Pathogenesis of Primary Biliary Cholangitis

项目总结/摘要 这项提案的主要目标是进行第一个多组学翻译研究的主要胆汁 胆管炎（PBC），从而确定系统水平的网络驱动的病理过程中，这种罕见的， 自身免疫性肝病迫切需要提高对PBC发病机制的认识， 护理和开发新的有效疗法。全面评估免疫力和 PBC目前缺乏环境影响。这种评价将提供重要的知识， 利用该领域最近的进展，改变免疫系统的能力，从而提供 给PBC患者带来了新的希望。对理解基因组学做出了重大贡献， PBC自身免疫发展的基础架构，我们提出了一种新的，以患者为导向的，多 组学方法。在这个新的应用程序中，我们将破译外周细胞免疫和非细胞免疫是如何发生的。 循环因素参与PBC的发病。我们假设多组学分析整合 细胞和非细胞因素将鉴定驱动PBC发病机制的系统水平途径。测试 基于这一假设，我们开发了一个创新平台，将机器学习和量子 统计力学来识别PBC的基于组学的特征，当与临床特征整合时， 将揭示驱动疾病发病机制的生物学途径。 为了进行PBC的多组学研究，我们组建了一个世界级的多学科团队 整合PBC生物学和组学规模分析方面的专业知识以及马约诊所的资源， 哥伦比亚大学。从300个深海中收集到的各种生物标本- 表型PBC患者和300名匹配良好的对照，我们的研究已经在进行中，初步 证明可测量的免疫组、甲基化组、炎症蛋白、代谢组和代谢组的数据 PBC患者和对照组之间的差异。在目标1中，我们彻底评估了外周免疫 组成（免疫组）和激活状态（甲基化组，转录组，炎性蛋白） 质谱-细胞术（CyTOF）、基于测序和邻近延伸的方法。在目标2中，我们执行 外源性化学物质"干扰组"和内源性代谢物"代谢组"的前沿研究 使用高分辨质谱来发现PBC中代谢的致病性改变。我们 还开发了一种测定方法，以定量血液中肝脏特异性无细胞DNA，作为疾病严重程度的衡量标准。在aim中 3，我们使用一种新的方法来识别PBC并对其进行优先级排序， 相关特征，以进行进一步的生物学研究。然后，我们推断PBC的临床相关亚组 患者进行相似性网络融合分析。总之，使用最先进的多组学 分析，我们将发现系统级网络驱动PBC发病机制，刺激新的发展， 旨在阐明PBC病理生物学和确定新疗法的假设和研究。

PROJECT SUMMARY/ABSTRACT The major goal of this proposal is to conduct the first multi-omics translational study of Primary Biliary Cholangitis (PBC), thereby identifying the systems-level networks driving pathological processes in this rare, autoimmune liver disease. Improved understanding of PBC pathogenesis is urgently needed to inform tailored care and the development of new effective therapies. Comprehensive assessments of immunity and the role of environmental influence in PBC are currently lacking. Such evaluations would provide critical knowledge to leverage recent advances in the field’s ability to pharmacologically alter the immune system, thereby providing new hope to PBC patients. Having made significant contributions to the understanding of the genomic architecture underlying development of autoimmunity in PBC, we propose a novel, patient-oriented, multi- omics approach. In this new application, we will decipher how peripheral cellular immunity and non-cellular circulating factors contribute to PBC pathogenesis. We hypothesize that multi-omic analyses integrating cellular and non-cellular factors will identify systems-level pathways driving PBC pathogenesis. To test this hypothesis, we develop an innovative platform that combines aspects of machine learning and quantum statistical mechanics to identify omics-based signatures of PBC that when integrated with clinical features will unveil biological pathways driving disease pathogenesis. To perform this multi-omic study of PBC, we have assembled a world-class, multi-disciplinary team synergizing expertise in PBC biology and omics-scale analytics as well as resources across Mayo Clinic and Columbia University. With a new, in-hand collection of diverse biological specimens from 300 deeply- phenotyped PBC patients and 300 well-matched controls, our studies are already underway with preliminary data demonstrating measureable immunome, methylome, inflammatory protein, exposome, and metabolome differences between PBC patients and controls. In Aim 1, we thoroughly evaluate peripheral immune composition (the immunome) and activation state (methylome, transcriptome, inflammatory proteins) using mass-cytometry (CyTOF), sequencing- and proximity extension-based methods. In Aim 2, we perform a cutting-edge study of exogenous chemicals “the exposome” and endogenous metabolites “the metabolome” using ultrahigh resolution mass spectroscopy to discover pathogenic alterations in metabolism in PBC. We also develop an assay to quantify liver-specific cell-free DNA in blood as a measure of disease severity. In Aim 3, we integrate omic-specific signatures (Aims 1 and 2) using a novel approach to identify and prioritize PBC- associated features for further biological investigation. We then infer clinically-relevant subgroups of PBC patients by performing similarity network fusion analysis. In summary, using state-of-the-art, multi-omic analyses, we will discover systems-level networks driving PBC pathogenesis, spurring development of new hypotheses and studies designed to elucidate PBC pathobiology and identify novel therapies.