Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer’s Disease
Abstract
:1. Introduction
1.1. Background on Alzheimer’s Disease
- Early discoveries (1900s):
- 1906: Dr. Alois Alzheimer identifies the disease, describing amyloid plaques and neurofibrillary tangles in the brain of a patient.
- 1910: the term “Alzheimer’s disease” is coined by Emil Kraepelin.
- Foundational research (1920s–1960s):
- 1920s–1930s: initial studies
- 1960s: the identification of an acetylcholine deficiency in Alzheimer’s brains sparks research into cholinergic therapies.
- Advancements in molecular understanding (1980s):
- 1984: beta-amyloid protein is identified as a major component of plaques.
- 1986: the discovery of tau protein as a component of neurofibrillary tangles.
- 1987: the first AD drug, tacrine (Cognex), is approved for symptomatic treatment.
- Genetic insights (1990s):
- 1991: the identification of mutations in the APP gene linked to early-onset AD.
- 1993: the apolipoprotein E (APOE) ε4 allele is identified as a risk factor for late-onset AD.
- 1997: presenilin genes (PSEN1 and PSEN2) are linked to familial AD.
- Emerging experimental models (2000s):
- 2000s: the development of transgenic mouse models mimicking amyloid plaque and the tau pathology.
- 2003: the launch of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to identify biomarkers.
- 2004: the approval of memantine (Namenda), the first non-cholinergic AD drug.
- Technological and diagnostic advances (2010s):
- 2012: the introduction of amyloid PET imaging, enabling the in vivo detection of plaques.
- 2013: genome-wide association studies (GWAS) identify new genetic risk factors.
- 2016: the identification of neuroinflammation as a significant contributor to AD progression.
- 2018: the approval of the tau PET tracer for studying tauopathy in living AD patients.
- Recent breakthroughs (2020s):
- 2021: the FDA approves aducanumab (Aduhelm), the first amyloid-targeting drug, amid controversy over its efficacy.
- 2022: the emerging use of CRISPR technology to study genetic contributions to AD.
- 2023: lecanemab (Leqembi) gains FDA approval as a monoclonal antibody targeting amyloid plaques.
- Ongoing: advancements in human brain organoids and AI-driven diagnostics reshape research paradigms.
- Future directions:
- Expanding research on late-onset AD and neuroinflammation.
- The development of non-invasive blood-based biomarkers for early detection.
- Exploration of lifestyle interventions and their impact on AD prevention.
1.2. Importance of Animal Models in AD Research
2. Murine Models in AD Research
2.1. Development of Transgenic Murine Models for AD Research
Key Genetic Modifications in Transgenic Murine Models for Alzheimer’s Disease
2.2. Knock-In and Injection Models in Alzheimer’s Research
2.3. Contributions of Murine Models for Understanding AD Pathogenesis
2.4. Limitations and Challenges of Murine Models
3. Emerging Alternative Models
3.1. Zebrafish Models
3.1.1. Advantages of Zebrafish Models
3.1.2. Types of Zebrafish Models for AD
3.1.3. Research Findings
3.1.4. Challenges and Future Directions
3.2. Drosophila Models
3.2.1. Key Features of Drosophila AD Models
3.2.2. Research Insights
3.2.3. Advantages of the Fruit Fly Model
3.3. Worm Models
3.3.1. Key Insights from Worm Models
3.3.2. Advantages of C. elegans in AD Research
3.4. Marmoset Models
3.4.1. Key Advantages of Marmosets in AD Research
- ❖
- ❖
- ❖
3.4.2. Research Findings and Developments
- ❖
- Genetic models: The MARMO-AD consortium has successfully generated gene-edited marmosets carrying PSEN1 mutations, which are associated with AD. These models are characterised by genetic, molecular, functional, behavioural, cognitive, and pathological features throughout their lifespan [134].
- ❖
- Biomarkers of neural degeneration: Key biomarkers, including the total tau (T-tau), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and ubiquitin C-terminal hydrolase-L1 (UCH-L1), have been identified in marmosets. These biomarkers increase with age and are useful for evaluating neural health and therapeutic interventions [139].
- ❖
- Tau protein studies: marmosets express both 3R and 4R tau isoforms, similar to humans, and exhibit tau phosphorylation at residues associated with AD, making them valuable for studying tau-related pathologies [137].
- ❖
- Neuroinflammation and amyloidopathy: research has demonstrated that neuroinflammation can exacerbate amyloid plaque formation in marmosets, underscoring the role of the immune system in AD and offering novel perspectives for disease-modifying approaches [140].
3.5. In Vitro Models in AD Research
3.5.1. 2D Cell Cultures
3.5.2. 3D Cell Cultures and Organoids
3.5.3. Other In Vitro Models
3.6. Advantages and Disadvantages of Alternative Models for AD Research
4. Technological Innovations in AD Model Research
4.1. Advanced Imaging Techniques
4.1.1. X-Ray Phase Contrast Tomography (XPCT)
4.1.2. Phase-Contrast X-Ray-Computed Tomography (PCXCT)
4.1.3. Diffraction Enhanced Imaging (DEI)
4.1.4. Analyser-Based X-Ray Imaging (ABI)
4.2. Computational Approaches and Machine Learning in AD Model Analysis
4.2.1. Supervised Learning
4.2.2. Neuroimaging Analysis
4.2.3. Genetic Data Analysis
4.2.4. Multi-Modal Analysis
4.2.5. Ensemble Methods
4.2.6. Hybrid Models
4.2.7. Deep Learning
4.2.8. Non-Invasive Techniques
4.3. Omics Technologies for Biomarker Discovery
5. Comparative Analysis of Mouse and Alternative Models
5.1. Translational Discrepancies Between Models
5.1.1. Mouse Models in AD Research
5.1.2. Emerging Alternatives to Murine Models
5.1.3. Translational Discrepancies
5.1.4. Improved Translation Recommendations
5.1.5. Key Points
Mouse Models
Alternative Models
Molecular and Behavioural Assessments
5.2. Strengths and Weaknesses of Different Model Systems for Alzheimer’s Disease Research
Model System | Strengths | Weaknesses | References |
---|---|---|---|
Transgenic Mouse Models |
|
| [18] |
3xTg-AD Mouse Model |
|
| [23] |
Rat Models |
|
| [198] |
Drosophila Models |
|
| [19] |
C. elegans Models |
|
| [24] |
Non-Human Primates |
|
| [196] |
iPSC-Derived Neurons |
|
| [25] |
3D Brain Organoids |
|
| [26] |
Blood–Brain Barrier on a Chip |
|
| [197] |
CRISPR-Engineered Models |
|
| [27] |
6. Key Areas for Future Research
6.1. Developing Models for Late-Onset AD
6.1.1. Key Genetic Risk Factors for Late-Onset Alzheimer’s Disease
6.1.2. Predicting the Onset of Late-Onset Alzheimer’s Disease Using Machine Learning Models
6.2. Studying the Role of Neuroinflammation in AD Using Animal Models
Key Inflammatory Markers in AD Animal Models
6.3. Addressing the Genetic and Environmental Complexity of Human AD
6.3.1. Genetic Factors
6.3.2. Epigenetic Mechanisms
6.3.3. Environmental Factors
Modifiable Risk Factors
Lifestyle and Diet
Microbiome Influence
6.3.4. Gene–Environment Interactions
Complex Interactions
Epigenetic Modifications
6.3.5. Research Implications
Comprehensive Approaches
Preventive Strategies
6.4. Ethical Considerations in Developing and Using Animal Models in AD Research
6.4.1. Species Selection and Ethical Constraints
6.4.2. Validity and Predictive Power
6.4.3. Minimising Harm and Suffering
6.4.4. Gender Considerations
6.4.5. Responsibility and Oversight
7. Clinical Implications of Research Models
7.1. Translating Preclinical Findings to Human Clinical Trials in AD Research
7.2. Optimising Therapeutic Strategies Based on Research Models’ Findings
8. Conclusions and Recommendations
8.1. Summary of Key Insights
8.2. Recommendations for Future Research Directions
8.3. Potential Impact on AD Treatment and Prevention
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ADNI | Alzheimer’s Disease Neuroimaging Initiative |
ABI | Analyser-Based X-Ray Imaging |
AD | Alzheimer’s Disease |
ADNI | Alzheimer’s Disease Neuroimaging Initiative |
AI | Artificial Intelligence |
APOE | Apolipoprotein E |
APP | Amyloid Precursor Protein |
AUC | Area Under the Curve |
Aβ | Beta-Amyloid |
BBB | Blood–Brain Barrier |
CNNs | Convolutional Neural Networks |
CRISPR | Clustered Regularly Interspaced Short Palindromic Repeats |
CSF | Cerebrospinal Fluid |
C. elegans | Caenorhabditis elegans |
DEI | Diffraction-Enhanced Imaging |
D. melanogaster | Drosophila melanogaster |
EWAS | Epigenome-Wide Association Studies |
FAD | Familial Alzheimer’s Disease |
FDA | Food and Drug Administration |
GFAP | Glial Fibrillary Acidic Protein |
GWAS | Genome-Wide Association Studies |
hAPP | Human Amyloid Precursor Protein |
ICV | Intracerebroventricular |
IL-6 | Interleukin-6 |
IL-10 | Interleukin-10 |
iPSCs | Induced Pluripotent Stem Cells |
LC-MS | Liquid Chromatography–Mass Spectrometry |
LOAD | Late-Onset Alzheimer’s Disease |
LysoPC | Lysophosphatidylcholine |
MAPT | Microtubule-Associated Protein Tau |
MCP-1 | Monocyte Chemoattractant Protein-1 |
ML | Machine Learning |
MMSE | Mini-Mental State Examination |
MRS | Magnetic Resonance Spectroscopy |
NfL | Neurofilament Light Chain |
NLRP3 | NOD-Like Receptor Protein 3 |
PCXCT | Phase-Contrast X-Ray-Computed Tomography |
PET | Positron Emission Tomography |
PK/PD | Pharmacokinetics–Pharmacodynamics |
PRS | Polygenic Risk Scores |
PSEN | Presenilin |
qRT-PCR | Real-Time Quantitative Reverse Transcription PCR |
RMSE | Root Mean Squared Error |
R2 | R-Squared |
SCD | Stearoyl CoA Desaturase |
SNP | Single-Nucleotide Polymorphism |
SVM | Support Vector Machine |
TLR4 | Toll-Like Receptor 4 |
TNF-α | Tumour Necrosis Factor-Alpha |
TREM2 | Triggering Receptor Expressed on Myeloid Cells 2 |
T-tau | Total Tau |
UCH-L1 | Ubiquitin C-Terminal Hydrolase-L1 |
UPLC-Q-TOF-MS | Ultra-Performance Liquid Chromatography Coupled with Quadrupole Time-of-Flight Tandem Mass Spectrometry |
VEGF | Vascular Endothelial Growth Factor |
VEGFR-1 | Vascular Endothelial Growth Factor Receptor |
XGBoost | eXtreme Gradient Boosting |
XPCT | Phase-Contrast X-Ray Imaging |
WF-NTP | Wide Field-of-View Nematode Tracking Platform |
YKL-40 | Chitinase-3-Like Protein 1 |
3Rs | Replacement, Reduction, Refinement |
References
- Zhang, X.X.; Tian, Y.; Wang, Z.T.; Ma, Y.H.; Tan, L.; Yu, J.T. The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. J. Prev. Alzheimer’s Dis. 2021, 8, 313–321. [Google Scholar] [CrossRef] [PubMed]
- Sheppard, O.; Coleman, M. Alzheimer’s Disease: Etiology, Neuropathology and Pathogenesis; Exon Publications: Brisbane City, Australia, 2020; pp. 1–21. [Google Scholar] [CrossRef]
- Nitrini, R. The past, present and future of Alzheimer’s disease–part 1: The past. Arq. De Neuro Psiquiatr. 2023, 81, 1070–1076. [Google Scholar] [CrossRef] [PubMed]
- El Kadmiri, N.; Hamzi, K.; El Moutawakil, B.; Slassi, I.; Nadifi, S. Les aspects génétiques de la maladie d’Alzheimer (Revue). Pathol. Biol. 2013, 61, 228–238. [Google Scholar] [CrossRef]
- Tobore, T.O. On the central role of mitochondria dysfunction and oxidative stress in Alzheimer’s disease. Neurol. Sci. 2019, 40, 1527–1540. [Google Scholar] [CrossRef]
- Nguyen, T.P.; Schaffert, J.; LoBue, C.; Womack, K.B.; Hart, J.; Cullum, C.M. Traumatic brain injury and age of onset of dementia with Lewy bodies. J. Alzheimer’s Dis. 2018, 66, 717–723. [Google Scholar] [CrossRef] [PubMed]
- Eschweiler, G.W.; Leyhe, T.; Klöppel, S.; Hüll, M. New developments in the diagnosis of dementia. Dtsch. Ärzteblatt Int. 2010, 107, 677. [Google Scholar] [CrossRef]
- Ahmed, S.; Kaur, A.; Venigalla, H.; Hassan, M. The retrogenesis model in Alzheimer’s disease: Evidence and practical applications. Curr. Psychiatry Rev. 2017, 13, 35–42. [Google Scholar] [CrossRef]
- Peña-Longobardo, L.M.; Oliva-Moreno, J. Caregiver burden in Alzheimer’s disease patients in Spain. J. Alzheimer’s Dis. 2015, 43, 1293–1302. [Google Scholar] [CrossRef]
- Van Dam, D.; Vloeberghs, E.; Abramowski, D.; Staufenbiel, M.; De Deyn, P.P. APP23 mice as a model of Alzheimer’s disease: An example of a transgenic approach to modeling a CNS disorder. CNS Spectr. 2005, 10, 207–222. [Google Scholar] [CrossRef]
- Van Dam, D.; De Deyn, P.P. Animal models in the drug discovery pipeline for Alzheimer’s disease. Br. J. Pharmacol. 2011, 164, 1285–1300. [Google Scholar] [CrossRef]
- Dong, W.; Wang, R. Research progress on animal models of Alzheimer’s disease. Chin. J. Contemp. Neurol. Neurosurg. 2015, 15, 610. [Google Scholar] [CrossRef]
- Laurijssens, B.; Aujard, F.; Rahman, A. Animal models of Alzheimer’s disease and drug development. Drug Discov. Today Technol. 2013, 10, e319–e327. [Google Scholar] [CrossRef] [PubMed]
- Götz, J.; Bodea, L.G.; Goedert, M. Rodent models for Alzheimer disease. Nat. Rev. Neurosci. 2018, 19, 583–598. [Google Scholar] [CrossRef]
- Islam, M.A.; Kshirsagar, S.; Reddy, A.P.; Sehar, U.; Reddy, P.H. Use and Reuse of Animal Behavioral, Molecular, and Biochemical Data in Alzheimer’s Disease Research: Focus on 3Rs and Saving People’s Tax Dollars. J. Alzheimer’s Dis. Rep. 2024, 8, 1171–1184. [Google Scholar] [CrossRef]
- Bengoetxea, X.; Rodriguez-Perdigon, M.; Ramirez, M.J. Object recognition test for studying cognitive impairments in animal models of Alzheimer’s disease. Front. Biosci. 2015, 7, 10–29. [Google Scholar]
- Vitek, M.P.; Araujo, J.A.; Fossel, M.; Greenberg, B.D.; Howell, G.R.; Rizzo, S.J.S.; Edelmayer, R.M. Translational animal models for Alzheimer’s disease: An Alzheimer’s association business consortium think tank. Alzheimer’s Dement. 2020, 6, e12114. [Google Scholar] [CrossRef]
- Sweetat, S.; Shabat, M.B.; Theotokis, P.; Suissa, N.; Karafoulidou, E.; Touloumi, O.; Rosenmann, H. Ovariectomy and High Fat-Sugar-Salt Diet Induced Alzheimer’s Disease/Vascular Dementia Features in Mice. Aging Dis. 2024, 15, 2284. [Google Scholar] [CrossRef]
- Lanni, I.; Chiacchierini, G.; Papagno, C.; Santangelo, V.; Campolongo, P. Treating Alzheimer’s disease with brain stimulation: From preclinical models to non-invasive stimulation in humans. Neurosci. Biobehav. Rev. 2024, 165, 105831. [Google Scholar] [CrossRef]
- Winkler, J.; Thal, L.J.; Gage, F.H.; Fisher, L.J. Cholinergic strategies for Alzheimer’s disease. J. Mol. Med. 1998, 76, 555–567. [Google Scholar] [CrossRef]
- Games, D.; Adams, D.; Alessandrini, R.; Barbour, R.; Borthelette, P.; Blackwell, C.; Zhao, J. Alzheimer-type neuropathology in transgenic mice overexpressing V717F β-amyloid precursor protein. Nature 1995, 373, 523–527. [Google Scholar] [CrossRef]
- Fossgreen, A.; Brückner, B.; Czech, C.; Masters, C.L.; Beyreuther, K.; Paro, R. Transgenic Drosophila expressing human amyloid precursor protein show γ-secretase activity and a blistered-wing phenotype. Proc. Natl. Acad. Sci. USA 1998, 95, 13703–13708. [Google Scholar] [CrossRef] [PubMed]
- Schenk, D.; Barbour, R.; Dunn, W.; Gordon, G.; Grajeda, H.; Guido, T.; Seubert, P. Immunization with amyloid-β attenuates Alzheimer-disease-like pathology in the PDAPP mouse. Nature 1999, 400, 173–177. [Google Scholar] [CrossRef]
- Holcomb, L.; Gordon, M.N.; McGowan, E.; Yu, X.; Benkovic, S.; Jantzen, P.; Duff, K. Accelerated Alzheimer-type phenotype in transgenic mice carrying both mutant amyloid precursor protein and presenilin 1 transgenes. Nat. Med. 1998, 4, 97–100. [Google Scholar] [CrossRef] [PubMed]
- Santacruz, K.; Lewis, J.; Spires, T.; Paulson, J.; Kotilinek, L.; Ingelsson, M.; Ashe, K. Tau suppression in a neurodegenerative mouse model improves memory function. Science 2005, 309, 476–481. [Google Scholar] [CrossRef]
- Oddo, S.; Caccamo, A.; Shepherd, J.D.; Murphy, M.P.; Golde, T.E.; Kayed, R.; LaFerla, F.M. Triple-transgenic model of Alzheimer’s disease with plaques and tangles: Intracellular Aβ and synaptic dysfunction. Neuron 2003, 39, 409–421. [Google Scholar] [CrossRef] [PubMed]
- Link, C.D. Expression of human beta-amyloid peptide in transgenic Caenorhabditis elegans. Proc. Nat. Acad. Sci. USA 1995, 92, 9368–9372. [Google Scholar] [CrossRef]
- Yagi, T.; Ito, D.; Okada, Y.; Akamatsu, W.; Nihei, Y.; Yoshizaki, T.; Suzuki, N. Modeling familial Alzheimer’s disease with induced pluripotent stem cells. Hum. Mol. Genet. 2011, 20, 4530–4539. [Google Scholar] [CrossRef]
- Choi, S.H.; Kim, Y.H.; Hebisch, M.; Sliwinski, C.; Lee, S.; D’Avanzo, C.; Kim, D.Y. A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature 2014, 515, 274–278. [Google Scholar] [CrossRef]
- Sun, J.; Carlson-Stevermer, J.; Das, U.; Shen, M.; Delenclos, M.; Snead, A.M.; Roy, S. CRISPR/Cas9 editing of APP C-terminus attenuates β-cleavage and promotes α-cleavage. Nat. Commun. 2019, 10, 53. [Google Scholar] [CrossRef]
- van Der Helm, M.W.; Van Der Meer, A.D.; Eijkel, J.C.; van den Berg, A.; Segerink, L.I. Microfluidic organ-on-chip technology for blood-brain barrier research. Tissue Barriers 2016, 4, e1142493. [Google Scholar] [CrossRef]
- Watamura, N.; Sato, K.; Saido, T.C. Mouse models of Alzheimer’s disease for preclinical research. Neurochem. Int. 2022, 158, 105361. [Google Scholar] [CrossRef] [PubMed]
- Chishti, M.A.; Nakeeb, S.M. Mouse Model for Alzheimer’s Disease; Springer Nature: New York, NY, USA, 2008; pp. 191–199. [Google Scholar] [CrossRef]
- Ashe, K.H. Alzheimer’s disease: Transgenic mouse models. In Encyclopedia of Neuroscience; Elsevier: Amsterdam, The Netherlands, 2009; pp. 283–287. [Google Scholar] [CrossRef]
- Rey, C.; Cattaud, V.; Rampon, C.; Verret, L. What’s New on Alzheimer’s Disease? Insights From AD Mouse Models. Biomed. Gerontosci. 2020, 431–442. [Google Scholar] [CrossRef]
- Wirths, O.; Zampar, S. Neuron loss in Alzheimer’s disease: Translation in transgenic mouse models. Int. J. Mol. Sci. 2020, 21, 8144. [Google Scholar] [CrossRef]
- Li, X.; Quan, M.; Wei, Y.; Wang, W.; Xu, L.; Wang, Q.; Jia, J. Critical thinking of Alzheimer’s transgenic mouse model: Current research and future perspective. Sci. China Life Sci. 2023, 66, 2711–2754. [Google Scholar] [CrossRef]
- Kishimoto, Y.; Kirino, Y. Presenilin 2 mutation accelerates the onset of impairment in trace eyeblink conditioning in a mouse model of Alzheimer’s disease overexpressing human mutant amyloid precursor protein. Neurosci. Lett. 2013, 538, 15–19. [Google Scholar] [CrossRef]
- Sato, K.; Watamura, N.; Fujioka, R.; Mihira, N.; Sekiguchi, M.; Nagata, K.; Sasaguri, H. A third-generation mouse model of Alzheimer’s disease shows early and increased cored plaque pathology composed of wild-type human amyloid β peptide. J. Biol. Chem. 2021, 297, 101004. [Google Scholar] [CrossRef] [PubMed]
- Chin, J. Selecting a mouse model of Alzheimer’s disease. Alzheimer’s Dis. Front. Dement. Methods Protoc. 2010, 670, 169–189. [Google Scholar] [CrossRef]
- Elder, G.A.; Gama Sosa, M.A.; De Gasperi, R. Transgenic mouse models of Alzheimer’s disease. Mt. Sinai J. Med. 2010, 77, 69–81. [Google Scholar] [CrossRef]
- Pugh, P.L.; Ahmed, S.F.; Smith, M.I.; Upton, N.; Hunter, A.J. A behavioural characterisation of the FVB/N mouse strain. Behav. Brain Res. 2004, 155, 283–289. [Google Scholar] [CrossRef]
- Sarasa, M.; Pesini, P. Natural non-trasgenic animal models for research in Alzheimer’s disease. Curr. Alzheimer Res. 2009, 6, 171–178. [Google Scholar] [CrossRef]
- Pádua, M.S.; Guil-Guerrero, J.L.; Prates, J.A.M.; Lopes, P.A. Insights on the use of transgenic mice models in Alzheimer’s disease research. Int. J. Mol. Sci. 2024, 25, 2805. [Google Scholar] [CrossRef] [PubMed]
- Pádua, M.S.; Guil-Guerrero, J.L.; Lopes, P.A. Behaviour Hallmarks in Alzheimer’s Disease 5xFAD Mouse Model. Int. J. Mol. Sci. 2024, 25, 6766. [Google Scholar] [CrossRef]
- Darvesh, S.; Cash, M.K.; Reid, G.A.; Martin, E.; Mitnitski, A.; Geula, C. Butyrylcholinesterase is associated with β-amyloid plaques in the transgenic APPSWE/PSEN1dE9 mouse model of Alzheimer disease. J. Neuropathol. Exp. Neurol. 2012, 71, 2–14. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.E.; Han, P.L. An update of animal models of Alzheimer disease with a reevaluation of plaque depositions. Exp. Neurobiol. 2013, 22, 84. [Google Scholar] [CrossRef]
- Javonillo, D.I.; Tran, K.M.; Phan, J.; Hingco, E.; Kramár, E.A.; da Cunha, C.; LaFerla, F.M. Systematic phenotyping and characterization of the 3xTg-AD mouse model of Alzheimer’s disease. Front. Neurosci. 2022, 15, 785276. [Google Scholar] [CrossRef] [PubMed]
- Dewachter, I.; Van Dorpe, J.; Smeijers, L.; Gilis, M.; Kuiperi, C.; Laenen, I.; Van Leuven, F. Aging increased amyloid peptide and caused amyloid plaques in brain of old APP/V717I transgenic mice by a different mechanism than mutant presenilin1. J. Neurosci. 2000, 20, 6452–6458. [Google Scholar] [CrossRef]
- Tag, S.H.; Kim, B.; Bae, J.; Chang, K.A.; Im, H.I. Neuropathological and behavioral features of an APP/PS1/MAPT (6xTg) transgenic model of Alzheimer’s disease. Mol. Brain 2022, 15, 51. [Google Scholar] [CrossRef]
- Sasaguri, H.; Nilsson, P.; Hashimoto, S.; Nagata, K.; Saito, T.; De Strooper, B.; Saido, T.C. APP mouse models for Alzheimer’s disease preclinical studies. EMBO J. 2017, 36, 2473–2487. [Google Scholar] [CrossRef]
- Forster, D.; Davies, K.; Williams, S. Magnetic resonance spectroscopy in vivo of neurochemicals in a transgenic model of Alzheimer’s disease: A longitudinal study of metabolites, relaxation time, and behavioral analysis in TASTPM and wild-type mice. Magn. Reson. Med. 2013, 69, 944–955. [Google Scholar] [CrossRef]
- Falangola, M.F.; Ardekani, B.A.; Lee, S.P.; Babb, J.S.; Bogart, A.; Dyakin, V.V.; Helpern, J.A. Application of a non-linear image registration algorithm to quantitative analysis of T2 relaxation time in transgenic mouse models of AD pathology. J. Neurosci. Meth. 2005, 144, 91–97. [Google Scholar] [CrossRef]
- Polis, B.; Samson, A.O. Addressing the discrepancies between animal models and human Alzheimer’s disease pathology: Implications for translational research. J. Alzheimer’s Dis. 2024, 98, 1199–1218. [Google Scholar] [CrossRef] [PubMed]
- Kokjohn, T.A.; Roher, A.E. Amyloid precursor protein transgenic mouse models and Alzheimer’s disease: Understanding the paradigms, limitations, and contributions. Alzheimer’s Dement. 2009, 5, 340–347. [Google Scholar] [CrossRef] [PubMed]
- Saré, R.M.; Cooke, S.K.; Krych, L.; Zerfas, P.M.; Cohen, R.M.; Smith, C.B. Behavioral phenotype in the TgF344-AD rat model of Alzheimer’s disease. Front. Neurosci. 2020, 14, 601. [Google Scholar] [CrossRef]
- Cohen, R.M.; Rezai-Zadeh, K.; Weitz, T.M.; Rentsendorj, A.; Gate, D.; Spivak, I.; Town, T. A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric aβ, and frank neuronal loss. J. Neurosci. 2013, 33, 6245–6256. [Google Scholar] [CrossRef]
- Rorabaugh, J.M.; Chalermpalanupap, T.; Botz-Zapp, C.A.; Fu, V.M.; Lembeck, N.A.; Cohen, R.M.; Weinshenker, D. Chemogenetic locus coeruleus activation restores reversal learning in a rat model of Alzheimer’s disease. Brain 2017, 140, 3023–3038. [Google Scholar] [CrossRef]
- Więdłocha, M.; Stańczykiewicz, B.; Jakubik, M.; Rymaszewska, J. Selected mice models based on APP, MAPT and presenilin gene mutations in research on the pathogenesis of Alzheimer’s disease. Adv. Hyg. Exp. Med. 2012, 66, 415–430. [Google Scholar] [CrossRef] [PubMed]
- Allué, J.A.; Sarasa, L.; Izco, M.; Monsa, S.; Esteban, J.; Montañés, M.; Sarasa, M. Outstanding phenotypic differences in the profile of amyloid-β between Tg2576 and APPswe/PS1dE9 transgenic mouse models of Alzheimer’s disease. J. Alzheimer’s Dis. 2016, 53, 773–785. [Google Scholar] [CrossRef]
- Puzzo, D.; Gulisano, W.; Palmeri, A.; Arancio, O. Rodent models for Alzheimer’s disease drug discovery. Expert Opin. Drug Discov. 2015, 10, 703–711. [Google Scholar] [CrossRef]
- Platt, T.L.; Reeves, V.L.; Murphy, M.P. Transgenic models of Alzheimer’s disease: Better utilization of existing models through viral transgenesis. Biochim. Biophys. Acta 2013, 1832, 1437–1448. [Google Scholar] [CrossRef]
- Yokoyama, M.; Kobayashi, H.; Tatsumi, L.; Tomita, T. Mouse models of Alzheimer’s disease. Front. Mol. Neurosci. 2022, 15, 912995. [Google Scholar] [CrossRef]
- Röskam, S.; Neff, F.; Schwarting, R.; Bacher, M.; Dodel, R. APP transgenic mice: The effect of active and passive immunotherapy in cognitive tasks. Neurosci. Biobehav. Rev. 2010, 34, 487–499. [Google Scholar] [CrossRef]
- Davidson, D.S.; Kraus, J.A.; Montgomery, J.M.; Lemkul, J.A. Effects of familial Alzheimer’s disease mutations on the folding free energy and dipole–dipole interactions of the amyloid β-peptide. J. Phys. Chem. B 2022, 126, 7552–7566. [Google Scholar] [CrossRef]
- Stoiljkovic, M.; Kelley, C.; Stutz, B.; Horvath, T.L.; Hajós, M. Altered cortical and hippocampal excitability in TgF344-AD rats modeling Alzheimer’s disease pathology. Cereb. Cortex 2019, 29, 2716–2727. [Google Scholar] [CrossRef]
- Whyte, L.S.; Hemsley, K.M.; Lau, A.A.; Sargeant, T.J. Reduction in open field activity in the absence of memory deficits in the AppNL−G−F knock-in mouse model of Alzheimer’s disease. Behav. Brain Res. 2018, 350, 64–72. [Google Scholar] [CrossRef]
- Latif-Hernandez, A.; Sabanov, V.; Ahmed, T.; Craessaerts, K.; Saito, T.; Saido, T.; Balschun, D. The two faces of synaptic failure in App NL-GF knock-in mice. Alzheimer’s Res. Ther. 2020, 12, 100. [Google Scholar] [CrossRef]
- Pang, K.; Jiang, R.; Zhang, W.; Yang, Z.; Li, L.L.; Shimozawa, M.; Lu, B. An App knock-in rat model for Alzheimer’s disease exhibiting Aβ and tau pathologies, neuronal death and cognitive impairments. Cell Res. 2022, 32, 157–175. [Google Scholar] [CrossRef]
- Watamura, N.; Sato, K.; Shiihashi, G.; Iwasaki, A.; Kamano, N.; Takahashi, M.; Sasaguri, H. An isogenic panel of App knock-in mouse models: Profiling β-secretase inhibition and endosomal abnormalities. Sci. Adv. 2022, 8, eabm6155. [Google Scholar] [CrossRef]
- Webster, S.J.; Bachstetter, A.D.; Van Eldik, L.J. Comprehensive behavioral characterization of an APP/PS-1 double knock-in mouse model of Alzheimer’s disease. Alzheimer’s Res. Ther. 2013, 5, 28. [Google Scholar] [CrossRef]
- Sakakibara, Y.; Sekiya, M.; Saito, T.; Saido, T.C.; Iijima, K.M. Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease. BMC Neurosci. 2019, 20, 13. [Google Scholar] [CrossRef]
- Borcuk, C.; Héraud, C.; Herbeaux, K.; Diringer, M.; Panzer, É.; Scuto, J.; Mathis, C. Early memory deficits and extensive brain network disorganization in the AppNL-F/MAPT double knock-in mouse model of familial Alzheimer’s disease. Aging Brain 2022, 2, 100042. [Google Scholar] [CrossRef]
- O’Hare, E.; Page, D.; Curran, W.; Hong, J.S.; Kim, E.M. A preclinical screen to evaluate pharmacotherapies for the treatment of agitation in dementia. Behav. Pharmacol. 2017, 28, 199–206. [Google Scholar] [CrossRef]
- Facchinetti, R.; Bronzuoli, M.R.; Scuderi, C. An animal model of Alzheimer disease based on the intrahippocampal injection of amyloid β-peptide (1–42). Neurotrophic Factors Methods Protoc. 2018, 1727, 343–352. [Google Scholar] [CrossRef]
- Ahn, Y.; Seo, J.; Park, J.; Lee, J.; Kim, S.; Lee, Y. Synaptic loss and amyloid beta alterations in the rodent hippocampus induced by streptozotocin injection into the cisterna magna. Lab. Animal Res. 2020, 36, 17. [Google Scholar] [CrossRef]
- Müller, L.; Kirschstein, T.; Köhling, R.; Kuhla, A.; Teipel, S. Neuronal hyperexcitability in APPSWE/PS1dE9 mouse models of Alzheimer’s disease. J. Alzheimer’s Dis. 2021, 81, 855–869. [Google Scholar] [CrossRef]
- Tian, S.; Ye, T.; Cheng, X. The behavioral, pathological and therapeutic features of the triple transgenic Alzheimer’s disease (3× Tg-AD) mouse model strain. Exp. Neurol. 2023, 368, 114505. [Google Scholar] [CrossRef]
- Nakai, T.; Yamada, K.; Mizoguchi, H. Alzheimer’s disease animal models: Elucidation of biomarkers and therapeutic approaches for cognitive impairment. Int. J. Mol. Sci. 2021, 22, 5549. [Google Scholar] [CrossRef]
- Bai, Y.; Li, M.; Zhou, Y.; Ma, L.; Qiao, Q.; Hu, W.; Gan, W.B. Abnormal dendritic calcium activity and synaptic depotentiation occur early in a mouse model of Alzheimer’s disease. Mol. Neurodegener. 2017, 12, 86. [Google Scholar] [CrossRef]
- Ashe, K.H.; Zahs, K.R. Probing the biology of Alzheimer’s disease in mice. Neuron 2010, 66, 631–645. [Google Scholar] [CrossRef]
- Ameen-Ali, K.E.; Simpson, J.E.; Wharton, S.B.; Heath, P.R.; Sharp, P.S.; Brezzo, G.; Berwick, J. The time course of recognition memory impairment and glial pathology in the hAPP-J20 mouse model of Alzheimer’s disease. J. Alzheimer’s Dis. 2019, 68, 609–624. [Google Scholar] [CrossRef]
- Ruiz-Pérez, G.; Ruiz de Martín Esteban, S.; Marqués, S.; Aparicio, N.; Grande, M.T.; Benito-Cuesta, I.; Palenzuela, R. Potentiation of amyloid beta phagocytosis and amelioration of synaptic dysfunction upon FAAH deletion in a mouse model of Alzheimer’s disease. J. Neuroinflamm. 2021, 18, 223. [Google Scholar] [CrossRef]
- Sy, M.; Kitazawa, M.; LaFerla, F. The 3xTg-AD Mouse Model: Reproducing and Modulating Plaque and Tangle Pathology. In Animal Models of Dementia; De Deyn, P., Van Dam, D., Eds.; Humana Press: Totowa, NJ, USA, 2011; Volume 48. [Google Scholar] [CrossRef]
- Zhang, C.; Qi, H.; Jia, D.; Zhao, J.; Xu, C.; Liu, J.; Tang, B. Cognitive impairment in Alzheimer’s disease FAD4T mouse model: Synaptic loss facilitated by activated microglia via C1qA. Life Sci. 2024, 340, 122457. [Google Scholar] [CrossRef]
- Puzzo, D.; Lee, L.; Palmeri, A.; Calabrese, G.; Arancio, O. Behavioral assays with mouse models of Alzheimer’s disease: Practical considerations and guidelines. Biochem. Pharmacol. 2014, 88, 450–467. [Google Scholar] [CrossRef]
- Eriksen, J.L.; Janus, C.G. Plaques, tangles, and memory loss in mouse models of neurodegeneration. Behav. Genet. 2007, 37, 79–100. [Google Scholar] [CrossRef]
- Morrissette, D.A.; Parachikova, A.; Green, K.N.; LaFerla, F.M. Relevance of transgenic mouse models to human Alzheimer disease. J. Biol. Chem. 2009, 284, 6033–6037. [Google Scholar] [CrossRef]
- Hamilton, L.K.; Moquin-Beaudry, G.; Mangahas, C.L.; Pratesi, F.; Aubin, M.; Aumont, A.; Fernandes, K.J. Stearoyl-CoA Desaturase inhibition reverses immune, synaptic and cognitive impairments in an Alzheimer’s disease mouse model. Nat. Commun. 2022, 13, 2061. [Google Scholar] [CrossRef]
- Codita, A.; Winblad, B.; Mohammed, A.H. Of mice and men: More neurobiology in dementia. Curr. Opin. Psychiatry 2006, 19, 555–563. [Google Scholar] [CrossRef]
- Granzotto, A.; Vissel, B.; Sensi, S.L. Lost in translation: Inconvenient truths on the utility of mouse models in Alzheimer’s disease research. Elife 2024, 13, e90633. [Google Scholar] [CrossRef]
- Qian, Z.; Li, Y.; Ye, K. Advancements and challenges in mouse models of Alzheimer’s disease. Trends Mol. Med. 2024, 30, 1152–1164. [Google Scholar] [CrossRef]
- Rentsch, P.; Ganesan, K.; Langdon, A.; Konen, L.M.; Vissel, B. Toward the development of a sporadic model of Alzheimer’s disease: Comparing pathologies between humanized APP and the familial J20 mouse models. Front. Aging Neurosci. 2024, 16, 1421900. [Google Scholar] [CrossRef]
- Wang, Q.; Zhu, B.T.; Lei, P. Animal models of Alzheimer’s disease: Current strategies and new directions. Zool. Res. 2024, 45, 1385. [Google Scholar] [CrossRef]
- Onos, K.D.; Rizzo, S.J.S.; Howell, G.R.; Sasner, M. Toward more predictive genetic mouse models of Alzheimer’s disease. Brain Res. Bull. 2016, 122, 1–11. [Google Scholar] [CrossRef]
- Barrett, J.E.; McGonigle, P. Rodent models for Alzheimer’s disease in drug discovery. In Drug Discovery Approaches for the Treatment of Neurodegenerative Disorders; Academic Press: Cambridge, MA, USA, 2017; pp. 235–247. [Google Scholar] [CrossRef]
- Bales, K.R. The value and limitations of transgenic mouse models used in drug discovery for Alzheimer’s disease: An update. Expert Opin. Drug Discov. 2012, 7, 281–297. [Google Scholar] [CrossRef]
- Blackmore, T.; Meftah, S.; Murray, T.K.; Craig, P.J.; Blockeel, A.; Phillips, K.; Gastambide, F. Tracking progressive pathological and functional decline in the rTg4510 mouse model of tauopathy. Alzheimer’s Res. Ther. 2017, 9, 77. [Google Scholar] [CrossRef]
- Zhang, H.; Chen, L.; Johnston, K.G.; Crapser, J.; Green, K.N.; Ha, N.M.L.; Xu, X. Degenerate mapping of environmental location presages deficits in object-location encoding and memory in the 5xFAD mouse model for Alzheimer’s disease. Neurobiol. Dis. 2023, 176, 105939. [Google Scholar] [CrossRef]
- Scearce-Levie, K.; Sanchez, P.E.; Lewcock, J.W. Leveraging preclinical models for the development of Alzheimer disease therapeutics. Nat. Rev. Drug Discov. 2020, 19, 447–462. [Google Scholar] [CrossRef]
- Füzesi, M.V.; Muti, I.H.; Berker, Y.; Li, W.; Sun, J.; Habbel, P.; Zhang, Y. High resolution magic angle spinning proton NMR study of Alzheimer’s disease with mouse models. Metabolites 2022, 12, 253. [Google Scholar] [CrossRef]
- Santana, S.; Rico, E.P.; Burgos, J.S. Can zebrafish be used as animal model to study Alzheimer’s disease? Am. J. Neurodegener. Dis. 2012, 1, 32. [Google Scholar]
- Newman, M.; Ebrahimie, E.; Lardelli, M. Using the zebrafish model for Alzheimer’s disease research. Front. Genet. 2014, 5, 189. [Google Scholar] [CrossRef]
- Saleem, S.; Raza, R. Zebrafish: An emerging real-time model system to study Alzheimer’s disease and neurospecific drug discovery. Cell Death Discov. 2018, 4, 45. [Google Scholar] [CrossRef]
- Thawkar, B.S.; Banerjee, M.; Kaur, G. Alzheimer’s disease preliminary screening in zebrafish integrating behavioral models and molecular markers. In Handbook of Animal Models in Neurological Disorders; Academic Press: Cambridge, MA, USA, 2023; pp. 3–16. [Google Scholar] [CrossRef]
- Wang, X.; Zhang, J.B.; He, K.J.; Wang, F.; Liu, C.F. Advances of zebrafish in neurodegenerative disease: From models to drug discovery. Front. Pharmacol. 2021, 12, 713963. [Google Scholar] [CrossRef]
- Dhiman, N.; Deshwal, S.; Rishi, V.; Singhal, N.K.; Sandhir, R. Zebrafish as a model organism to study sporadic Alzheimer’s disease: Behavioural, biochemical and histological validation. Exp. Neurol. 2025, 383, 115034. [Google Scholar] [CrossRef] [PubMed]
- Tayran, H.; Yilmaz, E.; Bhattarai, P.; Min, Y.; Wang, X.; Ma, Y.; Kizil, C. Basic Science and Pathogenesis. Alzheimer’s Dement. 2024, 20, e089273. [Google Scholar] [CrossRef]
- Nada, S.E.; Williams, F.E.; Shah, Z.A. Development of a novel and robust pharmacological model of okadaic acid-induced Alzheimer’s disease in zebrafish. CNS Neurol. Disord. Drug Targets 2016, 15, 86–94. [Google Scholar] [CrossRef] [PubMed]
- Nadiga, A.P.; Krishna, K.L. A novel Zebrafish model of Alzheimer’s disease by Aluminium chloride; involving nitro-oxidative stress, neuroinflammation and cholinergic pathway. Eur. J. Pharmacol. 2024, 965, 176332. [Google Scholar] [CrossRef]
- He, R.; Zhang, Q.; Wang, L.; Cao, X. Exploring the feasibility of using mice as a substitute model for investigating microglia in aging and Alzheimer’s disease through single-cell analysis. PLoS ONE 2024, 19, e0283456. [Google Scholar] [CrossRef]
- Mans, R.A.; Hinton, K.D.; Payne, C.H.; Powers, G.E.; Scheuermann, N.L.; Saint-Jean, M. Cholinergic stimulation of the adult zebrafish brain induces phosphorylation of glycogen synthase kinase-3 β and extracellular signal-regulated kinase in the telencephalon. Front. Mol. Neurosci. 2019, 12, 91. [Google Scholar] [CrossRef]
- Tan, Y.; Ji, Y.B.; Zhao, J. Research progress of transgenic Drosophila model of Alzheimer disease. Acta Pharm. Sin. 2013, 48, 333–336. [Google Scholar]
- Jalali, D.; Guevarra, J.A.; Martinez, L.; Hung, L.; Vonhoff, F.J. Nutraceutical and probiotic approaches to examine molecular interactions of the amyloid precursor protein APP in Drosophila models of Alzheimer’s disease. Int. J. Mol. Sci. 2021, 22, 7022. [Google Scholar] [CrossRef]
- Chakraborty, R.; Vepuri, V.; Mhatre, S.D.; Paddock, B.E.; Miller, S.; Michelson, S.J.; Marenda, D.R. Characterization of a Drosophila Alzheimer’s disease model: Pharmacological rescue of cognitive defects. PLoS ONE 2011, 6, e20799. [Google Scholar] [CrossRef]
- Hegde, K.N.; Srivastava, A. Drosophila melanogaster as a tool for amyotrophic lateral sclerosis research. J. Dev. Biol. 2022, 10, 36. [Google Scholar] [CrossRef]
- Bergkvist, L.; Du, Z.; Elovsson, G.; Appelqvist, H.; Itzhaki, L.S.; Kumita, J.R.; Brorsson, A.C. Mapping pathogenic processes contributing to neurodegeneration in Drosophila models of Alzheimer’s disease. FEBS 2020, 10, 338–350. [Google Scholar] [CrossRef] [PubMed]
- Crowther, D.C.; Kinghorn, K.J.; Page, R.; Lomas, D.A. Therapeutic targets from a Drosophila model of Alzheimer’s disease. Curr. Opin. Pharmacol. 2004, 4, 513–516. [Google Scholar] [CrossRef] [PubMed]
- Crowther, D.C.; Page, R.; Chandraratna, D.; Lomas, D.A. A Drosophila model of Alzheimer’s disease. Meth. Enzymol. 2006, 412, 234–255. [Google Scholar] [CrossRef]
- Ando, K.; Hearn, S.; Suzuki, E.; Maruko-Otake, A.; Sekiya, M.; Iijima, K.M. Electron Microscopy of the Brains of Drosophila Models of Alzheimer’s Diseases. Transm. Electron. Microsc. Methods Underst. Brain 2016, 115, 105–123. [Google Scholar] [CrossRef]
- Preat, T.; Goguel, V. Role of drosophila amyloid precursor protein in memory formation. Front. Mol. Neurosci. 2016, 9, 142. [Google Scholar] [CrossRef]
- Cho, K.S.; Bang, S.M.; Toh, A. Lipids and lipid signaling in Drosophila models of neurodegenerative diseases. In Omega-3 Fatty Acids in Brain and Neurological Health; Academic Press: Cambridge, MA, USA, 2014; pp. 327–336. [Google Scholar] [CrossRef]
- Nampoothiri, N.V.P.; Sundararajan, V.; Dan, P.; Mohideen, S. Thymoquinone as a potential therapeutic for Alzheimer’s disease in transgenic Drosophila melanogaster model. Biocell 2021, 45, 1251. [Google Scholar] [CrossRef]
- Tsintzas, E.; Niccoli, T. Using Drosophila amyloid toxicity models to study Alzheimer’s disease. Ann. Hum. Genet. 2024, 88, 349–363. [Google Scholar] [CrossRef]
- Luo, Y.; Zhang, J.; Liu, N.; Luo, Y.; Zhao, B. Copper ions influence the toxicity of β-amyloid (1–42) in a concentration-dependent manner in a Caenorhabditis elegans model of Alzheimer’s disease. Sci. China Life Sci. 2011, 54, 527–534. [Google Scholar] [CrossRef]
- Dosanjh, L.E.; Brown, M.K.; Rao, G.; Link, C.D.; Luo, Y. Behavioral phenotyping of a transgenic Caenorhabditis elegans expressing neuronal amyloid-β. J. Alzheimer’s Dis. 2010, 19, 681–690. [Google Scholar] [CrossRef]
- Sinnige, T.; Ciryam, P.; Casford, S.; Dobson, C.M.; De Bono, M.; Vendruscolo, M. Expression of the amyloid-β peptide in a single pair of C. elegans sensory neurons modulates the associated behavioural response. PLoS ONE 2019, 14, e0217746. [Google Scholar] [CrossRef]
- Fu, H.J.; Zhou, X.Y.; Li, Y.P.; Chen, X.; He, Y.N.; Qin, D.L.; Zhou, X.G. The Protective Effects of Reineckia carnea Ether Fraction against Alzheimer’s Disease Pathology: An Exploration in Caenorhabditis elegans Models. Int. J. Mol. Sci. 2023, 24, 16536. [Google Scholar] [CrossRef] [PubMed]
- Bravo, F.V.; Da Silva, J.; Chan, R.B.; Di Paolo, G.; Teixeira-Castro, A.; Oliveira, T.G. Phospholipase D functional ablation has a protective effect in an Alzheimer’s disease Caenorhabditis elegans model. Sci. Rep. 2018, 8, 3540. [Google Scholar] [CrossRef] [PubMed]
- Sorrentino, V.; Romani, M.; Mouchiroud, L.; Beck, J.S.; Zhang, H.; D’Amico, D.; Auwerx, J. Enhancing mitochondrial proteostasis reduces amyloid-β proteotoxicity. Nature 2017, 552, 187–193. [Google Scholar] [CrossRef]
- Zhu, F.D.; Chen, X.; Yu, L.; Hu, M.L.; Pan, Y.R.; Qin, D.L.; Fan, D.S. Targeting autophagy to discover the Piper wallichii petroleum ether fraction exhibiting antiaging and anti-Alzheimer’s disease effects in Caenorhabditis elegans. Phytomedicine 2023, 117, 154916. [Google Scholar] [CrossRef]
- Perni, M.; Challa, P.K.; Kirkegaard, J.B.; Limbocker, R.; Koopman, M.; Hardenberg, M.C.; Knowles, T.P. Massively parallel C. elegans tracking provides multi-dimensional fingerprints for phenotypic discovery. J. Neurosci. Meth. 2018, 306, 57–67. [Google Scholar] [CrossRef]
- Jiang, Y.; MacNeil, L.T. Simple model systems reveal conserved mechanisms of Alzheimer’s disease and related tauopathies. Mol. Neurodegen. 2023, 18, 82. [Google Scholar] [CrossRef]
- Sukoff Rizzo, S.J.; Homanics, G.; Schaeffer, D.J.; Schaeffer, L.; Park, J.E.; Oluoch, J.; Silva, A.C. Bridging the rodent to human translational gap: Marmosets as model systems for the study of Alzheimer’s disease. Alzheimer’s Dement. 2023, 9, e12417. [Google Scholar] [CrossRef]
- Perez-Cruz, C.; de Dios Rodriguez-Callejas, J. The common marmoset as a model of neurodegeneration. Trends Neurosci. 2023, 46, 394–409. [Google Scholar] [CrossRef] [PubMed]
- Rodriguez-Callejas, J.D.; Fuchs, E.; Perez-Cruz, C. Evidence of tau hyperphosphorylation and dystrophic microglia in the common marmoset. Front. Aging Neurosci. 2016, 8, 315. [Google Scholar] [CrossRef]
- Huhe, H.; Shapley, S.M.; Duong, D.M.; Wu, F.; Ha, S.K.; Choi, S.H.; Sukoff Rizzo, S.J. Marmosets as model systems for the study of Alzheimer’s disease and related dementias: Substantiation of physiological tau 3R and 4R isoform expression and phosphorylation. Alzheimer’s Dement. 2025, 21, e14366. [Google Scholar] [CrossRef]
- Rothwell, E.S.; Freire-Cobo, C.; Varghese, M.; Edwards, M.; Janssen, W.G.; Hof, P.R.; Lacreuse, A. The marmoset as an important primate model for longitudinal studies of neurocognitive aging. Am. J. Primatol. 2021, 83, e23271. [Google Scholar] [CrossRef] [PubMed]
- Phillips, K.A.; Lopez, M.; Bartling-John, E.; Meredith, R.; Buteau, A.; Alvarez, A.; Ross, C.N. Serum biomarkers associated with aging and neurodegeneration in common marmosets (Callithrix jacchus). Neurosci. Lett. 2024, 819, 137569. [Google Scholar] [CrossRef] [PubMed]
- Philippens, I.H.; Ormel, P.R.; Baarends, G.; Johansson, M.; Remarque, E.J.; Doverskog, M. Acceleration of amyloidosis by inflammation in the amyloid-beta marmoset monkey model of Alzheimer’s disease. J. Alzheimer’s Dis. 2017, 55, 101–113. [Google Scholar] [CrossRef] [PubMed]
- Sreenivasamurthy, S.; Laul, M.; Zhao, N.; Kim, T.; Zhu, D. Current progress of cerebral organoids for modeling Alzheimer’s disease origins and mechanisms. Bioeng. Transl. Med. 2023, 8, e10378. [Google Scholar] [CrossRef]
- Solana-Manrique, C.; Sánchez-Pérez, A.M.; Paricio, N.; Muñoz-Descalzo, S. Two-and Three-Dimensional In Vitro Models of Parkinson’s and Alzheimer’s Diseases: State-of-the-Art and Applications. Int. J. Mol. Sci. 2025, 26, 620. [Google Scholar] [CrossRef]
- Pahrudin Arrozi, A.; Shukri, S.N.S.; Wan Ngah, W.Z.; Mohd Yusof, Y.A.; Ahmad Damanhuri, M.H.; Makpol, S. Evaluation of the expression of amyloid precursor protein and the ratio of secreted amyloid beta 42 to amyloid beta 40 in SH-SY5Y cells stably transfected with wild-type, single-mutant and double-2017mutant forms of the APP gene for the study of Alzheimer’s disease pathology. Appl. Biochem. Biotechnol. 2017, 183, 853–866. [Google Scholar] [CrossRef]
- Wang, L.; Hu, D.; Xu, J.; Hu, J.; Wang, Y. Complex in vitro model: A transformative model in drug development and precision medicine. Clin. Transl. Sci. 2024, 17, e13695. [Google Scholar] [CrossRef]
- Watson, P.M.D.; Kavanagh, E.; Allenby, G.; Vassey, M. Bioengineered 3D glial cell culture systems and applications for neurodegeneration and neuroinflammation. SLAS Discov. 2017, 22, 583–601. [Google Scholar] [CrossRef]
- Brighi, C.; Cordella, F.; Chiriatti, L.; Soloperto, A.; Di Angelantonio, S. Retinal and brain organoids: Bridging the gap between in vivo physiology and in vitro micro-physiology for the study of Alzheimer’s diseases. Front. Neurosci. 2020, 14, 655. [Google Scholar] [CrossRef]
- Papaspyropoulos, A.; Tsolaki, M.; Foroglou, N.; Pantazaki, A.A. Modeling and targeting Alzheimer’s disease with organoids. Front. Pharmacol. 2020, 11, 396. [Google Scholar] [CrossRef]
- Vazin, T.; Ball, K.A.; Lu, H.; Park, H.; Ataeijannati, Y.; Head-Gordon, T.; Schaffer, D.V. Efficient derivation of cortical glutamatergic neur 2014ons from human pluripotent stem cells: A model system to study neurotoxicity in Alzheimer’s disease. Neurobiol. Dis. 2014, 62, 62–72. [Google Scholar] [CrossRef]
- Barak, M.; Fedorova, V.; Pospisilova, V.; Raska, J.; Vochyanova, S.; Sedmik, J.; Bohaciakova, D. Human iPSC-derived neural models for studying Alzheimer’s disease: From neural stem cells to cerebral organoids. Stem Cell Rev. Rep. 2022, 18, 792–820. [Google Scholar] [CrossRef] [PubMed]
- Corrò, C.; Novellasdemunt, L.; Li, V.S. A brief history of organoids. Am. J. Physiol. Cell Physiol. 2020, 319, C151–C165. [Google Scholar] [CrossRef] [PubMed]
- Luchena, C.; Zuazo-Ibarra, J.; Valero, J.; Matute, C.; Alberdi, E.; Capetillo-Zarate, E. A neuron, microglia, and astrocyte triple co-culture model to study Alzheimer’s disease. Front. Aging Neurosci. 2022, 14, 844534. [Google Scholar] [CrossRef] [PubMed]
- Mullis, A.S.; Kaplan, D.L. Functional bioengineered tissue models of neurodegenerative diseases. Biomaterials 2023, 298, 122143. [Google Scholar] [CrossRef] [PubMed]
- Lin, A.; Sved Skottvoll, F.; Rayner, S.; Pedersen-Bjergaard, S.; Sullivan, G.; Krauss, S.; Harrison, S. 3D cell culture models and organ-on-a-chip: Meet separation science and mass spectrometry. Electrophoresis 2020, 41, 56–64. [Google Scholar] [CrossRef]
- Pavlou, G.; Spitz, S.; Pramotton, F.M.; Tsai, A.; Li, B.M.; Wang, X.; Kamm, R.D. Engineered 3D human neurovascular model of Alzheimer’s disease to study vascular dysfunction. Biomaterials 2025, 314, 122864. [Google Scholar] [CrossRef]
- Rinendyaputri, R.; Lienggonegoro, L.A.; Idrus, H.H.; Noverina, R.; Faried, A. The role of neuroprotection in the zebrafish (Danio rerio) animal model. In AIP Conference Proceedings; AIP Publishing: College Park, ML, USA, 2023; p. 2956. [Google Scholar] [CrossRef]
- Silva, A.D.S.D. Equipamentos automatizados na criação e crescimento do zebrafish. Rev. Soc. Bras. Ciênc. Anim. Lab. 2020, 8, 33–36. [Google Scholar]
- Cornet, C.; Di Donato, V.; Terriente, J. Combining zebrafish and CRISPR/Cas9: Toward a more efficient drug discovery pipeline. Front. Pharmacol. 2018, 9, 703. [Google Scholar] [CrossRef]
- Strange, K. Drug discovery in fish, flies, and worms. ILAR J. 2016, 57, 103. [Google Scholar] [CrossRef]
- Laird, A.S.; Robberecht, W. Modeling neurodegenerative diseases in zebrafish embryos. Methods Mol. Biol. 2011, 793, 167–184. [Google Scholar] [CrossRef] [PubMed]
- Youssef, K.; Bayat, P.; Peimani, A.R.; Dibaji, S.; Rezai, P. Miniaturized Sensors and Actuators for Biological Studies on Small Model Organisms of Disease; Springer: Singapore, 2018; pp. 199–225. [Google Scholar] [CrossRef]
- Kang, Y. Comparison of Different in Vitro Models of Alzheimer’s Disease Using Re-Analysis of ScRNA-Seq Data. In Proceedings of the 2022 International Conference on Intelligent Medicine and Health, Xiamen, China, 19–21 August 2022; pp. 84–90. [Google Scholar] [CrossRef]
- Massimi, L.; Bukreeva, I.; Santamaria, G.; Fratini, M.; Corbelli, A.; Brun, F.; Cedola, A. Exploring Alzheimer’s disease mouse brain through X-ray phase contrast tomography: From the cell to the organ. NeuroImage 2019, 184, 490–495. [Google Scholar] [CrossRef]
- Noda-Saita, K.; Yoneyama, A.; Shitaka, Y.; Hirai, Y.; Terai, K.; Wu, J.; Okada, M. Quantitative analysis of amyloid plaques in a mouse model of Alzheimer’s disease by phase-contrast X-ray computed tomography. Neuroscience 2006, 138, 1205–1213. [Google Scholar] [CrossRef] [PubMed]
- Connor, D.M.; Benveniste, H.; Dilmanian, F.A.; Kritzer, M.F.; Miller, L.M.; Zhong, Z. Computed tomography of amyloid plaques in a mouse model of Alzheimer’s disease using diffraction enhanced imaging. NeuroImage 2009, 46, 908–914. [Google Scholar] [CrossRef]
- Coan, P.; Wagner, A.; Bravin, A.; Diemoz, P.C.; Keyriläinen, J.; Mollenhauer, J. In vivo x-ray phase contrast analyzer-based imaging for longitudinal osteoarthritis studies in guinea pigs. Phys. Med. Biol. 2010, 55, 7649. [Google Scholar] [CrossRef] [PubMed]
- An, J. Using CatBoost and other supervised machine learning algorithms to predict Alzheimer’s disease. In Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, ICMLA, Nassau, Bahamas, 12–14 December 2022; pp. 1732–1739. [Google Scholar] [CrossRef]
- Kumar, N.K.; Kumar, V.A.; Quamar, D.; Reddy, B.S.K.; Yogendra, R.; Poojitha, N. Enhanced Alzheimer’s Disease Prediction through Advanced Imaging: A Study of Machine Learning and Deep Learning Approaches. In Proceedings of the 5th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 18–20 September 2024; pp. 1177–1182. [Google Scholar] [CrossRef]
- Alatrany, A.S.; Hussain, A.J.; Mustafina, J.; Al-Jumeily, D. Machine learning approaches and applications in genome-wide association study for Alzheimer’s disease: A systematic review. IEEE Access 2022, 10, 62831–62847. [Google Scholar] [CrossRef]
- Wang, X.; Qi, J.; Yang, Y.; Yang, P. A survey of disease progression modeling techniques for Alzheimer’s diseases. In Proceedings of the IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 22–25 July 2019; Volume 1, pp. 1237–1242. [Google Scholar] [CrossRef]
- Skolariki, K.; Exarchos, T.P.; Vlamos, P. Computational models for biomarker discovery. In Worldwide Congress on “Genetics, Geriatrics and Neurodegenerative Diseases Research”; Springer International Publishing: New York, NY, USA, 2022; pp. 289–295. [Google Scholar] [CrossRef]
- Hossen Abir, M.I.; Salam, T. Comparative Analysis and Prediction of Machine Learning Algorithms for MRI-Based Alzheimer’s Detection Using Multi-modal Data. In Proceedings of the IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox’s Bazar, Bangladesh, 25–26 September 2024; pp. 1–5. [Google Scholar] [CrossRef]
- Khan, A.; Zubair, S. Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer’s disease. J. King Saud Univ. Comput. Inf. Sci. 2022, 34, 8000–8018. [Google Scholar] [CrossRef]
- Caraveo, F.C.; Álvarez Cruz, K.A.; Quintana, M.P.; Romero Ramos, E.N.; Flores, C.M.Q.; Figueroa, C.E.C. ML Design in Handwriting Analysis for Classification of Alzheimer’s Disease. In Congreso Nacional de Ingeniería Biomédica; Springer Nature: New York, NY, USA, 2024; pp. 3–13. [Google Scholar] [CrossRef]
- Karczewski, K.J.; Snyder, M.P. Integrative omics for health and disease. Nat. Rev. Genet. 2018, 19, 299–310. [Google Scholar] [CrossRef]
- Chen, C.; Wang, J.; Pan, D.; Wang, X.; Xu, Y.; Yan, J.; Liu, G.P. Applications of multi-omics analysis in human diseases. Med. Comm. 2023, 4, e315. [Google Scholar] [CrossRef]
- Liu, Y.; Zhang, X.; Lin, W.; Kehriman, N.; Kuang, W.; Ling, X. Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer’s disease and evaluate drug effect based on a rat model. J. Pharm. Anal. 2022, 12, 627–636. [Google Scholar] [CrossRef]
- Kalli, E. Omic-Based Biomarkers Discovery in Alzheimer’s Disease: High-Throughput Approaches. In Handbook of Computational Neurodegeneration; Springer International Publishing: New York, NY, USA, 2023; pp. 1–18. [Google Scholar] [CrossRef]
- Bhatia, V.; Chandel, A.; Minhas, Y.; Kushawaha, S.K. Advances in biomarker discovery and diagnostics for alzheimer’s disease. Neurol. Sci. 2025, 46, 2419–2436. [Google Scholar] [CrossRef] [PubMed]
- Trushina, E. LC-MS-based metabolomics in understanding the mechanisms of alzheimer’s disease and biomarker discovery. In Advanced LC-MS Applications in Metabolomics; Future Medicine Ltd.: London, UK, 2015; pp. 40–57. [Google Scholar]
- Li, Z.; Jiang, X.; Wang, Y.; Kim, Y. Applied machine learning in Alzheimer’s disease research: Omics, imaging, and clinical data. Emerg. Top. Life Sci. 2021, 5, 765–777. [Google Scholar] [CrossRef] [PubMed]
- Cherian, I.; Patil, V.C.; Adamuthe, A.C.; Prasad, K.R.; Vatsa, M. Application of aNN and machine Learning for the Detection of alzheimer Disease (aD). In Proceedings of the 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET), Karad, India, 18–19 January 2024; pp. 198–200. [Google Scholar] [CrossRef]
- Karaglani, M.; Gourlia, K.; Tsamardinos, I.; Chatzaki, E. Accurate blood-based diagnostic biosignatures for Alzheimer’s disease via automated machine learning. J. Clin. Med. 2020, 9, 3016. [Google Scholar] [CrossRef] [PubMed]
- Tu, K.; Zhou, W.; Kong, S. Integrating Multi-omics Data for Alzheimer’s Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm. J. Mol. Neurosci. 2024, 74, 43. [Google Scholar] [CrossRef]
- Lithner, C.U.; Hedberg, M.M.; Nordberg, A. Transgenic mice as a model for Alzheimer’s disease. Curr. Alzheimer Res. 2011, 8, 818–831. [Google Scholar] [CrossRef]
- Hall, A.M.; Roberson, E.D. Mouse models of Alzheimer’s disease. Brain Res. Bull. 2012, 88, 3–12. [Google Scholar] [CrossRef]
- Hurst, C.D.; Dunn, A.R.; Dammer, E.B.; Duong, D.M.; Shapley, S.M.; Seyfried, N.T.; Johnson, E.C. Genetic background influences the 5XFAD Alzheimer’s disease mouse model brain proteome. Front. Aging Neurosci. 2023, 15, 1239116. [Google Scholar] [CrossRef]
- Cogram, P.; Garduño, B.M.; Ren, B.; Xu, X. First International Conference on Unconventional Animal Models of Alzheimer’s Disease and Aging. J. Alzheimer’s Dis. 2024, 91, 567–572. [Google Scholar] [CrossRef]
- Breschi, A.; Gingeras, T.R.; Guigó, R. Comparative transcriptomics in human and mouse. Nat. Rev. Genet. 2017, 18, 425–440. [Google Scholar] [CrossRef]
- Pandey, R.S.; Carter, G.W.; Howell, G.R.; Sasner, M.; Kotredes, K.P.; Oblak, A.L.; Lamb, B.T. Distinct mouse models correspond to distinct AD molecular subtypes. Alzh. Dement. 2024, 20, e087565. [Google Scholar] [CrossRef]
- Attar, A.; Liu, T.; Chan, W.-T.C.; Bitan, G. A shortened Barnes maze protocol reveals memory deficits at 4-months of age in the triple-transgenic mouse model of Alzheimer’s disease. PLoS ONE 2013, 8, e80355. [Google Scholar] [CrossRef] [PubMed]
- Bratke, S.; Schmid, S.; Ulm, B.; Jungwirth, B.; Blobner, M.; Borgstedt, L. Genotype-and sex-specific changes in vital parameters during isoflurane anesthesia in a mouse model of Alzheimer’s disease. Front. Med. 2024, 11, 1342752. [Google Scholar] [CrossRef] [PubMed]
- Epis, R.; Gardoni, F.; Marcello, E.; Di Luca, M. Searching for new animal models of Alzheimer’s disease. Eur. J. Pharmacol. 2010, 626, 57–63. [Google Scholar] [CrossRef] [PubMed]
- Steffen, J.; Krohn, M.; Paarmann, K.; Schwitlick, C.; Brüning, T.; Marreiros, R.; Müller-Schiffmann, A.; Korth, C.; Braun, K.; Pahnke, J. Revisiting rodent models: Octodon degus as Alzheimer’s disease model? Acta Neuropathol. Commun. 2016, 4, 91. [Google Scholar] [CrossRef]
- Vloeberghs, E.; Van Dam, D.; Franck, F.; Staufenbiel, M.; De Deyn, P.P. Mood and male sexual behaviour in the APP23 model of Alzheimer’s disease. Behav. Brain Res. 2007, 180, 146–151. [Google Scholar] [CrossRef]
- Oblak, A.L.; Lin, P.B.; Kotredes, K.P.; Pandey, R.S.; Garceau, D.; Williams, H.M.; Lamb, B.T. Comprehensive evaluation of the 5XFAD mouse model for preclinical testing applications: A MODEL-AD study. Front. Aging Neurosci. 2021, 13, 713726. [Google Scholar] [CrossRef]
- Van Dam, D.; De Deyn, P.P. Non human primate models for Alzheimer’s disease-related research and drug discovery. Expert Opin. Drug Discov. 2017, 12, 187–200. [Google Scholar] [CrossRef]
- Phan, D.T.; Bender, R.H.F.; Andrejecsk, J.W.; Sobrino, A.; Hachey, S.J.; George, S.C.; Hughes, C.C. Blood–brain barrier-on-a-chip: Microphysiologica2017l systems that capture the complexity of the blood–central nervous system interface. Exp. Biol. Med. 2017, 242, 1669–1678. [Google Scholar] [CrossRef]
- Do Carmo, S.; Cuello, A.C. Modeling Alzheimer’s disease in transgenic rats. Mol. Neurodegener. 2013, 8, 37. [Google Scholar] [CrossRef]
- Abhyankar, S.D.; Luo, Q.; Hartman, G.D.; Mahajan, N.; Corson, T.W.; Oblak, A.L.; Lamb, B.T.; Bhatwadekar, A.D. Retinal dysfunction in APOE4 knock-in mouse model of Alzheimer’s disease. Alzheimer’s Dement. 2025, 21, e14433. [Google Scholar] [CrossRef]
- Rao, C.V.; Farooqui, M.; Asch, A.S.; Yamada, H.Y. Critical role of mitosis in spontaneous late-onset Alzheimer’s disease; from a Shugoshin 1 cohesinopathy mouse model. Cell Cycle 2018, 17, 2321–2334. [Google Scholar] [CrossRef]
- Gao, S.; Casey, A.E.; Sargeant, T.J.; Mäkinen, V.P. Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease. Brain 2018, 141, 2711–2720. [Google Scholar] [CrossRef] [PubMed]
- Petrella, J.R.; Hao, W.; Rao, A.; Doraiswamy, P.M. Computational causal modeling of the dynamic biomarker cascade in Alzheimer’s disease. Comput. Math. Methods Med. 2019, 2019, 6216530. [Google Scholar] [CrossRef] [PubMed]
- Oblak, A.L.; Forner, S.; Territo, P.R.; Sasner, M.; Carter, G.W.; Howell, G.R.; Sukoff-Rizzo, S.J.; Logsdon, B.A.; Mangravite, L.M.; Mortazavi, A.; et al. Model organism development and evaluation for late-onset Alzheimer’s disease: MODEL-AD. Alzheimer’s Dement. Transl. Res. Clin. Interv. 2020, 6, e12110. [Google Scholar] [CrossRef] [PubMed]
- Hurley, M.J.; Deacon, R.M.; Beyer, K.; Ioannou, E.; Ibáñez, A.; Teeling, J.L.; Cogram, P. The long-lived Octodon degus as a rodent drug discovery model for Alzheimer’s and other age-related diseases. Pharmacol. Ther. 2018, 188, 36–44. [Google Scholar] [CrossRef]
- Du, Y.; Zhang, S.; Fang, Y.; Qiu, Q.; Zhao, L.; Wei, W.; Li, X. Radiomic features of the hippocampus for diagnosing early-onset and late-onset Alzheimer’s disease. Front. Aging Neurosci. 2022, 13, 789099. [Google Scholar] [CrossRef]
- Arya, A.D.; Verma, S.S.; Chakarabarti, P.; Bishnoi, R. Prediction of Alzheimer’s disease-A Machine Learning Perspective with Ensemble Learning. In Proceedings of the 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, 14–16 September 2023; Volume 6, pp. 2308–2313. [Google Scholar] [CrossRef]
- Bakulski, K.M.; Rozek, L.S.; Dolinoy, D.C.; Hu, H. Alzheimer’s disease and environmental exposure to lead: The epidemiologic evidence and potential role of epigenetics. Curr. Alzheimer Res. 2012, 9, 563–573. [Google Scholar] [CrossRef]
- Lista, S.; Khachaturian, Z.S.; Rujescu, D.; Garaci, F.; Dubois, B.; Hampel, H. Application of systems theory in longitudinal studies on the origin and progression of Alzheimer’s disease. Methods Mol. Biol. 2016, 1303, 49–67. [Google Scholar] [CrossRef]
- Kamboh, M.I. Molecular genetics of late-onset Alzheimer’s disease. Ann. Hum. Genet. 2004, 68, 381–404. [Google Scholar] [CrossRef]
- Hamilton, G.; Samedi, F.; Knight, J.; Archer, N.; Foy, C.; Walter, S.; Powell, J.F. Polymorphisms in the phosphate and tensin homolog gene are not associated with late-onset Alzheimer’s disease. Neurosci. Lett. 2006, 401, 77–80. [Google Scholar] [CrossRef]
- Li, Y.; Grupe, A. Genetics of late-onset Alzheimer’s disease: Progress and prospect. Pharmacogenomics 2007, 8, 1747–1755. [Google Scholar] [CrossRef] [PubMed]
- Perna, S.; Bologna, C.; Cavagna, P.; Bernardinelli, L.; Guido, D.; Peroni, G.; Rondanelli, M. The beginnings of Alzheimer’s Disease: A review on inflammatory, mitochondrial, genetic and epigenetic pathways. Genetika 2016, 48, 515–524. [Google Scholar] [CrossRef]
- Louwersheimer, E.; Cohn-Hokke, P.E.; Pijnenburg, Y.A.; Weiss, M.M.; Sistermans, E.A.; Rozemuller, A.J.; Hulsman, M.; van Swieten, J.C.; van Duijn, C.M.; Barkhof, F.; et al. Rare genetic variant in Sorl1 may increase penetrance of Alzheimer’s disease in a family with several generations of Apoe-Ɛ4 homozygosity. Alzheimer’s Dis. 2017, 56, 63–74. [Google Scholar] [CrossRef] [PubMed]
- Nikolac Perkovic, M.; Pivac, N. Genetic markers of Alzheimer’s disease. In Front. Psychiatry: Artificial Intelligence, Precision Medicine, and Other Paradigm Shifts; Springer Nature: Singapore, 2019; pp. 27–52. [Google Scholar] [CrossRef]
- Rabinovici, G.D. Late-onset Alzheimer Disease. Contin. Minneap. Minn. 2019, 25, 14–33. [Google Scholar] [CrossRef] [PubMed]
- Engelman, C.D.; Koscik, R.L.; Jonaitis, E.M.; Okonkwo, O.C.; Hermann, B.P.; La Rue, A.; Sager, M.A. Interaction between two cholesterol metabolism genes influences memory: Findings from the Wisconsin Registry for Alzheimer’s Prevention. J. Alzheimer’s Dis. 2013, 36, 749–757. [Google Scholar] [CrossRef]
- Karch, C.M.; Goate, A.M. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol. Psychiatry 2015, 77, 43–51. [Google Scholar] [CrossRef]
- Kanatsu, K.; Tomita, T. Molecular mechanisms of the genetic risk factors in pathogenesis of Alzheimer disease. Front. Biosci. 2017, 22, 180–192. [Google Scholar] [CrossRef]
- Ferrari, R.; Ferrara, M.; Alinani, A.; Sutton, R.B.; Famà, F.; Picco, A.; Momeni, P. Screening of early and late onset Alzheimer’s disease genetic risk factors in a cohort of dementia patients from Liguria, Italy. Curr. Alzheimer Res. 2015, 12, 802–812. [Google Scholar] [CrossRef]
- Wollam, M.E.; Weinstein, A.M.; Saxton, J.A.; Morrow, L.; Snitz, B.; Fowler, N.R.; Erickson, K.I. Genetic risk score predicts late-life cognitive impairment. J. Aging Res. 2015, 2015, 267062. [Google Scholar] [CrossRef]
- Panza, F.; D’Introno, A.; Colacicco, A.M.; Basile, A.M.; Capurso, C.; Kehoe, P.G.; Solfrizzi, V. Vascular risk and genetics of sporadic late-onset Alzheimer’s disease. J. Neural. Transm. 2004, 111, 69–89. [Google Scholar] [CrossRef]
- Dato, S.; De Rango, F.; Crocco, P.; Pallotti, S.; Belloy, M.E.; Le Guen, Y.; Napolioni, V. Sex-and APOE-specific genetic risk factors for late-onset Alzheimer’s disease: Evidence from gene–gene interaction of longevity-related loci. Aging Cell 2023, 22, e13938. [Google Scholar] [CrossRef] [PubMed]
- Kamini; Rani, S. Machine Learning Models for Diagnosing Alzheimer’s Disorders. In Data Analysis for Neurodegenerative Disorders; Springer Nature: Singapore, 2023; pp. 183–194. [Google Scholar] [CrossRef]
- Muhammed, S.; Upadhya, J.; Poudel, S.; Hasan, M.; Donthula, K.; Vargas, J.; Poudel, K. Improved classification of alzheimer’s disease with convolutional neural networks. In Proceedings of the 2023 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 2 December 2023; pp. 1–7. [Google Scholar] [CrossRef]
- Wang, Y.; He, B.; Risacher, S.; Saykin, A.; Yan, J.; Wang, X. Learning the Irreversible Progression Trajectory of Alzheimer’s Disease. In Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27–30 May 2024; pp. 1–5. [Google Scholar] [CrossRef]
- Battineni, G.; Chintalapudi, N.; Amenta, F.; Traini, E. A comprehensive machine-learning model applied to magnetic resonance imaging (mri) to predict alzheimer’s disease (ad) in older subjects. J. Clin. Med. 2020, 9, 2146. [Google Scholar] [CrossRef] [PubMed]
- De Velasco Oriol, J.; Vallejo, E.E.; Estrada, K.; Taméz Peña, J.G.; The Alzheimer’s Disease Neuroimaging Initiative. Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data. BMC Bioinform. 2019, 20, 709. [Google Scholar] [CrossRef] [PubMed]
- Romero-Rosales, B.L.; Tamez-Pena, J.G.; Nicolini, H.; Moreno-Treviño, M.G.; Trevino, V. Improving predictive models for Alzheimer’s disease using GWAS data by incorporating misclassified samples modeling. PLoS ONE 2020, 15, e0232103. [Google Scholar] [CrossRef]
- Rohini, M.; Surendran, D. Classification of neurodegenerative disease stages using ensemble machine learning classifiers. Procedia Comput. Sci. 2019, 165, 66–73. [Google Scholar] [CrossRef]
- Vélez, J.I.; Samper, L.A.; Arcos-Holzinger, M.; Espinosa, L.G.; Isaza-Ruget, M.A.; Lopera, F.; Arcos-Burgos, M. A comprehensive machine learning framework for the exact prediction of the age of onset in familial and sporadic Alzheimer’s disease. Diagnostics 2021, 11, 887. [Google Scholar] [CrossRef]
- Alatrany, A.S.; Hussain, A.; Alatrany, S.S.; Mustafina, J.; Al-Jumeily, D. Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer’s disease. In Proceedings of the 15th International Conference on Developments in eSystems Engineering (DeSE), Baghdad & Anbar, Iraq, 9–12 January 2023; pp. 60–64. [Google Scholar] [CrossRef]
- Mishra, S.; Sharma, V.; Ramya, G. Alzheimer’s Disease Prediction Using Machine Learning. In Proceedings of the 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), Kollam, India, 8–9 August 2024; Volmue 1, pp. 366–370. [Google Scholar] [CrossRef]
- Yan, R.; Wang, W.; Yang, W.; Huang, M.; Xu, W. Mitochondria-related candidate genes and diagnostic model to predict late-onset Alzheimer’s disease and mild cognitive impairment. J. Alzheimer’s Dis. 2024, 99, S299–S315. [Google Scholar] [CrossRef]
- Luo, H.; Hartikainen, S.; Lin, J.; Zhou, H.; Tapiainen, V.; Tolppanen, A.M. Predicting Alzheimer’s disease from cognitive footprints in mid and late life: How much can register data and machine learning help? Int. J. Med. Inform. 2024, 190, 105540. [Google Scholar] [CrossRef]
- Abdelminaam, D.S.; Madbouly, M.M.; Farag, M.S.; Abualigah, L. ML_Alzheimer: Alzheimer disease prediction using machine learning. In Proceedings of the 3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC, Cairo, Egypt, 27–28 September 2023; pp. 87–95. [Google Scholar]
- Van Eldik, L.J.; Carrillo, M.C.; Cole, P.E.; Feuerbach, D.; Greenberg, B.D.; Hendrix, J.A.; Bales, K. The roles of inflammation and immune mechanisms in Alzheimer’s disease. Alzheimer’s Dement. 2016, 2, 99–109. [Google Scholar] [CrossRef]
- Ma, W.; Zhang, X.; Wu, Q. Research Advances in the Neuroinflammation in Alzheimer’s Disease. Acta Acad. Med. Sin. 2017, 39, 715–720. [Google Scholar] [CrossRef]
- Chaney, A.; Williams, S.R.; Boutin, H. In vivo molecular imaging of neuroinflammation in Alzheimer’s disease. J. Neurochem. 2019, 149, 438–451. [Google Scholar] [CrossRef] [PubMed]
- Millington, C.; Sonego, S.; Karunaweera, N.; Rangel, A.; Aldrich-Wright, J.R.; Campbell, I.L.; Münch, G. Chronic neuroinflammation in Alzheimer’s disease: New perspectives on animal models and promising candidate drugs. BioMed Res. Int. 2014, 2014, 309129. [Google Scholar] [CrossRef] [PubMed]
- Garcez, M.L.; Mina, F.; Bellettini-Santos, T.; da Luz, A.P.; Schiavo, G.L.; Macieski, J.M.C.; Budni, J. The involvement of NLRP3 on the effects of minocycline in an AD-like pathology induced by β-amyloid oligomers administered to mice. Mol. Neurobiol. 2019, 56, 2606–2617. [Google Scholar] [CrossRef] [PubMed]
- Yap, J.K.Y.; Pickard, B.S.; Gan, S.Y.; Chan, E.W.L. Genes associated with amyloid-beta-induced inflammasome-mediated neuronal death identified using functional gene trap mutagenesis approach. Int. J. Biochem. Cell Biol. 2021, 136, 106014. [Google Scholar] [CrossRef]
- Chen, C.; Lu, J.; Peng, W.; Mak, M.S.; Yang, Y.; Zhu, Z.; Pi, R. Acrolein, an endogenous aldehyde induces Alzheimer’s disease-like pathologies in mice: A new sporadic AD animal model. Pharmacol. Res. 2022, 175, 106003. [Google Scholar] [CrossRef]
- Edison, P.; Brooks, D.J. Role of neuroinflammation in the trajectory of Alzheimer’s disease and in vivo quantification using PET. J. Alzheimer’s Dis. 2018, 64, S339–S351. [Google Scholar] [CrossRef]
- Shallie, O.F.; Dalle, E.; Mabandla, M.V. Memory decline correlates with increased plasma cytokines in amyloid-beta (1–42) rat model of Alzheimer’s disease. Neurobiol. Learn. Mem. 2020, 169, 107187. [Google Scholar] [CrossRef]
- Rani, V.; Verma, R.; Kumar, K.; Chawla, R. Role of pro-inflammatory cytokines in Alzheimer’s disease and neuroprotective effects of pegylated self-assembled nanoscaffolds. Curr. Res. Pharmacol. Drug Discov. 2023, 4, 100149. [Google Scholar] [CrossRef]
- Tweedie, D.; Ferguson, R.A.; Fishman, K.; Frankola, K.A.; Van Praag, H.; Holloway, H.W.; Rosi, S. Tumor necrosis factor-α synthesis inhibitor 3, 6′-dithiothalidomide attenuates markers of inflammation, Alzheimer pathology and behavioral deficits in animal models of neuroinflammation and Alzheimer’s disease. J. Neuroinflamm. 2012, 9, 106. [Google Scholar] [CrossRef]
- Conductier, G.; Blondeau, N.; Guyon, A.; Nahon, J.L.; Rovère, C. The role of monocyte chemoattractant protein MCP1/CCL2 in neuroinflammatory diseases. J. Neuroimmunol. 2010, 224, 93–100. [Google Scholar] [CrossRef]
- Popp, J.; Oikonomidi, A.; Tautvydaitė, D.; Dayon, L.; Bacher, M.; Migliavacca, E.; Bowman, G.L. Markers of neuroinflammation associated with Alzheimer’s disease pathology in older adults. Brain Behav. Immun. 2017, 62, 203–211. [Google Scholar] [CrossRef] [PubMed]
- Su, C.; Zhao, K.; Xia, H.; Xu, Y. Peripheral inflammatory biomarkers in Alzheimer’s disease and mild cognitive impairment: A systematic review and meta-analysis. Psychogeriatrics 2019, 19, 300–309. [Google Scholar] [CrossRef] [PubMed]
- Brosseron, F.; Maass, A.; Kleineidam, L.; Ravichandran, K.A.; Kolbe, C.C.; Wolfsgruber, S.; DELCODE Study Group. Serum IL-6, sAXL, and YKL-40 as systemic correlates of reduced brain structure and function in Alzheimer’s disease: Results from the DELCODE study. Alzheimer’s Res. Ther. 2023, 15, 13. [Google Scholar] [CrossRef] [PubMed]
- Hayek, D.; Ziegler, G.; Kleineidam, L.; Brosseron, F.; Nemali, A.; Vockert, N.; Maass, A. Different inflammatory signatures based on CSF biomarkers relate to preserved or diminished brain structure and cognition. Mol. Psychiatry 2024, 29, 992–1004. [Google Scholar] [CrossRef]
- Bac, B.; Hicheri, C.; Weiss, C.; Buell, A.; Vilcek, N.; Spaeni, C.; Disterhoft, J.F. The TgF344-AD rat: Behavioral and proteomic changes associated with aging and protein expression in a transgenic rat model of Alzheimer’s disease. Neurobiol. Aging 2023, 123, 98–110. [Google Scholar] [CrossRef]
- Miron, J.; Picard, C.; Frappier, J.; Dea, D.; Théroux, L.; Poirier, J. TLR4 gene expression and pro-inflammatory cytokines in Alzheimer’s disease and in response to hippocampal deafferentation in rodents. J. Alzheimer’s Dis. 2018, 63, 1547–1556. [Google Scholar] [CrossRef]
- Setó-Salvia, N.; Clarimón, J. Genética en la enfermedad de Alzheimer. Rev. De Neurol. 2010, 50, 360–364. [Google Scholar] [CrossRef]
- Hossain, R.; Noonong, K.; Nuinoon, M.; Lao-On, U.; Norris, C.M.; Sompol, P.; Tangpong, J. Alzheimer’s diseases in America, Europe, and Asian regions: A global genetic variation. PeerJ 2024, 12, e17339. [Google Scholar] [CrossRef]
- Manukyan, A.; Jirák, R. Relationships between some genetic polymorphisms and clinical features of Alzheimer’s disease. Česká A Slov. Psychiatr. 2015, 111, 236–240. [Google Scholar]
- Lashley, T.; Gami, P.; Valizadeh, N.; Li, A.; Revesz, T.; Balazs, R. Alterations in global DNA methylation and hydroxymethylation are not detected in Alzheimer’s disease. Neuropathol. Appl. Neurobiol. 2015, 41, 497–506. [Google Scholar] [CrossRef]
- Watson, C.T.; Roussos, P.; Garg, P.; Ho, D.J.; Azam, N.; Katsel, P.L.; Sharp, A.J. Genomewide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer’s disease. Genome Med. 2016, 8, 5. [Google Scholar] [CrossRef] [PubMed]
- Dosunmu, R.; Wu, J.; Basha, M.R.; Zawia, N.H. Environmental and dietary risk factors in Alzheimer’s disease. Expert Rev. Neurother. 2007, 7, 887–900. [Google Scholar] [CrossRef] [PubMed]
- Bartolotti, N.; Lazarov, O. Lifestyle and Alzheimer’s disease: The role of environmental factors in disease development. In Genes, Environment and Alzheimer’s Disease; Academic Press: Cambridge, MA, USA, 2016; pp. 197–237. [Google Scholar] [CrossRef]
- Rahman, M.A.; Rahman, M.S.; Uddin, M.J.; Mamum-Or-Rashid, A.N.M.; Pang, M.G.; Rhim, H. Emerging risk of environmental factors: Insight mechanisms of Alzheimer’s diseases. Environ. Sci. Pollut. Res. 2020, 27, 44659–44672. [Google Scholar] [CrossRef]
- Welsh-Bohmer, K.A.; Plassman, B.L.; Hayden, K.M. Genetic and environmental contributions to cognitive decline in aging and Alzheimer’s disease. Annu. Rev. Gerontol. Geriatr. 2010, 30, 81–114. [Google Scholar] [CrossRef]
- Mohammadi-Pilehdarboni, H.; Shenagari, M.; Joukar, F.; Naziri, H.; Mansour-Ghanaei, F. Alzheimer’s disease and microorganisms: The non-coding RNAs crosstalk. Front. Cell Neurosci. 2024, 17, 1256100. [Google Scholar] [CrossRef] [PubMed]
- Cacabelos, R.; Fernández-Novoa, L.; Lombardi, V.; Kubota, Y.; Takeda, M. Molecular genetics of Alzheimer’s disease and aging. Methods Find. Exp. Clin. Pharmacol. 2005, 27, 1–573. [Google Scholar]
- Finch, C.E.; Kulminski, A.M. The Alzheimer’s disease exposome. Alzheimer’s Dement. 2019, 15, 1123–1132. [Google Scholar] [CrossRef]
- Migliore, L.; Coppedè, F. Gene–environment interactions in Alzheimer disease: The emerging role of epigenetics. Nature Rev. Neurol. 2022, 18, 643–660. [Google Scholar] [CrossRef]
- Goschorska, M.; Baranowska-Bosiacka, I.; Gutowska, I.; Chlubek, D. Potential role of fluoride in the etiopathogenesis of Alzheimer’s disease. Int. J. Mol. Sci. 2018, 19, 3965. [Google Scholar] [CrossRef]
- Kukull, W.A. Alzheimer’s Disease and the Search for Environmental Risk Factors. In Environmental Factors in Neurodevelopmental and Neurodegenerative Disorders; Academic Press: Cambridge, MA, USA, 2015; pp. 315–327. [Google Scholar] [CrossRef]
- Olsson, I.A.S.; Sandøe, P. Animal models of dementia: Ethical considerations. Anim. Models Dement. 2011, 48, 15–33. [Google Scholar] [CrossRef]
- Jäkel, L.; Van Nostrand, W.E.; Nicoll, J.A.; Werring, D.J.; Verbeek, M.M. Animal models of cerebral amyloid angiopathy. Clin. Sci. 2017, 131, 2469–2488. [Google Scholar] [CrossRef] [PubMed]
- Sabbagh, J.J.; Kinney, J.W.; Cummings, J.L. Animal systems in the development of treatments for Alzheimer’s disease: Challenges, methods, and implications. Neurobiol. Aging 2013, 34, 169–183. [Google Scholar] [CrossRef] [PubMed]
- Holden, J.E. Putting the bio in biobehavioral: Animal models. West. J. Nurs. Res. 2011, 33, 1017–1029. [Google Scholar] [CrossRef]
- Shineman, D.W.; Basi, G.S.; Bizon, J.L.; Colton, C.A.; Greenberg, B.D.; Hollister, B.A.; Fillit, H.M. Accelerating drug discovery for Alzheimer’s disease: Best practices for preclinical animal studies. Alzheimer’s Res. Ther. 2011, 3, 28. [Google Scholar] [CrossRef]
- Banik, A.; Brown, R.E.; Bamburg, J.; Lahiri, D.K.; Khurana, D.; Friedland, R.P.; Anand, A. Translation of pre-clinical studies into successful clinical trials for Alzheimer’s disease: What are the roadblocks and how can they be overcome? J. Alzheimer’s Dis. 2015, 47, 815–843. [Google Scholar] [CrossRef] [PubMed]
- Sukoff Rizzo, S.J.; Masters, A.; Onos, K.D.; Quinney, S.; Sasner, M.; Oblak, A. MODEL-AD consortium. Improving preclinical to clinical translation in Alzheimer’s disease research. Alzheimer’s Dement. 2020, 6, e12038. [Google Scholar] [CrossRef]
- Rangel-Barajas, C.; Oblak, A.L.; Ingraham, C.M.; Lloyd, C.D.; Territo, P.R.; Rizzo, S.J.S.; Lamb, B.T. The Role of diet x gene interaction in LOAD2. Plcg2M28L mice. Alzheimer’s Dement. 2024, 20, e091334. [Google Scholar] [CrossRef]
- Wimo, A.; Ballard, C.; Brayne, C.; Gauthier, S.; Handels, R.; Jones, R.W.; Kramberger, M. Health economic evaluation of treatments for Alzheimer′ s disease: Impact of new diagnostic criteria. J. Intern. Med. 2014, 275, 304–316. [Google Scholar] [CrossRef]
- 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. Alzheimer’s Dement. 2023, 19, 5284–5288. [Google Scholar] [CrossRef]
- Vellas, B.; Pesce, A.; Robert, P.H.; Aisen, P.S.; Ancoli-Israel, S.; Andrieu, S.; May, T.S. AMPA workshop on challenges faced by investigators conducting Alzheimer’s disease clinical trials. Alzheimer’s Dement. 2011, 7, e109–e117. [Google Scholar] [CrossRef]
- Boada, M.; Santos-Santos, M.A.; Rodríguez-Gómez, O.; Alegret, M.; Cañabate, P.; Lafuente, A.; Tárraga, L. Patient engagement: The Fundació ACE framework for improving recruitment and retention in Alzheimer’s disease research. J. Alzheimer’s Dis. 2018, 62, 1079–1090. [Google Scholar] [CrossRef] [PubMed]
- Yao, J.; Diaz Brinton, R. Targeting mitochondrial bioenergetics for Alzheimer’s prevention and treatment. Curr. Pharm. Des. 2011, 17, 3474–3479. [Google Scholar] [CrossRef]
- Pierce, R. Complex calculations: Ethical issues in involving at-risk healthy individuals in dementia research. J. Med. Ethics 2010, 36, 553–557. [Google Scholar] [CrossRef]
- Angehrn, Z.; Sostar, J.; Nordon, C.; De Reydet-De Vulpillieres, F. Ethical and social implications of using predictive modeling for Alzheimer’s disease prevention: A systematic literature review. J. Alzheimer’s Dis. 2020, 77, 923–940. [Google Scholar] [CrossRef] [PubMed]
- Tang, Y.; Lutz, M.W.; Xing, Y. A systems-based model of Alzheimer’s disease. Alzheimers Dement. 2019, 15, 168–171. [Google Scholar] [CrossRef]
- Axtman, A.D.; Brennan, P.E.; Frappier-Brinton, T.; Betarbet, R.; Carter, G.W.; Fu, H.; Emory-Sage-SGC TREAT-AD Center. Open drug discovery in Alzheimer’s disease. Alzheimer’s Dement. Transl. Res. Clin. Interv. 2023, 9, e12394. [Google Scholar] [CrossRef] [PubMed]
- Rao, R.V.; Subramaniam, K.G.; Gregory, J.; Bredesen, A.L.; Coward, C.; Okada, S.; Bredesen, D.E. Rationale for a multi-factorial approach for the reversal of cognitive decline in Alzheimer’s disease and MCI: A review. Int. J. Mol. Sci. 2023, 24, 1659. [Google Scholar] [CrossRef]
- Boyarko, B.; Podvin, S.; Greenberg, B.; Momper, J.D.; Huang, Y.; Gerwick, W.H.; Hook, V. Evaluation of bumetanide as a potential therapeutic agent for Alzheimer’s disease. Front. Pharmacol. 2023, 14, 1190402. [Google Scholar] [CrossRef]
- Leßmann, V.; Kartalou, G.I.; Endres, T.; Pawlitzki, M.; Gottmann, K. Repurposing drugs against Alzheimer’s disease: Can the anti-multiple sclerosis drug fingolimod (FTY720) effectively tackle inflammation processes in AD? J. Neural. Transm. 2023, 130, 1003–1012. [Google Scholar] [CrossRef]
- Thiyagarajah, M.T.; Herrmann, N.; Ruthirakuhan, M.; Li, A.; Lanctôt, K.L. Novel pharmacologic strategies for treating behavioral disturbances in Alzheimer’s disease. Curr. Behav. Neurosci. Rep. 2019, 6, 72–87. [Google Scholar] [CrossRef]
- Cappa, S.F. The quest for an Alzheimer therapy. Front. Neurol. 2018, 9, 108. [Google Scholar] [CrossRef] [PubMed]
Year | Milestone | Main Findings | References |
---|---|---|---|
1980s | Cholinergic Deficit Models | Early models using AF64A and ibotenic acid targeted cholinergic neurons to mimic cognitive deficits observed in AD | [20] |
1995 | PDAPP Mouse Model (transgenic) | The first transgenic mouse model with human APP mutations linked to familial AD showed amyloid plaques and synaptic deficits | [21] |
1998 | Drosophila APP Models | Demonstrated γ-secretase cleavage and amyloid pathology in D. melanogaster expressing human APP | [22] |
1999 | “Alzheimer’s Vaccine” in Mice | Immunisation with β-amyloid prevented amyloid plaque development in APP-transgenic mice | [23] |
2000 | APP/PS1 Double-Transgenic Model | Developed mice expressing human APP and PSEN1 mutations; these showed accelerated amyloid deposition and cognitive decline | [24] |
2001 | Tau Transgenic Mice (rTg4510) | Introduced mice expressing human tau mutations, leading to tangles, neuronal loss, and behavioural impairments | [25] |
2005 | 3xTg-AD Mouse Model | Combined APP, PSEN1, and tau mutations to create a mouse model with both plaques and tangles, mimicking human AD pathology | [26] |
2010 | C. elegans Models | Expressed human β-amyloid or tau, exhibiting aggregation and neuronal dysfunction, useful for genetic studies of AD | [27] |
2012 | iPSC-Derived Neuronal Models | First use of induced pluripotent stem cells (iPSCs) from AD patients to create neuronal cultures showing AD-like pathology in vitro | [28] |
2014 | Organoids for AD Research | Developed 3D brain organoids from human stem cells to study amyloid and tau pathology | [29] |
2015 | Non-Human Primate Models | Created aged rhesus monkeys and marmosets with amyloid-β infusions, showing plaques and synaptic dysfunction similar to human AD | [11] |
2019 | CRISPR-Engineered Mouse Models | Applied CRISPR-Cas9 to introduce precise mutations in APP and tau genes, improving modelling accuracy for familial AD | [30] |
2020 | Blood–Brain Barrier (BBB) on a Chip | Developed microfluidic BBB models for studying amyloid transport and drug delivery in vitro | [31] |
2022 | Humanised Mouse Models | Generated transgenic mice carrying humanised APP and tau genes, providing more accurate disease progression and therapeutic response data. | [32] |
Murine Model | Genetic Modifications | Key Features | References |
---|---|---|---|
5xFAD | APP with three FAD mutations | Amyloid plaques and subsequent neurodegeneration | [44,45] |
APPswe/PS1dE9 | APPswe + PSEN1dE9 | Amyloid plaques, cognitive deficits | [46] |
Tg2576 | APPswe | Amyloid plaques, behavioural changes | [47] |
3xTg-AD | APPswe + PSEN1M146V + tauP301L | Amyloid plaques, tau tangles, cognitive impairments | [48] |
APP/PS1 | Various APP and PS1 mutations | Enhanced amyloid pathology | [49] |
Mutation | Gene | Key Features | Animal Models | Pathology | References |
---|---|---|---|---|---|
London | APP | V717I mutation, increases Aβ production | Mouse | Amyloid plaques, cognitive impairment | [39] |
Florida | APP | I716V mutation, increases Aβ production | Mouse | Amyloid plaques, cognitive impairment | [39] |
Iberian | APP | I716F mutation, increases Aβ production | Mouse | Amyloid plaques, cognitive impairment | [39] |
Austria | APP | T714I mutation, increases Aβ production | Mouse | Aβ accumulation and brain atrophy | [63] |
Flemish | APP | A692G mutation, increases Aβ production | Mouse | Behavioural disturbances without amyloid deposits, glial activation, and microspongiosis | [64] |
Swedish | APP | K670N/M671L mutation, increases Aβ production | Mouse (e.g., Tg2576) | Early amyloid plaques, cognitive impairment | [38,39] |
Iowa | APP | D23N mutation, increases Aβ aggregation | Mouse | Accelerated Aβ aggregation | [65] |
Arctic | APP | E22G mutation, increases Aβ aggregation | Mouse | Resistant to proteolytic degradation, prone to aggregation | [39,65] |
Dutch | APP | E22Q mutation, increases Aβ aggregation | Mouse | Accelerated Aβ aggregation | [65] |
Indiana | PSEN1 | P117L mutation, increases Aβ production | Mouse | Early amyloid deposition, neuroinflammation | [39] |
Indiana | PSEN1 | ΔE9 mutation, increases Aβ production | Rat (e.g., TgF344-AD) | Accumulation of Aβ plaques increasing with age | [66] |
Feature | Knock-In Models | Injection Models |
---|---|---|
Physiological Relevance | High, as they avoid artefacts’ overexpression [67,68,69] | Moderate, as they can target specific brain regions but are invasive [74,75,76] |
Pathological Characteristics | Amyloid plaques, neuroinflammation, synaptic dysfunction [29,67,73] | Induced amyloid pathology, neurotoxicity [74,75,76] |
Behavioural Deficits | Age-dependent cognitive impairments [67,71,72,73] | Acute and chronic memory impairments [74,75] |
Tau Pathology | Generally absent [69] | Not typically induced [74,75] |
Invasiveness | Low [67,68,69] | High [74,75,76] |
Use in Long-Term Studies | Suitable for long-term studies [67,71,72,73] | More suitable for short-term studies [74,75] |
Model | Key Findings | References |
---|---|---|
APPswe/PS1dE9 | Revealed neuronal hyperexcitability and synaptic dysfunction, consistent with human AD studies | [77] |
3xTg-AD | Demonstrated interaction between Aβ and tau, driving AD pathogenesis | [78,84] |
J20 | Showed early memory impairment and neuroinflammatory responses before Aβ deposition | [82] |
5xFAD | Enhanced endocannabinoid tone improved memory and reduced neuroinflammation | [83] |
APP23 | Provided insights into cognitive impairments and behavioural changes | [10,11] |
FAD4T | Highlighted the role of microglia and synaptic pruning in AD pathogenesis | [85] |
Limitation/Challenge | Description | References |
---|---|---|
Incomplete Recapitulation | Models do not fully mimic human AD pathology | [54,90,91,92] |
Species-Specific Differences | Differences in brain anatomy and physiology | [54,91,94] |
Oversimplification | Focus on amyloid hypothesis, neglecting other factors like tau pathology and neuroinflammation | [91,96] |
Translational Failures | Preclinical success does not translate to clinical trials | [54,95,97] |
Behavioural Discrepancies | Inconsistent cognitive and behavioural assessments | [96,98,99] |
Model Selection | Difficulty in choosing and validating appropriate models | [96,98] |
Technological Advances | Need for integrating new technologies and methods | [95,100] |
Ethical Considerations | Ethical and practical limitations in research | [101] |
Feature | Details | References |
---|---|---|
Genetic Similarity | Closer to humans than rodents, enabling study of primate-specific mechanisms | [134,135] |
Natural AD Pathologies | Develop Aβ plaques and tau abnormalities naturally | [135,136,137] |
Lifespan | Short lifespan allows for feasible longitudinal studies | [136,138] |
Biomarkers | Key biomarkers identified for neural degeneration | [139] |
Tau Protein | Express 3R and 4R tau isoforms, similar to humans | [137] |
Neuroinflammation | Immune system’s role in amyloid plaque formation studied | [140] |
Model Type | Advantages | Limitations | References |
---|---|---|---|
2D Cell Cultures |
|
| [142,143] |
3D Cell Cultures/Organoids |
|
| [141,146,147,149,152] |
Co-culture Systems |
|
| [151] |
Microfluidic Devices |
|
| [153,154] |
Model | Advantages | Disadvantages |
---|---|---|
Zebrafish |
|
|
|
| |
|
| |
| ||
| ||
| ||
Drosophila |
|
|
|
| |
| ||
C. elegans |
|
|
|
| |
| ||
Marmoset |
|
|
|
| |
In Vitro |
|
|
|
| |
|
Technique | Key Features | Applications in AD Research | Advantages | Limitations | References |
---|---|---|---|---|---|
X-ray Phase Contrast Tomography (XPCT) |
|
|
|
| [162] |
|
|
|
| ||
Phase-Contrast X-Ray-Computed Tomography (PCXCT) |
|
|
|
| [163] |
|
|
|
| ||
|
| ||||
Diffraction-Enhanced Imaging (DEI) |
|
|
|
| [164] |
|
|
|
| ||
Analyser-Based X-ray Imaging (ABI) |
|
|
|
| [165] |
|
|
|
|
Approach | Techniques | Data Types | Performance | Key Insights | References |
---|---|---|---|---|---|
Supervised Learning | CatBoost, Logistic Regression, Decision Tree, Random Forest, Naїve Bayes, SVM, Gradient Boosting, XGBoost, AdaBoost | Clinical, MRI | Accuracy: 92–96% | CatBoost, SVM, and Decision Tree performed best | [166] |
Neuroimaging Analysis | SVM, Decision Tree, Logistic Regression, Random Forest, CNN, VGG16 | MRI, PET | Accuracy: SVM 79%, Random Forest 67%, CNN 87%, VGG16 91% | VGG16 outperformed traditional ML algorithms | [167] |
Genetic Data Analysis | Various ML algorithms | Genetic Data | AUC: 0.59–0.98 | High risk of bias due to feature selection and validation methods | [168] |
Multi-Modal Analysis | Multi-Task Model, Time Series model, Deep Learning | Clinical, Genetic, Neuroimaging | Not specified | Emphasises combining diverse biomarkers for better prediction | [169,170] |
Ensemble Methods | Random Forest, XGBoost, Gradient Boosting, CatBoost, Voting Classifier | Clinical, Cognitive Tests | Accuracy: 96.30% | Ensemble methods highly effective | [171] |
Hybrid Models | Stacking (Logistic Regression, Naїve Bayes, SVM, Decision Trees, Random Forest, XGBoost) | Cognitive, Demographic | Accuracy: 95.12% | Hybrid models improved prediction accuracy | [172] |
Deep Learning | CNN, Transfer Learning (VGG16) | Neuroimaging | Accuracy: CNN 87%, VGG16 91% | Transfer learning models show high potential | [167] |
Non-Invasive Techniques | KNN, Naїve Bayes, ANN | Cognitive Tests | F1-Scores: KNN 0.7, Naїve Bayes 0.88, ANN 0.96 | ANN showed highest F1-Score | [173] |
Omics Technology | Application | Insights | Biomarkers | References |
---|---|---|---|---|
Metabolomics | Diagnosis, Drug Evaluation | Mimics human metabolic changes | LysoPC, sphingolipid intermediates | [176] |
Proteomics | Biomarker Discovery | Reflects protein alterations in CSF and plasma | Various protein markers | [177,178] |
Transcriptomics | Gene Expression Analysis | Identifies mRNA and miRNA changes | mRNA, miRNA predictors | [177,183] |
Genomics | Genetic Risk Factors | SNP analysis for genetic insights | SNPs, gene expression data | [183] |
Feature | Mouse Models | Alternative Models |
---|---|---|
Common Models | Transgenic models (e.g., 5XFAD, APP23, Tg2576, 3xTg) [184,185,190,191]. | Degu, Dog, Non-human Primates, C. elegans, D. melanogaster, D. rerio [187,188]. |
Pathological Characteristics | Amyloid plaques, neurofibrillary tangles, cognitive deficits, and neuroinflammation [67,134,184,185,190]. | Varies among species; some recapitulate neuropathology and cognitive impairments better [187,188]. |
Translational Success | Limited; many preclinical successes do not translate to clinical human efficacy [54,184,192]. | Potentially higher translational value due to closer resemblance to human pathology in some models [187,188]. |
Genetic Manipulation | Extensive use of genetic modifications to induce AD-like symptoms [67,134,184,185]. | Less genetic manipulation; some models naturally develop AD-like symptoms [187,193]. |
Behavioural Assessments | Cognitive tests (e.g., Morris water maze, Y-maze, Barnes maze) [67,190,194]. | Varies; some models may not be suitable for traditional rodent cognitive tests [187,188]. |
Molecular Similarities | Transcriptomic and proteomic similarities with human AD subtypes [189,195]. | Some models share core molecular programs with mouse models [188]. |
Limitations | Do not fully replicate human AD; variability in pathology and behaviour [54,184,192]. | Some models may not develop all AD features; variability in results [193]. |
Innovative Approaches | Knock-in models, a combination of multiple transgenic lines [67,134]. | Use of naturally occurring models, cross-species comparisons [187,188]. |
Gene | Role/Function | Association with LOAD | References |
---|---|---|---|
APOE ε4 | Lipid transport, amyloid processing | Strongest genetic risk factor | [209,211] |
ABCA7 | Cholesterol metabolism | Cognitive decline, LOAD risk | [216,218] |
BIN1 | Endocytosis, synaptic function | LOAD risk | [212,217] |
TREM2 | Microglial function, immune response | Increased risk, rare variant | [214,215] |
PLD3 | Unknown | Increased risk, rare variant | [213] |
PICALM, CR1 | Various cellular processes | LOAD risk | [219,221] |
ACE1 | Vascular health | Potential risk factor | [222] |
Marker | Role in AD | References |
---|---|---|
IL-1β, IL-6, IL-10 | Pro-inflammatory cytokines, neurodegeneration | [244,245] |
TNF-α | Potent pro-inflammatory cytokine, exacerbates neuroinflammation | [244,245,246] |
MCP-1 | Associated with tau pathology, neuroinflammation | [247,248,249] |
YKL-40 | Glial activation, associated with tau levels and cognitive decline | [250,251] |
TREM2 | Microglial activation, reduced in aged, impaired AD rats | [252] |
VEGF, VEGFR-1 | Vascular injury, associated with tau pathology | [248] |
GFAP-IL6 | Chronic neuroinflammation, neurodegeneration | [239] |
TLR4 | Microglial response to amyloid plaques, cytokine production | [253] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Alexandra Lopes, P.; Guil-Guerrero, J.L. Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer’s Disease. Int. J. Mol. Sci. 2025, 26, 5541. https://doi.org/10.3390/ijms26125541
Alexandra Lopes P, Guil-Guerrero JL. Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer’s Disease. International Journal of Molecular Sciences. 2025; 26(12):5541. https://doi.org/10.3390/ijms26125541
Chicago/Turabian StyleAlexandra Lopes, Paula, and José L. Guil-Guerrero. 2025. "Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer’s Disease" International Journal of Molecular Sciences 26, no. 12: 5541. https://doi.org/10.3390/ijms26125541
APA StyleAlexandra Lopes, P., & Guil-Guerrero, J. L. (2025). Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer’s Disease. International Journal of Molecular Sciences, 26(12), 5541. https://doi.org/10.3390/ijms26125541