Ecology

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Different picocyanobacterial communities exhibit distinct gene repertoiresTo analyze the distribution of Prochlorococcus and Synechococcus reads along the Tara Oceans transect, metagenomic reads corresponding to the bacterial size fraction were recruited against 256 picocyanobacterial reference genomes, including SAGs and MAGs representative of uncultured lineages (e.g., Prochlorococcus HLIII-IV, Synechococcus EnvA or EnvB). This yielded a total of 1.07 billion recruited reads, of which 87.7% mapped onto Prochlorococcus genomes and 12.3% onto Synechococcus genomes, which were then functionally assigned by mapping them onto the manually curated Cyanorak v2.1 CLOG database [19]. In order to identify picocyanobacterial genes potentially involved in niche adaptation, we analyzed the distribution across the oceans of flexible (i.e. non-core) genes. Clustering of Tara Oceans stations according to the relative abundance of flexible genes resulted in three well-defined clusters for Prochlorococcus (Fig. 1A), which matched those obtained when stations were clustered according to the relative abundance of Prochlorococcus ESTUs, as assessed using the high-resolution marker gene petB, encoding cytochrome b6 (Fig. 1A; [24]). Only a few discrepancies can be observed between the two trees, including stations TARA-070 that displayed one of the most disparate ESTU compositions and TARA-094, dominated by the rare HLID ESTU (Fig. 1A). Similarly, for Synechococcus, most of the eight assemblages of stations discriminated based on the relative abundance of ESTUs (Fig. 1B) were also retrieved in the clustering based on flexible gene abundance, except for a few intra-assemblage switches between stations, notably those dominated by ESTU IIA (Fig. 1B). Despite these few variations, four major clusters can be clearly delineated in both Synechococcus trees, corresponding to four broadly defined ecological niches, namely (i) cold, nutrient-rich, pelagic or coastal environments (blue and light red in Fig. 1B), (ii) Fe-limited environments (purple and grey), (iii) temperate, P-depleted, Fe-replete areas (yellow) and (iv) warm, N-depleted, Fe-replete regions (dark red). This correspondence between taxonomic and functional information was also confirmed by the high congruence between distance matrices based on ESTU relative abundance and on CLOG relative abundance (p-value  0.01) are marked by a cross. Φsat: index of iron limitation derived from satellite data. PAR30: satellite-derived photosynthetically available radiation at the surface, averaged on 30 days. DCM: depth of the deep chlorophyll maximum.Full size imageIdentification of individual genes potentially involved in niche partitioningTo identify genes relevant for adaptation to a specific set of environmental conditions and enriched in specific ESTU assemblages, we selected the most representative genes from each module (Dataset 5; Figs. 3, S2). Most genes retrieved this way encode proteins of unknown or hypothetical function (85.7% of 7,485 genes). However, among the genes with a functional annotation (Dataset 6), a large fraction seems to have a function related to their realized environmental niche (Figs. 3, S2). For instance, many genes involved in the transport and assimilation of nitrite and nitrate (nirA, nirX, moaA-C, moaE, mobA, moeA, narB, M, nrtP; [6]) as well as cyanate, an organic form of nitrogen (cynA, B, D, S), are enriched in the Prochlorococcus blue module, which is correlated with the HLIIA-D ESTU and to low inorganic N, P, and silica levels and anti-correlated with Fe availability (Fig. 2A–C). This is consistent with previous studies showing that while only a few Prochlorococcus strains in culture possess the nirA gene and even less the narB gene, natural Prochlorococcus populations inhabiting N-poor areas do possess one or both of these genes [40,41,42]. Similarly, numerous genes amongst the most representative of Prochlorococcus brown, red and turquoise modules are related to adaptation of HLIIIA/IVA, HLIA and LLIA ESTUs to Fe-limited, cold P-limited, and cold, mixed waters, respectively (Fig. 3). Comparable results were obtained for Synechococcus, although the niche delineation was less clear than for Prochlorococcus since genes within each module exhibited lower correlations with the module eigenvalue (Fig. S2). These results therefore constitute a proof of concept that this network analysis was able to retrieve niche-related genes from metagenomics data.Fig. 3: Violin plots highlighting the most representative genes of each Prochlorococcus module.For each module, each gene is represented as a dot positioned according to its correlation with the eigengene for each module, the most representative genes being localized on top of each violin plot. Genes mentioned in the text and/or in Dataset 6 have been colored according to the color of the corresponding module, indicated by a colored bar above each module. The text above violin plots indicates the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageIdentification of eCAGs potentially involved in niche partitioningIn order to better understand the function of niche-related genes, notably of the numerous unknowns, we then integrated global distribution data with gene synteny in reference genomes using a network approach (Datasets 7, 8). This led us to identify clusters of adjacent genes in reference genomes, and thus potentially involved in the same metabolic pathway (Figs. 4, S3, S4; Dataset 6). These clusters were defined within each module and thus encompass genes with similar distribution and abundance in situ. Hereafter, these environmental clusters of adjacent genes will be called “eCAGs”.Fig. 4: Delineation of Prochlorococcus eCAGs, defined as a set of genes that are both adjacent in reference genomes and share a similar in situ distribution.Nodes correspond to individual genes with their gene name (or significant numbers of the CK number, e.g. 1234 for CK_00001234) and are colored according to their WGCNA module. A link between two nodes indicates that these two genes are less than five genes apart in at least one genome. The bottom insert shows the most significant environmental parameter(s) and/or ESTU(s) for each module, as derived from Fig. 2.Full size imageeCAGs related to nitrogen metabolismThe well-known nitrate/nitrite gene cluster involved in uptake and assimilation of inorganic forms of N (see above), which is present in most Synechococcus genomes (Dataset 6), was expectedly not restricted to a particular niche in natural Synechococcus populations, as shown by its quasi-absence from WGCNA modules. In Prochlorococcus, this cluster is separated into two eCAGs enriched in low-N areas (Fig. S5A, B), most genes being included in Pro-eCAG_002, present in only 13 out of 118 Prochlorococcus genomes, while nirA and nirX form an independent eCAG (Pro-eCAG_001) due to their presence in many more genomes. The quasi-core ureA-G/urtB-E genomic region was also found to form a Prochlorococcus eCAG (Pro-eCAG_003) that was impoverished in low-Fe compared to other regions (Fig. S5C, D), in agreement with its presence in only two out of six HLIII/IV genomes. We also uncovered several other Prochlorococcus and Synechococcus eCAGs that seem to be involved in the transport and/or assimilation of more unusual and/or complex forms of nitrogen, which might either be degraded into elementary N molecules or possibly directly used by cells for e.g. the biosynthesis of proteins or DNA. Indeed, we detected in both genera an eCAG (Pro-eCAG_004 and Syn-eCAG_001; Fig. S6A, B; Dataset 6) that encompasses speB2, an ortholog of Synechocystis PCC 6803 sll1077, previously annotated as encoding an agmatinase [29, 43] and which was recently characterized as a guanidinase that degrades guanidine rather than agmatine to urea and ammonium [44]. E. coli produces guanidine under nutrient-poor conditions, suggesting that guanidine metabolism is biologically significant and potentially prevalent in natural environments [44, 45]. Furthermore, the ykkC riboswitch candidate, which was shown to specifically sense guanidine and to control the expression of a variety of genes involved in either guanidine metabolism or nitrate, sulfate, or bicarbonate transport, is located immediately upstream of this eCAG in Synechococcus reference genomes, all genes of this cluster being predicted by RegPrecise 3.0 to be regulated by this riboswitch (Fig. S6C; [45, 46]). The presence of hypA and B homologs within this eCAG furthermore suggests that, in the presence of guanidine, these homologs could be involved in the insertion of Ni2+, or another metal cofactor, in the active site of guanidinase. The next three genes of this eCAG, which encode an ABC transporter similar to the TauABC taurine transporter in E. coli (Fig. S6C), could be involved in guanidine transport in low-N areas. Of note, the presence in most Synechococcus/Cyanobium genomes possessing this eCAG of a gene encoding a putative Rieske Fe-sulfur protein (CK_00002251) downstream of this gene cluster, seems to constitute a specificity compared to the homologous gene cluster in Synechocystis sp. PCC 6803. The presence of this Fe-S protein suggests that Fe is used as a cofactor in this system and might explain why this gene cluster is absent from picocyanobacteria thriving in low-Fe areas, while it is present in a large proportion of the population in most other oceanic areas (Fig. S6A, B).Another example of the use of organic N forms concerns compounds containing a cyano radical (C ≡ N). The cyanate transporter genes (cynABD) were indeed found in a Prochlorococcus eCAG (Pro-eCAG_005, also including the conserved hypothetical gene CK_00055128; Fig. S7A, B). While only a small proportion of the Prochlorococcus community possesses this eCAG in warm, Fe-replete waters, it is absent from other oceanic areas in accordance with its low frequency in Prochlorococcus genomes (present in only two HLI and five HLII genomes). In Synechococcus these genes were not included in a module, and thus are not in an eCAG (Dataset 6; Fig. S7C), but seem widely distributed despite their presence in only a few Synechococcus genomes (mostly in clade III strains; [6, 47, 48]). Interestingly, we also uncovered a 7-gene eCAG (Pro-eCAG_006 and Syn-eCAG_002), encompassing a putative nitrilase gene (nitC), which also suggests that most Synechococcus cells and a more variable fraction of the Prochlorococcus population could use nitriles or cyanides in warm, Fe-replete waters and more particularly in low-N areas such as the Indian Ocean (Fig. 5A, B). The whole operon (nitHBCDEFG; Fig. 5C), called Nit1C, was shown to be upregulated in the presence of cyanide and to trigger an increase in the rate of ammonia accumulation in the heterotrophic bacterium Pseudomonas fluorescens [49], suggesting that like cyanate, cyanide could constitute an alternative nitrogen source in marine picocyanobacteria as well. However, given the potential toxicity of these C ≡ N-containing compounds [50], we cannot exclude that these eCAGs could also be devoted to cell detoxification [45, 47]. Such an example of detoxification has been described for arsenate and chromate that, as analogs of phosphate and sulfate respectively, are toxic to marine phytoplankton and must be actively exported out of the cells [51, 52].Fig. 5: Global distribution map of the eCAG involved in nitrile or cyanide transport and assimilation.A Prochlorococcus Pro-eCAG_006. B Synechococcus Syn-eCAG_002. C The genomic region in Prochlorococcus marinus MIT9301. The size of the circle is proportional to relative abundance of each genus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus or Synechococcus read abundance was too low to reach the threshold limit.Full size imageWe detected the presence of an eCAG encompassing asnB, pyrB2, and pydC (Pro-eCAG_007, Syn-eCAG_003, Fig. S8), which could contribute to an alternative pyrimidine biosynthesis pathway and thus provide another way for cells to recycle complex nitrogen forms. While this eCAG is found in only one fifth of HLII genomes and in quite specific locations for Prochlorococcus, notably in the Red Sea, it is found in most Synechococcus cells in warm, Fe-replete, N and P-depleted niches, consistent with its phyletic pattern showing its absence only from most clade I, IV, CRD1, and EnvB genomes (Fig. S8; Dataset 6). More generally, most N-uptake and assimilation genes in both genera were specifically absent from Fe-depleted areas, including the nirA/narB eCAG for Prochlorococcus, as mentioned by Kent et al. [36] as well as guanidinase and nitrilase eCAGs. In contrast, picocyanobacterial populations present in low-Fe areas possess, in addition to the core ammonium transporter amt1, a second transporter amt2, also present in cold areas for Synechococcus (Fig. S9). Additionally, Prochlorococcus populations thriving in HNLC areas also possess two amino acid-related eCAGs that are present in most Synechococcus genomes, the first one involved in polar amino acid N-II transport (Pro-eCAG_008; natF-G-H-bgtA; [53]; Fig. S10A, B) and the second one (leuDH-soxA-CK_00001744, Pro-eCAG_009, Fig. S10C, D) that notably encompasses a leucine dehydrogenase, able to produce ammonium from branched-chain amino acids. This highlights the profound difference in N acquisition mechanisms between HNLC regions and Fe-replete, N-deprived areas: the primary nitrogen sources for picocyanobacterial populations dwelling in HNLC areas seem to be ammonium and amino acids, while N acquisition mechanisms are more diverse in N-limited, Fe-replete regions.eCAGs related to phosphorus metabolismAdaptation to P depletion has been well documented in marine picocyanobacteria showing that while in P-replete waters Prochlorococcus and Synechococcus essentially rely on inorganic phosphate acquired by core transporters (PstSABC), strains isolated from low-P regions and natural populations thriving in these areas additionally contain a number of accessory genes related to P metabolism, located in specific genomic islands [6, 14, 30,31,32, 54]. Here, we indeed found in Prochlorococcus an eCAG containing the phoBR operon (Pro-eCAG_010) that encodes a two-component system response regulator, as well as an eCAG including the alkaline phosphatase phoA (Pro-eCAG_011), both present in virtually the whole Prochlorococcus population from the Mediterranean Sea, the Gulf of Mexico and the Western North Atlantic Ocean, which are known to be P-limited [30, 55] (Fig. S11A, B). By comparison, in Synechococcus, we only identified the phoBR eCAG (Syn-eCAG_005, Fig. S11C) that is systematically present in warm waters whatever the limiting nutrient, in agreement with its phyletic pattern in reference genomes showing its specific absence from cold thermotypes (clades I and IV, Dataset 6). Furthermore, although our analysis did not retrieve them within eCAGs due to the variability of gene content and synteny in this genomic region, even within each genus, several other P-related genes were enriched in low-P areas but partially differed between Prochlorococcus and Synechococcus (Figs. 3, S2, S11; Dataset 6). While the genes putatively encoding a chromate transporter (ChrA) and an arsenate efflux pump ArsB were present in both genera in different proportions, a putative transcriptional phosphate regulator related to PtrA (CK_00056804; [56]) was specific to Prochlorococcus. Synechococcus in contrast harbors a large variety of alkaline phosphatases (PhoX, CK_00005263 and CK_00040198) as well as the phosphate transporter SphX (Fig. S11).Phosphonates, i.e. reduced organophosphorus compounds containing C–P bonds that represent up to 25% of the high-molecular-weight dissolved organic P pool in the open ocean, constitute an alternative P form for marine picocyanobacteria [57]. We indeed identified, in addition to the core phosphonate ABC transporter (phnD1-C1-E1), a second previously unreported putative phosphonate transporter phnC2-D2-E2-E3 (Pro-eCAG_012; Fig. 6A). Most of the Prochlorococcus population in strongly P-limited areas of the ocean harbored these genes, while they were absent from other areas, consistent with their presence in only a few Prochlorococcus and no Synechococcus genomes. Furthermore, as previously described [58,59,60], we found a Prochlorococcus eCAG encompassing the phnYZ operon involved in C-P bond cleavage, the putative phosphite dehydrogenase ptxD, and the phosphite and methylphosphonate transporter ptxABC (Pro-eCAG_0013, Dataset 6; Fig. 6B, [60,61,62]). Compared to these previous studies that mainly reported the presence of these genes in Prochlorococcus cells from the North Atlantic Ocean, here we show that they actually occur in a much larger geographic area, including the Mediterranean Sea, the Gulf of Mexico, and the ALOHA station (TARA_132) in the North Pacific, even though they were present in a fairly low fraction of Prochlorococcus cells. These genes occurred in an even larger proportion of the Synechococcus population, although not found in an eCAG for this genus (Fig. S12; Dataset 6). Synechococcus cells from the Mediterranean Sea, a P-limited area dominated by clade III [24], seem to lack phnYZ, in agreement with the phyletic pattern of these genes in reference genomes, showing the absence of this two-gene operon in the sole clade III strain that possesses the ptxABDC gene cluster. In contrast, the presence of the complete gene set (ptxABDC-phnYZ) in the North Atlantic, at the entrance of the Mediterranean Sea, and in several clade II reference genomes rather suggests that it is primarily attributable to this clade. Altogether, our data indicate that part of the natural populations of both Prochlorococcus and Synechococcus would be able to assimilate phosphonate and phosphite as alternative P-sources in low-P areas using the ptxABDC-phnYZ operon. Yet, the fact that no picocyanobacterial genome except P. marinus RS01 (Fig. 6C) possesses both phnC2-D2-E2-E3 and phnYZ, suggests that the phosphonate taken up by the phnC2-D2-E2-E3 transporter could be incorporated into cell surface phosphonoglycoproteins that may act to mitigate cell mortality by grazing and viral lysis, as recently suggested [63].Fig. 6: Global distribution map of eCAGs putatively involved in phosphonate and phosphite transport and assimilation.A Prochlorococcus Pro-eCAG_012 putatively involved in phosphonate transport. B Prochlorococcus Pro-eCAG_013, involved in phosphonate/phosphite uptake and assimilation and phosphonate C-P bond cleavage. C The genomic region encompassing both phnC2-D2-E2-E3 and ptxABDC-phnYZ specific to P. marinus RS01. The size of the circle is proportional to relative abundance of Prochlorococcus as estimated based on the single-copy core gene petB and this gene was also used to estimate the relative abundance of other genes in the population. Black dots represent Tara Oceans stations for which Prochlorococcus read abundance was too low to reach the threshold limit.Full size imageeCAGs related to iron metabolismAs for macronutrients, it has been hypothesized that the survival of marine picocyanobacteria in low-Fe regions was made possible through several strategies, including the loss of genes encoding proteins that contain Fe as a cofactor, the replacement of Fe by another metal cofactor, and the acquisition of genes involved in Fe uptake and storage [14, 15, 36, 39, 64]. Accordingly, several eCAGs encompassing genes encoding proteins interacting with Fe were found in modules anti-correlated to HNLC regions in both genera. These include three subunits of the (photo)respiratory complex succinate dehydrogenase (SdhABC, Pro-eCAG_014, Syn-eCAG_006, Fig. S13; [65]) and Fe-containing proteins encoded in most abovementioned eCAGs involved in N or P metabolism, such as the guanidinase (Fig. S6), the NitC1 (Fig. 5), the pyrB2 (Fig. S8), the phosphonate (Fig. 6, S12), and the urea and inorganic nitrogen eCAGs (Fig. S5). Most Synechococcus cells thriving in Fe-replete areas also possess the sodT/sodX eCAG (Syn-eCAG_007, Fig. S14A, B) involved in nickel transport and maturation of the Ni-superoxide dismutase (SodN), these three genes being in contrast core in Prochlorococcus. Additionally, Synechococcus from Fe-replete areas, notably from the Mediterranean Sea and the Indian Ocean, specifically possess two eCAGs (Syn-eCAG_008 and 009; Fig. S14C, D), involved in the biosynthesis of a polysaccharide capsule that appear to be most similar to the E. coli groups 2 and 3 kps loci [66]. These extracellular structures, known to provide protection against biotic or abiotic stress, were recently shown in Klebsiella to provide a clear fitness advantage in nutrient-poor conditions since they were associated with increased growth rates and population yields [67]. However, while these authors suggested that capsules may play a role in Fe uptake, the significant reduction in the relative abundance of kps genes in low-Fe compared to Fe-replete areas (t-test p-value  More

Genome reduction is associated with a termite comb-associated lifestyleFor our studies, we collected fungus comb samples originating from mounds of Macrotermes natalensis, Odontotermes spp., and Microtermes spp. termites and were able to obtain seven viable Pseudoxylaria cultures (X802 [Microtermes sp.], Mn132, Mn153, X187, X3-2 [Macrotermes natalensis], and X167, X170LB [Odontotermes spp.], Table S1-S3).To test if a fungus comb-associated lifestyle of Pseudoxylaria was reflected in differences at the genome level, we sequenced the genomes of all seven isolates using a combination of paired-end shotgun sequencing (BGISEQ-500, BGI) and long-read sequencing (PacBio sequel, BGI or Oxford Nanopore Technologies, Oxford, UK). In addition, we sequenced the transcriptomes (BGISEQ, BGI) of two isolates (X802, X170LB). Eleven publicly available genomes of free-living Xylaria (Fig. 2A, B) were used as reference genomes (Table S4). Hybrid draft genomes were comprised on average of 33–742 scaffolds with total haploid assembly lengths of 33.2–40.4 Mb, and a high BUSCO completeness of genomes ( > 95 %) with a total number of predicted proteins ranging from 8.8 to 12.1 × 103. The GC content was comparable to reference genomes with 49.7–51.6%. To verify the phylogenetic placement of the isolates, different genetic loci encoding conserved protein sequences (α-actin (ACT), second largest subunit of RNA polymerase (RPB2), β-tubulin (TUB) and the internal transcribed spacer (ITS) were used as genetic markers [7, 13].Fig. 2: Geographic and comparative phylogenomic analysis of termite-associated Pseudoxylaria isolates (strains 1-7) and free-living Xylaria (strains 8–18).A Geographic origins of genome-sequenced free-living Xylaria and termite-associated Pseudoxylaria isolates, B phylogenomic placement based on single-copy ortholog protein sequences, and C comparison of genome assembly length, and numbers of predicted proteins per genome.Full size imagePhylogenies were reconstructed from ITS sequences and three aligned sequence datasets (RPB2, TUB, ACT) using reference sequences of twelve different taxa (Table S4–S7). Consistent with previous findings, all isolates grouped within the monophyletic termite-associated Pseudoxylaria group [9,10,11,12,13], which diverged from the free-living members of the genus Xylaria (Fig. 2B, Figure S1–S4).As our seven isolates covered a larger portion of the previously reported phylogenetic diversity of the termite-associated subgenus, we elaborated on genomic characteristics of our isolates to uncover features of the termite-associated ecology of Pseudoxylaria. Indeed, comparative genome analysis of the South African Pseudoxylaria isolates with publicly available genomes of free-living Xylaria species of similar genome quality revealed significantly reduced genome assembly lengths in Pseudoxylaria with reduced numbers of predicted genes per genome (Table S4). Comparison of the annotated mitochondrial (mt) genomes (Figure S5, Table S8) also indicated that all seven mt genomes were shorter in length (assembly lengths: 18.5–63.8 kbp) compared to the, albeit few, publicly available mitochondrial genomes of free-living species (48.9–258.9 kbp). The reduction in mitochondrial genome size also corresponded to a significantly reduced mean number of annotated genes (7.6) and tRNAs (14.3) in Pseudoxylaria spp. compared to on average 30.0 (annotated genes) and 25.8 (tRNAs) found in free-living species.Analysis of the abundance and composition of transposable elements (TEs), which account for up to 30–35% of the genomes of (endo)parasitic fungi due to the expansion of certain gene families [20, 21], showed that the mean total numbers of TEs across Pseudoxylaria spp. genomes were comparable (1530), but the numbers were reduced compared to free-living Xylaria species (3690) (Table S9). We also identified high variation in the TE composition across genomes (1.5–9.9 %), comparable to what was observed in free-living Xylaria spp. (1.3–8.1 %), with reductions in long terminal repeat retrotransposons (LTRs: Copia and unknown LTRs) in two inverted tandem repeat DNA transposons (TIRs; CACTA, Mutator and hAT). As Pseudoxylaria spp. contained increased numbers of non-ITR transposons of the helitron class and LTRs of the Gypsy class compared to Xylaria strains, we concluded that Pseudoxylaria exhibits no typical features of an (endo)parasitic lifestyle, but that the overall composition and the reduced numbers of TEs could serve as a fingerprint to distinguish the genetically divergent Pseudoxylaria taxa.Repertoire of carbohydrate-active enzymes indicates specialized substrate useAs the fungus comb is mostly composed of partially-digested plant material interspersed with fungal mycelium of the termite mutualist [3], we anticipated that Pseudoxylaria should exhibit features of a substrate specialist similar to the fungal mutualist Termitomyces, which should be reflected in a Carbohydrate-Active enzyme (CAZyme) repertoire distinguishable from  free-living saprophytic Xylaria species [22,23,24]. In particular, numbers and composition of redox-active enzymes (e.g., benzoquinone reductase (EC 1.6.5.6/EC 1.6.5.7), catalase (EC 1.11.1.6), glutathione reductase (EC 1.11.1.9), hydroxy acid oxidase (EC 1.1.3.15), laccase (EC 1.10.3.2), manganese peroxidase (EC 1.11.1.13), peroxiredoxin (EC 1.11.1.15), superoxide dismutase (EC 1.15.1.1), dye-decolorization or unspecific peroxygenase (EC 1.11.2.1), Table S10), which catalyze the degradation of lignin-rich biomass, were expected to differ between free-living strains and substrate specialists [22].Identification of CAZymes using Peptide Pattern Recognition (PPR) revealed that Pseudoxylaria genomes encoded on average a reduced number of CAZymes (mean 264) compared to the free-living taxa in the family Xylaria (mean 367 CAZymes, pANOVA; F = 41.4, p = 3.5 × 10–8, pairwise p = 1.69 × 10–7) (Fig. 3A, B, Figure S6), but similar numbers to those identified in Termitomyces (mean 265, pairwise p = 0.949).Fig. 3: Comparison of carbohydrate-active enzymes (CAZymes) in Xylaria, Pseudoxylaria and the fungal mutualist Termitomyces.A Predicted CAZymes, B Principal Coordinates Analysis (PCoA) of predicted CAZyme families, and C heatmap of representatives CAZyme families in the predicted proteomes of free-living Xylaria, Termitomyces and Pseudoxylaria species.Full size imageOverall, significant differences in the composition of CAZymes were observed [8], most notably in the reduction of auxiliary activity enzymes (AA), carbohydrate esterases (CE), glycosyl hydrolases (GH), and polysaccharide lyases (PL). The most significant reduction was observed in the AA3 family (Fig. 3C), which typically displays a high multigenicity in wood-degrading fungi as many  enzymes of this family catalyze the oxidation of alcohols or carbohydrates with the concomitant formation of hydrogen peroxide or hydroquinones thereby supporting lignocellulose degradation by other AA-enzymes, such as peroxidases (AA2). Similarly, although to a lesser extent, reduced numbers within the related AA1 family were detected, which included oxidizing enzymes like laccases, ferroxidases, and laccase-like multicopper oxidases. Along these lines, glycosyl hydrolases of the GH3 and GH5 family, including enzymes responsible for degradation of cellulose-containing biomass and xylose, were less abundant. We also noted that all Pseudoxylaria lacked homologs of the unspecific peroxygenases (UPO; EC 1.11.2.1), while almost all free-living Xylaria spp. and the fungal symbiont Termitomyces harbored at least one or two copies of similar gene sequences.
Pseudoxylaria shows reduced biosynthetic capacity for secondary metabolite productionA healthy termite colony is engulfed in several layers of social immunity [5, 6], which pose a constant selection pressure on associated and potentially antagonistic microbes. As Pseudoxylaria evolved measures to remain inconspicuously present within the comb environment, we hypothesized that one of the possible adaptations to evade hygiene measures of termites could be reflected in a reduced biosynthetic capability to produce antibiotic or volatile natural products, which often serve as infochemicals triggering defense mechanisms [25,26,27], or as alarm pheromones [4, 28].The biosynthesis of secondary metabolites is encoded in so called Biosynthetic Gene Cluster (BGC) regions. We explored the abundance and diversity of encoded BGCs using FungiSMASH 6.0.0 and manually cross-checked the obtained data set by BLAST to account for possible biases due to varying genome qualities across strains of both groups [29]. Overall, the herein investigated Xylaria genomes harbored on average 90 BGCs per genome, while Pseudoxylaria encoded on average 45 BGCs (Fig. 4, Figure S7). Fig. 4: Similarity network analysis of biosynthetic gene clusters.Comparative analysis of termite associated-associated Pseudoxylaria isolates (strains 1–7, red circles) and free-living Xylaria (strains 8–18, green circles) with BiG-SCAPE 1.0 annotations (blue hexagon) ACR ACR toxin, Alt alternariol, Bio biotin, Chr chromene, Cyt cytochalasins, Cur curvupalide, Dep depiudecin, Fus fusarin, Gri griseofulvin, Mon monascorubin, MSA 6-methylsalicylic acid, Pho phomasetin, Sol solanapyrone, Swa swasionine, Xen xenolozoyenone, Xsp xylasporins, Xyl xylacremolide. Singletons are not shown.Full size imageThe nature and relatedness of the BGCs were analyzed by creating a curated similarity network analysis using BiG-SCAPE 1.0 [30]. Overall, 28 orthologous BGCs were shared across all genomes, including the biosynthesis of polyketides like 6-methylsalicylic acid (MSA), chromenes (Chr) and polyketide-non-ribosomal peptide (PKS-NRPS) hybrids like the cytochalasins (Cyt) [31]. Furthermore, five BGC networks, which were shared by Pseudoxylaria and Xylaria, contained genes encoding natural product modifying dimethylallyltryptophan synthases (DMATS). In contrast, and despite the significant reduction in the biosynthetic capacity within Pseudoxylaria genomes [29], about 29 BGC networks were unique to Pseudoxylaria and thus could possibly relate to the comb-associated lifestyle (Figure S8 and S9). Notably, Pseudoxylaria genomes lacked genes encoding ribosomally synthesized and posttranslationally modified peptides (RiPPs) or halogenases. In comparision, free-living Xylaria spp. harbored at least one sequence encoding a RiPP, and up to two orthologous sequences encoding putative halogenases. In contrast, a reduced average number of terpene synthases (TPS) in Pseudoxylaria (9 TPS) compared to free-living Xylaria (18 TPS) was detected, which included three BGCs encoding TPSs that were unique to Pseudoxylaria.  In comparison, genomes of the fungal mutualist Termitomyces were reported to encode for about 20-25 terpene cyclases, but haboured only about two loci containing genes for a PKS and NRPS each [24].Manual BLAST searches were conducted to identify BGCs that could be putatively assigned to previously isolated metabolites from Pseudoxylaria (vide infra Fig. 7, Figure S8) [32, 33]. Using e.g., the known NRPS-PKS-hybrid cluster sequence ccs (Aspergillus clavatus) of cytochalasins as query, an orthologous BGC, here named cytA, was identified in the cytochalasin-producing strain X802 [34]. Although the putative PKS-NRPS hybrid and CcsA shared 60 % identical amino acids (aa), the sequences of the accessory enzymes were less related to CcsC-G (45–47% identical aa) and the BGC in X802 lacked a gene of a homologue to ccsB. Similarly, five free-living Xylaria species carried orthologous gene loci (Xylaria sp. BCC 1067, Xylaria sp. MSU_SB201401, X. flabelliformis G536, X. grammica EL000614, and X. multiplex DSM 110363) supporting previous isolation reports of cytochalasins with varying structural features. Furthermore, three Pseudoxylaria strains (X187, and closely related Mn153, and Mn132) were found to share a highly similar PKS-NRPS hybrid BGC (99–100 % identical aa, named xya), which likely encodes for the enzymatic production of previously identified xylacremolides [32]. Four Pseudoxylaria strains (X802, Mn132, Mn153, and X187) also shared a BGC (50–98 % amino acid identity) resembling the fog BGC (Aspergillus ruber) [35, 36], which putatively encodes the biosynthetic machinery to produce xylasporin/cytosporin-like metabolites. In this homology search, we also uncovered that fog-like BGC arrangements are likely more common than previously anticipated, as clusters with similar arrangements and identity were also found in genomes of Rosellinia necatrix, Pseudomasariella vexata, Stachybotrys chartarum, and Hyaloscypha bicolor (Fig. 4, Figure S8).A detailed analysis of the fog-like cluster arrangements within Pseudoxylaria genomes revealed – similar to homologs of the ccs cluster – variation in the abundance and arrangement of several accessory genes coding for a cupin protein (pxF), a short chain oxidoreductase (pxB; SDR), and an additional SnoaL-like polyketide cyclase (pxP), which could account for the production of strain-specific structural congeners (vide infra, Fig. 7).Change of nutrient sources causes dedicated transcriptomic changes in Pseudoxylaria
To further solidify our in silico indications of substrate specialization with comb material as preferred substrate and fungus garden as environment, we analyzed Pseudoxylaria growth on different media (PDA, and reduced medium 1/3-PDA) including comb-like agar matrices (wood-rice medium (WRM), agar-agar or 1/3-PDA medium containing lyophilized (dead) Termitomyces sp. T112 biomass (T112, respectively T112-PDA), PDB covering glass-based surface-structuring elements (GB), Table S11–S14).Cultivation of Pseudoxylaria on agar-agar containing lyophilized biomass of Termitomyces (T112) as the sole nutrient source allowed Pseudoxylaria to sustain growth, although to a reduced extent compared to growth on nutrient-rich PDA medium (Table S3). Wood-rice medium (WRM) induced comparable growth rates as observed on PDA and also the appearance of phenotypic stromata.To investigate the influence of these growth conditions on the transcriptomic level, we harvested RNA from vegetative mycelium after growth on comb-like media (WRM, T112, T112-PDA, and GB), PDA, and reduced medium 1/3-PDA (Fig. 5A). The most significant transcript changes (normalized to data obtained from growth on PDA) were observed for genes coding for specific CAZymes including several redox active enzymes (Fig. 5B). The 30 most variable transcripts coded for specific glycoside hydrolases (GH), lytic polysaccharide monooxygenases (AA), ligninolytic enzymes, and a glycoside transferase (GT). Similarly, chitinases (CHT2; CHT4; CHI2; CHI4) were upregulated (up to 243-fold on T112) under almost all conditions compared to PDA, but some of these specific transcript changes were exclusive to growth on Termitomyces biomass or artificial comb material (WRM) suggesting the ability to regulate and increase chitin metabolism if necessary [37].Fig. 5: Transcriptomic analysis of Pseudoxylaria sp. X802 in dependence of growth conditions.A Representative pictures of Pseudoxylaria sp. X802 growing on PDA, PDB on glass beads (GB), wood-rice medium (WRM), and agar-agar medium containing lyophilized Termitomyces sp. T112 biomass (T112). B Heatmap of the most variable transcripts coding for CAZymes (red), redox enzymes (orange), secondary metabolite-related core genes (green), and more specifically on key genes within the boundaries of cytochalasin (turquoise) and xylasporin/cytosporin BGCs (blue). RNA was obtained from vegetative mycelium after growth on PDA, reduced medium (1/3-PDA), PDB on glass beads (GB), wood-rice medium (WRM), 1/3-PDA-medium enriched with Termitomyces sp. T112 biomass (T112-PDA) and agar-agar medium containing lyophilized Termitomyces biomass (T112). Transcript counts are shown as log10 transformed transcripts per million (top; TPM). Significance of the changes in transcript counts are compared to control (X802 grown on PDA) and depicted in log-10 transformed p values.Full size imageWhen X802 was grown on T112 (agar matrix containing lyophilized Termitomyces sp. T112 biomass), we observed a >400-fold increase in the expression of transcripts encoding glycoside hydrolases in the GH43 family, GH7 (~140-fold), GH3, and GH64 (5–12-fold). Similarly, transcripts for a putative mannosyl-oligosaccharide-α-1,2-mannosidase (MNS1B; 8.2-fold), chitinase CHT4 (2.9-fold), β-glucosidase BGL4 (5.7-fold), and copper-dependent lytic polysaccharide monooxygenase AA11 (1.6-fold) were significantly upregulated. Growth on WRM (wood-rice medium) or T112 (Termitomyces sp. T112 biomass) also caused a significant upregulation of genes coding for glycoside transferase GT2, glycoside hydrolases GH15, GH3, and aldehyde oxidase AOX1, which indicated the ability to expand the degradation portfolio if necessary. Along these lines, specific transcript levels were reduced when X802 was grown on T112, in particular class II lignin-modifying peroxidases (AA2), carbohydrate-binding module family 21 (CBM21), multicopper oxidases (AA1), secreted β-glucosidases (SUN4), and glycoside hydrolases GH16, and GH128.When the fungus was challenged with lignocellulose-rich WRM medium, higher transcript levels putatively assigned to glutathione peroxidase (GXP2), superoxide dismutase (SOD2), and laccases (LCC5) were observed, which indicated that despite the reduced wood-degrading capacity, Pseudoxylaria activates available enzymatic mechanisms to degrade the provided material and respond to the resulting oxidative stress. Cultivation on GB (glass-based surfaces covered in liquid PD broth) influenced the expression of certain genes coding for glycoside hydrolases (GH64, GH76, GH72, GH128, BGL4) and lytic polysaccharide monooxygenases (AA1, AA2, AA11), presumably enabling the fungus to utilize soluble carbohydrates.To test the hypothesis that the presence of Termitomyces biomass stimulates secondary metabolite production in Pseudoxylaria to eventually displace the mutualist, we also analyzed changes in the transcript levels of core BGC genes that encode the production of bioactive secondary metabolites. Overall, only slight transcript variations were detectable within the  most variable expressed genes. (Fig. 5B). Cultivation on GB, WRM, and T112 media caused lower transcript levels of genes coding for terpene synthase TC1, polyketide synthases (PKS7, PKS8), and the NRPS-like1, while an upregulation of NRPS-like2 on WRM (2.5-fold), and of PKS7 (1.7-fold) on reduced 1/3-PDA medium was observed.Transcript levels of core genes within BGCs assigned to cytochalasines (cyt) or xylasporins/cytosporins (px), e.g., remained nearly constant, while minor transcript level variations of neighboring genes and reduced transcript levels for pxI (flavin-dependent monooxygenase), pxH (ABBA-type prenyltransferase), pxF (cupin fold oxidoreductase), and pxJ (short-chain dehydrogenase) were detectable. Hence, it was concluded that the presence of Termitomyces biomass only weakly triggers secondary metabolite production in general, but varying transcript levels coding for decorating enzymes could cause substantial structural alterations within the produced natural product composition. It was also notable that transcript levels of the terpene synthase TC1 were downregulated, which could cause a reduced production level of specific volatiles.
Pseudoxylaria antagonizes Termitomyces growth and metabolizes fungal biomassThe growth behavior of Pseudoxylaria isolates was also analyzed in co-culture assays with Termitomyces. As expected from prior studies, both fungi showed reduced growth when co-cultured on agar plates, often causing the formation of zones of inhibition (ZOI) between the fungal colonies (Fig. 6A–D, Table S11–S14) [7]. When fungus-fungus co-cultures were maintained for longer than two weeks on agar plates, Pseudoxylaria started to overcome the ZOI and overgrew Termitomyces via the extension of aerial mycelium. The observation was even more pronounced when co-cultures were performed on wood-rice medium (WRM), where Pseudoxylaria remained the only visible fungus after two weeks.Fig. 6: Co-cultivation of Pseudoxylaria sp. X170LB and Termitomyces sp. T112 and results of isotope fractionation experiments.Representative pictures of fungal growth and co-cultivation of A Termitomyces sp. T112, B Pseudoxylaria sp. X170LB, C co-culture of Pseudoxylaria sp. X802 and Termitomyces sp. T153 exhibiting a ZOI, in which X802 overgrowths T153 in proximity to the interaction zone (red arrow), and D Pseudoxylaria sp. X802 growing on the surface of a living Termitomyces sp. T153 culture. E, F Shown is the relative change in the carbon isotope pattern (δ13C values, ± standard deviation, with n = 3) of lipid and carbohydrate fractions isolated from fungal biomass of Termitomyces sp. T112, Pseudoxylaria sp. X170LB, and Pseudoxylaria sp. X170LB cultivated on vegetative Termitomyces sp. T112 biomass (T112ǂ), or on lyophilized Termitomyces sp. T112 biomass (T112). Fungal strains were grown on E medium with natural 13C abundance and F medium artificially enriched in 13C content.Full size imageTo verify whether Pseudoxylaria consumes Termitomyces or even partially degrades specific metabolites present within the fungal biomass, we pursued stable isotope fingerprinting commonly used to analyse trophic relations [38, 39]. This diagnostic method relies on measurable changes in the bulk stable isotope composition, because biosynthetic enzymes preferentially convert lighter metabolites enriched in 12C compared to their heavier 13C-enriched congeners. This intrinsic kinetic isotope effect results in an overall change in the 13C/12C ratio of the respective educts and products, in particular in biomarkers such as phospholipid fatty acids, carbohydrates, and amino acids. Using this isotope enrichment effect, we determined the natural trophic isotope fractionation of 13C in lipids and carbohydrates produced by Termitomyces sp. T112 and Pseudoxylaria sp. X170LB. For clearer differentiation, both fungi were cultivated on PDA medium containing naturally abundant 13C/12C, Fig. 6E) and on PDA medium enriched with 13C-glucose (Fig. 6F). Lipids and carbohydrates were isolated from mycelium harvested after 21 days (Fig. 6E, Table S15).Analysis of fungal carbohydrate and lipid-rich metabolite fractions by Elemental Analysis-Isotope Ratio Mass Spectrometry (EA-IRMS) [40, 41] uncovered that under normal growth conditions (full medium), Termitomyces sp. T112 and Pseudoxylaria sp. X170LB showed only a slight negative trophic fractionation of stable carbon isotopes (δ13C/12C ratio (expressed as δ13C values [‰]), Fig. 6F) within the carbohydrate fractions (T112: −1.2 ‰; for X170LB: −1.3 ‰), and expectedly a stronger depletion in the lipid fraction (T112: −6.7 ‰, and less pronounced for X170LB: −3.1 ‰). To determine if Pseudoxylaria metabolizes Termitomyces biomass, the isotope pattern of metabolites derived from Pseudoxylaria thriving on living biomass of Termitomyces (T112ǂ) was analysed next. Here, an overall positive carbon isotope (13C/12C) fractionation by approximately +0.6 ‰ relative to the control medium was detectable, while the δ13C values of lipids remained largely unchanged (Fig. 6F, Table S15). These results suggested that Pseudoxylaria might pursue a preferential uptake of Termitomyces-derived carbohydrates.In a last experiment, Pseudoxylaria was grown on lyophilized (dead) Termitomyces biomass (T112) as sole food source. In this experiment, the isotope fingerprint showed converging δ13C values of −1.9 ‰ (relative to the media) for both carbohydrate and lipid fractions, which indicated that Pseudoxylaria is able to simultaneously metabolize and cycle carbohydrates as well as lipids resulting in the equilibration of isotopic levels between carbohydrates and lipids. Thus, it was concluded that in nature, Pseudoxylaria likely harvests nutrients firstly from vegetative Termitomyces, and then—if possible—subsequently degrades dying or dead mycelium.
Pseudoxylaria produces antimicrobial secondary metabolitesBased on the observation that Pseudoxylaria antagonizes growth of Termitomyces, we questioned if the formation of a ZOI might be caused by the secretion of Pseudoxylaria-derived antimicrobial metabolites [26, 42]. Thus, we performed an ESI(+)-HRMS/MS based metabolic survey using the web-based platform “Global Natural Product Social Molecular Networking” (GNPS) [43] to correlate the encoded biosynthetic repertoire of Pseudoxylaria with secreted metabolites.A partial similar metabolic repertoire across the six analyzed strains was detectable and allowed us to match some of the detectable chemical features and previously isolated metabolites to the predicted shared BGCs, such as antifungal and histone deacetylase inhibitory xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides (Psa; X187/Mn132) [32], antibacterial pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) [36], and cytotoxic cytochalasins (X802/OD126) (Fig. 7A and B) [18].Fig. 7: Comparative metabolomic analysis of six Pseudoxylaria strains (OD126 (red), Mn132 (orange), X170 (black), X187 (green), X3.2 (yellow) and X802 (blue)).A Overview of the GNPS network. Identified metabolite clusters xylacremolides (Xyl; X187/Mn132) [32, 33], pseudoxylaramides [32] (Psa; X187/Mn132), pseudoxylallemycins (Psm; X802/OD126) [18], xylasporin/cytosporins (Xsp; X802/OD126/X187/Mn132) and cytochalasins (X802/OD126) [18]. B xylasporin/cytosporin-related cluster formed by nodes from X802 (blue), OD126 (red), X187 (green) and Mn132 (orange). C Chemical structures of natural products isolated from Pseudoxylaria species and related compounds. Red box highlights proposed structures of isolated xylasporin G and I in this study.Full size imageA cluster that contained MS2 signals of molecular ions assigned to the cytosporin/xylasporin family, which was shared by at least four strains, caught our attention as a certain degree of structural diversity of xylasporin/cytosporin family was predicted from the comparison of their respective BGCs. The assigned nodes of this GNPS cluster split into two subclusters with only very little overlap between both regions. Analysis of the mass fragment shifts suggested that both subclusters belong to two different families of xylasporin/cytosporin congeners (Figure S9). To verify these deductions, we pursued an MS-guided purification of xylasporin/cytosporins from chemical extracts of Pseudoxylaria sp. X187, which yielded xylasporin G (3.23 mg, pale-yellow solid) and xylasporin I (1.75 mg, pale-yellow solid). The sum formulas of xylasporin G and xylasporin I were determined to be C17H22O5 (calcd. for [M + H]+ C17H23O5+ = 307.1540, found 307.15347, −1.726 ppm) and C17H24O5 (calcd. for [M + H]+ C17H25O5+ = 309.1697, found 309.1691, −1.68 ppm) by ESI-(+)-HRMS and were predicted to have six degrees of unsaturation (Fig. 7B, Figure S10, Table S16-S17). Planar structures were deduced by comparative 1D and 2D NMR analyses, which revealed the presence of an unsaturated polyketide chain that matched the unsaturation degree and the anticipated structural variation from cytosporins (Fig. 7C, Figure S11-S25).To evaluate if Pseudoxylaria-derived culture extracts and produced natural products (e.g., cytochalasins) are responsible for the observed antimicrobial activity, standardized antimicrobial activity assays were performed (Table S17, S18 and Figure S26). As neither culture extracts nor single compounds exhibited significant antimicrobial activity, they could not be held fully accountable for the antagonistic behavior in co-cultures. Thus, we hypothesized that the observed ZOI might be caused by yet unknown effects like nutrient depletion or bioactive enzymes.
Pseudoxylaria has a negative impact on the fitness of insect larvaeDue to the production of structurally diverse and weakly antimicrobial secondary metabolites, we questioned if mycelium of Pseudoxylaria exhibits intrinsic insecticidal or other insect-detrimental activities, which could discourage or ward off grooming behavior of termite workers. Due to the technical challenges associated with behavioral studies of termites, we evaluated instead the effect of Pseudoxylaria biomass on Spodoptera littoralis, a well-established insect model system and a destructive agricultural lepidopterous pest [44, 45]. When S. littoralis larvae were fed with mycelium-covered agar plugs of Pseudoxylaria sp. X802, a clear decrease of the relative growth rate (RGR) and decline in survival was observed (Fig. 8: treatment D (green), Table S19, S20) compared to feeding with untreated agar plugs (treatment A (black)). In comparison, when larvae were fed with agar plugs covered with the fungal mutualist Termitomyces sp. T153 (treatment B (blue)) an increased growth rate of larvae was observed.Fig. 8: Effect of Termitomyces sp. T153 and Pseudoxylaria sp. X802 mycelia on the relative growth rate and survival of S. littoralis larvae.Insects were fed with either A PDA, B PDA agar plug covered with vegetative Termitomyces sp. T153, C PDA agar plug from which vegetative Termitomyces sp. T153 was removed prior to feeding, D PDA agar plug covered with vegetative Pseudoxylaria sp. X802 mycelium, and E PDA agar plug from which vegetative Pseudoxylaria sp. X802 mycelium was removed prior to feeding. All experiments were performed with 25 replicates per treatment, a duration of 10 days, and larval weights and survival rates were recorded every day. Statistical significances were determined using ANOVA on ranks (p  More

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Extraction of total active proteomes from sediment samplesWe sampled 14 sediments along the coastlines of the Irish Sea, the Mediterranean Sea, and the Red Sea (from 16°N to 53°N), applying uniform sampling and storage procedures. Location details and sediment temperature fluctuations are summarized in Supplementary Table S1. We collected sediments (5 Kg) in triplicate and extracted the total proteins using a well-established microbial detachment procedure67, with some modifications. We mixed 100 g of sediment with 300 ml of sterilized saline solution (5 mM sodium pyrophosphate and 35 g L−1 of NaCl) containing 150 mg L−1 of Tween 80 (from Merck Life Science S.L.U., Madrid, Spain) in an ice water bath. After re-suspension, samples were kept in a water bath ultra-sonicator (Bandelin SONOREX, Berlin, Germany) on ice and sonicated (60 W output) for 120 min. We repeated this procedure twice, with an ice water bath incubation of 60 min between each cycle. We then centrifuged the samples at 500 g for 15 min at 4 °C to remove the sediments in a centrifuge 5810 R (Eppendorf AG, Hamburg, Germany). Supernatants were carefully transferred to a new tube, minimizing disruption of the sediments, and the resulting supernatants were centrifuged at 13,000 g for 15 min at 4 °C to produce microbial cell pellets. We used the resulting cell mix to extract the total protein by mixing the cells with 1.2 ml BugBuster® Protein Extraction Reagent (Novagen, Darmstadt, Germany) for 30 min with shaking (250 rpm). Subsequently, samples were disrupted by sonication using a pin Sonicator® 3000 (Misonix, New Highway Farmingdale, NY, USA) for a total time of 2 min (10 watts) on ice (4 cycles × 0.5 min with 1 min ice-cooling between each cycle). Extracts were centrifuged for 10 min at 12,000 g at 4 °C to separate cellular debris and intact cells. Supernatants were carefully aspirated (to avoid disturbing the pellet), transferred to new tubes, and stored at –80 °C until use. The protein solution was filtered at 15 °C for 7 h using Vivaspin filters (Sartorius, Goettingen, Germany) with a molecular weight (MW) cut-off of 3,000 Da to concentrate the proteins up to a final concentration of 10 mg ml−1, according to the Bradford Protein Assay (Bio-Rad Laboratories, S.A., Madrid, Spain)68. The average total amount of proteins extracted per each 100 g of sediment was 612 µg (interquartile range, 31 µg, see details in Supplementary Fig. S2). In all cases, extensive dialysis of protein solutions against 40 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer was performed using a Pur-A-LyzerTM Maxi 1200 dialysis kit (Merck Life Science S.L.U., Madrid, Spain)69, and active proteins stored at a concentration of 10 mg ml−1at –86 °C until use. As reported previously70, 2DE was performed using GE Healthcare reagents and equipment, 11 cm IPG strips in the pH range of 3–10 and molecular weight ranging from 10 to 250 kDa (Precision Plus Protein Dual Color Standards #1610374, Bio-Rad Laboratories, S.A., Madrid, Spain). The 2-DE was performed using a validated pooling strategy71, in which proteins extracted from three independent biological replicates (i.e., sediments) were mixed in equal amounts and a total of 150 µg of protein were further loaded per gel. Staining was performed with SYPRO Ruby Protein Gel Stain (Invitrogen, Waltham, MA, USA). The two-dimensional SDS-PAGE (12% acrylamide) gels of extracted proteins are reported in Supplementary Fig. S2 (original gels in Source Data). The same protocol was applied to extract and analyse by SDS-PAGE the total active proteins extracted from sediment samples with different temperature variability levels (HTV, ITV, and LTV) collected in the Red Sea (Supplementary Table S4). The total amount of protein extracted per each 100 g of sediment is given in Supplementary Table S8. Coomassie-stained one-dimension SDS-PAGE (1-DE) gels of extracted proteins are shown in Supplementary Fig. S9 (original gel in Source Data).Source, expression and purification of esterases and EXDOs from a wide geographical rangeWe recovered 83 enzymes (78 esterases and 5 EXDO) from microbial communities inhabiting marine sediments across ten distinct locations from the latitudinal transect described above: Ancona harbour (Anc), Priolo Gargallo (Pri), Gulf of Genoa, Messina harbour (Mes), Milazo harbour (Mil), Mar Chica lagoon (MCh), Bizerte lagoon (Biz), El-Max site (ElMax), Gulf of Aqaba (Aq), and Menai Strait (MS); further details are provided in Supplementary Data S3. Sources of the enzymes were the corresponding shotgun metagenomes (see Supplementary Table S3) and the metagenome clone libraries generated from the extracted DNA71. The sediment sample from the Gulf of Genoa was not used for activity tests and metaproteome analysis because no raw sample material was available; however, because of the possibility to access its shotgun metagenome (see Supplementary Table S3) and a metagenome clone library72, we used the sample for screening esterases to incorporate an additional latitude in our transect. In the case of Menai Strait (Irish Sea), five additional esterases were retrieved from a metagenome obtained from enriched cultures prepared with samples collected on 22nd June 2019 from Menai Strait (School of Ocean Sciences, Bangor University, St. George’s Pier, Menai Bridge, N53°13′31.3″; W4°09′33.3”). The water temperature was 14 °C and the salinity was 32 p.s.u. Two enrichment cultures were set up at 20 °C: (i) SW: seawater enrichment with 0.1% lignin; the enrichment was set up using 50 ml of the sample as inoculum with the addition of 0.1% lignin (Sigma-Aldrich, Gillingham, United Kingdom) (w/v); (ii) AW: algal surface wash-off in seawater, enriched with 0.1% lignin; the enrichment was set up using 50 ml of surface wash-off after mixing of ca. 10 g of Fucus (brown algae) in the seawater and removal of plant tissue, 0.1% lignin (w/v) was added. After 92 days of incubation, 5 ml of each enrichment cultures were transferred into the new flask containing 45 ml autoclaved and filtered seawater with 0.1% lignin. This procedure was repeated on days 185 and 260, and the incubation was stopped on day 365. The DNA was extracted using 12 months using MetaGnome extraction kit (EpiCentre, Biotechnologies, Madison, WI, USA), sequenced on Illumina MiSeq™ platform (Illumina Inc., San Diego, CA, USA) using paired-end 250 bp reads at the Centre for Environmental Biotechnology (Bangor, UK), and sequencing reads were processed and analysed as described previously73.The screening, cloning and activity of a subset of 35 identified esterases have been reported previously72. The remaining 48 enzymes are reported for the first time in this study and were identified using naive and in silico metagenomic approaches, as detailed below. The environmental site from which each enzyme originated and the method employed for its identification are detailed in Supplementary Data S3. For naive screens addressing the recovery of new sequences encoding esterases and EXDO, the large-insert pCCFOS1 fosmid libraries made using the corresponding DNA samples, the CopyControl Fosmid Library Kit (Epicentre Biotechnologies, Madison, WI, USA) and the Escherichia coli EPI300-T1R strain were used. The nucleic acid extraction, construction and the functional screens of such libraries have been previously described72. In brief, fosmid clones were plated onto large (22.5 × 22.5 cm) Petri plates with Luria Bertani (LB) agar containing chloramphenicol (12.5 µg ml−1) and induction solution (Epicentre Biotechnologies; WI, USA), at a quantity recommended by the supplier to induce a high fosmid copy number. Clones were scored by the ability to hydrolyze α-naphthyl acetate and tributyrin (for esterase activity), and catechol (for EXDO activity)72,74. Positive clones presumed to contain esterases and EXDOs were selected, and their DNA inserts were sequenced using a MiSeq Sequencing System (Illumina, San Diego, USA) with a 2 × 150-bp sequencing v2 kit at Lifesequencing S.L. (Valencia, Spain). After sequencing, the reads were quality-filtered and assembled to generate nonredundant meta-sequences, and genes were predicted and annotated via BLASTP and the PSI-BLAST tool72. For in silico screens, addressing the recovery of new sequences encoding esterases, the predicted protein-coding genes, obtained after the sequencing of DNA material from resident microbial communities in each of the samples, were used. The meta-sequences are available from the National Center for Biotechnology Information (NCBI) nonredundant public database (accession numbers reported in Supplementary Data S3). Protein-coding genes identified from the DNA inserts of positive clones (naive screen) or from the meta-sequences were screened for enzymes of interest using the Blastp algorithm via the DIAMOND v2.0.9 program with default parameters (percentage of identity ≥60%; alignment length ≥70; e-value ≤1e−5)29, against the Lipase Engineering sequence databases (to screen for esterases) and AromaDeg database (for EXDO)74. Since the collection of sediments across locations experiencing different MATs was limited by our sampling capacity, to expand our range of exploration at a global scale and to validate our dataset, we added our single enzyme analysis to the seawater metagenomes retrieved from the Tara Ocean Expedition database (accession number in Supplementary Data S4). Due to the volume of sequences generated, this database provides access to a large number of enzymes, including those studied here through homology search. Esterases were selected as target sequences, and the following pipeline was used. First, we selected a sequence encoding an esterase reported as one of the most substrate-ambiguous esterases out of 145 tested (EH1, Protein Data Bank acc. nr. 5JD4) and well-distributed in the marine environment72. Second, we performed a homology search of this sequence against the Tara Ocean metagenome21 to retrieve similar sequences, using the Blastp algorithm via the DIAMOND v2.0.9 program30 (e-value 98% using SDS-PAGE analysis in a Mini PROTEAN electrophoresis system (Bio-Rad Laboratories, S.A., Madrid, Spain). Purified protein was stored at –86 °C until use at a concentration of 10 mg ml−1 in 40 mM HEPES buffer (pH 7.0). A total of approximately 5–40 mg of total purified recombinant protein was obtained from 1 L of culture. Supplementary Fig. S1 illustrates a schematic representation of the pipeline implemented in this work to investigate enzyme activities in a large set of marine samples, starting from samples collected (sediments) and available metagenomes.Enzyme activity assessmentsAll substrates used for activity tests were of the highest purity and, if not indicated otherwise, were obtained from Merck Life Science S.L.U. (Madrid, Spain): 4-nitrophenyl-propionate (ref. MFCD00024664), 4-nitrophenyl phosphate (ref. 487663), 4-nitrophenyl β-D-galactose (ref. N1252), bis(p-nitrophenyl) phosphate (ref. 123943), benzaldehyde (ref. B1334), 2-(4-nitrophenyl)ethan-1-amine (ref. 184802-5G), pyridoxal phosphate (ref. P9255), acetophenone (ref. A10701), NADPH (ref. N5130) and catechol (ref. PHL82372). We directly tested total protein extracts for esterase, phosphatase, beta-galactosidase, and nuclease activity using 4-nitrophenyl-propionate, 4-nitrophenyl phosphate, 4-nitrophenyl β-D-galactose, and bis(p-nitrophenyl) phosphate, respectively, by following the production of 4-nitrophenol at 348 nm (extinction coefficient [ε], 4147 M−1 cm−1), as previously described69. For determination: [total protein]: 5 μg ml−1; [substrate]: 0.8 mM; reaction volume: 200 μl; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of 4-nitrophenyl-propionate was used to determine, under these standard conditions, the effects of temperature on the purified esterase. Transaminase activity was determined using benzaldehyde as amine acceptor, 2-(4-nitrophenyl)ethan-1-amine as amine donor, and pyridoxal phosphate as a cofactor, by following the production of a colour amine at 600 nm (extinction coefficient, 537 M−1 cm−1), as previously described75. For determination, [total protein]: 5 μg ml−1; [substrates]: 25 mM; [pyridoxal phosphate]: 1 mM; reaction volume: 200 μL; T: 4-85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). Aldo-keto reductase activity was determined using acetophenone as a substrate and NADPH as a cofactor, by following the consumption of NADPH at 340 nm (extinction coefficient, 6220 M−1 cm−1), as described76. For determination, [total protein]: 5 μg ml−1; [substrate]: 1 mM; [cofactor]: 1 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). We determined EXDO activity using catechol as substrate, by following the increase of absorbance at 375 nm of the ring fission products (extinction coefficient, 36000 M−1 cm−1), as previously described74. For determination, [protein]: 5 μg ml−1; [catechol]: 0.5 mM; reaction volume: 200 μL; T: 4–85 °C; and pH: 8.0 (50 mM Tris-HCl buffer). The hydrolysis of catechol was used to determine, under these standard conditions, the effects of temperature on the purified EXDOs. All measurements were performed in 96-well plates (ref. 655801, Greiner Bio-One GmbH, Kremsmünster, Austria), in biological triplicates over 180 min in a Synergy HT Multi-Mode Microplate Reader (Biotek Instruments, Winooski, VT, USA) in continuous mode (measurements every 30 s) and determining the absorbance per minute from the slopes generated and applying the formula (1). All values were corrected for nonenzymatic transformation.$${Rate}left(frac{mu {mol}}{{{min }}{mg},{protein}}right)= frac{frac{triangle {{{{{rm{Abs}}}}}}}{{{min }}}}{{{{{{rm{varepsilon }}}}}},{{{{{rm{M}}}}}}-1{{{{{rm{cm}}}}}}-1}*frac{1}{0.4,{cm}}*frac{{10}^{6},mu M}{1{{{{{rm{M}}}}}}}\ *0.0002,L*frac{1}{{mg},{protein}}$$
(1)
Shotgun proteomicsProteomics was performed by using total active proteins (extracted as above), which were then subjected to protein precipitation, protein digestion and Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometric (LC-ESI-MS/MS) analysis, as previously described77. High-quality reference metagenomes corresponding to each sample (BioProject number in Supplementary Table S3) were used for protein calling, with a threshold of only one identified peptide per protein identification because False Discovery Rates (FDR) controlled experiments counter-intuitively suffer from the two-peptide rule. The confidence interval for protein identification was set to ≥95% (p  50 °C for which the second phase transition was chosen to focus on the decomposition of the core. It is important to note that applying CNA to MD simulations at room temperature may lead to an evening out of Tp values for esterases that transition around this temperature, i.e., systems with a Tp at or below room temperature might all be influenced similarly by loosening their bonding network. By contrast, systems with a transition temperature at or above room temperature would still be discriminated against. The data generated in this study for analyzing Tp values have been deposited at researchdata.hhu.de under accession code DOI: 10.25838/d5p-42101 [https://doi.org/10.25838/d5p-42].Relationship of temperature-induced changes in enzymeRelationship between MAT and enzyme response to temperature (i.e., Topt, Td and Tp) were evaluated by performing linear regression in R. In the case of enzymes retrieved from the Tara ocean dataset we calculated first the break point (flexus) using the package segmented in R102 and then we computed separately the linear model describing the two linear regressions before and after the breakpoint. To evaluate the possible relation between enzyme thermal response and other environmental parameters, salinity and pH data were retrieved from Bio-ORACLE52 using GPS coordinates of each location.Environmental characterization and sediment collection from different temperature variability levels in the Red SeaWe recorded the temperatures of surface sediments from March 2015 to September 2016 along the coast of the Red Sea using HOBO data loggers (Onset, USA) in nine stations located at 3, 25, and 50 m depth. Details on the location, depth and temperature fluctuations of the studied sediments are reported in Supplementary Table S4 and Source Data. We first assess the differences in the homogeneity of the temperature variance in the three types of sediments to evaluate the magnitude of thermal variation and then we test the difference among their MATs using a non-parametric ANOVA (Dunnett’s multiple comparisons tests). We identified three different levels of temperature variability (Fig. 3a–c; Supplementary Table S5): high, intermediate, and low thermal variability (HTV, ITV, and LTV, respectively), where sediments experienced temperature variations of 12.8 °C, 8.8 °C, and 6.7 °C, respectively. From each station, we sampled 200 g of surface sediment (0–5 cm depth) in triplicate in August and December 2015 with a Van der Venn grab (1 dm3) equipped with a MicroCat 250 Seabird CTD (Conductivity, Temperature, Depth), which was assembled on board the research vessel R/V Explorer (KAUST). During sampling, we measured the temperature of the sediments and the water layer covering the sediments using a digital thermometer and the CTD, respectively. We conducted all sampling in compliance with the guidelines specified by KAUST and Saudi Arabian authorities.Sediment processing for analysis of bacterial communitiesFrom each sample (in triplicate), we immediately removed subsamples of sediment (n = 54, ~10 g) and stored them at –20 °C for molecular analysis. Separately, sediment 25 ± 1 g was transferred to 50 ml tubes and added 30 ml of filtered (0.2 µm) water from the Red Sea. The tubes were shaken at 500 rpm for one hour and then centrifuged them at 300 g for 15 min to detach the microbial cells in the sediments without affecting their vitality103,104. The supernatant containing the extracted cells was collected in sterile tubes and was immediately used to measure microbial growth rates.Evaluation of bacterial growth in sediments at different temperaturesWe evaluated the microbial growth rate of the heterotrophic community extracted from the sediments under HTV, ITV, and LTV at 10 °C, 20 °C, 30 °C, 40 °C and 50 °C, using Marine Broth as the cultivation medium (Zobell Marine Broth 2216) supplemented with 0.1 g/L cycloheximide; a rich-medium was selected to avoid the nutrient limitation effect that can affect bacterial physiology63,105. We inoculated 96-well plates with 200 µl of cultivation medium and 25 µl of the cell suspension extracted from the sediments. We inoculated the three biological replicates from each station and each level of temperature variability in eight wells, giving a total of 72 wells for each plate, with 24 wells used as a negative control inoculated with water. We assembled a total of three plates for each incubation temperature from August and December. Plates were spectrophotometrically measured at 3 h intervals using an optical density of 600 nm (Spectramax® M5) for 72 h. Wells with optical density 90%) for further analysis (Supplementary Tables S9 and S10). We calculated the compositional similarity matrix (Bray-Curtis of the log-transformed OTU table) with Primer 6109. Using the same software, canonical analysis of principal coordinates (CAP)110 was used to compare the temperature variability samples (temperature variability levels: HTV, ITV, and LTV; season levels: August and December) based on the compositional similarity matrix. We applied permutational multivariate analyses of variance to the matrix (PERMANOVA; main and multiple comparison tests). We tested the occurrence of thermal-decay patterns in sediments with different temperature variability levels using linear regression (Prism 9.2 software, La Jolla California USA, www.graphpad.com) between the bacterial community similarities (Bray-Curtis) and the temperature differences among sediments (∆T°C) at the time of sampling. We calculated alphadiversity indices (richness and evenness) using the paleontological statistics (PAST) software, and their correlation with temperature was modelled using linear regression in Prism 9.2. Spearman correlation among temperature and relative abundance of OTUs within each sediment sample was evaluated; OTUs were classified based on their positive (enriched) and negative (depleted) correlation with sediment temperature.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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