Aspergillus flavus pangenome (AflaPan) uncovers novel aflatoxin and secondary metabolite associated gene clusters.

BACKGROUND: Aspergillus flavus is an important agricultural and food safety threat due to its production of carcinogenic aflatoxins. It has high level of genetic diversity that is adapted to various environments. Recently, we reported two reference genomes of A. flavus isolates, AF13 (MAT1-2 and highly aflatoxigenic isolate) and NRRL3357 (MAT1-1 and moderate aflatoxin producer). Where, an insertion of 310 kb in AF13 included an aflatoxin producing gene bZIP transcription factor, named atfC. Observations of significant genomic variants between these isolates of contrasting phenotypes prompted an investigation into variation among other agricultural isolates of A. flavus with the goal of discovering novel genes potentially associated with aflatoxin production regulation. Present study was designed with three main objectives: (1) collection of large number of A. flavus isolates from diverse sources including maize plants and field soils; (2) whole genome sequencing of collected isolates and development of a pangenome; and (3) pangenome-wide association study (Pan-GWAS) to identify novel secondary metabolite cluster genes. RESULTS: Pangenome analysis of 346 A. flavus isolates identified a total of 17,855 unique orthologous gene clusters, with mere 41% (7,315) core genes and 59% (10,540) accessory genes indicating accumulation of high genomic diversity during domestication. 5,994 orthologous gene clusters in accessory genome not annotated in either the A. flavus AF13 or NRRL3357 reference genomes. Pan-genome wide association analysis of the genomic variations identified 391 significant associated pan-genes associated with aflatoxin production. Interestingly, most of the significantly associated pan-genes (94%; 369 associations) belonged to accessory genome indicating that genome expansion has resulted in the incorporation of new genes associated with aflatoxin and other secondary metabolites. CONCLUSION: In summary, this study provides complete pangenome framework for the species of Aspergillus flavus along with associated genes for pathogen survival and aflatoxin production. The large accessory genome indicated large genome diversity in the species A. flavus, however AflaPan is a closed pangenome represents optimum diversity of species A. flavus. Most importantly, the newly identified aflatoxin producing gene clusters will be a new source for seeking aflatoxin mitigation strategies and needs new attention in research.

High-throughput diagnostic markers for foliar fungal disease resistance and high oleic acid content in groundnut.

BACKGROUND: Foliar diseases namely late leaf spot (LLS) and leaf rust (LR) reduce yield and deteriorate fodder quality in groundnut. Also the high oleic acid content has emerged as one of the most important traits for industries and consumers due to its increased shelf life and health benefits. RESULTS: Genetic mapping combined with pooled sequencing approaches identified candidate resistance genes (LLSR1 and LLSR2 for LLS and LR1 for LR) for both foliar fungal diseases. The LLS-A02 locus housed LLSR1 gene for LLS resistance, while, LLS-A03 housed LLSR2 and LR1 genes for LLS and LR resistance, respectively. A total of 49 KASPs markers were developed from the genomic regions of important disease resistance genes, such as NBS-LRR, purple acid phosphatase, pentatricopeptide repeat-containing protein, and serine/threonine-protein phosphatase. Among the 49 KASP markers, 41 KASPs were validated successfully on a validation panel of contrasting germplasm and breeding lines. Of the 41 validated KASPs, 39 KASPs were designed for rust and LLS resistance, while two KASPs were developed using fatty acid desaturase (FAD) genes to control high oleic acid levels. These validated KASP markers have been extensively used by various groundnut breeding programs across the world which led to development of thousands of advanced breeding lines and few of them also released for commercial cultivation. CONCLUSION: In this study, high-throughput and cost-effective KASP assays were developed, validated and successfully deployed to improve the resistance against foliar fungal diseases and oleic acid in groundnut. So far deployment of allele-specific and KASP diagnostic markers facilitated development and release of two rust- and LLS-resistant varieties and five high-oleic acid groundnut varieties in India. These validated markers provide opportunities for routine deployment in groundnut breeding programs.

Linkage-Mapping and Genome-wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using a Nested Association Mapping.

Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping (NAM) population has been genotyped with the peanut 58 K SNP array and phenotyped for LLS severity in the field for three years. Joint linkage-based QTL mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained (PVE) up to 47.7%. A genome-wide association study (GWAS) identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 Kb to 1,520 Kb (184 Kb) on chromosome B02 and from 1,026.9 Kb to 1,793.2 Kb (767 Kb) on chromosome B03, designated as peanut late leaf spot resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 NBS-LRR disease resistant genes. An NBS-LRR disease resistance gene Arahy.VKVT6A was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat (PPR), acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as NAM for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

High-density bin-based genetic map reveals a 530-kb chromosome segment derived from wild peanut contributing to late leaf spot resistance.

KEY MESSAGE: Twenty-eight QTLs for LLS disease resistance were identified using an amphidiploid constructed mapping population, a favorable 530-kb chromosome segment derived from wild species contributes to the LLS resistance. Late leaf spot (LLS) is one of the major foliar diseases of peanut, causing serious yield loss and affecting the quality of kernel and forage. Some wild Arachis species possess higher resistance to LLS as compared with cultivated peanut; however, ploidy level differences restrict utilization of wild species. In this study, a synthetic amphidiploid (Ipadur) of wild peanuts with high LLS resistance was used to cross with Tifrunner to construct TI population. In total, 200 recombinant inbred lines were collected for whole-genome resequencing. A high-density bin-based genetic linkage map was constructed, which includes 4,809 bin markers with an average inter-bin distance of 0.43 cM. The recombination across cultivated and wild species was unevenly distributed, providing a novel recombination landscape for cultivated-wild Arachis species. Using phenotyping data collected across three environments, 28 QTLs for LLS disease resistance were identified, explaining 4.35-20.42% of phenotypic variation. The major QTL located on chromosome 14, qLLS14.1, could be consistently detected in 2021 Jiyang and 2022 Henan with 20.42% and 12.12% PVE, respectively. A favorable 530-kb chromosome segment derived from Ipadur was identified in the region of qLLS14.1, in which 23 disease resistance proteins were located and six of them showed significant sequence variations between Tifrunner and Ipadur. Allelic variation analysis indicating the 530-kb segment of wild species might contribute to the disease resistance of LLS. These associate genomic regions and candidate resistance genes are of great significance for peanut breeding programs for bringing durable resistance through pyramiding such multiple LLS resistance loci into peanut cultivars.

Optimizing speed breeding and seed/pod chip based genotyping techniques in pigeonpea: A way forward for high throughput line development.

BACKGROUND: The challenge of pigeonpea breeding lies in its photosensitivity and seasonal specificity. This poses a problem to the breeder, as it restricts to single generation advancement in a year. Currently, the cross to cultivar gap is twelve to thirteen years resulting in a limited number of varietal releases over the past six decades. Shortening the breeding cycle was need of the hour, unlikely achieved by conventional breeding. To overcome these hindrances speed breeding was a necessary leap. An experiment was planned to optimize the speed breeding coupled with single seed descent and seed or pod chip-based genotyping to shorten the breeding cycle in pigeonpea at ICRISAT, Hyderabad. Monitored photoperiod, light wavelength, temperature and crop management regime were the indicators attributing to the success of speed breeding. RESULT: A photoperiod of 13 h: 8 h: 13 h at vegetative: flowering and pod filling stages is ideal for shortening the breeding cycle. Broad spectrum light (5700 K LED) hastened early vegetative growth and pod formation. Whereas far-red (735 nm) light favoured early flowering. A significant difference between the photoperiods, genotypes as well as photoperiod x genotype interaction for both days to flowering and plant height was noted. CONCLUSION: The optimized protocol serves as a road map for rapid generation advancement in pigeonpea. Deploying this protocol, it is possible to advance 2-4 generations per year. The breeding cycle can be reduced to 2-4 years which otherwise takes 7 years under conventional breeding. Single Seed Descent and seed or pod chip-based genotyping for early generation marker assisted selection, strengthened the precision of this technique aiding in high throughput line development.

A genomic variation map provides insights into peanut diversity in China and associations with 28 agronomic traits.

Peanut (Arachis hypogaea L.) is an important allotetraploid oil and food legume crop. China is one of the world's largest peanut producers and consumers. However, genomic variations underlying the migration and divergence of peanuts in China remain unclear. Here we reported a genome-wide variation map based on the resequencing of 390 peanut accessions, suggesting that peanuts might have been introduced into southern and northern China separately, forming two cultivation centers. Selective sweep analysis highlights asymmetric selection between the two subgenomes during peanut improvement. A classical pedigree from South China offers a context for the examination of the impact of artificial selection on peanut genome. Genome-wide association studies identified 22,309 significant associations with 28 agronomic traits, including candidate genes for plant architecture and oil biosynthesis. Our findings shed light on peanut migration and diversity in China and provide valuable genomic resources for peanut improvement.

Genome-Wide Mapping of Quantitative Trait Loci for Yield-Attributing Traits of Peanut.

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality.

Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms.

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique based on imaging spectroscopy that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871,87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time of a large area with high accuracy.

Celebrating Professor Rajeev K. Varshney's transformative research odyssey from genomics to the field on his induction as Fellow of the Royal Society.

Professor Rajeev K. Varshney's transformative impact on crop genomics, genetics, and agriculture is the result of his passion, dedication, and unyielding commitment to harnessing the potential of genomics to address the most pressing challenges faced by the global agricultural community. Starting from a small town in India and reaching the global stage, Professor Varshney's academic and professional trajectory has inspired many scientists active in research today. His ground-breaking work, especially his effort to list orphan tropical crops to genomic resource-rich entities, has been transformative. Beyond his scientific achievements, Professor Varshney is recognized by his colleagues as an exemplary mentor, fostering the growth of future researchers, building institutional capacity, and strengthening scientific capability. His focus on translational genomics and strengthening seed system in developing countries for the improvement of agriculture has made a tangible impact on farmers' lives. His skills have been best utilized in roles at leading research centres where he has applied his expertise to deliver a new vision for crop improvement. These efforts have now been recognized by the Royal Society with the award of the Fellowship (FRS). As we mark this significant milestone in his career, we not only celebrate Professor Varshney's accomplishments but also his wider contributions that continue to transform the agricultural landscape.

Long non-coding RNA-mediated epigenetic response for abiotic stress tolerance in plants.

Plants perceive environmental fluctuations as stress and confront several stresses throughout their life cycle individually or in combination. Plants have evolved their sensing and signaling mechanisms to perceive and respond to a variety of stresses. Epigenetic regulation plays a critical role in the regulation of genes, spatiotemporal expression of genes under stress conditions and imparts a stress memory to encounter future stress responses. It is quintessential to integrate our understanding of genetics and epigenetics to maintain plant fitness, achieve desired genetic gains with no trade-offs, and durable long-term stress tolerance. The long non-coding RNA >200 nts having no coding potential (or very low) play several roles in epigenetic memory, contributing to the regulation of gene expression and the maintenance of cellular identity which include chromatin remodeling, imprinting (dosage compensation), stable silencing, facilitating nuclear organization, regulation of enhancer-promoter interactions, response to environmental signals and epigenetic switching. The lncRNAs are involved in a myriad of stress responses by activation or repression of target genes and hence are potential candidates for deploying in climate-resilient breeding programs. This review puts forward the significant roles of long non-coding RNA as an epigenetic response during abiotic stresses in plants and the prospects of deploying lncRNAs for designing climate-resilient plants.

Enhancing climate change resilience in agricultural crops.

Climate change threatens global food and nutritional security through negative effects on crop growth and agricultural productivity. Many countries have adopted ambitious climate change mitigation and adaptation targets that will exacerbate the problem, as they require significant changes in current agri-food systems. In this review, we provide a roadmap for improved crop production that encompasses the effective transfer of current knowledge into plant breeding and crop management strategies that will underpin sustainable agriculture intensification and climate resilience. We identify the main problem areas and highlight outstanding questions and potential solutions that can be applied to mitigate the impacts of climate change on crop growth and productivity. Although translation of scientific advances into crop production lags far behind current scientific knowledge and technology, we consider that a holistic approach, combining disciplines in collaborative efforts, can drive better connections between research, policy, and the needs of society.

Identification and application of a candidate gene AhAftr1 for aflatoxin production resistance in peanut seed (Arachis hypogaea L.).

INTRODUCTION: Peanut is susceptible to infection of Aspergillus fungi and conducive to aflatoxin contamination, hence developing aflatoxin-resistant variety is highly meaningful. Identifying functional genes or loci conferring aflatoxin resistance and molecular diagnostic marker are crucial for peanut breeding. OBJECTIVES: This work aims to (1) identify candidate gene for aflatoxin production resistance, (2) reveal the related resistance mechanism, and (3) develop diagnostic marker for resistance breeding program. METHODS: Resistance to aflatoxin production in a recombined inbred line (RIL) population derived from a high-yielding variety Xuhua13 crossed with an aflatoxin-resistant genotype Zhonghua 6 was evaluated under artificial inoculation for three consecutive years. Both genetic linkage analysis and QTL-seq were conducted for QTL mapping. The candidate gene was further fine-mapped using a secondary segregation mapping population and validated by transgenic experiments. RNA-Seq analysis among resistant and susceptible RILs was used to reveal the resistance pathway for the candidate genes. RESULTS: The major effect QTL qAFTRA07.1 for aflatoxin production resistance was mapped to a 1.98 Mbp interval. A gene, AhAftr1 (Arachis hypogaea Aflatoxin resistance 1), was detected structure variation (SV) in leucine rich repeat (LRR) domain of its production, and involved in disease resistance response through the effector-triggered immunity (ETI) pathway. Transgenic plants with overexpression of AhAftr1(ZH6) exhibited 57.3% aflatoxin reduction compared to that of AhAftr1(XH13). A molecular diagnostic marker AFTR.Del.A07 was developed based on the SV. Thirty-six lines, with aflatoxin content decrease by over 77.67% compared to the susceptible control Zhonghua12 (ZH12), were identified from a panel of peanut germplasm accessions and breeding lines through using AFTR.Del.A07. CONCLUSION: Our findings would provide insights of aflatoxin production resistance mechanisms and laid meaningful foundation for further breeding programs.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Prospects for developing allergen-depleted food crops.

In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio-molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life-threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy-based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.

Genetic mapping identified major main-effect and three co-localized quantitative trait loci controlling high iron and zinc content in groundnut.

Malnutrition is a major challenge globally, and groundnut is a highly nutritious self-pollinated legume crop blessed with ample genomic resources, including the routine deployment of genomic-assisted breeding. This study aimed to identify genomic regions and candidate genes for high iron (Fe) and zinc (Zn) content, utilizing a biparental mapping population (ICGV 00440 × ICGV 06040;). Genetic mapping and quantitative trait locus (QTL) analysis (474 mapped single-nucleotide polymorphism loci; 1536.33 cM) using 2 seasons of phenotypic data together with genotypic data identified 5 major main-effect QTLs for Fe content. These QTLs exhibited log-of-odds (LOD) scores ranging from 6.5 to 7.4, explaining phenotypic variation (PVE) ranging from 22% (qFe-Ah01) to 30.0% (qFe-Ah14). Likewise, four major main effect QTLs were identified for Zn content, with LOD score ranging from 4.4 to 6.8 and PVE ranging from 21.8% (qZn-Ah01) to 32.8% (qZn-Ah08). Interestingly, three co-localized major and main effect QTLs (qFe-Ah01, qZn-Ah03, and qFe-Ah11) were identified for both Fe and Zn contents. These genomic regions harbored key candidate genes, including zinc/iron permease transporter, bZIP transcription factor, and vacuolar iron transporter which likely play pivotal roles in the accumulation of Fe and Zn contents in seeds. The findings of this study hold potential for fine mapping and diagnostic marker development for high Fe and Zn contents in groundnut.

Genetic mapping identified three hotspot genomic regions and candidate genes controlling heat tolerance-related traits in groundnut.

Groundnut productivity and quality have been impeded by rising temperatures in semi-arid environments. Hence, understanding the effects and molecular mechanisms of heat stress tolerance will aid in tackling yield losses. In this context, a recombinant inbred line (RIL) population was developed and phenotyped for eight seasons at three locations for agronomic, phenological, and physiological traits under heat stress. A genetic map was constructed using genotyping-by-sequencing with 478 single-nucleotide polymorphism (SNP) loci spanning a map distance of 1,961.39 cM. Quantitative trait locus (QTL) analysis using phenotypic and genotypic data identified 45 major main-effect QTLs for 21 traits. Intriguingly, three QTL clusters (Cluster-1-Ah03, Cluster-2-Ah12, and Cluster-3-Ah20) harbor more than half of the major QTLs (30/45, 66.6%) for various heat tolerant traits, explaining 10.4%-38.6%, 10.6%-44.6%, and 10.1%-49.5% of phenotypic variance, respectively. Furthermore, important candidate genes encoding DHHC-type zinc finger family protein (arahy.J0Y6Y5), peptide transporter 1 (arahy.8ZMT0C), pentatricopeptide repeat-containing protein (arahy.4A4JE9), Ulp1 protease family (arahy.X568GS), Kelch repeat F-box protein (arahy.I7X4PC), FRIGIDA-like protein (arahy.0C3V8Z), and post-illumination chlorophyll fluorescence increase (arahy.92ZGJC) were the underlying three QTL clusters. The putative functions of these genes suggested their involvement in seed development, regulating plant architecture, yield, genesis and growth of plants, flowering time regulation, and photosynthesis. Our results could provide a platform for further fine mapping, gene discovery, and developing markers for genomics-assisted breeding to develop heat-tolerant groundnut varieties.

Application of CRISPR/Cas9-mediated gene editing for abiotic stress management in crop plants.

Abiotic stresses, including drought, salinity, cold, heat, and heavy metals, extensively reducing global agricultural production. Traditional breeding approaches and transgenic technology have been widely used to mitigate the risks of these environmental stresses. The discovery of engineered nucleases as genetic scissors to carry out precise manipulation in crop stress-responsive genes and associated molecular network has paved the way for sustainable management of abiotic stress conditions. In this context, the clustered regularly interspaced short palindromic repeat-Cas (CRISPR/Cas)-based gene-editing tool has revolutionized due to its simplicity, accessibility, adaptability, flexibility, and wide applicability. This system has great potential to build up crop varieties with enhanced tolerance against abiotic stresses. In this review, we summarize the latest findings on understanding the mechanism of abiotic stress response in plants and the application of CRISPR/Cas-mediated gene-editing system towards enhanced tolerance to a multitude of stresses including drought, salinity, cold, heat, and heavy metals. We provide mechanistic insights on the CRISPR/Cas9-based genome editing technology. We also discuss applications of evolving genome editing techniques such as prime editing and base editing, mutant library production, transgene free and multiplexing to rapidly deliver modern crop cultivars adapted to abiotic stress conditions.