The GeneNet database is designed for accumulation of information on gene networks. Original technology applied in GeneNet enables description of not only a gene network structure and functional relationships between components, but also metabolic and signal transduction pathways. Specialised software, GeneNet Viewer, automatically displays the graphical diagram of gene networks described in the database. Current release 3.0 of GeneNet database contains descriptions of 25 gene networks, 945 proteins, 567 genes, 151 other substances and 1364 relationships between components of gene networks. Information distributed between 14 interlinked tables was obtained by annotating 968 scientific publications. The SRS-version of GeneNet database is freely available (http://wwwmgs.bionet.nsc.ru/mgs/systems/genenet/).
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http://dx.doi.org/10.1093/nar/30.1.398 | DOI Listing |
Mol Biol Rep
January 2025
Medical Sociology and Psychobiology, Department of Health and Physical Activity, University of Potsdam, 14469, Potsdam, Germany.
Background: Depression constitutes a risk factor for osteoporosis, but underlying molecular and cellular mechanisms are not fully understood. MiRNAs influence gene expression and are carried by extracellular vesicles (EV), affecting cell-cell communication.
Aims: (1) Identify the difference in miRNA expression between depressed patients and healthy controls; (2) Analyze associations of these miRNAs with bone turnover markers; (3) Analyze target genes of differentially regulated miRNAs and predict associated pathways regarding depression and bone metabolism.
Neurochem Res
January 2025
Department of Neurology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China.
Our aim was to evaluate the regulation of messenger RNAs (mRNAs) and biological pathways by long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in ischemic stroke. We employed weighted gene co-expression network analysis (WGCNA) to construct two co-expression networks for mRNAs with circRNAs and lncRNAs, respectively, to investigate their association with ischemic stroke. We compared the overlap of mRNAs and biological pathways in the stroke-associated modules of the two networks.
View Article and Find Full Text PDFEmerg Microbes Infect
January 2025
Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, China.
In Guangxi, the number of newly diagnosed HIV-1 infections among students is continuously increasing, highlighting the need for a detailed understanding of local transmission dynamics, particularly focusing on key drivers of transmission. We recruited individuals newly diagnosed with HIV-1 in Nanning, Guangxi, and amplified and sequenced the HIV-1 pol gene to construct a molecular network. Bayesian phylogenetic analysis was utilized to identify migration events, and multivariable logistic regression was employed to analyze factors influencing clustering and high linkage.
View Article and Find Full Text PDFBiol Aujourdhui
January 2025
Sorbonne Université, CNRS, Inserm U1156, Institut de Biologie Paris Seine, Laboratoire de Biologie du Développement/UMR7622, 9 Quai St-Bernard, 75005 Paris, France.
The advent of high-throughput omics data and the generation of new algorithms provide the biologists with the opportunity to explore living processes in the context of systems biology aiming at revealing the gene interactions, the networks underlying complex cellular functions. In this article, we discuss two methods for gene network reconstruction, WGCNA (Weighted Gene Correlation Network Analysis) developed by Steve Horvath and collaborators in 2008, and MIIC (Multivariate Information-based Inductive Causation) developed by Hervé Isambert and his team in 2017 and 2024. These two methods are complementary, WGCNA generating undirected networks in which most gene-to-gene interactions are indirect, while MIIC reveals direct interactions and some causal links.
View Article and Find Full Text PDFJ Exp Bot
January 2025
Advanced Genomics Unit, Center for Research and Advanced Studies (Cinvestav), Irapuato, Mexico.
Arabidopsis has served as a model plant for studying the genetic networks that guide gynoecium development. However, less is known about other species such as tomato, a model for fleshy fruit development and ripening. Here, we study in tomato the transcription factor SPATULA (SPT), a bHLH-family member that in Arabidopsis is known to be important for gynoecium development.
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