Depth-Discrete Eco-Genomics of Lake Tanganyika Reveals Roles of Diverse Microbes, 1 Including Candidate Phyla, in Tropical Fresh

bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-ND 4.0 International license.

1 Depth-discrete eco-genomics of Lake Tanganyika reveals roles of diverse microbes, 2 including candidate phyla, in tropical freshwater nutrient cycling 3 4 Patricia Q. Tran1,2, Peter B. McIntyre3, Benjamin M. Kraemer4, Yvonne Vadeboncoeur5, Ismael 5 A. Kimirei6, Rashid Tamatamah6, Katherine D. McMahon1,7, Karthik Anantharaman1 6 1. Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA 7 2. Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 8 USA 9 3. Department of Ecology and Evolution, Cornell University, Ithaca, NY, USA 10 4. Department of Ecosystem Research, Leibniz Institute for Freshwater Ecology and Inland 11 Fisheries, Berlin, Germany 12 5. Department of Biological Sciences, Wright State University, Dayton, OH, USA 13 6. Tanzania Fisheries Research Institute (TAFIRI), Dar es Salaam, Tanzania 14 7. Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 15 Madison, WI, USA 16 17 Corresponding author: Karthik Anantharaman ([email protected]) 18 19 Abstract 20 Lake Tanganyika, the largest tropical freshwater lake and the second largest by volume 21 on Earth is characterized by strong oxygen and redox gradients. In spite of the majority of its 22 water column being anoxic, Tanganyika hosts some of the most diverse and prolific fisheries and 23 ecosystems on Earth. Yet, little is known about microorganisms inhabiting this lake, and their 24 impacts on biogeochemistry and nutrient cycling underlying ecosystem structure and 25 productivity. Here, we apply depth-discrete metagenomics, single-cell genomics, and 26 environmental analyses to reconstruct and characterize 3996 microbial genomes representing 802 27 non-redundant organisms from 81 bacterial and archaeal phyla, including two novel bacterial 28 candidate phyla, Tanganyikabacteria and Ziwabacteria. We found sharp contrasts in community 29 composition and metabolism between the oxygenated mixed upper layer compared to deep 30 anoxic waters, with core freshwater taxa in the former, and Archaea and uncultured Candidate 31 Phyla in the latter. Microbially-driven nitrogen cycling increased in the anoxic zone, highlighting 32 microbial contribution to the productive surface layers, via production of upwelled nutrients, and 33 greenhouse gases such as nitrous oxide. Overall, we provide a window into how oxygen 34 gradients shape microbial community metabolism in widespread anoxic tropical freshwaters, and 35 advocate for the importance of anoxic freshwater habitats in the context of global 36 biogeochemical cycles and nutrient cycling. bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license. Tran et al. Lake Tanganyika’s microbiome

37 Introduction 38 Located in the East African Rift Valley, Lake Tanganyika (LT) holds 16% of the Earth’s 39 freshwater, and is the second largest lake by volume1. By its sheer size and magnitude, LT exerts 40 a major influence on biogeochemical cycling on regional and global scales2,3. Over the past 41 centuries, LT has been extensively studied as a model system for its rich animal biodiversity4,5 42 which have revealed important insights on species radiation and evolution. In contrast, the 43 microbial community of LT that drives the productivity of this ecosystem largely remains a 44 mystery. 45 LT provides a unique ecosystem to study microbial diversity and function in freshwater 46 lakes, specifically tropical lakes. This ancient lake harbors some of the most spectacular and 47 well-studied adaptive radiations of metazoan species diversity on Earth5, but these organisms 48 exist in a thin layer of oxygenated surface water. Lake Tanganyika is estimated to be at least 10 49 million years old and is considered oligotrophic (low nutrient concentrations). Among Lake 50 Tanganyika’s defining features is that approximately 80% of the 1890 km3 of water is anoxic. 51 Being meromictic, its water column is permanently stratified, with a large volume of anoxic and 52 nutrient-rich bottom waters (hypolimnion), separated from the upper ~70m of well-lit, nutrient- 53 depleted water (epilimnion). Despite stratification, periodic pulses of phosphorus, nitrogen and 54 iron from upwelling of deep-waters replenish the epilimnion and sustain its productivity. Lake 55 Tanganyika’s influence in biogeochemical cycling regionally and globally is valuable, for 56 instance it stores over 23 TG of methane below the oxycline3, and stores about 14,000,000Tg of 57 C in its sediments2. 58 Only a handful of previous work has been done on the lake’s microbial ecology6,7. Previous 59 studies have found microbial community composition to be heterogeneous along both vertical 60 and horizontal spans7. The differences were primarily related to thermal stratification, which 61 leads to strong gradients in oxygen and nutrient concentrations. However, the contribution of 62 microbes to nutrient cycling in Lake Tanganyika remains to be studied. Here we investigated the 63 microbial community composition, metabolic interactions, and biogeochemical contributions 64 along ecological gradients from high light, oxygenated surface waters to dark, oxygen-free and 65 nutrient-rich bottom waters of Lake Tanganyika. Our comprehensive analyses include genome- 66 resolved metagenomics and single-cell genomics to generate thousands of bacterial and archaeal 67 genomes, metabolic reconstructions at the resolution of individual organisms and the entire lake 68 ecosystem, and measurements of lake biogeochemistry to infer the roles of microorganisms in 69 nutrient cycling across different layers in the water column. Our work offers a window into the 70 understudied microbial diversity of LT and serves as a case study for investigating microbial 71 roles and links to biogeochemistry in globally distributed anoxic freshwater lakes. 72 73 Results 74 We collected 24 samples from the LT water column spanning 0 to 1200m, at two stations 75 (Kigoma: 13 samples, Mahale: 11 samples) in July and October 2015 (Table S1). Detailed 76 environmental profiles from 2010-2013 were used to guide sampling location and depth (Figure

77 1A). Water column temperatures ranged from 24 to 28°C, and changes in dissolved oxygen (DO) 78 were greatest at depths ranging from ~50 to 100 m during the time of sampling (Figure 1A, 79 Figure 1B), dropping to 0% DO around 100m. A consistent chlorophyll-a peak was detected at a 80 depth of ~120m throughout the years (Figure S1). Nitrate concentrations increased up to ~100 81 µg/L at a depth of 100m, followed by rapid depletion with the onset of anoxia (Figure 1D). 82 Secchi depth, a measure of how deep light penetrates through the water column was on average 83 12.2m in July and 12.0m in October (Figure S2). Despite not having paired environmental data 84 in 2015, environmental data from 2010 to 2013 was highly consistent and showed minimal 85 interannual variability, particularly during our sampling period (July and October) (Figure S1, 86 Figure S2). Additionally, the environmental profiles are similar to those previously collected (see 87 references 1,8,9). 88 Metagenomic sequencing and binning of 24 individual samples resulted in 3948 draft- 89 quality metagenome-assembled genomes (MAGs) that were dereplicated (using a threshold of 90 98% average nucleotide identity (ANI)) into a set of 802 non-redundant genomes for 91 downstream analyses (Table S2, Table S7). We also sequenced 48 single-cell amplified genomes 92 (SAGs) from a depth of 1200m to complement the MAGs. To assign taxonomic classifications to 93 the organisms represented by the genomes, we combined two complimentary genome-based 94 phylogenetic approaches: a manual 16 ribosomal protein concatenated gene phylogeny (16RP) 95 and GTDB-tk10, an automated program which uses 120 concatenated protein coding genes. 96 Overall, we observed congruence between the two approaches (Table S2). Six bacterial genomes 97 clustered away from known genomes and likely represent two monophyletic lineages at the 98 phylum-level. On this basis, we propose the candidate phyla (CP) Candidatus 99 Tanganyikabacteria (named after the lake) and Candidatus Ziwabacteria (from the Swahili word 100 for “lake” which is “ziwa”). Overall, our genomes represented 34 Archaea and 769 Bacteria from 101 81 phyla, including our two proposed phyla (Figure 2, Table S2). 102 To elucidate the stratification of microbial populations and metabolic processes in the 103 water column of LT, we identified three zones referred to as “oxic”, “sub-oxic”, and “anoxic” 104 which we operationally define based on general oxygen percent saturation (Figure 1C). These 105 zones coincidently correspond broadly to the epilimnion, metalimnion and hypolimnion 106 respectively. To relate microbial community composition to relative abundance in our samples, 107 we mapped metagenomic reads from each depth-discrete metagenome against all 802 MAGs and 108 computed the average normalized coverage (abundance) (Table S3). 109 110 The microbiome of Lake Tanganyika is shaped by oxygen and redox gradients 111 Broadly speaking, the tropical microbiome of LT was similar to the epilimnions of 112 temperate worldwide11. Major phylogenetic groups found in high abundance included 113 Actinobacteria, Bacteroidetes, Betaproteobacteria, Cyanobacteria, Gammaproteobacteria, 114 Planctomycetes and Verrucomicrobia (Figure 2), many of which are cosmopolitan and canonical 115 freshwater lineages. However, LT also hosts organisms less frequently observed elsewhere in 116 freshwater systems such as Ignavibacteria and Thaumarchaeota. Ignavibacteria were among the

117 most abundant organisms in 23 out of 24 samples while Thaumarchaeota were present at all 118 depths but peaked in the sub-oxic zone. 119 Cyanobacteria constituted the most abundant phylum across all our samples (Figure 3, 120 Table S2). Cyanobacterial MAGs were also abundant in the hypolimnion, including the deepest 121 samples. While these Cyanobacteria encoded both photosystems I and II, we postulate that these 122 organisms likely conduct dark fermentative metabolism below the photic zone similar to that 123 observed in Cyanobacteria from deep terrestrial ecosystems12. Analysis of replication rates13 124 suggests that Cyanobacteria are indeed growing, but we cannot quantify absolute rates without 125 calibration using cultures. Cells were estimated to be replicating more quickly in the photic zone 126 as compared to the deep hypolimnion (Figure S3). 127 The dominant community members became more distinct with depth, as oxygen and light 128 limitations increased (Figure 1, Figure 3, Figure S1, Figure S2). We noticed some differences 129 between stations, but hesitate to attribute these to local factors without more extensive spatial 130 sampling. The presence of Euryarchaeota in the epilimnion in Mahale might be a result from the 131 upwelling, though more temporally resolved sampling is required to properly examine this. 132 Green sulfur bacteria (phylum Chlorobi) were the sixteenth most abundant group in samples just 133 below the sub-oxic zone (Figure S1). Chlorobi are observed to be highly abundant in other 134 freshwater lakes where strong light and sulfide gradients exist, such as the meromictic Lake 135 Cadagno14,15. We hypothesize that the lower abundance of Chlorobi observed in LT at and in the 136 anoxic zone may be attributed to the presence of anoxygenic Cyanobacteria and competition 137 between these groups (Figure S4). Nitrifying Nitrospira were present but only below 100m. We 138 identified a total of ninety-five organisms from CP with Eisenbacteria, Kaiserbacteria and 139 Rokubacteria comprising the most abundant lineages below 100m. Among CP, only 140 Tanganyikabacteria, Tectomicrobia and TM6 were present in both the oxic and anoxic zones. 141 Much remains to be learnt about microbial biodiversity inhabiting lakes’ hypolimnions 142 and deep lakes worldwide. With few microbial ecology studies focused on lake hypolimnions16– 143 19, we cannot speculate if microbes in the hypolimnion of Lake Tanganyika are truly endemic. 144 Nevertheless, we note the contrast between typical freshwater microbes dominating the 145 epilimnion, and the high prominence of Archaea and CP, including Candidate Phyla Radiation 146 (CPR) in the anoxic zones. 147 148 Shared microbiome between tropical and temperate freshwater lakes 149 We examined MAGs from the well-sampled and globally distributed bacterial lineage, 150 Actinobacteria to ask whether endemic strains were present in Lake Tanganyika. Two specific 151 lineages (acI, followed by acIV) within the Actinobacteria are typically constrained to freshwater 152 systems and are cosmopolitan11. Previously, acI lineages were found in Lake Tanganyika by 16S 153 rRNA gene sequencing7. In comparison to other available acI from lakes in Japan, Wisconsin 154 and Switzerland20,21, Lake Tanganyika MAGs only cover a small fraction of the Actinobacteria 155 phylogenetic diversity, dominated by freshwater groups acI-B1, acI-C, acIII, acIV, acSTL, acTH 156 and Iluma-A (Table S2). Overall, Actinobacteria lineages show possible niche partitioning with

157 depth in Lake Tanganyika (Figure S5). All from LT were most abundant in the epilimnion, 158 except acIII, acIV and two Iluma-A2. Non-canonical freshwater groups were present throughout 159 the water column. Several cosmopolitan clade members were virtually indistinguishable in the 160 16RP phylogeny from genomes recovered in US and European lakes (acI lineage), while others 161 constituted unique groups previously unsampled in other lakes (Figure S5). 162 163 Archaea and candidate phyla are prominent in the anoxic zone of Lake Tanganyika 164 Little is known about the distribution and ecology of organisms belonging to CP and the 165 CPR in freshwater lakes. The CPR consists of mostly uncultivated bacteria and are 166 phylogenetically distinct from major bacterial lineages13,22. In a hypersaline soda lake, 167 uncultured CPR were found to be a major component of the bacterial community. Similarly, 168 Parcubacteria dominated the anaerobic bacterial community of boreal lakes23. 169 Amongst non-CPR candidate phyla, Candidatus Aegiribacteria was first described below the 170 chemocline of Lake Mahoney (British-Columbia, Canada) in the euxinic hypolimnion, which is 171 anoxic, saline and sulfur-rich. 172 Here, we reconstructed 95 CP genomes (40 phyla, including CP Tanganyikabacteria and 173 CP Ziwabacteria) (Figure 2A) including 22 genomes from CPR organisms. Thirty-seven archaeal 174 MAGs accounted for 3.4% of microbial abundance (Figure 2A). We reconstructed a full circular 175 genome of a CP Saccharibacteria. Only Euryarchaeota and Thaumarchaeota were present 176 throughout the water column while most of archaea, including members of the DPANN lineages, 177 resided in the anoxic zone. Verstrarchaeota, Bathyarchaeota, Woesearchaeota and 178 Micrarchaeota genomes were recovered for the first time from tropical freshwaters, allowing for 179 future comparative genomics within these groups. 180 181 The novel candidate phyla Tanganyikabacteria and Ziwabacteria 182 Recently, the discovery of non-photosynthetic relatives of Cyanobacteria and their 183 metabolic diversity24,25 has changed our understanding of metabolic repertoire of these 184 organisms. A total of fifteen MAGs (3 dereplicated MAGs) from Tanganyika were distinct from 185 Cyanobacteria, and closely related to other non-photosynthetic Cyanobacteria-like organisms. 186 We placed these MAGs in the context of published reference genomes26,27 to ascertain their 187 phylogeny. All 3 MAGs clustered away from Cyanobacteria, CP Melainabacteria, CP 188 Blackallbacteria, CP Saganbacteria and CP Margulisbacteria (Figure 4A). The 3 MAGs formed a 189 distinct monophyletic clade closely related to the CP, yet their 16S rRNA sequences shared less 190 than 80% ANI to them. Therefore, we propose that these MAGs belong to a new candidate 191 phylum that we designate as CP Tanganyikabacteria. 192 To assess CP Tanganyikabacteria’s global distribution, we searched the 16S rRNA 193 sequences from the CP Tanganyikabacteria MAGs against publicly available datasets. Sequences 194 from (>75% 16S rRNA sequence identity phylum cutoff28) CP Tanganyikabacteria were widely 195 distributed across environments, but dominant in freshwater, marine and soil natural systems 196 (Figure 4B).

197 Organisms from CP Tanganyikabacteria are facultative denitrifiers with heterotrophic 198 metabolism supplemented with the ability to oxidize sulfide (Figure 4C). All CP 199 Tanganyikabacteria in Lake Tanganyika had flagellar machinery and pili, chemotaxis proteins, 200 and several transporters including iron complex outer membrane receptor and transport proteins, 201 ferrous iron, sugar and biopolymer transporters. Like other CP Melainabacteria, they generally 202 do not have a TCA cycle, cytochrome c for the electron transfer chain, nor glycolysis 203 pathways25. However, the one denitrifying CP Tanganyikabacteria had a TCA cycle, and cyt c 204 cbb3 type and certain genes in the glycolysis pathway were found. None had Ni-Fe 205 hydrogenases, unlike its CP relatives which have a hydrogen-dependent metabolism26. 206 Another group of organisms were distantly related to the Acidobacteria group, CP 207 Raymondbacteria and Candidate division TG3 (Figure S6). Outside of Lake Tanganyika, one 208 single genome was identified for this lineage from metagenomes reconstructed from a deep 209 subsurface aquifer, Crystal Geyser near Green River, Utah27. This second CP named 210 Ziwabacteria contains facultative organisms with heterotrophic metabolism supplemented by 211 hydrogen metabolism (FeFe hydrogenase, Ni-Fe Hydrogenase Group 1, Group 3c), oxygen 212 metabolism, and sulfide oxidation (sqr). 213 214 Nitrogen cycling 215 Nitrogen is a major nutrient shaping the ecology of Lake Tanganyika9. While 477 T of 216 N/year flow into LT, dissolved inorganic nitrogen at the surface waters remains low suggesting 217 rapid nitrogen depletion with important implications for primary productivity that limits yields of 218 critical fisheries29. Operating in both oxic and anoxic conditions present in Lake Tanganyika, the 219 microbially-driven nitrogen cycle produces biologically-relevant compounds like ammonia, 220 amino acids, proteins, urea, nitrate, and nitrite. The spatial dynamics of nitrogen forms in Lake 221 Tanganyika have been studied from biogeochemical data and show that upwelling from the 222 chemocline (~50-100m) can deliver fixed N to surface8,29–31. Such vertical transfers are critical to 223 supporting productive food webs and the ecosystem32. Here, we identify microorganisms that 224 catalyze these processes while emphasizing specific physiologies that may have implications for 225 ecosystem-level nitrogen budgets. 226 We identified microbes involved in ammonia oxidation, nitrite oxidation, complete 227 ammonia oxidation (comammox), denitrification, urea utilization, anaerobic ammonia oxidation 228 (anammox), dissimilatory nitrate reduction to ammonia (DNRA), and nitrogen fixation (Figure 229 S7). In total, nitrogen cycling organisms accounted for 23% of the microbial community 230 abundance across all the samples. Nitrification (ammonia oxidation to nitrite, nitrite oxidation to 231 nitrate) is catalyzed by oxygen-dependent enzymes (amoABC, nxrAB), and expected to be 232 prevalent in the surface. Ammonia oxidation was found in bacteria (Alpha-, Gamma- and 233 Betaproteobacteria), and also in Thaumarchaeota. The potential for nitrite oxidation was found 234 in Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Deltaproteobacteria, 235 Fibrobacteres, Nitrospira, CP Ziwabacteria, Planctomycetes, and Verrucomicrobia. Organisms 236 from many CP were found to be capable of nitrite oxidation including Poribacteria,

237 Handelsmanbacteria, Eisenbacteria, Rokubacteria, Hydrogenedentes, Tanganyikabacteria and 238 Ziwabacteria. 239 We recovered two Nitrospira genomes with amoABC and nxrAB suggesting comammox 240 ability for the first time in a freshwater lake water column (to our knowledge), although previous 241 studies have hinted at their existence33. These genomes belong to the previously described 242 lineage II Clade A, consistent with known Nitrospira comammox lineages (Figure 5A). 243 Additionally, we performed a full genome-wide metabolic comparison with Nitrospira 244 (comammox and not) from other environments (wastewater treatment plants, iron pipes, 245 biofilm)34(Figure 5B). When comparing comammox, nitrite oxidizing bacteria (NOB) and 246 anammox bacteria, which are competing among themselves for either ammonia or nitrite, we 247 observed that they were present also in the anoxic layer (Figure 5C). Ammonia oxidizers have a 248 versatile metabolism and can be heterotrophs35. In anoxic environment, comammox Nitrospira 36 249 can potentially use H2, which provides an advantage in hypoxic and anoxic habitats . 250 Despite a prominently anoxic water column, our data does not suggest that full 251 denitrification and DNRA are prevalent genomic features of the microbial community. 252 Denitrification and DNRA can co-occur under oxygen-limited or anoxic conditions, such as in 253 the anoxic hypolimnion of lakes. Unlike denitrification which removes reactive nitrogen from

254 the system by converting fully to N2 gas, DNRA conserves nitrogen within the system by 255 reducing nitrate to ammonium. DNRA therefore does not produce N2 nor N2O that is released 256 into the atmosphere. Nitrate reduction to nitrite (napAB, narGH) is the first step of denitrification 257 and DNRA. Six MAGs (Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, 258 Planctomycetes and CP Tanganyikabacteria) had potential for nitrate reduction (Table S4). Only 259 3 organisms from Betaproteobacteria and CP Tanganyikabacteria possessed the capacity for 260 complete DNRA. Meanwhile, a single organism from Betaproteobacteria was capable of

261 complete denitrification to N2, while one from CP Tanganyikabacteria could undertake partial 262 denitrification to nitrous oxide (N2O). Individual steps in denitrification such as nitrite reduction 263 to nitric oxide (NO), NO reduction to N2O, and N2O reduction to N2 were inconsistently 264 distributed in organisms suggesting the presence of widespread metabolic handoffs in the 265 reductive cycle of nitrogen cycle37 (Table S4). 266 Lake water column anammox was first discovered in Lake Tanganyika38. Anammox

267 bypasses the steps of nitrite oxidation and nitrite reduction to directly produce N2. We identified 268 one MAG closely related to Candidatus Brocadiacea in the phylum Planctomycetes that 269 contained the hydrazine oxidase (hzoA) and hydrazine synthase (hzsA) genes necessary for 270 anammox. Like observed previously, anammox organisms peaked in abundance around 100- 271 150m (Figure 5C)38. In LT, nitrite oxidizing bacteria (NOB) and anammox, which both compete 272 for nitrite, peak in abundance at 100-150m, but NOB are relatively more abundant than 273 anammox bacteria (Figure 5C). Other important components of the nitrogen cycle in LT include 274 nitrogen fixation and transformations of organic nitrogen including urea, amino acids, and 275 proteins (Table S4). 276

277 Sulfur Cycling 278 The sulfur cycle spans the entire water column of LT and is characterized by 279 intermediates that transcend and connect other biogeochemical cycles (Figure S7). Sulfide 280 oxidation to elemental sulfur was identified by the presence of sulfide quinone oxidoreductases 281 (sqr) and was found in 94 MAGs, including Cyanobacteria. We compared the distribution of 282 sulfide oxidizing Cyanobacteria with non-Cyanobacteria (Figure S4). We noted that non- 283 Cyanobacterial sulfide oxidizers were more prominent in the dark anoxic zone, whereas 284 anoxygenic photosynthetic Cyanobacteria that were able to oxidize sulfide were more abundant 285 at the oxic-photic zone. We propose that competition for sulfide results in higher abundance of 286 anoxygenic photosynthetic cyanobacteria at the surface (where light is present), whereas below 287 the euphotic zone, other sulfide oxidizers such as Chlorobi, might have an advantage. The 288 process of anoxygenic photosynthesis coupled with sulfide oxidation has been studied in 289 microbial mats39, but rarely in pelagic freshwater environment. Interestingly, we did not find any 290 purple sulfur bacteria in Lake Tanganyika. Finally, thiosulfate disproportionation (using the sox 291 complex without soxCD) and thiosulfate oxidation (using the sox complex) were abundant in LT 292 MAGs representing organisms from Alphaproteobacteria, Betaproteobacteria, 293 Gammaproteobacteria, Chloroflexi, Planctomycetes, and CP Rokubacteria. 294 Sulfate reduction was found to be highly prominent in the anoxic water column of LT. In 295 total, we identified 28 MAGs representing organisms from six different phyla, namely 296 Deltaproteobacteria, Acidobacteria, Nitrospirae, Calditrochaeota, Aminicenantes, Chloroflexi 297 that possessed the dissimilatory sulfite reductase complex. No organisms capable of only sulfite 298 reduction or containing the anaerobic sulfite reductase complex were identified. Organisms 299 involved in Sulfur metabolism were the third most diverse after carbon and oxygen metabolism 300 (Figure S8). 301 302 Carbon transformations as a direct link to the food chain

303 Autotrophy involving carbon fixation can take many forms. In photosynthesis, CO2 is 304 fixed into organic carbon and involves Rubisco enzymes. Rubisco Form I and II are found in 305 algae, plants and bacteria. Form IV are found in both Bacteria and Archaea and perform 306 functions distinct from carbon fixation, such as methionine salvage, sulfur metabolism and D- 307 apiode catabolism. In LT, Form I Rubisco were prevalent in MAGs from Betaproteobacteria, 308 Chloroflexi, Alphaproteobacteria, Cyanobacteria while Form II was only found in 309 Betaproteobacteria (Figure S9). A well supported clade of Rubisco sequences from LT could not 310 be classified to any known forms. This newly discovered Form IV-like clade was composed of 311 sequences from Planctomycetes, Verrucomicrobia, Chloroflexi, Poribacteria, CP 312 Handelsmanbacteria, and CP Hydrogenedentes. In addition to the CBB cycle, the Wood- 313 Ljungdahl pathway was also found to be prevalent in LT. This pathway is dominant in anoxic 314 environments and was found in 48 MAGs, representing organisms from Actinobacteria, 315 Armatimonadetes, Chloroflexi, Deltaproteobacteria, Euryarchaeota, Firmicutes, Lentisphaerae, 316 Nitrospirae, Planctomycetes, and Omnitrophica. Most organisms with the Wood-Ljungdahl

317 pathway decreased in abundance with increased depth. While is it counterintuitive that 318 fermentative organisms are present in the oxic waters of Lake Tanganyika, this could provide an 319 advantage for them under microoxic or anoxic conditions. 320 Aerobic and anaerobic methanotrophs consume methane. While previously thought of as 321 limited to the sediments, it is now known that both aerobic and anaerobic methane oxidation 322 occur in the water column40,41. Methane oxidation is prevalent in the well-oxygenated part of the 323 water column in many lakes including Lake Kivu, also part of the African Rift Valley lakes42,43. 324 Methanogenesis (production of methane) is generally expected to be restricted to the 325 hypolimnion, but can also be found in the oxygenated water column44. Understanding the 326 potential for microbially-driven methanotrophy and methanogenesis in the water column is 327 important since it modulates methane fluxes, as they travels from the sediment, through the water 328 column, to the atmosphere. We identified three MAGs (Verstraetaerchaeota, Euryarchaeaota) 329 that possessed the capacity for methanogenesis and were most abundant in the hypolimnion 330 (Figure S7). The capacity for methane oxidation was identified in organisms from 331 Betaproteobacteria whose abundance was highest in the oxic-anoxic interface, suggesting an 332 aerobic methanotrophic lifestyles. Similar to observations in marine systems where capacities for 333 methane oxidation coexist with sulfur oxidation, we propose that these Betaproteobacteria 334 adapted to thrive in low-oxygen, transient environments characteristic of the sub-oxic zone, with 335 its ability to utilize many reduced compounds such as sulfide, sulfur, and hydrogen. 336 To investigate the heterotrophic potential of microbes to breakdown polysaccharides in 337 Lake Tanganyika, we annotated the carbohydrate-degrading enzymes (CAZYmes) in all MAGs 338 (Figures S10). Overall, Planctomycetes seemed to be a major contributor to carbon degradation 339 since this group had the largest number of protein families associated with auxiliary activities 340 (AAs), carbohydrate-binding modules (CBMs), carbohydrates esterases (CEs), glycoside 341 hydrolases (GHs), and Polysaccharide Lyases (PLs). Deltaproteobacteria had the most glycosyl 342 transferases (GTs). Despite their small genome sizes, CPR especially CP Shapirobacteria have 343 relatively many CAZYme hits per genome size45. We observed the total number of identified 344 GTs to be more abundant that GHs which begs the question as to why GT are more abundant 345 than GH in this system, and if the same pattern is found in similar or contrasting environments. 346 347 Metabolic connections across vertical gradients 348 Overall, we identified and assessed the distribution of organisms involved in nitrogen, 349 carbon and sulfur cycles through their genomic content. Along a vertical oxygen depth gradient, 350 we observed a prominence of both phototrophic and heterotrophic metabolism in the photic zone 351 (Figure 6, Figure S11). At the sub-oxic zone, we noticed the start of the increase in nitrogen- 352 based metabolism, and complex carbon degradation, and hydrogen metabolism (Figure 6, Figure 353 S11). Methane metabolism was found throughout the water column but increased below the sub- 354 oxic zone suggesting the prominent use of non-oxygen electron acceptors for this process. Our 355 overall findings about microbial metabolisms in LT in relation to light, oxygen and temperature 356 are reminiscent of classical understanding of partitioning between aerobic and anaerobic

357 metabolism. Yet, while it is classically understood that certain metabolic processes such as nitrite 358 oxidation are extremely limited in their distribution (e.g. in Nitrospirae, Nitrospinae, 359 Proteobacteria, Nitrospira), we found that diverse co-occurring groups are actually capable of 360 performing these transformations. Similarly, identification and characterization of common 361 freshwater lineages such as Thaumarchaeota and Actinobacteria below the sub-oxic zone in LT 362 presents new insights in the ecology of these organisms. These novel findings demonstrate that 363 genomic-based investigations, when combined with environmental chemistry, add significant 364 value in comparison to taxonomy-based assumptions of microbial metabolism in the 365 environment and highlight potential inter-species metabolic dependencies. 366 Here, we identify connections between organisms in the water column via the 367 intermediate substrates, or end compounds that they produce. In other words, each cycle can be 368 seen not in isolation, but in terms of intersections among cycles. This serves as a conceptual map 369 for studying microbial metabolism in LT and can be used as a framework in other meromictic 370 lakes with strong oxygen and nutrient gradients. The connections between microbial groups and 371 metabolisms vary within and across the different lake layers. Finally, we perform a community 372 wide profiling of microbes with intersecting “metabolic guilds”. For example, we found 373 methanotrophs that can denitrify (Alphaproteobacteria), chemolithoautotrophs like anammox 374 bacteria (Planctomycetes) and nitrifiers (Nitrospira), and anoxygenic phototrophs 375 (Cyanobacteria) that use hydrogen sulfide which is known to also happen in stable mats 376 environments39, but unknown of in meromictic lakes. These findings point to the complexity and 377 diverse microbial metabolisms in natural ecosystems and for our understanding of pelagic 378 freshwater microbial paradigms. 379 380 Discussion 381 Our study of bacterial and archaeal depth-discrete community offers a whole water 382 column perspective of this incredibly deep, voluminous, and ancient freshwater lake. Our study 383 encompassed the LT’s continuous vertical redox gradient. Microbial communities were 384 dominated by from core freshwater taxa at the surface to Archaea and uncultured Candidate 385 Phyla in the latter. We provide genomic evidence for the capabilities of Bacteria and Archaea in 386 biogeochemical cycling and describe links between spatial distribution of organisms and 387 biogeochemical processes. These processes are essential in replenishing the water surface in 388 nutrients, and possibly impact critical food webs that are renowned for their high biodiversity 389 and that serve as important protein sources for local human populations in four surrounding 390 African countries46. 391 Finally, this study serves as a baseline of microbial community ecology and 392 biogeochemistry of tropical lakes and highlights the importance of characterizing and linking 393 microbial processes in deep anoxic waters. Future follow-up studies including genome informed 394 cultivation efforts, stable isotope probing and biogeochemical modeling, can be employed to 395 validate our conceptual model of transformations in tropical lake waters, and quantitatively

396 model the flow of nutrients from microorganisms to the food web, especially the impacts of the 397 little-studied globally distributed sub-oxic and anoxic freshwaters. 398 399 Methods 400 Sample collection 401 Water samples were collected from two stations, Kigoma and Mahale in 2015 (Table 1). 402 Two casts were employed at each station (Kigoma Deep Cast, Kigoma Surface, Mahale Deep 403 Cast, Mahale Offshore). Geographic coordinates of the sample sites are listed in Table S1. 404 Water samples were collected with a vertically oriented Van Dorn bottle in 2015. 405 Environmental data was collected in 2013, on a previous research cruise, using a YSI 6600 sonde 406 with optical DO and chl-a sensors. All data including temperature, dissolved oxygen (DO), 407 conductivity, chlorophyll a) and was collected down to approximately 150m. 408 409 DNA extraction and sequencing 410 DNA extractions were performed using the MP Biomedicals FastDNA Spin Kit with 411 minor protocol modifications as described previously47. Metagenome sequencing was conducted 412 at the DOE Joint Genome Institute (JGI). DNA was sequenced on the Illumina HiSeq 2500 413 platform (Illumina, San Diego, CA, U.S.A.), which produces 2 x 150 base pairs (bp) reads with a 414 targeted insert size of ~240 bp. Cells collected at 1200m at the Kigoma station were sent to the 415 DOE JGI for single-cell sequencing. 416 Single amplified genomes (SAGs) were generated following the Department of Energy 417 Joint Genome Institute's (JGI) standard protocol48. Briefly, individual cells were sorted using an 418 Influx flow cytometer (BD Biosciences) and treated with Ready‐Lyse lysozyme (Epicenter; 5 419 U μL−1 final concentration) for 15 min at room temperature. Next, cell lysis and whole‐genome 420 amplification were performed with the REPLI‐g Single Cell Kit (Qiagen) in 2 μL reactions. 421 Lysis and stop reagents from the REPLI‐g kit received UV treatment to remove potential DNA 422 contamination49. An Illumina shotgun library was constructed from each single cell and 423 sequenced on the Illumina NextSeq platform (Illumina, San Diego, CA, U.S.A.). Sequencing 424 reads were filtered using BBTools50 and assembled into SAGs using SPAdes51. 425 426 Metagenome Assembly and binning 427 Each of the 24 individual samples were assembled de novo to obtain 24 metagenomes 428 assemblies. Metagenomes were quality filtered, then assembled using MetaSPADEs52. 429 Sequencing coverage for each assembled scaffold was performed using Bowtie253. Each 24 430 metagenomes were binned individually using three binning program: MetaBat154, MetaBat255 431 and MaxBin256 test each program’s ability to recover bins, and to consolidate bins generated 432 using different binners using DASTool57. DAStool was run with default parameters except for -- 433 score_threshold 0.4. Using this approach, we reconstructed 3948 MAGs, and calculated genomes 434 statistics using CheckM58. We then used dRep59 to dereplicate these 3948 MAGs to generate a 435 non-redundant set of genomes for downstream analyses using default parameters. dRep identifies

436 the highest quality genomes from a set of similar genomes, and reduces overall redundancy for 437 the downstream analyses. Finally, we used CheckM58 lineage_wf and ssu_finder functions to 438 generate basic genome statistics and find 16S rRNA sequences for all 802 dereplicated genomes. 439 440 Identification of phylogenetic markers 441 We used a curated set of Hidden Markov Models (HMM) for 16 single-copy ribosomal proteins 442 (rpL2, rpL3, rpL4, rpL5, rpL6, rpL14, rpL14, rpL15, rpL16, rpL18, rpL22, rpL24, rpS3, rpS3, 443 rpS8, rpS10, rpS17, rpS19). Initially, the nucleotides of all genomes were translated into amino 444 acid sequences using Prodigal60 (V2.6.3). Then the HMM models were searched against the 445 MAGs and SAGs amino acid sequences using hmmsearch (HMMER 3.1b2)61 with the setting -- 446 cut_tc and by saving the alignment as a multiple alignment for all hits (-A option). The cut_tc 447 option was manually determined for each HMM by the threshold resulting in the sharpest decline 448 in score during the initial run (no --cut_tc option); and are hardcoded in the HMM profiles. The 449 esl-reformat.sh script available with hmmsearch was used to extract alignment hits. 450 451 Phylogenetic tree 452 To create the concatenated gene phylogeny, we used publicly available metagenome- 453 assembled genomes which represented a wide range of environments including marine, soil, 454 hydrothermal environments, coastal and estuarine environments. We used 16 genes for the 455 bacterial tree, and 14 genes for the archaeal tree as described previously22. The amino acid 456 sequences corresponding to each gene were imported to Geneious Prime V.2019.0.04 457 (https://www.geneious.com) separately for each Bacterial or Archaeal tree. For each gene, we 458 aligned the sequences using MAFFT (v7.388, with parameters: Automatic algorithm, 459 BLOSUM62 scoring matrix, 1.53 gap open penalty, 0.123 offset value) 62. In the case that more 460 than one copy of the ribosomal protein was found, we performed a sequence alignment of that 461 gene using MAFFT (same settings) and compared the alignments for those copies. For example, 462 if they correspond exactly to a split gene, we concatenated them to obtain a full-length gene. If 463 they were the same section (overlap) of the gene, but one was shorter than the other, the longer 464 fragment was retained. 465 We used Geneious Prime (https://www.geneious.com) to apply a 50% gap masking and 466 concatenated the 16 (or 14) proteins. The concatenated alignment was exported into the fasta 467 format and used as an input for RAxML-HPC, which was run on the CIPRES server63 with the 468 following settings: datatype = protein, maximum-likelihood search = TRUE, no bfgs = FALSE, 469 print br length = false, protein matrix spec: JTT, runtime=168 hours, use bootstrapping = TRUE. 470 The resulting Newick format tree was visualized with FigTree 471 (http://tree.bio.ed.ac.uk/software/figtree/ ). The Newick format trees for the Archaea and 472 Bacteria are available as Supplementary Data 1 and Supplementary Data 2. The concatenated 473 ribosomal protein alignments can be found in Supplementary Data 3 and 4. 474 The same procedure was followed to create the taxon-specific tree, for example for the 475 CP Tanganyikabacteria, CP Ziwabacteria, Actinobacteria ac groups, LD12, and Nitrospira, using

476 phylum-specific references, except with no gap masking since sequences were highly similar and 477 had few gaps. The amoA gene phylogeny was performed using UniProt amoA sequences with the 478 two comammox genomes and with 90% applied masking. 479 480 Taxonomic assignment and comparison of manual versus automated methods 481 Taxonomic classification of MAGs and SAGs was manually performed by careful 482 inspection of the concatenated-RP gene phylogeny, bootstrap values of each group, and closest 483 named representatives. For comparative purposes, we also assigned taxonomy using GTDB-tk10, 484 which uses ANI comparisons to reference genomes and 120 marker genes. While most 485 taxonomic assignment was consistent between methods, we noted that automatic assignment was 486 inconsistent for phyla with few representatives in the databases, such Archaea or CP. In total, 12 487 of the 48 SAGs had enough marker ribosomal sequences to be included in the bacterial tree, and 488 taxonomic classification was manually assigned, confirming classification by JGI-IMG. In most 489 cases, we were able to resolve a finer taxonomic identity for the SAGs manually. Additionally, 490 16S rRNA genes were found in 444 out of 802 MAGs using CheckM58. We used the curated 491 freshwater bacteria database “FreshTrain”, implemented in TaxAss64, to assigned taxonomy to 492 the 16S rRNA sequences. This freshwater-specific database, albeit focused on epilimnions of 493 temperate lakes is useful for comparable terminology between the Lake Tanganyika genomes 494 and the “typical” freshwater bacterial clades, lineages and tribes terminology defined 495 previously11. Taken together, the concatenated gene phylogeny, manual curation, automated 496 taxonomic classification and 16S rRNA sequences were used to provide support for phylogenetic 497 classification. 498 499 Confirmation and support for novel candidate phyla 500 To ensure that candidate phyla (CP Tanganyikabacteria and CP Ziwabacteria) described 501 in this study are indeed novel, we combined evidence from genome-wide phylogenetic analysis, 502 16S rRNA sequences and phylogeny of the MAGs, and average nucleotide identity (ANI) with 503 closely related genomes in the literature. Taken together, this providence evidence of the 504 monophyly and deep-branching of these lineages suggesting that they represent novel bacterial 505 phyla. 506 To visualize the distribution of CP Tanganyikabacteria globally, we annotated the 16S 507 rRNA sequence using ssu_finder from CheckM. Then we searched the LT’s 16S rRNA sequence 508 against all the 16S rRNA sequence database from IMG/M65 (accessed June 2019). We extracted 509 the latitude and longitude of each hit and used R to visualize the results. The points shown are 510 results with 81-100% percent ID using BLASTn66 and have a e-value between 0 and 0.0000653. 511 512 Relative abundance across water column depths 513 To obtain a matrix of relative coverage as a proxy for abundance across the samples, we 514 first mapped each metagenomic paired-read sets to its respective metagenome assemblies using 515 BBMAP50, using default settings. BBMap uses pileup.sh, which normalizes the coverage per

516 scaffold and genome size. We combined the mapping table from the 24 metagenomes, and 517 summed the total coverage based on the associated MAG ID for each scaffold. To normalize the 518 coverage by the metagenome size, we obtained the number of raw reads per metagenome. We 519 also classified the metagenomes by depth as oxic (0-50m, 100% to 80% DO), sub-oxic (50- 520 100m, 80% to 0% DO) and anoxic (100-1200m, 0% DO). We divided each coverage value by 521 the sum of reads in that layer. To determine whether a taxonomic group was “abundant” we 522 applied the criterion that the lineage must be among the 20 most abundant taxa in each of the 24 523 metagenomes 524 525 Metabolic potential analysis 526 Metabolic potential of the Tanganyika MAGs was assessed using 143 custom HMM 527 profiles67 , made using hmm-build and searched with hmmsearch (HMMER 3.1b2)61. Threshold 528 cutoffs were manually created for each 143 HMMs by identifying the score at which the sharpest 529 drop happened and manually writing the TC value in the HMM database file. Hmmsearch -- 530 use_tc and esl-reformat (part of the “easel” tool suite that comes with hmmsearch) were used to 531 export the alignments for the HMM hits. We classified the number of genes involved in sulfur 532 metabolism, hydrogen metabolism, methane, nitrogen, oxygen, C1-metabolism, C monoxide 533 (CO) metabolism, carbon fixation, organic nitrogen (urea), halogenated compounds, arsenic, 534 selenium, nitriles, and metals metabolism/utilization. To determine if a MAG could perform a 535 metabolic function, one copy of each representative gene of the pathway must have been present, 536 which a value of 1 (presence) was written, as opposed to 0 (absence) (Supplementary Table 3). 537 For novel candidate phyla, we also used kofamKOALA68 to further annotate the genomes 538 (Supplementary Table 5). To investigate heterotrophy, carbohydrates degrading enzymes were 539 annotated using dbCan269 (Supplementary Table 6). The dbCAN-HMMdb-V7 databases was 540 downloaded (June 2019), and hmmscan and hmmscan-parser.sh were used to run dbCAN2. 541 542 Annotation and differentiation of amoA and pmoA genes 543 We annotated pmoA using custom HMMs as described above. However, we did not have 544 a custom HMM for amoA. amoA and pmoA can be difficult to distinguish using gene calling 545 methods. Therefore, we selected sequences annotated as “pmoA” (amoA-like sequences) and 546 built a single-gene phylogeny using references in Supplementary File 3 from 70. We first called 547 the protein coding sequences using Prodigal60 on the File 1 named 548 “AamoA.db_an96.aln_tax.annotated.fasta”. Then we aligned the sequences using MAFFT62 in 549 Geneious Prime, and applied 90% gap masking. We used RAxML71 to build a comprehensive 550 tree. Based on the phylogenetic position of the sequences, we delineated amoA and pmoA 551 sequences (Figure S12). We noted that the pmoA custom HMM did not pick up any Archaeal 552 sequences. To identify archaeal ammonia oxidizers in LT, we searched the amoA, amoB and 553 amoC subunits of Candidatus Nitrosophaera gargensis Ga9.2 (Phylum Thaumarchaeota, 554 Kingdom Crenarchaeota, Class Nitrososphaeria) using BLASTp 2.2.3166 . We identified 5

555 Thaumarchaeota MAGs with either subunits, and 2 MAGs (with all 3 subunits). We added the 556 amoA sequences from these Thaumarchaeota to the tree in Figure S12. 557 558 Annotation and classification of Rubisco sequences 559 Following the methodology described in reference 72, we further classified the Rubisco 560 enzymes in Lake Tanganyika MAGs. We used manually curated HMMs (described in our 561 Methods) to retrieve Form I, II, II/III, III and IV. We only retained sequences over 200 basepairs. 562 To generate the concatenated tree, we retrieved the unmasked sequences from Jaffe et al., 2019, 563 and used MAFFT to include our sequences from Lake Tanganyika. A 95% sequence alignment 564 masking was described as mentioned in the methods in Jaffe et al., 2019. We used RAxML71 on 565 the CIPRES server63 to generate the concatenated tree. Rubisco forms were classified based on 566 their position in the phylogenetic tree. 567 568 Data availability 569 The 802 MAGs can be accessed under the NCBI BioProject ID: PRJNA523022. The 24 570 metagenomes can be accessed on the Integrated Microbial Genomes & Microbiomes IMG/M 571 portal using the following IMG Genome ID’s: 3300020220, 3300020083, 3300020183, 572 3300020200, 3300021376, 3300021093, 3300021091, 3300020109, 3300020074, 3300021092, 573 3300021424, 3300020179, 3300020193, 3300020204, 3300020221, 3300020196, 3300020190, 574 3300020197, 3300020222, 3300020214, 3300020084, 3300020198, 3300020603, 3300020578. 575 An interactive version of the Archaeal and Bacteria trees (Supplementary Data 1 and 2) can be 576 accessed at iTOL at the url: https://itol.embl.de/shared/patriciatran . Code to generate the figures 577 can be accessed at https://github.com/patriciatran/LakeTanganyika/. 578 579 Acknowledgments 580 We thank the University of Wisconsin - Office of the Vice Chancellor for Research and 581 Graduate Education, University of Wisconsin – Department of Bacteriology, and University of 582 Wisconsin – College of Agriculture and Life Sciences for their support. The Lake Tanganyika 583 project was supposed by the United States National Science Foundation (DEB-1030242 to 584 P.B.M and DEB-0842253 to Y.V.). We are thankful to the Tanzania Commission for Science 585 and Technology (COSTECH) for providing the research permits to collect the samples. This 586 research was also supported by the U.S. Department of Energy Joint Genome Institute (JGI) 587 through a JGI-Community Science Program Award to K.D.M (Proposal ID: CSP 2796). The 588 work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of 589 Science User Facility, is supported by the Office of Science of the U.S. Department of Energy 590 under Contract No. DE-AC02-05CH11231. K.D.M. received funding from the United States 591 National Science Foundation via an INSPIRE award (DEB-1344254) and the Wisconsin Alumni 592 Research Foundation at UW-Madison. B.M.K was supported by funding from the Leibniz 593 Institute for Freshwater Ecology and Inland Fisheries’ International Postdoctoral Research 594 Fellowship and from the German Research Foundation through the LimnoScenES project (AD

595 91/22-1). P.Q.T is supported by the Natural Sciences and Engineering Research Council of 596 Canada (NSERC) Alexander Graham Bell Fellowship Canadian Graduate Scholarship – 597 Doctoral (CGS-D). We would like to thank our colleagues for valuable feedback throughout the 598 project. 599 600 Contributions 601 P.Q.T, K.D.M, and K.A designed the study. P.B.M and B.K. conducted the sampling and 602 provided environmental data. P.Q.T. and K.A. performed genome-resolved metagenomics 603 analyses. P.Q.T. performed phylogenetic, metabolic and community analyses. P.Q.T, K.D.M, 604 and K.A. wrote the manuscript. All authors provided feedback and revised the manuscript. 605 606 Corresponding author 607 Correspondence to Karthik Anantharaman 608 609 Competing interests 610 The authors declare no competing financial interests. 611 612 Supplementary Information 613 Supplementary Methods, Supplementary Figures 1–12, Supplementary Tables 1-7 and 614 Supplementary Data 1-4

615 List of Figures 616 617 Figure 1. Sampling site and environmental data 618 A. Sampling sites in Lake Tanganyika, collected in July and October 2015 619 B. Weekly temperature profiles from January to July 2013. 620 C. Weekly dissolved oxygen profiles from January to July 2013. 621 Contours in Figure 1A and 1B were generated interp in the package Akima (Akima and 622 Gebhardt 2016), and plots were done with the fields package (Nychka et al., 2017) in R. 623 Thermocline depths are calculated from the rLakeAnalyzer (Winslow et al., 2019) package in R, 624 and are shown on the plots as points. Each point represents a calculated thermocline depth. 625 Figure 1D. Nitrate profiles (ug/L) in Mahale and Kigoma collected in 2015. 626 627 Figure 2. Phylogeny of Archaea and Bacteria 628 Concatenated gene phylogeny showing the distribution of (Figure 2A) Archaeal and (Figure 2B) 629 Bacterial metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) 630 in Lake Tanganyika, using 14 and 16 concatenated ribosomal proteins respectively, and 631 visualized using FigTree. The scale bar shows branch length corresponding to 0.3 and 0.4 632 substitutions per site. 633 A. Colored names are a subset of groups from Lake Tanganyika, with the number dereplicated of 634 MAGs listed in parenthesis. 635 B. Colored groups are the most abundant groups in Lake Tanganyika. Additionally, the 636 Candidate Phyla Radiation (CPR) and candidate phyla (CP) are listed. The two novel phyla from 637 this study are italicized. In both panels, the color scale of the circles represents the normalized 638 abundance of that taxa across depths, summarized by the values in the oxic (0-50m), sub-oxic 639 (50-100m) and anoxic (100-12000m) zones. 640 641 Figure 3. Rank abundance curve of major taxonomic groups. The total sum of coverage was 642 calculated for each taxonomic group, then the top 20 most abundant groups overall were selected 643 to be plotted here. The y axis represents the relative abundance (sum of abundance of specific 644 taxonomic group in a given layer divided by total sum of abundance across all taxa in that given 645 layer). Alphaproteobacteria and Actinobacteria are shown as classes, lineages, or tribes for 646 freshwater microbiologists/ecologists to relate to their ecosystem. 647 648 Figure 4. Novel Candidate Phyla Tanganyikabacteria 649 A. Genome-wide phylogeny of the 3 CP Tanganyikabacteria MAGs from Lake Tanganyika in 650 the context of Cyanobacteria and the sister lineages Melainabacteria, Saganbacteria, 651 Margulisbacteria and Blackallbacteria. 652 B. Global distribution of the 16S rRNA gene from CP Tanganyikabacteria. 653 C. Cellular map overview of genes common to all CP Tanganyikabacteria, with some common 654 features of CP Tanganyikabacteria.

655 656 Figure 5. Comammox Nitrospira Genome Comparison and Phylogeny. 657 A. Concatenated gene phylogeny of 16 ribosomal proteins of Nitrospira genomes from Lake 658 Tanganyika (renamed LTC-1 and LTC-2) and other environments, including comammox. 659 B. Presence (filled circles) and absence (empty circles) of selected genes in ammonia and nitrite 660 oxidation to nitrate, comammox, ammonium transporters and urea utilization. amoABC were 661 annotated using single-gene phylogenies, ammonium transporters and ureases were annotated 662 with Interpro, whereas nxrAB were annotated with custom HMM. 663 C. Distribution of MAGs identified as nitrite oxidizing bacteria (NOB), comammox and 664 anammox bacteria in Lake Tanganyika. The grey dashed lines at 50 and 100m represent the 665 oxygen layer boundaries. The red line (70m) represents the depth of the photic zone. 666 667 Figure 6. Overview of vertical biogeochemical cycling 668 Samples were collected along a vertical profile to capture changes in environmental variables. 669 The lake is separated into three operational zones: oxic, sub-oxic and anoxic. A selected group of 670 cycles (e.g. methane, fermentation, nitrogen, sulfur) are shown with the approximate vertical 671 distribution are depicted on the right. 672 673 List of Supplementary Tables 674 675 Supplementary Table 1. List of metagenomes 676 List of the 24 metagenome samples collected in Lake Tanganyika with accession ID’s. 677 678 Supplementary Table 2. Metagenome-assembled genomes information 679 Information for the 802 Metagenome-assembled genomes (MAGs) from Lake Tanganyika, 680 including taxonomic assignment, genome statistics (completeness, size, number of coding DNA 681 sequences), and more. 682 683 Supplementary Table 3. Relative abundances of MAGs 684 Relative abundances of each 802 MAGs through depths and strata. The relative abundances 685 (Columns D to AA) were obtained by first dividing each coverage value obtained by BBMap 686 pileup.sh by the number of total reads in that given metagenome. Then the value was multiplied 687 by the average number of reads of the 24 metagenomes. Column “AB”,”AC”, and “AB”, are the 688 values obtained divided by the maximum number of reads of all given metagenomes per layer 689 (oxic, sub-oxic and anoxic). 690 691 Supplementary Table 4. HMM metabolic markers in MAGs 692 The number of hits for each curated HMM of selected metabolic marker genes, in all 802 MAGs. 693

694 Supplementary Table 5. CP Tanganyikabacteria and CP Ziwabacteria KOfam KEGG 695 annotations 696 Six genomes belonging to CP Tanganyikabacteria and CP Ziwabacteria were further annotated 697 using KOFAM KEGG via the online website. 698 699 Supplementary Table 6. The 802 genomes were annotated using dbCAN. 700 The genome sizes and percent coding DNA sequences (CDS) were obtained by CheckM. The 701 number of CAZYme hits were normalized by the genome size of each MAG (Column “E”). The 702 sum, and the detailed hits for Auxiliary Activities (AA), Carbohydrate-binding domains (CBD), 703 cohesin, glycoside hydrolases (GH), glycosyl transferases (GT) and Polysaccharide lyases (PL) 704 are reported. 705 706 Supplementary Table 7. All >3000 MAGs generated, and dereplication results. Each MAG was 707 assigned to a cluster, then one representative MAG was selected for each cluster. 708 709 List of Supplementary Figures 710 Figure S1. Environmental data collected in 2010, 2011, 2012 and 2013 for dissolved oxygen, 711 temperature, chlorophyll a and conductivity. Data in was collected during 6 days in 2010 [Julian 712 days 189 to 230], 8 days in 2011 [Julian day 182 to 237], 26 days in 2012 [Julian day 194 to 362] 713 and 33 days in 2013 [Julian day 3 to 227]. Data in 2013 covers the largest day span annually. 714 Data was plotted with ggplot2 (Wickham 2016) in R, dates were converted using lubridate 715 package (Grolemund and Wickham 2011). 716 717 Figure S2. Light information 718 A. Box plots showing the Secchi depths distribution by month, for data collected in 2012 and 719 2013. Secchi depths is a visual measure of lake transparency. The coefficient of light extinction 720 (kd) is estimated from the empirical relationship 1.7/SecchiDepth(m) = kd. The water is clearest 721 in April. The 24 metagenomes from our study were collected in July and October. These months 722 are shown in red on the x-axis. 723 B. Boxplot showing the range of light extinction coefficient. The coefficient of light extinction 724 (kd) is estimated from the empirically derived relationship 1.7/SecchiDepth(meters) = kd. The 725 values are derived from the Secchi depths values A. The lower the kd value, the clearer the lake, 726 and the higher the kd value, the less transparent the lake. The 24 metagenomes were collected in 727 July and October, as shown in red on the x-axis. 728 729 Figure S3. Index of replication (iRep) values for three high-quality Cyanobacterial MAGs from 730 Lake Tanganyika, along with their abundance (coverage) values in the 24 metagenomes (color 731 coded in pink and blue for Kigoma and Mahale). Dashed grey lines at 50 and 100m represent 732 oxygen layer boundaries, and red 70m dashed line represents approximate photic zone depth 733 based in previously published environmental profiles. Note that the most abundant

734 Cyanobacteria MAG did not meet the thresholds to calculate iRep values, therefore we were 735 unable to assess its replication value. 736 737 Figure S4. A. Distribution of all Cyanobacterial MAGs in Lakes Tanganyika. In blue are 738 Cyanobacterial MAGs which are sulfide oxidizers, based on sqr presence in their genomes. In 739 Pannel B, the zoomed-in distribution on Cyanobacterial sulfide oxidizers, and all other sulfide 740 oxidizers in Lake Tanganyika. The x-axis the now the sum of coverage group by the taxonomic 741 group in the legend. The dominating sulfide oxidizers in the oxic layer (grey dashed lines) are 742 Cyanobacteria, whereas a wider diversity of organisms, including Chlorobi, reach higher 743 abundance in the anoxic layer. Pannel C. Conceptual diagram hypothesing that competition for 744 sulfide results in higher abundance of anoxygenic photosynthetic cyanobacteria at the surface 745 (where light is present), whereas below the euphotic zone, other sulfide oxidizers might have an 746 advantage. Note that purple sulfur bacteria (Gammaproteobacteria, Order Chromatiales) were not 747 identified in Lake Tanganyika. 748 749 Figure S5. Detailed Actinobacteria genome phylogeny with a focus on common freshwater 750 lineages, using MAGs from Lake Tanganyika, published datasets (see text). 751 752 Figure S6. Zoom into the CP Ziwabacteria. See Newick tree (Supplementary Data 2) for detailed 753 phylogeny. 754 755 Figure S7. Overview of the biogeochemical cycles using the data show in Table S3. Overall, the 756 taxa involved in nitrogen, sulfur, hydrogen, oxygen, carbon and other (e.g. metals) metabolism 757 are shown. 758 759 Figure S8. As a supplement to Figure S7, a bar plot shows the number of distinct taxa involved 760 in each biogeochemical category, versus the number of distinct MAGs corresponding to these 761 categories. 762 763 Figure S9. Phylogeny of Rubisco genes in Lake Tanganyika, using the sequences in72 as the 764 backbone reference tree. 765 766 Figure S10. Distribution of carbohydrate-degrading enzymes (CAZymes) among the Lake 767 Tanganyika MAGs, organized alphabetically. The CAZyme density is the total number of 768 Cazyme hits divided by the respective MAG’s genome size. 769 770 Figure 11. Distribution of organisms involved in the various metabolic cycles from Figure S7 771 and S8. 772

773 Figure S12. Single-gene phylogeny of bacterial and archaeal amoA genes (See Methods). Lake 774 Tanganyika genomes are bolded. 775 776 List of Supplementary Data 777 778 Supplementary Data 1. Archaeal tree in Newick format. 779 780 Supplementary Data 2. Bacterial tree in Newick format. 781 782 Supplementary Data 3. Concatenated alignment of 14 ribosomal proteins for Archaea. 783 784 Supplementary Data 4. Concatenated alignment of 16 ribosomal proteins for Bacteria 785 786

787 References 788 1. Verburga, P. & Hecky, R. E. The physics of the warming of Lake Tanganyika by climate 789 change. Limnol. Oceanogr. 54, 2418–2430 (2009). 790 2. Alin, S. R. & Johnson, T. C. Carbon cycling in large lakes of the world: A synthesis of 791 production, burial, and lake-atmosphere exchange estimates: CARBON CYCLING IN 792 LARGE LAKES. Glob. Biogeochem. Cycles 21, n/a-n/a (2007). 793 3. Durisch-Kaiser, E. et al. What prevents outgassing of methane to the atmosphere in Lake 794 Tanganyika? J. Geophys. Res. 116, (2011). 795 4. Cohen, A. S. et al. Climate warming reduces fish production and benthic habitat in Lake 796 Tanganyika, one of the most biodiverse freshwater ecosystems. Proc. Natl. Acad. Sci. 113, 797 9563–9568 (2016). 798 5. Salzburger, W., Van Bocxlaer, B. & Cohen, A. S. Ecology and Evolution of the African 799 Great Lakes and Their Faunas. Annu. Rev. Ecol. Evol. Syst. 45, 519–545 (2014). 800 6. Pirlot, S., Unrein, F., Descy, J.-P. & Servais, P. Fate of heterotrophic bacteria in Lake 801 Tanganyika (East Africa): Fate of bacteria in Lake Tanganyika. FEMS Microbiol. Ecol. 62, 802 354–364 (2007). 803 7. De Wever, A. et al. Bacterial Community Composition in Lake Tanganyika : Vertical and 804 Horizontal Heterogeneity. Society 71, 5029–5037 (2005). 805 8. Edmond, J. M. et al. Nutrient chemistry of the water column of Lake Tanganyika. Limnol. 806 Oceanogr. 38, 725–738 (1993). 807 9. Järvinen, M., Salonen, K., Sarvala, J., Vuorio, K. & Virtanen, A. The stoichiometry of 808 particulate nutrients in Lake Tanganyika – implications for nutrient limitation of 809 phytoplankton. Hydrobiologia 407, 81–88 (1999). 810 10. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny 811 substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018). 812 11. Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the 813 natural history of freshwater lake bacteria. vol. 75 (2011). 814 12. Stal, L. J. & Moezelaar, R. Fermentation in cyanobacteria. FEMS Microbiol. Rev. 21, 179– 815 211 (1997). 816 13. Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial 817 replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016). 818 14. Tonolla, M., Peduzzi, S., Demarta, A., Peduzzi, R. & Hahn, D. Phototropic sulfur and 819 sulfate-reducing bacteria in the chemocline of meromictic Lake Cadagno, Switzerland. J. 820 Limnol. 63, 161 (2004). 821 15. Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural 822 bacterial populations. ISME J. 10, 1589–1601 (2016). 823 16. Cabello-Yeves, P. J. et al. Genomes of novel microbial lineages assembled from the sub-ice 824 waters of Lake Baikal. Appl. Environ. Microbiol. AEM.02132-17 (2017) 825 doi:10.1128/AEM.02132-17. 826 17. Tran, P. et al. Microbial life under ice: Metagenome diversity and in situ activity of 827 Verrucomicrobia in seasonally ice-covered lakes. Environ. Microbiol. 20, 2568–2584 (2018). 828 18. Linz, A. M., He, S., Stevens, S. L. R., Anantharaman, K. & Robin, R. Connections between 829 freshwater carbon and nutrient cycles revealed through. 221, (2018). 830 19. Haas, S., Desai, D. K., LaRoche, J., Pawlowicz, R. & Wallace, D. Geomicrobiology of the 831 Carbon, Nitrogen and Sulfur Cycles in Powell Lake: A Permanently Stratified Water

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Figure 1 A B D Temperature (°C) 5 -3.338477 28 0 20 Burundi 35 27 Mahale 50

Tanzania 65 26 80 Kigoma Depth(m) 95 Kigoma 100 110 25 125 . Lake Tanganyika The copyright holder for this preprint (which was (which preprint this for holder copyright The 140 24 155 Mahale

Latitude Date

Tanzania

2013−01−03 2013−02−07 2013−03−12 2013−04−10 2013−05−15 2013−06−27 2013−07−29 200 C Dissolved Oxygen (%) Democratic Republic 5 100 of Congo 20 35 80 50 CC-BY-NC-ND 4.0 International license International 4.0 CC-BY-NC-ND this version posted November 8, 2019. 2019. 8, November posted version this

; 65 60 300

under a under 80 Zambia 95 40 Depth(m) -8.791309 110 800 27.55762 32.69844 125 20 Axis break Longitude 140 0 155 1200 https://doi.org/10.1101/834861 0 30 60 90

doi: doi: Date - 2013−07−29 2013−05−15 2013−06−27 2013−02−07 2013−03−12 2013−04−10 2013−01−03 NO3 (μg/L) 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 available made It is perpetuity. in preprint the display to a license bioRxiv granted has who the author/funder, is review) peer by certified not bioRxiv preprint preprint bioRxiv A. bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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 availableFigure 2 under aCC-BY-NC-ND 4.0 International license.

Euryarchaeota CP Parvarchaeota (3 MAGs) Diapherotrites (2 MAGs) (2 MAGs)

DPANN CP Altiarchaeales (1 MAG) CP Micrarchaeota (2 MAGs) CP Aenigmarchaeota (1 MAG) Vestraetearchaeota (2 MAGs)

Woesearchaeota (5 MAGs) CP Bathyarchaeota (2 MAGs)

Thaumarchaeota (8 MAGs)

0.3 Pacearchaeota (6 MAGs) TACK

Bacteria Symbol legend Relative abundance of each lineage in each layer: Oxic (0-50 m) B. 75%-100% Sub-oxic (50-100m) 50%-75% Cyanobacteria 25%-50% CP Fraserbacteria (1) (53) Anoxic (50-1200m) CP Tanganyikabacteria (3) 0%-25% Actinobacteria (59)

Chloroﬂexi (65)

Nitrospirae (10) Planctomycetes (45) Deltaproteobacteria (66) CP Tectomicrobia (3)

Verrucomicrobia (34) Alphaproteobacteria (87) Archaea

Gammaproteobacteria Candidate Phyla Radiation (47) (CPR) CP WWE3 (2) CP Shapirobacteria (2) (6) ) CP Roizmanbacteria (1) (OP8 s (2) a CP Gottesmanbacteria (1) (2) 1 CP Saccharibacteria (1)

Betaproteobacteria (41) AminicenenteRokubacteriWWE CP Peribacteria (1) P P P C C C CP Perigrinibacteria (1) CP Eisenbacteria (4) CP Urhbacteria (2) CP Handelsmanbacteria (2) CP Grilbaldobacteria (1) CP Ziwabacteria( 3) CP Staskawiczbacteria (2) CP Nealsonbacteria (3) Ignavibacteria (9) CP Kaiserbacteria (2) CP Parcubacteria (2) CP Liptonbacteria (1) CP Harrisonbacteria (1) CP Moranbacteria (1)

CP TM6 (4) Bacteroidetes (80) CP Hydrogenedentes (1) CP BRC1 (1) Chlorobi (2) CP Zixibacteria (1)

0.4 bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license.

0.8 Layer Oxic Layer (0−50m) Sub−oxic Layer (50−100m) 0.6 Anoxic Layer (100−1200m)

0.4

0.2 Relative Abundance (%) Abundance Relative

0.0

Chlorobi Chloroflexi Nitrospirae Cyanobacteria Bacteroidetes Ignavibacteria AcidobacteriaEuryarchaeota Planctomycetes Verrucomicrobia Actinobacteria acI Thaumarchaeota Betaproteobacteria Actinobacteria acIV DeltaproteobacteriaGemmatimonadetes Gammaproteobacteria Alphaproteobacteria LD12

Actinobacteria (non "freshwater")Alphaproteobacteria (non LD12) Taxonomic Group, ranked by total abundance across all layers Figure 4 A B

100 Cyanobacteria(71)

100 CP Margulisbacteria (9) 100 100 50 CP Saganbacteria (9)

K_Offshore_40m_m2_103

100 100 K_DeepCast_65m_m2_236 CP Tanganyikabacteria 100 . 77 Lake Tanganyika The copyright holder for this preprint (which was (which preprint this for holder copyright The M_DeepCast_65m_m2_071 0 100 CP Blackallbacteria (3) Latitude 69 Lake Malawi

96 CP Melainabacteria (14) 0.2

C −50

Sugar transporter proteins

amino acid transporters CC-BY-NC-ND 4.0 International license International 4.0 CC-BY-NC-ND this version posted November 8, 2019. 2019. 8, November posted version this

; 2 x MFS transporters iron complex Ferrous Iron Transporter AB Biopolymer transport system (exbBD) outermembrane 2 x Magnesium transporters recepter and transport protein under a under chemotaxis proteins cheXR, motAB −100 0 100 200 pili CpaB, Flp, PilA cyt c oxidase cbb3 partial Longitude TCA Cycle NO3-NO2 CO dehydrogenase transporter NO2 Map Legend NO3 F-type H+-transporting ATPase subunit nirK alpha, b, c, delta, epsilon Aquatic freshwater Engineered NO2 https://doi.org/10.1101/834861 NO chemotaxis cheWYA, Aquatic freshwater sediment Host−associated NO3 peptide/nickel transport system doi: doi: nxrAB NO2 ATP-binding protein, permease proteins Aquatic (e.g. groundwater) Terrestrial and substrate binding protein NO2 NO3 narGH Aquatic sediment napAB NO3 NO2 Marine 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 available made It is perpetuity. in preprint the display to a license bioRxiv granted has who the author/funder, is review) peer by certified not bioRxiv preprint preprint bioRxiv Flagella rod shape-determining protein MreBCD and related proteins (FlgADCGHI, FliHEFGNYOZS, FlhAFB, FlbD) Figure 5

A B Nitrogen Cycling Nitrification and comammox amoABC nxrAB NH3/NH4 NO2 NO3 (alpha, beta, gamma) 100 Candidatus Rokubacteria (8 genomes) ammonium trannsporter ureBCA comammox amoA amoB amoC nxrA nxrB

Thermodesulfobacteria yellowstonii [ GCA_000020985.1] 100 K_DeepCast_300m_m2_067 100 K_DeepCast_250m_m1_045 9 0 Nitrospira bacteria SM23_35 [GCA_001303745.1] 100 6 0 K_DeepCast_150m_m2_200 100 M_DeepCast_200m_m2_046 C . Nitrospira sp. bin 75 [NIUT01000208.1] Clade IV The copyright holder for this preprint (which was (which preprint this for holder copyright The K_Offshore_80m_m2_040 5 0 comammox NOB Anammox Nitrospira_10061_58_17 [VBOJ01000001.1] 6 3 K_Offshore_80m_m2_121 100 9 9 0 M_DeepCast_65m_m2_186 6 0 M_DeepCast_65m_m2_155

100 Nitrospira defluvi [GCA_000196815.1] 100 Nitrospira sp. ND1 [GCA_900170025.1] 100 Clade I 100 Nitrospira sp. UW-LDO-02 [GCA_002254325.1] Nitrospira sp. OLB3 [GCA_001567445.1] N. sp. CG24A [GCA_002869925.2] 100 N. sp. RCB SPAX01000025 [GCA_005239475.1] Taxonomy 6 2 Clade IIB 100 N. sp. CG24C [GCA_002869885.2] 100 4 3 Acidobacteria N. sp. CG24E [GCA_002869895.2] 400 Actinobacteria N. japonica [GCA_900169565.1] 100 Alphaproteobacteria N. sp. UBA5699 [GCA_002420105.1] Bacteroidetes Nitrospira sp. ST-bin5 [GCA_002083555.1] Chloroflexi 100 8 8 Clade II canonical CP Eisenbacteria N. lenta [GCA_900403705.1] 100 CP Handelsmanbacteria CC-BY-NC-ND 4.0 International license International 4.0 CC-BY-NC-ND Nitrospira sp. CG24D [GCA_002869855.2] CP Hydrogenedentes this version posted November 8, 2019. 2019. 8, November posted version this

; N. moscoviensis [GCA_001273775.1] CP Rokubacteria 100 Nitrospira sp. UBA2082 [GCA_002331335.1] CP Tanganyikabacteria 100

Depth (m) CP Ziwabacteria M_DeepCast_65m_mx_150 (Nitrospira sp. LTC-2) under a under 9 9 Deltaproteobacteria 100 M_DeepCast_50m_m2_151 (Nitrospira sp. LTC-1) 9 9 Fibrobacteres acidobacteria N. inopinata [GCA_001458695.1] 800 Nitrospirae 9 3 Nitrospira sp. ST-bin4 [GCA_002083565.1] Phycisphaerae 100 Planctomycetes 5 9 Nitrospira sp. SG-bin2 [GCA_002083405.1] 9 9 Poribacteria Nitrospira sp. UBA6909 [GCA_002451055.1] Verrucomicrobia V3 Nitrospira sp. RCA [GCA_005239465.1] Ca. N. nitrificans [GCA_001458775.1] 100 Clade IIA Nitrospira sp. bin 8 [GCA_001464735.1] 4 4 100 9 1 Nitrospira sp. CG24B [GCA_002869845.2] Nitrospira sp. SG-bin1 [GCA_002083365.1] 100 Nitrospira sp. UBA2083 [GCA_002331625.1] https://doi.org/10.1101/834861 100 Nitrospira sp. UBA5702 [GCA_002420045.1] 1200 100 Nitrospira sp. UBA5698 [GCA_002420115.1] 100 doi: doi: Ca. N. nitrosa [GCA_001458735.1] 100 0.2 Nitrospira sp. UW-LDO-01 [GCA_002254365.1] 0 20 40 0 20 40 0 20 40 Coverage in metagenome 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 available made It is perpetuity. in preprint the display to a license bioRxiv granted has who the author/funder, is review) peer by certified not bioRxiv preprint preprint bioRxiv Figure 6 Lake Surface

0m Photosynthesis 10m Light Organosulfur Oxic Heterotrophs - Aerobic respiration NO (0-50m) CO CO 3 35m 2 2 NO - 40m O2 2 50m Nitrifiers Biomass Comammox 65m Sub-oxic Sulfur oxidizers (50-100m) (Organic C, N, P) 80m Denitrification H S S0 - + 2 SO3 NH4 DNRA 100m CH2O 120m Anammox - NO Sulfate reducers H O 2 Anoxic 2 150m (100-1200m) NO . The copyright holder for this preprint (which was (which preprint this for holder copyright The 200m CH 4 Methanotrophs N2O

250m

N2 Methanogens

300m 2- SO4 CC-BY-NC-ND 4.0 International license International 4.0 CC-BY-NC-ND this version posted November 8, 2019. 2019. 8, November posted version this ; Formate under a under Acetate

400m

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doi: doi: Metagenomes Autotrophs (Photolithotrophs) Samples collected at: Heterotrophs (Chemoorganotrophs) Both stations Methane Cycle Mahale Fermentation Cycle H Nitrogen Cycle 1200m Kigoma 2 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 available made It is perpetuity. in preprint the display to a license bioRxiv granted has who the author/funder, is review) peer by certified not bioRxiv preprint preprint bioRxiv Sulfur Cycle

Bottom of the lake Dissolved Oxygen (% saturation) Figure S1 2010 2011 2012 2013 Julian Days 0 bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable 50 300

100 200 Depth (m) Depth 100 150 doi:

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150 670 680 690 700 670 680 690 700 670 680 690 700 670 680 690 700 Conductivity bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. The copyright holder for this preprint (which was A. not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It isFigure made available S2 (more under aCC-BY-NC-ND 4.0 International license. transparent)

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January March May July September November February April June August October December Month Figure S3 Location Kigoma Mahale A Index of replication of Cyanobacteria MAGs in Lake Tanganyika bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable K_DeepCast_35m_m2_155 K_Offshore_surface_m2_011 M_surface_10_m2_119 0 doi: https://doi.org/10.1101/834861 400

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A. B. Distribution of all Cyanobacteria Distribution of Sulfide Oxidizers C. doi: Cyanobacteria Cyanobacteria (57 MAGs) Cyanobacteria with sqr Others with sqr Sulfide https://doi.org/10.1101/834861 Purple Sulfur sqr Bacteria 0 0 Betaproteobacteria Other sulfide oxidizers Elemental sulfur (e.g. Chlorobi) Alphaproteobacteria non LD12

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800 800 Chloroflexi (6 MAGs) CP Tanganyikabacteria (1 MAG) CP Ziwabacteria (1 MAG)

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0 100 200 300 400 0 25 50 75 100 125 0 25 50 75 100 125 Coverage in metagenome Sum of Taxonomic Groups' Coverage in metagenome bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license.

Figure S5 Tree scale: 0.1 0 - 50m Clade Coverage 50 - 100 m K.Offshore.40m.m2.139 100-1200m SAG.MEInt.Actinobacteria-bacterium-acTH1-A-JGI.MCM14ME106.Actinobacteria.Actinobacteria.Actinomycetales.acTHL.acTHL-A.acTH1-A1 Tang.SAG.Actinobacteria.bacterium.JGI.MCMTA15005.contamination.screened 100 M.surface.9.m2.101 K.DeepCast.35m.m1.047

100 K.Offshore.80m.m2.226 94 K.Offshore.40m.m2.150 96 acSTL K.Offshore.40m.m1.268 93

100 Actinobacteria Actinobacteridae

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100 100

100 Lake Tanganyika Actinobacterium

100 100 Lake Tanganyika Actinobacterium

100 92 Actinobacteria Acidimicrobidae

MEint.metabat.1953.2582580544.Actinobacteria.Actinobacteria.Acidimicrobiales 99 Bacteria.Actinobacteria.Actinobacteria.unclassified.Actinobacteria.Candidatus.Microthrix.parvicella.Bio17.1 96 K.Offshore.80m.m1.195 94 K.Offshore.40m.m2.105 Bacteria.Actinobacteria.Actinobacteria.Acidimicrobidae.Acidimicrobiales.Acidimicrobineae.Acidimicrobiaceae.Ilumatobacter.coccineum.YM16.304 MEint.metabat.3163.2582580565.Actinobacteria.Actinobacteria.Acidimicrobiales SAG.MEInt.Actinobacteria-bacterium-acIV-C-JGI.MCM14ME150.Actinobacteria.Acidimicrobiia.Acidimicrobiales-.acIV.acIV-C.Iluma-C1 100 M.surface.8.m2.026 K.Offshore.surface.m2.010 acIV 97 M.surface.10.m2.188

97 MEint.metabat.14260.2582580530.Actinobacteria.Actinobacteria.Acidimicrobiales 94 MEint.metabat.1091.2582580517.Actinobacteria.Actinobacteria.Acidimicrobiales 93 SAG.AAA027-L17.Actinobacteria.Actinobacteria.Actinomicrobiales.acIV.acIV-B.Iluma-B1 MEint.metabat.11576.2582580520.Actinobacteria.Actinobacteria.Acidimicrobiales 100 K.Offshore.80m.m1.031 100 K.Offshore.120m.m1.107 100 SAG.AAA027-E14.Actinobacteria.Actinobacteria.Actinomicrobiales.acIV.acIV-A.Iluma-A2 K.DeepCast.300m.m2.027 100 M.surface.7.m2.239 acIV Iluma A1 Bacteria.Actinobacteria.Actinobacteria.Actinobacteridae.Actinomycetales.Frankineae.Sporichthyaceae.Sporichthya.polymorpha.DSM.43042 CP015606.1 100 K.Offshore.0m.m1.007 acI-C SAG.AAA044-O16.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-A.acI-A5 100 SAG.AAA028-G02.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-A.acI-A5 SAG.AAA028-E20.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-A.acI-A6 CP015605.1 100 CP016776.1 CP016782.1 CP015604.1

100 CP016783.1 100 100 CP016780.1 CP016769.1

100 CP016774.1

100 CP016781.1 CP016775.1 96 CP016778.1 100 CP016773.1 100 CP015603.1 99 100 CP016777.1 100 CP016770.1 CP016772.1 M.surface.10.m2.076 M.surface.8.mx.219 100 M.surface.9.m1.009 acI-C 100 100 M.surface.9.m1.017 100 M.surface.9.m2.080 100 MEint.metabat.3864.2582580572.Actinobacteria.Actinobacteria

94 K.DeepCast.65m.m2.221

99 M.surface.10.m2.136

97 SAG.MEInt.Actinobacteria-bacterium-acI-C-JGI.MCM14ME159.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-C.acI-C2

94 SAG.MEInt.Actinobacteria-bacterium-acI-C-JGI.MCM14ME133.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-C.acI-C2 92 SAG.MEInt.Actinobacteria-bacterium-acI-C-JGI.MCM14ME182.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-C.acI-C2 SAG.MEInt.Actinobacteria-bacterium-acI-C-JGI.MCM14ME034.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-C.acI-C2 TBepi.metabat.3475.2582580632.Actinobacteria.Actinobacteria SAG.AAA044-D11.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B4 SAG.TBHyp.Actinobacteria-bacterium-acI-B-JGI.MCM14TBH026.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B2 SAG.TBHyp.Actinobacteria-bacterium-acI-B-JGI.MCM14TBH075.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B2 SAG.TBHyp.Actinobacteria-bacterium-acI-B-JGI.MCM14TBH061.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B2 TBepi.metabat.3207.2582580630.Actinobacteria.Actinobacteria 100 TBhypo.metabat.3463.2582580675.Actinobacteria.Actinobacteria M.DeepCast.50m.m2.266 M.surface.9.m1.114 100 K.DeepCast.1200m.m2.173 M.DeepCast.10m.mx.089 acI-B M.surface.7.m2.032 SAG.MEInt.Actinobacteria-bacterium-acI-B-JGI.MCM14ME048.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B1 100 99 SAG.MEInt.Actinobacteria-bacterium-acI-B-JGI.MCM14ME025.Actinobacteria.Actinobacteria.Actinomycetales.acI.acI-B.acI-B1 CP016768.2 100 CP016771.1 100 CP016779.1 M.DeepCast.10m.m2.141

100 K.Offshore.40m.m2.034 M.surface.6.mx.034 K.DeepCast.65m.m2.120 Tang.SAG.Actinobacteria.bacterium.JGI.MCMTA15072.contamination.screened K.DeepCast.250m.m2.310 bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license.

Figure S6

CP Ziwabacteria bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Nitrogen Figure S7 A 41 taxonomic groups and 276 distinct MAGs

Octaheme c− nitric nitrous Ammonia nitrate nitrite nitrite nitrogen type anammox comammox oxide oxide

oxidation reduction oxidation reduction fixation cytochrome doi: reduction reduction Shewanella− type https://doi.org/10.1101/834861

Woesearchaeota Archaea Thaumarchaeota Euryarchaeota Diapherotrites

Verrucomicrobia V4 Verrucomicrobia V3 Planctomycetes Phycisphaerae Nitrospirae Lentisphaerae

Kirimatiellacea under a Ignavibacteria Gemmatimonadetes ;

Gammaproteobacteria this versionpostedNovember8,2019. CC-BY-NC-ND 4.0Internationallicense Firmicutes Fibrobacteres acidobacteria Deltaproteobacteria Nb.of.genes Deinococcus thermus 4 Cyanobacteria CP Zixibacteria 3 CP Ziwabacteria Bacteria CP WOR−3

Taxonomy 2 CP Tectomicrobia CP Tanganyikabacteria CP Rokubacteria 1 CP Poribacteria CP Hydrogenedentes CP Handelsmanbacteria CP Eisenbacteria CP Aminicemantes (OP8) Chloroflexi The copyrightholderforthispreprint(whichwas

Chlorobi . Calditrichaeota Betaproteobacteria Bacteroidetes Armatimonadetes Alphaproteobacteria non LD12 Actinobacteria acIII Actinobacteria acI−B1 Actinobacteria Acidobacteria nirB nirK nifH nirD nrfA nrfH octR narH narG nosZ nosD napA napB amoA nifA_Mo nifB_Mo Comammox nitrite_reductase_nirS hydrazine_oxidase_hzoA hydrazine_synthase_hzsA nitrite_oxidoreductase_nxrA nitrite_oxidoreductase_nxrB nitric_oxide_reductase_norB nitric_oxide_reductase_norC Genes bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Sulfur Figure S7 B 49 taxonomic groups and 502 distinct MAGs

associated strongly with associated sulfide sulfur sulfate sulfite the ancillary core with

oxidation oxidation reduction reduction electron genes genes doi: sulfur transport oxidation chain https://doi.org/10.1101/834861

Woesearchaeota Archaea Thaumarchaeota Euryarchaeota Diapherotrites CP Verstraetaerchaeota Verrucomicrobia V6 Verrucomicrobia V4 Verrucomicrobia V3 Unclassified Planctomycetes Phycisphaerae Nitrospirae Lentisphaerae

Ignavibacteria under a Gemmatimonadetes Gammaproteobacteria

Firmicutes ; this versionpostedNovember8,2019.

Fibrobacteres acidobacteria CC-BY-NC-ND 4.0Internationallicense Deltaproteobacteria Deinococcus thermus Nb.of.genes Cyanobacteria 6 CP Zixibacteria CP Ziwabacteria 5 CP WOR−2 Omnitrophica CP TM6 4 Bacteria CP Tectomicrobia 3

Taxonomy CP Tanganyikabacteria CP Rokubacteria 2 CP Poribacteria CP Kaiserbacteria 1 CP Handelsmanbacteria CP Gribaldobacteria CP Eisenbacteria CP BRC1 CP Aminicemantes (OP8) Chloroflexi Chlorobi Chlamydiae Calditrichaeota

Betaproteobacteria The copyrightholderforthispreprint(whichwas

Bacteroidetes . Armatimonadetes Alphaproteobacteria non LD12 Actinobacteria Iluma−A1 Actinobacteria acIV−C Actinobacteria acIV Actinobacteria acIII Actinobacteria Acidobacteria sat dsrJ fccB dsrF dsrT dsrE dsrA dsrB dsrK dsrP dsrS dsrH dsrD dsrC dsrR aprA soxZ dsrO soxA soxB soxX soxY dsrM soxD sulfur_dioxygenase_sdo thiosulfate_reductase_phsA sulfide_quinone_oxidoreductase_sqr Genes bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Hydrogen Figure S7 C 46 taxonomic groups and 220 distinct MAGs

NiFe FeFeHydrogenase Hydrogenase Woesearchaeota Euryarchaeota doi:

Diapherotrites Archaea CP Verstraetaerchaeota https://doi.org/10.1101/834861 CP Parvarchaeota CP Pacearchaeota CP Bathyarchaeota CP Altiarchaeota

Verrucomicrobia V4 Verrucomicrobia V3 Planctomycetes Phycisphaerae Nitrospirae Lentisphaerae under a Ignavibacteria Gemmatimonadetes Gammaproteobacteria ; this versionpostedNovember8,2019.

Fibrobacteres acidobacteria CC-BY-NC-ND 4.0Internationallicense Deltaproteobacteria Nb.of.genes Cyanobacteria 4 CP Ziwabacteria CP WWE1 CP WOR−3 3 CP WOR−2 Omnitrophica

Taxonomy CP Shapirobacteria 2

CP Roizmanbacteria Bacteria CP Poribacteria 1 CP Nealsonbacteria CP Moranbacteria CP Handelsmanbacteria CP Gottesmanbacteria CP Fraserbacteria CP Eisenbacteria CP BRC1

CP Aminicemantes (OP8) The copyrightholderforthispreprint(whichwas Chloroflexi . Chlorobi Chlamydiae Calditrichaeota Betaproteobacteria Bacteroidetes Armatimonadetes Alphaproteobacteria non LD12 Actinobacteria acSTL Actinobacteria Acidobacteria FeFeHydrogenase FeFeHydrogenase_2 Hydrogenase_Group_1 Hydrogenase_Group_4 Hydrogenase_Group_3c Hydrogenase_Group_2a Hydrogenase_Group_2b Hydrogenase_Group_3b Hydrogenase_Group_3d Genes bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Oxygen Figure S7 D 49 taxonomic groups and 556 distinct MAGs

Oxygen Oxygen Oxygen Oxygen Oxygen metabolism metabolism metabolism metabolism metabolism − − − − −

cytochrome doi: cytochrome cytochrome cytochrome cytochrome (quinone) (quinone) (quinone) c c oxidase,

oxidase, oxidase, oxidase, oxidase, https://doi.org/10.1101/834861 aa3 bd bo caa3− cbb3− type, type type type type

QoxABCD Archaea Woesearchaeota Thaumarchaeota

Verrucomicrobia V6 Verrucomicrobia V4 Verrucomicrobia V3 Verrucomicrobia V2 Verrucomicrobia V1 Planctomycetes

Phycisphaerae under a Nitrospirae Lentisphaerae ;

Ignavibacteria this versionpostedNovember8,2019. Gemmatimonadetes CC-BY-NC-ND 4.0Internationallicense Gammaproteobacteria Fibrobacteres acidobacteria Deltaproteobacteria Deinococcus thermus Cyanobacteria CP Zixibacteria CP Ziwabacteria Nb.of.genes CP Tectomicrobia CP Tanganyikabacteria 7 CP Rokubacteria 6

CP Poribacteria Bacteria 5 CP Kaiserbacteria 4 CP Hydrogenedentes Taxonomy CP Handelsmanbacteria 3 CP Eisenbacteria 2 CP Aminicemantes (OP8) 1 Chloroflexi The copyrightholderforthispreprint(whichwas

Chlorobi . Chlamydiae Calditrichaeota Betaproteobacteria Bacteroidetes Armatimonadetes Alphaproteobacteria non LD12 Alphaproteobacteria LD12 Actinobacteria Iluma−A1 Actinobacteria acTH1 Actinobacteria acSTL Actinobacteria acIV−C Actinobacteria acIV Actinobacteria acIII Actinobacteria acI−C2 Actinobacteria acI−C Actinobacteria acI−B1 Actinobacteria Acidobacteria coxA coxB cyoA cyoE cydA cydB ccoP qoxA cyoD ccoN ccoO Genes bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Carbon Figure S7 E 66 taxonomic groups and 618 distinct MAGs

CBB Methylamine particulate Reverse soluble Wood 3HP/ cycle CO Formaldehyde Formate methane methanol −−> methane TCA methane Ljundahl 4HB − Oxidation oxidation oxidation production oxidation Formaldehyde oxidation cycle oxidation pathway

Rubisco doi: Woesearchaeota Thaumarchaeota https://doi.org/10.1101/834861

Euryarchaeota Archaea Diapherotrites CP Verstraetaerchaeota CP Parvarchaeota CP Pacearchaeota CP Micrarchaeota CP Bathyarchaeota CP Altiarchaeota CP Aenigmarchaeota Verrucomicrobia V6 Verrucomicrobia V4 Verrucomicrobia V3 Unclassified Planctomycetes Phycisphaerae Nitrospirae Lentisphaerae Kirimatiellacea

Ignavibacteria under a Gemmatimonadetes Gammaproteobacteria Firmicutes Fibrobacteres acidobacteria ;

Deltaproteobacteria Nb.of.genesthis versionpostedNovember8,2019.

Deinococcus thermus CC-BY-NC-ND 4.0Internationallicense Cyanobacteria CP Zixibacteria CP Ziwabacteria 30 CP WWE1 CP WOR−3 CP WOR−2 Omnitrophica 20 CP Tectomicrobia CP Tanganyikabacteria Bacteria

Taxonomy CP Staskawiczbacteria CP Rokubacteria 10 CP Roizmanbacteria CP Poribacteria CP Peribacteria CP Moranbacteria CP Kaiserbacteria CP Hydrogenedentes CP Handelsmanbacteria CP Fraserbacteria CP Eisenbacteria CP BRC1 CP Aminicemantes (OP8) Chloroflexi Chlorobi Chlamydiae Calditrichaeota Betaproteobacteria Bacteroidetes Armatimonadetes Alphaproteobacteria non LD12 Actinobacteria Iluma−A1 The copyrightholderforthispreprint(whichwas

Actinobacteria acTH1 . Actinobacteria acSTL Actinobacteria acIV−C Actinobacteria acIV Actinobacteria acI−C2 Actinobacteria acI−C Actinobacteria acI−B1 Actinobacteria Acidobacteria sfh fae fmtf fdhA fdhB fdhC sgdh mcrA mcrB mcrC mtmc smdh madA madB pmoA pmoB pmoC mmoB codhC codhD mmoD fdh_thiol_id codh_catalytic rubisco_form_I rubisco_form_II rubisco_form_III rubisco_form_IV rubisco_form_II_III acetate_citrate_lyase_aclA acetate_citrate_lyase_aclB ndma_methanol_dehydrogenase Four.hydroxybutyryl.CoA.synthetase Four.hydroxybutyryl.CoA.dehydratase carbon_monoxide_dehydrogenase_coxL carbon_monoxide_dehydrogenase_coxS carbon_monoxide_dehydrogenase_coxM Genes bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Other Figure S7 F 47 taxonomic groups and 393 distinct MAGs

Metal Halogenated (Iron/ Arsenate Arsenite Chlorite Nitrile Selenate compounds Manganese) reduction oxidation reduction hydratase Reduction

breakdown oxidation/ doi: Reduction

Woesearchaeota https://doi.org/10.1101/834861 Archaea Thaumarchaeota Euryarchaeota CP Micrarchaeota CP Bathyarchaeota

Verrucomicrobia V4 Verrucomicrobia V3 Verrucomicrobia V2 Verrucomicrobia V1 Unclassified Planctomycetes

Phycisphaerae under a Nitrospirae Lentisphaerae ;

Kirimatiellacea this versionpostedNovember8,2019. Ignavibacteria CC-BY-NC-ND 4.0Internationallicense Gemmatimonadetes Gammaproteobacteria Fibrobacteres acidobacteria Deltaproteobacteria Nb.of.genes Deinococcus thermus 6 Cyanobacteria 5 CP Zixibacteria CP Ziwabacteria 4 CP Tectomicrobia Bacteria 3 Taxonomy CP Tanganyikabacteria 2 CP Rokubacteria CP Poribacteria 1 CP Hydrogenedentes CP Handelsmanbacteria CP Fraserbacteria CP Eisenbacteria

CP Aminicemantes (OP8) The copyrightholderforthispreprint(whichwas Chloroflexi . Chlorobi Calditrichaeota Betaproteobacteria Bacteroidetes Armatimonadetes Alphaproteobacteria non LD12 Alphaproteobacteria LD12 Actinobacteria acSTL Actinobacteria acIV−C Actinobacteria acIV Actinobacteria acIII Actinobacteria Acidobacteria cld rdh hdh ygfK nthA nthB arsC mtrA mtrC ygfM ars_ox sel_mo ars_ox_2 ars_thioredoxin ars_glutaredoxin Genes Figure S8 600 bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable doi: https://doi.org/10.1101/834861

400 under a ; this versionpostedNovember8,2019. CC-BY-NC-ND 4.0Internationallicense Shown Number of MAGs

Count Number of Taxa

200 The copyrightholderforthispreprint(whichwas .

Carbon Hydrogen Nitrogen Other Oxygen Sulfur Biogeochemical category bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license. Figure S9

ABA23512_Anabaena_variabilis_ATCC_29413_I_REF_reference_I Tanganyika_M_DeepCast_200m_mx_041_Ga0194113_10325264_1_Cyanobacteria Form I Tanganyika_K_DeepCast_0m_mx_142_Ga0194124_10207389_1_Cyanobacteria

Allochromatium_vinosum_YP_00344469_REF_reference_I Thiocapsa_marina_ZP_0877298_REF_reference_I Nitrococcus_mobilis_ZP_0112568_REF_reference_I Tanganyika_K_Offshore_surface_m2_239_Ga0194130_10004910_4_Cyanobacteria Tanganyika_K_Offshore_surface_m2_011_Ga0194130_10000239_109_Cyanobacteria Tanganyika_M_DeepCast_200m_mx_001_Ga0194113_10001418_7_Cyanobacteria Tanganyika_M_surface_7_m2_037_Ga0194118_10002759_1_Cyanobacteria Tanganyika_M_surface_10_m1_298_Ga0194123_10011154_11_Cyanobacteria Tanganyika_K_DeepCast_150m_m2_036_Ga0194126_10004014_23_Cyanobacteria Tanganyika_K_DeepCast_250m_m2_038_Ga0194125_10027049_3_Cyanobacteria Tanganyika_K_DeepCast_100m_m1_010_Ga0194127_10007202_5_Cyanobacteria Tanganyika_M_DeepCast_10m_m2_174_Ga0194122_10001851_1_Cyanobacteria Tanganyika_K_DeepCast_0m_m1_088_Ga0194124_10002279_1_Cyanobacteria Tanganyika_K_DeepCast_35m_m2_155_Ga0194129_10001660_15_Cyanobacteria Tanganyika_M_surface_7_m2_040_Ga0194118_10024976_1_Cyanobacteria Tanganyika_K_DeepCast_250m_m2_308_Ga0194125_10039555_1_Cyanobacteria Form I Tanganyika_K_Offshore_40m_m2_224_Ga0194133_10043047_1_Cyanobacteria Tanganyika_M_surface_10_m2_146_Ga0194123_10051515_1_Cyanobacteria Tanganyika_K_DeepCast_35m_m2_023_Ga0194129_10005969_9_Cyanobacteria uncultured_marine_typeA_Synechococcus_ABD9625_REF_reference_I Synechococcus_sp_WH8102_NP_89780_REF_reference_I Prochlorococcus_marinus_YP_00109080_REF_reference_I Thiorhodovibrio_sp_970_ZP_0894247_REF_reference_I Bradyrhizobium_sp_ORS_278_YP_00120434_REF_reference_I Tanganyika_K_DeepCast_150m_m1_387_Ga0194126_10000133_112_Betaproteobacteria Q59613_Nitrobacter_vulgaris_I_REF_reference_I Nitrosomonas_eutropha_YP_74703_REF_reference_I Form I Cupriavidus_metallidurans_YP_58365_REF_reference_I Tanganyika_K_Offshore_120m_m1_203_Ga0194131_10090287_1_Betaproteobacteria Tanganyika_K_DeepCast_150m_m2_250_Ga0194126_10004264_8_Betaproteobacteria Tanganyika_K_DeepCast_300m_mx_197_Ga0194111_10131075_2_Chloroflexi Tanganyika_M_DeepCast_65m_m1_275_Ga0194120_10053553_4_Alphaproteobacteria Tanganyika_M_DeepCast_65m_m2_091_Ga0194120_10030983_5_Woesearchaeota Form IIIc rifcsplowo2_01_scaffold_4908_46_Aenigmarchaeota_unknown rifcsphigho2_01_scaffold_100504_5_Micrarchaeota_unknown Form IIIb RIFCSPHIGHO2_01_FULL_OD1_39_14_rifcsphigho2_01_scaffold_71123_4_Buchananbacteria_IIIb

Tanganyika_M_DeepCast_400m_m2_277_Ga0194112_10058330_4_Woesearchaeota CG09_land_8_20_14_0.10_scaffold_6172_c_6_Diapherotrites_IIIb rifcsplowo2_01_scaffold_53096_8_Woesearchaeota_IIIb Tanganyika_M_DeepCast_400m_m2_303_Ga0194112_10018026_6_Pacearchaeota Tanganyika_M_DeepCast_65m_mx_140_Ga0194120_10020200_3_Parvarchaeota CG06_land_8_20_14_3.00_150_scaffold_71672_c_2_Micrarchaeota_IIIb CG08_land_8_20_14_0.20_scaffold_7644_c_2_Micrarchaeota_IIIb Tanganyika_K_DeepCast_300m_mx_218_Ga0194111_10166674_2_Bathyarchaeota Tanganyika_M_DeepCast_65m_m1_115_Ga0194120_10008406_7_Woesearchaeota RifSed_csp2_10ft_2_scaffold_141884_1_Pacearchaeota_IIIb RifSed_csp2_10ft_3_scaffold_402847_1_Pacearchaeota_IIIb Form IIIb rifcsplowo2_01_scaffold_159415_2_Pacearchaeota_IIIb rifcsplowo2_01_scaffold_401896_1_Pacearchaeota_IIIb rifcsphigho2_01_scaffold_319644_1_Pacearchaeota_IIIb

rifcsplowo2_02_scaffold_407950_1_Pacearchaeota_IIIb GWB1_scaffold_6749_11_Pacearchaeota_IIIb rifcsplowo2_01_scaffold_412156_2_Pacearchaeota_IIIb

rifcsphigho2_01_sub10_scaffold_4260_8_Pacearchaeota_IIIb rifcsplowo2_01_scaffold_22357_10_Pacearchaeota_IIIb Form IIIb rifcsplowo2_02_scaffold_295114_1_Pacearchaeota_IIIb rifcsphigho2_02_scaffold_369452_2_Pacearchaeota_IIIb rifcsphigho2_01_scaffold_15374_6_Pacearchaeota_IIIb rifcsphigho2_01_scaffold_6966_11_Pacearchaeota_IIIb Tanganyika_M_DeepCast_50m_m2_169_Ga0194121_10001633_19_Pacearchaeota Form IIIb Form IIIb Form IIIb

RifSed_csp2_19ft_2_scaffold_258162_1_Micrarchaeota_IIIb

CG10_big_fil_rev_8_21_14_0.10_scaffold_7288_19_Amesbacteria_IIIb rifcsphigho2_01_scaffold_12654_5_Amesbacteria_IIIb Tanganyika_M_DeepCast_400m_m2_082_Ga0194112_10000156_108_Pacearchaeota

Form IIIb rifcsplowo2_01_scaffold_118545_1_Amesbacteria_IIIb

Tanganyika_K_DeepCast_250m_m2_117_Ga0194125_10011911_9_Micrarchaeota Tanganyika_K_DeepCast_150m_m2_121_Ga0194126_10051330_4_Micrarchaeota CG10_big_fil_rev_8_21_14_0.10_scaffold_22081_3_Pacearchaeota_IIIb Form IIIb Form IIIb

rifcsplowo2_01_scaffold_269943_3_Pacearchaeota_IIIb Tanganyika_M_DeepCast_200m_m2_220_Ga0194113_10004659_13_Altiarchaeota CG10_big_fil_rev_8_21_14_0.10_scaffold_3334_6_Micrarchaeota_IIIb CG_201501_scaffold_106789_1_Micrarchaeota_IIIb cg1_0.2_scaffold_83715_1_Micrarchaeota_IIIb

CG10_big_fil_rev_8_21_14_0.10_scaffold_1834_23_Micrarchaeota_IIIb Tanganyika_M_DeepCast_200m_m2_220_Ga0194113_10150992_1_Altiarchaeota CG13_big_fil_rev_8_21_14_2.50_scaffold_6700_4_CPR_IIIb rifcsplowo2_01_scaffold_4893_79_Micrarchaeota_IIIb LSDeep1_scaffold_289_96_Peregrinibacteria_unknown Form III-like rifcsphigho2_01_scaffold_7865_10_Woesearchaeota_IIIb UBA153contig_35505_2_Woesearchaeota_IIIb rifoxyd1_full_scaffold_54939_2_Micrarchaeota_IIIb rifcsphigho2_12_scaffold_331687_2_Pacearchaeota_IIIb CG_4_9_14_3_um_filter_150_scaffold_9172_2_Micrarchaeota_IIIb rifcsphigho2_02_scaffold_33630_15_Pacearchaeota_IIIb UBA93contig_1572_78_Micrarchaeota_unknown UBA96contig_21807_17_Micrarchaeota_unknown Tanganyika_M_DeepCast_400m_m2_128_Ga0194112_10004753_21_Verstraetaerchaeota Tanganyika_M_DeepCast_400m_m2_325_Ga0194112_10050238_3_Euryarchaeota Form IIIa-like RBG_13_scaffold_9498_7_unknown_IVlike imgVR_3300009058_____Ga0102854_1000016_27_PHAGE_putative_phage_IVlike Form IV-like Form IV-like Tanganyika_M_surface_7_m1_073_Ga0194118_10000235_26_Bacteroidetes Tanganyika_M_surface_8_m1_077_Ga0194117_10000308_26_Bacteroidetes Tanganyika_M_surface_6_mx_051_Ga0194115_10000314_73_Bacteroidetes Tanganyika_M_surface_10_m1_166_Ga0194123_10000502_86_Bacteroidetes Tanganyika_M_DeepCast_10m_m2_123_Ga0194122_10003462_6_Planctomycetes imgVR_3300005662_____Ga0078894_10000568_30_PHAGE_putative_phage_IVlike imgVR_3300009183_____Ga0114974_10000026_102_PHAGE_putative_phage_IVlike imgVR_3300002835_____B570J40625_100027887_6_PHAGE_putative_phage_IVlike imgVR_3300001346_____JGI20151J14362_10002242_5_PHAGE_putative_phage_IVlike imgVR_3300006790_____Ga0098074_1002816_5_PHAGE_putative_phage_IVlike Form IV-like imgVR_3300009181_____Ga0114969_10002748_1_PHAGE_putative_phage_IVlike imgVR_3300009164_____Ga0114975_10000143_17_PHAGE_putative_phage_IVlike Tanganyika_M_surface_9_mx_081_Ga0194116_10199735_1_Cyanobacteria imgVR_3300006810_____Ga0070754_10000068_66_PHAGE_putative_phage_IVlike imgVR_3300006810_____Ga0070754_10000142_45_PHAGE_putative_phage_IVlike

imgVR_3300006030_____Ga0075470_10000039_31_PHAGE_putative_phage_IVlike Tanganyika_M_DeepCast_200m_mx_047_Ga0194113_10002768_13_Cyanobacteria Form IV-like Tanganyika_M_DeepCast_200m_mx_047_Ga0194113_10034094_7_Cyanobacteria imgVR_3300005805_____Ga0079957_1000649_25_PHAGE_putative_phage_IVlike Form IV-like

Form IV-like Tanganyika_M_DeepCast_200m_m1_287_Ga0194113_10166727_1_Planctomycetes Tanganyika_M_DeepCast_65m_m2_001_Ga0194120_10004792_11_Verrucomicrobia_V3_2 Tanganyika_K_Offshore_80m_m1_157_Ga0194132_10133525_2_Verrucomicrobia_V3 Tanganyika_K_DeepCast_65m_m1_133_Ga0194128_10000654_62_CP_Handelsmanbacteria Tanganyika_K_Offshore_80m_m1_122_Ga0194132_10044716_3_CP_Handelsmanbacteria Tanganyika_M_DeepCast_65m_m2_204_Ga0194120_10080312_2_CP_Hydrogenedentes Tanganyika_K_DeepCast_1200m_m1_255_Ga0194110_10151798_2_Chloroflexi Tanganyika_K_DeepCast_300m_mx_113_Ga0194111_10163340_1_Poribacteria Tanganyika_K_DeepCast_1200m_m1_294_Ga0194110_10105532_1_Poribacteria Tanganyika_M_DeepCast_400m_m1_357_Ga0194112_10100198_3_Poribacteria Tanganyika_K_DeepCast_300m_m1_220_Ga0194111_10076275_1_Poribacteria CAE31534_Bordetella_bronchiseptica_RB50_IV_Non_phot_REF_reference_IV Tanganyika_K_Offshore_120m_m2_141_Ga0194131_10052419_2_Betaproteobacteria Tanganyika_K_Offshore_80m_m2_139_Ga0194132_10005031_18_Betaproteobacteria Tanganyika_M_DeepCast_400m_m1_328_Ga0194112_10107749_3_Alphaproteobacteria BAB53192_Mesorhizobium_loti_IV_Non_phot_REF_reference_IV CAC48779_Sinorhizobium_meliloti_1021_IV_Non_phot_REF_reference_IV Tanganyika_K_DeepCast_150m_m2_164_Ga0194126_10000423_26_Verrucomicrobia_V3 Tanganyika_K_Offshore_80m_m2_019_Ga0194132_10047262_1_Verrucomicrobia_V3 Tanganyika_K_Offshore_80m_m1_178_Ga0194132_10016584_4_Betaproteobacteria Tanganyika_K_Offshore_80m_m1_165_Ga0194132_10003362_13_Verrucomicrobia_V3 Tanganyika_M_DeepCast_50m_m2_266_Ga0194121_10002356_6_Actinobacteria_acI_B1 Tanganyika_K_DeepCast_65m_m2_120_Ga0194128_10011676_3_Actinobacteria_acI_B1 ZP_01056409_Roseobacter_sp_MED193_IV_Non_phot_REF_reference_IV YP_511005_Jannaschia_sp_CCS1_IV_Non_phot_REF_reference_IV ZP_01438569__Fulvimarina_pelagi_HTCC2506_IV_Non_phot_REF_reference_IV Tanganyika_M_surface_10_m1_233_Ga0194123_10025256_2_Betaproteobacteria Tanganyika_K_Offshore_surface_m1_273_Ga0194130_10036456_5_Betaproteobacteria Tanganyika_M_DeepCast_100m_m2_066_Ga0194119_10003877_24_Fibrobacteres_acidobacteria Tanganyika_K_DeepCast_65m_m1_022_Ga0194128_10000227_75_Acidobacteria Tanganyika_K_DeepCast_65m_m2_251_Ga0194128_10002943_18_Fibrobacteres_acidobacteria Tanganyika_M_DeepCast_100m_m2_066_Ga0194119_10029084_7_Fibrobacteres_acidobacteria Form IV Tanganyika_M_DeepCast_200m_mx_065_Ga0194113_10033010_5_Fibrobacteres_acidobacteria Mesorhizobium_sp._L2C085B00_REF_reference_IV

Tanganyika_M_DeepCast_50m_m2_086_Ga0194121_10006673_10_Armatimonadetes BAB44150_Allochromatium_vinosum_IV_Phot_REF_reference_IV YP_530146_Rhodopseudomonas_palustris_BisB18_IV_Phot_REF_reference_IV AAM72993_Chlorobium_tepidum_TLS1_IV_Phot_REF_reference_IV ABB28892_Chlorobium_chlorochromatii_CaD3_IV_Phot_REF_reference_IV Tanganyika_K_DeepCast_1200m_m2_283_Ga0194110_10001012_6_Bacteroidetes_Chlorobi Tanganyika_M_DeepCast_65m_m2_268_Ga0194120_10000556_2_Bacteroidetes_Chlorobi Tanganyika_K_DeepCast_150m_m2_167_Ga0194126_10171529_1_Chloroflexi Tanganyika_K_DeepCast_150m_m2_167_Ga0194126_10051812_3_Chloroflexi Tanganyika_M_DeepCast_65m_m1_226_Ga0194120_10003825_14_Chloroflexi Tanganyika_K_DeepCast_1200m_m2_204_Ga0194110_10004078_5_Actinobacteria Tanganyika_K_DeepCast_100m_m1_326_Ga0194127_10003260_8_Deltaproteobacteria Tanganyika_K_Offshore_40m_m2_139_Ga0194133_10024898_1_Actinobacteria Tanganyika_M_DeepCast_50m_m2_266_Ga0194121_10002897_5_Actinobacteria_acI_B1 Tanganyika_K_Offshore_0m_m2_159_Ga0194134_10004672_8_Bacteroidetes Tanganyika_M_surface_10_m2_076_Ga0194123_10001101_16_Actinobacteria Tanganyika_M_DeepCast_65m_m1_011_Ga0194120_10003339_7_Deltaproteobacteria Tanganyika_M_DeepCast_65m_m1_099_Ga0194120_10017470_1_Deltaproteobacteria Tanganyika_M_DeepCast_65m_m2_011_Ga0194120_10000501_34_Planctomycetes Tanganyika_M_DeepCast_65m_m2_155_Ga0194120_10006129_12_Nitrospirae Tanganyika_K_Offshore_80m_m2_121_Ga0194132_10002640_31_Nitrospirae Tanganyika_M_DeepCast_200m_m2_133_Ga0194113_10089527_3_Deltaproteobacteria ABH04879_Heliobacillus_mobilis_IV_DeepYk_REF_reference_IV Tanganyika_K_DeepCast_100m_mx_210_Ga0194127_10061580_1_Deltaproteobacteria Tanganyika_M_DeepCast_65m_m1_226_Ga0194120_10001323_29_Chloroflexi Tanganyika_K_DeepCast_100m_m2_324_Ga0194127_10001954_1_Deltaproteobacteria Tanganyika_K_Offshore_80m_m2_043_Ga0194132_10021490_3_Betaproteobacteria Tanganyika_K_Offshore_40m_m2_105_Ga0194133_10060886_4_Actinobacteria Tanganyika_M_surface_10_m2_141_Ga0194123_10007936_9_Gammaproteobacteria

Tanganyika_K_Offshore_80m_m1_220_Ga0194132_10051217_3_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_228_Ga0194132_10007819_7_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_191_Ga0194132_10005337_22_Alphaproteobacteria Tanganyika_K_DeepCast_35m_m2_271_Ga0194129_10005783_13_Alphaproteobacteria Tanganyika_K_Offshore_surface_m1_194_Ga0194130_10043210_1_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_187_Ga0194132_10035125_4_Alphaproteobacteria ABC22798_Rhodospirillum_rubrum_IV_DeepYk_REF_reference_IV

Tanganyika_K_Offshore_40m_mx_120_Ga0194133_10003588_13_Alphaproteobacteria Tanganyika_K_DeepCast_250m_m1_099_Ga0194125_10009714_7_Alphaproteobacteria Tanganyika_K_DeepCast_250m_m1_099_Ga0194125_10004831_13_Alphaproteobacteria Tanganyika_K_Offshore_40m_mx_105_Ga0194133_10000378_36_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_218_Ga0194132_10000300_93_Chloroflexi Tanganyika_M_surface_10_m2_064_Ga0194123_10000004_330_Alphaproteobacteria BAD64310_Bacillus_clausii_KSM_K16_IV_Ykr_REF_reference_IV AAU23062_Bacillus_licheniformis_ATCC_14580_IV_Ykr_REF_reference_IV CAB13232_Bacillus_subtilis_subsp_subtilis_str_168_IV_Ykr_REF_reference_IV AAU16474_Bacillus_cereus_E33L_IV_Ykr_REF_reference_IV Tanganyika_K_DeepCast_150m_m1_051_Ga0194126_10087204_1_Actinobacteria Tanganyika_K_DeepCast_100m_m1_216_Ga0194127_10168609_1_Actinobacteria Tanganyika_K_DeepCast_150m_m2_097_Ga0194126_10011515_10_Actinobacteria Tanganyika_K_DeepCast_300m_m1_095_Ga0194111_10080453_2_Actinobacteria Tanganyika_M_DeepCast_200m_m2_194_Ga0194113_10004796_6_Chloroflexi Tanganyika_M_DeepCast_400m_m2_252_Ga0194112_10039032_2_Chloroflexi Tanganyika_K_DeepCast_150m_m1_063_Ga0194126_10026522_3_Chloroflexi Tanganyika_K_DeepCast_250m_m2_195_Ga0194125_10040700_2_Chloroflexi Tanganyika_K_DeepCast_1200m_m1_340_Ga0194110_10112248_1_Chloroflexi Tanganyika_K_DeepCast_150m_m1_015_Ga0194126_10004483_17_Chloroflexi Tanganyika_K_DeepCast_100m_m2_152_Ga0194127_10009541_5_Chloroflexi Tanganyika_K_DeepCast_1200m_m1_340_Ga0194110_10001418_25_Chloroflexi Tanganyika_M_DeepCast_400m_m2_044_Ga0194112_10005539_11_Chloroflexi Tanganyika_M_DeepCast_200m_m1_335_Ga0194113_10003594_6_Chloroflexi Tanganyika_M_DeepCast_100m_mx_191_Ga0194119_10089764_2_Chloroflexi Tanganyika_K_DeepCast_300m_m2_134_Ga0194111_10006059_9_Chloroflexi Tanganyika_K_DeepCast_250m_m1_047_Ga0194125_10006674_11_Chloroflexi Tanganyika_M_DeepCast_200m_m2_208_Ga0194113_10000482_9_Chloroflexi Tanganyika_M_DeepCast_400m_m2_027_Ga0194112_10000198_34_Chloroflexi Tanganyika_M_DeepCast_200m_mx_194_Ga0194113_10000725_39_Chloroflexi Tanganyika_M_DeepCast_400m_m2_207_Ga0194112_10000015_110_Chloroflexi Tanganyika_K_DeepCast_150m_m1_051_Ga0194126_10082287_1_Actinobacteria Tanganyika_M_DeepCast_200m_m1_155_Ga0194113_10079552_1_Actinobacteria Tanganyika_K_DeepCast_1200m_m1_044_Ga0194110_10091210_1_Actinobacteria Tanganyika_M_DeepCast_200m_m1_155_Ga0194113_10035520_1_Actinobacteria Tanganyika_K_DeepCast_1200m_m1_044_Ga0194110_10008704_2_Actinobacteria Tanganyika_K_DeepCast_150m_m1_088_Ga0194126_10010990_2_Actinobacteria Tanganyika_K_DeepCast_100m_m1_278_Ga0194127_10036268_2_Actinobacteria Clostridium_asparagiforme_REF_reference_IV Tanganyika_K_DeepCast_65m_m2_187_Ga0194128_10000001_1048_Thaumarchaeota

Form II/III UBA4787contig_1245_80_Patescibacteria_II/III Form II/III YP_004385218_Methanosaeta_concilii_GP6_Putative_IIandII_REF_reference_II/III rifcsplowo2_02_scaffold_6002_1_Woesearchaeota_II/III rifcsphigho2_02_scaffold_6968_22_Woesearchaeota_II/III rifcsplowo2_01_scaffold_240_75_Woesearchaeota_II/III rifoxyb1_full_scaffold_364_31_Woesearchaeota_II/III Tanganyika_K_DeepCast_100m_m1_175_Ga0194127_10068671_1_Woesearchaeota Tanganyika_K_DeepCast_100m_m1_149_Ga0194127_10000860_33_Pacearchaeota rifoxyb1_full_scaffold_3633_1_Woesearchaeota_II/III Form II/III cg1_0.2_scaffold_107_c_58_Micrarchaeota_II/III rifoxyc1_full_scaffold_4459_6_Pacearchaeota_II/III Tanganyika_K_DeepCast_300m_m2_023_Ga0194111_10000334_54_Woesearchaeota Tanganyika_K_DeepCast_250m_m1_172_Ga0194125_10012662_6_Aenigmarchaeota Form II/III bjp_ig2599_sub10_scaffold_1302_18_Micrarchaeota_II/III Ig5771_scaffold_652_7_Pacearchaeota_II/III rifoxyc1_full_scaffold_336_27_Pacearchaeota_II/III 07M_4_2014_scaffold_3235_2_unknown_II/III rifcsplowo2_02_scaffold_81990_4_Gottesmanbacteria_II/III RIFCSPLOWO2_02_FULL_OP11_38_8_rifcsplowo2_02_scaffold_81990_5_Gottesmanbacteria_II/III Form II/III AAN52766_Rhodopseudomonas_palustris_I_REF_reference_II AAC38280_Riftia_pachyptila_endosymbiont_I_REF_reference_II Q59462_Hydrogenovibrio_marinus_I_REF_reference_II Mariprofundus_ferrooxydans_ZP_0145121_REF_reference_II ABB41020_Thiomicrospira_crunogena_XCL_2_II__REF_reference_II Tanganyika_K_DeepCast_150m_m2_250_Ga0194126_10004264_15_Betaproteobacteria Form II Form II Tanganyika_M_DeepCast_100m_m1_373_Ga0194119_10000930_29_Betaproteobacteria AAA98748_Gonyaulax_polyedra_I_REF_reference_II AAG37859_Symbiodinium_sp_I_REF_reference_II Form II P50922_Rhodobacter_capsulatus_ATCC11166_I_REF_reference_II Tanganyika_M_DeepCast_65m_m1_226_Ga0194120_10000029_101_Chloroflexi WP_029927633_Nocardia_otitidiscaviaru_REF_reference_I Form I Tanganyika_M_DeepCast_65m_m2_058_Ga0194120_10013346_4_Betaproteobacteria Tanganyika_M_DeepCast_65m_m2_266_Ga0194120_10003907_25_Alphaproteobacteria_rhodospirillales Tanganyika_K_DeepCast_65m_m1_262_Ga0194128_10106391_2_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_035_Ga0194132_10002439_2_Acidobacteria Tanganyika_K_DeepCast_300m_mx_266_Ga0194111_10177893_1_Alphaproteobacteria Tanganyika_K_Offshore_80m_m2_162_Ga0194132_10000444_32_Alphaproteobacteria Galdieria_Partita_1IW_REF_reference_I Chondrophycus_papillosus_ABO3124_REF_reference_I Pyropia_dentata_Q760S_REF_reference_I Tanganyika_K_Offshore_40m_m1_111_Ga0194133_10022995_2_Verstraetaerchaeota Tanganyika_M_surface_10_mx_133_Ga0194123_10014745_3_Cyanobacteria Tanganyika_K_Offshore_40m_mx_205_Ga0194133_10000757_62_Cyanobacteria Tanganyika_K_Offshore_surface_m1_122_Ga0194130_10000572_5_Cyanobacteria Tanganyika_M_surface_10_m2_157_Ga0194123_10000280_67_Cyanobacteria Q08051_Pleurochrysis_carterae_I_REF_reference_I Tanganyika_K_Offshore_40m_mx_246_Ga0194133_10000623_58_Cyanobacteria Form I Tanganyika_K_Offshore_surface_mx_199_Ga0194130_10000657_56_Cyanobacteria Tanganyika_K_DeepCast_0m_mx_006_Ga0194124_10001293_16_Cyanobacteria NP_043654_Odontella_sinensis_I_REF_reference_I Tanganyika_M_DeepCast_100m_mx_041_Ga0194119_10000101_62_Cyanobacteria Tanganyika_M_DeepCast_10m_m2_062_Ga0194122_10000137_60_Cyanobacteria Tanganyika_M_surface_9_mx_081_Ga0194116_10000470_61_Cyanobacteria Tanganyika_M_DeepCast_200m_m1_110_Ga0194113_10018601_8_Cyanobacteria Tanganyika_K_Offshore_surface_mx_266_Ga0194130_10004070_10_Cyanobacteria ABA56859_Nitrosococcus_oceani_ATCC_19707_I_REF_reference_I YP_411385_Nitrosospira_multiformis_ATCC_251966_I_REF_reference_I Burkholderia_xenovorans_YP_55288_REF_reference_I Form I Tanganyika_K_DeepCast_35m_m1_018_Ga0194129_10000142_44_Betaproteobacteria Tanganyika_M_surface_10_mx_121_Ga0194123_10123036_1_Betaproteobacteria Tanganyika_K_Offshore_40m_mx_035_Ga0194133_10000926_25_Betaproteobacteria Form I Form I gi_518884741_ref_WP_020040616_ribulose_bisophosphate_carboxylase_Salipiger_mucosu_REF_reference_I Tanganyika_M_DeepCast_400m_m1_328_Ga0194112_10036460_4_Alphaproteobacteria emb_CDO21134_Mycobacterium_mageritense_DSM_4447_REF_reference_I

0.5 bioRxiv preprint doi: https://doi.org/10.1101/834861Cazyme; this version posted November 8, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, whoDensity has grantedAA bioRxiv a CBMlicense to displayCE the preprintcohesin in perpetuity.GH It is madeGT available PL Figure S10 under aCC-BY-NC-ND 4.0 International license. Woesearchaeota (5) Thaumarchaeota (8) Euryarchaeota (3) Archaea Diapherotrites (2) CP Verstraetaerchaeota (2) CP Parvarchaeota (2) CP Pacearchaeota (6) CP Micrarchaeota (2) CP Bathyarchaeota (2) CP Altiarchaeota (1) CP Aenigmarchaeota (1) Verrucomicrobia V6 (10) Verrucomicrobia V4 (7) Verrucomicrobia V3 (15) Verrucomicrobia V2 (1) Verrucomicrobia V1 (1) Unclassified (1) Planctomycetes (45) Phycisphaerae (14) Nitrospirae (10) Lentisphaerae (5) Kirimatiellacea (1) Ignavibacteria (9) Gemmatimonadetes (11) Gammaproteobacteria (47) Firmicutes (2) Fibrobacteres acidobacteria (11) Deltaproteobacteria (66) Deinococcus thermus (2) Cyanobacteria (53) CP Zixibacteria (1) CP Ziwabacteria (3) CP WWE3 (2) CP WWE1 (2) CP WOR−3 (5) CP WOR−2 Omnitrophica (9) CP Urhbacteria (2) CP TM6 (4) CP Tectomicrobia (3) CP Tanganyikabacteria (3) CP Staskawiczbacteria (2) CP Shapirobacteria (2) CP Saccharibacteria (1) CP Rokubacteria (2) Bacteria CP Roizmanbacteria (1) CP Poribacteria (8)

Taxonomic group CP Perigrinibacteria (1) CP Peribacteria (1) CP Parcubacteria (2) CP Nealsonbacteria (3) CP Moranbacteria (1) CP Liptonbacteria (1) CP Kaiserbacteria (2) CP Hydrogenedentes (1) CP Harrisonbacteria (1) CP Handelsmanbacteria (2) CP Gribaldobacteria (1) CP Gottesmanbacteria (1) CP Fraserbacteria (1) CP Eisenbacteria (4) CP BRC1 (1) CP Aminicemantes (OP8) (6) Chloroflexi (65) Chlorobi (2) Chlamydiae (10) Calditrichaeota (4) Betaproteobacteria (41) Bacteroidetes (80) Armatimonadetes (7) Alphaproteobacteria non LD12 (73) Alphaproteobacteria LD12 (14) Actinobacteria Iluma−A1 (3) Actinobacteria acTH1 (1) Actinobacteria acSTL (3) Actinobacteria acIV−C (4) Actinobacteria acIV (6) Actinobacteria acIII (1) Actinobacteria acI−C2 (3) Actinobacteria acI−C (3) Actinobacteria acI−B1 (9) Actinobacteria (30) Acidobacteria (14) 0 0 0 0 0 0 5 20 40 60 80 10 20 30 40 20 40 60 10 15 20 25 50 75 1.0 1.5 2.0 2.5 3.0 100 125 100 200 300 0e+00 2e−05 4e−05 6e−05 8e−05 % Number of hits Figure S11 bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable

Nitrogen Sulfur Hydrogen 0 0 0 ●●●●●●●●●●● ● ●●● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●● ● ● ● ●● ● ● ● ●●● ● ●●●●●●●●●●●●●●● ●●●●●●●● ● ●●●●●●●●●●●●●● ● ● ● ● ● ● ●●●●● ● ●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●● ● ●●●●● ● ● ● ●●●● ● ●● ●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●● ●●● ● ● ● ● ● ● ● ●●●● ●● ●●●●●●● ● ●●●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●● ●●●●●●●●●●●●●●●● ● ● ●● ● ● ●●●●●●●●●●●●●●●●●●●● ●● ●● ●●● ● ● ●●●●●●● ● ●●●●●●●●●●●● ●● ●● ●● ● ●● ● ● ●●●●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ●●●●●●

●●●●●●●●●●●● ● ●●● ● ● ●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ● ● ●●●●●●●●● ●● ● ● doi:

●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●● ●●●● ● ● ● ●●●●●●●●●●●●● ● ● 200 200 200 − − −

●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●● ●●●● ● ● ● ● ● ●●●●●●●●●●●●● ● https://doi.org/10.1101/834861

●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●● ●●●●● ● ● ● ● ●●●●●●●●●●●● ● ●

●●●●●●●●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ● ●●●●●●●●●●●●● ● 400 400 400 − − − 600 600 600 − − − Depth Depth Depth 800 800 800 − − − under a ; 1000 1000 1000 this versionpostedNovember8,2019. − − − CC-BY-NC-ND 4.0Internationallicense

●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●●●●●● ●● ● 1200 1200 1200 − − −

0 100 200 300 400 0 100 200 300 400 0 100 200 300 400

Coverage Coverage Coverage

Oxygen Carbon Other 0 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●●●● ●● ● ●● ● ●●● ● 0 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●● ● ● ● ●● ● ● ● ●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●● ●●●●● ● ●●● ●● ● ●●●●●●●●●●●●●●●●● ●● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●● ●● ●● ● ● ●●●●●●●●●●● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ●● ● ● ●●●●●●●●●●●● ●●●● ●●●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●● ● ●●●●●●●●●●●●●●●●●●●●●● ●●● ● ● ●●●●●●●●●●● ● ●●● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●● ● ● ●●●●●●●●●●●●●●●●●●●●● ●● ●● ●●● ● ● ●●●●●●●●●●●●●●●●●●● ●● ● ●● ● ●●●●●●●●●●●●●●●●● ●● ●●●●●●●●● ● ● ●●●●●●●●●●●●●●●● ●● ● ●●●●●●●● ● ● ● ●●●●●●●●●●●●●●● ● ●●●●●● ● ● ●●●●●●●●●●●●●●●●● ●●●●●●●●● ●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●● ●●●●●● ● ● ●

●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●●● ● ● ● ● 200 200 200 − − ●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ● ● ● − ●●●●●●●●●●●●●●●●●● ●●● ● ● ● ● ●●●●●●●●●●●●●●●● ● ● ● ● ● ● The copyrightholderforthispreprint(whichwas ●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ●●●●●●●●●●●●●●● ●●●● ● ● ● .

●●●●●●●●●●●●●●●●●●●●● ●●●● ●● ●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●● ●● ●● ● ● ● ● ●●●●●●●●●●●●●●● ● ●● ●● ● ● ● 400 400 400 − − − 600 600 600 − − − Depth Depth Depth 800 800 800 − − − 1000 1000 1000 − − −

●●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ●●●●●●●●●●●●●● ●●● ● ● ● ● 1200 1200 1200 − − −

0 100 200 300 400 0 100 200 300 400 0 100 200 300 400

Coverage Coverage Coverage bioRxiv preprint doi: https://doi.org/10.1101/834861; this version posted November 8, 2019. 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-ND 4.0 International license.

Figure S12

Candidatus Nitrospira inopinata LN885086.1_1707 Nitrospira sp. Ga0074138 LNDU01000063.1_90 CZPZ01000032.1_161 Candidatus Nitrospira nitrificans NEWS02000008.1_106 Nitrospira sp. UBA2083 DCZN01000130.1_15 Nitrospira sp. UBA5702 DIHG01000031.1_3 Candidatus Nitrospira nitrosa CZQA01000001.1_65 Candidatus Nitrospira nitrosa CZQA01000011.1_92 Nitrospira sp. UW-LDO-01 NIUT01000005.1_7 Nitrospira sp. SG-bin1 LVWS01000001.1_58 Nitrospira sp. SG-bin2 LVWT01000004.1_39 Nitrospira sp. ST-bin4 MSXM01000087.1_1 Nitrospira sp. UBA2082 DCZO01000016.1_277 amoA gene phylogeny M_DeepCast_50m_m2_151 M_DeepCast_65m_mx_150

0.3