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

项目摘要/摘要 表皮葡萄球菌是人体皮肤常见的共生菌，但也是医院获得性感染。 病原体。这种二元性使这种微生物成为一种相当大的病原体，可能是由于巨大的基因 在它的许多品系中存在的多样性。然而，我们对这一功能后果的理解 遗传多样性有限的部分原因是基因功能特征方面的重大差距(超过25%的基因 没有已知的功能)并且部分是由于复杂的多菌种环境的动态环境效应， 其中几乎总是发现表皮葡萄球菌，这会影响基因的表达、功能和毒力。 然而，对所有表皮葡萄球菌菌株的所有基因功能的综合分析跨越多个 与致病性相关的环境条件将提供一个巨大而棘手的搜索空间。 基因功能的系统评估可以通过多种组学方法产生：例如，转录 可以容易地为所有基因生成数据和基因重要性屏幕数据，而不考虑它们的注释 状态，可以在遗传背景和环境条件的背景下解释，是一种 大规模基因表征的有力工具。目前，一套有限的转录和基因适应性 有一些表皮葡萄球菌菌株的数据存在，但更多的类似数据已经产生。 深入研究的表亲，皮肤病原体金黄色葡萄球菌。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，到较少被探索的基因，包括 菌株特有的基因，可以快速预测相关的基因功能，然后进行实验测试。 因此，我在这个提案中的目标是开发利用现有转录的计算工具 以及来自金黄色葡萄球菌和表皮葡萄球菌的基因重要性数据来鉴定未表征的功能 表皮葡萄球菌中可能决定致病与共生生活方式的基因。在《目标1》中，我将使用 转移学习来推导假定的基因功能，用rna-seq数据对该方法的局限性进行基准测试。 从多菌群落中生长的多株表皮葡萄球菌中收集的重组人 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 在具有应激源和与应激反应转录因子srA的上位性相互作用的表型阵列中 作为一个案例研究，基因在多重应激反应中被认为是重要的。在《目标2》中，我将使用类似的 算法与目标2相同，但包括基因重要性数据以导出特定于条件的基因重要性集团 然后使用基因敲除和表型阵列来验证基因表征集团。建议的工作 这里提出了一种开发工具的框架，该工具用于快速生成假设， 实验假设检验，以确定不同菌株间遗传多样性的功能后果 令人困惑的病原菌表皮葡萄球菌。

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.