sulfitobacter Sphingobium Haliangium Plesiocystis arsB (66) Oceanicoccus Hydrogenophaga Hydrogenophagabeta Hydrogenophaga arsJ (48) Hydrogenophaga Enhydrobacter Oceanococcus Sedimenticola arsP (20) ComamonadaceaeAzoarcus Paucibacter Thiobacillus Chitinilyticum Sulfurivermis Thiohalobacter Thiohalophilus Thiohalobacter 306 different sequences Thiocapsa Rhodomicrobium Thermoanaerobaculum Thermoanaerobaculum 3,635 detections Haloarcula Marinobacter Halomonas Alkalitalea Marinobacter Alkalitalea Marinobacter Alkalitalea Marinobacter Alkalitalea Marinobacter Rufibacter Marinilabilia Oceanospirillales Desulfobacter Alcanivorax Desulfosoma Halomonas Candidatus-Brocadia Halomonas Desulfofustis Halomonas Desulfococcus Pseudomonas Desulfococcus Pseudomonas Candiadtus Pseudomonas Richelia Nostoc Simplicispira Arthrospira Nitrosomonas Methanosarcina Nitrosomonas Natrialbaceae Nitrosomonas Halomicrobium Opitutaceae Thioalkalivibrio Desulfonatronovibrio ThioalkalivibrioNitrosomonas Desulfonatronovibrio Nitrosomonas delta-proteobacterium Cyanothece Desulfonatronospira Desulfonatronovibrio Haloterrigena delta Desulforhopalus Desulfonatronovibrio Marinobacter Desulfonatronovibrio MarinobacterAzonexus Desulfonatronovibrio Rhodothermaceae Laceyella Desulfobacula Desulfuromusa Desulforhopalus Roseiflexus Desulforhopalus Thermoflavimicrobium Geobacter Desulfomicrobium Anaerolineales Geoalkalibacter Anaerolineales Desulfomicrobium Desulfomicrobium Halorubrum Desulforegula DesulfotomaculumPseudomonas Desulfobacter Desulfobacter Ilumatobacter Desulfonatronum Ilumatobacter Desulfopila Roseovarius Syntrophus Planktotalea Syntrophus Chlorobaculum Sediminimonas Chlorobaculum Sediminimonas Chlorobaculum Desulfosporosinus Ignavibacterium RhodobacteraceaeRoseovarius Acetobacteroides PseudooceanicolaRoseovarius Acetobacteroides Roseovarius Algoriphagus Roseicyclus Flavobacterium Marinifilaceae Marinifilaceae Hydrogenophaga Syntrophobacter Marinifilum Hydrogenophaga Labilibaculum Hydrogenophaga Pseudomonas Hydrogenophaga Erythrobacter Wenzhouxiangella Porticoccus Methylocella Porticoccus Methylocella PseudomonasHalomonas Methylocella Halomonas Gemmatimonas beta-proteobacterium Cryobacterium Halomonas Euzebya beta-proteobacterium Nitriliruptor Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacteraceae Thioalkalivibrio Thiocapsa Ilumatobacter Ilumatobacteraceae Thioalkalivibrio Thiocapsa Ectothiorhodospira Thiocapsa Salinisphaera Thioalkalivibrio Ectothiorhodospira Thioalkalivibrio Ruegeria Ectothiorhodospira Methylomarinum Ectothiorhodospira Pseudaminobacter Thioalkalivibrio Pseudaminobacter Roseibaca Aneurinibacillus Bacillus Novibacillus Bacillus Bacillus Bacillus Bacillus Bacillus Haloarcobacter Arcobacter Microcella Halomicrobium Halomicrobium Pseudomonas Pseudomonas P Marinobacter P Marinobacter r s r e MarinobacterPseudomonas

e e Halocynthiibacter e t Roseibaca Roseibaca t u Roseibaca t r

c d Roseibaca c o Roseibaca

a o Roseibaca a t Roseibaca Roseibaca

b m Alicycliphilus Roseibaca b o Roseibaca

o o o g Saccharospirillum

n n n a

Pseudomonas i a i Pseudogulbenkiania Pseudomonas Pseudomonas Pseudomonas

r s Pseudomonas r Pseudomonas

a a

Marinobacter Marinobacter Marinobacter Marinobacter Marinobacter M M 314

17 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

315 Figure 5. Arsenic metabolism in the Salar de Huasco. A) Distribution and abundance of 316 detected genes related to arsenic (As) metabolism in the five SH metagenomes; Stacked 317 bars represent the proportion of all the genes grouped by mechanism type based on relative 318 abundance (%), and the line represents total As concentration in each corresponding site 319 (mg/Kg of sediment). B) Stacked bars represent the proportion of all detected As efflux 320 pumps in the five metagenomes, based on genes relative abundance (%). C) Phylogenetic 321 analysis of 306 non-redundant sequences of arsenic efflux pumps detected in the SH, layers 322 surrounding the phylogenomic tree indicate: detection level, type of efflux pump, 323 taxonomy, the phylum level, and the species. 324 325 Novel genomes from SH belong undescribed genera 326 The binning the process reconstructed 195 bins (which were manually curated and 327 evaluated for completion and redundancy) to gain insights of non-culturable bacteria. This 328 resulted in 19 metagenome-assembled genomes (MAGs) that met the completion ≥ 80% 329 and redundancy ≤ 10% criteria (Figure 6); that clustered 4.99% of the contigs in the 330 metagenome profile database. The MAGs taxonomic affiliation resulted in 1/19 belonging 331 to the Eukaryota domain and the 18/19 remaining to the Bacteria domain, distributed in 4 332 different phyla (9 Proteobacteria, 7 Bacteroidota, 1 Cyanobacteria and 1 333 Gemmatimonadetes (Figure 6A). We only were able to assign 22% (4/18) of the Bacteria 334 MAGs to previously described genera, suggesting a great diversity of novel species that are 335 yet to be described. Also, MAGs contigs number was very variable from 208 to 626 among 336 the Bacteria (Eukaryota being substantially larger: 4,174), the same tendency was observed 337 for the GC-content (36.1% - 69.4%). On the other hand, most of the reconstructed MAGS 338 were represented or detected in the H3, H4 and H5 metagenomes, showing differential 339 abundance patterns in each one (Figure 6B; Supplementary Table S4). 340

18 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

!A !!!!!!!!!"#$%&'!(%()*%#)! +,)-./! 012341.! !!!!!!!!! ! ! ! ! ! ! ! ! ! ! ! '#7%?@ + ! ;8! ):;#:<#-= '8(*)7%'# '8(*)7%'#

5)6-)#7 + 5)6-)#7 9 > >AB 0+!-'#7)#7 5)6-)#7 + 5)6-)#7 +'#7<8%#<7%'# 5,=*;8! +*<@@! C6:)6! D<8%*=! 0)#;@! ! ! ! ! ! ! ! ! ! ! ! ! "! EF4/"0GGG! HAIJJK! LIMLK! NNL! GGOGLJ! PBIJPK! MGIHAK! GIAQK! 3<-7)6'%:'7

SH-MAG144 B SH-MAG148

Redundancy SH-MAG131 Completion H5 H3 SH-MAG78 H4 H0 Clustering by MAGs abundance H1 GC-content SH-MAG111 Length

96174371

Total Mapped Reads

SH-MAG3

SH-MAG129 SH-MAG169

SH-MAG96 SH-MAG29

SH-MAG143 SH-MAG193

SH-MAG93 SH-MAG89

SH-MAG12

SH-MAG116 SH-MAG162

SH-MAG130

SH-MAG192 341

19 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

342 Figure 6. Salar de Huasco Metagenome-Assembled Genomes. A) Genomic features 343 summary of the SH 19 MAGs, quality indices, and taxonomic affiliation (the results of 344 *Anvi'o pipeline and/or **CheckM meet the quality standard of completion ≥ 90% and 345 redundancy/contamination ≤ 10% (according to 125). B) Presence and abundance of the 19 346 MAGs on the five SH metagenomes: the central tree represents the MAGs organization on 347 Ward’s linkage with Euclidean distances, the seven circle layers (from bottom-up) 348 represent, for the corresponding MAG: its length, GC-content, and abundance on the five 349 metagenomes. The top-right bars represent the total number of mapped reads for the 350 corresponding metagenome and the dendrogram their clustering by absence/presence 351 profile. 352 353 Novel genomes from SH are mostly arsenic reducers 354 The functional potential of reconstructed MAGs was evaluated globally and 355 particularly in relation to their repertoire of arsenic associated genes (Figure 7). For these 356 evaluations, only the 12 MAGs that meet the quality standard of completion ≥90% and 357 redundancy/contamination ≤10% were considered. Therefore, a total of 267 KEGG modules 358 were detected among the MAGs, presenting variable absence/presence and completion 359 patterns (Figure 7A; Supplementary Table S5). This variation reflects the MAGs taxonomic 360 affiliations, being all the Bacteroidota (MAG111, MAG12, MAG116, MAG130 and MAG193) 361 clustered together, as well as the Deltaproteobacteria (MAG148, MAG143 and MAG93) and 362 Gammaproteobacteria (MAG129 and MAG144). On the other hand, the only Cyanobacteria 363 (MAG29) is close to the Gammaproteobacteria and the only Gemmatimonadota (MAG3) is 364 close to the Bacteroidota. Particularly, the MAGs presented a considerable number of genes 365 regarding arsenic metabolism, where the same tendency shown before was observed; with 366 a dominant presence of genes related to As expulsion (acr3, arsA, arsJ and arsR) throughout 367 all taxa, followed by reduction (arsC and arsH) and methylation (arsM) (Figure 7B, 368 Supplementary Table S6). Interestingly, arsJ was only detected in one Proteobacteria, which 369 agrees with its lower abundance found at the metagenomic level in the SH. While, arsH was 370 only detected in the Cyanobacteria, which could also reflect the abundance of this taxa and 371 this gene globally. In addition, the absence/presence patterns among the MAGs seems to 372 be group-specific, broadly associated by phyla. 373

20 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

MAG29 100% KEGG Module completeness MAG129 MAG144 80% MAG3 MAG111 60% MAG12 MAG116 MAG130 40% MAG193 MAG148 20% MAG143 MAG93 0% M00045 M00793 M00021 M00133 M00153 M00169 M00530 M00741 M00036 M00087 M00549 M00909 M00166 M00868 M00053 M00173 M00017 M00525 M00572 M00855 M00004 M00022 M00009 M00011 M00527 M00016 M00526 M00121 M00926 M00035 M00165 M00167 M00028 M00149 M00010 M00846 M00063 M00093 M00157 M00007 M00048 M00120 M00126 M00051 M00140 M00060 M00866 M00119 M00125 M00307 M00881 M00082 M00115 M00001 M00002 M00003 M00052 M00083 M00050 M00049 M00005 M00086 M00155 M00618 M00029 M00845 M00308 M00346 M00364 M00034 M00611 M00912 M00842 M00843 M00172 M00913 M00874 M00884 M00141 M00882 M00883 M00892 M00144 M00168 M00579 M00091 M00156 M00006 M00358 M00632 M00176 M00008 M00616 M00116 M00932 M00112 M00145 M00161 M00163 M00097 M00531 M00580 M00100 M00162 M00366 M00367 M00040 M00150 M00744 M00148 M00769 M00537 M00538 M00529 M00129 M00171 M00061 M00631 M00554 M00876 M00555 M00879 M00633 M00919 M00824 M00825 M00309 M00356 M00563 M00935 M00422 M00643 M00745 M00114 M00550 M00705 M00079 M00076 M00923 M00078 M00099 M00136 M00861 M00673 M00835 M00433 M00922 M00898 M00921 M00850 M00094 M00378 M00567 M00104 M00546 M00113 M00545 M00725 M00622 M00848 M00159 M00128 M00917 M00101 M00102 M00152 M00778 M00014 M00039 M00372 M00030 M00154 M00135 M00037 M00042 M00651 M00936 M00897 M00613 M00696 M00697 M00375 M00568 M00032 M00569 M00338 M00930 M00931 M00038 M00044 M00627 M00170 M00365 M00027 M00541 M00033 M00552 M00620 M00374 M00357 M00617 M00575 M00373 M00376 M00841 M00344 M00840 M00911 M00914 M00614 M00368 M00612 M00089 M00740 M00095 M00849 M00609 M00873 M00131 M00532 M00377 M00878 M00013 M00088 M00718 M00615 M00123 M00573 M00577 M00151 M00595 M00117 M00118 M00064 M00175 M00847 M00899 M00916 M00528 M00804 M00124 M00345 M00024 M00025 M00098 M00896 M00127 M00895 M00432 M00535 M00015 M00122 M00924 M00925 M00012 M00596 M00020 M00019 M00570 M00880 M00026 M00018 M00023 M00096 M00844 M00565 M00854

B MAG 116

MAG MAG 93 129

ArsC ArsC MAG 2 3 MAG 193 ArsR 148 2 ArsJ

ArsC ArsA

MAG MAG ArsR ArsH 29 143 3 ArsM Acr3

Acr3 2 MAG ArsR 3 ArsM ArsA 2 2

MAG 130 ArsA 3

MAG MAG 111 12

MAG 144 374 375 Figure 7. MAGs functional potential profiles: global and arsenic specific. A) Metabolic 376 capabilities of the 12 selected MAGs according to KOfam (KEGG Orthologs database); the 377 heatmap shows the KEGG Modules completition displayed by the color gradient for given 378 module in each corresponding MAG. B) Functional network analysis of the 12 MAGs 379 according to the presence and abundance of detected arsenic metabolism genes; size of 380 gene nodes represents the level of detection, and the size of the edges represents the 381 correlation level. Blue nodes represent genes, and orange nodes represent MAGs. 382 383 384 DISCUSSION

21 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

385 The Salar de Huasco has an important spatial variation of arsenic in sediments, which 386 we have described as a gradient from north to south (Figure 1) (9); being consistent with 387 the widely reported heterogeneity for this area and the impact of extreme conditions over 388 the inhabitant organisms (30). Previous studies have detected variations in conductivity, 389 organic matter, and dissolved oxygen even in geographically closed areas (6, 7, 31, 32, 33). 390 In aquatic ecosystems, the quantity of trace elements and physical-chemical states of some 391 elements, are related to their circulation/interaction, and with the mechanisms of 392 corresponding living organism (34). Salar flats as SH have the peculiarity of containing more

393 than 50% of CaCO3 and high levels of arsenic, which is very available and highly mobile. This

394 is because a fraction of the arsenic in sediments lies as soluble salts (Na2HAsO4); it also could

395 be associated to the calcium carbonate (CaHAsO4) and, adsorbed into Fe and Al oxides (35). 396 The metabolism of inhabiting microbial communities or the activity of primary producers 397 could be the source of the diversity of abiotic properties (36). It is hypothesized that this 398 spatial and functional heterogeneity is the cornerstone of resilience in these extreme 399 ecosystems. We must consider the presence of high concentrations of arsenic as a selective 400 pressure, which could have a modulating effect on the composition of the H3, H4 and H5 401 communities (grouping them together and separating them from the rest). 402 403 The five communities described from the SH metagenomes are dominated by 404 Proteobacteria, Bacteroidetes, and Cyanobacteria (Figure 2), similar to previous reports for 405 other Altiplanic environments (Socompa volcanic Lake in Argentina and La Brava Lagoon - 406 Salar de Atacama in Chile) mostly carried out by 16S rRNA amplicon sequencing (37, 38). 407 Specifically, the same pattern has been reported previously for the SH (3, 32). However, the 408 proportions are different, probably because the shotgun metagenomic sequencing used in 409 this study is more sensitive, which is evidenced by the alpha diversity indices´ magnitude. 410 Nevertheless, these values are also higher than those reported by other metagenomic 411 studies from similar areas (39). 412

22 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

413 Moreover, there is evidence of Proteobacteria enrichment in places with different 414 arsenic concentrations (12), as many microorganisms described in this group are capable of 415 interacting and tolerate arsenic. Some examples are Acidithiobacillus and Desulfovibrio 416 genera, which can solubilize arsenic from solid compounds or precipitate it by coupling 417 arsenic and sulfur reduction, respectively (10, 40). The phylum Bacteroidetes is widespread 418 and commonly found in hyper-saline wetlands and microbial mats (38, 41, 42, 43, 44, 45). 419 Furthermore, Cyanobacteria have fundamental roles in any community they belong to, as 420 primary producers and participating in bio-weathering and matrix transformation processes 421 (46). Also, they are usually abundant in places exposed to sunlight (47). Besides, previous 422 reports have proposed the cyanobacterial communities in SH are unique (6). In addition, 423 many recurrent representatives of these communities (Marinobacter and Halomonas) have 424 been cultured and isolated in laboratory conditions (29). Particularly, we isolated 425 Exiguobacterium strains from these communities to describe the mechanisms responsible 426 for their high arsenic resistance (9, 48, 49). 427 428 On the other hand, a high percentage of unidentified genera have also been 429 reported in the Chilean Atacama Desert, where those reach 66% putatively novel taxa 430 belonging to that bacterial “dark matter” (50), this is a direct consequence of database 431 shortage, reinforcing the need to keep exploring these unique environments and describe 432 the novel, highly adapted microorganisms that remain unknown to science and that are of 433 fundamental importance to the maintenance of these ecosystems. The co-occurrence 434 networks show clear evidence of overlapping among the bacterial taxonomic composition 435 of the metagenomes or “core microbiome”, mainly composed of unknown and little- 436 studied/described taxa. As an interaction example, we have Seohaeicola and 437 Anoxynatronum, belonging to an isolated in the network involved in nitrogen cycling as 438 autotrophic and heterotrophic taxa. From the co-occurrence networks, we can infer that 439 this is a very fragile and susceptible ecosystem as there are no keystone nodes detected 440 (51). 441

23 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

442 At the lower available taxonomic rank, we found some bacteria that are typical for 443 extreme environments, such as Thiobacillus, a well-known sulfur-oxidizing bacterium, and 444 Desulfotignum an anaerobic group known to be sulphate-reductors, as well as other less 445 studied groups. The most abundant belong to the Roseovarius genus, an aerobic, non- 446 photoautotroph bacterium; Rhodohalobacter is a facultative anaerobic, moderately 447 halophilic group, and Truepera comprises aerobic quimio-organotropic alkaliphile 448 bacterium. This is evidence of the lifestyle diversity that these microorganisms have, 449 suggesting a cooperative and specialized community. Likewise, this is in coherence with our 450 other findings, the enrichment of photosynthetic organisms in sites H0 and H1, as well as 451 Proteobacteria as the most abundant group in all the other sampled sites. Other similarities 452 are evidenced, as the genus Methylibium is only found on the H0 site, where the methane- 453 related metabolism function was found to be enriched in the MEBS analyses. In addition, 454 the importance of some of these abundant taxa is highlighted, as they are structural parts 455 in the co-occurrence network, namely Desulfomicrobium, Thiobacillus, and 456 Brumimicrobium. 457 458 The functional potential of the five SH communities showed similar patterns in 459 general, with particular differences in some categories following the same association 460 pattern described before (Figure 3). Comparing each site, we found significant differences 461 in the enrichment of some important functional categories related to the maintenance of 462 critical geochemical cycles, including carbon, nitrogen, and phosphate metabolism, stress 463 resistance, transport of Zinc, Nickel, Cobalt, Iron, Manganese and osmotic stress. Notably, 464 the two functions with statistical differences between almost all communities were the 465 Circadian Clock in Cyanobacteria and the Calvin Benson cycle, suggesting differences in the

466 primary production and CO2 fixation. The Circadian Rhythm is directly influenced by several 467 abiotic conditions, such as temperature and atmospheric pressure (52). Moreover, the

468 Calvin Benson cycle was the only metabolic pathway involved in CO2 fixation reported in an 469 endolithic halite metagenome analysis, underlining its importance for microbial 470 communities living in these extreme conditions (53). This agrees with the MEBS analysis

24 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

471 results, where the carbon cycle relevant reactions presented a greater variation between 472 the metagenomes. Particularly, carbon flux control in these communities seems to be 473 different especially in regard to methanogenesis processes, as the far greater detection of 474 specific markers in H0 and H1 could account for a high presence of methanogenic species 475 in these two communities in comparison to the other metagenomes (54). This could be 476 related to the S decrease in H1, implying that methanogens outcompete sulfate reducers in 477 this community (55). 478 479 Furthermore, among these significant differences, arsenic resistance is enriched in 480 the metagenomes from the sites with the highest As concentration, which was expected 481 and has been reported previously (56, 57). Particularly, the distribution and abundance of 482 arsenic-related genes in the five SH communities showed significant differences among the 483 communities (Figure 4), as the presence of arsenic affects the dynamics of inhabiting 484 microorganisms (18, 58). Therefore, arsenic would promote the selection and abundance 485 of tolerant microorganisms (59, 60). Our results agree with this premise since the 486 abundance of all these genes increases following the gradient of arsenic concentration 487 among the five sites, indicating a different dynamic in each niche, even though there are 488 geographically closely located. 489 490 Overall, we found that most of the detected genes related to arsenic belong to 491 expulsion and reduction mechanisms (arsR, arsC, and acr3), which correlates with what was 492 reported in the Argentinian Altiplano (22). The transcriptional repressor ArsR was the one 493 with the highest abundance detected; as this protein participates in the regulation of 494 different arsenic pumps, namely ArsB, ACR3, and ArsP (61, 62); and is also reported to be in 495 operon conformations along with ArsC or ArsM (63). Hence, it was expected to be the most 496 abundant and broadly distributed. Moreover, to better assess the proportion of the arsenic 497 mechanisms found in SH, we separated the genes related to As expulsion out of the cell 498 from those related to reduction mechanism (usually reported together). This is due to the 499 fact that gene clusters including arsB, arsP, arsK, arsJ, and acr3 are commonly

25 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

500 found/reported independent of arsenate reductase arsC (9, 62, 64, 65). Also, arsenic 501 methyltransferase (ArsM) was the next most abundant protein, and particularly enriched in 502 H3, H4, and H5, suggesting this protein is part of a complementary mechanism used to 503 thrive in sites with high arsenic concentrations (66). 504 505 Even though proteins related to oxidation and respiration of As are less abundant; 506 their distribution is interesting, which could be due to niche-specific conditions. Despite 507 these mechanisms are somewhat known and recurrent, much remains unclear, mostly 508 regarding their function and interconnection with the bacterial central metabolism (67, 68). 509 This problem is exacerbated when working in unexplored environments, where unknown 510 or unclassified organisms abound. Besides, the amount of misclassified and/or unclassified 511 protein sequences in databases is a major blockage for these investigations. Nonetheless, 512 this is fully acknowledged and is being addressed; thus, helping to generate important 513 findings (69, 70, 71, 72). For example, our group was able to manually identify the ArsP and 514 ArsK transporters, as well as a possible arsenic respiration system, additionally a missing 515 arsenic methylase was evidenced in Exiguobacterium genomes using a combination of 516 bioinformatic tools (9). Nevertheless, database shortage and the classification by homology 517 problems must always be considered when gene- or metabolism- detection is being carried 518 out. 519 520 Broadly, the most abundant mechanism found in the SH was arsenic expulsion, with 521 around 75% of all the arsenic-related sequences (Figure 5). Nonetheless, by merging the 522 expulsion and reduction mechanisms, we would get results consistent with previous reports 523 for paddy, contaminated, and natural arsenic-rich soils (around 85%) for the reduction 524 mechanism (56). Likewise, here we report that mechanism proportions are relatively 525 equivalent between the five SH sites, and those are independent of the total sequence 526 abundance as well as the arsenic concentration. Whereas respiration and oxidation 527 mechanisms showed some variation, which could be reflecting the taxonomic composition 528 of the communities. Furthermore, we also found that the Organoarsenical Permease arsP

26 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

529 was detected in the higher arsenic concentration sites. Therefore, this may be a 530 counteraction against a possible greater production of these highly toxic organic species at 531 these sites, where consistently the Arsenite Methyltransferase (arsM) gene was enriched 532 as well. Thus, these markers could be proposed as an arsenic contamination bioindicator 533 like acr3 (73). Additionally, the distribution and phylogenetic relationships of arsenic pumps 534 among the metagenomes revealed distinct groups, which is consistent with those 535 previously reported: I) ArsB - Acr3 [ion/BART], II) ArsP [Permease]; III) ArsK - ArsJ [MFS] (65). 536 It has been hypothesized that the divergence presented by the arsenic resistance 537 mechanisms originated from Earth's geological changes to adapt for a particular function or 538 As emergent species (74). Besides, we found that Proteobacteria possess a broader gene 539 repertoire for arsenic response, which has also been described before (22). 540 541 The twelve reconstructed high-quality MAGs provide information about the 542 abundance and distribution of undescribed or unknown microorganisms (Figure 6), as well 543 as genomic insights into widespread arsenic metabolism. Most of these are affiliated within 544 Proteobacteria and Bacteroidetes phyla, reflecting what we described at the community 545 level. As stated before, the recovery of MAGs complements decades of cultivation and PCR- 546 survey efforts by providing information about taxa missing in culture collections (Candidate 547 Phyla Radiation), improving our understanding of microbial communities (75); particularly 548 those inhabiting extreme environments. Nonetheless, four MAGs were classified at the 549 genus rank: Arthrospira, a Cyanobacteria with great commercial interest as a pigment 550 source and to make spirulina supplements; Thioalkalivibrio: an aerobic sulfur-oxidizer; 551 Desulfatitalea and Desulfosarcina: two anaerobic sulfate reducers (76, 77, 78). 552 553 In the same way that these MAGs only cover the most abundant phyla of SH, we 554 only detected genes that belong to the most prevalent arsenic resistance mechanisms on 555 them (Figure 7). Thus, supporting expulsion, reduction, and methylation as the most 556 recurrent widespread mechanisms in the SH to cope with the high arsenic concentrations. 557 Conversely, no arsenic oxidation and/or respiration-related gene was detected on the

27 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

558 recovered MAGs. This could indicate that the different ways used to expel the toxic out of 559 the cell would be more straightforward and direct as a first line of defense against arsenic; 560 while the oxidation and respiration would involve a coupling with the bacteria central 561 metabolism and a larger machinery to benefit from this compound, which seems to be 562 associated with less abundant and highly adapted or specialized bacteria. Also, arsenic 563 oxidation and respiration have been reported in association with nitrate and sulfate 564 reduction, respectively (10, 79). Nonetheless, we need to take into consideration the MAGs 565 completion levels as a possible source for missing genes. 566 567 Overall, our results reveal that populations of Proteobacteria, Bacteroidetes and 568 Cyanobacteria are abundant across wide ecological niches in the SH spanning a challenging 569 ensemble of environmental conditions and physico-chemical parameters, among which 570 arsenic is highly relevant. As the arsenic cycle and the bacterial contribution to it, has yet to 571 be completely understood, more environmental studies are needed, while metagenomic 572 approaches have been shedding light on the possible role of unknown or undescribed 573 microorganisms additional transcriptomic, metabolomic and cultivation approaches will be 574 essential to define these phenomena and its relevance in a global context. 575 576 577 CONCLUSION 578 The Altiplano array of ecological niches is a reservoir for microbial diversity, showing 579 great richness in adapted organisms capable of facing these challenging conditions. 580 Particularly, Salar de Huasco is a highly diverse ecosystem, where salinity and As 581 concentration contribute to shaping the community composition, mainly represented by 582 Proteobacteria and Bacteroidetes. Also, the interaction networks within these communities 583 showed three distinct groups of related taxa, but no “key stone” nodes were found. 584 Nonetheless, little niche overlap was determined. Altogether, these indicate that SH studied 585 niches harbor highly diverse communities, being H1 and H5 the most contrasting ones. 586 Moreover, the most abundant arsenic-related genes found in these communities indicate

28 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

587 that the As(V) reduction and subsequent As(III) expulsion would be the most common 588 strategy used to detoxify the cell of arsenic; furthermore, regarding to the expulsion pumps, 589 the most abundant were Acr3, followed by ArsB; however, in sites with high As 590 concentration ArsP begins to be enriched. In addition, 12 high-quality, non-redundant 591 MAGs were reconstructed from the metagenomes; those represented the dominant 592 diversity detected across the communities as well as the metabolic variability and the 593 presence of marker-genes related to the most recurrent As resistance mechanisms 594 (expulsion, reduction and methylation). Finally, in order to further elucidate the strategies 595 and relationships between the microbial taxa and among biotic and abiotic components 596 within the ecosystem, further multidisciplinary studies are required, as well as the use of 597 the ever evolving NGS approaches with the increasing database information to better 598 understand the evolutionary process of adaptation to the extreme conditions presented by 599 these unique ecosystems. 600 601 602 MATERIALS AND METHODS 603 Study Area and Sampling 604 Salar de Huasco National Park (SH) is an area located on the Chilean Altiplano that is 605 known for its spatial heterogeneity, as well as great biodiversity and physicochemical 606 characteristics. This ecosystem is mostly composed of streams, salt crusts, peatlands, and 607 shallow (permanent and non-permanent) lakes with salinity and arsenic gradients from 608 north to south (9). We sampled surface sediments (to a depth of 5 cm) in sterile tubes from 609 five different sites (H0 to H5 previously described in (6)) by duplicate during fieldwork done 610 in June 2018; those samples were kept and transported in a cooler until stored at -20ºC for 611 subsequent DNA extraction. Physicochemical parameters like temperature, salinity, and pH 612 were recorded (HI 98192 and HI 2211 - HANNA Instruments) in situ (Figure 1). 613 614 DNA extraction and high throughput shotgun sequencing

29 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

615 Total DNA was extracted from the sediment samples from each SH site using the 616 DNeasy PowerSoil Kit (Qiagen Inc., Hilden, Germany) following manufacturer's instructions. 617 DNA integrity, quality, and quantity were verified through 1% agarose gel electrophoresis,

618 OD260/280 ratio, and fluorescence using a Qubit® 3.0 Fluorometer along with the Qubit dsDNA 619 HS Assay Kit (Thermo Fisher Scientific, MA, USA). Next, Paired-end (150bp) libraries were 620 constructed for each sample in duplicates, at the Centro de Biotecnología Vegetal, 621 Universidad Andrés Bello (Santiago, Chile) using the TruSeq Nano DNA Kit (Illumina Inc., CA, 622 USA.) following the TruSeq Nano DNA Sample Preparation Guide 15041110 Rev. D. Libraries 623 were sent for sequencing at Macrogen Inc. (Seoul, Korea) on a HiSeq 4000 platform 624 (Illumina Inc., CA, USA). Then, raw data was evaluated using FastQC v0.11.8 (80) for quality 625 control, adapters were removed from the reads of all samples using Trimmomatic v0.30 626 (81) and the filtering and trimming (length ≤ 100bp, Ns = 0, and Q ≤ 30 thresholds) was 627 performed with PRINSEQ v0.20.4 (82). The whole raw data sets have been deposited at 628 DDBJ/ENA/GenBank under the Bioproject: PRJNA573913. 629 630 Taxonomic profiling analysis 631 Quality-controlled reads for each sample were profiled using the phyloFlash pipeline 632 (83) to obtain all reads that align with the bacterial SSU rRNA (small-subunit rRNA) SILVA 633 v138 database (84). Later, these sequences (FASTQ files) were processed using R v3.5.2 and 634 RStudio v1.1.463 (85, 86) following the DADA2 v1.16.0 R package pipeline (87) to infer 635 amplicon sequence variants (ASVs) present in each sample. Briefly, after dereplication, 636 denoising, and paired reads merge steps, the ASV table was built with 97% clustering, the 637 chimeras removed, and the taxonomic was assigned against the Silva v138 database (84) 638 using DADA2 Ribosomal Database Project’s (RDP) naive Bayesian classifier (88). Then, data 639 was normalized by variance stabilizing transformation using the R package DESeq2 v1.28.1 640 (89). Also, a multiple sequence alignment was created using the R package DECIPHER 641 v2.16.1 (90) to infer a phylogeny with FastTree v2.1.10 (91). Furthermore, a phyloseq-object 642 (containing the ASVs, taxonomy assignation, phylogenetic tree, and samples meta-data) 643 was created using the R package Phyloseq v1.32.0 (92); in order to calculate the alpha

30 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

644 diversity indexes, along with btools v0.0.1 R package. Also, beta diversity (PCoA - Bray Curtis 645 distance with environmental variables fit) was calculated using the R package ampvis2 646 v2.4.5 (93), and visualizations were generated with ggplot2 v2.2.1 (94) R package. 647 648 Co-occurrence networks 649 We used the same phyloseq object, which was agglomerated by best hit using the 650 microbiomeutilities v1.00.11 R package (95); filtered by tax abundance (0.5% in at least one 651 sample) using the Genefilter v 1.72.0 (96) and Phyloseq v1.32.0 (92) R packages. Then, the 652 co-occurrence network was estimated using the SPIEC-EASI v0.1.4 R package (SParse 653 InversE Covariance Estimation for Ecological Association Inference) (97), using 654 neighborhood selection model (lambda.min.ratio=1e-2, nlambda=20, and 50 replicates 655 parameters). Finally, the network was visualized using the ggnet2 function of GGally v1.5.0 656 R package (a ggplot2 extension) (98). 657 658 Functional profiling analysis 659 The patterns of functional potential as subsystems with different specificity levels 660 for each community were determined by the presence/absence and relative abundance of 661 the quality-controlled reads that aligned against the metabolic pathways and functions in 662 the SEED database (99) with SUPER-FOCUS v0.35 software (100), which uses DIAMOND 663 v2.0.6 (101) for fast and efficient alignment. Statistical analysis was carried out in STAMP 664 v2.3.1 (Software package for Analyzing Taxonomic or Metabolic Profiles) (102) using 665 Welch’s t-test to compare all samples. 666 667 Metagenome co-assembly and read mapping 668 As we need for subsequent analyses: i) a co-assembly (the reads from all samples 669 assembled together: .fasta file), ii) its annotations (.gff, .ffn and .faa files) and iii) an 670 alignment per samples (all the reads from each sample mapped against the co-assembly: 671 .bam files); we proceeded to assemble the quality-controlled reads from all samples using 672 MEGAHIT v1.1.3 (103), with the –presets meta-large option and a minimum contig length

31 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

673 of 1 kb. The co-assembly was then evaluated with MetaQUAST v5.0.2 (104) and annotated 674 with Prokka v1.11 (105) using the metagenome mode. Furthermore, we mapped the 675 quality-controlled reads from each sample against the co-assembly using Bowtie2 v2.3.4 676 (106) and stored the recruited reads (sorted and indexed) as BAM files using SAMtools v1.3 677 (107). 678 679 Metagenome profiling 680 We followed the anvi’o v7 pipeline (108). First, we created a contig database with 681 the co-assembed contigs, which uses Prodigal v2.6.3 (109), HMMER v3.3.1 (110) and NCBI 682 COGs (111) to identify genes calls and functionally annotate them (anvi-gen-contigs- 683 database, anvi-run-hmms, anvi-run-ncbi-cogs). Secondly, we profiled each sample’s BAM 684 file against the contigs database to estimate the detection and coverage statistics for each 685 contig (anvi-profile). Then, we combined the profiles in a single merged metagenomic 686 profile database, which uses all individual statistics to compute hierarchical clustering of 687 the contigs (anvi-merge). Finally, we visualized the merged profile on the anvi’o interactive 688 interface, which allows easy exploration and curation of the metagenomes (anvi- 689 interactive). On the other hand, we used MEBS (Multigenomic Entropy Based Score) v1.0

690 package (112) to evaluate and compare the S, N, O, CH4, and Fe biogeochemical cycles on 691 each metagenome using the predicted proteins (annotated .faa file from each individual 692 assembly). 693 694 Metagenome target gene search 695 To estimate the abundance of genes related to arsenic metabolism, the read counts 696 for each predicted gene of each sample were obtained using the corresponding BAM file 697 and the co-assembly annotated GFF file, with HTSeq-Counts v0.13.5 (113). Also, we 698 constructed a database from the GenBank – “Identical Protein Groups” with all available 699 prokaryotic proteins of arsenic metabolism (Acr3, AioA, AioB, AioR, AioS, AioX, AoxA, AoxB, 700 AoxC, AoxD, ArrA, ArrB, ArrC, ArsA, ArsB, ArsC, ArsD, ArsH, ArsJ, ArsK, ArsM, ArsN, ArsO, 701 ArsP, ArsR, ArsT, ArxA, ArxB, ArxR, ArxS and ArxX) and this was targeted with the co-

32 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

702 assembly predicted proteins (.faa file) using CRB-BLAST v0.6.6 (E-value ≥1E−05, Identity ≥70% 703 and Query Coverage ≥70%) (114). Finally, matching the hits with the gene counts 704 (normalized by the target gene length and the corresponding library size), we calculated the 705 relative abundance of the interest genes for the five metagenomes. Visualizations were 706 made with R packages ggplot2 v2.2.1 (94) and pheatmap v1.0.12 (115). Furthermore, all the 707 detected sequences that corresponded to arsenic efflux pumps were extracted from the 708 .ffn file and aligned using MAFFT v7 (116). As phylogeny inference was calculated with 709 FastTree v2.1.10 (91) and visualized with the anvi’o v7 interactive interface (108). 710 711 Metagenomic binning 712 The contigs in the metagenome profile were clustered through anvi’o v7 (108) using 713 CONCOCT v1.1.0 (117) binning program, which adds a bins collection the profile (anvi- 714 cluster-contigs). The resulting bins were evaluated for completion/redundancy, and manual 715 curation/refinement was carried out in the interactive interface (anvi-estimate-genome- 716 completeness and anvi-refine). Finally, the refined bins were displayed in the interactive 717 interface and summarized to obtain all the statistics and files for downstream analysis (anvi- 718 interactive and anvi-summarize). 719 720 MAGs evaluation, taxonomy, and functional estimation 721 We define MAGs (Metagenome Assembles Genomes) as bins with a completion 722 >80% and redundancy <10%. Then, we used CheckM v1.0.13 (118) for a more robust 723 evaluation. Later, to infer MAGs taxonomy, we used GTDB-Tk v0.3.2 (119) along with The 724 Genome Taxonomy Database (120). Moreover, we estimated the MAGs metabolic potential 725 by evaluating their gene content with anvi’o v7 (108); First, functions and metabolic 726 pathways were annotated to the MAGs using HMM hits from KOfam - KEGG Orthologs (KO) 727 database (121, 122) (anvi-run-kegg-kofams). Secondly, relying on these KO annotations, the 728 metabolic pathways were predicted considering those defined by KOs in the KEGG 729 MODULES resource (123), where a KO represents a gene function, and a module represent 730 a group of KOs that together carry out the reactions in a metabolic path (anvi-estimate-

33 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

731 metabolism). The MAGs module completition was visualized using pheatmap v1.0.12 R 732 package (115). 733 734 MAGs target gene search 735 All MAGs were queried against the same previously constructed database (with the 736 arsenic metabolism genes) using CRB-BLAST v0.6.6 (E-value ≥1E−05, Identity ≥70% and Query 737 Coverage ≥70%) (114). Furthermore, the resulting hits matrix was compared and 738 supplemented with the detected KOfams related to arsenic, and the final matrix was used 739 to generate a functional network using Gephi v0.9.2 (124) to connect MAGs and detected 740 genes. 741 742 743 DATA AVAILABILITY 744 The whole raw data sets have been deposited at DDBJ/ENA/GenBank under the Bioproject: 745 PRJNA573913. 746 747 748 AUTHOR CONTRIBUTIONS 749 JC-S, CP-E, EC-N, FR, and CPS conceived and designed the study. JC-S, JF, FM, FR, and CPS 750 performed the field work. JC-S and CP-E processed the samples, performed the 751 experimental procedures and carried out the bioinformatics analyzes. KM, SM and EC-N 752 contributed with critical bioinformatics advice. CPS, FR, and EC-N contributed with reagents, 753 materials, and analysis tools. JC-S and CP-E interpreted the results and wrote the first 754 manuscript draft. All authors read and approved the final manuscript. 755 756 757 ACKNOWLEDGEMENTS 758 We would like to thank the illustrator Florence Gutzwiller for the SH landscape painting 759 (https://spideryscrawl-illustration.webnode.com/). Also, we would like to thank

34 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

760 Universidad Andres Bello’s high-performance computing cluster, Dylan 761 (http://www.castrolab.org/), for providing data storage, support, and computing power for 762 bioinformatic analyses. Also, We would also like to thank the MerenLab group 763 (https://merenlab.org/) for their reachability in providing help, advice and solutions related 764 to the use of Anvi'o. In addition, we´d like to thank to Dra. Valerie de Anda 765 (https://valdeanda.github.io/) for the guidance and advice to implement the MEBS 766 software. 767 768 769 FUNDING 770 This research was sponsored by ANID (Agencia Nacional de Investigación y Desarrollo de 771 Chile) grants. CPS was funded by ANID-FONDECYT regular 1210633 and ECOS-ANID 170023. 772 EC-N was funded by ANID-FONDECYT Regular 1200834 and ANID-PIA-Anillo INACH 773 ACT192057. JC-S was founded by ANID 2021 Post-Doctoral FONDECYT 3210156. CP-E was 774 founded by Universidad Católica del Norte 2021 Post-Doctoral fellowship. The funders had 775 no role in study design, data collection and analysis, decision to publish, or preparation of 776 the manuscript. 777 778 779 CONFLICT OF INTEREST STATEMENT 780 The authors declare that the research was conducted in the absence of any commercial or 781 financial relationships that could be construed as a potential conflict of interest. 782 783 784 REFERENCES 785 1. Bahram M, Netherway T, Frioux C, Ferretti P, Coelho LP, Geisen S, Bork P, Hildebrand 786 F. 2020. Metagenomic assessment of the global diversity and distribution of bacteria 787 and fungi. Env Microbiol 23(1):316-326 788 2. Albarracín V, Kurth D, Ordoñez O, Belfiore C, Luccini E, Salum G, Piacentini R, Farías, 789 ME. 2015. High-Up: A remote reservoir of Microbial Extremophiles in Central 790 Andean Wetlands. Front Microbiol 6, 1404.

35 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

791 3. Dorador C, Vila I, Witzel KP, Imhoff JF. 2013. Bacterial and archaeal diversity in high 792 altitude wetlands of the Chilean Altiplano. Fundam Appl Limnol 182, 135–159. 793 4. Aceituno P. 1997. Aspectos generales del clima en el Altiplano Sudamericano. In R 794 Charrier, P Aceituno, M Castro, A Llanos, LA Raggi (Eds.), El Altiplano: ciencia y 795 conciencia de los Andes, Actas del 21 simposio internacional de estudios altiplánicos 796 (pp. 63–69). Santiago, Chile: Universidad de Chile. 797 5. Risacher F, Alonso H, Salazar C. 2003. The origin of brines and salts in Chilean salars: 798 A hydrochemical review. Earth Sci Rev 63, 249–293. 799 6. Dorador C, Vila I, Imhoff JF, Witzel KP. 2008. Cyanobacterial diversity in Salar de 800 Huasco, a high altitude saline wetland in northern Chile: an example of geographical 801 dispersion?. FEMS microbiol 64(3), 419-432. 802 7. Hernández KL, Yannicelli B, Olsen LM, Dorador C, Menschel EJ, Molina, V, 803 Remonsellez F, Hengst M, Jeffrey WH. 2016. Microbial activity response to solar 804 radiation across contrasting environmental conditions in Salar de Huasco, Northern 805 Chilean Altiplano. Front Microbiol 7, 1857. 806 8. Molina V, Hernández K, Dorador C, Eissler Y, Hengst M, Pérez V, Harrod C. 2016. 807 Bacterial active community cycling in response to solar radiation and their influence 808 on nutrient changes in a high- altitude wetland. Front Microbiol 7, 1–15. 809 9. Castro-Severyn J, Pardo-Esté C, Mendez KN, Morales N, Marquez SL, Molina F, 810 Remonsellez F, Castro-Nallar E, Saavedra CP. 2020. Genomic Variation and Arsenic 811 Tolerance Emerged as Niche Specific Adaptations by Different Exiguobacterium 812 Strains Isolated From the Extreme Salar de Huasco Environment in Chilean– 813 Altiplano. Front Microbiol 11, 1632. 814 10. Demergasso CS, Guillermo CD, Lorena EG, Mur JJP, Pedrós-Alió C. 2007. Microbial 815 precipitation of arsenic sulfides in Andean salt flats. Geomicrobiol J 24, 111–123. 816 11. Escalante G, Campos V, Valenzuela C, Yañez J, Zaror C, Mondaca M. 2009. Arsenic 817 resistant bacteria isolated from arsenic contaminated river in the Atacama Desert 818 (Chile). Bull Environ Contam Toxicol 83(5):657H661. 819 12. Escudero LV, Casamayor EO, Chong G, Pedrós-Alió C, Demergasso C. 2013. 820 Distribution of microbial arsenic reduction, oxidation and extrusion genes along a 821 wide range of environmental arsenic concentrations. PLoS One 8(10), e78890. 822 13. Finstad KM, Probst AJ, Thomas BC, Andersen GL, Demergasso C, Echeverría A, 823 Amundson R, Banfield JF. 2017. Microbial community structure and the persistence 824 of cyanobacterial populations in salt crusts of the hyperarid Atacama Desert from 825 genome-resolved metagenomics. Front Microbiol 8, 1435. 826 14. Wang L, Zhipeng Y, Jing C. 2020. Metagenomic insights into microbial arsenic 827 metabolism in shallow groundwater of Datong basin, China. Chemosphere. 245. 828 125603. 829 15. Berhe A, Barnes R, Six J, Marin-Spiotta E. 2018. Role of soil erosion in biogeochemical 830 cycling of essential elements: carbon, nitrogen, and phosphorus. Annu Rev Earth Pl 831 Sci 46, 521–548. 832 16. Zhang J, Shi Q, Fan S, Zhang Y, Zhang M, Zhang J. 2020. Distinction between Cr and 833 other heavy-metal-resistant bacteria involved in C/N cycling in contaminated soils 834 of copper producing sites. J Hazard Mater 402, 123454.

36 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

835 17. Li Y, Zhang M, Xu R, Lin H, Sun X, Xu F, Gao P, Kong T, Xiao E, Yang N, Sun W. 2021. 836 Arsenic and antimony co-contamination influences on soil microbial community 837 composition and functions: Relevance to arsenic resistance and carbon, nitrogen, 838 and sulfur cycling. Env Int 153, 106522. 839 18. Andres J, Bertin PN. 2016. The microbial genomics of arsenic. FEMS Microbiol Rev. 840 40:299–322. 841 19. Desoeuvre A, Casiot C, Héry M. 2016. Diversity and distribution of arsenic-related 842 genes along a pollution gradient in a river affected by acid mine drainage. Microb 843 Ecol 71(3), 672-685. 844 20. Rosen BP. 2002. Biochemistry of arsenic detoxification. FEBS Lett 529:86–92. 845 21. Sheik CS, Mitchell TW, Rizvi FZ, Rehman Y, Faisal M, Hasnain S, McInerney M, 846 Krumholz, LR. 2012. Exposure of soil microbial communities to chromium and 847 arsenic alters their diversity and structure. PloS one 7(6), e40059. 848 22. Kurth D, Amadio A, Ordoñez O, Albarracín V, Gärtner W, Farías ME. 2017. Arsenic 849 metabolism in high altitude modern stromatolites revealed by metagenomic 850 analysis. Sci Reports. 7: 1024. 851 23. Danczak RE, Johnston MD, Kenah C, Slattery M, Wilkins MJ. 2019. Capability for 852 arsenic mobilization in groundwater is distributed across broad phylogenetic 853 lineages. PloS one 14(9), e0221694. 854 24. Dunivin TK, Yeh SY, Shade A. 2019. A global survey of arsenic-related genes in soil 855 microbiomes. BMC biology 17(1), 1-17. 856 25. Aykanat T, Lindqvist M, Pritchard VL, Primmer CR. 2016. From population genomics 857 to conservation and management: A workflow for targeted analysis of markers 858 identified using genome-wide approaches in Atlantic salmon Salmo salar. J Fish 859 Biol 89(6), 2658-2679. 860 26. Ezeokoli OT, Bezuidenhout CC, Maboeta MS, Khasa DP, Adeleke RA. 2020. Structural 861 and functional differentiation of bacterial communities in post-coal mining 862 reclamation soils of South Africa: bioindicators of soil ecosystem restoration. Sci 863 Rep 10(1), 1-14. 864 27. Dorador C, Vila I, Remonsellez F, Imhoff JF, Witzel KP. 2010. Unique clusters of 865 Archaea in Salar de Huasco, an athalassohaline evaporitic basin of the Chilean 866 Altiplano. FEMS Microbiol 73(2), 291-302. 867 28. Eissler Y, Dorador C, Kieft B, Molina V, Hengst M. 2020. Virus and Potential Host 868 Microbes from Viral-Enriched Metagenomic Characterization in the High-Altitude 869 Wetland, Salar de Huasco, Chile. Microorganisms 8(7), 1077. 870 29. Remonsellez F, Castro-Severyn J, Pardo-Esté C, Aguilar P, Fortt J, Salinas C, Barahona 871 S, León J, Fuentes B, Areche C, Hernández K, Saavedra CP. 2018. Characterization 872 and salt response in recurrent halotolerant Exiguobacterium sp. SH31 isolated from 873 sediments of Salar de Huasco, Chilean Altiplano. Front Microbiol 9, 2228. 874 30. Vásquez-Dean J, Maza F, Morel I, Pulgar R, González M. 2020. Microbial 875 communities from arid environments on a global scale. A systematic review. Biol Res 876 53(1), 1-12. 877 31. Cabrol NA, Grin EA, Chong G, Minkley E, Hock AN, Yu Y, Bebout L, Fleming E, Häder 878 D, Demergasso C, Gibson J, Escudero L, Dorador C, Lim D, Woosley C, Morris R,

37 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

879 Tambley T, Gaete V, Galvez M, Smith E, Uskin-Peate I, Salazar C, Dawidowicz G, 880 Majerowicz J. 2009. The high-lakes project. J Geophys Res 114(G2). 881 32. Aguilar P, Acosta E, Dorador C, Sommaruga R. 2016. Large differences in bacterial 882 community composition among three nearby extreme waterbodies of the high 883 Andean plateau. Front Microbiol 7, 976. 884 33. Cortés-Albayay C, Silber J, Imhoff JF, Asenjo JA, Andrews B, Nouioui I, Dorador C. 885 2019. The polyextreme ecosystem, Salar de Huasco at the Chilean Altiplano of the 886 Atacama Desert houses diverse Streptomyces spp. with promising pharmaceutical 887 potentials. Diversity 11(5), 69. 888 34. Bourg ACM. 1989. Adsorption of Trace Inorganic and Organic Contaminants by Solid 889 Particulate Matter, in Aquatic Ecotoxicology: Fundamental Concepts and 890 Methodologies. Boudou A and Ribeyre F. eds. CRC Press, Inc. Boca Raton, Florida; 891 pp.107-148. 892 35. Herrera V, De Gregori I, Pinochet H. 2009. Assessment of trace elements and 893 mobility of arsenic and manganese in lagoon sediments of the salars of Huasco and 894 Coposa, Chilean Altiplano. J Chil Chem Soc 54(3), 282-288. 895 36. Oren A. 2013. Life at high salt concentrations, intracellular KCl concentrations, and 896 acidic proteomes. Front Microbiol 4, 315. 897 37. Farías ME, Rascovan N, Toneatti DM, Albarracín VH, Flores MR, Poiré DG, Collavino 898 M, Aguilar M, Vazquez M, Polerecky L. 2013. The discovery of stromatolites 899 developing at 3570 m above sea level in a high-altitude volcanic lake Socompa, 900 Argentinean Andes. PloS one 8(1), e53497. 901 38. Farías ME, Contreras M, Rasuk MC, Kurth D, Flores MR, Poire DG, Novoa F, Visscher 902 PT. 2014. Characterization of bacterial diversity associated with microbial mats, 903 gypsum evaporites and carbonate microbialites in thalassic wetlands: Tebenquiche 904 and La Brava, Salar de Atacama, Chile. Extremophiles 18(2), 311-329. 905 39. Saona LA, Soria M, Durán-Toro V, Wörmer L, Milucka J, Castro-Nallar E, Meneses C, 906 Contreras M, Farías ME. 2021. Phosphate-Arsenic Interactions in Halophilic 907 Microorganisms of the Microbial Mat from Laguna Tebenquiche: from the 908 Microenvironment to the Genomes. Microb Ecol 1-13. 909 40. Ko MS, Park HS, Kim KW, Lee JU. 2013. The role of Acidithiobacillus ferrooxidans and 910 Acidithiobacillus thiooxidans in arsenic bioleaching from soil. Environ Geochem 911 HLTH 35(6), 727-733. 912 41. Demergasso C, Casamayor EO, Chong G, Galleguillos P, Escudero L, Pedros-Alió C. 913 2004 Distribution of prokaryotic genetic diversity in athalassohaline lakes of the 914 Atacama Desert, Northern Chile. FEMS Microbiol Ecol 48:57–69 915 42. Demergasso C, Escudero L, Casamayor EO, Chong G, Balague V, Pedros-Alió C. 2008 916 Novelty and spatio-temporal heterogeneity in the bacterial diversity of hypersaline 917 Lake Tebenquiche (Salar de Atacama). Extremophiles 12:491–504 918 43. Demergasso C, Dorador C, Meneses D, Blamey J, Cabrol N, Escudero L, Chong G. 919 2010 Prokaryotic diversity pattern in high-altitude ecosystems of the Chilean 920 Altiplano. J Geophys Res 115:G00D09 921 44. Dorador C. 2007. Microbial communities in high altitude altiplanic wetlands in 922 northern Chile: phytogeny, diversity and function. Doctoral dissertation, Christian-

38 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

923 Albrechts-Universität, Kiel, Germany, p 166 924 45. Jiang H, Dong H, Yu B, Liu X, Li Y, Ji S, Zhang CL .2007. Microbial response to salinity 925 change in Lake Chaka, a hypersaline lake on Tibetan plateau. Environ Microbiol 926 9:2603–2621 927 46. Jung P, Baumann K, Lehnert LW, Samolov E, Achilles S, Schermer M, Wraase L, 928 Eckhardt KU, Bader M, Leinweber P, Karsten U, Bendix J, Büdel B. 2020. Desert 929 breath—How fog promotes a novel type of soil biocenosis, forming the coastal 930 Atacama Desert’s living skin. Geobiology 18(1), 113-124. 931 47. Samolov E, Baumann K, Büdel B, Jung P, Leinweber P, Mikhailyuk T, Karsten U, Glaser 932 K. 2020. Biodiversity of algae and cyanobacteria in biological soil crusts collected 933 along a climatic gradient in Chile using an integrative approach. Microorganisms 934 8(7), 1047. 935 48. Castro-Severyn J, Remonsellez F, Valenzuela SL, Salinas C, Fortt J, Aguilar P, Pardo- 936 Esté C, Dorador C, Quatrini R, Molina F, Aguayo D, Castro-Nallar E, Saavedra CP. 937 2017. Comparative genomics analysis of a new Exiguobacterium strain from Salar de 938 Huasco reveals a repertoire of stress-related genes and arsenic resistance. Front 939 Microbiol 8, 456. 940 49. Castro-Severyn J, Pardo-Esté C, Sulbaran Y, Cabezas C, Gariazzo V, Briones A, 941 Morales N, Séveno M, Decourcelle, Salvetat N, Remonsellez F, Castro-Nallar E, 942 Saavedra CP. 2019. Arsenic response of three altiplanic Exiguobacterium strains with 943 different tolerance levels against the metalloid species: a proteomics study. Front 944 Microbiol 10, 2161. 945 50. Idris H, Goodfellow M, Sanderson R, Asenjo JA, Bull AT. 2017. Actinobacterial rare 946 biospheres and dark matter revealed in habitats of the Chilean Atacama Desert. Sci 947 Rep 7(1), 1-11. 948 51. Agler MT, Ruhe J, Kroll S, Morhenn C, Kim ST, Weigel D, Kemen EM. 2016. Microbial 949 hub taxa link host and abiotic factors to plant microbiome variation. PLoS 950 biology 14(1), e1002352. 951 52. Kitahara R, Oyama K, Kawamura T, Mitsuhashi K, Kitazawa S, Yasunaga K, Sagara N, 952 Fujimoto M, Terauchi, K. 2019. Pressure accelerates the circadian clock of 953 cyanobacteria. Sci Rep 9(1), 1-8. 954 53. Crits-Christoph A, Gelsinger DR, Wierzchos J, Ravel J, Davila A, Casero MC, 955 DiRuggiero J. 2016. Functional interactions of archaea, bacteria and viruses in a 956 hypersaline endolithic community. Env Microbiol 18(6), 2064-2077. 957 54. Luton PE, Wayne JM, Sharp RJ, Riley PW. 2002. The mcrA gene as an alternative to 958 16S rRNA in the phylogenetic analysis of methanogen populations in landfill. 959 Microbiology 148(11), 3521-3530. 960 55. Sela-Adler M, Ronen Z, Herut B, Antler G, Vigderovich H, Eckert W, Sivan O. 2017. 961 Co-existence of methanogenesis and sulfate reduction with common substrates in 962 sulfate-rich estuarine sediments. Front Microbiol 8, 766. 963 56. Xiao KQ, Li LG, Ma LP, Zhang SY, Bao P, Zhang T, Zhu YG. 2016. Metagenomic analysis 964 revealed highly diverse microbial arsenic metabolism genes in paddy soils with low- 965 arsenic contents. Env Poll 211, 1-8.

39 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

966 57. Zhai W, Qin T, Li L, Guo T, Yin X, Khan MI, Hashmi MZ, Liu X, Tang X, Xu, J. 2020. 967 Abundance and diversity of microbial arsenic biotransformation genes in the sludge 968 of full-scale anaerobic digesters from a municipal wastewater treatment plant. Env 969 Int 138, 105535. 970 58. Slyemi D, Bonnefoy V. 2012. How prokaryotes deal with arsenic. Env Microbiol Rep 971 4(6), 571-586. 972 59. Jia Y, Huang H, Chen Z, Zhu YG. 2014. Arsenic uptake by rice is influenced by microbe- 973 mediated arsenic redox changes in the rhizosphere. Env Sci Tech 48(2), 1001-1007. 974 60. Zhang SY, Zhao FJ, Sun GX, Su JQ, Yang XR, Li H, Zhu YG. 2015. Diversity and 975 abundance of arsenic biotransformation genes in paddy soils from southern China. 976 Env Sci Tech 49(7), 4138-4146. 977 61. Osman D, Cavet JS. 2010. Bacterial metal-sensing proteins exemplified by ArsR– 978 SmtB family repressors. Nat Prod Rep 27(5), 668-680. 979 62. Shen Z, Luangtongkum T, Qiang Z, Jeon B, Wang L, Zhang Q. 2014. Identification of 980 a novel membrane transporter mediating resistance to organic arsenic in 981 Campylobacter jejuni. Antimicrob Agents Chemother 58(4), 2021-2029. 982 63. Fekih BI, Zhang C, Li YP, Zhao Y, Alwathnani HA, Saquib Q, Rensing C, Cervantes C. 983 2018. Distribution of Arsenic Resistance Genes in Prokaryotes. Front Microbiol 9, 984 2473. 985 64. Mateos LM, Villadangos AF, Alfonso G, Mourenza A, Marcos-Pascual L, Letek M, 986 Pedre B, Messens J, Gil JA. 2017. The arsenic detoxification system in 987 corynebacteria: basis and application for bioremediation and redox control. Adv 988 Appl Microbiol 99, 103-137. 989 65. Shi K, Li C, Rensing C, Dai X, Fan X, Wang G. 2018. Efflux transporter ArsK is 990 responsible for bacterial resistance to arsenite, antimonite, trivalent roxarsone, and 991 methylarsenite. App Env Microbiol 84(24). 992 66. Xue S, Jiang X, Wu C, Hartley W, Qian Z, Luo X, Li W. 2020. Microbial driven iron 993 reduction affects arsenic transformation and transportation in soil-rice system. Env 994 Poll 260, 114010. 995 67. Oremland RS, Saltikov CW, Wolfe-Simon F, Stolz JF. 2009. Arsenic in the evolution of 996 earth and extraterrestrial ecosystems. J Geomicrobiol 26(7), 522-536. 997 68. Mazumder P, Sharma SK, Taki K, Kalamdhad AS, Kumar M. 2020. Microbes involved 998 in arsenic mobilization and respiration: a review on isolation, identification, isolates 999 and implications. Environ Geochem HLTH 1-27. 1000 69. Lobb B, Kurtz DA, Moreno-Hagelsieb G, Doxey AC. 2015. Remote homology and the 1001 functions of metagenomic dark matter. Front Genet 6, 234. 1002 70. da Costa WLO, Araújo CLDA, Días LM, Pereira LCDS, Alves JTC, Araujo FA, Folador EL, 1003 Henriques I, Silva A, Folador ARC. 2018. Functional annotation of hypothetical 1004 proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved 1005 in adaptation to extreme environments, including high arsenic resistance. PloS one 1006 13(6), e0198965. 1007 71. Antczak M, Michaelis M, Wass MN. 2019. Environmental conditions shape the 1008 nature of a minimal bacterial genome. Nat comm 10(1), 1-13.

40 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1009 72. Makarova KS, Wolf YI, Koonin EV. 2019. Towards functional characterization of 1010 archaeal genomic dark matter. Biochem Soc Trans 47(1), 389-398. 1011 73. Cai L, Liu G, Rensing C, Wang G. 2009. Genes involved in arsenic transformation and 1012 resistance associated with different levels of arsenic-contaminated soils. BMC 1013 Microbiol 9(1), 1-11. 1014 74. Chen SC, Sun GX, Yan Y, Konstantinidis KT, Zhang SY, Deng Y, Li XM, Cui HL, Musat F, 1015 Popp D, Rosen B, Zhu YG. 2020. The Great Oxidation Event expanded the genetic 1016 repertoire of arsenic metabolism and cycling. PNAS 117(19), 10414-10421. 1017 75. Delmont TO, Quince C, Shaiber A, Esen ÖC, Lee ST, Rappé MS, McLellan S, Lücker S, 1018 Eren AM. 2018. Nitrogen-fixing populations of Planctomycetes and Proteobacteria 1019 are abundant in surface ocean metagenomes. Nat Microbiol 3(7), 804-813. 1020 76. Leema JM, Kirubagaran R, Vinithkumar NV, Dheenan PS, Karthikayulu S. 2010. High 1021 value pigment production from Arthrospira (Spirulina) platensis cultured in 1022 seawater. Bioresour Technol 101(23), 9221-9227. 1023 77. Sorokin DY, Muntyan MS, Panteleeva AN, Muyzer G. 2012. Thioalkalivibrio 1024 sulfidiphilus sp. nov., a haloalkaliphilic, sulfur-oxidizing gammaproteobacterium 1025 from alkaline habitats. Int J Syst Evol Microbiol 62(8), 1884-1889. 1026 78. Purcell AM, Mikucki JA, Achberger AM, Alekhina IA, Barbante C, Christner BC, Ghosh 1027 D, Michaud A, Mitchell A, Priscu J, Scherer R, Skidmore M, Vick-Majors TJ. 2014. 1028 Microbial sulfur transformations in sediments from Subglacial Lake Whillans. Front 1029 Microbiol 5, 594. 1030 79. Zargar K, Hoeft S, Oremland R, Saltikov CW. 2010. Identification of a novel arsenite 1031 oxidase gene, arxA, in the haloalkaliphilic, arsenite-oxidizing bacterium 1032 Alkalilimnicola ehrlichii strain MLHE-1. J Bacteriol 192(14), 3755-3762. 1033 80. Andrews S. 2010. FastQC a quality-control tool for high-throughput sequence data 1034 http://www. Bioinformaticsbabraham. ac. uk/projects/fastqc. 1035 81. Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for Illumina 1036 sequence data. Bioinformatics 30(15), 2114–2120. 1037 82. Schmieder R, Edwards R. 2011. Quality control and preprocessing of metagenomic 1038 datasets. Bioinformatics 27(6), 863-864. 1039 83. Gruber-Vodicka HR, Seah BK, Pruesse E. 2020. phyloFlash: Rapid Small-Subunit rRNA 1040 Profiling and Targeted Assembly from Metagenomes. Msystems, 5(5). 1041 84. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 1042 2012. The SILVA ribosomal RNA gene database project: improved data processing 1043 and web-based tools. Nucleic Acids Res 41(D1), D590-D596. 1044 85. R Core Team. 2018. R: A language and environment for statistical computing. R 1045 Foundation for Statistical Computing, Vienna, Austria. URL https://www.R- 1046 project.org/. 1047 86. RStudio Team. 2016. RStudio: Integrated development for R. Boston, MA: RStudio 1048 Inc. Retrieved from http://www.rstudio.com/ 1049 87. Callahan B, McMurdie P, Rosen M, Han A, Johnson A, Holmes S. 2016. DADA2: High- 1050 resolution sample inference from Illumina amplicon data. Nat Methods 13, 581– 1051 583.

41 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1052 88. Wang Q, Garrity GM, Tiedje JM, Cole JR. 2007. Naive Bayesian classifier for rapid 1053 assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ 1054 Microbiol 73(16), 5261-5267. 1055 89. Love M, Huber W, Anders S. 2014. Moderated estimation of fold change and 1056 dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. 1057 90. Wright ES. 2016. Using DECIPHER v2. 0 to analyze big biological sequence data in R. 1058 R Journal 8(1). 1059 91. Price MN, Dehal PS, Arkin AP. 2009. FastTree: computing large minimum evolution 1060 trees with profiles instead of a distance matrix. Mol Biol Evol 26(7), 1641-1650. 1061 92. McMurdie PJ, Holmes S. 2013. phyloseq: an R package for reproducible interactive 1062 analysis and graphics of microbiome census data. PloS one 8(4), e61217. 1063 93. Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. 2018. ampvis2: an R package to 1064 analyse and visualise 16S rRNA amplicon data. BioRxiv 299537. 1065 94. Wickham H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New 1066 York. 1067 95. Lahti L, Shetty S, Blake T, Salojarvi J. 2017. Tools for microbiome analysis in R. 1068 Version, 1, 10013. 1069 96. Gentleman R, Carey V, Huber W, Hahne F. 2011. Genefilter: Methods for filtering 1070 genes from microarray experiments. R package version, 1(0). 1071 97. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. 2015. Sparse 1072 and compositionally robust inference of microbial ecological networks. PLoS 1073 Comput Biol 11(5), e1004226. 1074 98. Schloerke B, Crowley J, Cook D, Briatte F, Marbach M, Thoen E, Elberg A, Toomet O, 1075 Crowley J, Hofmann H, Wickman H. 2018. GGally: Extension to ‘ggplot2’. R package 1076 version 1.4.0. https://CRAN.R-project.org/package=GGally. 1077 99. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, Edwards R, Gerdes S, 1078 Parrello B, Shukla M, Vonstein V, Wattam A, Xia F, Stevens R. 2014. The SEED and 1079 the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). 1080 Nucleic Acids Res 42(D1), D206-D214. 1081 100. Silva GGZ, Green KT, Dutilh BE, Edwards RA. 2016. SUPER-FOCUS: a tool for agile 1082 functional analysis of shotgun metagenomic data. Bioinformatics 32(3), 354-361. 1083 101. Buchfink B, Xie C, Huson DH. 2015. Fast and sensitive protein alignment using 1084 DIAMOND. Nat Methods 12(1), 59-60. 1085 102. Parks DH, Tyson GW, Hugenholtz P, Beiko RG. 2014. STAMP: statistical analysis of 1086 taxonomic and functional profiles. Bioinformatics 30(21), 3123-3124. 1087 103. Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, Yamashita H, Lam TW. 2016. 1088 MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced 1089 methodologies and community practices. Methods 102, 3-11. 1090 104. Mikheenko A, Saveliev V, Gurevich A. 2016. MetaQUAST: evaluation of metagenome 1091 assemblies. Bioinformatics 32(7), 1088-1090. 1092 105. Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 1093 30(14), 2068-2069. 1094 106. Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat 1095 Methods 9(4), 357.

42 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1096 107. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, 1097 Durbin R. 2009. The sequence alignment/map format and SAMtools. Bioinformatics 1098 25(16), 2078-2079. 1099 108. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. 2015. 1100 Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, 1101 e1319. 1102 109. Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: 1103 prokaryotic gene recognition and translation initiation site identification. BMC 1104 Bioinformatics 11(1), 119. 1105 110. Eddy SR. 2011. Accelerated profile HMM searches. PLoS Comput Biol 7(10), 1106 e1002195. 1107 111. Tatusov RL, Galperin MY, Natale DA, Koonin EV. 2000. The COG database: a tool for 1108 genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28(1), 1109 33-36. 1110 112. De Anda V, Zapata-Peñasco I, Poot-Hernandez AC, Eguiarte LE, Contreras-Moreira B, 1111 Souza V. 2017. MEBS, a software platform to evaluate large (meta) genomic 1112 collections according to their metabolic machinery: unraveling the sulfur cycle. 1113 GigaScience 6(11), gix096. 1114 113. Anders S, Pyl PT, Huber W. 2015. HTSeq—a Python framework to work with high- 1115 throughput sequencing data. Bioinformatics 31(2), 166-169. 1116 114. Aubry S, Kelly S, Kümpers BM, Smith-Unna RD, Hibberd JM. 2014. Deep evolutionary 1117 comparison of gene expression identifies parallel recruitment of trans-factors in two 1118 independent origins of C4 photosynthesis. PLoS Genet 10(6), e1004365. 1119 115. Kolde R, Kolde MR. 2015. Package ‘pheatmap’. R Package 1(7), 790. 1120 116. Katoh K, Misawa K, Kuma KI, Miyata T. 2002. MAFFT: a novel method for rapid 1121 multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 1122 30(14), 3059-3066. 1123 117. Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman N, 1124 Andersson A, Quince C. 2014. Binning metagenomic contigs by coverage and 1125 composition. Nat Methods 11(11), 1144-1146. 1126 118. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: 1127 assessing the quality of microbial genomes recovered from isolates, single cells, and 1128 metagenomes. Genome Res 25(7), 1043-1055. 1129 119. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2020. GTDB-Tk: a toolkit to classify 1130 genomes with the Genome Taxonomy Database. Bioinformatics 36(6), btz848. 1131 120. Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. 2020. A 1132 complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotech 1-8. 1133 121. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. 2016. KEGG as a 1134 reference resource for gene and protein annotation. Nucleic Acids Res 44(D1), D457- 1135 D462. 1136 122. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, Ogata H. 2020. 1137 KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score 1138 threshold. Bioinformatics 36(7), 2251-2252.

43 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1139 123. Muto A, Kotera M, Tokimatsu T, Nakagawa Z, Goto S, Kanehisa M. 2013. Modular 1140 architecture of metabolic pathways revealed by conserved sequences of reactions. 1141 J Chem Inf Model 53(3), 613-622. 1142 124. Bastian M, Heymann S, Jacomy M. 2009. Gephi: an open-source software for 1143 exploring and manipulating networks. ICWSM 2, 361–362. 1144 125. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, 1145 Schulz F, Jarett J, Rivers A, Eloe-Fadrosh EA, Tringe SG, Ivanova NN, Copeland A, Clum 1146 A, Becraft ED, Malmstrom RR, Birre B, Podar M, Bork P, Weinstock GM, Garrity G, 1147 Dodsworth JA, Yooseph S, Sutton G, Glöckner F, Gilbert J, Nelson W, Hallam S, 1148 Jungbluth SP, Ettema TJ, Tighe S, Konstantinidis KT, Liu WT, Baker BJ, Rattei T, Eisen 1149 JA, Hedlund B, McMahon KD, Fierer N, Knight R, Finn R, Cochrane G, Karsch-Mizrachi 1150 I, Tyson GW, Rinke C, Lapidus A, Meyer F, Yilmaz P, Parks D, Eren AM, Schriml L, 1151 Hugenholtz P, Woyke T. 2017. Minimum information about a single amplified 1152 genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and 1153 archaea. Nat Biotechnol 35(8), 725-731. 1154 1155 1156 SUPPLEMENTARY MATERIAL 1157 Supplementary Table S1. Relative abundance of all detected phyla in the five SH 1158 communities. 1159 Supplementary Table S2. Statistical values of the SH co-assembly, representing the five 1160 metagenomes. 1161 Supplementary Table S3. Statistical evaluation of enriched functional categories (SEED 1162 subsystem 1) in each of the five communities regarding the rest according to Welch’s t-test. 1163 Supplementary Table S4. Indexes of relative abundance and detection of each recovered 1164 MAG across the five SH metagenomes. 1165 Supplementary Table S5. Completion index of all detected KEGG modules each analyzed 1166 MAG. 1167 Supplementary Table S6. Presence and copy number of arsenic resistance related genes in 1168 each analyzed MAG. 1169

44 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

Most abundant taxa Proteobacteria; G: Roseovarius 0 0 3.2 10.4 7.8 Proteobacteria; G: Desulfotignum 0.2 0 4.3 11.3 5.3 Bacteroidetes; G: Rhodohalobacter 0 0 4.3 2.3 10.4 Deinococcus−Thermus; G: Truepera 2 0.1 5 5.1 2.8 Proteobacteria; G: Thiobacillus 0.1 12.3 1.3 0 0.1 Proteobacteria; G: Marinobacter 0 0 4.4 0.7 3 Chloroflexi 0 0 2.8 1.3 2.2 Bacteroidetes; G: Psychroflexus 0 0 0.9 0.7 2.8 Proteobacteria; G: Halomonas 0.2 0 1 0.5 2.1 Bacteroidetes; G: Brumimicrobium 0 0 0.1 2 0.8 Verrucomicrobia; G: Luteolibacter 0.8 0.1 0.6 0.5 0.2 Euryarchaeota; G: Halorubrum 0 0 0.6 1.1 0.4 % Read Abundance Proteobacteria; G: Desulfomicrobium 2.1 0 0 0 0.1 12 Cyanobacteria; O: Chloroplast-ASV6012 1.1 1 0 0 0 9 Proteobacteria; G: Desulfobacter 0.6 0 0.2 0.6 0.7 6 Cyanobacteria; O: Chloroplast-ASV339 1.8 0 0 0 0 Proteobacteria; G: Methylibium 1.8 0 0 0 0 3 Cyanobacteria; G: Arthrospira 1.8 0 0 0 0 Proteobacteria; G: Defluviicoccus 0 0.6 1 0 0.1 0 Proteobacteria; G: Thiocapsa 1 0 0 0.6 0 Gemmatimonadetes; G: Gemmatimonas 0.6 1 0 0 0 Proteobacteria; G: Shimia 0 0 0.6 0.6 0.4 Proteobacteria; G: Hydrogenophaga 1.2 0.4 0 0 0 Proteobacteria; C: Gammaproteobacteria 1.6 0 0 0 0 Actinobacteria; G: Ilumatobacter 0.2 0.7 0.3 0.1 0.2 Bacteroidetes; F: Bacteroidetes 0 1.5 0 0 0 Proteobacteria; G: Thioalkalivibrio 0 0 0.4 0.8 0.3 Cyanobacteria; O: Chloroplast-ASV6009 1.4 0 0 0 0 Proteobacteria; F: Burkholderiaceae 0 1.3 0 0 0 Bacteroidetes; G: Flavobacterium 0.9 0 0.2 0 0.1

1170 H0 H1 H3 H4 H5 1171 Supplementary Figure S1. Composition and structure of SH bacterial communities. 1172 Heatmap showing the 30 most abundant ASVs (bacterial lineages). Taxonomic is showed at 1173 the phylum rank plus the best hit available (C: class; O: order; F: family; G: genus). 1174

45 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

Observed Chao1 Shannon Simpson Phylogenetic 2300 775

2250 7.2 0.9989 2100 750

0.9988 2000 1900 7.1 725 ersity Measures v 0.9987 1700 1750 700 7.0 Alpha Di 0.9986 1500 1500 675 H0 H1 H3 H4 H5 H0 H1 H3 H4 H5 H0 H1 H3 H4 H5 H0 H1 H3 H4 H5 H0 H1 H3 H4 H5

1175 1176 Supplementary Figure S2. Alpha diversity indices for the five SH communities. 1177

46 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

Completion

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

9.41* 9.41* 9.40* 8.84* 9.40* Sulfur

e 33.23* 32.79* 37.08* 75.07* 67.30*

l Carbon

c 9.27 9.27 9.27 9.39 9.27

y Iron

C 18.80* 18.32* 20.36* 20.36* 19.18* Nitrogen aprAB (Marker gene) Apt/Sat (Marker gene) DsrABC (Marker gene) Sulfur oxidation (Sox system) Sulfur oxidation (Sor system) Sulfur oxidation (FccB) Sulfur oxidation (DoxAD) Sulfur oxidation (DsrEFH) DsrKMJOP (Marker gene) Sulfur reduction (QmoABC) Sulfur oxidation (Puf reaction center) Sulfur assimilation (CysACDJNPQU)

u Tetrathionate reduction (asrABC)

f l Tetrathionate reduction (ttrABC)

S Thiosulfate disproportionation (phsABC) Thiosulfate disproportionation (Rhodanase) S° reduction (hydACD) S° reduction (sreABC) DMS degradation (DdhABC) DMS degradation (DsoABCDEF) DMS degradation (DmoAB) Sulfoacetaldehyde degradation (isfD) Sulfoacetaldehyde degradation (Xsc) Sulfoacetaldehyde degradation (SafD) Methanesulfonate degradation Sulfolactate degradation Taurine degradation

)

4 coB/coM regeneration Methane oxidation Methanogenesis Methanogenesis (methanol) Methylamine degradation

Carbon (CH mcrABC (Marker gene)

n Fe(II) oxidation

r Fe reduction absorption

I Ammonia assimilation I Ammonia assimilation II L glutamine biosynthesis I Superpathway Ammonia assimilation Ammonia oxidation I (aerobic) Ammonia oxidation II (anaerobic) Ammonia oxidation IV (Autotrophic Ammonia oxidizers) Nitrifier denitrification Nitrate reduction I (denitrification) Nitrate reduction III (denitrification) Nitrate reduction IV (dissimilatory) Nitrate reduction V (assimilatory)

e Nitrate reduction VI (assimilatory)

g Nitrate reduction VIII (dissimilatory)

t Nitrate reduction VIIIb (dissimilatory)

N Nitrate reduction IX (dissimilatory) Nitrate reduction X (dissimilatoryperiplasmic) Nitrogen fixation I (ferredoxin) Nitrogen fixation II (flavodoxin) Urea degradation II Caffeine degradation V (bacteria via trimethylurate) 4 aminobutanoate degradation I 4 aminobutanoate degradation II 4 aminobutanoate degradation V Allantoin degradation IV (anaerobic) Ammonia monoxygenase AmoABC (Marker gene) nirBD GABA biosynthesis prokaryotes putrescine 1178 H5 H4 H3 H1 H0 1179 Supplementary Figure S3. MEBS analysis heatmap displaying the completeness of N, Fe, S 1180 and CH4 pathways: as a whole (first top section) and particular reactions (bottom four 1181 sections). The color gradient shows the percentage of completition for each pathway (from 1182 lowest to highest) and the values at the top section represent the corresponding MEBS 1183 score (* FDR ≤ 0.01). 1184

47 H0 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. VenezuelaIt is made H1 available under aCC-BY-NC 4.0 International license. Colombia

H3 Ecuador

H4 Perú Brasil Salar de Huasco

Bolivia

SH Paraguay H5

Chile Argentina Uruguay

3 Km

H5 H4 H3 H1 H0 [As] 321 mg/kg [As] 155 mg/kg [As] 49.2 mg/kg [As] 16.3 mg/kg [As] 9 mg/kg Salinity 84.5% Salinity 77.2% Salinity 2.2% Salinity 8.1% Salinity 11.9% pH 9.2 pH 8.4 pH 8.5 pH 9.4 pH 8.8 Temp 24.3ºC Temp 18.8ºC Temp 19.9ºC Temp 14ºC Temp 14.6ºC Altitud 3,781 masl Altitud 3,787 masl Altitud 3,783 masl Altitud 3,785 masl Altitud 3,792 masl A 100 B Principal Coordinates Analysis 0.75 Altitude Phylum Chloroflexi Omnitrophicaeota 0.50 75 Cyanobacteria Spirochaetes H0 Gemmatimonadetes H0 Epsilonbacteraeota Sample Halanaerobiaeota bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright0.25 holder for this preprint (which H0 was not certified by peer review) is the author/funder, who has grantedBacteroidetes bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NCFirmicutes 4.0 International license. H1 undance (%) undance 50 Nanoarchaeaeota H3 Deinococcus−Thermus H4 H5 Patescibacteria H3 H5 T ºC H5 PCo2 [23.4%] 0.00 Salinity Actinobacteria H3 H4 Relative Ab Acidobacteria H4 [As] Tenericutes Verrucomicrobia 25 Kiritimatiellaeota −0.25 Planctomycetes Proteobacteria H1 Euryarchaeota H1 −0.50 pH 0 −0.5 0.0 0.5 H0 H1 H3 H4 H5 PCo1 [54.9%] Distance measure: Bray-Curtis

C G: Seohaeicola G: Anoxynatronum Phylum Interaction degree Acidobacteria 1 2 Actinobacteria G: Woeseia P: Gemmatimonadetes 3 Bacteroidetes F: Ruminococcaceae 4 Chloroflexi F: Rhodothermaceae 5

G: Prolixibacteraceae Kiritimatiellaeota

F: Geminicoccaceae Nanoarchaeaeota G: Geobacter G: Desulfatirhabdium G: Anaerolinea Nitrospirae

F: Hydrogenophilaceae Omnitrophicaeota O: Aminicenantales F: Rhodothermaceae F: Anaerolineaceae Patescibacteria C: Thermodesulfovibrionia

F: Desulfobacteraceae Proteobacteria G: Rhodohalobacter Spirochaetes Tenericutes Verrucomicrobia G: Brumimicrobium Unclassified A H5 H3 H4 H0 H1

GC-content Length bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder forClustering this preprint (which by read was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. recruitment profile

96,174,371 Total 148,394 mapped Contigs reads 0

Arsenic Metabolism Genes arsR 6142.15 4214.04 12356.33 18499.32 13750.63

1327.42 1188.22 2117.04 2345.06 2057.75 Normalized reads arsC 20000

1099.45 698.09 2375.33 2003.96 2183.94 acr3 15000 arsM 427.13 495.38 1491.54 2215.36 1827.64 10000 arsB 367.57 200.46 583.55 542.77 487.89 5000 1000 arsD 259.69 57.08 534.59 682.07 600.08 500 100 0 arsJ 202.04 75.23 531.96 717.95 480.64 arsA 601.44 93.67 337.78 438.65 338.12 Mechanism aoxB 113.32 0.89 519.98 613.91 485.76 Expulsion arsP 2.14 0.60 178.54 564.49 341.63 Reduction arsH 265.30 92.76 217.13 253.17 237.18 Respiration Oxidation aioB 46.54 50.63 153.38 171.91 134.68 Methylation arxB 65.25 2.50 205.48 105.97 147.89 aoxA 0.00 0.00 156.08 200.40 165.10 aioA 110.20 86.16 106.81 114.45 84.01 arxA 177.41 2.07 158.71 35.78 92.44 arxR 240.74 0.00 0.00 0.00 0.00 arrA 8.58 60.09 0.00 0.00 0.00 H0 H1 H3 H4 H5 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755; this version posted April 16, 2021. The copyright holder for this preprint (which was notA certifiedArsenic by peer Metabolism review) is the author/funder, who has granted bioRxivB Arsenic a license Efflux to display Pumps the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 100 100

300

75 75

200 [Arsenic mg/Kg]

50 50 Relative Abundance (%) Relative Abundance Relative Abundance (%) Relative Abundance

100 25 25

0 0 0

H0 H1 H3 H4 H5 Mechanism H0 H1 H3 H4 H5 arsJ arsB acr3 Respiration Oxidation Methylation Reduction Expulsion As Pumps: arsP

Prolixibacteraceae

Cyclobacterium Salinimicrobium Prolixibacter Caldisalinibacter Clostridiisalibacter Tangfeifania Mongoliibacter Pontibacter Mongoliibacter Pontibacter Clostridiisalibacter Cyclobacterium Pontibacter Clostridiisalibacter C gnaT Rhodohalobacter Psychroflexus Rhodohalobacter Psychroflexus Aquiflexum

Gillisia Gillisia Crocinitomix Gillisia Genus Proteobacteria (189) Geoalkalibacter Fervidicola Geoalkalibacter

ainafief Clostridiisalibacter Chrysiogenes ainafiefgnaT Chrysiogenes ClostridiisalibacterGeoalkalibacter

Chrysiogenes Bacteroidetes (41) Catabacter Blautia Phylum Tenericutes CaldisalinibacterTenericutes Methanocorpusculum Bacteroides Cytophaga Firmicutes (24)

Cytophaga Clostridium As Pump

Halomonas NitrincolaHalomonas Actinobacteria (17) Halomonas AlkalilimnicolaHalomonas HalorhodospiraHalomonas Halomonas Halorhodospira Euryarchaeota (9) Marinobacter Counts Thioalkalivibrio Marinobacter

Caldicoprobacter Egicoccus Egicoccus Salinibacter Others (26) Salinibacter Salinibacter Pseudomonas Pseudomonas Pusillimonas Pusillimonas OceanibaculumPorticoccus OceanibaculumPontibaca acr3 (172) OceanibaculumPontibaca Sulfitobacter Sphingobium Haliangium Plesiocystis arsB (66) Oceanicoccus Hydrogenophaga Hydrogenophagabeta Hydrogenophaga arsJ (48) Hydrogenophaga Enhydrobacter Oceanococcus Sedimenticola arsP (20) ComamonadaceaeAzoarcus Paucibacter Thiobacillus Chitinilyticum Sulfurivermis Thiohalobacter Thiohalophilus Thiohalobacter 306 different sequences Thiocapsa Rhodomicrobium Thermoanaerobaculum Thermoanaerobaculum 3,635 detections Haloarcula Marinobacter Halomonas Alkalitalea Marinobacter Alkalitalea Marinobacter Alkalitalea Marinobacter Alkalitalea Marinobacter Rufibacter Marinilabilia Oceanospirillales Desulfobacter Alcanivorax Desulfosoma Halomonas Candidatus-Brocadia Halomonas Desulfofustis Halomonas Desulfococcus Pseudomonas Desulfococcus Pseudomonas Candiadtus Pseudomonas Richelia Nostoc Simplicispira Arthrospira Nitrosomonas Methanosarcina Nitrosomonas Natrialbaceae Nitrosomonas Halomicrobium Opitutaceae Thioalkalivibrio Desulfonatronovibrio ThioalkalivibrioNitrosomonas Desulfonatronovibrio Nitrosomonas delta-proteobacterium Cyanothece Desulfonatronospira Desulfonatronovibrio Haloterrigena delta Desulforhopalus Desulfonatronovibrio Marinobacter Desulfonatronovibrio MarinobacterAzonexus Desulfonatronovibrio Rhodothermaceae Laceyella Desulfobacula Desulfuromusa Desulforhopalus Roseiflexus Desulforhopalus Thermoflavimicrobium Geobacter Desulfomicrobium Anaerolineales Geoalkalibacter Anaerolineales Desulfomicrobium Desulfomicrobium Halorubrum Desulforegula Desulfotomaculum Pseudomonas Desulfobacter Desulfobacter Ilumatobacter Desulfonatronum Ilumatobacter Desulfopila Roseovarius Syntrophus Planktotalea Syntrophus Chlorobaculum Sediminimonas Chlorobaculum Sediminimonas Chlorobaculum Desulfosporosinus Ignavibacterium RhodobacteraceaeRoseovarius Acetobacteroides PseudooceanicolaRoseovarius Acetobacteroides Roseovarius Algoriphagus Roseicyclus Flavobacterium Marinifilaceae Marinifilaceae Hydrogenophaga Syntrophobacter Marinifilum Hydrogenophaga Labilibaculum Hydrogenophaga Pseudomonas Hydrogenophaga Erythrobacter Wenzhouxiangella Porticoccus Methylocella Porticoccus Methylocella PseudomonasHalomonas Methylocella Halomonas Gemmatimonas beta-proteobacterium Cryobacterium Halomonas Euzebya beta-proteobacterium Nitriliruptor Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacter Ilumatobacteraceae Thioalkalivibrio Thiocapsa Ilumatobacter Thioalkalivibrio Ilumatobacteraceae Thiocapsa Ectothiorhodospira Thioalkalivibrio Thiocapsa Salinisphaera Ectothiorhodospira Thioalkalivibrio Ruegeria Ectothiorhodospira Methylomarinum Ectothiorhodospira Pseudaminobacter Thioalkalivibrio Pseudaminobacter Roseibaca Aneurinibacillus Bacillus Novibacillus Bacillus Bacillus Bacillus Bacillus Bacillus Haloarcobacter Arcobacter Microcella Halomicrobium Halomicrobium Pseudomonas Marinobacter Pseudomonas Marinobacter

retcaboniraM retcaboniraM Halocynthiibacter MarinobacterPseudomonas Roseibaca Roseibaca Roseibaca Roseibaca Roseibaca Roseibaca

torteP Roseibaca Roseibaca Alicycliphilus Roseibaca Roseibaca Saccharospirillum

ago

Pseudomonas Pseudogulbenkiania Pseudomonas Pseudomonas Pseudomonas Pseudomonas sanomoduesP Pseudomonas

Marinobacter Marinobacter Marinobacter Marinobacter Marinobacter bioRxiv preprint doi: https://doi.org/10.1101/2021.04.13.439755! ; this version posted April 16, 2021. The copyright holder for this preprint (which !A was not certified!!!!!!!!! by"#$%&' peer!(%()*%#) review)! is the author/funder,+,)-./ who! has granted bioRxiv a license to display0123 the41. !preprint in perpetuity. It is made !!!!!!!!! available under a! CC-BY-NC 4.0 International license. ! !

! ! ! ! ! ! ! ! ! '#7%?@ + ! ;8! ):;#:<#-= '8(*)7%'# '8(*)7%'# 5)6-)#7 5)6-)#7 0+!-'#7)#7 >AB 5)6-)#7 5)6-)#7 +'#7<8%#<7%'# ! ! !> ! ! ! ! ! !+ !9 !+ ! 5,=*;8! +*<@@! C6:)6! D<8%*=! 0)#;@! "! EF4/"0GGG! HAIJJK! LIMLK! NNL! GGOGLJ! PBIJPK! MGIHAK! GIAQK! 3<-7)6'%:'7

SH-MAG144 B SH-MAG148

Redundancy SH-MAG131 Completion H5 H3 SH-MAG78 H4

H0 Clustering by MAGs abundance H1 GC-content SH-MAG111 Length

96174371

Total Mapped Reads

SH-MAG3

SH-MAG129 SH-MAG169

SH-MAG96 SH-MAG29

SH-MAG143 SH-MAG193

SH-MAG93 SH-MAG89

SH-MAG12

SH-MAG116 SH-MAG162

SH-MAG130

SH-MAG192 B A

M00045 M00793 M00021 M00133

MAG M00153

143 M00169 M00530 M00741 M00036

M00087 bioRxiv preprint

MAG M00549

193 M00909 M00166 M00868 M00053 M00173 M00017

M00525 was notcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmade

MAG M00572

130 M00855 M00004 M00022 M00009 M00011 M00527 M00016 M00526 M00121 M00926 M00035 M00165 M00167 M00028 M00149 M00010 M00846

Acr3 M00063 M00093 M00157 M00007 M00048 2 M00120 doi: M00126 M00051 M00140 M00060 ArsR M00866 M00119 M00125

M00307 https://doi.org/10.1101/2021.04.13.439755 M00881 M00082

3 M00115 M00001 M00002 M00003 MAG M00052 M00083 93 M00050 M00049 M00005 M00086 M00155 M00618 M00029 M00845 M00308 M00346 M00364 M00034 M00611 M00912 M00842 M00843 M00172 M00913 M00874 M00884 M00141 M00882 ArsC

Acr3 M00883 M00892 M00144 M00168 M00579 M00091 M00156

MAG M00006 M00358 M00632

12 M00176 ArsC M00008 ArsM M00616 M00116 M00932 M00112 2

M00145 available undera 2 M00161 M00163 M00097 M00531 M00580 M00100 M00162 M00366 M00367 M00040 M00150 M00744 M00148 M00769 M00537 M00538 M00529 M00129 M00171 M00061 M00631 M00554 M00876 M00555 M00879 M00633 M00919 M00824 M00825 M00309 M00356 M00563 M00935

M00422 CC-BY-NC 4.0Internationallicense M00643 M00745 M00114 ; M00550

M00705 this versionpostedApril16,2021. M00079

ArsR M00076

ArsR M00923 M00078 MAG M00099

116 M00136 M00861

ArsA M00673 M00835 M00433

2 M00922 M00898

3 M00921 MAG M00850 144 M00094 M00378 M00567 M00104 M00546 M00113 M00545 M00725 M00622 M00848 M00159 M00128 M00917 M00101 M00102 M00152 M00778 M00014 M00039 M00372 M00030 M00154 M00135 M00037 M00042 M00651 M00936 M00897 M00613 M00696 M00697 M00375 M00568 M00032 M00569 M00338 M00930 M00931 ArsM M00038 M00044 M00627 ArsC ArsA M00170 M00365 M00027 M00541 ArsA

3 M00033 M00552 M00620 M00374 M00357 . 2 M00617 M00575 M00373

M00376 The copyrightholderforthispreprint(which M00841 M00344 M00840 M00911 M00914 M00614 M00368 M00612 M00089 M00740 M00095 M00849 M00609 M00873 M00131 M00532

MAG M00377

129 M00878 M00013 M00088 M00718 M00615 M00123 M00573 M00577 M00151

MAG M00595 M00117

111 M00118 M00064 M00175 ArsJ M00847 M00899

ArsH M00916 M00528 M00804 M00124 M00345 M00024 M00025 M00098 M00896 M00127 M00895 M00432 M00535 M00015 M00122 M00924 M00925 M00012 M00596 M00020 M00019 M00570 M00880 M00026 M00018 M00023 M00096 M00844 M00565 M00854 MAG93 MAG143 MAG148 MAG193 MAG130 MAG116 MAG12 MAG111 MAG3 MAG144 MA MAG29 G129 MAG 3 MAG 148 MAG 29

0% 20% 40% 60% 80% 100% KEGG Module completeness Module KEGG