Fuzzless Gossypium hirsutum mutants are ideal materials for investigating cotton fiber initiation and development. In this study, we used the fuzzless G. hirsutum mutant Xinluzao 50 FLM as the research material and combined it with other fuzzless materials for verification by RNA sequencing to explore the gene expression patterns and differences between genes in upland cotton during the fuzz period. A gene ontology (GO) enrichment analysis showed that differentially expressed genes (DEGs) were mainly enriched in the metabolic process, microtubule binding, and other pathways. A weighted gene co-expression network analysis (WGCNA) showed that two modules of Xinluzao 50 and Xinluzao 50 FLM and four modules of CSS386 and Sicala V-2 were highly correlated with fuzz. We selected the hub gene with the highest KME value among the six modules and constructed an interaction network. In addition, we selected some genes with high KME values from the six modules that were highly associated with fuzz in the four materials and found 19 common differential genes produced by the four materials. These 19 genes are likely involved in the formation of fuzz in upland cotton. Several hub genes belong to the arabinogalactan protein and GDSL lipase, which play important roles in fiber development. According to the differences in expression level, 4 genes were selected from the 19 genes and tested for their expression level in some fuzzless materials. The modules, hub genes, and common genes identified in this study can provide new insights into the formation of fiber and fuzz, and provide a reference for molecular design breeding for the genetic improvement of cotton fiber.

DVL is one of the small polypeptides which plays an important role in regulating plant growth and development, tissue differentiation, and organ formation in the process of coping with stress conditions. So far, there has been no comprehensive analysis of the expression profile and function of the cotton DVL gene. According to previous studies, a candidate gene related to the development of fuzz was screened, belonging to the DVL family, and was related to the development of trichomes in Arabidopsis thaliana. However, the comprehensive identification and systematic analysis of DVL in cotton have not been conducted. In this study, we employed bioinformatics approaches to conduct a novel analysis of the structural characteristics, phylogenetic tree, gene structure, expression pattern, evolutionary relationship, and selective pressure of the DVL gene family members in four cotton species. A total of 117 DVL genes were identified, including 39 members in G. hirsutum. Based on the phylogenetic analysis, the DVL protein sequences were categorized into five distinct subfamilies. Additionally, we successfully mapped these genes onto chromosomes and visually represented their gene structure information. Furthermore, we predicted the presence of cis-acting elements in DVL genes in G. hirsutum and characterized the repeat types of DVL genes in the four cotton species. Moreover, we computed the Ka/Ks ratio of homologous genes across the four cotton species and elucidated the selective pressure acting on these homologous genes. In addition, we described the expression patterns of the DVL gene family using RNA-seq data, verified the correlation between GhMDVL3 and fuzz development through VIGS technology, and found that some DVL genes may be involved in resistance to biotic and abiotic stress conditions through qRT-PCR technology. Furthermore, a potential interaction network was constructed by WGCNA, and our findings demonstrated the potential of GhM_A05G1032 to interact with numerous genes, thereby playing a crucial role in regulating fuzz development. This research significantly contributed to the comprehension of DVL genes in upland cotton, thereby establishing a solid basis for future investigations into the functional aspects of DVL genes in cotton.

Drought stress severely constrains cotton yield and fiber quality, but conventional evaluation methods are inefficient and time-consuming. To address this, we developed a high-throughput, non-destructive phenotyping framework by integrating UAV-based multispectral remote sensing with machine learning, using 225 upland cotton (Gossypium hirsutum L.) accessions. The accessions were subjected to well-watered (CK) and drought stress (DS) treatments at the flowering and boll-setting stage. Canopy multispectral imagery (Green/Red/Red_edge/Near-infrared bands) was acquired via DJI Mavic 3 Multispectral UAV, and 16 vegetation indices (VIs) were derived. Concurrently, 15 agronomic and fiber quality traits were measured to calculate drought resistance coefficients (DRCs), which were used for principal component analysis (PCA) and comprehensive drought tolerance index (D) construction. Hierarchical clustering categorized the accessions into 6 drought tolerance grades (Groups I–VI). Variable importance analysis identified GNDVI, NGRVI, and NDRE as the most drought-sensitive VIs (% IncMSE > 11). Among four regression models (LR, KNN, LGBM, XGBoost), XGBoost achieved the best performance for D prediction (test set: R² = 0.785, RMSE = 0.032, MAE = 0.024). This study demonstrates that UAV multispectral data coupled with XGBoost enables accurate, efficient drought tolerance assessment, providing a robust tool for high-throughput germplasm screening and smart agricultural management.

Background: Drought is one of the major abiotic stresses that affect and limit cotton growth and production. However, transcriptome differences between drought-tolerant and drought-susceptible cotton lines remain largely unknown. Methods and Results: In this study, two upland cotton cultivars, the drought-tolerant XLZ80 and drought-sensitive XLZ61, were subjected to comparative phenotypic and transcriptomic analyses under drought stress. Phenotypic evaluation showed that XLZ80 exhibited only mild leaf wilting, whereas XLZ61 displayed severe wilting symptoms after drought stress. RNA-seq analysis revealed that differentially expressed genes in XLZ80 were specifically enriched in pathways related to phosphatidylinositol signaling, phenylalanine metabolism, MAPK signaling, and betaine biosynthesis, while DEGs in XLZ61 were primarily involved in basal metabolic processes. A total of 9302 core DEGs were identified across and between the cultivars and were grouped into eight dynamic expression clusters containing 841 transcription factors. Weighted gene co-expression network analysis further identified three key modules associated with drought tolerance. Twelve hub genes, including GH_D02G2153 (MADS-box) and GH_A05G1087 (bZIP), were identified as central regulators. qRT-PCR validation confirmed that these genes exhibited faster and stronger induction in the tolerant cultivar. In summary, this study deepens the transcriptional-level understanding of drought stress responses in cotton and provides valuable gene resources for breeding drought-resistant cultivars.

The height of the first fruiting branch node (HFFBN) is a core indicator for mechanical harvesting of cotton, and the development of molecular markers for this trait is important for accelerating the breeding process. In this study, using bulked segregant analysis coupled with whole-genome sequencing (BSA-seq), one quantitative trait locus (QTL) associated with the HFFBN was mapped; a molecular marker, qFBH7, associated with the HFFBN of cotton was developed; and its application value was systematically evaluated. A total of 20 lines with extreme phenotypes were selected from the recombinant inbred lines constructed using upland cotton Z3-146 and Z3-147 as parental lines. The screened lines with extreme phenotypes were used to construct the extreme high-HFFBN pool and the extreme low-HFFBN pool, which were subsequently used for BSA-seq. Using the upland cotton genome as a reference, relevant QTLs were mapped by BSA-seq. One relevant candidate region was identified, with a total length of 2.25 Mb. The validation experiments revealed that the genotyping results of the KASP_FBH7_03 molecular marker in the parental lines Z3-146 and Z3-147 were completely consistent with the BSA-seq data: Z3-146 had the TT genotype, and Z3-147 had the CC genotype. Among the 66 samples from the natural population, there was a significant difference (p < 0.05) in the HFFBN between the CC and TT genotypes, and the mean HFFBN of the TT genotype was greater than that of the CC genotype. In summary, the KASP_FBH7_03 molecular marker can be effectively used for selective breeding for the HFFBN of cotton, and the TT genotype has a positive regulatory effect on the HFFBN. This study not only provides resources for breeding cotton varieties suited to mechanical harvesting but also offers a robust tool for molecular marker-assisted selection.