Leaves are the main places for photosynthesis and organic synthesis of cotton. Leaf shape has important effects on the photosynthetic efficiency and canopy formation, thereby affecting cotton yield. Previous studies have shown that LMI1 is the main gene regulating leaf shape. In this study, the LMI1 gene (LATE MERISTEM IDENTITY1) was inserted into the 35S promoter expression vector, and cotton plants overexpressing LMI1(OE) were obtained through genetical transformation. Statistical analysis of the biological traits of T1 and T2 populations showed that compared to wild type (WT), OE plants had significant larger leaves, thicker stems and significantly increased dry weight. Furthermore, plant sections of the main vein and petiole showed that the number of cell in those tissues of OE plants increased significantly. In addition, RNA-seq analysis revealed differential expression of genes related to gibberellin synthesis and NAC gene family (genes containing the NAC domain) in OE and WT plants, suggesting that LMI1 is involved in secondary wall formation and cell proliferation, and promotes stem thickening. Moreover, GO (Gene Ontology) analysis enriched the terms of calcium ion binding, and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis enriched the terms of fatty acid degradation, phosphatidylinositol signal transduction system, and cAMP signal pathway. These results suggested that LMI1 OE plants were responsive to gibberellin hormone signals, and altered messenger signal (cAMP, Ca2+) which amplified this function, to promote the stronger above ground vegetative growth. This study found the LMI1 soared the nutrient growth in cotton, which is the basic for higher yield.
Cotton fiber quality is a persistent concern that determines planting benefits and the quality of finished textile products. However, the limitations of measurement instruments have hindered the accurate evaluation of some important fiber characteristics (such as fiber maturity, fineness, neps), which in turn has impeded the genetic improvement and industrial utilization of cotton fiber. Here, twelve single fiber quality traits were measured using Advanced Fiber Information System (AFIS) equipment among 383 accessions of upland cotton (Gossypium hirsutum L.). Also, eight conventional fiber quality traits were assessed by the High Volume Instrument (HVI) system. Genome-Wide Association Study (GWAS), linkage disequilibrium (LD) block genotyping and functional identification were conducted sequentially to uncover the associated elite loci and candidate genes of fiber quality traits. As a result, the pleiotropic locus FL_D11 regulating fiber length related traits was again identified in this study. More importantly, three novel pleiotropic loci (FM_A03, FF_A05, FN_A07) regulating fiber maturity, fineness and neps respectively were detected on the basis of AFIS traits. Numerous highly-promising candidate genes were screened out by integrating RNA-seq and qRT-PCR analyses, including the reported GhKRP6 for fiber length and newly identified GhMAP8 for maturity and GhDFR for fineness. The origin and evolution analysis of pleiotropic loci indicated that the selection pressure on FL_D11, FM_A03 and FF_A05 increased as the breeding period approached and the origins of FM_A03 and FF_A05 were traced back to cotton landraces. These findings reveal the genetic basis underlying fiber quality and provide insight into genetic improvements and textile utilization of fiber in G. hirsutum.
EPSPS is a key gene in the shikimic acid synthesis pathway and has been widely used in breeding crops with herbicide resistance. However, its role in regulating cell elongation is poorly understood. Through the overexpression of EPSPS genes, we generated lines resistant to glyphosate that exhibited an unexpected dwarf phenotype. A representative line, DHR1, exhibits a stable dwarf phenotype throughout its entire growth period. Except for plant height, the other agronomic traits of DHR1 were similar to its transgenic explants ZM24. Paraffin section experiments showed that DHR1 internodes were shortened due to reduced elongation and division of internode cells. Exogenous hormones confirmed that DHR1 is not a classical BR- or GA-related dwarfing mutant. Hybridization analysis and fine mapping confirmed that the EPSPS gene is the causal gene for dwarfism, and the phenotype can be inherited in different genotypes. Transcriptome and metabolome analyses showed that genes associated with the phenylpropanoid synthesis pathway were enriched in DHR1 when compared with ZM24. Flavonoid metabolites were enriched in DHR1, whereas lignin metabolites were decreased. The enhancement of flavonoids likely resulted in differential expression of auxin signal pathway genes and altered the auxin response, subsequently affecting cell elongation. This study provides a new strategy for generating dwarfs and will accelerate advancements in light simplification of cultivation and mechanized harvesting for cotton.
Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production. Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases. Therefore, this paper proposes a novel method to identify banana leaf diseases. First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images. The proposed algorithm introduces a new decomposition scale k based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image. Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50. In this, the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model's computational speed is increased using the hybrid activation function of RReLU and Swish. Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.
Seed size is an important agronomic trait in melons that directly affects the seed germination and subsequent seedling growth. However, the genetic mechanism underlying seed size in melon has remained unclear. In the present study, we employed Bulked-Segregant Analysis sequencing (BSA-seq) to identify a candidate region (~1.35 Mb) on chromosome 6 that corresponds to seed size. This interval was confirmed by QTL mapping of three seed size-related traits from an F2 population across three environments. This mapping represented nine QTLs that shared an overlapping region on chromosome 6, collectively referred to as qSS6.1. New InDel markers were developed in the qSS6.1 region, narrowing it down to a 68.35-kb interval that contains eight annotated genes. Sequence variation analysis of the eight genes identified a SNP with a C to T transition mutation in the promoter region of MELO3C014002, a leucine-rich repeat receptor-like kinase (LRR-RLK) gene. This mutation affected promoter activity of the MELO3C014002 gene and was successfully used to differentiate the large-seeded accessions (C-allele) from the small-seeded accessions (T-allele). qRT-PCR revealed differential expression of MELO3C014002 between the two parental lines. Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the homologs in several plant species. Altogether, these findings suggest MELO3C014002 as the most likely candidate gene involved in melon seed size regulation. Our results will be helpful for better understanding the genetic mechanism regulating seed size in melons and for genetically improving this important trait through molecular breeding pathways.
Improving plant resistance to Verticillium wilt (VW), which causes massive losses in Gossypium hirsutum, is a global challenge. Crop needs to efficiently allocate their limited energy resources to balance growth and defense. However, few transcriptional regulators specifically response to V. dahliae and the underlying mechanism in cotton has not been identified. In this study, we found that the that expression of the majority R2R3-MYB in cotton is significantly changed relative to other MYB types by V. dahliae infection. Of which, a novel R2R3-MYB TF GhMYB3D5, specifically response to V. dahliae, was identified. GhMYB3D5 did not express across 15 cotton tissues at normal condition, but drastically induced by V. dahliae stress. We functionally characterized its positive role and underlying mechanism in VW resistance. Upon V. dahliae infection, the up-regulated GhMYB3D5 bound the GhADH1 promoter and activated GhADH1 expression, moreover, GhMYB3D5 physically interacted with GhADH1 and furtherly enhanced the transcriptional activation to GhADH1. Consequently, the transcriptional regulatory module GhMYB3D5-GhADH1 promoted lignin accumulation via improving the transcriptional levels of genes related to lignin biosynthesis (GhPAL, GhC4H, Gh4CL, and GhPOD/GhLAC) in cotton, thereby enhancing the cotton VW resistance. Taken together, our results demonstrated that the GhMYB3D5 promoted a defense-induced lignin accumulation, which regarded as an effective manner in orchestrating plant immunity and growth.
Sucrose transporters (SUTs) play a crucial role in carbon allocation from the source leaf to the sink end, and the function of SUTs varies among family members. However, the genome-wide identification of SUT superfamily in Camellia oleifera is lacking, and their biological function remains elusive. In this study, a total of four SUT genes, named CoSUT1-4, were identified in C. oleifera through a genome-wide analysis and classified into three subfamilies. We used a combination of cis-acting elements analysis, mRNA quantification, histochemical analyses, and heterologous transformation to evaluate the expression profiles and functions of these SUTs. A key finding is CoSUT4 that is localized on the plasma membrane is highly expressed in mature leaves and early stage of seed development in C. oleifera. In-vitro culture C. oleifera seed revealed the responsiveness of CoSUT4 to various exogenous hormones such as ABA and GA. CoSUT4 was able to restore the growth of the yeast strain SUSY7/ura3 (sucrose transport-deficient mutant) on sucrose-containing media, and specifically contributed to sucrose translocation and tissue growth in CoSUT4 overexpressed apple calli. In situ hybridization identified chalazal nucellus and transfer cells as the action sites of CoSUT4 at the maternal-filial interface mediating sucrose transportation in oil tea seeds. CoSUT4 overexpression in Arabidopsis thaliana atsuc4 mutant restored the growth and seed yield deficiencies of the mutant, leading to an increase in filled seeds and oil content. Additionally, CoSUT4 overexpression enhanced the drought and salt stress tolerance by augmenting sugar content. Overall, these findings provide valuable insights into the function of SUTs and present promising candidates for the genetic enhancement of seed production in C. oleifera.
Potato Cyst Nematodes (PCNs) are a significant threat to potato production, having caused substantial damage in many countries. Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies, especially given the impact of climate change on pest species invasion and distribution. Machine-Learning (ML), specifically ensemble models, has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets. Thus, this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions, providing the initial element for invasion risk assessment. We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors. Then, five machine learning models were employed to build two groups of ensembles, single-algorithm ensembles (ESA) and multi-algorithm ensembles (EMA), and compared their performances. In this research, the EMA did not always perform better than the ESA, and the ESA of Artificial Neural Network gave the highest performance while being cost-effective. Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes. However, the total area of suitable regions will not change significantly, occupying 16-20% of the total land surface (18% under current conditions). This research alerts policymakers and practitioners to the risk of PCNs’ incursion into new regions. Additionally, this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.
