Search

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research. [ABSTRACT FROM AUTHOR]

Competing Interests: All authors declare no conflicts of interest. No significant funding was received for this work.

Paenibacillus sp. 32O-W, which is attributed for biodesulfurization of petroleum, has 56.34% genomic G+C content. Correspondence analysis on Relative Synonymous Codon Usage (RSCU) of the Paenibacillus sp. 32O-W genome has revealed the two different trends of codon usage variation. Two sets of genes have been identified representing the two distinct pattern of codon usage in this bacterial genome. We have measured several codon usage indices to understand the influencing factors governing the differential pattern of codon usage variation in this bacterial genome. We also observed significant differences in many protein properties between the two gene sets (e.g., hydrophobicity, protein biosynthetic cost, protein aggregation propensity). The compositional difference between the two sets of genes and the difference in their potential gene expressivity are the driving force for the differences in protein biosynthetic cost and aggregation propensity. Based on our results we argue that codon usage variation in Paenibacillus sp. 32O-W genome is actually influenced by both mutational bias and translational selection.