A multi-scale expression and regulation knowledge base for Escherichia coli

Abstract

Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.

Understanding and exploitation of host stress responses to protein production using novel transcriptomic analytics

“…This was calculated by taking the iModulon activities of overexpression samples (Cluster 1 and 2) and subtracting them from the activities of the control samples (Cluster 3). For the analysis of iModulons, we filtered out the Rhamnose iModulon as well as iModulons under the system categories Genetic Alterations (22 iModulons) and ALE effects (13), based annotations from Precise-1K (Lamoureux et al, 2023). The resulting top 21 differentially activated iModulons are shown in Figure 2a.…”

“…Log-TPM data was batch corrected using pyComBat (Behdenna et al, 2023), then inspected for quality and appended to the Precise-1K RNAseq compendium detailed in https://github.com/SBRG/Precise1k-analyze (Lamoureux et al, 2023). This resulted in a total of 1,081 expression profiles, 46 of which were generated for this study.…”

“…For the analysis, iModulons related to genetic differences were filtered out. This was done by removing iModulons under the system categories Genetic alterations (22 iModulons) and ALE effects (13 iModulons) based on iModulon annotations from Precise-1K (Lamoureux et al, 2023).…”

“…Here, gene expression data is decomposed into independently modulated sets of genes known as iModulons (Sastry et al, 2019). A knowledge base has been developed for transcriptomics datasets for different organisms (iModulonDB), and recently, a large library of 1035 high-quality E. coli samples was assembled, known as Precise-1K (Lamoureux et al, 2023; Rychel et al, 2021). Before this, ICA was used on E. coli transcriptomes to study transcriptional responses to overexpression of 40 heterologous enzymes (Tan et al, 2020).…”

EnhancingEscherichia coliAbiotic Stress Resistance through Ornithine Lipid Formation

“…The filters used for the DEGs were: absolute log2FoldChange >= 1 and p -value adjust <= 0.05. The iModulon analysis consists of adding the differential expression data to each iModulon, and the order within the graphs was made based on the "system category" field (Rychel et al 2021; Lamoureux et al 2023). The accession number for the RNAseq data reported in this study is GEO: GSE233657.…”