Publications

 

  1. 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
  2. Tran, K. M. 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
  3. Sasner, M., Territo, P. R. & Sukoff Rizzo, S. J. 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
  4. 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
  5. Quinney, S. K. 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.13108
  6. Pandey, 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
  7. 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
  8. Milinkeviciute, G. & Green, K. N. Clusterin/apolipoprotein J, its isoforms and Alzheimer’s disease. Front Aging Neurosci 15, 1167886 (2023). https://doi.org/10.3389/fnagi.2023.1167886
  9. 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
  10. Bohlson, S. S. & Tenner, A. J. 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
  11. Tsai, A. P. 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
  12. Reagan, A. M., Onos, K. D., Heuer, S. E., Sasner, M. & Howell, G. R. 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
  13. Reagan, A. M. 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
  14. Onos, K. D. 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
  15. Oblak, A. L. 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
  16. Oblak, A. L. 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
  17. 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
  18. Kotredes, K. P. 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
  19. Jullienne, A., Trinh, M. V. & Obenaus, A. Neuroimaging of Mouse Models of Alzheimer’s Disease. Biomedicines 10 (2022). https://doi.org/10.3390/biomedicines10020305
  20. 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
  21. Henningfield, C. M., Arreola, M. A., Soni, N., Spangenberg, E. E. & Green, K. N. 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
  22. Gordon, M. N. 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
  23. Foley, K. E. 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
  24. Foley, K. E. 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
  25. Dunham, S. J. B. 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
  26. Tsai, A. P. 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
  27. Szu, J. I. & 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
  28. Oblak, A. L. 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
  29. 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
  30. 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
  31. Kotredes, K. P. 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
  32. Javonillo, D. I. 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
  33. 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
  34. Crapser, J. D., Arreola, M. A., Tsourmas, K. I. & Green, K. N. 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
  35. 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
  36. 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
  37. Arreola, M. A. 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
  38. 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
  39. Wan, Y. W. 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
  40. Vitek, M. P. 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
  41. Sukoff Rizzo, S. J. 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
  42. Silverman, J. L., Nithianantharajah, J., Der-Avakian, A., Young, J. W. & Sukoff Rizzo, S. J. 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
  43. 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
  44. Oblak, A. L. 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
  45. 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
  46. 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
  47. Jadhav, V. S. 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
  48. Greenwood, A. K. 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
  49. Fernández-Mendívil, C., Arreola, M. A., Hohsfield, L. A., Green, K. N. & Lopez, M. G. 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
  50. Crapser, J. D. 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
  51. Chintapaludi, S. R. 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
  52. Pandey, R. S. 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
  53. Onos, K. D. 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
  54. 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
  55. Cheng-Hathaway, P. J. 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
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