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Elucidating genetic mechanisms of Clostridioides difficile pathogenesis and patient immune manipulation

阐明艰难梭菌发病机制和患者免疫操作的遗传机制

基本信息

批准号：
10601891
负责人：
Emily Maggioncalda
金额：
$ 7.45万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-12-01 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10601891
关键词：
Adult Antibiotic Resistance Antibiotic Therapy Antibiotics Bacteria Bacterial Genome Base Sequence Big Data Bioinformatics Biological Markers Biological Models Centers for Disease Control and Prevention (U.S.)Clinical Clostridium Clostridium difficile Complement Data Data Analyses Data Set Disease Electronic Health Record Epidemiology Exhibits Genes Genetic Genetic Variation Genome Genomics Germination Health Healthcare Healthcare Systems Immune Immune response In Vitro Incidence Individual Infection Integration Host Factors Investigation Knowledge Machine Learning Measurement Measures Mediating Medicine Metadata Methods Michigan Morbidity - disease rate Outcome Pathogenesis Pathway interactions Patient Care Patient risk Patient-Focused Outcomes Patients Performance Phylogeny Predictive Value Process Recording of previous events Reporting Reproduction spores Resolution Risk Factors Role Sampling Serum Severities Structure Symptoms Testing Toxin Training Validation Variant Virulence Factors Work adverse outcome bacterial genetics biomarker identification care burden cost cytokine data repository experience genetic makeup genetic strain genetic variant genome wide association study gut colonization gut inflammation gut microbiome gut microbiota healthcare-associated infections improved in vivo insight microbiome mouse model new therapeutic target novel novel therapeutics pathogen predictive modeling repository response treatment strategy whole genome

项目摘要

Project Summary In their 2019 Antibiotic Resistance Threats Report, the Centers for Disease Control and Prevention listed Clostridioides (formerly Clostridium) difficile as an urgent threat. As the most common healthcare-associated infection, it has an enormous impact on both the lives of individuals and the healthcare system at large. Developing a C. difficile infection (CDI) is most often associated with the recent use of antibiotics, as broad spectrum antibiotics can lead to a disruption of the normal gut microbiota, which in turn allows C. difficile spores to germinate and overwhelm the remaining microbiome that normally keeps the vegetative C. difficile at bay. Although the patient risk factors for CDI are fairly well understood, the potential roles of genetic variation in the infecting strain in influencing the progression to severe CDI are less so. Given the extensive diversity in both the nucleotide sequences of core genes and variation in gene content among common C. difficile strains, it is likely that there are significant differences in how different strains of C. difficile interact with the host. Indeed, there have been numerous reports of variation in the propensity for certain sequence types to cause severe disease, although the genetic variation mediating strain-level differences is largely unknown. In this proposal I take a data driven approach to identify genetic variants influencing patient immune responses and clinical trajectories. To accomplish this, I will leverage a massive data repository created through comprehensive sampling of all C. difficile positive cases at Michigan Medicine. Included in this repository are 1,678 C. difficile whole genome sequenced isolates, associated processed electronic health record data from 1,516 patients, and banked serum during the instance of CDI for 1178 patients. Serum cytokine levels have already been determined for 220 of these patients. Preliminary studies conducted in support of this proposal demonstrate that variation encoded in the genomes of infecting strains are predictive of both initial patient immune responses and subsequent severe infections, supporting the contribution of strain genetic background to patient clinical trajectories. I will build upon these studies and attempt to identify the specific variants, genes and pathways that are mediating variation in clinical outcomes. To this end I will employ a combination of machine learning and bacterial genome-wide association studies (bGWAS) to gain insight into bacterial genetic features that influence patient immune response as quantified by serum cytokine measurements, as well as bacterial genetic variation associated with severe outcomes. I will then validate these bioinformatic findings by evaluating the accuracy of model predictions by comparison of predicted and actual 1) cytokine measures on withheld serum samples, and 2) in vivo severity outcome in a mouse model of CDI. The resulting understanding of the genetic factors of C. difficile that impact patient cytokine response and severity outcome can then be leveraged to improve current treatment strategies, as well as indicate novel targets for therapy against CDI.

项目总结