High variation across E. coli hybrid isolates identified in metabolism-related biological pathways co-expressed with virulent genes

ABSTRACT

Virulent genes present in Escherichia coli (E. coli) can cause significant human diseases. These enteropathogenic E. coli (EPEC) and enterotoxigenic E. coli (ETEC) isolates with virulent genes show different expression levels when grown under diverse laboratory conditions. In this research, we have performed differential gene expression analysis using publicly available RNA-seq data on three pathogenic E. coli hybrid isolates in an attempt to characterize the variation in gene interactions that are altered by the presence or absence of virulent factors within the genome. Almost 26.7% of the common genes across these strains were found to be differentially expressed. Out of the 88 differentially expressed genes with virulent factors identified from PATRIC, nine were common in all these strains. A combination of Weighted Gene Co-Expression Network Analysis and Gene Ontology Enrichment Analysis reveals significant differences in gene co-expression involving virulent genes common among the three investigated strains. The co-expression pattern is observed to be especially variable among biological pathways involving metabolism-related genes. This suggests a potential difference in resource allocation or energy generation across the three isolates based on genomic variation.

KEYWORDS:

• E. coli
• EPEC
• ETEC
• WGCNA
• enrichment analysis
• differential expression

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

This is a secondary data analysis. The original data that support the findings of this study are openly available at https://www.ncbi.nlm.nih.gov/. The accession numbers can be found in⁵¹ https://doi.org/10.1038/s41598-017-03489-z.

Additional information

Funding

NDSU Bioinformatics SEED grant/ND INBRE Health and the Environment project grant [UND0022287] and by the National Institute of General Medical Sciences of the National Institutes of Health COBRE grant [1P20GM109024]. NIH grant [P30 CA77598] utilizing the Biostatistics and Bioinformatics Core shared resources of the Masonic Cancer Center, University of Minnesota, and the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number [UL1TR002494]