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Review

Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress

1
Department of Bioenvironmental Chemistry, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea
2
Department of Agriculture Technology & Agri-Informatics, Shobhit Institute of Engineering & Technology, Meerut 250110, India
3
Department of Agricultural Chemistry, Jeonbuk National University, Jeonju 54896, Republic of Korea
4
School of Chemical Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
5
Institute of Agricultural Science & Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea
*
Author to whom correspondence should be addressed.
Biology 2023, 12(11), 1400; https://doi.org/10.3390/biology12111400
Submission received: 25 August 2023 / Revised: 1 November 2023 / Accepted: 1 November 2023 / Published: 3 November 2023

Abstract

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Simple Summary

Plants are subjected to various environmental stresses that negatively impact growth and development and limit crop productivity. Therefore, in order to meet the requirements of the growing world population and food security, it is essential to develop cultivars resistant to abiotic stresses. In recent years, with the availability of genetic databases and the advancement in genome editing techniques, it is feasible to edit target genes with precision and create new opportunities for crop improvement that conventional breeding methods could not achieve. The genome-editing method using CRISPR-Cas systems is very powerful and confers exceptional versatility to develop improved cultivars at abiotic stresses. These efficient gene editing techniques facilitate the cultivation of superior-performing genotypes in challenging environmental conditions without compromising yield.

Abstract

Abiotic stresses extensively reduce agricultural crop production globally. Traditional breeding technology has been the fundamental approach used to cope with abiotic stresses. The development of gene editing technology for modifying genes responsible for the stresses and the related genetic networks has established the foundation for sustainable agriculture against environmental stress. Integrated approaches based on functional genomics and transcriptomics are now expanding the opportunities to elucidate the molecular mechanisms underlying abiotic stress responses. This review summarizes some of the features and weblinks of plant genome databases related to abiotic stress genes utilized for improving crops. The gene-editing tool based on clustered, regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has revolutionized stress tolerance research due to its simplicity, versatility, adaptability, flexibility, and broader applications. However, off-target and low cleavage efficiency hinder the successful application of CRISPR/Cas systems. Computational tools have been developed for designing highly competent gRNA with better cleavage efficiency. This powerful genome editing tool offers tremendous crop improvement opportunities, overcoming conventional breeding techniques’ shortcomings. Furthermore, we also discuss the mechanistic insights of the CRISPR/Cas9-based genome editing technology. This review focused on the current advances in understanding plant species’ abiotic stress response mechanism and applying the CRISPR/Cas system genome editing technology to develop crop resilience against drought, salinity, temperature, heavy metals, and herbicides.

1. Introduction

Agricultural production faces global challenges due to climate change, insufficient arable land, population growth, and abiotic and biotic stresses. Abiotic stress adversely impacts plant growth and development by hampering essential biochemical and physiological activities [1,2]. Climate change, such as extreme temperature, drought, water logging, flooding, and increased soil salinity, adversely affect global agricultural crop production. The production of excessive greenhouse gases is the most significant contributor to global climate change, resulting in intense drought, infrequent rain, and high temperatures. Drought stress negatively impacts crop plants by modifying physiological and biochemical processes such as plant growth habit and duration, metabolism, and resistance mechanisms [3,4,5]. Salinity stress is one of the significant constraints to crop production because it causes oxidative stress due to the generation of reactive oxygen species (ROS) that damage plant cells [6,7]. Similarly, plants respond to heat stress by producing reactive oxygen species (ROS), heat shock proteins (HSPs), and genes encoding scavenger proteins [8,9]. Temperature stress strongly impacts grain filling, leading to poor grain yield [5,10]. In addition, overusing chemical fertilizers/herbicides contaminates agricultural fields with heavy metals such as arsenic (As), copper (Cu), cobalt (Co), cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni) zinc (Zn), mercury (Hg), lead (Pb) accumulating inside plants, reducing grain fullness, and increasing risk to human and animal health. Arsenic causes a loss of functionality in plant cells, and cadmium inhibits plant growth, as evidenced by stunted plant growth, a decrease in leaf size, shoot growth, and root dry matter [11,12,13,14]. Plants have evolved several defense mechanisms to withstand stress by activating stress-responsive genes via secondary messengers and finally activating several stress-responsive genes and their products [15].
Conventional breeding approaches, including cross-breeding and mutation breeding, have enhanced crop performance under climate change scenarios. However, even with marker-assisted selection, breeding programs can be time-consuming and labor-intensive [16]. Therefore, more efficient and advanced technologies with immediate effects are needed to overcome the drawbacks of traditional breeding methods. The availability of genome sequence data of numerous crop plants and precise genome editing tools has revolutionized plant breeding programs. By genome editing tools, desired changes are possible within the DNAs by the formation of an insertion/deletion (indel) and mutation in the sequences of particular genes via recruiting specific nucleases such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) or Clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system [17]. CRISPR/Cas comprises single-guide RNA (sgRNA) and RNA-guided Cas endonuclease that protects bacteria and archaea from being invaded by mobile genetic elements and bacteriophages [18]. During the process of genome editing, sgRNA recruits Cas endonuclease to a specific site of the genome to catalyze a DNA double-stranded break (DSB), which can be repaired by various DNA repair mechanisms, including non-homologous end joining (NHEJ), homology-directed repairs (HDR), and microhomology-mediated end joining [MMEJ], leading to gene knockout, DNA fragment insertion, deletion, and replacement [19,20,21,22]. Different Cas enzymes recognize Protospacer-Adjacent Motif (PAM) sequences and show unique specificity [23]. Moreover, the gRNA spacer sequence could be readily programmed using online tools to target DNA sites with PAM. The online computational tools also minimize off-target effect (guide specificity) and maximize on-target effect (guide efficiency) by avoiding gRNA sequences showing significant homology with genomic loci at multiple sites [24].
CRISPR technology has revolutionized life science research since it was first applied in 2012. CRISPR–Cas9 and CRISPR–Cpf1 are plants’ best-studied and most widely used CRISPR systems [25,26,27]. In addition, Cas12a and Cas12b systems were also developed for plant genome editing [28]. The applications of CRISPR/Cas9 system in plants have been found in studies about Nicotiana benthamiana [29], Nicotiana tabacum [30], Arabidopsis thaliana [31], Zea mays [32], Oryza sativa [33], Triticum aestivum [34], Hordeum vulgare [35], Setaria italica [36], Lycopersicon esculentum [37], Solanum tuberosum [38], Capsicum annuum [39], Brassica napus [40] Glycine max [41], and Saccharum spp. [42]. This review summarizes plant genome databases related to abiotic stress and the potential for genome editing technology applications using the CRISPR/Cas9 system in managing abiotic stresses such as drought, salinity, temperature, environmental pollutants, and heavy metal toxicity in important agricultural crop species.

2. Genome Databases of Abiotic Stress Gene

Genome databases dedicated to plant abiotic stress genes and genome data provide helpful information on essential plant species. The availability of a genome database of agronomically important plant species facilitates targeted genome modification using gene editing tools, which offers tremendous opportunities to develop improved cultivars with higher yield and resistance to abiotic stress. The publicly available specialized bioinformatics database resources contain valuable information on plant stress genes, such as PlantStress, Plant Stress Gene, Plant Stress Proteome (PlantPReS), Plant miRNA ENcyclopedia (PmiREN), Network-based Rice Expression Analysis (NetREx), PncStress, and Pearl Millet Drought Transcriptome (PMDTDb) databases.

2.1. PlantStress

The PlantStress (https://plantstress.com/, accessed on 31 October 2023) website was launched in the year 2000 to serve as a web-based information resource for a meeting place and a consultation facility, and a source of professional updates on the most important issues of plant environmental abiotic stress, including drought, salinity, heat, mineral deficiency, oxidative stress, cold, water logging, and stress combination [43].

2.2. Plant Stress Gene Database

The Plant Stress Gene Database (http://ccbb.jnu.ac.in/stressgenes/, accessed on 23 August 2023) contains 259 stress-related genes from different plant species (Arabidopsis thaliana, Arachis hypogaea, Glycine max, Hordeum vulgare, Oryza sativa, Pennisetum glaucum, Phaseolus vulgaris, Saccharum officinarum, Lycopersicon esculentum, Triticum aestivum, and Zea mays). Additionally, this database offers details on paralogous or orthologous proteins encoded by stress-related genes [44].

2.3. Plant Stress Proteome Database (PlantPReS)

PlantPReS (http://www.proteome.ir/, accessed on 23 August 2023) contains a database of 10,600 unique stress-responsive proteins and 20,413 entries from 456 research articles [45]. It is an open online proteomic database comprising >35,086 entries from 577 manually curated articles containing >10,600 unique stress-responsive proteins. A customized BLAST tool has been made available, which is helpful in retrieving the homologous sequences from the database. The interface is user-friendly and features several analysis tools such as a gene ontology, cross-referencing, and information on the expression patterns of stress-responsive proteins.

2.4. Plant miRNA ENcyclopedia (PmiREN)

PmiREN (Plant miRNA Encyclopedia) is a functional plant miRNA database available at https://www.pmiren.com/, accessed on 23 August 2023. PmiREN contains 38,186 miRNA loci belonging to 7838 families, 141,327 predicted miRNA-target pairs and phylogenetic trees of conserved miRNA families in 179 species spanning from chlorophytes to angiosperms. It also provides tools for in-depth data mining. Additionally, 116 PARE-Seq libraries were utilized to confirm predicted miRNA-target pairs, and 2331 fully sequenced small RNA libraries were used to quantify miRNA expression patterns [46,47].

2.5. Network-Based Rice Expression Analysis (NetREx)

Network-based Rice Expression Analysis server (NetREx) provides information on the expression and interaction of rice genes under various environmental stress and hormonal treatment conditions. NetREx is a web-based server available at https://bioinf.iiit.ac.in/netrex/index.html, accessed on 23 August 2023. It offers a choice of interactable data viewers and modules for analyzing genes under drought, flood, cold conditions, and hormonal treatments (abscisic and jasmonic acid). The server can also explore the expression fold change, gene annotations, and gene pathway analysis. This web server also allows the search of orthologous genes from A. thaliana, T. aestivum, Z. mays, H. vulgare, and S. bicolor [48].

2.6. PncStress

PncStress (https://bis.zju.edu.cn/pncstress/, accessed on 23 August 2023) is a manually curated database of circRNAs, lncRNAs, and microRNAs related to plants’ abiotic and biotic stress conditions. PncStress contains 4227 entries, with 114 species responding to abiotic and biotic stresses. PncStress contains the database of the following plant species, including Arabidopsis thaliana, Brassica sp., Gossypium sp., Hordeum vulgare, Oryza sativa, Solanum lycopersicum, Solanum tuberosum, Triticum aestivum, Vitis vinifera, and Zea mays. These entries include 2523 miRNAs, 444 lncRNAs, and 52 circRNAs verified through various experimental techniques [49].

2.7. Pearl Millet Drought Transcriptome Database (PMDTDb)

PMDTDb (Pearl Millet Drought Transcriptome Database) is the database of the drought transcriptome of pearl millet available at http://webtom.cabgrid.res.in/pmdtdb/, accessed on 23 August 2023. It catalogs the differentially expressed genes in leaf and root tissues of millet in response to drought, along with transcription factors (TFs), gene regulatory networks (GRNs) with hub genes, and discovery of putative gene region markers such as simple sequence repeats (SSRs), single nucleotide polymorphism (SNP) and, InDels (insertions and deletions). This database is based on a 3-tier architecture developed in PHP and MySQL [50].

3. Functional Genomic Approaches and Abiotic Stress Tolerance

Functional genomics approaches have been employed to understand the precise regulatory gene networks associated with complex abiotic stress responses, benefiting crop improvement programs. Several stress-related genes/pathways and regulatory networks have been worked out in the past decades using various functional genomic approaches, including expressed sequence tags (ESTs), transcriptome analysis, and targeted random mutagenesis. Recently advanced sequencing technologies have provided a cost-effective and high-throughput method for generating DNA/RNA sequence data, facilitating the identification of genes/transcription factors mediating stress tolerance. The identified genes can be successfully used in the programs for crop improvement by employing a transgenic method or exploiting genetic variation indicating the trait of interest. Additionally, functional validation of stress-related genes may facilitate unraveling the stress-tolerance networks and designing different functional markers for crop improvement programs.

3.1. Sequencing-Based Approaches

The single-pass sequencing of cDNA clones generates partial gene coding sequences, which are the Expressed Sequence Tags (ESTs) [51]. EST databases are efficient tools for discovering genes, comparing interspecies sequences, and providing markers for physical and genetic mapping and clones for expression analysis. Functional genomics studies utilize ESTs due to their quick and cost-effectiveness compared to the whole genome sequencing method. Currently, over a million ESTs of different crop species are available in the EST database at the National Center for Biotechnological Information (NCBI) (http://www.ncbi.nlm.nih.gov/dbEST/, accessed on 31 October 2023), which serves as the reservoir of differentially expressed genes. Additionally, the relative abundance of cDNA libraries created from various plant species and organs under various physiological situations offers early insights into the expression patterns for the more abundant transcripts [15]. Several functional genomics studies have been performed to identify the abiotic stress-responsive transcripts using EST sequencing, Serial analysis of gene expression (SAGE), Super serial analysis of gene expression (SuperSAGE), and massively parallel signature sequencing (MPPS). Plant cDNA libraries were screened for identifying genes involved in abiotic stress response. Screening of the cDNA library from a salt-tolerant rice genotype showed differential expression of two genes in response to multiple stresses [52]. In Cicer arietinum (Chickpea), to identify differentially expressed genes in drought-tolerant and -susceptible genotypes, 5494 high-quality drought-responsive EST sequence was generated by suppression subtraction hybridization (SSH) [53].
The SAGE is a highly competent technology that isolates unique sequence tags from individual mRNAs for transcriptome research. Although SAGE is not widely utilized in plants, it is more sensitive than EST in detecting rarely expressed transcripts [54]. SAGE helps to identify a set of specific genes to the cellular conditions and study the gene expression profile of a particular type of cells or organs. By transcriptome analysis using SuperSAGE and high-throughput sequencing, 17,493 SAGE UniTags have been generated from the roots of the drought-tolerant C. arietinum variety [55]. Another tag-based method, massively parallel signature sequencing (MPSS), is an open-ended platform for conducting in-depth expression profiling [56]. MPSS allows the identification of millions of signatures per experiment, surpassing even the most extensive SAGE applications, covering hundreds of thousands of tags. Due to more extended tags and high-throughput analysis, MPSS identifies genes with greater specificity and sensitivity [57]. Plant MPSS databases contain publicly available MPSS expression data for many plant species, including A. thaliana, rice, grape, Z. mays, and soybean [58]. MPSS databases quantify the absolute expression level of most genes. It also provides information about novel transcripts, including regulatory intergenic transcripts, alternative splice isoforms, and antisense transcripts [59].

3.2. Genome-Wide Association Studies (GWAS)

Genome-wide association studies (GWAS) have emerged as a powerful tool in identifying variations at the DNA level related to stress tolerance. GWAS provides a robust and potent tool successfully applied in germplasm collections that identifies the regulatory loci associated with resistant phenotypic traits [51]. The two major approaches were used to study the interaction between genotype and phenotype (trait) based on mapping populations, including linkage mapping and association mapping [60,61,62,63,64,65,66]. GWAS can be effectively used for fine genome-wide mapping and also enables finding a higher diversity of alleles at the corresponding loci, which exploits historical recombination events in a population, leading to a better association between the marker and the phenotype with a desired trait. GWAS has identified 155 significant SNPs and 275 genes associated with salt sensitivity in O. sativa [67]. Combining QTL mapping and GWAS with transcriptome profiling complements the identification of differentially expressed candidate genes in various crop species. GWAS combination with Meta-QTL analysis can be used to investigate the critical genomic regions and major quantitative traits in T aestivum [68,69]. In T. aestivum, meta-analysis was performed using previously identified QTLs associated with abiotic stress, including drought, heat, salinity, temperature, and aluminum stress, resulting in 76 meta-QTLs verified using genome-wide association studies [70]. In Z. mays, 86 candidate genes and 5 SNPs related to salt tolerance were identified by GWAS [71]. Phosphorus (P) is the essential macronutrient in crop growth and production, and its deficiency is one of the major limiting factors in G. max production, especially at the reproductive stage. GWAS and a combination of high-density SoySNP analysis identified 27 P-efficiency-related single nucleotide polymorphisms (SNPs), which can be utilized in breeding high-P-efficiency varieties of G. max [72]. GWAS has been used in detecting genetic variations underlying diverse, complex traits in barley, such as cadmium stress [73], cold tolerance [74], drought tolerance [75,76], aluminum tolerance [77,78], and salinity tolerance [79,80]. Heat stress caused a significant decrease in grain nutrient content in C. arietinum. GWAS revealed that grain yield negatively correlated with Fe and Zn content and non-significantly with protein content. In C. arietinum, 181 marker-trait associated with grain nutrient content (Fe, Zn, and protein) under drought and stress conditions was identified using GWAS [81]. Similarly, GWAS revealed SNPs associated with QTLs involved in drought stress by evaluating the drought tolerance ability of horsegram (Macrotyloma uniflorum) germplasms [82].

4. Mechanisms of CRISPR/CAS9 Genome Editing

The CRISPR/Cas system relies on an adaptive immune system found in the genomes of bacteria and archaea to protect against the invasion of foreign plasmids or viral DNA [30]. CRISPR/Cas9 is composed of two components of Cas9 and a single guide RNA (sgRNA). The CRISPR/Cas system can be divided into two classes based on the structure and functions of Cas-proteins: Class I (type I, III, and IV) and Class II (type II, V, and VI) [83]. Class I systems contain multi-subunit protein complexes, whereas Class II systems include single effector proteins. Due to its relatively simple structure, type II CRISPR/Cas9 has been well-studied and widely used in genetic engineering. The first Cas protein (Cas9) used in genome editing was originated from Streptococcus pyogenes (SpCas9). The Cas9 is a large multi-domain DNA endonuclease, composed of 1368 amino acids, that cleaves the target DNA to generate a double-strand break (DSB) [84]. The genome editing mechanism of CRISPR/Cas9 is generally divided into three steps: recognition, cleavage, and repair. The designed sgRNA guides Cas9 and identifies the target sequence within the interest gene through its complementary base pair of 5′crRNA. As a component of the sgRNA/Cas9 complex, 20 nucleotides at the 5’ end of a sgRNA bind to the target site. Without sgRNA, the Cas9 protein remains inactive. The Cas9 nuclease-induced double-strand break (DSB) is formed at a three-base pair upstream site to the protospacer adjacent motif (PAM). This specific target location should be immediately at the upstream of PAM, a conserved DNA sequence downstream of the cleavage site. The most commonly used nuclease in the genome-editing tool, Cas9 protein recognizes the PAM sequence at 5ʹ-NGG-3ʹ. Several studies have been conducted to improve the efficiency of the CRISPR/Cas system. For example, plants’ unique PAM sites (NGG and NG) were discovered using SpCas9-NG and Cas9-NG variants [85]. SpCas9 orthologues have been recognized from Streptococcus thermophiles (St1Cas9) [86] and Staphylococcus aureus (SaCas9) [87]. Engineered SaCas9 has been developed to target plant genomic loci with the PAM sequence of NNNRRT [88]. Additionally, engineered SpCas9 has been developed to amend plant genomic loci with PAM sequences of NGG, NG, NRNH, NGN, NRN, or NYN [89,90,91,92,93,94,95] [Table 1].
Two mechanisms repair DSBs created by Cas9 protein: homology-directed repair (HDR) and non-homologous end joining (NHEJ) (Figure 1). The HDR is exact and requires a homologous DNA donor template with a target sequence. The HDR mechanism is primarily active in the G2 and late S phases of the cell cycle. HDR executes the specific gene insertion or gene replacement by adding a donor DNA template with sequence homology at the predicted DSB site [21]. The NHEJ is the leading and most efficient cellular repair mechanism and is active in all cell cycle phases. Unlike HDR, NHEJ is an error-prone mechanism that may result in indels (short insertions and deletions) at the cleavage site, leading to frameshift mutation or premature stop codon in the sequence. NHEJ accelerates the repairs created by DSBs by joining the cleaved DNAs through an enzymatic activity in the absence of exogenous homologous DNA [19,20,22].
Targeted gene modifications were performed using the Cas9-gRNA system in multiple plant species, which suggests the system can function in different organisms [29,30,31,32,33,34,37,38,39,41,42]. Two main criteria for CRISPR/Cas genome editing are efficacy and specificity. Numerous computational in silico tools have been developed for designing guide RNAs to predict cleavage efficiency and accurate target specificity (Table 2). Online computational tools also facilitate the design of specific guide RNAs for particular genes of interest to avoid potential off-target effects. The freely available online tools for sgRNA design and quality check are CHOPCHOP (https://chopchop.cbu.uib.no/, accessed on 23 August 2023) [103], Cas-OFFinder (http://www.rgenome.net/cas-offinder/, accessed on 23 August 2023) [104], CCTop (https://cctop.cos.uni-heidelberg.de/, accessed on 23 August 2023) [105], CRISTA (https://crista.tau.ac.il/, accessed on 23 August 2023) [106], CRISPR-GE (http://skl.scau.edu.cn/, accessed on 23 August 2023) [107], CRISPR-P (http://crispr.hzau.edu.cn/CRISPR2/, accessed on 23 August 2023) [108], CRISPR-PLANT V2 (http://omap.org/crispr2/, accessed on 23 August 2023) [109], CRISPRlnc (http://www.crisprlnc.org/, accessed on 23 August 2023) [110], SNP-CRISPR (https://www.flyrnai.org/tools/snp_crispr/web/, accessed on 23 August 2023) [111], and PnB Designer (https://fgcz-shiny.uzh.ch/PnBDesigner/, accessed on 23 August 2023) [112].

5. Impact of CRISPR/Cas9-Based Genome Editing on Abiotic Stress Tolerance

Due to abiotic stresses, plant growth and development are affected, which can cause crop yield reduction at approximately 50% [113]. Though productivity increases to a large extent by traditional breeding, it may cause a loss of genetic variety and fitness. In addition to the development time period, it relies on natural allelic variants, which makes it challenging to develop the desired characteristic and to ensure the sustainability of production. Genome editing must include precise modifications at specific sites to perform desired changes to the DNA sequence [20,21,30]. Therefore, genome-editing techniques employing sequence-specific nucleases (SSNs) have become popular in plant research to develop improved cultivars in terms of yield, nutrition content, and resistance to environmental stresses. The SSNs introduce DNA DSBs at a target site, stimulating the cellular DNA repair and resulting in genome alterations, including targeted mutagenesis, gene insertion, and gene replacement [114]. In recent years, three types of genome-editing techniques have been widely used, such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced palindromic repeat CRISPR/Cas protein systems. Due to complex procedures and high failure rates, ZFN and TALEN have not been utilized extensively, whereas CRISPR/Cas was successfully used in various crop improvement programs. CRISPR/Cas9-based genome editing is accurate because it targets specific sites of particular genes involved in stress response pathways and modifies genes to develop plants’ ability to withstand environmental stress [30,115]. Additionally, the CRISPR/Cas system has been used to introduce critical agricultural traits, including plant resistance against abiotic and biotic stress, and other agronomically important traits (increased grain size and grain weight) into many economically important crops, such as O. sativa, T. aestivum, Z. mays, L. esculentum, S. tuberosum, N. tabacum, Gossypium spp., G. max, Brassica sp., S. italica, and Saccharum spp. [5,30,31,32,33,34,37,39,40].

5.1. Improvement in Drought Stress Tolerance using CRISPR/Cas System

Drought stress can reduce crop yields by 50–70% in different crops due to significant reductions in plant growth and development. For example, a 27–40% yield reduction has been observed in C. arietinum, 42% in G. max, 50% in O. sativa, 21% in T. aestivum, 68% in V. unguiculata, and 40% in Z. mays [5]. Plants experience morphological, physiological, biochemical, and molecular changes in response to drought stress. CRISPR/Cas technique was successfully applied to enhance the drought resistance of rice crops by modifying the expression of drought and other stress-related genes [116]. The potential of CRISPR/Cas gene editing has been documented in various crop species against drought stress (Table 3). Researchers aimed to enhance plants’ ability to withstand drought stress and reduce crop losses by altering drought-related genes. A truncated version of gRNAs (<20 nucleotides) with target sequences in plant cells was used to improve the specificity of CRISPR/Cas9 and eventually generate altered alleles for OST2 (Open Stomata 2). The novel mutant alleles for OST2 exhibited drought tolerance by altered stomatal closing in response to environmental stress in A. thaliana [117]. Similarly, the remodeled CRISPR/Cas9 activation system activates vacuolar H+-pyro phosphatase AVP1, leading to an increase in single-leaf area, an increase in leaf numbers, and an enhancement of stress tolerance to drought [3]. Improved drought resistance was found in homozygous CRISPR/Cas9-edited MIR169a T3 plants using a combinatory dual-sgRNA/Cas9 vector containing deleted miRNA gene regions (MIR169a and MIR827a) [118]. Histone acetyltransferase (HAT) modifies chromatin histone, exposing DNA to the transcriptional machinery and regulating gene expression. Stable transgenic plants expressing chimeric dCas9HAT in A. thaliana showed higher chlorophyll content, faster stomatal aperture, and an improved survival rate under drought-stress conditions [119]. Trehalase (TRE1) gene silencing through the use of the CRISPR/Cas9 system developed drought tolerance in A. thaliana [120]. Transcriptome analysis using microarray technology is the best technique that has proven helpful in discovering many stress-inducible genes/stress-inducible transcription factors including the DRE-binding protein (DREB) members, ethylene-responsible element binding factor (ERF), zinc-finger, WRKY, MYB, basic helix-loop-helix (bHLH), basic-domain leucine zipper (bZIP), NAC (NAM, ATAF1, and CUC2), and homeodomain transcription factor families [4]. Overexpressing AtNAC07, AtNAC019, and AtNAC055 can enhance tolerance to drought in A. thaliana [121]. Dehydration-responsive element binding (DREB) proteins are one of the most prominent transcription factors and have a significant role in signaling networks regulating various plant development processes and stress responses. The overexpression of DREB1A/CBF3 (C-repeat binding factor) under the stress-inducible RD29A promoter improved drought tolerance in transgenic T. aestivum [122]. Drought tolerance in T. aestivum was enhanced by altering Dehydration-responsive element binding 2 (TaDREB2) and Ethylene Responsive Factor 3 (TaERF3) using the CRISPR/Cas system [123].
Abscisic acid (ABA) plays a vital role in drought tolerance by regulating the expression of many drought-related genes. ABA regulates the expression of genes through ABA-responsive element (ABRE) binding protein/ABRE binding factor (AREB/ABF). Drought stress tolerance has been demonstrated by over-expression of AREB1, as compared to the AREB1 knockout mutant [124]. In A. thaliana, ABF1, ABF3, AREB1/ABF2, and AREB2/ABF4 are expressed in response to ABA and drought stress in vegetative tissues, whereas ABI5, AREB3, DPBF2, and EEL are expressed during seed maturation [124,125,126]. Abscisic acid (ABA) signaling is regulated by ABA-induced transcription repressors (AITRs). The CRISPR/Cas9 system was used in soybean (Glycine max) to target the six GmAITR genes and generated Cas9-free gmaitr36 double and gmaitr23456 quintuple mutants, enhancing salinity tolerance [41]. Similarly, the Dehydration-responsive element [DREB1]/CBF is responsible for the ABA-independent induction of several genes in response to osmotic and cold stress, for example, RD29A/COR78/LTI78 gene in A. thaliana. The lateral organ boundaries domain (LBD) genes play essential roles in lateral organ development. CRISPR/Cas9 knockout of SlLBD40 improved drought tolerance in L. esculentum compared with overexpressing transgenic and wild-type plants [127]. Mitogen-activated protein kinases (MAPKs) are important signaling molecules that respond to drought stress. Similarly, the CRISPR-Cas knockout mutant for the SlMAPK3 gene down-regulated the expressions of drought stress-responsive genes: SlLOX, SlGST, and SlDREB [128,129]. The CRISPR-Cas9 mediated dst∆184–305 mutation in the DST (drought and salt tolerance) gene of O. indica cv. MTU1010 produced mutants having broader leaves and reduced stomatal density, resulting in improved leaf water retention under drought stress [130]. The SNF1-related protein kinase 2 (SnRK2) is the primary regulator of hyper-osmotic stress signaling and abscisic acid (ABA)-dependent plant development. A knockout mutant of the SAPK2 gene improved drought tolerance in O. sativa by affecting ABA signaling [131]. The CRISPR/Cas9-mediated knockout of SRL1 and SRL2 (Semi-rolled leaf 1, 2) and ERA1 (Enhanced Response to ABA1) genes improved drought tolerance in O. sativa. OSERA1 mutant lines display similar leaf growth as wild-type plants but enhanced primary root growth [132]. The SRL1 and SRL12 knockout mutants had fewer stomata, a slower rate of transpiration, less chlorophyll, vascular bundles, and rolled leaves than the wild type [133]. Plant ITPKs (Inositol trisphosphate 5/6 kinases) participate in abiotic stress signaling, and the itpk1 mutant created using programmable nuclease Cas9 displayed higher tolerance to salinity stress than deletion mutants in H. vulgare [134]. In B. napus, the bnaa6.rga mutant generated through CRISPR/Cas9 showed enhanced tolerance to drought stress by promoting stomatal closure through increased ABA sensitivity [40].
ARGOS is a negative regulator of the ethylene response, and CRISPR/Cas9-mediated editing of the ethylene response factor ARGOS8 improved drought tolerance in Z. mays [135]. WRKY are plant-specific transcription factors that play essential roles in abiotic stress response. Several WRKY transcription factors were identified in plant species, including A. thaliana, O. sativa, G. max, T. aestivum, and H. vulgare [136,137,138]. Overexpression of ZmWRKY40 promoted root growth and reduced the water loss rates in transgenic A. thaliana under drought stress [137]. Overexpression of the T. aestivum TaWRKY33 enhanced the drought and heat tolerance in transgenic A. thaliana [136]. OsWRKY5 is expressed in developing leaves at the seedling and heading stages of O. sativa. It is the negative regulator of drought, and its expression was reduced under drought stress and by treatment with NaCl, mannitol, and abscisic acid (ABA) [138]. These studies indicated the efficiency of the CRISPR/Cas system in developing drought-tolerant cultivars by knocking out or overexpressing target genes through precise genome editing.

5.2. Improvement in Salinity Stress Tolerance Using CRISPR/Cas System

Genome editing and genetic engineering tools have been utilized to target genes involved in ion transport for regulating osmotic adjustment under salt stress. Soil salinity is a critical abiotic stress affecting crop productivity worldwide. Plant salt tolerance is the ultimate manifestation of several physiologic processes, including Na+ uptake and exclusion, ionic balance (especially Na+/K+ ratio), and distribution [6]. Salt Overly Sensitive 1 (SOS1) is an extensively characterized Na+ efflux transporter in G. max, A. thaliana, and T. aestivum. The gmsos1 mutants were generated using the CRISPR-Cas9 system in G. max, and the resulting mutant displays a significant accumulation of Na+ in the roots and increased salt sensitivity [139]. In A. thaliana, the SOS signal transduction pathway (including SOS1, SOS2, and SOS3 genes) is essential for ion homeostasis and salt tolerance. The SOS1 gene isolated from durum wheat (T. durum) conferred salinity tolerance to the sos1 mutant of A. thaliana [140]. Similarly, the CRISPR/Cas9 knockout of the AITR family genes (AITR3 and AITR4) in A. thaliana enhanced tolerance to drought and salinity stress without fitness costs [7]. A gene cluster containing (T5G46490, AT5G46500, AT5G46520) and (NLRs; AT5G46510) is involved in osmotic stress tolerance. CRISPR/Cas9-mediated mutagenesis generated single and double knockout lines for ACQOS alleles in A. thaliana. A. thaliana plants containing complete deletions or pseudogenization-induced polymorphisms in ACQOS and AT5G46510 show considerable tolerance to salt stress, suggesting the role of ACQOS in salt stress tolerance [141].
Nitric oxide (NO) plays a vital role in cytoprotection by regulating the level of ROS and inducing transcriptional changes, leading to the modulation of protein function [142]. Reactive oxygen species (ROS) are highly reactive molecules typically produced in response to environmental stress, such as salinity and drought. Histone acetyltransferase TaHAG1 is a vital regulator to strengthen the salt tolerance of T. aestivum. TaHAG1 contributed to salt tolerance by modulating ROS production and signal specificity. CRISPR-mediated mutagenesis of TaHAG1 validated the role of TaHAG1 in salt tolerance in T. aestivum [143]. Salt stress increases ROS production and is responsible for oxidative damage, membrane injury, lipid peroxidation (malondialdehyde), and ultimately cell death. CRISPR/Cas9-mediated mutagenesis of the osbhlh024 gene negatively regulates the functions of Na+ and K+ transporter genes, suppressing the higher accumulation of MDA and H2O2, leading to salt tolerance in O. sativa [144].
Several quantitative trait loci (QTLs) and genes associated with regulating salt stress tolerance have been identified in O. sativa, including the NHX family (OsNHX1, OsNHX2, OsNHX3) [145,146,147], HKT family (OsHKT1, OsHKT2, OsHKT7) [148,149,150,151], DCA1 [152], DST1 [130,153], OsKAT1 [154], OsBADH1 [155], OsNAC5 [156], OsbZIP71 [157], SKC1, OsHAL3, P5CS, SNAC2, OsNAP, OsRRY [158,159], and OsSALP1 [113,160]. CRISPR/Cas9-mediated knockout of several salt stress genes significantly improved salinity tolerance in various crops. CRISPR/Cas9 and third-generation hybrid rice system approaches were employed to generate the OsRR22 mutant, which exhibited enhanced salinity tolerance without any morphological and physiological changes relative to the wild-type [159]. A receptor-like kinase gene OSBBS1/OsRLCK109 played vital roles in leaf senescence and salt stress response [161]. CRISPR/Cas9-mediated editing of the SAPK1 and SAPK2 genes showed resistance to salt stress in O. sativa [131]. The mutant alleles of DST (drought and salt tolerance) generated using the CRISPR/Cas9 method showed reduced stomatal density by downregulating stomatal developmental genes (SPCH1, MUTE, ICE1), resulting in a high level of salt tolerance in the seedling stage of O. sativa [130]. Argonaute (AGO) proteins primarily function in gene silencing by forming RNA-induced silencing complexes. CRISPR/Cas9-mediated AGO2-knockout mutant lines showed few morphological changes compared to wild-type rice. The overexpression of AGO2 under the control of the cauliflower mosaic virus 35S led to a simultaneous increase in salt tolerance and grain length [162]. Transcription factors such as AP2/ERF, NAC (NAM, ATAF1/2, CUC2), and WRKY families induce stress-responsive gene expression in response to environmental signals. APETALA2/ethylene response factor (AP2/ERF) plays crucial roles in transcriptional regulation and defense response against biotic and abiotic stress. Editing of the OsRAV2 (AP2/ERF domain-containing RAV) gene using CRISPR/Cas9 showed tolerance to salt stress [163]. DOF transcription factor (DNA-binding with one finger) regulates the elongation of the primary root positively by controlling cell proliferation in the root meristem by restricting ethylene biosynthesis. O. sativa mutant osdof15 showed reduced cell proliferation and primary root elongation in the root meristem [164]. A knockout mutant (ospqt3) with CRISPR-Cas9 technology displayed greater resistance to oxidative and salt stress with high expression of OsGPX1, OsAPX1, and OsSOD1 [165]. Similarly, CRISPR/Cas9 knockout of OsmiR535 demonstrated salinity tolerance in O. sativa against NaCl, ABA, dehydration, and PEG stresses [166]. OsNAC45 plays a vital role in ABA signal responses, and overexpression of NAC45 enhances salt tolerance in O. sativa. OsNAC45 may regulate the expression of seven genes namely CYP89G1, DREB1F, EREBP2, ERF104, PM1, SAMDC2, and SIK1 [167]. Targeted mutagenesis of the OsOTS1 gene using the CRISPR/Cas9 system in the O. sativa cv. Kitaake enhanced sensitivity to salt with reduced root and shoot biomass, indicating that OsOTS1 has a major role in salt stress tolerance [168].
Hormones like Gibberellic and Absiscic acid signaling pathways significantly affect salt stress. OsPIL14-SLR1 (Phytochrome Interacting Factor-Like14–DELLA protein, SLENDER RICE1) controls seedling growth in response to salt stress. CRISPR/Cas9 mediated ospil14 mutants produce normal mesocotyls and longer roots than wild-type plants [169]. ZmWRKY114 is a negative regulator of salt-stress responses, and overexpressed WRKY114 exhibited enhanced salt-stress sensitivity and reduced ABA sensitivity [170]. Salinity stress tolerance was identified in several stress-related genes like HyPRP1 (Hybrid proline-rich protein 1), HKT1, HKT1 (High-affinity potassium transporter1;2), RAD51/54 (DNA repair and recombination protein 51/54) and PR-1 (Pathogenesis-related protein 1) [37,151]. HyPRP1 is a negative regulator of salt stress responses, and CRISPR-Cas9 mediated genome editing of HyPRP1 in L. esculentum resulted in the elimination of the functional domain of proline-rich protein. Plants carrying such variants, PR1v1 lacking proline-rich domain, PR2v2 and PR2v3 lacking eight cysteine motifs, showed improved germination compared to wild type under osmosis stress [37]. A significant improvement in Homology-directed repair (HDR) using CRISPR/LbCpf1-geminiviral multi-replicons was reported to target marker-free salt-tolerant HKT1, HKT2 alleles in L. esculentum [151]. Self-pollinated offspring plants carrying the HKT1, and HKT2 allele showed stable inheritance and germination tolerance under salt stress conditions (100 mm NaCl concentration). In Z. mays, Na+ Content1 (ZmNC1) encodes an HKT-type transporter ZmHKT1, preferentially expressed in root stele. CRISPR/Cas9 knockout lines of ZmHKT1 increase Na+ concentration in xylem sap and cause increased root-to-shoot Na+ delivery, indicating that ZmHKT1 promotes leaf Na+ exclusion and salt tolerance by withdrawing Na+ from the xylem sap [150]. Mutations in genes OsRR9 and OsRR10 generated using the CRISPR/Cas9 system enhanced salinity tolerance but reduced panicle and spikelet numbers per panicle in O. sativa [171]. CRISPR/Cas9 mediated mutagenesis of the ARF (Auxin Response Factors) gene generates a slarf4 mutant that displayed salinity and drought tolerance in L. esculentum by stimulating root development and stomatal function [172] (Table 3). These studies demonstrate the potential role of CRISPR/Cas mutagenesis in knocking out genes responsible for salinity tolerance in plants.

5.3. Improvement in Heat Stress Tolerance Using CRISPR/Cas System

Heat stress is the third most crucial abiotic factor that adversely affects the yield and quality of plants during entire growth stages, from germination to harvesting. Plants respond to heat stress in various ways, including alterations in enzymes that generate reactive oxygen species (ROS), heat shock proteins (HSPs), and genes encoding scavenger proteins [8]. The advancement of structural and functional genomics technologies in plants has led to the identification and characterization of various temperature-stress-related genes to enhance plant ability to withstand heat [9]. The heat-shock-induced CRISPR/Cas9-mediated genome editing efficiently produces heritable targeted mutations. In O. sativa, a heat-shock-inducible CRISPR/Cas9 system was employed to generate targeted and heritable mutations [173]. Similarly, CRISPR/Cas9-based genome editing targeted the heat-sensitive gene, Slagamous-Like 6 (SIAGL6), resulting in increased fruit setting under heat stress conditions in L. esculentum [174]. Calcium-dependent protein kinase 28 (cpk28) mutant was generated using CRISPR/Cas9 mediated editing and displayed thermotolerance in L. esculentum [175]. Brassinazole Resistant 1 (BZR1) is involved in thermo-tolerance by regulating the Feronia (FER) homologs. CRISPR/Cas9-based bzr1 mutant reduced apoplastic reactive oxygen species (H2O2) production and enhanced heat tolerance L. esculentum [176]. Photosynthetic apparatus is highly susceptible to thermal damage. Heat-sensitive albino1 (hsa1) mutant harbors a recessive mutation in a gene encoding fructokinase-like protein2 (FLN2), resulting in a severe albino phenotype with defects in early chloroplast development. In O. sativa, hsa1 mutants showed increased sensitivity to heat stress but had a faster greening phenotype than wild-type plants [177]. Knockout of the ZmTMS5 gene of Z. mays using the CRISPR/Cas9 system generated homozygous T1 tms5 thermosensitive male-sterile plants that are male-sterile at 32 °C but are male-fertile at 24 °C [178]. NCED4 (9-cis-Epoxycarotenoid Dioxygenase4) is a key regulatory enzyme in the biosynthesis of abscisic acid (ABA). Similarly, stable homozygous NCED4 mutants generated using CRISPR/Cas9 were capable of germinating seeds at a higher temperature (>70% germination at 37°) in Lettuce (Lactuca sativa) [179]. Another transcription factor, OsNAC006, is regulated by temperature stress, hormones (abscisic acid, indole-3-acetic acid, and gibberellin), NaCl, polyethylene glycol, and reactive oxygen species. Furthermore, CRISPR-Cas9 mediated knockout of OsNAC006 causes drought and heat sensitivity in O. sativa [180] (Table 3). These studies highlight the application of the CRISPR/Cas9 system to target heat-sensitive genes for developing plant resistance against heat stress.

5.4. Improvement in Cold Stress Tolerance Using CRISPR/Cas System

Cold stress due to chilling and freezing temperatures hinders plant growth and development. Low temperature inhibited plant metabolic activities, producing osmotic and oxidative stress [5]. Mechanical damage and metabolic dysfunction caused by freezing temperatures reduced plant growth and development. In A. thaliana, the two subclasses, namely DREB1/CBF and DREB2, are induced by cold and dehydration, respectively [4]. Expression of T. aestivum TaICE41 and TaICE87 in transgenic A. thaliana activated the expression of COR genes and consequently led to the enhancement of cold tolerance, but only after cold acclimation [181]. The overexpression of AtDREB1A under the RD29A promoter conferred increased drought and freezing tolerance to transgenic A. thaliana plants without affecting growth and development [182]. Several studies have demonstrated that WRKY transcription factors are essential in cold, heat, drought, and salinity stress [183]. In Cucumber (Cucumis sativus), CsWRKY46 is a WRKY transcription factor that confers cold resistance in transgenic plants by controlling cold-stress responsive genes in an ABA-dependent manner. Overexpression of CsWRKY46 regulates freezing and chilling resistance and increases the expression of stress-inducible genes, including RD29A and COR47 [184]. In strawberries (Fragaria vesca), FvICE1 is a positive regulator of cold and drought resistances, and the overexpressed FvICE1 gene improved cold and drought tolerance at the phenotypic and physiological levels. Mutant (fvice1) generated using the CRISPR/Cas9 system demonstrated lower tolerance to cold and drought. This study indicates the potential of CRISPR/Cas9 system in understanding the function of stress-related genes [185]. A. thaliana HOS1 (High Expression of Osmotically Responsive Genes 1) is a Ring finger E3 ubiquitin ligase, a key regulator of cold signaling. CRISPR/Cas9-mediated knockout of the HOS1 gene showed abiotic stress tolerance, accumulation of secondary metabolites, and expression of the biosynthetic genes [10].
The C-repeat binding factors (CBF) are highly conserved CBF cold-response-system components in many plant species. It has a major role in cold acclimation and freezing tolerance in response to low temperatures. CRISPR–Cas9-mediated SlCBF1 mutagenesis reduced chilling tolerance of L. esculentum because of higher electrolyte leakage, increased indole acetic acid contents, decreased abscisic acid, methyl jasmonate, and down-regulated CBF-related genes [186]. Similarly, CRISPR–Cas9-mediated mutagenesis of CGFS-type GRXs (SlGRXS14, SlGRXS15, SlGRXS16, and SlGRXS17) genes showed the sensitivity of Slgrxs mutants to various abiotic stresses as compared to wild-type in L. esculentum [187]. Plant annexins are Ca2+-dependent phospholipid-binding proteins that play a role in development and protection from environmental stresses. CRISPR/Cas9-mediated knockout mutant of annexin gene OsAnn3 decreased cold tolerance in O sativa [188]. OsMYB30 confers cold sensitivity by interacting with an OsJAZ9 protein and downregulating the expression of β-amylase genes in O. sativa [189]. Novel mutants were generated by simultaneously editing three genes, OsPIN5b (panicle length gene), GS3 (grain size gene), and OsMYB30, using the CRISPR–Cas9 system showed higher yield and excellent cold tolerance [190]. PYR1]/PYR1-like [PYL]/regulatory components of the ABA receptor detects abscisic acid during abiotic stress. CRISPR/Cas9 technology was used to edit PYL1PYL6 and PYL12 (group I) and PYL7PYL11 and PYL13 (group II) genes of O. sativa [191]. A knockout mutant of the OsPRP1 gene of O. sativa generated by CRISPR/Cas9 demonstrated less antioxidant enzyme activity and accumulated lower levels of proline, chlorophyll, abscisic acid (ABA), and ascorbic acid (AsA) content relative to wild-type plants under low-temperature stress [192]. CRISPR/Cas9-mediated base editing technology generated the point mutations in two genes (OsWSL5 and OsZEBRA3) in protoplasts and regenerated plants of O. sativa. OsWSL5 encodes a novel chloroplast-targeted pentatricopeptide repeat protein essential in rice chloroplast biogenesis under cold stress [193,194] (Table 3). These studies indicated the potential of CRISPR/Cas9–mediated mutagenesis in developing resistance to chilling and freezing temperatures in drought-tolerant cultivars by knocking out or overexpressing target genes through precise genome editing.

5.5. Improvement in Metal and Herbicide Stress Tolerance Using CRISPR/Cas System

Heavy metals, including arsenic (As), copper (Cu), cobalt (Co), cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni), zinc (Zn), mercury (Hg), lead (Pb) have accumulated in soils as a result of various human activities such as the overuse of agricultural chemicals (fertilizer, herbicides, and pesticides), improper disposal of industrial and sewage waste [11]. Heavy metals are responsible for causing oxidative stress in plants and generate reactive oxygen species, leading to cellular injury. CRISPR-Cas9-mediated mutagenesis was used to improve heat metal stress in plants. OXP1 is one of the enzymes involved in 5-oxoproline metabolism and the pathway for glutathione recycling. The oxp1/CRISPR tolerated plants tolerated heavy metals, such as Cd and amisulbrom (a sulfonamide) [13]. Cadmium stress activates the antioxidant defense system and increases the production of abscisic acid (ABA), glutathione (GSH), salicylic acid (SA), jasmonic acid (JA), and nitric oxide (NO) [195,196]. Absorption of Cd by the roots is mediated by O. sativa genes (OsNramp1, OsCd1, and OsNramp5). In O. sativa, OsHMA2, OsCCX2, and CAL1 regulate Cd transport to the xylem, and OsHMA3 negatively regulates Cd xylem loading. Manipulation in the expression of these genes through CRISPR/Cas9 can minimize the Cd concentration in O. sativa [197]. CRISPR/Cas9 knockout mutants of OsLCT1 and OsNramp5 exhibited reduced levels of Cd in O. sativa [198]. OsNRAMP1 modulates metal ion and reactive oxygen species (ROS) homeostasis. Osnramp1 mutants generated through CRISPR/Cas9 displayed reduced levels of heavy metals (Cd and Pb) in leaves and grains of O. sativa [14]. The OsHAK1 gene encodes a Cs+-permeable K+ transporter that regulates the absorption and translocation of cesium [Cs+] in O. sativa. CRISPR/Cas9 knockout mutant of OsHAK1 exhibited a significant reduction in Cs+ uptake in O. sativa [199]. Potassium [K+] is a critical macronutrient for plant growth and development. ROS was strongly induced and accumulated in K+-deficient plants. Gene Prxs have been involved in the toxic reduction and intracellular H2O2 scavenging. The overexpressed OsPRX2 gene improved tolerance to K+ deficiency by affecting stomatal movement in O. sativa [200]. OsARM1 (Arsenite-Responsive MYB1) is the R2R3 MYB transcription factor that regulates arsenic-associated transporters genes in O. sativa, and the knockout mutant (osarm1) generated using CRISPR/Cas system displayed improved tolerance to arsenic [201].
Herbicides destroy weeds and crop plants by interfering with or altering their metabolic processes, resulting in low yields. Thus, tolerance to herbicides can be one of the essential traits for crop plants that could improve farming practices and crop productivity. CRISPR/Cas-based genome editing techniques efficiently modify target genes and hold great potential in engineering plants with herbicide resistance [202]. In recent years, CRISPR-Cas9-based technology has been used to generate herbicide-tolerant crops, including O. sativa, Z. mays, and G. max [203,204,205,206]. Acetolactate synthase (ALS) catalyzes the step in the biosynthesis of the branched-chain amino acids, including leucine (Leu), isoleucine (Ile), and valine (Val). Enzyme ALS is the target enzyme for two classes of herbicides: sulfonylurea and imidazolinone. Tolerance to ALS-inhibiting herbicides has been developed using a genome editing system in A. thaliana, O. sativa, T. aestivum, Z. mays, S. lycopersicon, and Saccharum spp. [42,203,204,205,206,207,208]. Herbicide-resistant plants were generated through CRISPR/Cas9-mediated homologous recombination of ALS in O. sativa [204]. Similarly, chlorsulfuron resistance was enhanced in Z. mays by editing the ALS2 gene (substitution P165 with Ser) using either single-strand oligonucleotides or double-strand DNA vectors as repair templates [203]. Moreover, P171F substitution in the OsALS1 allele was performed in the O. sativa cultivar Nangeng 46 by precise base editing, resulting in tolerance to the herbicide bispyribac-sodium [208]. Four different missense mutations (P171S, P171A, P171Y, and P171F) in the P171 codon of the ALS gene showed different degrees of tolerance towards five typical herbicides (Sulfonylurea, imidazolinone, triazolopyrimidine, pyr-imidinyl-thiobenzoates, and sulfonyl-aminocarbonyl-triazolinone) belongs to five chemical families of ALS inhibitors in O. sativa [206]. A novel allele (G628W) was developed from a G-to-T transversion at position 1882 of the OsALS gene and conferred resistance to herbicide stress. These mutants of rice plants conferred resistance to herbicides of imazethapyr (IMT) and imazapic (IMP) [209]. The CRISPR/Cas9 system was also successfully used to edit the ALS1 gene of G. max to obtain chlorsulfuron-resistant plants [207]. Mutation of the Proline-186 residue in the ALS gene conferred chlorsulfuron resistance in L. esculentum [210]. An enzyme of activation-induced cytidine deaminase (AID) converts C to U in DNA/RNA by deamination. A synthetic complex of nuclease-deficient Cas9 fused to an AID, which is target-AID enables targeted nucleotide substitutions (C to T or G to A). The point mutation C287T of the ALS gene in rice plants resisted the herbicide imazamox [211]. In T. aestivum, herbicide-tolerant plants were produced by nucleotide editing of the acetolactate synthase (ALS) gene and acetyl-coenzyme A carboxylase gene, which conferred resistance to sulfonylurea, imidazolinone-, and aryloxy phenoxy propionate-type herbicides [205]. Co-editing three copies of the ALS gene resulted in herbicide tolerance in Saccharum spp. [42].
Glyphosate is one of the well-known and broad-spectrum herbicides used in the weed management of resistant crops, such as C. annum, G. max, O. sativa, and Z. mays. Glyphosate inhibits the enzyme EPSPS (5-enolpyruvylshikimate-3-phosphate synthase), involved in the biosynthesis of aromatic amino acids and secondary metabolites. Site-specific gene replacements and insertions in the rice endogenous EPSPS gene resulted in glyphosate-resistant plants [212,213]. The CRISPR/Cas9 tool creates a structural variation (genomic duplication or inversion) in chromosomes. The resulting mutant developed through CRISPR/Cas technology showed the increased accumulation of transcripts of CP12 and Ubiquitin2 genes and the 10-fold upregulated expression of HPPD (4-hydroxyphenyl pyruvate dioxygenase) and PPO1 (protoporphyrinogen oxidase) resulted in herbicide resistance without affecting the yield and other agronomically important traits in O. sativa [214]. CRISPR-Cas9 system was used to edit the target genes of herbicides (ALS and EPSPS) in L. esculentum cv. Micro-Tom [215]. Another herbicide resistance gene, Bentazon Sensitive Lethal (BEL), gives resistance to herbicides of bentazon and sulfonylurea in O. sativa. CRISPR/Cas9-based mutation of the BEL gene was evaluated in rice using the Agrobacterium-mediated stable transformation [216]. The efficiency of mutagenesis ranged from 2% to 16%, and the phenotypic analysis indicated that the mutated plant was sensitive to the herbicide bentazon. Precise editing of the endogenous α-tubulin homolog gene OsTubA2 using CRIPSR-mediated adenine base editors at the T1981 site. The point mutation in the OsTubA2 gene transformed the O. sativa cultivar into a herbicide (dinitroaniline) tolerant cultivar [217] (Table 3). These studies summarised the application of CRISPR/Cas-mediated editing of genes responsible for metal and herbicide stress tolerance in plants.
Table 3. Application of the CRISPR-based genome editing approach in tailoring abiotic stress tolerant plants.
Table 3. Application of the CRISPR-based genome editing approach in tailoring abiotic stress tolerant plants.
CropsTargeted GeneTraitReferences
Arabidopsis thalianaOST2Drought tolerance[117]
Arabidopsis thalianaAVP1Drought tolerance[3]
Arabidopsis thalianaMIR169a and MIR827aDrought tolerance[118]
Arabidopsis thalianaHATDrought tolerance[119]
Arabidopsis thalianaTRE1Drought tolerance[120]
Arabidopsis thalianaNAC07, NAC019, NAC055Drought tolerance[121]
Arabidopsis thalianaAITR3 and AITR4Drought and salinity tolerance[7]
Arabidopsis thalianaACQOSalinity tolerance[141]
Arabidopsis thalianaOxp1Metal Stress tolerance[13]
Brassica napusBnaA6.RGADrought tolerance[40]
Glycine maxAITRSalinity tolerance[41]
Glycine maxSOS1Salinity tolerance[139]
Glycine maxALS1Resistance to chlorsulfuron herbicide[207]
Hordeum vulgareITPK1Salinity tolerance[134]
Lactuca sativaNCED4Heat tolerance[179]
Lycopersicon esculentumSlLBD40Drought tolerance[127]
Lycopersicon esculentumSlMAPK3Drought tolerance[128,129]
Lycopersicon esculentumSlHyPRP1Salinity tolerance[37]
Lycopersicon esculentumSlARF4Drought and salinity tolerance[172]
Lycopersicon esculentumSlCBF1Cold tolerance[186]
Lycopersicon esculentumSIAGL6Heat tolerance[174]
Lycopersicon esculentumCPK28, APX2Heat tolerance[175]
Lycopersicon esculentumBZR1Heat tolerance[176]
Lycopersicon esculentumALSResistance to chlorsulfuron herbicide[210]
Oryza sativaSRL1, SRL2Drought tolerance[133]
Oryza sativaOsDSTDrought and salinity tolerance[130]
Oryza sativaOsERA1Drought tolerance[132]
Oryza sativaSAPK2Drought and salinity tolerance[131]
Oryza sativaRR22Salinity tolerance[159]
Oryza sativamiR535Drought and salinity tolerance[166]
Oryza sativaRAV2Salinity tolerance[163]
Oryza sativaRR9, RR10Salinity tolerance[171]
Oryza sativaNAC006Drought and heat tolerance[180]
Oryza sativaOTS1Salinity tolerance[168]
Oryza sativaHSPHeat tolerance[173]
Oryza sativaHSA1Heat tolerance[177]
Oryza sativaMYB30Cold tolerance[190]
Oryza sativaAnn3Cold tolerance[188]
Oryza sativaPRP1Cold tolerance[192]
Oryza sativaWSL5Cold tolerance[193,194]
Oryza sativaHAK1Low cesium accumulation[199]
Oryza sativaLCT1,Nramp5Reduced cadmium accumulation[198]
Oryza sativaNRAMP1Reduced levels of heavy metals (Cd and Pb)[14]
Oryza sativaPRX2Potassium deficiency tolerance[200]
Oryza sativaARM1Increase tolerance to high Arsenic[201]
Oryza sativaALSResistance to Imazethapyr and imazapic herbicides[209]
Oryza sativaALSHerbicide resistance[204]
Oryza sativaALS1Resistance to bispyribac-sodium herbicide[208]
Oryza sativaALSResistance to Sulfonylurea, imidazolinone, triazolopyrimidine, pyr-imidinyl-thiobenzoates and sulfonyl-aminocarbonyl-triazolinone herbicides[206]
Oryza sativaEPSPSResistance to glyphosate resistance[212]
Oryza sativaC287TResistance to imazamox herbicide[211]
Oryza sativaALS, EPSPSHerbicide resistance[215]
Oryza sativaBELResistance to bentazon herbicide[216]
Oryza sativaOsTubA2Resistance to dinitroaniline herbicide[217]
Oryza sativaOsbhlh024Salinity tolerance[144]
Oryza sativaOsDERF1Drought tolerance[116]
Triticum aestivumDREB1A/CBF3Drought tolerance[122]
Triticum aestivumDREB2, ERF3Drought tolerance[123]
Triticum aestivumHAG1Salinity tolerance[143]
Zea maysARGOS8Drought tolerance[135]
Zea maysHKTISalinity tolerance[150]
Zea maysTMS5Heat tolerance[178]
Zea maysALS2Resistance to chlorsulfuron herbicide[203]

6. Conclusions

The CRISPR/Cas genome editing tool provides a unique and innovative approach to producing crops with enhanced tolerance to environmental stress. It is considered the best genome editing method compared to ZFNs and TALENs due to its simplicity, low cost, and high efficiency. CRISPR/Cas can be used for gene knockout, knockdown, point mutation, replacement, and insertion to develop abiotic stress-tolerant plants. Multiplex genome editing systems can edit a single gene or a few genes, but editing polyploid crops is challenging because their entire genomes have been duplicated or triplicated. The availability of plant genome sequences allows researchers to tailor the genome precisely, facilitating the use of CRISPR/Cas9 in resistance breeding. However, some limitations are associated with this CRISPR/Cas system, such as off-target, which can be reduced by carefully designing sgRNAs and using specific nucleases. Also, PAM sequences provide target specificity and a guide to the genome editing sites. The application of the CRISPR/Cas system will increase with the development of the PAM-independent CRISPR/Cas systems in crop improvement. Numerous studies demonstrate that Cas enzyme modifications may decrease the PAM requirement and create a novel tool for gene functional studies. Several efficient computational tools, including CRISPR-GE, CRISPR-P, and CRISPR-PLANT-V2, have been developed to design the guide RNA precisely.
Genetic modification can integrate with efficient plant transformation and regeneration systems to attain the desired improvement. Efficient and robust transformation techniques are essential for introducing the CRISPR-Cas system into plant cells. Several methods, including Agrobacterium-mediated transformation or particle bombardment, are used for genetic manipulation, but many crops are recalcitrant or exceptionally hard to transform. In order to build an effective tissue culture and plant regeneration system, an attempt has been made to culture immature embryos that can transform into a complete plant. The commercialization and regulation of genome-edited crops are highly debated and vary across countries. Moreover, the generation of transgene-free crops takes longer due to several repeated back-crossings of the edited plant. The development of a DNA-free CRISPR/Cas system is expanding the usefulness and effectiveness of genome editing technology. However, the DNA-free genome editing technique is still evolving, and innovative strategies are urgently required to effectively implement in crop improvement.
The CRISPR/Cas-based genome editing technology presents excellent potential for fundamental and applied research, becoming a sustainable tool for resolving important biological and agricultural problems. The fundamental areas of plant research and improvement will depend on accurate information about the various aspects of the CRISPR/Cas9 system, such as the high-throughput genome sequencing method, guide RNA designing tools, and plant cell transformation and regeneration protocols.

Author Contributions

Conceptualization, A.J. and J.-H.L.; investigation, A.J.; resources, A.J., S.-Y.Y. and H.-G.S.; writing—original draft preparation, A.J.; writing—review and editing, A.J., S.-Y.Y., H.-G.S., J.M. and J.-H.L.; supervision, J.-H.L.; project administration, J.-H.L.; funding acquisition, J.-H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by research funds of Jeonbuk National University in 2023 and also supported by the Cooperative Research Programs for Agricultural Science and Technology Development [Project No. PJ015716032023 or RS-2021-RD009903], Rural Development Administration, Republic of Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank Min-Joo Choi from the Department of Bioenvironmental Chemistry, Jeonbuk National University for her help in the publication search.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Asati, R.; Tripathi, M.K.; Tiwari, S.; Yadav, R.K.; Tripathi, N. Molecular Breeding and Drought Tolerance in Cicer arietinum. Life 2022, 12, 1846. [Google Scholar] [CrossRef] [PubMed]
  2. Karunarathne, S.; Walker, E.; Sharma, D.; Li, C.; Han, Y. Genetic resources and precise gene editing for targeted improvement of barley abiotic stress tolerance. J. Zhejiang Univ. Sci. B 2023, 28, 1–24. [Google Scholar] [CrossRef]
  3. Park, J.J.; Dempewolf, E.; Zhang, W.; Wang, Z.Y. RNA-guided transcriptional activation via CRISPR/dCas9 mimics overexpression phenotypes in Arabidopsis. PLoS ONE 2017, 12, e0179410. [Google Scholar] [CrossRef] [PubMed]
  4. Shinozaki, K.; Yamaguchi-Shinozaki, K. Functional genomics in plant abiotic stress responses and tolerance: From gene discovery to complex regulatory networks and their application in breeding. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci. 2022, 98, 470–492. [Google Scholar] [CrossRef] [PubMed]
  5. Yadav, R.K.; Tripathi, M.K.; Tiwari, S.; Tripathi, N.; Asati, R.; Chauhan, S.; Tiwari, P.N.; Payasi, D.K. Genome Editing and Improvement of Abiotic Stress Tolerance in Crop Plants. Life 2023, 13, 1456. [Google Scholar] [CrossRef] [PubMed]
  6. Zhang, Y.; Fang, J.; Wu, X.; Dong, L. Na+/K+ Balance and Transport Regulatory Mechanisms in Weedy and Cultivated Rice (Oryza sativa L.) Under Salt Stress. BMC Plant Biol. 2018, 18, 375. [Google Scholar] [CrossRef]
  7. Chen, S.; Zhang, N.; Zhou, G.; Hussain, S.; Ahmed, S.; Tian, H.; Wang, S. Knockout of the entire family of AITR genes in Arabidopsis leads to enhanced drought and salinity tolerance without fitness costs. BMC Plant Biol. 2021, 21, 137. [Google Scholar] [CrossRef]
  8. Parmar, N.; Singh, K.H.; Sharma, D.; Singh, L.; Kumar, P.; Nanjundan, J.; Khan, Y.J.; Chauhan, D.K.; Thakur, A.K. Genetic engineering strategies for biotic and abiotic stress tolerance and quality enhancement in horticultural crops: A comprehensive review. Biotech 2017, 7, 239. [Google Scholar] [CrossRef]
  9. Erdoğan, İ.; Cevher-Keskin, B.; Bilir, Ö.; Hong, Y.; Tör, M. Recent Developments in CRISPR/Cas9 Genome-Editing Technology Related to Plant Disease Resistance and Abiotic Stress Tolerance. Biology 2023, 12, 1037. [Google Scholar] [CrossRef]
  10. Shkryl, Y.; Yugay, Y.; Avramenko, T.; Grigorchuk, V.; Gorpenchenko, T.; Grischenko, O.; Bulgakov, V. CRISPR/Cas9-Mediated Knockout of HOS1 Reveals Its Role in the Regulation of Secondary Metabolism in Arabidopsis thaliana. Plants 2021, 10, 104. [Google Scholar] [CrossRef] [PubMed]
  11. Rascio, N.; Navari-Izzo, F. Heavy metal hyperaccumulating plants: How and why do they do it? And what makes them so interesting? Plant Sci. 2011, 180, 169–181. [Google Scholar] [CrossRef] [PubMed]
  12. Shah, K.; Nahakpam, S.; Chaturvedi, V.; Singh, P. Cadmium-Induced Anatomical Abnormalities in Plants. In Cadmium Toxicity and Tolerance in Plants; Hasanuzzaman, M., Prasad, M.N.V., Fujita, M., Eds.; Academic Press: Cambridge, MA, USA, 2019; pp. 111–139. [Google Scholar]
  13. Baeg, G.J.; Kim, S.H.; Choi, D.M.; Tripathi, S.; Han, Y.J.; Kim, J. CRISPR/Cas9-Mediated Mutation of 5-Oxoprolinase Gene Confers Resistance to Sulfonamide Compounds in Arabidopsis. Plant Biotechnol. Rep. 2021, 15, 753–764. [Google Scholar] [CrossRef]
  14. Chu, C.; Huang, R.; Liu, L.; Tang, G.; Xiao, J.; Yoo, H.; Yuan, M. The rice heavy-metal transporter OsNRAMP1 regulates disease resistance by modulating ROS homoeostasis. Plant Cell Environ. 2022, 45, 1109–1126. [Google Scholar] [CrossRef] [PubMed]
  15. Roychowdhury, R.; Das, S.P.; Gupta, A.; Parihar, P.; Chandrasekhar, K.; Sarker, U.; Kumar, A.; Ramrao, D.P.; Sudhakar, C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses. Genes 2023, 14, 1281. [Google Scholar] [CrossRef]
  16. Gao, C. Genome engineering for crop improvement and future agriculture. Cell 2021, 184, 1621–1635. [Google Scholar] [CrossRef]
  17. Wada, N.; Ueta, R.; Osakabe, Y.; Osakabe, K. Precision genome editing in plants: State-of-the-art in CRISPR/Cas9-based genome engineering. BMC Plant Biol. 2020, 20, 234. [Google Scholar] [CrossRef]
  18. Hille, F.; Richter, H.; Wong, S.P.; Bratovič, M.; Ressel, S.; Charpentier, E. The Biology of CRISPR-Cas: Backward and Forward. Cell 2018, 172, 1239–1259. [Google Scholar] [CrossRef]
  19. Liu, M.; Rehman, S.; Tang, X.; Gu, K.; Fan, Q.; Chen, D.; Ma, W. Methodologies for Improving HDR Efficiency. Front. Genet. 2019, 9, 691. [Google Scholar] [CrossRef]
  20. Lu, G.; Wang, C.; Wang, G.; Mao, G.; Habben, J.E.; Chen, G.; Liu, M.; Shi, Y.; Wang, W.; Wang, X.; et al. Knockouts of Drought Sensitive Genes Improve Rice Grain Yield Under Both Drought and Well-Watered Field Conditions. Adv. Crop. Sci. Technol. 2020, 8, 444. [Google Scholar]
  21. Yang, H.; Ren, S.; Yu, S.; Pan, H.; Li, T.; Ge, S.; Zhang, J.; Xia, N. Methods Favoring Homology-Directed Repair Choice in Response to CRISPR/Cas9 Induced-Double Strand Breaks. Int. J. Mol. Sci. 2020, 21, 6461. [Google Scholar] [CrossRef] [PubMed]
  22. Asmamaw, M.; Zawdie, B. Mechanism and Applications of CRISPR/Cas-9-Mediated Genome Editing. Biologics 2021, 15, 353–361. [Google Scholar] [CrossRef] [PubMed]
  23. Tan, J.; Zhao, Y.; Wang, B.; Hao, Y.; Wang, Y.; Li, Y.; Luo, W.; Zong, W.; Li, G.; Chen, S.; et al. Efficient CRISPR/Cas9-based plant genomic fragment deletions by microhomology-mediated end joining. Plant Biotechnol. J. 2020, 11, 2161–2163. [Google Scholar] [CrossRef]
  24. Wiles, M.V.; Qin, W.; Cheng, A.W.; Wang, H. CRISPR-Cas9-mediated genome editing and guide RNA design. Mamm Genome 2015, 26, 501–510. [Google Scholar] [CrossRef] [PubMed]
  25. Zetsche, B.; Gootenberg, J.S.; Abudayyeh, O.O.; Slaymaker, I.M.; Makarova, K.S.; Essletzbichler, P.; Volz, S.E.; Joung, J.; van der Oost, J.; Regev, A.; et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell. 2015, 163, 759–771. [Google Scholar] [CrossRef] [PubMed]
  26. Tang, X.; Lowder, L.G.; Zhang, T.; Malzahn, A.A.; Zheng, X.; Voytas, D.F.; Zhong, Z.; Chen, Y.; Ren, Q.; Li, Q.; et al. A CRISPR-Cpf1 system for efficient genome editing and transcriptional repression in plants. Nat. Plants. 2017, 3, 17018. [Google Scholar] [CrossRef] [PubMed]
  27. Matres, J.M.; Hilscher, J.; Datta, A.; Armario-Nájera, V.; Baysal, C.; He, W.; Huang, X.; Zhu, C.; Valizadeh-Kamran, R.; Trijatmiko, K.R.; et al. Genome editing in cereal crops: An overview. Transgenic Res. 2021, 30, 461–498. [Google Scholar] [CrossRef]
  28. Ming, M.; Ren, Q.; Pan, C.; He, Y.; Zhang, Y.; Liu, S.; Zhong, Z.; Wang, J.; Malzahn, A.A.; Wu, J.; et al. CRISPR-Cas12b enables efficient plant genome engineering. Nat. Plants 2020, 6, 202–208. [Google Scholar] [CrossRef]
  29. Li, J.F.; Norville, J.E.; Aach, J.; McCormack, M.; Zhang, D.; Bush, J.; Church, G.M.; Sheen, J. Multiplex and homologous recombination-mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9. Nat. Biotechnol. 2013, 31, 688–691. [Google Scholar] [CrossRef]
  30. Kumar, M.; Ayzenshtat, D.; Marko, A.; Bocobza, S. Optimization of T-DNA configuration with UBIQUITIN10 promoters and tRNA-sgRNA complexes promotes highly efficient genome editing in allotetraploid tobacco. Plant Cell Rep. 2022, 41, 175–194. [Google Scholar] [CrossRef]
  31. Nekrasov, V.; Staskawicz, B.; Weigel, D.; Jones, J.D.G.; Kamoun, S. Targeted mutagenesis in the model plant Nicotiana benthamiana using Cas9 RNA-guided endonuclease. Nat. Biotechnol. 2013, 31, 691–693. [Google Scholar] [CrossRef]
  32. Char, S.N.; Neelakandan, A.K.; Nahampun, H.; Frame, B.; Main, M.; Spalding, M.H.; Becraft, P.W.; Meyers, B.C.; Walbot, V.; Wang, K.; et al. An Agrobacterium-delivered CRISPR/Cas9 system for high-frequency targeted mutagenesis in maize. Plant Biotechnol. J. 2017, 15, 257–268. [Google Scholar] [CrossRef]
  33. Zafar, K.; Sedeek, K.E.M.; Rao, G.S.; Khan, M.Z.; Amin, I.; Kamel, R.; Mukhtar, Z.; Zafar, M.; Mansoor, S.; Mahfouz, M.M. Genome Editing Technologies for Rice Improvement: Progress, Prospects, and Safety Concerns. Front. Genome Ed. 2020, 2, 5. [Google Scholar] [CrossRef]
  34. Smedley, M.A.; Hayta, S.; Clarke, M.; Harwood, W.A. CRISPR-Cas9 Based Genome Editing in Wheat. Curr Protoc. 2021, 1, e65. [Google Scholar] [CrossRef]
  35. Garcia-Gimenez, G.; Jobling, S.A. Gene editing for barley grain quality improvement. J. Cereal Sci. 2022, 103, 103394. [Google Scholar] [CrossRef]
  36. Liang, Z.; Wu, Y.; Ma, L.; Guo, Y.; Ran, Y. Efficient Genome Editing in Setaria italica Using CRISPR/Cas9 and Base Editors. Front. Plant Sci. 2022, 12, 815946. [Google Scholar] [CrossRef]
  37. Tran, M.T.; Son, G.H.; Song, Y.J.; Nguyen, N.T.; Park, S.; Thach, T.V.; Kim, J.; Sung, Y.W.; Das, S.; Pramanik, D.; et al. CRISPR-Cas9-based precise engineering of SlHyPRP1 protein towards multi-stress tolerance in Tomato. Front. Plant Sci. 2023, 14, 1186932. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, S.; Zhang, S.; Wang, W.; Xiong, X.; Meng, F.; Cui, X. Efficient targeted mutagenesis in potato by the CRISPR/Cas9 system. Plant Cell Rep. 2015, 34, 1473–1476. [Google Scholar] [CrossRef] [PubMed]
  39. Park, S., II; Kim, H.B.; Jeon, H.J.; Kim, H. Agrobacterium-mediated capsicum annuum gene editing in two cultivars, hot pepper CM334 and bell pepper dempsey. Int. J. Mol. Sci. 2021, 22, 3921. [Google Scholar] [CrossRef] [PubMed]
  40. Wu, J.; Yan, G.; Duan, Z.; Wang, Z.; Kang, C.; Guo, L.; Liu, K.; Tu, J.; Shen, J.; Yi, B.; et al. Roles of the Brassica napus DELLA Protein BnaA6.RGA, in Modulating Drought Tolerance by Interacting with the ABA Signaling Component BnaA10.ABF2. Front. Plant Sci. 2020, 11, 577. [Google Scholar] [CrossRef]
  41. Wang, T.; Xun, H.; Wang, W.; Ding, X.; Tian, H.; Hussain, S.; Dong, Q.; Li, Y.; Cheng, Y.; Wang, C.; et al. Mutation of GmAITR Genes by CRISPR/Cas9 Genome Editing Results in Enhanced Salinity Stress Tolerance in Soybean. Front. Plant Sci. 2021, 12, 779598. [Google Scholar] [CrossRef] [PubMed]
  42. Oz, M.T.; Altpeter, A.; Karan, R.; Merotto, A.; Altpeter, F. CRISPR/Cas9-mediated multi-allelic gene targeting in sugarcane confers herbicide tolerance. Front. Genome Ed. 2021, 3, 673566. [Google Scholar] [CrossRef]
  43. Ambrosino, L.; Colantuono, C.; Diretto, G.; Fiore, A.; Chiusano, M.L. Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving-Omics Era. Plants 2020, 9, 591. [Google Scholar] [CrossRef] [PubMed]
  44. Prabha, R.; Ghosh, I.; Singh, D.P. Plant Stress Gene Database: A collection of plant genes responding to stress condition. ARPN J. Sci. Technol. 2011, 1, 28–31. [Google Scholar]
  45. Mousavi, S.A.; Pouya, F.M.; Ghaffari, M.R.; Mirzaei, M.; Ghaffari, A.; Alikhani, M.; Ghareyazie, M.; Komatsu, S.; Haynes, P.A.; Salekdeh, G.H. PlantPReS: A database for plant proteome response to stress. J. Proteom. 2016, 143, 69–72. [Google Scholar] [CrossRef]
  46. Naika, M.; Shameer, K.; Mathew, O.K.; Gowda, R.; Sowdhamini, R. STIFDB2: An Updated Version of Plant Stress-Responsive TranscrIption Factor DataBase with Additional Stress Signals, Stress-Responsive Transcription Factor Binding Sites and Stress-Responsive Genes in Arabidopsis and Rice. Plant Cell Physiol. 2013, 54, e8. [Google Scholar] [CrossRef] [PubMed]
  47. Guo, Z.; Kuang, Z.; Zhao, Y.; Deng, Y.; He, H.; Wan, M.; Tao, Y.; Wang, D.; Wei, J.; Li, L.; et al. PmiREN2.0: From data annotation to functional exploration of plant microRNAs. Nucleic Acids Res. 2022, 50, D1475–D1482. [Google Scholar] [CrossRef]
  48. Sircar, S.; Musaddi, M.; Parekh, N. NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions. Database 2022, 2022, baac060. [Google Scholar] [CrossRef]
  49. Wu, W.; Wu, Y.; Hu, D.; Zhou, Y.; Hu, Y.; Chen, Y.; Chen, M. PncStress: A manually curated database of experimentally validated stress-responsive non-coding RNAs in plants. Database 2020, 2020, baaa001. [Google Scholar] [CrossRef]
  50. Jaiswal, S.; Antala, T.J.; Mandavia, M.K.; Chopra, M.; Jasrotia, R.S.; Tomar, R.S.; Kheni, J.; Angadi, U.B.; Iquebal, M.A.; Golakia, B.A.; et al. Transcriptomic signature of drought response in pearl millet (Pennisetum glaucum (L.) and development of web-genomic resources. Sci. Rep. 2018, 8, 3382. [Google Scholar] [CrossRef] [PubMed]
  51. Akpınar, B.A.; Lucas, S.J.; Budak, H. Genomics approaches for crop improvement against abiotic stress. Sci. World J. 2013, 2013, 361921. [Google Scholar] [CrossRef]
  52. Kumar, R.; Mustafiz, A.; Sahoo, K.K.; Sharma, V.; Samanta, S.; Sopory, S.K.; Pareek, A.; Singla-Pareek, S.L. Functional screening of cDNA library from a salt tolerant rice genotype Pokkali identifies mannose-1-phosphate guanyl transferase gene (OsMPG1) as a key member of salinity stress response. Plant Mol. Biol. 2012, 79, 555–568. [Google Scholar] [CrossRef] [PubMed]
  53. Deokar, A.A.; Kondawar, V.; Jain, P.K.; Karuppayil, S.M.; Raju, N.L.; Vadez, V.; Varshney, R.K.; Srinivasan, R. Comparative analysis of expressed sequence tags (ESTs) between drought-tolerant and-susceptible genotypes of Cicer arietinum under terminal drought stress. BMC Plant Biol. 2011, 11, 70. [Google Scholar] [CrossRef]
  54. Sun, M.; Zhou, G.; Lee, S.; Chen, J.; Shi, R.Z.; Wang, S.M. SAGE is far more sensitive than EST for detecting low-abundance transcripts. BMC Genom. 2004, 5, 1. [Google Scholar] [CrossRef] [PubMed]
  55. Molina, C.; Rotter, B.; Horres, R.; Udupa, S.M.; Besser, B.; Bellarmino, L.; Baum, M.; Matsumura, H.; Terauchi, R.; Kahl, G.; et al. SuperSAGE: The drought stress-responsive transcriptome of chickpea roots. BMC Genom. 2008, 9, 553. [Google Scholar] [CrossRef] [PubMed]
  56. Brenner, S.; Johnson, M.; Bridgham, J.; Golda, G.; Lloyd, D.H.; Johnson, D.; Luo, S.; McCurdy, S.; Foy, M.; Ewan, M.; et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat. Biotechnol. 2000, 18, 630–634. [Google Scholar] [CrossRef]
  57. Reinartz, J.; Bruyns, E.; Lin, J.Z.; Burcham, T.; Brenner, S.; Bowen, B.; Kramer, M.; Woychik, R. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Brief Funct. Genom. Proteomic 2002, 1, 95–104. [Google Scholar] [CrossRef]
  58. Nakano, M.; Nobuta, K.; Vemaraju, K.; Tej, S.S.; Skogen, J.W.; Meyers, B.C. Plant MPSS databases: Signature-based transcriptional resources for analyses of mRNA and small RNA. Nucleic Acids Res. 2006, 34, D731–D735. [Google Scholar] [CrossRef]
  59. Nobuta, K.; Vemaraju, K.; Meyers, B.C. Methods for analysis of gene expression in plants using MPSS. Methods Mol. Biol. 2007, 406, 387–408. [Google Scholar] [CrossRef]
  60. Zimmermann, P.; Hirsch-Hoffmann, M.; Hennig, L.; Gruissem, W. GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol. 2004, 136, 2621–2632. [Google Scholar] [CrossRef]
  61. Kawaura, K.; Mochida, K.; Ogihara, Y. Genome-wide analysis for identification of salt-responsive genes in common wheat. Funct. Integr. Genom. 2008, 8, 277–286. [Google Scholar] [CrossRef] [PubMed]
  62. Pervaiz, T.; Amjid, M.W.; El-kereamy, A.; Niu, S.-H.; Wu, H.X. MicroRNA and cDNA-Microarray as Potential Targets against Abiotic Stress Response in Plants: Advances and Prospects. Agronomy 2022, 12, 11. [Google Scholar] [CrossRef]
  63. Wong, C.E.; Li, Y.; Labbe, A.; Guevara, D.; Nuin, P.; Whitty, B.; Diaz, C.; Golding, G.B.; Gray, G.R.; Weretilnyk, E.A. Transcriptional profiling implicates novel interactions between abiotic stress and hormonal responses in Thellungiella, a close relative of Arabidopsis. Plant Physiol. 2006, 140, 1437–1450. [Google Scholar] [CrossRef] [PubMed]
  64. Jangam, A.P.; Pathak, R.R.; Raghuram, N. Microarray Analysis of Rice d1 (RGA1) Mutant Reveals the Potential Role of G-Protein Alpha Subunit in Regulating Multiple Abiotic Stresses Such as Drought, Salinity, Heat, and Cold. Front. Plant Sci. 2016, 7, 11. [Google Scholar] [CrossRef] [PubMed]
  65. Xu, Y.; Li, P.; Yang, Z.; Xu, C. Genetic mapping of quantitative trait loci in crops. Crop. J. 2017, 5, 175–184. [Google Scholar] [CrossRef]
  66. Ibrahim, A.K.; Zhang, L.; Niyitanga, S.; Afzal, M.Z.; Xu, Y.; Zhang, L.; Qi, J. Principles and approaches of association mapping in plant breeding. Trop. Plant Biol. 2020, 13, 212–224. [Google Scholar] [CrossRef]
  67. Lv, Y.; Ma, J.; Wei, H.; Xiao, F.; Wang, Y.; Jahan, N.; Hazman, M.; Qian, Q.; Shang, L.; Guo, L. Combining GWAS, Genome-Wide Domestication and a Transcriptomic Analysis Reveals the Loci and Natural Alleles of Salt Tolerance in Rice (Oryza sativa L.). Front. Plant Sci. 2022, 13, 912637. [Google Scholar] [CrossRef] [PubMed]
  68. Bilgrami, S.S.; Ramandi, H.D.; Shariati, V.; Razavi, K.; Tavakol, E.; Fakheri, B.A.; Mahdi Nezhad, N.; Ghaderian, M. Detection of genomic regions associated with tiller number in Iranian bread wheat under different water regimes using genome-wide association study. Sci. Rep. 2020, 10, 14034. [Google Scholar] [CrossRef] [PubMed]
  69. Saini, D.K.; Srivastava, P.; Pal, N.; Gupta, P.K. Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.). Theoret. Appl. Genet. 2022, 135, 1049–1081. [Google Scholar] [CrossRef] [PubMed]
  70. Tanin, M.J.; Saini, D.K.; Sandhu, K.S.; Pal, N.; Gudi, S.; Chaudhary, J.; Sharma, A. Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding. Sci. Rep. 2022, 12, 13680. [Google Scholar] [CrossRef] [PubMed]
  71. Liu, P.; Zhu, Y.; Liu, H.; Liang, Z.; Zhang, M.; Zou, C.; Yuan, G.; Gao, S.; Pan, G.; Shen, Y.; et al. A Combination of a Genome-Wide Association Study and a Transcriptome Analysis Reveals circRNAs as New Regulators Involved in the Response to Salt Stress in Maize. Int. J. Mol. Sci. 2022, 23, 9755. [Google Scholar] [CrossRef] [PubMed]
  72. Wang, Q.; Ning, L.; Yu, W.; Zhao, W.; Huang, F.; Yu, D.; Wang, H.; Cheng, H. Detection of Candidate Loci and Genes Related to Phosphorus Efficiency at Maturity through a Genome-Wide Association Study in Soybean. Agronomy 2022, 12, 2031. [Google Scholar] [CrossRef]
  73. Wu, D.Z.; Sato, K.; Ma, J.F. Genome-wide association mapping of cadmium accumulation in different organs of barley. New Phytol. 2015, 208, 817–829. [Google Scholar] [CrossRef]
  74. Visioni, A.; Tondelli, A.; Francia, E.; Pswarayi, A.; Malosetti, M.; Russell, J.; Thomas, W.; Waugh, R.; Pecchioni, N.; Romagosa, I.; et al. Genome-wide association mapping of frost tolerance in barley (Hordeum vulgare L.). BMC Genom. 2013, 14, 424. [Google Scholar] [CrossRef] [PubMed]
  75. Pham, A.; Maurer, A.; Pillen, K.; Brien, C.; Dowling, K.; Berger, B.; Eglinton, J.K.; March, T.J. Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC Plant Biol. 2019, 19, 134. [Google Scholar] [CrossRef] [PubMed]
  76. Tarawneh, R.A.; Alqudah, A.M.; Nagel, M.; Börner, A. Genome-wide association mapping reveals putative candidate genes for drought tolerance in barley. Environ. Exp. Bot. 2020, 180, 104237. [Google Scholar] [CrossRef]
  77. Cai, S.G.; Wu, D.H.; Jabeen, Z.; Huang, Y.Q.; Huang, Y.C.; Zhang, G.P. Genome-wide association analysis of aluminum tolerance in cultivated and Tibetan wild barley. PLoS ONE. 2013, 8, e69776. [Google Scholar] [CrossRef]
  78. Zhou, G.F.; Broughton, S.; Zhang, X.Q.; Zhou, M.X.; Li, C.D. Genome-wide association mapping of acid soil resistance in barley (Hordeum vulgare L.). Front. Plant Sci. 2016, 7, 406. [Google Scholar] [CrossRef] [PubMed]
  79. Long, N.V.; Dolstra, O.; Malosetti, M.; Kilian, B.; Graner, A.; Visser, R.G.; van der Linden, C.G. Association mapping of salt tolerance in barley (Hordeum vulgare L.). Theor. Appl. Genet. 2013, 126, 2335–2351. [Google Scholar] [CrossRef]
  80. Fan, Y.; Zhou, G.; Shabala, S.; Chen, Z.; Cai, S.; Li, C.; Zhou, M. Genome-wide association study reveals a new QTL for salinity tolerance in barley (Hordeum vulgare L.). Front. Plant Sci. 2016, 7, 94. [Google Scholar] [CrossRef]
  81. Samineni, S.; Mahendrakar, M.D.; Hotti, A.; Chand, U.; Rathore, A.; Gaur, P.M. Impact of heat and drought stresses on grain nutrient content in Cicer arietinum: Genome-wide marker-trait associations for protein, Fe and Zn. Environ. Exp. Bot. 2022, 194, 104688. [Google Scholar] [CrossRef]
  82. Choudhary, S.; Isobe, S.; Chahota, R.K. Elucidation of drought tolerance potential of horsegram (Macrotyloma uniflorum var.) germplasm using genome wide association studies. Gene 2022, 819, 146241. [Google Scholar] [CrossRef]
  83. Makarova, K.S.; Haft, D.H.; Barrangou, R.; Brouns, S.J.; Charpentier, E.; Horvath, P.; Moineau, S.; Mojica, F.J.; Wolf, Y.I.; Yakunin, A.F.; et al. Evolution and Classification of the CRISPR-Cas Systems. Nat. Rev. Microbiol. 2011, 9, 467–477. [Google Scholar] [CrossRef]
  84. Gasiunas, G.; Barrangou, R.; Horvath, P.; Siksnys, V. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl. Acad. Sci. USA 2012, 109, E2579–E2586. [Google Scholar] [CrossRef]
  85. Nishimasu, H.; Shi, X.; Ishiguro, S.; Gao, L.; Hirano, S.; Okazaki, S.; Noda, T.; Abudayyeh, O.O.; Gootenberg, J.S.; Mori, H.; et al. Engineered CRISPR-Cas9 nuclease with expanded targeting space. Science 2018, 361, 1259–1262. [Google Scholar] [CrossRef] [PubMed]
  86. Magadán, A.H.; Dupuis, M.È.; Villion, M.; Moineau, S. Cleavage of phage DNA by the Streptococcus thermophilus CRISPR3-Cas system. PLoS ONE 2012, 7, e40913. [Google Scholar] [CrossRef] [PubMed]
  87. Ran, F.A.; Cong, L.; Yan, W.X.; Scott, D.A.; Gootenberg, J.S.; Kriz, A.J.; Zetsche, B.; Shalem, O.; Wu, X.; Makarova, K.S.; et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 2015, 520, 186–191. [Google Scholar] [CrossRef] [PubMed]
  88. Kleinstiver, B.P.; Prew, M.S.; Tsai, S.Q.; Nguyen, N.T.; Topkar, V.V.; Zheng, Z.; Joung, J.K. Broadening the targeting range of Staphylococcus aureus CRISPR-Cas9 by modifying PAM recognition. Nat. Biotechnol. 2015, 33, 1293–1298. [Google Scholar] [CrossRef]
  89. Kleinstiver, B.P.; Pattanayak, V.; Prew, M.S.; Tsai, S.Q.; Nguyen, N.T.; Zheng, Z.; Joung, J.K. High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects. Nature 2016, 529, 490–495. [Google Scholar] [CrossRef]
  90. Slaymaker, I.M.; Gao, L.; Zetsche, B.; Scott, D.A.; Yan, W.X.; Zhang, F. Rationally engineered Cas9 nucleases with improved specificity. Science 2016, 351, 84–88. [Google Scholar] [CrossRef] [PubMed]
  91. Lee, J.K.; Jeong, E.; Lee, J.; Jung, M.; Shin, E.; Kim, Y.H.; Lee, K.; Jung, I.; Kim, D.; Kim, S.; et al. Directed evolution of CRISPR-Cas9 to increase its specificity. Nat. Commun. 2018, 9, 3048. [Google Scholar] [CrossRef]
  92. Casini, A.; Olivieri, M.; Petris, G.; Montagna, C.; Reginato, G.; Maule, G.; Lorenzin, F.; Prandi, D.; Romanel, A.; Demichelis, F.; et al. A highly specific SpCas9 variant is identified by in vivo screening in yeast. Nat. Biotechnol. 2018, 36, 265–271. [Google Scholar] [CrossRef]
  93. Chen, J.S.; Dagdas, Y.S.; Kleinstiver, B.P.; Welch, M.M.; Sousa, A.A.; Harrington, L.B.; Sternberg, S.H.; Joung, J.K.; Yildiz, A.; Doudna, J.A. Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature 2017, 550, 407–410. [Google Scholar] [CrossRef] [PubMed]
  94. Miller, S.M.; Wang, T.; Randolph, P.B.; Arbab, M.; Shen, M.W.; Huang, T.P.; Matuszek, Z.; Newby, G.A.; Rees, H.A.; Liu, D.R. Continuous evolution of SpCas9 variants compatible with non-G PAMs. Nat. Biotechnol. 2020, 38, 471–481. [Google Scholar] [CrossRef]
  95. Walton, R.T.; Christie, K.A.; Whittaker, M.N.; Kleinstiver, B.P. Unconstrained genome targeting with near-PAMless engineered CRISPR-Cas9 variants. Science 2020, 368, 290–296. [Google Scholar] [CrossRef] [PubMed]
  96. Zhang, D.; Zhang, Z.; Unver, T.; Zhang, B. CRISPR/Cas: A powerful tool for gene function study and crop improvement. J. Adv. Res. 2020, 29, 207–221. [Google Scholar] [CrossRef] [PubMed]
  97. Hou, Z.; Zhang, Y.; Propson, N.E.; Howden, S.E.; Chu, L.F.; Sontheimer, E.J.; Thomson, J.A. Efficient genome engineering in human pluripotent stem cells using Cas9 from Neisseria meningitidis. Proc. Natl. Acad. Sci. USA 2013, 110, 15644–15649. [Google Scholar] [CrossRef] [PubMed]
  98. Hirano, H.; Gootenberg, J.S.; Horii, T.; Abudayyeh, O.O.; Kimura, M.; Hsu, P.D.; Nakane, T.; Ishitani, R.; Hatada, I.; Zhang, F.; et al. Structure and Engineering of Francisella novicida Cas9. Cell 2016, 164, 950–961. [Google Scholar] [CrossRef]
  99. Kim, E.; Koo, T.; Park, S.W.; Kim, D.; Kim, K.; Cho, H.Y.; Song, D.W.; Lee, K.J.; Jung, M.H.; Kim, S.; et al. In vivo genome editing with a small Cas9 orthologue derived from Campylobacter jejuni. Nat. Commun. 2017, 8, 14500. [Google Scholar] [CrossRef]
  100. Abudayyeh, O.O.; Gootenberg, J.S.; Konermann, S.; Joung, J.; Slaymaker, I.M.; Cox, D.B.; Shmakov, S.; Makarova, K.S.; Semenova, E.; Minakhin, L.; et al. C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Science 2016, 353, aaf5573. [Google Scholar] [CrossRef]
  101. Harrington, L.B.; Burstein, D.; Chen, J.S.; Paez-Espino, D.; Ma, E.; Witte, I.P.; Cofsky, J.C.; Kyrpides, N.C.; Banfield, J.F.; Doudna, J.A. Programmed DNA destruction by miniature CRISPR-Cas14 enzymes. Science 2018, 362, 839–842. [Google Scholar] [CrossRef]
  102. Kleinstiver, B.P.; Prew, M.S.; Tsai, S.Q.; Topkar, V.V.; Nguyen, N.T.; Zheng, Z.; Gonzales, A.P.; Li, Z.; Peterson, R.T.; Yeh, J.R.; et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 2015, 523, 481–485. [Google Scholar] [CrossRef] [PubMed]
  103. Labun, K.; Montague, T.G.; Gagnon, J.A.; Thyme, S.B.; Valen, E. CHOPCHOP v2: A web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 2016, 44, W272–W276. [Google Scholar] [CrossRef]
  104. Bae, S.; Park, J.; Kim, J.S. Cas-OFFinder: A fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 2014, 30, 1473–1475. [Google Scholar] [CrossRef] [PubMed]
  105. Stemmer, M.; Thumberger, T.; Del Sol Keyer, M.; Wittbrodt, J.; Mateo, J.L. CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS ONE 2015, 10, e0124633. [Google Scholar] [CrossRef]
  106. Abadi, S.; Yan, W.X.; Amar, D.; Mayrose, I. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput. Biol. 2017, 13, e1005807. [Google Scholar] [CrossRef] [PubMed]
  107. Xie, X.; Ma, X.; Zhu, Q.; Zeng, D.; Li, G.; Liu, Y.G. CRISPR-GE: A Convenient Software Toolkit for CRISPR-Based Genome Editing. Mol. Plant. 2017, 10, 1246–1249. [Google Scholar] [CrossRef] [PubMed]
  108. Lei, Y.; Lu, L.; Liu, H.Y.; Li, S.; Xing, F.; Chen, L.L. CRISPR-P: A web tool for synthetic single-guide RNA design of CRISPR-system in plants. Mol. Plant. 2014, 7, 1494–1496. [Google Scholar] [CrossRef]
  109. Minkenberg, B.; Zhang, J.; Xie, K.; Yang, Y. CRISPR-PLANT v2: An online resource for highly specific guide RNA spacers based on improved off-target analysis. Plant Biotechnol. J. 2019, 17, 5–8. [Google Scholar] [CrossRef] [PubMed]
  110. Chen, W.; Zhang, G.; Li, J.; Zhang, X.; Huang, S.; Xiang, S.; Hu, X.; Liu, C. CRISPRlnc: A manually curated database of validated sgRNAs for lncRNAs. Nucl. Acid. Res. 2019, 47, D63–D68. [Google Scholar] [CrossRef] [PubMed]
  111. Chen, C.L.; Rodiger, J.; Chung, V.; Viswanatha, R.; Mohr, S.E.; Hu, Y.; Perrimon, N. SNP-CRISPR: A Web Tool for SNP-Specific Genome Editing. G3 2020, 10, 489–494. [Google Scholar] [CrossRef] [PubMed]
  112. Siegner, S.M.; Karasu, M.E.; Schröder, M.S.; Kontarakis, Z.; Corn, J.E. PnB Designer: A web application to design prime and base editor guide RNAs for animals and plants. BMC Bioinform. 2021, 22, 101. [Google Scholar] [CrossRef]
  113. Liu, C.T.; Mao, B.G.; Yuan, D.Y.; Chu, C.C.; Duan, M.J. Salt tolerance in rice: Physiological responses and molecular mechanisms. Crop. J. 2022, 10, 13. [Google Scholar] [CrossRef]
  114. Chen, K.; Wang, Y.; Zhang, R.; Zhang, H.; Gao, C. CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu. Rev. Plant Biol. 2019, 70, 667–697. [Google Scholar] [CrossRef]
  115. Joshi, A.; Song, H.G.; Yang, S.Y.; Lee, J.H. Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants. Plants 2023, 12, 2454. [Google Scholar] [CrossRef] [PubMed]
  116. Zhang, H.; Zhang, J.; Wei, P.; Zhang, B.; Gou, F.; Feng, Z.; Mao, Y.; Yang, L.; Zhang, H.; Xu, N.; et al. The CRISPR/Cas9 System Produces Specific and Homozygous Targeted Gene Editing in Rice in One Generation. Plant Biotechnol. J. 2014, 12, 797–807. [Google Scholar] [CrossRef] [PubMed]
  117. Osakabe, Y.; Watanabe, T.; Sugano, S.S.; Ueta, R.; Ishihara, R.; Shinozaki, K.; Osakabe, K. Optimization of CRISPR/Cas9 genome editing to modify abiotic stress responses in plants. Sci. Rep. 2016, 6, 26685. [Google Scholar] [CrossRef] [PubMed]
  118. Zhao, Y.; Zhang, C.; Liu, W.; Gao, W.; Liu, C.; Song, G.; Li, W.X.; Mao, L.; Chen, B.; Xu, Y.; et al. An alternative strategy for targeted gene replacement in plants using a dual-sgRNA/Cas9 design. Sci. Rep. 2016, 6, 23890. [Google Scholar] [CrossRef]
  119. Roca Paixão, J.F.; Gillet, F.X.; Ribeiro, T.P.; Bournaud, C.; Lourenço-Tessutti, I.T.; Noriega, D.D.; Melo, B.P.; de Almeida-Engler, J.; Grossi-de-Sa, M.F. Improved drought stress tolerance in arabidopsis by CRISPR/dCas9 fusion with a histone Acetyl Transferase. Sci. Rep. 2019, 9, 8080. [Google Scholar] [CrossRef]
  120. Nuñez-Muñoz, L.; Vargas-Hernández, B.; Hinojosa-Moya, J.; Ruiz-Medrano, R.; Xoconostle-Cázares, B. Plant Drought Tolerance Provided Through Genome Editing of the Trehalose Gene. Plant Signal. Behav. 2021, 16, 1877005. [Google Scholar] [CrossRef]
  121. Tran, L.S.; Nakashima, K.; Sakuma, Y.; Simpson, S.D.; Fujita, Y.; Maruyama, K.; Fujita, M.; Seki, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Isolation and functional analysis of Arabidopsis stress-inducible NAC transcription factors that bind to a drought-responsive cis-element in the early responsive to dehydration stress 1 promoter. Plant Cell. 2004, 16, 2481–2498. [Google Scholar] [CrossRef]
  122. Pellegrineschi, A.; Reynolds, M.; Pacheco, M.; Brito, R.M.; Almeraya, R.; Yamaguchi-Shinozaki, K.; Hoisington, D. Stress-induced expression in wheat of the Arabidopsis thaliana DREB1A gene delays water stress symptoms under greenhouse conditions. Genome 2004, 47, 493–500. [Google Scholar] [CrossRef] [PubMed]
  123. Kim, D.; Alptekin, B.; Budak, H. CRISPR/Cas9 Genome Editing in Wheat. Funct. Integr. Genom. 2018, 18, 31–41. [Google Scholar] [CrossRef]
  124. Singh, D.; Laxmi, A. Transcriptional regulation of drought response: A tortuous network of transcriptional factors. Front. Plant Sci. 2015, 6, 895. [Google Scholar] [CrossRef]
  125. Finkelstein, R.R.; Lynch, T.J. The Arabidopsis abscisic acid response gene ABI5 encodes a basic leucine zipper transcription factor. Plant Cell. 2000, 12, 599–609. [Google Scholar] [CrossRef]
  126. Fujita, Y.; Fujita, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. ABA-mediated transcriptional regulation in response to osmotic stress in plants. J. Plant Res. 2011, 124, 509–525. [Google Scholar] [CrossRef] [PubMed]
  127. Liu, L.; Zhang, J.; Xu, J.; Li, Y.; Guo, L.; Wang, Z.; Zhang, X.; Zhao, B.; Guo, Y.D.; Zhang, N. CRISPR/Cas9 targeted mutagenesis of SlLBD40, a lateral organ boundaries domain transcription factor, enhances drought tolerance in tomato. Plant Sci. 2020, 301, 110683. [Google Scholar] [CrossRef] [PubMed]
  128. Wang, L.; Chen, L.; Li, R.; Zhao, R.; Yang, M.; Sheng, J.; Shen, L. Reduced Drought Tolerance by CRISPR/Cas9-Mediated SlMAPK3 Mutagenesis in Tomato Plants. J. Agric. Food Chem. 2017, 65, 8674–8682. [Google Scholar] [CrossRef] [PubMed]
  129. Yu, W.; Wang, L.; Zhao, R.; Sheng, J.; Zhang, S.; Li, R.; Shen, L. Knockout of SlMAPK3 enhances tolerance to heat stress involving ROS homeostasis in Tomato plants. BMC Plant Biol. 2019, 19, 354. [Google Scholar] [CrossRef]
  130. Santosh Kumar, V.V.; Verma, R.K.; Yadav, S.K.; Yadav, P.; Watts, A.; Rao, M.V.; Chinnusamy, V. CRISPR-Cas9 mediated genome editing of drought and salt tolerance (OsDST) gene in indica mega rice cultivar MTU1010. Physiol. Mol. Biol. Plants 2020, 26, 1099–1110. [Google Scholar] [CrossRef]
  131. Lou, D.; Wang, H.; Liang, G.; Yu, D. OsSAPK2 Confers Abscisic Acid Sensitivity and Tolerance to Drought Stress in Rice. Front. Plant Sci. 2017, 8, 993. [Google Scholar] [CrossRef]
  132. Ogata, T.; Ishizaki, T.; Fujita, M.; Fujita, Y. CRISPR/Cas9-targeted mutagenesis of OsERA1 confers enhanced responses to abscisic acid and drought stress and increased primary root growth under nonstressed conditions in rice. PLoS ONE 2020, 15, e0243376. [Google Scholar] [CrossRef]
  133. Liao, S.; Qin, X.; Luo, L.; Han, Y.; Wang, X.; Usman, B.; Nawaz, G.; Zhao, N.; Liu, Y.; Li, R. CRISPR/Cas9-Induced Mutagenesis of Semi-Rolled Leaf1,2 Confers Curled Leaf Phenotype and Drought Tolerance by Influencing Protein Expression Patterns and ROS Scavenging in Rice (Oryza sativa L.). Agronomy 2019, 9, 728. [Google Scholar] [CrossRef]
  134. Vlčko, T.; Ohnoutková, L. Allelic Variants of CRISPR/Cas9 Induced Mutation in an Inositol Trisphosphate 5/6 Kinase Gene Manifest Different Phenotypes in Barley. Plants 2020, 9, 195. [Google Scholar] [CrossRef] [PubMed]
  135. Shi, J.; Gao, H.; Wang, H.; Lafitte, H.R.; Archibald, R.L.; Yang, M.; Hakimi, S.M.; Mo, H.; Habben, J.E. ARGOS 8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol. J. 2017, 15, 207–216. [Google Scholar] [CrossRef] [PubMed]
  136. He, G.H.; Xu, J.Y.; Wang, Y.X.; Liu, J.M.; Li, P.S.; Chen, M.; Ma, Y.Z.; Xu, Z.S. Drought-responsive WRKY transcription factor genes TaWRKY1 and TaWRKY33 from wheat confer drought and/or heat resistance in Arabidopsis. BMC Plant Biol. 2016, 16, 116. [Google Scholar] [CrossRef] [PubMed]
  137. Wang, C.T.; Ru, J.N.; Liu, Y.W.; Yang, J.F.; Li, M.; Xu, Z.S.; Fu, J.D. The Maize WRKY Transcription Factor ZmWRKY40 Confers Drought Resistance in Transgenic Arabidopsis. Int. J. Mol. Sci. 2018, 19, 2580. [Google Scholar] [CrossRef] [PubMed]
  138. Lim, C.; Kang, K.; Shim, Y.; Yoo, S.C.; Paek, N.C. Inactivating transcription factor OsWRKY5 enhances drought tolerance through abscisic acid signaling pathways. Plant Physiol. 2022, 188, 1900–1916. [Google Scholar] [CrossRef]
  139. Zhang, M.; Cao, J.; Zhang, T.; Xu, T.; Yang, L.; Li, X.; Ji, F.; Gao, Y.; Ali, S.; Zhang, Q.; et al. A Putative Plasma Membrane Na+/H+ Antiporter GmSOS1 Is Critical for Salt Stress Tolerance in Glycine max. Front. Plant Sci. 2022, 13, 870695. [Google Scholar] [CrossRef]
  140. Feki, K.; Quintero, F.J.; Khoudi, H.; Leidi, E.O.; Masmoudi, K.; Pardo, J.M.; Brini, F. A constitutively active form of a durum wheat Na+/H+ antiporter SOS1 confers high salt tolerance to transgenic Arabidopsis. Plant Cell Rep. 2014, 33, 277–288. [Google Scholar] [CrossRef]
  141. Kim, S.T.; Choi, M.; Bae, S.J.; Kim, J.S. The Functional Association of ACQOS/VICTR with Salt Stress Resistance in Arabidopsis thaliana Was Confirmed by CRISPR-Mediated Mutagenesis. Int. J. Mol. Sci. 2021, 22, 11389. [Google Scholar] [CrossRef]
  142. Lee, H.M.; Choi, J.W.; Choi, M.S. Role of Nitric Oxide and Protein S-Nitrosylation in Ischemia-Reperfusion Injury. Antioxidants 2022, 11, 57. [Google Scholar] [CrossRef] [PubMed]
  143. Zheng, M.; Lin, J.; Liu, X.; Chu, W.; Li, J.; Gao, Y.; An, K.; Song, W.; Xin, M.; Yao, Y.; et al. Histone acetyltransferase TaHAG1 acts as a crucial regulator to strengthen salt tolerance of hexaploid wheat. Plant Physiol. 2021, 186, 1951–1969. [Google Scholar] [CrossRef] [PubMed]
  144. Alam, M.S.; Kong, J.; Tao, R.; Ahmed, T.; Alamin, M.; Alotaibi, S.S.; Abdelsalam, N.R.; Xu, J.H. CRISPR/Cas9 Mediated Knockout of the OsbHLH024 Transcription Factor Improves Salt Stress Resistance in Rice (Oryza sativa L.). Plants 2022, 11, 1184. [Google Scholar] [CrossRef] [PubMed]
  145. Uddin, M.I.; Qi, Y.; Satoshi, Y.; Izumi, S.; Deng, X.P.; Soo Sang, K.; Hironori, K.; Kiyoshi, T. Overexpression of a new rice vacuolar antiporter regulating protein OsARP improves salt tolerance in tobacco. Plant Cell Physiol. 2008, 49, 880–890. [Google Scholar] [CrossRef]
  146. Liu, S.; Zheng, L.; Xue, Y.; Qian, Z.; Lu, W.; Shou, H. Overexpression of OsVP1 and OsNHX1 increases tolerance to drought and salinity in rice. J. Plant Biol. 2010, 53, 444–452. [Google Scholar] [CrossRef]
  147. Fukuda, A.; Nakamura, A.; Hara, N.; Toki, S.; Tanaka, Y. Molecular and functional analyses of rice NHX-type Na+/H+ antiporter genes. Planta 2011, 233, 175–188. [Google Scholar] [CrossRef]
  148. Wang, R.; Jing, W.; Xiao, L.; Jin, Y.; Shen, L.; Zhang, W. The rice high-affinity potassium Transporter1;1 is involved in salt tolerance and regulated by an MYB-type transcription factor. Plant Physiol. 2015, 168, 1076–1090. [Google Scholar] [CrossRef]
  149. Suzuki, K.; Yamaji, N.; Costa, A.; Okuma, E.; Kobayashi, N.I.; Kashiwagi, T.; Katsuhara, M.; Wang, C.; Tanoi, K.; Murata, Y.; et al. OsHKT1;4-mediated Na(+) transport in stems contributes to Na(+) exclusion from leaf blades of rice at the reproductive growth stage upon salt stress. BMC Plant Biol. 2016, 16, 22. [Google Scholar] [CrossRef]
  150. Zhang, M.; Cao, Y.; Wang, Z.; Wang, Z.Q.; Shi, J.; Liang, X.; Song, W.; Chen, Q.; Lai, J.; Jiang, C. A retrotransposon in an HKT1 family sodium transporter causes variation of leaf Na+ exclusion and salt tolerance in maize. New Phytol. 2017, 217, 1161–1176. [Google Scholar] [CrossRef]
  151. Vu, T.V.; Sivankalyani, V.; Kim, E.J.; Doan, D.T.H.; Tran, M.T.; Kim, J.; Sung, Y.W.; Park, M.; Kang, Y.J.; Kim, J.Y. Highly efficient homology-directed repair using CRISPR/Cpf1-geminiviral replicon in Tomato. Plant Biotechnol. J. 2020, 18, 2133–2143. [Google Scholar] [CrossRef] [PubMed]
  152. Cui, L.G.; Shan, J.X.; Shi, M.; Gao, J.P.; Lin, H.X. DCA1 acts as a transcriptional co-activator of DST and contributes to drought and salt tolerance in rice. PLoS Genet. 2015, 11, e1005617. [Google Scholar] [CrossRef]
  153. Huang, X.Y.; Chao, D.Y.; Gao, J.P.; Zhu, M.Z.; Shi, M.; Lin, H.X. A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes Dev. 2009, 23, 1805–1817. [Google Scholar] [CrossRef] [PubMed]
  154. Huang, S.; Spielmeyer, W.; Lagudah, E.S.; Munns, R. Comparative mapping of HKT genes in wheat, barley, and rice, key determinants of Na+ transport, and salt tolerance. J. Exp. Bot. 2008, 59, 927–937. [Google Scholar] [CrossRef] [PubMed]
  155. Hasthanasombut, S.; Supaibulwatana, K.; Mii, M.; Nakamura, I. Genetic manipulation of Japonica rice using the OsBADH1 gene from Indica rice to improve salinity tolerance. Plant Cell Tissue Organ Cult. 2011, 104, 79–89. [Google Scholar] [CrossRef]
  156. Song, S.Y.; Chen, Y.; Chen, J.; Dai, X.Y.; Zhang, W.H. Physiological mechanisms underlying OsNAC5-dependent tolerance of rice plants to abiotic stress. Planta 2011, 234, 331–345. [Google Scholar] [CrossRef]
  157. Liu, C.T.; Mao, B.G.; Ou, S.J.; Wang, W.; Liu, L.C.; Wu, Y.B.; Chu, C.C.; Wang, X.P. OsbZIP71, a bZIP transcription factor, confers salinity and drought tolerance in rice. Plant Mol. Biol. 2014, 84, 19–36. [Google Scholar] [CrossRef]
  158. Takagi, H.; Tamiru, M.; Abe, A.; Yoshida, K.; Uemura, A.; Yaegashi, H.; Obara, T.; Oikawa, K.; Utsushi, H.; Kanzaki, E.; et al. MutMap accelerates breeding of a salt-tolerant rice cultivar. Nat. Biotechnol. 2015, 33, 445–449. [Google Scholar] [CrossRef]
  159. Zhang, A.; Liu, Y.; Wang, F.; Li, T.; Chen, Z.; Kong, D.; Bi, J.; Zhang, F.; Luo, X.; Wang, J.; et al. Enhanced rice salinity tolerance via CRISPR/Cas9-targeted mutagenesis of the OsRR22 gene. Mol. Breed. 2019, 39, 47. [Google Scholar] [CrossRef]
  160. Yuan, X.; Sun, H.; Tang, Z.; Tang, H.; Zhang, H.; Huang, J. A novel little membrane protein confers salt tolerance in rice (Oryza sativa L.). Plant Mol. Biol. Report. 2016, 34, 524–532. [Google Scholar] [CrossRef]
  161. Zeng, D.D.; Yang, C.C.; Qin, R.; Alamin, M.; Yue, E.K.; Jin, X.L.; Shi, C.H. A guanine insert in OsBBS1 leads to early leaf senescence and salt stress sensitivity in rice (Oryza sativa L.). Plant Cell Rep. 2018, 37, 933–946. [Google Scholar] [CrossRef] [PubMed]
  162. Yin, W.; Xiao, Y.; Niu, M.; Meng, W.; Li, L.; Zhang, X.; Liu, D.; Zhang, G.; Qian, Y.; Sun, Z.; et al. ARGONAUTE2 Enhances Grain Length and Salt Tolerance by Activating BIG GRAIN3 to Modulate Cytokinin Distribution in Rice. Plant Cell 2020, 32, 2292–2306. [Google Scholar] [CrossRef]
  163. Duan, Y.B.; Li, J.; Qin, R.Y.; Xu, R.F.; Li, H.; Yang, Y.C.; Ma, H.; Li, L.; Wei, P.C.; Yang, J.B. Identification of a regulatory element responsible for salt induction of rice OsRAV2 through ex situ and in situ promoter analysis. Plant Mol. Biol. 2016, 90, 49–62. [Google Scholar] [CrossRef] [PubMed]
  164. Qin, H.; Wang, J.; Chen, X.; Wang, F.; Peng, P.; Zhou, Y.; Miao, Y.; Zhang, Y.; Gao, Y.; Qi, Y.; et al. Rice OsDOF15 contributes to ethylene-inhibited primary root elongation under salt stress. New Phytol. 2019, 223, 798–813. [Google Scholar] [CrossRef] [PubMed]
  165. Alfatih, A.; Wu, J.; Jan, S.U.; Zhang, Z.S.; Xia, J.Q.; Xiang, C.B. Loss of rice PARAQUAT TOLERANCE 3 confers enhanced resistance to abiotic stresses and increases grain yield in field. Plant Cell Environ. 2020, 43, 2743–2754. [Google Scholar] [CrossRef] [PubMed]
  166. Yue, E.; Cao, H.; Liu, B. OsmiR535, a Potential Genetic Editing Target for Drought and Salinity Stress Tolerance in Oryza sativa. Plants 2020, 9, 1337. [Google Scholar] [CrossRef]
  167. Zhang, X.; Long, Y.; Huang, J.; Xia, J. OsNAC45 is Involved in ABA Response and Salt Tolerance in Rice. Rice 2020, 13, 79. [Google Scholar] [CrossRef] [PubMed]
  168. Srivastava, A.K.; Zhang, C.; Caine, R.S.; Gray, J.; Sadanandom, A. Rice SUMO protease Overly Tolerant to Salt 1 targets the transcription factor, OsbZIP23 to promote drought tolerance in rice. Plant J. 2017, 92, 1031–1043. [Google Scholar] [CrossRef]
  169. Mo, W.; Tang, W.; Du, Y.; Jing, Y.; Bu, Q.; Lin, R. PHYTOCHROME-INTERACTING FACTOR-LIKE14 and SLENDER RICE1 Interaction Controls Seedling Growth under Salt Stress. Plant Physiol. 2020, 184, 506–517. [Google Scholar] [CrossRef]
  170. Bo, C.; Chen, H.; Luo, G.; Li, W.; Zhang, X.; Ma, Q.; Cheng, B.; Cai, R. Maize WRKY114 gene negatively regulates salt-stress tolerance in transgenic rice. Plant Cell Rep. 2020, 39, 135–148. [Google Scholar] [CrossRef]
  171. Wang, W.C.; Lin, T.C.; Kieber, J.; Tsai, Y.C. Response Regulators 9 and 10 Negatively Regulate Salinity Tolerance in Rice. Plant Cell Physiol. 2019, 60, 2549–2563. [Google Scholar] [CrossRef]
  172. Bouzroud, S.; Gasparini, K.; Hu, G.; Barbosa, M.A.M.; Rosa, B.L.; Fahr, M.; Zouine, M. Down Regulation and Loss of Auxin Response Factor 4 Function Using CRISPR/Cas9 Alters Plant Growth, Stomatal Function and Improves Tomato Tolerance to Salinity and Osmotic Stress. Genes 2020, 11, 272. [Google Scholar] [CrossRef]
  173. Nandy, S.; Pathak, B.; Zhao, S.; Srivastava, V. Heat-shock-inducible CRISPR/Cas9 system generates heritable mutations in rice. Plant Direct. 2019, 3, e00145. [Google Scholar] [CrossRef] [PubMed]
  174. Klap, C.; Yeshayahou, E.; Bolger, A.M.; Arazi, T.; Gupta, S.K.; Shabtai, S.; Usadel, B.; Salts, Y.; Barg, R. Tomato facultative parthenocarpy results from SlAGAMOUS-LIKE 6 loss of function. Plant Biotechnol. J. 2017, 15, 634–647. [Google Scholar] [CrossRef] [PubMed]
  175. Hu, Z.; Li, J.; Ding, S. The protein kinase CPK28 phosphorylates ascorbate peroxidase and enhances thermotolerance in Tomato. Plant Physiol. 2021, 186, 1302–1317. [Google Scholar] [CrossRef]
  176. Yin, Y.; Qin, K.; Song, X.; Zhang, Q.; Zhou, Y.; Xia, X.; Yu, J. BZR1 Transcription Factor Regulates Heat Stress Tolerance Through FERONIA Receptor-Like Kinase-Mediated Reactive Oxygen Species Signaling in Tomato. Plant Cell Physiol. 2018, 59, 2239–2254. [Google Scholar] [CrossRef]
  177. Qiu, Z.; Kang, S.; He, L.; Zhao, J.; Zhang, S.; Hu, J.; Zeng, D.; Zhang, G.; Dong, G.; Gao, Z.; et al. The newly identified heat-stress sensitive albino 1 gene affects chloroplast development in rice. Plant Sci. 2018, 267, 168–179. [Google Scholar] [CrossRef] [PubMed]
  178. Li, J.; Zhang, H.; Si, X.; Tian, Y.; Chen, K.; Liu, J.; Chen, H.; Gao, C. Generation of thermosensitive male-sterile maize by targeted knockout of the ZmTMS5 gene. J. Genet. Genom. 2017, 44, 465–468. [Google Scholar] [CrossRef] [PubMed]
  179. Bertier, L.D.; Ron, M.; Huo, H.; Bradford, K.J.; Britt, A.B.; Michelmore, R.W. High-Resolution Analysis of the Efficiency, Heritability, and Editing Outcomes of CRISPR/Cas9-Induced Modifications of NCED4 in Lettuce (Lactuca sativa). G3 2018, 8, 1513–1521. [Google Scholar] [CrossRef]
  180. Wang, B.; Zhong, Z.; Wang, X.; Han, X.; Yu, D.; Wang, C.; Song, W.; Zheng, X.; Chen, C.; Zhang, Y. Knockout of the OsNAC006 Transcription Factor Causes Drought and Heat Sensitivity in Rice. Int. J. Mol. Sci. 2020, 21, 2288. [Google Scholar] [CrossRef]
  181. Chinnusamy, V.; Schumaker, K.; Zhu, J.K. Molecular genetic perspectives on cross-talk and specificity in abiotic stress signaling in plants. J. Exp. Bot. 2004, 55, 225–236. [Google Scholar] [CrossRef]
  182. Kasuga, M.; Miura, S.; Shinozaki, K.; Yamaguchi-Shinozaki, K. A combination of the Arabidopsis DREB1A gene and stress-inducible rd29A promoter improved drought- and low-temperature stress tolerance in tobacco by gene transfer. Plant Cell Physiol. 2004, 45, 346–350. [Google Scholar] [CrossRef] [PubMed]
  183. Chen, L.; Yang, Y.; Liu, C.; Zheng, Y.; Xu, M.; Wu, N.; Sheng, J.; Shen, L. Characterization of WRKY transcription factors in Solanum lycopersicum reveals collinearity and their expression patterns under cold treatment. Biochem. Biophys. Res. Commun. 2015, 464, 962–968. [Google Scholar] [CrossRef] [PubMed]
  184. Zhang, Y.; Yu, H.; Yang, X.; Li, Q.; Ling, J.; Wang, H.; Gu, X.; Huang, S.; Jiang, W. CsWRKY46, a WRKY transcription factor from cucumber, confers cold resistance in transgenic-plant by regulating a set of cold-stress responsive genes in an ABA-dependent manner. Plant Physiol. Biochem. 2016, 108, 478–487. [Google Scholar] [CrossRef]
  185. Han, J.; Li, X.; Li, W.; Yang, Q.; Li, Z.; Cheng, Z.; Lv, L.; Zhang, L.; Han, D. Isolation and preliminary functional analysis of FvICE1, involved in cold and drought tolerance in Fragaria vesca through overexpression and CRISPR/Cas9 technologies. Plant Physiol. Biochem. 2023, 196, 270–280. [Google Scholar] [CrossRef]
  186. Li, R.; Zhang, L.; Wang, L.; Chen, L.; Zhao, R.; Sheng, J.; Shen, L. Reduction of Tomato-Plant Chilling Tolerance by CRISPR-Cas9-Mediated SlCBF1 Mutagenesis. J. Agric. Food Chem. 2018, 66, 9042–9051. [Google Scholar] [CrossRef] [PubMed]
  187. Kakeshpour, T.; Tamang, T.M.; Motolai, G.; Fleming, Z.W.; Park, J.E.; Wu, Q.; Park, S. CGFS-type glutaredoxin mutations reduce tolerance to multiple abiotic stresses in Tomato. Physiol Plant. 2021, 173, 1263–1279. [Google Scholar] [CrossRef] [PubMed]
  188. Shen, C.; Que, Z.; Xia, Y.; Tang, N.; Li, D.; He, R.; Cao, M. Knock out of the annexin gene OsAnn3 via CRISPR/Cas9-mediated genome editing decreased cold tolerance in rice. J. Plant Biol. 2017, 60, 539–547. [Google Scholar] [CrossRef]
  189. Lv, Y.; Yang, M.; Hu, D.; Yang, Z.; Ma, S.; Li, X.; Xiong, L. The OsMYB30 Transcription Factor Suppresses Cold Tolerance by Interacting with a JAZ Protein and Suppressing β-Amylase Expression. Plant Physiol. 2017, 173, 1475–1491. [Google Scholar] [CrossRef] [PubMed]
  190. Zeng, Y.; Wen, J.; Zhao, W.; Wang, Q.; Huang, W. Rational Improvement of Rice Yield and Cold Tolerance by Editing the Three Genes OsPIN5b, GS3, and OsMYB30 With the CRISPR-Cas9 System. Front. Plant Sci. 2020, 10, 1663. [Google Scholar] [CrossRef]
  191. Miao, C.; Xiao, L.; Hua, K.; Zou, C.; Zhao, Y.; Bressan, R.A.; Zhu, J.K. Mutations in a Subfamily of Abscisic Acid Receptor Genes Promote Rice Growth and Productivity. Proc. Natl. Acad. Sci. USA 2018, 115, 6058–6063. [Google Scholar] [CrossRef]
  192. Nawaz, G.; Han, Y.; Usman, B.; Liu, F.; Qin, B.; Li, R. Knockout of OsPRP1, a gene encoding proline-rich protein, confers enhanced cold sensitivity in rice (Oryza sativa L.) at the seedling stage. Biotech 2019, 9, 254. [Google Scholar] [CrossRef]
  193. Liu, X.; Lan, J.; Huang, Y.; Cao, P.; Zhou, C.; Ren, Y.; He, N.; Liu, S.; Tian, Y.; Nguyen, T.; et al. WSL5, a pentatricopeptide repeat protein, is essential for chloroplast biogenesis in rice under cold stress. J. Exp. Bot. 2018, 69, 3949–3961. [Google Scholar] [CrossRef]
  194. Molla, K.A.; Shih, J.; Yang, Y. Single-nucleotide editing for zebra3 and wsl5 phenotypes in rice using CRISPR/Cas9-mediated adenine base editors. aBiotech 2020, 1, 106–118. [Google Scholar] [CrossRef]
  195. Ramazan, A.K.N.; Najafi, F.; Rezaei, M. The influence of cadmium toxicity on some physiological parameters as affected by iron in rice (Oryza Sativa L.) plant. J. Plant Nutr. 2014, 37, 1202–1213. [Google Scholar] [CrossRef]
  196. Asgher, M.; Khan, M.I.; Anjum, N.A.; Khan, N.A. Minimising toxicity of cadmium in plants-role of plant growth regulators. Protoplasma 2015, 252, 399–413. [Google Scholar] [CrossRef] [PubMed]
  197. Chen, J.; Zou, W.; Meng, L.; Fan, X.; Xu, G.; Ye, G. Advances in the Uptake and Transport Mechanisms and QTLs Mapping of Cadmium in Rice. Int. J. Mol. Sci. 2019, 20, 3417. [Google Scholar] [CrossRef] [PubMed]
  198. Tang, L.; Mao, B.; Li, Y.; Lv, Q.; Zhang, L.; Chen, C.; He, H.; Wang, W.; Zeng, X.; Shao, Y. Knockout of OsNramp5 using the CRISPR/Cas9 system produces low Cd-accumulating indica rice without compromising yield. Sci. Rep. 2017, 7, 14438. [Google Scholar] [CrossRef] [PubMed]
  199. Nieves Cordones, M.; Mohamed, S.; Tanoi, K.; Kobayashi, N.I.; Takagi, K.; Vernet, A.; Guiderdoni, E.; Périn, C.; Sentenac, H.; Véry, A.A. Production of low-Cs+ rice plants by inactivation of the K+ transporter OsHAK1 with the CRISPR-Cas system. Plant J. 2017, 92, 43–56. [Google Scholar] [CrossRef] [PubMed]
  200. Mao, X.; Zheng, Y.; Xiao, K.; Wei, Y.; Zhu, Y.; Cai, Q.; Chen, L.; Xie, H.; Zhang, J. OsPRX2 contributes to stomatal closure and improves potassium deficiency tolerance in rice. Biochem. Biophys. Res. Commun. 2018, 495, 461–467. [Google Scholar] [CrossRef]
  201. Wang, F.Z.; Chen, M.X.; Yu, L.J.; Xie, L.J.; Yuan, L.B.; Qi, H.; Xiao, M.; Guo, W.; Chen, Z.; Yi, K.; et al. OsARM1, an R2R3 MYB Transcription Factor, Is Involved in Regulation of the Response to Arsenic Stress in Rice. Front. Plant Sci. 2017, 8, 1868. [Google Scholar] [CrossRef] [PubMed]
  202. Dong, H.; Huang, Y.; Wang, K. The Development of Herbicide Resistance Crop Plants Using CRISPR/Cas9-Mediated Gene Editing. Genes 2021, 12, 912. [Google Scholar] [CrossRef] [PubMed]
  203. Svitashev, S.; Young, J.K.; Schwartz, C.; Gao, H.; Falco, S.C.; Cigan, A.M. Targeted Mutagenesis, Precise Gene Editing, and Site-Specific Gene Insertion in Maize Using Cas9 and Guide RNA. Plant Physiol. 2015, 169, 931–945. [Google Scholar] [CrossRef] [PubMed]
  204. Sun, Y.; Zhang, X.; Wu, C.; He, Y.; Ma, Y.; Hou, H.; Guo, X.; Du, W.; Zhao, Y.; Xia, L. Engineering Herbicide-Resistant Rice Plants through CRISPR/Cas9-Mediated Homologous Recombination of Acetolactate Synthase. Mol. Plant. 2016, 9, 628–631. [Google Scholar] [CrossRef]
  205. Zhang, R.; Liu, J.; Chai, Z.; Chen, S.; Bai, Y.; Zong, Y.; Chen, K.; Li, J.; Jiang, L.; Gao, C. Generation of herbicide tolerance traits and a new selectable marker in wheat using base editing. Nat. Plants 2019, 5, 480–485. [Google Scholar] [CrossRef] [PubMed]
  206. Zhang, R.; Chen, S.; Meng, X.; Chai, Z.; Wang, D.; Yuan, Y.; Chen, K.; Jiang, L.; Li, J.; Gao, C. Generating broad-spectrum tolerance to ALS-inhibiting herbicides in rice by base editing. Sci. China Life Sci. 2021, 64, 1624–1633. [Google Scholar] [CrossRef] [PubMed]
  207. Li, Z.; Liu, Z.B.; Xing, A.; Moon, B.P.; Koellhoffer, J.P.; Huang, L.; Ward, R.T.; Clifton, E.; Falco, S.C.; Cigan, A.M. Cas9-Guide RNA Directed Genome Editing in Soybean. Plant Physiol. 2015, 169, 960–970. [Google Scholar] [CrossRef] [PubMed]
  208. Kuang, Y.; Li, S.; Ren, B.; Yan, F.; Spetz, C.; Li, X.; Zhou, X.; Zhou, H. Base-Editing-Mediated Artificial Evolution of OsALS1 In Planta to Develop Novel Herbicide-Tolerant Rice Germplasms. Mol. Plant. 2020, 13, 565–572. [Google Scholar] [CrossRef] [PubMed]
  209. Wang, F.; Xu, Y.; Li, W.; Chen, Z.; Wang, J.; Fan, F.; Tao, Y.; Jiang, Y.; Zhu, Q.H.; Yang, J. Creating a novel herbicide-tolerance OsALS allele using CRISPR/Cas9-mediated gene editing. Crop. J. 2021, 9, 305–312. [Google Scholar] [CrossRef]
  210. Veillet, F.; Perrot, L.; Chauvin, L.; Kermarrec, M.P.; Guyon-Debast, A.; Chauvin, J.E.; Nogué, F.; Mazier, M. Transgene-Free Genome Editing in Tomato and Potato Plants Using Agrobacterium-Mediated Delivery of a CRISPR/Cas9 Cytidine Base Editor. Int. J. Mol Sci. 2019, 20, 402. [Google Scholar] [CrossRef]
  211. Shimatani, Z.; Kashojiya, S.; Takayama, M.; Terada, R.; Arazoe, T.; Ishii, H.; Teramura, H.; Yamamoto, T.; Komatsu, H.; Miura, K.; et al. Targeted base editing in rice and Tomato using a CRISPR-Cas9 cytidine deaminase fusion. Nat. Biotechnol. 2017, 35, 441–443. [Google Scholar] [CrossRef]
  212. Li, J.; Meng, X.; Zong, Y.; Chen, K.; Zhang, H.; Liu, J.; Li, J.; Gao, C. Gene replacements and insertions in rice by intron targeting using CRISPR-Cas9. Nat. Plants 2016, 2, 16139. [Google Scholar] [CrossRef]
  213. Sony, S.K.; Kaul, T.; Motelb, K.F.A.; Thangaraj, A.; Bharti, J.; Kaul, R.; Verma, R.; Nehra, M. CRISPR/Cas9-mediated homology donor repair base editing confers glyphosate resistance to rice (Oryza sativa L.). Front. Plant Sci. 2023, 14, 1122926. [Google Scholar] [CrossRef] [PubMed]
  214. Lu, Y.; Wang, J.; Chen, B.; Mo, S.; Lian, L.; Luo, Y.; Ding, D.; Ding, Y.; Cao, Q.; Li, Y.; et al. A donor-DNA-free CRISPR/Cas-based approach to gene knock-up in rice. Nat. Plants. 2021, 7, 1445–1452. [Google Scholar] [CrossRef] [PubMed]
  215. Yang, S.H.; Kim, E.; Park, H.; Koo, Y. Selection of the high efficient sgRNA for CRISPR-Cas9 to edit herbicide related genes, PDS, ALS, And EPSPS in Tomato. Appl. Biol. Chem. 2022, 65, 13. [Google Scholar] [CrossRef]
  216. Xu, R.; Li, H.; Qin, R.; Wang, L.; Li, L.; Wei, P.; Yang, J. Gene targeting using the Agrobacterium tumefaciens mediated CRISPR-Cas system in rice. Rice 2014, 7, 5. [Google Scholar] [CrossRef] [PubMed]
  217. Liu, L.; Kuang, Y.; Yan, F.; Li, S.; Ren, B.; Gosavi, G.; Spetz, C.; Li, X.; Wang, X.; Zhou, X.; et al. Developing a novel artificial rice germplasm for dinitroaniline herbicide resistance by base editing of OsTubA2. Plant Biotechnol. J. 2021, 19, 5–7. [Google Scholar] [CrossRef]
Figure 1. Schematic illustration of the mechanism of genome editing using CRISPR/Cas9 system. The single guide RNA (sgRNA)/CRISPR-associated protein 9 (Cas9) complex binds to the target site at a complementary region of the genomic DNA. The protospacer adjacent motif (PAM) is recognized by Cas9 nuclease, which introduces the double-stranded breaks (DSBs) within the target DNA at a site three base pair upstream to the PAM. Double-stranded breaks (DSBs) of the target DNA are repaired by non-homologous end joining (NHEJ), resulting InDel mutations (insertion or deletion) or homology-directed repair (HDR) in the presence of a donor DNA, resulting in precise gene editing.
Figure 1. Schematic illustration of the mechanism of genome editing using CRISPR/Cas9 system. The single guide RNA (sgRNA)/CRISPR-associated protein 9 (Cas9) complex binds to the target site at a complementary region of the genomic DNA. The protospacer adjacent motif (PAM) is recognized by Cas9 nuclease, which introduces the double-stranded breaks (DSBs) within the target DNA at a site three base pair upstream to the PAM. Double-stranded breaks (DSBs) of the target DNA are repaired by non-homologous end joining (NHEJ), resulting InDel mutations (insertion or deletion) or homology-directed repair (HDR) in the presence of a donor DNA, resulting in precise gene editing.
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Table 1. Type of protospacer adjacent motifs (PAMs) sequences used in CRISPR/Cas genome editing system (adapted from [96]).
Table 1. Type of protospacer adjacent motifs (PAMs) sequences used in CRISPR/Cas genome editing system (adapted from [96]).
NameCasResourcesPAM SequencePAM LocationReference
SpCas9Cas9Streptococcus pyogenesNGG3′[84]
St1Cas9Cas9Streptococcus thermophilusNNAGAAW or 3′[86]
NGGNG
SaCas9Cas9Streptococcus aureusNNGRRT3′[87]
NmCas9Cas9Neisseria meningitidisNNNNGATT3′[97]
FnCas9Cas9Francisella novicidaNGG3′[98]
CjCas9Cas9Campylobacter jejuniNNNNRYAC3′[99]
AsCas12aCas12a(cpf1)Acidaminococcus sp.TTTV5′[25]
LbCas12aCas12a(cpf1)Lachnospiraceae bacteriumTTTV5′[25]
FnCas12aCas12a(cpf1)Francisella novicidaTTTN or YTN5′[25]
LsCas13Cas13(C2c2)Leptotrichia shahii [100]
Cas14Cas14Archaea [101]
FnCas9 variantCas9Modified FnCas9YG3′[98]
Modified SpCas9Cas9Engineered SpCas9NGA or NAG3′[102]
SaCas9-KKHCas9Engineered SaCas9NNNRRT3′[88]
SpCas9-HFCas9Engineered SpCas9NGG3′[89]
eSpCas9Cas9Engineered SpCas9NGG3′[90]
SpCas9-NGCas9Engineered SpCas9NG3′[85]
Sniper-Cas9Cas9Engineered SpCas9NGG3′[91]
evoCas9Cas9Mutated SpCas9NGG3′[92]
HypaCas9Cas9Mutated SpCas9-HFNGG3′[93]
Cas9-NRNHCas9Engineered SpCas9NRNH3′[94]
SpGCas9Engineered SpCas9NGN3′[95]
SpRYCas9Engineered SpCas9NRN or NYN3′[95]
“N” is any nucleotide [“A”, “T”, “G’, “C”]. “R” is “A” or “G”. “H” is “A”, “C” or “T”. “Y” is “C” or “T”. “W” is “A” or “T”. Cas13 targets RNA sequences instead of DNA; Cas14 targets single-stranded DNA (ssDNAs) instead of double-stranded DNA (dsDNAs) and does not require a Protospacer-Adjacent Motif (PAM). Note: Adapted from Zhang et al. [96].
Table 2. Computational tools for designing guide RNA 9 (sgRNA).
Table 2. Computational tools for designing guide RNA 9 (sgRNA).
ToolOrganismMajor FeatureWeblink
CHOPCHOP>100 species, including plants Provides several predictive models and primers. Visualizing the genomic location of genes and targets [103].https://chopchop.cbu.uib.no/, accessed on 23 August 2023
Cas-OFFinder>100 species, including plants Searches potential off-target sites [104].http://www.rgenome.net/cas-offinder/, accessed on 23 August 2023
CCTop>100 speciesPredictes off-target impacts and sgRNA efficiency using CRISPRater with custom in vitro transcription. Searching for single and multiple queries [105].https://cctop.cos.uni-heidelberg.de/, accessed on 23 August 2023
CRISTA>100 speciesDetectes off-target, providing machine learning framework, including DNA/RNA genomic information and RNA thermodynamics [106].https://crista.tau.ac.il/, accessed on 23 August 2023
CRISPR-GE>40 plant speciesPCR sequencing result analysis. Provides software toolkits, primer design, and on-target amplification [107].http://skl.scau.edu.cn/, accessed on 23 August 2023
CRISPR-P49 plant speciesProviding on-target and off-target scoring and gRNA sequence analysis [108]http://crispr.hzau.edu.cn/CRISPR2/, accessed on 23 August 2023
CRISPR-PLANT V27 plant speciesAllows selection of particular chromosomes and a resource for specific gRNA spacer sequences [109].http://omap.org/crispr2/, accessed on 23 August 2023
CRISPRlnc10 speciesProvides hundreds of lncRNAs and thousands of validated sgRNA [110].http://www.crisprlnc.org/, accessed on 23 August 2023
SNP-CRISPR9 plants and animal speciesDesigning sgRNAs (NGG and NAG) for targeting SNPs or Indels [111].https://www.flyrnai.org/tools/snp_crispr/web/, accessed on 23 August 2023
PnB DesignerO. sativa, V. viniferaDesigning sgRNAs for base editors and pegRNAs for prime editors [112].https://fgcz-shiny.uzh.ch/PnBDesigner/, accessed on 23 August 2023
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Joshi, A.; Yang, S.-Y.; Song, H.-G.; Min, J.; Lee, J.-H. Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress. Biology 2023, 12, 1400. https://doi.org/10.3390/biology12111400

AMA Style

Joshi A, Yang S-Y, Song H-G, Min J, Lee J-H. Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress. Biology. 2023; 12(11):1400. https://doi.org/10.3390/biology12111400

Chicago/Turabian Style

Joshi, Alpana, Seo-Yeon Yang, Hyung-Geun Song, Jiho Min, and Ji-Hoon Lee. 2023. "Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress" Biology 12, no. 11: 1400. https://doi.org/10.3390/biology12111400

APA Style

Joshi, A., Yang, S. -Y., Song, H. -G., Min, J., & Lee, J. -H. (2023). Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress. Biology, 12(11), 1400. https://doi.org/10.3390/biology12111400

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