In Silico Identification and Validation of Potential microRNAs in Kinnow Mandarin (Citrus reticulata Blanco)

In silico identification of conserved miRNAs in the genome of fibre biogenesis crop Corchorus capsularis

Corchorus capsularis, commonly known as jute occupies the leading position in the production of natural fibre alongside lower environmental threat. Small noncoding ~21 to 24 nucleotides long microRNAs play significant roles in regulating the gene expression as well as different functions in cellular growth and development. Here, the study adopted a comprehensive in silico approach to identify and characterize the conserved miRNAs in the genome of C. capsularis including functional annotation of specific gene targets. Expressed Sequence Tags (ESTs) based homology search of 3350 known miRNAs of dicotyledons were allowed against 763 non-redundant ESTs of jute genome, resulted in the prediction of 5 potential miRNA candidates belonging five different miRNA families (miR1536, miR9567-3p, miR4391, miR11300, and miR8689). The putative miRNAs were composed of 18 nucleotides having a range of -0.49 to -1.56 MFEI values and 55%–61% of (A + U) content in their pre-miRNAs. A total of 1052 gene targets of putative miRNAs were identified and their functions were extensively analyzed. Most of the gene targets were involved in plant growth, cell cycle regulation, organelle synthesis, developmental process and environmental responses. Five gene targets, namely, NAC Domain Containing Protein, WRKY DNA binding protein, 3-dehydroquinate synthase, S-adenosyl-L-Met–dependent methyl transferase and Vascular-related NAC-Domain were found to be involved in the lignin biosynthesis, phenylpropanoid pathways and secondary wall formation. The present study might accelerate the more miRNA discovery, strengthening the complete understanding of miRNAs association in the cellular basis of lignin biosynthesis towards the production of high standard jute products.

miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification

Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.

Selection of Optimized Reference Genes for qRT-PCR Normalization in Xanthomonas campestris pv. campestris Cultured in Different Media

Black rot is a cruciferous disease caused by Xanthomonas campestris pv. campestris (Xcc) and results in significant economic losses worldwide; therefore, elucidation of the mechanism of Xcc pathogenesis is urgently required. In this study, we aimed to select optimized reference genes to verify the relative quantification of virulent genes in Xcc. Xcc strains were cultured in three different media [basic medium (MMX), hrp-inducing medium (MMXC) and rich medium (NYG)] and the expression stability of five candidate genes [thymidylate synthase (thyA), DNA gyrase subunit B (gyrB), DNA-directed RNA polymerase subunit beta, glyceraldehyde-3-phosphate dehydrogenase and 16S ribosomal RNA (16S rRNA)] was evaluated using BestKeeper, GeNorm, and NormFinder software programs. Quantitative real-time PCR (qRT-PCR) analysis confirmed that two Xcc effector genes were hrpX/hrpG-regulated in MMXC using selected genes as controls. Finally, gyrB and thyA were validated as the optimized reference genes of Xcc cultured in MMXC, and qRT-PCR analysis was demonstrated to be an efficient alternative to Gus-activity detection for the analysis of Xcc expression. This information will be useful in the future studies of Xcc, especially those seeking new functional genes.