Sukanta Kumar Pradhan

Rust, caused by Puccinia arachidis, is one among the most destructive fungal diseases constraining global groundnut (Arachis hypogaea L.) production. While the development of disease-resistant varieties stands as the most effective approach to preventing substantial yield losses, the genetic mechanisms underlying resistance to rust is not yet well understood, emphasizing the necessity for further detailed research. In this study, 184 accessions from the ICRISAT groundnut mini-core collection were evaluated for rust resistance at Dharwad, India, across multiple seasons, as well as in Vietnam for one season. Whole-genome resequencing-based genome-wide association study (GWAS) identified five highly significant marker trait associations (MTAs) for rust resistance (p = 5.22 × 10-13 to 7.21 × 10-08). Among these, two robust rust-associated kompetitive allele specific PCR (KASP) markers, snpAH00607 at chromosome Ah01 and snpAH00609 at chromosome Ah17, were validated across diverse set of breeding and pre-breeding lines. These markers were linked to candidate genes encoding sterol C4-methyl oxidase 1-2, implicated in brassinosteroid-mediated salicylic acid signalling, and MYB transcription factor known to be associated with defense responses. The identified SNPs, validated markers, and candidate genes will serve as important resources for marker-assisted breeding of rust disease resistant groundnut varieties.

Climate change negatively influences the productive and reproductive abilities of goats. There is a need to understand the relationship between heat stress and genes that may aid in the development of climate-resilient goats. Melanism variation in goats plays a role in thermoregulation, in which the melanogenic genes have a pleiotropic effect on the regulation of physiological responses and behavior that are altered due to heat stress in the animals. Thus, the present study was conducted to establish a possible association between the coat color gene (MC1R) and heat stress characteristics. The physiological responses and cortisol levels were recorded in forty different coat-colored goats. The genotyping of the animals revealed four SNPs at the 183^rd (C/T), 332^nd (C/G), 748^th (G/T), and 801^st (C/G) positions, among which the black and brown goat populations had novel SNPs at the 332^nd position. Eight haplotypes were constructed, and an association study revealed that haplotypes (CCGG, TCGG, and CCTC) that were linked to white animals had lower cortisol values, rectal temperature, skin temperature, and respiration rate. The multivariate and cluster analyses revealed that the white goats were distinct from the rest of the goats. In addition, the docking results revealed the residues that were forming the interaction complex, which could play a role in melanogenesis in the animals and, in turn, the heat stress ability of the goats. Altogether, the results of the present study could pave the way for more research into coat color genes and their relationship with heat stress traits.

In plants, GIGANTEA (GI) protein plays different biological functions including carbon and sucrose metabolism, cell wall deposition, transpiration and hypocotyl elongation. This suggests that GI is an important class of proteins. So far, the resource-intensive experimental methods have been mostly utilized for identification of GI proteins. Thus, we made an attempt in this study to develop a computational model for fast and accurate prediction of GI proteins. Ten different supervised learning algorithms i.e., SVM, RF, JRIP, J48, LMT, IBK, NB, PART, BAGG and LGB were employed for prediction, where the amino acid composition (AAC), FASGAI features and physico-chemical (PHYC) properties were used as numerical inputs for the learning algorithms. Higher accuracies i.e., 96.75% of AUC-ROC and 86.7% of AUC-PR were observed for SVM coupled with AAC + PHYC feature combination, while evaluated with five-fold cross validation. With leave-one-out cross validation, 97.29% of AUC-ROC and 87.89% of AUC-PR were respectively achieved. While the performance of the model was evaluated with an independent dataset of 18 GI sequences, 17 were observed as correctly predicted. We have also performed proteome-wide identification of GI proteins in wheat, followed by functional annotation using Gene Ontology terms. A prediction server “GIpred” is freely accessible at http://cabgrid.res.in:8080/gipred/ for proteome-wide recognition of GI proteins.

Background: Circadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes.Results: Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely,Oryza sativaandSorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms.Conclusions: To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG (https://cran.r-project.org/web/packages/PredCRG/index.html) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.