Transcriptome organization for chronic alcohol abuse in human brain.

paper Cited Public

Authors: Farris, S P; Arasappan, D; Hunicke-Smith, S; Harris, R A; Mayfield, R D
Year: 2015
Journal: Molecular psychiatry
PMID: 25450227
DOI: 10.1038/mp.2014.159
PMCID: PMC4452464

Figure 1

Global comparison of prefrontal cortex (PFC) transcriptome architecture in alcoholic and matched controls(a) Scatter plots of ranked expression (left) and connectivity (right) in alcoholics (n=16) and matched controls (n=15). The scatter plots demonstrate that overall transcriptome features are robustly correlated for overall expression for alcoholics (y-axis) and controls (x-axis), regardless of expression for genes, alternatively spliced transcripts, or individual exons; however, disease is related to increasingly disparate patterns in overall connectivity across genes, alternatively spliced transcripts, and individual exons. (b) Average connectivity of coding features is significantly reduced in alcoholic PFC compared to controls, showing diminished homeostatic interconnection among genes (left), transcripts (middle), and exons (right). Bar plots show the mean ± se, with asterisks (***) denoting a significant difference between controls and alcoholics at P < 1e-16.

Figure 2

Analysis of gene coexpression modules for estimates of lifetime alcohol consumption(a) Ranking of alcoholic gene coexpression networks in relation to a specific phenotypic trait, the degree of lifetime alcohol drinking; dividing modules into larger cohorts which are most (Upper Quartile) and least (Lower Quartile) associated with human alcohol consumption. (b) Ranked inter-correlation of modules in the upper quartile (left) and lower quartile (right) of alcohol consumption. Heatmaps demonstrate modules within the lower quartile are largely unorganized whereas those in the upper quartile form two distinct factions. Mean absolute correlation among the two categories are significantly different t(88)=4.56, P = 1.65 e-05. (c) Average connectivity of matched gene coexpression patterns within the upper and lower quartiles for controls (blue) and alcoholics (red), showing the lifetime amount of alcohol consumed influences transcriptome architecture F(1,14964)=111.5, P < 1.0 e-16 leading to higher average connectivity patterns associated with disease (Bar plots show the mean ± se). (d) Mosaic plot of the three major cell types in mammalian brain (Neurons: top, Astrocytes: middle, Oligodendrocytes: bottom) with ten-fold enrichment for the upper (left) and lower (right) quartiles; size of boxes reflects the number of overlapping genes. The upper quartile cohort is significantly enriched for neuronal classified genes (P = 4.66 e-23, Fisher’s exact test). (e) Scatter plot of relevant over-represented gene ontology categories, plotted based on semantic similarity and scaled according to −log10 P-values for the upper quartile (complete gene ontology results provided in Table S2; see methods for additional details).

Figure 3

Network diagrams for Group1 gene coexpression modules associated with lifetime alcohol consumptionVisualization demonstrates the inter-connection of genes for the top ten percent of module members with ≥ |r = 0.80| correlation strength. Relative size of the depicted networks is based upon the size of the four modules. Size of the nodes reflects the module connectivity ranking for individual genes. The degree of node opacity is proportional to the correlation with lifetime consumption, with alcohol hubs defined as those genes within the top ten percent of alcohol-associated genes in our human sample. Networks were further overlapped using a prior meta-analysis of alcohol drinking behavior in mice45 to identify potential points of convergent validity in rodent models.

Figure 4

Correlation of the quantitative trait gene candidate (QTG) SCN4B (voltage-gated sodium channel type IV beta subunit) isoformsScatter plots for three coding variants (NM_001142349, NM_001142348, and NM_174934) of SCN4B, demonstrating a significant negative correlation for only the shortest coding isoform (NM_001142349) for lifetime alcohol consumption in humans.

Figure 5

Over-representation for single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) related to alcohol dependenceEnrichment of groups (left to right) upper quartile Group1, upper quartile Group2, lower quartile, and differentially expressed genes within two GWAS involving alcohol dependence (COGA – Collaborative Studies on Genetics of Alcoholism and SAGE – Study of Addiction: Genetics and Environment) and three non-psychiatric negative control datasets (Neg.1 – age related macular degeneration and age related cataracts, Neg.2 – Crohn’s disease, and Neg.3 – type 1 diabetes). The −log10 P-value is plotted along the y-axis, with dashed horizontal lines designating the cut-off level for over-representation at P = 0.05, 0.01, and 0.001. Only the aggregate of genes within Group1 of the upper quartile associated with lifetime alcohol consumption is significantly over-represented for SNPs underlying alcohol dependence.

#	Section	Preview
0	INTRODUCTION	Dysregulation of neurocircuitry and brain structure through altered neurotransmitter systems,…
1	INTRODUCTION	Human postmortem brain tissue is a valuable resource for determining the role of gene expression in…
2	INTRODUCTION	Coordinate expression of genes form highly organized biological networks within specific regions of…
3	MATERIALS AND METHODS — Case selection and postmortem tissue collection	Human autopsy brain samples were obtained from the New South Wales Tissue Resource Centre at the…
4	MATERIALS AND METHODS — Sample preparation and read counting	Total RNA from postmortem human prefrontal cortex was extracted from frozen tissue, excluding any…
5	MATERIALS AND METHODS — Bioinformatics analyses	Scale-free coexpression networks were constructed using the weighted gene coexpression network…
6	MATERIALS AND METHODS — Bioinformatics analyses	functionally annotated against known ontological categories, and additional features such as…
7	MATERIALS AND METHODS — Bioinformatics analyses	Representative gene modules were examined for group-wise overrepresentation of genetic variants…
8	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	Paired-end sequencing (50/35 bps) fragments from ribosome-depleted RNA, spanning an average of ~35…
9	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	Gene-level expression data for controls and alcoholics were assigned separately into organized…
10	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	alcoholics. RNA-Seq defined modules were significantly conserved in relation to previous…
11	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	contrast the global connectivity, a measure of interrelationship among all the features within a…
12	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	In order to further refine gene modules (GM) in the context of alcohol dependence, GM network…
13	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	residing in the lower quartile (t(88)=4.56, P = 1.65 e-05) (Fig. 2b). This correlation structure…
14	RESULTS — RNA-Seq Assessment of Gene Coexpression Networks in PFC	restructuring of the transcriptome in alcoholic PFC favoring GMs associated with an individual’s…
15	RESULTS — Gene Modules Associated with Lifetime Consumption of Alcohol	A number of isolated model system approaches have implicated ion channels 39 and signaling networks…
16	RESULTS — Gene Modules Associated with Lifetime Consumption of Alcohol	Group1 of the upper quartile was the least variable contingent of modules, with the greatest…
17	RESULTS — Gene Modules Associated with Lifetime Consumption of Alcohol	regulated by common substrates and closely associated endophenotypes governing the pathophysiology…
18	RESULTS — Gene Modules Associated with Lifetime Consumption of Alcohol	Leveraging network properties for the pairwise relationship among genes allows for the…
19	RESULTS — Gene Modules Associated with Lifetime Consumption of Alcohol	determinants of alcohol dependence. Hub genes within these molecular networks conceivably act across…

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Cross-species molecular dissection across alcohol behavioral domains.	Farris SP et al.	—	2018	→
Gabapentin for the treatment of alcohol use disorder.	Mason BJ et al.	—	2018	→
Histone deacetylases mediate GABA<sub>A</sub> receptor expression, physiology, and behavioral maladaptations in rat models of alcohol dependence.	Bohnsack JP et al.	—	2018	→
Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses.	Luo J et al.	—	2018	→
Long-term ethanol exposure: Temporal pattern of microRNA expression and associated mRNA gene networks in mouse brain.	Osterndorff-Kahanek EA et al.	—	2018	→
Molecular Neuropathology of Astrocytes and Oligodendrocytes in Alcohol Use Disorders.	Miguel-Hidalgo JJ	—	2018	→
New steps for treating alcohol use disorder.	Campbell EJ et al.	—	2018	→
On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome.	Iancu OD et al.	—	2018	→
Prenatal Alcohol Exposure: Profiling Developmental DNA Methylation Patterns in Central and Peripheral Tissues.	Lussier AA et al.	—	2018	→
Dependence-induced increase of alcohol self-administration and compulsive drinking mediated by the histone methyltransferase PRDM2.	Barbier E et al.	—	2017	→
DNA methylation signatures of chronic alcohol dependence in purified CD3<sup>+</sup> T-cells of patients undergoing alcohol treatment.	Brückmann C et al.	—	2017	→
Emerging roles for ncRNAs in alcohol use disorders.	Mayfield RD	—	2017	→
Ethanol Exposure Regulates <i>Gabra1</i> Expression via Histone Deacetylation at the Promoter in Cultured Cortical Neurons.	Bohnsack JP et al.	—	2017	→
Gene expression profiling in the human alcoholic brain.	Warden AS et al.	—	2017	→
Genetic studies of alcohol dependence in the context of the addiction cycle.	Reilly MT et al.	—	2017	→
GLOBAL OPIOID EPIDEMIC: DOOMED TO FAIL WITHOUT GENETICALLY BASED PRECISION ADDICTION MEDICINE (PAM<sup>™</sup>): LESSONS LEARNED FROM AMERICA.	Blum K et al.	—	2017	→
Promising pharmacogenetic targets for treating alcohol use disorder: evidence from preclinical models.	Rinker JA et al.	—	2017	→
Reprogramming of mPFC transcriptome and function in alcohol dependence.	Heilig M et al.	—	2017	→
RNA sequencing in post-mortem human brains of neuropsychiatric disorders.	Wu C et al.	—	2017	→
The Sodium Channel β4 Auxiliary Subunit Selectively Controls Long-Term Depression in Core Nucleus Accumbens Medium Spiny Neurons.	Ji X et al.	—	2017	→
Alcohol resistance in Drosophila is modulated by the Toll innate immune pathway.	Troutwine BR et al.	—	2016	→
FMRP regulates an ethanol-dependent shift in GABA<sub>B</sub>R function and expression with rapid antidepressant properties.	Wolfe SA et al.	—	2016	→
GABBR1 and SLC6A1, Two Genes Involved in Modulation of GABA Synaptic Transmission, Influence Risk for Alcoholism: Results from Three Ethnically Diverse Populations.	Enoch MA et al.	—	2016	→
Genes and Alcohol Consumption: Studies with Mutant Mice.	Mayfield J et al.	—	2016	→
THE BENEFITS OF CUSTOMIZED DNA DIRECTED NUTRITION TO BALANCE THE BRAIN REWARD CIRCUITRY AND REDUCE ADDICTIVE BEHAVIORS.	Blum K et al.	—	2016	→
The neuroimmune transcriptome and alcohol dependence: potential for targeted therapies.	Warden A et al.	—	2016	→
The NSW brain tissue resource centre: Banking for alcohol and major neuropsychiatric disorders research.	Sutherland GT et al.	—	2016	→
Voluntary wheel running reduces voluntary consumption of ethanol in mice: identification of candidate genes through striatal gene expression profiling.	Darlington TM et al.	—	2016	→
Advances in Medications and Tailoring Treatment for Alcohol Use Disorder.	Seneviratne C et al.	—	2015	→
Applying the new genomics to alcohol dependence.	Farris SP et al.	—	2015	→
Epigenetic modulation of brain gene networks for cocaine and alcohol abuse.	Farris SP et al.	—	2015	→
Molecular Genetic Testing in Pain and Addiction: Facts, Fiction and Clinical Utility.	Blum K et al.	—	2015	→
Molecular Genetic Testing in Reward Deficiency Syndrome (RDS): Facts and Fiction.	Blum K et al.	—	2015	→
Neurogenetics and gene therapy for reward deficiency syndrome: are we going to the Promised Land?	Blum K et al.	—	2015	→