Sequence-specific DNA-binding transcription factors have widespread biological significance in the regulation of gene expression. However, in lower prokaryotes and eukaryotic metazoans, it is usually difficult to find transcription regulatory factors that recognize specific target promoters. To address this, we have developed in this study a new bacterial one-hybrid reporter vector system that provides a convenient and rapid strategy to determine the specific interaction between target DNA sequences and their transcription factors. Using this system, we have successfully determined the DNA-binding specificity of the transcription regulator Rv3133c to a previously reported promoter region of the gene Rv2031 in Mycobacterium tuberculosis. In addition, we have tested more than 20 promoter regions of M. tuberculosis genes using this approach to determine if they interact with ;150 putative regulatory proteins. A variety of transcription factors are found to participate in the regulation of stress response and fatty acid metabolism, both of which comprise the core of in vivo-induced genes when M. tuberculosis invades macrophages. Interestingly, among the many new discovered potential transcription factors, the WhiB-like transcriptional factor WhiB3 was identified for the first time to bind with the promoter sequences of most in vivo-induced genes. Therefore, this study offers important data in the dissection of the transcription regulations in M. tuberculosis, and the strategy should be applicable in the study of DNA-binding factors in a wide range of biological organisms.

Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).

No abstract

Analysis of the protein-protein interaction network of a pathogen is a powerful approach for dissecting gene function, potential signal transduction, and virulence pathways. This study looks at the construction of a global protein-protein interaction (PPI) network for the human pathogen Mycobacterium tuberculosis H37Rv, based on a high-throughput bacterial two-hybrid method. Almost the entire ORFeome was cloned, and more than 8000 novel interactions were identified. The overall quality of the PPI network was validated through two independent methods, and a high success rate of more than 60% was obtained. The parameters of PPI networks were calculated. The average shortest path length was 4.31. The topological coefficient of the M. tuberculosis B2H network perfectly followed a power law distribution (correlation = 0.999; R-squared = 0.999) and represented the best fit in all currently available PPI networks. A cross-species PPI network comparison revealed 94 conserved subnetworks between M. tuberculosis and several prokaryotic organism PPI networks. The global network was linked to the protein secretion pathway. Two WhiB-like regulators were found to be highly connected proteins in the global network. This is the first systematic noncomputational PPI data for the human pathogen, and it provides a useful resource for studies of infection mechanisms, new signaling pathways, and novel antituberculosis drug development.

RelBE represents a typical bacterial toxin-antitoxin (TA) system. Mycobacterium tuberculosis H37Rv, the pathogen responsible for human tuberculosis, contains three RelBE-like modules, RelBE, RelFG, and RelJK, which are at least partly expressed in human macrophages during infection. RelBE modules appear to be autoregulated in an atypical manner compared to other TA systems; however, the molecular mechanisms and potential interactions between different RelBE modules remain to be elucidated. In the present study, we characterized the interaction and cross-regulation of these Rel toxin-antitoxin modules from this unique pathogen. The physical interactions between the three pairs of RelBE proteins were confirmed and the DNA-binding domain recognized by three RelBE-like pairs and domain structure characteristics were described. The three RelE-like proteins physically interacted with the same RelB-like protein, and could conditionally regulate its binding with promoter DNA. The RelBE-like modules exerted complex cross-regulation effects on mycobacterial growth. The relB antitoxin gene could replace relF in cross-neutralizing the relG toxin gene. Conversely, relF enhanced the toxicity of the relE toxin gene, while relB increased the toxicity of relK. This is the first report of interactions between different pairs of RelBE modules of M. tuberculosis.