Abstract Background Numerous AD risk loci and variants have been identified by large‐scale genetic studies, but few have been functionally verified and models to study their mechanism of action are lacking. The Model Organism Development and Evaluation for Late‐onset AD (MODEL‐AD) Center was created to develop, characterize, and distribute more precise preclinical models for late‐onset AD (LOAD) by engineering these disease‐associated variants into mouse models, and characterizing the resulting phenotypes using clinically relevant assays. Method Diverse criteria including replication in multiple studies, predicted pathogenicity of variant, sequence conservation between human and mouse, and expression in relevant cell types were used to prioritize risk variants, which were then engineered into a mouse model that expresses the AD risk variants APOE4 and Trem2*R47H. Primary screening was done by transcriptomic analysis using the nanoString Mouse AD Panel at 4, 8, and 12 months. Result Bioinformatics studies enabled us to prioritize coding variants in the Abca7, Clasp2, Mthfr, Mtmr4, Plcg2, Shc2, Slc6a17, Snx1, and Sorl1 loci. Knockouts of Abca7 , Ceacam1 , Il1rap , and Plcg2 have also been created to model human loss of function variants. Transcriptomic analysis demonstrated that the Abca7*A1527G and Mthfr*C677T models exhibited some age‐dependent similarities to transcriptomic changes seen in post‐mortem samples from the AMP‐AD cohort. These will move on to deep phenotyping out to 24 months of age. Analysis of other models are still in progress and will be presented. Humanized APP and Tau ( MAPT ) models are also in progress. Conclusion We have prioritized Abca7*A1527G and Mthfr*C677T models to be comprehensively characterized up to 24 months of age in the MODEL‐AD program. We will discuss our approach to prioritize genetic variants, develop novel models relevant to LOAD, and use clinically relevant measures to determine which models are most useful for translational studies. All models are made available for both academic and for‐profit use from The Jackson Laboratory, and all validation data will be shared via the AMP‐AD knowledge portal. For more information see www.model‐ad.org.
Abstract Background Late‐onset Alzheimer’s disease (LOAD) is the most common form of dementia without an approved therapy. Transgenic overexpression animal models do not effectively produce the heterogeneity observed clinically in LOAD patients and thus are not ideal for therapy development. Hence, the Model Organism Development and Evaluation for Late‐onset AD (MODEL‐AD) Center is developing, characterizing, and distributing novel mouse models expressing humanized, clinically relevant risk factors. Analysis of aging LOAD2 mice that better phenocopy human disease will inform the further development of subsequent generations of models and reveal more appropriate molecular targets useful in the treatment of LOAD. Method Two risk factors of LOAD, APOEe4 and Trem2 * R47H, were incorporated into C57BL/6J mice along with humanized amyloid‐beta to produce the LOAD2 model. Mice were aged up to 24 months. In some mice, high‐fat diet replaced normal mouse chow. An animal phenotyping pipeline was employed to qualify and align with neurodegenerative disease states and phenotypes observed in human patients. In vivo imaging in addition to behavior and wellness assays were performed, as were analyses of blood and brain tissue for risk‐factor‐related alterations in neuropathology, transcriptomics, metabolomics, and proteomics. Result Aged LOAD2 mice on high‐fat diet present altered cytokine profiles in the brain, and peripherally, however no overt hippocampal pathology nor differences in long‐term potentiation were observed. Expression of human amyloid did not yield plaque formation in 18‐month mice. High‐fat diet was a strong determinant during behavioral phenotyping. Correlation of transcriptional profiling with human AMP‐AD modules determined individual and synergistic effects between genetic and environmental risk factors compared to human data. Continuing efforts to dissect the clinically‐relevant downstream effects of sex, age, genotype, and diet in these animals remain. Conclusion The MODEL‐AD consortium has established the LOAD2 mouse model to study the effects of genetic and environmental risk factors of LOAD. This strain serves as a platform for the incorporation of additional AD risk factors (both genetic and environmental) to more closely align phenotypes in the mouse to outcomes observed in the clinic. Here we show the evidence of emerging disease pathology in an animal model exposed to genetic and environmental risk factors.
Abstract Background Alzheimer’s disease (AD) is the most common form of dementia without an effective treatment. Animal models of AD have been valuable tools to understand familial or early onset AD, but to date have not been predictive for translational research. The Model Organism Development and Evaluation for Late‐onset AD (MODEL‐AD) Center is developing, validating, and distributing novel mouse models of late‐onset AD (LOAD) that can be used to develop novel therapeutics. Using the 5XFAD model of early onset AD, we have established pipelines to characterize models across multiple sites using biochemistry, histology, functional assays, in vivo MRI and PET imaging, and Serial Two‐Photon (STP) tomography whole brain imaging. Methods At designated time points, animals were subjected to the following assays: Functional ‐ behavioral testing paradigms, and in vivo MRI and PET scanning followed by secondary validation with autoradiography. Biochemistry/Histology ‐ mice were sacrificed, perfused, and tissue harvested, with half of the brain frozen and the other half fixed. On frozen tissue samples we performed bulk RNA‐seq. On fixed tissue samples, we assessed AD relevant changes using the following stains and antibodies: X34 (plaques and NFTs), Lamp1 (dystrophic neurites), Iba1 (microglia). STP Whole Brain Imaging ‐ on additional fixed tissue samples we performed whole brain imaging and regional analysis of Methoxy‐X04 labeled plaques using the TissueCyte® imaging platform. Results Combined assessment of the 5XFAD model revealed systematic neurodegenerative changes in the mouse brain. Multiple data sets including histology, biochemistry, RNA‐seq, transcriptomics, in vivo imaging, and STP whole brain analysis show differences throughout disease progression. Conclusions We utilized the well characterized 5XFAD model of AD to develop an integrated pipeline for deep phenotyping of novel mouse models of AD. This pipeline combines novel functional and anatomical assays across sites to track the ontology of AD progression, and targeted understanding of the molecular, biochemical and functional progression of AD pathology. Together, this pipeline provides a novel platform for greater understanding of LOAD mouse models and potential therapeutic approaches for AD.
Abstract 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.
The immune system of the gastrointestinal (GI) tract manages the significant task of recognizing and eliminating pathogens while maintaining tolerance of commensal bacteria. Dysregulation of this delicate balance can be detrimental, resulting in severe inflammation, intestinal injury, and cancer. Therefore, mechanisms to relay important signals regulating cell growth and immune reactivity must be in place to support GI homeostasis. Type I interferons (IFN-I) are a family of pleiotropic cytokines, which exert a wide range of biological effects including promotion of both pro- and anti-inflammatory activities. Using animal models of colitis, investigations into the regulation of intestinal epithelium inflammation highlight the role of IFN-I signaling during fine modulation of the immune system. The intestinal epithelium of the gut guides the immune system to differentiate between commensal and pathogenic microbiota, which relies on intimate links with the IFN-I signal-transduction pathway. The current paradigm depicts an IFN-I-induced antiproliferative state in the intestinal epithelium enabling cell differentiation, cell maturation, and proper intestinal barrier function, strongly supporting its role in maintaining baseline immune activity and clearance of damaged epithelia or pathogens. In this review, we will highlight the importance of IFN-I in intestinal homeostasis by discussing its function in inflammation, immunity, and cancer.
Obesity is recognized as a significant risk factor for Alzheimer's disease (AD). Studies have supported the notion that obesity accelerates AD-related pathophysiology in mouse models of AD. The majority of studies, to date, have focused on the use of early-onset AD models. Here, we evaluate the impact of genetic risk factors on late-onset AD (LOAD) in mice fed with a high fat/high sugar diet (HFD). We focused on three mouse models created through the IU/JAX/PITT MODEL-AD Center. These included a combined risk model with
Genetic association studies identified the R47H coding variant of triggering receptor expressed on myeloid cells-2 (TREM2) increasing the risk of late-onset Alzheimer's disease (LOAD). The Trem2-/- (KO) mouse models demonstrated alterations on microglial function in brain, whereas the effects of the Trem2*R47H point mutation remain incompletely understood. Transcriptomic profiling of mouse brains carrying the Trem2*R47H variant on different amyloid, tau, and late-onset transgenic backgrounds can help characterize the effects of the R47H allele in response to transgene-driven pathology, thereby dissecting variant function. We have generated Trem2 KO and Trem2*R47H mice on 5xFAD and hTau transgenic and APOE4 knock-in backgrounds. Cortex, hippocampus and cerebellum brain regions were collected from 3 and 6 months-old male and female mice for RNA-Seq in a ‘Genotype x Sex x Age’ experimental design. We performed DESeq2 differential expression analysis, and KEGG and GO pathway enrichment analysis to determine gene sets and molecular processes altered in the assessed mouse models. The effect of R47H mutation demonstrated differences across genetic backgrounds and tissues. Immunity related pathways were significantly enriched in amyloid related APOE4 and 5xFAD models carrying R47H mutation, whereas R47H effect altered various metabolic pathways in presence of hTau transgene. We also mapped the R47H related transcriptional signatures to co-expression gene modules of human LOAD from the AMP-AD consortium and observed correlations with AMP-AD expression modules specific to R47H on each distinct background. This study provides a detailed view of how the Trem2*R47H variant interacts with genetic drivers of AD-relevant pathologies at the transcriptome level. Alignment with human LOAD signatures further specifies potential disease pathways affected by the variant in the context of human disease.
Abstract Background Alzheimer’s disease (AD) is the most common cause of dementia in the United States, with approximately 95% of patients exhibiting sporadic Late‐Onset AD (LOAD), which lacks an inheritance pattern. Therefore, identifying phenotypic patterns are critical for understanding disease progression. Apolipoprotein E4 (APOE4), the strongest genetic risk factor of LOAD, increases AD risk by 3‐12 fold depending on copy number. Recent GWAS analysis elucidated the LOAD‐associated loci on the triggering receptor expressed on myeloid cell 2 (TREM2). APOE is a known ligand to the TREM2 receptor, and the R47H variant could increase the AD risk by 2‐3 fold. Preclinical studies of these risk alleles and their phenotypes are underway by MODEL‐AD. Methods Using PET/MRI and a novel analytical scheme, we established the perfusion‐metabolism profiles across 27 brain regions in both sexes by using 64 Cu‐PTSM and 18 F‐FDG in the APOE4 ( APOE E4/E4 ), TREM2 ( TREM2 R47H ), and double ( APOE E4/E4 . TREM2 R47H ) knockin (KI) mice, and compared these to blood chemistry and nanoString transcriptomic analysis. Results Longitudinal analysis comparing 12mo to 4mo time point revealed that male APOE4 mice and both sexes of TREM2 had hypo‐ perfusion and metabolism, while female APOE4 mice showed an uncoupled hyper‐perfusion and hypo‐metabolism phenotype, and was correlated to human AD pathology. In double KI mice, perfusion and metabolism showed a mixed phenotype which was region dependent. Cross‐sectional analysis of KI compared to C57BL/6J mice at 12mo showed an overall reduced glucose metabolism. Intriguingly, male APOE4, TREM2, and double mice showed hypo‐perfusion and metabolism, while female double mice showed metabolic uncoupling compared to C57BL/6J. Analysis of blood biochemistry in non‐fasted mice revealed no significant difference between genotypes in blood glucose with age. However, APOE4 decreased the blood cholesterol level (LDL, and HDL). RNAseq and immunohistology was peformed, and key genes involved in the regulation of cerebral perfusion, glucose transportation, and metabolism were altered. Conclusions These data suggest that the new perfusion‐metabolism strategy may be able to identify AD‐related patterns. Moreover, they replicate clinical manifestations of subjects with the same variants, suggesting additional mechanistic studies are needed to elucidate the mechanisms and etiology of this uncoupling phenomenon.
Abstract Background Late‐onset Alzheimer’s disease (LOAD) is caused by interactions between genes and environment. However, current mouse models do not fully recapitulate LOAD phenotypes, limiting translatability to clinical needs. To address this, MODEL‐AD (Model Organism Development and Evaluation for Late‐Onset Alzheimer’s Disease) Consortium was established to create and phenotype new mouse models. The IU/JAX/PITT MODEL‐AD Center focuses on developing models with combinations of genetic risk factors, as well as humanizing APP (hAb) and MAPT. One use of these models is to determine the contribution of environmental factors to LOAD risk. Studies show associations between LOAD risk and exposure to ubiquitous environmental neurotoxicants, including arsenic (As), lead (Pb), and cadmium (Cd). All three are ranked in the “Top 10 Toxicants” of public health concern (As #1, Pb #2, Cd #7; ATSDR Toxic Substance Priority List). However, mechanisms driving increased risk are unknown. Method Novel mouse models were created by genetic engineering or editing in C57BL/6J. Four to 24 months old mice and controls were phenotyped using a human‐relevant battery that includes cognitive performance, imaging, biometrics, transcriptomics, proteomics, metabolomics, neuropathology, and fluid biomarkers. Toxicants (As, Pb, Cd) were delivered using human‐relevant doses in drinking water. Exogenous toxicants (Pb, Cd, As) and endogenous biometals (Zn, Cu, Fe) in brain and blood samples were analyzed by elemental mass spectrometry mapping. Result More than 40 mouse models have been created carrying combinations of genetic risk factors in genes including APOE, Trem2, Abca7 and Mthfr. One strain, LOAD2 (triple homozygous for APOE4, Trem2*R47H, and hAb) shows molecular and transcriptomic changes associated with human LOAD. Short‐term exposure to As, Cd, or Pb also modified expression of LOAD‐relevant genes in LOAD2 mice and controls, including App and Vgf, in a toxicant‐specific manner. Elevated toxicant levels were detected in both brain and blood samples from exposed mice. Conclusion These data indicate that exposure to common environmental toxicants may increase LOAD risk by modifying expression of specific genes and molecular pathways previously implicated in human LOAD. Studies are underway using additional MODEL‐AD models to determine the long‐term effects of neurotoxicant exposures.
