Seed development is an intricate process regulated via a complex transcriptional regulatory network. To understand the molecular mechanisms governing seed development and seed size/weight in chickpea, we performed a comprehensive analysis of transcriptome dynamics during seed development in two cultivars with contrasting seed size/weight (small-seeded, Himchana 1 and large-seeded, JGK 3). Our analysis identified stage-specific expression for a significant proportion (>13%) of the genes in each cultivar. About one half of the total genes exhibited significant differential expression in JGK 3 as compared with Himchana 1. We found that different seed development stages can be delineated by modules of coexpressed genes. A comparative analysis revealed differential developmental stage specificity of some modules between the two cultivars. Furthermore, we constructed transcriptional regulatory networks and identified key components determining seed size/weight. The results suggested that extended period of cell division during embryogenesis and higher level of endoreduplication along with more accumulation of storage compounds during maturation determine large seed size/weight. Further, we identified quantitative trait loci-associated candidate genes harboring single nucleotide polymorphisms in the promoter sequences that differentiate small- and large-seeded chickpea cultivars. The results provide a valuable resource to dissect the role of candidate genes governing seed development and seed size/weight in chickpea.

Long non-coding RNAs (lncRNAs) make up a significant portion of non-coding RNAs and are involved in a variety of biological processes. Accurate identification/annotation of lncRNAs is the primary step for gaining deeper insights into their functions. In this study, we report a novel tool, PLncPRO, for prediction of lncRNAs in plants using transcriptome data. PLncPRO is based on machine learning and uses random forest algorithm to classify coding and long non-coding transcripts. PLncPRO has better prediction accuracy as compared to other existing tools and is particularly well-suited for plants. We developed consensus models for dicots and monocots to facilitate prediction of lncRNAs in non-model/orphan plants. The performance of PLncPRO was quite better with vertebrate transcriptome data as well. Using PLncPRO, we discovered 3714 and 3457 high-confidence lncRNAs in rice and chickpea, respectively, under drought or salinity stress conditions. We investigated different characteristics and differential expression under drought/salinity stress conditions, and validated lncRNAs via RT-qPCR. Overall, we developed a new tool for the prediction of lncRNAs in plants and showed its utility via identification of lncRNAs in rice and chickpea.

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Seed development is orchestrated via complex gene regulatory networks and pathways. Epigenetic factors may also govern seed development and seed size/weight. Here, we analyzed DNA methylation in a large-seeded chickpea cultivar (JGK 3) during seed development stages. Progressive gain of CHH context DNA methylation in transposable elements (TEs) and higher frequency of small RNAs in hypermethylated TEs during seed development suggested a role of the RNA-dependent DNA methylation pathway. Frequency of intragenic TEs was higher in CHH context differentially methylated region (DMR) associated differentially expressed genes (DEGs). CG context hyper/hypomethylation within the gene body was observed for most of DMR-associated DEGs in JGK 3 as compared to small-seeded chickpea cultivar (Himchana 1). We identified candidate genes involved in seed size/weight determination exhibiting CG context hypermethylation within the gene body and higher expression in JGK 3. This study provides insights into the role of DNA methylation in seed development and seed size/weight determination in chickpea.

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