*Department of Microbiology, New York University School of Medicine, New York, New York; †Department of Medicine, Division of Gastroenterology, New York University School of Medicine, New York, New York; ‡Department of Veterans Affairs, New York Harbor Healthcare System, New York, New York; §Department of Pathology, New York University School of Medicine, New York, New York; ‖Department of Medicine, Division of Gastroenterology, Mount Sinai School of Medicine, New York, New York; ¶Immunology Institute, Mount Sinai School of Medicine, New York, New York; **Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York; and ††Simons Center for Data Analysis, Simons Foundation, New York, New York.
Inflamm Bowel Dis. 2017 Sep;23(9):1544-1554. doi: 10.1097/MIB.0000000000001208.
Inflammatory bowel diseases (IBD) are believed to be driven by dysregulated interactions between the host and the gut microbiota. Our goal is to characterize and infer relationships between mucosal T cells, the host tissue environment, and microbial communities in patients with IBD who will serve as basis for mechanistic studies on human IBD.
We characterized mucosal CD4 T cells using flow cytometry, along with matching mucosal global gene expression and microbial communities data from 35 pinch biopsy samples from patients with IBD. We analyzed these data sets using an integrated framework to identify predictors of inflammatory states and then reproduced some of the putative relationships formed among these predictors by analyzing data from the pediatric RISK cohort.
We identified 26 predictors from our combined data set that were effective in distinguishing between regions of the intestine undergoing active inflammation and regions that were normal. Network analysis on these 26 predictors revealed SAA1 as the most connected node linking the abundance of the genus Bacteroides with the production of IL17 and IL22 by CD4 T cells. These SAA1-linked microbial and transcriptome interactions were further reproduced with data from the pediatric IBD RISK cohort.
This study identifies expression of SAA1 as an important link between mucosal T cells, microbial communities, and their tissue environment in patients with IBD. A combination of T cell effector function data, gene expression and microbial profiling can distinguish between intestinal inflammatory states in IBD regardless of disease types.
炎症性肠病（IBD）被认为是由宿主与肠道微生物群之间失调的相互作用驱动的。我们的目标是对 IBD 患者的黏膜 T 细胞、宿主组织环境和微生物群落进行特征描述和推断，为人类 IBD 的机制研究提供基础。
我们使用流式细胞术对黏膜 CD4 T 细胞进行了特征描述，同时还对来自 35 个 IBD 患者的黏膜全基因表达和微生物群落数据进行了匹配。我们使用集成框架分析了这些数据集，以确定炎症状态的预测因子，然后通过分析儿科 RISK 队列的数据来再现这些预测因子之间形成的一些假定关系。
我们从组合数据集中共鉴定出 26 个预测因子，这些预测因子可有效区分处于活跃炎症状态的肠段和正常肠段。对这 26 个预测因子的网络分析表明，SAA1 是将拟杆菌属丰度与 CD4 T 细胞产生的 IL17 和 IL22 联系起来的最相关节点。这些 SAA1 相关的微生物和转录组相互作用在儿科 IBD RISK 队列的数据中得到了进一步重现。
本研究确定了 SAA1 的表达作为 IBD 患者黏膜 T 细胞、微生物群落及其组织环境之间的重要联系。T 细胞效应功能数据、基因表达和微生物分析的组合可以区分 IBD 中的肠道炎症状态，而与疾病类型无关。
