Transcriptomics compendia for the study of strain-level genetic diversity of the human skin microbiome

用于研究人类皮肤微生物组菌株水平遗传多样性的转录组学概要

基本信息

批准号：
10751097
负责人：
Georgia Doing
金额：
$ 6.91万
依托单位：
JACKSON LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-30 至 2026-09-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10751097
关键词：
Affect Algorithms Bacteria Benchmarking Biological Assay CRISPR interference Case Study Communities Complex Cues Data Data Set Dependence Drug Tolerance Epidermis Essential Genes Gene Expression Generations Genes Genetic Genetic Transcription Genetic Variation Genomics Goals Growth Hospitalization Hospitals Hour Human Human Microbiome In Vitro Knock-out Knowledge Laboratory Finding Life Style Link Machine Learning Methodology Methods Microbe Microbial Genetics Modeling Mus Nature Organism Outcome Pathogenesis Pathogenicity Pattern Phenotype Process Research Role Skin Staphylococcus aureus Staphylococcus epidermidis Stress System Techniques Technology Testing Virulence Work biological adaptation to stress computational pipelines computerized tools data integration experimental study fitness gene function genetic manipulation genome-wide knock-down member microbial microbiome multiple omics overexpression pathogen phenotypic data response skin microbiome stress tolerance stressor therapeutic target tool tool development transcription factor transcriptome transcriptome sequencing transcriptomics transfer learning unsupervised learning whole genome

项目摘要

PROJECT SUMMARY/ABSTRACT Staphylococcus epidermidis is found across human skin as a common commensal but is also a hospital-acquired pathogen. This duality makes this microbe a considerable pathogen and is likely due to the immense genetic diversity that exists across its many strains. However, our understanding of the functional consequences of this genetic diversity is limited in part due to significant gaps in gene functional characterization (over 25% of genes have no known functions) and in part due to dynamic environmental effects of complex polymicrobial settings, in which S. epidermidis is nearly always found, that can influence gene expression, function and virulence. However, a comprehensive analysis of all gene functions in all S. epidermidis strains across multiple pathogenicity-relevant environmental conditions would present a massive and intractable search space. Systematic assessments of gene function can be generated by multiple `omics approaches: e.g., transcriptional data and gene essentiality screen data can be readily generated for all genes irrespective of their annotation status and can be interpreted within the context of genetic background and environmental conditions, is a powerful tool for large-scale gene characterization. Currently, a limited set of transcriptional and gene fitness data exists for a few strains of S. epidermidis, but extensive analogous data has been generated for its more deeply studied cousin, skin pathogen S. aureus. New algorithms that could use existing data to transfer knowledge from characterized genes, including those present in S. aureus, to lesser explored genes, including strain-specific genes, would rapidly predict relevant gene functions that could then be tested experimentally. Thus, my goal in this proposal is to develop computational tools that leverage existing transcriptomic and gene essentiality data from S. aureus and S. epidermidis to identify functions for uncharacterized genes in S. epidermidis that could determine a pathogenic vs. commensal lifestyle. In Aim 1 I will use transfer learning to derive putative gene functions, benchmark the limits of this method with RNA-seq data collected from multiple strains of S. epidermidis grown in polymicrobial communities on reconstructed human epidermis, and assess the functional characterizations produced by this tool by testing the contributions to growth in a phenotypic array with stressors and epistatic interactions with stress responsive transcription factor SrrA of genes suggested to be important in multiple stress responses, as a case study. In Aim 2 I will use similar algorithms as in Aim 2 but include gene essentiality data to derive condition-specific gene essentiality cliques then validate gene characterization cliques using gene knock-downs and phenotype arrays. The work proposed here presents a framework for the development of tools for rapid hypothesis generation paired with focused, experimental hypothesis testing to identify functional consequences of genetic diversity across strains of the perplexing pathogen S. epidermidis.

项目概要/摘要表皮葡萄球菌作为一种常见的共生菌存在于人类皮肤中，但也是医院获得性的病原。这种二元性使这种微生物成为一种重要的病原体，并且可能是由于巨大的遗传基因其许多品系中存在多样性。然而，我们对这种功能后果的理解遗传多样性受到限制，部分原因是基因功能特征存在显着差距（超过 25% 的基因没有已知的功能），部分原因是复杂的多微生物环境的动态环境影响，其中几乎总是发现表皮葡萄球菌，它可以影响基因表达、功能和毒力。然而，对跨多个表皮葡萄球菌菌株的所有基因功能进行全面分析与致病性相关的环境条件将带来巨大且棘手的搜索空间。基因功能的系统评估可以通过多种“组学”方法进行：例如转录组学方法可以轻松生成所有基因的数据和基因必要性筛选数据，无论其注释如何状态并且可以在遗传背景和环境条件的背景下进行解释，是一个用于大规模基因表征的强大工具。目前，一组有限的转录和基因适应性存在一些表皮葡萄球菌菌株的数据，但更多的类似数据已经生成。深入研究了皮肤病原体金黄色葡萄球菌的表亲。可以使用现有数据传输的新算法从特征基因（包括金黄色葡萄球菌中存在的基因）到较少探索的基因（包括菌株特异性基因将快速预测相关基因功能，然后可以通过实验进行测试。因此，我在本提案中的目标是开发利用现有转录组学的计算工具以及来自金黄色葡萄球菌和表皮葡萄球菌的基因必要性数据，以确定未表征的功能表皮葡萄球菌中可以决定致病性与共生生活方式的基因。在目标 1 中我将使用转移学习推导假定的基因功能，用 RNA-seq 数据衡量该方法的局限性从在重建人体上的多种微生物群落中生长的多种表皮葡萄球菌菌株中收集表皮，并通过测试对生长的贡献来评估该工具产生的功能特征在具有应激源的表型阵列中以及与应激反应转录因子 SrrA 的上位相互作用作为案例研究，基因在多种应激反应中发挥着重要作用。在目标 2 中我将使用类似的算法与目标 2 相同，但包含基因必要性数据以导出特定条件的基因必要性团然后使用基因敲除和表型阵列验证基因表征派系。拟议的工作这里提出了一个开发工具的框架，用于快速生成假设，并结合有针对性的、实验假设检验，以确定跨菌株遗传多样性的功能后果令人困惑的病原体表皮葡萄球菌。