Identification of unique essential proteins from a Mycobacterium tuberculosis F15/LAM4/KZN phage secretome library

Devising Isolation Forest-Based Method to Investigate the sRNAome of Mycobacterium tuberculosis Using sRNA-seq Data

Small non-coding RNAs (sRNAs) regulate the synthesis of virulence factors and other pathogenic traits, which enables the bacteria to survive and proliferate after host infection. While high-throughput sequencing data have proved useful in identifying sRNAs from the intergenic regions (IGRs) of the genome, it remains a challenge to present a complete genome-wide map of the expression of the sRNAs. Moreover, existing methodologies necessitate multiple dependencies for executing their algorithm and also lack a targeted approach for the de novo sRNA identification. We developed an Isolation Forest algorithm-based method and the tool Prediction Of sRNAs using Isolation Forest for the de novo identification of sRNAs from available bacterial sRNA-seq data (http://posif.ibab.ac.in/). Using this framework, we predicted 1120 sRNAs and 46 small proteins in Mycobacterium tuberculosis. Besides, we highlight the context-dependent expression of novel sRNAs, their probable synthesis, and their potential relevance in stress response mechanisms manifested by M. tuberculosis.

Development of multi-epitope based subunit vaccine against Mycobacterium Tuberculosis using immunoinformatics approach

The etiological agent of tuberculosis (TB), Mycobacterium tuberculosis, is a deadly pathogen that adapts to thrive within the host. Since 2020, the COVID-19 pandemic has had colossal health, societal, and economic consequences, which have affected the reporting of new incidences and mortality cases of TB. As per the WHO 2022 report, 10.6 million people were diagnosed with TB, and 1.6 million died worldwide. The increase in resistant strains of tuberculosis is making it more burdensome to reach the End TB strategy. A reliable and efficient TB vaccine that may avert both primary infection and recurrence of latent TB in adults and adolescents is of the utmost importance. In this study, we used computational techniques to predict the ability of HLA molecules to display epitopes for six TB proteins (PPE68, PE_PGRS17, EspC, LDT4, RpfD, and RpfC) to design the multi-epitope subunit vaccine. From the aimed proteins, the potential B-cell, helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL) epitopes were predicted and linked together with LPA adjuvant, and the vaccine was designed. The vaccine's physicochemical analysis demonstrates that it is non-allergic, non-toxic, and antigenic. Then, the vaccine structure was predicted, improved, and verified to yield the optimal structure. The developed vaccine's binding mechanism with distinct immunogenic receptors (Tlr2 and MHC-II) was assessed utilizing molecular docking. The molecular dynamic simulation and MMPBSA analysis were performed to comprehend the complexes' dynamics and stability. The immune simulation was utilized to anticipate the vaccine's immunogenic attributes. In silico cloning was employed to demonstrate the efficient expression of the designed vaccine in E. coli as a host. Moreover, in vitro and in vivo animal testing is required to determine the efficacy of the in silico developed vaccine.Communicated by Ramaswamy H. Sarma.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.

Towards Reverse Vaccinology for Bovine TB: High Throughput Expression of Full Length Recombinant Mycobacterium bovis Proteins

Bovine tuberculosis caused by Mycobacterium bovis, is a significant global pathogen causing economic loss in livestock and zoonotic TB in man. Several vaccine approaches are in development including reverse vaccinology which uses an unbiased approach to select open reading frames (ORF) of potential vaccine candidates, produce them as recombinant proteins and assesses their immunogenicity by direct immunization. To provide feasibility data for this approach we have cloned and expressed 123 ORFs from the M. bovis genome, using a mixture of E. coli and insect cell expression. We used a concatenated open reading frames design to reduce the number of clones required and single chain fusion proteins for protein pairs known to interact, such as the members of the PPE-PE family. Over 60% of clones showed soluble expression in one or the other host and most allowed rapid purification of the tagged bTB protein from the host cell background. The catalogue of recombinant proteins represents a resource that may be suitable for test immunisations in the development of an effective bTB vaccine.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Resonance assignments and secondary structure prediction of secretory protein Rv0603 from Mycobacterium tuberculosis H37Rv

We report the NMR resonance assignments of N-terminal signal sequence deleted secretory protein Rv0603 (∆_1-28-Rv0603) from Mycobacterium tuberculosis H37Rv. ∆_1-28-Rv0603 displayed good peak yield and signal dispersion in 2D [¹⁵N-¹H] HSQC spectrum, which prompted us to proceed for resonance assignments on this construct. Standard triple-resonance experiments for resonance assignments were recorded on [U-¹⁵N]-∆Rv0603 and [U-¹⁵N, ¹³C]-∆Rv0603 samples. We obtained 97% of backbone ¹H^N, 98% of ¹³C^α, 98% of ¹H^α, 96% of ¹³C´, 100% of ¹³C^β, 100% of ¹H^β and 98% of side-chain ¹H chemical shifts. This protein does not show any sequence similarity to any other protein of known structure. Determination of its solution structure would facilitate understanding of its biological function.

Bayesian reconstruction of Mycobacterium tuberculosis transmission networks in a high incidence area over two decades in Malawi reveals associated risk factors and genomic variants

Understanding host and pathogen factors that influence tuberculosis (TB) transmission can inform strategies to eliminate the spread of Mycobacterium tuberculosis (Mtb). Determining transmission links between cases of TB is complicated by a long and variable latency period and undiagnosed cases, although methods are improving through the application of probabilistic modelling and whole-genome sequence analysis. Using a large dataset of 1857 whole-genome sequences and comprehensive metadata from Karonga District, Malawi, over 19 years, we reconstructed Mtb transmission networks using a two-step Bayesian approach that identified likely infector and recipient cases, whilst robustly allowing for incomplete case sampling. We investigated demographic and pathogen genomic variation associated with transmission and clustering in our networks. We found that whilst there was a significant decrease in the proportion of infectors over time, we found higher transmissibility and large transmission clusters for lineage 2 (Beijing) strains. By performing evolutionary convergence testing (phyC) and genome-wide association analysis (GWAS) on transmitting versus non-transmitting cases, we identified six loci, PPE54, accD2, PE_PGRS62, rplI, Rv3751 and Rv2077c, that were associated with transmission. This study provides a framework for reconstructing large-scale Mtb transmission networks. We have highlighted potential host and pathogen characteristics that were linked to increased transmission in a high-burden setting and identified genomic variants that, with validation, could inform further studies into transmissibility and TB eradication.

Immunoscreening of the M. tuberculosis F15/LAM4/KZN secretome library against TB patients' sera identifies unique active- and latent-TB specific biomarkers

Tuberculosis (TB) protein biomarkers are urgently needed for the development of point-of-care diagnostics, new drugs and vaccines. Mycobacterium tuberculosis extracellular and secreted proteins play an important role in host-pathogen interactions. Antibodies produced against M. tuberculosis proteins before the onset of clinical symptoms can be used in proteomic studies to identify their target proteins. In this study, M. tuberculosis F15/LAM4/KZN strain phage secretome library was screened against immobilized polyclonal sera from active TB patients (n = 20), TST positive individuals (n = 15) and M. tuberculosis uninfected individuals (n = 20) to select and identify proteins recognized by patients' antibodies. DNA sequence analysis from randomly selected latent TB and active TB specific phage clones revealed 118 and 96 ORFs, respectively. Proteins essential for growth, virulence and metabolic pathways were identified using different TB databases. The identified active TB specific biomarkers included five proteins, namely, TrpG, Alr, TreY, BfrA and EspR, with no human homologs, whilst latent TB specific biomarkers included NarG, PonA1, PonA2 and HspR. Future studies will assess potential applications of identified protein biomarkers as TB drug or vaccine candidates/targets and diagnostic markers with the ability to discriminate LTBI from active TB.