A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data
A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data
复制标题
DOI：
10.1142/9789813279827_0022
复制
发表时间：
2018-11
影响因子：
--
通讯作者：
Wontack Han;Yuzhen Ye
中科院分区：
文献类型：
--
作者：
Wontack Han;Yuzhen Ye
微生物组研究正在经历一个进化的转变，从关注与不同环境/宿主相关的参考微生物组的特征到翻译应用，包括利用微生物组进行疾病诊断，提高癌症治疗的疗效，以及预防疾病(例如，使用益生菌)。已经从来自不同疾病、治疗反应等的患者队列的微生物组数据中识别出微生物标记，并且通常基于这些标记的预测器被构建用于在给定微生物组数据的情况下预测宿主表型(例如，在给定他或她的微生物组数据的情况下预测一个人是否患有2型糖尿病)。不幸的是，这些微生物标记物和预报器往往没有发表，因此不能被其他人重复使用。在本文中，我们报告了用于基于微生物组预测宿主表型的微生物标记基因库和由这些标记构建的预测器的管理，以及用于使用该库的称为Mi2P(从微生物组到表型)的计算流水线。作为最初的努力，我们专注于与两种疾病相关的微生物标记基因，以及对两种类型的癌症，非小细胞肺癌(NSCLC)和肾细胞癌(RCC)的免疫治疗效果。我们使用我们最近开发的消减组装方法从元基因组数据中鉴定了标记基因。我们表明，根据这些微生物标记基因构建的预测器可以在给定微生物组数据的情况下提供对宿主表型的快速和相当准确的预测。随着我们在这个精确健康和精确医学时代的前进，理解和利用微生物组数据(我们的第二个基因组)变得至关重要，我们相信这样一个储存库将有助于微生物组数据的翻译应用。
The microbiome research is going through an evolutionary transition from focusing on the characterization of reference microbiomes associated with different environments/hosts to the translational applications, including using microbiome for disease diagnosis, improving the efficacy of cancer treatments, and prevention of diseases (e.g., using probiotics). Microbial markers have been identified from microbiome data derived from cohorts of patients with different diseases, treatment responsiveness, etc, and often predictors based on these markers were built for predicting host phenotype given a microbiome dataset (e.g., to predict if a person has type 2 diabetes given his or her microbiome data). Unfortunately, these microbial markers and predictors are often not published so are not reusable by others. In this paper, we report the curation of a repository of microbial marker genes and predictors built from these markers for microbiome-based prediction of host phenotype, and a computational pipeline called Mi2P (from Microbiome to Phenotype) for using the repository. As an initial effort, we focus on microbial marker genes related to two diseases, type 2 diabetes and liver cirrhosis, and immunotherapy efficacy for two types of cancer, non-small-cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We characterized the marker genes from metagenomic data using our recently developed subtractive assembly approach. We showed that predictors built from these microbial marker genes can provide fast and reasonably accurate prediction of host phenotype given microbiome data. As understanding and making use of microbiome data (our second genome) is becoming vital as we move forward in this age of precision health and precision medicine, we believe that such a repository will be useful for enabling translational applications of microbiome data.