In a world where climate change is real and its consequences are unprecedented, understanding of the plant adaptive capacity and native stress-responsive machinery is crucial. In recent years, universal stress proteins (USPs) have received much attention in the field of plant science due to their stress-specific transcriptional regulation. This study focuses on the extensive characterization of the USP gene family members in the monocot crop rice (Oryza sativa L. var. japonica). Here, we report a total of 44 USP genes in the rice genome. In silico characterization of these genes showed that domain architecture played a major role in the functional diversification of the USP gene family which holds for all plant USPs. On top of that, a higher conservation of OsUSP members has been exhibited with a monocot genome (Zea mays L.) as compared to a dicot genome (Arabidopsis thaliana L.). Expression profiling of the identified genes led to the discovery of multiple OsUSP genes that showed pronounced transcript alteration under various abiotic stress conditions, indicating their potential role as multi-functional stress-specific modules. Furthermore, expression validation of OsUSP genes using qRT-PCR provided a strong evidence for the utility OsUSP genes in building multi-stress tolerant plants. Altogether, this study provides leads to suitable USP candidates that could be targeted for plant breeding and genetic engineering experiments to develop stress resilient crop species.

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Jute (Corchorus sp.), is a versatile, naturally occurring, biodegradable material that holds the promising possibility of diminishing the extensive use of plastic bags. One of the major components of the cell wall, lignin plays both positive and negative roles in fiber fineness and quality. Although it gives mechanical strength to plants, an excess amount of it is responsible for the diminution of fiber quality. Among various gene families involved in the lignin biosynthesis, Caffeoyl-CoA 3-O-methyltransferase (CCoAOMT) is the most significant and has remained mostly unexplored. In this study, an extensive in-silico characterization of the CCoAOMT gene family was carried out in two jute species (C. capsularis L. and C. olitoroius L.) by analyzing their structural, functional, molecular and evolutionary characteristics. A total of 6 CCoAOMT gene members were identified in each of the two species using published reference genomes. These two jute species showed high syntenic conservation and the identified CCoAOMT genes formed four clusters in the phylogenetic tree. Histochemical assay of lignin in both jute species could shed light on the deposition pattern in stems and how it changes in response to abiotic stresses. Furthermore, expression profiling using qPCR showed considerable alteration of CCoAOMT transcripts under various abiotic stresses and hormonal treatment. This study will lay a base for further analysis and exploration of target candidates for overexpression of gene silencing using modern biotechnological techniques to enhance the quality of this economically important fiber crop.

Expression quantitative trait locus (eQTL) mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of eQTL data has been generated for this model plant; however, only a few causal eQTL genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal eQTL genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype QTLs to be used for eQTLs by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize sixteen out of twenty-five potential eQTL causal genes within the 20% rank percentile. Several new features are important in prioritizing causal eQTL genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate eQTL causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.