Mycobacterium tuberculosis: Pathogenesis and therapeutic targets

Hypoxia-inducible factor-driven glycolytic adaptations in host-microbe interactions

Mammalian cells utilize glucose as a primary carbon source to produce energy for most cellular functions. However, the bioenergetic homeostasis of cells can be perturbed by environmental alterations, such as changes in oxygen levels which can be associated with bacterial infection. Reduction in oxygen availability leads to a state of hypoxia, inducing numerous cellular responses that aim to combat this stress. Importantly, hypoxia strongly augments cellular glycolysis in most cell types to compensate for the loss of aerobic respiration. Understanding how this host cell metabolic adaptation to hypoxia impacts the course of bacterial infection will identify new anti-microbial targets. This review will highlight developments in our understanding of glycolytic substrate channeling and spatiotemporal enzymatic organization in response to hypoxia, shedding light on the integral role of the hypoxia-inducible factor (HIF) during host-pathogen interactions. Furthermore, the ability of intracellular and extracellular bacteria (pathogens and commensals alike) to modulate host cellular glucose metabolism will be discussed.

One-Pot Assembly of Mannose-Capped Lipoarabinomannan Motifs up to 101-Mer from the Mycobacterium tuberculosis Cell Wall

Lipoarabinomannan (LAM) from the Mycobacterium tuberculosis cell envelope represents important targets for the development of new therapeutic agents against tuberculosis, which is a deadly disease that has plagued mankind for a long time. However, the accessibility of long, branched, and complex lipoarabinomannan over 100-mer remains a long-standing challenge. Herein, we report the modular synthesis of mannose-capped lipoarabinomannan 101-mer from the M. tuberculosis cell wall using a one-pot assembly strategy on the basis of glycosyl ortho-(1-phenylvinyl)benzoates (PVB), which not only accelerates the modular synthesis but also precludes the potential problems associated with one-pot glycosylation with thioglycosides. Shorter sequences including 18-mer, 19-mer, and 27-mer are also synthesized for in-depth structure-activity relationship biological studies. Current synthetic routes also highlight the following features: (1) streamlined synthesis of various linear and branched glycans using one-pot orthogonal glycosylation on the combination of glycosyl N-phenyltrifluoroacetimidates, glycosyl ortho-alkynylbenzoates, and glycosyl PVB; (2) highly stereoselective construction of 10 1,2-cis-arabinofuranosyl linkages using 5-O-(2-quinolinecarbonyl)-directing 1,2-cis-arabinofuranosylation via a hydrogen-bond-mediated aglycone delivery strategy; and (3) convergent [(18 + 19) × 2 + 27] one-pot synthesis of the 101-mer LAM polysaccharide. The present work demonstrates that this orthogonal one-pot glycosylation strategy can highly streamline the chemical synthesis of long, branched, and complex polysaccharides.

The uncharted territory of host-pathogen interaction in tuberculosis

Mycobacterium tuberculosis (M.tb) effectively manipulates the host processes to establish the deadly respiratory disease, Tuberculosis (TB). M.tb has developed key mechanisms to disrupt the host cell health to combat immune responses and replicate efficaciously. M.tb antigens such as ESAT-6, 19kDa lipoprotein, Hip1, and Hsp70 destroy the integrity of cell organelles (Mitochondria, Endoplasmic Reticulum, Nucleus, Phagosomes) or delay innate/adaptive cell responses. This is followed by the induction of cellular stress responses in the host. Such cells can either undergo various cell death processes such as apoptosis or necrosis, or mount effective immune responses to clear the invading pathogen. Further, to combat the infection progression, the host secretes extracellular vesicles such as exosomes to initiate immune signaling. The exosomes can contain M.tb as well as host cell-derived peptides that can act as a double-edged sword in the immune signaling event. The host-symbiont microbiota produces various metabolites that are beneficial for maintaining healthy tissue microenvironment. In juxtaposition to the above-mentioned mechanisms, M.tb dysregulates the gut and respiratory microbiome to support its replication and dissemination process. The above-mentioned interconnected host cellular processes of Immunometabolism, Cellular stress, Host Microbiome, and Extracellular vesicles are less explored in the realm of exploration of novel Host-directed therapies for TB. Therefore, this review highlights the intertwined host cellular processes to control M.tb survival and showcases the important factors that can be targeted for designing efficacious therapy.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.