Associations between catechol-O-methyltransferase (COMT) genotypes at rs4818 and rs4680 and gene expression in human dorsolateral prefrontal cortex.
Brian DeanGeorgia M ParkinAndrew S GibbonsPublished in: Experimental brain research (2020)
Having reported associations between catechol-O-methyltransferase (COMT) genotypes at SNPs rs4818 and rs4680 with levels of soluble COMT (S-COMT) in human dorsolateral prefrontal cortex (DLPFC), we postulated that changes in the levels of cortical S-COMT could impact on behavioural abilities associated with COMT genotype through S-COMT-mediated changes in gene expression. To test this hypothesis, we have examined the relationships between COMT genotypes and gene expression measured using the Affymetrix™ Human Exon 1.0 ST Array in the DLPFC from 141 individuals, some of whom had had a psychiatric disorder. There were significant differences in levels of expression of 15 genes between individuals with a homozygous genotype at rs4818 (GG vs CC), compared to differences in levels of expression of 6 genes between homozygotes at rs4680 (GG vs AA); levels of expression of CEP128, EFCAB13, and FAM133A differed between homozygotes at both SNPs. Fourteen of the genes differentially expressed in the DLPFC according to COMT genotypes have oestrogen receptor elements and their expression could, therefore, be regulated by catecholestrogens, which are substrates for COMT that occupy and activate oestrogen receptors. In addition, the changes in gene expression between the homozygotes at rs4818 or rs4680 would be expected to impact on neuronal function, synaptic plasticity, cognition, and attention. These data would support a hypothesis that the mechanism underlying the association between COMT genotype and cognition involves differential changes in cortical gene expression.
Keyphrases
- gene expression
- prefrontal cortex
- dna methylation
- poor prognosis
- genome wide
- endothelial cells
- working memory
- binding protein
- long non coding rna
- mild cognitive impairment
- big data
- transcranial magnetic stimulation
- deep learning
- high throughput
- pluripotent stem cells
- electronic health record
- genome wide identification