Verticillium wilt caused by Verticillium dahliae is a serious vascular disease in cotton (Gossypium spp.). V. dahliae induces the expression of the CAROTENOID CLEAVAGE DIOXYGENASE 7 (GauCCD7) gene involved in strigolactone (SL) biosynthesis in Gossypium australe, suggesting a role for SLs in Verticillium wilt resistance. We found that the SL analog rac-GR24 enhanced while the SL biosynthesis inhibitor TIS108 decreased cotton resistance to Verticillium wilt. Knock-down of GbCCD7 and GbCCD8b genes in island cotton (Gossypium barbadense) decreased resistance, whereas overexpression of GbCCD8b in upland cotton (Gossypium hirsutum) increased resistance to Verticillium wilt. Additionally, Arabidopsis (Arabidopsis thaliana) SL mutants defective in CCD7 and CCD8 putative orthologs were susceptible, whereas both Arabidopsis GbCCD7- and GbCCD8b-overexpressing plants were more resistant to Verticillium wilt than wild-type (WT) plants. Transcriptome analyses showed that several genes related to the jasmonic acid (JA)- and abscisic acid (ABA)-signaling pathways, such as MYELOCYTOMATOSIS 2 (GbMYC2) and ABA-INSENSITIVE 5, respectively, were upregulated in the roots of WT cotton plants in responses to rac-GR24 and V. dahliae infection but downregulated in the roots of both GbCCD7- and GbCCD8b-silenced cotton plants. Furthermore, GbMYC2 suppressed the expression of GbCCD7 and GbCCD8b by binding to their promoters, which might regulate the homeostasis of SLs in cotton through a negative feedback loop. We also found that GbCCD7- and GbCCD8b-silenced cotton plants were impaired in V. dahliae-induced reactive oxygen species (ROS) accumulation. Taken together, our results suggest that SLs positively regulate cotton resistance to Verticillium wilt through crosstalk with the JA- and ABA-signaling pathways and by inducing ROS accumulation.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user‐friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large‐scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user‐friendly, full‐functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large‐scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant‐specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co‐expression network analysis strategy for efficient biomarker metabolite screening in large‐scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Background: F-box proteins are substrate-recognition components of the Skp1-Rbx1-Cul1-F-box protein (SCF) ubiquitin ligases. By selectively targeting the key regulatory proteins or enzymes for ubiquitination and 26S proteasome mediated degradation, F-box proteins play diverse roles in plant growth/development and in the responses of plants to both environmental and endogenous signals. Studies of F-box proteins from the model plant Arabidopsis and from many additional plant species have demonstrated that they belong to a super gene family, and function across almost all aspects of the plant life cycle. However, systematic exploration of F-box family genes in the important fiber crop cotton (Gossypium hirsutum) has not been previously performed. The genome-wide analysis of the cotton F-box gene family is now possible thanks to the completion of several cotton genome sequencing projects. Results: In current study, we first conducted a genome-wide investigation of cotton F-box family genes by reference to the published F-box protein sequences from other plant species. 592 F-box protein encoding genes were identified in the Gossypium hirsutume acc.TM-1 genome and, subsequently, we were able to present their gene structures, chromosomal locations, syntenic relationships with their parent species. In addition, duplication modes analysis showed that cotton F-box genes were distributed to 26 chromosomes, with the maximum number of genes being detected on chromosome 5. Although the WGD (whole-genome duplication) mode seems play a dominant role during cotton F-box gene expansion process, other duplication modes including TD (tandem duplication), PD (proximal duplication), and TRD (transposed duplication) also contribute significantly to the evolutionary expansion of cotton F-box genes. Collectively, these bioinformatic analysis suggest possible evolutionary forces underlying F-box gene diversification. Additionally, we also conducted analyses of gene ontology, and expression profiles in silico, allowing identification of F-box gene members potentially involved in hormone signal transduction. Conclusion: The results of this study provide first insights into the Gossypium hirsutum F-box gene family, which lays the foundation for future studies of functionality, particularly those involving F-box protein family members that play a role in hormone signal transduction.