Exploring microRNAs, Target mRNAs and their Functions in Leguminous Plant Arachis hypogaea.

BACKGROUND: MicroRNAs (miRNAs) are a class of small non-coding, endogenous RNAs that regulate gene expression at post-transcriptional level. In plants, miRNAs are usually of 18-24 nucleotide in length and play humongous role by aiding in development, growth, defense, biotic and abiotic stress responses, etc. Objective: Arachis hypogaea is an economically important oil seed crop and human dietary source cultivated mostly in tropical and subtropical regions. In the present study, an initiative was taken to uncover miRNAs, their targets and functions in this important plant species. METHOD: Comparative genomics strategy coupled with bioinformatics approaches was deployed for the identification of miRNAs, their corresponding targets and functions by exploiting biological databases and tools. RESULTS: The study was able to identify 34 conserved miRNA candidates, belonging to 17 miRNA families, contributed by 23 and 3 precursor miRNAs from A. hypogaea Expressed Sequence Tags (EST) and Genome Survey Sequences (GSS), respectively. As well, 495 EST and 917 unigene sequences were predicted as targets for the identified miRNAs. Herein, psRNAtarget server and TargetFinder tool were used to predict unigene targets, whereas comparative genomics strategy was used for identifying EST targets. Functional annotation of the identified targets revealed that the identified miRNAs regulate mRNAs that participate in key biological and metabolic processes. Pathway enrichment analysis using KEGG database also revealed that they regulate important metabolic pathways including antibiotic biosynthesis, biosynthesis of unsaturated fatty acids, amino acids metabolism and flavonoid biosynthesis. CONCLUSION: The outcome of the study would aid experimental biologists to focus on these miRNAs to facilitate improved crop development and yield.

Computational Approaches and Related Tools to Identify MicroRNAs in a Species: A Bird's Eye View.

MicroRNAs (miRNAs) are a family of non-coding RNAs that play a central role in fine-tuning gene expression regulation. Over the past decade, identification and annotation of miRNAs have become a major focus in epigenomics research. However, detection and characterization of miRNA are challenging due to its small size (~22 nucleotide-long) and susceptibility to degradation. The difficulties involved in experimental prediction and characterization of miRNA coding genes have led to the development of in silico-based approaches. Although several algorithms have been developed in recent years, a comprehensive assessment of the principles, methodological insights, and estimate of the strengths and weaknesses of computational methods are limited. The present review is dealt with the detailed methodological insights of different tools used for identifying miRNA coding genes falling under four computational approaches. The parameters considered in these tools along with their specificity are also delineated. Furthermore, the strengths and weaknesses of these four computational approaches, and the bioinformatics resources pertaining to target identification, expression analysis, regulatory network analysis, and SNP identification are stated in this review. The methodological details of miRNA prediction methods and bioinformatics resources related to miRNA research in one platform would facilitate the miRNA research community to develop efficient tools for uncovering novel miRNAs and understanding their role in regulatory networks.

Computational analysis of protein interaction networks for infectious diseases.

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens.