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[学术文献 ] Pangenome analysis reveals yield- and fiber-related diversity and interspecific gene flow in Gossypium barbadense L. 进入全文
NATURE COMMUNICATIONS
Gossypium barbadense is renowned for its superior fiber quality, particularly its extra-long fibers, although its fiber yield is lower compared to G. hirsutum. Here, to further reveal fiber-related genomic variants of G. barbadense, we de novo assemble 12 genomes of G. barbadense that span the wild-to-domesticated continuum, and construct a graph-based pangenome by integrating these assemblies and 17 publicly available tetraploid cotton genome assemblies. We uncover the divergent evolutionary trajectories and subsequent exchanges between G. barbadense and G. hirsutum through investigation of structural variants (SVs). We perform the SV-based GWAS analysis in G. barbadense and identify four, three, and seven candidate SVs for fiber length, fiber strength, and lint percentage, respectively. Furthermore, we detect the underlying candidate genes and uncover the origin and distribution of favorable alleles, and reveal the tradeoff between lint percentage and fiber quality. These pangenome and trait-associated SVs provide insights into and resources for improving cotton fiber.
[前沿资讯 ] How will the “water footprint” of Xinjiang cotton change under climate change? 进入全文
EurekAlert!
According to the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), human activities have significantly intensified global warming, leading to more frequent, intense, and prolonged extreme weather events, which pose a major threat to agricultural production. Xinjiang, as one of the driest regions in China, has an average annual precipitation of less than 270 mm and an evaporation rate exceeding 1000 mm, yet it produces 25% of the world’s cotton, contributing 91.0% of the national cotton production and 35% of farmers’ income. Cotton cultivation in this region heavily relies on irrigation, and climate change is likely to exacerbate aridity in Xinjiang. In this context, how will the water use structure of cotton production in Xinjiang change? How can the water-saving potential of different irrigation technologies be assessed? A study conducted by Dr. La Zhuo and colleagues from the Institute of Soil and Water Conservation at Northwest A&F University, published in Frontiers of Agricultural Science and Engineering, provides the answers to these questions (DOI: 10.15302/J-FASE-2024585). This study focuses on the “water footprint” of cotton production in Xinjiang—specifically, the amount of freshwater consumed to produce one ton of cotton, divided into “blue water footprint” (relying on groundwater or surface water) and “green water footprint” (relying on precipitation). Unlike previous studies that primarily focused on food crops or single irrigation methods, this research innovatively simulates three mainstream irrigation technologies—furrow irrigation, micro-irrigation (drip irrigation), and sprinkler irrigation—at a fine grid scale of 5 arcminutes (approximately 9 km × 9 km), analyzing the spatiotemporal changes in cotton’s water footprint under two climate change scenarios for the 2050s and 2090s (SSP2-4.5 moderate emissions and SSP5-8.5 high emissions). The study first reveals the future climate trends in Xinjiang: compared to the baseline period of 2000–2018 (with a reference crop evapotranspiration ET0 of 1080 mm), evaporation demand in Xinjiang significantly increases under both scenarios. In the SSP5-8.5 scenario of the 2090s, ET0 increases by 14.3% annually, with the largest increases occurring in January and November, while the summer increase is only about 8%. Annual precipitation decreases by 15.1% overall, with only July to September slightly exceeding the baseline period. This indicates that Xinjiang will become increasingly arid in the future, and the pressure on agricultural water use may further intensify. However, a key finding is that the total water footprint of cotton shows a downward trend. The total water footprint for cotton in Xinjiang during the baseline period is 4264 m3·t-1, of which blue water accounts for 83% (3560 m3·t-1). By the 2090s, the total water footprint is expected to decrease by 19.3% under the SSP2-4.5 scenario, and by 35.7% under the SSP5-8.5 high emissions scenario. This is mainly attributed to the effects of increased atmospheric CO2 concentration—under the SSP5-8.5 scenario, CO2 concentration is significantly higher than under SSP2-4.5, and higher CO2 levels can enhance the photosynthetic efficiency of cotton while reducing transpiration water loss. It is noteworthy that the structure of the water footprint is changing: the proportion of blue water in the total water footprint is expected to increase slightly. Although the total amount of blue water is also decreasing—by 16.5% and 33.4% under the SSP2-4.5 and SSP5-8.5 scenarios, respectively—the contribution of green water declines due to reduced precipitation, leading to an increased proportion of blue water. The decline in green water footprint is more pronounced, decreasing by 33.7% and 47.2% under the SSP2-4.5 and SSP5-8.5 scenarios, with only a few areas experiencing slight increases due to minor precipitation increases. There are significant differences in the water-saving potential of the three irrigation technologies: sprinkler irrigation shows a reduction in water footprint of 24.8% and 40.1% under the SSP2-4.5 and SSP5-8.5 scenarios, respectively, demonstrating the most notable water-saving effects; furrow and micro-irrigation show relatively smaller reductions. This indicates that sprinkler irrigation technology has higher water-saving potential for future cotton cultivation in Xinjiang. The cotton industry in Xinjiang is crucial for the regional economy, but water resource scarcity is a long-term challenge. This study not only quantifies the dynamic patterns of cotton water consumption under climate change but also clarifies the adaptive differences among various irrigation technologies, providing scientific support for optimizing water resource allocation and promoting water-saving measures. In the future, combined with variety improvement and agronomic upgrades, Xinjiang cotton is expected to achieve more efficient water resource utilization in an increasingly arid environment.
[前沿资讯 ] Cotton virus circulated undetected for nearly 20 years, study finds：Discovery reshapes understanding of disease emergence and highlights new opportunities for virus surveillance in U.S. agriculture 进入全文
EurekAlert!
A virus responsible for damaging cotton crops across the southern United States has been lurking in U.S. fields for nearly 20 years – undetected. According to new research, cotton leafroll dwarf virus (CLRDV), long believed to be a recent arrival, was infecting plants in cotton-growing states as early as 2006. The findings, published in Plant Disease by USDA Agricultural Research Service researchers and cooperators at Cornell University, challenge long-standing assumptions about when and how the virus emerged in U.S. cotton. They also demonstrate how modern data-mining tools can uncover hidden threats in samples collected well before the virus was on anyone’s radar. “CLRDV was officially detected in 2017, so the assumption was that it had only recently entered the U.S.,” said Alejandro Olmedo-Velarde, formerly a Cornell postdoctoral associate and now Assistant Professor in the Department of Plant Pathology, Entomology, and Microbiology at Iowa State. “Our study shows that this virus was actually present in the country’s Cotton Belt long before that. We found clear evidence of the virus in samples from 2006 in Mississippi, 2015 in Louisiana, and 2018 in California.” To confirm the findings, the team conducted field surveys in 2023, collecting fresh cotton samples in Southern California. Lab testing confirmed that CLRDV is currently present in California—marking the state’s first official report of the virus. The team’s approach relied heavily on reanalyzing existing data in public genetic databases. By mining these datasets, the researchers uncovered viral sequences that closely matched current U.S. strains, offering a more complete picture of CLRDV’s spread over time and geography. The study underscores the importance of maintaining easily accessible, publicly available databases for improving future disease surveillance and preparedness. In an unexpected twist, the researchers also identified traces of the virus in an unusual location: a sample from the gut of a cow studied by researchers in California. Their data are consistent with the hypothesis that the cow ingested CLRDV-infected plant-based animal feed. While this does not suggest that animals are infected, it adds a new dimension to understanding the timeline and extent of CLRDV infection in the U.S. prior to the official first report. The study also reignites interest in an unresolved issue in cotton pathology: bronze wilt. The researchers propose a potential connection between CLRDV and bronze wilt symptoms, a topic that has sparked debate in the past. “Now, as more studies align with our findings, the idea is gaining traction,” said Olmedo-Velarde. “It could help explain long-standing crop losses and inform virus monitoring strategies moving forward.” Agricultural Research Service Scientist Dr. Michelle Heck explains, “For growers, the findings offer both a caution and a call to action. CLRDV has been in U.S. fields far longer than anyone realized, and it may be more widespread than current reports suggest. Understanding how and why the virus remained under the radar for so long – and why it’s becoming more of a problem now – will be critical for developing effective management strategies.” The research highlights the growing role of bioinformatics, plant pathology, and cross-disciplinary collaboration in modern agriculture – and shows that existing data may already contain the clues we need to detect emerging threats earlier.
[学术文献 ] RNAi-mediated down-regulation of the endogenous GhAIP10.1 and GhAIP10.2 genes in transgenic cotton (Gossypium hirsutum) enhances the earliness and yield of flower buds 进入全文
PLANT PHYSIOLOGY AND BIOCHEMISTRY
Armadillo BTB Arabidopsis protein 1 (AtABAP1) plays a central role in the cell cycle. ABAP1-interacting protein 10 (AtAIP10, a Snf1 kinase interactor-like protein) is a protein that interacts with AtABAP1. Down-regulation of the AtAIP10 gene in A. thaliana resulted in an altered cell cycle and increased photosynthesis, chlorophyll content, metabolites, plant growth, root system, seed yield, and drought tolerance. Herein, aimed to test whether the down-regulation of GhAIP10 genes can stimulate the cotton plants in a manner similar to those observed in A. thaliana. Cotton transgenic events containing transgenes carrying RNA interfering (RNAi) or artificial miRNA (amiRNA) strategies were successfully generated to down-regulate the endogenous GhAIP10.1 and GhAIP10.2 genes. From these 15 transgenic events, five RNAi-based transgenic lines and five amiRNA-based transgenic events were selected for further analyses. The down-regulation of the GhAIP10.1 and GhAIP10.2 genes was confirmed by real-time RT-PCR. Phenotypic and physiological analyses revealed that these transgenic lines exhibited earlier production and opening of flower buds, increased vegetative growth over time and root biomass, no reduction in susceptibility to root-knot nematodes, and improved drought tolerance indicated by a higher photosynthetic rate and better intrinsic water-use efficiency. Based on the high identity of amino acid sequences, motifs, domains, subcellular localization, tertiary structure, down-regulation of GhABAP1 (partner of GhAIP10), up-regulation of GhCdt1 (a marker of the ABAP1 network), up-regulation of GhCyclinB1 (a marker of the cell cycle), up-regulation of GhAP3 (involved in vegetative to reproductive transition), and the up-regulation of CAB3, NDA1, DJC22, and DNAJ11 genes (involved in plant resilience) suggested that GhAIP10.1 and GhAIP10.2 proteins may act in cotton similarly to the AtAIP10 protein in A. thaliana. Furthermore, GhAIP10.1 and GhAIP10.2 genes are suggested as biotechnological targets for cotton genetic engineering based on genome editing.
[学术文献 ] Coupling effects of silicon and calcium foliar application and potassium soil fertilization on growth and yield production of cotton plants under drought stress conditions 进入全文
SILICON
Drought significantly affects cotton production, decreasing both yield and fiber quality. This study investigated how foliar applications of calcium (Ca) or silicon (Si), along with varying potassium (K) levels in the soil, can improve drought tolerance in cotton. The foliar treatments involved calcium nitrate at 4 g/L or silicon oxide at 1 ml/L, combined with 106.6 and 160 kg K2SO4 ha(-)1 as soil fertilizer. These treatments were compared to potassium-only applications, with irrigation intervals of 30 days during the 2021 and 2022 growing seasons. The 160 kg K2SO4 ha(-)1 treatment notably improved plant growth, including increased plant height, dry weight, leaf area, and the number of fruiting branches, compared to the 106.6 kg K2SO4treatment. It also enhanced chlorophyll content, antioxidant enzyme activity, leaf phenol and proline levels, and relative water content (RWC). Additionally, the 160 kg K2SO4 ha(-)1 treatment improved yield-related traits, such as the number of open bolls, lint percentage, seed index, and fiber quality, including fiber length, strength, and micronaire. The number of open bolls, lint percentage, and seed index increased by 2.38%, 1.71%, and 1.68% in the first season, and by 4.29%, 1.57%, and 1.38% in the second season, respectively. The combination of Ca or Si foliar applications with K treatments further enhanced plant growth, chlorophyll, antioxidant activity, RWC, seed index, boll weight, and fiber quality. These treatments also raised nutrient levels of N, P, K, Ca, and Si compared to the control. Overall, combining Ca or Si sprays with 160 kg K2SO4 effectively mitigated drought stress and improved cotton growth and productivity.
[学术文献 ] Mapping of cotton bolls and branches with high-granularity through point cloud segmentation 进入全文
PLANT METHODS
High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R2) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.
