Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators and role in key physiological responses

Abstract

The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its Transcriptional Regulatory Network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying Independent Component Analysis (ICA) to a compendium of 108 RNAseq expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed (1) high confidence associations between 21 i-modulons and known regulators; (2) an association between an i-modulon and σS, whose regulatory role was previously undefined; (3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR and Vim-3, (4) the roles of three key transcription factors (codY, Fur and ccpA) in coordinating the metabolic and regulatory networks; and (5) a low dimensional representation, involving the function of few transcription factors, of changes in gene expression between two laboratory media (RPMI, CAMHB) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation. Significance StatementStaphylococcus aureus infections impose an immense burden on the healthcare system. To establish a successful infection in a hostile host environment, S. aureus must coordinate its gene expression to respond to a wide array of challenges. This balancing act is largely orchestrated by the Transcriptional Regulatory Network (TRN). Here, we present a model of 29 independently modulated sets of genes that form the basis for a segment of the TRN in clinical USA300 strains of S. aureus . Using this model, we demonstrate the concerted role of various cellular systems (e.g. metabolism, virulence and stress response) underlying key physiological responses, including response during blood infection. framework to reevaluate the RNA-seq data accelerates discovery by (1) quantitatively formulating TRN organization, (2) simplifying complex changes across hundreds of genes into a few changes in regulator activities, (3) allowing for analysis of interactions among different regulators, (4) connecting transcriptional regulation to metabolism, and (5) defining previously unknown regulons. Main

Laboratory evolution of multipleE. colistrains reveals unifying principles of adaptation but diversity in driving genotypes

“…Distribution of differences in iModulon activities between biological replicates were first calculated and a log-norm distribution was fit to the differences (Poudel et al , 2020) . In order to test statistical significance, absolute value of difference in activity level of each iModulon between the two samples were calculated.…”

Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility

“…The cAMP receptor protein, CRP, is activated when glucose is absent from the media, recruiting RNA polymerase to genes encoding catabolic pathways for alternative carbon sources (Kolb et al, 1993) . Although glucose uptake was not previously detected in CA-MHB (Poudel et al, 2020) , some of its CRP-related i-modulon activities are closer to M9 media than R10LB. Biosynthetic pathways for vitamins and amino acids contributed to 19% and 15% of the expression deviation from M9 for R10LB and CA-MHB, respectively (Figure 3c).…”

“…In each of these strains the SOS-response regulator LexA was no longer activated by hydrogen peroxide treatment, indicating that the mutations reduced DNA damage from reactive oxygen species through constitutive activation of OxyR. I-modulons have also been successfully extracted for additional species, disentangling the transcriptional trajectory of a Bacillus subtilis sporulation time-course (Rychel et al, 2020) , and identifying media-specific transcriptional responses in Staphylococcus aureus (Poudel et al, 2020) .…”

Characterizing the response of Acinetobacter baumannii ATCC 17978 to azithromycin in multiple in vitro growth conditions

“…Fluorescence microscopy was done as previously described with modifications 11,14,15,17 . In brief, at the 2.5 h timepoint, 20 µL cells were added to 1 µL dye mix containing 80 µg/mL DAPI, 10 µM SYTOX Green, and 200 µg/mL FM4-64 in 1x T-base.…”