Reyazul Rouf Mir

Wheat production is increasingly threatened by biotic and abiotic stresses, with stripe rust, caused by Puccinia striiformis f. sp. tritici being among the most devastating diseases. To dissect stripe rust resistance mechanisms, 329 diverse wheat genotypes were evaluated across six distinct environments in India (three locations over two years). The panel exhibited wide variation for stripe rust resistance and was genotyped using a 35K SNP-array. Genome-wide association study (GWAS) revealed 49 significant marker-trait associations (MTAs), explaining 1.58% to 29.7% of phenotypic variation, with notable quantitative-trait locus (QTL) hotspots on chromosomes 2A, 3B and 4B. Several MTAs co-localized with known resistance loci, while AX-92621629 appeared novel, suggesting new genomic region contributing to adult plant resistance. Candidate genes near significant single-nucleotide polymorphisms (SNPs) were enriched for defense-related functions, including nucleotide-binding site leucine-rich repeat (NBS-LRR) proteins, receptor-like kinases and transcription factors involved in defense signaling. To further investigate resistance mechanisms, metabolomic profiling, phytohormone and flavonoid dynamics were conducted on two contrasting wheat genotypes (resistant SKUA_415; susceptible SKUA_246) using untargeted Gas Chromatography-Mass Spectrometry (GC‒MS) and Liquid Chromatography-Mass Spectrometry (LC‒MS) approaches. Key defense-related metabolites, including myo-inositol, ketoglutaric acid, rutin and schaftoside and kaempferol derivatives were identified. These metabolites were downregulated in SKUA_246 following infection, while SKUA_415 showed up-regulation of defense phytohormones, anthocyanins and flavonoids. The two contrasting genotypes also exhibited clear allelic differentiation at key resistance-linked SNP loci, consistent with their divergent metabolomic responses. This study highlights identification of promising genes/QTLs/MTAs and metabolic markers for breeding next-generation stripe rust resistant wheat cultivars.

Wheat, a major staple crop, contributes significantly to global protein and calorie intake. However, the increasing challenges posed by climate change and a growing population threaten its stable production. Pre-harvest sprouting (PHS), triggered by prolonged rainfall and humidity before harvest, significantly reduces wheat grain yield and quality. This study is the first to assess PHS tolerance (PHST) in a global collection of 116 T. sphaerococcum accessions, which were characterized at three different locations. A 35 K Axiom single nucleotide polymorphism (SNP) array was used to genotype these accessions and finally 15, 308 high quality SNPs were used to perform Genome-wide association studies (GWAS) employing two single-locus GWAS (SL-GWAS) and four multi-locus GWAS (ML-GWAS) models. Consequently, twelve marker-trait associations (MTAs) controlling PHST were identified using SL-GWAS and ML- GWAS models (p < 0.001). Among these, five MTAs (AX-94415302, AX-94919611, AX-94403953, AX-95220897, and AX-94756068), were consistently found across all the tested environments. In silico analysis revealed that these SNPs were located within the candidate genes (CGs) containing domains such as LRR, NAC, serine/threonine kinase, F-box, WRKY, SANT/Myb, cytochrome P450, homeobox-like, and WD40, which are involved in regulating seed germination, dormancy, and abiotic stress tolerance. Furthermore, haplotype analysis led to the identification of a variable number of haplotypes across 10 MTAs. Notably, three haplotypes, namely H005, H006, and H007 were present in PHS tolerant accessions, TS28, TS64 and TS81 respectively, representing favourable allelic combinations for PHST. These findings provide valuable genetic resources and potential targets for breeding strategies to enhance PHST in wheat.

Three field experiments were conducted to assess the performance of various spring wheat genotypes, viz., SKAU-101 (V1), SKAU-102 (V2) and Shalimar Wheat2 (V3). These genotypes were sown on different dates: 15 October (S1), 1 November(S2) and 15November(S3) during the 2020–2021winter(rabi) season. Randomised complete block design (RCBD) was employed for the experimental setup in North Kashmir, India. This design facilitated a thorough examination of each genotype’s response to the different planting dates within the specified agroecological context. The crops sown on 15 October showed superior growth, phenology and yield (4.01tha-1 for grain). The SKAU-102 variety required less time to reach various phenological stages, matured 5–12 days earlier than the other two genotypes, and produced a higher yield; the highest yield (4.4 tha-1) was observed with the SKAU-102 genotype when sown early (V2S1). Furthermore, climate change trends in the region from 1980 to 2021 revealed statistically significant increases in maximum and minimum temperatures at a rate of 0.02°C per year, accompanied by a decreasing trend in precipitation at a rate of-4.53mm per year, which, if they continue, can adversely affect wheat growth, development and yield. Considering the ongoing climate changes and the findings from field experiments, it is advisable to sow the wheat genotype SKAU-102by15Octobertoachievethe earliest maturity and the highest yield, in contrast to the typical sowing date of mid-November in the region.

The Kashmir Valley, in the Pir Panjal range, is bordered by the Himalayas to the east and the Karakoram to the north, creating a unique geographical setting in the Western Himalayas. Its temperate climate with high rainfall, temperature fluctuations, warm summers, and cool winters fosters various wheat diseases. Wheat, a vital cereal crop supporting the livelihoods and food security of Kashmir's population, is significantly impacted by diseases, particularly spot blotch caused by Bipolaris sorokiniana. Although B. sorokiniana has been reported in warmer, humid regions including Bihar, Gujarat, Rajasthan, and Uttar Pradesh (Acharya et al. 2011), its presence in the cooler climate of Kashmir has not been documented. In April 2023 and 2024, spot blotch symptoms were consistently detected in the wheat research fields of SKUAST- Kashmir, with an incidence ranging from 40-50%. Initial symptoms included light brown, oval to elliptical necrotic spots that enlarged, leading to chlorosis and leaf death. To identify the causal pathogen, symptomatic leaf tissue was excised from the diseased-healthy tissue interface, surface disinfected in 1% NaOCl for 1 min, rinsed thrice with sterile water, dried, and plated onto PDA amended with 100 ppm streptomycin sulfate. After five days at 25°C, the putative causal agent was isolated from 90% of symptomatic samples and purified via single hyphal tip technique. The culture initially appeared velvety and olive brown with a loose cottony mass of white mycelium, turning black with profuse sporulation after 8 days. The conidia were dark olivaceous brown and obclavate to cylindrical or broadly ellipsoidal with tapered ends, featuring three to eight distosepta and measuring 50-100 × 10-17.5 µm. Based on symptoms and morphological characteristics described by Manamgoda et al. (2014), the fungus was tentatively identified as B. sorokiniana. For molecular identification, eight representative isolates were amplified and Sanger sequenced using primer pairs for the internal transcribed spacer (ITS) region (ITS1/ITS4) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene (GPD1/GPD2) (White et al. 1990; Berbee et al. 1999). Sequences were deposited in GenBank (ITS:PV162803-PV162810; GAPDH:PV295261-PV295268). BLAST searches of the obtained sequences revealed 98-100% for ITS and 97-100% for GAPDH homology with B. sorokiniana sequences in GenBank, OR575723 (isolate HUBS-54) and OR260701 (isolate LB-22), respectively. To confirm pathogenicity, 4 replicates of 21-day old plants of the susceptible variety Shalimar Wheat-2 were sprayed with a conidial suspension (1 × 10⁵ conidia/ml) and incubated in plastic bags for 24 h in a greenhouse (temperature: ~25±2°C). Control plants were sprayed with sterile water. Inoculated plants developed symptoms similar to those observed in the field within 7 days, while controls remained healthy. The pathogen was reisolated from lesions and confirmed as B. sorokiniana based on morphology and resequencing, fulfilling Koch's postulates. Pathogenicity tests were conducted twice. To our knowledge, this is the first report confirming B. sorokiniana causing spot blotch on wheat in the Kashmir Valley, India. The expanded range of this pathogen, likely due to elevated temperature and relative humidity associated with climate change, poses a significant threat to wheat production in the region. Our study provides important reference information for controlling this disease.

Papaya is a nutritionally valuable fruit crop cultivated globally in tropical and subtropical regions. Conventional breeding efforts have prioritized enhancing traits such as yield and fruit size, with notable success in developing high-yielding cultivars. However, other critical areas in papaya improvement, such as enhancing genetic diversity, improving disease resistance, optimizing post-harvest management, and addressing consumer preferences for fruit quality and flavor, have experienced relatively limited progress. Addressing these gaps is essential for meeting both production challenges and market demands. Achieving substantial genetic gains in these traits in the shortest timeframe will require integrating traditional breeding practices with emerging genomics tools. Over the past two decades, substantial advancements in papaya genomics have been achieved, resulting in resources including high-density genetic maps, high-quality reference genomes, and transcriptomic and resequencing datasets. These resources have been utilized to develop genome-wide markers and identify marker-trait associations, supporting the development of disease-resistant varieties and uncovering the genetics of consumer-preferred traits. By utilizing these resources in combination with innovative approaches such as genomic selection and speed breeding, sequence-based breeding approaches can significantly accelerate genetic gains in papaya. This enables the rapid development of elite (high-performance) papaya cultivars that meet both agronomic and consumer expectations.

Excessive nitrogen use and low nitrogen use efficiency (NUE) in current agroecosystems are disrupting the global nitrogen cycle. Chemical inhibitors offer only temporary relief, while plant‐derived biological nitrification inhibitors (BNIs) remain safer but underexplored. Identifying biological nitrification inhibition (BNI) traits in nitrogen‐demanding crops like wheat is key to improving sustainability. In this study, a combined GC‐ and LC–MS platform was used to determine the metabolome of the root exudates of 44 diverse wheat genotypes originating from India and Austria. With more than 6000 metabolic features, a pronounced genotype‐specific variation, a clear geographic pattern and an unexpected complexity of the root exudate metabolome were observed. A novel high‐throughput assay utilizing diverse ammonia‐oxidizing bacteria (AOB) and archaea (AOA) was developed for rapid BNI testing, highlighting distinct inhibition and even growth stimulation capacities between genotypes. Network analysis and advanced machine and deep learning analysis identified combinations of 32 metabolites linked to high BNI activity, including phenylpropanoids sinapinic acid, syringic acid and others, as well as glycosylated flavones isoschaftoside and others. This indicates that the concurrent presence of specific metabolites, rather than a single compound, drives nitrification inhibition in the rhizosphere. Variation in BNI activity among wheat genotypes, classified as either spring or winter types, suggests that root architecture modulates the dynamics of root exudation and the potential for nitrification inhibition. The unique combination of high‐throughput metabolomics analysis and the BNI fast‐track assay allows for screening of large germplasm collections as an essential requirement to introduce BNI and related NUE traits into modern breeding programmes.

Custard apples (Annona spp.) are among the most important horticultural crops in the world, including Australia. The genus Annona comprises several economically and nutritionally significant species, including atemoya, cherimoya, sugar apple, ilama, soursop, bullock’s heart, and bibra. These fruits are valued for their exotic taste and are popular backyard fruit crops in many countries. While some species are commercially cultivated and exported, the broader potential of these crops remains largely untapped. Despite their historical significance, these Annona species remain neglected or underutilised, with breeding efforts restricted to only a few countries. Extensive genetic resources, including germplasm collections, candidate genotypes, and mapping populations, are available for crop improvement. Traditional breeding methods - such as selection, crossbreeding, and mutation breeding – have been widely applied alongside modern breeding approaches like marker-assisted selection (MAS). However, several challenges, such as a lack of information regarding the crop and a long juvenile period, hinder crop improvement in custard apples. Recent advancements and affordability of sequencing technologies have enabled an increase in the number of multiomics studies, especially genomics and transcriptomics within Annona species. Integrating these data with proteomics, metabolomics, and phenomics will facilitate the genetic dissection of important traits in Annona. This review provides a comprehensive overview of the current advancements and future prospects of multiomics tools and technologies developed and their potential to accelerate custard apple breeding programs.

The present study was conducted to determine variability for complex traits of yield and yield attributes by measuring different morphologically related metric traits and evaluation of the traits which that are closely related to yield. Grain yield showed high heritability along with high genetic advance yield and the characters which high heritability along with high genetic advance and genetic gain were spike length, 1000-grain weight, spikelet per spike, grains per spike, seed size, awn length, peduncle length, and stem weight would be effective for selection in the breeding programme. However, highest estimates of heritability (b.s.) accompanied by high genetic advance as per cent of mean were recorded for grain yield/ ha. The estimates of genotypic coefficient of variation (GCV) were highest for grain yield (35.24%) followed by 1000 grain weight (23.42%), grains per spike (19. 34%) and a number of spikelet spike (19.32%).It is obvious that yield is a polygenic trait that results from the contribution of many interacting factors. The phenotypic and genotypic correlations for yield and yield attributing traits revealed that grain yield exhibited positive and highly significant genotypic and phenotypic correlation with 1000 grain weight, grains per spike, and spikelets per spike but exhibited negative and significant genotypic and phenotypic correlation with days to flowering and days to maturity. The highest indirect positive effects of a number of grains per spike on grain yield was recorded via a number of spikelets per spike (0.368) followed by spike length (0.136) and flag leaf length (0.123) whereas grains per spike recorded a moderate negative indirect effect via length breadth ratio (-0.014), awn length (- 0.011) and peduncle length (-0.145) on grain yield. This study offers treasured acumens for breeders and researchers working on enhancing wheat productivity to meet the mounting demands of increasing human population

Food legume crops, including common bean, faba bean, mungbean, cowpea, chickpea, and pea, have long served as vital sources of energy, protein, and minerals worldwide, both as grains and vegetables. Advancements in high-throughput phenotyping, next-generation sequencing, transcriptomics, proteomics, and metabolomics have significantly expanded genomic resources for food legumes, ushering research into the panomics era. Despite their nutritional and agronomic importance, food legumes still face constraints in yield potential and genetic improvement due to limited genomic resources, complex inheritance patterns, and insufficient exploration of key traits such as quality and stress resistance. This highlights the need for continued efforts to comprehensively dissect the phenome, genome, and regulome of these crops. This review summarizes recent advances in technological innovations and multi-omics applications in food legumes research and improvement. Given the critical role of germplasm resources and the challenges in applying phenomics to food legumes—such as complex trait architecture and limited standardized methodologies—we first address these foundational areas. We then discuss recent gene discoveries associated with yield stability, seed composition, and stress tolerance and their potential as breeding targets. Considering the growing role of genetic engineering, we provide an update on gene-editing applications in legumes, particularly CRISPR-based approaches for trait enhancement. We advocate for integrating chemical and biochemical signatures of cells ('molecular phenomics') with genetic mapping to accelerate gene discovery. We anticipate that combining panomics approaches with advanced breeding technologies will accelerate genetic gains in food legumes, enhancing their productivity, resilience, and contribution to sustainable global food security.

Global crop productivity faces a significant threat from climate change-induced drought stress (DS), which is vital for sustainable agriculture and global food security. Uncovering DS adaptation and tolerance mechanisms in crops is necessary to alleviate climate challenges. Innovative plant breeding demands revolutionary approaches to develop stress-smart plants. Metabolomics, a promising field in plant breeding, offers a predictive tool to identify metabolic markers associated with plant performance under DS, enabling accelerated crop improvement. Central to DS adaptation is metabolomics-driven metabolic regulation, which is critical for maintaining cell osmotic potential in crops. Recent innovations allow rapid mapping of specific metabolites to their genetic pathways, providing a valuable resource for plant scientists. Metabolomics-driven molecular breeding, integrating techniques such as mQTL and mGWAS, enhances our ability to discover key genetic elements linked to stress-responsive metabolites. This integration offers a beneficial platform for plant scientists, yielding significant insights into the complex metabolic networks underlying DS tolerance. Therefore, this review discusses (1) insights into metabolic regulation for DS adaptation, (2) the multifaceted role of metabolites in DS tolerance and nutritional/yield trait improvement, (3) the potential of single-cell metabolomics and imaging, (4) metabolomics-driven molecular breeding, and (5) the application of metabolic and genetic engineering for DS-tolerant crops. We finally propose that the metabolomics-driven approach positions drought-smart crops as key contributors to future food production, supporting the vital goal of achieving zero hunger.