Animal models of Alzheimer’s disease: preclinical insights and challenges

Abstract

Alzheimer’s disease (AD), an irreversible neurodegenerative disease that progressively impairs memory and cognitive judgment, severely affects the quality of life and imposes a heavy burden on the healthcare system. No cure is currently available for AD, in part because the pathogenesis of this disease has not been established. Animal models are essential for investigating AD pathogenesis and evaluating potential therapeutic strategies for AD. Some phenotypic and neuropathologic changes in AD patients can be recapitulated with genetic and pharmacologic approaches in animals. This article systematically reviews the animal models available for AD research, including transgenic, chemical- or drug-induced, and spontaneous animal models, and the characteristics of these animal models. In this review we also discuss the challenges and constraints when using AD animal models. Although no single animal model can reproduce all pathologic aspects and behavioral features in AD patients, the currently available AD models are valuable tools for deciphering the pathogenic mechanisms underlying AD and developing new anti-AD therapeutics.

Main article text

1. INTRODUCTION

Alzheimer’s disease (AD) is the most common neurodegenerative disease that progressively impairs memory and cognitive judgment [1]. AD severely affects the quality of life and imposes a heavy burden on the healthcare system [1]. The global incidence of AD currently exceeds 50 million, and the mortality rate due to AD has increased two-fold over the last two decades [2]. No cure for AD is currently available, and current therapeutics commonly used in the clinical setting only exert a modest effect in symptomatic relief, but fall short in reversing or even slowing down AD progression [3, 4].

The pathogenesis of AD is complex, much of which is unknown. There are two well-recognized pathologic hallmarks in AD (extracellular amyloid plaques and intracellular neurofibrillary tangles [NFTs]) [4]. The aggregation of beta-amyloid (Aβ) and hyperphosphorylated tau protein contribute to the formation of amyloid plaques ( Figure 1 ) and NFTs ( Figure 2 ), respectively, which are thought to have important roles in AD progression [4].

Figure 1 |

Amyloidogenic and non-amyloidogenic pathways for APP processing in AD.

There are two pathways of APP processing: (a) The non-amyloidogenic pathway, in which APP is cleaved by α-secretase, leads to the release of cytoplasmic tail fragment α (CTFα) and soluble APPα (sAPPα). sAPPα has neuroprotective effects due to prevention of APP degradation mediated by β-secretase. (b) The other pathway is the amyloidogenic pathway, in which β-secretase cleaves APP to produce CTFβ and soluble APPβ (sAPPβ), followed by γ-secretase-mediated cleavage to generate several types of Aβ. Aβ40 and Aβ42 are the two major Aβ peptides that have an important role in AD pathology. The ratio of these two isoforms is influenced by the pattern of cleavage from APP by α-, β-, and γ secretases. These two pathways are parallel and kept in balance under physiologic conditions. Dysfunction of Aβ clearance and degradation causes the deposition of Aβ in the AD brain, and sequentially generates Aβ oligomers and fibrils, ultimately leading to the formation of Aβ plaques.

Figure 2 |

Intracellular neurofibrillary tangles (NFTs) pathway in AD.

Hyperphosphorylation occurs in an epitope-specific manner during the course of AD, and is known to underlie the mis-sorting of tau from a primarily axonal-to-a somato-dendritic location in neurons. Hyperphosphorylated tau loses its affinity to microtubules and destabilizes microtubule dynamics. After pathologic aggregation, abnormal folding of tau protein leads to the generation of a paired helical filament (PHF) and neurofibrillary tangles (NFTs), ultimately causing neurodegeneration and cognitive impairment in patients with AD.

The formation of amyloid plaques, mainly consisting of Aβ peptide, is one of the pathologic hallmarks in the brains of patients with AD. The amyloid cascade hypothesis proposes that Aβ accumulates and aggregates in the brain during the initial stage of AD pathology, and ultimately leads to neurodegeneration [5, 6]. Aβ is a 38-43-residue fragment formed by proteolysis of amyloid precursor protein (APP) through cleavage of β- and γ-secretases [7]. The following two pathways are associated with APP processing: the non-amyloidogenic pathway, in which APP is cleaved by α-secretase, leading to the release of cytoplasmic tail fragment α (CTFα) and soluble APPα (sAPPα) [8], which has neuroprotective effects due to its ability to prevent APP degradation mediated by β-secretase [9]; and the amyloidogenic pathway, through which β-secretase cleaves APP to produce CTFβ and soluble APPβ (sAPPβ), followed by γ-secretase-mediated generation of several types of Aβ [10]. Among the types of Aβ, Aβ₄₀ and Aβ₄₂ are the two major Aβ peptides that have important roles in AD pathology. The ratio of the Aβ₄₀ and Aβ₄₂ isoforms is influenced by the pattern of cleavage from APP by α, β, and γ secretases [11]. These two pathways are parallel and kept in balance in under physiologic conditions. Aβ clearance and degradation dysfunction can cause the deposition of Aβ in the brains of AD patients, and sequentially generate Aβ oligomers and fibrils, ultimately leading to the formation of Aβ plaques [6, 12]. In addition, the genetic mutations of the APP gene on chromosome 21, the presenilin-1 (PS1) gene on chromosome 14, and the presenilin-2 (PS2) gene on chromosome 1 are positively correlated with the increased levels of Aβ [13]. Accumulating evidence suggests that Aβ oligomers have significant neurotoxicity [14], and the Aβ plaques are usually surrounded by activated glial cells and damaged neurons [15–17]. It has been shown that soluble oligomeric and deposited Aβ in amyloid plaques interact with neurons, microglia, astroglia, and blood vessels, causing a variety of detrimental cellular responses that eventually lead to neuronal death [18].

NFTs are largely composed of hyperphosphorylated tau protein. NFTs are another pathologic hallmark of AD and contribute significantly to AD progression [19]. Tau protein is a glycoprotein related to microtubule assembly that has a crucial role in stabilizing microtubules, maintaining neuronal morphology, and modulating neurotransmitter transport under physical conditions [20]. Progression of AD is closely associated with abnormal post-translational modifications and aggregation of tau protein, including phosphorylation, acetylation, N-glycosylation, and truncation [21, 22]. Phosphorylation is the most common post-translational modification of tau protein. Accumulating evidence suggests that phosphorylated tau is closely correlated with AD symptom severity and may drive progression of AD [23]. In the brains of patients with AD, hyperphosphorylated tau protein loses affinity for microtubules and progressively forms insoluble intracellular NFTs, thus leading to neuronal dysfunction, synaptic loss, and ultimately cognitive impairment [24].

Tau and phosphorylated tau proteins have been reported to be associated with Aβ directly and indirectly. According to the amyloid cascade hypothesis, Aβ accumulation promotes the hyperphosphorylation of tau protein and the formation of NFTs [25]. Tau immunotherapy, however, alleviates amyloid pathology and memory impairment in a transgenic APP mouse model of AD [26], indicating that tau pathologies have important roles in Aβ-induced neurotoxicity. Multiple factors, such as calcium homeostasis, kinases (glycogen synthase kinase-3β [GSK-3β] and cyclin-dependent protein kinase-5 [CDK-5]), and free fatty acid disruptions, are responsible for the association between tau and Aβ pathologies. Moreover, tau and Aβ-associated pathologies exert synergistic effects on neurotoxicity and synaptic dysfunction [27]. The development of tau-targeted therapeutics may be a promising strategy for AD treatment, although the relationship between Aβ and tau has not been completely determined.

Other neuropathologic features, such as neuroinflammation, neuronal loss, oxidative stress, and synaptic impairment, are also believed to contribute to disease progression [28]. All of these mechanisms may interact, leading to amplification of the mechanisms and resulting in AD pathogenesis.

Animal models are important for investigating AD pathogenesis and evaluating potential therapies for AD. In the past few decades, many potential drug candidates have shown promising anti-AD effects in preclinical models, but surprisingly failed in clinical trials [29]. One of the reasons for this finding may be partially attributed to the inappropriate choice of preclinical models. Mammals, such as rodents, share extensive physiologic and genetic homology with humans. Thus, preclinical findings acquired from animal models may provide insight into human disease conditions. Although no single animal model can reproduce all aspects of the pathologic and behavioral features of AD patients, currently available AD animal models can, at least to some extent, replicate several important features of AD. This review aimed to summarize the characteristics and features of the animal models commonly in use for AD research. It is hoped that the information summarized in this article may help AD researchers choose appropriate animal models to achieve their objectives.

2. TRANSGENIC RODENT MODELS

Genetics is one of the most salient known risk factors for AD. AD can be divided into sporadic AD (sAD) and familial AD (fAD). The ε4 allele of the apolipoprotein E (ApoE) gene is considered the most relevant gene for sAD [30, 31], while fAD is mainly associated with familial gene mutations, including APP, PS1 and PS2 gene mutations [32, 33]. Compared with the most common ApoE genotype (ε3/ε3), ε4 heterozygosity raises the risk of developing AD 3-fold and ε4 homozygosity increases the risk 8-12 times [1]. Approximately two-thirds of pathology-confirmed AD cases are ε4-positive (homozygous or heterozygous), while the percentage of ε4-positive cases is 15%-20% in the general population [31]. Although pathogenic variants of APP, PS1, and PS2 genes contribute to ≤ 1% of AD cases [1], nearly 100% of individuals inheriting APP and PS1 and approximately 95% of individuals inheriting PS2 develop the disease, assuming an average lifespan [34].

The identification of APP, PS1, and PS2 gene mutations related to fAD [32, 33] has resulted in the development of numerous transgenic rodent models of amyloid pathology [35, 36], while the human MAPT gene mutations related to neurodegeneration [37] have been utilized to induce tau pathology. To date, > 200 transgenic rodent models of AD have been established (data obtained from https://www.alzforum.org/, accessed in November 2022), most of which are generated based on the mutations associated with amyloid and tau pathologies. Because ApoE4 and triggering receptor expressed on myeloid cells 2 (TREM2) genes are considered the most relevant genes for sAD [30, 31, 38], transgenic models associated with these genes have been established. The most commonly used transgenic rodent models of AD are listed in Table 1 .

Table 1 |

Transgenic animal models used in the preclinical studies of AD.

Model	Mutations	Genetic background and promoter	AD-related pathology			Behavioral impairment	Advantages	Disadvantages	References
			Amyloid pathology	Tau pathology	Other neuropathologies
APP-based
TgCRND8	APPSwe, APPInd	Hybrid C3H/He-C57BL/6, hamster PrP promoter	Aβ deposition in cerebral cortex (2-3 months); hippocampus, midbrain, brainstem, and cerebellum with age	Hyperphosphorylated and aggregation (7-12 months)	1. Gliosis (9-20 weeks) 2. Synaptic loss (6 months) 3. Cholinergic basal forebrain neurons (7 months) 4. Changes in LTP/LTD (6-12 months)	Learning and spatial memory deficits (3 months)	1. Early-appearing Aβ deposition (2-3 months) 2. Hyperphosphorylated tau (7 months) 3. Early-appearing learning and spatial memory deficits (3 months)	No NFTs observed.	[39–43]
APP23	APPSwe	C57BL/6, murine Thy1 promoter	Aβ deposition (6 months); plaques in cerebral cortex, hippocampus, thalamus, olfactory nucleus, and putamen (24 months)	Hyperphosphorylated tau in proximity to Aβ plaques; no NFTs observed	1. Gliosis 2. Neuronal loss (14-18 months)	Learning and spatial memory deficits (3 months) and progress with age	Early-appearing learning and spatial memory deficits (3 months)	No NFTs observed	[44–47]
Tg2576	APPSwe	C57BL/6×SJL, hamster prion protein promoter	Numerous parenchymal Aβ plaques (by 11-13 months).	None	1. Gliosis (10-16 months) 2. Changes in LTP/LTD (5-15 months)	Impaired spatial learning, working memory, and contextual fear conditioning at < 6 months, although other studies have reported normal cognition at this age with progressive impairment by > 12 months	Early impaired spatial learning and memory functions (less than 6 months)	The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs	[48–51]
PDAPP	APPInd	C57BL/6×DBA2, human PDGF-β promoter	Aβ deposition in hippocampus and cerebral cortex (6-9 months); extensive plaques with age	None	1. Gliosis 2. Synaptic loss 3. Changes in LTP/LTD (4-5 months)	1. Robust deficits (3 months) 2. Objective recognition deficits (6, 9-10 months) 3. Operative learning deficits (3-6 months)	Early changes in LTP/LTD (4-5 months)	The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs	[52–56]
APP(V717I)	APPLon	Originally generated on FVB/N background; available at reMYND as C57BL/6×FVB/N, murine Thy1 promoter	Aβ deposition (2-4 months); plaque formation in hippocampus and cerebral cortex (13-18 months).	Hyperphosphorylated tau; no NFTs observed	1. Gliosis (by 10 months) 2. Changes in LTP/LTD in hippocampus (6 months)	Learning and memory deficits (from 6 months)	1. Early Aβ deposition (2-4 months) 2. Learning and memory deficits (from 6 months) 3. Changes in LTP/LTD in hippocampus (6 months)	No NFTs observed	[57, 58]
J20	APPSwe, APPInd	C57BL/6, human PDGF-β promoter	Aβ deposition in cerebral cortex and hippocampus (5-6 months); progressive Aβ deposition with widespread plaques (by 8-10 months)	None	1. Gliosis (24-36 weeks) 2. Synaptic loss (by 3 months) 3. Neuronal loss in CA1 region (12-36 weeks). 4. Changes in LTP/LTD (3-6 months)	Learning and spatial memory deficits (by 4 months)	1. Early changes in LTP/LTD (3-6 months) 2. Early learning and spatial memory deficits (by 4 months) 3. Early synaptic loss (3 months)	The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs	[59–62]
mThy1-hAPP751	APPSwe, APPLon	C57BL/6×DBA, murine Thy1 promoter	Aβ deposition in frontal cortex (3-6 months); accumulation in cerebral cortex, hippocampus, thalamus, and cerebellum with age	None	1. Gliosis (by 6 months) 2. Synaptic loss (from 12 months)	Learning and spatial memory deficits (by 6 months)	1. Early appeared Aβ deposition in frontal cortex (3-6 months); 2. Early learning and spatial memory deficits (by 6 months); 3. Early gliosis (6 months)	The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs	[63–65]
APPNL-G-F knock-in rats	APPSwe, APPArc, APPIbe	Sprague-Dawley, created using CRISPR/Cas9 technology	Amyloid plaques (1 month in homozygous knock-ins; 4 months in heterozygotes)	Hyperphosphorylated tau; no NFTs observed	1. Gliosis (6 months in homozygous; 12 months in heterozygotes) 2. Synaptic loss 3. Neuronal loss (12 months)	Learning and spatial memory deficits (5-7 months)	1. Early amyloid plaques (1 month in homozygous knock-ins; 4 months in heterozygotes) 2. Early learning and spatial memory deficits (5-7 months)	No NFTs observed	[66]
Tau-based
Tau P301S	MAPT P301S	(C57BL/6 × C3H) F1, mouse prion protein promoter	None	NFTs in the hippocampus, neocortex, amygdala, brainstem, and spinal cord (6 months) with progressive accumulation	1. Gliosis (by 6 months) 2. Synaptic loss (3-6 months) 3. Neuronal loss in hippocampus and cortex (9-12 months) 4. Changes in LTP/LTD (3-6 months)	1. Learning and spatial memory deficits (6 months) 2. Impaired memory in assays of contextual fear conditioning	1. Early synaptic loss (3-6 months) 2. Early changes in LTP/LTD (3-6 months) 3. Early learning and spatial memory deficits (6 months) 4. Early gliosis (by 6 months)	1. The absence of amyloid pathology 2. No association between genetic mutation and tau pathology is found in AD patients	[67–69]
rTg(tauP301L)4510	MAPT P301S	Mixed: 129S6 (activator)×FVB (responder), human CaMKIIα promoter	None	Pre-tangles (2.5 months); argyrophilic tangle-like inclusions in hippocampus (5.5 months) and cortex (4 months)	1. Neuronal loss in hippocampus (from 5.5 months) 2. Synaptic loss (8-9 months) 3. Changes in LTP/LTD (4.5 months)	Learning and spatial memory deficits (2.5-4 months)	1. Early pre-tangles (2.5 months) 2. Early learning and spatial memory deficits (2.5-4 months) 3. Early changes in LTP/LTD (4.5 months) 4. Early neuronal loss in hippocampus (from 5.5 months)	1. The absence of amyloid pathology 2. No association between genetic mutation and tau pathology is found in AD patients. 3. The neuropathologic deficits and behavior phenotypes in this model may be partially attributed to its overexpression of mutant human tau	[70–74]
hTau	MAPT (all 6 isoforms)	The targeted allele was created in 129S4/SvJae-derived J1 embryonic stem cells that were subsequently injected into C57BL/6 blastocysts. The transgenic allele was generated in embryos derived from a cross between Swiss Webster and B6D2F1. Mice containing both alleles were back-crossed to C57BL/6 mice.	None	Hyperphosphorylated tau (from 6 months); aggregated tau and PHFs (9 months)	1. Neuronal loss (8-18 months) 2. Changes in LTP/LTD (12 months)	1. Learning and spatial memory deficits (6-12 months) 2. Impaired burrowing (4 months)	1. Early impaired burrowing (4 months) 2. Early hyperphosphorylated tau (from 6 months)	The absence of amyloid pathology	[75–79]
THY-Tau22	MAPT P301S, MAPT G272V	C57BL6/CBA; backcrossed to C57BL6. Thy1.2 promoter	None	Tau aggregation in cerebral cortex and hippocampus (3 months); hyperphosphorylated tau and NFTs in hippocampus and frontal cortex (6 months); age-related increase of tau pathology.	1. Gliosis (age-dependent increase) 2. Neuronal loss in CA1 region of the hippocampus (from 12 months) 3. Changes in LTP/LTD (9-10 months)	1. Spatial memory deficits (after 9 months) 2. Impaired appetitive responding	Early appeared Tau aggregation	1. The absence of amyloid pathology 2. No association between genetic mutation and tau pathology is found in AD patients.	[80–83]
PSEN-based
PS cDKO	PSEN1 conditional KO; PSEN2-/-	C57BL6/129 hybrid	Decreased Aβ deposition	Hyperphosphorylated tau (6 m).	1. Gliosis (6 months) 2. Synaptic loss (6-9 months) 3. Neuronal loss (2-4 months)	1. Learning and spatial memory deficits (mild at 2 months, age-dependent) 2. Contextual fear conditioning deficits (mild at 2 months, age-dependent)	1. Early-appearing learning and spatial memory deficits, as well as contextual fear conditioning deficits (2 months) 2. Early-appearing neuronal loss (2-4 months)	This model cannot exhibit pronounced AD-like pathologies.	[84–86]
Multi-transgenic
APP/PS1	APPSwe, PSEN1 M146L	Tg2576×C3H, hamster prion protein promoter/PDGF-β promoter	Large amounts of Aβ accumulate in the cerebral cortex and hippocampus (from 6 months, increasing with age)	Hyperphosphorylated tau in proximity to Aβ deposits (from 6 months)	1. Gliosis (6-12 months, increasing with age) 2. Neuronal loss in the CA1 region of the hippocampus (22 months)	Learning and spatial memory deficits (from 12-14 weeks)	1. Early-appearing large amounts of Aβ accumulate (6 months) 2. Early-appearing hyperphosphorylated tau in proximity to Aβ deposits (from 6 months)	No NFTs observed.	[87–91]
PS2APP	APPSwe, PSEN2 N141I	C57BL/6, mouse prion protein promoter/Thy1.2 promoter	Aβ deposition (6 months); plaques in cerebral cortex and hippocampus (10 months)	None.	1. Gliosis 2. Changes in LTP/LTD (10 months)	Learning and spatial memory deficits (8-12 months)	Early appeared Aβ deposition (6 months)	The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs	[92–94]
5×FAD	APPSwe, APPFlo, APPLon, PSEN1 M146 L, PSEN1 L286V	C57BL/6×SJL, mouse Thy1 promoter	Aβ plaques in deep cortical layers and subiculum (2 months); Aβ deposition in spinal cord (4 months); plaques throughout cerebral cortex and hippocampus (6 months).	None.	1. Gliosis 2. Synaptic loss 3. Neuronal loss (12 months) 4. Changes in LTP/LTD	Learning and spatial memory deficits (3-6 months, age-dependent)	1. Early-appearing Aβ plaques 2. Early-appearing learning and spatial memory deficits (3-6 months)	1. The absence of comprehensive tau pathology, including hyperphosphorylated tau and NFTs. 2. Gender-dependently differences in phenotypes	[95–98]
3×Tg	APPSwe, MAPT P301S, PSEN1 M146L	C7BL/6;129X1/SvJ;129S1/Sv, mouse Thy1.2 promoter	Aβ deposition in cerebral cortex (6-12 months); accumulation in cerebral cortex and hippocampus (12-20 months)	Hyperphosphorylated tau in hippocampus (12 months) and cerebral cortex (18 months)	1. Gliosis (7 months) 2. Changes in LTP/LTD (6 months)	1. A retention/retrieval deficit (by 4 months) 2. Learning and memory deficits (6.5 months)	1. Early-appearing a retention/retrieval deficit (by 4 months) 2. Early-appearing learning and memory deficits (6.5 months) 3. Early-appearing changes in LTP/LTD (6 months) 4. Early-appearing Aβ deposition (6-12 months)	No association between genetic mutation and tau pathology is found in AD patients.	[99–101]
PLB1-triple	APPSwe, APPLon, MAPT P301 L, MAPT R406W, PSEN1 A246E	C57BL/6, mouse CaMKII-α promoter	Aβ deposition in cerebral cortex and hippocampus (5-6 months); plaque formation (12 months)	Hyperphosphorylated tau in the hippocampus and cortex (6 months).	Changes in LTP/LTD (6-12 months).	1. Social recognition memory deficits (from 5 months) 2. Objective recognition memory deficits (by 8 months) 3. Spatial memory deficits (12 months)	1. Early-appearing social recognition memory deficits (from 5 months) 2. Early-appearing Aβ deposition (5-6 months) 3. Early-appearing hyperphosphorylated tau in the hippocampus and cortex (6 months)	1. The neurodegenerative pathologies progress slowly and are mild. 2. This model expresses low levels of mutant human APP, tau and PS1, with no NFTs and virtually absent Aβ deposition	[102–106]
E4FAD	APPSwe, APPFlo, APPLon, PSEN1 M146L (A>C), PSEN1 L286V, APOE Knock-in	C57BL/6	Aβ plaques in the subiculum and deep cortical layers (by 4 months)	No data	1. Gliosis (6 months) 2. Synaptic loss (by 4 months)	Modest learning and spatial memory deficits (by 2 months); worsen with age	1. Early-appearing modest learning and spatial memory deficits (2 months) 2. Early-appearing Aβ plaques (4 months) 3. Early-appearing synaptic loss (by 4 months)	1. The AD phenotypes are generally less severe than the original 5×FAD mouse model. 2. Tau pathology is not reported.	[107–109]
Trem2 R47H KI (Lamb/Landreth) ×APPPS1-21	TREM2 R47H, APPSwe, PSEN1 L166P, Trem2 Knock-In	C57BL6/J, Thy1 promoter	Reduction in the number and burden of fibrillar amyloid plaques in the hippocampus	Hyperphosphorylated tau in dystrophic neurites surrounding plaques (4 months); no NFTs observed	Fewer plaque-associated myeloid cells	No data	Early-appearing hyperphosphorylated tau in dystrophic neurites surrounding plaques (4 months)	1. No NFTs observed 2. Behavior deficits are not reported	[110–114]
PS19 with humanized TREM2 (R47H)	MAPT P301S, TREM2 R47H, Trem2 Knock-out	C57BL/6	No data	Tangles revealed using antibody PG5 (9 months)	1. Decreased astroglia and microglial reactivity (9 months) 2. More synapses and fewer dystrophic synapses (9 months)	No data	More synapses (9 months)	1. No NFTs observed. 2. Amyloid pathology and behavior deficits are not reported	[59, 115, 116]

Note: APP_Flo, APP I716V; APP_Ind, APP V717F; APP_Lon, APP V717I; APP_Swe, APP KM670/671NL.

2.1 APP-based transgenic model

The PDAPP transgenic mouse model, the first reported transgenic AD mouse model, was generated in the mid-1990s [52–56], followed by the development of the currently most commonly used APP transgenic models, including Tg2576 [117], APP23 [44–47], TgCRND8 [39–43], and J20 [118] transgenic mouse models. These five models overexpress human APP with the Indiana mutation (V717F) and/or the Swedish mutation (KM670/671NL). Other APP transgenic murine models, such as APP(V717I) and mThy1-hAPP751, encode a mutant form of human APP with the London mutation (V717I) driven by the murine Thy-1 promoter [57, 58, 63–65]. APP-based transgenic rodent models exhibit comprehensive Aβ pathology, Aβ-associated neuroinflammation, synaptic dysfunction, neuronal loss, and cognitive and behavioral impairment (also shown in Table 1 ), thus supporting the amyloid cascade hypothesis and recapitulating several key pathologies observed in AD patients. Indeed, these AD models are ideal experimental tools for amyloid-related research, not only for evaluating the potential efficacy of new therapeutics, but also investigating the underlying mechanisms, including amyloid pathology and amyloid-associated neuropathogenesis. It is worth noting, however, that the importance of Aβ in AD pathogenesis has been increasingly questioned due to the continuous clinical failures of anti-Aβ or amyloid-centric therapies [119–121]. Although aducanumab, a monoclonal antibody targeting Aβ pathology, was conditionally approved in 2021 by the United States Food and Drug Administration (US FDA) for the treatment of AD patients, although the clinical efficacy and safety have yet to be verified [122]. This therapeutic dilemma may be attributed to the complex pathologies of AD [123], while APP transgenic rodent models only display amyloid pathology. It is conceivable that the absence of comprehensive tau pathology in APP transgenic models, another crucial pathologic feature of AD, may result in the promising therapeutic effects of potential drug candidates demonstrated in animal models, but shown to be negative in AD patients. Although tau protein is hyperphosphorylated in some models, such as TgCRND8 [124], APP23 [44–47], and APP(V717I) mice [57, 58, 63–65], as well as APP^NL-G-F knock-in rats [66], no NFTs were observed in APP-based transgenic rodent models. Therefore, the lack of NFT development in the above-mentioned amyloidosis models, at least to some extent, limits preclinical use [36, 125].

2.2 Tau-based transgenic models

Apart from amyloid plaques, another pathologic hallmark of AD is the formation of NFTs, which results from the hyperphosphorylation and pathologic aggregation of tau proteins [19]. Because the formation of NFTs is absent in transgenic rodents harboring APP or APP/PS mutations [126], mutated MAPT models are often selected to investigate tau pathology in preclinical studies on AD.

2.2.1 hTau.P310S

As the most widely used tauopathy model, tau P310S mice express mutant human tau 5-fold higher than the endogenous murine tau on a C57BL/6×C3H background, and display widespread NFTs in the hippocampus, neocortex, amygdala, brainstem, and spinal cord at 6 months of age, with progressive hippocampal synaptic dysfunction at 3-6 months of age and neuronal loss by 9-12 months of age [67]. Tau pathology is also accompanied by microglial and astrocytic activation by 6 months of age [67]. Moreover, 6-month-old P301S transgenic mice exhibit learning and spatial memory deficits, when undergoing the Morris water maze test (MWMT) and impaired spontaneous alternating behavior in the Y-maze test [68].

2.2.2 rTg (tauP301L) 4510

After the initial description in 2005 [70, 71], the rTg4510 mouse model has gradually become a popular and commonly used tauopathic model. rTg4510 mice encode a repressible mutant of human tau with a P301L mutation, expressing mutant human tau at a level 13 times higher than endogenous murine tau [71]. NFT-like pathologic changes appear in the cortex and hippocampus at 4 months of age and 5.5 months of age, respectively [70, 71]. Apart from an early onset of tau pathology, this model shows progressive and age-related spatial memory impairment beginning at 2.5 months of age [71, 72], while females exhibit more severe deterioration than males [72]. Hyperactivity and recognition memory deficits are also observed, as demonstrated by the open field and novel objective recognition tests [127–129]; however, a recent study suggested that the neuropathologic deficits and behavior phenotypes in this model may be partially attributed to overexpression of mutant human tau [130]. Thus, researchers are advised to take factors other than hTau overexpression into consideration when using rTg4510 mice.

2.2.3 hTau

Unlike the above-mentioned animal models of tau pathology, hTau mice develop tangles without mutations and exclusively express all six isoforms of non-mutant human tau, including 3R and 4R forms, but no endogenous mouse tau is detected [75]. Tau protein is progressively hyperphosphorylated and accumulated from 6 months of age, while paired helical filaments (PHFs) and thioflavin-S-positive NFTs can be detected in 9-15-month-old hTau mice [75]. Compared with the wild-type littermates, hTau mice exhibit significant deficits in spontaneous burrowing behavior at 4 months of age [76], as well as spatial learning and memory at 6-12 months of age [77, 78], with neuronal loss detected at 8-18 months of age [79].

2.2.4 THY-Tau22

This model encodes a transgene containing human tau with P301S and G272V mutations driven by a Thy1.2 promotor. The tau pathologies with an onset at 3-6 months develop progressively with age in THY-Tau22 mice, including hyperphosphorylation of tau protein and the formation of NFTs in the hippocampus and cortex [80]. In addition, this model shows neuronal loss, synaptic dysfunction, and an age-dependent increase in GFAP-positive astrocytes in the hippocampus [80, 81, 131]. THY-Tau22 mice exhibit spatial memory impairment at 9 months of age [81] and impaired appetitive responding at 9-10 months [82].

Although tau-based rodent models display pronounced tau pathology, Aβ plaques and amyloid pathology are absent in these models [70, 71, 75, 80, 132]. Of note, pathogenic mutations of the tau gene are only present in patients with frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17), but not in individuals affected with AD [133]. Due to the lack of amyloid pathology in tau-based models and the lack of tau pathology in APP-based models, these AD models cannot recapitulate the association between tau and amyloid pathologies observed in AD patients. Thus, some multi-transgenic AD models, such as triple transgenic (3 × Tg) and PLB1-triple mice, have been generated to counterbalance the above-mentioned shortcomings.

2.3 PSEN-based transgenic models

As major components of the γ-secretase enzyme, PS1 and PS2 are closely associated with APP processing and AD pathology [134, 135]. Mutations of these two genes contribute to the early onset of fAD, in which PS1 mutations play a more prominent role [134, 135]. Transgenic models only expressing mutant PS1 or PS2 produce a significantly elevated level of Aβ₄₂, a major Aβ peptide preceding Aβ plaque deposition, but Aβ plaques and NFTs are not developed [136]. In addition, age-related tau inclusions, synaptic dysfunction, and neuronal loss are present in transgenic animals expressing mutant PS1 or PS2 [137–139]. These models generally do not exhibit pronounced AD-like pathologies because the models have a lower Aβ₄₂ level than APP transgenic mice [136, 140]. Thus, the application of PS mutant mice is limited; however, the development of APP/PS transgenic mice has drawn much attention because the PS1 or PS2 mutations have synergistic effects with APP on accelerating amyloidosis [136].

2.4 Multi-transgenic models

2.4.1 APP/PS1

Compared to single APP transgenic models, APP/PS mice have a more extensive and earlier onset of amyloid pathologies and cognitive deficits, in addition to more pronounced synaptic and neuronal loss [136, 137]. Specifically, APP/PS1 mice harboring mutant APP_swe and PS1 (M146L) display Aβ depositions much earlier than the original monogenic APP transgenic mice (Tg2576 mice) [141]. Other APP/PS1 lines, such as APP_Swe × PS1_△E9, APP_Swe × PS1_A246E, and APP_Lon × PS1_A246E, have also been developed and widely used. The extensive applications of APP/PS1 mice in preclinical AD research are mainly attributed to rapid and age-dependent Aβ accumulation and deposition in the hippocampus and cortex at an early age, with an enhanced Aβ₄₂/Aβ₄₀ ratio and behavioral impairments. Like monogenic APP transgenic mice, however, tau pathologies are absent in APP/PS1 mice.

2.4.2 5×FAD

The 5×FAD model is used commonly in preclinical AD-associated research. These mice harbor five AD-related human mutant genes: mutant APP (with Swedish, Florida, and London mutations); and PS1 (with M146 L and L286V mutations). Like other APP/PS1 mice, this model has a rapid and early onset of amyloid deposition, making APP/PS1 mice a valuable preclinical tool for amyloid-associated studies. Extracellular Aβ plaques appear in the hippocampus and cortex beginning at 2 months of age and progress in an age-dependent manner, while accompanied with neuroinflammation [95–98]. Of note, female mice generate higher Aβ levels than males and display more numerous Aβ plaques in the hippocampus and cortex, showing more aggressive amyloid pathology [142–144]. This gender-dependent difference in phenotypes is generally similar to AD patients, as women have been demonstrated to be more susceptible to AD than men [1]. Moreover, 5×FAD mice exhibit age-dependent cognitive deficits, including impaired spatial memory and working memory [95–98], with neuronal loss and synaptic dysfunction [95–98, 145].

2.4.3 3×Tg-AD

As the most commonly used transgenic AD mouse model, 3×Tg-AD mice encode three mutant forms of genes associated with fAD (APP_Swe , MAPT P301L, and PSEN1 M146V) and develop both amyloid and tau pathologies [99]. Extracellular Aβ deposition occurs in the cerebral cortex by 6 months of age and becomes more extensive by 12 months, while tau protein is hyperphosphorylated and aggregated in the hippocampus by 12-15 months of age, which is later than amyloid deposition [99, 100]. Neuroinflammation and synaptic deficits appear at 7 and 6 months of age, respectively; both neuroinflammation and synaptic deficits occur prior to amyloid and tau pathologies [99, 146]. These mice show an early onset of intraneuronal Aβ-associated cognitive impairment at 4 months of age that manifests as a deficit with long-term retention [100]. Thus, this model recapitulates many AD-associated phenotypes and is well-recognized as one of the most appropriate models for mechanistic investigations and drug discovery in AD.

2.4.4 PLB1-triple

The PLB1-triple mice have five transgenic mutations on a C57BL/6J background, including APP _Swe, APP _Lon, MAPT P301 L, MAPT R406W, and PSEN1 A246E. Unlike models with high overexpression of disease genes, this model expresses low levels of mutant human APP, tau, and PS1, with no NFTs and virtually no Aβ deposition [102]. The neurodegenerative pathologies in this model progress slowly and are mild; however, spatial memory deficits are reported in 12-month-old PLB1-triple mice, with sparse Aβ plaques and hyperphosphorylated tau in the hippocampus and cortex from 6 months onwards [102, 103].

2.4.5 E4FAD

As mentioned above, the APOE4 gene is currently the most relevant gene associated with the development of sAD [30, 31]. The APOE4-elicited increase in the risk of developing AD has been reported to be closely related to the high avidity and specific binding of Aβ peptide with APOE4, sequentially leading to enhanced Aβ aggregation and reduced Aβ clearance [30, 31, 147]. Thus, some animal models have been established to investigate the role of APOE4 in AD, such as the E4FAD mouse model. The E4FAD mice are generated through cross-breeding of 5×FAD mice with the APOE4-targeted replacement mice on a C57BL/6 background. In this model, the AD phenotypes are generally less severe than the original 5×FAD mouse model. While Aβ plaques can be detected in 5×FAD mice at 2 months of age, the E4FAD mouse model exhibits progressive amyloid plaques in the cerebral cortex at 4 months [107]. The E4FAD mice have increased compact plaques and decreased diffuse plaques in the brain relative to mice with other APOE isoforms, such as E2FAD mice expressing APOE2 and E3FAD mice expressing APOE3 [107]. Moreover, age-dependent cognitive impairments in MWMT and Y maze are found in this model at 2 months, with elevated neuroinflammation at 6 months and synaptic loss at 4 months [108, 109] compared to the E2FAD and E3FAD models.

3. NON-TRANSGENIC ANIMAL MODELS

Most transgenic animal models are generated based on genetic mutations, bringing valuable insight into the roles of amyloid and tau pathogenesis in AD; however, > 95% of AD patients are diagnosed with sAD and are rarely caused by familial gene mutations [148, 149]. Although the etiology of sAD has yet to be fully elucidated, some non-transgenic animal models have been established to mimic the neuropathologic changes in sAD. The most commonly used non-transgenic animal models of AD are shown in Table 2 .

Table 2 |

Non-transgenic animal models used in the preclinical studies of AD.

Model	AD-related pathophysiology	Behavioral impairment	Mechanisms	Advantages	Disadvantages	References
Natural aging-based AD-like models	1. Cholinergic hypofunction and other neurotransmitter alterations 2. Morphologic alterations	Learning and memory deficits (mice older than 18 months, rats older than 24 months and monkeys older than 16 years)	Aging	1. Increased knowledge of neural mechanisms of cognitive deficits 2. Defining normal ageing versus pathological states	1. Long experimental cycle 2. High maintenance cost	[150]
Senescence-accelerated mouse (SAM) models	1. Hyperphosphorylated tau, Aβ deposition, and neurotransmitter dysfunctions in SAMP8 mice 2. Aβ deposition, neuronal loss, and neuroinflammation in SAMP10 mice	Learning and memory deficits (after 6 months for SAMP8 mice and 4-6 months for SAMP10 mice)	Aging	1. Increased knowledge of neural mechanisms of cognitive deficits 2. Defining normal ageing versus pathologic states	1. Poor drug absorption. 2. High mortality rate 3. The pathologies in these models are inconsistent with the pathogenesis of AD	[36, 151–157]
Streptozotocin (STZ)-induced AD model	1. Hyperphosphorylation tau in the hippocampus and cortex (3 weeks) 2. Increased Aβ40 and Aβ42 levels; increased BACE1 expression; Aβ deposition 3. Insulin deficiency 4. Activated glial cells and increased pro-inflammatory cytokines (after 7 days) 5. Neuronal loss (after 2 weeks) 6. Oxidative stress	Learning and spatial memory deficits (after 2 weeks)	Insulin signaling pathway/neuroinflammation	1. Increased insight into roles of specific pathophysiologic pathways in neurodegeneration 2. Preclinical evaluation of drugs targeting a specific pathway, such as the insulin signaling pathway	1. The absence of obvious NFTs 2. Repeated or prolonged exposure to STZ is a health hazard and possibly carcinogenic to humans 3. The STZ solution is unstable and has a short half-life 4. High resistance to STZ injection in the female animals	[158–167]
D-galactose-induced AD animal model	1. Oxidative stress (8 weeks) 2. Activated microglia and astrocyte (after 6 weeks) 3. Abnormal acetylcholine levels and choline acetyltransferase activity	Spatial memory impairment (after 6 weeks)	Oxidative stress	This model is applicable to study the AD pathogenesis in aging	1. No development of NFTs 2. No Aβ pathology 3. Gender-dependent differences in pathologic changes	[168–173]
Aluminum-induced rodent model	1. Hyperphosphorylated tau 2. Overexpression of the APP gene (4 months); increased Aβ42 level and BACE1 expression (6 weeks) 3. Oxidative stress; increased MDA level, decreased SOD activity, and DNA oxidative damage in the hippocampus and cortex (60-100 days)	Learning and memory deficits (after 6 weeks)	Metal ions disorder	Preclinical evaluation of the relationship between metal ion dysfunction and AD pathology.	The absence of Aβ plaques and NFTs	[174–183]
Scopolamine-induced model	Cholinergic hypofunction	Cognitive deficits	Brain atrophy	1. Increased insight into the role of the cholinergic system in cognition 2. Preclinical evaluation of cholinomimetics	1. No pathologic AD hallmarks 2. No disease progression	[184–189]
Aβ infusion (single or chronic)	Increased Aβ levels (after 2 weeks)	Learning and memory deficits (after 2 weeks)	Aβ deposits	1. Increased insight into Aβ toxicity 2. Preclinical evaluation of Aβ targeting drugs	1. No development of NFTs 2. Hyper-physiologic Aβ levels required	[190–197]
Acrolein-induced model	1. Hyperphosphorylated tau 2. Increased secretion of Aβ; increased BACE1 expression 3. Increased MDA level	Learning and memory deficits	Oxidative stress	NA	No typical Aβ plaques and NFTs observed	[183]
Traumatic brain injury (TBI)-induced models	1. Aβ deposition 2. Abnormal tau aggregation and hyperphosphorylation 3. Activated glial cells and increased pro-inflammatory cytokines 4. BBB dysfunctions 5. Neuronal loss	Learning and spatial memory deficits	TBI-induced phosphorylation of tau protein	This model is applicable for studies on the regulatory mechanism of hyperphosphorylation of tau protein.	1. No obvious Aβ plaques and NFTs 2. Poor comparability among different TBI models due to the absence of officially neurologic criteria for trauma	[198]
High cholesterol diet-induced AD animal model	1. Increased Aβ levels (after 3 weeks); increased APP expression (after 2 weeks) 2. Activated microglia and increased pro-inflammatory cytokines (after 1 week) 3. Oxidative stress 4. BBB dysfunction	None.	Hypocholesteremia	This model is applicable to study the relationship between lipid metabolism disorders and AD pathologic progress	1. Seldom occurrence of tau hyperphosphorylation 2. No behavior impairments observed	[199–202]

3.1 Spontaneous models

3.1.1 Senescence-accelerated mouse (SAM) models

The most salient known risk factors for AD are the non-modifiable contributors of older age, gender, genetics, and family history. Of these risk factors, advanced age has the most significant known impact on the risk of developing AD [1]. Thus, some AD models are established based on aging. The SAM, a mouse model of AD established in the early 1980s through phenotypic selection from a genetic pool of AKR/J mice, has mostly duplicated natural age-associated deterioration [36]. The SAM model consists of nine major sub-strains of SAM-prone (SAMP) mice, including the SAMP8 and SAMP10 sub-strains. Accumulating evidence demonstrated that SAMP8 mice display progressive memory deficits from 6 months of age and other aging features at a young age, such as sparse hair and osteoporosis [151, 152], with age-related hyperphosphorylated tau, Aβ deposition, and neurotransmitter dysfunction [153–155]. Therefore, the SAMP8 mouse model has been used as an aging-associated AD model. Another sub-strain, the SAMP10 strain, has also been proven to be a relevant model for AD, as these mice show age-associated learning and memory deficits at 4-6 months of age and exhibit Aβ deposition, neuronal loss and neuroinflammation during the aging process [156, 157]. Due to the high mortality rate and poor drug absorption, the use of SAMP models is limited. Moreover, the pathologic alterations in these models are not consistent with AD pathogenesis [33].

3.1.2 Natural aging models

Natural aging animals have also been used for AD research. Although natural aging rodents show age-related cognitive impairments morphologic changes, and cholinergic hypofunction [150], aging rodents do not develop amyloid and tau pathologies, making them less useful for AD studies targeting these two pathologic features. Furthermore, the high cost of maintenance and long experimental cycle largely restrict preclinical use. Some other species, including dogs, goats, cats, polar bears, sheep, wolverines, and several non-human primate species, can spontaneously develop Aβ plaque pathology, and some species even exhibit tau pathology [36]. These histopathologic changes can be accompanied by learning and memory impairment; however, there are some shortcomings or obstacles when using these species for experimental studies, such as limited availability, ethical problems, difficulty of large-scale breeding, and the high maintenance cost.

3.2 Streptozotocin (STZ)-induced AD model

As a natural alkylating anti-neoplastic agent isolated from Streptomyces achromogenes, STZ is selectively toxic for insulin-producing cells in the brain and periphery. The administration of STZ via intracerebroventricular (ICV) injection progressively and in a dose-dependent fashion causes AD-like pathologies, including cerebral glucose/energy metabolism impairments, decreased brain insulin resistance state, Aβ plaque deposition, hyperphosphorylation of tau protein, synaptic dysfunction, neuroinflammation, oxidative stress, and neuronal loss [158–161]. Moreover, behavioral deficits, such as progressive memory impairment, anxiety- and depression-like behaviors, and social dysfunction, can also be induced by ICV injection of STZ in rodents [162–164]. These pathologic alterations are generally consistent with the features of sAD patients. Thus, STZ has been extensively used to establish the sAD rodent model for the mechanistic assessment and preclinical evaluation of potential therapeutics.

Sone features or disadvantages in this model can hinder preclinical use. First, the STZ-induced AD model does not develop pronounced NFTs, although tau protein is hyperphosphorylated in the hippocampus and cortex 3 weeks after injection [165]. Second, repeated or prolonged exposure to STZ is considered a health hazard and possibly carcinogenic to humans [166]. Therefore, qualified experimental laboratories and appropriate personal protective equipment are required during the establishment of the STZ-induced AD model. Third, the STZ solution is unstable and has a short half-life (15-30 min), thereby easily leading to failure of model establishment if not handled properly. In addition, females are more resistant to pathologic changes induced by STZ injection than male rats, thus there are significant sex differences in cognitive impairment, glucose uptake, glutathione level, hippocampal astrocyte activation, and the choline acetyltransferase level [167]. The sex-dependent phenotypes in this model are inconsistent with the clinical findings that showed women have a nearly two-fold increased risk of developing AD than males [1].

3.3 D-galactose-induced AD model

D-galactose (D-gal) is a naturally-occurring monosaccharide sugar that serves as an energy-providing source and essential glycosylation component in mammals. As a reducing sugar, D-gal can be metabolized in the body under physiologic conditions, while high levels of D-gal can be changed to aldose and hydroperoxide under catalysis of galactose oxidase [168, 169]. The increased production of reactive oxygen species (ROS) induced by high D-gal levels cause oxidative stress, ultimately accelerating the process of aging [168, 170]. Accumulating evidence has shown that rodents receiving a continuous subcutaneous or intraperitoneal injection of D-gal exhibit progressive learning and memory impairment, accompanied by mitochondrial dysfunction, oxidative stress, microglial and astrocytic activation, and impaired cholinergic neurons in the brain [169, 171, 172]. All of these pathologic alterations are generally consistent with the natural brain aging process. Thus, long-term injection of rodents with D-gal has been widely used to study the mechanisms involved with aging and age-associated neurodegeneration and evaluate potential therapeutic strategies. Because increasing age is the most salient known risk factor for developing AD [1], the D-gal-induced model is applicable to the research on AD pathogenesis in aging.

The D-gal-induced model does not develop NFTs and amyloid plaques, the two neuropathologic hallmarks in the AD brain, or tau and Aβ pathologies [169, 171]. Moreover, gender-dependent differences have been shown with respect to the pathologic changes induced by D-gal [173]. Chronic injection of 100 mg/kg D-gal for 6 consecutive weeks causes spatial memory deficits and cerebral oxidative stress in male mice, but not female mice [173], indicating that male mice are more vulnerable to D-gal-elicited oxidative damage.

3.4 Aluminum-induced rodent model

As the most abundant neurotoxic metal, aluminum (Al) is closely associated with AD pathogenesis [203, 204]. Mounting evidence demonstrates that Al can cross the blood-brain barrier (BBB) and accumulate in the brain in a semi-permanent manner [205, 206]. The high consumption of Al from drinking water has been reported to increase both the incidence of cases and AD mortality [207, 208], and Al has been shown to promote the neurofibrillary degeneration and accelerate the formation of NFT-like structures in AD patients [209–211], suggesting that Al is a potential causative factor for AD. The association between Al exposure and tau pathology was later confirmed by experimental findings. Oshima et al. [174] reported that chronic oral administration of Al accelerates tau aggregation, promotes apoptosis, and exacerbates motor dysfunction in a tauopathy mouse model, showing that Al has an aggravating effect on tau pathologies. Moreover, Al administration induce taus or tau-related pathologies in experimental animals by suppressing the dephosphorylation of tau protein, increasing tau aggregation, and enhancing the activities of GSK-3β and CDK5, the two important tau kinases [204]. Apart from the development of tau pathologies, Al exposure affects Aβ by increasing the aggregation and Aβ-associated endothelial cell toxicity [175, 176]. The APP gene has been reported to be overexpressed in rat brain after treatment with 1.7 mg/kg aluminum chloride (AlCl₃) for 4 consecutive months [177], while the oral administration of 17 mg/kg AlCl₃ for 6 consecutive weeks causes increased BACE-1 and Aβ₄₂ levels in rats [178]. Other prominent pathologic features, such as oxidative damage [179], neuroinflammation [180, 204], neuronal apoptosis [181], and increased AChE activity [182], are also observed in an Al-treated AD model. All these neurologic alternations induced by Al exposure are attributable to the impaired cognitive functions in animals, which are generally congruent with that which is observed in the early stage of AD [203, 204]. Although Al causes neuropathologic and neurobehavioral changes in in vivo experimental models, poor bioavailability hinders use of Al due to low solubility at a physiologic pH. Thus, AlCl₃ rather than Al is often used to establish the animal model of AD; however, it is worth noting that an aqueous solution of AlCl₃ partially hydrolyzes and releases volatile hydrogen chloride [183], affecting its bioavailability. Furthermore, this Al-induced AD model does not develop Aβ plaques and NFTs, two typical hallmarks of AD.

3.5 Scopolamine-induced rodent model

Cholinergic neurons are extensively distributed in the central nervous system (CNS) and have a predominant role in regulating critical physiologic processes, such as attention, learning, memory, and the stress response [212]. dysfunction of the cholinergic transmission system contributes to AD pathology [213]. Cholinergic antagonists impair learning and memory, while the administration of cholinergic agonists increases the availability of acetylcholine (ACh) at synapses and ameliorates cognitive deficits [198, 213]. Scopolamine, a known non-selective antagonist of the postsynaptic muscarinic receptor, disrupts the cholinergic tracts for cognition and induces learning and memory impairment in animals via intraperitoneal or ICV injection [184–186]. Animals treated with scopolamine partially exhibit the pathologic features of patients with AD; specifically, the Ach level is decreased, cholinergic neuron loss, amnesia, oxidative damage, and neuroinflammation [187–189]. Thus, the scopolamine-induced animal model provides an experimental tool to investigate the role of the cholinergic system in cognition and assess the preclinical efficacy of cholinomimetics; however, this model lacks typical AD pathologic hallmarks and disease progression, imposing limitations on preclinical use.

3.6 Aβ infusion (single or chronic)

The amyloid cascade hypothesis proposes that cerebral accumulation and aggregation of Aβ in the initial stage of AD is the primary culprit driving pathogenesis, regardless of sporadic or familiar AD, which ultimately results in neurodegeneration [5, 6, 12]. The Aβ-associated pathologies of the AD brain can be mimicked by intrahippocampal, intracerebral, or ICV injection of different Aβ species into the rodent brain [214]. Aβ peptides, including Aβ₄₀, Aβ₄₂, and Aβ_25-35, can be administered in single or multiple injections to induce AD-like pathologies and behavioral deficits [190–192], including cholinergic dysfunction, Aβ deposition, and learning and memory impairment [191, 193–195]. In addition, the severity of cognitive impairment is associated with the species of Aβ infused and the interval between Aβ administration and behavioral tests.

In addition to the APP transgenic animal models, this Aβ infusion rodent model provides researchers with alternative means to study the mechanisms underlying Aβ toxicity in vivo and assess the preclinical effects of Aβ-targeting drugs. Moreover, this model has a much shorter experimental cycle than transgenic models. As alluded to above, it takes several months to observe Aβ plaque deposition in APP-based transgenic rodents, while plaque deposition can be found within a few weeks after Aβ infusion in animals [196]; however, like APP transgenic models, the formation of NFTs are not observed in this Aβ infusion model, suggesting that Aβ infusion does not induce essential neuropathologic features of AD. Furthermore, hyperphysiologic Aβ levels required to establish this model are much higher than the Aβ concentrations in the cerebrospinal fluid or brains of AD patients [197].

3.7 Other non-transgenic AD models

In addition to the spontaneous and chemical/drug-induced models described above, AD-like pathologies can also be induced by acrolein [215] and traumatic brain injury [216] ( Table 2 ).

Moreover, in addition to rodents, other non-rodent animal species have also been used as AD animal models, such as Caenorhabditis elegans [217], zebrafish [218], Drosophila melanogaster (also known as fruit flies) [219], and non-human primates [215, 220]. These species, however, are much less used than rodents in AD studies due to the limited availability, the vast phenotypic or pathologic differences from humans, difficult large-scale breeding, ethical problems, and the high maintenance cost. In addition, C. elegans and D. melanogaster are valuable and advantageous AD models for genetic manipulation and high-throughput drug screening due to the short lifespan, ease of maintenance, and genetic tractability [221, 222]. These two models can be genetically modified to overexpress human APP and MAPT genes, and are useful for studying the amyloid and tau pathologies in AD [217, 219]. C. elegans has a short life cycle of 3 days from egg-to-adult at 25 °C [222], while D. melanogaster has a short lifespan that includes four different morphologic stages (embryo, larva, pupa, and adult) [223], each of which allows genetic dissection of the mechanisms that affect aging and lifespan. Thus, although higher animals (e.g., rodents and non-human primates) are necessary for a more comprehensive understanding of AD pathology and for developing and translating potential therapeutics, lower species, such as C. elegans and D. melanogaster, can replace higher species for specific studies.

4. CHALLENGES AND PERSPECTIVES

Currently, numerous animal models are available for AD research, all of which have their own advantages and disadvantages. Of note, no experimental model can perfectly replicate all aspects of the neuropathologic and behavioral features that occur in AD patients. For example, the absence of Aβ plaques is commonly noted in in tau-based transgenic models, and the lack of NFTs formation is a feature of most sAD models and APP-based transgenic models. Thus, the experimental findings from animal studies may not be directly translated into clinical use. Moreover, because of the dramatic increase in the incidence of AD and the absence of a promising cure for AD at present, some potential drugs have been evaluated in clinical trials without reliable and robust preclinical data from animal studies, hence heightening the likelihood of failure in a clinical study. To validate a successful translation, the potential therapeutic strategies or drug candidates should be evaluated in different AD models to test efficacy and safety because pathologic differences are detected in different AD animal models. In addition, to avoid the preclinical inconsistency among different researchers or laboratories, it is necessary to follow standard procedures or protocols to assure the results are comparable and repeatable. Some guidelines, such as the Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) [224] and the Animal Research: Reporting of In Vivo Experiments (ARRIVE) [225] guidelines, should be considered for improving the methodologic quality during the design, execution, and reporting of further animal studies in AD.

Some key points should also be considered when using AD animal models. First, like AD patients, gender often affects not only the onset but also the progression of the disease in AD models, as reviewed above. In some AD models, females display earlier AD phenotypic and pathologic characteristics than males, such as 5×FAD transgenic mice. In contrast, males are more vulnerable to AD and exhibit more severe deterioration in other models, such as the STZ-induced AD animal model. Second, because most AD-like pathologies and cognitive deficits in animal models are time- or age-dependent, the appropriate timing of behavioral tests and tissue/sample collection should be designed with an intention to obtain better and more promising results. Third, although animal models can mimic AD-like phenotypes, interspecies variability can cause marked pathologic differences, and the pharmacokinetics are potentially different between species [226]. Nevertheless, up to now, the use of AD animal models for preclinical research is well-recognized and well-accepted because the phenotypic similarities between humans and animals are more prominent than the differences [227].

5. CONCLUSIONS

This review comprehensively summarized the AD animal models and the model characteristics, including transgenic animal models, chemical- or drug-induced animal models, and spontaneous animal models. Some phenotypic and neuropathological changes in AD patients can be recapitulated with genetic and pharmacologic manipulations in animals. Despite the challenges and constraints, the currently available AD models remain very valuable tools for deciphering the pathogenic mechanisms underlying AD and developing new therapeutic strategies.

ABBREVIATIONS

Aβ, Beta-amyloid; Ach, Acetylcholine; AD, Alzheimer’s disease; Al, Aluminum; AlCl₃, Aluminum chloride; ApoE, Apolipoprotein E; APP, Amyloid precursor protein; ARRIVE, Animal Research: Reporting of In Vivo Experiments; BBB, Blood-brain barrier; CNS, Central nervous system; D-gal, D-Galactose; fAD, familial AD; FTDP-17, Frontotemporal dementia and parkinsonism linked to chromosome 17; ICV, Intracerebroventricular; MWMT, Morris water maze test; NFT, Neurofibrillary tangle; PHFs, Paired helical filaments; PREPARE, Planning Research and Experimental Procedures on Animals: Recommendations for Excellence; PS1, presenilin-1; PS2, presenilin-2; ROS, Reactive oxygen species; sAD, Sporadic AD; SAM, Senescence-accelerated mouse; SAMP, SAM-prone; STZ, Streptozotocin; TREM2, Triggering receptor expressed on myeloid cells 2.

ACKNOWLEDGEMENTS

Not applicable.

CONFLICT OF INTEREST STATEMENTS

The authors declare that they have no competing interests.

REFERENCES

1. Alzheimer’s Association: 2022 Alzheimer’s Disease Facts and Figures. Alzheimer’s & Dementia. 2022. Vol. 18:700–789

2. The top 10 causes of death. December. 2020. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-deathAccessed on 10 Febrary 2023

3. Schmidt R, Hofer E, Bouwman FH, Buerger K, Cordonnier C, Fladby T, et al.. EFNS-ENS/EAN Guideline on Concomitant Use of Cholinesterase Inhibitors and Memantine in Moderate to Severe Alzheimer’s Disease. European Journal of Neurology. 2015. Vol. 22:889–898

4. Huang LK, Chao SP, Hu CJ. Clinical Trials of New Drugs for Alzheimer Disease. Journal of Biomedical Science. 2020. Vol. 27:18

5. Frisoni GB, Altomare D, Thal DR, Ribaldi F, van der Kant R, Ossenkoppele R, et al.. The Probabilistic Model of Alzheimer Disease: The Amyloid Hypothesis Revised. Nature Reviews: Neuroscience. 2022. Vol. 23:53–66

6. Lee SJ, Nam E, Lee HJ, Savelieff MG, Lim MH. Towards an Understanding of Amyloid-β Oligomers: Characterization, Toxicity Mechanisms, and Inhibitors. Chemical Society Reviews. 2017. Vol. 46:310–323

7. Kang J, Lemaire HG, Unterbeck A, Salbaum JM, Masters CL, Grzeschik KH, et al.. The Precursor of Alzheimer’s Disease Amyloid A4 Protein Resembles a Cell-Surface Receptor. Nature. 1987. Vol. 325:733–736

8. Sisodia SS, Koo EH, Beyreuther K, Unterbeck A, Price DL. Evidence that Beta-Amyloid Protein in Alzheimer’s Disease is Not Derived by Normal Processing. Science. 1990. Vol. 248:492–495

9. Obregon D, Hou H, Deng J, Giunta B, Tian J, Darlington D, et al.. Soluble Amyloid Precursor Protein-α Modulates β-Secretase Activity and Amyloid-β Generation. Nature Communications. 2012. Vol. 3:777

10. Wu T, Lin D, Cheng Y, Jiang S, Riaz MW, Fu N, et al.. Amyloid Cascade Hypothesis for the Treatment of Alzheimer’s Disease: Progress and Challenges. Aging and Disease. 2022. Vol. 13:1745–1758

11. Hardy J. Has the Amyloid Cascade Hypothesis for Alzheimer’s Disease Been Proved? Current Alzheimer Research. 2006. Vol. 3:71–73

12. Selkoe DJ, Hardy J. The Amyloid Hypothesis of Alzheimer’s Disease at 25 Years. EMBO Molecular Medicine. 2016. Vol. 8:595–608

13. Hardy J. The Amyloid Hypothesis for Alzheimer’s Disease: A Critical Reappraisal. Journal of Neurochemistry. 2009. Vol. 110:1129–1134

14. Haass C, Selkoe DJ. Soluble Protein Oligomers in Neurodegeneration: Lessons from the Alzheimer’s Amyloid Beta-Peptide. Nature Reviews: Molecular Cell Biology. 2007. Vol. 8:101–112

15. Rostami J, Mothes T, Kolahdouzan M, Eriksson O, Moslem M, Bergström J, et al.. Crosstalk between Astrocytes and Microglia Results in Increased Degradation of α-Synuclein and Amyloid-β Aggregates. Journal of Neuroinflammation. 2021. Vol. 18:124

16. Leng F, Edison P. Neuroinflammation and Microglial Activation in Alzheimer Disease: Where Do We Go from Here? Nature Reviews: Neurology. 2021. Vol. 17:157–172

17. Vandenbark AA, Offner H, Matejuk S, Matejuk A. Microglia and Astrocyte Involvement in Neurodegeneration and Brain Cancer. Journal of Neuroinflammation. 2021. Vol. 18:298

18. Karran E, De Strooper B. The Amyloid Hypothesis in Alzheimer Disease: New Insights from New Therapeutics. Nature Reviews: Drug Discovery. 2022. Vol. 21:306–318

19. James BD, Bennett DA. Causes and Patterns of Dementia: An Update in the Era of Redefining Alzheimer’s Disease. Annual Review of Public Health. 2019. Vol. 40:65–84

20. Lasagna-Reeves CA, Castillo-Carranza DL, Sengupta U, Sarmiento J, Troncoso J, Jackson GR, et al.. Identification of Oligomers at Early Stages of Tau Aggregation in Alzheimer’s Disease. FASEB Journal. 2012. Vol. 26:1946–1959

21. Congdon EE, Sigurdsson EM. Tau-Targeting Therapies for Alzheimer Disease. Nature Reviews: Neurology. 2018. Vol. 14:399–415

22. Zempel H, Mandelkow E. Lost after Translation: Missorting of Tau Protein and Consequences for Alzheimer Disease. Trends in Neurosciences. 2014. Vol. 37:721–732

23. Dujardin S, Commins C, Lathuiliere A, Beerepoot P, Fernandes AR, Kamath TV, et al.. Tau Molecular Diversity Contributes to Clinical Heterogeneity in Alzheimer’s Disease. Nature Medicine. 2020. Vol. 26:1256–1263

24. Gao YL, Wang N, Sun FR, Cao XP, Zhang W, Yu JT. Tau in Neurodegenerative Disease. Annals of Translational Medicine. 2018. Vol. 6:175

25. Hardy J, Selkoe DJ. The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics. Science. 2002. Vol. 297:353–356

26. Castillo-Carranza DL, Guerrero-Muñoz MJ, Sengupta U, Hernandez C, Barrett AD, Dineley K, et al.. Tau Immunotherapy Modulates Both Pathological Tau and Upstream Amyloid Pathology in an Alzheimer’s Disease Mouse Model. Journal of Neuroscience. 2015. Vol. 35:4857–4868

27. Guerrero-Muñoz MJ, Gerson J, Castillo-Carranza DL. Tau Oligomers: The Toxic Player at Synapses in Alzheimer’s Disease. Frontiers in Cellular Neuroscience. 2015. Vol. 9:464

28. Querfurth HW, LaFerla FM. Alzheimer’s Disease. New England Journal of Medicine. 2010. Vol. 362:329–344

29. Anand A, Patience AA, Sharma N, Khurana N. The Present and Future of Pharmacotherapy of Alzheimer’s Disease: A Comprehensive Review. European Journal of Pharmacology. 2017. Vol. 815:364–375

30. Elias-Sonnenschein LS, Viechtbauer W, Ramakers IH, Verhey FR, Visser PJ. Predictive Value of APOE-ε4 Allele for Progression from MCI to AD-Type Dementia: A Meta-Analysis. Journal of Neurology, Neurosurgery and Psychiatry. 2011. Vol. 82:1149–1156

31. Mattsson N, Groot C, Jansen WJ, Landau SM, Villemagne VL, Engelborghs S, et al.. Prevalence of the Apolipoprotein E ε4 Allele in Amyloid β Positive Subjects Across the Spectrum of Alzheimer’s Disease. Alzheimer’s & Dementia. 2018. Vol. 14:913–924

32. Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer’s Disease: Clinical Trials and Drug Development. Lancet Neurology. 2010. Vol. 9:702–716

33. Zhang L, Chen C, Mak MS, Lu J, Wu Z, Chen Q, et al.. Advance of Sporadic Alzheimer’s Disease Animal Models. Medicinal Research Reviews. 2020. Vol. 40:431–458

34. Goldman JS, Hahn SE, Catania JW, LaRusse-Eckert S, Butson MB, Rumbaugh M, et al.. Genetic Counseling and Testing for Alzheimer Disease: Joint Practice Guidelines of the American College of Medical Genetics and the National Society of Genetic Counselors. Genetics in Medicine. 2011. Vol. 13:597–605

35. Bilkei-Gorzo A. Genetic Mouse Models of Brain Ageing and Alzheimer’s Disease. Pharmacology and Therapeutics. 2014. Vol. 142:244–257

36. Van Dam D, De Deyn PP. Animal Models in the Drug Discovery Pipeline for Alzheimer’s Disease. British Journal of Pharmacology. 2011. Vol. 164:1285–1300

37. Forrest SL, Kril JJ, Stevens CH, Kwok JB, Hallupp M, Kim WS, et al.. Retiring the Term FTDP-17 as MAPT Mutations are Genetic Forms of Sporadic Frontotemporal Tauopathies. Brain. 2018. Vol. 141:521–534

38. Guerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, Majounie E, et al.. TREM2 Variants in Alzheimer’s Disease. New England Journal of Medicine. 2013. Vol. 368:117–127

39. Chishti MA, Yang DS, Janus C, Phinney AL, Horne P, Pearson J, et al.. Early-Onset Amyloid Deposition and Cognitive Deficits in Transgenic Mice Expressing a Double Mutant form of Amyloid Precursor Protein 695. Journal of Biological Chemistry. 2001. Vol. 276:21562–21570

40. Woodhouse A, Vickers JC, Adlard PA, Dickson TC. Dystrophic Neurites in TgCRND8 and Tg2576 Mice Mimic Human Pathological Brain Aging. Neurobiology of Aging. 2009. Vol. 30:864–874

41. Dudal S, Krzywkowski P, Paquette J, Morissette C, Lacombe D, Tremblay P, et al.. Inflammation Occurs Early During the Abeta Deposition Process in TgCRND8 Mice. Neurobiology of Aging. 2004. Vol. 25:861–871

42. Krantic S, Isorce N, Mechawar N, Davoli MA, Vignault E, Albuquerque M, et al.. Hippocampal GABAergic Neurons are Susceptible to Amyloid-β Toxicity In Vitro and are Decreased in Number in the Alzheimer’s Disease TgCRND8 Mouse Model. Journal of Alzheimer’s Disease. 2012. Vol. 29:293–308

43. Kimura R, MacTavish D, Yang J, Westaway D, Jhamandas JH. Beta Amyloid-Induced Depression of Hippocampal Long-Term Potentiation is Mediated through the Amylin Receptor. Journal of Neuroscience. 2012. Vol. 32:17401–17406

44. Sturchler-Pierrat C, Abramowski D, Duke M, Wiederhold KH, Mistl C, Rothacher S, et al.. Two Amyloid Precursor Protein Transgenic Mouse Models with Alzheimer Disease-Like Pathology. Proceedings of the National Academy of Sciences of the United States of America. 1997. Vol. 94:13287–13292

45. Calhoun ME, Wiederhold KH, Abramowski D, Phinney AL, Probst A, Sturchler-Pierrat C, et al.. Neuron Loss in APP Transgenic Mice. Nature. 1998. Vol. 395:755–756

46. Van Dam D, D’Hooge R, Staufenbiel M, Van Ginneken C, Van Meir F, De Deyn PP. Age-Dependent Cognitive Decline in the APP23 Model Precedes Amyloid Deposition. European Journal of Neuroscience. 2003. Vol. 17:388–396

47. Kelly PH, Bondolfi L, Hunziker D, Schlecht HP, Carver K, Maguire E, et al.. Progressive Age-Related Impairment of Cognitive Behavior in APP23 Transgenic Mice. Neurobiology of Aging. 2003. Vol. 24:365–378

48. Lanz TA, Carter DB, Merchant KM. Dendritic Spine Loss in the Hippocampus of Young PDAPP and Tg2576 Mice and its Prevention by the ApoE2 Genotype. Neurobiology of Disease. 2003. Vol. 13:246–253

49. Jacobsen JS, Wu CC, Redwine JM, Comery TA, Arias R, Bowlby M, et al.. Early-Onset Behavioral and Synaptic Deficits in a Mouse Model of Alzheimer’s Disease. Proceedings of the National Academy of Sciences of the United States of America. 2006. Vol. 103:5161–5166

50. Jung JH, An K, Kwon OB, Kim HS, Kim JH. Pathway-Specific Alteration of Synaptic Plasticity in Tg2576 Mice. Molecules and Cells. 2011. Vol. 32:197–201

51. Chapman PF, White GL, Jones MW, Cooper-Blacketer D, Marshall VJ, Irizarry M, et al.. Impaired Synaptic Plasticity and Learning in Aged Amyloid Precursor Protein Transgenic Mice. Nature Neuroscience. 1999. Vol. 2:271–276

52. Games D, Adams D, Alessandrini R, Barbour R, Berthelette P, Blackwell C, et al.. Alzheimer-Type Neuropathology in Transgenic Mice Overexpressing V717F Beta-Amyloid Precursor Protein. Nature. 1995. Vol. 373:523–527

53. Masliah E, Sisk A, Mallory M, Games D. Neurofibrillary Pathology in Transgenic Mice Overexpressing V717F Beta-Amyloid Precursor Protein. Journal of Neuropathology and Experimental Neurology. 2001. Vol. 60:357–368

54. Larson J, Lynch G, Games D, Seubert P. Alterations in Synaptic Transmission and Long-Term Potentiation in Hippocampal Slices from Young and Aged PDAPP Mice. Brain Research. 1999. Vol. 840:23–35

55. Dodart JC, Meziane H, Mathis C, Bales KR, Paul SM, Ungerer A. Behavioral Disturbances in Transgenic Mice Overexpressing the V717F Beta-Amyloid Precursor Protein. Behavioral Neuroscience. 1999. Vol. 113:982–990

56. Hartman RE, Izumi Y, Bales KR, Paul SM, Wozniak DF, Holtzman DM. Treatment with an Amyloid-Beta Antibody Ameliorates Plaque Load, Learning Deficits, and Hippocampal Long-Term Potentiation in a Mouse Model of Alzheimer’s Disease. Journal of Neuroscience. 2005. Vol. 25:6213–6220

57. Van Dorpe J, Smeijers L, Dewachter I, Nuyens D, Spittaels K, Van Den Haute C, et al.. Prominent Cerebral Amyloid Angiopathy in Transgenic Mice Overexpressing the London Mutant of Human APP in Neurons. American Journal of Pathology. 2000. Vol. 157:1283–1298

58. Moechars D, Dewachter I, Lorent K, Reversé D, Baekelandt V, Naidu A, et al.. Early Phenotypic Changes in Transgenic Mice that Overexpress Different Mutants of Amyloid Precursor Protein in Brain. Journal of Biological Chemistry. 1999. Vol. 274:6483–6492

59. Hong S, Beja-Glasser VF, Nfonoyim BM, Frouin A, Li S, Ramakrishnan S, et al.. Complement and Microglia Mediate Early Synapse Loss in Alzheimer Mouse Models. Science. 2016. Vol. 352:712–716

60. Wright AL, Zinn R, Hohensinn B, Konen LM, Beynon SB, Tan RP, et al.. Neuroinflammation and Neuronal Loss Precede Aβ Plaque Deposition in the hAPP-J20 Mouse Model of Alzheimer’s Disease. PloS One. 2013. Vol. 8:e59586

61. Saganich MJ, Schroeder BE, Galvan V, Bredesen DE, Koo EH, Heinemann SF. Deficits in Synaptic Transmission and Learning in Amyloid Precursor Protein (APP) Transgenic Mice Require C-Terminal Cleavage of APP. Journal of Neuroscience. 2006. Vol. 26:13428–13436

62. Cheng IH, Scearce-Levie K, Legleiter J, Palop JJ, Gerstein H, Bien-Ly N, et al.. Accelerating Amyloid-Beta Fibrillization Reduces Oligomer Levels and Functional Deficits in Alzheimer Disease Mouse Models. Journal of Biological Chemistry. 2007. Vol. 282:23818–23828

63. Rockenstein E, Mallory M, Mante M, Sisk A, Masliaha E. Early Formation of Mature Amyloid-Beta Protein Deposits in a Mutant APP Transgenic Model Depends on Levels of Abeta(1-42). Journal of Neuroscience Research. 2001. Vol. 66:573–582

64. Rockenstein E, Mante M, Alford M, Adame A, Crews L, Hashimoto M, et al.. High Beta-Secretase Activity Elicits Neurodegeneration in Transgenic Mice Despite Reductions in Amyloid-Beta Levels: Implications for the Treatment of Alzheimer Disease. Journal of Biological Chemistry. 2005. Vol. 280:32957–32967

65. Havas D, Hutter-Paier B, Ubhi K, Rockenstein E, Crailsheim K, Masliah E, et al.. A Longitudinal Study of Behavioral Deficits in an AβPP Transgenic Mouse Model of Alzheimer’s Disease. Journal of Alzheimer’s Disease. 2011. Vol. 25:231–243

66. Pang K, Jiang R, Zhang W, Yang Z, Li LL, Shimozawa M, et al.. An App Knock-In Rat Model for Alzheimer’s Disease Exhibiting Aβ and Tau Pathologies, Neuronal Death and Cognitive Impairments. Cell Research. 2022. Vol. 32:157–175

67. Yoshiyama Y, Higuchi M, Zhang B, Huang SM, Iwata N, Saido TC, et al.. Synapse Loss and Microglial Activation Precede Tangles in a P301S Tauopathy Mouse Model. Neuron. 2007. Vol. 53:337–351

68. Takeuchi H, Iba M, Inoue H, Higuchi M, Takao K, Tsukita K, et al.. P301S Mutant Human Tau Transgenic Mice Manifest Early Symptoms of Human Tauopathies with Dementia and Altered Sensorimotor Gating. PloS One. 2011. Vol. 6:e21050

69. Lasagna-Reeves CA, de Haro M, Hao S, Park J, Rousseaux MW, Al-Ramahi I, et al.. Reduction of Nuak1 Decreases Tau and Reverses Phenotypes in a Tauopathy Mouse Model. Neuron. 2016. Vol. 92:407–418

70. Santacruz K, Lewis J, Spires T, Paulson J, Kotilinek L, Ingelsson M, et al.. Tau Suppression in a Neurodegenerative Mouse Model Improves Memory Function. Science. 2005. Vol. 309:476–481

71. Ramsden M, Kotilinek L, Forster C, Paulson J, McGowan E, SantaCruz K, et al.. Age-Dependent Neurofibrillary Tangle Formation, Neuron Loss, and Memory Impairment in a Mouse Model of Human Tauopathy (P301L). Journal of Neuroscience. 2005. Vol. 25:10637–10647

72. Yue M, Hanna A, Wilson J, Roder H, Janus C. Sex Difference in Pathology and Memory Decline in rTg4510 Mouse Model of Tauopathy. Neurobiology of Aging. 2011. Vol. 32:590–603

73. Kopeikina KJ, Polydoro M, Tai HC, Yaeger E, Carlson GA, Pitstick R, et al.. Synaptic Alterations in the rTg4510 Mouse Model of Tauopathy. Journal of Comparative Neurology. 2013. Vol. 521:1334–1353

74. Hoover BR, Reed MN, Su J, Penrod RD, Kotilinek LA, Grant MK, et al.. Tau Mislocalization to Dendritic Spines Mediates Synaptic Dysfunction Independently of Neurodegeneration. Neuron. 2010. Vol. 68:1067–1081

75. Andorfer C, Kress Y, Espinoza M, de Silva R, Tucker KL, Barde YA, et al.. Hyperphosphorylation and Aggregation of Tau in Mice Expressing Normal Human Tau Isoforms. Journal of Neurochemistry. 2003. Vol. 86:582–590

76. Geiszler PC, Barron MR, Pardon MC. Impaired Burrowing is the Most Prominent Behavioral Deficit of Aging hTau mice. Neuroscience. 2016. Vol. 329:98–111

77. Phillips M, Boman E, Osterman H, Willhite D, Laska M. Olfactory and Visuospatial Learning and Memory Performance in Two Strains of Alzheimer’s Disease Model Mice--a Longitudinal Study. PloS One. 2011. Vol. 6:e19567

78. Polydoro M, Acker CM, Duff K, Castillo PE, Davies P. Age-Dependent Impairment of Cognitive and Synaptic Function in the hTau Mouse Model of Tau Pathology. Journal of Neuroscience. 2009. Vol. 29:10741–10749

79. Andorfer C, Acker CM, Kress Y, Hof PR, Duff K, Davies P. Cell-Cycle Reentry and Cell Death in Transgenic Mice Expressing Nonmutant Human Tau Isoforms. Journal of Neuroscience. 2005. Vol. 25:5446–5454

80. Schindowski K, Bretteville A, Leroy K, Bégard S, Brion JP, Hamdane M, et al.. Alzheimer’s Disease-Like Tau Neuropathology Leads to Memory Deficits and Loss of Functional Synapses in a Novel Mutated Tau Transgenic Mouse without any Motor Deficits. American Journal of Pathology. 2006. Vol. 169:599–616

81. Van der Jeugd A, Vermaercke B, Derisbourg M, Lo AC, Hamdane M, Blum D, et al.. Progressive Age-Related Cognitive Decline in Tau Mice. Journal of Alzheimer’s Disease. 2013. Vol. 37:777–788

82. Lo AC, Iscru E, Blum D, Tesseur I, Callaerts-Vegh Z, Buée L, et al.. Amyloid and Tau Neuropathology Differentially Affect Prefrontal Synaptic Plasticity and Cognitive Performance in Mouse Models of Alzheimer’s Disease. Journal of Alzheimer’s Disease. 2013. Vol. 37:109–125

83. Van der Jeugd A, Ahmed T, Burnouf S, Belarbi K, Hamdame M, Grosjean ME, et al.. Hippocampal Tauopathy in Tau Transgenic Mice Coincides with Impaired Hippocampus-Dependent Learning and Memory, and Attenuated Late-Phase Long-Term Depression of Synaptic Transmission. Neurobiology of Learning and Memory. 2011. Vol. 95:296–304

84. Wines-Samuelson M, Schulte EC, Smith MJ, Aoki C, Liu X, Kelleher RJ 3rd, et al.. Characterization of Age-Dependent and Progressive Cortical Neuronal Degeneration in Presenilin Conditional Mutant Mice. PloS One. 2010. Vol. 5:e10195

85. Beglopoulos V, Sun X, Saura CA, Lemere CA, Kim RD, Shen J. Reduced Beta-Amyloid Production and Increased Inflammatory Responses in Presenilin Conditional Knock-Out Mice. Journal of Biological Chemistry. 2004. Vol. 279:46907–46914

86. Saura CA, Choi SY, Beglopoulos V, Malkani S, Zhang D, Shankaranarayana Rao BS, et al.. Loss of Presenilin Function Causes Impairments of Memory and Synaptic Plasticity followed by Age-Dependent Neurodegeneration. Neuron. 2004. Vol. 42:23–36

87. Gordon MN, Holcomb LA, Jantzen PT, DiCarlo G, Wilcock D, Boyett KW, et al.. Time Course of the Development of Alzheimer-Like Pathology in the Doubly Transgenic PS1+APP Mouse. Experimental Neurology. 2002. Vol. 173:183–195

88. Kurt MA, Davies DC, Kidd M, Duff K, Howlett DR. Hyperphosphorylated Tau and Paired Helical Filament-Like Structures in the Brains of Mice Carrying Mutant Amyloid Precursor Protein and Mutant Presenilin-1 Transgenes. Neurobiology of Disease. 2003. Vol. 14:89–97

89. Sadowski M, Pankiewicz J, Scholtzova H, Ji Y, Quartermain D, Jensen CH, et al.. Amyloid-Beta Deposition is Associated with Decreased Hippocampal Glucose Metabolism and Spatial Memory Impairment in APP/PS1 Mice. Journal of Neuropathology and Experimental Neurology. 2004. Vol. 63:418–428

90. Holcomb LA, Gordon MN, Jantzen P, Hsiao K, Duff K, Morgan D. Behavioral Changes in Transgenic Mice Expressing Both Amyloid Precursor Protein and Presenilin-1 Mutations: Lack of Association with Amyloid Deposits. Behavior Genetics. 1999. Vol. 29:177–185

91. Arendash GW, King DL, Gordon MN, Morgan D, Hatcher JM, Hope CE, et al.. Progressive, Age-Related Behavioral Impairments in Transgenic Mice Carrying Both Mutant Amyloid Precursor Protein and Presenilin-1 Transgenes. Brain Research. 2001. Vol. 891:42–53

92. Ozmen L, Albientz A, Czech C, Jacobsen H. Expression of Transgenic APP mRNA is the Key Determinant for Beta-Amyloid Deposition in PS2APP Transgenic Mice. Neuro-Degenerative Diseases. 2009. Vol. 6:29–36

93. Weidensteiner C, Metzger F, Bruns A, Bohrmann B, Kuennecke B, von Kienlin M. Cortical Hypoperfusion in the B6.PS2APP Mouse Model for Alzheimer’s Disease: Comprehensive Phenotyping of Vascular and Tissular Parameters by MRI. Magnetic Resonance in Medicine. 2009. Vol. 62:35–45

94. Poirier R, Veltman I, Pflimlin MC, Knoflach F, Metzger F. Enhanced Dentate Gyrus Synaptic Plasticity but Reduced Neurogenesis in a Mouse Model of Amyloidosis. Neurobiology of Disease. 2010. Vol. 40:386–393

95. Richard BC, Kurdakova A, Baches S, Bayer TA, Weggen S, Wirths O. Gene Dosage Dependent Aggravation of the Neurological Phenotype in the 5xFAD Mouse Model of Alzheimer’s Disease. Journal of Alzheimer’s Disease. 2015. Vol. 45:1223–1236

96. Giannoni P, Arango-Lievano M, Neves ID, Rousset MC, Baranger K, Rivera S, et al.. Cerebrovascular Pathology during the Progression of Experimental Alzheimer’s Disease. Neurobiology of Disease. 2016. Vol. 88:107–117

97. Jawhar S, Trawicka A, Jenneckens C, Bayer TA, Wirths O. Motor Deficits, Neuron Loss, and Reduced Anxiety Coinciding with Axonal Degeneration and Intraneuronal Aβ Aggregation in the 5xFAD Mouse Model of Alzheimer’s Disease. Neurobiology of Aging. 2012. Vol. 33:196.e129–e140

98. Gu L, Wu D, Tang X, Qi X, Li X, Bai F, et al.. Myelin Changes at the Early Stage of 5XFAD Mice. Brain Research Bulletin. 2018. Vol. 137:285–293

99. Oddo S, Caccamo A, Shepherd JD, Murphy MP, Golde TE, Kayed R, et al.. Triple-Transgenic Model of Alzheimer’s Disease with Plaques and Tangles: Intracellular Abeta and Synaptic Dysfunction. Neuron. 2003. Vol. 39:409–421

100. Billings LM, Oddo S, Green KN, McGaugh JL, LaFerla FM. Intraneuronal Abeta Causes the Onset of Early Alzheimer’s Disease-Related Cognitive Deficits in Transgenic Mice. Neuron. 2005. Vol. 45:675–688

101. Stover KR, Campbell MA, Van Winssen CM, Brown RE. Early Detection of Cognitive Deficits in the 3xTg-AD Mouse Model of Alzheimer’s Disease. Behavioural Brain Research. 2015. Vol. 289:29–38

102. Platt B, Drever B, Koss D, Stoppelkamp S, Jyoti A, Plano A, et al.. Abnormal Cognition, Sleep, EEG and Brain Metabolism in a Novel Knock-In Alzheimer Mouse, PLB1. PloS One. 2011. Vol. 6:e27068

103. Ryan D, Koss D, Porcu E, Woodcock H, Robinson L, Platt B, et al.. Spatial Learning Impairments in PLB1Triple Knock-In Alzheimer Mice are Task-Specific and Age-Dependent. Cellular and Molecular Life Sciences. 2013. Vol. 70:2603–2619

104. Borchelt DR, Ratovitski T, van Lare J, Lee MK, Gonzales V, Jenkins NA, et al.. Accelerated Amyloid Deposition in the Brains of Transgenic Mice Coexpressing Mutant Presenilin 1 and Amyloid Precursor Proteins. Neuron. 1997. Vol. 19:939–945

105. Jyoti A, Plano A, Riedel G, Platt B. EEG, Activity, and Sleep Architecture in a Transgenic AβPPswe/PSEN1A246E Alzheimer’s Disease Mouse. Journal of Alzheimer’s Disease. 2010. Vol. 22:873–887

106. Koss DJ, Drever BD, Stoppelkamp S, Riedel G, Platt B. Age-Dependent Changes in Hippocampal Synaptic Transmission and Plasticity in the PLB1Triple Alzheimer Mouse. Cellular and Molecular Life Sciences. 2013. Vol. 70:2585–2601

107. Youmans KL, Tai LM, Nwabuisi-Heath E, Jungbauer L, Kanekiyo T, Gan M, et al.. APOE4-Specific Changes in Aβ Accumulation in a New Transgenic Mouse Model of Alzheimer Disease. Journal of Biological Chemistry. 2012. Vol. 287:41774–41786

108. Liu DS, Pan XD, Zhang J, Shen H, Collins NC, Cole AM, et al.. APOE4 Enhances Age-Dependent Decline in Cognitive Function by Down-Regulating an NMDA Receptor Pathway in EFAD-Tg Mice. Molecular Neurodegeneration. 2015. Vol. 10:7

109. Rodriguez GA, Tai LM, LaDu MJ, Rebeck GW. Human APOE4 Increases Microglia Reactivity at Aβ Plaques in a Mouse Model of Aβ Deposition. Journal of Neuroinflammation. 2014. Vol. 11:111

110. Cheng-Hathaway PJ, Reed-Geaghan EG, Jay TR, Casali BT, Bemiller SM, Puntambekar SS, et al.. The Trem2 R47H Variant Confers Loss-of-Function-Like Phenotypes in Alzheimer’s Disease. Molecular Neurodegeneration. 2018. Vol. 13:29

111. Jay TR, Hirsch AM, Broihier ML, Miller CM, Neilson LE, Ransohoff RM, et al.. Disease Progression-Dependent Effects of TREM2 Deficiency in a Mouse Model of Alzheimer’s Disease. Journal of Neuroscience. 2017. Vol. 37:637–647

112. Jay TR, Miller CM, Cheng PJ, Graham LC, Bemiller S, Broihier ML, et al.. TREM2 Deficiency Eliminates TREM2+ Inflammatory Macrophages and Ameliorates Pathology in Alzheimer’s Disease Mouse Models. Journal of Experimental Medicine. 2015. Vol. 212:287–295

113. Wang Y, Cella M, Mallinson K, Ulrich JD, Young KL, Robinette ML, et al.. TREM2 Lipid Sensing Sustains the Microglial Response in an Alzheimer’s Disease Model. Cell. 2015. Vol. 160:1061–1071

114. Wang Y, Ulland TK, Ulrich JD, Song W, Tzaferis JA, Hole JT, et al.. TREM2-Mediated Early Microglial Response Limits Diffusion and Toxicity of Amyloid Plaques. Journal of Experimental Medicine. 2016. Vol. 213:667–675

115. Turnbull IR, Gilfillan S, Cella M, Aoshi T, Miller M, Piccio L, et al.. Cutting Edge: TREM-2 Attenuates Macrophage Activation. Journal of Immunology. 2006. Vol. 177:3520–3524

116. Gratuze M, Leyns CE, Sauerbeck AD, St-Pierre MK, Xiong M, Kim N, et al.. Impact of TREM2R47H Variant on Tau Pathology-Induced Gliosis and Neurodegeneration. Journal of Clinical Investigation. 2020. Vol. 130:4954–4968

117. Hsiao K, Chapman P, Nilsen S, Eckman C, Harigaya Y, Younkin S, et al.. Correlative Memory Deficits, Abeta Elevation, and Amyloid Plaques in Transgenic Mice. Science. 1996. Vol. 274:99–102

118. Mucke L, Masliah E, Yu GQ, Mallory M, Rockenstein EM, Tatsuno G, et al.. High-Level Neuronal Expression of Abeta 1-42 in Wild-Type Human Amyloid Protein Precursor Transgenic Mice: Synaptotoxicity without Plaque Formation. Journal of Neuroscience. 2000. Vol. 20:4050–4058

119. Castello MA, Soriano S. On the Origin of Alzheimer’s Disease. Trials and Tribulations of the Amyloid Hypothesis. Ageing Research Reviews. 2014. Vol. 13:10–12

120. Morris GP, Clark IA, Vissel B. Questions Concerning the Role of Amyloid-β in the Definition, Aetiology and Diagnosis of Alzheimer’s Disease. Acta Neuropathologica. 2018. Vol. 136:663–689

121. Makin S. The Amyloid Hypothesis on Trial. Nature. 2018. Vol. 559:S4–S7

122. Jeremic D, Jiménez-Díaz L, Navarro-López JD. Past, Present and Future of Therapeutic Strategies Against Amyloid-β Peptides in Alzheimer’s Disease: A Systematic Review. Ageing Research Reviews. 2021. Vol. 72:101496

123. Ferrari C, Sorbi S. The Complexity of Alzheimer’s Disease: An Evolving Puzzle. Physiological Reviews. 2021. Vol. 101:1047–1081

124. Bellucci A, Rosi MC, Grossi C, Fiorentini A, Luccarini I, Casamenti F. Abnormal Processing of Tau in the Brain of Aged TgCRND8 Mice. Neurobiology of Disease. 2007. Vol. 27:328–338

125. Van Dam D, De Deyn PP. Drug Discovery in Dementia: The Role of Rodent Models. Nature Reviews: Drug Discovery. 2006. Vol. 5:956–970

126. Duyckaerts C, Potier MC, Delatour B. Alzheimer Disease Models and Human Neuropathology: Similarities and Differences. Acta Neuropathologica. 2008. Vol. 115:5–38

127. Cook C, Carlomagno Y, Gendron TF, Dunmore J, Scheffel K, Stetler C, et al.. Acetylation of the KXGS Motifs in Tau is a Critical Determinant in Modulation of Tau Aggregation and Clearance. Human Molecular Genetics. 2014. Vol. 23:104–116

128. Blackmore T, Meftah S, Murray TK, Craig PJ, Blockeel A, Phillips K, et al.. Tracking Progressive Pathological and Functional Decline in the rTg4510 Mouse Model of Tauopathy. Alzheimer’s Research & Therapy. 2017. Vol. 9:77

129. Wes PD, Easton A, Corradi J, Barten DM, Devidze N, DeCarr LB, et al.. Tau Overexpression Impacts a Neuroinflammation Gene Expression Network Perturbed in Alzheimer’s Disease. PloS One. 2014. Vol. 9:e106050

130. Gamache J, Benzow K, Forster C, Kemper L, Hlynialuk C, Furrow E, et al.. Factors Other than hTau Overexpression that Contribute to Tauopathy-Like Phenotype in rTg4510 Mice. Nature Communications. 2019. Vol. 10:2479

131. Belarbi K, Burnouf S, Fernandez-Gomez FJ, Desmercières J, Troquier L, Brouillette J, et al.. Loss of Medial Septum Cholinergic Neurons in THY-Tau22 Mouse Model: What Links with Tau Pathology? Current Alzheimer Research. 2011. Vol. 8:633–638

132. Allen B, Ingram E, Takao M, Smith MJ, Jakes R, Virdee K, et al.. Abundant Tau Filaments and Nonapoptotic Neurodegeneration in Transgenic Mice Expressing Human P301S Tau Protein. Journal of Neuroscience. 2002. Vol. 22:9340–9351

133. Iqbal K, Alonso Adel C, Chen S, Chohan MO, El-Akkad E, Gong CX, et al.. Tau Pathology in Alzheimer Disease and Other Tauopathies. Biochimica et Biophysica Acta. 2005. Vol. 1739:198–210

134. Sherrington R, Rogaev EI, Liang Y, Rogaeva EA, Levesque G, Ikeda M, et al.. Cloning of a Gene Bearing Missense Mutations in Early-Onset Familial Alzheimer’s Disease. Nature. 1995. Vol. 375:754–760

135. Ertekin-Taner N. Genetics of Alzheimer’s Disease: A Centennial Review. Neurologic Clinics. 2007. Vol. 25:611–667, v

136. Esquerda-Canals G, Montoliu-Gaya L, Güell-Bosch J, Villegas S. Mouse Models of Alzheimer’s Disease. Journal of Alzheimer’s Disease. 2017. Vol. 57:1171–1183

137. Elder GA, Gama Sosa MA, De Gasperi R, Dickstein DL, Hof PR. Presenilin Transgenic Mice as Models of Alzheimer’s Disease. Brain Structure & Function. 2010. Vol. 214:127–143

138. Lazarov O, Peterson LD, Peterson DA, Sisodia SS. Expression of a Familial Alzheimer’s Disease-Linked Presenilin-1 Variant Enhances Perforant Pathway Lesion-Induced Neuronal Loss in the Entorhinal Cortex. Journal of Neuroscience. 2006. Vol. 26:429–434

139. Tanemura K, Chui DH, Fukuda T, Murayama M, Park JM, Akagi T, et al.. Formation of tau Inclusions in Knock-In Mice with Familial Alzheimer Disease (FAD) Mutation of Presenilin 1 (PS1). Journal of Biological Chemistry. 2006. Vol. 281:5037–5041

140. Jankowsky JL, Younkin LH, Gonzales V, Fadale DJ, Slunt HH, Lester HA, et al.. Rodent A Beta Modulates the Solubility and Distribution of Amyloid Deposits in Transgenic Mice. Journal of Biological Chemistry. 2007. Vol. 282:22707–22720

141. Holcomb L, Gordon MN, McGowan E, Yu X, Benkovic S, Jantzen P, et al.. Accelerated Alzheimer-Type Phenotype in Transgenic Mice Carrying Both Mutant Amyloid Precursor Protein and Presenilin 1 Transgenes. Nature Medicine. 1998. Vol. 4:97–100

142. Oakley H, Cole SL, Logan S, Maus E, Shao P, Craft J, et al.. Intraneuronal Beta-Amyloid Aggregates, Neurodegeneration, and Neuron Loss in Transgenic Mice with Five Familial Alzheimer’s Disease Mutations: Potential Factors in Amyloid Plaque Formation. Journal of Neuroscience. 2006. Vol. 26:10129–10140

143. Maarouf CL, Kokjohn TA, Whiteside CM, Macias MP, Kalback WM, Sabbagh MN, et al.. Molecular Differences and Similarities Between Alzheimer’s Disease and the 5xFAD Transgenic Mouse Model of Amyloidosis. Biochemistry Insights (Online). 2013. Vol. 6:1–10

144. Bhattacharya S, Haertel C, Maelicke A, Montag D. Galantamine Slows Down Plaque Formation and Behavioral Decline in the 5xFAD Mouse Model of Alzheimer’s Disease. PloS One. 2014. Vol. 9:e89454

145. Crowe SE, Ellis-Davies GC. Spine Pruning in 5xFAD Mice Starts on Basal Dendrites of Layer 5 Pyramidal Neurons. Brain Structure & Function. 2014. Vol. 219:571–580

146. Caruso D, Barron AM, Brown MA, Abbiati F, Carrero P, Pike CJ, et al.. Age-Related Changes in Neuroactive Steroid Levels in 3xTg-AD Mice. Neurobiology of Aging. 2013. Vol. 34:1080–1089

147. Kim J, Basak JM, Holtzman DM. The Role of Apolipoprotein E in Alzheimer’s Disease. Neuron. 2009. Vol. 63:287–303

148. Mattsson N, Zetterberg H, Hansson O, Andreasen N, Parnetti L, Jonsson M, et al.. CSF Biomarkers and Incipient Alzheimer Disease in Patients with Mild Cognitive Impairment. JAMA. 2009. Vol. 302:385–393

149. Bali J, Gheinani AH, Zurbriggen S, Rajendran L. Role of Genes Linked to Sporadic Alzheimer’s Disease Risk in the Production of β-Amyloid Peptides. Proceedings of the National Academy of Sciences of the United States of America. 2012. Vol. 109:15307–15311

150. Erickson CA, Barnes CA. The Neurobiology of Memory Changes in Normal Aging. Experimental Gerontology. 2003. Vol. 38:61–69

151. Butterfield DA, Poon HF. The Senescence-Accelerated Prone Mouse (SAMP8): A Model of Age-Related Cognitive Decline with Relevance to Alterations of the Gene Expression and Protein Abnormalities in Alzheimer’s Disease. Experimental Gerontology. 2005. Vol. 40:774–783

152. Pallas M, Camins A, Smith MA, Perry G, Lee HG, Casadesus G. From Aging to Alzheimer’s Disease: Unveiling “the switch” with the Senescence-Accelerated Mouse Model (SAMP8). Journal of Alzheimer’s Disease. 2008. Vol. 15:615–624

153. Canudas AM, Gutierrez-Cuesta J, Rodríguez MI, Acuña-Castroviejo D, Sureda FX, Camins A, et al.. Hyperphosphorylation of Microtubule-Associated Protein Tau in Senescence-Accelerated Mouse (SAM). Mechanisms of Ageing and Development. 2005. Vol. 126:1300–1304

154. Del Valle J, Duran-Vilaregut J, Manich G, Casadesús G, Smith MA, Camins A, et al.. Early Amyloid Accumulation in the Hippocampus of SAMP8 Mice. Journal of Alzheimer’s Disease. 2010. Vol. 19:1303–1315

155. Flood JF, Farr SA, Uezu K, Morley JE. Age-Related Changes in Septal Serotonergic, GABAergic and Glutamatergic Facilitation of Retention in SAMP8 Mice. Mechanisms of Ageing and Development. 1998. Vol. 105:173–188

156. Suzuki T, Aoki K, Shimokobe K, Omiya S, Funayama C, Takahashi T, et al.. Age-Related Morphological and Functional Changes in the Small Intestine of Senescence-Accelerated Mouse. Experimental Gerontology. 2022. Vol. 163:111795

157. Miyamoto M. Characteristics of Age-Related Behavioral Changes in Senescence-Accelerated Mouse SAMP8 and SAMP10. Experimental Gerontology. 1997. Vol. 32:139–148

158. Ravelli KG, Rosário BD, Camarini R, Hernandes MS, Britto LR. Intracerebroventricular Streptozotocin as a Model of Alzheimer’s Disease: Neurochemical and Behavioral Characterization in Mice. Neurotoxicity Research. 2017. Vol. 31:327–333

159. Halawany AME, Sayed NSE, Abdallah HM, Dine RSE. Protective Effects of Gingerol on Streptozotocin-Induced Sporadic Alzheimer’s Disease: Emphasis on Inhibition of β-Amyloid, COX-2, Alpha-, Beta - Secretases and APH1a. Scientific Reports. 2017. Vol. 7:2902

160. Zhang Y, Ding R, Wang S, Ren Z, Xu L, Zhang X, et al.. Effect of Intraperitoneal or Intracerebroventricular Injection of Streptozotocin on Learning and Memory in Mice. Experimental and Therapeutic Medicine. 2018. Vol. 16:2375–2380

161. Mehan S, Arora R, Sehgal V, Sharma D, Sharma G. Dementia – A Complete Literature Review on Various Mechanisms Involves in Pathogenesis and an Intracerebroventricular Streptozotocin Induced Alzheimer’s Disease. IntechOpen. 2012.

162. Moreira-Silva D, Vizin RCL, Martins TMS, Ferreira TL, Almeida MC, Carrettiero DC. Intracerebral Injection of Streptozotocin to Model Alzheimer Disease in Rats. Bio-Protocol. 2019. Vol. 9:e3397

163. Kamat PK, Kalani A, Rai S, Tota SK, Kumar A, Ahmad AS. Streptozotocin Intracerebroventricular-Induced Neurotoxicity and Brain Insulin Resistance: A Therapeutic Intervention for Treatment of Sporadic Alzheimer’s Disease (sAD)-Like Pathology. Molecular Neurobiology. 2016. Vol. 53:4548–4562

164. Xu QQ, Su ZR, Hu Z, Yang W, Xian YF, Lin ZX. Patchouli Alcohol Ameliorates the Learning and Memory Impairments in an Animal Model of Alzheimer’s Disease via Modulating SIRT1. Phytomedicine. 2022. Vol. 106:154441

165. Chen Y, Liang Z, Blanchard J, Dai CL, Sun S, Lee MH, et al.. A Non-Transgenic Mouse Model (icv-STZ mouse) of Alzheimer’s Disease: Similarities to and Differences from the Transgenic Model (3xTg-AD mouse). Molecular Neurobiology. 2013. Vol. 47:711–725

166. Abdollahi M, Hosseini A. StreptozotocinWexler P. Encyclopedia of Toxicology. 3rd edition. Vol. vol 4. Elsevier Inc., Academic Press. 2014. p. 402–404. [Cross Ref]

167. Biasibetti R, Almeida Dos Santos JP, Rodrigues L, Wartchow KM, Suardi LZ, Nardin P, et al.. Hippocampal Changes in STZ-Model of Alzheimer’s Disease are Dependent on Sex. Behavioural Brain Research. 2017. Vol. 316:205–214

168. Azman KF, Zakaria R. D-Galactose-Induced Accelerated Aging Model: An Overview. Biogerontology. 2019. Vol. 20:763–782

169. Shwe T, Pratchayasakul W, Chattipakorn N, Chattipakorn SC. Role of D-Galactose-Induced Brain Aging and its Potential used for Therapeutic Interventions. Experimental Gerontology. 2018. Vol. 101:13–36

170. Haider S, Liaquat L, Shahzad S, Sadir S, Madiha S, Batool Z, et al.. A High Dose of Short Term Exogenous D-Galactose Administration in Young Male Rats Produces Symptoms Simulating the Natural Aging Process. Life Sciences. 2015. Vol. 124:110–119

171. Sadigh-Eteghad S, Majdi A, McCann SK, Mahmoudi J, Vafaee MS, Macleod MR. D-Galactose-Induced Brain Ageing Model: A Systematic Review and Meta-Analysis on Cognitive Outcomes and Oxidative Stress Indices. PloS One. 2017. Vol. 12:e0184122

172. Qu Z, Zhang J, Yang H, Huo L, Gao J, Chen H, et al.. Protective Effect of Tetrahydropalmatine Against D-Galactose Induced Memory Impairment in Rat. Physiology and Behavior. 2016. Vol. 154:114–125

173. Hao L, Huang H, Gao J, Marshall C, Chen Y, Xiao M. The Influence of Gender, Age and Treatment Time on Brain Oxidative Stress and Memory Impairment Induced by D-Galactose in Mice. Neuroscience Letters. 2014. Vol. 571:45–49

174. Oshima E, Ishihara T, Yokota O, Nakashima-Yasuda H, Nagao S, Ikeda C, et al.. Accelerated Tau Aggregation, Apoptosis and Neurological Dysfunction Caused by Chronic Oral Administration of Aluminum in a Mouse Model of Tauopathies. Brain Pathology. 2013. Vol. 23:633–644

175. Ricchelli F, Drago D, Filippi B, Tognon G, Zatta P. Aluminum-Triggered Structural Modifications and Aggregation of Beta-Amyloids. Cellular and Molecular Life Sciences. 2005. Vol. 62:1724–1733

176. Drago D, Folin M, Baiguera S, Tognon G, Ricchelli F, Zatta P. Comparative Effects of Abeta(1-42)-Al Complex from Rat and Human Amyloid on Rat Endothelial Cell Cultures. Journal of Alzheimer’s Disease. 2007. Vol. 11:33–44

177. Walton JR, Wang MX. APP Expression, Distribution and Accumulation are Altered by Aluminum in a Rodent Model for Alzheimer’s Disease. Journal of Inorganic Biochemistry. 2009. Vol. 103:1548–1554

178. Alawdi SH, El-Denshary ES, Safar MM, Eidi H, David MO, Abdel-Wahhab MA. Neuroprotective Effect of Nanodiamond in Alzheimer’s Disease Rat Model: A Pivotal Role for Modulating NF-κB and STAT3 Signaling. Molecular Neurobiology. 2017. Vol. 54:1906–1918

179. Yu L, Jiang R, Su Q, Yu H, Yang J. Hippocampal Neuronal Metal Ion Imbalance Related Oxidative Stress in a Rat Model of Chronic Aluminum Exposure and Neuroprotection of Meloxicam. Behavioral and Brain Functions. 2014. Vol. 10:6

180. Erazi H, Sansar W, Ahboucha S, Gamrani H. Aluminum Affects Glial System and Behavior of Rats. Comptes Rendus Biologies. 2010. Vol. 333:23–27

181. Prakash D, Sudhandiran G. Dietary Flavonoid Fisetin Regulates Aluminium Chloride-Induced Neuronal Apoptosis in Cortex and Hippocampus of Mice Brain. The Journal of Nutritional Biochemistry. 2015. Vol. 26:1527–1539

182. Prakash A, Kumar A. Effect of N-Acetyl Cysteine against Aluminium-Induced Cognitive Dysfunction and Oxidative Damage in Rats. Basic & Clinical Pharmacology & Toxicology. 2009. Vol. 105:98–104

183. Aluminum chloride. https://pubchem.ncbi.nlm.nih.gov/compound/Aluminum-chlorideAccessed on 10 Febrary 2023

184. Klinkenberg I, Blokland A. The Validity of Scopolamine as a Pharmacological Model for Cognitive Impairment: A Review of Animal Behavioral Studies. Neuroscience and Biobehavioral Reviews. 2010. Vol. 34:1307–1350

185. Drummond E, Wisniewski T. Alzheimer’s Disease: Experimental Models and Reality. Acta Neuropathologica. 2017. Vol. 133:155–175

186. Rahimzadegan M, Soodi M. Comparison of Memory Impairment and Oxidative Stress Following Single or Repeated Doses Administration of Scopolamine in Rat Hippocampus. Basic and Clinical Neuroscience. 2018. Vol. 9:5–14

187. Abu Almaaty AH, Mosaad RM, Hassan MK, Ali EHA, Mahmoud GA, Ahmed H, et al.. Urtica Dioica Extracts Abolish Scopolamine-Induced Neuropathies in Rats. Environmental Science and Pollution Research International. 2021. Vol. 28:18134–18145

188. Riedel G, Kang SH, Choi DY, Platt B. Scopolamine-Induced Deficits in Social Memory in Mice: Reversal by Donepezil. Behavioural Brain Research. 2009. Vol. 204:217–225

189. Xian YF, Ip SP, Mao QQ, Su ZR, Chen JN, Lai XP, et al.. Honokiol Improves Learning and Memory Impairments Induced by Scopolamine in Mice. European Journal of Pharmacology. 2015. Vol. 760:88–95

190. Harkany T, Abrahám I, Timmerman W, Laskay G, Tóth B, Sasvári M, et al.. Beta-Amyloid Neurotoxicity is Mediated by a Glutamate-Triggered Excitotoxic Cascade in Rat Nucleus Basalis. European Journal of Neuroscience. 2000. Vol. 12:2735–2745

191. Yamada M, Chiba T, Sasabe J, Nawa M, Tajima H, Niikura T, et al.. Implanted Cannula-Mediated Repetitive Administration of Abeta25-35 into the Mouse Cerebral Ventricle Effectively Impairs Spatial Working Memory. Behavioural Brain Research. 2005. Vol. 164:139–146

192. Sipos E, Kurunczi A, Kasza A, Horváth J, Felszeghy K, Laroche S, et al.. Beta-Amyloid Pathology in the Entorhinal Cortex of Rats Induces Memory Deficits: Implications for Alzheimer’s Disease. Neuroscience. 2007. Vol. 147:28–36

193. Nag S, Yee BK, Tang F. Reduction in Somatostatin and Substance P Levels and Choline Acetyltransferase Activity in the Cortex and Hippocampus of the Rat after Chronic Intracerebroventricular Infusion of Beta-Amyloid (1-40). Brain Research Bulletin. 1999. Vol. 50:251–262

194. Nakamura S, Murayama N, Noshita T, Annoura H, Ohno T. Progressive Brain Dysfunction Following Intracerebroventricular Infusion of Beta(1-42)-Amyloid Peptide. Brain Research. 2001. Vol. 912:128–136

195. Olariu A, Yamada K, Mamiya T, Hefco V, Nabeshima T. Memory Impairment Induced by Chronic Intracerebroventricular Infusion of Beta-Amyloid (1-40) Involves Downregulation of Protein Kinase C. Brain Research. 2002. Vol. 957:278–286

196. Frautschy SA, Yang F, Calderón L, Cole GM. Rodent Models of Alzheimer’s Disease: Rat A Beta Infusion Approaches to Amyloid Deposits. Neurobiology of Aging. 1996. Vol. 17:311–321

197. Vickers JC, Dickson TC, Adlard PA, Saunders HL, King CE, McCormack G. The Cause of Neuronal Degeneration in Alzheimer’s Disease. Progress in Neurobiology. 2000. Vol. 60:139–165

198. Bracco L, Bessi V, Padiglioni S, Marini S, Pepeu G. Do Cholinesterase Inhibitors Act Primarily on Attention Deficit? A Naturalistic Study in Alzheimer’s Disease Patients. Journal of Alzheimer’s Disease. 2014. Vol. 40:737–742

199. Ullrich C, Pirchl M, Humpel C. Hypercholesterolemia in Rats Impairs the Cholinergic System and Leads to Memory Deficits. Molecular and Cellular Neurosciences. 2010. Vol. 45:408–417

200. Xue-Shan Z, Juan P, Qi W, Zhong R, Li-Hong P, Zhi-Han T, et al.. Imbalanced Cholesterol Metabolism in Alzheimer’s Disease. Clinica Chimica Acta. 2016. Vol. 456:107–114

201. Prasanthi JR, Dasari B, Marwarha G, Larson T, Chen X, Geiger JD, et al.. Caffeine Protects against Oxidative Stress and Alzheimer’s Disease-Like Pathology in Rabbit Hippocampus Induced by Cholesterol-Enriched Diet. Free Radical Biology and Medicine. 2010. Vol. 49:1212–1220

202. Grimm MO, Grimm HS, Tomic I, Beyreuther K, Hartmann T, Bergmann C. Independent Inhibition of Alzheimer Disease Beta- and Gamma-Secretase Cleavage by Lowered Cholesterol Levels. Journal of Biological Chemistry. 2008. Vol. 283:11302–11311

203. Das N, Raymick J, Sarkar S. Role of Metals in Alzheimer’s Disease. Metabolic Brain Disease. 2021. Vol. 36:1627–1639

204. Huat TJ, Camats-Perna J, Newcombe EA, Valmas N, Kitazawa M, Medeiros R. Metal Toxicity Links to Alzheimer’s Disease and Neuroinflammation. Journal of Molecular Biology. 2019. Vol. 431:1843–1868

205. Priest ND. The Biological Behaviour and Bioavailability of Aluminium in Man, with Special Reference to Studies Employing Aluminium-26 as a Tracer: Review and Study Update. Journal of Environmental Monitoring. 2004. Vol. 6:375–403

206. Yumoto S, Nagai H, Kobayashi K, Tamate A, Kakimi S, Matsuzaki H. 26Al Incorporation into the Brain of Suckling Rats through Maternal Milk. Journal of Inorganic Biochemistry. 2003. Vol. 97:155–160

207. Gauthier E, Fortier I, Courchesne F, Pepin P, Mortimer J, Gauvreau D. Aluminum Forms in Drinking Water and Risk of Alzheimer’s Disease. Environmental Research. 2000. Vol. 84:234–246

208. Ferreira PC, Tonani KA, Julião FC, Cupo P, Domingo JL, Segura-Muñoz SI. Aluminum Concentrations in Water of Elderly People’s Houses and Retirement Homes and its Relation with Elderly Health. Bulletin of Environmental Contamination and Toxicology. 2009. Vol. 83:565–569

209. Boni UD, Otvos A, Scott JW, Crapper DR. Neurofibrillary Degeneration Induced by Systemic Aluminum. Acta Neuropathologica. 1976. Vol. 35:285–294

210. Bouras C, Giannakopoulos P, Good PF, Hsu A, Hof PR, Perl DP. A Laser Microprobe Mass Analysis of Brain Aluminum and Iron in Dementia Pugilistica: Comparison with Alzheimer’s Disease. European Neurology. 1997. Vol. 38:53–58

211. McDermott JR, Smith AI, Iqbal K, Wisniewski HM. Brain Aluminum in Aging and Alzheimer Disease. Neurology. 1979. Vol. 29:809–814

212. Woolf NJ, Butcher LL. Cholinergic Systems Mediate Action from Movement to Higher Consciousness. Behavioural Brain Research. 2011. Vol. 221:488–498

213. Hampel H, Mesulam MM, Cuello AC, Farlow MR, Giacobini E, Grossberg GT, et al.. The Cholinergic System in the Pathophysiology and Treatment of Alzheimer’s Disease. Brain. 2018. Vol. 141:1917–1933

214. Lawlor PA, Young D. Aβ Infusion and Related Models of Alzheimer DementiaAnimal Models of Dementia. De Deyn PP, Van Dam D. Humana Press. 2011. p. 347–370

215. Schoenfeld HA, West T, Verghese PB, Holubasch M, Shenoy N, Kagan D, et al.. The Effect of Angiotensin Receptor Neprilysin Inhibitor, Sacubitril/Valsartan, on Central Nervous System Amyloid-β Concentrations and Clearance in the Cynomolgus Monkey. Toxicology and Applied Pharmacology. 2017. Vol. 323:53–65

216. Ramos-Cejudo J, Wisniewski T, Marmar C, Zetterberg H, Blennow K, de Leon MJ, et al.. Traumatic Brain Injury and Alzheimer’s Disease: The Cerebrovascular Link. EBioMedicine. 2018. Vol. 28:21–30

217. Shen P, Yue Y, Zheng J, Park Y. Caenorhabditis Elegans: A Convenient In Vivo Model for Assessing the Impact of Food Bioactive Compounds on Obesity, Aging, and Alzheimer’s Disease. Annual Review of Food Science and Technology. 2018. Vol. 9:1–22

218. Newman M, Ebrahimie E, Lardelli M. Using the Zebrafish Model for Alzheimer’s Disease Research. Frontiers in Genetics. 2014. Vol. 5:189

219. Prüßing K, Voigt A, Schulz JB. Drosophila Melanogaster as a Model Organism for Alzheimer’s Disease. Molecular Neurodegeneration. 2013. Vol. 8:35

220. Chen X, Errangi B, Li L, Glasser MF, Westlye LT, Fjell AM, et al.. Brain Aging in Humans, Chimpanzees (Pan troglodytes), and Rhesus Macaques (Macaca mulatta): Magnetic Resonance Imaging Studies of Macro- and Microstructural Changes. Neurobiology of Aging. 2013. Vol. 34:2248–2260

221. Tan FHP, Azzam G. Drosophila Melanogaster: Deciphering Alzheimer’s Disease. Malaysian Journal of Medical Sciences. 2017. Vol. 24:6–20

222. Brenner S. The Genetics of Caenorhabditis Elegans. Genetics. 1974. Vol. 77:71–94

223. Pandey UB, Nichols CD. Human Disease Models in Drosophila Melanogaster and the Role of the Fly in Therapeutic Drug Discovery. Pharmacological Reviews. 2011. Vol. 63:411–436

224. Smith AJ, Clutton RE, Lilley E, Hansen KEA, Brattelid T. PREPARE: Guidelines for Planning Animal Research and Testing. Laboratory Animals. 2018. Vol. 52:135–141

225. Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al.. The ARRIVE Guidelines 2.0: Updated Guidelines for Reporting Animal Research. British Journal of Pharmacology. 2020. Vol. 177:3617–3624

226. Miller JA, Horvath S, Geschwind DH. Divergence of Human and Mouse Brain Transcriptome Highlights Alzheimer Disease Pathways. Proceedings of the National Academy of Sciences of the United States of America. 2010. Vol. 107:12698–12703

227. Hall AM, Roberson ED. Mouse Models of Alzheimer’s Disease. Brain Research Bulletin. 2012. Vol. 88:3–12