Abstract
Because of its high pathogenicity and infectivity, tuberculosis is a serious threat to human health. Some information about the functions of the genes in Mycobacterium tuberculosis genome was currently available, but it was not enough to explore transcriptional regulatory mechanisms. Here, we applied the WGCNA (Weighted Gene Correlation Network Analysis) algorithm to mine pooled microarray datasets for the M. tuberculosis H37Rv strain. We constructed a co-expression network that was subdivided into 78 co-expression gene modules. The different response to two kinds of vitro models (a constant 0.2% oxygen hypoxia model and a Wayne model) were explained based on these modules. We identified potential transcription factors based on high Pearson’s correlation coefficients between the modules and genes. Three modules that may be associated with hypoxic stimulation were identified, and their potential transcription factors were predicted. In the validation experiment, we determined the expression levels of genes in the modules under hypoxic condition and under overexpression of potential transcription factors (Rv0081, furA (Rv1909c), Rv0324, Rv3334, and Rv3833). The experimental results showed that the three identified modules related to hypoxia and that the overexpression of transcription factors could significantly change the expression levels of genes in the corresponding modules.
Highlights
Mycobacterium tuberculosis (MTB) is a pathogenic bacterium that causes tuberculosis
We identified some modules related to hypoxia and predict the potential transcription factors (TFs) involved
The results show that MTB responses to the hypoxia models had shared values in some modules and different values in other modules, which provided clues to the stress response mechanism of MTB
Summary
Mycobacterium tuberculosis (MTB) is a pathogenic bacterium that causes tuberculosis. MTB infects about a third of the global population and leads to more than two million deaths each year[1]. The functions of some essential MTB genes are still not well known, and the regulatory mechanisms need to be further investigated. We studied the MTB transcriptional regulation networks reported previously[2,3,4,5,6,7] and identified some limitations. The results of Rustad et al suggested that additional genes may be essential for entry into and maintenance of bacteriostasis. This controversy has not yet been successfully explained. We present the results of an extensive study of the MTB transcriptional regulation network. We conducted a validation experiment to confirm the accuracy of the bioinformatic predictions
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