Analysis of hyaluronic acid metabolism gene expression profiles in neurodegenerative disorders using transcriptome analysis methodsстатья Электронная публикация Тезисы

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Дата последнего поиска статьи во внешних источниках: 10 апреля 2019 г.

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[1] Analysis of hyaluronic acid metabolism gene expression profiles in neurodegenerative disorders using transcriptome analysis methods / N. V. Azbukina, V. O. Gorbatenko, D. V. Chistyakov et al. // Journal of Bioenergetics and Biomembranes. — 2018. — Vol. 50, no. 6. — P. 57. Connections between alteration of extracellular matrix composition and the emergence and development of various pathologies has been detected for many diseases, but the mechanisms of these alterations has been poorly studied in the context of inflammatory states of the central nervous system. Understanding molecular mechanisms for regulating composition of the matrix, its interaction with cells, is considered promising for development new therapeutic strategies that promote regeneration and synaptic plasticity. At the moment, distribution of the enzymes of metabolism of hyaluronic acid, crucial component of extracellular matrix, which is considered an endogenous ligand that activates intracellular signaling cascades through interaction with a number of receptors, among which are Toll-like receptors (TLR-2, TLR-4), CD44 receptors along the nervous system cells remains an open question. Earlier in our group were shown that genome-wide transcriptome analysis allows to make reliable predictions about system’s behavior and estimate changes in particular metabolic pathways [ 1–3 ]. The goals of this research were to analyze different aspects of hyaluronic acid metabolism and to draw up a list of involved genes; to choose appropriate genome-wide transcriptome arrays of patients diagnosed with Parkinson and Alzheimer disease from GEO database; to process these data using DEG (differential gene expression according to T-test) analysis and WGCNA (weighted gene correlation network analysis) to trace variations in chosen genes expression profiles. The study was supported by Russian Science Foundation grant 18-74-00069. [ DOI ]

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