Gene expression in brain and liver produced by three different regimens of alcohol consumption in mice: comparison with immune activation.

paper Cited Public

Authors: Osterndorff-Kahanek, Elizabeth; Ponomarev, Igor; Blednov, Yuri A; Harris, R Adron
Year: 2013
Journal: PloS one
PMID: 23555817
DOI: 10.1371/journal.pone.0059870
PMCID: PMC3612084

Chronically available alcohol escalates drinking in mice and a single injection of the immune activator lipopolysaccharide can mimic this effect and result in a persistent increase in alcohol consumption. We hypothesized that chronic alcohol drinking and lipopolysaccharide injections will produce some similar molecular changes that play a role in regulation of alcohol intake. We investigated the molecular mechanisms of chronic alcohol consumption or lipopolysaccharide insult by gene expression profiling in prefrontal cortex and liver of C57BL/6J mice. We identified similar patterns of transcriptional changes among four groups of animals, three consuming alcohol (vs water) in different consumption tests and one injected with lipopolysaccharide (vs. vehicle). The three tests of alcohol consumption are the continuous chronic two bottle choice (Chronic), two bottle choice available every other day (Chronic Intermittent) and limited access to one bottle of ethanol (Drinking in the Dark). Gene expression changes were more numerous and marked in liver than in prefrontal cortex for the alcohol treatments and similar in the two tissues for lipopolysaccharide. Many of the changes were unique to each treatment, but there was significant overlap in prefrontal cortex for Chronic-Chronic Intermittent and for Chronic Intermittent-lipopolysaccharide and in liver all pairs showed overlap. In silico cell-type analysis indicated that lipopolysaccharide had strongest effects on brain microglia and liver Kupffer cells. Pathway analysis detected a prefrontal cortex-based dopamine-related (PPP1R1B, DRD1, DRD2, FOSB, PDNY) network that was highly over-represented in the Chronic Intermittent group, with several genes from the network being also regulated in the Chronic and lipopolysaccharide (but not Drinking in the Dark) groups. Liver showed a CYP and GST centered metabolic network shared in part by all four treatments. We demonstrate common consequences of chronic alcohol consumption and immune activation in both liver and brain and show distinct genomic consequences of different types of alcohol consumption.

Figure 1

Ethanol consumption and gene expression changes in liver and prefrontal cortex.(A) Average daily ethanol intake for three ethanol treatments. The Chronic treatment (open circles) is continuous two bottle choice drinking, CI is chronic intermittent two bottle choice with access to alcohol every other day and DID is a limited daily access (2 hr or 4 hr) to alcohol. Only data for days of ethanol consumption are shown. (B) Change in ethanol intake between the first and last 4 days of each treatment (mean ± SEM). Asterisks identify a significant change in ethanol intake (paired t test, p≤0.05). (C) Total ethanol consumed (average of all animals) and the number of genes differentially expressed (DE, p<0.05) in PFC and liver in each treatment (open bars are prefrontal cortex, filled bars are liver). Values are mean ± SEM, for n = 10 (Chronic and DID), n = 11 (CI). For some values, error bars are smaller than the symbols.

LLM interpretation

This figure consists of three panels analyzing ethanol intake and gene expression. Panel A is a line graph showing daily ethanol intake (g/kg) over time, where Chronic and CI groups show higher, similar intake levels compared to the significantly lower, fluctuating intake of the DID group. Panel B is a bar chart showing a significant increase in intake between the first and last four days for all three groups (p≤0.05). Panel C is a combined bar and line graph comparing total ethanol consumed with the number of differentially expressed (DE) genes in the prefrontal cortex (PFC) and liver across treatments.

Figure 2

Overlap of differentially expressed genes in pairs of studies for each tissue.(Panel A, PFC; Panel B, liver). Filled bars show the number of genes expected to be differentially expressed in both compared studies. Open bars show the observed number of shared, differentially expressed genes, with a horizontal line indicating the observed number of genes regulated in the same direction. Observed values are significantly greater than expected by chance (Bonferroni-corrected Chi square goodness-of-fit) at p<0.05 (*) and p<0.0001 (**).

LLM interpretation

This figure consists of two bar charts (Panel A for PFC and Panel B for liver) showing the overlap of differentially expressed genes between pairs of studies. For each pair, filled bars represent the expected overlap and open bars represent the observed overlap, with horizontal lines within the open bars indicating genes regulated in the same direction. In both tissues, the observed overlap is significantly greater than expected for several pairs, most notably for CI/LPS (**p<0.0001).

Figure 3

Mean t values of cell-type specific genes expressed in PFC and liver.Bars show mean t values (+/− SEM) of cell-type specific genes identified in each treatment. Asterisks identify cell-type specific t-means that are significantly different (by z test, Bonferroni-corrected, p<0.05) from the mean t value of all cell-type specific genes [n] detected in the given study. A. PFC (Astrocyte, n = 451–468; Microglia, n = 144–156; Neuron, n = 695–750; Oligodendrocyte, n = 264–297) B. Liver (Hepatocyte, n = 210–226; HSC, n = 11–15; Kupffer, n = 21–32).

LLM interpretation

This figure consists of several bar charts showing mean t-values (± SEM) of cell-type specific genes across four treatment conditions (Chronic, CI, DID, LPS) for different cell types in the PFC (Panel A) and Liver (Panel B). In the PFC, significant differences (p<0.05) are observed in various directions depending on the cell type, such as increased t-values for Microglia and Astrocytes under LPS treatment and decreased values for Neurons and Oligodendrocytes under LPS. In the liver, Kupffer cells show a significant increase in mean t-value under LPS treatment and a decrease under Chronic treatment, while Hepatocytes show significant decreases across all four conditions.

Figure 4

Gene networks derived from Chronic Intermittent (CI) data.Red and green fill indicate up- and down regulation, respectively in treated animals relative to controls (fold change ≥1.2, p<0.05). Gray fill indicates gene was not differentially expressed using these thresholds. White fill indicates genes not detected in our data, but added to the network due to their connectedness with other genes. Orange arrows point to members of a gene family. Solid lines indicate direct relationships; hashed lines are indirect relationships. Dark blue outlines identify genes that regulate cytokines or are regulated by cytokines or LPS. Tables show the fold changes of network genes up regulated (red) and down regulated (green) in all four data sets at p<0.05. Shapes represent molecule types. Genes are identified with human gene symbols. See Figure S1 for legend of molecule shapes. A. Neuronal network derived from PFC. B. Top network derived from liver.

LLM interpretation

This figure presents two gene networks (A: PFC neuronal network; B: liver network) accompanied by tables listing fold changes across four conditions (Chr, CI, DID, LPS). The networks use color-coded nodes (red for up-regulated, green for down-regulated, gray for non-differential, and white for undetected) and various line styles to indicate direct or indirect relationships. Tables provide quantitative fold change data, with red and green highlighting indicating statistical significance (p<0.05).

#	Section	Preview
20	Results — Gene Expression Changes	Overlap of regulated transcripts in pairs of studies for each tissue type was evaluated by comparing…
21	Results — Correlation of Gene Expression and Alcohol Consumption	Using data from individual mice, total ethanol consumption was correlated with PFC gene expression…
22	Results — Cell-type Analysis of Expression	We next used cell-type specific databases to ask which treatments might disproportionately affect…
23	Results — Ingenuity Pathway Analysis (IPA)	IPA identified the CI treatment as containing the most statistically significant networks and…
24	Results — Ingenuity Pathway Analysis (IPA)	The top network identified in CI liver (Figure 4B) was associated with drug metabolism, glutathione…
25	Discussion	One of the most surprising findings from this work is the distinct effects of the three alcohol…
26	Discussion	of every other day access appear to be distinct from continuous access. For example, our cell type…
27	Discussion	Another notable result from this study is that the LPS treatment produced twice as many changes in…
28	Discussion	the CI gene networks assembled for both PFC and liver, the overlap of genes was greater for LPS than…
29	Discussion	A novel aspect of this work is that our pathway analysis of the PFC transcriptome highlights a…
30	Discussion	as well as heroin and cocaine addiction in humans [61]. Genetic manipulation in PDYN or κ-opioid…
31	Discussion	For liver, a network was constructed that features cytochrome monooxygenases, glutathione…
32	Discussion	Several groups have published changes in brain gene expression related to alcohol and we have…
33	Discussion	top genes in the Wolstenholme et al [10] study. FOS, a main hub gene in our pathway analysis, was…
34	Discussion	In summary, comparison of three different models of voluntary alcohol consumption in mice shows…
35	Discussion	the strong immune component found in gene expression studies of PFC in human alcoholism. We also…

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Long-term alcohol drinking in High Drinking in the Dark mice is stable for many months and does not show alcohol deprivation effects.	Crabbe JC et al.	—	2022	→
The role of alcohol intake in the pharmacogenetics of treatment with clozapine.	Monroy-Jaramillo N et al.	—	2022	→
Transcriptional and Epigenetic Regulation of Monocyte and Macrophage Dysfunction by Chronic Alcohol Consumption.	Malherbe DC et al.	—	2022	→
Binge-like ethanol drinking activates anaplastic lymphoma kinase signaling and increases the expression of STAT3 target genes in the mouse hippocampus and prefrontal cortex.	Hamada K et al.	—	2021	→
Cortical astrocytes regulate ethanol consumption and intoxication in mice.	Erickson EK et al.	—	2021	→
Deletion of Tlr3 reduces acute tolerance to alcohol and alcohol consumption in the intermittent access procedure in male mice.	Blednov YA et al.	—	2021	→
Differential regulation of alcohol consumption and reward by the transcriptional cofactor LMO4.	Maiya R et al.	—	2021	→
Repetitive binge-like consumption based on the Drinking-in-the-Dark model alters the microglial population in the mouse hippocampus.	Nelson JC et al.	—	2021	→
Apoptosis-triggered decline in hippocampal microglia mediates adolescent intermittent alcohol exposure-induced depression-like behaviors in mice.	Hu P et al.	—	2020	→
Daily alcohol intake triggers aberrant synaptic pruning leading to synapse loss and anxiety-like behavior.	Socodato R et al.	—	2020	→
Inbred Substrain Differences Influence Neuroimmune Response and Drinking Behavior.	Warden AS et al.	—	2020	→
Role of toll-like receptor 7 (TLR7) in voluntary alcohol consumption.	Grantham EK et al.	—	2020	→
Systems genetics analysis of the LXS recombinant inbred mouse strains:Genetic and molecular insights into acute ethanol tolerance.	Radcliffe RA et al.	—	2020	→
Timing Eclipses Amount: The Critical Importance of Intermittency in Alcohol Exposure Effects.	Spear LP	—	2020	→
A comparison of hippocampal microglial responses in aged and young rodents following dependent and non-dependent binge drinking.	Grifasi IR et al.	—	2019	→
Alcohol intake triggers aberrant synaptic pruning leading to synapse loss and anxiety-like behavior	Socodato R et al.	—	2019	—
Calcium/calmodulin-stimulated adenylyl cyclases 1 and 8 regulate reward-related brain activity and ethanol consumption.	Bosse KE et al.	—	2019	→
Changes in brain kynurenine levels <i>via</i> gut microbiota and gut-barrier disruption induced by chronic ethanol exposure in mice.	Giménez-Gómez P et al.	—	2019	→
Cross-Species Co-analysis of Prefrontal Cortex Chronic Ethanol Transcriptome Responses in Mice and Monkeys.	Bogenpohl JW et al.	—	2019	→
Dissecting Brain Networks Underlying Alcohol Binge Drinking Using a Systems Genomics Approach.	Ferguson LB et al.	—	2019	→
GABA<sub>A</sub>R α2-activated neuroimmune signal controls binge drinking and impulsivity through regulation of the CCL2/CX3CL1 balance.	Aurelian L et al.	—	2019	→
Glial gene networks associated with alcohol dependence.	Erickson EK et al.	—	2019	→
Its complicated: The relationship between alcohol and microglia in the search for novel pharmacotherapeutic targets for alcohol use disorders.	Melbourne JK et al.	—	2019	→
Neuroimmune signaling in alcohol use disorder.	Erickson EK et al.	—	2019	→
Neuroinvasion and cognitive impairment in comorbid alcohol dependence and chronic viral infection: An initial investigation.	Loftis JM et al.	—	2019	→
Systemic Administration of the Cyclin-Dependent Kinase Inhibitor (S)-CR8 Selectively Reduces Escalated Ethanol Intake in Dependent Rats.	Goulding SP et al.	—	2019	→
Toll-like receptor 3 activation increases voluntary alcohol intake in C57BL/6J male mice.	Warden AS et al.	—	2019	→
Toll-like receptor 3 dynamics in female C57BL/6J mice: Regulation of alcohol intake.	Warden AS et al.	—	2019	→
Antagonising TLR4-TRIF signalling before or after a low-dose alcohol binge during adolescence prevents alcohol drinking but not seeking behaviour in adulthood.	Jacobsen JHW et al.	—	2018	→
Apremilast Alters Behavioral Responses to Ethanol in Mice: I. Reduced Consumption and Preference.	Blednov YA et al.	—	2018	→
Astrocyte-specific transcriptome responses to chronic ethanol consumption.	Erickson EK et al.	—	2018	→
Chronic ethanol consumption: role of TLR3/TRIF-dependent signaling.	McCarthy GM et al.	—	2018	→
Inhibition of phosphodiesterase 2 by Bay 60-7550 decreases ethanol intake and preference in mice.	Shi J et al.	—	2018	→
Microglia activation in the offspring of prenatal Poly I: C exposed rats: a PET imaging and immunohistochemistry study.	Li X et al.	—	2018	→
Microglial-specific transcriptome changes following chronic alcohol consumption.	McCarthy GM et al.	—	2018	→
Persistent escalation of alcohol consumption by mice exposed to brief episodes of social defeat stress: suppression by CRF-R1 antagonism.	Newman EL et al.	—	2018	→
The influence of adolescent nicotine exposure on ethanol intake and brain gene expression.	Silva CP et al.	—	2018	→
Transcriptome analysis of alcohol-treated microglia reveals downregulation of beta amyloid phagocytosis.	Kalinin S et al.	—	2018	→
DNA modifications in models of alcohol use disorders.	Tulisiak CT et al.	—	2017	→
Ethanol Consumption in Mice Lacking CD14, TLR2, TLR4, or MyD88.	Blednov YA et al.	—	2017	→
Evaluation of intoxicating effects of liquor products on drunken mice.	Wu Z et al.	—	2017	→
Gene expression profiling in the human alcoholic brain.	Warden AS et al.	—	2017	→
Loss of control over the ethanol consumption: differential transcriptional regulation in prefrontal cortex.	de Paiva Lima C et al.	—	2017	→
Maternal folate depletion during early development and high fat feeding from weaning elicit similar changes in gene expression, but not in DNA methylation, in adult offspring.	McKay JA et al.	—	2017	→
Mechanistic insights into epigenetic modulation of ethanol consumption.	Ponomarev I et al.	—	2017	→
The allostatic impact of chronic ethanol on gene expression: A genetic analysis of chronic intermittent ethanol treatment in the BXD cohort.	van der Vaart AD et al.	—	2017	→
Transcriptional profiling of the rat nucleus accumbens after modest or high alcohol exposure.	Morud J et al.	—	2017	→
Alcohol consumption induces global gene expression changes in VTA dopaminergic neurons.	Marballi K et al.	—	2016	→
Ethanol alters the expression of ion channel genes in Daphnia pulex.	Hu AM et al.	—	2016	→
Genes and Alcohol Consumption: Studies with Mutant Mice.	Mayfield J et al.	—	2016	→
Inhibition of IKKβ Reduces Ethanol Consumption in C57BL/6J Mice.	Truitt JM et al.	—	2016	→
Label-Free Proteomic Analysis of Protein Changes in the Striatum during Chronic Ethanol Use and Early Withdrawal.	Ayers-Ringler JR et al.	—	2016	→
Mechanisms of neuroimmune gene induction in alcoholism.	Crews FT et al.	—	2016	→
PPAR Agonists: I. Role of Receptor Subunits in Alcohol Consumption in Male and Female Mice.	Blednov YA et al.	—	2016	→
The neuroimmune transcriptome and alcohol dependence: potential for targeted therapies.	Warden A et al.	—	2016	→
Chronic ethanol exposure produces time- and brain region-dependent changes in gene coexpression networks.	Osterndorff-Kahanek EA et al.	—	2015	→
Novel candidate genes for alcoholism--transcriptomic analysis of prefrontal medial cortex, hippocampus and nucleus accumbens of Warsaw alcohol-preferring and non-preferring rats.	Stankiewicz AM et al.	—	2015	→
Peroxisome proliferator-activated receptors α and γ are linked with alcohol consumption in mice and withdrawal and dependence in humans.	Blednov YA et al.	—	2015	→
Phosphodiesterase regulation of alcohol drinking in rodents.	Logrip ML	—	2015	→
The synaptoneurosome transcriptome: a model for profiling the emolecular effects of alcohol.	Most D et al.	—	2015	→
Traumatic brain injury induces neuroinflammation and neuronal degeneration that is associated with escalated alcohol self-administration in rats.	Mayeux JP et al.	—	2015	→
Genetics and genomics of alcohol sensitivity.	Morozova TV et al.	—	2014	→
Inhibition of phosphodiesterase 4 reduces ethanol intake and preference in C57BL/6J mice.	Blednov YA et al.	—	2014	→
Neuroimmune pathways in alcohol consumption: evidence from behavioral and genetic studies in rodents and humans.	Robinson G et al.	—	2014	→