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Genomic and metabolic adaptations of biofilms to ecological windows of opportunities in glacier-fed streams

Microorganisms dominate life in cryospheric ecosystems. In glacier-fed streams (GFSs), ecological windows of opportunities allow complex microbial biofilms to develop and transiently form the basis of the food web, thereby controlling key ecosystem processes. Here, using high-resolution metagenomics, we unravel strategies that allow biofilms to seize this opportunity in an ecosystem otherwise characterized by harsh environmental conditions. We found a diverse microbiome spanning the entire tree of life and including a rich virome. Various and co-existing energy acquisition pathways point to diverse niches and the simultaneous exploitation of available resources, likely fostering the establishment of complex biofilms in GFSs during windows of opportunity. The wide occurrence of rhodopsins across metagenome-assembled genomes (MAGs), besides chlorophyll, highlights the role of solar energy capture in these biofilms. Concomitantly, internal carbon and nutrient cycling between photoautotrophs and heterotrophs may help overcome constraints imposed by the high oligotrophy in GFSs. MAGs also revealed mechanisms potentially protecting bacteria against low temperatures and high UV-radiation. The selective pressure of the GFS environment is further highlighted by the phylogenomic analysis, differentiating the representatives of the genus Polaromonas, an important component of the GFS microbiome, from those found in other ecosystems. Our findings reveal key genomic underpinnings of adaptive traits that contribute to the success of complex biofilms to exploit environmental opportunities in GFSs, now rapidly changing owing to global warming.


Introduction
Metagenomics unveils the complexity of epilithic biofilms. To unveil the full complexity of the 136 epilithic biofilms, we performed whole genome shotgun metagenomics on 21 epilithic samples 137 from four GFSs each in NZ and CC (Supp. Fig. 1a-b); low biomass associated with sandy 138 sediments precluded epipsammic biofilms from metagenomic analysis. High-resolution 139 sequencing, after quality filtering yielded on average 1.2 x 10 8 (± 1.4 x10 7 s.d.) reads per sample 140 which were assembled to obtain an average of 8.7 x 10 5 contigs per sample, that were subsequently 141 binned. Bacteria and eukaryotes dominated the biofilm communities across all samples (Supp. Fig.   142 2a). Seventy-three (70 bacteria and 3 archaea) medium-to-high quality (>70% completion, <5% 143 contamination) metagenome-assembled genomes (MAGs) from a total of 662 MAGs formed the 144 pool of the prokaryotes. As seen from the phylogenomic analysis, the high-quality MAGs span the 145 bacterial tree of life and based on this, along with the taxonomic information, many of these 146 plausibly represent novel species (Fig. 2a). Aggregated at the genus level, Polaromonas was both 147 abundant and prevalent in the biofilms along with representatives of Flavobacterium, 148 Cyanobacteria, and unclassified MAGs from the Bacteroidota and Candidate Phyla Radiation 149 (CPR; Patescibacteria) (Fig. 2b). These taxa were found in over half of the samples, irrespective Alongside these bacteria, archaea contributed less than 1% to the microbiome of epilithic 156 biofilms, with representatives of Asgardarchaeota, Crenarchaeota and Nanoarchaeota. 157 Intriguingly, the recently discovered lineages of Asgardarchaeota 25,26 have been reported from 158 freshwater sediments, yet not from cryospheric environments. Algae, mostly diatoms and 159 Hydrurus (Ochrophyta phylum), as well as dinoflagellata were the most important photoautotrophs 160 of the eukaryotic domain (Fig. 2c). The prevalence of Hydrurus (~87% relative abundance) 161 underscores the function of this filamentous alga as a resource to higher trophic levels in GFS 27 .

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Our metagenomic insights further support the notion that phototrophic biofilms are highly diverse 163 with representatives from all three domains of life 28 . 164 In addition to the archaeal, bacterial and eukaryotic community members, we also found a 165 diverse viral community associated with epilithic biofilms (Supp. Fig. 2b). Most of the viruses 166 were bacteriophages targeting abundant MAGs such as Flavobacterium, Pseudomonas,and 167 Bacillus genera, but we also identified eukaryotic phages (i.e., Paramecium bursaria Chlorella the 'green' food web during the window of opportunity. While these photoautotrophs are well 177 known to use chlorophyll to capture solar energy, little is known on retinal-based phototrophy 178 using rhodopsins in GFSs. Intriguingly, we found that MAGs from sixteen out of twenty phyla in 179 the epilithic biofilms, including the abundant groups, such as Proteobacteria (Polaromonas) and 180 Bacteroidota (Flavobacterium), encoded for (bacterio-)rhodopsins (Fig. 3a). These also included 181 genes encoding for light-harvesting complex 1 (LH1), reaction centre (RC) subunits (pufBALM), 182 and transcriptional regulators (ppsR) required for aerobic anoxygenic phototrophs along with 183 rhodopsins as a signature of energy-limitation adaptations (Fig. 3a). Recently, rhodopsins were 184 also reported to serve as a photoprotectant in Flavobacterium from glaciers 32 . Collectively, our 185 7 findings unveil multiple strategies of photoautotrophy, which may help cyanobacteria and algae in 186 maximising the exploitation of solar energy and to thrive during windows of opportunity. 187 Rapid growth should be advantageous for primary producers, such as cyanobacteria, to best 188 exploit the short windows of opportunity in GFSs. Moreover, functional independence from other 189 microorganisms could thereby allow them to efficiently react to a window of opportunity. To test  Specifically, Polaromonas had a strong interaction with algae, while Methylotenera co-occurred 210 with Chytridiomycetes (Fig. 3b). These results support our hypothesis of heterotrophic bacteria co-211 occurring with eukaryotes, primarily algae, for metabolic cross-feeding, similar to those occurring 212 in the phycosphere 10 . 213 Furthermore, our results hint at the existence of a more cryptic interaction in epilithic 214 biofilms between parasitic fungi Chytridiomycetes and algae (i.e., Ochrophyta). Fungal parasitism 215 on pelagic algae has been recently reported to be more important than expected, even with 216 8 consequences for carbon and nutrient cycling as mediated by the fungal shunt 33,34 . The possibility 217 of fungal parasitism on algae in epilithic biofilms further underlines the role of photoautotrophs as 218 the foundation of a complex food web in GFSs as a typically energy-limited ecosystem.

220
Genomic underpinnings of algae-bacteria metabolic interactions. As photoautotrophs grow 221 and senesce, they increasingly exude intracellular material into their ambient environment, where 222 it can be metabolized by heterotrophic bacteria through the action of extracellular enzyme activity 223 (EEA). To explore this metabolic cross-feeding between bacterial heterotrophs and algae, we 224 assessed the MAGs for genes encoding five common EEAs required for cleaving complex 225 polysaccharides, phosphomonoesters and proteins 35 . Not unexpectedly, these genes were 226 predominantly associated with bacterial heterotrophs, rather than with the photoautotrophs (Supp. 227 Fig. 4), which suggests adapted genomic traits to meet specific metabolic needs of the 228 heterotrophs. However, based on the presence of the EEA genes, especially among Cyanobacteria, 229 we cannot discount the possibility of mixotrophy in the epilithic biofilms (Supp. Fig. 4a), including 230 in other abundant members of the epilithic microbiome (Supp. Fig. 1c-d). The widespread 231 occurrence of mixotrophy in planktonic communities, including Cyanobacteria, and the ensuing 232 food web dichotomy is considered as an adaptive strategy to oligotrophic and cold ecosystems 233 (e.g., the polar sea). Therefore, we argue that mixotrophy may also be an important trait of 234 Cyanobacteria within GFS biofilms.

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Carbohydrate-active enzymes (CAZymes) are the prime tools used by heterotrophic 236 bacteria to initiate the degradation of polysaccharides, largely algae-derived in the GFS epilithic 237 biofilms. To shed light on this potential trophic interaction identified through specific EEAs, we 238 tested if all the CAZymes in the metagenomes covaried with the abundance of eukaryotes. Overall, 239 we found positive correlations between eukaryote abundances and CAZymes, particularly 240 carbohydrate-binding modules (CBM) and glycoside hydrolases (GH) (Supp. Fig. 4d). More 241 specifically, these correlations were particularly pronounced for GH and some of the algal groups 242 (e.g.,Ochrophyta, Haptophyta, Cryptophyta) that we found at relatively high abundances in the 243 epilithic biofilms (Fig. 4c). As some of these algae are known to copiously produce sulfated 244 carbohydrates 36 , we suggest a similar involvement of CAZymes (Supp. carbohydrates 37 , our findings suggest that they are still relevant to carbon turnover in an ecosystem 248 that is inherently carbon limited.

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In order to understand whether functions potentially geared towards cross-domain 250 interactions were enriched solely in epilithic biofilms, we compared the KEGG orthology (KO) 251 annotations from our metagenomes to 105 metagenomes from a wide range of ecosystems (Supp. 252 Table 2). Strikingly, we found that KOs associated with quorum sensing, vitamin B12 (cobalamin) 253 transporters and thiamine biosynthesis were enriched in epilithic biofilms compared to other 254 ecosystems (Supp. Table 3). The associated pathways and their completion levels were evaluated  Furthermore, several MAGs were found to encode genes (e.g., quorum sensing, cobalamin 260 metabolism, tryptophan synthesis) potentially facilitating algal-bacterial interactions (Fig. 4a).

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Particularly, cobalamin metabolism may be relevant for nutrient acquisition in algal-bacterial 262 relationships 40 , whereas tryptophan was reported as a key signalling molecule involved in 263 interactions between bacteria and associated phytoplankton 11,41 . Collectively these genomic 264 insights stress cross-domain interactions as an adaptive potential that the epilithic microorganisms 265 have developed to exploit the window of opportunity in GFSs.

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Energy acquisition and biogeochemical pathways in epilithic biofilm MAGs. The dominance 268 (~88%) of MAGs encoding for organic carbon metabolism highlights the relevance of a 'green 269 food web' during the windows of opportunity, potentially sustaining metabolic interactions 270 between primary producers and heterotrophs. Further exploring the gene repertoire of the epilithic 271 biofilms, we found that Cyanobacteria were one of the largest bacterial contributors to carbon 272 fixation along with Bacteroidota and few Gammaproteobacteria (Fig. 4a). An in-depth analysis 273 across the 662 MAGs revealed that 583 MAGs encoded genes involved in organic carbon 274 oxidation, while 120 MAGs encoded genes involved in CO2 fixation. In line with the above 275 findings, the majority of these MAGs was identified as Cyanobacteria along with few other phyla 276 such as Proteobacteria, Asgardarchaeota, Crenarchaeota and Huberarchaeota. We also note that 277 10 351 MAGs encoded genes for fermentation ( Fig. 4b) spanning several phyla, including 278 Actinobacteriota, Bacteroidota, Patescibacteria,Planctomycetota and Verrucomicrobiota. 279 For biofilms to thrive in GFSs, even during the windows of opportunity, it appears 280 opportune to diversify the exploitation of energy sources. Therefore, we performed an in-depth 281 characterisation of chemolithotrophic pathways to disclose the potential role of minerals derived 282 from the glacial comminution of bedrock as an energy source for microorganisms 42 . The 283 prevalence of the sox gene cluster in representatives of the Bacteriodota (UBA7662) and 284 Bdellovibrionota reveals the potential importance of inorganic sulfur oxidation in epilithic 285 biofilms. This notion is supported by the broad occurrence of sulfur dioxygenases (SDOs) across 286 the various phyla that facilitate sulfur oxidation (Fig. 4c) hydrogen may also contribute energy to bacteria within the epilithic biofilms.

296
Further genomic insights into the nitrogen cycle revealed the Dissimilatory Nitrate 297 Reduction to Ammonium (DNRA, or nitrite ammonification) and, to a lesser extent, by 298 denitrification, as major pathways (Fig. 4d). Relatively little is known regarding these two 299 competing pathways in stream biofilms or sediments 48 . However, our insights into the cross-300 domain metabolic interactions suggest that epilithic algae provide significant amounts of organic 301 carbon (i.e., electron donors), which may favour bacteria to grow using DNRA. This is in line with 302 other ecosystems where DNRA is favoured over denitrification when alternate electron donors 303 prevail over nitrate 49 . Our analyses revealed Burkholderiales (Gammaproteobacteria) as the largest 304 contributor to nitrate assimilation and ammonia-oxidation genes (Fig. 5a). DNRA, if not conducive 305 to N2O production, would enhance nitrogen recycling within epilithic biofilms through ammonia 306 assimilation by algae and cyanobacteria, for instance. Our genomic evidence for nitrogen recycling 307 that potentially overwhelms nitrogen losses through denitrification is corroborated by flux 308 measurements from microbial mats in Antarctic GFSs 50 , and highlights recycling as a strategy to 309 cope with nutrient limitation in glacier ecosystems [50][51][52] . 310 Strikingly, we found only few MAGs, mostly belonging to Deinococcota,  Polaromonas has evolved traits that facilitates its success in GFS, both in NZ and CC. To identify 328 such traits, we created a pangenome and performed an enrichment analysis for clusters of 329 orthologous genes. We found three categories that were significantly enriched in GFS

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Polaromonas compared to those from other environments (Supp. Table 4). Two categories are 331 related to defense mechanisms, both general and transcription, and one to energy production ( Fig.   332 5b). It is plausible that these mechanisms are related to high UV-radiation 55,56 and oxidative 333 stress 57 , as well as to cold stress responses as previously reported from other bacteria 58-60 .

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Furthermore, the presence of CRISPR-Cas proteins in the enriched clusters of orthologous genes 335 (COGs) hint at defense mechanisms against phages (Supp. Table 4), which we showed to be 336 present in the epilithic biofilms. This is in accordance with reports demonstrating that cryospheric response to stressors, such as UV and reactive oxygen species (Fig. 5c).

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In conclusion, our genome-resolved metagenomics analyses have set the stage for a 363 mechanistic understanding of how the diversification of energy and matter acquisition pathways 364 as well as metabolic interactions allow biofilms to thrive during windows of opportunity in GFSs. 365 We acknowledge that a metagenomic time series outside and throughout windows of opportunity 366 would be required to substantiate some of our observations. Nevertheless, our findings shed light 367 on boulders as important habitats that confer stability of biofilms even outside the typical windows 368 of opportunity. GFSs count among the ecosystems that are most vulnerable to climate change.  Table 5). Epilithic biofilms were sampled using a sterilized metal spatula. All samples were 382 immediately flash-frozen in liquid nitrogen in the field and transported and stored frozen pending 383 DNA extraction. Streamwater turbidity, conductivity, temperature, and pH were measured in situ 384 during the sampling (Supplementary Table 2).  Subsequently, to determine the growth rates of prokaryotes, we used codon usage statistics for 457 detecting optimization of genes that are highly expressed, as an indicator of maximal growth rates 458 with gRodon v1.0 94 . All the parameters, databases, and relevant code for the analyses described     Acknowledgements 554 We are thankful for the assistance of Audrey Frachet Bour, Lea Grandmougin, Janine Habier, 555 Laura Lebrun (LCSB) and Emmy Marie Oppliger (EPFL) for laboratory support. We are grateful 556 to Alex Washburne for his feedback on the draft, and we also acknowledge the valuable input from 557 Rashi Halder at the LCSB Sequencing Platform with respect to library preparation. We are equally 558 grateful for the valuable insights into metagenomic processing from Patrick May and Cedric                             InterJournal, Complex Systems 1695, 1-9 (2006).