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Filtering threshold for handling spurious sequencesWe first used bacterial communities of known composition (simplified communities) to assess the occurrence of spurious taxa and to determine at which relative abundances they begin to appear. To propose a cutoff that is potentially applicable to different 16S rRNA gene amplicon studies, we included reference data obtained with different variable regions and sequencing pipelines and originating from both in vitro an in vivo communities varying in number and type of species (max. 58) (Tables 1 and 2). To determine a filtering threshold that allowed exclusion of most spurious taxa, we recorded the relative abundance of the first spurious OTU occurring in each of the reference community datasets (Fig. 2a). Median values of approx. 0.12% relative abundance were observed (Fig. 2b). Besides one outlier in the mock communities (0.44% relative abundance), all values were below 0.25% relative abundance.Fig. 2: Determination of filtering thresholds using artificial communities of known composition in vitro (mock; n = 9 different types; 21 replicates in total) and in mice (gnotobiotes; n = 4 different communities; 28 mice in total).a Example of the occurrence of all molecular species detected without filtering in the gut of a gnotobiotic mouse [49]. The arrow indicates the position of the first spurious molecular species, all following taxa being considered as having a high risk of being spurious (light gray bars in the enlarged inset). b Distribution of the relative abundances of first occurring spurious molecular species (as shown in panel a) across all mock communities and samples from gnotobiotes. The orange dashes on the y-axis indicate the consensus threshold of 0.25% relative abundance, above which no spurious taxa occurred with the exception of one outlier in a mock community at a relative abundance of 0.44%. c Comparison of various standard filtering cutoffs (see explanations in the text) in terms of spurious taxa (i.e., those molecular species not matching sequences of the known species contained in the artificial communities). d Corresponding percentages of positive hits retained by the different filtering strategies, with positive hits being defined as the reference sequences found in the respective amplicon datasets. e Percentage of spurious taxa and positive hits in the same reference communities using the DADA2 pipeline for analysis based on amplicon sequence variants (ASVs) [6]. f Effect of filtering thresholds at increments of 0.05% relative abundance on the detection of spurious taxa and positive hits in all mock and gnotobiotic datasets for OTUs (upper panel) and ASVs (lower panel). Lines correspond to mean values; ribbons represent standard deviations.Full size imageWithout any filtering, sequence clustering generated an average of 508 ± 355 OTUs (min. 52; max. 1081) per mock community (10–58 target species in theory) and 105 ± 50 OTUs (min, 55; max. 215) per gnotobiotic community (4–12 target species in theory). Up to 87% of these OTUs were spurious (i.e., they did not match the expected classification of species contained in the corresponding artificial community) (Fig. 2c). On average, the proportion of spurious OTUs in both the mock communities and samples from gnotobiotic mice was slightly lower after removing singletons, although this did not reach statistical significance (50.8 vs. 64.3%, p = 0.227; 57.5% vs. 65.7%; p = 0.70, pairwise comparison by t-test, including Benjamini–Hochberg correction following ANOVA). Interestingly, the proportion of spurious molecular species was higher in gnotobiotic mice independent of filtering (p  0.50) (Fig. 2d). Note that the diversity of reference communities in the gnotobiotic mice was relatively low (4–12 members; Table 2), resulting in a marked drop in the percentage of positive hit (8–25%) when even just one true member is excluded after filtering because of its low relative abundance (which is an expectable event considering a classical, exponentially decreasing distribution of species occurrence in gut environments).We next employed the widely used ASV analysis approach to confirm the aforementioned results. Processing of the same simplified communities generated a total number of 42 ± 25 ASVs (min. 16; max. 98) for mock communities (10–58 target species) and 14 ± 8 ASVs (min. 4; max. 25) for gnotobiotes (4–12 target species). Altogether, a marked decrease in spurious taxa was observed compared with OTU clustering, with an average of 8.6 ± 11.8 and 4.4 ± 6.4% spurious sequences for mock and gnotobiotic communities, respectively (comparison of purple box plots in Fig. 2e, top panels, and Fig. 2c). Of note, the DADA2 pipeline used for the ASV approach does not infer sequence variants that are only supported by a single read (singletons) due to a lack of confidence in their existence relative to sequencing errors. Consequently, data corresponding to “no filtering” with the OTU-based approach were not generated. On average, the first spurious ASV occurred at a relative abundance of 0.10 ± 0.32%. By applying the cutoff of 0.25% relative abundance, spurious sequences were completely removed (except for three outlying samples), albeit with a slight drop in positive hits for both mock and gnotobiotic communities (Fig. 2e).To obtain a more comprehensive view on how filtering thresholds affect the detection of spurious taxa, all datasets (mock and gnotobiotic mice) were processed using a range of relative abundance filtering thresholds (from 0 to 0.5% at increments of 0.05%) after either OTU- or ASV-based processing of raw sequence reads (Fig. 2f). These data indicate that filtering thresholds between 0.1 and 0.3% are appropriate to reduce the occurrence of spurious taxa to 600 of the 678 spurious OTUs occurred in fewer than five of the ten sequencing runs tested, with approximately 450 of them occurring in only one run (Fig. 3c). This observation indicates that the majority of spurious taxa are sporadic cross-contaminations rather than generalist artifacts across sequencing runs, suggesting that fully independent technical replicates would improve data quality. Although most of the spurious taxa were characterized by relative abundances between 0.25 and 2% in the IMNGS-amplicon datasets tested, they represented very dominant populations in a few samples (Fig. 3d).Fig. 3: Origin and occurrence of spurious taxa.a Taxonomic profile and ecological distribution. Inner ring: SILVA-based classification of all non-redundant spurious molecular species at the phylum and family level. Outer colored ring: sample type characterized by the highest prevalence for the given taxon. Outer bars: corresponding highest prevalence values. Only samples with relative abundances >0.25% for any given OTU were counted as positive for prevalence calculation. The total numbers of samples considered were: human, 46,153; soil, 29,864; freshwater, 13,977; mouse, 10,409; marine, 8478. b Distribution of the spurious taxa across sample types. The exclusivity of each OTU for any given sample type was assessed using a Z-test: those assumed to be non-specific for any given sample type appear in red (p 0.25% in at least one replicate were kept). Richness was calculated using ampvis2 [29]. Applying the 0.25% cutoff decreased the number of observed ASVs from 408 ± 71 to 139 ± 5 and, more importantly, the IQR from 101 to 7 (Fig. 6b). Unweighted UniFrac distances within and between runs as calculated using ampvis2 were also compared before and after filtering. Sequences were aligned using MAFFT [30] and phylogeny was inferred using FastTree. Whilst the community makeup in the soil sample varied substantially between sequencing runs without additional filtering, the 0.25% cutoff reduced this variation to the level observed within runs without filtering (Fig. 6c). Replicates within a run were very similar after applying the 0.25% cutoff. Altogether, these data serve as an independent confirmation that stringent filtering delivers more stable values obtained for the exact same sample sequenced in replicates across several sequencing runs. More

2,809 draft MAGs from the rumen ecosystemWe amassed 3.2 terabase pairs (Tbp) of data from 346 publicly available and 66 new rumen metagenome datasets (Supplementary Table 1). The metagenomes were from cattle (312 samples, 2.1 Tbp), sheep (75 samples, 888.4 gigabase pairs (Gbp)), moose (9 samples, 108.8 Gbp), deer (8 samples, 62.9 Gbp), and bison (8 samples, 52.3 Gbp). Metagenomes were assembled independently to reduce the influence of strain variation and improve the recovery of closely related genomes18,19. Following refinement, dereplication, and filtering of resulting population genomes, we identified 2,809 nonredundant MAGs satisfying the following criteria: dRep20 genome quality score ≥60, ≥75% complete, ≤10% contamination, N50 ≥5 kbp, and ≤500 contigs.The median estimated completeness and contamination of the MAGs were 89.7% and 0.9%, respectively (Fig. 1a and Supplementary Data 1). Further, recovered MAGs had a median genome size of 2.2 Mbp, a median of 131 contigs, and a median N50 of 28.3 kbp (Fig. 1b). The proposed minimum information about a MAG (MIMAG) specifies high-quality draft genomes to have an estimated ≥90% completeness, ≤5% contamination, at least 18 tRNAs, and contain 23S, 16S, and 5S rRNA genes21. It remains challenging to reconstruct rRNA genes from short metagenomic reads due to the high sequence similarity of rRNA genes in closely related species. As a result, despite high estimated completeness and low contamination rates, only 20 MAGs meet the MIMAG standards for a high-quality draft genome. We identified a 16S rRNA gene in 197 of the MAGs. The remaining MAGs are characterized as medium-quality MAGs under the MIMAG standards.Fig. 1: Genomic properties of 2,809 rumen MAGs.a CheckM completeness and contamination estimates for the 2,809 population genomes recovered from rumen metagenomes. The size of the point on the scatter plot corresponds to the dRep genome quality score, where Quality = Completeness − (5 ⋅ Contamination) + (Contamination ⋅ (Strain Heterogeneity/100)) + 0.5 ⋅ (({mathrm{log}},)(N50). The reported MAGs meet the following minimum criteria: genome quality score ≥60, ≥75% complete, ≤10% contamination, N50 ≥5 kbp, and ≥500 contigs. b The frequency distribution of the number of contigs and genome sizes of reconstructed MAGs.Full size imageThe majority of bacterial MAGs belonged to phyla Firmicutes or Bacteroidota (2,326; Fig. 2a and Supplementary Data 1). Additionally, we assembled 12 bacterial genomes from the superphylum Patescibacteria. At lower taxonomic ranks, Lachnospiraceae (415) and Prevotella (398) were the dominant family and genus identified among the assembled bacterial genomes. The most prevalent archaeal family and genus were Methanobacteriaceae (45) and Methanobrevibacter (35), respectively (Fig. 2b). The recovered MAGs represent several new taxonomic lineages, as four genomes could not be classified at the rank of order, 16 at the rank of family, and 243 at the genus rank.Fig. 2: Phylogenetic relationships and coverage patterns of near-complete MAGs.a Phylogenomic analysis of 1,163 near-complete (≥90% complete, ≤5% contamination, and N50 ≥15 kbp) bacterial MAGs and (b) 20 near-complete archaeal MAGs inferred from the concatenation of phylogenetically informative proteins. Layers below the genomic trees designate bacterial phylum or archaeal genus based on GTDB taxonomic assignments, genomic size (0–5 Mbp), and the mean number of bases with ≥1× coverage in a rumen metagenomic dataset (layer color indicates the ruminant the data was collected from). The mean number of bases with ≥1× coverage was used as input for hierarchical clustering of rumen metagenomic datasets based on Euclidean distance and Ward linkage. The bacterial and archaeal phylogenetic trees are provided as Supplementary Data 6 and Supplementary Data 7, respectively.Full size imageSpecies-level overlap between reference genomes, the Hungate1000 Collection, and rumen MAGsTo further characterize the assembled genomes, we compared the MAGs to other rumen-specific genome collections, specifically genomes generated from the Hungate1000 project3 and MAGs identified from the Stewart et al. studies4,5. We clustered genomes based on approximate species-level thresholds (≥95% ANI) and calculated the intersection between MAGs in the current study and the Hungate1000 Collection (410 genomes)3, MAGs from Stewart et al. (4,941 genomes)4,5, and a dereplicated genome collection from the GTDB (22,441 genomes, see Methods)22, which includes reference isolate genomes and some environmental MAGs23. It should be noted that we used the raw data from the first of the Stewart et al. studies4 (Supplementary Table 1), but with different assembly and binning approaches. Approximately one-third of the MAGs (1,007) did not exhibit ≥95% ANI with a genome in the GTDB, Stewart et al. MAGs, or the Hungate1000 isolates (Fig. 3a). When considering the pairwise intersections between the datasets, 98 (3.5%), 933 (33.2%), and 1,438 (51.2%) of the MAGs in the current study had ≥95% ANI with a genome in the Hungate1000 Collection3, GTDB22, and Stewart et al.4,5, respectively. One hundred twenty-one (29.5%), 552 (2.5%), and 3,125 (63.2%) of the genomes from the Hungate1000 Collection3, GTDB22, and Stewart et al.4,5 displayed ≥95% ANI with a MAG from the current study. Together, these results indicate that we recovered a majority of previous rumen genomic diversity with additional lineages not previously identified in other major rumen genomic collections.Fig. 3: Genomes sharing ≥95% ANI between databases and the characterization of rumen-specific 95% ANI clusters.a The approximate number of species overlapping amongst rumen-specific and reference genomic datasets. Genomes demonstrating ≥95% ANI were considered to be shared between two datasets. Presented are a subset of intersections in which a MAG from the current study was the query genome. b The number of genomes comprising each of the 3,541 95% ANI clusters generated from 8,160 rumen microbial genomes in the current study, the Hungate1000 Collection3, and Stewart et al. studies4, 5. c Rarefaction analysis based on subsampling 95% ANI clusters at steps of 500 genomes indicates the 8,160 genomes from recently published rumen genomic collections still only represent a fraction of expected microbial species diversity in the rumen ecosystem. Phylogenomic relationships of the 1,781 near-complete bacterial (d) and 35 near-complete archaeal (e) representative genomes with the highest dRep genome quality score from the 3,541 95% ANI clusters generated from 8,160 rumen-specific genomes. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. Layers surrounding the genomic trees indicate the bacterial phyla or archaeal genera and the log normalized number of genomes from each rumen genomic collection belonging to the same 95% ANI cluster. The bacterial and archaeal phylogenetic trees are provided as Supplementary Data 8 and Supplementary Data 9, respectively.Full size imageWe applied an additional clustering approach to identify the approximate number of species represented by the rumen-specific genomes assembled in this study, in the Hungate1000 Collection3, and Stewart et al.4,5. A 95% ANI threshold yielded 3,541 clusters from the combination of the datasets (Supplementary Data 2). Of the 3,541 clusters, 2,024 contained a MAG from the current study, and 1,135 were composed exclusively of MAGs from the current study. In comparison, 2,175 and 286 clusters were comprised of genomes from Stewart et al.4,5 and the Hungate1000 Collection3, respectively. The majority of 95% ANI clusters (2,166) are only comprised of a single genome (Fig. 3b). Furthermore, a rarefaction curve suggests the 8,160 genomes from the genomic collections analyzed here only represent a fraction of the estimated microbial species diversity in the rumen (Fig. 3c). The genome with the best dRep score from each cluster was used to generate a phylogenetic tree highlighting the species diversity within each rumen genomic collection and represents the vast diversity of rumen bacterial (Fig. 3d) and archaeal (Fig. 3e) genomes published to date.As stated previously, the median genome size of reconstructed MAGs was 2.2 Mbp, smaller than the median size of genomes from the Hungate1000 project (3.1 Mbp)3. To provide an assessment at a finer resolution, genome sizes of MAGs and Hungate1000 genomes3 belonging to the same 95% ANI cluster were compared (Supplementary Fig. 1). Adjusted sizes of MAGs and Hungate1000 genomes that are ≥95% complete displayed a regression coefficient of 0.96 with a slope of 0.86, indicating the binning process likely did not lead to extensive losses and systematic biases in the reconstructed genomes. Instead, it further highlights that current culturing approaches have not brought large portions of rumen microbial diversity into culture and putatively supports previous findings from the human gut that revealed genome-reduction in uncultured bacteria24.Rumen metagenome classification rates using reference and rumen-specific genomesUtilizing an approach similar to Stewart et al.4,5, we investigated the influence of MAGs on rates of metagenomic read classification. The baseline for read classification was the standard Kraken database containing bacterial, archaeal, fungal, and protozoal RefSeq genomes25. Each rumen-specific dataset was incrementally added to the Kraken RefSeq genomic database in the following order to build new databases: the Hungate1000 Collection3, MAGs from Stewart et al.4,5, and MAGs from the current study. Each individual and collective database was used for classification of sample reads that underpinned metagenomic binning and from a rumen metagenomic dataset not used in the reconstruction of MAGs26. MAGs from the current work classified more reads from deer, moose, and sheep metagenomes, while the more numerous MAGs from Stewart et al.4,5 classified more reads from bison and cattle metagenomes (Supplementary Fig. 2a). The addition of MAGs improves classification relative to databases primarily based on cultured isolates, like the Hungate1000 Collection3 (Supplementary Fig. 2b). Using the combination of all reference and rumen-specific genomes, the median classification rate on an independent set of cattle metagenomes was 62.6%.Phylogenetic characterization of biosynthetic gene clustersMicrobial genome mining is a powerful tool for natural product discovery. We sought to explore the extent of secondary metabolite diversity coded by the MAGs in the current study, the Hungate1000 Collection3, and Stewart et al. MAGs4,5. We identified 14,814 BGCs encoded by the 8,160 rumen-specific genomes using antiSMASH27 (Fig. 4a and Supplementary Data 3). The majority of BGCs were NRPS (5,346), followed by aryl polyenes (2,800), sactipeptides (2,126), and bacteriocins (1,943). Only a few PKS were identified (75). Firmicutes harbored the vast majority of clusters for NRPS, sactipeptide, lantipeptide, lassopeptide, and bacteriocin synthesis (Fig. 4b). At lower taxonomic ranks, DTU089 (979), Bacteroidaceae (934), and Lachnospiraceae (923) coded for the bulk of NRPS gene clusters. Moreover, Acidaminococcaceae genomes contained 21.2% of identified bacteriocins and Ruminococcus spp. possessed the bulk of sactipeptides and lantipeptides. Archaea were predicted to code 737 BGCs, including an average of 3.8 NRPS gene clusters per genome (Fig. 4a).Fig. 4: Characterization of BGCs from 8,160 rumen genomes and MAGs.a Number and types of BGCs identified from select phyla in genomes from the Hungate1000 Collection3, Stewart et al. studies4, 5, and the current study. b Phylogenomic analysis of 1,766 near-complete Firmicutes genomes inferred from the concatenation of phylogenetically informative proteins. The inner layer surrounding the genomic tree designates taxonomic annotations, while the remaining layers depict the log normalized number of BGCs in the genome with the ascribed function. Bacterial class and order labels are displayed for those lineages in which more than 50 genomes were identified. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. The phylogenetic tree is provided as Supplementary Data 10. c A relational network of NRPS gene clusters in Firmicutes, Bacteroidota, and Euryarchaeota highlights the similarity of NRPS BGCs from Euryarchaeota and Firmicutes. Edge weight represents the similarity of two BGCs, as determined by BiG-SCAPE (i.e. darker edges demonstrate more similarity between two BGCs). Edges are only shown for BGCs with ≥0.3 BiG-SCAPE similarity. Nodes from each phylum are duplicated to illustrate intra-phylum relationships and nodes along a given axis are ordered alphabetically by taxonomic family. d The association between genome phylogeny and the similarity of NRPS gene clusters coded by near-complete Euryarchaeota genomes. BGCs designated as NRPS were clustered with BiG-SCAPE. The relationship between NRPS clusters was portrayed through the hierarchical clustering of pairwise inter-cluster similarities. The number of NRPS clusters coded by each genome (range of 0–3) is presented alongside the assigned genus. A group of Methanobrevibacter genomes, likely of the same species (≥95% ANI), possessed very similar NRPS clusters (highlighted in blue). Yet, phylogenetically closely related genomes, belonging to two different 95% ANI clusters, did not code for any identified NRPS gene clusters (highlighted in red). The phylogenetic tree is based on the concatenation of 122 phylogenetically informative archaeal proteins and is available as Supplementary Data 11.Full size imageNRPS exhibit high molecular and structural diversity resulting in a wide array of biological activities. The diversity of NRPS, combined with their proteolytic stability and selective bioactivity, has resulted in the development of many NRPS as antimicrobials and other therapeutic agents28. Given the prevalence of NRPS among the recovered MAGs (Fig. 4a), the peptides appear to be important bioactive metabolites in the rumen. To gain fundamental insight into the phylogenetic diversity of rumen NRPS, we built a network based on BGC similarity using BiG-SCAPE29. BiG-SCAPE uses protein domain content, order, copy number, and sequence identity to calculate a distance metric. We assessed the similarity of NRPS gene clusters identified in Firmicutes, Bacteroidota, and Euryarchaeota, as these three phyla coded for 96.4% of assembled NRPS gene clusters from rumen genomes. With a BiG-SCAPE similarity threshold of 0.3, the resulting network consisted of 3,436 nodes (NRPS BGCs on contigs ≥10 kbp) and 79,112 edges (Fig. 4c and Supplementary Data 4). As expected, the network analysis depicted high inter- and intra-phylum genetic diversity among the NRPS gene clusters. The median intra-phylum, -family, and -genus similarity was 0.40, 0.44, and 0.46, respectively, while the median inter-phylum, -family, and -genus similarity was 0.32, 0.34, and 0.34, respectively. Further, only 2.6% of edges were inter-phylum and 69.0% were intra-family. Of the 6,594 Euryarchaeota edges, 8.1% were Euryarchaeota-Firmicutes (median similarity of 0.32) and 2.0% of edges were Euryarchaeota-Bacteroidota (median similarity of 0.31). To further examine the phylogenetic relationships of rumen Euryarchaeota NRPS, we clustered 265 NRPS gene clusters (≥10 kbp) from 85 near-complete Euryarchaeota genomes at a higher similarity threshold of 0.75, yielding 57 NRPS clusters (Fig. 4d). The distribution of NRPS clusters amongst the genomes suggests there exists a strong relationship between methanogen phylogeny and NRPS similarity. Only Methanobrevibacter genomes contain NRPS gene clusters, and genomes of the same species often possessed many of the same NRPS clusters (see genomes highlighted in blue in Fig. 4d). However, there are instances in which closely related methanogens code for a contrasting pattern of NRPS clusters or no NRPS clusters at all (see genomes highlighted in red in Fig. 4d).Bacteriocins likely serve as regulatory elements in complex microbial communities such as the rumen. Consequently, bacteriocins have been studied and characterized for their bactericidal activity and as agents that modulate the microbiota structure and function30. In particular, lanthipeptides, a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) with thioether cross-linked amino acids31, are of pharmaceutical, preservative, and agricultural interest due to their strong antimicrobial properties against gram-positive pathogens31,32,33, low levels of antimicrobial resistance34, and stability35. We identified 195 rumen lanthipeptide BGCs from the Hungate1000 genomes and MAGs from Stewart et al. and the current study. Rumen lanthipeptide BGCs were clustered with 22,870 lanthipeptide BGCs from RefSeq genomes36,37 into gene cluster families (GCFs; groups of BGCs that may generate highly similar products). Clustering with BiG-SCAPE29 yielded 4,565 GCFs, 120 of which contained a rumen lanthipeptide. The 120 GCFs were composed of 519 lanthipeptide BGCs, where 324 were from RefSeq isolates and 195 from rumen genomes (Fig. 5a). The 324 RefSeq BGCs fell into only 18 GCFs. Lanthipeptides from the Hungate1000 isolates clustered into 36 GCFs, while rumen MAG lanthipeptides belonged to 92 GCFs, 82 of which were exclusively composed of MAG lanthipeptides. Together, this evidence suggests rumen MAGs code for diverse and novel lanthipeptides not represented in cultured isolates, including the Hungate Collection.Fig. 5: Phylogenetic diversity of 195 lanthipeptide BGCs coded by rumen genomes.a Network depicting the similarity between lanthipeptide BGCs identified from complete and draft isolate genomes in RefSeq and rumen genomes of the Hungate1000 collection, Stewart et al. MAGs, and MAGs from the current study. The BGCs were clustered into gene cluster families (GCFs) with BiG-SCAPE29. Only the GCFs containing a rumen genome and at least two BGCs were visualized. Nodes in the network represent BGCs and edges connect BGCs with BiG-SCAPE defined similarity ≥0.3. b Phylogenetic relationships of 120 near-complete rumen bacterial genomes coding for lanthipeptide BGCs. Near-complete genomes were defined as being ≥90% complete, having ≤5% contamination, and contig N50 ≥15 kbp. Layers surrounding the genomic trees indicate the bacterial phyla and family, if the genome is a MAG or Hungate Collection isolate, and the class of lanthipeptide, as predicted by antiSMASH27. Genomes without an indicated lanthipeptide class were not classified by antiSMASH. The phylogenetic tree is based on the concatenation of 120 phylogenetically informative bacterial proteins and is available as Supplementary Data 12.Full size imageWe sought to further examine the differences in rumen MAG lanthipeptides relative to isolates and the taxonomic diversity of rumen microbes coding for lanthipeptides. The 195 rumen lanthipeptides were mainly found in Firmicutes genomes, with a subset from Bacteroidota and Actinobacteriota (Fig. 5b). Fifty-two of the 55 lanthipeptides from the Hungate Collection isolates were from Firmicutes (94.5%). At the family-level, these 52 Firmicutes BGCs were distributed evenly between Lachnospiraceae and Streptococcaceae. In contrast, 19.2% and 8.6% of lanthipeptides from rumen MAGs belonged to Bacteroidota and Actinobacteriota, respectively. Lanthipeptides from MAGs were also found in Muribaculaceae and Oscillospiraceae. Moreover, 26.4% of rumen MAG lanthipeptides, compared to 3.6% of Hungate Collection isolates, were found in Eubacterium genomes. The majority of Eubacterium MAG lanthipeptides (62.1%) belonged to a single GCF, suggesting they code for very similar products. Lastly, antiSMASH predicted the bulk of the rumen lanthipeptides were Class II lanthipeptides, with fewer Class I and Class III types (Fig. 5b). Nearly all of the Class I lanthipeptides were from Hungate isolates. The above analysis of lanthipeptide diversity further supports that rumen MAGs code for novel secondary metabolites not represented in cultured isolates.We aligned previously published rumen metatranscriptome data from steers characterized as having high and low feed efficiency to the BGCs to demonstrate if the identified BGCs are active and to explore potential ecological roles of secondary metabolites. Despite data from the metatranscriptome study not being applied to reconstruct genomes in the current study, we identified the expression of 554 gene clusters from rumen-specific genomes in the 20 metatranscriptomes (≥100 aligned reads). Metatranscriptome read count data were normalized independently for each genome to better account for the variation in taxonomic composition across samples38. Genome-specific normalization resulted in the identification of 17 differentially expressed gene clusters between steers with high and low feed efficiency (DESeq239 false discovery rate adjusted P  More

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