Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria

Abstract

SUMMARY Bacterial genes that change in expression upon environmental disturbance have commonly been seen as those that must also phenotypically matter. However, several studies suggest that differentially expressed genes are rarely phenotypically important. We demonstrate, for Gram-positive and Gram-negative bacteria, that these seemingly uncoordinated gene sets are involved in responses that can be linked through topological network analysis. However, the level of coordination is stress-dependent. While a well-coordinated response is triggered in response to nutrient stress, antibiotics trigger an uncoordinated response in which transcriptionally and phenotypically important genes are neither linked spatially nor in their magnitude. Moreover, a gene expression meta-analysis reveals that genes with large fitness changes during stress have low transcriptional variation across hundreds of other conditions, and vice versa. Our work suggests that cellular responses can be understood through network models that incorporate regulatory and genetic relationships, which could aid drug target predictions and genetic network engineering.

ShinyOmics: collaborative exploration of omics-data

“…Comparison of Tn-Seq and RNA-Seq data from S. pneumoniae antibiotic experiments and a comparison of microarray and proteomic data from M. tuberculosis shows a lack of similarity in the responses in the different screens (Additional file 1: Figure S1). This is in accordance with previous findings that systems-level data are often quite distinct, and different systems should not be taken as substitutes of one another, but rather complementary parts of the organism as a whole [18,40].…”

“…Similarly, proteomic screens can identify proteins relevant for virulence, or cancer biomarkers [9][10][11][12]. Furthermore, phenotypic profiling using transposon insertion sequencing (Tn-Seq) in human pathogens has identified genes involved in colonization, infection, and intrinsic antibiotic resistance; and has been used in genetic interaction mapping [13][14][15][16][17][18].…”

The landscape of intrinsic and evolved fluoroquinolone resistance inAcinetobacter baumanniiincludes suppression of drug-induced prophage replication

“…RNA samples were diluted, combined with SUPERase-in (Invitrogen), and processed via the RNAtag-seq method (42). Illumina cDNA sequencing libraries were sequenced and reads processed as described (63). Differential expression was calculated using DESeq2 (64).…”

Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

“…We tested if the enzymes in the smaller E sets show stronger functional connections. We previously used a collection of 214 gene expression datasets for P. aeruginosa to calculate the correlation coefficient for all pairs of metabolic genes [27]. Overall, pairs of genes assigned to the same E are more correlated than either pairs of genes selected from the entire metabolic network or the E(R) sets (Figure 3b).…”

“…B. A meta-analysis of P. aeruginosa expression data[27] reveals strong pairwise correlations between enzymes in directly computed sets. The pairwise correlations are weaker among enzymes associated with the same coupled reaction set.…”