Identification of Quantitative Trait Loci Controlling Root and Shoot Traits Associated with Drought Tolerance in a Lentil (Lens culinaris Medik.) Recombinant Inbred Line Population

Integrated Bulk Segregant Analysis, Fine Mapping, and Transcriptome Revealed QTLs and Candidate Genes Associated with Drought Adaptation in Wild Watermelon

Drought stress has detrimental effects on crop productivity worldwide. A strong root system is crucial for maintaining water and nutrients uptake under drought stress. Wild watermelons possess resilient roots with excellent drought adaptability. However, the genetic factors controlling this trait remain uninvestigated. In this study, we conducted a bulk segregant analysis (BSA) on an F2 population consisting of two watermelon genotypes, wild and domesticated, which differ in their lateral root development under drought conditions. We identified two quantitative trait loci (qNLR_Dr. Chr01 and qNLR_Dr. Chr02) associated with the lateral root response to drought. Furthermore, we determined that a small region (0.93 Mb in qNLR_Dr. Chr01) is closely linked to drought adaptation through quantitative trait loci (QTL) validation and fine mapping. Transcriptome analysis of the parent roots under drought stress revealed unique effects on numerous genes in the sensitive genotype but not in the tolerant genotype. By integrating BSA, fine mapping, and the transcriptome, we identified six genes, namely L-Ascorbate Oxidase (AO), Cellulose Synthase-Interactive Protein 1 (CSI1), Late Embryogenesis Abundant Protein (LEA), Zinc-Finger Homeodomain Protein 2 (ZHD2), Pericycle Factor Type-A 5 (PFA5), and bZIP transcription factor 53-like (bZIP53-like), that might be involved in the drought adaptation. Our findings provide valuable QTLs and genes for marker-assisted selection in improving water-use efficiency and drought tolerance in watermelon. They also lay the groundwork for the genetic manipulation of drought-adapting genes in watermelon and other Cucurbitacea species.

The Prospects of gene introgression from crop wild relatives into cultivated lentil for climate change mitigation

Crop wild relatives (CWRs), landraces and exotic germplasm are important sources of genetic variability, alien alleles, and useful crop traits that can help mitigate a plethora of abiotic and biotic stresses and crop yield reduction arising due to global climatic changes. In the pulse crop genus Lens, the cultivated varieties have a narrow genetic base due to recurrent selections, genetic bottleneck and linkage drag. The collection and characterization of wild Lens germplasm resources have offered new avenues for the genetic improvement and development of stress-tolerant, climate-resilient lentil varieties with sustainable yield gains to meet future food and nutritional requirements. Most of the lentil breeding traits such as high-yield, adaptation to abiotic stresses and resistance to diseases are quantitative and require the identification of quantitative trait loci (QTLs) for marker assisted selection and breeding. Advances in genetic diversity studies, genome mapping and advanced high-throughput sequencing technologies have helped identify many stress-responsive adaptive genes, quantitative trait loci (QTLs) and other useful crop traits in the CWRs. The recent integration of genomics technologies with plant breeding has resulted in the generation of dense genomic linkage maps, massive global genotyping, large transcriptomic datasets, single nucleotide polymorphisms (SNPs), expressed sequence tags (ESTs) that have advanced lentil genomic research substantially and allowed for the identification of QTLs for marker-assisted selection (MAS) and breeding. Assembly of lentil and its wild species genomes (~4Gbp) opens up newer possibilities for understanding genomic architecture and evolution of this important legume crop. This review highlights the recent strides in the characterization of wild genetic resources for useful alleles, development of high-density genetic maps, high-resolution QTL mapping, genome-wide studies, MAS, genomic selections, new databases and genome assemblies in traditionally bred genus Lens for future crop improvement amidst the impending global climate change.

The Key to the Future Lies in the Past: Insights from Grain Legume Domestication and Improvement Should Inform Future Breeding Strategies

Crop domestication is a co-evolutionary process that has rendered plants and animals significantly dependent on human interventions for survival and propagation. Grain legumes have played an important role in the development of Neolithic agriculture some 12,000 years ago. Despite being early companions of cereals in the origin and evolution of agriculture, the understanding of grain legume domestication has lagged behind that of cereals. Adapting plants for human use has resulted in distinct morpho-physiological changes between the wild ancestors and domesticates, and this distinction has been the focus of several studies aimed at understanding the domestication process and the genetic diversity bottlenecks created. Growing evidence from research on archeological remains, combined with genetic analysis and the geographical distribution of wild forms, has improved the resolution of the process of domestication, diversification and crop improvement. In this review, we summarize the significance of legume wild relatives as reservoirs of novel genetic variation for crop breeding programs. We describe key legume features, which evolved in response to anthropogenic activities. Here, we highlight how whole genome sequencing and incorporation of omics-level data have expanded our capacity to monitor the genetic changes accompanying these processes. Finally, we present our perspective on alternative routes centered on de novo domestication and re-domestication to impart significant agronomic advances of novel crops over existing commodities. A finely resolved domestication history of grain legumes will uncover future breeding targets to develop modern cultivars enriched with alleles that improve yield, quality and stress tolerance.

Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Omics Path to Increasing Productivity in Less-Studied Crops Under Changing Climate-Lentil a Case Study

Conventional breeding techniques for crop improvement have reached their full potential, and hence, alternative routes are required to ensure a sustained genetic gain in lentils. Although high-throughput omics technologies have been effectively employed in major crops, less-studied crops such as lentils have primarily relied on conventional breeding. Application of genomics and transcriptomics in lentils has resulted in linkage maps and identification of QTLs and candidate genes related to agronomically relevant traits and biotic and abiotic stress tolerance. Next-generation sequencing (NGS) complemented with high-throughput phenotyping (HTP) technologies is shown to provide new opportunities to identify genomic regions and marker-trait associations to increase lentil breeding efficiency. Recent introduction of image-based phenotyping has facilitated to discern lentil responses undergoing biotic and abiotic stresses. In lentil, proteomics has been performed using conventional methods such as 2-D gel electrophoresis, leading to the identification of seed-specific proteome. Metabolomic studies have led to identifying key metabolites that help differentiate genotypic responses to drought and salinity stresses. Independent analysis of differentially expressed genes from publicly available transcriptomic studies in lentils identified 329 common transcripts between heat and biotic stresses. Similarly, 19 metabolites were common across legumes, while 31 were common in genotypes exposed to drought and salinity stress. These common but differentially expressed genes/proteins/metabolites provide the starting point for developing high-yielding multi-stress-tolerant lentils. Finally, the review summarizes the current findings from omic studies in lentils and provides directions for integrating these findings into a systems approach to increase lentil productivity and enhance resilience to biotic and abiotic stresses under changing climate.

Root Trait Variation in Lentil (Lens culinaris Medikus) Germplasm under Drought Stress

Drought is the most critical environmental factor across the continents affecting food security. Roots are the prime organs for water and nutrient uptake. Fine tuning between water uptake, efficient use and loss determines the genotypic response to water limitations. Targeted breeding for root system architecture needs to be explored to improve water use efficiency in legumes. Hence, the present study was designed to explore root system architecture in lentil germplasm in response to drought. A set of 119 lentil (Lens culinaris Medik.) genotypes was screened in controlled conditions to assess the variability in root traits in relation to drought tolerance at seedling stage. We reported significant variation for different root traits in lentil germplasm. Total root length, surface area, root volume and root diameter were correlated to the survival and growth under drought. Among the studied genotypes, the stress tolerance index varied 0.19–1.0 for survival and 0.09–0.90 for biomass. Based on seedling survival and biomass under control and drought conditions, 11 drought tolerant genotypes were identified, which may be investigated further at a physiological and molecular level for the identification of the genes involved in drought tolerance. Identified lines may also be utilised in a lentil breeding program.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Root-omics for drought tolerance in cool-season grain legumes

Root traits can be exploited to increase the physiological efficiency of crop water use under drought. Root length, root hairs, root branching, root diameter, and root proliferation rate are genetically defined traits that can help to improve the water productivity potential of crops. Recently, high-throughput phenotyping techniques/platforms have been used to screen the germplasm of major cool-season grain legumes for root traits and their impact on different physiological processes, including nutrient uptake and yield potential. Advances in omics approaches have led to the dissection of genomic, proteomic, and metabolomic structures of these traits. This knowledge facilitates breeders to improve the water productivity and nutrient uptake of cultivars under limited soil moisture conditions in major cool-season grain legumes that usually face terminal drought. This review discusses the advances in root traits and their potential for developing drought-tolerant cultivars in cool-season grain legumes.

Physiological and Biochemical Characterization of the GABA Shunt Pathway in Pea (Pisum sativum L.) Seedlings under Drought Stress

The physiological and biochemical role of the γ-aminobutyric acid (GABA) shunt pathway in green pea seedlings (Pisum sativum L.) was studied in response to soil water holding capacity levels: 80%, 60%, 40%, 20%, and 10% grown under continuous light at 25 °C for 7 days and 14 days, separately. Characterization of seeds germination pattern, seedlings growth (plant height, fresh and dry weight, and chlorophyll contents), GABA shunt metabolite (GABA, glutamate, and alanine) levels, total protein and carbohydrate levels, and oxidative damage (MDA level) were examined. Data showed a significant effect of drought stress on seed germination, plant growth, GABA shunt metabolites level, total protein and carbohydrate contents, and MDA level. A significant decline in seed germination percentage was recorded at a 20% drought level, which indicated that 20% of soil water holding capacity is the threshold value of water availability for normal germination after 14 days. Seedling fresh weight, dry weight, and plant height were significantly reduced with a positive correlation as water availability was decreased. There was a significant decrease with a positive correlation in Chl a and Chl b contents in response to 7 days and 14 days of drought. GABA shunt metabolites were significantly increased with a negative correlation as water availability decreased. Pea seedlings showed a significant increase in protein content as drought stress was increased. Total carbohydrate levels increased significantly when the amount of water availability decreased. MDA content increased slightly but significantly after 7 days and sharply after 14 days under all water stress levels. The maximum increase in MDA content was observed at 20% and 10% water levels. Overall, the significant increases in GABA, protein and carbohydrate contents were to cope with the physiological impact of drought stress on Pisum sativum L. seedlings by maintaining cellular osmotic adjustment, protecting plants from oxidative stress, balancing carbon and nitrogen (C:N) metabolism, and maintaining cell metabolic homeostasis and cell turgor. The results presented in this study indicated that severe (less than 40% water content of the holding capacity) and long-term drought stress should be avoided during the germination stage to ensure proper seedling growth and metabolism in Pisum sativum L.

Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan

Mungbean (Vigna radiata L.) is an important food grain legume, but its production capacity is threatened by global warming, which can intensify plant stress and limit future production. Identifying new variation of key root traits in mungbean will provide the basis for breeding lines with effective root characteristics for improved water uptake to mitigate heat and drought stress. The AVRDC mungbean mini core collection consisting of 296 genotypes was screened under modified semi-hydroponic screening conditions to determine the variation for fourteen root-related traits. The AVRDC mungbean mini core collection displayed wide variations for the primary root length, total surface area, and total root length, and based on agglomerative hierarchical clustering eight homogeneous groups displaying different root traits could be identified. Germplasm with potentially favorable root traits has been identified for further studies to identify the donor genotypes for breeding cultivars with enhanced adaptation to water-deficit stress and other stress conditions.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Validation of reference genes for qRT-PCR data normalisation in lentil (Lens culinaris) under leaf developmental stages and abiotic stresses

Lentil (Lens culinaris) is one of the most important staple food crops of developing countries. Transcriptome based global gene expression profiling followed by validation of expression of important genes through quantitative real time-PCR (qRT-PCR) has achieved significance in recent years. However, there is a severe scarcity of information regarding stable reference genes in lentil, which is mandatory for qRT-PCR data normalisation. Hence, the present study was under-taken to identify the most stable reference gene(s) in lentil. Expression stability of eight candidate genes viz. ribulose 1,5-bisphosphate carboxylase large subunit (Rbcl), ribosomal protein L2 (RPL2), 18S rRNA, tubulin (Tub), elongation factor 1α (EF1α), glyceraldehydes-3-phosphate dehydrogenase (GAPDH), heat shock protein (HSP70), and Maturase (mat K) was evaluated in five varieties of lentil at three different stages of leaf development and abiotic stress conditions using qRT-PCR. The results were analysed using four types of statistical software viz., geNorm, BestKeeper, NormFinder and RefFinder; all softwares identified RPL2 as most stable under abiotic stress conditions and developmental stages followed by Tub and Rbcl; while, HSP70 was identified as least stable. Relative expression of the target genes, defensin and PR4, was evaluated under abiotic stress conditions and data normalisation was done using two stable reference genes, RPL2 and Tub, either alone or in combination and with two least stable genes, HSP70 and 18S. The present work provides a list of potential reference genes in lentil, which will help in selection of appropriate reference gene for qRT-PCR data normalization depending upon the experiment.

An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.)

Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.

Silicon improves seed germination and alleviates drought stress in lentil crops by regulating osmolytes, hydrolytic enzymes and antioxidant defense system

Silicon (Si) has been widely reported to have beneficial effect on mitigating drought stress in plants. However, the effect of Si on seed germination under drought conditions is still poorly understood. This research was carried out to ascertain the role of Si to abate polyethylene glycol-6000 mediated drought stress on seed germination and seedling growth of lentil. Results showed that drought stress significantly decreased the seed germination traits and increased the concentration of osmolytes (proline, glycine betaine and soluble sugars), reactive oxygen species (hydrogen peroxide and superoxide anion) and lipid peroxides in lentil seedlings. The activities of hydrolytic enzymes and antioxidant enzymes increased significantly under osmotic stress. The application of Si significantly enhanced the plants ability to withstand drought stress conditions through increased Si content, improved antioxidants, hydrolytic enzymes activity, decreased concentration of osmolytes and reactive oxygen species. Multivariate data analysis showed statistically significant correlations among the drought-tolerance traits, whereas cluster analysis categorised the genotypes into distinct groups based on their drought-tolerance levels and improvements in expression of traits due to Si application. Thus, these results showed that Si supplementation of lentil was effective in alleviating the detrimental effects of drought stress on seed germination and increased seedling vigour.