MYB-related transcription factors are known to regulate different branches of flavonoid metabolism in plants and are believed to play wider roles in the regulation of phenylpropanoid metabolism in general. Here, we demonstrate that overexpression of two MYB genes from Antirrhinum represses phenolic acid metabolism and lignin biosynthesis in transgenic tobacco plants. The inhibition of this branch of phenylpropanoid metabolism appears to be specific to AmMYB308 and AmMYB330, suggesting that they recognize their normal target genes in these transgenic plants. Experiments with yeast indicate that AmMYB308 can act as a very weak transcriptional activator so that overexpression may competitively inhibit the activity of stronger activators recognizing the same target motifs. The effects of the transcription factors on inhibition of phenolic acid metabolism resulted in complex modifications of the growth and development of the transgenic plants. The inhibition of monolignol production resulted in plants with at least 17% less lignin in their vascular tissue. This reduction is of importance when designing strategies for the genetic modification of woody crops.
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http://dx.doi.org/10.1105/tpc.10.2.135 | DOI Listing |
Mol Hortic
January 2025
Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, South China Botanical Garden, Chinese Academy of Sciences, 510650, Guangzhou, China.
Banana is sensitive to cold stress and often suffers from chilling injury with browning peel and failure to normal ripening. We have previously reported that banana chilling injury is accompanied by a reduction of miR528 accumulation, alleviating the degradation of its target gene MaPPO and raising ROS levels that cause peel browning. Here, we further revealed that the miR528-MaPPO cold-responsive module was regulated by miR156-targeted SPL transcription factors, and the miR156c-MaSPL4 module was also responsive to cold stress in banana.
View Article and Find Full Text PDFGenome Biol
January 2025
College of Agriculture & Biotechnology, Zhejiang University, Hangzhou, 310058, China.
Background: Fruit acidity and color are important quality attributes in peaches. Although there are some exceptions, blood-fleshed peaches typically have a sour taste. However, little is known about the genetic variations linking organic acid and color regulation in peaches.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Pathology, Faculty of Medicine, Shahed University, Tehran, Iran.
Background: Cytokeratins are intracellular proteins known as diagnostic biomarkers or prognostic factors for certain cancers. Cytokeratin 19 (CK-19) expression has been proven to have prognostic value for some cancers, but its relationship with others, such as prostate cancer (PCa), remains unclear. This systematic review article aimed to examine the relationship between CK-19 expression and prostate adenocarcinoma (PAC).
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
Proper differentiation of bone marrow stromal cells (BMSCs) into adipocytes is crucial for maintaining skeletal homeostasis. However, the underlying regulatory mechanisms remain incompletely understood, posing a challenge for the treatment of age-related osteopenia and osteoporosis. Here, through comprehensive gene expression analysis during BMSC differentiation into adipocytes, we identified the forkhead transcription factor Foxk2 as a key regulator of this process.
View Article and Find Full Text PDFNat Biomed Eng
January 2025
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.
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