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Abstract

Infection caused by ) is still a leading cause of mortality worldwide with estimated 1.4 million deaths annually.

Despite macrophages' ability to kill bacterium, can grow inside these innate immune cells and the exploration of the infection has traditionally been characterized by a one-sided relationship, concentrating solely on the host or examining the pathogen in isolation.

Because of only a handful of –host interactions have been experimentally characterized, our main goal is to predict protein–protein interactions during the early phases of the infection.

In this work, we performed an integrative computational approach that exploits differentially expressed genes obtained from Dual RNA-seq analysis combined with known domain–domain interactions.

A total of 2381 and 7214 genes were identified as differentially expressed in and in THP-1-like macrophages, respectively, revealing different transcriptional profiles in response to infection. Over 48 h of infection, the host–pathogen network revealed 25 016 PPIs. Analysis of the resulting predicted network based on cellular localization information of proteins, indicated the implication of interacting nodes including the bacterial PE/PPE/PE_PGRS family. In addition, proteins interacted with host proteins involved in NF-kB signalling pathway as well as interfering with the host apoptosis ability via the potential interaction of TB16.3 with human TAB1 and GroEL2 with host protein kinase C delta, respectively.

The prediction of the full range of interactions between and host will contribute to better understanding of the pathogenesis of this bacterium and may provide advanced approaches to explore new therapeutic targets against tuberculosis.

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/content/journal/jmm/10.1099/jmm.0.001803
2024-02-05
2025-02-09
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