Genetic insights into cold tolerance in cotton: GWAS identified GhPRL1 gene responsible for cold tolerance in cotton at seedling stage

Genetic insights into cold tolerance in cotton: GWAS identified GhPRL1 gene responsible for cold tolerance in cotton at seedling stage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic insights into cold tolerance in cotton: GWAS identified GhPRL1 gene responsible for cold tolerance in cotton at seedling stage Aamir Abro, Mubashir Abbas, Qiankun Liu, Zheng Jie, Yanchao Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6283010/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cold stress during the seedling emergence stage severely affects the growth and development of cotton (Gossypium hirsutum), leading to reduced yield and plant health. Despite its importance, the molecular mechanisms underlying cold tolerance in cotton remain poorly understood. In this study, we analyzed 302 cotton accessions from the Cotton Research Institute in Anyang, China, to assess phenotypic and genetic responses to cold stress. Statistical analysis revealed significant reductions in primary root length (PRL) under cold stress, with a notable increase in genetic variation in root growth. Genome-wide association studies (GWAS) identified key genetic loci associated with cold tolerance, particularly on chromosome A11, where a cluster of SNPs exhibited strong associations with PRL. Fine mapping revealed high linkage disequilibrium in this region, indicating evolutionary selection for cold tolerance. Among the candidate genes, Gh_A11G315100 (GhPRL1) was identify as a major gene linked to cold tolerance. Virus-Induced Gene Silencing (VIGS) of GhPRL1 confirmed its essential role in maintaining plant health under cold stress, with GhPRL1-silenced plants showing greater phenotypic damage, increased ion leakage, and reduced antioxidant activity. This study provides valuable insights into the genetic basis of cold tolerance in cotton and identifies GhPRL1 as a critical target for future breeding efforts aimed at enhancing cold resilience. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key message This study identifies the GhPRL1 gene as a key factor in cotton's cold tolerance, providing insights for breeding cold-resistant varieties. Introduction Cotton ( Gossypium hirsutum ) is a major cash crop for the textile industry, accounting for 35% of the total fiber consumption worldwide (Salimath et al., 2021). Cotton plant faces substantial difficulties because of biotic and abiotic stress particularly when they originate from tropical or subtropical regions (Kamburova et al., 2022). The inherent sensitivity of cotton crops leads to reduced seed germination rates and delayed plant emergence as well as weakened seedling vigor (Atta et al., 2023; Bhattacharya, 2022). Cotton cultivation in suboptimal climatic areas of high-latitude means this vulnerability worsens (Rezaei et al., 2023). The discovery of molecular mechanisms responsible for cold tolerance in cotton remains crucial for creators of resistant cotton plant varieties (Ijaz et al., 2024). Cotton exposed to cold conditions experience numerous physiological-related processes affected particularly in membrane stability enzyme together with photosynthetic mechanisms (Xia et al., 2020; Abro et al., 2024). Plant organisms have developed two protective methods including transcriptional reprogramming along with osmoprotectant accumulation to reduce the effects of cold stress according to (Raza et al., 2023; Wang et al., 2024) Cotton plants turn on CBF/DREB1 signaling pathways, which start downstream cold regulated gene expression due to pathway activation improvements in stress adaptation mechanisms (Liu et al., 2021; Wang et al., 2021). Research has extensively studied the pathway in model plants Arabidopsis and cereals rice and maize but there is still limited knowledge about this pathway in cotton (Feng et al., 2025; Zhang et al., 2023). Research achievements included the isolation of GhCBF1 and its demonstration to provide freezing tolerance to transgenic systems thus showing cotton has potential for genetic improvement (Liu et al., 2021). Two important cold response mediator genes named GhAGP31 and GTOM1 have been discovered along with their role in mediating cold responses but the complete regulatory pathway must still be clarified (Wang et al., 2024). GWAS technology has proven effective for investigating genes affecting stress resistance levels in crop plants such as pea and maize and sorghum (Osuna-Caballero et al., 2024; Sing et al., 2023; Baye et al., 2020). A GWAS study of maize uncovered 159 QTLs and 106 candidate genes, which play a role in organelle biosynthesis during the seedling stage of cold tolerance (Yi et al., 2021). Studies on rice led to discoveries that QTLs that control different aspects of germination versus seedling development stage (Wang et al., 2020). The application of GWAS to investigate fiber quality and yield and tolerance against drought and salt stress has increased but cold stress research on seedling emergence specifically remains poorly understood (Sun et al., 2023; Zhu et al., 2020). In our research trail, we utilized Genome wide association study (GWAS) to study how Cotton controls cold tolerance during seedling emergence through primary root length PRL assessment in 302 wild and semi-wild G hirsutum accessions. This research examines three essential objectives: determining genetic diversity with association patterns to ecological patterns and performing GWAS analysis to discover the genetic essence of cold tolerance under stress and identifying genetic regulators for cold tolerance management in cotton. In this study GhPRL1 gene was identify as a key gene by GWAS placement for cold tolerance in cotton, and upregulated expression was also confirm by real-time quantitative PCR (RT-qPCR) to be a candidate gene for enhancing cold tolerance in cotton. This study explore the function and validation of GhPRL1 gene under the cold stress through virus-induces gene silencing (VIGS) in cotton. Materials and methods Plant materials and growth conditions Diverse wild and semi-wild G hirsutum 302 accessions from labeled Latifolium, Punctatum, Marigarant and Ycatan formed the association-mapping panel at the Cotton Research Institute Chinese Academy of Agricultural Sciences in Anyang, Henan province, China. The population contained 156 Latifolium varieties together with 64 Punctatum , 54 Marigarant accessions alongside 28 Ycatan accessions. We needed to select a method that increased environmental repetition because the low-temperature culture environment provided stability along with uniformity. Research on other cotton characters used seed number as their experimental basis. Furthermore, were chose full and uniform seeds from all materials before planting to decrease seed vitality, in our research trail IMAGE J software was used for analysis and measurement of primary root length PRL. The seeds of cotton received 3 minutes of exposure to sulphuric acid solution with its concentration set at 98 percent for delinting purposes. A dry place stored seeds following repeated water washing to minimize sulfuric acid residues while seeds remained there for two days. Fifty-germination seeds placed onto Petri dishes with two filter paper layers for placement. The petri plates received placement inside a Sanyo Japanese light incubator using a daily night schedule of 16:8 hours and a temperature range from 28 to 30 degrees Celsius and 60% relative humidity R.H. The samples underwent forty-eight hours of seeding development. A length of 1 cm for cotton radicles served than seed transfer into a cold stress chamber for both cold tolerance analysis and assessment of the cold-sensitive cotton genotypes. The collection of samples included all study items at the end of the 96-hour cold stress period using biological replicates that integrated multiple Petri dish samples. Three repeated trials functioned to boost biological replicate numbers while producing robust data. Furthermore, we froze all samples in liquid nitrogen immediately after cold stress then placed these samples at -80 degrees Celsius for future molecular and physiological analysis. The experimental design implemented in this system delivered thorough testing of cold stress responses in cotton genotypes by producing results, which consistently used for future investigations. Genome‑wide association study (GWAS) There are 302 accessions along with SNP data are available in the Sequence Read Archive database under accession number PRJNA1191690 at https://www.ncbi.nlm.nih.gov/sra/. The single nucleotide polymorphism (SNPs) and insertion-deletion (InDels) variations of each cotton accession were called separately using Genome Analysis Toolkit (GATK, version 3.1, HaplotypeCaller) software. Based on the reads site in the reference genome, redundant reads were filtered using Picard package (http://sourceforge. net/projects/picard/) to ensure the accuracy of the results. GATK was use for recalibrating the base quality score to obtain more accurate quality scores for each base, using the co-current variations set as known sites. Data obtain by recalibrating the base quality score used for variation detection by Haplotype Caller in GATK. In total, 2 606 757 SNPs and 1 084 616 InDels were identified across 200 cotton accessions. The 2 606 757 SNPs were used to perform GWAS using a mixed linear model in TASSEL. Principal component analysis based on filtered SNPs was perform using EIGENSOFT. Relative kinship was analyses using ADMIXTURE. Linkage disequilibrium (LD) decay was evaluated using PLINK software. The phylogenetic tree of each sample was construct using the neighbour-joining method and Kimura 2-parameter model in MEGA X software. Identification of significant association SNPs A definite sequence of highly linked SNPs recognizing various cold tolerance traits in significant markers detected within 200 kb ranges found to indicate strong effects at common loci. The assessment indicated significant SNP association when the − log10 (p) value exceeded 6.41 (P ≤ 0.0000004) using a Bonferroni correction (p = 1/n). The applied threshold value exceeded the values used for significant thresholds in other GWAS studies operating at low temperatures. Furthermore, we team selected P ≤ 0.0001 (− log10 (p) ≥ 4) as the threshold value to identify the most significant SNPs that appeared in multiple environments. When using a − log10 (p) filter value of 6.41 only a minimal number of SNPs would demonstrate environmental stability. The study screened significantly related SNPs using a − log10 (p) threshold threshold of 6.41 but examined SNPs with stable spots using a − log10 (p) threshold of four. Transcriptome sequencing The cold-tolerant Latifolium307 and together with the cold-sensitive Punctatum-131 were chosen according to the results of haplotype analysis on Gh_A11G315100 and Gh_A11G305200 from the same geographical region. Low-temperature treatment performed after 2 d of germination at normal temperature. Samples collected before treatment (0 h) and at 48 and 96 h after 4 °C treatment. Total RNA was extracted, eukaryotic mRNA was enriched using Oligo (dT) beads, whereas prokaryotic mRNA was enriched by removing the rRNA using the Ribo-Zero™ Magnetic Kit (Epicenter), which have been deposited in the Sequence Read Archive database (https:// www.ncbi.nlmnih.gov/sra/) under accession number PRJNA1191690. The RNA-seq library production required use of high quality RNA. Each gene received expression values as FPKM, which stands for expected fragments per kilobase of transcripts per million mapped fragments (FPKM). The unigenes characterized through FPKM to FDR ratios in calculating their abundance differences. A gene was considered differentially expressed when its FDR value was below 0.05 and its FPKM value exceeded 10. The investigators performed GO functional analysis and KEGG pathway analysis for each detected module. WEGO analysis at http://wego.genomics.org.cn/cgi-bin/wego/ index.pl revealed each module gene could obtain molecular function and biological process or cellular component classification. A GO analysis determined the primary biological functions within the DEGs. KEGG pathway analysis determined enriched pathways that included DEGs by comparing their numbers against the reference gene set through hypergeometric tests. The selection of consider ably enriched GO terms and KEGG enrichment pathways required a corrected P value below 0.01 as the threshold value. RNA extraction, cDNA preparation, and qRT-PCR analyses The study utilized RNA prep pure plant reagent and extraction operated by following the manufacturer guidelines through the RNA-prep Pure Plant kit (Tiangen Biotech, Beijing, China). A reverse transcription quantitative PCR procedure was using primers of proper design (Supplement Table 1). To check RNA integrity samples ran on a 1.2% agarose gel and RNA quality determination done through Nanodrop 1000 spectrophotometer measurement. RNA conversion to cDNA occurred through the EasyScript First strand cDNA Synthesis SuperMix kit by Transgene, Beijing, China according to their instructions for cDNA synthesis. Furthermore, we team applied actin gene as an internal control to normalize expression levels in their examination. The real time PCR reactions contained 25 μL of final solution using SYBR green master mix. The PCR amplification process executed on ABI7500 thermal cycler (Applied Biosystems) through manufacturer-defined parameters (https://www.bioz.com/). The experimental process involved three separate biological repeats for every single test. The analysis of gene expression used the 2−ΔΔCT to enable us to perform relative quantification of gene expression data based on CT values. Virus‑induced gene silencing A 300-bp of GhPRL1 genetic sequence were designed through the website tool 'https://vigs.solgenomics.net/' to achieve VIGS treatment. The experimental primers can be find in (Supplement Table 2). The specific coding sequence of GhPRL1 underwent amplification and cloning into TRV2 using the ClonExpress II One Step Cloning Kit from Vazyme (China) (C112-01). The scientists used Agrobacterium tumefaciens strain LBA4404 to transfer TRV2:GhPRL1. TRV2::00 was use as negative control. The vectors containing Agrobacterium tumefaciens strains was mix with Agrobacterium tumefaciens strains that contained pTRV1 into a mix of 1:1 ratio. An injection using a 1-mL needleless syringe was perform on cotton seedling cotyledons before the dark period lasted 24 hours while the plants later received constant temperature light culture (28 °C under 16 h/8 h day/night conditions). Morphological, Physiological, and Biochemical Trait Determination Physiological index determination was taken for dehydration rate assessment included excised leaf water loss (ELWL) while relative moisture content was evaluated through relative leaf water content (RLWL) and ion permeability through cell membrane stability (CMS) and chlorophyll content was measured through soil plant analysis development (SPAD). The evaluation of biochemical indices through SOD, H2O2, CAT and MDA tests in leaf samples occurred at 0 h and 96 h time points using the Solarbio (Solarbio Beijing China) test kit following the provided procedures in the instructions. Statistical Analysis Microsoft Excel (2010 version) from Microsoft Corporation helped analyze the obtained data which is displayed as mean ± SD. The statistical analysis process used SPSS software version 20.0 for its operations. The analysis used one-way ANOVA followed by Student's t test for group comparison. The selected P value smaller than 0.05 to determine statistical significance levels during analysis. Results Phenotypic variation under cold stress. In our research trail, we used the wild and semi-wild cotton accessions from Cotton Research institute Anyang China for conducting GWAS on 302 divers' collections. ANOVA results showed that significant differences occurred, as indicated by the F-statistics of 9.016 for interaction and 21.39 for genotypes together with 14,022 for Control vs Cold, where all p-values reached below 0.0001 ( Supplement Table 3). The data analysis showed PRLs displayed significant variations throughout 302 accessions during germination stage cold stress assessment as determined by the CV results between 27.44% under control and 33.63% under cold stress condition (Supplement Table 4). Cold exposure caused an established decrease in PRL levels as compared to the regular control condition. The wide spread of data values in the control group shows higher RL values while the lower spread from the cold treatment indicates a statistically significant reduction of RL difference at p-value less than 2.22e-16 based on the violin plot analysis (Fig. 1a). The data analysis confirming these results through histograms demonstrates that the control group reached a mean RL value of 11.3 and standard deviation (SD) of 2.98 yet the cold group barely reached a mean RL of 5.27 accompanied by a lower standard deviation of 1.66 (Fig. 1b,c). Most genotypes display moderate responses to cold temperature as shown by the Cold Sensitivity Index results which present a mean value of 0.98 and standard deviation of 0.27 (Fig. 1d). The data shows root lengths primarily explained through genetic factors because the heritability values did not decrease under any experimental settings. The experiment revealed that the genetic diversity enhanced at room temperature compared to cold conditions leading to higher genetic heterogeneity at diverse controls (Supplement Fig. 1a). The data in (Supplement Fig. 1b) reveals that genetic variability reached higher levels under control conditions when compared to the limited values observed during cold conditions. The histogram shows environmental stress through the narrow distribution combined with high overlap, which characterizes the cold treatment data. Root ength displays greater environmental susceptibility to cold stress according to the higher residual variance exhibited in the cold treatment (Supplement Fig. 1c). Genetic Diversity Analysis and Population Structure GWAS analysis needs population structure control since population stratification would create misleading associations between genotypes and phenotypes. The heterozygosity measurements indicated that 95% of cotton accessions displayed less than 60% heterozygosity whereas 80% of individual materials showed less than 5% heterozygosity as shown in (Supplement Fig. 2a). Evidence shows HEs occurs commonly in heterologous tetraploid crops yet remain at minimal levels in cotton populations despite earlier studies confirming this fact. Most cotton genotypes showed weak genetic connections while only few had close relationships with one another (the figure shows yellow background with small dark red shapes). A four-species subgroup classification exists for all 302 examined cotton genotypes (Supplement Fig. 2b). The re-sequencing analysis yielded 10,180 highly reliable SNPs among 17 total. The data analysis regarding chromosome distribution appears in (Fig 2). The 302 cotton plant accessions contributed to four phylogenetic subgroups according to the analysis of SNP-derived tree (Fig 3a). PCA separated 302 individuals of cotton Gossypium hirsutum into four distinct subgroups wherein subgroup 1 included 200 materials while subgroup 2 held 50 samples and subgroup 3 included 40 individuals and subgroup 4 contained 13 materials (Fig 3b). Structure analysis software utilized the hybrid model for its operations. The model applied three iterations at each level of subgroups (K) ranging from 1 to 10. During the non-count iteration period the Markov chain Monte Carlo (MCMC) began running for 10,000 iterations as its burn-in period before continuing its execution for 1,000,000 iterations. The analysis used maximum likelihood value to detect subgroup structure along with their numbers from the optimal K value. Analysis of cross-validation error (CV error) happened through different K value combinations (2–10). The findings from Structure software and Q value calculation indicated K being at 4 (Fig 3c). The GWAS results would achieve better accuracy after using the k = 4 Q-matrix as the covariate to eliminate potential spurious associations. Furthermore, we findings revealed that the material origin influenced the structural distribution of subgroups because the genetic compositions within resources were mainly homogeneous (Fig 3d). GWAS for Primary root length PRL under cold stress An analysis of cold sensitivity in cotton ( Gossypium hirsutum ) primary root length (PRL) through the best linear unbiased prediction (BLUP) model were performed on 302 diverse accessions in a genome-wide association study (GWAS) (Supplement Table 5). Furthermore, we approach simplified the genetic complexities of PRL under cold stress which had not been studied in genetic models previously. A significant cluster of SNPs reached -log₁₀ (P) levels greater than 7 on chromosome A11 in the Manhattan plot thus indicating this region as a fundamental factor for cold adaptation (Fig. 4a). Furthermore, we conducted subsequent fine mapping within a 2Mb interval (chrA10:108.407–110.407 Mb) to study the linkage disequilibrium (LD) pattern found in A10 Region1.01. The LD heatmap displayed distinct haplotype blocks showing almost complete allelic correlation (D′ ≈ 1.0) of genomic regions that escaped historical recombination events (Fig. 4d). The regions situated between linkage groups displayed D′ values less than 0.5, which confirmed both the occurrence of recombination hotspots and ancestral allele transmission decline. The measured findings might result from either functional adaptations that happened through natural selection or selection-based evolutionary adjustments. The Hap-1 through Hap-4 haplotype-stratified association analysis showed significant non-random variation in −log₁₀ (P) observations through quantile-quantile (QQ) analysis (Fig. 4b). Hap-4 (n=28) showed strong statistical evidence of low P-values reaching (−log₁₀ (P) = 4.5; P = 3.2×10⁻⁵) and surpassing the genome-wide threshold (P < 0.0001) despite having a small cohort size indicating a powerful cold tolerance allele (Fig. 4e). The Hap-1 group (n=156) generated less prominent signals at −log₁₀ (P) ≈ 2.0 (P = 0.01) yet the Hap-2 (n=64) and Hap-3 (n=54) groups produced intermediate signatures that indicated polygenic influence. Candidate Gene Screening under cold stress In our research trail we furfure investigate about the screening of candidate gene under cold stress where we found that 135 genes associated with cold tolerance appeared in genotype 307 through the analysis of the 2MB region (Supplement Table 6). An analysis of these genes during germination under cold stress detection yielded results to identify potential candidate genes influencing the cold response mechanism. Furthermore, we evaluated the expression patterns of these genes which reside within identified regions across germination stages through primary root length PRL candidate gene identification under cold tolerant genotype 307 (Fig5). Post-sequence analysis showed that nine genes including Gh_A11G315100 Gh_A11G309000, Gh_A11G315400, Gh_A11G312800, Gh_A11G305200, Gh_A11G312600, Gh_A11G302700, Gh_A11G305000, and Gh_A11G311900 were present within the area spanning from A11:109232146-104617544. During the expression level, filtering step and haplotype analysis the number of remaining genes reached 3 for the found changes in SNPs. The genes Gh_A11G315100 and Gh_A11G305200 showed significant potential regarding primary root length because they displayed distinct expression patterns between cold tolerant genotype 307 (Fig 5a). Research indicates that Gh_A11G315100 (SECB13) and Gh_A11G305200 ( SYP4) demonstrate different variations in accessions distinguished by their cold-tolerant or cold-sensitive characteristics. The expression of GhPRL1 gene was evaluated in cold tolerant and cold sensitive accessions through reverse transcription quantitative PCR. Furthermore, we findings demonstrated that GhPRL1 displayed elevated expression patterns within cold-tolerant accessions compared to the accessions with cold-sensitive characteristics. The GhPRL1 gene emerged as the leading candidate that connects to cold tolerance diversity between different Gossypium hirsutum accessions since the allele GhPRL1 Hap1 shows a potential link to cold responses in cold-tolerant accessions according to these data (Fig 5b). The confirmations of gene expression data verified through qrt-PCR assessment. Gene expression of SECB13 and SYP4 reached its highest point during the 96-hour period of the resistant condition based on the experimental results (Fig 5c). During the 48-hour period, SECB13 recorded its highest expression point, which decreased slightly by the end of 96 hours yet SYP4 expression levels progressively rose from 48 hours to deliver pronounced elevation at 96 hours. The expression levels of investigated genes parallel each other but stay mainly at lower levels during the study period. The expression of SECB13 decreased throughout the period from 48 to 96 hours although SYP4 expression levels rapidly increased during this same period. The resistant interactions exhibit increased and sustained gene expression patterns during the analysis at 96hours according to the results (Fig 5c). Validation of candidate gene silence in cotton plants under cold stress. GhPRL1 appears to represent the main gene linked to cold tolerance and shows its highest expression level during seed germination according to (Fig. 5). Therefore, we used VIGS methodology to silence the GhPRL1 gene under cold stress conditions. The phenotypic changes of seedling cotton plants in two-leaf development evaluated following different treatments during a specific observation period. The plants that received TRV2: PDS bacterial solution developed clear whitening of their leaves about 10 days after cultivation. The successful gene silencing procedure produced stable results that gave rise to a whitening phenotype, which acquired a lasting effect during the next ten days of plant culture (Fig 6a). To evaluate GhPRL1 expression changes caused by gene silencing in TRV2:00 and TRV2: GhPRL1 cotton seedlings we used quantitative real-time PCR (qRT-PCR) to measure the levels of Gh_A11G315100 ( GhPRL1 ) expression. The research found that GhPRL1 maintained a similar expression between wild type plants and TRV2:00 samples under both conditions. The TRV2: GhPRL1 plants displayed significantly diminished GhPRL1 expression levels beneath WT and TRV2:00 expression levels. The suppression of GhPRL1 gene regulation demonstrated the successful silencing of this gene within the transgenic lines (Fig 6b). The research team evaluated both normal WT, TRV2:00 and TRV2: GhPRL1 cotton seedlings under cold stress conditions. Every plant line maintained healthy development without showing signs of wilting and withstanding normal growth conditions properly. All subjected plants developed leaf-wilting symptoms after their exposure to cold stress for twenty-four hours. Phenotypic changes became more distinguished during the 48-hour exposure to cold conditions. The WT and TRV2:00 plants exhibited equal levels of wilting to each other but both groups maintained comparable cold stress responses. By contrast the damage in TRV2: GhPRL1 plants reached a worse stage. The stems softened and the plants slightly lodged as they showed major decreased cold resistance following the GhPRL1 gene silencing (Fig 6c). Impact of GhPRL1 Gene Silencing on physiological index under Cold Stress in Cotton The examination of physiological and oxidative stress responses in GhPRL1 -silenced cotton plants during cold stress included measurements of chlorophyll content, relative water content, ion leakage, excised leaf water loss together, superoxide dismutase (SOD), catalase (CAT), hydrogen peroxide (H2O2) and malondialdehyde (MDA) content in WT TRV2:00 and TRV2: GhPRL1 cotton under cold stress (Fig 7). TRV2: GhPRL1 plants revealed higher amounts of ion leakage compared to WT and TRV2:00 plants thus indicating extensive damage to cell membranes and reduced maintenance of cellular integrity during cold stress. TRV2: GhPRL1 plants experienced more rapid water loss from excised leaves, which indicated damages to their leaf tissues alongside dehydration. The silencing of GhPRL1 through transgenic engineering resulted in identical chlorophyll content and relative water content when plants were expos to cold stress (Fig 7a, b, c and d). The activities of SOD and CAT together with the contents of MDA and H2O2 remained unchanged in TRV2: GhPRL1 plants, WT, and TRV2:00 control plants under standard (CK) growth conditions (Fig 7e, f, g, and h). The chlorophyll content together with relative water content, ion leakage, and excised leaf water loss from the three groups remained statistically similar when observing normal conditions (Fig 9). The groups demonstrated substantial variations during cold stress tests according to experimental results. TRV2: GhPRL1 plants presented reduced activities of SOD and CAT that resulted in impaired reactive oxygen species detoxification capabilities and limited management of oxidative stress. The TRV2: GhPRL1 plant line displayed elevated levels of H2O2 and MDA together with increased ROS accumulation and oxidative damage when tested against cold stress. This demonstrates that blocking GhPRL1 expression had no substantial impact on plant response to these criteria related to cotton plant health under cold stress (Fig 7). Discussion Cotton ( Gossypium hirsutum ) is a globally significant crop that is highly susceptible to cold stress, which can adversely affect its growth and yield (Zhao et al., 2024; Abro et al., 2024). In this study, we aimed to explore the genetic and phenotypic characteristics of cold tolerance in cotton by focusing on genetic loci associated with primary root length (PRL) and candidate genes that mediate cold stress responses. We examined genetic and phenotypic characteristics of cold tolerance in cotton by analyzing 302 diverse accessions from the Cotton Research Institute in Anyang, China. The large set of cotton accessions demonstrated that primary root length (PRL) measurements progressively decreased as exposure to cold stress intensified. The cold stress resulted in a significant reduction of PRL, reaching 5.27 cm under cold stress conditions compared to 11.3 cm in control conditions. Statistical analysis confirmed that both genetic type and cold treatment had a substantial impact on PRL (F-statistics: genotype differences = 21.39, cold stress = 14.022), with p-values below 0.0001. These results support the observation that cold stress negatively affects root growth and highlights the genetic variability in root growth responses under cold conditions, which has been documented in previous studies (Zhang et al., 2024; Fan et al., 2022; Lu et al., 2019). Additionally, genotypic analysis revealed that most cotton accessions exhibited moderate sensitivity to cold stress, which is consistent with the results reported by (Ge et al., 2022; Shen et al., 2023). This variability in response to cold stress is likely due to the differing genetic backgrounds of the accessions, further emphasizing the importance of identifying genetic markers for cold tolerance. Heritability estimates for root length under cold stress indicated that genetic factors govern PRL under these conditions, suggesting that cold stress response in cotton was primarily control by genetic factors rather than environmental influences (Windpassinger et al., 2017; Jähne et al., 2019; Sthapit and Witcombe 1998). To ensure the reliability of the Genome-Wide Association Studies (GWAS), we conducted a genetic diversity analysis and kinship evaluation among the 302 accessions. The heterozygosity analysis revealed that 95% of the accessions had less than 60% of heterozygous alleles, indicating relatively low genetic diversity. Despite this, PCA analysis identified four distinct genetic clusters that reflected the genetic relationships among the cotton accessions. These findings are in line with previous studies, such as (Ge et al., 2024; Baytar et al., 2022; Shen et al., 2022), which also documented distinct genetic patterns in cotton accessions from different geographical regions. GWAS identified key genomic regions associated with cold tolerance, with a major candidate gene located on chromosome A11. SNP analysis revealed a significant SNP (−log₁₀ (P) > 7) in this region, indicating a strong correlation with cold stress tolerance. The linkage disequilibrium (LD) heatmap for this region demonstrated a high correlation (D′ ≈ 1.0), suggesting that the cotton genome has undergone strong selection pressure for cold tolerance. This aligns with findings by (Sun et al., 2019; Khatab et al., 2022; Li et al., 2022), who also identified cold tolerance loci on chromosome A11. In addition to GWAS, we further explored the functional role of GhPRL1 in cold stress tolerance by silencing the gene using Virus-Induced Gene Silencing (VIGS) technology. The results of this experiment confirmed the critical role of GhPRL1 in cotton's response to cold stress. The successful silencing of GhPRL1 resulted in notable phenotypic and physiological changes, as evidenced by the albino phenotype observed in PDS-silenced plants and the reduced expression of GhPRL1 in TRV2: GhPRL1 plants, confirmed by qRT-PCR (Fig. 6b). These results validate that GhPRL1 plays a critical role in cold tolerance in cotton plants, also align with findings by (Ge et al., 2024; Shen et al., 2022; Ge et al., 2022; Shen et al., 2023). Under cold stress conditions, plants that underwent GhPRL1 silencing (TRV2: GhPRL1 ) exhibited significantly greater damage compared to the wild type (WT) and TRV2:00 control plants. This damage was evident through increased wilting, lodging of the stems, and a general decline in plant health, particularly after 48 hours of cold exposure (Fig. 6c). These results suggest that the loss of GhPRL1 expression hampers the plant's ability to withstand cold stress, in line with previous studies on PRL genes in other species (Li et al., 2024; Wu et al., 2024; Zhanfg et al., 2024). Physiological measurements also supported these findings. TRV2: GhPRL1 plants showed higher ion leakage, increased water loss from excised leaves, and reduced chlorophyll content under cold stress, indicating greater cellular damage and dehydration compared to WT and TRV2:00 plants (Fig. 7). These results underscore the importance of GhPRL1 in maintaining cellular integrity and water retention during cold stress, a finding consistent with other research on stress-associated genes in plants (Cai et al., 2019; Cao et al., 2021; Wei wr al., 2024). Furthermore, the activities of antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) were significantly reduced in TRV2: GhPRL1 plants under cold stress (Fig. 7e, g). This suggests that silencing GhPRL1 impairs the plant's ability to detoxify reactive oxygen species (ROS), which accumulate during cold stress. Elevated levels of H2O2 and malondialdehyde (MDA) in TRV2: GhPRL1 plants further corroborate the increased oxidative stress and cellular damage observed (Fig. 7f, h). These results emphasize the role of GhPRL1 in modulating antioxidant defense mechanisms and maintaining cellular homeostasis during cold stress, as observed in other plant species (Cai et al., 2019). Conclusion In this study, we evaluated the phenotypic and genetic responses of 302 cotton accessions to cold stress, focusing on primary root length (PRL) as a key trait. Our findings demonstrated that cold tolerance in cotton influenced by both genetic and environmental factors, with significant genetic variation observed in response to cold stress. GWAS identified a major locus on chromosome A11 associated with cold tolerance, and fine mapping highlighted a critical region linked to PRL. GhPRL1 emerged as a key candidate gene, with its silencing leading to severe phenotypic damage and reduced cold tolerance. The results emphasize the importance of GhPRL1 in cotton's response to cold stress, suggesting its potential as a target for improving cold resilience through genetic breeding. Future research should focus on the functional validation of GhPRL1 and other candidate genes identified in this study to further elucidate their role in cold stress adaptation and enhance breeding strategies for cold-tolerant cotton cultivars. Declarations Authorship contributions statement : Conceptualization and methodology, A.A.A. and M.A.; Experimentation, Q.L and J.Z., Initial draft, A.A.A. and M.A. Review and editing, Y.X., Y.H., Z.Z., F.L., and X.C, Formal analysis, Y.X., Y.H., Z.Z.; All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China (32401824, 32171994, 32272090, 32201875), Natural Science Foundation of Henan (242300421595), Nanfan special project, CAAS (YBXM2439) and the National Key R&D Program of China (2024YFD1200300). Data availability : The datasets analyzed during this study are included in this manuscript. Declaration of competing interests: The authors stated that they had no interest, which perceived as posing a conflict or bias. References Abro AA, Qasim M, Abbas M, Muhammad N, Ali I, Khalid S, Liu F (2024) Integrating physiological and molecular insights in cotton under cold stress conditions. 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Singh B, Wani SH, Kukreja S, Kumar V, Goutam U (2023) Genome-wide association studies (GWAS) for agronomic traits in maize. In: Maize Improvement: Current Advances in Yield, Quality, and Stress Tolerance under Changing Climatic Scenarios (pp. 83–98). Cham: Springer International Publishing. Sthapit BR, Witcombe JR (1998) Inheritance of tolerance to chilling stress in rice during germination and plumule greening. Crop Sci 38(3):660–665. Sun F, Ma J, Shi W, Yang Y (2023) Genome-wide association analysis revealed genetic variation and candidate genes associated with the yield traits of upland cotton under drought conditions. Front Plant Sci 14:1135302. Sun H, Meng M, Yan Z, Lin Z, Nie X, Yang X (2019) Genome-wide association mapping of stress-tolerance traits in cotton. Crop J 7(1):77–88. Wang Q, Tang J, Han B, Huang X (2020) Advances in genome-wide association studies of complex traits in rice. Theor Appl Genet 133:1415–1425. Wang Z, Peng Z, Khan S, Qayyum A, Rehman A, Du X (2024) Unveiling the power of MYB transcription factors: Master regulators of multi-stress responses and development in cotton. Int J Biol Macromol 133885. Wei W, Ju J, Zhang X, Ling P, Luo J, Li Y, Wang C (2024) GhBRX1, GhBRX2, and GhBRX4.3 improve resistance to salt and cold stress in upland cotton. Front Plant Sci 15:1353365. Windpassinger S, Friedt W, Deppé I, Werner CH, Snowdon R, Wittkop B (2017) Towards enhancement of early‐stage chilling tolerance and root development in sorghum F1 hybrids. J Agron Crop Sci 203(2):146–160. Wu H, Lian B, Lv X, Sun M, Wei F, An L, Wei H (2024) Xyloglucan endotransglucosylase-hydrolase 22 positively regulates response to cold stress in upland cotton ( Gossypium hirsutum L.). Ind Crops Prod 220:119273. Xia J, Kong X, Shi X, Hao X, Li N, Khan A, Luo H (2020) Physio-biochemical characteristics and correlation analysis of the seeds of some cotton ( Gossypium hirsutum L.) genotypes under cold temperature stress. Appl Ecol Environ Res 18(1). Yi Q, Álvarez-Iglesias L, Malvar RA, Romay MC, Revilla P (2021) A worldwide maize panel revealed new genetic variation for cold tolerance. Theor Appl Genet 134:1083–1094. Zhang J, Liu R, Zhang S, Ge C, Liu S, Ma H, Shen Q (2024) Integrating physiological and transcriptomic analyses explored the regulatory mechanism of cold tolerance at seedling emergence stage in upland cotton ( Gossypium hirsutum L.). Plant Physiol Biochem 217:109297. Zhang X, Wu C, Guo Y, Ren X, Meng Y, Gao Q, Guo J (2024) Genome-wide analysis elucidates the roles of GhTIR1/AFB genes and reveals the function of Gh_D08G0763 (GhTIR1) in cold stress in Gossypium hirsutum . Plants 13(8):1152. Zhao Y, Zhu Y, Feng S, Zhao T, Wang L, Zheng Z, Guan X (2024) The impact of temperature on cotton yield and production in Xinjiang, China. npj Sustainable Agric 2(1):33. Zhu G, Gao W, Song X, Sun F, Hou S, Liu N, Guo W (2020) Genome-wide association reveals genetic variation of lint yield components under salty field conditions in cotton ( Gossypium hirsutum L.). BMC Plant Biol 20:1–13. Supplementary Files suplementTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6283010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435469251,"identity":"d2239f11-d8e2-4e20-bfce-6db90aa88864","order_by":0,"name":"Aamir Abro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYJCCA2AkwdjA8AHIY2MnRQvjDJAWZqItkmBgYOYBsQlp4W8/nXi4ouZOPv/s5tbNNr+2yfMxMzB++JiDW4vEmdwNB88ce2Y5487Bttu5fbcN25gZmCVnbsPnJKCWBrbDBgw3EoFaem4zArWwMfPi0SJ//i1Qy7/DBvIgLZY9t+0JajG4AbSlse2wgQFIC8OP24kEtRjeANrS2PfMwBCo5WZvw+3kNmbGZrx+kTufu/ljw7c7BnI30p/d+PHntu389uaDHz7i8z4KYGwDkw3EqgeBP6QoHgWjYBSMgpECAO8VX8xmnrDxAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5566-2306","institution":"Institute for Cotton Research: Cotton Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Aamir","middleName":"","lastName":"Abro","suffix":""},{"id":435469252,"identity":"ee963de9-6247-48b0-8262-86bdd459ec73","order_by":1,"name":"Mubashir Abbas","email":"","orcid":"","institution":"Institute for Cotton Research: Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mubashir","middleName":"","lastName":"Abbas","suffix":""},{"id":435469253,"identity":"f2057321-8b05-4a33-8cbe-a021d0d1f347","order_by":2,"name":"Qiankun Liu","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qiankun","middleName":"","lastName":"Liu","suffix":""},{"id":435469254,"identity":"2823c85a-eaa8-4085-a279-15d38aaf93a4","order_by":3,"name":"Zheng Jie","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Jie","suffix":""},{"id":435469255,"identity":"9f22db65-e997-4105-88eb-43eda1e70ffc","order_by":4,"name":"Yanchao Xu","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Xu","suffix":""},{"id":435469256,"identity":"df19c531-69f8-4d68-8983-51488cf0a665","order_by":5,"name":"Yuqing Hou","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Hou","suffix":""},{"id":435469257,"identity":"84b57974-9f8d-4008-b219-2142db322ece","order_by":6,"name":"Zhongli Zhou","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhongli","middleName":"","lastName":"Zhou","suffix":""},{"id":435469258,"identity":"f8f2e6ce-9f22-466f-b2f7-6c69c8804668","order_by":7,"name":"Fang Liu","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Liu","suffix":""},{"id":435469259,"identity":"901231e4-e830-4cda-9d2d-146dbd7ac059","order_by":8,"name":"Xiaoyan Cai","email":"","orcid":"","institution":"Cotton Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2025-03-22 10:29:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6283010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6283010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80758168,"identity":"30380feb-11f0-4fb6-bb7f-2d30e47e522e","added_by":"auto","created_at":"2025-04-16 18:26:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256904,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots and histograms illustrating the effects of cold stress on root length (RL) and related distributions. (a) Comparison of root length (RL) under control (CK) and cold (Cold stress) treatments, showing a significant reduction in RL under cold stress (p \u0026lt; 2.22e-16). (b) Distribution of control root length (RL), with a mean of 11.3 and standard deviation of 2.98. (c) Distribution of cold-stressed root length (RL), with a mean of 5.27 and standard deviation of 1.65. (d) Distribution of Cold Stress Index (CSI), with a mean of 0.98 and standard deviation of 0.27\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/c26db054c67f89224ed1251e.png"},{"id":80758167,"identity":"c3029d07-9660-45a6-bd2a-b618fb3e1f92","added_by":"auto","created_at":"2025-04-16 18:26:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":422238,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide distribution of SNPs (Single Nucleotide Polymorphisms) within 1Mb sliding window sizes across 26 chromosomes. Heatmap illustrates the number of SNPs per window, with the color gradient representing SNP density, ranging from low (green) to high (red) density. This distribution provides insights into the genetic variability across the genome, with specific regions showing higher concentrations of SNPs, particularly on chromosomes 6, 7, and 10.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/87e8be418a93dfc070aefce9.png"},{"id":80758976,"identity":"105929d1-eef9-4684-b112-8fa4de08594a","added_by":"auto","created_at":"2025-04-16 18:42:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":512837,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic structure and clustering analysis. (a) Phylogenetic tree showing genetic relationships between samples, with distinct color-coded groups representing different clusters. (b) Principal Component Analysis (PCA) plots, with the first two principal components (PC1 and PC2) showing the clustering of samples into four distinct groups, corresponding to the colors red, green, blue, and yellow. (c) Delta K plot indicating the optimal number of genetic clusters (K = 4), based on STRUCTURE analysis. (d) STRUCTURE bar plot illustrating the genetic composition of each sample, with individual samples assigned to one of the four groups based on their genetic profile.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/2d49327a9f3c5fa7e1cb09bc.png"},{"id":80758169,"identity":"d9e1d7fa-5857-4e9b-9490-77d552fe7d6f","added_by":"auto","created_at":"2025-04-16 18:26:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358240,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic association and haplotype analysis for root length. (a) Manhattan plot showing the -log10 (p) values across chromosomes, with a significant peak highlighted on chromosome 11 (red box) indicating a strong association with root length. (b) QQ-plot displaying the observed versus expected -log10 (p) values, with the red dashed line representing the null hypothesis and deviations indicating significant associations. (c) Zoomed-in view of the peak on chromosome 11 from the Manhattan plot, showing strong association signals. (d) Linkage disequilibrium (LD) heatmap highlighting the relationship between SNPs, with a color gradient indicating varying degrees of LD (from red to green). (e) Violin plot comparing root length across four haplotypes, with significant differences observed (p \u0026lt; 0.0001) among haplotypes, showing genotype-specific variations in root length.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/15c53b28ae8d26b2146991c2.png"},{"id":80758178,"identity":"e53a8b03-4848-4fcc-9285-0c9515e24581","added_by":"auto","created_at":"2025-04-16 18:26:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":240699,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression and cold stress analysis. (a) heatmap showing the relative expression of several genes across different cotton genotypes under cold stress, with color-coded values representing upregulation (red) and downregulation (green). (b) Boxplots comparing the Cold Stress Index (CSI) between two haplotypes (Ha1 and Ha2) for the \u003cem\u003eGhPRL1\u003c/em\u003e gene, indicating significant differences in cold stress response (p \u0026lt; 0.05). (c) Relative gene expression levels of resistance (\u003cem\u003eGh_A11G315100\u003c/em\u003e SECB13 and \u003cem\u003eGh_A11G305200 \u003c/em\u003eSYP4)) and susceptible (SECB13, SYP4) genes under 0 hr, 48 hr, and 96 hr cold stress, showing varying expression patterns over time.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/d8aec61dda2dac6383586717.png"},{"id":80758498,"identity":"fe8ecd4a-c280-4c24-bc18-b71ee663f9cc","added_by":"auto","created_at":"2025-04-16 18:34:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":359153,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of silencing efficiency of Virus-Induced Gene Silencing (VIGS) and phenotypic changes of cotton plants under cold stress. (A) PDS Albino Phenotype: The albino phenotype observed in PDS-silenced plants, highlighting the visual effects of gene silencing on plant coloration. (B) VIGS Interference and Detection of \u003cem\u003eGhPRL1\u003c/em\u003e Gene Expression: The second panel demonstrates the successful interference with \u003cem\u003eGhPRL1\u003c/em\u003e gene expression through VIGS, confirming the silencing of \u003cem\u003eGhPRL1\u003c/em\u003e in the cotton plants. (C) Cold Stress Tolerance Phenotype after \u003cem\u003eGhPRL1\u003c/em\u003e Gene Silencing: The third panel presents the evaluation of cold stress tolerance in \u003cem\u003eGhPRL1\u003c/em\u003e-silenced plants, highlighting the phenotypic changes in plant performance under cold stress conditions.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/f0a8e38c7803d5ec2bd194ed.png"},{"id":80758501,"identity":"3011f294-9b1e-4321-a084-ba0d303676b4","added_by":"auto","created_at":"2025-04-16 18:34:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":666137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of Virus-Induced Gene Silencing (VIGS) efficiency and physiological responses of cotton plants under Cold Stress\u003c/strong\u003e. (A) \u003cstrong\u003eChlorophyll Content\u003c/strong\u003e: Chlorophyll levels in WT, TRV2:00 and TRV2:\u003cem\u003eGhPRL1\u003c/em\u003eplants under normal and cold stress conditions. (B) \u003cstrong\u003eRelative Water Content:\u003c/strong\u003e No significant difference in water content among groups under normal conditions; varying responses to cold stress. (C) \u003cstrong\u003eIon Leakage\u003c/strong\u003e: Increased ion leakage in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, indicating greater cell damage. (D) \u003cstrong\u003eExcised Leaf Water Loss\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eHigher water loss in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, suggesting increased dehydration. (E) \u003cstrong\u003eSOD:\u003c/strong\u003e Reduced SOD activity in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, pointing to decreased antioxidant defense. (F) \u003cstrong\u003eH2O2 Levels\u003c/strong\u003e: Elevated H2O2 levels in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, indicating increased oxidative stress. (G) \u003cstrong\u003eCAT\u003c/strong\u003e: Lower CAT activity in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, confirming reduced antioxidant enzyme function. (H) \u003cstrong\u003eMDA\u003c/strong\u003e: Increased MDA content in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress, reflecting higher oxidative damage.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/31d7b0699b55477014c04a81.png"},{"id":81695674,"identity":"b353ee19-c6de-478c-8710-15b775550003","added_by":"auto","created_at":"2025-04-30 12:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3674017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/71a59b14-2a70-496a-9983-3b37b5d2faf6.pdf"},{"id":80758173,"identity":"b5e56bcf-edc0-43b1-b84a-4ce29e24b33d","added_by":"auto","created_at":"2025-04-16 18:26:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":55829,"visible":true,"origin":"","legend":"","description":"","filename":"suplementTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6283010/v1/5bd89d2b8b7c3df95c619df8.docx"}],"financialInterests":"","formattedTitle":"Genetic insights into cold tolerance in cotton: GWAS identified GhPRL1 gene responsible for cold tolerance in cotton at seedling stage","fulltext":[{"header":"Key message","content":"\u003cp\u003eThis study identifies the \u003cem\u003eGhPRL1\u003c/em\u003e gene as a key factor in cotton\u0026apos;s cold tolerance, providing insights for breeding cold-resistant varieties.\u003c/p\u003e"},{"header":"Introduction ","content":"\u003cp\u003eCotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e) is a major cash crop for the textile industry, accounting for 35% of the total fiber consumption worldwide (Salimath et al., 2021). Cotton plant faces substantial difficulties because of biotic and abiotic stress particularly when they originate from tropical or subtropical regions (Kamburova et al., 2022). The inherent sensitivity of cotton crops leads to reduced seed germination rates and delayed plant emergence as well as weakened seedling vigor (Atta et al., 2023; Bhattacharya, 2022). Cotton cultivation in suboptimal climatic areas of high-latitude means this vulnerability worsens (Rezaei et al., 2023). The discovery of molecular mechanisms responsible for cold tolerance in cotton remains crucial for creators of resistant cotton plant varieties (Ijaz et al., 2024). Cotton exposed to cold conditions experience numerous physiological-related processes affected particularly in membrane stability enzyme together with photosynthetic mechanisms (Xia et al., 2020; Abro et al., 2024). Plant organisms have developed two protective methods including transcriptional reprogramming along with osmoprotectant accumulation to reduce the effects of cold stress according to (Raza et al., 2023; Wang et al., 2024)\u003c/p\u003e\n\u003cp\u003eCotton plants turn on CBF/DREB1 signaling pathways, which start downstream cold regulated gene expression due to pathway activation improvements in stress adaptation mechanisms (Liu et al., 2021; Wang et al., 2021). Research has extensively studied the pathway in model plants Arabidopsis and cereals rice and maize but there is still limited knowledge about this pathway in cotton (Feng et al., 2025; Zhang et al., 2023). Research achievements included the isolation of GhCBF1 and its demonstration to provide freezing tolerance to transgenic systems thus showing cotton has potential for genetic improvement (Liu et al., 2021). Two important cold response mediator genes named GhAGP31 and GTOM1 have been discovered along with their role in mediating cold responses but the complete regulatory pathway must still be clarified \u0026nbsp;(Wang et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGWAS technology has proven effective for investigating genes affecting stress resistance levels in crop plants such as pea and maize and sorghum (Osuna-Caballero et al., 2024; Sing et al., 2023; Baye et al., 2020). A GWAS study of maize uncovered 159 QTLs and 106 candidate genes, which play a role in organelle biosynthesis during the seedling stage of cold tolerance (Yi et al., 2021). Studies on rice led to discoveries that QTLs that control different aspects of germination versus seedling development stage (Wang et al., 2020). The application of GWAS to investigate fiber quality and yield and tolerance against drought and salt stress has increased but cold stress research on seedling emergence specifically remains poorly understood (Sun et al., 2023; Zhu et al., 2020). In our research trail, we utilized Genome wide association study (GWAS) to study how Cotton controls cold tolerance during seedling emergence through primary root length PRL assessment in 302 wild and semi-wild \u003cem\u003eG hirsutum\u003c/em\u003e accessions. This research examines three essential objectives: determining genetic diversity with association patterns to ecological patterns and performing GWAS analysis to discover the genetic essence of cold tolerance under stress and identifying genetic regulators for cold tolerance management in cotton. In this study \u003cem\u003eGhPRL1\u003c/em\u003e gene was identify as a key gene by GWAS placement for cold tolerance in cotton, and upregulated expression was also confirm by real-time quantitative PCR (RT-qPCR) to be a candidate gene for enhancing cold tolerance in cotton. This study explore the function and validation of \u003cem\u003eGhPRL1\u003c/em\u003e gene under the cold stress through virus-induces gene silencing (VIGS) in cotton.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods ","content":"\u003cp\u003e\u003cstrong\u003ePlant materials and growth conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiverse wild and semi-wild \u003cem\u003eG hirsutum\u003c/em\u003e 302 accessions from labeled \u003cem\u003eLatifolium, Punctatum, Marigarant\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Ycatan\u003c/em\u003e formed the association-mapping panel at the Cotton Research Institute Chinese Academy of Agricultural Sciences in Anyang, Henan province, China. The population contained 156 \u003cem\u003eLatifolium\u003c/em\u003e varieties together with 64 \u003cem\u003ePunctatum\u003c/em\u003e, 54 \u003cem\u003eMarigarant\u003c/em\u003e accessions alongside 28 \u003cem\u003eYcatan\u003c/em\u003e accessions. We needed to select a method that increased environmental repetition because the low-temperature culture environment provided stability along with uniformity. Research on other cotton characters used seed number as their experimental basis. Furthermore, were chose full and uniform seeds from all materials before planting to decrease seed vitality, in our research trail IMAGE J software was used for analysis and measurement of primary root length PRL. The seeds of cotton received 3 minutes of exposure to sulphuric acid solution with its concentration set at 98 percent for delinting purposes. A dry place stored seeds following repeated water washing to minimize sulfuric acid residues while seeds remained there for two days. Fifty-germination seeds placed onto Petri dishes with two filter paper layers for placement. The petri plates received placement inside a Sanyo Japanese light incubator using a daily night schedule of 16:8 hours and a temperature range from 28 to 30 degrees Celsius and 60% relative humidity R.H. The samples underwent forty-eight hours of seeding development. A length of 1 cm for cotton radicles served than seed transfer into a cold stress chamber for both cold tolerance analysis and assessment of the cold-sensitive cotton genotypes. The collection of samples included all study items at the end of the 96-hour cold stress period using biological replicates that integrated multiple Petri dish samples. Three repeated trials functioned to boost biological replicate numbers while producing robust data. Furthermore, we froze all samples in liquid nitrogen immediately after cold stress then placed these samples at -80 degrees Celsius for future molecular and physiological analysis. The experimental design implemented in this system delivered thorough testing of cold stress responses in cotton genotypes by producing results, which consistently used for future investigations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome‑wide association study (GWAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are 302 accessions along with SNP data are available in the Sequence Read Archive database under accession number PRJNA1191690 at https://www.ncbi.nlm.nih.gov/sra/. The single nucleotide polymorphism (SNPs) and insertion-deletion (InDels) variations of each cotton accession were called separately using Genome Analysis Toolkit (GATK, version 3.1, HaplotypeCaller) software. Based on the reads site in the reference genome, redundant reads were filtered using Picard package (http://sourceforge. net/projects/picard/) to ensure the accuracy of the results. GATK was use for recalibrating the base quality score to obtain more accurate quality scores for each base, using the co-current variations set as known sites. Data obtain by recalibrating the base quality score used for variation detection by Haplotype Caller in GATK. In total, 2 606 757 SNPs and 1 084 616 InDels were identified across 200 cotton accessions. The 2 606 757 SNPs were used to perform GWAS using a mixed linear model in TASSEL. Principal component analysis based on filtered SNPs was perform using EIGENSOFT. Relative kinship was analyses using ADMIXTURE. Linkage disequilibrium (LD) decay was evaluated using PLINK software. The phylogenetic tree of each sample was construct using the neighbour-joining method and Kimura 2-parameter model in MEGA X software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of significant association SNPs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA definite sequence of highly linked SNPs recognizing various cold tolerance traits in significant markers detected within 200 kb ranges found to indicate strong effects at common loci. The assessment indicated significant SNP association when the − log10 (p) value exceeded 6.41 (P ≤ 0.0000004) using a Bonferroni correction (p = 1/n). The applied threshold value exceeded the values used for significant thresholds in other GWAS studies operating at low temperatures. Furthermore, we team selected P ≤ 0.0001 (− log10 (p) ≥ 4) as the threshold value to identify the most significant SNPs that appeared in multiple environments. When using a − log10 (p) filter value of 6.41 only a minimal number of SNPs would demonstrate environmental stability. The study screened significantly related SNPs using a − log10 (p) threshold threshold of 6.41 but examined SNPs with stable spots using a − log10 (p) threshold of four.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cold-tolerant \u003cem\u003eLatifolium307\u003c/em\u003e and together with the cold-sensitive\u0026nbsp;\u003cem\u003ePunctatum-131\u003c/em\u003e were chosen according to the results of haplotype analysis on\u0026nbsp;\u003cem\u003eGh_A11G315100\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Gh_A11G305200\u003c/em\u003e from the same geographical region. Low-temperature treatment performed after 2 d of germination at normal temperature. Samples collected before treatment (0 h) and at 48 and 96 h after 4 °C treatment. Total RNA was extracted, eukaryotic mRNA was enriched using Oligo (dT) beads, whereas prokaryotic mRNA was enriched by removing the rRNA using the Ribo-Zero™ Magnetic Kit (Epicenter), which have been deposited in the Sequence Read Archive database (https:// www.ncbi.nlmnih.gov/sra/) under accession number\u0026nbsp;PRJNA1191690. The RNA-seq library production required use of high quality RNA. Each gene received expression values as FPKM, which stands for expected fragments per kilobase of transcripts per million mapped fragments (FPKM). The unigenes characterized through FPKM to FDR ratios in calculating their abundance differences. A gene was considered differentially expressed when its FDR value was below 0.05 and its FPKM value exceeded 10. The investigators performed GO functional analysis and KEGG pathway analysis for each detected module. WEGO analysis at http://wego.genomics.org.cn/cgi-bin/wego/ index.pl revealed each module gene could obtain molecular function and biological process or cellular component classification. A GO analysis determined the primary biological functions within the DEGs. KEGG pathway analysis determined enriched pathways that included DEGs by comparing their numbers against the reference gene set through hypergeometric tests. The selection of consider ably enriched GO terms and KEGG enrichment pathways required a corrected P value below 0.01 as the threshold value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction, cDNA preparation, and qRT-PCR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized RNA prep pure plant reagent and extraction operated by following the manufacturer guidelines through the RNA-prep Pure Plant kit (Tiangen Biotech, Beijing, China). A reverse transcription quantitative PCR procedure was using primers of proper design (Supplement Table 1). To check RNA integrity samples ran on a 1.2% agarose gel and RNA quality determination done through Nanodrop 1000 spectrophotometer measurement. RNA conversion to cDNA occurred through the EasyScript First strand cDNA Synthesis SuperMix kit by Transgene, Beijing, China according to their instructions for cDNA synthesis. Furthermore, we team applied actin gene as an internal control to normalize expression levels in their examination. The real time PCR reactions contained 25 μL of final solution using SYBR green master mix. The PCR amplification process executed on ABI7500 thermal cycler (Applied Biosystems) through manufacturer-defined parameters (https://www.bioz.com/). The experimental process involved three separate biological repeats for every single test. The analysis of gene expression used the 2−ΔΔCT to enable us to perform relative quantification of gene expression data based on CT values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVirus‑induced gene silencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 300-bp of \u003cem\u003eGhPRL1\u003c/em\u003e genetic sequence were designed through the website tool 'https://vigs.solgenomics.net/' to achieve VIGS treatment. The experimental primers can be find in (Supplement Table 2). The specific coding sequence of GhPRL1 underwent amplification and cloning into TRV2 using the ClonExpress II One Step Cloning Kit from Vazyme (China) (C112-01). The scientists used Agrobacterium tumefaciens strain LBA4404 to transfer TRV2:GhPRL1. TRV2::00 was use as negative control. The vectors containing Agrobacterium tumefaciens strains was mix with Agrobacterium tumefaciens strains that contained pTRV1 into a mix of 1:1 ratio. An injection using a 1-mL needleless syringe was perform on cotton seedling cotyledons before the dark period lasted 24 hours while the plants later received constant temperature light culture (28 °C under 16 h/8 h day/night conditions).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMorphological, Physiological, and Biochemical Trait Determination\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysiological index determination was taken for dehydration rate assessment included excised leaf water loss (ELWL) while relative moisture content was evaluated through relative leaf water content (RLWL) and ion permeability through cell membrane stability (CMS) and chlorophyll content was measured through soil plant analysis development (SPAD). The evaluation of biochemical indices through SOD, H2O2, CAT and \u0026nbsp;MDA tests in leaf samples occurred at 0 h and 96 h time points using the Solarbio (Solarbio Beijing China) test kit following the provided procedures in the instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrosoft Excel (2010 version) from Microsoft Corporation helped analyze the obtained data which is displayed as mean ± SD. The statistical analysis process used SPSS software version 20.0 for its operations. The analysis used one-way ANOVA followed by Student's t test for group comparison. The selected P value smaller than 0.05 to determine statistical significance levels during analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic variation\u003c/strong\u003e \u003cstrong\u003eunder cold stress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our research trail, we used the wild and semi-wild cotton accessions from Cotton Research institute Anyang China for conducting GWAS on 302 divers\u0026apos; collections. ANOVA results showed that significant differences occurred, as indicated by the F-statistics of 9.016 for interaction and 21.39 for genotypes together with 14,022 for Control vs Cold, where all p-values reached below 0.0001 ( Supplement Table 3). The data analysis showed PRLs displayed significant variations throughout 302 accessions during germination stage cold stress assessment as determined by the CV results between 27.44% under control and 33.63% under cold stress condition (Supplement Table 4). Cold exposure caused an established decrease in PRL levels as compared to the regular control condition. The wide spread of data values in the control group shows higher RL values while the lower spread from the cold treatment indicates a statistically significant reduction of RL difference at p-value less than 2.22e-16 based on the violin plot analysis (Fig. 1a). The data analysis confirming these results through histograms demonstrates that the control group reached a mean RL value of 11.3 and standard deviation (SD) of 2.98 yet the cold group barely reached a mean RL of 5.27 accompanied by a lower standard deviation of 1.66 (Fig. 1b,c). Most genotypes display moderate responses to cold temperature as shown by the Cold Sensitivity Index results which present a mean value of 0.98 and standard deviation of 0.27 (Fig. 1d). The data shows root lengths primarily explained through genetic factors because the heritability values did not decrease under any experimental settings. The experiment revealed that the genetic diversity enhanced at room temperature compared to cold conditions leading to higher genetic heterogeneity at diverse controls (Supplement Fig. 1a). The data in (Supplement Fig. 1b) reveals that genetic variability reached higher levels under control conditions when compared to the limited values observed during cold conditions. The histogram shows environmental stress through the narrow distribution combined with high overlap, which characterizes the cold treatment data. Root ength displays greater environmental susceptibility to cold stress according to the higher residual variance exhibited in the cold treatment (Supplement Fig. 1c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Diversity Analysis and Population Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS analysis needs population structure control since population stratification would create misleading associations between genotypes and phenotypes. The heterozygosity measurements indicated that 95% of cotton accessions displayed less than 60% heterozygosity whereas 80% of individual materials showed less than 5% heterozygosity as shown in (Supplement Fig. 2a). Evidence shows HEs occurs commonly in heterologous tetraploid crops yet remain at minimal levels in cotton populations despite earlier studies confirming this fact. Most cotton genotypes showed weak genetic connections while only few had close relationships with one another (the figure shows yellow background with small dark red shapes). A four-species subgroup classification exists for all 302 examined cotton genotypes (Supplement Fig. 2b). The re-sequencing analysis yielded 10,180 highly reliable SNPs among 17 total. The data analysis regarding chromosome distribution appears in (Fig 2). The 302 cotton plant accessions contributed to four phylogenetic subgroups according to the analysis of SNP-derived tree (Fig 3a). PCA separated 302 individuals of cotton \u003cem\u003eGossypium hirsutum\u003c/em\u003e into four distinct subgroups wherein subgroup 1 included 200 materials while subgroup 2 held 50 samples and subgroup 3 included 40 individuals and subgroup 4 contained 13 materials (Fig 3b). Structure analysis software utilized the hybrid model for its operations. The model applied three iterations at each level of subgroups (K) ranging from 1 to 10. During the non-count iteration period the Markov chain Monte Carlo (MCMC) began running for 10,000 iterations as its burn-in period before continuing its execution for 1,000,000 iterations. The analysis used maximum likelihood value to detect subgroup structure along with their numbers from the optimal K value. Analysis of cross-validation error (CV error) happened through different K value combinations (2\u0026ndash;10). The findings from Structure software and Q value calculation indicated K being at 4 (Fig 3c). The GWAS results would achieve better accuracy after using the k = 4 Q-matrix as the covariate to eliminate potential spurious associations. Furthermore, we findings revealed that the material origin influenced the structural distribution of subgroups because the genetic compositions within resources were mainly homogeneous (Fig 3d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS for Primary root length PRL under cold stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis of cold sensitivity in cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e) primary root length (PRL) through the best linear unbiased prediction (BLUP) model were performed on 302 diverse accessions in a genome-wide association study (GWAS) (Supplement Table 5). Furthermore, we approach simplified the genetic complexities of PRL under cold stress which had not been studied in genetic models previously. A significant cluster of SNPs reached -log₁₀ (P) levels greater than 7 on chromosome A11 in the Manhattan plot thus indicating this region as a fundamental factor for cold adaptation (Fig. 4a). Furthermore, we conducted subsequent fine mapping within a 2Mb interval (chrA10:108.407\u0026ndash;110.407 Mb) to study the linkage disequilibrium (LD) pattern found in A10 Region1.01. The LD heatmap displayed distinct haplotype blocks showing almost complete allelic correlation (D\u0026prime; \u0026asymp; 1.0) of genomic regions that escaped historical recombination events (Fig. 4d). The regions situated between linkage groups displayed D\u0026prime; values less than 0.5, which confirmed both the occurrence of recombination hotspots and ancestral allele transmission decline. The measured findings might result from either functional adaptations that happened through natural selection or selection-based evolutionary adjustments. The Hap-1 through Hap-4 haplotype-stratified association analysis showed significant non-random variation in \u0026minus;log₁₀ (P) observations through quantile-quantile (QQ) analysis (Fig. 4b). Hap-4 (n=28) showed strong statistical evidence of low P-values reaching (\u0026minus;log₁₀ (P) = 4.5; P = 3.2\u0026times;10⁻⁵) and surpassing the genome-wide threshold (P \u0026lt; 0.0001) despite having a small cohort size indicating a powerful cold tolerance allele (Fig. 4e). The Hap-1 group (n=156) generated less prominent signals at \u0026minus;log₁₀ (P) \u0026asymp; 2.0 (P = 0.01) yet the Hap-2 (n=64) and Hap-3 (n=54) groups produced intermediate signatures that indicated polygenic influence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate Gene Screening\u003c/strong\u003e \u003cstrong\u003eunder cold stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our research trail we furfure investigate about the screening of candidate gene under cold stress where we found that 135 genes associated with cold tolerance appeared in genotype 307 through the analysis of the 2MB region (Supplement Table 6). An analysis of these genes during germination under cold stress detection yielded results to identify potential candidate genes influencing the cold response mechanism. Furthermore, we evaluated the expression patterns of these genes which reside within identified regions across germination stages through primary root length PRL candidate gene identification under cold tolerant genotype 307 (Fig5). Post-sequence analysis showed that nine genes including \u003cem\u003eGh_A11G315100 Gh_A11G309000, Gh_A11G315400, Gh_A11G312800, Gh_A11G305200, Gh_A11G312600, Gh_A11G302700, Gh_A11G305000,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGh_A11G311900\u0026nbsp;\u003c/em\u003ewere present within the area spanning from A11:109232146-104617544. During the expression level, filtering step and haplotype analysis the number of remaining genes reached 3 for the found changes in SNPs. The genes \u003cem\u003eGh_A11G315100\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Gh_A11G305200\u003c/em\u003e showed significant potential regarding primary root length because they displayed distinct expression patterns between cold tolerant genotype 307 (Fig 5a). Research indicates that \u003cem\u003eGh_A11G315100\u003c/em\u003e (SECB13) and \u003cem\u003eGh_A11G305200 (\u003c/em\u003eSYP4) demonstrate different variations in accessions distinguished by their cold-tolerant or cold-sensitive characteristics. The expression of \u003cem\u003eGhPRL1\u003c/em\u003e gene was evaluated in cold tolerant and cold sensitive accessions through reverse transcription quantitative PCR. Furthermore, we findings demonstrated that \u003cem\u003eGhPRL1\u003c/em\u003e displayed elevated expression patterns within cold-tolerant accessions compared to the accessions with cold-sensitive characteristics. The \u003cem\u003eGhPRL1\u003c/em\u003e gene emerged as the leading candidate that connects to cold tolerance diversity between different \u003cem\u003eGossypium hirsutum\u003c/em\u003e accessions since the allele \u003cem\u003eGhPRL1\u003c/em\u003e Hap1 shows a potential link to cold responses in cold-tolerant accessions according to these data (Fig 5b). The confirmations of gene expression data verified through qrt-PCR assessment. Gene expression of SECB13 and SYP4 reached its highest point during the 96-hour period of the resistant condition based on the experimental results (Fig 5c). During the 48-hour period, SECB13 recorded its highest expression point, which decreased slightly by the end of 96 hours yet SYP4 expression levels progressively rose from 48 hours to deliver pronounced elevation at 96 hours. The expression levels of investigated genes parallel each other but stay mainly at lower levels during the study period. The expression of SECB13 decreased throughout the period from 48 to 96 hours although SYP4 expression levels rapidly increased during this same period. The resistant interactions exhibit increased and sustained gene expression patterns during the analysis at 96hours according to the results (Fig 5c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of candidate gene silence in cotton plants under cold stress.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGhPRL1\u003c/em\u003e appears to represent the main gene linked to cold tolerance and shows its highest expression level during seed germination according to (Fig. 5). Therefore, we used VIGS methodology to silence the \u003cem\u003eGhPRL1\u003c/em\u003e gene under cold stress conditions. The phenotypic changes of seedling cotton plants in two-leaf development evaluated following different treatments during a specific observation period. The plants that received TRV2: PDS bacterial solution developed clear whitening of their leaves about 10 days after cultivation. The successful gene silencing procedure produced stable results that gave rise to a whitening phenotype, which acquired a lasting effect during the next ten days of plant culture (Fig 6a).\u003c/p\u003e\n\u003cp\u003eTo evaluate \u003cem\u003eGhPRL1\u003c/em\u003e expression changes caused by gene silencing in TRV2:00 and TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e cotton seedlings we used quantitative real-time PCR (qRT-PCR) to measure the levels of \u003cem\u003eGh_A11G315100\u003c/em\u003e (\u003cem\u003eGhPRL1\u003c/em\u003e) expression. The research found that \u003cem\u003eGhPRL1\u003c/em\u003e maintained a similar expression between wild type plants and TRV2:00 samples under both conditions. The TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants displayed significantly diminished \u003cem\u003eGhPRL1\u003c/em\u003e expression levels beneath WT and TRV2:00 expression levels. The suppression of \u003cem\u003eGhPRL1\u003c/em\u003e gene regulation demonstrated the successful silencing of this gene within the transgenic lines (Fig 6b).\u003c/p\u003e\n\u003cp\u003eThe research team evaluated both normal WT, TRV2:00 and TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e cotton seedlings under cold stress conditions. Every plant line maintained healthy development without showing signs of wilting and withstanding normal growth conditions properly. All subjected plants developed leaf-wilting symptoms after their exposure to cold stress for twenty-four hours. Phenotypic changes became more distinguished during the 48-hour exposure to cold conditions. The WT and TRV2:00 plants exhibited equal levels of wilting to each other but both groups maintained comparable cold stress responses. By contrast the damage in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants reached a worse stage. The stems softened and the plants slightly lodged as they showed major decreased cold resistance following the \u003cem\u003eGhPRL1\u003c/em\u003e gene silencing (Fig 6c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of \u003cem\u003eGhPRL1\u003c/em\u003e Gene Silencing on physiological index under Cold Stress in Cotton\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe examination of physiological and oxidative stress responses in \u003cem\u003eGhPRL1\u003c/em\u003e-silenced cotton plants during cold stress included measurements of chlorophyll content, relative water content, ion leakage, excised leaf water loss together, superoxide dismutase (SOD), catalase (CAT), hydrogen peroxide (H2O2) and malondialdehyde (MDA) content in WT TRV2:00 and TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e cotton under cold stress (Fig 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants revealed higher amounts of ion leakage compared to WT and TRV2:00 plants thus indicating extensive damage to cell membranes and reduced maintenance of cellular integrity during cold stress. TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants experienced more rapid water loss from excised leaves, which indicated damages to their leaf tissues alongside dehydration. The silencing of \u003cem\u003eGhPRL1\u003c/em\u003e through transgenic engineering resulted in identical chlorophyll content and relative water content when plants were expos to cold stress (Fig 7a, b, c and d). \u0026nbsp;The activities of SOD and CAT together with the contents of MDA and H2O2 remained unchanged in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants, WT, and TRV2:00 control plants under standard (CK) growth conditions (Fig 7e, f, g, and h). The chlorophyll content together with relative water content, ion leakage, and excised leaf water loss from the three groups remained statistically similar when observing normal conditions (Fig 9). The groups demonstrated substantial variations during cold stress tests according to experimental results. TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants presented reduced activities of SOD and CAT that resulted in impaired reactive oxygen species detoxification capabilities and limited management of oxidative stress. The TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plant line displayed elevated levels of H2O2 and MDA together with increased ROS accumulation and oxidative damage when tested against cold stress. This demonstrates that blocking \u003cem\u003eGhPRL1\u003c/em\u003e expression had no substantial impact on plant response to these criteria related to cotton plant health under cold stress (Fig 7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e) is a globally significant crop that is highly susceptible to cold stress, which can adversely affect its growth and yield (Zhao et al., 2024; Abro et al., 2024). In this study, we aimed to explore the genetic and phenotypic characteristics of cold tolerance in cotton by focusing on genetic loci associated with primary root length (PRL) and candidate genes that mediate cold stress responses. We examined genetic and phenotypic characteristics of cold tolerance in cotton by analyzing 302 diverse accessions from the Cotton Research Institute in Anyang, China. The large set of cotton accessions demonstrated that primary root length (PRL) measurements progressively decreased as exposure to cold stress intensified. The cold stress resulted in a significant reduction of PRL, reaching 5.27 cm under cold stress conditions compared to 11.3 cm in control conditions. Statistical analysis confirmed that both genetic type and cold treatment had a substantial impact on PRL (F-statistics: genotype differences = 21.39, cold stress = 14.022), with p-values below 0.0001. These results support the observation that cold stress negatively affects root growth and highlights the genetic variability in root growth responses under cold conditions, which has been documented in previous studies (Zhang et al., 2024; Fan et al., 2022; Lu et al., 2019). Additionally, genotypic analysis revealed that most cotton accessions exhibited moderate sensitivity to cold stress, which is consistent with the results reported by (Ge et al., 2022; Shen et al., 2023). This variability in response to cold stress is likely due to the differing genetic backgrounds of the accessions, further emphasizing the importance of identifying genetic markers for cold tolerance. Heritability estimates for root length under cold stress indicated that genetic factors govern PRL under these conditions, suggesting that cold stress response in cotton was primarily control by genetic factors rather than environmental influences (Windpassinger et al., 2017;\u0026nbsp;Jähne et al., 2019;\u0026nbsp;Sthapit and Witcombe 1998).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of the Genome-Wide Association Studies (GWAS), we conducted a genetic diversity analysis and kinship evaluation among the 302 accessions. The heterozygosity analysis revealed that 95% of the accessions had less than 60% of heterozygous alleles, indicating relatively low genetic diversity. Despite this, PCA analysis identified four distinct genetic clusters that reflected the genetic relationships among the cotton accessions. These findings are in line with previous studies, such as (Ge et al., 2024; Baytar et al., 2022; Shen et al., 2022), which also documented distinct genetic patterns in cotton accessions from different geographical regions. GWAS identified key genomic regions associated with cold tolerance, with a major candidate gene located on chromosome A11. SNP analysis revealed a significant SNP (−log₁₀ (P) \u0026gt; 7) in this region, indicating a strong correlation with cold stress tolerance. The linkage disequilibrium (LD) heatmap for this region demonstrated a high correlation (D′ ≈ 1.0), suggesting that the cotton genome has undergone strong selection pressure for cold tolerance. This aligns with findings by (Sun et al., 2019; Khatab et al., 2022; Li et al., 2022), who also identified cold tolerance loci on chromosome A11. In addition to GWAS, we further explored the functional role of \u003cem\u003eGhPRL1\u003c/em\u003e in cold stress tolerance by silencing the gene using Virus-Induced Gene Silencing (VIGS) technology. The results of this experiment confirmed the critical role of \u003cem\u003eGhPRL1\u003c/em\u003e in cotton's response to cold stress. The successful silencing of \u003cem\u003eGhPRL1\u003c/em\u003e resulted in notable phenotypic and physiological changes, as evidenced by the albino phenotype observed in PDS-silenced plants and the reduced expression of \u003cem\u003eGhPRL1\u003c/em\u003e in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants, confirmed by qRT-PCR (Fig. 6b). These results validate that \u003cem\u003eGhPRL1\u003c/em\u003e plays a critical role in cold tolerance in cotton plants, also align with findings by (Ge et al., 2024; Shen et al., 2022; Ge et al., 2022; Shen et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder cold stress conditions, plants that underwent \u003cem\u003eGhPRL1\u003c/em\u003e silencing (TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e) exhibited significantly greater damage compared to the wild type (WT) and TRV2:00 control plants. This damage was evident through increased wilting, lodging of the stems, and a general decline in plant health, particularly after 48 hours of cold exposure (Fig. 6c). These results suggest that the loss of \u003cem\u003eGhPRL1\u003c/em\u003e expression hampers the plant's ability to withstand cold stress, in line with previous studies on PRL genes in other species (Li et al., 2024; Wu et al., 2024; Zhanfg et al., 2024). Physiological measurements also supported these findings. TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants showed higher ion leakage, increased water loss from excised leaves, and reduced chlorophyll content under cold stress, indicating greater cellular damage and dehydration compared to WT and TRV2:00 plants (Fig. 7). These results underscore the importance of \u003cem\u003eGhPRL1\u003c/em\u003e in maintaining cellular integrity and water retention during cold stress, a finding consistent with other research on stress-associated genes in plants (Cai et al., 2019; Cao et al., 2021; Wei wr al., 2024). Furthermore, the activities of antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) were significantly reduced in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants under cold stress (Fig. 7e, g). This suggests that silencing \u003cem\u003eGhPRL1\u003c/em\u003e impairs the plant's ability to detoxify reactive oxygen species (ROS), which accumulate during cold stress. Elevated levels of H2O2 and malondialdehyde (MDA) in TRV2:\u003cem\u003eGhPRL1\u003c/em\u003e plants further corroborate the increased oxidative stress and cellular damage observed (Fig. 7f, h). These results emphasize the role of \u003cem\u003eGhPRL1\u003c/em\u003e in modulating antioxidant defense mechanisms and maintaining cellular homeostasis during cold stress, as observed in other plant species (Cai et al., 2019).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we evaluated the phenotypic and genetic responses of 302 cotton accessions to cold stress, focusing on primary root length (PRL) as a key trait. Our findings demonstrated that cold tolerance in cotton influenced by both genetic and environmental factors, with significant genetic variation observed in response to cold stress. GWAS identified a major locus on chromosome A11 associated with cold tolerance, and fine mapping highlighted a critical region linked to PRL. \u003cem\u003eGhPRL1\u003c/em\u003e emerged as a key candidate gene, with its silencing leading to severe phenotypic damage and reduced cold tolerance. The results emphasize the importance of \u003cem\u003eGhPRL1\u003c/em\u003e in cotton\u0026apos;s response to cold stress, suggesting its potential as a target for improving cold resilience through genetic breeding. Future research should focus on the functional validation of \u003cem\u003eGhPRL1\u003c/em\u003e and other candidate genes identified in this study to further elucidate their role in cold stress adaptation and enhance breeding strategies for cold-tolerant cotton cultivars.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contributions statement :\u003c/strong\u003e Conceptualization and methodology, A.A.A. and M.A.; Experimentation, Q.L and J.Z., Initial draft, A.A.A. and M.A. Review and editing, Y.X., Y.H., Z.Z., \u0026nbsp;F.L., and X.C, Formal analysis, Y.X., Y.H., Z.Z.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by National Natural Science Foundation of China (32401824, 32171994, 32272090, 32201875), Natural Science Foundation of Henan (242300421595), Nanfan special project, CAAS (YBXM2439) and the National Key R\u0026amp;D Program of China (2024YFD1200300).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets analyzed during this study are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests:\u0026nbsp;\u003c/strong\u003eThe authors stated that they had no interest, which perceived as posing a conflict or bias.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbro AA, Qasim M, Abbas M, Muhammad N, Ali I, Khalid S, Liu F (2024) Integrating physiological and molecular insights in cotton under cold stress conditions. 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BMC Plant Biol 20:1\u0026ndash;13.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6283010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6283010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cold stress during the seedling emergence stage severely affects the growth and development of cotton (Gossypium hirsutum), leading to reduced yield and plant health. Despite its importance, the molecular mechanisms underlying cold tolerance in cotton remain poorly understood. In this study, we analyzed 302 cotton accessions from the Cotton Research Institute in Anyang, China, to assess phenotypic and genetic responses to cold stress. Statistical analysis revealed significant reductions in primary root length (PRL) under cold stress, with a notable increase in genetic variation in root growth. Genome-wide association studies (GWAS) identified key genetic loci associated with cold tolerance, particularly on chromosome A11, where a cluster of SNPs exhibited strong associations with PRL. Fine mapping revealed high linkage disequilibrium in this region, indicating evolutionary selection for cold tolerance. Among the candidate genes, Gh_A11G315100 (GhPRL1) was identify as a major gene linked to cold tolerance. Virus-Induced Gene Silencing (VIGS) of GhPRL1 confirmed its essential role in maintaining plant health under cold stress, with GhPRL1-silenced plants showing greater phenotypic damage, increased ion leakage, and reduced antioxidant activity. This study provides valuable insights into the genetic basis of cold tolerance in cotton and identifies GhPRL1 as a critical target for future breeding efforts aimed at enhancing cold resilience.","manuscriptTitle":"Genetic insights into cold tolerance in cotton: GWAS identified GhPRL1 gene responsible for cold tolerance in cotton at seedling stage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 18:26:52","doi":"10.21203/rs.3.rs-6283010/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f202ba4-4a08-4b50-b783-647916edc349","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T13:49:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 18:26:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6283010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6283010","identity":"rs-6283010","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}