Comparative Transcriptomic Analysis of Gossypium hirsutum Fiber Development in Mutant Materials (xin w 139) Provides New Insights into Cotton Fiber Development.

Cotton is the most widely planted fiber crop in the world, and improving cotton fiber quality has long been a research hotspot. The development of cotton fibers is a complex process that includes four consecutive and overlapping stages, and although many studies on cotton fiber development have been reported, most of the studies have been based on cultivars that are promoted in production or based on lines that are used in breeding. Here, we report a phenotypic evaluation of Gossypium hirsutum based on immature fiber mutant (xin w 139) and wild-type (Xin W 139) lines and a comparative transcriptomic study at seven time points during fiber development. The results of the two-year study showed that the fiber length, fiber strength, single-boll weight and lint percentage of xin w 139 were significantly lower than those of Xin W 139, and there were no significant differences in the other traits. Principal component analysis (PCA) and cluster analysis of the RNA-sequencing (RNA-seq) data revealed that these seven time points could be clearly divided into three different groups corresponding to the initiation, elongation and secondary cell wall (SCW) synthesis stages of fiber development, and the differences in fiber development between the two lines were mainly due to developmental differences after twenty days post anthesis (DPA). Differential expression analysis revealed a total of 5131 unique differentially expressed genes (DEGs), including 290 transcription factors (TFs), between the 2 lines. These DEGs were divided into five clusters. Each cluster functional category was annotated based on the KEGG database, and different clusters could describe different stages of fiber development. In addition, we constructed a gene regulatory network by weighted correlation network analysis (WGCNA) and identified 15 key genes that determined the differences in fiber development between the 2 lines. We also screened seven candidate genes related to cotton fiber development through comparative sequence analysis and qRT-PCR; these genes included three TFs (GH_A08G1821 (bHLH), GH_D05G3074 (Dof), and GH_D13G0161 (C3H)). These results provide a theoretical basis for obtaining an in-depth understanding of the molecular mechanism of cotton fiber development and provide new genetic resources for cotton fiber research.

GhWRKY41 forms a positive feedback regulation loop and increases cotton defence response against Verticillium dahliae by regulating phenylpropanoid metabolism.

Despite the established significance of WRKY proteins and phenylpropanoid metabolism in plant immunity, how WRKY proteins modulate aspects of the phenylpropanoid pathway remains undetermined. To understand better the role of WRKY proteins in plant defence, we identified a cotton (Gossypium hirsutum) protein, GhWRKY41, that is, universally and rapidly induced in three disease-resistant cotton cultivars following inoculation with the plant pathogenic fungus, Verticillium dahliae. We show that overexpression of GhWRKY41 in transgenic cotton and Arabidopsis enhances resistance to V. dahliae, while knock-down increases cotton more susceptibility to the fungus. GhWRKY41 physically interacts with itself and directly activates its own transcription. A genome-wide chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq), in combination with RNA sequencing (RNA-seq) analyses, revealed that 43.1% of GhWRKY41-binding genes were up-regulated in cotton upon inoculation with V. dahliae, including several phenylpropanoid metabolism master switches, receptor kinases, and disease resistance-related proteins. We also show that GhWRKY41 homodimer directly activates the expression of GhC4H and Gh4CL, thereby modulating the accumulation of lignin and flavonoids. This finding expands our understanding of WRKY-WRKY protein interactions and provides important insights into the regulation of the phenylpropanoid pathway in plant immune responses by a WRKY protein.

PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.

Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after staining, which is laborious and hinders high-throughput screening. We developed an automatic detection tool (PollenDetect) to distinguish viable and nonviable pollen based on the YOLOv5 neural network, which is adjusted to adapt to the small target detection task. Compared with manual work, PollenDetect significantly reduced detection time (from approximately 3 min to 1 s for each image). Meanwhile, PollenDetect can maintain high detection accuracy. When PollenDetect was tested on cotton pollen viability, 99% accuracy was achieved. Furthermore, the results obtained using PollenDetect show that high temperature weakened cotton pollen viability, which is highly similar to the pollen viability results obtained using 2,3,5-triphenyltetrazolium formazan quantification. PollenDetect is an open-source software that can be further trained to count different types of pollen for research purposes. Thus, PollenDetect is a rapid and accurate system for recognizing pollen viability status, and is important for screening stress-resistant crop varieties for the identification of pollen viability and stress resistance genes during genetic breeding research.

Genomic and GWAS analyses demonstrate phylogenomic relationships of Gossypium barbadense in China and selection for fibre length, lint percentage and Fusarium wilt resistance.

Sea Island cotton (Gossypium barbadense) is the source of the world's finest fibre quality cotton, yet relatively little is understood about genetic variations among diverse germplasms, genes underlying important traits and the effects of pedigree selection. Here, we resequenced 336 G. barbadense accessions and identified 16 million SNPs. Phylogenetic and population structure analyses revealed two major gene pools and a third admixed subgroup derived from geographical dissemination and interbreeding. We conducted a genome-wide association study (GWAS) of 15 traits including fibre quality, yield, disease resistance, maturity and plant architecture. The highest number of associated loci was for fibre quality, followed by disease resistance and yield. Using gene expression analyses and VIGS transgenic experiments, we confirmed the roles of five candidate genes regulating four key traits, that is disease resistance, fibre length, fibre strength and lint percentage. Geographical and temporal considerations demonstrated selection for the superior fibre quality (fibre length and fibre strength), and high lint percentage in improving G. barbadense in China. Pedigree selection breeding increased Fusarium wilt disease resistance and separately improved fibre quality and yield. Our work provides a foundation for understanding genomic variation and selective breeding of Sea Island cotton.

A Single-Nucleotide Mutation in a GLUTAMATE RECEPTOR-LIKE Gene Confers Resistance to Fusarium Wilt in Gossypium hirsutum.

Fusarium wilt (FW) disease of cotton, caused by the fungus Fusarium oxysporum f. sp. vasinfectum (Fov), causes severe losses in cotton production worldwide. Though significant advancements have been made in development of FW-resistant Upland cotton (Gossypium hirsutum) in resistance screening programs, the precise resistance genes and the corresponding molecular mechanisms for resistance to Fov remain unclear. Herein it is reported that Fov7, a gene unlike canonical plant disease-resistance (R) genes, putatively encoding a GLUTAMATE RECEPTOR-LIKE (GLR) protein, confers resistance to Fov race 7 in Upland cotton. A single nucleotide polymorphism (SNP) (C/A) in GhGLR4.8, resulting in an amino acid change (L/I), is associated with Fov resistance. A PCR-based DNA marker (GhGLR4.8SNP(A/C) ) is developed and shown to cosegregate with the Fov resistance. CRISPR/Cas9-mediated knockout of Fov7 results in cotton lines extremely susceptible to Fov race 7 with a loss of the ability to induce calcium influx in response to total secreted proteins (SEPs) of Fov. Furthermore, coinfiltration of SEPs with GhGLR4.8A results in a hypersensitive response. This first report of a GLR-encoding gene that functions as an R gene provides a new insight into plant-pathogen interactions and a new handle to develop cotton cultivars with resistance to Fov race 7.

A Calmodulin-Like Gene (GbCML7) for Fiber Strength and Yield Improvement Identified by Resequencing Core Accessions of a Pedigree in Gossypium barbadense.

Sea Island cotton (Gossypium barbadense) is world-renowned for its superior natural fiber. Although fiber strength is one of the most important fiber quality traits, genes contributing to fiber strength are poorly understood. Production of sea island cotton also is inextricably linked to improving its relatively low yield, thus enhancing the importance of joint improvement of both fiber quality and yield. We used genomic variation to uncover the genetic evidence of trait improvement resulting from pedigree breeding of Sea Island cotton. This pedigree was aimed at improving fiber strength and yielded an elite cultivar, XH35. Using a combination of genome-wide association study (GWAS) and selection screens, we detected 82 putative fiber-strength-related genes. Expression analysis confirmed a calmodulin-like gene, GbCML7, which enhanced fiber strength in a specific haplotype. This gene is a major-effect gene, which interacts with a minor-effect gene, GbTUA3, facilitating the enhancement of fiber strength in a synergistic fashion. Moreover, GbCML7 participates in the cooperative improvement of fiber strength, fiber length, and fiber uniformity, though a slight compromise exists between the first two of these traits and the latter. Importantly, GbCML7 is shown to boost yield in some backgrounds by increasing multiple yield components to varying degrees, especially boll number. Our work provides valuable genomic evidence and a key genetic factor for the joint improvement of fiber quality and yield in Sea Island cotton.