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Thompson JC, Levis Rabi M, Novoa M, Nash KR, Joly-Amado A. Evaluating the Efficacy of Levetiracetam on Non-Cognitive Symptoms and Pathology in a Tau Mouse Model. Biomedicines 2024; 12:2891. [PMID: 39767797 PMCID: PMC11727630 DOI: 10.3390/biomedicines12122891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Revised: 12/13/2024] [Accepted: 12/15/2024] [Indexed: 01/16/2025] Open
Abstract
Background/Objectives: Alzheimer's disease (AD) is marked by amyloid-β plaques and hyperphosphorylated tau neurofibrillary tangles (NFTs), leading to cognitive decline and debilitating non-cognitive symptoms. This study aimed to evaluate compounds from four different classes in a short-term (7-day) study using transgenic tau mice to assess their ability to reduce non-cognitive symptoms. The best candidate was then evaluated for longer exposure to assess non-cognitive symptoms, cognition, and pathology. Methods: Tg4510 mice, expressing mutated human tau (P301L), were administered with levetiracetam, methylphenidate, diazepam, and quetiapine for 7 days at 6 months old, when pathology and cognitive deficits are established. Drugs were given in the diet, and non-cognitive symptoms were evaluated using metabolic cages. Levetiracetam was chosen for longer exposure (3 months) in 3-month-old Tg4510 mice and non-transgenic controls to assess behavior and pathology. Results: After 3 months of diet, levetiracetam mildly reduced tau pathology in the hippocampus but did not improve cognition in Tg4510 mice. Interestingly, it influenced appetite, body weight, anxiety-like behavior, and contextual fear memory in non-transgenic animals but not in Tg4510 mice. Conclusions: While levetiracetam has shown benefits in amyloid deposition models, it had limited effects on tau pathology and behavior in an animal model of tau deposition, which is crucial for AD context. The differential effects on non-transgenic versus Tg4510 mice warrant further investigation.
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Affiliation(s)
| | | | | | | | - Aurelie Joly-Amado
- Department of Molecular Pharmacology and Physiology, Morsani College of Medicine, University of South Florida, 12901 Bruce B Downs Blvd, Tampa, FL 33612, USA; (J.C.T.); (M.L.R.); (M.N.); (K.R.N.)
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Onos K, Lin PB, Pandey RS, Persohn SA, Burton CP, Miner EW, Eldridge K, Kanyinda JN, Foley KE, Carter GW, Howell GR, Territo PR. Assessment of Neurovascular Uncoupling: APOE Status is a Key Driver of Early Metabolic and Vascular Dysfunction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.13.571584. [PMID: 38168292 PMCID: PMC10760108 DOI: 10.1101/2023.12.13.571584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia worldwide, with apolipoprotein ε4 (APOEε4) being the strongest genetic risk factor. Current clinical diagnostic imaging focuses on amyloid and tau; however, new methods are needed for earlier detection. METHODS PET imaging was used to assess metabolism-perfusion in both sexes of aging C57BL/6J, and hAPOE mice, and were verified by transcriptomics, and immunopathology. RESULTS All hAPOE strains showed AD phenotype progression by 8 mo, with females exhibiting the regional changes, which correlated with GO-term enrichments for glucose metabolism, perfusion, and immunity. Uncoupling analysis revealed APOEε4/ε4 exhibited significant Type-1 uncoupling (↓ glucose uptake, ↑ perfusion) at 8 and 12 mo, while APOEε3/ε4 demonstrated Type-2 uncoupling (↑ glucose uptake, ↓ perfusion), while immunopathology confirmed cell specific contributions. DISCUSSION This work highlights APOEε4 status in AD progression manifest as neurovascular uncoupling driven by immunological activation, and may serve as an early diagnostic biomarker.
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Affiliation(s)
- Kristen Onos
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Scott A. Persohn
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Charles P. Burton
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Ethan W. Miner
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Kierra Eldridge
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | | | - Kate E. Foley
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gregory W. Carter
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | | | - Paul R. Territo
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA
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Courson J, Quoy M, Timofeeva Y, Manos T. An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control. Front Comput Neurosci 2024; 18:1360009. [PMID: 38468870 PMCID: PMC10925689 DOI: 10.3389/fncom.2024.1360009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/08/2024] [Indexed: 03/13/2024] Open
Abstract
Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.
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Affiliation(s)
- Juliette Courson
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, Cergy-Pontoise, France
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Mathias Quoy
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- IPAL CNRS Singapore, Singapore, Singapore
| | - Yulia Timofeeva
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Thanos Manos
- ETIS Lab, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
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Burton CP, Chumin EJ, Collins AY, Persohn SA, Onos KD, Pandey RS, Quinney SK, Territo PR. Levetiracetam modulates brain metabolic networks and transcriptomic signatures in the 5XFAD mouse model of Alzheimer's disease. Front Neurosci 2024; 17:1336026. [PMID: 38328556 PMCID: PMC10847229 DOI: 10.3389/fnins.2023.1336026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/13/2023] [Indexed: 02/09/2024] Open
Abstract
Introduction Subcritical epileptiform activity is associated with impaired cognitive function and is commonly seen in patients with Alzheimer's disease (AD). The anti-convulsant, levetiracetam (LEV), is currently being evaluated in clinical trials for its ability to reduce epileptiform activity and improve cognitive function in AD. The purpose of the current study was to apply pharmacokinetics (PK), network analysis of medical imaging, gene transcriptomics, and PK/PD modeling to a cohort of amyloidogenic mice to establish how LEV restores or drives alterations in the brain networks of mice in a dose-dependent basis using the rigorous preclinical pipeline of the MODEL-AD Preclinical Testing Core. Methods Chronic LEV was administered to 5XFAD mice of both sexes for 3 months based on allometrically scaled clinical dose levels from PK models. Data collection and analysis consisted of a multi-modal approach utilizing 18F-FDG PET/MRI imaging and analysis, transcriptomic analyses, and PK/PD modeling. Results Pharmacokinetics of LEV showed a sex and dose dependence in Cmax, CL/F, and AUC0-∞, with simulations used to estimate dose regimens. Chronic dosing at 10, 30, and 56 mg/kg, showed 18F-FDG specific regional differences in brain uptake, and in whole brain covariance measures such as clustering coefficient, degree, network density, and connection strength (i.e., positive and negative). In addition, transcriptomic analysis via nanoString showed dose-dependent changes in gene expression in pathways consistent 18F-FDG uptake and network changes, and PK/PD modeling showed a concentration dependence for key genes, but not for network covariance modeling. Discussion This study represents the first report detailing the relationships of metabolic covariance and transcriptomic network changes resulting from LEV administration in 5XFAD mice. Overall, our results highlight non-linear kinetics based on dose and sex, where gene expression analysis demonstrated LEV dose- and concentration-dependent changes, along with cerebral metabolism, and/or cerebral homeostatic mechanisms relevant to human AD, which aligned closely with network covariance analysis of 18F-FDG images. Collectively, this study show cases the value of a multimodal connectomic, transcriptomic, and pharmacokinetic approach to further investigate dose dependent relationships in preclinical studies, with translational value toward informing clinical study design.
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Affiliation(s)
- Charles P. Burton
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Evgeny J. Chumin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Alyssa Y. Collins
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Scott A. Persohn
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | | | - Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States
| | - Sara K. Quinney
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Paul R. Territo
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN, United States
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Burton CP, Chumin EJ, Collins AY, Persohn SA, Onos KD, Pandey RS, Quinney SK, Territo PR. Levetiracetam Modulates Brain Metabolic Networks and Transcriptomic Signatures in the 5XFAD Mouse Model of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566574. [PMID: 38014102 PMCID: PMC10680636 DOI: 10.1101/2023.11.10.566574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
INTRODUCTION Subcritical epileptiform activity is associated with impaired cognitive function and is commonly seen in patients with Alzheimer's disease (AD). The anti-convulsant, levetiracetam (LEV), is currently being evaluated in clinical trials for its ability to reduce epileptiform activity and improve cognitive function in AD. The purpose of the current study was to apply pharmacokinetics (PK), network analysis of medical imaging, gene transcriptomics, and PK/PD modeling to a cohort of amyloidogenic mice to establish how LEV restores or drives alterations in the brain networks of mice in a dose-dependent basis using the rigorous preclinical pipeline of the MODEL-AD Preclinical Testing Core. METHODS Chronic LEV was administered to 5XFAD mice of both sexes for 3 months based on allometrically scaled clinical dose levels from PK models. Data collection and analysis consisted of a multi-modal approach utilizing 18F-FDG PET/MRI imaging and analysis, transcriptomic analyses, and PK/PD modeling. RESULTS Pharmacokinetics of LEV showed a sex and dose dependence in Cmax, CL/F, and AUC0-∞, with simulations used to estimate dose regimens. Chronic dosing at 10, 30, and 56 mg/kg, showed 18F-FDG specific regional differences in brain uptake, and in whole brain covariance measures such as clustering coefficient, degree, network density, and connection strength (i.e. positive and negative). In addition, transcriptomic analysis via nanoString showed dose-dependent changes in gene expression in pathways consistent 18F-FDG uptake and network changes, and PK/PD modeling showed a concentration dependence for key genes, but not for network covariance modeling. DISCUSSION This study represents the first report detailing the relationships of metabolic covariance and transcriptomic network changes resulting from LEV administration in 5XFAD mice. Overall, our results highlight non-linear kinetics based on dose and sex, where gene expression analysis demonstrated LEV dose- and concentration- dependent changes, along with cerebral metabolism, and/or cerebral homeostatic mechanisms relevant to human AD, which aligned closely with network covariance analysis of 18F-FDG images. Collectively, this study show cases the value of a multimodal connectomic, transcriptomic, and pharmacokinetic approach to further investigate dose dependent relationships in preclinical studies, with translational value towards informing clinical study design.
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Affiliation(s)
- Charles P. Burton
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA
| | - Evgeny J. Chumin
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis IN 46202
| | - Alyssa Y. Collins
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA
| | - Scott A. Persohn
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA
| | | | - Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032
| | - Sara K. Quinney
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA
| | - Paul R. Territo
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis IN 46202 USA
- Department of Medicine, Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis IN 46202 USA
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Acri DJ, You Y, Tate MD, Karahan H, Martinez P, McCord B, Sharify AD, John S, Kim B, Dabin LC, Philtjens S, Wijeratne HS, McCray TJ, Smith DC, Bissel SJ, Lamb BT, Lasagna-Reeves CA, Kim J. Network analysis identifies strain-dependent response to tau and tau seeding-associated genes. J Exp Med 2023; 220:e20230180. [PMID: 37606887 PMCID: PMC10443211 DOI: 10.1084/jem.20230180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/05/2023] [Accepted: 07/27/2023] [Indexed: 08/23/2023] Open
Abstract
Previous research demonstrated that genetic heterogeneity is a critical factor in modeling amyloid accumulation and other Alzheimer's disease phenotypes. However, it is unknown what mechanisms underlie these effects of genetic background on modeling tau aggregate-driven pathogenicity. In this study, we induced tau aggregation in wild-derived mice by expressing MAPT. To investigate the effect of genetic background on the action of tau aggregates, we performed RNA sequencing with brains of C57BL/6J, CAST/EiJ, PWK/PhJ, and WSB/EiJ mice (n = 64) and determined core transcriptional signature conserved in all genetic backgrounds and signature unique to wild-derived backgrounds. By measuring tau seeding activity using the cortex, we identified 19 key genes associated with tau seeding and amyloid response. Interestingly, microglial pathways were strongly associated with tau seeding activity in CAST/EiJ and PWK/PhJ backgrounds. Collectively, our study demonstrates that mouse genetic context affects tau-mediated alteration of transcriptome and tau seeding. The gene modules associated with tau seeding provide an important resource to better model tauopathy.
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Affiliation(s)
- Dominic J. Acri
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Medical Neuroscience Graduate Program, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Yanwen You
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Mason D. Tate
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Medical Neuroscience Graduate Program, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Hande Karahan
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Pablo Martinez
- Department of Anatomy, Cell Biology and Physiology, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Brianne McCord
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - A. Daniel Sharify
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Sutha John
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Byungwook Kim
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Luke C. Dabin
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Stéphanie Philtjens
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - H.R. Sagara Wijeratne
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Tyler J. McCray
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Medical Neuroscience Graduate Program, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Daniel C. Smith
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Medical Neuroscience Graduate Program, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Stephanie J. Bissel
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Bruce T. Lamb
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Cristian A. Lasagna-Reeves
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Center for Computational Biology and Bioinformatics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
| | - Jungsu Kim
- Stark Neurosciences Research Institute, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana UniversitySchool of Medicine, Indianapolis, IN, USA
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Jullienne A, Szu JI, Quan R, Trinh MV, Norouzi T, Noarbe BP, Bedwell AA, Eldridge K, Persohn SC, Territo PR, Obenaus A. Cortical cerebrovascular and metabolic perturbations in the 5xFAD mouse model of Alzheimer's disease. Front Aging Neurosci 2023; 15:1220036. [PMID: 37533765 PMCID: PMC10392850 DOI: 10.3389/fnagi.2023.1220036] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/03/2023] [Indexed: 08/04/2023] Open
Abstract
Introduction The 5xFAD mouse is a popular model of familial Alzheimer's disease (AD) that is characterized by early beta-amyloid (Aβ) deposition and cognitive decrements. Despite numerous studies, the 5xFAD mouse has not been comprehensively phenotyped for vascular and metabolic perturbations over its lifespan. Methods Male and female 5xFAD and wild type (WT) littermates underwent in vivo 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging at 4, 6, and 12 months of age to assess regional glucose metabolism. A separate cohort of mice (4, 8, 12 months) underwent "vessel painting" which labels all cerebral vessels and were analyzed for vascular characteristics such as vessel density, junction density, vessel length, network complexity, number of collaterals, and vessel diameter. Results With increasing age, vessels on the cortical surface in both 5xFAD and WT mice showed increased vessel length, vessel and junction densities. The number of collateral vessels between the middle cerebral artery (MCA) and the anterior and posterior cerebral arteries decreased with age but collateral diameters were significantly increased only in 5xFAD mice. MCA total vessel length and junction density were decreased in 5xFAD mice compared to WT at 4 months. Analysis of 18F-FDG cortical uptake revealed significant differences between WT and 5xFAD mice spanning 4-12 months. Broadly, 5xFAD males had significantly increased 18F-FDG uptake at 12 months compared to WT mice. In most cortical regions, female 5xFAD mice had reduced 18F-FDG uptake compared to WT across their lifespan. Discussion While the 5xFAD mouse exhibits AD-like cognitive deficits as early as 4 months of age that are associated with increasing Aβ deposition, we only found significant differences in cortical vascular features in males, not in females. Interestingly, 5xFAD male and female mice exhibited opposite effects in 18F-FDG uptake. The MCA supplies blood to large portions of the somatosensory cortex and portions of motor and visual cortex and increased vessel length alongside decreased collaterals which coincided with higher metabolic rates in 5xFAD mice. Thus, a potential mismatch between metabolic demand and vascular delivery of nutrients in the face of increasing Aβ deposition could contribute to the progressive cognitive deficits seen in the 5xFAD mouse model.
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Affiliation(s)
- Amandine Jullienne
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Jenny I. Szu
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Ryan Quan
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Michelle V. Trinh
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Tannoz Norouzi
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Brenda P. Noarbe
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Amanda A. Bedwell
- Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, United States
| | - Kierra Eldridge
- Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, United States
| | - Scott C. Persohn
- Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, United States
| | - Paul R. Territo
- Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, United States
- Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN, United States
| | - Andre Obenaus
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
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