GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

Metabolic modeling predicts unique drug targets in Borrelia burgdorferi

IMPORTANCE

Lyme disease is often treated using long courses of antibiotics, which can cause side effects for patients and risks the evolution of antimicrobial resistance. Narrow-spectrum antimicrobials would reduce these risks, but their development has been slow because the Lyme disease bacterium, Borrelia burgdorferi, is difficult to work with in the laboratory. To accelerate the drug discovery pipeline, we developed a computational model of B. burgdorferi's metabolism and used it to predict essential enzymatic reactions whose inhibition prevented growth in silico. These predictions were validated using small-molecule enzyme inhibitors, several of which were shown to have specific activity against B. burgdorferi. Although the specific compounds used are not suitable for clinical use, we aim to use them as lead compounds to develop optimized drugs targeting the pathways discovered here.

A Sensitive and Quantitative Assay to Enumerate and Measure Mycobacterium leprae Viability in Clinical and Experimental Specimens

Mycobacterium leprae, the etiologic agent of leprosy, cannot be cultured on artificial media. This characteristic, coupled with its long generation time, presents a number of unique challenges to studying this pathogen. One of the difficulties facing both researchers and clinicians is the absence of a rapid test to measure the viability of M. leprae in clinical or experimental specimens. The lack of such a tool limits the understanding of M. leprae immunopathogenesis and makes determining the efficacy of drug treatments difficult. With this in mind, we developed a robust two-step molecular viability assay (MVA) that first enumerates the M. leprae in the tissue; then, this data is used to normalize bacterial RNA quantities for the second step, in which the expression of M. leprae esxA and hsp18 are measured. This assay is specific and sensitive enough to be used on most clinical samples. This protocol describes the steps required to extract DNA and RNA from M. leprae-infected tissue, enumerate M. leprae, and measure M. leprae viability based on the normalized expression of two M. leprae-specific genes (hsp18 and esxA). This protocol also outlines an optimal laboratory design and workflow for performing this assay. © 2022 The Leprosy Mission Nepal. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Basic Protocol 1: DNA and RNA P purification from M. leprae-infected tissue Basic Protocol 2: Enumeration of M. leprae by RLEP qPCR on the DNA fraction Basic Protocol 3: Calculation of M. leprae per tissue and normalization of RNA Basic Protocol 4: Reverse-transcription of normalized RNA to generate cDNA Basic Protocol 5: Determination of M. leprae viability using HSP18 and ESXA qPCR on the cDNA Support Protocol 1: M. leprae qPCR primer/probe stock preparation Support Protocol 2: Preparation of plasmid stocks and standard curves.

Dissecting Host-Pathogen Interactions in TB Using Systems-Based Omic Approaches

Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

Strategies for drug target identification in Mycobacterium leprae

Hansen's disease (HD), or leprosy, continues to be endemic in many parts of the world. Although multidrug therapy (MDT) is successful in curing a large number of patients, some of them abandon it because it is a long-term treatment. Therefore, identification of new drug targets in Mycobacterium leprae is considered of high importance. Here, we introduce an overview of in silico and in vitro studies that might be of help in this endeavor. The essentiality of M. leprae proteins is reviewed with discussion of flux balance analysis, gene expression, and knockout articles. Finally, druggability techniques are proposed for the validation of new M. leprae protein targets (see Fig. 1).