Epigenomic Mechanisms & STAT Networks in Persistent CA Candidemia

表观基因组机制

• 批准号: 10551709
• 负责人: Scott G Filler
• 金额: $ 37.83万
• 依托单位: LUNDQUIST INSTITUTE FOR BIOMEDICAL INNOVATION AT HARBOR-UCLA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551709
• 关键词: Address; Adult; Affect; Aneuploidy; Antifungal Agents; Antifungal Therapy; Bioinformatics; Blood; Blood Circulation; Candida; Candida albicans; Candidate Disease Gene; Cell Differentiation process; Cell physiology; Characteristics; Chromatin; Chromosomes; Clinical; Computer Models; DNA Sequence Rearrangement; Data; Diagnosis; Disease; Disseminated candidiasis; Endothelial Cells; Epigenetic Process; Filament; Fungal Drug Resistance; Gene Silencing; Genetic; Genetic Polymorphism; Genetic Variation; Genomics; Genotype; Growth; Hematogenous; Hospitalization; Host Defense Mechanism; Human; IL12B gene; IL8RA gene; Immune; Immune response; Immune signaling; Immune system; Individual; Infection; Interleukin-10; Invaded; Knowledge; Life; Link; Loss of Heterozygosity; Medical Economics; Microbial Biofilms; Modeling; Molecular; Monitor; Mucous Membrane; Mus; Mutation; Nosocomial Infections; Outcome; Pathogenesis; Patient Selection; Patient-Focused Outcomes; Patients; Pattern; Phagocytosis; Phenotype; Predisposition; Production; Public Health; Research; Risk; Risk Factors; Role; STAT protein; Sampling; Sepsis; Signal Pathway; Signal Transduction; Stream; System; TLR1 gene; Testing; Transcriptional Activation; Variant; Virulence; Virulence Factors; Whole Blood; biobank; candidemia; cohort; cytokine; epigenomics; fungus; genome sequencing; genome-wide; immune activation; insight; mortality; mouse model; neonate; novel; novel diagnostics; novel strategies; novel therapeutic intervention; oropharyngeal thrush; pathogen; predictive modeling; prevent; response; socioeconomics; transcription factor; transcriptome sequencing; transcriptomics; treatment response; virtual; whole genome

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.

项目概要/摘要 在住院患者中，真菌白色念珠菌侵入血液并导致血源性感染 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 目前的抗真菌治疗。因为念珠菌属。构成第三个最常见的原因 医院内血流感染、HDC 是日益增加的医疗和医疗服务所面临的一个严重的公共卫生挑战。 社会经济重要性。需要更好地了解真菌和宿主来开发新的 诊断、预防和治疗 HDC 的方法。真菌和宿主的特征决定了 哪些患者出现 HDC，哪些患者出现持续性念珠菌血症和/或死于 HDC 感染。然而，这些特征尚不完全了解。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本的现有生物储存库 300 名念珠菌血症患者。我们将比较 35 名死于念珠菌血症的患者的样本 来自 35 名匹配的幸存念珠菌患者。我们还将分析来自选定患者的连续分离株 患有持续性念珠菌血症。使用这个生物样本库和相关的临床信息，我们将链接真菌 基因型/表型，以及宿主表观遗传学、免疫反应和影响患者结果的信号通路。 具体来说，我们将 1) 确定白色念珠菌基因组、表观遗传、转录组和表型特征 与念珠菌血症期间的致死率和持续性相关，2) 确定宿主对致死性与非致命性的反应。 非致命和持久性与解决性白色念珠菌临床分离株，重点是信号转换器和激活剂 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) 核心并由计算和预测建模 (CPM) 核心进行分析，以识别 与持续性和致命性念珠菌血症相关的真菌和宿主特征。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 将以迭代方式进行测试。这项综合分析的结果有望带来新的结果 预测、预防和治疗 HDC 的方法。