Advances and opportunities in unraveling cold-tolerance mechanisms in the world's primary staple food crops.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end-use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold-responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low-temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold-tolerant crops for the future.

Do diverse wheat genotypes unleash their biochemical arsenal differentially to conquer cold stress? A comprehensive study in the Western Himalayas.

Wheat is one of the most important cereal crops in the world. Cold stress is a major constraint in production of wheat grown in cold climate regions. In this study, we conducted a comprehensive assessment of cold stress tolerance in wheat genotypes through field screening, cell membrane stability through electrolyte leakage assay and biochemical profiling. A core set comprising 4560 genotypes was evaluated for two years (2021-2022), revealing substantial genetic variation for cold stress tolerance. Most genotypes exhibited moderate tolerance, while a smaller proportion showed susceptibility to cold stress. Based on the cold screening data in the field, a mini-core set of 350 genotypes was selected for membrane stability analysis using electrical conductivity assays. Significant differences were observed in membrane stability among the genotypes, indicating the presence of genetic variation for this trait. Furthermore, a mini-core set was narrowed down to 50 diverse candidate genotypes that were subsequently profiled for various biochemicals, including reactive oxygen species (ROS) like lipid peroxidation (MDA) and hydrogen peroxide (H2 02 ), osmoprotectant (proline) and enzymatic antioxidants including ascorbate peroxidase (APX), superoxide dismutase (SOD), guaiacol peroxidase (GPX), and catalase (CAT). Correlation analysis of the biochemicals revealed negative associations between antioxidants and reactive oxygen species (ROS), highlighting their role in mitigating oxidative damage under cold stress. This study enhances our understanding of the physiological and biochemical mechanisms underlying cold stress tolerance in wheat. The identified genotypes with superior cold stress tolerance can serve as valuable genetic resources for wheat breeding.

Meta-QTL analysis in wheat: progress, challenges and opportunities.

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat.

BACKGROUND: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. RESULTS: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. CONCLUSION: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Phylogenomic analysis of 20S proteasome gene family reveals stress-responsive patterns in rapeseed (Brassica napus L.).

The core particle represents the catalytic portions of the 26S proteasomal complex. The genes encoding α- and ß-subunits play a crucial role in protecting plants against various environmental stresses by controlling the quality of newly produced proteins. The 20S proteasome gene family has already been reported in model plants such as Arabidopsis and rice; however, they have not been studied in oilseed crops such as rapeseed (Brassica napus L.). In the present study, we identified 20S proteasome genes for α- (PA) and ß-subunits (PB) in B. napus through systematically performed gene structure analysis, chromosomal location, conserved motif, phylogenetic relationship, and expression patterns. A total of 82 genes, comprising 35 BnPA and 47 BnPB of the 20S proteasome, were revealed in the B. napus genome. These genes were distributed on all 20 chromosomes of B. napus and most of these genes were duplicated on homoeologous chromosomes. The BnPA (α1-7) and BnPB (ß1-7) genes were phylogenetically placed into seven clades. The pattern of expression of all the BnPA and BnPB genes was also studied using RNA-seq datasets under biotic and abiotic stress conditions. Out of 82 BnPA/PB genes, three exhibited high expression under abiotic stresses, whereas two genes were overexpressed in response to biotic stresses at both the seedling and flowering stages. Moreover, an additional eighteen genes were expressed under normal conditions. Overall, the current findings developed our understanding of the organization of the 20S proteasome genes in B. napus, and provided specific BnPA/PB genes for further functional research in response to abiotic and biotic stresses.

DNA marker based diversity across rice genotypes and advanced breeding lines bred for temperate regions of North-West India.

BACKGROUND: Characterization and evaluation of plant genetic resources play an important role for their utilization in the crop improvement programmes. METHODS AND RESULTS: This study involves the agro-morphological and cooking quality besides, molecular characterization of 51 genotypes/advance breeding lines of rice from Kashmir Himalayas. Significant variability was observed for all agro-morphological and cooking quality traits among all the studied genotypes. Cluster analysis using UPGMA method divided the genotypes into two major clusters having 15 and 36 genotypes. Thirty eight genotypes screened using 24 SSR markers detected 48 alleles with 2.0 alleles for each locus with average polymorphism information content (PIC) of 0.37. High polymorphism information content (PIC) values was observed for the primers RM263 (0.67), RM159 (0.59) and RM333 (0.50). Furthermore, out of 38 SSR markers screened on 192 temperate rice germpalsm lines, R4M17 accurately differentiated indica and temperate japonica genotypes amplifying 220 bp and 169 bp, respectively. Accordingly, 15 genotypes were reported as indica and 28 temperate japonica in addition to 149 genotypes as intermediate types. CONCLUSION: The information on marker-based diversity and performance based on cooking quality and agronomic traits helped to select the most divergent lines for crossing. Also the analysis was useful to classify the temperate germplasm into indica and temperate japonica. The classification could be helpful to devise a strategy for inter-sub species hybridization to breed for improved rice varieties.

Allelic Diversity, Structural Analysis, and Genome-Wide Association Study (GWAS) for Yield and Related Traits Using Unexplored Common Bean (Phaseolus vulgaris L.) Germplasm From Western Himalayas.

The north-western Indian Himalayas possesses vast diversity in common bean germplasm due to several years of natural adaptation and farmer's selection. Systematic efforts have been made for the first time for the characterization and use of this huge diversity for the identification of genes/quantitative trait loci (QTLs) for yield and yield-contributing traits in common bean in India. A core set of 96 diverse common bean genotypes was characterized using 91 genome-wide genomic and genic simple sequence repeat (SSR) markers. The study of genetic diversity led to the identification of 691 alleles ranging from 2 to 21 with an average of 7.59 alleles/locus. The gene diversity (expected heterozygosity, He) varied from 0.31 to 0.93 with an average of 0.73. As expected, the genic SSR markers detected less allelic diversity than the random genomic SSR markers. The traditional clustering and Bayesian clustering (structural analysis) analyses led to a clear cut separation of a core set of 96 genotypes into two distinct groups based on their gene pools (Mesoamerican and Andean genotypes). Genome-wide association mapping for pods/plant, seeds/pod, seed weight, and yield/plant led to the identification of 39 significant marker-trait associations (MTAs) including 15 major, 15 stable, and 13 both major and stable MTAs. Out of 39 MTAs detected, 29 were new MTAs reported for the first time, whereas the remaining 10 MTAs were already identified in earlier studies and therefore declared as validation of earlier results. A set of seven markers was such, which were found to be associated with multiple (two to four) different traits. The important MTAs will be used for common bean molecular breeding programs worldwide for enhancing common bean yield.