Background: Small non-coding RNAs (sRNAs) are regulatory molecules, present in all forms of life, known to regulate various biological processes in response to the different environmental signals. In recent years, deep sequencing and various other computational prediction methods have been employed to identify and analyze sRNAs. Results: In the present study, we have applied an improved sRNA scanner method to predict sRNAs from the genome of Rhizobium etli, based on PWM matrix of conditional sigma factor 32. sRNAs predicted from the genome are integrated with the available stress specific transcriptome data to predict putative conditional specific sRNAs. A total of 271 sRNAs from the genome and 173 sRNAs from the transcriptome are computationally predicted. Of these, 25 sRNAs are found in both genome and transcriptome data. Putative targets for these sRNAs are predicted using TargetRNA2 and these targets are involved in a wide array of cellular functions such as cell division, transport and metabolism of amino acids, carbohydrates, energy production and conversion, translation, cell wall/membrane biogenesis, posttranslation modification, protein turnover and chaperones. Predicted targets are functionally classified based on COG analysis and GO annotations.
Conclusion:sRNAs predicted from the genome, using PWM matrices for conditional sigma factor 32 could be a better method to identify the conditional specific sRNAs which expand the list of putative sRNAs from the intergenic regions (IgRs) of R. etli and closely related α-proteobacteria. sRNAs identified in this study would be helpful to explore their regulatory role in biological cellular process during the stress.
Plant lectins are the heterogenous group of glycoproteins extensively studied for their potent insecticidal property against Hemipteran pests. In this present study, the full-length cDNA of monocot mannose-binding insecticidal lectin gene was isolated from Allium ascalonicum leaves. The isolated Allium ascalonicum Lectin (AAL) gene was cloned in pGEM-T vector, sequenced and the sequence was submitted to GenBank (KM096570.1). Sequence analysis revealed a 468 bp open reading frame (ORF) encoding a putative 155 amino acids agglutinin precursor. Multiple sequence alignment and phylogenetic analysis of AAL amino acid with those of 30 other Mannose binding lectin (MBL) sequences in NCBI revealed a high similarity of 85-95% indicating that AAL is a member of the MBL super family and forms a cluster with other onion lectins. Secondary structure prediction and the homology modeling showed that AAL protein possess predominantly β-sheets and three potential mannose-binding motifs consisting of 5 amino acid residues QDNVY like other GNA lectins. The results of the insilico analysis predict that the Allium ascalonicum lectin gene can be another potent insecticidal protein.
Rhizobium-legume symbiosis is considered as the major contributor of biological nitrogen xation. In the present study, we have identi ed sigma factor 54regulated sRNAs from the genome of ve Rhizobium strains and integrated with the free-living and symbiotic speci c transcriptome data to identify the novel putative sRNAs that are over expressed during the regulation of nitrogen xation. A total of 1059 sRNAs were predicted from each genome of the select set of Rhizobium strains and 1,375 sRNAs were predicted from the transcriptome data of Bradyrhizobium japonicum. Target mRNA analysis revealed the functional role of putative novel sRNAs from different free-living and symbiotic strains. Those novel sRNAs were inferred to target several nodulation and nitrogen xation genes including nodC, nodJ, nodY, nodJ, nodM, nodW, nodZ, nifD, nifN, nifQ, xK, xL, Fdx, nolB, and several cytochrome proteins. Further, sRNAs of Bradyrhizobium japonicum which targeted the regulatory genes of nitrogen xation were experimentally con rmed with semi-quantitative reverse transcription polymerase chain reaction. Predicted target mRNAs were functionally classi ed based on the COG analysis and GO annotations. Studies on this sigma factor 54-regulated sRNA identi cation could be a better method to relate the role of sRNAs in nitrogen metabolism during free-living and symbiotic association with legumes.
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