RNA-Seq reveals novel transcriptional reorganization in human alcoholic brain.

paper Cited Public

Authors: Farris, Sean P; Mayfield, R Dayne
Year: 2014
Journal: International review of neurobiology
PMID: 25172479
DOI: 10.1016/B978-0-12-801105-8.00011-4
PMCID: PMC4267562

Figure 11.1

RNA-Seq detection of biological features for alcoholics and matched controls. Bar plot demonstrates the percentage of features detected in a representative control (blue (black in the print version)) and alcoholic (red (dark gray in the print version)) sample from a cohort of the prefrontal cortex. The left axis shows percentage of features for the top three biotypes with the right axis showing percentage of remaining biotypes (separated by dotted green (light gray in the print version) vertical line). Protein-coding transcripts were the predominant feature detected in both groups.

LLM interpretation

This figure consists of two side-by-side bar plots comparing the percentage of RNA-Seq biological features detected in a control sample (blue) and an alcoholic sample (red) from the prefrontal cortex. The x-axes list various biotypes, with protein-coding transcripts being the most predominant feature in both groups. The plots distinguish between the percentage of features in the genome (gray), those detected (patterned), and the percentage in the sample (solid color), with a vertical dotted green line separating the top three biotypes from the remaining categories.

Figure 11.10

Example of a novel three prime untranslated region (3′-UTR) from RNA-Seq data. Snapshot of RNA-Seq counts for three representative samples of a novel long form of a 3′-UTR. Blue (black in the print version) boxes indicate the presence of the last exon and currently annotated 3′-UTR; however, RNA-Seq may detect previously unknown details regarding transcript expression that may impact function.

LLM interpretation

This figure consists of three genomic track snapshots showing RNA-Seq read counts for three representative samples. A blue gene model at the bottom indicates the currently annotated last exon and 3′-UTR, while a dashed line marks a proposed "Novel 3' UTR" extending upstream. The visualization demonstrates the presence of RNA-Seq reads in the region preceding the annotated 3′-UTR, suggesting a longer transcript form.

Figure 11.11

Classifying genetic variants within alcoholic prefrontal cortex. Genetic variants detected within alcoholic prefrontal cortex classified by currently annotated gene regions: intergenic, upstream, 5′-UTR, exon, splice site donor, intron, splice site acceptor, 3′-UTR, and downstream. Genetic variants are primarily located in unannotated genomic areas or noncoding/intronic regions.

LLM interpretation

This is a bar chart showing the distribution of genetic variants detected within the alcoholic prefrontal cortex across different genomic regions. The y-axis represents the "% Variant detected," while the x-axis lists various gene regions, including intergenic, upstream, UTRs, exons, splice sites, and introns. The data shows a dominant peak in the "Intron" region, which accounts for over 40% of the detected variants, with all other regions remaining below 10%.

Figure 11.12

Utilizing RNA-Seq in the context of a multiscale systems approach for understanding the neurobiology of alcohol dependence. Differing brain regions of alcoholics and controls can be evaluated using high-throughput sequencing for regulation of DNA and RNA expression. Information from sequencing data can then be layered with current protein data, brain imaging, physiological function, a variety of phenotypic traits (i.e., drinking behavior, withdrawal, craving), and additional influences (non-CNS tissues, human microbiota, and environmental pressures). Pooling resources from both clinical and preclinical sources can clarify points of convergent validity to determine individualized treatment plans incorporating behavioral therapy, current FDA approved compounds, or designer compounds that best target the underlying structure of an individual’s disease.

LLM interpretation

This figure is a conceptual diagram illustrating a multiscale systems approach to understanding alcohol dependence across three panels: "Human brain," "Model system," and "Convergent evidence." Each panel displays layered biological data—DNA regulation, RNA expression, protein expression, anatomical-physiological data, and phenotypes—represented by colored nodes and networks connected by vertical dashed lines. The bottom of the diagram indicates external influences (other tissues, environment, microbiome) and potential clinical outcomes, including designer drugs, targeted therapeutics, behavioral interventions, and FDA-approved treatments.

Figure 11.2

Expression values of the detected biological features for alcoholics and matched controls. Box and whisker plots for expression of biological features in representative controls (A) and alcoholics (B) from the prefrontal cortex. Shown along the x-axis is the number of corresponding biotypes determined for all samples having greater than zero counts. The two groups have similar overall expression values for biological features.

LLM interpretation

This figure consists of two box and whisker plots comparing the expression values of various biological features in the prefrontal cortex of control subjects (A) and alcoholics (B). The y-axes represent expression values on a logarithmic scale, while the x-axis lists different biotypes (e.g., protein coding, snRNA, lincRNA) along with the number of corresponding biotypes detected. The overall distribution and median expression values across the different biological features appear similar between the control and alcoholic groups.

Figure 11.3

Comparison of individual samples for overall expression and sensitivity. (A) Box and whisker plots for overall expression within individual samples and (B) stacked bar plot of binned expression based on counts per million (CPM) mapped reads across individuals to determine intersample consistency and the percentage of low expression values that may interfere with downstream analyses. Horizontal lines depict the percentage of CPM expressed in at least one specimen to help determine an appropriate range of sensitivity.

LLM interpretation

Figure A consists of box and whisker plots showing expression values for 12 individual samples (6 CTL and 6 ALC), with most values concentrated between 10 and 100 and several high-value outliers. Figure B is a stacked bar plot displaying sensitivity percentages across the same samples, binned by counts per million (CPM) thresholds ranging from >0 to >10. Horizontal lines in Figure B indicate the percentage of CPM expressed in at least one specimen across different sensitivity ranges.

Figure 11.4

Saturation plots for assessing quality control across individual samples and disease groups. Number of detected features compared with sequencing depth in million mapped reads for controls (left), alcoholics (middle), and all samples (right) for all biological features detected (A) and “protein-coding” transcripts only (B). Figures demonstrate all samples have comparable saturation slopes and can be included in downstream analysis.

LLM interpretation

This figure consists of six saturation plots (line graphs) organized into two rows (A and B) and three columns (Controls, Alcoholics, and All Samples). The x-axis represents sequencing depth in million mapped reads, and the y-axis represents the number of detected features. Row A shows all biological features, while row B shows only protein-coding transcripts; in all plots, the curves plateau as sequencing depth increases, indicating comparable saturation slopes across all individual samples and groups.

Figure 11.5

Assessment of gene expression for bias in sequencing length. The size of detected biotypes is binned along the x-axis and compared to mean expression values of raw counts (top) and normalized expression by reads per kilobase per million (RPKM) mapped reads (bottom). Alcoholic and control samples follow similar trends in raw mean expression values (A) and RPKM values (C). Mean raw count values in controls and alcoholics are highly associated with feature length (B); however, feature length is not strongly correlated with mean RPKM (D).

LLM interpretation

This figure consists of four panels (A-D) of line and scatter plots comparing mean gene expression against length bins for "Controls" (green/black) and "Alcoholics" (red/black). Panels A and B show raw mean expression, which increases sharply with feature length, showing a strong correlation ($R^2$ values of 90.93% and 92.43% in panel B). Panels C and D show RPKM-normalized expression, where the correlation with feature length is significantly reduced ($R^2$ values of 8.71% and 8.62% in panel D).

Figure 11.6

Assessment of gene expression for bias in GC content. The GC% of detected biotypes is binned along the x-axis and compared to mean expression values of raw counts (top) and normalized expression by reads per kilobase per million (RPKM) mapped reads (bottom). Alcoholic and control samples follow similar trends in raw mean expression values (A) and RPKM values (C) in relation to GC%. Mean raw count values in controls and alcoholics are persistently correlated with % of GC content (B); however, GC% shows lower correlation with normalized expression (D).

LLM interpretation

This figure consists of four panels (A-D) showing the relationship between GC content bins (x-axis) and mean gene expression (y-axis) for control and alcoholic groups. Panels A and B display raw counts, showing a strong negative correlation between GC content and expression ($R^2$ values of 93.70% and 92.31%). Panels C and D display RPKM normalized expression, which shows a significantly weaker correlation with GC content ($R^2$ values of 43.21% and 34.29%).

Figure 11.7

Comparison of normalization strategies for assessing gene expression in controls and alcoholics. Box and whisker plots for intersample Pearson correlation coefficients of control and alcoholic prefrontal cortex gene expression across multiple strategies for normalizing RNA-Seq count data. Differing methods of normalization exhibit differing median intersample consistency and within group variation, which may affect experimental outcomes. The appropriate method should be based on the quality control measures and hypothesis in question.

LLM interpretation

This figure consists of two side-by-side box and whisker plots comparing the median intersample Pearson correlation coefficients for gene expression in "Controls" and "Alcoholics" across various RNA-Seq normalization strategies. The y-axis represents the median correlation coefficient, ranging from 0.6 to 1.0, while the x-axis lists different normalization methods (e.g., Raw Counts, RPKM, DESeq2). The plots show that different normalization methods result in varying levels of intersample consistency and within-group variation for both groups.

Figure 11.8

Differential expression for gene, transcript, and exon models of RNA-Seq data. The mean log CPM is plotted against the log fold change in gene (left), alternatively spliced transcript (middle), and exon (right) expression for alcoholics versus controls in the prefrontal cortex. Horizontal blue (light gray in the print version) lines depict a twofold change in expression (increased or decreased) between disease groups and red (dark gray in the print version) dots indicate features with a p-value ≤ 0.05.

LLM interpretation

This figure consists of three MA plots showing differential expression for gene, transcript, and exon models in the prefrontal cortex of alcoholics versus controls. The x-axis represents the mean log counts per million (logCPM) and the y-axis represents the log fold change (logFC). Red dots indicate features with a p-value $\le$ 0.05, while horizontal blue lines mark a twofold change in expression.

Figure 11.9

Fold change of long noncoding RNAs within the prefrontal cortex. Bar plot of fold change in expression of the top 15 long noncoding RNAs (lncRNAs) between alcoholics and controls within prefrontal cortex. Eleven lncRNAs show increased expression in the prefrontal cortex of alcoholics compared to controls, while four lncRNAs show decreased expression.

LLM interpretation

This bar plot displays the fold change in expression of the top 15 long noncoding RNAs (lncRNAs) in the prefrontal cortex, comparing alcoholics to controls. The y-axis represents the "Fold change in expression," with red bars indicating increased expression (11 lncRNAs) and dark blue bars indicating decreased expression (4 lncRNAs). The x-axis labels the specific lncRNAs, with NCRNA00051 showing the highest positive fold change and NCRNA00290 showing the greatest negative fold change.

#	Section	Preview
0	1. OVERVIEW	RNA-Seq utilizes highly efficient sequencing techniques and subsequent mapping of short sequence…
1	1. OVERVIEW	This review addresses three critical barriers to progress in alcohol research: (1) Regulation of…
2	2. RNA-SEQ OF POSTMORTEM BRAIN TISSUE	Transcriptome profiling of postmortem brain tissue from alcoholics and matched controls has revealed…
3	2. RNA-SEQ OF POSTMORTEM BRAIN TISSUE	A first-pass examination of the transcriptome of alcoholics and matched controls identified a number…
4	2. RNA-SEQ OF POSTMORTEM BRAIN TISSUE	Comparing the overall expression of detected biological RNA categories within individual samples…
5	3. DETECTION OF TECHNICAL BIASES IN RNA-SEQ DATA	Obtaining an accurate assessment of RNA molecules that correspond with disease is not a trivial…
6	3. DETECTION OF TECHNICAL BIASES IN RNA-SEQ DATA	Williams, McCue, Schaeffer, & Wold, 2008), effectively alleviated the significant bias introduced by…
7	4. NORMALIZATION OF RNA-SEQ DATA	No single procedure has yet emerged as a gold standard for RNA-Seq analyses. Differing methodologies…
8	4. NORMALIZATION OF RNA-SEQ DATA	controls. A comprehensive evaluation of normalization techniques for RNA-Seq data has previously…
9	5. ALTERNATIVE SPLICING AND DIFFERENTIAL EXPRESSION	Once an acceptable normalization method is determined, summarized read counts can be evaluated for…
10	5. ALTERNATIVE SPLICING AND DIFFERENTIAL EXPRESSION	Differential expression between alcoholics and controls is able to distinguish ~1000 genes and ~1200…
11	5. ALTERNATIVE SPLICING AND DIFFERENTIAL EXPRESSION	prefrontal cortex of alcoholics (Fig. 11.8). Additionally, a greater number of exon features have…
12	6. LONG NONCODING RNA	In the absence of forming a functional protein, intracellular molecules can still function as ncRNAs…
13	6. LONG NONCODING RNA	an increase in the expression of the lncRNA MALAT1 within multiple brain regions (Kryger, Fan,…
14	7. NOVEL THREE PRIME UNTRANSLATED REGIONS	RNA-Seq can generate rich expression maps for annotated and unannotated regions of the…
15	7. NOVEL THREE PRIME UNTRANSLATED REGIONS	short 3′-UTR of Bdnf RNA is restricted to neuronal somata, while a long form of the 3′-UTR is…
16	8. GENETIC VARIATION AND ALCOHOL DEPENDENCE	Single nucleotide polymorphisms (SNPs) within GABA receptors, and several other candidate genes, are…
17	8. GENETIC VARIATION AND ALCOHOL DEPENDENCE	human populations, as well as other model systems, is beginning to pinpoint numerous sites of…
18	9. BIOLOGICAL COEXPRESSION NETWORKS	The number of genes implicated in alcohol dependence and other psychiatric illnesses continues to…
19	9. BIOLOGICAL COEXPRESSION NETWORKS	The human brain transcriptome represents highly organized gene coexpression networks that are…

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In this knowledge base

Title	Year	PMID
Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases.	2021	34417470
Alcohol exposure alters pre-mRNA splicing of antiapoptotic Mcl-1L isoform and induces apoptosis in neural progenitors and immature neurons.	2019	31171771
Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism.	2019	30765688
Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice.	2017	28058793
Gene expression profiling in the human alcoholic brain.	2017	28254370

External

Title	Authors	Journal	Year	Link
Cortical reactive microglia activate astrocytes, increasing neurodegeneration in human alcohol use disorder.	Crews FT et al.	—	2026	→
Integrative Genomics Approach Identifies Glial Transcriptomic Dysregulation and Risk in the Cortex of Individuals With Alcohol Use Disorder.	Warden AS et al.	—	2026	→
Epigenetic Dysregulation in Alcohol-Associated Behaviors: Preclinical and Clinical Evidence.	Domi E et al.	—	2025	→
Potential Molecular Mechanisms of Alcohol Use Disorder with Non-Coding RNAs and Gut Microbiota for the Development of Superior Therapeutic Application.	Nakashima M et al.	—	2024	→
Ethanol exposure alters Alzheimer's-related pathology, behavior, and metabolism in APP/PS1 mice.	Day SM et al.	—	2023	→
Ethanol-induced cerebellar transcriptomic changes in a postnatal model of fetal alcohol spectrum disorders: Focus on disease onset.	Holloway KN et al.	—	2023	→
Functional roles, regulatory mechanisms and theranostics applications of ncRNAs in alcohol use disorder.	Wang JQ et al.	—	2023	→
Prefrontal cortex glutamatergic adaptations in a mouse model of alcohol use disorder.	Siddiqi MT et al.	—	2023	→
Ethanol exposure alters Alzheimer’s-related pathology, behavior, and metabolism in APP/PS1 mice	Gironda SC et al.	—	2022	—
FACTORS CONTRIBUTING TO THE ESCALATION OF ALCOHOL CONSUMPTION.	Bowen MT et al.	—	2022	→
Noncoding RNA therapeutics for substance use disorder.	Seyednejad SA et al.	—	2022	→
The interactions of alcohol and cocaine regulate the expression of genes involved in the GABAergic, glutamatergic and endocannabinoid systems of male and female rats.	Marcos A et al.	—	2022	→
Chronic Alcohol Use Induces Molecular Genetic Changes in the Dorsomedial Thalamus of People with Alcohol-Related Disorders.	Hade AC et al.	—	2021	→
Chronic Ethanol Exposure Enhances Facial Stimulation-Evoked Mossy Fiber-Granule Cell Synaptic Transmission <i>via</i> GluN2A Receptors in the Mouse Cerebellar Cortex.	Li BX et al.	—	2021	→
Downregulation of Synaptotagmin 1 in the Prelimbic Cortex Drives Alcohol-Associated Behaviors in Rats.	Barbier E et al.	—	2021	→
Effect of chronic ethanol consumption in rhesus macaques on the nucleus accumbens core transcriptome.	Walter N et al.	—	2021	→
Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases.	Kapoor M et al.	—	2021	→
The importance of non-coding RNAs in environmental stress-related developmental brain disorders: A systematic review of evidence associated with exposure to alcohol, anesthetic drugs, nicotine, and viral infections.	Arzua T et al.	—	2021	→
What can we learn from PWS and SNORD116 genes about the pathophysiology of addictive disorders?	Salles J et al.	—	2021	→
Alcohol Causes Lasting Differential Transcription in <i>Drosophila</i> Mushroom Body Neurons.	Petruccelli E et al.	—	2020	→
Chronic Voluntary Ethanol Drinking in Cynomolgus Macaques Elicits Gene Expression Changes in Prefrontal Cortical Area 46.	Walter NAR et al.	—	2020	→
Neural Mechanisms Underlying the Rewarding and Therapeutic Effects of Ketamine as a Treatment for Alcohol Use Disorder.	Strong CE et al.	—	2020	→
Sex differences: Transcriptional signatures of stress exposure in male and female brains.	Brivio E et al.	—	2020	→
Alcohol exposure alters pre-mRNA splicing of antiapoptotic Mcl-1L isoform and induces apoptosis in neural progenitors and immature neurons.	Donadoni M et al.	—	2019	→
Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism.	Kapoor M et al.	—	2019	→
Insights from intoxicated Drosophila.	Petruccelli E et al.	—	2019	→
The lncRNA BDNF-AS is an epigenetic regulator in the human amygdala in early onset alcohol use disorders.	Bohnsack JP et al.	—	2019	→
Alcohol Activates Scabrous-Notch to Influence Associated Memories.	Petruccelli E et al.	—	2018	→
GABA<sub>A</sub> receptor polymorphisms in alcohol use disorder in the GWAS era.	Koulentaki M et al.	—	2018	→
Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs.	Iancu OD et al.	—	2018	→
On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome.	Iancu OD et al.	—	2018	→
On the safety of repeated ketamine infusions for the treatment of depression: Effects of sex and developmental periods.	Strong CE et al.	—	2018	→
Alcohol-Mediated Missplicing of Mcl-1 Pre-mRNA is Involved in Neurotoxicity.	Sariyer R et al.	—	2017	→
Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice.	Colville AM et al.	—	2017	→
Emerging roles for ncRNAs in alcohol use disorders.	Mayfield RD	—	2017	→
Gene expression profiling in the human alcoholic brain.	Warden AS et al.	—	2017	→
Genetic divergence in the transcriptional engram of chronic alcohol abuse: A laser-capture RNA-seq study of the mouse mesocorticolimbic system.	Mulligan MK et al.	—	2017	→
Possible involvement of ACSS2 gene in alcoholism.	Ribeiro AF et al.	—	2017	→
Synaptic targets: Chronic alcohol actions.	Roberto M et al.	—	2017	→
A Genetic Animal Model of Alcoholism for Screening Medications to Treat Addiction.	Bell RL et al.	—	2016	→
A novel heat shock protein alpha 8 (Hspa8) molecular network mediating responses to stress- and ethanol-related behaviors.	Urquhart KR et al.	—	2016	→
Applying the new genomics to alcohol dependence.	Farris SP et al.	—	2015	→
Chronic ethanol exposure produces time- and brain region-dependent changes in gene coexpression networks.	Osterndorff-Kahanek EA et al.	—	2015	→
Epigenetic modulation of brain gene networks for cocaine and alcohol abuse.	Farris SP et al.	—	2015	→
RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles.	Webb A et al.	—	2015	→
Role of microRNAs in Alcohol-Induced Multi-Organ Injury.	Natarajan SK et al.	—	2015	→