Document Type

Article

Original Publication Date

2016

Journal/Book/Conference Title

PLOS ONE

Volume

11

Issue

1

DOI of Original Publication

10.1371/journal.pone.0146257

Comments

Originally published at: http://dx.doi.org/10.1371/journal.pone.0146257

Date of Submission

March 2016

Abstract

Long lasting abusive consumption, dependence, and withdrawal are characteristic features of alcohol use disorders (AUD). Mechanistically, persistent changes in gene expression are hypothesized to contribute to brain adaptations leading to ethanol toxicity and AUD. We employed repeated chronic intermittent ethanol (CIE) exposure by vapor chamber as a mouse model to simulate the cycles of ethanol exposure and withdrawal commonly seen with AUD. This model has been shown to induce progressive ethanol consumption in rodents. Brain CIE-responsive expression networks were identified by microarray analysis across five regions of the mesolimbic dopamine system and extended amygdala with tissue harvested from 0-hours to 7-days following CIE. Weighted Gene Correlated Network Analysis (WGCNA) was used to identify gene networks over-represented for CIE-induced temporal expression changes across brain regions. Differential gene expression analysis showed that long-lasting gene regulation occurred 7-days after the final cycle of ethanol exposure only in prefrontal cortex (PFC) and hippocampus. Across all brain regions, however, ethanol-responsive expression changes occurred mainly within the first 8-hours after removal from ethanol. Bioinformatics analysis showed that neuroinflammatory responses were seen across multiple brain regions at early time-points, whereas co-expression modules related to neuroplasticity, chromatin remodeling, and neurodevelopment were seen at later time-points and in specific brain regions (PFC or HPC). In PFC a module containing Bdnf was identified as highly CIE responsive in a biphasic manner, with peak changes at 0 hours and 5 days following CIE, suggesting a possible role in mechanisms underlying long-term molecular and behavioral response to CIE. Bioinformatics analysis of this network and several other modules identified Let-7 family microRNAs as potential regulators of gene expression changes induced by CIE. Our results suggest a complex temporal and regional pattern of widespread gene network responses involving neuroinflammatory and neuroplasticity related genes as contributing to physiological and behavioral responses to chronic ethanol.

Rights

Copyright © 2016 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Is Part Of

VCU Pharmacology and Toxicology Publications

Additional Files
S1_Fig.pdf (1279 kB)
Multi-dimensional scale plots of the first and second principal component of each module identified by WGCNA in the prefrontal cortex (PFC); hierarchical cluster dendrogram using the module eigengenes (first principal component) of each PFC module; and line graphs of average module eigengene expression in Ctrl and CIE samples at each time-point.
S1_Table.xlsx (36707 kB)
Detailed results of linear models for microarray analysis (LIMMA).
S2_Fig.pdf (905 kB)
Multi-dimensional scale plots of the first and second principal component of each module identified by WGCNA in the nucleus accumbens (NAC); hierarchical cluster dendrogram using the module eigengenes (first principal component) of each NAC module; and line graphs of average module eigengene expression in Ctrl and CIE samples at each time-point.
S2_Table.xlsx (33879 kB)
Detailed results of linear models for microarray analysis (LIMMA).
S3_Fig.pdf (1144 kB)
Multi-dimensional scale plots of the first and second principal component of each module identified by WGCNA in the hippocampus (HPC); hierarchical cluster dendrogram using the module eigengenes (first principal component) of each HPC module; and line graphs of average module eigengene expression in Ctrl and CIE samples at each time-point.
S3_Table.xlsx (35653 kB)
Detailed results of linear models for microarray analysis (LIMMA).
S4_Fig.pdf (1101 kB)
Multi-dimensional scale plots of the first and second principal component of each module identified by WGCNA in the bed nucleus of the stria terminalis (BNST); hierarchical cluster dendrogram using the module eigengenes (first principal component) of each BNST module; and line graphs of average module eigengene expression in Ctrl and CIE samples at each time-point.
S4_Table.xlsx (34344 kB)
Detailed results of linear models for microarray analysis (LIMMA).
S5_Fig.pdf (807 kB)
Multi-dimensional scale plots of the first and second principal component of each module identified by WGCNA in the central nucleus of the amygdala (CEA); hierarchical cluster dendrogram using the module eigengenes (first principal component) of each CEA module; and line graphs of average module eigengene expression in Ctrl and CIE samples at each time-point.
S5_Table.xlsx (35552 kB)
Detailed results of linear models for microarray analysis (LIMMA).
S6_Fig.pdf (3921 kB)
Network representation of the PFC Yellow module based on adjacency.
S6_Table.docx (64 kB)
Time point comparisons of CIE-regulated genes within brain regions.
S7_Fig.pdf (3145 kB)
Network representation of HPC Magenta module based on adjacency.
S7_Table.xlsx (37752 kB)
Connectivity measures, RMA-values, LIMMA log-ratios, WGCNA module assignments, and LIMMA FDR adjusted p-values of 10,072 probesets used for WGCNA.
S8_Fig.pdf (1519 kB)
Network representation of BNST Lightgreen module based on adjacency.
S8_Table.xlsx (691 kB)
DAVID bioinformatics results obtained from each WGCNA module identified in the PFC.
S9_Fig.pdf (2751 kB)
Network representation of BNST Tan module based on adjacency.
S9_Table.xlsx (648 kB)
DAVID bioinformatics results obtained from each WGCNA module identified in the NAC.
S10_Fig.pdf (2139 kB)
Network representation of CEA Salmon module based on adjacency.
S10_Table.xlsx (794 kB)
DAVID bioinformatics results obtained from each WGCNA module identified in the HPC.
S11_Table.xlsx (644 kB)
DAVID bioinformatics results obtained from each WGCNA module identified in the BNST.
S12_Table.xlsx (632 kB)
DAVID bioinformatics results obtained from each WGCNA module identified in the CEA.
S13_Table.xlsx (83 kB)
Results of overlap analysis between WGCNA modules and LIMMA significant results including number of overlapping genes, p-values, odds ratios, and representation factor.
S14_Table.xlsx (772 kB)
Count of number of WGCNA modules in each brain-region significantly overlapping with Gene Ontology categories (DAVID p-value ≤ 0.05 and number of overlapping genes between 3 and 300).
S15_Table.xlsx (1959 kB)
miRvestigator results for PFC modules significantly overlapping with genes significantly differentially expressed at 0 hours or 7 days (significantly differentially expressed = LIMMA FDR ≤ 0.01, significantly overlapping = Fisher’s Exact test, p-value ≤ 0.005 and odds ratio ≥ 3).
S16_Table.xlsx (1579 kB)
miRvestigator results for HPC modules significantly overlapping with genes significantly differentially expressed at 0 hours or 7 days (significantly differentially expressed = LIMMA FDR ≤ 0.01, significantly overlapping = Fisher’s Exact test, p-value ≤ 0.005 and odds ratio ≥ 3).
S17_Table.xlsx (2353 kB)
Connectivity measures, RMA-values, LIMMA log-ratios, WGCNA module assignments, and LIMMA FDR adjusted p-values of probesets significant at 7 days (FDR ≤ 0.01) in the PFC (tab 1) and HPC (tab 2).
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