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Mol Syst Biol. 2008; 4: 225.
Published online 2008 Nov 4. doi:  10.1038/msb.2008.63
PMCID: PMC2600667

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


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.

Keywords: tuberculosis, network, origon, growth arrest, hypoxia


A hallmark of Mycobacterium tuberculosis infection is the switching of tubercle bacilli between replicative (growth) and non-replicative (dormancy) states in response to environmental cues generated by the host immune response (Wayne and Sohaskey, 2001; Warner and Mizrahi, 2007). When infection has progressed enough to induce adaptive immune responses, the bacilli survive by slowing down their growth and eventually entering a phenotypic state called dormancy, which enables M. tuberculosis to persist in the immunocompetent host for many years, causing asymptomatic (latent) infection. When host immunity falters, tubercle bacilli can resume growth and reactivate disease (Wayne and Sohaskey, 2001; Warner and Mizrahi, 2007).

Little is known about the dormant state of tubercle bacilli in human infection. A tractable surrogate for dormancy is the arrest (or drastic slowdown) of bacterial growth in particular stress conditions in vitro, including gradual O2 depletion, treatment with nitric oxide (NO), and nutrient starvation. Microarray studies of in vitro cultures have defined transcriptional changes during hypoxia (Sherman et al, 2001; Voskuil et al, 2004), NO treatment (Voskuil et al, 2003), nutrient starvation (Betts et al, 2002; Hampshire et al, 2004), altered pH (Fisher et al, 2002), and treatment with detergents such as SDS (Manganelli et al, 2001). Robust markers of dormancy have emerged, such as the upregulation of the dosR regulon (Park et al, 2003). The induction of dosR-regulated genes in various dormancy models was further underscored by a recent meta-analysis of published microarray data (Murphy and Brown, 2007). Nevertheless, most of these studies have focused only on changes in the expression of individual genes in M. tuberculosis dormancy. Little, if any, attention has been given to the dynamic series of events that occur at the level of the gene regulatory network (Albert, 2005). To understand these aspects of gene regulation during transition to dormancy, time course microarray data should be overlaid with the large-scale transcriptional-regulatory (TR) network of M. tuberculosis, as done earlier for Escherichia coli (Balázsi et al, 2005; Ernst et al, 2008) and Saccharomyces cerevisiae (Ihmels et al, 2004; Farkas et al, 2006). However, the current database of M. tuberculosis gene regulation (Jacques et al, 2005) contains far fewer interactions than the TR network of S. cerevisiae (Harbison et al, 2004; Balaji et al, 2006) and E. coli (Salgado et al, 2006).

To address this problem, we assembled a large M. tuberculosis TR network and used previously published microarray data (Voskuil et al, 2004) to analyze the network-level response of M. tuberculosis to hypoxia and transition into stationary phase. Although the goal of most microarray data analysis methods is to identify individual genes that are significantly up- or downregulated, we aimed to identify significantly responsive subnetworks. This is motivated by the modular structure of biological networks (Wagner et al, 2007), where various sets of modules respond specifically to various types of environmental change. We found a distinct set of transcriptional subnetworks (origons) affected early and late during adaptation to hypoxia and stationary phase, indicating a progressive shift of modular network response to growth arrest. Most of the origons were affected in both conditions, suggesting the existence of a general, condition-independent repertoire of transcriptional modules utilized in M. tuberculosis growth arrest.

Results and discussion

Assembly of a large-scale M. tuberculosis TR network

We compiled a large-scale M. tuberculosis TR network using three main sources. The core of the TR network consists of 381 gene regulatory interactions documented in the literature, 222 of which have been collected in MtbRegList (Jacques et al, 2005), whereas 159 links were added in this study (see Materials and methods). We enlarged this core network including 223 M. tuberculosis gene pairs that have orthologs with confirmed TR relationship in E. coli (Babu et al, 2006). Finally, we augmented the network based on the full list of M. tuberculosis operons (Roback et al, 2007), assuming that transcription factor (TF) binding to the promoter region affects the expression of all genes within an operon. This is a reasonable assumption, as TF-promoter binding dictates the rate at which genes in a typical operon are co-transcribed into polycistronic mRNA (Jacob et al, 1960), although still allowing for post-transcriptional modulation of individual gene expression (Nudler and Gottesman, 2002; Li and Altman, 2004; Isaacs et al, 2006; Pfleger et al, 2006).

The 783 nodes in the TR network (see Figure 1 and Supplementary information) correspond to M. tuberculosis genes and their protein products, whereas the 937 links correspond to 45 TFs (Table I) directly regulating the expression of target genes. Remarkably, 29 of these 45 TFs regulate their own expression, demonstrating the importance of autoregulation in prokaryotic gene networks (Thieffry et al, 1998). In addition, the gene pairs Rv2358-furB, Rv1404-Rv1931c, and mprA-sigE participate in two-gene feedback loops (Figure 1).

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 ...
Table 1
List of transcription factors in the large-scale TR network of M. tuberculosis

We consider 381 (41%) of the 937 interactions in this network relatively reliable, because they are based on experimental studies of 26 TFs binding and regulating the expression of 355 target genes. The operon-based extension of the literature-derived network has 581 links (62%) among 518 genes, which are somewhat less reliable. Finally, the 223 regulatory interactions among 201 genes inferred from orthology with E. coli TF–target gene pairs (Babu et al, 2006) might have the lowest confidence because orthologous TFs in bacteria can have different functions and can regulate different genes (Price et al, 2007). In fact, only 4 of the 223 orthology-based links are in common with the literature-based links. Still, the operon-based expansions of these two networks (581 and 410 links, respectively) share 54 links, supporting the inclusion of orthology-based links into the network.

To the best of our knowledge, this is the largest TR network of M. tuberculosis that has been assembled to date, comprising ∼20% of its genome. In comparison, the current version of the E. coli TR network (excluding sigma factors) contains 1364 genes (∼35%) of the E. coli genome (Salgado et al, 2006). We expect that this large-scale TR network will be a valuable resource for the M. tuberculosis research community, complementing existing efforts of genome-scale data integration (see, for example http://www.tbdb.org).

Topological properties of the M. tuberculosis TR network

To quantitatively characterize the topology of the newly assembled M. tuberculosis TR network, we analyzed and compared its connectivity distribution with that of other existing TR networks. The out-degree distribution (Albert, 2005) did not follow a power law (Khanin and Wit, 2006), but had a heavy tail, indicating that a small number of TF hubs regulate a very large number of targets, whereas most TFs regulate few or no targets. On the other hand, the in-degree distribution had a near-exponential tail to the right of a peak for genes with one regulator, indicating that most genes have only one known transcriptional regulator (Figure 1). Such differences between in- and out-degree distributions have been observed for other TR networks (Thieffry et al, 1998; Guelzim et al, 2002), suggesting a general property of TR network topology (see the Supplementary information for a detailed analysis and comparison with the TR networks of E. coli and S. cerevisiae).

The 783 genes in the M. tuberculosis TR network can be arranged hierarchically (Balázsi et al, 2005) into four layers, which reflect the flow of information from the 34 input nodes (representing 15 TFs that are transcriptionally unregulated and 19 TFs that are regulated only by feedback loops) to the 735 output nodes (representing genes that do not directly regulate the expression of other genes). The 11 nodes that are neither input nor output nodes are transit nodes (Table I). Input and transit nodes mediate information entry into the TR network because their TR activity is affected by various intra- or extracellular changes (Martinez-Antonio et al, 2006). Most (34/45) TFs are input nodes, similar to E. coli, but unlike S. cerevisiae (see the Supplementary information). This may reflect the simplicity of bacterial TR networks as compared with eukaryotes, indicating that bacteria are equipped with a specialized sensing apparatus for diverse environmental stimuli that undergo relatively simple processing before a response is developed.

Because of the directionality and sparseness of links, TFs control the expression of only a limited number of genes in the current version of the TR network. The set of genes regulated directly or indirectly by a given TF forms an origon (an example is shown in Figure 1). This is a generalization of the earlier concept of regulatory subnetworks originating only at the input layer (Balázsi et al, 2005). By contrast, here we allow origons to originate at either input or transit TFs, because any TF can be affected by intra- or extracellular signal(s) (Martinez-Antonio et al, 2006) and relay the perturbation to target genes directly or indirectly. Thus, the number of origons is equal to the number of TFs in the network, and we will refer to the resulting 45 origons by the name of the TF at which they originate (see the Materials and methods).

Origons significantly affected by growth arrest

Having assembled a large-scale TR network of M. tuberculosis, we set out to identify transcriptional subnetworks affected by various conditions. The reason for shifting focus from individual genes to subnetworks is that particular TFs can mediate the up- or downregulation of downstream target genes by post-translational modification while maintaining relatively constant mRNA expression levels. Traditional approaches focusing only on individual genes with significantly altered mRNA expression could miss such TFs (Ideker et al, 2002).

We developed a new method, NetReSFun (Network Response to Step Functions), which takes a network and time course data as inputs, and generates a list of significantly affected subnetworks for each time point as output. NetReSFun is the extension of an earlier approach (Balázsi et al, 2005), with a new scope and modified methodology (see the Materials and methods). We have tested NetResFun on random data, and showed that it can reliably detect the time when a major expression change occurs in a group of genes, such as an origon (see the Supplementary information).

We identified significantly affected M. tuberculosis origons during hypoxia-induced growth arrest by feeding the newly assembled TR network and the recently published time course microarray data GSE8786 (Voskuil et al, 2004) into NetReSFun. Briefly, the program calculates scaled cross-covariances covi(τ) between the expression profile xi(t) of each gene i and a set of step functions s(τ, t) that jump at subsequent time points τ of microarray data collection, e.g., τ∈{4, 6, 8, 10, 12, 14, 20, 30, 80 days} in hypoxia (Figure 2A). Next, the responsiveness ∣zi(τ)∣ of each gene at time point τ is determined as the z-score of covi(τ) when compared with covj(τ) for all other genes (Figure 2A). Finally, the program calculates the responsiveness ZI(τ) of each origon I as the z-score of the average <∣zi(τ)∣>I over all genes in the origon (Figure 2B), when compared to the average <∣zi(τ)∣>R of the same number of genes chosen randomly from the network (see the Materials and methods). The output of NetReSFun consists of origons with ZI(τ)>2, considered ‘significantly responsive' at time point τ. Importantly, the responsiveness ZI(τ) of origon I peaks at times when many genes within the origon have a large expression change (Figure 2C and D). Therefore, the times τ when ZI(τ) peaks occur can be used to classify origons as early or late responders.

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 ...

We classified significantly responding origons as ‘early', ‘intermediate' or ‘late' based on the peak in their responsiveness ZI(τ) over the time course (Figure 2C). For example, the dosR origon was most responsive at day 4, as nearly all dosR-controlled genes changed their expression at this time point (Figure 2B and and3A).3A). Rv0494 and sigD were also early origons, with a ZI(τ) peak on or before day 6. Most of the significantly responsive origons peaked between days 8 and 14. These intermediate origons included furB/zur, crp, sigH, kstR, and sigE-mprA. Finally, late origons such as nadR, Rv1956, and hrcA were most responsive on or after day 20 (Figure 2C). Interestingly, the dosR origon had a second prominent ZI(τ) peak at day 80, corresponding to a gene expression change opposite to day 4 (Figure 2B).

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 ...

We performed a similar analysis for the time course microarray data collected by the same authors at days 0, 6, 8, 14, 24, and 60 in aerated cultures (Voskuil et al, 2004). Surprisingly, 11 of the origons responsive in hypoxia were also significantly responsive during transition to stationary phase (Figure 2D). We found that dosR was again the most prominently responding early origon, but it remained significant longer than in hypoxia (until day 14), presumbly because aerated cultures reach stationary phase later (day 20) than hypoxic cultures stop growing (day 10) (Voskuil et al, 2004), and prompted us to classify origons with a ZI(τ) peak on or before day 8 as ‘early' in the aerated time course. In addition to dosR, other early origons during aerated growth were hrcA and hspR. The origons sigD, nadR, and Rv0494 were most prominently responsive at intermediate time points (days 14 and 24) (Figure 2D), whereas the origons sigC and furB had a ZI(τ) peak on day 60. This indicates that, although the two types of growth arrest elicit response from the same origons, the temporal sequence of these responses is not always identical.

The most consistent early responder is the dosR origon (Figure 3A and C), which seems to be upregulated immediately before the bacteria stop growing in both time courses. By contrast, the origons sigD, hrcA, and Rv0494 respond early in only one of the time courses, raising the possibility that they are condition-dependent initiators of growth arrest along with dosR. Finally, the origons nadR (Figure 3B and D), sigE, sigC, and furB peak consistently after dosR in both time courses (Figure 2C and D), suggesting that they orchestrate the maintenance (rather than the initiation) of dormancy. It will be important to experimentally test how inhibiting early versus late TFs affects the transition to dormancy.

In particular, the condition-dependent activation of other, alternate early origons in addition to dosR might explain the controversy between the early upregulation of the dosR regulon during growth arrest in vitro (Park et al, 2003) and in vivo (Shi et al, 2003) with the ability of a dosR deletion mutant to stop growing in hypoxic cultures and in mice weeks into the time course (Rustad et al, 2008). Considering the likelihood of alternate origon partners joining dosR to initiate dormancy in a condition-dependent manner, the ability of appropriate multiple deletion mutants (including dosR, sigD, hrcA, and Rv0494) to prevent growth arrest should be tested experimentally. Also, the apparent contradiction between the early hyper-virulence and fast growth of the dosR deletion mutant (Parish et al, 2003) and its unaltered dormancy after weeks of culture (Rustad et al, 2008) could be resolved by late origons governing growth arrest regardless of dosR status.

We performed additional control analyses to test the sensitivity of these results to random network rewiring and node removal. Specifically, we used NetReSFun to detect significantly responding regulons instead of origons, and we performed the same analysis on a higher confidence (literature-based) network. All these tests supported the robustness of our findings (see the Supplementary information).


In summary, we have assembled the largest M. tuberculosis TR network available to date, and analyzed its topology, comparing it with two other large-scale TR networks. We have developed a novel method to unravel the temporal network response to a cellular program (growth arrest), and identified early, intermediate, and late origons based on their peak responsiveness during the time course. We found that the sets of TFs governing temporal network response to growth arrest in two different conditions (hypoxia and stationary phase) were highly similar. As growth arrest is key to M. tuberculosis virulence, these regulators can be regarded as potential drug targets.

The present work has several limitations. First, the network-level analysis presented here would benefit from microarray data collected more frequently during the transition of M. tuberculosis into dormancy. The lower the number of samples, the higher the chance of observing high covariance values by pure chance. Second, time course data obtained under additional growth-arresting conditions, such as NO treatment and nutrient starvation, are needed to confirm that the observed repertoire of transcriptional modules generally governs growth arrest. However, no other time course data on M. tuberculosis growth arrest with sufficient time points is currently available. Third, a more complete TR network would improve our analysis significantly. The current version of the M. tuberculosis TR network contains only 45 of the 194 TFs listed in TubercuList. A systematic effort to identify the genes directly regulated by each of the 149 TFs is necessary to obtain an unbiased TR network. Future studies would also benefit from including non-TF regulators of gene expression in the network, such as signaling kinases, the alarmone (p)ppGpp, small peptides, and so on. Fourth, the majority of TFs implicated in network response are feedback-regulated, implying that their expression dynamics during growth arrest needs to be studied at the single cell level to better understand their role in adaptation and cell decision-making (Maamar et al, 2007; Sureka et al, 2008).

Despite the limitations mentioned above, our analysis defines an early, transient involvement of the dosR origon (Rustad et al, 2008), along with origons sigD, hrcA, and Rv0494 in a condition-dependent manner during growth arrest. We also observed that the response of the origons nadR, sigE, sigC, and furB consistently replace dosR late in the time course, independent of the growth arrest conditions. This is in agreement with the proposition that the hypoxic response is maintained by genes that are not dosR-regulated (Rustad et al, 2008). However, our results also indicate that these ‘later' origons are associated not specifically with hypoxia, but rather with the growth arrest per se, largely independent of the initiating stimulus.

Combining time course microarray data and large-scale gene regulatory networks might provide new means to dissect the cellular response to environmental changes at the network level. Such analyses should provide important novel insights into microbial biology and will likely suggest new drug targets.

Materials and methods

Assembly of the large-scale M. tuberculosis TR network

The TR network used in this study was assembled in several steps as follows. First, we created a gene regulatory network consisting of 222 links among 216 genes based on MtbRegList (Jacques et al, 2005), a database that lists the binding sites of 21 TFs and sigma factors. Next, we added to this network 159 links among 164 genes, based on recent studies on the transcriptional regulatory activity of mprA, dosR, Rv1395, Rv2358, furB, Rv0967, kstR, pknH, embR, trcR, and crp (Zahrt and Deretic, 2001; Park et al, 2003; Kendall et al, 2004, 2007; Bai et al, 2005; Canneva et al, 2005; Haydel and Clark-Curtiss, 2006; Sharma et al, 2006; Liu et al, 2007). We also downloaded and included an M. tuberculosis TR network (223 links among 201 genes) inferred from gene orthology with 29 E. coli TFs and their targets (Babu et al, 2006). Finally, we completed the network based on the list of M. tuberculosis operons (Roback et al, 2007), assuming that if a TF regulates a gene within an operon, it also regulates all other gene members of the operon. Following a similar procedure, we have also assembled a separate, purely literature-derived network, with 581 links among 518 genes that should have higher confidence than those in the full network.

The full M. tuberculosis TR network is available for download as Supplementary Table S1. This file contains regulator and target gene pairs identified by their GenBank IDs, their Rv numbers and traditional names whenever available. The last two columns provide information about the source of each regulatory interaction with respect to the literature and gene orthology, respectively. For example, the numbers 0, 1 and 2 in the last column indicate whether a link is not orthology-based (0), is from the original orthology-based network (1) or has been inferred by operon-based extension of the original orthology-based network (2). We used the software Pajek (Batagelj and Brandes, 2005) for network visualization.

Naming of genes and origons

Throughout this paper, we used gene names obtained from FTGPRED (http://www.imtech.res.in/raghava/ftgpred/ANNOTATION/), TubercuList (http://genolist.pasteur.fr/TubercuList/) and the recent literature whenever possible. When the gene name was unknown, we used the Rv number instead.

We mapped origons as subtrees reachable from 45 of the 47 TFs. Two TFs (Rv0144 and Rv3744) regulate no other genes except themselves, and therefore were not considered as origons. Origons were named based on the TF at which they originate. For the feedback loops involving more than one TF (Rv2358-furB, Rv1404-Rv1931c, and mprA-sigE), we chose the TF with more target genes to name the corresponding origons furB, Rv1931c, and sigE, respectively.

Origons significantly affected during transition to non-replicative persistence

The GSE8786 microarray dataset that we used (Voskuil et al, 2004) consisted of two time series: aerated growth and growth arrest in hypoxia. For each gene, we used its expression at day 0 in aerated growth as a control intensity value. We determined the log10 ratios of expression, dividing the intensity on both the hypoxia and aerated growth arrays by this control intensity value.

The tool NetReSFun (available for download as Supplementary information) measures the effect of various stages of growth arrest on each gene's expression by the scaled covariance covi(τ) between the expression (log10 ratio) profile xi(t) of gene i and a step function s(τ, t) that jumps at time point τ:

An external file that holds a picture, illustration, etc.
Object name is msb200863-i1.jpg

where the brackets indicate averaging over genes, the horizontal bar indicates averaging over time, and the letter σ denotes standard deviation. Thus, the covariance covi(τ) is scaled by the standard deviation of the step function s(τ, t):

An external file that holds a picture, illustration, etc.
Object name is msb200863-i2.jpg

ensuring that only the variance of gene expression contributes to gene responsiveness at time τ, defined as the z-score

An external file that holds a picture, illustration, etc.
Object name is msb200863-i3.jpg

Similarly, the responsiveness of origon I at time point τ was defined as the z-score of z-scores, or ‘double Z-score':

An external file that holds a picture, illustration, etc.
Object name is msb200863-i4.jpg

The subscripts I and R indicate averaging over all genes in the origon, and over the same number of genes chosen randomly from the network.

Using the scaled covariance to determine gene affectedness offers the advantage of simultaneously measuring the amplitude of gene expression changes as well as their similarity to a pre-defined signal. More widely used measures, such as the cross-correlation coefficient would only measure the similarity of the expression profile to the external signal, regardless of the amplitude of gene expression changes, ignoring an important characteristic of gene response.

Supplementary Material

Supplementary Information

Supplementary Table

Supplementary Data


We thank Bhaskar Dutta for discussions. This study was supported by MD Anderson start-up funds (GB) and by the NIH (MLG).


The authors declare that they have no conflict of interest.


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