presence/absence of functional metabolic pathways (curator-inferred binary phenotypes). The consensus phenotype predictions were combined into a binary phenotype matrix (BPM) containing 1,000 MAGs and 106 phenotypes (Supplementary Fig. 1, Supplementary Table 7a).

**Gene annotation and phenotype prediction for *Bifidobacterium-specific carbohydrate-utilization pathways.** We adapted the annotation pipeline described above (Supplementary Fig. 1) to obtain functional annotations of genes comprising 25 additional carbohydrate-utilization pathways for a set of 34 *Bifidobacterium* MAGs, followed by inference of their respective binary phenotypes. As input data for this set of *Bifidobacterium*-specific phenotypes, we curated a set of 14 metabolic subsystems in 387 reference human-gut-derived *Bifidobacterium* genomes using the mcSEED framework. The reconstructed metabolic pathways and a corresponding BPM for reference *Bifidobacterium* genomes were used to predict carbohydrate-utilization phenotypes in the 34 *Bifidobacterium* MAGs. Finally, the automatically generated BPM was further manually curated to account for the variability of certain pathways in this taxonomically restricted set of predictions.

**Applying enrichment analyses to predicted MAG phenotypes.** Not all successfully annotated MAG genes were components of an intact functional pathway. To enable inferred phenotype-based analysis, we filtered gene annotations to those that were part of a complete functional pathway (with a respective binary phenotype value denoted as '1'). This filter resulted in a list of 208,246 genes used for microbiome and metatranscriptome phenotype enrichment analyses.

## Annotation of CAZymes and PULs in *P. copri* MAGs and cultured *P. copri* strains

CAZymes were annotated according to the CAZy classification scheme⁸⁴. Amino acid sequences from MAGs and isolate genomes were analysed using a bioinformatics workflow that performs homology searches against the CAZy database⁸⁵ and specifically accommodates the modular structure of CAZymes (which often carry a variable number of ancillary modules in addition to their catalytic domain). Details of this workflow and its application are provided in a previous publication⁸⁶.

PULs were identified/predicted by combining information from marker genes (SusC/SusD pairs), operon structure and CAZyme annotation information, as previously described²⁴. The experimentally validated substrate specificities of CAZyme homologues contained in each PUL were used to infer the carbohydrate substrate(s) of each PUL.

We investigated the conservation of PULs within WLZ-associated *P. copri* MAGs and, subsequently, between these MAGs and all other *P. copri* MAGs and cultured *P. copri* isolate genomes from this study. As no automated method has been described to perform such a pan-genome PUL comparison, we proceeded manually through the following steps. First, we predicted the PUL repertoire of Bg0018 and Bg0019 using PULDB²⁵. Next, using Bg0019 as a reference, we selected PULs in Bg0018 and in other genomes for additional analysis if they displayed the same gene components in the same genomic order in both genomes. To do so, we conducted pairwise searches (BLASTp⁵¹) to identify homologous components (for example, SusC, SusD, transcriptional regulators, genes encoding CAZymes) of PULs in other *P. copri* genomes. The level of conservation for each PUL across the set of MAGs was categorized as (1) conserved (pairwise comparisons of ORFs satisfy the requirements that their protein products have >90% amino acid identity and that the ORFs comprising the PULs being compared are organized in an identical way within the respective genomes); (2) structurally distinct (a given PUL is present in the genomes being compared but one or more CAZymes or one or both SusC/SusD proteins are missing or fragmented in a way that is likely to impact their function, or where extra PUL ORFs are present); or (3) not conserved/absent

(PULs present in the respective genomes but with mutations that are likely to completely compromise function or where no PUL identified).

To relate the pattern of PUL conservation in *P. copri* MAGs to the WLZ response of study participants, we re-encoded each cell in the matrix shown in Fig. 4a as '1' for conserved PULs, '0.5' for structurally distinct PULs and '0' for not conserved/absent PULs. We imported this matrix into R, calculated a Euclidean distance between the PUL conservation pattern of Bg0019 and each additional MAG, then hierarchically clustered these patterns to generate a tree. Finally, we used Pearson correlation to relate the distance between the patterns of PUL conservation in Bg0019 and each additional *P. copri* MAG to the WLZ association ($\beta_1$ coefficient) of each MAG (Fig. 4b).

## In vitro screening of glycan substrate specificity

We selected a variety of growth substrates representing putative glycan components of MDCF-2 plus glucose (Sigma-Aldrich, G8270) as a positive control and chondroitin sulfate (Thermo Fisher Scientific, J66156.06) as a negative control. Growth substrates included arabinan, arabinoxylan, β-glucan, galactan, galactomannan, rhamnogalacturonan, xylologucan and xylan (all obtained from Neogen Megazyme; Supplementary Table 16a). A 2% (w/v) solution of each polysaccharide was prepared by mixing the polysaccharide with autoclaved, filter-purified water as recommended by the supplier (see Supplementary Table 16a for additional details of the preparation of each solution). Each solubilized substrate was allowed to equilibrate in the anaerobic growth chamber for around 5 days before use. Sterility was checked by (1) plating 10 µl aliquots of each polysaccharide preparation onto BHI + 10% horse blood agar and (2) preparing 1:1 mixtures of preparation in *P. copri* defined medium (PCDM). This growth assay medium was selected because (1) it was fully defined, enabling supplementation with specific carbon sources and (2) it successfully supported the growth of *P. copri* strains in preliminary test cultures containing 5% glucose as the sole carbon source. PCDM was prepared according to the published recipe⁸⁷ with slight modifications (1 ml of a solution containing 1.9 mM haemin (H3281, Sigma-Aldrich) and 0.2 M L-histidine (Sigma-Aldrich, H6034) was added to each litre of the medium). Plates and broth were incubated for ≥3 days before checking for growth. The galactan and rhamnogalacturonan solutions displayed contamination. Thus, a fresh 2% (w/v) solution of each of these polysaccharides was prepared and autoclaved at 121 °C for 20 min at 15 psi before transferring them to the anaerobic chamber and confirming their sterility as described above. Autoclaved stocks were again checked for sterility before use.

We selected five *P. copri* isolates, representing a diversity of PUL conservation profiles, mcSEED BPMs and phylogenetic assignments for in vitro growth assays. Freezer stocks of each isolate were plated (-200 µl) onto BHI + 10% horse blood and grown for 48 h at 37 °C. Isolated colonies were picked into 3 ml Wilkins-Chalgren medium and grown for 15 h at 37 °C. Each culture was then diluted 1:100 into fresh Wilkins-Chalgren medium and grown for an additional 6 h under the same anaerobic conditions at 37 °C to bring each culture into a similar phase of growth. The OD₆₀₀ was measured for each culture (Genesys 10S UV-Vis, Thermo Fisher Scientific) and the OD₆₀₀ was standardized to 0.02 by dilution into a final volume of 1 ml of PCDM with no added carbon source in a 96 × 1.3 ml deep-well plate (Nunc, Thermo Fisher Scientific); this procedure enables us to have a uniform inoculum for all test isolates. A total of 9 ml of a 1:1 mixture of 2× PCDM containing 2% (w/v) glycan or control substrate was prepared in 50 ml polypropylene tubes (Corning) yielding 11 tubes of medium, each with an individual carbon source. These media plus the OD-standardized cultures were sealed (Alumaseal, Excel Scientific) and transferred to an anaerobic chamber containing a BioTek Precision XS liquid-handling robot. The robot was used to aliquot 180 µl of each type of medium into corresponding columns of three 96-well flat-bottom tissue culture plates with a well capacity of 340 µl (TPP, Sigma-Aldrich). A separate
presence/absence of functional metabolic pathways (curator-inferred binary phenotypes). The consensus phenotype predictions were combined into a binary phenotype matrix (BPM) containing 1,000 MAGs and 106 phenotypes (Supplementary Fig. 1, Supplementary Table 7a).

**Gene annotation and phenotype prediction for *Bifidobacterium-specific carbohydrate-utilization pathways.** We adapted the annotation pipeline described above (Supplementary Fig. 1) to obtain functional annotations of genes comprising 25 additional carbohydrate-utilization pathways for a set of 34 *Bifidobacterium* MAGs, followed by inference of their respective binary phenotypes. As input data for this set of *Bifidobacterium*-specific phenotypes, we curated a set of 14 metabolic subsystems in 387 reference human-gut-derived *Bifidobacterium* genomes using the mcSEED framework. The reconstructed metabolic pathways and a corresponding BPM for reference *Bifidobacterium* genomes were used to predict carbohydrate-utilization phenotypes in the 34 *Bifidobacterium* MAGs. Finally, the automatically generated BPM was further manually curated to account for the variability of certain pathways in this taxonomically restricted set of predictions.

**Applying enrichment analyses to predicted MAG phenotypes.** Not all successfully annotated MAG genes were components of an intact functional pathway. To enable inferred phenotype-based analysis, we filtered gene annotations to those that were part of a complete functional pathway (with a respective binary phenotype value denoted as '1'). This filter resulted in a list of 208,246 genes used for microbiome and metatranscriptome phenotype enrichment analyses.

## Annotation of CAZymes and PULs in *P. copri* MAGs and cultured *P. copri* strains

CAZymes were annotated according to the CAZy classification scheme⁸⁴. Amino acid sequences from MAGs and isolate genomes were analysed using a bioinformatics workflow that performs homology searches against the CAZy database⁸⁵ and specifically accommodates the modular structure of CAZymes (which often carry a variable number of ancillary modules in addition to their catalytic domain). Details of this workflow and its application are provided in a previous publication⁸⁶.

PULs were identified/predicted by combining information from marker genes (SusC/SusD pairs), operon structure and CAZyme annotation information, as previously described²⁴. The experimentally validated substrate specificities of CAZyme homologues contained in each PUL were used to infer the carbohydrate substrate(s) of each PUL.

We investigated the conservation of PULs within WLZ-associated *P. copri* MAGs and, subsequently, between these MAGs and all other *P. copri* MAGs and cultured *P. copri* isolate genomes from this study. As no automated method has been described to perform such a pan-genome PUL comparison, we proceeded manually through the following steps. First, we predicted the PUL repertoire of Bg0018 and Bg0019 using PULDB²⁵. Next, using Bg0019 as a reference, we selected PULs in Bg0018 and in other genomes for additional analysis if they displayed the same gene components in the same genomic order in both genomes. To do so, we conducted pairwise searches (BLASTp⁵¹) to identify homologous components (for example, SusC, SusD, transcriptional regulators, genes encoding CAZymes) of PULs in other *P. copri* genomes. The level of conservation for each PUL across the set of MAGs was categorized as (1) conserved (pairwise comparisons of ORFs satisfy the requirements that their protein products have >90% amino acid identity and that the ORFs comprising the PULs being compared are organized in an identical way within the respective genomes); (2) structurally distinct (a given PUL is present in the genomes being compared but one or more CAZymes or one or both SusC/SusD proteins are missing or fragmented in a way that is likely to impact their function, or where extra PUL ORFs are present); or (3) not conserved/absent

(PULs present in the respective genomes but with mutations that are likely to completely compromise function or where no PUL identified).

To relate the pattern of PUL conservation in *P. copri* MAGs to the WLZ response of study participants, we re-encoded each cell in the matrix shown in Fig. 4a as '1' for conserved PULs, '0.5' for structurally distinct PULs and '0' for not conserved/absent PULs. We imported this matrix into R, calculated a Euclidean distance between the PUL conservation pattern of Bg0019 and each additional MAG, then hierarchically clustered these patterns to generate a tree. Finally, we used Pearson correlation to relate the distance between the patterns of PUL conservation in Bg0019 and each additional *P. copri* MAG to the WLZ association ($\beta_1$ coefficient) of each MAG (Fig. 4b).

## In vitro screening of glycan substrate specificity

We selected a variety of growth substrates representing putative glycan components of MDCF-2 plus glucose (Sigma-Aldrich, G8270) as a positive control and chondroitin sulfate (Thermo Fisher Scientific, J66156.06) as a negative control. Growth substrates included arabinan, arabinoxylan, β-glucan, galactan, galactomannan, rhamnogalacturonan, xyloglucan and xylan (all obtained from Neogen Megazyme; Supplementary Table 16a). A 2% (w/v) solution of each polysaccharide was prepared by mixing the polysaccharide with autoclaved, filter-purified water as recommended by the supplier (see Supplementary Table 16a for additional details of the preparation of each solution). Each solubilized substrate was allowed to equilibrate in the anaerobic growth chamber for around 5 days before use. Sterility was checked by (1) plating 10 µl aliquots of each polysaccharide preparation onto BHI + 10% horse blood agar and (2) preparing 1:1 mixtures of preparation in *P. copri* defined medium (PCDM). This growth assay medium was selected because (1) it was fully defined, enabling supplementation with specific carbon sources and (2) it successfully supported the growth of *P. copri* strains in preliminary test cultures containing 5% glucose as the sole carbon source. PCDM was prepared according to the published recipe⁸⁷ with slight modifications (1 ml of a solution containing 1.9 mM haemin (H3281, Sigma-Aldrich) and 0.2 M L-histidine (Sigma-Aldrich, H6034) was added to each litre of the medium). Plates and broth were incubated for ≥3 days before checking for growth. The galactan and rhamnogalacturonan solutions displayed contamination. Thus, a fresh 2% (w/v) solution of each of these polysaccharides was prepared and autoclaved at 121 °C for 20 min at 15 psi before transferring them to the anaerobic chamber and confirming their sterility as described above. Autoclaved stocks were again checked for sterility before use.

We selected five *P. copri* isolates, representing a diversity of PUL conservation profiles, mcSEED BPMs and phylogenetic assignments for in vitro growth assays. Freezer stocks of each isolate were plated (-200 µl) onto BHI + 10% horse blood and grown for 48 h at 37 °C. Isolated colonies were picked into 3 ml Wilkins-Chalgren medium and grown for 15 h at 37 °C. Each culture was then diluted 1:100 into fresh Wilkins-Chalgren medium and grown for an additional 6 h under the same anaerobic conditions at 37 °C to bring each culture into a similar phase of growth. The OD₆₀₀ was measured for each culture (Genesys 10S UV-Vis, Thermo Fisher Scientific) and the OD₆₀₀ was standardized to 0.02 by dilution into a final volume of 1 ml of PCDM with no added carbon source in a 96 × 1.3 ml deep-well plate (Nunc, Thermo Fisher Scientific); this procedure enables us to have a uniform inoculum for all test isolates. A total of 9 ml of a 1:1 mixture of 2× PCDM containing 2% (w/v) glycan or control substrate was prepared in 50 ml polypropylene tubes (Corning) yielding 11 tubes of medium, each with an individual carbon source. These media plus the OD-standardized cultures were sealed (Alumaseal, Excel Scientific) and transferred to an anaerobic chamber containing a BioTek Precision XS liquid-handling robot. The robot was used to aliquot 180 µl of each type of medium into corresponding columns of three 96-well flat-bottom tissue culture plates with a well capacity of 340 µl (TPP, Sigma-Aldrich). A separate
procedure on the Precision XS robot was used to mix and aliquot 20 µl of each OD₆₀₀-standardized culture into corresponding rows of each assay plate; one row was reserved as a no-inoculation control. Assay plates were then sealed with optically clear films (Axygen, UC-500) and placed into the input stack of a BioTek BioStack 4 microplate stacker configured to load a BioTek Eon microplate spectrophotometer. Before initiating the experiment, two heated catalyst boxes were used to warm the chamber to 37 °C. The microplate stacker and reader were used to perform 1 week of continuous monitoring, with OD₆₀₀ readings obtained every 15 min. Each assay plate was agitated for 5 s before each OD reading. At the conclusion of the experiment, data were exported in tabular form and analysed in R using custom scripts to determine maximal growth rate and OD₆₀₀ for each replicate of each culture in each glycan. Extracted curve parameters were normalized to growth rates determined in PCDM containing 5% glucose as the sole carbon source.

## Biological materials

Faecal specimens collected from Bangladeshi children are the property of icddr.b. Material transfer agreements exist between icddr.b and Washington University in St Louis for the use of these samples. Requests for materials should be made to J.I.G and T.A.

## Statistics and reproducibility

Statistical analyses were conducted using the approaches described in the Methods and the figure legends. Sample sizes and replicates are indicated along with each statistical test. All relevant statistical tests were two-tailed unless otherwise specified. All measurements were collected from distinct samples. Technical replicates were not collected and analysed unless otherwise noted.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

Shotgun DNA sequencing and microbial RNA-seq datasets generated from faecal samples, plus annotated *P. copri* isolate genome sequences are available at the European Nucleotide Archive (PRJEB45356). These data were analysed to generate Figs. 1, 3 and 4, Extended Data Figs. 1, 2, 4, 5, 7, 8 and 10 and Supplementary Figs. 4 and 5. Anthropometry data from a previous publication⁴ were used in combination with data from the current study to generate Figs. 1b and 4b. The mcSEED database (https://doi.org/10.5281/zenodo.10041395) that was used to predict the presence or absence of metabolic pathways shown in Fig. 3, Extended Data Fig. 2 and Supplementary Figs. 1, 4 and 5 was described in a previous publication¹⁶. The CAZy⁸⁴,⁸⁵ and PULdb²⁵ databases were used to identify and analyse genes encoding carbohydrate-active enzymes and PULs in Figs. 4 and 5 and Extended Data Figs. 5 and 7–9. LC-MS datasets of monosaccharide, glycoside linkage and polysaccharide data are deposited in GlycoPOST (GPST000244) and were used to generate Figs. 2 and 5 and Extended Data Figs. 3, 6 and 7. All other relevant and/or supporting data are available in the Supplementary Information.

## Code availability

Code detailing the steps in the MAG assembly workflow and analyses of microbial RNA-seq and glycan datasets are available at GitLab (https://gitlab.com/hibberdm/hibberd_webber et al_mdcf_poc_mags) and have been accessioned at Zenodo (https://doi.org/10.5281/zenodo.8000098). Code for annotation of bacterial genes and prediction of metabolic pathway presence/absence is available at GitHub (https://github.com/rodionovdima/PhenotypePredictor) and has been accessioned at Zenodo (https://doi.org/10.5281/zenodo.10049439).

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procedure on the Precision XS robot was used to mix and aliquot 20 µl of each OD₆₀₀-standardized culture into corresponding rows of each assay plate; one row was reserved as a no-inoculation control. Assay plates were then sealed with optically clear films (Axygen, UC-500) and placed into the input stack of a BioTek BioStack 4 microplate stacker configured to load a BioTek Eon microplate spectrophotometer. Before initiating the experiment, two heated catalyst boxes were used to warm the chamber to 37 °C. The microplate stacker and reader were used to perform 1 week of continuous monitoring, with OD₆₀₀ readings obtained every 15 min. Each assay plate was agitated for 5 s before each OD reading. At the conclusion of the experiment, data were exported in tabular form and analysed in R using custom scripts to determine maximal growth rate and OD₆₀₀ for each replicate of each culture in each glycan. Extracted curve parameters were normalized to growth rates determined in PCDM containing 5% glucose as the sole carbon source.

## Biological materials

Faecal specimens collected from Bangladeshi children are the property of icddr.b. Material transfer agreements exist between icddr.b and Washington University in St Louis for the use of these samples. Requests for materials should be made to J.I.G and T.A.

## Statistics and reproducibility

Statistical analyses were conducted using the approaches described in the Methods and the figure legends. Sample sizes and replicates are indicated along with each statistical test. All relevant statistical tests were two-tailed unless otherwise specified. All measurements were collected from distinct samples. Technical replicates were not collected and analysed unless otherwise noted.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

Shotgun DNA sequencing and microbial RNA-seq datasets generated from faecal samples, plus annotated *P. copri* isolate genome sequences are available at the European Nucleotide Archive (PRJEB45356). These data were analysed to generate Figs. 1, 3 and 4, Extended Data Figs. 1, 2, 4, 5, 7, 8 and 10 and Supplementary Figs. 4 and 5. Anthropometry data from a previous publication⁴ were used in combination with data from the current study to generate Figs. 1b and 4b. The mcSEED database (https://doi.org/10.5281/zenodo.10041395) that was used to predict the presence or absence of metabolic pathways shown in Fig. 3, Extended Data Fig. 2 and Supplementary Figs. 1, 4 and 5 was described in a previous publication¹⁶. The CAZy⁸⁴,⁸⁵ and PULdb²⁵ databases were used to identify and analyse genes encoding carbohydrate-active enzymes and PULs in Figs. 4 and 5 and Extended Data Figs. 5 and 7–9. LC-MS datasets of monosaccharide, glycoside linkage and polysaccharide data are deposited in GlycoPOST (GPST000244) and were used to generate Figs. 2 and 5 and Extended Data Figs. 3, 6 and 7. All other relevant and/or supporting data are available in the Supplementary Information.

## Code availability

Code detailing the steps in the MAG assembly workflow and analyses of microbial RNA-seq and glycan datasets are available at GitLab (https://gitlab.com/hibberdm/hibberd_webber et al_mdcf_poc_mags) and have been accessioned at Zenodo (https://doi.org/10.5281/zenodo.8000098). Code for annotation of bacterial genes and prediction of metabolic pathway presence/absence is available at GitHub (https://github.com/rodionovdima/PhenotypePredictor) and has been accessioned at Zenodo (https://doi.org/10.5281/zenodo.10049439).

30. Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. *PLoS ONE* **10**, e0128036 (2015).

31. Krueger, F., James, F., Ewels, P., Afyounian, E. & Schuster-Boeckler, B. TrimGalore, https://doi.org/10.5281/zenodo.5127899 (2021).

32. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. *Nat. Methods* **9**, 357–359 (2012).

33. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. *Bioinformatics* **31**, 1674–1676 (2015).

34. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. *Nat. Biotechnol.* **34**, 525–527 (2016).

35. Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. *Bioinformatics* **32**, 605–607 (2016).

36. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. *PeerJ* **7**, e7359 (2019).

37. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. *Nat. Microbiol.* **3**, 836–843 (2018).

38. Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. *Nat. Biotechnol.* **37**, 937–944 (2019).

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87. Li, J. et al. A versatile genetic toolbox for *Prevotella copri* enables studying polysaccharide utilization systems. *EMBO J.* **40**, e108287 (2021).

**Acknowledgements** We thank M. Meier, J. Serugo, S. Deng, K. Ahsan, J. Hoisington-Lopez and M. Crosby for technical assistance; members of the Genome Technology Access Center at

Washington University School of Medicine for Illumina NovaSeq-based generation of faecal DNA shotgun sequencing and microbial RNA-seq datasets; and E. Martin and B. Koebbe for high-performance computing support. This work was supported by the Bill & Melinda Gates Foundation, the National Institutes of Health (DK30292) and the Washington University-Centene Personalized Medicine Initiative. J.I.G. is the recipient of an Agilent Thought Leader Award.

**Author contributions** M.C.H. and D.M.W. generated short-read and long-read shotgun sequencing datasets of faecal DNAs and, together with H.M.L., assembled MAGs. D.A.R., A.L.O., M.D.K., S.A.L. and A.A.A. performed metabolic pathway reconstruction and annotation of MAGs, while S.H., V.L., N.T. and B.H. annotated PULs and CAZymes in MAGs. M.C.H. identified WLZ-correlated MAGs and conducted analyses of the enrichment of their encoded functions. M.C.H. and R.Y.C. conducted analyses of MAG abundance responses to treatment. D.M.W. generated and together with M.C.H., C.Z., D.A.R., A.L.O. and A.A.A. analysed microbial RNA-seq datasets. J.J.C., C.B.L., G.C., N.P.B. and Y.C. performed LC-MS analyses of food and faecal glycans. J.L.-G. isolated *P. copri* strains with assistance from D.M.W. and M.C.H. M.C.H., D.M.W. and S.H. designed the in vitro growth experiments, which were performed by M.C.H., D.M.W. and S.H. with assistance from J.L.-G. Y.W., H.-W.C. and E.M.L. integrated the results of MAG-based analysis described in this paper with findings from 'reverse translation' experiments involving gnotobiotic mice and cultured *P. copri* strains. I.M., S.D. and M.M., under the supervision of T.A., conducted the clinical study of MDCF-2 and RUSF and supplied all of the biospecimens used for the analyses conducted in this report. M.J.B. oversaw databases of clinical metadata and the biospecimen archive. T.A. and J.I.G. oversaw this research. M.C.H., D.M.W. and J.I.G. wrote the paper with input from all of the other authors.

**Competing interests** A.L.O. and D.A.R. are co-founders of Phenobiome, a company pursuing development of computational tools for predictive phenotype profiling of microbial communities. C.B.L. is a co-founder of Infinant Health, interVenn Bio and BCD Bioscience—companies involved in the characterization of glycans and developing carbohydrate applications for human health. A joint patent application between Washington University in St Louis and icddr,b has been filed, entitled "Synbiotic combination of selected strains of *P. copri* and dietary glycans to treat malnutrition", with J.I.G., M.C.H., D.M.W., H.-W.C., Y.W., M.J.B. and T.A. listed as co-inventors (PCT/US2023/018738). The authors are committed to the principle of Global Access; patented technologies will be made available and accessible at an affordable price to those in need throughout the world.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-06838-3.

**Correspondence and requests for materials** should be addressed to Jeffrey I. Gordon.

**Peer review information** *Nature* thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at http://www.nature.com/reprints.
68. Nandita, E. et al. Polysaccharide identification through oligosaccharide fingerprinting. *Carbohydr. Polym.* **257**, 117570 (2021).

69. Han, N. D. et al. Microbial liberation of *N*-methylserotonin from orange fiber in gnotobiotic mice and humans. *Cell* **185**, 2495–2509 (2022).

70. Kolmogorov, M. et al. Assembly of long, error-prone reads using repeat graphs. *Nat. Biotechnol.* **37**, 540–546 (2019).

71. Price, M. N. et al. FastTree 2—approximately maximum-likelihood trees for large alignments. *PLoS ONE* **5**, e9490 (2010).

72. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in *R. Bioinformatics* **35**, 526–528 (2018).

73. Yu, G. Using ggtree to visualize data on tree-like structures. *Curr. Protoc. Bioinform.* **69**, e96 (2020).

74. Davis, J. J. et al. The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. *Nucleic Acids Res.* **48**, D606–D612 (2020).

75. Ye, Y., Osterman, A., Overbeek, R. & Godzik, A. Automatic detection of subsystem/pathway variants in genome analysis. *Bioinformatics.* **21**, i478–i486 (2005).

76. Steinegger, M. & Söding, J. *MMseqs2* enables sensitive protein sequence searching for the analysis of massive data sets. *Nat. Biotechnol.* **35**, 1026–1028 (2017).

77. Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. *Nat. Methods* **18**, 366–368 (2021).

78. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. *BMC Bioinform.* **11**, 119 (2010).

79. Schubert, E. et al. DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. *ACM Trans. Database Syst.* **42**, 1–21 (2017).

80. Pedregosa, F. et al. Scikit-learn: machine learning in python. *J. Mach. Learn. Res.* **12**, 2825–2830 (2011).

81. Therneau, T., Atkinson, B. & Ripley, B., Rpart: recursive partitioning, github.com/bethatkinson/rpart/ (2013).

82. Kuhn, M. Building predictive models in R using the caret package. *J. Stat. Softw.* **28**, 1–26 (2008).

83. Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. *Genome Biol.* **17**, 132 (2016).

84. Drula, E. et al. The carbohydrate-active enzyme database: functions and literature. *Nucleic Acids Res.* **50**, D571–D577 (2021).

85. Lombard, V., Ramulu, H. G., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. *Nucleic Acids Res.* **42**, D490–D495 (2014).

86. Delannoy-Bruno, O. et al. Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans. *Nature* **595**, 91–95 (2021).

87. Li, J. et al. A versatile genetic toolbox for *Prevotella copri* enables studying polysaccharide utilization systems. *EMBO J.* **40**, e108287 (2021).

**Acknowledgements** We thank M. Meier, J. Serugo, S. Deng, K. Ahsan, J. Hoisington-Lopez and M. Crosby for technical assistance; members of the Genome Technology Access Center at

Washington University School of Medicine for Illumina NovaSeq-based generation of faecal DNA shotgun sequencing and microbial RNA-seq datasets; and E. Martin and B. Koebbe for high-performance computing support. This work was supported by the Bill & Melinda Gates Foundation, the National Institutes of Health (DK30292) and the Washington University-Centene Personalized Medicine Initiative. J.I.G. is the recipient of an Agilent Thought Leader Award.

**Author contributions** M.C.H. and D.M.W. generated short-read and long-read shotgun sequencing datasets of faecal DNAs and, together with H.M.L., assembled MAGs. D.A.R., A.L.O., M.D.K., S.A.L. and A.A.A. performed metabolic pathway reconstruction and annotation of MAGs, while S.H., V.L., N.T. and B.H. annotated PULs and CAZymes in MAGs. M.C.H. identified WLZ-correlated MAGs and conducted analyses of the enrichment of their encoded functions. M.C.H. and R.Y.C. conducted analyses of MAG abundance responses to treatment. D.M.W. generated and together with M.C.H., C.Z., D.A.R., A.L.O. and A.A.A. analysed microbial RNA-seq datasets. J.J.C., C.B.L., G.C., N.P.B. and Y.C. performed LC-MS analyses of food and faecal glycans. J.L.-G. isolated *P. copri* strains with assistance from D.M.W. and M.C.H. M.C.H., D.M.W. and S.H. designed the in vitro growth experiments, which were performed by M.C.H., D.M.W. and S.H. with assistance from J.L.-G. Y.W., H.-W.C. and E.M.L. integrated the results of MAG-based analysis described in this paper with findings from 'reverse translation' experiments involving gnotobiotic mice and cultured *P. copri* strains. I.M., S.D. and M.M., under the supervision of T.A., conducted the clinical study of MDCF-2 and RUSF and supplied all of the biospecimens used for the analyses conducted in this report. M.J.B. oversaw databases of clinical metadata and the biospecimen archive. T.A. and J.I.G. oversaw this research. M.C.H., D.M.W. and J.I.G. wrote the paper with input from all of the other authors.

**Competing interests** A.L.O. and D.A.R. are co-founders of Phenobiome, a company pursuing development of computational tools for predictive phenotype profiling of microbial communities. C.B.L. is a co-founder of Infinant Health, interVenn Bio and BCD Bioscience—companies involved in the characterization of glycans and developing carbohydrate applications for human health. A joint patent application between Washington University in St Louis and icddr,b has been filed, entitled "Synbiotic combination of selected strains of *P. copri* and dietary glycans to treat malnutrition", with J.I.G., M.C.H., D.M.W., H.-W.C., Y.W., M.J.B. and T.A. listed as co-inventors (PCT/US2023/018738). The authors are committed to the principle of Global Access; patented technologies will be made available and accessible at an affordable price to those in need throughout the world.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-06838-3.

**Correspondence and requests for materials** should be addressed to Jeffrey I. Gordon.

**Peer review information** *Nature* thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at http://www.nature.com/reprints.
a

<figure><img src="image_16.png" /></figure>

b

<figure><img src="image_17.png" /></figure>

**Extended Data Fig. 1** | Bioinformatic workflow for MAG assembly, refinement, and quantitation. (a) Pipeline for MAG assembly from short-read only or short-read plus long-read shotgun sequencing data. Steps are indicated alongside the boxes, while the bioinformatic tools employed to accomplish each step are described within each box. (b) Comparison of MAG assembly summary statistics derived from CheckM (completeness, contamination) or Quast (number, length and N50 of contigs) for n = 82 high-quality MAGs obtained

from short- plus long-read hybrid assemblies versus n = 918 high-quality MAGs from short-read only assembly methods. MAGs were assembled from shotgun sequencing data obtained from n = 942 biologically independent faecal samples as described in *Methods*. Boxplots show the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range.
***, *P* < 0.001 (Wilcoxon test, two-sided).
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

**Extended Data Fig. 1** | Bioinformatic workflow for MAG assembly, refinement, and quantitation. (a) Pipeline for MAG assembly from short-read only or short-read plus long-read shotgun sequencing data. Steps are indicated alongside the boxes, while the bioinformatic tools employed to accomplish each step are described within each box. (b) Comparison of MAG assembly summary statistics derived from CheckM (completeness, contamination) or Quast (number, length and N50 of contigs) for n = 82 high-quality MAGs obtained

from short- plus long-read hybrid assemblies versus n = 918 high-quality MAGs from short-read only assembly methods. MAGs were assembled from shotgun sequencing data obtained from n = 942 biologically independent faecal samples as described in *Methods*. Boxplots show the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range.
***, *P* < 0.001 (Wilcoxon test, two-sided).
<figure><img src="image_18.png" /></figure>

Model: WLZ ~ β₁(MAG) + β₂(study week) + (1|PID)

<figure><img src="image_19.png" /><figcaption>**Extended Data Fig. 2** | Taxonomy and functional characteristics of WLZ-associated MAGs. (a) Left subpanel, density plot showing WLZ-associated MAGs tabulated based on their genus-level classification. β₁ refers to the coefficient in the linear mixed effects model presented at the bottom of the figure. Genera containing >3 significantly WLZ-associated MAGs are shown. Right subpanel, number of statistically significant WLZ-associated MAGs assigned to each genus depicted in the left subpanel. (b) Enrichment of metabolic pathways in WLZ- and treatment-associated MAGs. MAGs were</figcaption></figure>

ranked by their WLZ association (negative to positive) or treatment association (RUSF-associated to MDCF-2-associated) and GSEA was employed to determine overrepresentation of pathways in MAGs at the extremes of each ranked list. The results (Normalized Enrichment Score, NES) only include pathways that display a statistically significant enrichment (q<0.05, GSEA) in both the WLZ-associated MAG and treatment-associated MAG analyses. For carbohydrate utilization pathways, disaccharides and oligosaccharides are indicated with an asterisk.
<figure><img src="image_20.png" /></figure>

Model: WLZ ~ β₁(MAG) + β₂(study week) + (1|PID)

<figure><img src="image_21.png" /><figcaption>**Extended Data Fig. 2** | Taxonomy and functional characteristics of WLZ-associated MAGs. (a) Left subpanel, density plot showing WLZ-associated MAGs tabulated based on their genus-level classification. β₁ refers to the coefficient in the linear mixed effects model presented at the bottom of the figure. Genera containing >3 significantly WLZ-associated MAGs are shown. Right subpanel, number of statistically significant WLZ-associated MAGs assigned to each genus depicted in the left subpanel. (b) Enrichment of metabolic pathways in WLZ- and treatment-associated MAGs. MAGs were</figcaption></figure>

ranked by their WLZ association (negative to positive) or treatment association (RUSF-associated to MDCF-2-associated) and GSEA was employed to determine overrepresentation of pathways in MAGs at the extremes of each ranked list. The results (Normalized Enrichment Score, NES) only include pathways that display a statistically significant enrichment (q<0.05, GSEA) in both the WLZ-associated MAG and treatment-associated MAG analyses. For carbohydrate utilization pathways, disaccharides and oligosaccharides are indicated with an asterisk.
a

Monosaccharides

<figure><img src="image_20.png" /></figure>

b

Glycosidic linkages

<figure><img src="image_21.png" /></figure>

**Extended Data Fig. 3** | LC-MS analysis of glycans present in MDCF-2, RUSF and their component ingredients. (a, b) Analysis of monosaccharides (panel a) and glycosidic linkages (panel b) liberated by hydrolysis of glycans present in

MDCF-2 and RUSF, and in the food ingredients used to formulate them.
Mean±SD are plotted for n = 4 independent samples. \*, P < 0.05, \*\*\*, P < 0.01
(t-test, two-sided). Points depict individual samples.
a

Monosaccharides

<figure><img src="image_22.png" /></figure>

b

Glycosidic linkages

<figure><img src="image_23.png" /></figure>

**Extended Data Fig. 3** | LC-MS analysis of glycans present in MDCF-2, RUSF and their component ingredients. (a, b) Analysis of monosaccharides (panel a) and glycosidic linkages (panel b) liberated by hydrolysis of glycans present in

MDCF-2 and RUSF, and in the food ingredients used to formulate them.
Mean±SD are plotted for n = 4 independent samples. \*, P < 0.05, \*\*\*, P < 0.01
(t-test, two-sided). Points depict individual samples.
<figure><img src="image_22.png" /><figcaption>**Extended Data Fig. 4 | Principal components analysis of transcript and MAG abundances in faecal specimens.** (a, b) Percent variance explained by the top 10 principal components of a PCA analysis including abundance of MAGs (panel a) at the 0, 2, 4, 8, 12, and 16 week time points in Fig. 1a, or transcripts</figcaption></figure>

(panel b) at 0, 4, and 12 weeks. (c) Significantly enriched taxa (q < 0.05, GSEA) along the first three principal components (PC1-PC3) of the faecal microbiome or meta-transcriptome PCA.
<figure><img src="image_24.png" /><figcaption>**Extended Data Fig. 4** | Principal components analysis of transcript and MAG abundances in faecal specimens. (a, b) Percent variance explained by the top 10 principal components of a PCA analysis including abundance of MAGs (panel a) at the 0, 2, 4, 8, 12, and 16 week time points in Fig. 1a, or transcripts</figcaption></figure>

(panel b) at 0, 4, and 12 weeks. (c) Significantly enriched taxa (q < 0.05, GSEA) along the first three principal components (PC1-PC3) of the faecal microbiome or meta-transcriptome PCA.
<figure><img src="image_23.png" /></figure>

Predicted growth based on CAZyme specificity
(predicted cleavage sites targeted by each strain / 
total possible cleavage sites)

b

<table><thead><tr><th rowspan="2">Polysaccharides added to defined medium</th><th rowspan="2">Cleavage sites in tested polysaccharides that are potential CAZyme targets</th><th colspan="2">Bgf5_2</th><th colspan="2">Bg2C6</th><th colspan="2">Bg2H3</th><th colspan="2">Bg131</th><th colspan="2">Bg5_1</th></tr><tr><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th></tr></thead><tbody><tr><td>Arabinan</td><td>a,b,c</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td></tr><tr><td>Arabinoxylan</td><td>c,d,e</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td></tr><tr><td>Glucan</td><td>f,g,h,i,j</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td></tr><tr><td>β-Galactan</td><td>k,l</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td></tr><tr><td>Galactomannan</td><td>m,o,p</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td></tr><tr><td>Rhamnogalacturonan</td><td>c,b,l,q,r,s,t,u</td><td>c,b,l,r,u</td><td>--</td><td>c,b,l,q,r,u</td><td>--</td><td>c,b,l,q,r,u</td><td>+++</td><td>c,b,l,q,r,u</td><td>--</td><td>c,b,l</td><td>--</td></tr><tr><td>Xyloglucan</td><td>f,i,w,x</td><td>f,i,w,x</td><td>+++</td><td>f,i,x</td><td>--</td><td>f,i,x</td><td>+++</td><td>f,i,w,x</td><td>--</td><td>f,i,x</td><td>--</td></tr><tr><td>Xylan</td><td>d,e,y</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td></tr></tbody></table>

<figure><img src="image_24.png" /></figure>

<table><thead><tr><th>Target linkage</th><th>Activity</th><th>CAZy families</th></tr></thead><tbody><tr><td>a</td><td>α-L-arabinofuranosidase (EC 3.2.1.55)</td><td>GH2, GH3, GH10, GH43, GH51, GH54, GH62, GH93, GH159</td></tr><tr><td>b</td><td>Endo-α-(1→5)-L-arabinanase (EC 3.2.1.99)</td><td>GH43</td></tr><tr><td>c</td><td>Exo-α-(1→2/1→3)-L-arabinofuranosidase (EC 3.2.1.-)</td><td>GH2, GH3, GH10, GH43, GH51, GH54, GH62, GH159</td></tr><tr><td>d</td><td>Endo-β-(1→4)-xylosidase (EC 3.2.1.37)</td><td>GH1, GH2, GH3, GH5, GH8, GH10, GH30, GH39, GH43, GH51, GH52, GH54, GH116, GH120</td></tr><tr><td>e</td><td>Endo-β-(1→4)-xylanase (EC 3.2.1.8)</td><td>GH5, GH8, GH10, GH11, GH30, GH43</td></tr><tr><td>f</td><td>β-glucosidase (EC 3.2.1.21 + 3.2.1.74)</td><td>GH1, GH2, GH3, GH5, GH9</td></tr><tr><td>g</td><td>Cellulose 1,4-β-cellobiosidase (EC 3.2.1.91)</td><td>GH5, GH6, GH9</td></tr><tr><td>h</td><td>Endo-β-(1→3/1→4)-glucosidase (EC 3.2.1.6)</td><td>GH3, GH5, GH8, GH9, GH16, GH51</td></tr><tr><td>i</td><td>Endo-β-(1→4)-glucanase (EC 3.2.1.4)</td><td>GH5, GH6, GH8, GH9, GH12, GH44, GH45, GH48, GH74, GH124</td></tr><tr><td>j</td><td>Reducing end cellulose exo-β-1,4-glucosidase (EC 3.2.1.176)</td><td>GH48</td></tr><tr><td>k</td><td>β-galactosidase (EC 3.2.1.23), galactan exo-β-1,4-galactosidase (EC 3.2.1.-)</td><td>GH1, GH2, GH5, GH16, GH35, GH42, GH50, GH54, GH147, GH165, GH173</td></tr><tr><td>l</td><td>Galactan endo-β-1,4-galactosidase (EC 3.2.1.89)</td><td>GH53, GH147</td></tr><tr><td>m</td><td>β-mannosidase (EC 3.2.1.25)</td><td>GH1, GH2, GH5, GH113, GH164</td></tr><tr><td>o</td><td>Mannan endo-β-1,4-mannosidase (EC 3.2.1.78)</td><td>GH5, GH26, GH44, GH113, GH134</td></tr><tr><td>p</td><td>Exo-α-(1→6)-galactosidase (EC 3.2.1.-)</td><td>GH27, GH36</td></tr><tr><td>q</td><td>Rhamnogalacturonan endolyase (EC 4.2.2.23); rhamnogalacturonan exolyase (EC 4.2.2.24); exo-α-(1→4)-L-rhamnosidase (EC 3.2.1.174)</td><td>PL4, PL9, PL11, PL26, GH78, GH106</td></tr><tr><td>r</td><td>Rhamnogalacturonan endo-α-(1→2)-galacturonidase (EC 3.2.1.171), rhamnogalacturonan exo-α-(1→2)-galacturonidase (EC 3.2.1.173)</td><td>GH28, GH138</td></tr><tr><td>s</td><td>Enzyme not previously characterized</td><td>n.a.</td></tr><tr><td>t</td><td>Enzyme not previously characterized</td><td>n.a.</td></tr><tr><td>u</td><td>Endo-polygalacturonidase (EC 3.2.1.15), exo-polygalacturonidase (EC 3.2.1.67), pectate lyase (EC 4.2.2.2), pectin lyase (EC 4.2.2.10), delta-4,5-unsaturated α-galacturonidase (EC 3.2.1.-)</td><td>GH28, PL1, PL2, PL3, PL9, PL10, GH105</td></tr><tr><td>w</td><td>α-xylosidase (EC 3.2.1.177)</td><td>GH31</td></tr><tr><td>x</td><td>β-galactosidase (EC 3.2.1.23)</td><td>GH1, GH2, GH35, GH42, GH147, GH165, GH173</td></tr><tr><td>y</td><td>Xylan exo-α-(1→2)-4 (OMethyl)-glucuronosidase (EC 3.2.1.131), α-glucuronidase (EC 3.2.1.139)</td><td>GH67, GH115</td></tr></tbody></table>

GH43

GH48

GH53, GH147

GH28, GH138

GH31

Extended Data Fig. 5 | Growth of Bangladeshi *P. copri* strains in defined medium supplemented with purified polysaccharides. (a) Growth curves in defined medium containing individual purified polysaccharides similar to those that are abundant in or unique to MDCF-2 compared to RUSF. Curves describe mean values ± sd for OD<sub>600</sub> measurements (n = 3 replicates/growth condition).

(b) Growth of *P. copri* strains versus a PUL-based prediction of their growth phenotypes. Growth is expressed as ‘+’ or ‘-’ for each of the triplicate cultures according to whether a threshold OD<sub>600</sub> > 0.25 was attained. The colour key

expresses ‘prediction’ as the fraction of possible cleavage sites in each polysaccharide that are known or predicted to be targeted by the PUL-associated CAZymes of a given strain. (c) CAZymes present in the PULs of each strain and their predicted activities against glycosidic linkages in each purified polysaccharide. Linkages in a polysaccharide that are predicted to be targeted by PUL-associated CAZymes are labelled ‘a,b...y’ in both panels b and c. All CAZyme family assignments for a given enzymatic activity are shown; the family that displays this activity most commonly is noted in bold font.
<figure><img src="image_25.png" /></figure>

Predicted growth based on CAZyme specificity
(predicted cleavage sites targeted by each strain / 
total possible cleavage sites)

b

<table><thead><tr><th rowspan="2">Polysaccharides added to defined medium</th><th rowspan="2">Cleavage sites in tested polysaccharides that are potential CAZyme targets</th><th colspan="2">Bgf5_2</th><th colspan="2">Bg2C6</th><th colspan="2">Bg2H3</th><th colspan="2">Bg131</th><th colspan="2">Bg5_1</th></tr><tr><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th><th>Predicted cleavage sites for CAZymes represented in strain</th><th>Growth observed</th></tr></thead><tbody><tr><td>Arabinan</td><td>a,b,c</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td><td>a,b,c</td><td>+++</td></tr><tr><td>Arabinoxylan</td><td>c,d,e</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td><td>c,d,e</td><td>+++</td></tr><tr><td>Glucan</td><td>f,g,h,i,j</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td><td>f,g,h,i</td><td>+++</td></tr><tr><td>β-Galactan</td><td>k,l</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td><td>k,l</td><td>+++</td></tr><tr><td>Galactomannan</td><td>m,o,p</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td><td>m,o,p</td><td>+++</td></tr><tr><td>Rhamnogalacturonan</td><td>c,b,l,q,r,s,t,u</td><td>c,b,l,r,u</td><td>--</td><td>c,b,l,q,r,u</td><td>--</td><td>c,b,l,q,r,u</td><td>+++</td><td>c,b,l,q,r,u</td><td>--</td><td>c,b,l</td><td>--</td></tr><tr><td>Xyloglucan</td><td>f,i,w,x</td><td>f,i,w,x</td><td>+++</td><td>f,i,x</td><td>--</td><td>f,i,x</td><td>+++</td><td>f,i,w,x</td><td>--</td><td>f,i,x</td><td>--</td></tr><tr><td>Xylan</td><td>d,e,y</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td><td>d,e,y</td><td>+++</td></tr></tbody></table>

<figure><img src="image_26.png" /></figure>

<table><thead><tr><th>Target linkage</th><th>Activity</th><th>CAZy families</th></tr></thead><tbody><tr><td>a</td><td>α-L-arabinofuranosidase (EC 3.2.1.55)</td><td>GH2, GH3, GH10, GH43, GH51, GH54, GH62, GH93, GH159</td></tr><tr><td>b</td><td>Endo-α-(1→5)-L-arabinanase (EC 3.2.1.99)</td><td>GH43</td></tr><tr><td>c</td><td>Exo-α-(1→2/1→3)-L-arabinofuranosidase (EC 3.2.1.-)</td><td>GH2, GH3, GH10, GH43, GH51, GH54, GH62, GH159</td></tr><tr><td>d</td><td>Exo-β-(1→4)-xylosidase (EC 3.2.1.37)</td><td>GH1, GH2, GH3, GH5, GH8, GH10, GH30, GH39, GH43, GH51, GH52, GH54, GH116, GH120</td></tr><tr><td>e</td><td>Endo-β-(1→4)-xylanase (EC 3.2.1.8)</td><td>GH5, GH8, GH10, GH11, GH30, GH43</td></tr><tr><td>f</td><td>β-glucosidase (EC 3.2.1.21 + 3.2.1.74)</td><td>GH1, GH2, GH3, GH5, GH9</td></tr><tr><td>g</td><td>Cellulose 1,4-β-cellobiosidase (EC 3.2.1.91)</td><td>GH5, GH6, GH9</td></tr><tr><td>h</td><td>Endo-β-(1→3/1→4)-glucosidase (EC 3.2.1.6)</td><td>GH3, GH5, GH8, GH9, GH16, GH51</td></tr><tr><td>i</td><td>Endo-β-(1→4)-glucanase (EC 3.2.1.4)</td><td>GH5, GH6, GH8, GH9, GH12, GH44, GH45, GH48, GH74, GH124</td></tr><tr><td>j</td><td>Reducing end cellulose exo-β-1,4-glucosidase (EC 3.2.1.176)</td><td>GH48</td></tr><tr><td>k</td><td>β-galactosidase (EC 3.2.1.23), galactan exo-β-1,4-galactosidase (EC 3.2.1.-)</td><td>GH1, GH2, GH5, GH16, GH35, GH42, GH50, GH54, GH147, GH165, GH173</td></tr><tr><td>l</td><td>Galactan endo-β-1,4-galactosidase (EC 3.2.1.89)</td><td>GH53, GH147</td></tr><tr><td>m</td><td>β-mannosidase (EC 3.2.1.25)</td><td>GH1, GH2, GH5, GH113, GH164</td></tr><tr><td>o</td><td>Mannan endo-β-1,4-mannosidase (EC 3.2.1.78)</td><td>GH5, GH26, GH44, GH113, GH134</td></tr><tr><td>p</td><td>Exo-α-(1→6)-galactosidase (EC 3.2.1.-)</td><td>GH27, GH36</td></tr><tr><td>q</td><td>Rhamnogalacturonan endolyase (EC 4.2.2.23); rhamnogalacturonan exolyase (EC 4.2.2.24); exo-α-(1→4)-L-rhamnosidase (EC 3.2.1.174)</td><td>PL4, PL9, PL11, PL26, GH78, GH106</td></tr><tr><td>r</td><td>Rhamnogalacturonan endo-α-(1→2)-galacturonidase (EC 3.2.1.171), rhamnogalacturonan exo-α-(1→2)-galacturonidase (EC 3.2.1.173)</td><td>GH28, GH138</td></tr><tr><td>s</td><td>Enzyme not previously characterized</td><td>n.a.</td></tr><tr><td>t</td><td>Enzyme not previously characterized</td><td>n.a.</td></tr><tr><td>u</td><td>Endo-polygalacturonidase (EC 3.2.1.15), exo-polygalacturonidase (EC 3.2.1.67), pectate lyase (EC 4.2.2.2), pectin lyase (EC 4.2.2.10), delta-4,5-unsaturated α-galacturonidase (EC 3.2.1.-)</td><td>GH28, PL1, PL2, PL3, PL9, PL10, GH105</td></tr><tr><td>w</td><td>α-xylosidase (EC 3.2.1.177)</td><td>GH31</td></tr><tr><td>x</td><td>β-galactosidase (EC 3.2.1.23)</td><td>GH1, GH2, GH35, GH42, GH147, GH165, GH173</td></tr><tr><td>y</td><td>Xylan exo-α-(1→2)-(4 Omethyl)-glucuronosidase (EC 3.2.1.131), α-glucuronidase (EC 3.2.1.139)</td><td>GH67, GH115</td></tr></tbody></table>

GH43

GH48

GH53, GH147

GH28, GH138

GH31

**Extended Data Fig. 5 | Growth of Bangladeshi *P. copri* strains in defined medium supplemented with purified polysaccharides.** (a) Growth curves in defined medium containing individual purified polysaccharides similar to those that are abundant in or unique to MDCF-2 compared to RUSF. Curves describe mean values ± sd for OD<sub>600</sub> measurements (n = 3 replicates/growth condition).

(b) Growth of *P. copri* strains versus a PUL-based prediction of their growth phenotypes. Growth is expressed as ‘+’ or ‘-’ for each of the triplicate cultures according to whether a threshold OD<sub>600</sub> > 0.25 was attained. The colour key

expresses ‘prediction’ as the fraction of possible cleavage sites in each polysaccharide that are known or predicted to be targeted by the PUL-associated CAZymes of a given strain. (c) CAZymes present in the PULs of each strain and their predicted activities against glycosidic linkages in each purified polysaccharide. Linkages in a polysaccharide that are predicted to be targeted by PUL-associated CAZymes are labelled ‘a,b...y’ in both panels b and c. All CAZyme family assignments for a given enzymatic activity are shown; the family that displays this activity most commonly is noted in bold font.
<figure><img src="image_25.png" /><figcaption>**Extended Data Fig. 6** | LC-MS analysis of monosaccharide consumption during growth of cultured Bangladeshi *P. copri* strains in defined medium supplemented with purified polysaccharides. Heatmaps representing bacterial consumption of monosaccharides present in the different polysaccharides. UHPLC-QqQ-MS-based monosaccharide analysis was performed using defined medium harvested from monocultures of the *P. copri*</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>stains. Control incubations did not contain added bacteria. Cells in each matrix show the mean difference (at the end of the 189 hour-long incubation) between the concentration of each monosaccharide in three technical replicates of each strain/polysaccharide combination compared to the corresponding uninoculated control.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Extended Data Fig. 6** | LC-MS analysis of monosaccharide consumption during growth of cultured Bangladeshi *P. copri* strains in defined medium supplemented with purified polysaccharides. Heatmaps representing bacterial consumption of monosaccharides present in the different polysaccharides. UHPLC-QqQ-MS-based monosaccharide analysis was performed using defined medium harvested from monocultures of the *P. copri*</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>stains. Control incubations did not contain added bacteria. Cells in each matrix show the mean difference (at the end of the 189 hour-long incubation) between the concentration of each monosaccharide in three technical replicates of each strain/polysaccharide combination compared to the corresponding uninoculated control.</figcaption></figure>
a WLZ-response quartile

<figure><img src="image_27.png" /></figure>

b

Consensus

<figure><img src="image_28.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

**Extended Data Fig. 7** | Changes in levels of faecal glycosidic linkages and expression of *P. copri* CAZyme genes after MDCF-2 treatment.

(a) Boxplot of changes in the levels of faecal glycosidic linkages relative to initiation of treatment among upper and lower WLZ quartile responders. Levels of these 14 linkages increased to a significantly greater extent over time in the upper vs lower WLZ response quartiles (Model: linkage abundance - WLZ-response quartile × study week + (1|PID)). Boxplots indicate the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range for 90 biologically independent faecal samples obtained

from n = 15 participants assigned to the upper quartile and n = 15 participants assigned to the lower quartile of WLZ-responses (n = 3 samples/participant).

(b) The β₃ coefficient for the interaction of WLZ-response quartile and study week is shown for CAZymes in consensus PULs in Bg0018 and Bg0019. Predicted PUL substrates and potential glycosidic linkages in each of these substrates are shown on the right. Glycosidic linkages whose abundances were significantly different in faecal samples from the upper versus lower WLZ quartile responders are highlighted in bold font (see Fig. 5a).
a WLZ-response quartile

<figure><img src="image_29.png" /></figure>

b

Consensus

<figure><img src="image_30.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

**Extended Data Fig. 7** | Changes in levels of faecal glycosidic linkages and expression of *P. copri* CAZyme genes after MDCF-2 treatment.

(a) Boxplot of changes in the levels of faecal glycosidic linkages relative to initiation of treatment among upper and lower WLZ quartile responders. Levels of these 14 linkages increased to a significantly greater extent over time in the upper vs lower WLZ response quartiles (Model: linkage abundance - WLZ-response quartile × study week + (1|PID)). Boxplots indicate the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range for 90 biologically independent faecal samples obtained

from n = 15 participants assigned to the upper quartile and n = 15 participants assigned to the lower quartile of WLZ-responses (n = 3 samples/participant).

(b) The β₃ coefficient for the interaction of WLZ-response quartile and study week is shown for CAZymes in consensus PULs in Bg0018 and Bg0019. Predicted PUL substrates and potential glycosidic linkages in each of these substrates are shown on the right. Glycosidic linkages whose abundances were significantly different in faecal samples from the upper versus lower WLZ quartile responders are highlighted in bold font (see Fig. 5a).
<figure><img src="image_29.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

**Extended Data Fig. 8** | Treatment-responsive glycosidic linkages and corresponding polysaccharide sources and structures, cleavage sites, and predicted products of CAZyme activity. (a,b) Predicted activities and expression of *P. copri* PUL CAZymes. Panel a depicts CAZymes assigned to PUL17b, including (i) the GH51 family CAZyme (blue) expected to cleave α-1,2- and α-1,3-linked arabinofuranose (Araf) side chains and (ii) GH43_4 and GH43_5 subfamily members (brown) predicted to cleave α-1,5-Araf-linked backbone of

<figure><img src="image_30.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

branched arabinan, yielding products containing *t*-Araf, 2-Araf, 2,3 Araf and 5-Araf linkages. Panel b depicts CAZymes assigned to PUL7, including GH26 and GH5_4 CAZymes (magenta) predicted to cleave β-1,4 linked mannose residues of galactomannan, yielding products containing 4,6-mannose which is the most significantly differentially abundant linkage in the upper quartile WLZ responders (see Fig. 5a). The CAZyme colouring scheme matches that used in Fig. 5b, c.
<figure><img src="image_31.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

**Extended Data Fig. 8** | Treatment-responsive glycosidic linkages and corresponding polysaccharide sources and structures, cleavage sites, and predicted products of CAZyme activity. (a,b) Predicted activities and expression of *P. copri* PUL CAZymes. Panel a depicts CAZymes assigned to PUL17b, including (i) the GH51 family CAZyme (blue) expected to cleave α-1,2- and α-1,3-linked arabinofuranose (Araf) side chains and (ii) GH43_4 and GH43_5 subfamily members (brown) predicted to cleave α-1,5-Araf-linked backbone of

<figure><img src="image_32.png" /></figure>

Model: transcriptᵢ ~ β₁(WLZ-response quartile) + β₂(study week) + β₃(quartile x study week)

branched arabinan, yielding products containing *t*-Araf, 2-Araf, 2,3 Araf and 5-Araf linkages. Panel b depicts CAZymes assigned to PUL7, including GH26 and GH5_4 CAZymes (magenta) predicted to cleave β-1,4 linked mannose residues of galactomannan, yielding products containing 4,6-mannose which is the most significantly differentially abundant linkage in the upper quartile WLZ responders (see Fig. 5a). The CAZyme colouring scheme matches that used in Fig. 5b, c.
**Amylopectin/Amylose**
**(enriched in green banana, chickpea, rice, lentil)**

Product resistant to further degradation

<figure><img src="image_31.png" /></figure>

Substrate cleavage sites for *P. copri*
MAG Bg0018, Bg0019 consensus PULs:

<figure><img src="image_32.png" /></figure>

↑ PUL4 and PUL 17a

<figure><img src="image_33.png" /></figure>

↑ PUL7, 8

<figure><img src="image_34.png" /></figure>

↑ PUL4

<figure><img src="image_35.png" /></figure>

↑ PUL16

Cleavage site accessibility:

<figure><img src="image_36.png" /></figure>

↑ high

<figure><img src="image_37.png" /></figure>

↑ low

<figure><img src="image_38.png" /></figure>

Greater increase (q < 0.05) over time
in upper- vs lower-WLZ quartile
MDCF-2 responders

**Xyloglucan (enriched in soybean)**

<figure><img src="image_39.png" /></figure>

***(Arabino)xylan (enriched in soybean)***

<figure><img src="image_40.png" /></figure>

**Arabinogalactan II (enriched in soybean, chickpea)**

<figure><img src="image_41.png" /></figure>

**Extended Data Fig. 9 | Polysaccharide structures, cleavage sites, and predicted products of CAZyme activity.** Glycosidic linkages highlighted with arrows are those predicted as sites of cleavage by CAZymes expressed by the set of PULs, described in Fig. 4a, that are present in *P. copri* MAG Bg0019 and/or Bg0018. Consensus PUL numbers are listed except in the case of Bg0019 PUL3,

which is not represented in Bg0018 (see Supplementary Table 15). The size of the arrows (large versus small) denotes the relative likelihood (high versus low, respectively) of cleavage of glycosidic linkages by *P. copri* CAZymes when considering steric hindrance at branch points.
**Amylopectin/Amylose**
**(enriched in green banana, chickpea, rice, lentil)**

Product resistant to further degradation

<figure><img src="image_33.png" /></figure>

Substrate cleavage sites for *P. copri*
MAG Bg0018, Bg0019 consensus PULs:

<figure><img src="image_34.png" /></figure>

↑ PUL4 and PUL 17a

<figure><img src="image_35.png" /></figure>

↑ PUL7, 8

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

↑ PUL16

Cleavage site accessibility:

<figure><img src="image_38.png" /></figure>

↑ high

<figure><img src="image_39.png" /></figure>

↑ low

<figure><img src="image_40.png" /></figure>

Greater increase (q < 0.05) over time
in upper- vs lower-WLZ quartile
MDCF-2 responders

**Xyloglucan (enriched in soybean)**

<figure><img src="image_41.png" /></figure>

***(Arabino)xylan (enriched in soybean)***

<figure><img src="image_42.png" /></figure>

**Arabinogalactan II (enriched in soybean, chickpea)**

<figure><img src="image_43.png" /></figure>

**Extended Data Fig. 9 | Polysaccharide structures, cleavage sites, and predicted products of CAZyme activity.** Glycosidic linkages highlighted with arrows are those predicted as sites of cleavage by CAZymes expressed by the set of PULs, described in Fig. 4a, that are present in *P. copri* MAG Bg0019 and/or Bg0018. Consensus PUL numbers are listed except in the case of Bg0019 PUL3,

which is not represented in Bg0018 (see Supplementary Table 15). The size of the arrows (large versus small) denotes the relative likelihood (high versus low, respectively) of cleavage of glycosidic linkages by *P. copri* CAZymes when considering steric hindrance at branch points.
<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

**Extended Data Fig. 10 | Validation of the MAG assembly pipeline.**

(a) Bioinformatic workflow for comparing the fidelity of MAG assembly from alignment-based (bowtie2) versus pseudoalignment-based (kallisto) contig quantitation. (b) A detailed description of the workflow is described in panel a. Each box includes a summary of the computational task plus the name of the program and, where relevant, the command used to complete the task (in parentheses). Colour of the text in parentheses: brown, default code from kallisto; purple, default code from bowtie2; blue, custom script written to achieve tasks described; black, default code used for programs to assemble MAGs and dereplicate contigs. Boxes with a black outline and the thick black arrows emanating from them indicate that binned contigs were used as input to AMBER for MAG assembly comparisons across methods. (c) Boxplot describing summary statistics (completeness and purity) for MAG assembly approaches.

<figure><img src="image_45.png" /></figure>

Boxplots indicate the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range. The number of individual MAGs generated using each MAG assembly tool-quantitation method was 671 for DAS tool-kallisto, 723 for MaxBin2-kallisto, 925 for MetaBAT2-kallisto, 362 for CONCOCT-kallisto, 540 for DAS tool-bowtie2, 780 for MaxBin2-bowtie2, 578 for MetaBAT2-bowtie2 and 340 for CONCOCT-bowtie2. (d) Number of MAGs, obtained from each assembly and quantitation strategy, that are distributed across two quality metrics (completeness, contamination). The number of 'gold standard' MAGs (theoretical maximum) in the simulated metagenomic dataset is indicated by a horizontal dashed line. \*, *P* < 0.05; \*,\*, *P* < 0.01; \*,\*,\*, *P* < 0.001 (two-tailed, unpaired Wilcoxon test for panel c; two-tailed Fisher's exact test for panel d).
<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

C Number of MAGs

<figure><img src="image_46.png" /></figure>

d

<figure><img src="image_47.png" /><figcaption>**Extended Data Fig. 10** | Validation of the MAG assembly pipeline.</figcaption></figure>

(a) Bioinformatic workflow for comparing the fidelity of MAG assembly from alignment-based (bowtie2) versus pseudoalignment-based (kallisto) contig quantitation. (b) A detailed description of the workflow is described in panel a. Each box includes a summary of the computational task plus the name of the program and, where relevant, the command used to complete the task (in parentheses). Colour of the text in parentheses: brown, default code from kallisto; purple, default code from bowtie2; blue, custom script written to achieve tasks described; black, default code used for programs to assemble MAGs and dereplicate contigs. Boxes with a black outline and the thick black arrows emanating from them indicate that binned contigs were used as input to AMBER for MAG assembly comparisons across methods. (c) Boxplot describing summary statistics (completeness and purity) for MAG assembly approaches.

Boxplots indicate the median, first and third quartiles; whiskers extend to the largest value no further than 1.5 × the interquartile range. The number of individual MAGs generated using each MAG assembly tool-quantitation method was 671 for DAS tool-kallisto, 723 for MaxBin2-kallisto, 925 for MetaBAT2-kallisto, 362 for CONCOCT-kallisto, 540 for DAS tool-bowtie2, 780 for MaxBin2-bowtie2, 578 for MetaBAT2-bowtie2 and 340 for CONCOCT-bowtie2. (d) Number of MAGs, obtained from each assembly and quantitation strategy, that are distributed across two quality metrics (completeness, contamination). The number of 'gold standard' MAGs (theoretical maximum) in the simulated metagenomic dataset is indicated by a horizontal dashed line. \*, *P* < 0.05; \*,\*, *P* < 0.01; \*,\*,\*, *P* < 0.001 (two-tailed, unpaired Wilcoxon test for panel c; two-tailed Fisher's exact test for panel d).
# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our [Editorial Policies](#) and the [Editorial Policy Checklist](#).

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a

☐ ☑ The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement

☐ ☑ A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly

☐ ☑ The statistical test(s) used AND whether they are one- or two-sided

*Only common tests should be described solely by name; describe more complex techniques in the Methods section.*

☐ ☑ A description of all covariates tested

☐ ☑ A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons

☐ ☑ A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)

☐ ☑ For null hypothesis testing, the test statistic (e.g. *F*, *t*, *r*) with confidence intervals, effect sizes, degrees of freedom and *P* value noted
*Give *P* values as exact values whenever suitable.*

☐ ☑ For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings

☐ ☑ For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes

☐ ☑ Estimates of effect sizes (e.g. Cohen's *d*, Pearson's *r*), indicating how they were calculated

*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

Policy information about [availability of computer code](#)

**Data collection** No specialized software or code was used for data collection

**Data analysis** The following software packages were used to analyze the data in this study: Trim Galore3 (v0.6.4), bowtie2 (v2.3.4.1), MegaHit (v1.1.4), MaxBin2 (v2.2.5), MetaBAT2 (v2.12.1), DAS Tool (v1.1.2), OPERA-MS (v0.9.0), CheckM (v1.1.3), MAGpurify (v2.1.2), dRep (v2.6.2), prokka (v1.14.6), GTDB-Tk (v1.3.0), Kraken2 (v2.0.8), Bracken (v2.5), Phylophlan (v3.0.60), Graphlan (v1.1.4), Roary (v3.12.0), MAFFT (v7.313), microseq R package (v2.1.4), IQ-TREE (v1.6.12), kallisto (v0.43.0), DESeq2 (v1.34.0), lme4 (v1.1-27.1), lmerTest (v3.1-3), dream variancePartition R package (v1.24.0), FastQC (v0.11.7), edgeR37 (v3.32.1), factoextra (v1.0.7), vegan R package (v2.5-7), ggtree (v3.2.1), ape (v5.6-2), MMSeqs (v1-c7a89), DIAMOND (v2.0.0), prodigal (v2.6.3), Scikit-learn (v0.22.1), Rpart (v4.1.15), Caret (v6.0.86). Code detailing the steps in the MAG assembly workflow and analyses of microbial RNA-Seq and glycan datasets are available from GitLab (https://gitlab.com/hibberdm/hibberd_webber_et_al_mdcf_poc_mags) and have been accessioned at Zenodo (DOI:10.5281/zenodo.8000098). Code for annotation of bacterial genes and prediction of metabolic phenotypes is available from GitHub (https://github.com/rodionovdima/PhenotypePredictor) and has been accessioned at Zenodo (DOI: 10.5281/zenodo.10049439).

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our [Editorial Policies](#) and the [Editorial Policy Checklist](#).

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a

☐ The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement

☐ A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly

☐ The statistical test(s) used AND whether they are one- or two-sided

*Only common tests should be described solely by name; describe more complex techniques in the Methods section.*

☐ A description of all covariates tested

☐ A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons

☐ A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)

☐ For null hypothesis testing, the test statistic (e.g. *F*, *t*, *r*) with confidence intervals, effect sizes, degrees of freedom and *P* value noted
*Give *P* values as exact values whenever suitable.*

☐ For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings

☐ For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes

☐ Estimates of effect sizes (e.g. Cohen's *d*, Pearson's *r*), indicating how they were calculated

*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

Policy information about [availability of computer code](#)

**Data collection** No specialized software or code was used for data collection

**Data analysis** The following software packages were used to analyze the data in this study: Trim Galore3 (v0.6.4), bowtie2 (v2.3.4.1), MegaHit (v1.1.4), MaxBin2 (v2.2.5), MetaBAT2 (v2.12.1), DAS Tool (v1.1.2), OPERA-MS (v0.9.0), CheckM (v1.1.3), MAGpurify (v2.1.2), dRep (v2.6.2), prokka (v1.14.6), GTDB-Tk (v1.3.0), Kraken2 (v2.0.8), Bracken (v2.5), Phylophlan (v3.0.60), Graphlan (v1.1.4), Roary (v3.12.0), MAFFT (v7.313), microseq R package (v2.1.4), IQ-TREE (v1.6.12), kallisto (v0.43.0), DESeq2 (v1.34.0), lme4 (v1.1-27.1), lmerTest (v3.1-3), dream variancePartition R package (v1.24.0), FastQC (v0.11.7), edgeR37 (v3.32.1), factoextra (v1.0.7), vegan R package (v2.5-7), ggtree (v3.2.1), ape (v5.6-2), MMSeqs (v1-c7a89), DIAMOND (v2.0.0), prodigal (v2.6.3), Scikit-learn (v0.22.1), Rpart (v4.1.15), Caret (v6.0.86). Code detailing the steps in the MAG assembly workflow and analyses of microbial RNA-Seq and glycan datasets are available from GitLab (https://gitlab.com/hibberdm/hibberd_webber_et_al_mdcf_poc_mags) and have been accessioned at Zenodo (DOI:10.5281/zenodo.8000098). Code for annotation of bacterial genes and prediction of metabolic phenotypes is available from GitHub (https://github.com/rodionovdima/PhenotypePredictor) and has been accessioned at Zenodo (DOI: 10.5281/zenodo.10049439).

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
# Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our [policy](#)

Shotgun DNA sequencing and microbial RNA-Seq datasets generated from fecal samples, plus annotated P. copri isolate genome sequences are available in the European Nucleotide Archive (accession PRJEB45356). Anthropometry data is available from a prior publication (ref. 4; DOI: 10.1056/NEJMoa2023294). The mcSEED database (https://zenodo.org/records/10041396) was used to predict the presence or absence of metabolic pathways. The Genome Taxonomy Database (GTDB) was used as a reference for taxonomic classification of MAGs. The CAZy and PUI db databases were used to identify and analyze carbohydrate-active genes and PULs. LC-MS datasets of monosaccharide, glycoside linkage, and polysaccharide data are deposited in GlycoPOST (accession GPST000244). All other relevant and/or supporting data is available in the Supplementary Information.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_46.png" /></figure>

Life sciences

<figure><img src="image_47.png" /></figure>

Behavioural & social sciences

<figure><img src="image_48.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Sample size**

Target enrollment for the clinical trial was set at 124 participants (n=62 per arm). This sample size was calculated to achieve 80% power at a 5% significance level for detecting a treatment effect. The anticipated effect size was derived from changes in Weight-for-Length Z-score (WLC) observed in a pilot randomized, double-blind controlled feeding study (ref. 3, DOI: 10.1126/science.aau4732). A total of 123 participants were randomized in the clinical trial (n=61 MDCF-2 diet, 62 RUSF diet), and 59 children in each group completed the 3-month intervention and 1-month follow-up and were included in our analyses. Additional details on the clinical trial design and sample size calculations are available elsewhere (ref. 4 [DOI: 10.1056/NEJMoa2023294], DOI 10.1186/s12889-020-8330-8).

**Data exclusions**

Participants who did not complete the trial were excluded as described previously (ref. 4 [DOI: 10.1056/NEJMoa2023294]). No additional data were excluded from the analyses described in the current study.

**Replication**

To enhance reproducibility, the MAG assembly pipeline was validated using synthetic sequence data from CAMI II as indicated in Extended Data Fig. 10. This validation measured the fidelity of MAG assembly, the accuracy of kallisto-based MAG quantification, and the effects of filtering as described in the supplementary information. We employed a consensus-based approach for assigning annotations as a means of enhancing the reliability of these metabolic pathway annotations. We utilized both short- and long-read MAG assembly methods and compared these assembly methods as illustrated in Extended Data Fig. 10. We compared results from two methods of taxonomic assignment and utilized a consensus approach to assign MAG taxonomy in a reproducible manner. The capacity of P. copri strains to utilize specific carbohydrate substrates was validated using a series of in vitro growth experiments as illustrated in Extended Data Fig. 5.

**Randomization**

Eligible participants, whose parents/guardians provided written informed consent, were randomly assigned to treatment groups. Participant randomization was performed by an independent researcher who has had no involvement in the trial (please see an additional description of randomization: DOI 10.1186/s12889-020-8330-8).

**Blinding**

Participants were blinded to their treatment group. Study staff were blinded to the extent that was possible as described in DOI 10.1186/s12889-020-8330-8. Investigators were not blinded to group allocation when analyzing the data presented in this paper.

# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
# Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A description of any restrictions on data availability

- For clinical datasets or third party data, please ensure that the statement adheres to our [policy](#)

Shotgun DNA sequencing and microbial RNA-Seq datasets generated from fecal samples, plus annotated P. copri isolate genome sequences are available in the European Nucleotide Archive (accession PRJEB45356). Anthropometry data is available from a prior publication (ref. 4; DOI: 10.1056/NEJMoa2023294). The mcSEED database (https://zenodo.org/records/10041396) was used to predict the presence or absence of metabolic pathways. The Genome Taxonomy Database (GTDB) was used as a reference for taxonomic classification of MAGs. The CAZy and PUI db databases were used to identify and analyze carbohydrate-active genes and PULs. LC-MS datasets of monosaccharide, glycoside linkage, and polysaccharide data are deposited in GlycoPOST (accession GPST000244). All other relevant and/or supporting data is available in the Supplementary Information.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_48.png" /></figure>

Life sciences

<figure><img src="image_49.png" /></figure>

Behavioural & social sciences

<figure><img src="image_50.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Sample size**

Target enrollment for the clinical trial was set at 124 participants (n=62 per arm). This sample size was calculated to achieve 80% power at a 5% significance level for detecting a treatment effect. The anticipated effect size was derived from changes in Weight-for-Length Z-score (WLC) observed in a pilot randomized, double-blind controlled feeding study (ref. 3, DOI: 10.1126/science.aau4732). A total of 123 participants were randomized in the clinical trial (n=61 MDCF-2 diet, 62 RUSF diet), and 59 children in each group completed the 3-month intervention and 1-month follow-up and were included in our analyses. Additional details on the clinical trial design and sample size calculations are available elsewhere (ref. 4 [DOI: 10.1056/NEJMoa2023294], DOI 10.1186/s12889-020-8330-8).

**Data exclusions**

Participants who did not complete the trial were excluded as described previously (ref. 4 [DOI: 10.1056/NEJMoa2023294]). No additional data were excluded from the analyses described in the current study.

**Replication**

To enhance reproducibility, the MAG assembly pipeline was validated using synthetic sequence data from CAMI II as indicated in Extended Data Fig. 10. This validation measured the fidelity of MAG assembly, the accuracy of kallisto-based MAG quantification, and the effects of filtering as described in the supplementary information. We employed a consensus-based approach for assigning annotations as a means of enhancing the reliability of these metabolic pathway annotations. We utilized both short- and long-read MAG assembly methods and compared these assembly methods as illustrated in Extended Data Fig. 10. We compared results from two methods of taxonomic assignment and utilized a consensus approach to assign MAG taxonomy in a reproducible manner. The capacity of P. copri strains to utilize specific carbohydrate substrates was validated using a series of in vitro growth experiments as illustrated in Extended Data Fig. 5.

**Randomization**

Eligible participants, whose parents/guardians provided written informed consent, were randomly assigned to treatment groups. Participant randomization was performed by an independent researcher who has had no involvement in the trial (please see an additional description of randomization: DOI 10.1186/s12889-020-8330-8).

**Blinding**

Participants were blinded to their treatment group. Study staff were blinded to the extent that was possible as described in DOI 10.1186/s12889-020-8330-8. Investigators were not blinded to group allocation when analyzing the data presented in this paper.

# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
## Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>Antibodies</td></tr><tr><td>☑</td><td>Eukaryotic cell lines</td></tr><tr><td>☑</td><td>Palaeontology and archaeology</td></tr><tr><td>☑</td><td>Animals and other organisms</td></tr><tr><td>☐</td><td>Human research participants</td></tr><tr><td>☐</td><td>Clinical data</td></tr><tr><td>☑</td><td>Dual use research of concern</td></tr></tbody></table>

## Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>ChIP-seq</td></tr><tr><td>☑</td><td>Flow cytometry</td></tr><tr><td>☑</td><td>MRI-based neuroimaging</td></tr></tbody></table>

## Human research participants

Policy information about studies involving human research participants

<table><tr><td>Population characteristics</td><td>Please refer to Chen, Mostafa, Hibberd et al., "A Microbiota-Directed Food Intervention for Undernourished Children", N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294) for population characteristics.</td></tr><tr><td>Recruitment</td><td>Please refer to Chen, Mostafa, Hibberd et al., "A Microbiota-Directed Food Intervention for Undernourished Children", N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294) for recruitment information.</td></tr><tr><td>Ethics oversight</td><td>The study protocol was approved by the Ethical Review Committee at the International Center for Diarrheal Disease Research, Bangladesh (icddr,b)</td></tr></table>

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Clinical data

Policy information about clinical studies

All manuscripts should comply with the ICMJE guidelines for publication of clinical research and a completed CONSORT checklist must be included with all submissions.

<table><tr><td>Clinical trial registration</td><td>ClinicalTrials.gov identifier: NCT04015999</td></tr><tr><td>Study protocol</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The protocol for this study is detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr><tr><td>Data collection</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The data collection methods for this study are detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr><tr><td>Outcomes</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The primary and secondary outcome measures for this study are detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr></table>
## Materials & experimental systems

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>Antibodies</td></tr><tr><td>☑</td><td>Eukaryotic cell lines</td></tr><tr><td>☑</td><td>Palaeontology and archaeology</td></tr><tr><td>☑</td><td>Animals and other organisms</td></tr><tr><td>☐</td><td>Human research participants</td></tr><tr><td>☐</td><td>Clinical data</td></tr><tr><td>☑</td><td>Dual use research of concern</td></tr></tbody></table>

## Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>ChIP-seq</td></tr><tr><td>☑</td><td>Flow cytometry</td></tr><tr><td>☑</td><td>MRI-based neuroimaging</td></tr></tbody></table>

## Human research participants

Policy information about studies involving human research participants

<table><tr><td>Population characteristics</td><td>Please refer to Chen, Mostafa, Hibberd et al., "A Microbiota-Directed Food Intervention for Undernourished Children", N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294) for population characteristics.</td></tr><tr><td>Recruitment</td><td>Please refer to Chen, Mostafa, Hibberd et al., "A Microbiota-Directed Food Intervention for Undernourished Children", N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294) for recruitment information.</td></tr><tr><td>Ethics oversight</td><td>The study protocol was approved by the Ethical Review Committee at the International Center for Diarrheal Disease Research, Bangladesh (icddr,b)</td></tr></table>

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Clinical data

Policy information about clinical studies

All manuscripts should comply with the ICMJE guidelines for publication of clinical research and a completed CONSORT checklist must be included with all submissions.

<table><tr><td>Clinical trial registration</td><td>ClinicalTrials.gov identifier: NCT04015999</td></tr><tr><td>Study protocol</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The protocol for this study is detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr><tr><td>Data collection</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The data collection methods for this study are detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr><tr><td>Outcomes</td><td>The human biospecimens analyzed in this manuscript are from a previously reported clinical study. The primary and secondary outcome measures for this study are detailed in ref. 4 (N Engl J Med 2021 (DOI: 10.1056/NEJMoa2023294)).</td></tr></table>