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Identification of Pre-symptomatic Gene Signatures That Predict Resilience to Cognitive Decline in the Genetically Diverse AD-BXD Model.
Neuner, Sarah M; Heuer, Sarah E; Zhang, Ji-Gang; Philip, Vivek M; Kaczorowski, Catherine C.
Afiliação
  • Neuner SM; University of Tennessee Health Science Center, Memphis, TN, United States.
  • Heuer SE; The Jackson Laboratory, Bar Harbor, ME, United States.
  • Zhang JG; The Jackson Laboratory, Bar Harbor, ME, United States.
  • Philip VM; Tufts University Sackler School of Graduate Biomedical Sciences, Boston, MA, United States.
  • Kaczorowski CC; The Jackson Laboratory, Bar Harbor, ME, United States.
Front Genet ; 10: 35, 2019.
Article em En | MEDLINE | ID: mdl-30787942
ABSTRACT
Across the population, individuals exhibit a wide variation of susceptibility or resilience to developing Alzheimer's disease (AD). Identifying specific factors that promote resilience would provide insight into disease mechanisms and nominate potential targets for therapeutic intervention. Here, we use transcriptome profiling to identify gene networks present in the pre-symptomatic AD mouse brain relating to neuroinflammation, brain vasculature, extracellular matrix organization, and synaptic signaling that predict cognitive performance at an advanced age. We highlight putative drivers of these observed relationships, including Itgb2, Fcgr2b, Slc6a14, and Gper1, which represent prime targets through which to promote resilience prior to overt symptom onset. In addition, we identify a genomic region on chromosome 2 containing variants that directly modulate resilience network expression. Overall, work here highlights new potential drivers of resilience to AD and contributes significantly to our understanding of early, potentially causal, disease mechanisms.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Genet Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Genet Ano de publicação: 2019 Tipo de documento: Article