*2.3. Heavy Metal Stress*

Heavy metal poses a global concern because of its significant technological implications in several industrial processes and applications. Different heavy metals from these sectors that severely contaminate wastewater have numerous long-term ecological and biological harmful impacts [116]. The heavy metals in this wastewater are hazardous, and if this discharged water is used for irrigation purposes, it disturbs the biological balance of the soil and the plants that are grown there. Since heavy metals are naturally prevalent in the earth's crust, they can be found in both polluted and unpolluted soils. Synthetic fertilizers, contaminated sewage/sludge, manure, and mining and industrial operations can all cause heavy metals to emerge in agricultural soil [117]. When sewage water is added to the soil, plant growth may increase, but it may also include toxic substances that

may threaten crops and the food chain. Many heavy metals, including Mo, Fe, Ni, Cu, Mn, and Zn, are advantageous or necessary for plant growth in low concentrations, but in high concentrations, they are all toxic [117]. In particular, a heavy dosage of these metals may cause oxidative stress, inhibit root elongation, displace other essential metals in a plant's enzymes, introduce pigments that cause the function of many metabolic processes to be disrupted, and ultimately compromise the yield and growth [118–120]. Heavy metals are proven to be toxic for oilseed crops in a variety of ways, and the symptoms vary greatly depending on the plant, metal, and its dose [121]. Oilseeds have the ability to reduce toxicity caused by metals optimizing their hemostasis. In oilseeds, heavy metals promote free radicals' generation, compete with metal cofactors of plant enzymes, alter enzyme action through binding sulfhydryl and N-containing groups, and cause the leakage of cellular contents through interactions with phospholipid-containing groups. *Brassica* cultivars have demonstrated decreased plant growth caused by Pb toxicity through altered cellular metabolism and nutrient uptake [122]. In fact, under Cr stress, such negative effects were also seen in shoot growth, leaf area, and length of leaf [123].

In order to facilitate MAS in RIL population (F6:8) obtained from a cross of AC Hime (high-Cd accumulation) × Westag-97 (low-Cd accumulation)' soybean, Jegadeesan et al. [100] conducted a study to develop markers for low-Cd accumulation. It was demonstrated by the use of 171 SSR markers that low-Cd accumulation in soybean seeds is regulated by a key gene (*Cda1*), with the low-accumulation allele being dominant. In soybean seeds, *Cda1* was found to be associated with 7 SSR markers, viz. SatK138, SatK139, SatK140, SatK147, SacK149, SaatK150, and SattK152. Each linked marker was assigned the same linkage group K. Markers SatK147, SacK149, and SattK152 distinguished studied genotypes with low and high Cd accumulation. Additionally, a significant QTL linked to a low Cd level in seeds was mapped to the same region at linkage group K as *Cda1*. This QTL was identified as the source of 57.3% of the phenotypic variation [100]. It has been experimentally proven that molecular markers can be used to locate particular loci regulating soybean resistance to Mn toxicity [101]. In a previous study, researchers demonstrated that RAPD markers could identify four QTLs, or hotspots, in an RIL population descended from the "Essex × Forrest" cross that may be responsible for resistance to manganese toxicity [124]. However, a study was conducted by using only high-quality scores generated by 240 microsatellite markers to detect the QTL that underlie tolerance to Mn toxicity in the F5-derived RIL population from "Essex × Forrest" (E × F, *n* = 100). The study was performed in order to rule out the errors occurring in RAPD maps and consequent errors in assigning QTL [101]. The necrosis of the leaves and roots served as markers. The findings showed that root necrosis at 7 days after treatment was strongly linked (*p* < 0.005, R2 = 20%) with the regions on linkage groups I (BARC Satt239), C2 (BARC Satt291), and G (OP OEO2); these three QTLs could explain about 58% of the total variation in root resistance to Mn toxicity. They also affirmed one of the previously identified RAPD-associated root necrosis QTLs, namely, sudden death syndrome QTL on LG (G). However, no QTL for leaf chlorosis were identified (*p* < 0.005), and none of the RAPD-associated with leaf chlorosis QTL could be confirmed [101].

#### *2.4. Flooding*

The main barrier to sustainable agriculture is flooding, and the plants exposed to flooding experience significant yield losses. Plants frequently encounter intermittent or persistent floods in their natural habitat. Physio-chemical soil characteristics that are crucial include redox potential, soil pH, and oxygen content, which are altered by flooding in a variety of ways. As a result, plants growing in wet soil endure stressful conditions, such as hypoxia (a lack of oxygen) or anoxia (the absence of O2). These low oxygen environments have a significant negative impact on plant growth, development, and survival. Metabolic changes under oxygen deprivation, including switching to anaerobic respiration and oxidative damage caused by reactive oxygen species (ROS), compromise membrane integrity, as well as damage photosystem II's efficiency, leading to a significant decline in net photosynthetic rates. To combat flooding-induced hypoxia/anoxia and oxidative stress, plants that endure waterlogging stress have mechanisms, such as the better availability of soluble sugars, formation of aerenchyma, enhanced glycolysis and fermentation activity, and the contribution of antioxidant defense mechanisms [125,126]. Many flooded plant species, including soybean, have shown evidence of developing adventitious roots [127–129]. Figure 4 illustrates a variety of responses and coping mechanisms used by plants to cope with flooding stress.

**Figure 4.** Response and the adaptive mechanisms of plants under flooding stress. [Abbreviations: *KCS*: 3-ketoacyl CoA synthase; *CYP*: cytochrome P450; *GPAT*: glycerol-3-phosphate acyltransferase; *ABC*: ATP-binding cassette; *LTP*: lipid transfer protein; *POD*: peroxidase; *RBOHB*: respiratory burst oxidase homolog B; *ERF*: ethylene response factor; *PIN*: PIN formed proteins; *SK1*: SNORKEL1; *SK2*: SNORKEL1; *LSD:* lesion simulating disease; *EDS*: enhanced disease susceptibility; *PAD*: phytoalexin deficient; *ARD:* acireductone dioxygenase; *MT*: metallothionein; *SOD:* superoxide dismutase; *POX*: peroxidise; *CAT*: catalase; *APX*: ascorbate peroxidise; *LDH*: lactate dehydrogenase; *HRE*: hypoxia response element; *SUB1A:* submergence tolerance regulator; *Amy3:* α-amylases; *ADH*: alcohol dehydrogenase.

It has now become simple for scientists to focus on altering or using the key genes that have been linked to flooding tolerance, to eventually develop new flood-tolerant plant varieties. Genomic areas linked to flooding tolerance can be detected using map-based gene cloning and QTL mapping. In the case of rice, it was able to introduce the *Sub1* gene to particular varieties by molecular-assisted backcrossing (MAB) to accommodate a different soil type and farmer preferences, as well as to add new variations through genetic engineering [126]. In an effort to map QTLs conferring flooding tolerance in soybean, two hundred and eight lines of two RIL populations descending from the 'Archer × Minsoy and Archer × Noir I' were placed in two different experimental setups: one under controlled condition (no flooding) and the other under flooding condition (waterlogging). Plants were subjected to 2 weeks of flooding at the early flowering stage in a water-logged setup, in order to identify the QTL linked to soybean flooding tolerance. Authors discovered a single QTL from the Archer parent, associated with marker Sat 064, that was responsible for the increased growth of plants (11 to 18%), as well as seed yield (47 to 180%), in a flood environment. Both RI populations included this highly significant QTL (*p* = 0.02–0.000001). Authors also reported that Sat\_064 QTL on Chromosome 18 was distinctively linked with flooding tolerance and was not linked with normal plant length, maturity, or seed yield. Although the Rps4 gene and Sat 064-QTL are co-localized for resistance to *Phytophthora*

*sojae* resistance, the donor parent Archer lacks the Rps4 resistance allele, proving that Sat 064-QTL is exclusive for flooding-stress tolerance [102]. Further, this QTL was verified in NILs at the F6 generation descended from heterogeneous inbred families [105]. Tolerant NILs produced around 60.9% greater yields under stress-free conditions compared to the yield of sensitive NILs (32.6%) under the same environment. Using bulked segregant analysis (BSA), as well as partial linkage mapping, two more QTLs concerning floodingtolerance traits were also identified and were found to be linked to markers Satt385 on Chromosome 5 and Satt 269 on Chromosome 13 [106]. The advantageous alleles of these two QTLs came from Archer.

In another investigation, 60 RILs of soybean were derived from cross 'Misuzudaizu (flooding tolerant cultivar) × Moshidou Gong-503 (flooding sensitive cultivar)' in order to study the genetics of tolerance to flooding stress at early vegetative stage. The plants were grown in pots and were subjected to flooding treatment at the two-leaf stage for 3 weeks. Pots were then put back in the greenhouse to mature there. The experiment was conducted for two consecutive years. In 2002, three QTLs for flooding tolerance, *ft1* to *ft3*, were identified, employing 360 genetic markers. Four other QTLs, numbered *ft4* to *ft7*, were discovered in 2003 in addition to the *ft1* (linkage group C2), which was reproducible. In both years, *ft1* possessed a high LOD (logarithm of the odds; relative probability that two loci are linked) score (15.41 and 7.57) and contributed 49.2% and 30.5%, respectively, of the overall variance. At a location identical to *ft1*, a major QTL for days to blooming was seen across all treatments and years [103]. It was further observed that the main QTL caused a prolonged recovery period prior to the reproductive stage by delayed flowering eventually resulting in a higher yield under stress condition. Using F7 RILs originating from cross 'S99-2281 × PI-408105A' at an early reproductive stage, two QTLs were recently identified and mapped on Chromosome 11 (FTS-11), as well as 13 (FTS-13); these QTLs were related to flood injury score and flood yield index. The significant QTL FTS-13 was reported to be linked to partial resistance to *P. sojae*, with an R2 of up to 18.3%, observed at several locations and years [104]. This provides definite evidence of the link between soybean flooding tolerance and *P. sojae* resistance. It implies that adding flooding-tolerance characteristics would boost resistance to rots caused by fungi, such as *P. sojae* [104]. The University of Missouri developed three improved germplasm lines of soybean for flood-tolerance through MAS. Under non-stress conditions, these germplasms have yielded a potential of 90% of commercial checks, and in severe flood condition, they were found to produce higher yield of 0.7–1.0 tonnes/hectare than commercial checks [130]. Dhungana et al. [115] reported QTLs linked with flooding stress at the V1–V2 stage of soybean. In this study, a RIL population derived from crossing a drought-susceptible (NTS116) and drought-tolerant (Danbaekkong) soybean cultivar was investigated. Based on composite interval mapping technique, they identified 10 QTLs associated with flood tolerance at the V1–V2 stage of soybean that possibly explained up to 30.7% phenotypic variations and can eventually be instrumental in soybean improvement programs. To summarize, marker-assisted mapping has been successful to some extent in identifying QTLs associated with flooding tolerance in oilseed crops, but more efforts are required to identify major QTLs explaining big phenotypic variance in large populations.

#### *2.5. Cold Stress*

Cold stress, which may include chilling temperatures (below 20 ◦C) and/or freezing temperatures (below 0 ◦C), has a severe influence on plant growth and development and greatly impedes plant spatial dispersion and agricultural production. Cold stress may be generated by either chilling (below 20 ◦C) or freezing (below 0 ◦C) conditions. Cold stress may also be caused by chilling temperatures (temperatures below 20 degrees Celsius) or freezing temperatures (temperatures below 0 degrees Celsius). It directly inhibits metabolic processes and has indirect effects in the form of cold-induced osmotic (freezing-induced cellular dehydration and chilling-induced reduction of water absorption), oxidative, and other stresses. Cold stress prevents plants from fully expressing their genetic potential by impeding metabolic activity first hand, while other stressors only do so indirectly. The great majority of plants that can thrive in temperate climates do so due to a process known as cold acclimation, which allows them to gain the capacity to endure temperatures as low as freezing.

The SSR, SNP, and EST markers have been successfully employed in achieving cold tolerance in plants [131,132]. Zhang et al. [133], Shinozaki et al. [134], and Lata and Prasad [135] identified the genes that play critical roles in the process by which plants develop tolerance to cold and osmotic stress. As a consequence of these investigations, as well as the use of molecular markers to build high-density physical and genetic maps of new genes, it is now feasible to enhance genetic diversity for desirable attributes, such as the ability to respond to cold stress. This is made feasible by the fact that molecular markers can now generate high-density maps of new genes [136]. At present, multiple genomics approaches are being employed to create new data through the utilization of genetic maps obtained from diverse *Brassica* and *Arabidopsis* species [137]. The accumulation of expressed sequence tags and single-nucleotide polymorphisms in *Brassica* species is generating critical information on genome polymorphism, as well as sequencing data for all stress-related traits.

Transcriptome adjustments from *Arabidopsis* have been exploited to discover genes related to cold treatment and other forms of stress. According to the findings, thirty percent of transcriptomes indicated sensitivity to regulation to common stress, with the majority clearly responding to particular stimuli [138]. In the first organ-specific cDNA fluorescence microarray investigation to evaluate coordinated transcriptional shifts in response to chilling and salinity stress in cultivated sunflower, Fernandez et al. [108] reported that eighty genes were found to be candidate genes for the early response of sunflowers to low temperature and salt stress. Microarray profiling of chilled and NaCltreated sunflower leaves by the authors revealed dynamic shifts in the abundance of transcripts, including transcription factors, proteins involved in defense and stress, and effectors of homeostasis, all of which emphasize the complexity of both stress responses.

In a similar study, nylon microarrays with more than 8000 putative unigenes was performed to evaluate the transcriptional profiles of two accessions of sunflower viz. Santiago II and Melody, with differential growth rate ability under low temperatures. The results showed that, between the plants developed at low temperature (15 ◦C and 7 ◦C) and the corresponding control plants at two-leaf and four-leaf stages, 108 cDNA clones were found to be differentially expressed across the two genotypes with a *p* value of 10−<sup>3</sup> [107]. Around 90% of these genes, including those involved in protein biosynthesis, signal transduction, and energy, as well as carbohydrate metabolism and transport, were downregulated. Only four genes were identified as being differentially expressed in both genotypes, which further suggests that the response of sunflower plants to these temperature regimes is driven by identical genetic processes. The authors came to the conclusion that the vulnerability of sunflower to cold stress may be caused by the downregulation and/or non-induction of genes playing a vital role in cold tolerance.

Another research group utilized 104 RILs (F6-derived lines) of soybean resulting from a cross 'Hayahikari (chilling-tolerant cultivar) × Toyomusume (chilling-sensitive cultivar)' in order to identify the QTL linked to freezing tolerance during reproductive stage. This was done to identify the QTL linked with soybean frost resistance during reproductive development. After conducting a genotypic evaluation of the population with 181 markers and correlating genotypic data with seed yield in two different conditions, i.e., chilling and optimal temperature, the researchers were able to identify three QTLs related to freezing tolerance on the basis of seed-yielding ability. These quantitative trait loci (QTLs) were essential for the plant's ability to withstand cold temperatures. Among these, qCTTSW1 and qCTTSW2 were found to be in close proximity to a QTL for flowering time. It was observed that qCTTSW2 interacted epistatically with a marker locus next to a second QTL for flowering time [109]. In fact, no significant QTL for cold tolerance was detected. An F2 generation descended from a cross 'Hayahikari' × 'RIL of

Hayahikari' demonstrated that qCTTSW1 was mostly independent of flowering time. The third quantitative trait locus, qCTTSW3, has been shown to influence chilling tolerance. Another research employed a BC2F3 population derived from Harosoy (donor parent) × Hongfeng 11 (recurrent parent) to screen soybeans during the germination stage for drought and low-temperature conditions [110]. This population was screened for lowtemperature and high-humidity circumstances. The objective of this study was to obtain a deeper knowledge of the genetic overlap between drought and low-temperature-tolerance QTLs in soybean during germination. There is a genetic overlap between drought and lowtemperature tolerance during germination, as indicated by the identification of twelve QTLs in soybean that were associated with both drought and low-temperature tolerance. This fact was corroborated by the ability of tolerant soybean to withstand both low temperatures and drought at the same time. On the other side, it was observed that 18 QTLs were associated with drought resistance and that 23 QTLs were associated with cold-temperature resistance. A study was conducted utilizing QTL analysis of seed-yielding capacity at low temperature in soybean, simulated climatic conditions at normal and low temperatures, and RILs obtained from the cross of two cultivars with differing chilling tolerances [111]. The aim of this study was to understand the genetic basis of freezing tolerance and to identify associated genomic regions.

In close proximity to marker Sat 162 at linkage group A2, a quantitative trait locus (QTL) with a substantial effect was identified (LOD more than 15, r2 larger than 0.3). This QTL was shown to be connected with the capacity to generate seeds only at low temperatures. A population of segregating varied inbred families that generated basically identical lineages gave further confirmation of the QTL's significance. This proof of ancestry was provided by the community of inbred families. It was further reported that the genomic region containing the QTL also influenced the node and pod numbers regardless of temperature condition, although this effect was not primarily associated with chilling tolerance. In summary, several studies successfully demonstrated the association of physiological traits with multiple QTLs (including major QTLs) in oilseed crops under chilling stress and can be a great boon for development of tolerant cultivars in the future.

### *2.6. Heat Stress*

One of the major abiotic factors that lower crop productivity is heat stress. More frequent heat waves are predicted to occur and with greater severity as a result of global warming, aggravating the existing conditions. Therefore, it is crucial to understand the molecular processes that increase crop plants' tolerance to heat, especially in their reproductive organs. For the manipulation and exploration of pertinent genes for application in crop development initiatives, precise molecular knowledge will be helpful. This can be accomplished by gaining an in-depth understanding of various plant responses to heat stress, deciphering mechanisms of heat tolerance, and developing potential interventions for improving heat tolerance. Reduced photosynthesis, increased photorespiration, decreased availability of water, loss of cell membrane integrity and function, generation of ROS, and many other detrimental impacts are all driven by heat stress. Plants deploy numerous defense mechanisms to combat heat stress, including the increased expression of different enzymatic and non-enzymatic antioxidants to scavenge ROS, maintaining membrane stability, the production of various compatible solutes and metabolites, and the activation of various signaling cascades (Figure 5). Understanding each of these mechanisms will enable us to develop transgenic, traditional, and molecular breeding methods to enhance plant heat tolerance [139]. Numerous studies have documented adverse impacts of high-temperature stress in oilseed crops, such as decreased pollen germination and pollen tube length, which led to pollen mortality and fruit setting in *Brassica* as a result of heat stress [140]., reduction in soybean yield at temperatures more than 26/20 ◦C [141], and a reduction in seed weight in soybean due to a rise in temperature from 30/25 ◦C (day/night) during the seed filling period [142].

**Figure 5.** Plant responses to heat stress.

Considering the uncontrolled nature of environmental factors and the influence of additional biotic stresses, selection for thermo-tolerance through conventional breeding could be extremely challenging. Better techniques are therefore required for carrying out more precise greenhouse tests. Over the past ten years, scientists have turned to various methods to find the genes and QTLs linked with heat stress tolerance. A foundation for identifying the precise chromosomal position of QTLs responsible for plant heat tolerance is currently being laid by breakthroughs in genotyping assays and marker identification [143].

In oilseed crops, several major or minor QTLs and related markers for heat tolerance have been identified, including in peanuts [144], sesame [145], and soybean [146]. Genome maps and molecular markers for major oilseed crops have been identified by many researchers [145,147–150]. Similar to this, various oilseed crops, including soybean, [151], rapeseed [152,153], cotton [154], sunflower [155], groundnut [156], and sesame [157], are using the genome-wide association mapping technique under heat stress. In a thermosensitive dominant genic male sterility (GMS)-based inbred line (TE5A), which originated through the spontaneous mutation of *Brassica napus*, Zeng et al. [112], reported the fine mapping of *BntsMs* (dominant thermo-sensitive GMS gene) using AFLP and intron polymorphism (IP) methodologies. The five AFLP markers associated with the *BntsMs* gene were found by the authors after screening with 1024 primer combinations; two of these markers were then transformed to SCAR markers. Two SCAR markers were found flanking the *BntsMs* gene at a distance of 3.5 and 4.8 cm after studying a sizable BC2 population of 700 recessive-fertility lines. Additionally, seven IP markers were also developed and used on the aforesaid population; two of these markers, IP004 and IP470, were placed at a distance of 0.3 and 0.2 cm, respectively, from the flanking region of the *BntsMs* gene.

#### **3. Advent of a New Era for Development of Molecular Markers**

With the advent of next-generation DNA sequencing technologies, including wholegenome sequencing (WGS), have opened new avenues for a comprehensive overview of the genetic diversity of oilseed crops and their genetic architecture in last decade. More recent advances involving expressed sequence tags (ESTs) also aided genome annotation and could further boost the molecular breeding program. Decent attempts that have been made in the genome sequencing of major oilseeds, such as sesame [158,159], safflower [160,161], rapeseed [162], mustard [163], sunflower [164,165], castor [166,167], flax seed [168], and peanut [169], have revolutionized the development of advanced co-dominant markers, such as SSR and SNPs, for the molecular mapping of abiotic resilience in oilseed crops. Soybean was sequenced through advanced high throughput technology in 2010 [170], and much progress has been made in this oilseed crop relevant to molecular breeding program for the achievement of abiotic stress tolerance and has been fairly covered in a review by Arya et al. [45]. WGS can provide detailed information about the genes associated with crucial and/or complex traits such as yield, oil content, and abiotic stress resistance, thus allowing for more precise selection of cultivars with desirable traits. Indeed, a novel method for SNP detection and mapping carry a huge potential to overcome the limitations of traditional MAS but are still far from being cost-efficient for the marker-assisted breeding of large populations [171]. Overall, WGS capacitates a system breeding approach with molecular markers that can be coupled with high-throughput phenotypic evaluation; such an approach has a potential to integrate gene function information with the improved field performance of oilseed crops.

#### **4. Conclusions**

Abiotic stresses have significant effect on the growth parameters of oilseed crops due to global climate change. Breeders of oilseeds should design their breeding programme to account for climate change and breed oilseed cultivars that are resilient to the changing climate. Developing reliable markers, which can be employed for different populations, could further enhance selection efficiency for breeding and could be a great milestone for breeding programs. The strong linkage of molecular markers to the desired attribute necessitates that they allow for preferred genotype selection. Abiotic stress tolerance in oilseed crops has also been established using emerging technologies like high-throughput marker systems and marker-based selection approaches, but their use is still limited. Not much work on MAS in this direction is being conducted. MAS is a highly promising strategy to achieve stress tolerance against abiotic stresses. Before beginning a breeding program, genetic diversity can also be assessed using molecular markers. In fact, several QTLs for economic features have already been reported. The use of a large sample size or the construction of multiple biparental cross populations could be useful to map rare alleles. To increase oilseed productivity, efforts should be made to use molecular breeding techniques, which can be expedited by current advancements in next-generation sequencing. The contemporary trend is to combine QTL mapping with the functional genomics methods (like ESTs and microarray) for gene expression studies that can be used to develop markers from genes itself [172]. This technique, called the "candidate gene approach", holds great potential in identifying the actual gene that controls the trait of interest. These methods can also be used to recognize SNP markers. The development of SNPs and EST-based markers has provided researchers a great tool for QTL mapping and MAS. Moreover, significant progress is being made in QTL mapping between related species through comparative mapping. To reduce the unfavorable effects of various abiotic stresses on oilseeds that are linked to climate change, modern molecular marker technologies must be adopted with traditional breeding techniques to create cultivars resistant to climatic change.

**Author Contributions:** Conceptualization, V.C., D.K., S.P., V.S., P.K. and R.B.D.; literature curation/investigation, V.C., S.P., P.K. and D.K.; validation, D.K., K., H.K. and A.R.; writing—draft preparation, V.C. and D.K.; writing—review and editing, V.C., P.K., V.S., K. and C.M.S.; supervision, R.B.D., P.K. and K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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