Browsing by Author "Mangravite, Lara M."

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    Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study
    (Frontiers Media, 2021-07-23) Oblak, Adrian L.; Lin, Peter B.; Kotredes, Kevin P.; Pandey, Ravi S.; Garceau, Dylan; Williams, Harriet M.; Uyar, Asli; O’Rourke, Rita; O’Rourke, Sarah; Ingraham, Cynthia; Bednarczyk, Daria; Belanger, Melisa; Cope, Zackary A.; Little, Gabriela J.; Williams, Sean-Paul G.; Ash, Carl; Bleckert, Adam; Ragan, Tim; Logsdon, Benjamin A.; Mangravite, Lara M.; Sukoff Rizzo, Stacey J.; Territo, Paul R.; Carter, Gregory W.; Howell, Gareth R.; Sasner, Michael; Lamb, Bruce T.; Radiology and Imaging Sciences, School of Medicine
    The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer’s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer’s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram, in vivo imaging, biochemical characterization, and behavioral assessments. The data from this study is publicly available through the AD Knowledge Portal.
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    Corrigendum: Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H
    (Frontiers Media, 2022-02-07) Kotredes, Kevin P.; Oblak, Adrian; Pandey, Ravi S.; Lin, Peter Bor-Chian; Garceau, Dylan; Williams, Harriet; Uyar, Asli; O’Rourke, Rita; O’Rourke, Sarah; Ingraham, Cynthia; Bednarczyk, Daria; Belanger, Melisa; Cope, Zackary; Foley, Kate E.; Logsdon, Benjamin A.; Mangravite, Lara M.; Sukoff Rizzo, Stacey J.; Territo, Paul R.; Carter, Gregory W.; Sasner, Michael; Lamb, Bruce T.; Howell, Gareth R.; Pharmacology and Toxicology, School of Medicine
    An author name was incorrectly spelled as “Daria Bednarycek”. The correct spelling is “Daria Bednarczyk”. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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    Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
    (Springer Nature, 2021-03-19) Sieberts, Solveig K.; Schaff, Jennifer; Duda, Marlena; Pataki, Bálint Ármin; Sun, Ming; Snyder, Phil; Daneault, Jean-Francois; Parisi, Federico; Costante, Gianluca; Rubin, Udi; Banda, Peter; Chae, Yooree; Neto, Elias Chaibub; Dorsey, E. Ray; Aydın, Zafer; Chen, Aipeng; Elo, Laura L.; Espino, Carlos; Glaab, Enrico; Goan, Ethan; Golabchi, Fatemeh Noushin; Görmez, Yasin; Jaakkola, Maria K.; Jonnagaddala, Jitendra; Klén, Riku; Li, Dongmei; McDaniel, Christian; Perrin, Dimitri; Perumal, Thanneer M.; Rad, Nastaran Mohammadian; Rainaldi, Erin; Sapienza, Stefano; Schwab, Patrick; Shokhirev, Nikolai; Venäläinen, Mikko S.; Vergara-Diaz, Gloria; Zhang, Yuqian; Parkinson’s Disease Digital Biomarker Challenge Consortium; Wang, Yuanjia; Guan, Yuanfang; Brunner, Daniela; Bonato, Paolo; Mangravite, Lara M.; Omberg, Larsson; Medicine, School of Medicine
    Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
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    Model organism development and evaluation for late‐onset Alzheimer's disease: MODEL‐AD
    (Wiley, 2020-11-23) Oblak, Adrian L.; Forner, Stefania; Territo, Paul R.; Sasner, Michael; Carter, Gregory W.; Howell, Gareth R.; Sukoff-Rizzo, Stacey J.; Logsdon, Benjamin A.; Mangravite, Lara M.; Mortazavi, Ali; Baglietto-Vargas, David; Green, Kim N.; MacGregor, Grant R.; Wood, Marcelo A.; Tenner, Andrea J.; LaFerla, Frank M.; Lamb, Bruce T.; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a major cause of dementia, disability, and death in the elderly. Despite recent advances in our understanding of the basic biological mechanisms underlying AD, we do not know how to prevent it, nor do we have an approved disease‐modifying intervention. Both are essential to slow or stop the growth in dementia prevalence. While our current animal models of AD have provided novel insights into AD disease mechanisms, thus far, they have not been successfully used to predict the effectiveness of therapies that have moved into AD clinical trials. The Model Organism Development and Evaluation for Late‐onset Alzheimer's Disease (MODEL‐AD; www.model-ad.org) Consortium was established to maximize human datasets to identify putative variants, genes, and biomarkers for AD; to generate, characterize, and validate the next generation of mouse models of AD; and to develop a preclinical testing pipeline. MODEL‐AD is a collaboration among Indiana University (IU); The Jackson Laboratory (JAX); University of Pittsburgh School of Medicine (Pitt); Sage BioNetworks (Sage); and the University of California, Irvine (UCI) that will generate new AD modeling processes and pipelines, data resources, research results, standardized protocols, and models that will be shared through JAX's and Sage's proven dissemination pipelines with the National Institute on Aging–supported AD Centers, academic and medical research centers, research institutions, and the pharmaceutical industry worldwide.
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    A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease
    (BMC, 2020-11-10) Preuss, Christoph; Pandey, Ravi; Piazza, Erin; Fine, Alexander; Uyar, Asli; Perumal, Thanneer; Garceau, Dylan; Kotredes, Kevin P.; Williams, Harriet; Mangravite, Lara M.; Lamb, Bruce T.; Oblak, Adrian L.; Howell, Gareth R.; Sasner, Michael; Logsdon, Benjamin A.; Carter, Gregory W.; Psychiatry, School of Medicine
    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. Supplementary information Supplementary information accompanies this paper at 10.1186/s13024-020-00412-5.
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    Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H
    (Frontiers Media, 2021-10-11) Kotredes, Kevin P.; Oblak, Adrian; Pandey, Ravi S.; Lin, Peter Bor-Chian; Garceau, Dylan; Williams, Harriet; Uyar, Asli; O’Rourke, Rita; O’Rourke, Sarah; Ingraham, Cynthia; Bednarczyk, Daria; Belanger, Melisa; Cope, Zackary; Foley, Kate E.; Logsdon, Benjamin A.; Mangravite, Lara M.; Sukoff Rizzo, Stacey J.; Territo, Paul R.; Carter, Gregory W.; Sasner, Michael; Lamb, Bruce T.; Howell, Gareth R.; Radiology and Imaging Sciences, School of Medicine
    Late-onset Alzheimer’s disease (AD; LOAD) is the most common human neurodegenerative disease, however, the availability and efficacy of disease-modifying interventions is severely lacking. Despite exceptional efforts to understand disease progression via legacy amyloidogenic transgene mouse models, focus on disease translation with innovative mouse strains that better model the complexity of human AD is required to accelerate the development of future treatment modalities. LOAD within the human population is a polygenic and environmentally influenced disease with many risk factors acting in concert to produce disease processes parallel to those often muted by the early and aggressive aggregate formation in popular mouse strains. In addition to extracellular deposits of amyloid plaques and inclusions of the microtubule-associated protein tau, AD is also defined by synaptic/neuronal loss, vascular deficits, and neuroinflammation. These underlying processes need to be better defined, how the disease progresses with age, and compared to human-relevant outcomes. To create more translatable mouse models, MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases beginning with APOEε4 and Trem2*R47H, two of the most powerful risk factors present in human LOAD populations. Mice expressing endogenous, humanized APOEε4 and Trem2*R47H gene sequences were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, as a sensitized baseline model with many additional alleles and environmental modifications already appended, the dataset from this initial MODEL-AD strain serves an important role in establishing the individual effects and interaction between two strong genetic risk factors for LOAD in a mouse host.