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与十二指肠狭窄相关的普通绒猴肠道微生物组的改变。

Alterations in common marmoset gut microbiome associated with duodenal strictures.

机构信息

Division of Comparative Medicine, Massachusetts Institute of Technology, Cambridge, MA, USA.

Merck Research Laboratories, Merck, South San Francisco, CA, USA.

出版信息

Sci Rep. 2022 Mar 28;12(1):5277. doi: 10.1038/s41598-022-09268-9.

DOI:10.1038/s41598-022-09268-9
PMID:35347206
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8960757/
Abstract

Chronic 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.

摘要

慢性胃肠道(GI)疾病是圈养普通狨猴(Callithrix jacchus)中最常见的疾病。尽管采用了标准化的饲养、饮食和管理方法,但最近在新英格兰灵长类动物研究中心(New England Primate Research Center)的一部分狨猴中观察到了一种以十二指肠溃疡和狭窄为特征的胃肠道综合征。由于肠道微生物组的变化与 GI 疾病有关,因此比较了 52 只健康无狭窄狨猴(153 个样本)的肠道微生物组与 21 只被诊断为十二指肠溃疡/狭窄的圈养狨猴(57 个样本)的肠道微生物组。使用 alpha 多样性指标没有观察到显著变化,尽管在比较健康和狭窄病例的 beta 多样性时,群落结构有显著差异,但由于两个数据集的分散性差异,结果不确定。使用 ANCOM 分析个体分类群的丰度,因为狭窄相关的失调表现为厌氧螺旋菌的丧失和梭状芽胞杆菌属的增加。为了确定有助于分类狭窄病例的微生物和血清生物标志物,我们使用机器学习算法(随机森林、分类和回归树、支持向量机和 k-最近邻)开发了模型,以分类微生物组、血清化学或全血细胞计数(CBC)数据。随机森林(RF)模型是最准确的模型,使用 9 个扩增子序列变体(ASVs)、4 个血清化学测试或 6 个 CBC 测试可正确分类狭窄病例。基于 RF 模型和 ANCOM 结果,鉴定梭状芽胞杆菌属为与狭窄发展相关的潜在致病因子。在组织学证实狭窄的 9 只狨猴的 4 个十二指肠样本中,通过微生物培养也分离到梭状芽胞杆菌属。由于 C. perfringens 在原位富集,我们使用 16S 微生物组分析和 RNAseq 分析了冷冻的十二指肠组织。麻省理工学院(MIT)种群中 29 只狨猴的十二指肠组织微生物组分析证实,狭窄病例中梭状芽胞杆菌属的丰度增加。比较狭窄和非狭窄狨猴的十二指肠基因表达发现,狭窄病例中与肠道吸收、脂质代谢、定位和运输相关的基因富集。使用机器学习,我们确定梭状芽胞杆菌属丰度增加,可能是狨猴 GI 疾病和肠道狭窄的潜在致病因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b942/8960757/2204f4664f7a/41598_2022_9268_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b942/8960757/7a7f4cdcd631/41598_2022_9268_Fig1_HTML.jpg
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