Gene expression profiling in the human alcoholic brain.

paper Cited Public

Authors: Warden, Anna S; Mayfield, R Dayne
Year: 2017
Journal: Neuropharmacology
PMID: 28254370
DOI: 10.1016/j.neuropharm.2017.02.017
PMCID: PMC5479716

Figure 1

A hypothetical diagram for the role of transcriptional regulation in AUD. Chronic alcohol consumption can alter regulation of transcription via epigenetic modifications, including miRNAs and lncRNAs. Alterations in these regulatory mechanisms result in aberrant downstream gene expression changes in functional systems known to contribute to alcohol dependence (e.g. neuronal transmission, ion channels, immune signaling, stress response).

LLM interpretation

This is a conceptual diagram illustrating the proposed mechanism by which chronic alcohol consumption leads to Alcohol Use Disorder (AUD). The flowchart shows that chronic alcohol consumption causes alterations in the regulation of transcription via three pathways: epigenetic markers/histone modifications, miRNAs, and long non-coding RNAs. These regulatory changes lead to aberrant gene expression changes across various biological systems (e.g., neurotransmission, stress and immune signaling, metabolism), ultimately resulting in AUD.

Figure 2

Total tissue versus cell-type specific analyses. Left Panel: Example volcano plot from an analysis of a cell-type specific isolation. The wider distribution of the plot signifies a greater number of statistically significant differentially expressed genes associated with chronic alcohol exposure. Right Panel: Example volcano plot from an analysis of a total tissue preparation. Note the smaller distribution and fewer statistically significant differentially expressed genes. Example data source: (https://raw.githubusercontent.com/brennanpincardiff/RforBiochemists/master/data/microArrayData.tsv”).

LLM interpretation

This figure consists of two volcano plots comparing gene expression changes associated with chronic alcohol exposure. The x-axis represents the $\log_2$ fold change and the y-axis represents the $-\log_{10}$ p-value. The left panel (cell-type specific isolation) shows a wider distribution of points with a higher density of statistically significant genes (blue) compared to the right panel (total tissue preparation), which exhibits a smaller distribution and fewer significant genes.

Figure 3

RNA-seq systems approach for understanding alcohol dependence. Sequencing can be used to evaluate changes in regulation of DNA and RNA expression in human alcoholic and control cases. Information from sequencing can then be superimposed with other variables such as protein data, physiological function, or other phenotypic information to identify mechanisms and potential treatment options for AUD. Adapted from (Farris and Mayfield, 2014).

LLM interpretation

This is a conceptual diagram illustrating a systems biology approach to understanding alcohol use disorder (AUD) in the human brain. The figure displays a hierarchical flow of biological information across six levels: DNA regulation/epigenetics, RNA expression, RNA regulation (lncRNAs, miRNAs), protein expression, anatomical-physiological data, and phenotypes. Interconnected nodes and vertical dashed lines indicate the integration of data across these different molecular and physiological layers, with additional external influences noted as other tissues, environment, and the microbiome.

#	Section	Preview
20	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.1: Epigenetic modifications in the human alcoholic brain	In addition to altered DNA methylation, histone modifications are also prevalent in human alcoholic…
21	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.1: Epigenetic modifications in the human alcoholic brain	(Alaux-Cantin et al., 2013; Sakharkar et al., 2014). Treatment with the HDAC inhibitor sodium…
22	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.1: Epigenetic modifications in the human alcoholic brain	The findings outlined above suggest that epigenetic alterations within addiction-related brain…
23	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	Protein-coding genes have traditionally been the most well-studied sequences in the human genome;…
24	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	up a large portion of the genome (Carninci et al., 2005; Consortium, 2012; Farris et al., 2015a),…
25	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	miRNAs were significantly up-regulated in the frontal cortex of human alcoholics (Lewohl et al.,…
26	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	through negative feedback loops involving down-regulation of IL-1 receptor-associated kinase 1…
27	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	In addition to innate immune responses, the differentially expressed miRNAs identified in human…
28	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	Up-regulated miRNAs in the frontal cortex of alcoholics are also involved in behavioral…
29	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	Differentially expressed miRNAs have also been found in the nucleus accumbens of human alcoholics…
30	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	(Lewohl et al., 2011). In the nucleus accumbens, down-regulation of the mir-34b/c family was…
31	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.2: MicroRNAs as transcriptional regulators	An additional complexity of miRNA transcriptional regulation is that miRNAs can also regulate other…
32	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.3: Long non-coding RNAs as transcriptional regulators	Long non-coding RNAs (lncRNAs) also regulate transcription of RNA and may be important in AUD.…
33	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.3: Long non-coding RNAs as transcriptional regulators	A continual challenge for gene expression profiling is that comparing expression levels of…
34	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.3: Long non-coding RNAs as transcriptional regulators	probable that causality can be established (Nica and Dermitzakis, 2013). Among the enriched eQTL…
35	3. Biological co-expression networks: Transcriptional regulation in alcohol use disorder — 3.3: Long non-coding RNAs as transcriptional regulators	Although the role of lncRNAs in alcohol dependence is still unclear, they are known to 1) regulate…
36	4. Functional systems associated with alcohol dependence	In addition to discrete mechanisms of transcriptional regulation, analysis of the human…
37	4. Functional systems associated with alcohol dependence — 4.1: GABA	Alcohol potentiates GABAA receptor-mediated responses and enhances inhibitory neurotransmission.…
38	4. Functional systems associated with alcohol dependence — 4.1: GABA	Some of the top candidate genes implicated in alcohol consumption in humans are transcripts for…
39	4. Functional systems associated with alcohol dependence — 4.1: GABA	Alcohol alters the expression of genes involved in neurotransmission, specifically those implicated…

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In this knowledge base

Title	Year	PMID
Allele-specific expression and high-throughput reporter assay reveal functional genetic variants associated with alcohol use disorders.	2021	31477794

External

Title	Authors	Journal	Year	Link
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Allele-specific expression and high-throughput reporter assay reveal functional genetic variants associated with alcohol use disorders.	Rao X et al.	—	2021	→
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Network preservation reveals shared and unique biological processes associated with chronic alcohol abuse in NAc and PFC.	Vornholt E et al.	—	2020	→
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RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models.	Kozell LB et al.	—	2020	→
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A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder.	Ferguson LB et al.	—	2019	→
Brain Imaging-Guided Analysis Reveals DNA Methylation Profiles Correlated with Insular Surface Area and Alcohol Use Disorder.	Zhao Y et al.	—	2019	→
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Regional Analysis of the Brain Transcriptome in Mice Bred for High and Low Methamphetamine Consumption.	Hitzemann R et al.	—	2019	→
Silencing synaptic MicroRNA-411 reduces voluntary alcohol consumption in mice.	Most D et al.	—	2019	→
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Persistent Neuroadaptations in the Expression of Genes Involved in Cholesterol Homeostasis Induced by Chronic, Voluntary Alcohol Intake in Rats.	Alsebaaly J et al.	—	2018	→
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Chronic intermittent ethanol exposure selectively alters the expression of Gα subunit isoforms and RGS subtypes in rat prefrontal cortex.	Luessen DJ et al.	—	2017	→
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The role of neuroimmune signaling in alcoholism.	Crews FT et al.	—	2017	→