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IntroductionAlzheimer'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.MethodsWe 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.ResultsGenetic 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.DiscussionIncreasing 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.HighlightsGenetic 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.

Background: Data from human and model organism studies suggest that genetic background influences susceptibility and resilience to Alzheimer's Disease (AD) neuropathology. We previously showed that, wild‐derived PWK/PhJ (PWK) mice carrying the APP/PS1 transgene (PWK.APP/PS1) exhibit cognitive and synaptic resilience compared to traditionally‐studied B6.APP/PS1 inbred mice in presence of amyloid beta (Aβ) plaque deposition. PWK.APP/PS1 mice also contain different proportions of transcriptionally‐defined microglia compared to B6.APP/PS1. The precise molecular mechanisms underlying these differences between strains remain unknown. In this study, we aim to identify and characterize cell populations in the hippocampi of genetically distinct B6 and PWK mouse strains, and investigate cell‐type specific gene expression patterns associated with cognitive resilience in presence of amyloid deposition. Method: We generated cohorts of B6.APP/PS1 and PWK.APP/PS1 transgenic female mice and wild‐type (WT) littermates as controls (n = 4 per strain/genotype group). At 8 months of age mice were euthanized, hippocampi collected, and single nuclear RNA sequencing (snRNAseq) was performed using 10x Genomics Chromium platform. Seurat R package (version 5.0.1) was used for major data analysis. Result: We identified subpopulations of microglial cells characterized by varying expression patterns of a set of genes including Hk2, Itgam and Dock8. Neuronal subtypes were clustered by top marker genes Plk5 and Adarb2. Oligodendrocytes, oligodendrocyte progenitor cells, and endothelial cells were marked by expression of Mog, Neu4 and Abcc9, respectively. Differential expression and pathway enrichment analysis revealed strain‐specific immune and cell signaling pathways altered in distinct cell populations in response to amyloid pathogenesis. Cross‐species mapping of these transcriptomic signatures with human hippocampal gene modules identified AD‐relevant molecular mechanisms observed in mouse models of AD at the cell‐type level. Conclusion: This study suggests that cell‐type specific transcriptomic response to amyloid is modulated by genetic background and our findings re‐iterate the importance of incorporating genetic diversity to model phenotypic and molecular heterogeneity in AD. Since the nuclei enriched in this dataset are enriched for neurons, future interrogations will provide evidence for specific neuronal populations and molecular mechanisms that differentiate cognitively resilient PWK from susceptible B6 mice, providing insights into mechanisms that can be leveraged to promote AD resilience. [ABSTRACT FROM AUTHOR]

Background: The genetic etiology of late‐onset Alzheimer's disease (LOAD) is complex, with over 75 identified loci contributing to disease risk. Recent efforts of the MODEL‐AD consortia have yielded several dozen mouse strains harboring variation designed to model LOAD risk alleles. Given the complex genetic architecture of LOAD, developing animal models that combine multiple risk alleles is likely essential to improving the fidelity of these models to human disease. However, it is not clear which combinations of risk alleles are optimal for modeling disease features in mice. In this study we utilize transcriptome data from existing strains to prioritize which variants should be combined in future model development. Method: We estimated the effects of genetic variants and environmental perturbations on gene expression using multiple linear regression. The computed gene‐level effects were used as features in separate regression models fit to changes in human transcriptomes from AMP‐AD cohorts and previously described disease subtypes. To mitigate multicollinearity and allow for non‐linear relationships, we employed both regularized linear models (LASSO, SVM) and non‐linear models (Random Forest, Neural Network). Variable importance was extracted from the fit models for each regression analysis. We evaluated the importance of each feature (e.g. genetic variant) across models in an ensemble approach. Result: The top mouse features prioritized by the different regression models were largely similar. Moreover, similar features were also prioritized across the different human cohorts. The top mouse features included a humanized Aβ and variants in SPI1, ABCA7, Epha1, and Sorl1. Intriguingly there were distinct sets of strains prioritized for different disease sub‐types. Looking across cohorts there were generally a typical (i.e. inflammatory) sub‐type and a non‐inflammatory sub‐type. The inflammatory subtypes prioritized similar features to the overall cohorts, while the priorities for non‐inflmmatory subtypes included Cd2ap, Shc2, and Mtmr4. Conclusion: We identified consistent predictions regarding which mouse factors are most influential in modeling disease across model classes and human cohorts. Ensembling the models was a useful approach for feature selection. These priorities informed the selection of mouse strains to breed in the development of polygenic LOAD mouse models by the IU‐JAX‐PITT MODEL‐AD center. [ABSTRACT FROM AUTHOR]

Author(s): Jonathan J. Kotzin [1, 2]; Sean P. Spencer [1, 2]; Sam J. McCright [1, 2]; Dinesh B. Uthaya Kumar [3, 4]; Magalie A. Collet [3]; Walter K. Mowel [1, [...]

Background: The Model Organism Development and Evaluation for Late‐onset AD (MODEL‐AD) Consortium was established to generate and characterize more translatable animal models for late‐onset Alzheimer's disease (LOAD) based on human genetic risk variants. While numerous genetic risk loci for LOAD have been identified, few have been experimentally verified in vivo and in many cases the risk variants have not been validated. The development of new models incorporating LOAD genetic risk should improve our understanding of disease mechanisms and improve preclinical testing of potential therapeutics. Method: Coding and non‐coding LOAD risk variants were prioritized based on human data sets, with the goal of targeting diverse pathways (e.g., neuroinflammation, vascular risk, metabolic function, lipid homeostasis). Risk variants in Abca7, Adamts4, Bin1, Erc2, Mthfr, Mtmr4, Plcg2, Ptk2b, Slc6a17, Snx1, Sorl1 and other loci were engineered into mouse models expressing humanized APOE4 and the Trem2*R47H risk variant; in more recent models, a humanized Abeta allele was also included. A novel transcriptomic panel, based on clinical LOAD samples (Preuss et al, 2020) was used to evaluate how well each model replicated clinical transcriptomic changes with disease. Models were aligned to ROSMAP clinical subtypes. Result: We have identified specific human AD‐related pathways disrupted in an age‐dependent manner in these novel mouse models. Specifically, mouse models carrying human AD risk variants Abca7*A1527G, Mthfr*C677T, and Plcg2*M28L exhibited transcriptomics changes similar to those seen in LOAD patients. Most models could be readily matched to either the inflammatory or non‐inflammatory subtype. Conclusion: We have prioritized mouse models expressing LOAD risk variants in Abca7, Mthfr, and Plcg2 for comprehensive phenotyping using clinically relevant measures including transcriptomics and proteomics, biomarkers, neuropathology and in vivo imaging at advanced ages (24 months). Ongoing projects will use human data to guide how to combine alleles to create models that match multi‐omic signatures of AD subtypes and test the effects of environmental risk factors such as high‐fat diet. Through this effort we aim to develop improved models for testing targeted therapeutics. [ABSTRACT FROM AUTHOR]

Background: The ability to effectively translate therapeutic efficacy from the bench to clinical success for Alzheimer's disease (AD) has been hampered in part due to limited recapitulation of the complexity of the disease in animal models. While analogous AD risk mutations have been engineered into animal models and have dominated the research field, these have primarily been familial, early onset risk alleles which do not capture the risk for AD for the majority of patients that present with sporadic late onset AD (LOAD). Method: The IU/JAX/PITT MODEL‐AD consortium is focused on developing mouse models with genetic risk variants associated with LOAD, in combination with environmental risk factors and aging to enable improved translation. The present studies aimed to characterize mice expressing humanized Aβ in combination with multiple genetic risk factors (APOE4 and the R47H risk variant in the Trem2 gene; LOAD2) and aged in the presence of a high‐fat, high‐sugar diet (HFD). Result: LOAD2 mice exposed to HFD from adolescence (LOAD2+HFD) demonstrated aging changes relative to LOAD2 mice in the absence of HFD, including presentation of insoluble Aβ42 in brain and plasma, and increased inflammatory cytokines. 12‐month aged LOAD2+HFD mice also demonstrated increased NfL in CSF, as well as vascular and perfusion changes as measured by PET/MRI. By 18 months, LOAD2+HFD mice demonstrated reductions in hippocampal neurons as well as cognitive impairment relative to LOAD2 mice in the absence of HFD on a translational touchscreen task. Intriguingly, gene expression profiles and proteomic signatures of aged LOAD2+HFD mice aligned with 'omics signatures of AD patients in the absence of core neuritic plaques, which were not detected up to 24 months of age. Conclusion: Mice with genetic risk for LOAD coupled with environmental risk factors demonstrate aging‐dependent changes in line with a spectrum and trajectory of features of clinical LOAD. From a precision medicine approach, our MODEL‐AD Preclinical Testing Core and our TREAT‐AD colleagues are prioritizing LOAD2+HFD mice as an important model system for evaluating the therapeutic potential of non‐amyloid targeting therapeutics as well as for prophylactic interventions initiated prior to significant amyloid accumulation. [ABSTRACT FROM AUTHOR]

Introduction: Genome‐wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. Methods: We use a transfer learning technique to train three‐dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer's Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression. Results: CNN‐derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding‐dependent synaptic loss, APP‐regulated inflammation response, and insulin resistance. Discussion: This is the first attempt to show that non‐invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Background: Late-onset Alzheimer’s disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimer’s have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes. Results: This resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of the 5xFAD mouse, a widely used amyloid pathology model, and three mouse models based on LOAD genetics carrying APOE4 and TREM2*R47H alleles demonstrated overlaps with distinct human AD modules that, in turn, were functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq showed strong correlation between gene expression changes independent of experimental platform. Conclusions: Taken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models. [ABSTRACT FROM AUTHOR]