Correction: Comparative transcriptome and metabolome profiling unveil genotype-specific strategies for drought tolerance in cotton

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Introduction Cotton is one of the most economically significant natural fiber crops globally (Geng et al., 2024). As the largest producer and consumer of cotton globally, China faces significant economic implications from potential shortages of cotton and cotton-derived products. Current cultivation practices concentrate cotton production in arid and semi-arid zones, rendering the crop highly susceptible to drought stress. Emerging evidence underscores drought as the primary abiotic constraint that impairs cotton growth, development, yield formation, and fiber quality (Geng et al., 2024). Addressing this challenge necessitates a systematic exploration of drought response mechanisms at the molecular level. By identifying candidate drought-resistant genes, characterizing key regulatory networks, and implementing marker-assisted breeding strategies, researchers can develop stress-tolerant cotton cultivars. This integrative approach holds promise for alleviating genetic resource limitations and optimizing water use efficiency in cotton production systems (Yin et al., 2024a). Current research has revealed that plants have evolved sophisticated multi-level response mechanisms to cope with drought stress, which can be systematically categorized as follows: (1) Rapidly initiating stomatal closure and stress-related gene expression via the ABA pathway (Yoshida et al., 2014); (2) Synthesizing osmoprotectants like proline and betaine for cellular turgor maintenance (Hennig, 2012); (3) Activating the antioxidant enzyme system, including superoxide dismutase (SOD) and catalase (CAT), to scavenge reactive oxygen species (ROS) (Ali et al., 2022); (4) Accumulating secondary metabolites such as flavonoids and terpenoids to enhance cell membrane stability (Jan et al., 2024). Notwithstanding these conserved adaptive strategies, drought responses involve intricate molecular networks, signaling cascades, and metabolic reprogramming that exhibit substantial interspecific and intraspecific variations. Therefore, gaining mechanistic insights into species-specific or genotype-specific drought responses is critical for precise identification of key regulatory genes and development of targeted breeding strategies. Transcriptomics is the study of the overall gene expression in a biological organism under specific conditions. It reveals the molecular mechanisms of gene regulatory networks and biological processes by analyzing the types and quantities of RNA molecules, such as mRNA, lncRNA, and miRNA. Recent advances in high-throughput sequencing technologies have revolutionized plant transcriptomics, enabling comprehensive investigations into growth regulation, stress adaptation, and metabolic networks (Yin et al., 2023). At the same time, the advancement of mass spectrometry technology has also promoted the widespread application of metabolomics in plant research. Metabolomics is a technology for the quantitative analysis of all metabolites in a biological organism. It can detect small molecules and exogenous substances, including endogenous substances in tissues or organs with a relative molecular mass usually less than 1000. Metabolites represent the final products of cellular regulatory processes, serving as the ultimate indicators of biological systems' responses to genetic or environmental changes (Liang et al., 2022). During the last few years, advancements in high-throughput sequencing technologies have driven a paradigmatic shift in plant drought research. Integrated transcriptomic and metabolomic analysis offers a systematic approach to elucideate cooperative networks between gene expression regulation and metabolite accumulation. Firstly, transcriptomic data provide genetic-level explanations for metabolite biosynthesis, transport, and regulation. For instance, transcriptomic profiling may identify upregulated expression of key enzyme-encoding genes involved in carbohydrate metabolism, which can be correlated with corresponding metabolite changes observed in metabolomic datasets. This establishes direct links between gene expression dynamics and metabolic reprogramming. Moreover, metabolomic data can corroborate transcriptomic findings by functional validation. When transcriptomics identifies DEGs under drought whose functions remain elusive, metabolomic analysis may reveal specific metabolite perturbations associated with these genes, enabling hypotheses regarding their roles in metabolic pathway regulation. Integrated transcriptomic and metabolomic analyses have revealed key molecular responses to drought in switchgrass. The drought-tolerant Alamo genotype upregulates diterpenoid biosynthetic genes, leading to root diterpenoid accumulation and enhanced stress tolerance (Tiedge et al., 2022). Metabolomic profiling demonstrated that both drought and cold stress stimulate raffinose, trehalose-6-phosphate, proline and monosaccharide accumulation, coupled with increased expression of corresponding biosynthetic genes (Guo et al., 2021). Correlation analysis between transcriptomics and metabolomics has revealed that LOC110713661 and LOC110738152 may be key genes for drought tolerance in quinoa, with DEGs and metabolites being annotated to starch and sucrose metabolism as well as flavonoid biosynthesis pathways, indicating that these metabolic pathways are crucial for enhancing quinoa's drought tolerance (Huan et al., 2022). Integrated transcriptomic and metabolomic analyses demonstrated that ZmGLK44 directly binds to and activates tryptophan synthase TSB2, regulating the tryptophan biosynthesis pathway in maize and playing a key role in metabolic regulation and drought response (Zhang et al., 2021). Transcriptomic and metabolomic profiling demonstrated that sweet potato boosts drought resistance through the activation of antioxidant-related genes and increased flavonoid production under drought stress (Yin et al., 2024b). Studies demonstrate that proline, tryptophan, and phenylalanine serve as crucial amino acids for maize drought adaptation, with enhanced expression of tryptophan biosynthesis genes (ZmAO1, ZmCAT1, and ZmYUC6) significantly contributing to drought resistance regulation (Li et al., 2024b). WGCNA has emerged as an effective tool for identifying functional genes by linking gene expression data with phenotypic traits, constructing gene coexpression networks, and identifying gene modules and hub genes closely associated with plant traits (Ma et al., 2024a). Through WGCNA analysis of transcriptomic data, the hub gene GhNAC072 was identified, and subsequent functional studies revealed that silencing GhNAC072 reduced cotton's tolerance to drought stress, while overexpressing this gene in Arabidopsis enhanced its drought tolerance (Mehari et al., 2021). WGCNA analysis identified 851 core drought-resistant genes in XZSN (the drought-resistant genotype of Tartary buckwheat), most of which were induced earlier and more rapidly by drought stress compared to genes in LK3 (the drought-sensitive genotype of Tartary buckwheat) (Meng et al., 2022). WGCNA analysis identified 22 distinct co-expression modules, among which the deep blue module exhibited the strongest positive association with drought tolerance characteristics. Further analysis identified twenty potential hub genes, including g47370 (AFP2), g14296 (CDKF), and g60091 (SPBC2A9), which were found to be potentially associated with drought resistance regulation in sweet potato (Zong et al., 2023). WGCNA revealed MsC3H29c as a key drought-responsive gene: its overexpression increased primary root length and biomass in Medicago sativa, whereas RNAi silencing reduced these traits (Dong et al., 2024). With ongoing advances in cotton molecular biology and functional genomics, researchers have uncovered key molecular pathways involved in drought response and identified several drought-tolerant genes. Nevertheless, the comparative analysis of adaptive mechanisms among different drought-resistant cotton genotypes remains

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