Salinity is one of the important abiotic factors for any crop management in irrigated as well as rainfed areas, which leads to poor harvests. This yield reduction in salt affected soils can be overcome by improving salt tolerance in crops or by soil reclamation. Salty soils can be reclaimed by leaching the salt or by cultivation of salt tolerance crops. Salt tolerance is a quantitative trait controlled by several genes. Poor knowledge about mechanism of its inheritance makes slow progress in its introgression into target crops. Brassica is known to be a good reclamation crop. Inter and intra specific variation within Brassica species shows potential of molecular breeding to raise salinity tolerant genotypes. Among the various molecular markers, SSR markers are getting high attention, since they are randomly sparsed, highly variable and show co-dominant inheritance. Furthermore, as sequencing techniques are improving and softwares to find SSR markers are being developed, SSR markers technology is also evolving rapidly. Comparative SSR marker studies targeting Arabidopsis thaliana and Brassica species which lie in the same family will further aid in studying the salt tolerance related QTLs and subsequent identification of the “candidate genes” and finding out the origin of important QTLs. Although, there are a few reports on molecular breeding for improving salt tolerance using molecular markers in Brassica species, usage of SSR markers has a big potential to improve salt tolerance in Brassica crops. In order to obtain best harvests, role of SSR marker driven breeding approaches play important role and it has been discussed in this review especially for the introgression of salt tolerance traits in crops.

Rice (Oryza sativa L.), a staple crop plant that is a major source of calories for approximately 50% of the human population, exhibits various physiological responses against temperature stress. These responses are known mechanisms of flexible adaptation through crosstalk with the intrinsic circadian clock. However, the molecular regulatory network underlining this crosstalk remains poorly understood. Therefore, we performed systematic transcriptome data analyses to identify the genes involved in both cold stress responses and diurnal rhythmic patterns. Here, we first identified cold-regulated genes and then identified diurnal rhythmic genes from those (119 cold-upregulated and 346 cold-downregulated genes). We defined cold-responsive diurnal rhythmic genes as CD genes. We further analyzed the functional features of these CD genes through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and performed a literature search to identify functionally characterized CD genes. Subsequently, we found that light-harvesting complex proteins involved in photosynthesis strongly associate with the crosstalk. Furthermore, we constructed a protein–protein interaction network encompassing four hub genes and analyzed the roles of the Stay-Green (SGR) gene in regulating crosstalk with sgr mutants. We predict that these findings will provide new insights in understanding the environmental stress response of crop plants against climate change.

Ubiquitination is an important environmental stress response, and E3 ubiquitin ligases play a major role in the process. T-DNA insertion mutants of rice, Oscbe1-1, and Oscbe1-2, were identified through the screening of cold stress tolerance at seedling stage. Oscbe1 mutants showed a significantly higher cold stress tolerance in the fresh weight, chlorophyll content, and photosynthetic efficiency than wild type. Molecular prediction showed that OsCBE1 (Oryza sativa Cullin4-Based E3 ubiquitin ligase1) encoded a novel substrate receptor of Cullin4-based E3 ubiquitin ligase complex (C4E3). Whereas Oscbe1 mutants had fewer panicles and grains than wild type in the paddy field, the overexpression lines of OsCBE1 had more panicles and grains, suggesting that OsCBE1 is involved in the regulation of both abiotic stress response and development. Oscbe1 mutants also showed ABA hypersensitivity during seed germination, suggesting OsCBE1 function for the stress response via ABA signaling. In silico analysis of OsCBE1 activity predicted a CCCH-type transcription factor, OsC3H32, as a putative substrate. Co-IP (Co-immunoprecipitation) study showed that OsCBE1 interacts with OsDDB1, an expected binding component of OsCBE1 and OsC3H32. Additionally, expression of OsOLE16, OsOLE18, and OsBURP5 were negatively related with expression of OsCBE1. These results suggest that OsCBE1 functions as a regulator of the abiotic stress response via CCCH as a member of the C4E3.

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low ac-curacy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction predic-tion method will broaden our understanding of protein interaction networks.

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on interspecies biological evidence (knowledge-based predictions) that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interactions using interaction energy distribution. Protein interactions with specific partners can be predicted by investigating narrow funnel-like interaction energy distribution, exhibiting a stable state in a specific orientation. In this study, it was demonstrated that various protein interactions with specific interaction partner, such as protein interaction with kinase and E3 ubiquitin ligases, have narrow funnel-like interaction energy distribution. Moreover, algorithm and deep learning model for prediction of general protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy of this method is similar to or even better than that of yeast two-hybrid screening, which is the most widely applied interaction screening method. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.