Cytoplasmic male sterility (CMS) is a maternally inherited trait failing to produce functional pollen. It plays a pivotal role in the exploitation of crop heterosis. The specific locus amplified fragment sequencing (SLAF-seq) as a high-resolution strategy for the identification of new SNPs on a large-scale is gradually applied for functional gene mining. The current study combined the bulked segregant analysis (BSA) with SLAF-seq to identify the candidate genes associated with fertility restorer gene (Rf) in CMS cotton. Illumina sequencing systematically investigated the parents. A segregating population comprising of 30 + 30 F2 individuals was developed using 3096A (female parent) as sterile and 866R (male parent) as a restorer. The original data obtained by dual-index sequencing were analyzed to obtain the reads of each sample that were compared to the reference genome in order to identify the SLAF tag with a polymorphism in parent lines and the SNP with read-associated coverage. Based on SLAF tags, SNP-index analysis, Euclidean distance (ED) correlation analysis, and whole genome resequencing, the hot regions were annotated. A total of 165,007 high-quality SLAF tags, with an average depth of 47.90× in the parents and 50.78× in F2 individuals, were sequenced. In addition, a total of 137,741 SNPs were detected: 113,311 and 98,861 SNPs in the male and female parent, respectively. A correlation analysis by SNP-index and ED initially located the candidate gene on 1.35 Mb of chrD05, and 20 candidate genes were identified. These genes were involved in genetic variations, single base mutations, insertions, and deletions. Moreover, 42 InDel markers of the whole genome resequencing were also detected. In this study, associated markers identified by super-BSA could accelerate the study of CMS in cotton, and as well as in other crops. Some of the 20 genes’ preliminary characteristics provided useful information for further studies on CMS crops.

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland region), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited the highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET (evapotranspiration) and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.

Plant SABATH methyltransferases contribute to volatile formation, hormone homeostasis and stress responses, but their family-wide organization in sunflower remains poorly characterized. Using the HanXRQr2.0-SUNRISE reference genome, we identified 33 high-confidence HaSABATH genes and integrated phylogenetic, duplication, synteny, promoter and protein-structure analyses with expression profiles from 200 public RNA sequencing (RNA-seq) libraries representing 87 distinct conditions. Twelve genes belonged to the salicylic acid/jasmonic acid methyltransferase-related Clade I, 19 to the benzoic acid/salicylic acid methyltransferase-related Clade II, and two to other lineages. Tandem, proximal, dispersed and whole-genome/segmental duplication contributed to the family, with overlapping tandem and segmental evidence for HaSABATH4 and HaSABATH15. Eleven reliable nonsynonymous-to-synonymous substitution ratios were below one. Most predicted proteins showed high model confidence. Across the sampled conditions, Clade I members generally showed higher expression levels and broader expression, while many Clade II members were weakly expressed or condition-biased. HaSABATH4 and HaSABATH6 were prioritized for follow-up under combined saline–alkali stress. This integrated analysis links evolutionary context to expression divergence and provides candidates for targeted biochemical and genetic studies.

Verticillium wilt, caused by Verticillium dahliae, is a major constraint on cotton production. The GASA (Gibberellic Acid-Stimulated in Arabidopsis) proteins are cysteine-rich peptides induced by gibberellin signaling; however, their systemic role in upland cotton (Gossypium hirsutum) resistance to Verticillium wilt remains elusive. Here, we identified 40 GhGASA members from the G. hirsutum genome, which were clustered into three subfamilies. All members possessed a typical gibberellin-regulated domain, and segmental duplication was the primary driver of family expansion. Transcriptomic analyses revealed tissue-specific expression, with high abundance in pistils and roots, and distinct responsive patterns to salt, drought, and cold stresses. Quantitative real-time PCR (qRT-PCR) showed that multiple GhGASA genes were significantly upregulated following V. dahliae inoculation, with transcript levels being much higher in roots and stems than in leaves. Notably, GhGASA17, harboring ethylene-responsive elements, was identified as a key candidate involved in the defense response. Overall, this study provides a comprehensive characterization of the GhGASA gene family, offering a theoretical foundation for understanding its regulatory functions in Verticillium wilt resistance and delivering potential genetic resources for molecular breeding in cotton.

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China's vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.