High variation across E. coli hybrid isolates identified in metabolism-related biological pathways co-expressed with virulent genes.
Rahul GomesAshleigh Denison KroschelStephanie DayRick J JansenPublished in: Gut microbes (2023)
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.
Keyphrases
- escherichia coli
- genome wide
- genome wide identification
- poor prognosis
- dna methylation
- genome wide analysis
- copy number
- gene expression
- rna seq
- network analysis
- bioinformatics analysis
- transcription factor
- endothelial cells
- long non coding rna
- binding protein
- genetic diversity
- magnetic resonance
- high resolution
- klebsiella pneumoniae
- electronic health record
- machine learning
- cystic fibrosis
- disease virus
- risk assessment
- single molecule
- staphylococcus aureus
- computed tomography
- human health
- artificial intelligence