Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition.
Journal
Nature microbiology
ISSN: 2058-5276
Titre abrégé: Nat Microbiol
Pays: England
ID NLM: 101674869
Informations de publication
Date de publication:
19 Mar 2024
19 Mar 2024
Historique:
received:
20
11
2023
accepted:
31
01
2024
medline:
20
3
2024
pubmed:
20
3
2024
entrez:
20
3
2024
Statut:
aheadofprint
Résumé
Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies.
Identifiants
pubmed: 38503977
doi: 10.1038/s41564-024-01628-7
pii: 10.1038/s41564-024-01628-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHGRI NIH HHS
ID : T32 HG000045
Pays : United States
Organisme : NIDDK NIH HHS
ID : F30 DK131866
Pays : United States
Organisme : NIDDK NIH HHS
ID : F30 DK123838
Pays : United States
Informations de copyright
© 2024. The Author(s).
Références
Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).
pubmed: 25974306
doi: 10.1016/j.chom.2015.04.004
Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).
pubmed: 30356187
pmcid: 6415775
doi: 10.1038/s41586-018-0617-x
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
pubmed: 22699611
pmcid: 3376388
doi: 10.1038/nature11053
Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).
pubmed: 24896187
pmcid: 4189846
doi: 10.1038/nature13421
Blanton, L. V. et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, aad3311 (2016).
pubmed: 26912898
doi: 10.1126/science.aad3311
Gehrig, J. L. et al. Effects of microbiota-directed foods in gnotobiotic animals and undernourished children. Science 365, eaau4732 (2019).
pubmed: 31296738
pmcid: 6683325
doi: 10.1126/science.aau4732
Brown, E. M. et al. Diet and specific microbial exposure trigger features of environmental enteropathy in a novel murine model. Nat. Commun. 6, 7806 (2015).
pubmed: 26241678
doi: 10.1038/ncomms8806
Chen, R. Y. et al. Duodenal microbiota in stunted undernourished children with enteropathy. N. Engl. J. Med. 383, 321–333 (2020).
pubmed: 32706533
pmcid: 7289524
doi: 10.1056/NEJMoa1916004
Malique, A. et al. NAD
pubmed: 38170788
doi: 10.1126/scitranslmed.abq4145
Chen, R. Y. et al. A microbiota-directed food intervention for undernourished children. N. Engl. J. Med. 384, 1517–1528 (2021).
pubmed: 33826814
pmcid: 7993600
doi: 10.1056/NEJMoa2023294
Hibberd, M. C. et al. Bioactive glycans in a microbiome-directed food for malnourished children. Nature 625, 157–165 (2024).
pubmed: 38093016
doi: 10.1038/s41586-023-06838-3
Underwood, M. A., German, J. B., Lebrilla, C. B. & Mills, D. A. Bifidobacterium longum subspecies infantis: champion colonizer of the infant gut. Pediatr. Res. 77, 229–235 (2015).
pubmed: 25303277
doi: 10.1038/pr.2014.156
Barratt, M. J. et al. Bifidobacterium longum subsp. infantis strains for treating severe acute malnutrition in Bangladeshi infants. Sci. Trans. Med. 14, eabk1107 (2022).
doi: 10.1126/scitranslmed.abk1107
Raman, A. S. et al. A sparse covarying unit that describes healthy and impaired human gut microbiota development. Science 365, eaau4735 (2019).
pubmed: 31296739
pmcid: 6683326
doi: 10.1126/science.aau4735
Sender, R. & Milo, R. The distribution of cellular turnover in the human body. Nat. Med. 27, 45–48 (2021).
pubmed: 33432173
doi: 10.1038/s41591-020-01182-9
Richter, M. & Rosselló-Móra, R. Shifting the genomic gold standard for the prokaryotic species definition. Proc. Natl Acad. Sci. USA 106, 19126–19131 (2009).
pubmed: 19855009
pmcid: 2776425
doi: 10.1073/pnas.0906412106
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
pubmed: 28742071
pmcid: 5702732
doi: 10.1038/ismej.2017.126
Karasov, W. H. & Douglas, A. E. Comparative digestive physiology. Compr. Physiol. 3, 741 (2013).
pubmed: 23720328
pmcid: 4458075
doi: 10.1002/cphy.c110054
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
pubmed: 31819264
doi: 10.1038/s41592-019-0667-5
Zheng, Y. et al. Intestinal epithelial cell-specific IGF1 promotes the expansion of intestinal stem cells during epithelial regeneration and functions on the intestinal immune homeostasis. Am. J. Physiol. Endocrinol. Metab. 315, E638–E649 (2018).
pubmed: 29783855
doi: 10.1152/ajpendo.00022.2018
Wagner, A. et al. Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 184, 4168–4185 (2021).
pubmed: 34216539
pmcid: 8621950
doi: 10.1016/j.cell.2021.05.045
Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).
pubmed: 23455439
doi: 10.1038/nbt.2488
Mihaylova, M. M. Fasting activates fatty acid oxidation to enhance intestinal stem cell function during homeostasis and aging. Cell Stem Cell 22, 769–778 (2018).
pubmed: 29727683
pmcid: 5940005
doi: 10.1016/j.stem.2018.04.001
Crenn, P., Messing, B. & Cynober, L. Citrulline as a biomarker of intestinal failure due to enterocyte mass reduction. Clin. Nutr. 27, 328–339 (2008).
pubmed: 18440672
doi: 10.1016/j.clnu.2008.02.005
Lanyero, B. Correlates of gut function in children hospitalized for severe acute malnutrition, a cross-sectional study in Uganda. J. Pediatr. Gastroenterol. Nutr. 69, 292–298 (2019).
pubmed: 31169661
doi: 10.1097/MPG.0000000000002381
Guerrant, R. L. et al. Biomarkers of environmental enteropathy, inflammation, stunting, and impaired growth in children in northeast Brazil. PLoS ONE 11, e0158772 (2016).
pubmed: 27690129
pmcid: 5045163
doi: 10.1371/journal.pone.0158772
Mostafa, I. et al. Effect of gut microbiota-directed complementary food supplementation on fecal and plasma biomarkers of gut health and environmental enteric dysfunction in slum-dwelling children with moderate acute malnutrition. Children 11, 69 (2024).
pubmed: 38255381
pmcid: 10814735
doi: 10.3390/children11010069
Clemente, T. E. & Cahoon, E. B. Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiol. 151, 1030–1040 (2009).
pubmed: 19783644
pmcid: 2773065
doi: 10.1104/pp.109.146282
Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167 (2018).
pubmed: 30270040
doi: 10.1016/j.cell.2018.08.063
Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826 (2021).
pubmed: 33406409
pmcid: 7864098
doi: 10.1016/j.cell.2020.12.016
Kolmgorov, M., Yuan, J. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).
doi: 10.1038/s41587-019-0072-8
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
pubmed: 25977477
pmcid: 4484387
doi: 10.1101/gr.186072.114
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
pubmed: 24642063
doi: 10.1093/bioinformatics/btu153
Aziz, R. K. et al. SEED servers: high-performance access to the SEED genomes, annotations, and metabolic models. PLoS ONE 7, e48053 (2012).
pubmed: 23110173
pmcid: 3480482
doi: 10.1371/journal.pone.0048053
Rodionov, D. A. et al. Micronutrient requirements and sharing capabilities of the human gut microbiome. Front. Microbiol. 10, 1316 (2019).
pubmed: 31275260
pmcid: 6593275
doi: 10.3389/fmicb.2019.01316
Frolova, M. S., Suvorova, I. A., Iablokov, S. N., Petrov, S. N. & Rodionov, D. A. Genomic reconstruction of short-chain fatty acid production by the human gut microbiota. Front. Mol. Biosci. 9, 949563 (2022).
pubmed: 36032669
pmcid: 9403272
doi: 10.3389/fmolb.2022.949563
Ashniev, G. A., Petrov, S. N., Iablokov, S. N. & Rodionov, D. A. Genomics-based reconstruction and predictive profiling of amino acid biosynthesis in the human gut microbiome. Microorganisms 10, 740 (2022).
pubmed: 35456791
pmcid: 9026213
doi: 10.3390/microorganisms10040740
Price, M. N. et al. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
pubmed: 20224823
pmcid: 2835736
doi: 10.1371/journal.pone.0009490
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).
doi: 10.1093/bioinformatics/bty633
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).
pubmed: 32162851
doi: 10.1002/cpbi.96
Pritchard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. & Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8, 12–24 (2016).
doi: 10.1039/C5AY02550H
Terrapon, N., Lombard, V., Gilbert, H. J. & Henrissat, B. Automatic prediction of polysaccharide utilization loci in Bacteroidetes species. Bioinformatics 31, 647–655 (2015).
pubmed: 25355788
doi: 10.1093/bioinformatics/btu716
Terrapon, N. et al. PULDB: the expanded database of polysaccharide utilization loci. Nucleic Acids Res. 46, D677–D683 (2018).
pubmed: 29088389
doi: 10.1093/nar/gkx1022
Sokol, H., Pigneur, B., Watterlot, L. & Langella, P. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).
pubmed: 18936492
pmcid: 2575488
doi: 10.1073/pnas.0804812105
Stammler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 21, 28 (2016).
doi: 10.1186/s40168-016-0175-0
McNulty, N. P. et al. Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome. PLoS Biol. 11, e1001637 (2013).
pubmed: 23976882
pmcid: 3747994
doi: 10.1371/journal.pbio.1001637
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020); https://www.R-project.org/
Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA) (Wiley, 2017); https://doi.org/10.1002/9781118445112.stat07841
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
Kuznetsova, A., Brockhoff, P. B. & Bojesen-Christensen, R. H. lmerTest: tests in linear mixed effects models. J. Stat. Softw. 82, 1–12 (2017).
doi: 10.18637/jss.v082.i13
Krueger F. FelixKrueger/TrimGalore: v0.6.7. Zenodo https://doi.org/10.5281/zenodo.5127899 (2021).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002
doi: 10.1038/nbt.3519
Waskom, M. L. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
doi: 10.21105/joss.03021
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Zhang, Y. et al. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics 37, i34–i41 (2021).
pubmed: 34252963
pmcid: 8275336
doi: 10.1093/bioinformatics/btab327
Klingenberg, H. & Meinicke, P. How to normalize metatranscriptomic count data for differential expression analysis. PeerJ 5, e3859 (2017).
pubmed: 29062598
pmcid: 5649605
doi: 10.7717/peerj.3859
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
pubmed: 29203879
pmcid: 5715110
doi: 10.1038/s41598-017-17204-5
Tosti, L. et al. Single-nucleus and in situ RNA–sequencing reveal cell topographies in the human pancreas. Gastroenterology 160, 1330–1344 (2021).
pubmed: 33212097
doi: 10.1053/j.gastro.2020.11.010
Zheng, G. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
pubmed: 28091601
pmcid: 5241818
doi: 10.1038/ncomms14049
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 20 (2022).
doi: 10.1186/s13059-021-02584-9
Capdevila, C. et al. Single-cell transcriptional profiling of the intestinal epithelium. Methods Mol. Biol. 2171, 129–153 (2020).
pubmed: 32705639
doi: 10.1007/978-1-0716-0747-3_8
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
pubmed: 29144463
pmcid: 6022292
doi: 10.1038/nature24489
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).
pubmed: 34584091
pmcid: 8479118
doi: 10.1038/s41467-021-25960-2
Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn (Lawrence Erlbaum Associates, 1988).
Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 6876 (2021).
pubmed: 34824236
pmcid: 8616929
doi: 10.1038/s41467-021-27150-6
An, J. et al. Hepatic expression of malonyl-CoA decarboxylase reverses muscle, liver and whole-animal insulin resistance. Nat. Med. 10, 268–274 (2004).
pubmed: 14770177
doi: 10.1038/nm995
Gray, N. et al. High-speed quantitative UPLC-MS analysis of multiple amines in human plasma and serum via precolumn derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate: application to acetaminophen-induced liver failure. Anal. Chem. 89, 2478–2487 (2017).
pubmed: 28194962
doi: 10.1021/acs.analchem.6b04623
Konieczna, L. et al. Bioanalysis of underivatized amino acids in non-invasive exhaled breath condensate samples using liquid chromatography coupled with tandem mass spectrometry. J. Chromatogr. A 1542, 72–81 (2018).
pubmed: 29477235
doi: 10.1016/j.chroma.2018.02.019