Large-scale profiling of microbial and metabolic interactions between C. difficile and gut microbiota using ultrahigh-throughput droplet microfluidics

使用超高通量液滴微流体对艰难梭菌和肠道微生物群之间的微生物和代谢相互作用进行大规模分析

• 批准号: 10473701
• 负责人: Ophelia Venturelli
• 金额: $ 23.33万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-23 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473701
• 关键词: 16S ribosomal RNA sequencing; Affect; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Automobile Driving; Bile Acids; Biological Assay; Biomass; Cells; Cessation of life; Clostridium difficile; Coculture Techniques; Colitis; Communities; Complex; Data; Development; Dimensions; Encapsulated; Environment; Environmental Risk Factor; Equilibrium; Fluorescence-Activated Cell Sorting; Fluorescent in Situ Hybridization; Foundations; Future; Genetic; Graph; Growth; Health; Hospitalization; Human; Infection; Internet; Intestines; Investigational Therapies; Knowledge; Lead; Machine Learning; Measurement; Mediating; Metabolic; Methods; Microbe; Microfluidics; Modeling; Molecular; Morbidity - disease rate; Nosocomial Infections; Nutrient; Outcome; Pathway Analysis; Patients; Pattern; Phenotype; Play; Property; Recurrence; Ribosomal RNA; Risk; Risk Factors; Role; Sampling; Sepharose; Signal Transduction; Stains; Techniques; Technology; Transplantation; Variant; Work; antibiotic-associated diarrhea; base; commensal microbes; design; effective therapy; enteric pathogen; fecal transplantation; gut bacteria; gut inflammation; gut microbiome; gut microbiota; healthcare-associated infections; high dimensionality; large scale data; machine learning method; member; metabolic profile; metabolomics; microbial; microbial community; microbiome; microbiota; microorganism interaction; mortality; novel; pathogen; prevent; random forest; recurrent infection; statistical and machine learning; success; tool; transmission process; 16S ribosomal RNA sequencing; Affect; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Automobile Driving; Bile Acids; Biological Assay; Biomass; Cells; Cessation of life; Clostridium difficile; Coculture Techniques; Colitis; Communities; Complex; Data; Development; Dimensions; Encapsulated; Environment; Environmental Risk Factor; Equilibrium; Fluorescence-Activated Cell Sorting; Fluorescent in Situ Hybridization; Foundations; Future; Genetic; Graph; Growth; Health; Hospitalization; Human; Infection; Internet; Intestines; Investigational Therapies; Knowledge; Lead; Machine Learning; Measurement; Mediating; Metabolic; Methods; Microbe; Microfluidics; Modeling; Molecular; Morbidity - disease rate; Nosocomial Infections; Nutrient; Outcome; Pathway Analysis; Patients; Pattern; Phenotype; Play; Property; Recurrence; Ribosomal RNA; Risk; Risk Factors; Role; Sampling; Sepharose; Signal Transduction; Stains; Techniques; Technology; Transplantation; Variant; Work; antibiotic-associated diarrhea; base; commensal microbes; design; effective therapy; enteric pathogen; fecal transplantation; gut bacteria; gut inflammation; gut microbiome; gut microbiota; healthcare-associated infections; high dimensionality; large scale data; machine learning method; member; metabolic profile; metabolomics; microbial; microbial community; microbiome; microbiota; microorganism interaction; mortality; novel; pathogen; prevent; random forest; recurrent infection; statistical and machine learning; success; tool; transmission process

PROJECT SUMMARY/ABSTRACT Clostridium difficile (C. difficile) is a major antibiotic resistant intestinal pathogen that is a leading cause of antibiotic associated diarrhea and colitis which, in severe cases, can lead to death. Treatment with antibiotics frequently leads to recurrence of the infection, which is then treated with fecal microbiota transplantation (FMT). In this case, a fecal sample from a healthy donor is transplanted into a patient with C. difficile infection, which has been shown to prevent recurrence of infections. Notably, FMT can lead to negative health outcomes including death due to the potential transmission of pathogens and uncharacterized factors in the samples. The observed efficacy of FMT suggests that commensal gut bacteria play a critical role in suppressing infection caused by C. difficile. Previous studies have elucidated specific molecular mechanisms that can influence C. difficile colonization including exploitation of key nutrients available in the inflamed gut and secondary bile acids. We postulate that there are diverse classes of ecological and molecular mechanisms mediating protection from C. difficile depending on the ecological and environmental context. Indeed, treatments of C. difficile infection based on defined microbiota have not proven successful. To elucidate the diverse classes of mechanisms, we propose to develop a droplet microfluidic workflow to construct millions of synthetic human gut communities, screen these consortia based on the abundance of C. difficile and determine species composition of the sorted “hits.” Combining this method with exo-metabolomics and machine learning techniques, we will infer microbial interactions and metabolite effectors impacting C. difficile growth. A detailed understanding of the diverse community types and metabolic properties that suppress C. difficile growth will be a major advance towards designing safe and effective treatments for this major intestinal pathogen.

项目摘要/摘要 艰难梭菌（艰难梭菌）是一种主要的抗生素抗生素肠道病原体，是导致的主要原因 抗生素相关的腹泻和结肠炎，在严重的情况下会导致死亡。用抗生素治疗 经常导致感染的复发，然后用粪便菌群移植（FMT）处理。 在这种情况下，将来自健康供体的粪便样本移植到艰难梭菌感染的患者中，该样本 已证明可以防止感染复发。值得注意的是，FMT会导致负面健康结果 包括由于病原体的潜在传播和样品中未表征的因子而导致的死亡。 观察到的FMT效率表明，共生肠道细菌在抑制感染中起着关键作用 由艰难梭菌引起。先前的研究阐明了可能影响C的特定分子机制。 艰难的殖民化包括对发炎的肠道和继发性胆汁酸中可用的关键营养素的开发。 我们假设有介导保护的生态和分子机制的潜水员类别 艰难梭菌取决于生态和环境环境。确实，艰难梭菌感染的治疗 基于确定的微生物群，尚未证明成功。为了阐明潜水员的机制类别，我们 提议开发液滴微流体工作流程，以构建数百万的合成人类肠道群落， 根据艰难梭菌的丰富度筛选这些联盟，并确定排序的物种组成 “命中。”将此方法与exo-Metabolomics和机器学习技术相结合，我们将推断微生物 相互作用和代谢物影响艰难梭菌的生长。对潜水员的详细理解 抑制艰难梭菌增长的社区类型和代谢特性将是向 为这种主要的肠道病原体设计安全有效的治疗方法。