Jaacks, L. M. et al. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 7, 231â240 (2019).
Google ScholarÂ
Chooi, Y. C., Ding, C. & Magkos, F. The epidemiology of obesity. Metabolism 92, 6â10 (2019).
Google ScholarÂ
Flegal, K. M., Kit, B. K., Orpana, H. & Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309, 71â82 (2013).
Google ScholarÂ
Crooks, B., Stamataki, N. S. & McLaughlin, J. T. Appetite, the enteroendocrine system, gastrointestinal disease and obesity. Proc. Nutr. Soc. 80, 50â58 (2021).
Google ScholarÂ
Kiela, P. R. & Ghishan, F. K. Physiology of intestinal absorption and secretion. Best Pract. Res. Clin. Gastroenterol. 30, 145â159 (2016).
Google ScholarÂ
Spiller, R. C. Intestinal absorptive function. Gut 35, S5âS9 (1994).
Google ScholarÂ
Wong, J. M., de Souza, R., Kendall, C. W., Emam, A. & Jenkins, D. J. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235â243 (2006).
Google ScholarÂ
Chey, W. Y. & Chang, T. M. Secretin: historical perspective and current status. Pancreas 43, 162â182 (2014).
Google ScholarÂ
Gribble, F. M. & Reimann, F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. Nat. Rev. Endocrinol. 15, 226â237 (2019).
Google ScholarÂ
Billing, L. J. et al. Single cell transcriptomic profiling of large intestinal enteroendocrine cells in miceâidentification of selective stimuli for insulin-like peptide-5 and glucagon-like peptide-1 co-expressing cells. Mol. Metab. 29, 158â169 (2019).
Google ScholarÂ
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333â339 (2017).
Google ScholarÂ
Beumer, J. et al. Enteroendocrine cells switch hormone expression along the crypt-to-villus BMP signalling gradient. Nat. Cell Biol. 20, 909â916 (2018).
Google ScholarÂ
Jenny, M. et al. Neurogenin3 is differentially required for endocrine cell fate specification in the intestinal and gastric epithelium. EMBO J. 21, 6338â6347 (2002).
Google ScholarÂ
Mellitzer, G. et al. Loss of enteroendocrine cells in mice alters lipid absorption and glucose homeostasis and impairs postnatal survival. J. Clin. Invest. 120, 1708â1721 (2010).
Google ScholarÂ
Blot, F. et al. Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice. Cell Mol. Gastroenterol. Hepatol. 15, 1443â1461 (2023).
Google ScholarÂ
Sanchez, J. G., Enriquez, J. R. & Wells, J. M. Enteroendocrine cell differentiation and function in the intestine. Curr. Opin. Endocrinol. Diabetes Obes. 29, 169â176 (2022).
Google ScholarÂ
Bethea, M., Bozadjieva-Kramer, N. & Sandoval, D. A. Preproglucagon products and their respective roles regulating insulin secretion. Endocrinology 162, bqab150 (2021).
Google ScholarÂ
Stojanovic, O., Miguel-Aliaga, I. & Trajkovski, M. Intestinal plasticity and metabolism as regulators of organismal energy homeostasis. Nat. Metab. 4, 1444â1458 (2022).
Google ScholarÂ
Duca, F. A., Waise, T. M. Z., Peppler, W. T. & Lam, T. K. T. The metabolic impact of small intestinal nutrient sensing. Nat. Commun. 12, 903 (2021).
Google ScholarÂ
Ramakrishna, B. S. Role of the gut microbiota in human nutrition and metabolism. J. Gastroenterol. Hepatol. 28, 9â17 (2013).
Google ScholarÂ
Rowland, I. et al. Gut microbiota functions: metabolism of nutrients and other food components. Eur. J. Nutr. 57, 1â24 (2018).
Google ScholarÂ
Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567â590 (1990).
Google ScholarÂ
Hills, R. D. Jr. et al. Gut microbiome: profound implications for diet and disease. Nutrients 11, 1613 (2019).
Google ScholarÂ
Kolodziejczyk, A. A., Zheng, D. & Elinav, E. Dietâmicrobiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17, 742â753 (2019).
Google ScholarÂ
Bastings, J., Venema, K., Blaak, E. E. & Adam, T. C. Influence of the gut microbiota on satiety signaling. Trends Endocrinol. Metab. 34, 243â255 (2023).
Google ScholarÂ
Wang, J. et al. Mutant neurogenin-3 in congenital malabsorptive diarrhea. N. Engl. J. Med. 355, 270â280 (2006).
Google ScholarÂ
Burnicka-Turek, O. et al. INSL5-deficient mice display an alteration in glucose homeostasis and an impaired fertility. Endocrinology 153, 4655â4665 (2012).
Google ScholarÂ
Panaro, B. L. et al. Intestine-selective reduction of Gcg expression reveals the importance of the distal gut for GLP-1 secretion. Mol. Metab. 37, 100990 (2020).
Google ScholarÂ
Tschop, M. et al. Physiology: does gut hormone PYY3â36 decrease food intake in rodents? Nature 430, 1â3 (2004).
Batterham, R. L. et al. Critical role for peptide YY in protein-mediated satiation and body-weight regulation. Cell Metab. 4, 223â233 (2006).
Google ScholarÂ
Boggiano, M. M. et al. PYY3â36 as an anti-obesity drug target. Obes. Rev. 6, 307â322 (2005).
Google ScholarÂ
Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).
Google ScholarÂ
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027â1031 (2006).
Google ScholarÂ
Liu, B. N., Liu, X. T., Liang, Z. H. & Wang, J. H. Gut microbiota in obesity. World J. Gastroenterol. 27, 3837â3850 (2021).
Google ScholarÂ
Israelyan, N. et al. Effects of serotonin and slow-release 5-hydroxytryptophan on gastrointestinal motility in a mouse model of depression. Gastroenterology 157, 507â521 (2019).
Google ScholarÂ
Maruvada, P., Leone, V., Kaplan, L. M. & Chang, E. B. The human microbiome and obesity: moving beyond associations. Cell Host Microbe 22, 589â599 (2017).
Google ScholarÂ
Rosenbaum, M., Knight, R. & Leibel, R. L. The gut microbiota in human energy homeostasis and obesity. Trends Endocrinol. Metab. 26, 493â501 (2015).
Google ScholarÂ
Liu, R. et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat. Med. 23, 859â868 (2017).
Google ScholarÂ
Suarez-Zamorano, N. et al. Microbiota depletion promotes browning of white adipose tissue and reduces obesity. Nat. Med. 21, 1497â1501 (2015).
Google ScholarÂ
Kameyama, K. & Itoh, K. Intestinal colonization by a Lachnospiraceae bacterium contributes to the development of diabetes in obese mice. Microbes Environ. 29, 427â430 (2014).
Google ScholarÂ
Membrez, M. et al. Gut microbiota modulation with norfloxacin and ampicillin enhances glucose tolerance in mice. FASEB J. 22, 2416â2426 (2008).
Google ScholarÂ
Ye, L. et al. High fat diet induces microbiota-dependent silencing of enteroendocrine cells. eLife 8, e48479 (2019).
Google ScholarÂ
Grosse, J. et al. Insulin-like peptide 5 is an orexigenic gastrointestinal hormone. Proc. Natl Acad. Sci. USA 111, 11133â11138 (2014).
Google ScholarÂ
Lee, Y. S. et al. Insulin-like peptide 5 is a microbially regulated peptide that promotes hepatic glucose production. Mol. Metab. 5, 263â270 (2016).
Google ScholarÂ
Lewis, J. E. et al. Selective stimulation of colonic L cells improves metabolic outcomes in mice. Diabetologia 63, 1396â1407 (2020).
Google ScholarÂ
Zaykov, A. N., Gelfanov, V. M., Perez-Tilve, D., Finan, B. & DiMarchi, R. D. Insulin-like peptide 5 fails to improve metabolism or body weight in obese mice. Peptides 120, 170116 (2019).
Google ScholarÂ
Brooks, L. et al. Fermentable carbohydrate stimulates FFAR2-dependent colonic PYY cell expansion to increase satiety. Mol. Metab. 6, 48â60 (2017).
Google ScholarÂ
Larraufie, P. et al. SCFAs strongly stimulate PYY production in human enteroendocrine cells. Sci. Rep. 8, 74 (2018).
Google ScholarÂ
Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648â652 (2017).
Google ScholarÂ
Holmes, E., Wilson, I. D. & Nicholson, J. K. Metabolic phenotyping in health and disease. Cell 134, 714â717 (2008).
Google ScholarÂ
Arora, T. et al. Microbial regulation of the L cell transcriptome. Sci. Rep. 8, 1207 (2018).
Google ScholarÂ
Sifuentes-Dominguez, L. F. et al. SCGN deficiency results in colitis susceptibility. eLife 8, e49910 (2019).
Google ScholarÂ
Hinoi, T. et al. Mouse model of colonic adenoma-carcinoma progression based on somatic Apc inactivation. Cancer Res. 67, 9721â9730 (2007).
Google ScholarÂ
Feng, Y. et al. Sox9 induction, ectopic Paneth cells, and mitotic spindle axis defects in mouse colon adenomatous epithelium arising from conditional biallelic Apc inactivation. Am. J. Pathol. 183, 493â503 (2013).
Google ScholarÂ
Maitra, R. et al. Development and characterization of a genetic mouse model of KRAS mutated colorectal cancer. Int. J. Mol. Sci. 20, 5677 (2019).
Google ScholarÂ
Quehenberger, O., Armando, A. M. & Dennis, E. A. High sensitivity quantitative lipidomics analysis of fatty acids in biological samples by gas chromatography-mass spectrometry. Biochim. Biophys. Acta 1811, 648â656 (2011).
Google ScholarÂ
Pendse, M. et al. Macrophages regulate gastrointestinal motility through complement component 1q. eLife 12, e78558 (2023).
Google ScholarÂ
Obata, Y. et al. Neuronal programming by microbiota regulates intestinal physiology. Nature 578, 284â289 (2020).
Google ScholarÂ
Roosen, L. et al. Specific hunger- and satiety-induced tuning of guinea pig enteric nerve activity. J. Physiol. 590, 4321â4333 (2012).
Google ScholarÂ
Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).
Google ScholarÂ
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90âW97 (2016).
Google ScholarÂ
Xie, Z. et al. Gene set knowledge discovery with Enrichr. Curr. Protoc. 1, e90 (2021).
Google ScholarÂ
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852â857 (2019).
Google ScholarÂ
Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).
Google ScholarÂ
Kim, J., Kim, M. S., Koh, A. Y., Xie, Y. & Zhan, X. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics 17, 420 (2016).
Google ScholarÂ
Kim, J. et al. MetaPrism: a versatile toolkit for joint taxa/gene analysis of metagenomic sequencing data. G3 11, jkab046 (2021).
Google ScholarÂ
Lu, J. et al. Metagenome analysis using the Kraken software suite. Nat. Protoc. 17, 2815â2839 (2022).
Google ScholarÂ
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754â1760 (2009).
Google ScholarÂ
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
Google ScholarÂ
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388âW396 (2021).
Google ScholarÂ
Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872â881 (2012).
Google ScholarÂ
Song, Y. et al. Gut-proglucagon-derived peptides are essential for regulating glucose homeostasis in mice. Cell Metab. 30, 976â986 (2019).
Google ScholarÂ