Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Abstract

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins (). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Computational Network Inference for Bacterial Interactomics

“…The resulting PPI networks often have confidence scores for putative interactions that allow thresholding and the use of network analysis algorithms that utilize these scores ( 112 ). Microbial PPI prediction has been carried out using several algorithms including random forests ( 113 – 117 ), support vector machines ( 118 – 120 ), and Bayesian classifiers ( 121 – 123 ). A related method is probabilistic functional integrated networks (PFINs), which combine multiple data types in a probabilistic framework to produce a network of confidence-weighted interactions ( 124 – 126 ).…”

“…A DDI-based network suggested that human– M. tuberculosis PPIs tend to have more domains than intraspecies interactions ( 138 ), and this trend was later observed in an interolog-based mapping study, which also revealed that hub proteins of intraspecies networks tend to be involved in host–pathogen PPI ( 148 ). Using a random forest framework, the cancer pathway was involved in M. tuberculosis infection ( 117 ), while a DDI network implicated several PPIs involving heat shock, redox proteins ( 224 ). Finally, a combination of interolog and DDI mapping associated several genes of the host immune responses to M. tuberculosis infection ( 65 ).…”

Allogeneic Vγ9Vδ2 T-Cell Therapy Promotes Pulmonary Lesion Repair: An Open-Label, Single-Arm Pilot Study in Patients With Multidrug-Resistant Tuberculosis

“…Tuberculosis (TB), a major chronic infectious disease of the lungs caused by Mycobacterium tuberculosis , has become a top killer among infectious diseases due to an epidemic of coinfections and drug resistance ( 1 , 2 ). According to the World Health Organization’s “Global tuberculosis report 2020,” in 2019, there were approximately two billion individuals infected with M. tuberculosis worldwide.…”

Shotgun EM of mycobacterial protein complexes during stationary phase stress

“…Although a large number of Mtb persistence mediators have been studied e.g. ( Wang et al., 2010 ; Huo et al., 2015 ; Sun et al., 2018 ), structural information is still lacking, particularly for those that form large assemblies. In fact, most protein interactions have only been detected indirectly and there is poor correlation between different detection methods ( Mackay et al., 2007 ).…”