Deciphering Metabolic Signatures of Cotton Seeds via High-Resolution Lc–Qtof–MsBased Untargeted Metabolomics

Plant metabolomics provides a powerful approach for characterizing biochemical variation associated with genotype and environmental adaptation.Here, we compared the seed metabolomic profiles of three Gossypium hirsutum materials with contrasting genetic backgrounds: the non-modified Coker-312 variety, the RNAi_FRS10 line, and the modified Matonat-2 variety.Three biological replicates were analyzed for each genotype (n = 3 per genotype) using untargeted high-resolution LC-QTOF-MS.A total of 302 molecular features were retained and metabolically annotated based on accurate mass, isotope-pattern information, MS/MS evidence, and database/in silico matching; these assignments should be regarded as putative annotations unless supported by authentic reference standards.Principal component analysis (PCA) separated the three genotypes, with PC1 and PC2 explaining 29% and 22% of the total variance, respectively (51% combined).One-way ANOVA identified 112 features with nominal significance (P≤0.05), of which 67 remained significant after Benjamini-Hochberg false discovery rate (FDR) correction and 28 after Bonferroni adjustment.The metabolic profiles differed across several compound classes.Matonat-2 showed higher relative abundances of phenolic compounds, including quercetin, rutin, and ferulic acid, together with glutathione and lipid-associated metabolites such as oleamide and linoleamide, whereas Coker-312 showed higher relative abundances of the amino acids L-asparagine and L-tyrosine.These patterns indicate genotype-associated differences in phenolic, redox-related, lipid-associated, and aminoacid metabolism, but do not by themselves demonstrate pathway activation or superior stress tolerance.The identified features, particularly those contributing to genotype discrimination, represent candidate discriminatory metabolites that warrant validation using authentic standards, targeted quantitative analysis, independent biological materials, and complementary physiological or molecular approaches.Overall, the findings provide a biochemical basis for further investigation of genotype-associated metabolic variation in cotton seeds.