Journal Article

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

Mei, Suyu, Zhang, Kun, Flemington, Erik K.

2018

Overview

Background Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. Methods In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l 2 -regularized logistic regression model. Results The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. Conclusions The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens , provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Analysis / Animal Genetics and Genomics / Area Under Curve / Bacteria / Bacterial infections / Bacterial proteins / Bacterial Proteins - metabolism / Bioinformatics / Biomedical and Life Sciences / Cellular signal transduction / Computation / Computer applications / Cytokines / Databases, Genetic / Drug discovery / Drug resistance / Drug Resistance, Bacterial - genetics / Gene Ontology / Genes / Genetic aspects / Genomes / Genomics / Host-bacteria relationships / Host-Pathogen Interactions - genetics / Humans / Immune response / Immune system / Immune System - metabolism / Immune System - microbiology / Immunosuppressive agents / Infections / Knowledge / l2-regularized logistic regression / Learning algorithms / Life Sciences / Logistic Models / Machine learning / Microarrays / Microbial Genetics and Genomics / Mycobacterium tuberculosis / Mycobacterium tuberculosis - metabolism / Networks / Ontology / Pathogen-host coevolution / Pathogen-host protein interaction networks / Pathogenesis / Pathogens / Plant Genetics and Genomics / Protein interaction / Protein Interaction Maps - genetics / Protein-protein interactions / Proteins / Proteomics / Regression analysis / Regression models / Research Article / ROC Curve / Salmonella / Signal transduction / Signal Transduction - genetics / Signaling / Signaling pathways / Studies / Transfer learning / Tuberculosis / Tuberculosis - genetics / Tuberculosis - immunology / Tuberculosis - microbiology / Tuberculosis - pathology