Laboratory models have been invaluable for the field of microbiology for over 100 years and have provided key insights into core aspects of bacterial physiology such as regulation and metabolism. However, it is important to identify the extent to which these models recapitulate bacterial physiology within a human infection environment. Here, we performed transcriptomics (RNA-seq), focusing on the physiology of the prominent pathogen Staphylococcus aureusin situ in human cystic fibrosis (CF) infection. Through principal-component and hierarchal clustering analyses, we found remarkable conservation in S. aureus gene expression in the CF lung despite differences in the patient clinic, clinical status, age, and therapeutic regimen. We used a machine learning approach to identify an S. aureus transcriptomic signature of 32 genes that can reliably distinguish between S. aureus transcriptomes in the CF lung and in vitro The majority of these genes were involved in virulence and metabolism and were used to improve a common CF infection model. Collectively, these results advance our knowledge of S. aureus physiology during human CF lung infection and demonstrate how in vitro models can be improved to better capture bacterial physiology in infection.IMPORTANCE Although bacteria have been studied in infection for over 100 years, the majority of these studies have utilized laboratory and animal models that often have unknown relevance to the human infections they are meant to represent. A primary challenge has been to assess bacterial physiology in the human host. To address this challenge, we performed transcriptomics of S. aureus during human cystic fibrosis (CF) lung infection. Using a machine learning framework, we defined a "human CF lung transcriptome signature" that primarily included genes involved in metabolism and virulence. In addition, we were able to apply our findings to improve an in vitro model of CF infection. Understanding bacterial gene expression within human infection is a critical step toward the development of improved laboratory models and new therapeutics.

中文翻译：

---

囊性纤维化肺部感染期间的金黄色葡萄球菌转录组。


100 多年来，实验室模型对于微生物学领域一直具有无价的价值，并为细菌生理学的核心方面（例如调节和代谢）提供了重要见解。然而，重要的是要确定这些模型在多大程度上概括了人类感染环境中的细菌生理学。在这里，我们进行了转录组学 (RNA-seq)，重点研究人类囊性纤维化 (CF) 感染中主要病原体金黄色葡萄球菌的生理学。通过主成分和层次聚类分析，我们发现尽管患者临床、临床状态、年龄和治疗方案存在差异，但 CF 肺中金黄色葡萄球菌基因表达具有显着的保守性。我们使用机器学习方法来识别 32 个基因的金黄色葡萄球菌转录组特征，这些基因可以可靠地区分 CF 肺中和体外的金黄色葡萄球菌转录组。这些基因中的大多数与毒力和代谢有关，并用于改善常见CF感染模型。总的来说，这些结果增进了我们对人 CF 肺部感染期间金黄色葡萄球菌生理学的了解，并证明了如何改进体外模型以更好地捕捉感染中的细菌生理学。 重要性 尽管对感染中的细菌进行了 100 多年的研究，但大多数这些研究利用了实验室和动物模型，这些模型通常与它们所代表的人类感染的相关性未知。主要挑战是评估人类宿主的细菌生理学。为了应对这一挑战，我们对人类囊性纤维化 (CF) 肺部感染期间的金黄色葡萄球菌进行了转录组学研究。 使用机器学习框架，我们定义了“人类 CF 肺转录组特征”，主要包括参与代谢和毒力的基因。此外，我们能够应用我们的研究结果来改进 CF 感染的体外模型。了解人类感染中的细菌基因表达是开发改进的实验室模型和新疗法的关键一步。