# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 4 Discussion  
In this study, we used a systems biology approach by analyzing hundreds of transcriptomic data sets to identify key genes and pathways involved in the stress tolerance of _E. coli_ K-12 MG1655, as a model for bacterial response to multiple stressors. Although genes involved in specific stress responses have been frequently studied in _E. coli_, the underlying mechanisms behind global stress response remain to be explored, as only a handful of studies explore multiple stress responses as a complex network, with central proteins controlling underlying mechanisms. Our approach offers a thorough investigation of transcriptomic data in response to five different stressors: heat, cold, oxidative, and nitrosative stress, along with antibiotic treatment--providing a comprehensive overview of _E. coli_ stress response. Our work is a step toward understanding the control of stress response, and toward identifying common gene sets connecting and regulating important biochemical pathways. Consequently, these pathways could be potential drug targets for overcoming antibiotic tolerance or multidrug resistance. Figure 7: Three main sub-clusters (numbered from **A**–**C**) were identified by the MCODE clustering algorithm from the network of the downregulated genes in at least four stressors. Examples of the hub genes are briefly discussed in the Discussion section. While looking for DEGs under five different stress types, we observed a substantial set of common DEGs. In most cases, all or most genes of an operon clustered together or showed the same expression profile; however, it was also common that some genes within the same operon were not detected in every stress, while their chromosomal neighbors were. This is expected with the use of inclusion/exclusion thresholds for DEGs (e.g., statistical significance thresholds, or a certain percentile rank to include). Among the other reasons are batch effects, inter-experiment variabilities, and the different sensitivities of different probes or primers used for transcript quantification. The variability between media types (e.g., rich LB vs. defined M9 or MOPS media) had different effects on sample clustering depending on the applied stressor. For example, in the heat stress category, samples from the GSE42675, GSE15534, and GSE20305 studies were clustered in the right panel of the heat map. The first two studies shared the same type of medium (M9) and time of exposure (10 min) with a small difference in the applied temperature. Samples from GSE15534 and GSE2030 studies shared the same time of exposure (10 min) and temperature (45 \({}^{\circ}\)C), but used different media types (M9 and MOPS, respectively). In the oxidative stress data sets, samples from studies GSE20305 and GSE61736 were clustered together in the left panel of the heatmap, as they shared the source of oxidative stress (H\({}_{2}\)O\({}_{2}\)) and the same culture medium (MOPS), while GSE56133 sample was clustered with them as a different culture medium was used (LB) with the same type of stress (H\({}_{2}\)O\({}_{2}\)). Among the sets of upregulated genes, six genes were upregulated under all the five studied conditions:  
* _osmB_ encodes an outer membrane lipoprotein, known to be upregulated by osmotic shock and stationary phase [16; 17; 18]. Although the function of _osmB_ is not fully characterized [19], its transcriptional regulation was thoroughly studied and found to depend on the response regulator, RcsB, and its transcription was found to increase following the activation of the RcsCDB phosphorelay [20]. Our analysis indicated that _osmY_, _osmF_, and _osmC_ were downregulated under three, two, and one stressor, respectively. * _nrdH_ and _nrdH_: NrdH, a glutaraldehyde-like protein, serves as the electron donor for NrdEF and is reduced by thioredoxin reductase [21].