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Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice.
Ferguson, Laura B; Roberts, Amanda J; Mayfield, R Dayne; Messing, Robert O.
Afiliación
  • Ferguson LB; Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America.
  • Roberts AJ; Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America.
  • Mayfield RD; Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America.
  • Messing RO; Animal Models Core Facility, The Scripps Research Institute, San Diego, California, United States of America.
PLoS Comput Biol ; 18(2): e1009800, 2022 02.
Article en En | MEDLINE | ID: mdl-35176017
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Consumo de Bebidas Alcohólicas / Expresión Génica / Etanol Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Consumo de Bebidas Alcohólicas / Expresión Génica / Etanol Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos