Alcohol-Induced Histone Acetylation Reveals a Gene Network Involved in Alcohol Tolerance

Alfredo Ghezzi, Harish R. Krishnan, Linda Lew, Francisco J. Prado III, Darryl S. Ong, Nigel S. Atkinson, Section of Neurobiology and Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, Texas, United States of AmericaSustained or repeated exposure to sedating drugs, such as alcohol, triggers homeostatic adaptations in the brain that lead to the development of drug tolerance and dependence. These adaptations involve long-term changes in the transcription of drug-responsive genes as well as an epigenetic restructuring of chromosomal regions that is thought to signal and maintain the altered transcriptional state. Alcohol-induced epigenetic changes have been shown to be important in the long-term adaptation that leads to alcohol tolerance and dependence endophenotypes. A major constraint impeding progress is that alcohol produces a surfeit of changes in gene expression, most of which may not make any meaningful contribution to the ethanol response under study. Here we used a novel genomic epigenetic approach to find genes relevant for functional alcohol tolerance by exploiting the commonalities of two chemically distinct alcohols. In Drosophila melanogaster, ethanol and benzyl alcohol induce mutual cross-tolerance, indicating that they share a common mechanism for producing tolerance. We surveyed the genome-wide changes in histone acetylation that occur in response to these drugs. Each drug induces modifications in a large number of genes. The genes that respond similarly to either treatment, however, represent a subgroup enriched for genes important for the common tolerance response. Genes were functionally tested for behavioral tolerance to the sedative effects of ethanol and benzyl alcohol using mutant and inducible RNAi stocks. We identified a network of genes that are essential for the development of tolerance to sedation by alcohol.This work was supported by National Institute of Health grant R01 AA018037 to NSA (http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.NeuroscienceWaggoner Center for Alcohol and Addiction ResearchEmail: [email protected] (AG)Email: [email protected] (NSA

Genes tested for drug tolerance using mutant analysis.

<p>(#) Cluster number. (+) Significantly blocked or reduced tolerance. (−) No effect on tolerance. (L) Lethal.</p

Clustering analysis by gene-expression patterns of genes identified in this study.

<p>Genes were clustered by Pearson correlation analysis of their mRNA expression patterns induced by 21 different environmental conditions. Shades of blue in heat map denote gene-expression levels for each condition, normalized for each gene using the sum of squares of all conditions (white is lowest, dark blue highest). After clustering, genes segregate into seven distinct clusters, four of which are highly correlated (r>0.65). These clusters are denoted by red brackets. Clusters with low or no correlation (r<0.3) are denoted by gray brackets. In this study, eighteen genes (16 of which fall within the highly correlated clusters, marked in bold) were tested for their role in behavioral alcohol tolerance. Of these, ten (marked in red) significantly reduced tolerance to alcohol, while eight (only six shown) had no effect (marked in blue).</p

Alcohol-induced upregulation of expression of candidate genes.

<p>The relative mRNA levels for candidate genes <i>eag</i>, <i>brp</i>, <i>Teh2</i>, <i>pum</i>, <i>nej</i>, and <i>para</i> in control animals, benzyl alcohol-sedated animals, and ethanol-sedated animals are shown. Abundance of mRNA for each gene was determined by quantitative reverse-transcription PCR analysis and expressed relative to the abundance of the <i>Cyp1</i> gene. Error bars are SEM. Asterisks denote statistically significant differences from the untreated controls (P<0.05, One-way ANOVA with Dunnett's post-hoc test). Overall statistical significance of the effects of alcohol treatment for the whole set of genes was determined by Two-way ANOVA (P = 0.013).</p

Genome-wide, drug-induced changes in H4 acetylation.

<p><b>A</b>) Annotated gene map of a 30 kb representative region of the Drosophila chromosome 3R. The coding region of depicted genes is shown as connected boxes. Genes in the top row are transcribed from the plus (+) strand (left to right); genes in the bottom row are transcribed from the minus (−) strand (right to left). <b>B</b>) Histone acetylation profile of chromatin isolated from wild-type fly heads. Plot shows histone H4 acetylation levels of untreated control flies across the same chromosomal region displayed in (<b>A</b>). Bars represent the normalized IP/input ratios of fluorescently labeled chromatin signals detected by a single DNA tilling array. <b>C–D</b>) Difference plot showing the changes in histone H4 acetylation between control flies and benzyl alcohol-treated flies (<b>C</b>) or control flies and ethanol-treated flies (<b>D</b>) across the same chromosomal region displayed in (<b>A</b>). Bars are log2 values of the normalized IP-treated/IP-control ratios of fluorescently labeled chromatin signals detected by a single DNA tilling array. The red rectangle highlights a representative example of a statistically significant drug-induced acetylation spike shared by both drug treatments. The depicted red gene identifies the closest gene loci associated with the drug-induced acetylation spike. <b>E</b>) Diagrammatic representation of the overlap between the cohorts of genes with significant changes in acetylation induced by benzyl alcohol, ethanol or both.</p

Gene ontology annotations for highly correlated gene clusters.

<p>Significant gene categories for each cluster were identified using DAVID (Pearson correlation coefficient shown in parenthesis next to cluster number). The top term in each cluster with a Fisher Exact/EASE Score p-value<0.05 are listed in this summary. The ‘Count’ column displays number of genes in each cluster associated with a particular GO term, percentage of the total number of genes in the cluster is shown in parenthesis.</p

Mutant and RNAi behavioral analysis of benzyl alcohol and ethanol tolerance.

<p><b>A</b>) Mutant alleles or inducible RNAi lines for eighteen genes were tested for the ability to acquire tolerance to benzyl alcohol. Eleven of these lines significantly block or reduced benzyl alcohol tolerance as compared to their appropriate controls. <b>B</b>) Mutant alleles or inducible RNAi lines for the same eighteen genes shown in (<b>A</b>) were tested for the ability to acquire tolerance to ethanol. Ten of these lines significantly block or reduced tolerance as compared to their appropriate controls. Controls were: wild-type (WT) CS strain for mutants, and the respective heterozygous Gal4-driver transgenic for RNAi lines. * denotes significant difference in magnitude of tolerance between subjected lines and controls (P<0.05).</p

Respiratory trajectories in type 2 and non-ambulant 3 Spinal muscular atrophy in the iSMAC cohort study.

OBJECTIVE To describe the respiratory trajectories and their correlation with motor function in an international paediatric cohort of patients with type 2 and non-ambulant type 3 spinal muscular atrophy (SMA). METHODS Eight-year retrospective observational study of patients in the iSMAc natural history study. We retrieved anthropometrics, forced vital capacity (FVC) absolute, FVC% predicted (FVC%P.), Non-Invasive ventilation (NIV) requirement. Hammersmith functional motor scale (HFMS) and Revised performance of upper limb (RULM) were correlated with respiratory function. We excluded patients in interventional clinical trials and on Nusinersen commercial therapy. RESULTS There were 437 patients with SMA: 348 type 2, 89 non-ambulant type 3. Mean age at first visit was 6.9(±4.4) and 11.1(±4) years. In SMA type 2 FVC%P declined by 4.2%/year from 5 to 13 years, followed by a slower decline (1.0%/year). In type 3 FVC%P declined by 6.3%/year between 8 and 13 years, followed by a slower decline (0.9%/year). 39% SMA type 2 and 9% type 3 required NIV at median age 5.0(1.8-16.6) and 15.1(13.8-16.3) years. 84% SMA type 2 and 80% type 3 had scoliosis, 54% and 46% required surgery, which did not significantly affect respiratory decline. FVC%P positively correlated with HFMS and RULM in both subtypes. CONCLUSIONS In SMA type 2 and non-ambulant type 3 lung function declines differently, with a common levelling after age 13 years. Lung and motor function correlated in both subtypes. Our data further defines the milder SMA phenotypes and provides novel information to benchmark the long-term efficacy of new treatments for SMA