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dc.creatorSheh, Alexander
dc.creatorArtim, Stephen C.
dc.creatorBurns, Monika A.
dc.creatorMolina Mora, José Arturo
dc.creatorLee, Mary Anne
dc.creatorDzink-Fox, JoAnn
dc.creatorMuthupalani, Sureshkumar
dc.creatorFox, James G.
dc.date.accessioned2022-04-05T15:47:42Z
dc.date.available2022-04-05T15:47:42Z
dc.date.issued2022-03-28
dc.identifier.citationhttps://www.nature.com/articles/s41598-022-09268-9es_ES
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10669/86411
dc.description.abstractChronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets (Callithrix jacchus). Despite standardized housing, diet and husbandry, a recently described gastrointestinal syndrome characterized by duodenal ulcers and strictures was observed in a subset of marmosets sourced from the New England Primate Research Center. As changes in the gut microbiome have been associated with GI diseases, the gut microbiome of 52 healthy, non-stricture marmosets (153 samples) were compared to the gut microbiome of 21 captive marmosets diagnosed with a duodenal ulcer/stricture (57 samples). No significant changes were observed using alpha diversity metrics, and while the community structure was significantly different when comparing beta diversity between healthy and stricture cases, the results were inconclusive due to differences observed in the dispersion of both datasets. Differences in the abundance of individual taxa using ANCOM, as stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. To identify microbial and serum biomarkers that could help classify stricture cases, we developed models using machine learning algorithms (random forest, classification and regression trees, support vector machines and k-nearest neighbors) to classify microbiome, serum chemistry or complete blood count (CBC) data. Random forest (RF) models were the most accurate models and correctly classified strictures using either 9 ASVs (amplicon sequence variants), 4 serum chemistry tests or 6 CBC tests. Based on the RF model and ANCOM results, C. perfringens was identified as a potential causative agent associated with the development of strictures. Clostridium perfringens was also isolated by microbiological culture in 4 of 9 duodenum samples from marmosets with histologically confirmed strictures. Due to the enrichment of C. perfringens in situ, we analyzed frozen duodenal tissues using both 16S microbiome profiling and RNAseq. Microbiome analysis of the duodenal tissues of 29 marmosets from the MIT colony confirmed an increased abundance of Clostridium in stricture cases. Comparison of the duodenal gene expression from stricture and non-stricture marmosets found enrichment of genes associated with intestinal absorption, and lipid metabolism, localization, and transport in stricture cases. Using machine learning, we identified increased abundance of C. perfringens, as a potential causative agent of GI disease and intestinal strictures in marmosets.es_ES
dc.description.sponsorshipNational Institutes of Health/[T32 OD010978]/NIH/Estados Unidoses_ES
dc.description.sponsorshipNational Institutes of Health/[P30-ES002109]/NIH/Estados Unidoses_ES
dc.description.sponsorshipUniversidad de Costa Rica/[803-C1-163]/UCR/Costa Ricaes_ES
dc.language.isoenges_ES
dc.sourceScientific Reports, vol.12, pp.1-14.es_ES
dc.subjectMarmosetes_ES
dc.subjectBioinformaticaes_ES
dc.subjectMachine learninges_ES
dc.titleAlterations in common marmoset gut microbiome associated with duodenal strictureses_ES
dc.typeartículo científicoes_ES
dc.identifier.doi10.1038/s41598-022-09268-9
dc.description.procedenceUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)es_ES
dc.identifier.codproyecto803-C1-163


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