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Hepatocyte-Derived FGF1 Alleviates Isoniazid and Rifampicin-Induced Liver Injury by Regulating HNF4α-Mediated Bile Acids Synthesis.
Adv Sci (Weinh)
December 2024
School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
Isoniazid and rifampicin co-therapy are the main causes of anti-tuberculosis drug-induced liver injury (ATB-DILI) and acute liver failure, seriously threatening human health. However, its pathophysiology is not fully elucidated. Growing evidences have shown that fibroblast growth factors (FGFs) play a critical role in diverse aspects of liver pathophysiology.
View Article and Find Full Text PDFThe OsMAPK6-OsWRKY72 module positively regulates rice leaf angle through brassinosteroid signals.
Plant Commun
December 2024
Rice Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou 350019, China; State Key Laboratory of Ecological Pest Control for Fujian and Taiwan' Crops/Key Laboratory of Germplasm Innovation and Molecular Breeding of Hybrid Rice in South China/Fujian Engineering Laboratory of Crop Molecular Breeding/Fujian Key Laboratory of Rice Molecular Breeding/Fuzhou Branch, National Center of Rice Improvement of China/National Engineering Laboratory of Rice/South Base of National Key Laboratory of Hybrid Rice of China, Fuzhou 350003, China; College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address:
Leaf angle is a major agronomic trait that determines plant architecture, which directly affects rice planting density, photosynthetic efficiency, and yield. The plant phytohormones brassinosteroids (BRs) and the MAPK signaling cascade are known to play crucial roles in regulating the leaf angle, but the underlying molecular mechanisms are not fully understood. Here, we report a rice WRKY family transcription factor gene, OsWRKY72, which positively regulates leaf angle by affecting lamina joint development and BR signaling.
View Article and Find Full Text PDFUnderstanding the triacylglycerol-based carbon anabolic differentiation in Cyperus esculentus and Cyperus rotundus developing tubers via transcriptomic and metabolomic approaches.
BMC Plant Biol
December 2024
College of Agronomy and Biotechnology, Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China.
Background: Yellow nutsedge (Cyperus esculentus, known as 'YouShaDou' in China, YSD) and purple nutsedge (Cyperus rotundus, known as 'XiangFuZi' in China, XFZ), closely related Cyperaceae species, exhibit significant differences in triacylglycerol (TAG) accumulation within their tubers, a key factor in carbon flux repartitioning that highly impact the total lipid, carbohydrate and protein metabolisms. Previous studies have attempted to elucidate the carbon anabolic discrepancies between these two species, however, a lack of comprehensive genome-wide annotation has hindered a detailed understanding of the underlying molecular mechanisms.
Results: This study utilizes transcriptomic analyses, supported by a comprehensive YSD reference genome, and metabolomic profiling to uncover the mechanisms underlying the major carbon perturbations between the developing tubers of YSD and XFZ germplasms harvested in Yunnan province, China, where the plant biodiveristy is renowned worldwide and may contain more genetic variations relative to their counterparts in other places.
Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.
Sci Rep
December 2024
Clinical Teaching Hospital of Medical School, Nanjing Children's Hospital, Nanjing University, Nanjing, 210008, China.
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored. This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features.
View Article and Find Full Text PDFInterpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.
Sci Rep
December 2024
Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University, 818 Fenghua Road, Jiangbei District, Ningbo, China.
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics methodologies with interpretable machine learning to identify reliable biomarkers for AD diagnosis and treatment. By leveraging differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and construction of Protein-Protein Interaction (PPI) Networks, we meticulously analyzed the AD dataset from the GEO database to pinpoint Hub genes.
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