Mapping the Transcriptional and Fitness Landscapes of a Pathogenic E. coli Strain: The Effects of Organic Acid Stress under Aerobic and Anaerobic Conditions

• Published in: Genes 2020-12, Vol.12 (1), p.53
• Main Authors: Bushell, Francesca, Herbert, John M J, Sannasiddappa, Thippeswamy H, Warren, Daniel, Turner, A Keith, Falciani, Francesco, Lund, Peter A
• Format: Article
• Language: English
• Subjects: Anaerobic conditions; Datasets; E coli; Fatty acids; Fermentation; Fitness; Gene expression; Gene mapping; Genomes; Genotype & phenotype; Infections; pH effects; Physical fitness; Transcription; Transposons
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes12010053

Several methods are available to probe cellular responses to external stresses at the whole genome level. RNAseq can be used to measure changes in expression of all genes following exposure to stress, but gives no information about the contribution of these genes to an organism's ability to survive the stress. The relative contribution of each non-essential gene in the genome to the fitness of the organism under stress can be obtained using methods that use sequencing to estimate the frequencies of members of a dense transposon library grown under different conditions, for example by transposon-directed insertion sequencing (TraDIS). These two methods thus probe different aspects of the underlying biology of the organism. We were interested to determine the extent to which the data from these two methods converge on related genes and pathways. To do this, we looked at a combination of biologically meaningful stresses. The human gut contains different organic short-chain fatty acids (SCFAs) produced by fermentation of carbon compounds, and is exposed to these in its passage through the gut. Their effect is likely to depend on both the ambient pH and the level of oxygen present. We, therefore, generated RNAseq and TraDIS data on a uropathogenic strain grown at either pH 7 or pH 5.5 in the presence or absence of three SCFAs (acetic, propionic and butyric), either aerobically or anaerobically. Our analysis identifies both known and novel pathways as being likely to be important under these conditions. There is no simple correlation between gene expression and fitness, but we found a significant overlap in KEGG pathways that are predicted to be enriched following analysis of the data from the two methods, and the majority of these showed a fitness signature that would be predicted from the gene expression data, assuming expression to be adaptive. Genes which are not in the core genome were found to be particularly likely to show a positive correlation between level of expression and contribution to fitness.