In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 2 ( 2022-2-17), p. e1009800-
Abstract:
Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67] ) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009800
DOI:
10.1371/journal.pcbi.1009800.g001
DOI:
10.1371/journal.pcbi.1009800.g002
DOI:
10.1371/journal.pcbi.1009800.g003
DOI:
10.1371/journal.pcbi.1009800.g004
DOI:
10.1371/journal.pcbi.1009800.g005
DOI:
10.1371/journal.pcbi.1009800.t001
DOI:
10.1371/journal.pcbi.1009800.t002
DOI:
10.1371/journal.pcbi.1009800.t003
DOI:
10.1371/journal.pcbi.1009800.t004
DOI:
10.1371/journal.pcbi.1009800.s001
DOI:
10.1371/journal.pcbi.1009800.s002
DOI:
10.1371/journal.pcbi.1009800.s003
DOI:
10.1371/journal.pcbi.1009800.s004
DOI:
10.1371/journal.pcbi.1009800.s005
DOI:
10.1371/journal.pcbi.1009800.s006
DOI:
10.1371/journal.pcbi.1009800.s007
DOI:
10.1371/journal.pcbi.1009800.s008
DOI:
10.1371/journal.pcbi.1009800.s009
DOI:
10.1371/journal.pcbi.1009800.s010
DOI:
10.1371/journal.pcbi.1009800.s011
DOI:
10.1371/journal.pcbi.1009800.s012
DOI:
10.1371/journal.pcbi.1009800.r001
DOI:
10.1371/journal.pcbi.1009800.r002
DOI:
10.1371/journal.pcbi.1009800.r003
DOI:
10.1371/journal.pcbi.1009800.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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