Functional and evolutionary significance of unknown genes from uncultivated taxa

ABSTRACT Many of the Earth’s microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain

largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2,3,4,5,6 and compiled a curated catalogue of 404,085 functionally and

evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection

and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits,

including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using

genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological

processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of

antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of

potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated

organisms. SIMILAR CONTENT BEING VIEWED BY OTHERS TOWARDS THE BIOGEOGRAPHY OF PROKARYOTIC GENES Article 15 December 2021 A GENOMIC CATALOG OF EARTH’S MICROBIOMES Article Open access 09

November 2020 ANALYSIS OF METAGENOMIC DATA Article 23 January 2025 MAIN Over the past few decades, metagenomics and metabarcoding studies have revolutionized microbial genomics, not only

discovering numerous new bacterial and archaeal lineages7,8 but also unveiling their unique genetic repertoire9,10,11. The fraction of environmental genes lacking homologues in cultured

organisms, hereafter referred to as ‘unknown’, varies from approximately 25% (ref. 6) to nearly 50% (ref. 3) depending on the methodology and specific environment. This translates to

millions of novel genes whose functional aspects and ecological and evolutionary significance remain unknown. This uncharted microbial sequence space can harbour new genes of great

biological significance10,12,13, including those that may have facilitated the emergence and diversification of major lineages14 and functional innovations such as novel enzymes15,

antibiotics16, versatile small peptides17 and metabolic pathways4. However, the systematic curation and analysis of unknown genes at a large scale remains challenging. The genetic pool of

uncultivated taxa is currently unrepresented in reference databases of gene functions, protein domains and orthologous groups, limiting our analyses and intercommunity comparisons to known

genes derived from cultured organisms. To date, this shortcoming has primarily been due to technical constraints that have been overcome only recently. First, the task of clustering genes

into families and conducting homology searches on a metagenomics scale, a crucial initial step for any comparative analysis, has been rendered achievable only by recent breakthroughs18,19.

Second, many comparative genomic analyses depend on the availability of complete genomic assemblies and have become feasible only with the recent publication of hundreds of thousands of

environmental genomes2,3,6. Several studies have leveraged these advances to successfully harness the concept of gene families for the identification and quantification of genetic novelty on

a broad metagenomic scale3,6,17. This has enabled the annotation of distant homologues20, the functional prediction of unknown genes based on coexpression patterns21 and the identification

of lineage-specific novel gene families defining entire new lineages22. However, recent work has shown that most genes within the global microbiome can be clustered into a limited number of

gene families primarily composed of neutral (or nearly neutral) gene variants2 (that is, lacking strong positive selection pressures). Additionally, various studies have indicated that both

known and unknown gene families are largely dominated by species-specific sequence clusters (orphans) of narrow ecological scope2,20. In this study we systematically analysed the genetic

repertoire of uncultured bacteria and archaea using a global-scale comparative genomics approach, aiming to identify novel gene families with clear signs of biological significance. A

CURATED CATALOGUE OF NOVEL GENE FAMILIES Employing a comparative genomics approach, we analysed a large multihabitat dataset comprising 149,842 medium- and high-quality metagenome-assembled

genomes (MAGs) and single-amplified genomes (SAGs), alongside 19,642 reference genomes, from isolated and fully sequenced species. This set includes nearly 400 million gene predictions and

was assembled by unification of five data sources representing 82 habitats (Supplementary Table 1): two MAG collections spanning thousands of samples of various origins (GEM3 and GMGC2), a

comprehensive human gut catalogue (UHGG)6, an extensive repository of global oceanic data (OMD)4 and the GTDB-r95 reference database5. To group sequences into gene families we employed a

deep-homology-clustering strategy operating at the amino acid level (30% minimum identity, coverage over 50%, expected value (_E_-value) under 0.001). The chosen approach was tailored to

maximize the grouping of distantly related genes and has been successfully employed in previous work2,20,21. Of the 12,132,456 clusters generated, 58% (7,052,473 clusters) lacked discernible

homologues in the databases Pfam-A/B23, eggNOG v5 (ref. 24) and RefSeq25—that is, they represent gene families that are exclusive to uncultivated taxa. To identify unknown gene family

clusters of maximal evolutionary and functional significance (Fig. 1a), we started by selecting clusters that encompassed a minimum of three complete genes from at least two distinct

uncultivated species, thereby excluding orphan gene families from our final catalogue. Furthermore, we required gene family members to exhibit a conserved aligned region spanning more than

20 contiguous amino acids. These criteria were important in regard to discarding clusters inferred from fragmented genes and to infer a distinct genomic signature (putative new protein

domain) for each novel gene family. To ensure the exclusion of pseudogene-based and viral-specific gene families, we eliminated clusters that matched either the AntiFam26 or pVOG27

databases. In addition, we required the selected novel gene families to exhibit signs of purifying selection (non-synonymous to synonymous substitutions ratio (dN/dS) below 0.5; Fig. 1b),

consistent with expectations for functional coding sequences28. Moreover, we ascertained their expressibility through either in silico predictions using the software RNAcode29 or empirical

evidence, identifying significant hits against recent metatranscriptome surveys21,30. From an evolutionary perspective these novel gene families are conserved, with an average amino acid

identity of 62.7% (Fig. 1c). Phylogeny-based orthology prediction31 suggests that none of the families were involved in gene duplication events at basal taxonomic ranks, probably

representing novel orthologous groups at the bacterial or archaeal level. In total, our analysis led to the identification of 404,085 clusters that potentially represent FESNov gene families

exclusively from uncultivated taxa. These families exhibit distributions of family size, sequence length and species content comparable to those observed in the eggNOG database (Fig. 1d,e).

In relative terms, the FESNov catalogue constitutes a small (5.7%) subset of the initial total of inferred unknown gene clusters, highlighting the strong impact of quality filters on the

analysis of the uncharted sequence space. However, our curated catalogue represents a roughly threefold increase in the total number of prokaryotic orthologous groups known to date (namely,

219,934 bacterial and archaeal eggNOG orthologous groups; Fig. 1d). This underscores the importance of incorporating the genetic repertory of uncultivated taxa into publicly accessible

functional genomics repositories. FUNCTIONAL PREDICTIONS For exploration of the putative functional roles of FESNov families we used genomic context reconstruction, synteny conservation

analysis, structural prediction and detection of intrinsic sequence signals. Historically, the preservation of gene order across species provided valuable insights into functional

interactions—an approach often referred to as the ‘guilty-by-association’ strategy. Although this technique has proved particularly effective for the accurate prediction of prokaryotic gene

functions32, its efficacy varies across functional categories33 and may be affected by the quality and completeness of environmental genomes. To account for these issues, we first conducted

a benchmarking exercise based on functionally annotated genes to establish a baseline for the minimum level of genomic context conservation required to generate dependable predictions for

different Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways34. To this end we implemented two scores: (1) ‘syntenic conservation’, to reflect the conservation of gene order across

species and (2) ‘functional relatedness of neighbouring genes’, measured as the number of contiguous genes belonging to the same KEGG pathway. Results indicate that genomic context analysis

has the capacity to predict accurately (confidence equal to or greater than 0.9) functional associations between genes spanning 55 KEGG pathways. However, the level of support needed in

terms of synteny and functional relatedness scores varied among different functional categories (Supplementary Table 2). Insights from the benchmarking exercise were used to predict KEGG

pathway associations for all novel gene families in our catalogue, fine-tuning the thresholds for both synteny and functional relatedness based on the requirements for each pathway. This

resulted in a total of 52,793 gene families successfully mapped to at least one specific KEGG pathway with confidence scores exceeding 50%. Of these gene families, 4,349 attained a

confidence score of 90% or above. Notably, the highest confidence scores were associated with conserved operon-like regions related to key cellular processes including central metabolism,

chemotaxis and degradation pathways (Fig. 2a and Supplementary Table 3). In addition, we identified 17,717 FESNov gene families located in the vicinity of various antibiotic-resistance

genes, indicating a possible role in cell defence systems. Next, we functionally annotated the FESNov catalogue based on each family’s predicted structural similarity to functionally known

genes. To this end we performed de novo protein structure prediction for the entire FESNov catalogue using ColabFold35, which yielded 389,638 protein structures, 226,991 with high-confidence

scores (predicted local distance difference test (PLDDT) score equal to or greater than 70). A total of 56,609 FESNov families showed significant structural similarities to known genes in

the databases PDB36 or Uniprot37. Interestingly, the same KEGG pathway annotation was predicted for 38.8% of FESNov gene families with both structural homologues and high-confidence (equal

to or greater than 90%) genomic context predictions. In addition, 8,804 families had structural similarities to various antibiotic-resistance genes, 3,637 of which had antibiotic-resistance

genomic neighbours in their context (Supplementary Table 4). We also screened FESNov families for inherent sequence features not captured by structural or homology-based searches, including

signal peptides, transmembrane regions and potential antimicrobial signals. It was found that 32.9 and 23.7% of FESNov gene families encoded for transmembrane and signal peptide-containing

proteins, respectively, suggesting that a significant fraction may play a role in environmental interactions. Additionally, we found 240 short (below 50 amino acid (aa)) FESNov families with

antimicrobial signatures (Supplementary Table 5), 17 of which were located in conserved antibiotic-related genomic contexts. Finally we mapped the FESNov catalogue against several databases

that could provide indirect functional hints. Searching through a collection of biosynthetic clusters recently reported from marine metagenomes4, we found 1,864 matches that may be actively

involved in the biosynthesis of natural products (Supplementary Table 6). Mapping FESNov gene families against a set of 11,779 unknown genes recently characterized using genome-wide mutant

fitness experiments38, we found 68 matches to genes associated with specific growth conditions (Supplementary Table 7). Similarly, we found 476 significant hits against a catalogue of

potentially functional small peptides17. All functional predictions, along with their confidence scores and supporting materials, are available in the online FESNov database

(https://novelfams.cgmlab.org). In addition, the FESNov catalogue has been integrated into the eggNOG-mapper tool39 to facilitate the detection and annotation of novel gene families in any

metagenome. HYPOTHESIS-DRIVEN FUNCTIONAL VALIDATIONS To further validate our functional annotation approach we experimentally tested the functional predictions of two novel gene families,

NOV3845Y and NOVOQR9B (Fig. 2c). NOV3845Y was found in at least two Gram-negative species from the unknown genus _UBA8309_ within the class Alphaproteobacteria, and it was predicted to be

involved in bacterial chemotaxis (confidence score of 0.8). A low-support protein structure was predicted for this family, and no structural similarity was found to any known gene in PDB or

Uniprot. However, given its position in the _che_ operon, a gene cluster canonically responsible for chemotaxis, we hypothesized that NOV3845Y played a role in cell motility in response to

environmental changes. To test our hypothesis we performed swimming chemotaxis assays on an _Escherichia coli_ strain heterologously expressing NOV3845Y and compared it against another _E.

coli_ strain transformed with the empty vector (pBSK). Results showed that the strain expressing NOV3845Y exhibited a larger swimming halo than the pBSK control strain (Extended Data Fig.

1), confirming the role of NOV3845Y in chemotaxis signalling and cell motility. NOVOQR9B, a gene family of short peptides found in 39 _Faecalibacillus_ genomes, is systematically conserved

in a genomic region predicted to be associated with antibiotic resistance (three conserved resistance neighbours within the CARD database). In addition, NOVOQR9B contains antimicrobial

signal motifs (antimicrobial probability by Macrel40, 0.545). We synthesized the 36-amino-acid-long peptide and tested, in vitro, its potential antimicrobial activity against _Paenibacillus

polymyxa_, _Bacillus subtilis_, _Lactobacillus_ sp., _E. coli_ and _Pseudomonas putida_ (three Gram-positive and two Gram-negative bacterial species). NOVOQR9B effectively inhibited growth

of all three Gram-positive bacterial species but had no inhibitory effect on Gram-negative species (Extended Data Fig. 2). We also found five FESNov families adjacent to known genes related

to nitrogen metabolism, covering operonic regions associated with three major pathways of the nitrogen cycle (Fig. 2b). Among these, NOV5WD8W appeared particularly intriguing because of its

direct proximity to a _NosZ_-encoding gene, which is responsible for the reduction of nitrous oxide (N2O) to molecular nitrogen (N2) in the final step of bacterial denitrification. The

maturation of _NosZ_ requires several accessory proteins, including a copper metal chaperone (_NosL_) involved in copper supply to _NosZ_41. Interestingly, NOV5WD8W folds similarly (PLDDT,

82.3; alignment score, 263) to the _E. coli_ copper metallochaperone _CusF_ (Extended Data Fig. 3), suggesting that this novel gene family could replace _NosL_ in the transfer of copper to

_NosZ_. DENSITY OF NOVEL FAMILIES PER GENOME Taxonomically, FESNov families are distributed across the entire microbial phylogeny (Fig. 3a) and comprise an important fraction of the genetic

repertoire of uncultivated taxa. On average, archaeal genomes encode a larger fraction of FESNov families than bacterial (3.4 versus 1.7%), reflecting the historical delay in

characterization of the archaeal domain. Despite the known widespread phylogenetic distribution of microbial dark matter20,42 and lineages with a high content of functionally unknown gene

families22, we further interrogated our data to identify potential novelty hubs—that is, lineages with a high number of FESNov families per genome (Supplementary Table 8). At the phylum

level, Riflebacteria, known for their high proportion of unidentified genes43, exhibit the highest co-occurrence of FESNov families per genome (431 ± 171), along with the unknown phylum

UBP17 (470 ± 106). Asgardarchaeota, the prokaryotic phylum closest to eukaryotes, also stands out with 228 ± 167 novel gene families per genome (ranked third among phyla). The

Patescibacteria phylum, previously reported to contain a high number of unknown genes20,22,42, exhibits a low novelty density per genome compared with other clades (62 ± 30, 62nd-ranked

phylum), although FESNov families represent a significant proportion of their gene set when normalizing by genome size (7.7% ± 2.9 s.d., 13th ranked phylum). Notably, Patescibacteria genomes

show a higher proportion (53%) of novel transmembrane and signal peptide proteins than other genomes (33%, two-sided Wilcoxon test _P_ < 1 × 10−15). Transmembrane proteins have been

shown to be important for this phylum22 and, together with secreted proteins involved in cell–cell interactions, they may be relevant because of their probable episymbiotic lifestyle22,43.

Among these we found several FESNov families in genomic contexts related to both cell wall disruption and DNA degradation and incorporation, indicating that they may indeed play a role in

microbe host parasitization44 (Extended Data Fig. 4). SYNAPOMORPHIES IN UNCULTIVATED TAXA To identify FESNov families with the greatest evolutionary significance we searched the catalogue

for potential synapomorphic traits, an approach previously applied to find evolutionarily relevant unknown genes in specific lineages14,22,45. A gene family is considered synapomorphic if it

is nearly ubiquitous within a certain clade (high coverage) but mostly absent in other clades (high specificity), therefore indicating common unique derived traits (Methods). To identify

the most prominent synapomorphies in uncultivated lineages we scanned the entire set of 12,132,456 clusters originally inferred with a minimum of 90% clade coverage and 100% clade

specificity. Although these thresholds offer very little tolerance for the potential incompleteness of environmental genomes used, and/or horizontal gene transfers, we found 2,210 clusters

that could be considered synapomorphic at the phylum, class or order level (Supplementary Table 9). Interestingly, almost half of these high-level synapomorphic clusters (1,034 families)

were included in our final FESNov catalogue, supporting the idea that our set is greatly enriched in evolutionarily relevant families. FESNov synapomorphic gene families allow precise

distinction of 18 uncultivated phyla, 20 classes and 90 orders, corresponding to 181, 128 and 725 synapomorphic novel gene families, respectively. We propose that these newly identified

synapomorphic proteins are probably functional innovations that fostered the ancestral divergence and selection of the underlying lineages. This hypothesis is supported by the fact that

these synapomorphic gene families exhibit stronger signs of purifying selection (indicated by a lower dN/dS ratio) and a higher genomic context conservation than other gene families in the

FESNov catalogue (Fig. 4b,c). Similarly, the number of unknown synapomorphic gene families was particularly high in uncultivated lineages with poorly understood biology. Examples include the

recently proposed Riflebacteria phylum, with 26 putative synapomorphic detections, or the Thorarchaeia and Lokiarchaeia classes, in which 14 detected putative synapomorphic families may

provide insight into their divergence from other Asgard members. For instance, the Thorarchaeia synapomorphic family NOV4IF0P is embedded within a highly conserved genomic region containing

eukaryotic-like genes associated with protein translation (_rpl35a_ and _elp3_; Fig. 4a). Similarly, we found all genomes within the HRBIN17 class to contain what we believe to be a highly

divergent version of _ccmD_, a small protein involved in cytochrome biosynthesis (prediction supported by genomic context analysis). We also found synapomorphic FESNov families in the UBP6

phylum potentially involved in DNA repair (_priA_ and _dksA_), or in the Riflebacteria phylum involved in chemotaxis (_mcp_ and _dgt_) (Fig. 4a). HABITAT DISTRIBUTION OF NOVEL FAMILIES For

estimation of the ecological range of FESNov families we mapped their genetic signatures to a larger and standardized set of 63,410 publicly accessible metagenomic samples covering 157 

habitats (Supplementary Table 10). Most FESNov gene families (59.4%) were detected in more than ten samples originating from at least two distinct habitats (Supplementary Table 11). The same

pattern was found even after consolidation of habitat annotations into ten less detailed ecological groups. For example, we identified various extreme cases in which 243, 16 and two FESNov

gene families, respectively, appeared in over half of all marine (3,441 total samples), soil (4,418) and human gut (32,544) samples (Supplementary Table 12). This result stands in stark

contrast to the habitat- and sample-specific patterns seen in most individual species-level genes2 and functionally unknown gene clusters20. This is remarkable considering that none of our

original filters used to identify FESNov families included ecological parameters. It also suggests that FESNov families may represent unknown molecular functions from uncultivated microbial

lineages spread over geographically and/or ecologically diverse habitats. Alternatively, they may derive from mobile elements. In an attempt to quantify the contribution of each scenario we

identified protein clusters that contained at least one member in a plasmid or viral-like contig. Findings indicated a strong correlation between mobility and the ecological breadth of gene

families across different habitats (blue and red lines in Fig. 3b and Extended Data Fig. 5). Similarly, we found that the proportion of mobile elements was correlated with the taxonomic

breadth of gene families (Fig. 3c and Extended Data Fig. 6). Nevertheless, the vast majority of families (90.4%) appear detached from obvious events of horizontal gene transfer, suggesting a

more constitutive role in their host genomes. Furthermore, the relative abundance profiles of FESNov families alone were sufficient to differentiate among environments and conditions,

highlighting their ecological significance. Specifically, FESNov profiles across samples had a predictive power similar to those based on KEGG orthologous groups when comparing metagenomic

samples from different environments using _t_-distributed stochastic neighbour-embedding (tSNE) multidimensional analysis (Extended Data Fig. 7). DISCOVERY OF NEW BIOMARKERS For assessment

of the potential benefits of integration of the FESNov catalogue into biomarker discovery pipelines, we investigated its capacity to differentiate between metagenomic samples drawn from

different conditions within the same environment, a common procedure in clinical studies. As a case study we revisited a recent microbiome investigation that assessed the predictive

capability of functional modules for colorectal cancer (CRC) detection, based on a cohort of 575 human gut samples from five populations46. By inclusion of the FESNov catalogue in the

analysis we were able to identify 69 novel gene families that were significantly overabundant in CRC samples compared with controls (false discovery rate (FDR) _q_ < 0.01; Fig. 5 and

Supplementary Table 13). Twenty-five of these overabundant gene families belong to the Acutalibacteraceae family, previously associated with CRC47. A further nine families are present in

seven different species enriched in the gut of CRC patients46. Moreover, our functional predictions suggest that six of these families might be involved in motility, adhesion and invasion,

processes potentially linked to colonization48 (Extended Data Fig. 8). The overall estimated accuracy of a CRC versus control diagnostic-oriented predictor, based solely on FESNov family

abundance profiles, reached an area under the curve of 0.74. Predictors built on relative abundance matrices of both FESNov and known gene families (for example, KEGG orthologous groups)

showed a slight but significant increase of around 1.2% compared with those using known gene families only (Extended Data Fig. 9a). Overall, these results underscore the high utility of

novel gene families for uncovering new functional biomarkers and their probable association with molecular mechanisms underpinning the observed differences. DISCUSSION With the genetic

material of most of our planet’s microbes remaining poorly studied, a wealth of genetic material with unknown potential for critical functional innovations remains largely overlooked and

untapped by most metagenomic studies. In an effort to narrow this knowledge gap, we focused on identification of a highly curated set of previously uncharacterized microbial gene families of

major evolutionary and functional significance. To this end we systematically addressed common problems such as sequencing artefacts, contaminations, gene fragments, false orphans,

pseudogenes or sequences of insufficient quality or evidence—typical issues that continue to hamper the comparative analysis of the so-called microbial dark matter1,49. Despite nearly

tripling the number of known prokaryotic orthologous groups described to date, our work has certain limitations that should be taken into account in future studies. For instance, the FESNov

catalogue accounts for only a relatively small portion of all unknown sequences from the original dataset. Considering that many sequences and clusters were discarded due to quality filters

and limited sampling depth, the catalogue probably represents only the proverbial tip of the iceberg, leaving many discoveries still to be made. We also demonstrated that the FESNov

catalogue can serve as a framework for the hypothesis-driven identification and experimental characterization of unknown but functionally important genes. However, functional associations

based on genomic context analysis are usually broad and do not allow for precise predictions of new molecular functions. Whereas structural alignments can facilitate additional and more

specific functional predictions, only 14% of FESNov gene families showed significant hits against known protein structures. This suggests that the majority of the unknown gene families

encode for novel protein folds rather than undetectable distant homologues in the known functional landscape, posing new challenges for in silico functional predictions. Similarly, we

identified many novel gene families that could be of great evolutionary significance for entire basal clades of uncultivated taxa; however, the taxonomic level at which these families were

found to be synapomorphic may change with the discovery and sequencing of new species. Finally we showed that, by integration of the FESNov catalogue into functional profiling tools such as

the eggNOG-mapper tool, new ecological and clinical biomarkers can be detected. It is therefore crucial that the unique genetic repertoire of hitherto uncultivated species is regularly

incorporated into reference databases and bioinformatic workflows, particularly as we advance in the exploration of understudied microbial ecosystems, micro-eukaryotic communities and

low-abundance species. METHODS DATA COLLECTION We retrieved medium- and high-quality MAGs, SAGs and reference genomes from five different studies: (1) 51,624 medium- and high-quality MAGs

(at least 50% complete, contamination 5% or less) from a planetary multihabitat catalogue including 10,450 samples and covering diverse habitats (GEM)3; (2) 46,655 high-quality MAGs (at

least 90% complete, contamination 5% or less) released by another multihabitat catalogue spanning 13,174 samples and 14 habitats (GMGC)2; (3) 13,975 MAGs and SAGs and 17,935 reference

genomes from the GTDB-r95 database5; (4) 26,975 medium- and high-quality MAGs (at least 50% complete, less than 10% contamination), 5,969 SAGs and 1,707 reference genomes obtained from ocean

samples4,50,51,52; and (5) 4,644 medium- and high-quality reference human gut MAGs (at least 50% complete, less than 5% contamination) from the UHGG human gut catalogue6. Overall, the mean

completeness and contamination of the collection of genomes were 86.15 and 1.13%, respectively (median 93 and 0.77%, respectively; Supplementary Table 14). RECALLING ORFS We noticed that

some MAG collections contained genes predicted under an incorrect codon table (for instance, those from the Gracilibacteria or Mycoplasma lineages were predicted under the standard codon

table), which could lead to incorrect conclusions53. We therefore recomputed ORF predictions for all MAGs in the GEM, GMGC and GTDB catalogues. For each MAG we used PROKKA54, selecting the

correct genetic table for each genome based on its taxonomic annotation. We further verified that the corrected Gracilibacteria and Mycoplasma ORFs were indeed longer than the original ones.

We obtained 116,208,548, 110,913,525 and 106,052,079 genes for GEM, GMGC and GTDB, respectively. Following their combinion with the 56,637,438 and 10,002,521 genes in the oceanic and UHGG

genomes, MAGs and SAGs we obtained a final catalogue of 399,814,111 genes. TAXONOMIC ANNOTATIONS For normalizitaion of taxonomic annotations of all genomes in our collection, we reannotated

all of them using GTDB-Tk v.1.6.0 (GTDB rev202 version)55. DEEP-HOMOLOGY CLUSTERING AND PHYLOGENETIC TREE RECONSTRUCTION For computation of gene family clusters we used MMseqs2 (ref. 19)

with relaxed thresholds: minimum percentage of amino acids identity 30, _E_-value 0.001 or less and minimum sequence coverage 50%. The parameters used were --min-seq-id 0.3 -c 0.5 --cov-mode

1 --cluster-mode 2 -e 0.001. Clusters with fewer than three sequences were discarded. We computed multiple-sequence alignments for each gene family with Clustal Omega56 using the translated

version of the genes and subsequently reconstructed their phylogeny with FastTree2 (ref. 57) with default parameters. We calculated alignment statistics (mean identity, most unrelated pair,

most distant sequence) on each gene family alignment using Alistat58. DETECTION OF PROTEIN CLUSTERS SPECIFICALLY FROM UNCULTIVATED TAXA For identification of genes/proteins without

homologues in current genomic databases we mapped the members of each gene family cluster against (1) EggNOG v.5 (ref. 24) using eggNOG-mapper v.2 (ref. 39) and all protein sequences in each

family as a query. Hits with an _E_-value below 0.001 were considered significant. (2) Pfam-A59, using HMMER60 hmmsearch against all protein sequences of each family. Hits with an _E_-value

below 1 × 10−5 were considered significant. (3) Pfam-B23, using HMMER60 hmmsearch searches against the representative protein sequence (longest sequence) of each family. Hits with an

_E_-value below 1 × 10−5 were considered significant. (4) Refseq61, using DIAMOND62 blastx (with the --sensitive flag) searches against the coding sequence (CDS) of all tmembers of the

family. Hits with an _E_-value below 0.001 and query coverage above 50% were considered significant. All gene family clusters with a significant hit in any of the above databases were

considered non-novel and discarded from the study. Additionally we mapped FESNov gene families against Eukprot63, finding only 1,127 distant hits (Supplementary Table 15). DETECTION OF

SPURIOUS DOMAINS IN GENE FAMILIES To discard potential sequencing errors and pseudogenes we mapped our catalogue against the AntiFAM26 database (HMMER search with the --cut_ga parameter, as

recommended) and discarded families with _E_-value 1 × 10−5 or less. We also discarded families with significant hits in the viral pVOG database27 using HMMER, an _E_-value threshold of

below 1 × 10−5 and minimum coverage of 50%. Searches were performed at the amino acid level. DETECTION OF CONSERVED DOMAINS We reconstructed protein-based, multiple-sequence alignments for

each gene family with Clustal Omega. For each gene family the most conserved domain was considered the longest aligned region in which 80% of amino acid residues were not gaps. Gene families

whose most conserved domain was shorter than 20 amino acid residues were discarded. CALCULATION OF DN/DS Multiple-sequence alignments from each gene family were back-translated into codon

alignments to reconstruct phylogenetic trees using FastTree2 with default parameters. The entire workflow was executed using ETE3 (ref. 64) with options ete3 build --nt-switch-threshold 0.0

--noimg --clearall --nochecks -w clustalo_default-none-none-none --no-seq-rename. For calculation of selective pressure per family we ran HyPhy using the BUSTED model65 with default

parameters, codon-based nucleotide alignment and the phylogenetic tree generated previously, retrieving the dN/dS ratio under the full codon model. We discarded gene families with dN/dS

values higher than 0.5. DETECTION OF PROTEIN-CODING FAMILIES We used back-translated, codon-based multiple-sequence alignments to run RNAcode29. Because the software calculates statistics on

the longest sequence, we rearranged alignments so that the longest sequence was the first to appear. We ran RNAcode with default options and the --stop-early flag. Gene families yielding

RNAcode _P_ values lower than 0.05 were considered coding and retained in our catalogue. Gene families without significant _P_ values were discarded from the study unless they were detected

in metatranscriptomics datasets (see following section). MAPPING TO METATRANSCRIPTOMIC DATASETS To obtain additional evidence of gene expression we mapped our gene families against (1) the

TARA oceanic metatranscriptomic catalogue21 and (2) 756 human gut metatranscriptomic samples66. For mapping the sequences of the novel families against TARA protein sequences we used DIAMOND

blastp with the _--_sensitive flag. We considered any hit with an _E_-value below 0.001 and query coverage above 50% as significant. For mapping of reads from the human gut

metatranscriptome samples against the sequences of novel families we used DIAMOND blastx sensitive mode. We considered any hit with an _E_-value below 0.001 as significant. Families were

considered expressible if at least one member had a significant match against the metatranscriptomic catalogues. ORTHOLOGY CALLING All FESNov gene family clusters were analysed to determine

whether they represented basal orthologous groups at the bacterial or archaeal level or, by contrast, contained duplication events leading to several orthologous groups within the same

family. To do so we rooted the phylogenetic tree of each family at midpoint and taxonomically annotated leaf nodes using GTDB v.202. Then, we used ETE64 to identify duplication events on

each phylogenetic tree with the ‘get_descendant_evol_events’ function and dated them according to their predicted common ancestry. Gene families without duplication events at basal taxonomic

ranks were considered as representing orthologous groups at the bacterial or archaeal level. COMPARISON OF GENE FAMILY LENGTH DISTRIBUTIONS We compared the distribution of protein lengths

in eggNOG v.5 orthologous groups24 and the small peptides catalogue described in ref. 17 with the length of the FESNov gene family clusters. The length of each gene family or orthologous

group was set to the longest sequence within each cluster at the protein level. SEQUENCE-BASED FUNCTIONAL PREDICTIONS We ran SignalP-5.0 (ref. 67) with Gram-positive and Gram-negative modes

on FESNov gene families. Genes predicted to encode for a signal peptide by either Gram-positive, Gram-negative or both were considered as secreted proteins. We also ran TMHMM68 with default

parameters to calculate transmembrane domains on the sequences. Similarly to the methods used in Sberro et al17, a gene family was considered to encode for transmembrane/secreted proteins if

at least 80% of its members were predicted to be so. SMALL PEPTIDE ANALYSES We considered FESNov gene families whose longest sequence was shorter than 50 residues to be small peptides. Our

set of small peptides was mapped against those described by Sberro et al.17 using DIAMOND (--sensitive flag), and considered as significant those with an _E_-value below 0.001 and coverage

above 50%. We ran antimicrobial predictions on our small peptide FESNov gene families using Macrel40. DETECTION OF MOBILE ELEMENTS For the detection of families potentially included in

plasmids we ran PlasFlow69 with the --threshold 0.95 flag on all contigs from all MAGs, SAGs and genomes. For the detection of gene families with potential viral origin we ran Seeker70 on

all contigs from all MAGs, SAGs and genomes, using a threshold of 0.9 for considering a sequence as viral. For the results shown in Fig. 3 we considered gene families as mobile or viral if

at least one member of the family was predicted to be so. The reported correlations were also found under more restrictive thresholds at which at least 30% of gene family members were

predicted as mobile or viral sequences (Extended Data Fig. 6a). ECOLOGICAL DISTRIBUTION OF GENE FAMILIES For expansion of the ecological profile of FESNov gene families we mapped the

representative sequence of each family (longest) against 63,410 public metagenomic samples using DIAMOND (-sensitive flag). In total, 303,515 hits with an _E_-value below 0.001 and target

coverage of at least 50% were considered significant. We grouped habitats into the following general groups: ‘Human gut’, ‘Human oral’, ‘Marine’, ‘Human skin’, ‘Non-marine aquatic’, ‘Animal

gut’, ‘Soil’, ‘Plant associated’ and ‘Anthropogenic’. Remaining samples were not included (that is, labelled as ‘Other). For building tSNE plots we mapped a representative sequence member of

each family against the GMGC catalogue using DIAMOND (--sensitive flag). Hits with an _E_-value below 0.001 and target coverage of at least 50% were considered significant. For calculation

of the abundance of each family on each sample we added the relative abundance of each gene family homologue. We restricted the tSNE analysis to families detected in more than 1,000 samples

and to a maximum of 150 samples per habitat/human population. tSNE was computed with the python scikit-learn manifold package following logarithmic transformation of the data. TAXONOMIC

BREADTH OF GENE FAMILIES For estimation of the taxonomic breadth of each FESNov gene family we calculated the last common ancestor (LCA) of their members using GTDB v.202 taxonomic

predictions as the most lineage-specific annotation shared by all members of the family. To ensure that our LCA predictions were not artefacts caused by a small proportion of misannotated

genes masking lineage-specific families to very basal levels we repeated the analysis, requiring the LCA lineage to be supported by 80% (Extended Data Fig. 6b) of the members of the family,

thereby obtaining comparable patterns. TAXONOMIC DISTRIBUTION OF FESNOV GENE FAMILIES To assess the distribution of novelty across the prokaryotic phylogeny we estimated the number of FESNov

gene families observed per clade. Figure 3a shows the GTDB phylogenetic trees bac120_r202 and ar122_r202 collapsed to the order level (each tree leaf represents a taxonomic order). For

represention of the percentage of uncultivated genomes per branch in Fig. 3a, we divided the number of uncultivated genomes per lineage by the total number of genomes under that lineage. The

final tree image was generated using Interactive Tree Of Life71. SYNAPOMORPHIC GENE FAMILIES For identification of synapomorphic gene families at different taxonomic levels we calculated

the clade specificity and coverage of each gene family across all GTDB v.202 lineages. For each gene family and clade, coverage was calculated as the number of genomes containing a specific

gene family over the total number of genomes under the target clade. Specificity was estimated as the percentage of protein members within a family that belonged to the target clade. Under

such criteria, very few synapomorphic gene family clusters would be expected at basal taxonomic levels, typically indicating lineage-specific functional traits. Preceding our analysis, and

to verify that our detection method was correct, we computed synapomorphic gene families out of the complete set of clusters obtained in the first step of the pipeline using a cutoff of 70%

coverage and 90% specificity. As expected, we detected a very low number (34,370) of synapomorphic clusters at high-level taxonomic ranks (phylum, class or order; Supplementary Table 16),

which included both known and unknown gene families. Among functionally known synapomorphies we found previously described examples of synapomorphies, such as the photosystem P840 reaction

centre protein (_pscD_) and the chlorosome envelope protein C (_csmC_), which are known to be synapomorphic at the _Chlorobium_ bacterial class72,73. Similarly, photosystem I subunits _psaD_

and _psaF_, photosystem II oxygen-evolving enhancer protein _psbO_ and the NHD1 complex subunit (_ndhN_) were also detected as synapomorphic for the entire Cyanobacteria phylum. In

addition, we validated our method for location of synapomorphic gene families by calculation of coverage and specificity within each lineage for each KEGG orthologous group and compared the

conservation values obtained with those in a recent study74, finding a correlation coefficient of 0.71 (_P_ < 1 × 10−15). To obtain the final set of synapomorphic gene families in the

FESNov catalogue (see above), and to focus only on the most prominent examples, we used the same validated method but with stricter specificity and coverage thresholds, requiring also a

minimum number of members per family. Thus we considered FESNov gene families as synapomorphic if they contained more than ten members (that is, protein sequences) and had a coverage higher

than 90% and specificity of 100% for a given lineage. We provide the coverage and specificity values of all families from every lineage in the downloads section of

https://novelfams.cgmlab.org/. Moreover, to further ensure that our final set of synapomorphic predictions is highly specific, we mapped them back against the whole catalogue using a more

sensitive mapping strategy based on HMMER searches60, excluding families with distant hits to genomes that might compromise our specificity and coverage thresholds. For comparison of the

degree of genomic context conservation between synapomorphic and non-synapomorphic FESNov gene families shown in Fig. 4c, we computed the syntenic conservation of adjacent neighbour genes

(+1 and −1 positions). FUNCTIONAL PREDICTIONS To infer the functional annotation of the neighbouring genes of FESNov gene families we ran eggNOG-mapper v.2 (ref. 39) with default parameters

on the 400 million proteins in our catalogue of MAGs, SAGs and reference genomes. We also mapped them against the CARD75 database using DIAMOND blastp, with _E_-value and coverage threshold

of 0.001 and over 50%, respectively. We next built a database with all the genomic locations of genes in their respective contigs and the annotated function of every gene. We then

established an association between a KEGG pathway (P) and a FESNov family if 30, 50, 80 or 90% of FESNov neighbour genes in a genomic window of plus or minus three genes of all FESNov

members were annotated to pathway P (synteny conservation), calculating also the proportion of neighbour genes annotated to pathway P over the total number of neighbours within the same

window (functional relatedness) and measuring the frequency of neighbour genes annotated to pathway P that are (1) on the same orientation as FESNov family members (strand conservation) and

(2) with no intergenic regions longer than 100 nucleotides (distance conservation). To determine an approximate confidence level for all such associations we computed the same calculations

for 108,823 gene family clusters with known and consistent KEGG pathway annotations. We then estimated the minimum synteny conservation, functional relatedness, strand conservation and

distance conservation necessary to successfully recover the function (KEGG pathway) of at least 50 or 90% of all families (confidence scores) assigned to that KEGG pathway. The

implementation of this method and benchmark is available at https://github.com/AlvaroRodriguezDelRio/nov-fams-pipeline. Using a confidence score of 90%, we were able to functionally annotate

1.2% of the FESNov gene families detected in host-associated samples but only 0.9% of pure free-environmental samples. For computation of FESNov gene families in resistance islands we

identified the number of positions in which the family had CARD genes within https://github.com/arpcard/aro with different evolutionary conservation values. To generate Fig. 2a we joined the

bacterial and archaeal GTDB phylogenetic trees, collapsed to the order level, using the ETE toolkit software. We then represented the presence/absence matrix of novel gene family

predictions associated with KEGG pathways with confidence larger than 0.9 across the different taxonomic orders. For readability, orders and KEGG pathways with no predictions are not shown

in the figure. We also mapped the representative sequence of each novel family against the collection of functionally annotated genes in Miller et al.33 with DIAMOND blastp (sensitive flag).

Hits with an _E_-value below 0.001 and coverage above 50% on the FESNov family were considered significant. We then compared our predictions with theirs, and used them to extend our

functional predictions. Both approaches reached 81% agreement for functional predictions tagged as high confidence. Mappings against Miller et al.33 provided genome context-based functional

predictions to 22,898 FESNov gene families. We also mapped FESNov gene family sequences against Fitness Browser38 genes (https://fit.genomics.lbl.gov/cgi_data/aaseqs) using DIAMOND blastp

with maximum _E_-value and minimum coverage thresholds of 0.001 and 50%, respectively. We considered hits with strong fitness changes (_t_-score above 4 or below −4; see original

publication) to be potentially associated with certain conditions. For location of FESNov gene families in biosynthetic clusters we screened the Antismash76 predictions presented in Paoli et

al.4, whose genes are included in this catalogue. STRUCTURAL PREDICTIONS As an initial attempt, we first queried all FESNov gene families against a large database of protein structural

models of environmental sequences77. To do so we mapped the longest member of each novel gene family against the sequences of High Confidence MGnify30 (ref. 78) using DIAMOND with _E_-value

below 0.001 and coverage above 50% significance thresholds. However, we found matches for only about one-third (33.6%) of our FESNov families. Given the low number of matches obtained, we

decided to run de novo structural predictions for all FESNov families by means of ColabFold35 using the representative sequence of each family as input. In total we computed structural

predictions for 389,638 FESNov gene families. For the remainder, ColabFold was not able to finish computations. For the FESNov gene families for which we could obtain structures, we ran

Foldseek79 against PDB36 (https://rcsb.org) and Uniprot37 (_-_e E-value, 0.001 -s sensitivity, 9.5 options). Structures were considered reliable if the PLDDT score was at least 70, as

suggested in the literature80. MOTILITY ASSAYS The coding sequence of a representative member of the NOV3845Y gene family (_nov3845y_) was manually codon optimized for expression in _E.

coli_ W3110 strain and subsequently cloned into pBlueScript II SK(+) using _Bam_HI and _Kpn_I sites. This plasmid contains an isopropyl-β-d-thiogalactoside (IPTG) inducible _lac_ promoter. A

Shine–Dalgarno consensus sequence (AGGAGG) was inserted at the beginning of the sequence to ensure ribosome binding and translation of the transcript. The plasmid was synthesized by BioCat.

The representative sequence from the NOV3845Y gene family was:

GTGGACGATCTGTTCATAATAGCCAGATGGAATGCGCAATATTTTGCCTGTGCGGAAGCAGACACGAAAGAAATTCTTGGTGGGCTGAATCAGGGCAGTCAAAGTCGAGGTGAAGAGGATGCCAGAGACGTTCAAAAGCATCATTTGTTTGCAGATCATGCGCTGGTGGAGTGCAACTTCGGTATCCTTCTAAACGACAATCAAATAGTGCCAAGTGACGATATTCCATCGCGGGTTTCGGCGACATTTTTGCCGCTGGACCCATATGGCGGTACGATCCTGAAAGACATCACCGGTATTATCCAGATTGACGAGGGTACATTCGCCCTTGTGCTGGCGCCTTCAAAAAACGATGCCAGAGGGGGATCGCTTGGAACAGGCCTTGATTAA,

which was codon optimized to

ATGGACGATCTGTTCATCATCGCCCGCTGGAATGCGCAGTATTTCGCCTGCGCGGAAGCAGACACGAAAGAAATCCTTGGTGGGCTGAATCAGGGCAGCCAGAGCCGCGGTGAAGAGGATGCCCGCGACGTGCAGAAGCATCATCTGTTCGCAGATCATGCGCTGGTGGAGTGCAACTTCGGTATCCTTCTGAACGACAATCAGATCGTGCCGAGCGACGATATCCCGTCGCGGGTGTCGGCGACCTTCCTGCCGCTGGACCCGTATGGCGGTACGATCCTGAAAGACATCACCGGTATCATCCAGATCGACGAGGGTACGTTCGCCCTTGTGCTGGCGCCGAGCAAAAACGATGCCCGCGGGGGATCGCTTGGAACCGGCCTTGATTAA.

A plasmid containing the selected sequence _nov3845y_ and the empty vector were transferred into _E. coli_ W3110 strain by electroporation. Strains that successfully incorporated plasmids

were selected for based on their resistance to ampicillin and checked by PCR with M13 universal primers. For swimming motility experiments, an _E. coli_ strain overexpressing NOV3845Y was

first streaked on solid lysogeny broth medium supplemented with ampicillin 100 µg ml−1 (Ap100). To prepare the swimming plates, 50 ml of semisolid lysogeny broth Ap100 containing 0.3% agar

and IPTG at concentrations of 0 µM, 10 µM, 100 µM and 1 mM was poured onto squared plates and left open to dry for 10 min in the biosafety laminar flow cabin. Isolated colonies were then

inoculated with a toothpick by perforation of the medium, avoiding reaching the bottom of the plate. Swimming area was quantified for each inoculated colony by measuring the diameter of the

bacterial ring following overnight growth (roughly 16 h) in a humid environment. ANTIMICROBIAL ACTIVITY ASSAY The candidate antimicrobial peptide NOVOQR9B (sequence:

MQTVNEPNVTGATPRGGCFVKTGCGKYKGSCTIHLA) was chemically synthesized by BioCat. For analysis of the putative antibacterial activity of the peptide, three Gram-positive bacterial strains (_P.

polymyxa_, _B. subtilis_ and _Lactobacillus_ sp.) and two Gram-negative bacterial strains (_E. coli_ K12 and _Ps. putida_ KT2440) were selected. Bacterial strains were grown overnight at 28 

°C in lysogeny broth agar plates. Cells were then scraped from the plates, resuspended in 1/3-diluted NB medium (1 g of yeast extract, 2 g of beef extract, 5 g of NaCl and 5 g of Bacto

Peptone (per litre)) and centrifuged at 5,000_g_ for 5 min. The resulting pellet was washed twice with 1/3-diluted NB medium and adjusted to an optical density at 600 nm (OD600) of 1.

Antimicrobial activity assays were performed in flat-bottomed, 96-well plates. The NOVOQR9B peptide was initially dissolved in 1/3-diluted NB medium to a concentration of 2 mM and then

serially diluted in the same medium to reach final concentrations ranging from 0.137 to 300 µM. Bacterial suspensions were then added to wells containing the peptide (final OD600 of 0.1).

Plates were incubated at 28 °C with shaking at 200 rpm, and the OD600 values of each well were measured following culture for 24 h using a SPECTROstar Nano microplate reader (BMG LABTECH).

The assay was carried out in triplicate. FUNCTIONAL PROFILING AND BIOMARKER DISCOVERY We calculated the relative abundance of FESNov gene families on 575 human gut samples from a clinical

study involving 285 CRC patients and 290 controls from five different populations46. For this we mapped the longest representative of each FESNov gene family against this dataset with

DIAMOND blastp and the --sensitive flag. Hits with _E_-value below 0.001 and coverage on the novel family sequence of over 50% were considered significant. For estimation of the relative

abundance of a FESNov family in a given sample we summed the relative abundance of all significant hits in that sample. We computed KEGG ortholog abundance by summing the relative abundances

of all genes annotated with a particular KEGG ortholog; KEGG ortholog annotations were obtained with eggNOG-mapper v.2. Differentially abundant FESNov gene families between CRC and control

samples were estimated, per gene family, using a blocked Wilcoxon test as implemented in the ‘coin’ R package, correcting study effects by blocking for ‘study’ and ‘colonoscopy’. We

corrected _P_ values by the FDR method to adjust for multiple-hypothesis testing to obtain _q_-values. A FESNov gene family was considered significantly over- or underabundant in CRC samples

compared with control samples if _q_-values were below 0.01. For comparison of the predictive power of the relative abundance profiles built on FESNov gene families versus KEGG orthologies,

and the combination of the two (FESNov + KEGG orthologies), we ran logistic regression models as implemented in the ‘gmlnet’ R package (Extended Data Fig. 9a) and random forest algorithms

as implemented in the ‘caret’ R package (Extended Data Fig. 9b). We ran the models independently for the relative abundance tables of FESNov, KEGG orthologies and FESNov + KEGG orthologies,

training the models with 70% of the samples and testing the prediction performance on the remaining 30%. For the random forest analysis, 1,000 random gene families were selected. Following

iteration of the analysis on different train and test data, we compared area under the curve values obtained when considering the relative abundance of FESNov gene families, KEGG orthologies

and the combination of the two. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA AVAILABILITY All

genomic data used in this study were downloaded from public sources as follows: UGHH MAGs from https://www.ebi.ac.uk/ena/browser/view/PRJEB33885, Ocean MAGs and SAGs from

https://www.ebi.ac.uk/ena/browser/view/PRJEB45951 and https://microbiomics.io/ocean/, GMGC MAGs from https://gmgc.embl.de, GEM MAGs from https://genome.jgi.doe.gov/GEMs and GTDB reference

genomes and MAGs from https://data.gtdb.ecogenomic.org/releases/release95/95.0. All the results derived from this study, including FESNov gene family fasta files, phylogenetic trees and

alignments, FESNov gene family statistics and evolutionary information, mobile element detections, taxonomic annotations, functional prediction summaries and protein structure predictions,

are available at https://zenodo.org/doi/10.5281/zenodo.10219528. Computer-generated structural models are also available at https://modelarchive.org/doi/10.5452/ma-fesnov. In addition, large

intermediate files from some analyses are provided at https://novelfams.cgmlab.org/downloads/, including: all standardized genomes, MAGs and SAGs downloaded from public sources,

consolidated FASTA files with predicted genes and proteins, functional annotations of all proteins by eggNOG-mapper v.2.1 and raw clustering results. Source data are provided with this

paper. CODE AVAILABILITY Custom code was developed for some of the analyses included in the manuscript and is available at https://zenodo.org/doi/10.5281/zenodo.10209798. In addition, this

code is also available as GitHub repositories at https://github.com/AlvaroRodriguezDelRio/NovFamilies and https://github.com/AlvaroRodriguezDelRio/nov-fams-pipeline, including a description

on how to apply different-quality filters, predict pathways based on genomic context conservation analysis and calculate taxonomic conservation values. Implementation of the FESNov-based

eggNOG-mapper functional profiler is available at https://github.com/eggnogdb/eggnog-mapper and can be used in the software from v.2.1.12 onwards and via the online service at

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University) for their help with the habitat annotation of metagenomes. We thank G. Zeller and J. Wirbel for critical discussion on the functional profiling of CRC samples. We thank X. Jiang,

L. Rubio, K. Patil, I. Roux and S. Blasche for their support and discussion on the experimental validations of novel functions. Editorial assistance, in the form of language editing and

correction, was provided by XpertScientific Editing and Consulting Services. This project has received funding from the National Programme for Fostering Excellence in Scientific and

Technical Research (grant no. PGC2018-098073-A-I00 MCIU/AEI/FEDER, UE) and, partially, by MCIN/AEI/10.13039/501100011033/ and FEDER Una manera de hacer Europa (grant no.

PID2021-127210NB-I00). A.R.d.R. was supported by a fellowship from la Caixa Foundation (ID 100010434, fellowship code no. LCF/BQ/DI18/11660009), cofunded by the European Union’s Horizon 2020

research and innovation programme under Marie Skłodowska-Curie grant agreement no. 713673. C.P.C., S.S.-H. and Z.D. acknowledge support by Severo Ochoa Centres of Excellence Programme from

the State Research Agency of Spain (grant nos. SEV-2016-0672 (2017–2021) and CEX2020-000999-S). J.B. acknowledges support by a grant from the Chan Zuckerberg Initiative DAF, an advised fund

of Silicon Valley Community Foundation (no. 2020-218584). A.H.-P. was supported by Research Technical Support Staff Aid (no. PTA2019-017593-I/AEI/10.13039/501100011033). M.M.-P., J.J.R.-H.

and E.L.-S. acknowledge support from Ministerio de Ciencia e Innovación, MCIN/AEI/10.13039/501100011033 (grant no. PID2021-125673OB-I00). S.S. acknowledges support from the Swiss National

Science Foundation project (grant no. 205321_184955) and NCCR Microbiomes (no. 51NF40_180575), and thanks the staff at ETH Zurich IT Services and HPC facilities. AUTHOR INFORMATION Author

notes * Joaquín Giner-Lamia Present address: Departamento de Bioquímica Vegetal y Biología Molecular, Facultad de Biología, Instituto de Bioquímica Vegetal y Fotosíntesis (IBVF), Universidad

de Sevilla-CSIC, Seville, Spain * Luis Pedro Coelho Present address: Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational

Research Institute, Woolloongabba, Queensland, Australia AUTHORS AND AFFILIATIONS * Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) – Instituto

Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain Álvaro Rodríguez del Río, Joaquín Giner-Lamia, Carlos P. Cantalapiedra, Jorge Botas, Ziqi Deng, Ana

Hernández-Plaza, Martí Munar-Palmer, Saray Santamaría-Hernando, José J. Rodríguez-Herva, Emilia López-Solanilla & Jaime Huerta-Cepas * Departamento de Biotecnología-Biología Vegetal,

Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain Joaquín Giner-Lamia, José J. Rodríguez-Herva & 

Emilia López-Solanilla * Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland Hans-Joachim Ruscheweyh, Lucas Paoli & 

Shinichi Sunagawa * Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Thomas S. B. Schmidt & Peer Bork * Max Delbrück Centre for

Molecular Medicine, Berlin, Germany Peer Bork * Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany Peer Bork * Institute of Science and Technology for

Brain-Inspired Intelligence, Fudan University, Shanghai, China Luis Pedro Coelho * MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center

for Brain Science, Shanghai, China Luis Pedro Coelho Authors * Álvaro Rodríguez del Río View author publications You can also search for this author inPubMed Google Scholar * Joaquín

Giner-Lamia View author publications You can also search for this author inPubMed Google Scholar * Carlos P. Cantalapiedra View author publications You can also search for this author

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CONTRIBUTIONS J.H.-C. conceived the project, designed and supervised experiments and performed part of the computational analyses. A.R.d.R. designed experiments, collected data and performed

all computational analyses, benchmarks and statistical tests. J.G.-L. designed and performed part of the genomic context analyses and curated functional predictions for experimental

validations. C.P.C. implemented the eggNOG-mapper functional annotation module for the FESNov catalogue. J.B. implemented part of the online resource. Z.D. computed functional annotations

based on protein structures and helped with online resource implementation. A.H.-P. participated in the clustering of gene families. M.M.-P., S.S.-H., J.J.R.-H. and E.L.-S. designed and

performed experimental validations of gene families NOV3845Y and NOVOAR9B. H.-J.R., L.P. and S.S. helped establish the genomic dataset, computed most of the protein structure predictions and

contributed mappings to biosynthetic clusters from marine metagenomes. T.S.B.S., P.B. and L.P.C. contributed ecological mappings and developed the habitat ontology used to compute

ecological distributions. J.H.-C., A.R.d.R. and J.G.-L. wrote the paper and prepared all figures with feedback from all authors. All of the authors read and approved the final manuscript.

CORRESPONDING AUTHOR Correspondence to Jaime Huerta-Cepas. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature_

thanks Ami Bhatt, Alexander Probst and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. ADDITIONAL INFORMATION

PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1

SWIMMING CHEMOTAXIS ASSAY OF _ESCHERICHIA COLI W311_0 STRAIN EXPRESSING NOV3845Y. (a) Representative image of a swimming chemotaxis assay of _Escherichia coli W3110_ strains containing

either _pBSK_ or _pNOV3845Y_. (b) Quantification of the swimming area (measured as diameter in cm) of _Escherichia coli W3110_ strains harboring _pBSK_ (n = 36, with 3 biological replicates,

each containing 12 technical replicates) and _pNOV3845Y_ (n = 36, with 3 biological replicates, each containing 12 technical replicates). Data are represented as mean values ± SEM. p-value

by two-tailed Mann-Whitney U test is indicated. Source Data EXTENDED DATA FIG. 2 ANTIMICROBIAL ACTIVITY OF NOVOQR9B PEPTIDE. Growth of 3 Gram-positive (a) and 2 Gram-negative (b) bacterial

strains in 1/3 diluted NB medium with peptide concentration as indicated. Control bars represent bacterial culture growth in the absence of the peptide. Only the results for the two highest

peptide concentrations are shown. Data from 3 technical replicates for all bacterial strains tested are represented as mean values ± SEM. p-values by One-way ANOVA test are indicated (* p

< 0.05; ** p < 0.01; *** p < 0.001). Source Data EXTENDED DATA FIG. 3 STRUCTURAL SIMILARITY OF NOV5WD8W WITH THE COPPER METALLOCHAPERONE CUSF. (a) Superposition of the structures of

FESNov protein sequence NOV5WD8W (blue) and _CusF_ (yellow) from _Escherichia coli_. (b) Zoom-in on the high-confidence structural homology domain in superposed structure of the FESNov

protein sequence NOV5WD8W (blue) and _CusF_ (yellow) from _Escherichia coli_. EXTENDED DATA FIG. 4 SCHEMATIC REPRESENTATION OF THE GENOMIC CONTEXT OF FOUR TRANSMEMBRANE FESNOV GENE FAMILIES

IN PATESCIBACTERIA GENOMES. Gene names correspond to _comEA_ (K02237): competence protein _ComEA_, _comEC_ (K02238) competence protein _ComEC_; _hsdM_ (K03427): type I restriction enzyme M

protein, _hsdS_ (K01154): type I restriction enzyme, S subunit, recombinase B; _pilB_ (K02652): type IV pilus assembly protein PilB, _pilC_ (K02653): type IV pilus assembly protein PilC,

_pilM_ (K02662): type IV pilus assembly protein PilM; _virB6_ (K03201: type IV secretion system protein VirB6). EXTENDED DATA FIG. 5 CORRELATION BETWEEN FESNOV GENE FAMILIES MOBILITY AND

ECOLOGICAL DISPERSION. Proportion of FESNov gene families linked to plasmids or viral contigs in relation to the number of habitats they were detected in. R indicates the Spearman

correlation coefficient. Source Data EXTENDED DATA FIG. 6 NUMBER OF FESNOV GENE FAMILIES CONFINED TO EACH TAXONOMIC RANK. Number of FESNov gene families confined to each taxonomic rank

attending to the last common ancestor (LCA) of their family members. The lines indicate the proportion of gene families detected in plasmids (red) or viral (blue) contigs. This figure is

equivalent to Fig. 3c with the following differences (A) requiring 30% of the members to be present in plasmids/viral contigs for considering the family as mobile and (B) calculating LCAs as

the most basal taxonomic group gathering 80% of the members of the family. Source Data EXTENDED DATA FIG. 7 SEPARATION OF HABITATS AND HUMAN GUT POPULATIONS WITH FESNOV FAMILIES AND KO

RELATIVE ABUNDANCES. tSNE representation of the log-transformed abundances of FESNov gene families and KOs detected in more than 1,000 samples within the GMGC catalog. To correct for

differences in the number of samples available per habitat and human gut sample available per country, we included a maximum of 150 random samples per habitat/country. (a) tSNE

representation of FESNov gene families per country; (b) tSNE representation of KOs per country; (c) tSNE representation of FESNov gene families per habitat: (d) tSNE representation of KOs

per habitat. Source Data EXTENDED DATA FIG. 8 EXAMPLES OF THE GENOMIC CONTEXT OF FESNOV FAMILIES OVER-ABUNDANT IN CRC SAMPLES. Schematic overview of the genomic context of NOVYGMIL,

NOV3ON7R, and NOVJ3S2O; FESNov gene families significantly more abundant in CRC samples with conserved genomic context related to cell adhesion, motility, and cell invasion. Gene names

correspond to: _yerB_ (COG1470), cell adhesion and biofilm formation; _ksgA_ (K02528), 16S rRNA (adenine1518-N6)-dimethyltransferase; _FliD_ (K02407), flagellar hook-associated protein,

_fliS_ (K02422), flagellar secretion chaperone; _bglA_ (K01223), 6-phospho-beta-glucosidase, ROK, sugar kinase protein (regulatory protein). EXTENDED DATA FIG. 9 PERFORMANCE OF PREDICTORS

BUILT UPON THE RELATIVE ABUNDANCE MATRICES OF BOTH FESNOV GENE FAMILIES AND KEGG ORTHOLOGS (KOS) FAMILIES. (A) Area Under the Curve (AUC) values obtained from 50 iterations of a logistic

regression model built on KEGG Orthologs (KOs), FESNov gene families, and the combination of both (B) AUC values obtained from 30 iterations of a random forest model built on KOs, FESNov

gene families and the combination of both, Data are represented as boxplots where the middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the

upper whisker extends from the hinge to the largest value no further than 1.5 × IQR from the hinge (where IQR is the interquartile range) and the lower whisker extends from the hinge to the

smallest value at most 1.5 × IQR of the hinge, while data beyond the end of the whiskers are outlying points that are plotted individually. _p_-values by two-sided Wilcoxon-test are

indicated. Source Data SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLE 1 Habitats represented by each original data source. SUPPLEMENTARY TABLE 2 Synteny-based functional

prediction benchmarking results. SUPPLEMENTARY TABLE 3 High-confidence, synteny-based functional predictions of FESNov gene families. SUPPLEMENTARY TABLE 4 FESNov gene families potentially

associated with antibiotic resistance. SUPPLEMENTARY TABLE 5 FESNov gene families with antimicrobial signatures. SUPPLEMENTARY TABLE 6 FESNov gene families in biosynthetic gene clusters.

SUPPLEMENTARY TABLE 7 FESNov gene families with homologues in the Fitness Browser database (mutant fitness assays). SUPPLEMENTARY TABLE 8 Number of FESNov gene families per genome and

lineage. SUPPLEMENTARY TABLE 9 List of synapomorphic gene families at basal taxonomic ranks. SUPPLEMENTARY TABLE 10 List of extended 157 habitat names. SUPPLEMENTARY TABLE 11 Number of

detections per habitat of FESNov gene families. SUPPLEMENTARY TABLE 12 FESNov gene families highly prevalent in human gut, marine and soil samples. SUPPLEMENTARY TABLE 13 FESNov gene

families significantly over- and underabundant in CRC study. SUPPLEMENTARY TABLE 14 Mean completeness and contamination per genome. SUPPLEMENTARY TABLE 15 FESNov gene families with hits to

the Eukprot database. SUPPLEMENTARY TABLE 16 Complete list of synapomorphic gene families. PEER REVIEW FILE SOURCE DATA SOURCE DATA FIG. 1 SOURCE DATA FIG. 2 SOURCE DATA FIG. 3 SOURCE DATA

FIG. 4 SOURCE DATA FIG. 5 SOURCE DATA EXTENDED DATA FIG. 1 SOURCE DATA EXTENDED DATA FIG. 2 SOURCE DATA EXTENDED DATA FIG. 5 SOURCE DATA EXTENDED DATA FIG. 6 SOURCE DATA EXTENDED DATA FIG. 7

SOURCE DATA EXTENDED DATA FIG. 9 RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,

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