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[学术文献 ] Evolutionary comparison of lncRNAs in four cotton species and functional identification of LncR4682-PAS2-KCS19 module in fiber elongation 进入全文
PLANT JOURNAL
Long non-coding RNAs (lncRNAs) play an important role in various biological processes in plants. However, there have been few reports on the evolutionary signatures of lncRNAs in closely related cotton species. The lncRNA transcription patterns in two tetraploid cotton species and their putative diploid ancestors were compared in this paper. By performing deep RNA sequencing, we identified 280 429 lncRNAs from 21 tissues in four cotton species. lncRNA transcription evolves more rapidly than mRNAs, and exhibits more severe turnover phenomenon in diploid species compared to that in tetraploid species. Evolutionarily conserved lncRNAs exhibit higher expression levels, and lower tissue specificity compared with species-specific lncRNAs. Remarkably, tissue expression of homologous lncRNAs in Gossypium hirsutum and G. barbadense exhibited similar patterns, suggesting that these lncRNAs may be functionally conserved and selectively maintained during domestication. An orthologous lncRNA, lncR4682, was identified and validated in fibers of G. hirsutum and G. barbadense with the highest conservatism and expression abundance. Through virus-induced gene silencing in upland cotton, we found that lncR4682 and its target genes GHPAS2 and GHKCS19 positively regulated fiber elongation. In summary, the present study provides a systematic analysis of lncRNAs in four closely related cotton species, extending the understanding of transcriptional conservation of lncRNAs across cotton species. In addition, LncR4682-PAS2-KCS19 contributes to cotton fiber elongation by participating in the biosynthesis of very long-chain fatty acids.
[学术文献 ] Natural variation at the cotton HIC locus increases trichome density and enhances resistance to aphids 进入全文
PLANT JOURNAL
Plant trichomes are an excellent model for studying cell differentiation and development, providing crucial defenses against biotic and abiotic stresses. There is a well-established inverse relationship between trichome density and aphid prevalence, indicating that higher trichome density leads to reduced aphid infestations. Here we present the cloning and characterization of a dominant quantitative trait locus, HIC (hirsute cotton), which significantly enhances cotton trichome density. This enhancement leads to markedly improved resistance against cotton aphids. The HIC encodes an HD-ZIP IV transcriptional activator, crucial for trichome initiation. Overexpression of HIC leads to a substantial increase in trichome density, while knockdown of HIC results in a marked decrease in density, confirming its role in trichome regulation. We identified a variant in the HIC promoter (-810 bp A to C) that increases transcription of HIC and trichome density in hirsute cotton compared with Gossypium hirsutum cultivars with fewer or no trichomes. Interestingly, although the -810 variant in the HIC promoter is the same in G. barbadense and hirsute cotton, the presence of a copia-like retrotransposon insertion in the coding region of HIC in G. barbadense causes premature transcription termination. Further analysis revealed that HIC positively regulates trichome density by directly targeting the EXPANSIN A2 gene, which is involved in cell wall development. Taken together, our results underscore the pivotal function of HIC as a primary regulator during the initial phases of trichome formation, and its prospective utility in enhancing aphid resistance in superior cotton cultivars via selective breeding.
[学术文献 ] Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data 进入全文
Frontiers in Plant Science
Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R2 = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPARf), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.
[学术文献 ] Effects of farmyard manure and chemical fertilizer application rates on soil biology, cotton and fiber yield 进入全文
NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA
Organic and inorganic fertilizers have significant effect on plant physiology, yield per unit area, available plant nutrient contents and extracellular enzyme activities of soils. This study was carried out in field conditions in arid and semi-arid regions between 2020-2021 years, May 01. The effects of farmyard manure (FM) (20, 40, 60 Mg ha(-1)) and chemical (CF) (350 kg urea ha-1, 100, 200, 300 kg DAP ha(-1)) fertilizers applied at different rates on plant nutrient (N, P, K, Ca, Mg, Fe, Cu, Zn and Mn) contents, SPAD value and NDVI of cotton plants, seed cotton yield and soil enzymes (urease, catalase, dehydrogenase, alkaline phosphatase) were investigated. The results showed that FM applications significantly (p<0.01) increased the plant macro and micronutrients compared to CF applications, except for N (200 + 150 kg urea ha-1), Zn and Cu (300 kg DAP + 200 + 150 kg urea ha-1) in the 2021 cotton growing season. Mineralization of FM is slow under natural conditions; therefore, the use of FM alone is not sufficient to meet the nutrient needs of high yielding varieties. Urease and dehydrogenase activities increased significantly in FM treated soils compared to CF, while no significant (p<0.01) increase was recorded in alkaline phosphatase and catalase activities. Farmyard manure is a useful management practice for increasing soil biological activity. Physiological parameters of NDVI, SPAD and seed cotton yield significantly increased in FM treated soils compared to CF applications. The increase in cotton yield was 29.15%, in NDVI value was 22.38% and in SPAD value was 121.7%. The main issues with cotton in the area are the low organic carbon content of the soils, high clay content, arid and semi-arid soils, and their detrimental impact on the uptake of particular nutrients (N, P and B).
[学术文献 ] Unveiling the genetic landscape: Exploring the SSR-based genetic architecture and amino acid dissection of Gossypium barbadense and G.darwinii genomes 进入全文
NOTULAE BOTANICAE HORTI AGROBOTANICI CLUJ-NAPOCA
Genetic maps highlight the genome organization and structure but also provide the chance of tagging superior traits for crop improvement through marker-assisted selection. Amino acids are building blocks of proteins and perform crucial function in regulating the signaling of molecules involved in the development and growth of plants. Plant architecture also have an impact on crop productivity. In order to select elite cultivars for breeding and identification of favorable alleles and their functional properties, a deep understanding of genetic architecture and development of genetic map is essential. In present investigation, an interspecific cross of Gossypium barbadense XH-18 x G. darwinii 5-7 was made to develop a genetic map utilizing single sequence repeat markers for the dissection of amino acids involved in genetic architecture of G. barbadense and G. darwinii. . We measured chromosomal distribution of 20 amino acids across the whole genome of both species. The map consists of 613 markers spread across all 26 chromosomes, covering 2371.4 cM of cotton genome with an average inter-marker distance of 9.35 cM. The marker number anchored on the chromosomes varied from 5 to 76 with an average of 23.57 on each chromosome. The Dt sub-genome had more markers (83.03%) than the At sub-genome (15.66%). Moreover, the longest chromosome was 143.387 cM, the shortest was 58.430 cM, and the average length was 91.207 cM. The Dt subgenome spans a greater genomic distance than the At subgenome. A sum of 21,035 genes were discovered, covering the complete genome of G. barbadense; ; G. darwinii and have been found to be involved in tRNA 3'-trailer cleavage, macromolecule modification, peptide deformylase activity, response to biotic stimulus and defense response. The minimum Glutamic acid (Glu), Histidine (His), and Lysine (Lys) were found on Chr.13 (0.00-17.74), Chr.02 (0.00-8.01), and Chr.06 (0.00-17.97), respectively found through chromosomal amino acid dissection. The genome-wide SSR interspecific genetic map of G. barbadense and G. darwinii is first of its kind, and studying chromosomal distribution of amino acids will set a landmark step to dissect the genome structure of G. darwinii.
[学术文献 ] Leveraging transcriptomics-based approaches to enhance genomic prediction: integrating SNPs and gene networks for cotton fibre quality improvement 进入全文
FRONTIERS IN PLANT SCIENCE
Cultivated cotton plants are the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains similar to 80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits.
