The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest

Abstract

The virulence of Mycobacterium tuberculosis depends on the ability of the bacilli to switch between replicative (growth) and non-replicative (dormancy) states in response to host immunity. However, the gene regulatory events associated with transition to dormancy are largely unknown. To address this question, we have assembled the largest M. tuberculosis transcriptional-regulatory network to date, and characterized the temporal response of this network during adaptation to stationary phase and hypoxia, using published microarray data. Distinct sets of transcriptional subnetworks (origons) were responsive at various stages of adaptation, showing a gradual progression of network response under both conditions. Most of the responsive origons were in common between the two conditions and may help define a general transcriptional signature of M. tuberculosis growth arrest. These results open the door for a systems-level understanding of transition to non-replicative persistence, a phenotypic state that prevents sterilization of infection by the host immune response and promotes the establishment of latent M. tuberculosis infection, a condition found in two billion people worldwide.

Figure 1

The M. tuberculosis TR network assembled from publicly available sources. Input nodes (genes with no known transcriptional regulators) are shown in blue, whereas transit nodes (TFs with known transcriptional regulators) are shown in green. The white nodes represent output nodes (genes encoding proteins with no TF activity). Triangles mark nodes that autoregulate their own expression, whereas diamonds represent nodes that are part of two-gene feedback loops. As an example, The oxyS origon is indicated by the dashed red line. The insets show the distributions of out-degree (number of target genes a TF can regulate, on the top) and in-degree (number of regulators a gene can have, on the bottom). The dashed line indicates the exponential fit f (x)=0.8e^{−1.78(x−1)}.

Figure 2

Responsiveness of genes and origons. (A) The gene expression profile of the gene devS (top row, left panel) combined with each of nine time-shifted step functions (bottom rows, left panel) give the normalized cross-covariance (middle panel), and then the responsiveness ∣z(τ)∣ (right panel) of devS at each of the nine hypoxia time points starting with day 4. The orange error bars indicate averages and standard deviations over all M. tuberculosis genes. (B) Similar to (A), except the cross-covariance and responsiveness are calculated by combining a single step function s(4, t) with the expression profile of each gene in the dosR origon. The yellow rectangles indicate identical values of cov(τ) and ∣z(τ)∣. (C) Z _I(τ) scores of significantly responsive origons during growth arrest in hypoxia (time points correspond to 4, 6, 8, 10, 12, 14, 20, 30, and 80 days). (D) Z _I(τ) scores of significantly responsive origons during aerated growth (time points correspond to days 6, 8, 14, 24, and 60). Eleven origons (nadR, hspR, Rv0494, sigE, sigC, furB, hrcA, ideR, dosR, sigD, and crp) responded significantly in both time courses. E and L denote the time points of peak response for early and late origons, respectively. Since a step function can only jump at time point 1 or later, time point 0 (day 0) is excluded from panels (C) and (D).

Figure 3

Gene expression profiles in two M. tuberculosis origons affected early and late during hypoxia and stationary phase. The log₁₀ ratios of all genes are shown for (A) the dosR origon during transition to dormancy in hypoxia, (B) the nadR origon during transition to dormancy in hypoxia, (C) the dosR origon during transition to stationary phase following aerated growth (white boxes indicate missing data) and (D) the nadR origon during transition to stationary phase following aerated growth.