MicroRNAs (miRNAs) are small endogenous RNAs of ~22 nucleotides that have been shown to play regulatory role by negatively affecting the expression of genes at the post-transcriptional level. Information of miRNAs on some important crops like soybean, Arabidopsis, and rice, etc. are available, but no study on heat-responsive novel miRNAs has yet been reported in wheat (Triticum aestivum L.). In the present investigation, a popular wheat cultivar HD2985 was used in small RNA library construction and Illumina HiSeq 2000 was used to perform high-throughput sequencing of the library after cluster generation; 110,896,604 and 87,743,861 reads were generated in the control (22 °C) and heat-treated (42 °C for 2 h) samples, respectively. Forty-four precursor and mature miRNAs were found in T. aestivum from miRBase v 19. The frequencies of the miRNA families varied from 2 (tae-miR1117) to 60,672 (tae-miR159b). We identify 1052 and 902 mature miRNA sequences in HD2985 control and HS-treated samples by mapping on reference draft genome of T. aestivum. Maximum identified miRNAs were located on IWGSC_CSS_3B_scaff (chromosome 3B). We could identify 53 and 46 mature miRNA in the control and HS samples and more than 516 target genes by mapping on the reference genome of Oryza sativa, Zea mays, and Sorghum bicolor. Using different pipelines and plant-specific criteria, 37 novel miRNAs were identified in the control and treated samples. Six novel miRNA were validated using qRT-PCR to be heat-responsive. A negative correlation was, however, observed between the expression of novel miRNAs and their targets. Target prediction and pathway analysis revealed their involvement in the heat stress tolerance. These novel miRNAs are new additions to miRNA database of wheat, and the regulatory network will be made use of in deciphering the mechanism of thermotolerance in wheat.

Yellow Mosaic Disease (YMD) in mungbean [Vigna radiata (L.) R. Wilczek] is one of the most damaging diseases in Asia. In the northern part of India, the YMD is caused by Mungbean Yellow Mosaic India Virus (MYMIV), while in southern India this is caused by Mungbean Yellow Mosaic Virus (MYMV). The molecular mechanism of YMD resistance in mungbean remains largely unknown. In this study, RNA-seq analysis was conducted between a resistant (PMR-1) and a susceptible (Pusa Vishal) mungbean genotype under infected and control conditions to understand the regulatory network operating between mungbean-YMV. Overall, 76.8 million raw reads could be generated in different treatment combinations, while mapping rate per library to the reference genome varied from 86.78% to 93.35%. The resistance to MYMIV showed a very complicated gene network, which begins with the production of general PAMPs (pathogen-associated molecular patterns), then activation of various signaling cascades like kinases, jasmonic acid (JA) and brassinosteroid (BR), and finally the expression of specific genes (like PR-proteins, virus resistance and R-gene proteins) leading to resistance response. The function of WRKY, NAC and MYB transcription factors in imparting the resistance against MYMIV could be established. The string analysis also revealed the role of proteins involved in kinase, viral movement and phytoene synthase activity in imparting YMD resistance. A set of novel stress-related EST-SSRs are also identified from the RNA-Seq data which may be used to find the linked genes/QTLs with the YMD resistance. Also, 11 defence-related transcripts could be validated through quantitative real-time PCR analysis. The identified gene networks have led to an insight about the defence mechanism operating against MYMIV infection in mungbean which will be of immense use to manage the YMD resistance in mungbean.

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The rising demand for popcorn necessitates improving the popping quality with higher yield of popcorn cultivars. Towards this direction several Quantitative Traits Loci (QTLs) for popping traits have been identified. However, identification of accurate and consistent QTLs across different genetic backgrounds and environments is necessary to effectively utilize the identified QTLs in marker-assisted breeding. In the current study, 99 QTLs related to popping traits reported in 8 different studies were assembled and projected on the reference map "Genetic 2005" using BioMercator v4.2 to identify metaQTLs with consistent QTLs. Total ten metaQTLs were identified on chromosome 1 (7 metaQTLs) and 6 (3 metaQTLs) with physical distance ranging between 0.43 and 12.75 Mb, respectively. Four identified metaQTLs, viz., mQTL1_1, mQTL1_5, mQTL1_7 and mQTL6_2 harboured 5–8 QTL clusters with moderately high R2 value. The clustered QTLs were from two or more experiments. Based on the expression pattern in endosperm and pericarp tissues, a total of 229 genes were selected. Nineteen of these genes are involved in carbohydrate metabolism. Of the 19 genes specifically involved in carbohydrate metabolism, 11 of them were in these regions, implying the importance of these clustered QTLs. MetaQTL1_1 at bin location 1.01 coincided with the reported QTLs related to various agronomic traits like stalk diameter, tassel length, leaf area and plant height. The identified metaQTLs can be further explored for fine mapping and candidate gene identification, which can be validated by loss or gain of function. Identified metaQTLs can be used for introgression of popping traits towards enhancing the popping ability.

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