Montgomery, S. | bioRxiv Science

bioRxiv2018

Zfp189 Mediates Stress Resilience Through a CREB-Regulated Transcriptional Network in Prefrontal Cortex

Stress resilience involves numerous brain-wide transcriptional changes. Determining the organization and orchestration of these transcriptional events may reveal novel antidepressant targets, but this remains unexplored. Here, we characterize the resilient transcriptome with co-expression analysis and identify a single transcriptionally-active uniquely-resilient gene network. Zfp189, a previously unstudied zinc finger protein, is the top network key driver and its overexpression in prefrontal cortical (PFC) neurons preferentially activates this network, alters neuronal activity and promotes behavioral resilience. CREB, which binds Zfp189, is the top upstream regulator of this network. To probe CREB-Zfp189 interactions as a network regulatory mechanism, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB selectively to the Zfp189 promoter. This single molecular interaction in PFC neurons recapitulates the pro-resilient Zfp189-dependent downstream effects on gene network activity, electrophysiology and behavior. These findings reveal an essential role for Zfp189 and a CREB-Zfp189 regulatory axis in mediating a central transcriptional network of resilience.

neuroscience↗

bioRxiv2017

Population-specific imputation of gene expression improves prediction of pharmacogenomic traits for African Americans

Genome-wide association studies (GWAS) are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power, and allows the effects of both protein coding variation as well as variation in expression to be combined into \"gene level\" effects.\n\nPrevious work has shown that warfarin dose can be predicted using information from genetic variation that affects protein coding regions. Here, we introduce a method that improves the predicted dose by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression--on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose, and identify a signature of eleven gene-tissue pairs that significantly augment the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction, in a population-specific manner.

genomics↗