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.

Peanut is one of the major source for human consumption worldwide and its seed contain approximately 50% oil. Improvement of oil content and quality traits (high oleic and low linoleic acid) in peanut could be accelerated by exploiting linked markers through molecular breeding. The objective of this study was to identify QTLs associated with oil content, and estimate relative contribution of FAD2 genes (ahFAD2A and ahFAD2B) to oil quality traits in two recombinant inbred line (RIL) populations. Improved genetic linkage maps were developed for S-population (SunOleic 97R × NC94022) with 206 (1780.6 cM) and T-population (Tifrunner × GT-C20) with 378 (2487.4 cM) marker loci. A total of 6 and 9 QTLs controlling oil content were identified in the S- and T-population, respectively. The contribution of each QTL towards oil content variation ranged from 3.07 to 10.23% in the S-population and from 3.93 to 14.07% in the T-population. The mapping positions for ahFAD2A (A sub-genome) and ahFAD2B (B sub-genome) genes were assigned on a09 and b09 linkage groups. The ahFAD2B gene (26.54%, 25.59% and 41.02% PVE) had higher phenotypic effect on oleic acid (C18:1), linoleic acid (C18:2), and oleic/linoleic acid ratio (O/L ratio) than ahFAD2A gene (8.08%, 6.86% and 3.78% PVE). The FAD2 genes had no effect on oil content. This study identified a total of 78 main-effect QTLs (M-QTLs) with up to 42.33% phenotypic variation (PVE) and 10 epistatic QTLs (E-QTLs) up to 3.31% PVE for oil content and quality traits. A total of 78 main-effect QTLs (M-QTLs) and 10 E-QTLs have been detected for oil content and oil quality traits. One major QTL (more than 10% PVE) was identified in both the populations for oil content with source alleles from NC94022 and GT-C20 parental genotypes. FAD2 genes showed high effect for oleic acid (C18:1), linoleic acid (C18:2), and O/L ratio while no effect on total oil content. The information on phenotypic effect of FAD2 genes for oleic acid, linoleic acid and O/L ratio, and oil content will be applied in breeding selection.

Lack of sufficient molecular markers hinders current genetic research in peanuts (Arachis hypogaea L.). It is necessary to develop more molecular markers for potential use in peanut genetic research. With the development of peanut EST projects, a vast amount of available EST sequence data has been generated. These data offered an opportunity to identify SSR in ESTs by data mining.

Since the early 1960’s, the fungal pathogen Aspergillus flavus (Link ex Fr.) has been the focus of intensive research due to the production of carcinogenic and highly toxic secondary metabolites collectively known as aflatoxins following pre-harvest colonization of crops. Given this recurrent problem and the occurrence of a severe aflatoxin outbreak in maize (Zea mays L.), particularly in the Southeast U.S. in the 1977 growing season, a significant research effort has been put forth to determine the nature of the interaction occurring between aflatoxin production, A. flavus, environment and its various hosts before harvest. Many studies have investigated this interaction at the genetic, transcript, and protein levels, and in terms of fungal biology at either pre- or post-harvest time points. Later experiments have indicated that the interaction and overall resistance phenotype of the host is a quantitative trait with a relatively low heritability. In addition, a high degree of environmental interaction has been noted, particularly with sources of abiotic stress for either the host or the fungus such as drought or heat stresses. Here, we review the history of research into this complex interaction and propose future directions for elucidating the relationship between resistance and susceptibility to A. flavus colonization, abiotic stress, and its relationship to oxidative stress in which aflatoxin production may function as a form of antioxidant protection to the producing fungus.

Construction and improvement of a genetic map for peanut (Arachis hypogaea L.) continues to be an important task in order to facilitate quantitative trait locus (QTL) analysis and the development of tools for marker-assisted breeding. The objective of this study was to develop a comparative integrated map from two cultivated × cultivated recombinant inbred line (RIL) mapping populations and to apply in mapping Tomato spotted wilt virus (TSWV) resistance trait in peanut. A total of 4,576 simple sequence repeat (SSR) markers from three sources: published SSR markers, newly developed SSR markers from expressed sequence tags (EST) and from bacterial artificial chromosome end-sequences were used for screening polymorphisms. Two cleaved amplified polymorphic sequence markers were also included to differentiate ahFAD2A alleles and ahFAD2B alleles. A total of 324 markers were anchored on this integrated map covering 1,352.1 cM with 21 linkage groups (LGs). Combining information from duplicated loci between LGs and comparing with published diploid maps, seven homoeologous groups were defined and 17 LGs (A1–A10, B1–B4, B7, B8, and B9) were aligned to corresponding A-subgenome or B-subgenome of diploid progenitors. One reciprocal translocation was confirmed in the tetraploid-cultivated peanut genome. Several chromosomal rearrangements were observed by comparing with published cultivated peanut maps. High consistence with cultivated peanut maps derived from different populations may support this integrated map as a reliable reference map for peanut whole genome sequencing assembling. Further two major QTLs for TSWV resistance were identified for each RILs, which illustrated the application of this map.

Peanut, a high-oil crop with about 50% oil content, is either crushed for oil or used as edible products. Fatty acid composition determines the oil quality which has high relevance to consumer health, flavor, and shelf life of commercial products. In addition to the major fatty acids, oleic acid (C18:1) and linoleic acid (C18:2) accounting for about 80% of peanut oil, the six other fatty acids namely palmitic acid (C16:0), stearic acid (C18:0), arachidic acid (C20:0), gadoleic acid (C20:1), behenic acid (C22:0), and lignoceric acid (C24:0) are accounted for the rest 20%. To determine the genetic basis and to improve further understanding on effect of FAD2 genes on these fatty acids, two recombinant inbred line (RIL) populations namely S-population (high oleic line ‘SunOleic 97R’ × low oleic line ‘NC94022’) and T-population (normal oleic line ‘Tifrunner’ × low oleic line ‘GT-C20’) were developed. Genetic maps with 206 and 378 marker loci for the S- and the T-population, respectively were used for quantitative trait locus (QTL) analysis. As a result, a total of 164 main-effect (M-QTLs) and 27 epistatic (E-QTLs) QTLs associated with the minor fatty acids were identified with 0.16% to 40.56% phenotypic variation explained (PVE). Thirty four major QTLs (>10% of PVE) mapped on five linkage groups and 28 clusters containing more than three QTLs were also identified. These results suggest that the major QTLs with large additive effects would play an important role in controlling composition of these minor fatty acids in addition to the oleic and linoleic acids in peanut oil. The interrelationship among these fatty acids should be considered while breeding for improved peanut genotypes with good oil quality and desired fatty acid composition.