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In order to explore the RNA/protein dynamics in a microbiome setting, we first needed to characterize our test community over time at the molecular level. We previously genomically reconstructed and resolved the SEM1b community, retrieving 11 metagenome-assembled genomes (MAGs) as well as two isolate genomes (see “Methods” section)10, covering the taxonomic and functional niches that are required to convert cellulosic material to methane/CO2 in an anaerobic biogas reactor15. Taxonomic analysis of both 16S rRNA genes and the MAGs of SEM1b inferred population-level affiliations to Rumini(Clostridium) thermocellum (RCLO1), Clostridium sp. (CLOS1), Coprothermobacter proteolyticus (COPR1, BWF2A, SW3C), Tepidanaerobacter (TEPI1-2), Synergistales (SYNG1-2), Tissierellales (TISS1), and the methanogen Methanothermobacter (METH1)10, as depicted in Fig. 1a. Herein we estimated that the total genomic potential of SEM1b includes 39,144 open reading frames (ORFs) (Supplementary Data 1). Since ORFs with very high sequence similarity may produce RNAs and proteins that are indistinguishable in MT and MP data, all the ORFs were gathered into ORF-groups (ORFGs) during the MT and MP data processing (see “Methods” section), where a singleton ORFG is defined as a group with a single ORF, and thus a single gene. Using this approach, our MT and MP data identified 12552 (96% singleton) and 3235 (78% singletons) highly transcribed and translated ORFGs, respectively. The discrepancy between the singleton percentages was as expected, due to the fact that variations in DNA/RNA sequences are greater than in proteins since different codons can code for the same amino acid (codon degeneracy). Degeneracy also implies that the chance to distinguish between homologous genes using MT is greater than using MP. Previous MG analyses using assembly algorithms have shown that genomic regions difficult to assemble in a given environmental contig can harbor variants from multiple, closely related strains, which can be further linked to normal strain-level variability within a population and species divergence16,17,18. Within SEM1b, the ORFGs that contained multiple homologous ORFs predominantly originated from several strains of a single species. For example, in the MT, 444 non-singleton ORFGs (88% of the total) contained ORFs from different strains of the same species, whilst this was the case for 294 ORFGs (32%) in the MP.
Fig. 1: Life cycle of the SEM1b consortium and sampling scheme.

a In the SEM1b consortium, seven major microbial populations perform the metabolic processes that lead from saccharification to methanogenesis. In the first phase, RCLO1 and CLOS1 degrade the spruce substrate (predominantly cellulose and hemicellulose), thanks to a sophisticated and flexible enzymatic array, which releases simple oligosaccharides and sugars. Subsequently, the consortium grows (the protein concentration in the samples increases) up to t4 (23 h post inoculum), alongside the degradation (and fermentation) of mono- and disaccharides by RCLO1, CLOS1, TEPI1, TISS1, and COPR1+ strains. One of the sugars released from the degradation of xylan, xylose, is briefly accumulated (Fig. 4a). In addition, SCFAs are accumulated as a byproduct of microbial fermentation. In the last step, the synergetic partnership between TEP1 and METH1 (syntrophic acetate oxidizer and methanogen, respectively) converts the SCFAs and H2 to methane. The bars in the protein profile represent the maxima and minima of the measurements. b To characterize SEM1b, 24 flasks containing spruce media were inoculated with a SEM1b culture at t0. Starting from t1 (8 h), three flasks were opened every 5 h and their content processed. From the eight time points (plus t0) different omic- and meta-data were collected (depicted in the table). Every dot represents a replicate sample, and most measurements are taken in triplicate (except for cellulose degradation).

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All ORFs were annotated using KEGG Ontology (KO), and at least one term was found for 19070 (49%) representatives from our complete data set (Supplementary Data 2). The predominant ORF annotations included Membrane transport, Carbohydrate metabolism, Translation, Amino acid metabolism, and Replication and repair (Supplementary Fig. 1). As expected, these functional categories were also among the top five most abundant for the MT, and top six in MP (plus Energy metabolism), although in a different order. The Membrane transport category is poorly represented in the MP (2% of the terms), which is likely explained by well-known technical issues with the gel-based sample preparation method that we used, which limits the extraction of transmembrane proteins19. The most abundant annotation categories mentioned above are all in line with the community function of cellulose degradation. The abundance ranking of the KO categories was assessed using the Kendall τ, which takes values from −1 (opposite direction of the ranking) to +1 (total agreement in ranking). Its score is interpreted as a correlation measure; however, it is more conservative. The ranking is largely preserved from MG to MT (Kendall τ: 0.77, p  More