Verticillium wilt is one of the most destructive diseases affecting global cotton production. The most effective way to control wilt disease has been the development of new cotton varieties that are resistant to VW. VW-resistant Upland cotton cultivars have been created in both the USA and China by Gossypium barbadense introgression. More than 100 VW resistance quantitative trait loci have been detected. Three clustered VW resistance-related QTL were detected in a 120-line association population and assigned to a genome region of 14,653,469–55,190,112 bp in Dt_chr9. A regional association analysis-based fine-mapping strategy was developed to narrow down the confidence intervals of the above QTL. The estimated LD decay of the genome region of interest was much faster than those of the Dt_chr9 chromosome and the whole genome, suggesting the existence of a recombination hotspot. Thirty-seven haplotype blocks were detected. The confidence intervals of the three clustered QTL were narrowed down to a region of 937,906 bp involving QTL-i23734Gh and a region of 1,389,417 bp involving QTL- i10740Gh, respectively. Each region contained the strongest association signal. Comparative analysis redefined the confidence intervals of the other three QTLs, qDL52T2-c19, QTL-BNL4069, and QTL-JESPR0001. The broad-spectrum VW resistance QTL qVW-D9–1 was demonstrated to be closely linked with the three redefined QTL, QTL-i23734Gh, QTL- i10740Gh and QTL-JESPR0001. Twelve functional genes were detected to be located within the redefined confidence intervals of VW resistance QTL. The mRNA CotAD_60243, encoding E3 ubiquitin-protein ligase UPL2-like, responsible for plant innate immunity and broad-spectrum disease resistance, was found to be overlapped with the strongest association signal i10740Gh. Six mRNAs encoding putative disease-resistance proteins were within the redefined confidence interval of QTL-JESPR0001, suggesting a tandem arrangement of R genes. Our results proved that the VW resistance effect related to three clustered VW resistance-related QTL was actually controled by two redefined major QTL and severlal minor loci. The broad-spectrum VW resistance QTL qVW-D9–1 may be closely linked with the two redefined major QTLs. The tandem arrangement of R genes were detected in the redefined confidence interval of QTL-JESPR0001. The candidate genes obtained should be helpful in identifying and characterizing defense genes related to VW resistance QTL.

Climate deterioration, water shortages, and abiotic stress are the main threats worldwide that seriously affect cotton growth, yield, and fiber quality. Therefore, research on improving cotton yield and tolerance to biotic and abiotic stresses is of great importance. The NAC proteins are crucial and plant-specific transcription factors (TFs) that are involved in cotton growth, development, and stress responses. The comprehensive utilization of cotton NAC TFs in the improvement of cotton varieties through novel biotechnological methods is feasible. Based on cotton genomic data, genome-wide identification and analyses have revealed potential functions of cotton NAC genes. Here, we comprehensively summarize the recent progress in understanding cotton NAC TFs roles in regulating responses to drought, salt, and Verticillium wilt-related stresses, as well as leaf senescence and the development of fibers, xylem, and glands. The detailed regulatory network of NAC proteins in cotton is also elucidated. Cotton NAC TFs directly bind to the promoters of genes associated with ABA biosynthesis and secondary cell-wall formation, participate in several biological processes by interacting with related proteins, and regulate the expression of downstream genes. Studies have shown that the overexpression of NAC TF genes in cotton and other model plants improve their drought or salt tolerance. This review elucidates the latest findings on the functions and regulation of cotton NAC proteins, broadens our understanding of cotton NAC TFs, and lays a fundamental foundation for further molecular breeding research in cotton.

Lysine 2-hydroxyisobutylation (K_hib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of K_hib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of K_hib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying K_hib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that K_hib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify K_hib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific K_hib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific K_hib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting K_hib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available K_hib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.

The Negative on TATA-less (NOT) 2_3_5 domain proteins play key roles in mRNA metabolism and transcription regulation, but few comprehensive studies have focused on this protein family in plants. In our study, a total of 30 NOT2_3_5 genes were identified in four cotton genomes: Gossypium. arboretum, G. raimondii, G. hirsutum and G. barbadense. Phylogenetic analysis showed that all the NOT2_3_5 domain proteins were divided into two classes. The NOT2_3_5 genes were expanded frequently, and segmental duplication had significant effects in their expansion process. The cis-regulatory elements analysis of NOT2_3_5 promoter regions indicated that NOT2_3_5 domain proteins might participate in plant growth and development processes and responds to exogenous stimuli. Expression patterns demonstrated that all of the GhNOT2_3_5 genes were expressed in the majority of tissues and fiber development stages, and that these genes were induced by multiple stresses. Quantitative real-time PCR showed that GbNOT2_3_5 genes were up-regulated in response to verticillium wilt and the silencing of GbNOT2_3_5-3/8 and GbNOT2_3_5-4/9 led to more susceptibility to verticillium wilt than controls. Identification and analysis of the NOT2_3_5 protein family will be beneficial for further research on their biological functions.