Exploring the transcription start sites and other genomic features facilitates the accurate identification and annotation of small RNAs across multiple stress conditions in Mycobacterium tuberculosis

• Published in: Functional and Integrative Genomics
• Main Author: Cheah H.-L.; Citartan M.; Lee L.-P.; Ahmed S.A.; Salleh M.Z.; Teh L.K.; Tang T.-H.
• Format: Article
• Language: English
• Published: Springer Nature 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203721858&doi=10.1007%2fs10142-024-01437-5&partnerID=40&md5=45e2c419a63d7cd18f988a9d62fab2c3

Mycobacterium tuberculosis (MTB) is a pathogen that is known for its ability to persist in harsh environments and cause chronic infections. Understanding the regulatory networks of MTB is crucial for developing effective treatments. Small regulatory RNAs (sRNAs) play important roles in gene expression regulation in all kingdoms of life, and their classification based solely on genomic location can be imprecise due to the computational-based prediction of protein-coding genes in bacteria, which often neglects segments of mRNA such as 5’UTRs, 3’UTRs, and intercistronic regions of operons. To address this issue, our study simultaneously discovered genomic features such as TSSs, UTRs, and operons together with sRNAs in the M. tuberculosis H37Rv strain (ATCC 27294) across multiple stress conditions. Our analysis identified 1,376 sRNA candidates and 8,173 TSSs in MTB, providing valuable insights into its complex regulatory landscape. TSS mapping enabled us to classify these sRNAs into more specific categories, including promoter-associated sRNAs, 5’UTR-derived sRNAs, 3’UTR-derived sRNAs, true intergenic sRNAs, and antisense sRNAs. Three of these sRNA candidates were experimentally validated using 3’-RACE-PCR: predictedRNA_0240, predictedRNA_0325, and predictedRNA_0578. Future characterization and validation are necessary to fully elucidate the functions and roles of these sRNAs in MTB. Our study is the first to simultaneously unravel TSSs and sRNAs in MTB and demonstrate that the identification of other genomic features, such as TSSs, UTRs, and operons, allows for more accurate and specific classification of sRNAs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.