Abstract
Regulatory small RNAs (sRNA) are RNA transcripts that are not translated into proteins but act as functional RNAs. Pathogenic Leptospira cause an epidemic spirochaetal zoonosis, Leptospirosis. It is speculated that Leptospiral sRNAs are involved in orchestrating their pathogenicity. In this study, biocomputational approach was adopted to identify Leptospiral sRNAs. In this study, two sRNA prediction programs, i.e., RNAz and nocoRNAc, were employed to screen the reference genome of Leptospira interrogans serovar Lai. Out of 126 predicted sRNAs, there are 96 cis-antisense sRNAs, 28 trans-encoded sRNAs and 2 sRNAs that partially overlap with protein-coding genes in a sense orientation. To determine whether these candidates are expressed in the pathogen, they were compared with the coverage files generated from our RNA-seq datasets. It was found out that 7 predicted sRNAs are expressed in mid-log phase, stationary phase, serum stress, temperature stress and iron stress while 2 sRNAs are expressed in mid-log phase, stationary phase, serum stress, and temperature stress. Besides, their expressions were also confirmed experimentally via RT-PCR. These experimentally validated candidates were also subjected to mRNA target prediction using TargetRNA2. Taken together, our study demonstrated that biocomputational strategy can serve as an alternative or as a complementary strategy to the laborious and expensive deep sequencing methods not only to uncover putative sRNAs but also to predict their targets in bacteria. In fact, this is the first study that integrates computational approach to predict putative sRNAs in L. interrogans serovar Lai. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12088-022-01050-9.