Whole blood vs serum-derived exosomes for host and pathogen-specific tuberculosis biomarker identification: RNA-seq based machine learning approach

Abstract

Mycobacterium tuberculosis (Mtb) is a successful intracellular pathogen that causes tuberculosis (TB) and remains a leading infectious disease responsible for millions of deaths. RNA sequencing is a rapidly growing technique and a great approach to understanding host and pathogen cross-talks via transcriptional responses in diverse biological samples. Even though RNA-seq studies are limited in application due to the high costs involved, this study suggests the collective need for extensive whole blood and exosome-based RNA-seq studies to understand the complete picture of the host and pathogen interplay during the TB infection through a machine learning approach. During the study, host-derived differentially expressed genes (DEGs) were identified in both whole blood and exosomes, whilst exosomes were successful in identifying pathogen-derived DEGs only in latent TB (LTB) individuals. The majority of the DEGs in whole blood were up-regulated between active TB (ATB) and healthy individuals (HC), and ATB and LTB, while down-regulated between LTB and HC, which was vice versa for the exosomes, showing the different mechanisms played in response to different states of TB infection across the two different biological samples. The pathway analysis revealed that whole blood gene signatures were mainly involved in the host immune responses, whilst the exosomal gene signatures were involved in manipulating the host’s cellular responses and Mtb survival. Overall, identifying both host and pathogen-derived gene signatures in different biological samples for intracellular pathogens like Mtb is vital to decipher the complex interplay between the host and the pathogen, ultimately leading to more successful future interventions.