mutant () ovules of upland cotton have been used to investigate cotton fiber development for decades. However, the molecular differences of green tissues between and wild-type (WT) cotton were barely reported. Here, we found that gossypol content, the most important secondary metabolite of cotton leaves, was higher in L. cv Xuzhou-142 (Xu142) WT than in . Then, we performed comparative proteomic analysis of the leaves from Xu142 WT and its . A total of 4506 proteins were identified, of which 103 and 164 appeared to be WT- and -specific, respectively. In the 4239 common-expressed proteins, 80 and 74 were preferentially accumulated in WT and , respectively. Pathway enrichment analysis and protein-protein interaction network analysis of both variety-specific and differential abundant proteins showed that secondary metabolism and chloroplast-related pathways were significantly enriched. Quantitative real-time PCR confirmed that the expression levels of 12 out of 16 selected genes from representative pathways were consistent with their protein accumulation patterns. Further analyses showed that the content of chlorophyll a in WT, but not chlorophyll b, was significantly increased compared to . This work provides the leaf proteome profiles of Xu142 and its mutant, indicating the necessity of further investigation of molecular differences between WT and leaves.
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http://dx.doi.org/10.3390/molecules24203769 | DOI Listing |
Nanotechnology
January 2025
Institute of Nano Science and Technology, Knowledge City, Sector 81, Mohali, 140306, INDIA.
This study investigates simple acetylenes substituted with phenylurea as a constant H-bonding unit (Alk-R) and varied hydrophobic units (R = H, Phenyl (Ph), Phenylacetylene (PA), Ph-NMe2) to understand self-assembly properties driven by synergistic non-covalent interactions. Our observations reveal hierarchical self-assembled fibrillar networks with luminescent needles, fibers, and flowers on nano- to micro-meter scales. Subtle changes in substituents led to significant differences: H, Ph, PA, and Ph-NMe2 produced needle-like crystals, dendritic nanofibers, microflakes, and no self-assembly, respectively.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Colour Science and Textile Chemistry Research Center, College of Textiles and Clothing, Qingdao University, Qingdao, Shandong 266071, China.
Superhydrophobic fabrics suffer from being commonly penetrated by moisture after laundering, seriously deteriorating their water repellency after air drying. Numerous researchers have successfully recovered superhydrophobicity by drying in fluid ovens; however, high energy consumption and equipment dependence limit practical applications. Herein, the superhydrophobic photothermal self-healing cotton fabric (SPS cotton fabric) was fabricated by depositing a composite layer of cellulose nanocrystal-MXene (C-MXene) and polyacrylate (PA) coatings on the cotton cloth.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Department of Chemistry and Biochemistry, Shahrood Branch, Islamic Azad University, 36714 Shahrood, Iran.
This study investigates the nature and interplay of noncovalent interactions (NCIs)─tetrel bonds (TB), hydrogen bonds (HB), and halogen bonds (XB)─in molecular assemblies formed between trifluorogermyl hypochlorite (FGeOCl) and hydrogen cyanide (HCN). Using a combination of high-level computational methods, we explored the geometric, energetic, and electronic properties of dimers, trimers, and tetramers formed in different molar ratios of interacting reagents. Various analyses reveal a significant cooperativity between TB and HB, which mutually reinforce each other, while XB interactions are diminished in the presence of TB and HB.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Geneis (Beijing) Co. Ltd., Beijing 100102, China.
Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement.
View Article and Find Full Text PDFEpidemiology
January 2025
Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway.
Background: Hospital regionalization involves balancing hospital volume and travel time. We investigated how hospital volume and travel time affect perinatal mortality and the risk of delivery in transit using three different study designs.
Methods: This nationwide cohort study used data from the Medical Birth Registry of Norway (1999-2016) and Statistics Norway.
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