Epigenomic Mechanisms & STAT Networks in Persistent CA Candidemia

项目总结/摘要 在住院患者中，真菌白色念珠菌侵入血液并引起造血功能障碍。 播散性念珠菌病（HDC）。与这种感染相关的死亡率接近40%，即使 抗真菌药物目前的抗真菌治疗。因为念珠菌属（Candida spp.）构成第三大常见原因 医院内血流感染，HDC是一个严重的公共卫生挑战，越来越多的医疗和 社会经济重要性。需要更好地了解真菌和宿主，以开发新的 诊断、预防和治疗HDC的方法。真菌和宿主的特性决定了 哪些患者发展为HDC，以及在这些患者中，哪些患者发展为持续性念珠菌血症和/或死于 感染然而，这些特征并不完全清楚。为了解决这一根本性差距， 知识，我们建议分析从队列中选择的宿主和真菌样品的现有生物储存库 300例念珠菌血症患者的临床资料。我们将比较35例死于念珠菌血症的患者的样本， 来自35名匹配的存活的念珠菌病患者。我们还将分析从选定的患者中分离的系列菌株 持续性念珠菌血症利用这个生物储存库和相关的临床信息，我们将真菌 基因型/表型和宿主表观遗传学、免疫应答和患者结果的信号传导途径。 具体来说，我们将1）确定C。白色念珠菌基因组、表观遗传、转录组和表型特征 与念珠菌血症期间的致死性和持久性相关，以及2）确定宿主对致死性vs. 非致死性和持续性与消退性C.白色念珠菌临床分离株，重点是信号转导和激活剂 转录（STAT）分子。该项目产生的数据将由生物信息学和 数据监控（BDM）核心，并通过计算和预测建模（CPM）核心进行分析， 真菌和宿主的特征都与持续和致命的念珠菌血症有关。这些签名将 然后通过突变和基因沉默方法进行测试，以完善模型并产生新的假设 将以迭代的方式进行测试。这一全面分析的结果有望导致新的 预测、预防和治疗HDC的方法。

PROJECT SUMMARY/ABSTRACT In hospitalized patients, the fungus Candida albicans invades the bloodstream and causes hematogenously disseminated candidiasis (HDC). The mortality associated with this infection approaches 40%, even with antifungal current antifungal therapy. Because Candida spp. constitute the third most common cause of nosocomial blood stream infections, HDC is a serious public health challenge of increasing medical and socioeconomic importance. A better understanding of both the fungus and the host is required to develop new approaches to diagnose, prevent, and treat HDC. Characteristics of both the fungus and the host determine which patients develop HDC, and among those, which ones develop persistent candidemia and/or succumb to infection. However, these characteristics are incompletely understood. To address this fundamental gap in knowledge, we propose to analyze an existing biorepository of host and fungal samples selected from a cohort of 300 patients with candidemia. We will compare samples from 35 patients with candidemia who died with those from 35 matched candidemic patients who survived. We will also analyze serial isolates from selected patients with persistent candidemia. Using this biorepository and associated clinical information, we will link fungal genotype/phenotype, and host epigenetics, immune responses, and signaling pathways to patient outcome. Specifically, we will 1) determine C. albicans genomic, epigenetic, transcriptomic, and phenotypic signatures associated with lethality and persistence during candidemia and 2) determine the host response to lethal vs. non-lethal and persisting vs. resolving C. albicans clinical isolates, with a focus on signal transducer and activator of transcription (STAT) molecules. The data generated by the project will be compiled by the Bioinformatics and Data Monitoring (BDM) core and analyzed by the Computation and Predictive Modeling (CPM) core to identify both fungal and host signatures that are associated with persistent and lethal candidemia. These signatures will then be tested by mutational and gene silencing approaches to refine the models and generate new hypotheses that will be tested in an iterative fashion. The results of this comprehensive analysis hold promise to lead to new approaches to predict, prevent, and treat HDC.