Identification of sigma factor 54-regulated small non-coding RNAs by employing genome-wide and transcriptome-based methods in rhizobium strains.

Rhizobium-legume symbiosis is considered as the major contributor of biological nitrogen fixation. Bacterial small non-coding RNAs are crucial regulators in several cellular adaptation processes that occur due to the changes in metabolism, physiology, or the external environment. Identifying and analysing the conditional specific/sigma factor-54 regulated sRNAs provides a better understanding of sRNA regulation/mechanism in symbiotic association. In the present study, we have identified sigma factor 54-regulated sRNAs from the genome of six rhizobium strains and from the RNA-seq data of free-living and symbiotic conditions of Bradyrhizobium diazoefficiens USDA 110 to identify the novel putative sRNAs that are over expressed during the regulation of nitrogen fixation. A total of 1351 sRNAs were predicted from the genome of six rhizobium strains and 1375 sRNAs were predicted from the transcriptome data of B. diazoefficiens USDA 110. Analysis of target mRNA for these novel sRNAs was inferred to target several nodulation and nitrogen fixation genes including nodC, nodJ, nodY, nodJ, nodM, nodW, nodZ, nifD, nifN, nifQ, fixK, fixL, fdx, nolB, and several cytochrome proteins. In addition, sRNAs of B. diazoefficiens USDA 110 which targeted the regulatory genes of nitrogen fixation were confirmed by wet-lab experiments with semi-quantitative reverse transcription polymerase chain reaction. Predicted target mRNAs were functionally classified based on the COG analysis and GO annotations. The genome-wide and transcriptome-based integrated methods have led to the identification of several sRNAs involved in the nodulation and symbiosis. Further validation of the functional role of these sRNAs can help in exploring the role of sRNAs in nitrogen metabolism during free-living and symbiotic association with legumes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03394-x.

Prediction and identification of novel sRNAs involved in Agrobacterium strains by integrated genome-wide and transcriptome-based methods.

Small RNAs (sRNAs) are a class of gene regulators in bacteria, playing a central role in their response to environmental changes. Bioinformatic prediction facilitates the identification of sRNAs expressed under different conditions. We propose a novel method of prediction of sRNAs from the genome of Agrobacterium based on a positional weight matrix of conditional sigma factors. sRNAs predicted from the genome are integrated with the virulence-specific transcriptome data to identify putative sRNAs that are overexpressed during Agrobacterial virulence induction. A total of 384 sRNAs are predicted from transcriptome data analysis of Agrobacterium fabrum and 100-500 sRNAs from the genome of different Agrobacterial strains. In order to refine our study, a final set of 10 novel sRNAs with best features across different replicons targeting virulence genes were experimentally identified using semi-quantitative polymerase chain reaction. Since Ti plasmid plays a major role in virulence, out of 10 sRNAs across the replicons, 4 novel sRNAs differentially expressed under virulence induced and non-induced conditions are predicted to be present in the Ti plasmid T-DNA region flanking virulence-related genes like agrocinopine synthase, indole 3-lactate synthase, mannopine synthase and tryptophan monooxygenase. Further validation of the function of these sRNAs in conferring virulence would be relevant to explore their role in Agrobacterium-mediated plant transformation.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.