Neuronal cell adhesion genes and antidepressant response in three independent samples.
C FabbriC CrisafulliD GurwitzJ StinglR CalatiDiego AlbaniG ForloniM CalabròR MartinesS KasperJ ZoharA Juven-WetzlerD SoueryS MontgomeryJ MendlewiczG D GirolamoA SerrettiPublished in: The pharmacogenomics journal (2015)
Drug-effect phenotypes in human lymphoblastoid cell lines recently allowed to identify CHL1 (cell adhesion molecule with homology to L1CAM), GAP43 (growth-associated protein 43) and ITGB3 (integrin beta 3) as new candidates for involvement in the antidepressant effect. CHL1 and ITGB3 code for adhesion molecules, while GAP43 codes for a neuron-specific cytosolic protein expressed in neuronal growth cones; all the three gene products are involved in synaptic plasticity. Sixteen polymorphisms in these genes were genotyped in two samples (n=369 and 90) with diagnosis of major depressive episode who were treated with antidepressants in a naturalistic setting. Phenotypes were response, remission and treatment-resistant depression. Logistic regression including appropriate covariates was performed. Genes associated with outcomes were investigated in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) genome-wide study (n=1861) as both individual genes and through a pathway analysis (Reactome and String databases). Gene-based analysis suggested CHL1 rs4003413, GAP43 rs283393 and rs9860828, ITGB3 rs3809865 as the top candidates due to their replication across the largest original sample and the STAR*D cohort. GAP43 molecular pathway was associated with both response and remission in the STAR*D, with ELAVL4 representing the gene with the highest percentage of single nucleotide polymorphisms (SNPs) associated with outcomes. Other promising genes emerging from the pathway analysis were ITGB1 and NRP1. The present study was the first to analyze cell adhesion genes and their molecular pathways in antidepressant response. Genes and biomarkers involved in neuronal adhesion should be considered by further studies aimed to identify predictors of antidepressant response.
Keyphrases
- genome wide
- cell adhesion
- genome wide identification
- dna methylation
- major depressive disorder
- copy number
- genome wide analysis
- depressive symptoms
- transcription factor
- emergency department
- pseudomonas aeruginosa
- machine learning
- sleep quality
- gene expression
- brain injury
- high speed
- small molecule
- deep learning
- weight loss
- big data
- systemic lupus erythematosus
- single molecule
- adverse drug
- atomic force microscopy