A genomics approach identifies selective effects of trans-resveratrol in cerebral cortex neuron and glia gene expression.
Gemma NavarroEva Martinez-PinillaAlejandro Sánchez-MelgarRaquel OrtizVéronique NoéMairena MartínCarlos J CiudadRafael FrancoPublished in: PloS one (2017)
The mode of action of trans-resveratrol, a promising lead compound for the development of neuroprotective drugs, is unknown. Data from a functional genomics study were retrieved with the aim to find differentially expressed genes that may be involved in the benefits provided by trans-resveratrol. Genes that showed a significantly different expression (p<0.05, cut-off of a two-fold change) in mice fed with a control diet or a control diet containing trans-resveratrol were different in cortex, heart and skeletal muscle. In neocortex, we identified 4 up-regulated (Strap, Pkp4, Rab2a, Cpne3) and 22 down-regulated (Actn1, Arf3, Atp6v01, Atp1a3, Atp1b2, Cacng7, Crtc1, Dbn1, Dnm1, Epn1, Gfap, Hap, Mark41, Rab5b, Nrxn2, Ogt, Palm, Ptprn2, Ptprs, Syn2, Timp2, Vamp2) genes upon trans-resveratrol consumption. Network analysis of gene products provided evidence of plakophilin 4 up-regulation as a triggering factor for down-regulation of events related to synaptic vesicle transport and neurotransmitter release via underexpression of dynamin1 and Vamp2 (synaptobrevin 2) as node-gene drivers. Analysis by RT-qPCR of some of the selected genes in a glioma cell line showed that dynamin 1 mRNA was down-regulated even in acute trans-resveratrol treatments. Taken all together, these results give insight on the glial-neuronal networks involved in the neuroprotective role of trans-resveratrol.
Keyphrases
- genome wide
- genome wide identification
- gene expression
- skeletal muscle
- dna methylation
- physical activity
- cerebral ischemia
- copy number
- heart failure
- single cell
- poor prognosis
- bioinformatics analysis
- functional connectivity
- drug induced
- type diabetes
- lymph node
- metabolic syndrome
- intensive care unit
- subarachnoid hemorrhage
- machine learning
- atrial fibrillation
- insulin resistance
- deep learning
- extracorporeal membrane oxygenation