Prediction of novel putative miRNAs and their targets in buffalo

D C MISHRA; SHUCHI SMITA; INDRA SINGH; M NANDHINI DEVI; SANJEEV KUMAR; M S FAROOQI; K K CHATURVEDI; ANIL RAI

doi:10.56093/ijans.v87i1.66861

Authors

D C MISHRA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
SHUCHI SMITA ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
INDRA SINGH ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
M NANDHINI DEVI ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
SANJEEV KUMAR ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
M S FAROOQI ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
K K CHATURVEDI ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India
ANIL RAI ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012 India

https://doi.org/10.56093/ijans.v87i1.66861

Keywords:

In-silico prediction, miRNA, Target genes, Water buffalo

Abstract

MicroRNAs (miRNAs) are ~22nt long non-coding RNAs, which regulate the gene regulation at the post transcriptional level in both plants and animals. These miRNA are conserved in nature and hence potential base for new miRNA prediction through homology search. No miRNAs in this species are identified so far in economically important water buffalo (Bubalus bubalis). In this study, EST-based homology search, an established computational approach is used to find the potential miRNAs in buffalo. Six potential miRNA in buffalo were identified utilizing publicly available buffalo ESTs against the already known mature miRNAs of closely related species i.e. Bos taurus. Based on their sequence complementarity, target genes were identified which encode transcription factors (8%), enzymes (30%) and transporters (14%) as well as other proteins involved in physiological and metabolic processes (48%). These target genes also encode the proteins for signal transduction and normal development. This study will accelerate the way for further research on miRNAs and their functions in Bubalus bubalis.

Downloads

Download data is not yet available.

References

Altschul S F , Madden T L, Schäffer A A, Zhang J, Zhang Z, Miller W, Lipman D J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids Research 25: 3389–3402. DOI: https://doi.org/10.1093/nar/25.17.3389

Bartel B and Bartel D P. 2003. MicroRNAs: at the root of plant development? Plant Physiology 132: 709–17. DOI: https://doi.org/10.1104/pp.103.023630

Betel D M, Wilson A, Gabow A, Marks D S and Sander C. 2008. The microRNA.org resource: targets and expression. Nucleic Acids Research 36: D149-D153. DOI: https://doi.org/10.1093/nar/gkm995

Bonnet E J, Wuyts P, Rouzé and Van de Peer Y. 2004. Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20: 2911–17. DOI: https://doi.org/10.1093/bioinformatics/bth374

Lau Nelson C L, Lim E Weinstein and Bartel D. 2001. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294: 858–62. DOI: https://doi.org/10.1126/science.1065062

Lee R C and Ambros V. 2001. An extensive class of small RNAs in Caenorhabditis elegans. Science 294: 862–64. DOI: https://doi.org/10.1126/science.1065329

Li X et al. 2014. Computational identification of conserved microRNAs and their targets from expression sequence tags of blueberry (Vaccinium corybosum). Plant Signaling and Behavior 9: e29462. DOI: https://doi.org/10.4161/psb.29462

Mendes N D, Freitas A T and Sagot M F. 2009. Current tools for the identification of miRNA genes and their targets. Nucleic Acids Research 37: 2419–33. DOI: https://doi.org/10.1093/nar/gkp145

Meyers B C et al. 2008. Criteria for annotation of plant MicroRNAs. Plant Cell 20: 3186–90. DOI: https://doi.org/10.1105/tpc.108.064311

Pfeffer S et al. 2004. Identification of virus-encoded microRNAs. Science 304: 734–36. DOI: https://doi.org/10.1126/science.1096781

Rhoades M W et al. 2002. Prediction of plant microRNA targets. Cell 110: 513–20. DOI: https://doi.org/10.1016/S0092-8674(02)00863-2

Yin Z C, Li X, Han and Shen F. 2008. Identification of conserved microRNAs and their target genes in tomato (Lycopersicon esculentum). Gene 414: 60–66. DOI: https://doi.org/10.1016/j.gene.2008.02.007

Zhang B, Pan X, Cannon C H, Cobb G P and Anderson T A. 2006. Conservation and divergence of plant microRNA genes. Plant Journal 46: 243–59. DOI: https://doi.org/10.1111/j.1365-313X.2006.02697.x