Network analysis links genome-wide phenotypic and transcriptional stress responses in a bacterial pathogen with a large pan-genome.

Abstract

BackgroundBacteria modulate subcellular processes to handle stressful environments. Genome-wide profiling of gene expression (RNA-Seq) and fitness (Tn-Seq) allows two views of the same genetic network underlying these responses. However, it remains unclear how they combine, enabling a bacterium to overcome a perturbation.\n\nResultsHere we generate RNA-Seq and Tn-Seq profiles in three strains of S. pneumoniae in response to stress defined by different levels of nutrient depletion. These profiles show that genes that change their expression and/or become phenotypically important come from a diverse set of functional categories, and genes that are phenotypically important tend to be highly expressed. Surprisingly, we find that expression and fitness changes rarely occur on the same gene, which we confirmed by over 140 validation experiments. To rationalize these unexpected results we built the first genome-scale metabolic model of S. pneumoniae showing that differential expression and phenotypic importance actually correlate between nearest neighbors, although they are distinctly partitioned into small subnetworks. Moreover, a meta-analysis of 234 S. pneumoniae gene expression studies reveals that essential genes and phenotypically important subnetworks rarely change expression, indicating that they are shielded from transcriptional fluctuations and that a clear distinction exists between transcriptional and phenotypic response networks.\n\nConclusionsWe present a genome-wide computational/experimental approach that contextualizes changes that occur on transcriptomic and phenomic levels in response to stress. Importantly, this highlights the need to connect disparate response networks, for instance in antibiotic target identification, where preferred targets are phenotypically important genes that would be overlooked by transcriptomic analyses alone.