Publications
2024:
Sasner M, Preuss C, Pandey RS, Uyar A, Garceau D, Kotredes KP, Williams H, Oblak AL, Lin PB-C, Perkins B, Soni D, Ingraham C, Lee-Gosselin A, Lamb BT, Howell GR, Carter GW. In vivo validation of late-onset Alzheimer’s disease genetic risk factors. Alzheimers Dement. 2024 Apr 30. PMID: 38687251. DO: 10.1002/alz.13840
Pandey RS, Arnold M, Batra R, Krumsiek J, Kotredes KP, Garceau D, Williams H, Sasner M, Howell GR, Kaddura-Daouk R, Carter GW. Metabolomics profiling reveals distinct, sex-specific signatures in serum and brain metabolomes in mouse models of Alzheimer’s disease. Alzheimers Dement. 2024 Apr 30. PMID: 38676929 DOI: 10.1002/alz.13851
Benzow K, Karanjeet K, Oblak AL, Carter GW, Sasner M, Koob MD. Gene replacement-Alzheimer’s disease (GR-AD): Modeling the genetics of human dementias in mice. Alzheimers Dement. 2024 Apr 30. PMID: 38343132. PMCID: PMC11032548 DOI: 10.1002/alz.13730
Cary GA, Wiley JC, Gockley J, Keegan S, Ganesh SSA, Heath L, Butler RR 3rd, Mangravite LM, Logsdon BA, Longo FJ, Levey A, Greenwood AK, Carter, GW. Genetic and multi-omic risk assessment of Alzheimer’s disease implicates core associated biological domains. Alzheimers Dement (N Y). 2024 Apr 22;10(2):e12461. PMID: 38650747 PMCID: PMC11033838 DOI: 10.1002/trc2.12461
Soni N, Hohsfield LA, Tran KM, Kawauchi S, Walker A, Javonillo D, Phan J, Matheos D, Da Cunha C, Uyar A, Milinkeviciute G, Gomez-Arboledas A, Tran K, Kaczorowski CC, Wood MA, Tenner AJ, LaFerla FM, Carter GW, Mortazavi A, Swarup V, MacGregor GR, Green KN. Genetic diversity promotes resilience in a mouse model of Alzheimer’s disease. Alzheimers Dement. 2024;20(4):2794-816. PMID: 38426371; PMCID: PMC11032575. https://libkey.io/libraries/3075/38426371
Sasner M, Onos KD, Territo PR, Sukoff Rizzo SJ. Meeting report of the fifth annual workshop on Principles and Techniques for Improving Preclinical to Clinical Translation in Alzheimer’s Disease Research. Alzheimers Dement. 2024 Feb 24. PMID: 38400713 DOI: 10.1002/alz.13742
2023:
Kotredes KP, Pandey RS, Persohn S, Elderidge K, Burton CP, Miner EW, Haynes KA, Santos DFS, Williams S-P, Heaton N, Ingraham CM, Lloyd C, Garceau D, O’Rourke R, Herrick S, Rangel-Barajas C, Maharjan S, Wang N, Sasner M, Lamb BT, Territo PR, Sukoff Rizzo SJ, Carter GW, Howell GR, Oblak AL. Characterizing Molecular and Synaptic Signatures in mouse models of Late-Onset Alzheimer’s Disease Independent of Amyloid and Tau Pathology. bioRxiv [Preprint]. 2023 Dec 20. 12.19.571985. PMID: 38187716. PMCID: PMC10769232 DOI: 10.1101/2023.12.19.571985
Zhang H et al. Degenerate mapping of environmental location presages deficits in object-location encoding and memory in the 5xFAD mouse model for Alzheimer’s disease. Neurobiol Dis 176, 105939 (2023). https://doi.org/10.1016/j.nbd.2022.105939
Tran KM et al. A Trem2(R47H) mouse model without cryptic splicing drives age- and disease-dependent tissue damage and synaptic loss in response to plaques. Mol Neurodegener 18, 12 (2023). https://doi.org/10.1186/s13024-023-00598-4
Sasner M, Territo PR, Sukoff Rizzo SJ. Meeting report of the annual workshop on Principles and Techniques for Improving Preclinical to Clinical Translation in Alzheimer’s Disease research. Alzheimers Dement (2023). https://doi.org/10.1002/alz.13093
Rezaie N, Reese F, Mortazavi A. PyWGCNA: a Python package for weighted gene co-expression network analysis. Bioinformatics 39 (2023). https://doi.org/10.1093/bioinformatics/btad415
Quinney SK et al. STOP-AD portal: Selecting the optimal pharmaceutical for preclinical drug testing in Alzheimer’s disease. Alzheimers Dement (2023). https://doi.org/10.1002/alz.1310Pandey, R. S., Kotredes, K. P., Sasner, M., Howell, G. R. & Carter, G. W. Differential splicing of neuronal genes in a Trem2*R47H mouse model mimics alterations associated with Alzheimer’s disease. BMC Genomics 24, 172 (2023). https://doi.org/10.1186/s12864-023-09280-x
Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 3, 100498 (2023). https://doi.org/10.1016/j.crmeth.2023.100498
Milinkeviciute G, Green KN. Clusterin/apolipoprotein J, its isoforms and Alzheimer’s disease. Front Aging Neurosci 15, 1167886 (2023). https://doi.org/10.3389/fnagi.2023.116788
Jullienne A et al. Cortical cerebrovascular and metabolic perturbations in the 5xFAD mouse model of Alzheimer’s disease. Front Aging Neurosci 15, 1220036 (2023). https://doi.org/10.3389/fnagi.2023.1220036
Bohlson SS, Tenner AJ. Complement in the Brain: Contributions to Neuroprotection, Neuronal Plasticity, and Neuroinflammation. Annu Rev Immunol 41, 431-452 (2023). https://doi.org/10.1146/annurev-immunol-101921-035639
2022:
Tsai AP et al. PLCG2 is associated with the inflammatory response and is induced by amyloid plaques in Alzheimer’s disease. Genome Med 14, 17 (2022). https://doi.org/10.1186/s13073-022-01022-0
Reagan AM, Onos KD, Heuer SE, Sasner M, Howell GR. Improving mouse models for the study of Alzheimer’s disease. Curr Top Dev Biol 148, 79-113 (2022). https://doi.org/10.1016/bs.ctdb.2021.12.005
Reagan AM et al. The 677C > T variant in methylenetetrahydrofolate reductase causes morphological and functional cerebrovascular deficits in mice. J Cereb Blood Flow Metab 42, 2333-2350 (2022). https://doi.org/10.1177/0271678×221122644
Onos KD et al. Pharmacokinetic, pharmacodynamic, and transcriptomic analysis of chronic levetiracetam treatment in 5XFAD mice: A MODEL-AD preclinical testing core study. Alzheimers Dement (N Y) 8, e12329 (2022). https://doi.org/10.1002/trc2.12329
Oblak AL et al. Plcg2(M28L) Interacts With High Fat/High Sugar Diet to Accelerate Alzheimer’s Disease-Relevant Phenotypes in Mice. Front Aging Neurosci 14, 886575 (2022). https://doi.org/10.3389/fnagi.2022.886575
Oblak AL et al. Prophylactic evaluation of verubecestat on disease- and symptom-modifying effects in 5XFAD mice. Alzheimers Dement (N Y) 8, e12317 (2022). https://doi.org/10.1002/trc2.12317
Lin X et al. Spatial coding defects of hippocampal neural ensemble calcium activities in the triple-transgenic Alzheimer’s disease mouse model. Neurobiol Dis 162, 105562 (2022). https://doi.org/10.1016/j.nbd.2021.105562
Kotredes KP et al. Corrigendum: Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2(*)R47H. Front Aging Neurosci 14, 857628 (2022). https://doi.org/10.3389/fnagi.2022.857628
Jullienne A, Trinh MV, Obenaus A. Neuroimaging of Mouse Models of Alzheimer’s Disease. Biomedicines 10 (2022). https://doi.org/10.3390/biomedicines10020305
Jullienne A et al. Progressive Vascular Abnormalities in the Aging 3xTg-AD Mouse Model of Alzheimer’s Disease. Biomedicines 10 (2022). https://doi.org/10.3390/biomedicines10081967
Henningfield CM, Arreola MA, Soni N, Spangenberg EE, Green KN. Microglia-specific ApoE knock-out does not alter Alzheimer’s disease plaque pathogenesis or gene expression. Glia 70, 287-302 (2022). https://doi.org/10.1002/glia.24105
Gordon MN et al. Impact of COVID-19 on the Onset and Progression of Alzheimer’s Disease and Related Dementias: A Roadmap for Future Research. Alzheimers Dement 18, 1038-1046 (2022). https://doi.org/10.1002/alz.12488
Foley KE et al. The APOE (ε3/ε4) Genotype Drives Distinct Gene Signatures in the Cortex of Young Mice. Front Aging Neurosci 14, 838436 (2022). https://doi.org/10.3389/fnagi.2022.838436
Foley KE et al. APOE ε4 and exercise interact in a sex-specific manner to modulate dementia risk factors. Alzheimers Dement (N Y) 8, e12308 (2022). https://doi.org/10.1002/trc2.12308
Dunham SJB et al. Longitudinal Analysis of the Microbiome and Metabolome in the 5xfAD Mouse Model of Alzheimer’s Disease. mBio 13, e0179422 (2022). https://doi.org/10.1128/mbio.01794-22
2021:
Tsai AP et al. INPP5D expression is associated with risk for Alzheimer’s disease and induced by plaque-associated microglia. Neurobiol Dis 153, 105303 (2021). https://doi.org/10.1016/j.nbd.2021.105303
Szu JI, Obenaus A. Cerebrovascular phenotypes in mouse models of Alzheimer’s disease. J Cereb Blood Flow Metab 41, 1821-1841 (2021). https://doi.org/10.1177/0271678×21992462
Oblak AL et al. Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study. Front Aging Neurosci 13, 713726 (2021). https://doi.org/10.3389/fnagi.2021.713726
Maguire E et al. PIP2 depletion and altered endocytosis caused by expression of Alzheimer’s disease-protective variant PLCγ2 R522. Embo j 40, e105603 (2021). https://doi.org/10.15252/embj.2020105603
Li Y et al. Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer’s disease progression. Alzheimers Dement (Amst) 13, e12140 (2021). https://doi.org/10.1002/dad2.12140
Kotredes KP et al. Uncovering Disease Mechanisms in a Novel Mouse Model Expressing Humanized APOEε4 and Trem2*R47H. Front Aging Neurosci 13, 735524 (2021). https://doi.org/10.3389/fnagi.2021.735524
Javonillo DI et al. Systematic Phenotyping and Characterization of the 3xTg-AD Mouse Model of Alzheimer’s Disease. Front Neurosci 15, 785276 (2021). https://doi.org/10.3389/fnins.2021.785276
Forner S et al. Systematic phenotyping and characterization of the 5xFAD mouse model of Alzheimer’s disease. Sci Data 8, 270 (2021). https://doi.org/10.1038/s41597-021-01054-y
Crapser JD, Arreola MA, Tsourmas KI, Green KN. Microglia as hackers of the matrix: sculpting synapses and the extracellular space. Cell Mol Immunol 18, 2472-2488 (2021). https://doi.org/10.1038/s41423-021-00751-3
Balderrama-Gutierrez G et al. Single-cell and nucleus RNA-seq in a mouse model of AD reveal activation of distinct glial subpopulations in the presence of plaques and tangles. bioRxiv, 2021.2009.2029.462436 (2021). https://doi.org/10.1101/2021.09.29.462436
Baglietto-Vargas D et al. Generation of a humanized Aβ expressing mouse demonstrating aspects of Alzheimer’s disease-like pathology. Nat Commun 12, 2421 (2021). https://doi.org/10.1038/s41467-021-22624-z
Arreola MA et al. Microglial dyshomeostasis drives perineuronal net and synaptic loss in a CSF1R(+/-) mouse model of ALSP, which can be rescued via CSF1R inhibitors. Sci Adv 7 (2021). https://doi.org/10.1126/sciadv.abg1601
2020:
Wyman D et al. A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification. bioRxiv, 672931 (2020). https://doi.org/10.1101/672931
Wan YW et al. Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models. Cell Rep 32, 107908 (2020). https://doi.org/10.1016/j.celrep.2020.107908
Vitek MP et al. Translational animal models for Alzheimer’s disease: An Alzheimer’s Association Business Consortium Think Tank. Alzheimers Dement (N Y) 6, e12114 (2020). https://doi.org/10.1002/trc2.12114
Sukoff Rizzo SJ et al. Improving preclinical to clinical translation in Alzheimer’s disease research. Alzheimers Dement (N Y) 6, e12038 (2020). https://doi.org/10.1002/trc2.12038
Silverman JL, Nithianantharajah J, Der-Avakian A, Young JW, Sukoff Rizzo SJ. Lost in translation: At the crossroads of face validity and translational utility of behavioral assays in animal models for the development of therapeutics. Neurosci Biobehav Rev 116, 452-453 (2020). https://doi.org/10.1016/j.neubiorev.2020.07.008
Preuss C et al. A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease. Mol Neurodegener 15, 67 (2020). https://doi.org/10.1186/s13024-020-00412-5
Oblak AL et al. Model organism development and evaluation for late-onset Alzheimer’s disease: MODEL-AD. Alzheimers Dement (N Y) 6, e12110 (2020). https://doi.org/10.1002/trc2.12110
Mukherjee S et al. Author Correction: Molecular estimation of neurodegeneration pseudotime in older brains. Nat Commun 11, 6307 (2020). https://doi.org/10.1038/s41467-020-20261-6
Milind N et al. Transcriptomic stratification of late-onset Alzheimer’s cases reveals novel genetic modifiers of disease pathology. PLoS Genet 16, e1008775 (2020). https://doi.org/10.1371/journal.pgen.1008775
Jadhav VS et al. Trem2 Y38C mutation and loss of Trem2 impairs neuronal synapses in adult mice. Mol Neurodegener 15, 62 (2020). https://doi.org/10.1186/s13024-020-00409-0
Greenwood AK et al. The AD Knowledge Portal: A Repository for Multi-Omic Data on Alzheimer’s Disease and Aging. Curr Protoc Hum Genet 108, e105 (2020). https://doi.org/10.1002/cphg.105
Fernández-Mendívil C, Arreola MA, Hohsfield LA, Green KN, Lopez MG. Aging and Progression of Beta-Amyloid Pathology in Alzheimer’s Disease Correlates with Microglial Heme-Oxygenase-1 Overexpression. Antioxidants (Basel) 9 (2020). https://doi.org/10.3390/antiox9070644
Crapser JD et al. Microglia facilitate loss of perineuronal nets in the Alzheimer’s disease brain. EBioMedicine 58, 102919 (2020). https://doi.org/10.1016/j.ebiom.2020.102919
Chintapaludi SR et al. Staging Alzheimer’s Disease in the Brain and Retina of B6.APP/PS1 Mice by Transcriptional Profiling. J Alzheimers Dis 73, 1421-1434 (2020). https://doi.org/10.3233/jad-190793
2019:
Pandey RS et al. Genetic perturbations of disease risk genes in mice capture transcriptomic signatures of late-onset Alzheimer’s disease. Mol Neurodegener 14, 50 (2019). https://doi.org/10.1186/s13024-019-0351-3
Onos KD et al. Enhancing face validity of mouse models of Alzheimer’s disease with natural genetic variation. PLoS Genet 15, e1008155 (2019). https://doi.org/10.1371/journal.pgen.1008155
2018:
Wang X et al. A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer’s Disease. Genetics 209, 51-64 (2018). https://doi.org/10.1534/genetics.117.300673
Cheng-Hathaway PJ et al. The Trem2 R47H variant confers loss-of-function-like phenotypes in Alzheimer’s disease. Mol Neurodegener 13, 29 (2018). https://doi.org/10.1186/s13024-018-0262-8