RESUMEN
INTRODUCTION: Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action. METHODS: Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE ε4/ε4 and Trem2*R47H. The potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts. RESULTS: We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes. DISCUSSION: These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics. HIGHLIGHTS: A novel approach to validate genetic risk factors for late-onset AD (LOAD) is presented. LOAD risk variants were knocked in to conserved mouse loci. Variant effects were assayed by transcriptional analysis. Risk variants in Abca7, Mthfr, Plcg2, and Sorl1 loci modeled molecular signatures of clinical disease. This approach should generate more translationally relevant animal models.
Asunto(s)
Enfermedad de Alzheimer , Modelos Animales de Enfermedad , Predisposición Genética a la Enfermedad , Ratones Transgénicos , Enfermedad de Alzheimer/genética , Animales , Ratones , Humanos , Factores de Riesgo , Predisposición Genética a la Enfermedad/genética , Estudio de Asociación del Genoma Completo , Masculino , Encéfalo/patología , Encéfalo/metabolismo , FemeninoRESUMEN
INTRODUCTION: Alzheimer's disease (AD) is a neurodegenerative disorder with multifactorial etiology, including genetic factors that play a significant role in disease risk and resilience. However, the role of genetic diversity in preclinical AD studies has received limited attention. METHODS: We crossed five Collaborative Cross strains with 5xFAD C57BL/6J female mice to generate F1 mice with and without the 5xFAD transgene. Amyloid plaque pathology, microglial and astrocytic responses, neurofilament light chain levels, and gene expression were assessed at various ages. RESULTS: Genetic diversity significantly impacts AD-related pathology. Hybrid strains showed resistance to amyloid plaque formation and neuronal damage. Transcriptome diversity was maintained across ages and sexes, with observable strain-specific variations in AD-related phenotypes. Comparative gene expression analysis indicated correlations between mouse strains and human AD. DISCUSSION: Increasing genetic diversity promotes resilience to AD-related pathogenesis, relative to an inbred C57BL/6J background, reinforcing the importance of genetic diversity in uncovering resilience in the development of AD. HIGHLIGHTS: Genetic diversity's impact on AD in mice was explored. Diverse F1 mouse strains were used for AD study, via the Collaborative Cross. Strain-specific variations in AD pathology, glia, and transcription were found. Strains resilient to plaque formation and plasma neurofilament light chain (NfL) increases were identified. Correlations with human AD transcriptomics were observed.
Asunto(s)
Enfermedad de Alzheimer , Resiliencia Psicológica , Ratones , Humanos , Femenino , Animales , Enfermedad de Alzheimer/patología , Placa Amiloide/patología , Ratones Endogámicos C57BL , Microglía/metabolismo , Variación Genética/genética , Modelos Animales de Enfermedad , Ratones Transgénicos , Péptidos beta-Amiloides/metabolismoRESUMEN
Introduction: Genome-wide association studies have identified over 70 genetic loci associated with late-onset Alzheimer's disease (LOAD), but few candidate polymorphisms have been functionally assessed for disease relevance and mechanism of action. Methods: Candidate genetic risk variants were informatically prioritized and individually engineered into a LOAD-sensitized mouse model that carries the AD risk variants APOE4 and Trem2*R47H. Potential disease relevance of each model was assessed by comparing brain transcriptomes measured with the Nanostring Mouse AD Panel at 4 and 12 months of age with human study cohorts. Results: We created new models for 11 coding and loss-of-function risk variants. Transcriptomic effects from multiple genetic variants recapitulated a variety of human gene expression patterns observed in LOAD study cohorts. Specific models matched to emerging molecular LOAD subtypes. Discussion: These results provide an initial functionalization of 11 candidate risk variants and identify potential preclinical models for testing targeted therapeutics.