A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder

Abstract

BackgroundLithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania.ResultsUsing microarray and rt-QPCR assays, we identified candidate genes that are differentially expressed with lithium treatment. We used a systems biology approach to identify interactions among these candidate genes and develop a network of genes that interact with the differentially expressed candidates. Notably, we also identified cocaine as having a potential influence on the network, consistent with the observed high rate of comorbidity for BD and cocaine abuse. The resulting network represents a novel hypothesis on how multiple genetic influences on bipolar disorder are impacted by both lithium treatment and cocaine use. Testing this network for association with BD and related phenotypes, we find that it is significantly over-represented for genes that participate in signal transduction, consistent with our hypothesized-gene-by environment interaction. In addition, it models related pharmacogenomic, psychiatric, and chemical dependence phenotypes.ConclusionsWe offer a network model of gene-by-environment interaction associated with lithium's effectiveness in treating BD mania, as well as the observed high rate of comorbidity of BD and cocaine abuse. We identified drug targets within this network that represent immediate candidates for therapeutic drug testing. Posing novel hypotheses for validation in future work, we prioritized SNPs near genes in the network based on functional annotation. We also developed a "concept signature" for the genes in the network and identified additional candidate genes that may influence the system because they are significantly associated with the signature.

High Throughput Transcriptome Profiling of Lithium Stimulated Human Mesenchymal Stem Cells Reveals Priming towards Osteoblastic Lineage

“…Among the differentially regulated genes, PLA2G4A (-1.4 fold) [27], CRIP1 (+5.39 fold) [28], ATF4 (+1.78 fold) and RRAD (+9.19 fold) [29] have been previously identified to be modulated in response to lithium in different cell types. Genes involved in cell adhesion (LAMA4, ITGA2, CLDN20), transport (TRPV6, GRIA3, SLC38A2, SLC22A3, CACNG6), transcription (EGR3, TSC22D1, BSX, HNF1A, FOXP1, RXRG, ETV1), metabolism (HK3, GK, AMY2B, ALDOB, KYNU, UPP1, PDE4D, PLD1, SULT1A2, PTGES), apoptosis (FAIM2, CARD17), tissue development (FGF7, NRG1, CHAT, TFPI2, ERRFI1, CYP26C1) and cellular homeostasis (BDKRB2, TXNDC2, NHLRC2) were altered upon lithium exposure.…”

Exploring lithium’s transcriptional mechanisms of action in bipolar disorder: a multi-step study

“…To relate our results to previous lithium findings, we compared the full set of DGE and DTE-only genes (n = 1108) with a set of genes associated with lithium in 18 previous transcriptomic reports [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] identified through a systematic literature search (Supplementary Materials and Methods). We found overlap with 112 genes (10.1%), many of them having similar direction and magnitude of effect (Table S3).…”

State‐related alterations of gene expression in bipolar disorder: a systematic review

“…Crucially, the RNA isolation method used must be the same throughout the study and among all individuals studied. Lastly, given that multiple genes are involved, probably each of small effect, some likely to be markers of vulnerability, some of disease activity, and some altered as a consequence of dynamic allostatic or homeostatic effects, a systems biology approach examining pathways of interest and interactions between candidate genes and networks of interacting genes may also be a viable approach (76, 77).…”