调控小RNA(sRNA)是不翻译成蛋白质但作为功能性RNA的RNA转录物。致病性钩端螺旋体引起流行性螺旋体人畜共患病,钩端螺旋体病.据推测,钩端螺旋体sRNA参与了它们的致病性。在这项研究中,采用生物计算方法鉴定钩端螺旋体sRNAs。在这项研究中,两个sRNA预测程序,即,RNAz和nocoRNAc,用于筛选钩端螺旋体血清型Lai的参考基因组。在126个预测的sRNA中,有96个顺式反义sRNAs,28个反式编码的sRNA和2个与蛋白质编码基因部分重叠的sRNA。为了确定这些候选物是否在病原体中表达,将它们与从我们的RNA-seq数据集生成的覆盖文件进行比较。发现7个预测的sRNAs在对数中期表达,固定相,血清应激,温度胁迫和铁胁迫,而2个sRNAs在对数中期表达,固定相,血清应激,和温度应力。此外,它们的表达也通过RT-PCR进行了实验证实。还使用TargetRNA2对这些实验验证的候选物进行mRNA靶标预测。一起来看,我们的研究表明,生物计算策略可以作为费力且昂贵的深度测序方法的替代或补充策略,不仅可以发现推定的sRNA,还可以预测其在细菌中的靶标.事实上,这是第一项整合计算方法来预测L.rologansserovarLai中推定的sRNAs的研究。
■在线版本包含补充材料,可在10.1007/s12088-022-01050-9获得。
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.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12088-022-01050-9.