Tolerance and resistance are complex biological phenotypes that are desirable bioengineering goals for those seeking to design industrial strains or prevent the spread of antibiotic resistance. Over decades of research, a wealth of information has been generated to attempt to decode a molecular basis for tolerance, but to fully achieve the goal of engineering tolerance, researchers must be able to easily learn from a variety of data sources. To this end, we here describe a resource designed to enable scrutiny of diverse tolerance phenotypes. We have curated hundreds of gene expression studies exploring the response of Escherichia coli to chemical and environmental perturbations, from antibiotics to biofuels and solvents and more. Overall, our efforts give rise to a database encompassing more than 56 000 gene expression changes across 89 different stress conditions. This resource is designed for compatibility with the Resistome database, which includes more than 5000 strains with mutations conferring resistance or sensitivity but no transcriptomic data. Thus, the work here results in the first combined resource specialized to tolerance and resistance in E. coli that supports investigations across genomic, transcriptomic, and phenotypic levels. We leverage the database to identify promising bioengineering targets by searching globally across multiple stress conditions as well as by narrowing the focus to fewer conditions of interest, such as biofuel stress and antibiotic stress. We discuss some of the most frequently differentially expressed or coexpressed genes, and predict which transcription factors and sigma factors most likely contribute to gene expression profiles in a wide array of conditions. We also compare profiles from sensitive and resistant strains, gaining knowledge of how responses differ per overrepresented gene ontology terms. Finally, we search for genes that are frequently differentially expressed but not mutated, with the expectation that these may present interesting targets for future engineering efforts. The curated data presented here is publicly available, and should be advantageous to those studying a variety of bacterial tolerance phenotypes.

中文翻译：

---

耐受性：转录组水平对多种大肠杆菌耐药性和耐受性表型的贡献的数据库。

耐受性和耐药性是复杂的生物表型，对于那些试图设计工业菌株或防止抗生素耐药性传播的人来说，是理想的生物工程目标。在数十年的研究中，已经生成了大量信息，试图对耐受性的分子基础进行解码，但是要完全实现工程耐受性的目标，研究人员必须能够轻松地从各种数据源中学习。为此，我们在此描述一种资源，旨在检查各种耐受性表型。我们已经策划了数百项探索大肠杆菌反应的基因表达研究化学和环境干扰，从抗生素到生物燃料和溶剂等等。总的来说，我们的努力建立了一个数据库，该数据库涵盖了89种不同压力条件下的超过56 000个基因表达变化。此资源旨在与Resistome数据库兼容，该数据库包括5000多个具有突变的菌株，这些菌株具有耐药性或敏感性，但没有转录组数据。因此，这里的工作产生了第一个专门针对大肠杆菌的耐受性和耐药性的组合资源支持跨基因组，转录组和表型水平的研究。我们通过在多个压力条件下进行全局搜索以及将关注范围缩小到较少的关注条件（例如生物燃料压力和抗生素压力）中，来利用数据库来确定有前途的生物工程目标。我们讨论了一些最经常差异表达或共表达的基因，并预测了在各种条件下最有可能影响基因表达谱的转录因子和西格玛因子。我们还比较了敏感菌株和抗性菌株的概况，从而了解了每个过度代表的基因本体术语的响应如何不同。最后，我们搜索经常差异表达但不会突变的基因，期望这些可能会为将来的工程工作提出有趣的目标。此处提供的精选数据可公开获得，并且对于研究多种细菌耐受表型的研究人员应该是有利的。