# Rodent models of Alzheimer's disease: Critical analysis of current hypotheses and pathways for future research

Pasindu Hansana Singhaarachchi, Peter Antal, Frédéric Calon, Carsten

Culmsee, Jean-Christophe Delpech, Martin Feldotto, Jorine Geertsema,

Emmy E Hoeksema, Aniko Korosi, Sophie Layé, et al.

► To cite this version:

Pasindu Hansana Singhaarachchi, Peter Antal, Frédéric Calon, Carsten Culmsee, Jean-Christophe Delpech, et al. Rodent models of Alzheimer's disease: Critical analysis of current hypotheses and pathways for future research. Progress in Neurobiology, 2025, 252, pp.102821. 10.1016/j.pneurobio.2025.102821. hal-05273054

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# Rodent models of Alzheimer's disease: Critical analysis of current hypotheses and pathways for future research

Pasindu Hansana Singhaarachchi<sup>a</sup>, Peter Antal<sup>b,1</sup>, Frédéric Calon<sup>c,d,1</sup>, Carsten Culmsee<sup>e,f,1</sup>, Jean-Christophe Delpech<sup>d,i,1</sup>, Martin Feldotto<sup>g,1</sup>, Jorine Geertsema<sup>h,1</sup>, Emmy E. Hoeksema<sup>h,1</sup>, Aniko Korosi<sup>h,1</sup>, Sophie Layé<sup>d,i,1</sup>, Jonathan McQualter<sup>a,1</sup>, Susanne R. de Rooij<sup>j,1</sup>, Christoph Rummel<sup>f,g,1</sup>, Mary Slayo<sup>f,g,1</sup>, Luba Sominsky<sup>k,l,1</sup>, Sarah J. Spencer<sup>a,*</sup> □

## ARTICLE INFO

### Keywords:

- Alzheimer's disease
- Amyloid beta
- Animal models
- Inflammation
- Tau

## ABSTRACT

Alzheimer's disease (AD) was first described over a century ago. However, the mechanisms underlying the disease are not well understood to this day. This has negatively impacted our ability to create animal models to design and test targeted reliable treatments for the disease. Amyloid β plaque accumulation, aggregation of neurofibrillary tangles, neuroinflammation, neurodegeneration, and, of course, cognitive decline, are few of the many observed pathological features associated with AD. However, there is a concern that the animal models of AD that are based on these frameworks may not be accurately representing AD in people. As such, the results from preclinical trials have not historically translated well to the clinic. In this article, we review the current major hypotheses to describe AD; we outline the major strengths and weaknesses of the commonly used rodent models used to replicate features of these hypotheses; and we provide a strategy for the field for future research.

## 1. Introduction

### 1.1. What is Alzheimer's disease?

Alzheimer's disease (AD) is a neurodegenerative disorder particularly prevalent within the aged population. It is the most common form of dementia, responsible for 60–80 % of cases (Duong et al., 2017). In 2019 the estimated global prevalence of dementia was 57.4 million cases. This is projected to grow to 152 million by 2050 (Nichols et al., 2022). AD is characterized by progressive cognitive decline caused by

neurodegeneration. A person with AD may experience characteristic cognitive impairments, including in the domains of memory, executive function, language skills, motor learning, and visuospatial skills (Weller and Budson 2018, Silva et al., 2019, Kumar et al., 2022). In addition, neuropsychiatric symptoms such as anxiety or depression may develop in the early stages of AD and further impact cognitive performance and the progression of the disease (Patel and Masurkar 2021, Sabates et al., 2023). The Consortium to Establish a Registry for Alzheimer's Disease (CERAD), has outlined the standards for clinical, neuropsychological and neuropathological diagnosis of AD (Fillenbaum et al., 2008),

<footnote><sup>a</sup> School of Health and Biomedical Sciences, RMIT University, Bundoora, Melbourne, Victoria 3083, Australia</footnote>

<footnote><sup>b</sup> Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics, 1111, Hungary</footnote>

<footnote><sup>c</sup> Faculty of Pharmacy, Centre de Recherche du CHU de Québec-Laval University, Quebec G1V 0A6, Canada</footnote>

<footnote><sup>d</sup> International Associated Laboratory OptiNutriBrain-NutriNeuro, Bordeaux, France, F-33000 and INAF, Quebec, G1V 0A6, Canada</footnote>

<footnote><sup>e</sup> Institute of Pharmacology and Clinical Pharmacy, Philipps University of Marburg, Marburg D-35032, Germany</footnote>

<footnote><sup>f</sup> Center for Mind, Brain and Behavior-CMBB, Giessen, D-35392, Marburg, D-35032, Germany</footnote>

<footnote><sup>g</sup> Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Giessen D-35392, Germany</footnote>

<footnote><sup>h</sup> Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam 1098 XH, the Netherlands</footnote>

<footnote><sup>i</sup> Université de Bordeaux, INRAE, Bordeaux INP, NutriNeuro, UMR 1286, Bordeaux F-33000, France</footnote>

<footnote><sup>j</sup> Department of Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, 1105, the Netherlands</footnote>

<footnote><sup>k</sup> Barwon Health, Geelong, Victoria 3220, Australia</footnote>

<footnote><sup>l</sup> IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria 3217, Australia</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: sarah.spencer@rmit.edu.au (S.J. Spencer).</footnote>

<footnote n="1">¹ Middle authors are in alphabetical order to reflect equal contribution.</footnote>
recently revised (Jack et al., 2024). The ATN framework can also be a useful tool for diagnosis using neuropathological markers at early stages of the disease (Heinzinger et al., 2023, Jack et al., 2024). Key histo-pathological features of AD include amyloid β (Aβ) plaque deposition, aggregation of neurofibrillary tangles (NFT), and neuroinflammation (Perl, 2010, Alzheimer's Association, 2021). Assessment of the extent of Aβ plaques and tau-laden NFT in key regions of the brain forms the basis of the neuropathological, usually postmortem, diagnosis (Hyman et al., 2012, Jack et al., 2018). However, AD is a multifactorial disease with poorly understood aetiology and pathophysiology. Causative factors are still unclear (Golde, 2022), and appropriate animal models are essential to further our understanding of this disease. Here, due to space constraints, we will focus on rodent models, but we acknowledge there are many additional species in which valuable models are emerging.

## 1.2. The aetiology of AD and the importance of rodent models

Around 95 % of AD cases are late-onset, with symptoms appearing past 65 years of age (Bali et al., 2012). Late-onset AD is largely sporadic, with the aetiology and the pathophysiology incompletely known. Early-onset AD, with symptoms developing around 45–64 years of age (Reitz et al., 2020), includes familial AD (FAD), which exhibits a Mendelian inheritance pattern and is associated with autosomal dominant mutations in genes that affect processing or production of Aβ such as APP and PSEN1 and PSEN2 (less than 1 % of cases). It also includes cases with sporadic or inconsistent inheritance patterns (Reitz et al., 2020). Several genetic risk factors have been identified, including E2, E3 and E4 isoforms present in the lipid transporting protein Apolipoprotein E (APOE). Dual copies of the E4 increases AD risk by 12–15-fold (Piaceri, 2013, Yamazaki et al., 2019, Lumsden et al., 2020). Similarly, the R47H mutation in the triggering receptor expressed in myeloid cells 2 (TREM2), a cell surface transmembrane protein that is expressed in dendritic cells, macrophage cells and microglia, can increase AD risk by 2–4 fold (Nazem et al., 2015, Gratuze et al., 2018, Hoogmartens et al., 2021, Li et al., 2021).

Multiple mechanisms are proposed to explain the aetiology of sporadic AD and its general pathophysiology. We will discuss here the Aβ cascade, the tau and the inflammation hypothesis. Other impactful hypotheses exist, including the cholinergic hypothesis, linking cholinergic function decline to AD development. Many modern pharmaceutical companies based their drug development on this mechanism to manage cognitive symptoms of AD (Terry and Buccafusco 2003, Hampel et al., 2019, Cacabelos, 2020). The neurovascular hypothesis and mitochondrial dysfunction hypothesis have also been used for clinical trial development (Cummings et al., 2024, Kazemeini et al., 2024, Santisteban and Iadecola 2025). However, we choose to focus on the three major hypotheses here.

Preclinical rodent models replicate some, but not all, of AD pathophysiology, most demonstrating reasonable face validity (Götz et al., 2018, Martini et al., 2018, Oblak et al., 2020, Trujillo-Estrada et al., 2021). These models have proven useful in testing hypotheses related to AD pathophysiology. However, their predictive value for clinical translation and the construct validity of these models are frequently questioned (Franco and Cedazo-Minguez 2014, Veening-Griffioen et al., 2019). Notably, current preclinical studies rely heavily on transgenic models that mimic the genetic origins of FAD, and these can be highly variable in their presentation and address only the AD cases representative of less than 1 % of patients (Zhu et al., 2015, Drummond and Wisniewski 2017). Improved, standardised animal models for AD preclinical research are strongly needed. In this review, we examine potential mechanisms underlying AD pathophysiology, the animal models used to test them, their strengths and weaknesses, and future directions for the field.

## 2. The amyloid β cascade hypothesis

The Aβ cascade has been a key target for drug development efforts for the past three decades (Morris et al., 2014, Selkoe and Hardy 2016). The Aβ hypothesis, which is founded on genetic links with FAD as well as slower clearance of Aβ in most AD patients (Mawuenyega et al., 2010, Lucey et al., 2017), posits that Aβ aggregation results in a cascade of events leading to sequential deposition of Aβ throughout the brain in a pattern described by Thal stages, eventually culminating in AD (Fig. 1). Aβ is produced by amyloid precursor protein (APP), a transmembrane protein that is cleaved by α-, γ-, and or β-secretase (Morris et al., 2014, Chen et al., 2017). Aβ can then undergo protease-mediated degradation and cell-mediated clearance or form aggregates (Chen et al., 2017). Observations of increased Aβ production, impaired clearance and increased aggregation of Aβ leading to insoluble plaques (Bateman et al., 2006, Sleegers et al., 2006, Mawuenyega et al., 2010, Chen et al., 2017) have fuelled claims that these plaques are the root cause of AD pathophysiology, including NFT, cell loss, vascular damage and cognitive decline (Hardy and Higgins 1992, Ricciarelli and Fedele 2017).

While the specific mechanisms underlying Aβ's role in AD remain unclear, Aβ seeding experiments and transgenic animal models replicating AD-like pathophysiology, alongside human genetics, provide strong support for the Aβ cascade hypothesis. Human evidence includes FAD conditions caused by mutations in the APP or presenilin (PSN1 and PSN2) genes, which can lead to increased production of Aβ or an increased Aβ42/Aβ40 ratio (Tcw and Goate 2017, Xiao et al., 2021, Takada et al., 2022). A reduction of Aβ42 in the cerebrospinal fluid, likely explained by defects in brain clearance, is now a well-accepted biomarker of AD (Gu and Guo 2013, Roe et al., 2013, Clark et al., 2016, Zhang et al., 2023). Therapeutic targeting to reduce the Aβ42 isoform with monoclonal antibodies, lecanemab or donanemab, has also shown some success in reducing plasma phosphorylated tau181 levels, as well as other key AD biomarkers. However, patients with a high tau pathology burden did not have a detectable clinical improvement upon donanemab treatment compared to the clinical and histopathological outcomes of patients with low/medium levels of tau pathology, highlighting the possibility that antibody therapies against the Aβ may be less effective at later stages of the disease (McDade et al., 2022, Sims et al., 2023). It is also important to note that the impressive Aβ reduction and plasma phosphorylated tau reduction associated with these anti-Aβ treatments (88 % reduction of Aβ plaques by donanemab by 76 weeks) have only caused ~20 % slowing of the clinical progression, highlighting a significant role by other pathways (for example inflammation and oxidative stress) in sustaining the progression of AD. Also challenging the amyloid cascade theory, postmortem histological studies (Chen et al., 2017, Kepp et al., 2023) and positron emission tomography scan studies of living participants have revealed the presence of significant Aβ depositions in 10–30 % of cognitively normal people (Chetelat et al., 2013).

Recent research on Aβ has concentrated on soluble Aβ oligomers being responsible for AD pathology (Benilova et al., 2012, Kametani and Hasegawa 2018). Aβ monomers may form dimers, trimers, tetramers, etc. (Benilova et al., 2012) and some studies have suggested multiple oligomer species to be the toxic form of Aβ (Ciudad et al., 2020). Transgenic mouse models have been produced to encourage the formation of Aβ oligomers, fibrils and Aβ plaques (e.g. the Aβ42-over-expressing BRI2-Aβ mice) (Kametani and Hasegawa 2018). However, these animals fail to exhibit NFT or cell death, two of the hallmarks of AD, thus questioning the broad validity of APP-based animal models (Kametani and Hasegawa 2018). Recent controversy regarding allegedly falsified results in a 2006 Nature paper on dodecamer Aβ, which was suggested to be a responsible oligomer for AD aetiology, has shed a negative light on the Aβ oligomer research in AD (Piller, 2022). Lack of consensus on the biochemical composition of Aβ oligomers in the brain, the validity of the studies in oligomer research due to non-physiological conditions under which these experiments are
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Thal stages of typical amyloid-β deposition throughout the brain in Alzheimer's disease (AD). Left: Accumulation of plaques in different brain regions with AD progression based on Thal stages of AD (Thal et al. 2002, Braak et al. 2015). Stage 1: plaques are observed in the temporal lobe. Stage 2: amyloid β (Aβ) pathology is observed in the neocortex and hippocampus. Stage 3–4: plaques are observed in subcortical regions. Stage 5: Aβ pathology has spread to the cerebellum. Note, aggregation of plaques is less well-correlated with the age than the Braak stages of tau. Ages indicate earliest observations of these pathologies. Right: Extracellular, soluble Aβ monomer accumulation, either due to impaired Aβ clearance or increased Aβ production; accumulation of monomers as insoluble oligomers and deposition of oligomers to form different types of plaques. Figure created with BioRender.com; Toronto, Canada.</figcaption></figure>

carried out, methodological challenges to identify and quantify oligomers *in vivo*, the potential false positives due to errors in experimental protocol, and comparatively less effectiveness on clinical outcomes of anti-Aβ treatment strategies despite a significant reduction in Aβ and tau are a few of the criticisms towards this hypothesis (Benilova et al., 2012, Morris et al., 2018). The contribution of Aβ oligomers to AD pathology is therefore still unclear.

Perhaps the clearest evidence for the Aβ cascade hypothesis lies in the involvement of ApoE. ApoE is a glycoprotein and one of its major functions is to transport cholesterol and other lipids within the circulatory system and the central nervous system (CNS) (Villeneuve et al., 2014, Lumsden et al., 2020). In the brain, ApoE is expressed mainly in astrocytes and microglia, but may also be found in neurons (Zhang et al., 2023). One of the functions of the ApoE protein is clearance of soluble Aβ from the brain interstitial fluid (Castellano et al., 2011). In humans, APOE has three major isoforms: APOE2, APOE3 and APOE4 coded by APOEε2, APOEε3 and APOEε4 alleles respectively. The APOEε4 polymorphism carries a higher risk for developing late-onset AD. Studies suggest ApoE polymorphisms are critical in influencing Aβ production and Aβ clearance (Castellano et al., 2011, Huang et al., 2017). In APP/PS1 mice that express the human APOEε4 gene, treatment with adeno-associated virus containing the APOEε2 gene and therefore expression of APOE2 protein in these mice reduced amyloid deposition, reduced the microgliosis near plaques, and reduced synaptic loss around the Aβ deposits compared to mice that only had the human APOE4 protein (Jackson et al., 2024). Considering ApoE protein is primarily expressed in glial cells, its role in microglia and astrocytes in exacerbating AD-associated pathology has been investigated. For instance, selective removal of astrocytic APOE4 protein in mice carrying the tau P301S mutation with the human APOEε4 gene, causes reduced tau pathology and reduced brain atrophy (Wang et al., 2021). Human APOE4 protein in APP/PS1 murine microglia causes different transcriptomic and epigenetic effects compared to mice with microglia carrying the human APOE3 protein, with several homeostatic genes upregulated resulting in reduced phagocytosis of dead neurons in the presence of APOE4, effects of which are mitigated upon conditional knock out of the APOEε4 gene (Yin et al., 2023). These results highlight the complex role of the ApoE protein in both the Aβ cascade and in Aβ-independent pathways in exacerbating the neuropathology observed in AD.

While many aspects of the Aβ cascade hypothesis remain under debate, numerous animal models targeting this pathway have been shown to replicate AD-like Aβ histopathology, including plaques, as well as neurodegeneration, cognitive deficits and increased indices of neuroinflammation such as increased microgliosis and astrogliosis (Table 1). NFT have not been detected in mouse models that are designed to target the Aβ cascade (although hyperphosphorylated, punitatively insoluble tau may be present in models such as the APP23 mice; Table 1). These models also do not reliably generate widespread neuronal loss such as is observed in AD (Wirths and Zampar 2020) and behavioural deficits can be variable. Thus, while offering valuable insights into discrete pathological processes, these animal models highlight the complex relationship between Aβ pathology and AD, suggesting a need for a multifaceted approach to understanding the disease. Targeting solely Aβ in animal models is unlikely to provide insight into the full spectrum of mechanisms involved in AD pathophysiology. We discuss each of the major Aβ models below. Knock in (KI) models are discussed in 4 because of the potential to demonstrate both Aβ and tau pathology.

## 2.1. The Tg2576 mouse model

The Tg2576 mouse model over-expresses a 695-amino acid isoform of the human APP gene, with the Swedish *K670N* and *M671L* mutations (Hsiao et al., 1996). Dense cored Aβ plaques are observed at around 8 months of age in the temporal cortex and by 12–15 months diffuse plaques appear (Kawarabayashi et al., 2001). Diffuse Aβ deposits have also been observed in other brain regions including hippocampus, entorhinal cortex, frontal cortex, amygdala and cerebellum, while cored plaques are present in the subiculum, presubiculum and cortex (Hsiao et al., 1996, Irizarry et al., 1997). Notably, NFT and neurodegeneration are not observed with this mutation (Irizarry et al., 1997). NFT formation can be challenging to recapitulate in murine models without a transgene, so it is noteworthy that phosphorylated tau increases with age in this model (Kawarabayashi et al., 2004), and tau oligomers are significantly higher in the Tg2576 mice than in wildtypes at the age of 5 months (Castillo-Carranza et al., 2015). Histopathological assessment also reveals an increase in phosphorylated tau-laden dystrophic neurites with age (Noda-Saita et al., 2004).
<table><caption>**Table 1**
Animal model pathologies.</caption><thead><tr><th rowspan="2">MODEL</th><th colspan="8">COGNITION</th><th colspan="3">Aβ</th><th colspan="2">TAU DISRUPTION</th><th rowspan="2">Neuro degene<br>ration</th><th colspan="2">NEUROIMMUNE RESPONSE</th></tr><tr><th>MWM<br>-L</th><th>MWM<br>-M</th><th>BM</th><th>RAM</th><th>YM</th><th>PA</th><th>NOR</th><th>APP</th><th>Aβ42</th><th>Aβ40</th><th>iAβ</th><th>Immun<br>oreacti<br>ve<br>plaque</th><th>p-tau</th><th>Tang<br>les</th><th>Microgli<br>osis</th><th>Astrogl<br>iosis</th></tr></thead><tbody><tr><td><strong>MICE</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tg2576</td><td>5m</td><td>6m</td><td>3m</td><td></td><td>3-4.5m</td><td>9-11m</td><td>12-15m</td><td>6x</td><td></td><td></td><td>7-8m</td><td>7-8m</td><td>(3)</td><td></td><td></td><td></td></tr><tr><td>APP23</td><td>3m</td><td>3m</td><td>12m<br>(1)</td><td>16m</td><td></td><td></td><td>25m</td><td>7x</td><td></td><td></td><td>6m</td><td>6m</td><td>(3)</td><td></td><td>HC,<br>FC</td><td></td></tr><tr><td>PDAPP</td><td>4-6m</td><td>4-6m</td><td>3-5m</td><td>3m</td><td></td><td></td><td>6m</td><td>&gt;10x</td><td></td><td></td><td>6-9m</td><td></td><td>plaque-<br>ass.<br>only</td><td></td><td>CA1</td><td></td></tr><tr><td>J20</td><td>4m</td><td>4m</td><td></td><td></td><td>4m</td><td></td><td>3m</td><td></td><td></td><td>6w</td><td>5-7m</td><td>8-9m</td><td></td><td></td><td></td><td></td></tr><tr><td>APP/PS1</td><td>5-8<br>months</td><td>3.5m</td><td></td><td></td><td></td><td></td><td></td><td></td><td>2.5-3.5<br>months</td><td></td><td>4m</td><td></td><td></td><td></td><td></td><td>3m</td></tr><tr><td>5xFAD</td><td>4-6m</td><td></td><td>6m</td><td></td><td></td><td>4-5m</td><td>6-8m</td><td>&lt;Tg257<br>6</td><td></td><td>1.5m</td><td>2m</td><td>2m</td><td></td><td></td><td>Sub</td><td>2m</td></tr><tr><td>hTau</td><td></td><td>12m</td><td>12m</td><td></td><td></td><td></td><td>12m</td><td></td><td></td><td></td><td></td><td></td><td>6w</td><td></td><td>HC,<br>FC</td><td></td></tr><tr><td>PS19</td><td>6m</td><td>6m</td><td></td><td></td><td></td><td>6m</td><td>3m</td><td></td><td></td><td></td><td></td><td></td><td>3-6m</td><td>5m</td><td>hind-<br>brain</td><td>8-9m</td></tr><tr><td>P301L</td><td></td><td></td><td></td><td></td><td></td><td>5&7m</td><td>4-9m</td><td></td><td></td><td></td><td></td><td></td><td></td><td>6m</td><td></td><td>6m</td></tr><tr><td>3xTg</td><td>6m</td><td>6m</td><td></td><td></td><td>12m</td><td>6m</td><td>9m</td><td></td><td></td><td></td><td></td><td></td><td></td><td>LC</td><td>7m</td><td>7m</td></tr><tr><td><strong>RATS</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>McGill</td><td>3m</td><td>3m</td><td></td><td></td><td>6m</td><td>4-6m</td><td>4m</td><td>~2x</td><td></td><td>1w</td><td>6m</td><td>6m</td><td></td><td></td><td>Sub</td><td>6m</td></tr><tr><td>TgF344</td><td></td><td></td><td>6-8m</td><td>15m</td><td></td><td></td><td></td><td>3x</td><td></td><td></td><td></td><td></td><td></td><td>16m</td><td>HC,<br>CC</td><td>6m</td></tr><tr><td><strong>Induced models</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>OKA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AlCl3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>MICE or RATS</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LPS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Colour coding: Light blue = Deficit. Dark Blue = Improvement. Yellow = No change or not observed. Orange = inconsistent & inconclusive findings or unreliable data. White = no data.

Table title and body abbreviations: Aβ: amyloid beta; iAβ: intraneuronal Aβ; APP: amyloid precursor protein; BM: Barnes maze; CC: corpus callosum; FC: frontal cortex; HC: hippocampus; m: months; MWM: Morris water maze; -L: learning; -M: memory; NOR: novel object recognition; Other: radial arm water maze or y maze; PA: passive avoidance test; p-tau: phosphorylated tau; RAM: radial arm maze; Sub: subiculum; w: weeks; Animal model abbreviations: McGill: McGill-R-Thy1 APP model; OKA: okadaic acid; P301L model: Tau transgenic models with P301L mutation in the MAPT gene; PS19: Tau transgenic model with P301S mutation in the MAPT gene; STZ: streptozotocin

Table notes:

(1) = change in memory but no change in learning at this time.

(2) = markers unspecific, e.g. H&E staining to detect tangles and plaques.

(3) = but no change in total-tau.

Age denotes the youngest age such a pathology was observed in the reviewed literature, this does not reflect the replicability of the said pathology in the model at the given age.

Dendritic spines in the CA1 region of the hippocampus typically diminish with age in healthy aging, so it is notable that dendritic spines are reduced in the Tg2576 mouse at 4.5 months and 11 months relative to wildtypes, but not at 2 months, when the phenotype is not yet established or at 20 months, when the effects of aging are equally affecting the wildtypes (Lanz et al., 2003). Notable microgliosis occurs in this model, and this particularly occurs around the Aβ deposits (Benzing et al., 1999).

Behavioural assessments of the Tg2576 mice suggest cognitive impairments. However, not all studies are able to replicate consistent behavioural deficits and the age of onset of cognitive impairments is not agreed upon. One of the earliest reported impairments in the Morris water maze (MWM) test for spatial learning is at 5 months (Fukumoto et al., 2010), but there is more consensus around the appearance of impairments in the MWM beyond 9 months (Hsiao et al., 1996, King et al., 1999, King and Arendash 2002, Lindner et al., 2006, Castellano et al., 2011, Stewart et al., 2011). One of the earliest reported ages for impairment in the Barnes maze test is 3 months in females, but there are no impairments in male mice at this time and no differences between the transgenic mice and wildtypes at 9 months (King et al., 1999). Fear-based associative memory deficits in female but not male mice are

also observed at 3 months with no deficits in either sex at 9 months in the passive avoidance test (King et al., 1999). Novel object recognition (NOR) tests have indicated recognition memory impairments in this model (Oules et al., 2012). A review by Stewart and colleagues compares behavioural studies conducted on these mice and reflects on the controversial nature of the behavioural outcomes (Stewart et al., 2011). Notably, Tg2576 animals with the B6.SJL background may have the Pde6brd1 mutation (Yassine et al., 2013), which can cause retinal degeneration and visual impairments, and this should be factored in when interpreting impairments observed in behavioural studies. However, there is substantial variability in cognitive performance for this transgenic model, with different studies calling for replication of the data across different ages and proper reporting of mouse strains.

## 2.2. The APP23 mouse model

APP23 mice have a 751-amino acid isoform of the human APP with the Swedish mutation, resulting in a 7-fold over-expression of the APP gene (Sturchler-Pierrat et al., 1997). In this model, rare Aβ deposits are observed at 6 months old, with Aβ appearance commencing in the frontal cortex and subiculum and extending to the neocortex and
hippocampus. By around one year of age, plaques are observed in the thalamus and amygdala. Plaques observed in this model are described as fibrillar and compact (Sturchler-Pierrat et al., 1997; Sturchler-Pierrat and Staufenbiel 2000, Van Dam et al., 2003). NFT are not present. However, Sturchler-Pierrat and colleagues reported increased expression of phosphorylated ser203 and thr205 tau (but not total tau) and paired helical filaments associated with congophilic plaques, indicating hyperphosphorylation of tau in this model (Sturchler-Pierrat et al., 1997).

APP23 mice have fewer neurons in the frontal cortex, subiculum, and hippocampus (Heneka et al., 2006). There is no specific quantitative evidence for neuron loss in the neocortex, however there may be a thinning of the pyramidal cell layer in the entorhinal and piriform cortices (Calhoun et al., 1998, Bondolfi et al., 2002). This model also presents with hypertrophic microglia in the neocortex and hippocampus that are juxtaposed with Congo red-positive Aβ plaques. However, no clear changes in microglial morphology have been noted close to diffuse Aβ deposits (Stalder et al., 1999).

In accordance with the neurodegeneration and indices of central inflammation, spatial learning and memory deficits in the MWM have been observed in APP23 mice, in some investigations as early as 3 months of age (Van Dam et al., 2003). However, this is not ubiquitous across studies (Huang et al., 2006), and still others find memory deficits in the MWM at 3 months, but no prominent spatial learning impairments at the same age (Kelly et al., 2003). There appears to be some sexual dimorphism complicating this response in that wildtype female mice have possible memory deficits in the probe trial of the MWM at 3 months of age relative to male wildtype mice (Kelly et al., 2003), cautioning against pooling male and female behavioural data. At 12 months, the APP23 mice demonstrate spatial learning deficits in the Barnes maze, but no memory issues during the retention trial and no impairments in the reversal learning phase of the trial (Prut et al., 2007). Spatial learning, working and reference memory deficits have been observed in the radial arm maze in APP23 mice at 16 months (Heneka et al., 2006). Reference memory is impaired in the NOR test in male APP23 mice aged 3-4 months (Huang et al., 2006), but this was not replicated in females at 16 months (Heneka et al., 2006). There are also possible impairments in fear memory in the passive avoidance test at 25 months but not at 18 (Kelly et al., 2003). These findings highlight the need for careful choice of behavioural paradigms for testing this model, as well as consideration of sex differences in the outcomes.

### 2.3. The PDAPP mouse model

The PDAPP mouse model has the Indiana mutation, i.e. a V717F mutation in the human APP gene (Games et al., 1995). At around 6-9 months Aβ plaques start appearing in the hippocampus, corpus callosum and the cortex in these mice and both diffuse and dense core plaques can be observed (Games et al., 1995). Again, NFT are not developed (Games et al., 1995).

Neuron numbers are not affected in this model, either in the CA1 hippocampal region, cingulate or in the entorhinal cortex, even at 18 months of age (Irizarry et al., 1997). However, there are some indicators of neurodegenerative pathology, including cell shrinkage in the locus coeruleus (one of the first areas of the CNS in humans to undergo AD pathology) at 25 months (German et al., 2005), and dendritic spine loss in the CA1 at 2-8 months (but not at 11 months) (Lanz et al., 2003). Game and colleagues report the presence of diffuse ameboid microglia and the presence of astrocytes surrounding the Aβ plaques, similar to the gliosis that occurs in human AD (Games et al., 1995).

Behavioural studies indicate potential spatial cognitive impairments at the age of 4-6 months in this model, that possibly worsen with age. However, these again seem to be test- and sex-dependent. Hartman et al. used the distance travelled in the MWM instead of the escape latency to conclude the presence of spatial memory deficits at 4-6 months in males (Hartman et al., 2005), while Evans and colleagues showed novel place

recognition deficits at 14-16 months, but not with younger age groups (Evans et al., 2019). In female mice there are subtle impairments at 3-5 months, with more substantial deficits at 20-26 months, as measured using the Barnes maze (Huitrón-Reséndiz et al., 2002). Reference memory, working memory and learning deficits appear at 3 months in the radial arm maze, in both homozygous and heterozygous groups (Dodart et al., 1999), while NOR deficits have been seen in some (Dodart et al., 1999) but not all (Evans et al., 2019) studies at 6 months or later.

### 2.4. The APP/PS1 mouse model

APP/PS1 is a bigenic model mouse based on the APP Swedish mutation and the PSEN1 ΔE9 mutation. Multiple strains of APP/PS1 mice have been generated with different genetic backgrounds and may present unique disease presentation profiles. The original strain (line 85) was developed in a C57BL/6 J and C3H/HeJ hybrid background. Line 85 mice had learning deficits at 5-8 months of age and memory deficits at 3 months of age in the MWM test, however, these were not replicated in 7-11 months old mice that were generated by breeding the line 85 strain with C3B6F1 females (Stover and Brown 2012, Janus et al., 2015). Similarly, the animals with the same mutations generated on the C57BL/6 background had memory impairments at 3.5 months of age without learning impairments (Zhang et al., 2012).

Plaques are observed around 6 months of age in the hippocampus in the hybrid line 85, while in the C57BL/6 strain, Aβ plaques are first observed around 4 months and progressively increase. Soluble Aβ increases have been observed in animals as young as 2.5-3.5 months of age (Zhang et al., 2012, Jackson et al., 2013). Moreover, this line causes AT8-positive phosphorylated tau in the CA3 region of the hippocampus (Jackson et al., 2013). Evidence for microgliosis at 3 months of age, i.e. before the Aβ plaque appearance, has been reported (Kater et al., 2023).

### 2.5. The 5xFAD mouse model

This model has five different mutations: the Swedish mutation (APP K670N and M671L), the Florida mutation (APP V717I), the London mutation (APP V717I), the PSEN1 M146L mutation and the PSEN1 L286V mutation (Oakley et al., 2006). Three lines of this model have been generated (Tg6799, Tg7031 and Tg7092). Aβ deposition begins at 2 months old in the Tg6799 line, and the plaques are stained by thioflavin S, indicating the presence of dense core plaques. Plaques first appear in the cortex and subiculum and spread to the hippocampus, thalamus, brainstem, and olfactory bulb with age (Oakley et al., 2006). Intraneuronal Aβ seems to appear before the plaques at around 1.5 months (Oakley et al., 2006). Again, no NFT are present in this model (Oakley et al., 2006). Oakley and colleagues were not able to detect significant phosphorylated tau with AT8 staining (Oakley et al., 2006). However, others have seen positive phosphorylated tau in the hippocampus and cortex (Shukla et al., 2013).

Whole brain synaptophysin is decreased with age in this model. By 9 months, total synaptophysin levels are reduced by 25%, similarly syn-taxin and synaptic density protein 95 (Psd-95) levels are significantly reduced compared to controls at ages 9 and 12 months, indicating synaptic loss. There are also fewer large pyramidal neurons in cortical layer 5, with possible neurodegeneration in cortical layer 1 and the subiculum, but neurons in the hippocampus and other cortical areas are reportedly intact (Oakley et al., 2006). Microgliosis and astrogliosis have been noted, with the number of F4/80-positive microglial cells proportional to the Aβ42 levels and amyloid deposition (Oakley et al., 2006). Microglial changes have been observed as early as 2 months in the Tg6799 strain, while the other strains have the same pathology around 4-6 months of age (Oakley et al., 2006). Ionized calcium binding adaptor molecule 1 (Iba-1)-immunoreactive microglial densities are reportedly increased in hippocampus by 4 months of age, however, the cortical areas may take up to 8 months to exhibit similar observable changes (Forner et al., 2021). Genotype differences between the
numbers of astrocytes (glial fibrillary acidic protein (Gfap)-positive cells) in hippocampus and cortex are reported after 8 months (Forner et al., 2021).

Spatial memory and learning deficits are also seen in this model, with
deficits in the MWM, Y maze and Barnes maze tests at 4–6 months
(Oakley et al., 2006, Ohno et al., 2006, Xu et al., 2014), but not earlier
(Oakley et al., 2006, Shukla et al., 2013, Xu et al., 2014). Deficits in
recognition memory in the NOR appear around the same time (6–8
months) (Tohda et al., 2012). Hyperactivity relative to controls is
another key behavioural deficit observed in this model (Oblak et al.,
2021).

2.6. The J20 mouse model

This model has the Swedish (APP K670N and M671L) and Indiana (APP V717F) mutations. By around 5–7 months, Aβ plaques are seen in most mice (Mucke et al., 2000, Fu et al., 2014). Diffuse plaques have been observed in the dentate gyrus of the hippocampus and dense de- posits are found in the neocortex (Mucke et al., 2000). Wright and col- leagues have reported thioflavin S-positive plaques at 8–9 months (Wright et al., 2013). By 8 months the dentate gyrus region and the stratum oriens of the CA1 have more cells expressing pThr205, but pSer262 tau is not changed at this time, and there are no differences when the whole of the CA1 region is considered, indicating these changes are subtle and region-specific (Dávila-Bouziguet et al., 2019).

Neuronal loss has been reported in the CA1 at 3, 6, and 9 months in this model, but there is no effect of these mutations on neurons in the CA3 (Wright et al., 2013). The effect on astrocytes is somewhat variable. Wright and colleagues have shown increases in Gfap at 6 and 9 months in the CA3 region of the hippocampus and at 3 and 6 months in the CA1 (Wright et al., 2013), but Ameen-Ali et al. have seen astrogliosis at 9 months, but not at 3–6 months or 12 months (Ameen-Ali et al., 2019). Fu and colleagues have shown reduced astrocyte coverage at 4 weeks relative to wildtypes and no changes at other ages (Fu et al., 2014). Microglial changes are similarly variable in this model. As with astrocytes, Ameen-Ali and colleagues showed specific microgliosis in the hippocampus at 9 months but not earlier or later, with no differences in the cortex (Ameen-Ali et al., 2019). Others have reported the expected pro-inflammatory microglial changes as these mice age, with microglial clusters accumulating in the hippocampus with age (Wright et al., 2013, Fu et al., 2014).

Spatial learning and memory deficits are well established for this model in MWM, starting from around 3–5 months of age (Palop et al., 2003, Meilandt et al., 2008, Cisse et al., 2011, Johnson et al., 2020). Spatial reference memory and learning deficits are seen in the radial arm maze at 4 and 24 months (Wright et al., 2013). NOR testing revealed a slight deficit in performance that develops as the J20 mice age, relative to their wildtype counterparts, but the mice are still able to successfully perform the test (i.e. achieve a positive discrimination ratio) (Ameen-Ali et al., 2019). Although most testing has been performed in males, female mice of this model develop memory impairments by 6 months of age (Quartey et al., 2019).

2.7. The McGill-R-Thy1-APP rat model

The McGill-R-Thy-1-APP rat model has the Swedish (APP K670N and M671L) and the Indiana (APP V717F) mutation in the human APP751 isoform under the control of the murine Thy1.2 promoter and is one of the few transgenic rat models of amyloidosis available. The earliest mature Aβ plaques in this model are observed in the subiculum at 6 months of age. Plaques are also found in entorhinal cortex, hippocampus and parietal cortex (Leon et al., 2010).

Surrounding the mature Aβ plaques are major histocompatibility
complex II (MHCII)-positive microglia, and neurodegeneration is also
seen (Leon et al., 2010). Stereological estimations of neuron numbers
reveal no differences in the CA1 region, or entorhinal cortex, but there

are fewer neurons in the subiculum at 18 months (Heggland et al., 2015). Astrocytes are increased in the CA1, CA2, CA3 and in the subiculum and cerebral cortex at 6 months (Hanzel et al., 2014). Microglia numbers and size are also correspondingly increased, likely indicative of a pro-inflammatory, reactive, profile (Hanzel et al., 2014).

Even as young as 3 months old, homozygous McGill rats exhibit
learning and memory impairments in the MWM (Leon et al., 2010,
Petrasek et al., 2018). Fear-related memory in the passive avoidance test
is also impaired at 4 and 6 months, but not as early as 3 months (Habif
et al., 2020). However, recognition memory is normal until at least 6–7
months in homozygous animals (Petrasek et al., 2018), with later ages
not yet being tested.

2.8. The TgF344 rat model

The TgF344 rat model has the Swedish mutation in the human APP gene (APP K670N and M671L) as well as a PSE11 ΔE9 mutation (Cohen et al., 2013, Nataraj et al., 2025). This model has age-dependent Aβ deposition in the brain (Cohen et al., 2013). By 16 months, diffuse and compact plaques are present in the cingulate cortex, hippocampus, striatum and the cerebellum (Cohen et al., 2013). Progressive accumulation of intraneuronal Aβ1-42 and soluble Aβ oligomers are observed. Importantly, hyperphosphorylated tau is also present in this model (Cohen et al., 2013, Rorabaugh et al., 2017). Mouse transgenic models typically do not produce the NFT observed in human AD pathology with the human PS1 and APP transgenes alone (without the MAPT transgene) (Cohen et al., 2013). However, TgF344 rats develop NFT-like structures at 16 months of age, and these are observed throughout the hippocampus and the cingulate cortex (Cohen et al., 2013).

Neuron numbers are reduced in these rats at 16 and 26 months, but not at 6 months, in the hippocampus and cingulate cortex, with loss emerging around 9 months (Morrone et al., 2022). Caspase-3 expression is also high in these regions, indicative of apoptosis (Cohen et al., 2013). Astro- and micro-gliosis may precede this neuron loss, with elevated levels of Gfap and Iba-1 at 6 months (Cohen et al., 2013).

Tests using the MWM at 6–8 months and 18–21 months indicate
possible cognitive deficits at these ages. However there is variability
within the model, with minimal impairments seen up to 12 months in
some studies (Hall et al., 2025) and around half of the 6–8 month old rats
unimpaired and 20 % of the 18–21 months old rats unimpaired (Bac
et al., 2023, Nataraj et al., 2025). These results are complicated by
deficits seen even in some of the aging wildtypes. In the Barnes maze, at
15 and 24 months, these rats made significantly more errors than
wildtypes during the learning phase, indicating learning impairments.
and performed worse during the probe trial, indicating reference
memory impairments (Cohen et al., 2013, Nataraj et al., 2025). Notably,
other deficits such as those related to motivation and anhedonia may
emerge earlier, particularly in females, which can show reduced moti-
vation as early as 3 months of age (Ostlund et al., 2025).

2.9. Brain seeding of Aβ

Chronic and acute administration of different species of Aβ (Aβ1-42, Aβ1-40 etc.) through intracerebroventricular (icv) injection or through hippocampal infusions have resulted in AD-like pathologies, including Aβ histopathology, neurodegeneration, increased inflammatory responses, and cognitive deficits (Frautschy et al., 1996, Kasza et al., 2017, Diddi et al., 2023). Together these outcomes are considered as further evidence for the Aβ cascade hypothesis. *In vivo* Aβ seeding was initially investigated using non-human primates (as discussed in (Friesen and Meyer-Luehmann 2019)). In these studies, human brain extracts from people with AD were deposited into primate brains using intracerebral inoculation. This model produced Aβ plaques in marmosets similar to those observed in naturally aged marmosets with cerebral amyloidosis, and to those seen in people with AD. This technique has subsequently been adapted for use with murine models to induce AD-like symptoms
and model AD.

Aβ seeding and the resulting pathologies can be influenced by the genetics of the host and the donor, the Aβ isoform(s) injected, the concentration of Aβ, and the route of administration of Aβ (Ulm et al., 2021). For example, human Aβ derived from transgenic mice does not produce any Aβ plaques or AD-like pathology when administered to non-transgenic host mice (Ulm et al., 2021). Seeding mouse Aβ to transgenic or non-transgenic mice has also not been efficacious (Ulm et al., 2021). These observations can be attributed to the difference in three amino acids in the mouse Aβ compared to the human (Ulm et al., 2021). On the other hand, injecting Aβ seeds derived from aged humans, and injecting synthetic Aβ, have resulted in Aβ pathology and AD-like symptoms in transgenic mice (Ulm et al., 2021). Therefore, in mouse models, this method may be best used in transgenic strains to exacerbate the amyloid pathology.

In a 2022 study by Baerends and colleagues, icv administration of Aβ oligomers to naïve Wistar rats reduced their social recognition memory with no impact on their spatial memory (Baerends et al., 2022). Administration, icv, of aggregated Aβ1-42 in Charles River-Harlan rats caused aggregation of NFT, cell death, and spatial memory deficits (Kasza et al., 2017). These findings are further supported by studies conducted with Wistar rats that used synthetic aggregated Aβ to induce AD-like symptoms including plaque formation, neurodegeneration and neuroinflammation, as well as spatial learning deficits in MWM and memory deficits in the NOR test (Zhang et al., 2017, Shamsipour et al., 2021, Diddi et al., 2023). Together these findings suggest rat models may be more appropriate for seeding experiments in non-transgenic lines.

## 2.10. Aβ summary

Collectively, most of the Aβ cascade-targeted animal models have characteristics that mimic many aspects of the human AD condition, including neurodegeneration, microgliosis and cognitive deficits. They also have profiles that are distinctly different to the human condition due to exaggerated expression of APP and other transgenes with rare mutations. As a result, these models exhibit diverse levels of Aβ pathology, ranging from very aggressive accumulation in the 5xFAD model to milder Aβ appearance in the 3xTg-AD model and more recent knock-in (KI) models (discussed below). Importantly, most of the models that target the Aβ cascade lack key elements of human AD pathology, including NFT. Additionally, behavioural outcomes are sometimes indistinguishable from normal aging in less sensitive cognitive tests. Transgenic models may help to create animal models with aggressive disease progression that allow investigators to study the pharmacological outcomes of drugs on different histopathological and behavioural outcomes associated with AD, where other models may prove to be difficult to work with due to their less pronounced behavioural and histopathological features. On the other hand, the more subtle models discussed below may provide a better avenue to understand the intricate relationships between different molecular and cellular mechanisms involved in AD as the native expression patterns are preserved compared to transgenic models with highly expressed APP and other AD-associated genes (Jankowsky and Zheng 2017).

## 3. The tau hypothesis

NFTs, a hallmark of AD pathology, are mainly composed of hyperphosphorylated tau, a microtubule-associated protein (MAP), that is encoded by the microtubule-associated protein tau gene (MAPT). The primary function of this protein is to maintain the stability of the microtubules in axons (Michalicova et al., 2020). In AD, tau undergoes phosphorylation at serine and threonine residues at an abnormal rate, leading microtubules to depolymerize, disrupting signal transmission in neurons (Liu et al., 2019). Hyperphosphorylated tau monomers detached from the microtubules may form oligomers, which upon further

hyperphosphorylation and conformational changes, form paired helical filaments (PHF). These PHF are the primary constituent of NFT (Kopeikina et al., 2012). Misfolded and fibrillar tau may also self-propagate (Liu et al., 2019).

In vitro studies have indicated that entrapping the APP protein in endosomes leads to Aβ formation (Paspalas et al., 2018). In one study in macaques, APP-containing endosomes in the entorhinal cortex were surrounded by phosphorylated tau (Paspalas et al., 2018), suggestive of a mechanism by which tau can induce Aβ formation and initiate AD (Paspalas et al., 2018). Post-mortem studies show that tau pathology (tangle counts and insoluble phosphorylated tau) may occur prior to symptom onset. However, there is a highly significant association with cognitive symptoms, typically even stronger than the association between Aβ plaques and cognitive indicators (Smith, 2002, Nelson et al., 2012, Tremblay et al., 2017, Jellinger, 2020) (Fig. 2). According to a large non-selected brain autopsy study (n = 2366) (Braak et al., 2015), AT8 immunoreactive tau pathology (hyperphosphorylated paired tau filaments) is first observed in brainstem (locus coeruleus) and in certain instances it is seen as early as the first two decades of life, while neurons associated with this tau pathology are not degenerated until much later (Braak et al., 2015). Cerebral tau pathology is first observed in the trans-entorhinal region and entorhinal cortex early in life. It then progresses into the hippocampus and by the late-stage AD to primary sensory cortex and motor cortex (Braak et al., 2015). Notably, tau accumulation is not exclusive for AD. It is also observed in other neurological disorders such as frontotemporal dementia, chronic traumatic encephalopathy and corticobasal syndrome, with different disease presentation from that of AD (Zhang et al., 2022). Tau pathology can also be a pathological feature in aging brains independent of Aβ pathology (Crary et al., 2014).

Although MAPT gene mutations have not been linked to FAD (Götz et al., 2007), they are associated with frontotemporal dementia, a cluster of non-AD dementias characterized by tau pathology (Götz et al., 2007, Vogels et al., 2020). For example, P301S and P301L MAPT mutations are observed in people with frontotemporal dementia with Parkinsonism-17 (FTDP-17), which is characterized by neurodegeneration and cognitive deficits, suggesting a link with tau pathology (Wszolek et al., 2006, Götz et al., 2007). Transgenic mouse models of these mutations have been generated to mimic this (Table 1). Mice with the P301S human tau gene have progressive accumulation of tau, including extensive NFT, gliosis, and cognitive deficits including spatial learning deficits in the MWM and decreased sociability (Goedert and Spillantini 2000, Takeuchi et al., 2011). However, the cognitive deficits seen in the P301L human tau mice are controversial, with some studies claiming memory and learning deficits (Liu et al., 2017) while other studies report no change (Harris et al., 2012, Kent et al., 2018).

Since the MAPT mutations are not observed to cause AD in humans, the tau-based animal models are not AD-specific models. While these transgenic models are appropriate to study tau pathology and to test potential interventions to disrupt the tau pathology present in AD, they may not be suitable to test interventions targeted at the Aβ cascade, cholinergic or inflammatory pathways. Notably, there are no Aβ plaques observed in any of the transgenic models that are specifically targeting only tau (Table 1).

Neuroinflammatory processes may also contribute to tau-related pathologies. For instance, different microglial subpopulations may have different roles in tau pathology under different molecular environments. Tau-mediated neurodegenerative pathology is attenuated by Trem2-independent microgliosis in the presence of murine ApoE in P301S mice, while the presence of human APOE4 protein has caused Trem2-independent microglia to exacerbate tau-mediated neurodegenerative phenotypes (Gratuze et al., 2023), though human APOE4 by itself may also exacerbate the AD-associated symptoms in P301S mice (Shi et al., 2017). Disease associated microglia (DAM) appear to have a protective effect on neurodegeneration in the very early phases of AD (Krasemann et al., 2017, Deczkowska et al., 2018). However, selectively
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Braak stages of Alzheimer's disease illustrating a proposed age-associated progression of tau pathologies based on post-mortem studies (Braak and Del Tredici, 2015). Upper panel: Pre-symptomatic stages a, b and c: detected by AT8-positive tau pathology in the locus coeruleus and non-thalamic nuclear regions, with progressive AT8 positive tau accumulation in neurons in the locus coeruleus spreading to non-thalamic nuclear regions by stage c. Cortical stages 1a and 1b: tau pathology is spread to the trans-entorhinal cortex with neurons burdened with AT8-positive tau observed. Neurofibrillary tangle (NFT) stages I-VI: Gallyas stain-positive NFT made of hyperphosphorylated tau are observed initially in the entorhinal region, then spreading to other regions of the cortex including temporal and frontal cortex, and finally affecting the sensory and motor areas. Lower panel: Formation of NFT. Initially, tau protein is attached to microtubules; tau then detaches due to hyperphosphorylation; hyperphosphorylated tau monomers form; monomers develop into polymers of hyperphosphorylated tau; and then form paired helical structures; upon further hyperphosphorylation and conformational changes NFT form (Kopeikina et al. 2012). Figure created with BioRender.com; Toronto, Canada.</figcaption></figure>

eliminating $p^{16INK4A}$-positive senescent microglia and astrocytes with INK-ATTAC can cause reduced tau and neurodegenerative phenotypes, although conflicting evidence is present, highlighting the possible worsening effects of senescent microglia on tau pathology (Bussian et al., 2018, Musi et al., 2018, Odfalk et al., 2022). Tau activates the nuclear factor (NF)-κB pathway in microglia, which in turn promotes the microglia to process and release tau with seeding activity (Wang et al., 2022). Inhibition of the NF-κB pathway has protective effects against cognitive decline, tau-mediated transcriptomic changes and microglia

autophagy deficits in the PS19 mice (Wang et al., 2022). Similarly, microglia depletion in the PS19 (human MAPT$^{P301S}$) mice and in a mixed APP and tau model (APP$^{NL-G-F}$ + AAV.P310L-tau), reduces tau propagation (Asai et al., 2015, Clayton et al., 2021). Here we discuss some of the main models currently used to address the tau hypothesis.

### 3.1. The hTau mouse model

The hTau mouse is a transgenic model that has expression of the
human MAPT gene in place of the rodent Mapt gene (Andorfer et al., 2003). In this case, tau is hyperphosphorylated and accumulates in cells in an age-dependent manner. In the hippocampus, phosphorylated CP13-positive (pSer203) tau is observed in the axons of the neurons at 6 weeks of age, and accumulation in the cell body is observed by 3 months (Andorfer et al., 2003).

Cell numbers are significantly reduced in the piriform cortex between 3 and 17 months, with DNA fragmentation, as seen with positive TUNEL staining. However, caspase-3 levels remained unchanged (Andorfer et al., 2005). In this hTau model, microglia remain homeostatic into aging in the hippocampus, cortex and cerebellum, with no evidence of microglial pathology (Zhu et al., 2020). Notably, depleting microglia in this model does not affect tau content (Zhu et al., 2020).

Spatial learning and spatial memory deficits in the MWM and recognition memory deficits in the NOR test have been observed at 12 months but not at 3 months (Polydoro et al., 2009). However, Geiszler and colleagues were not able to replicate these reported NOR impairments (Geiszler et al., 2016). An important aspect of this model is that the aforementioned pathologies are present due to the expression of human tau protein, not due to familial mutations.

## 3.2. The PS19 (MAPT P301S) mouse model

The PS19 mice have the human MAPT transgene with the P301S mutation (Yoshiyama et al., 2007). These mice have a shorter life span than wildtypes, with a median survival age of 9 months. More than 80% of the mice are not able to survive past 12 months of age (Yoshiyama et al., 2007). Severe neurological symptoms are observed with the model, including limb weakness and brain atrophy, that progress into paralysis by the age of 7–10 months. At 3 months, positive but weak T14 and 17026 immunostaining of tau is present in the hippocampus, amygdala, cortex, brain stem and spinal cord (Yoshiyama et al., 2007). At 5 months, Gallyas-positive NFT are observed (Yoshiyama et al., 2007), and at 6 months, PHF1-positive cells can be seen. Gallyas positive areas are also positive for thioflavin-S and Congo red, indicative of β-sheets of Aβ fibrils present in this model (Yoshiyama et al., 2007).

In these PS19 mice, neuron loss in the hippocampus is evident at 8 months (but not 6 months) (Yoshiyama et al., 2007). Hippocampal volume is significantly reduced by 9 months, and cortical volume by 12 months. Compared to wildtype mice, the PS19 mice have more astrocytes and microglia at 6 months of age in the hippocampus, amygdala and entorhinal cortex (Yoshiyama et al., 2007, Wang et al., 2022).

PS19 animals aged between 6 and 9 months have demonstrated learning and memory impairments in MWM tests (Takeuchi et al., 2011, Briggs et al., 2017). In the Y maze, there is also a deficit at 4–6 months, despite hyperactivity in the test (Takeuchi et al., 2011), and deficits in contextual fear conditioning (Ruan et al., 2020). At the same age, sociability and social memory are impaired (Takeuchi et al., 2011). Recognition memory deficits in the NOR have also been reported at 3 and 7 months (Perez-Garcia et al., 2023).

## 3.3. The MAPT P301L mouse model

The MAPT P301L mouse has the human MAPT transgene with the P301L mutation. AT180 (pThr231) immunoreactivity is present from 3 months of age in the entorhinal cortex (Maurin et al., 2014) and NFT are observed at 6 months (Terwel et al., 2005). The model exhibits some sex differences, with AT8-positive immunolabelling in the midbrain and cerebellum, and AT100 (pThr 212 and pSer 214) in the midbrain of 8-month-old mice being significantly higher in males than females (Camargo et al., 2021).

At 8 months of age, the male MAPT P301L mouse has fewer NeuN-positive cells in the hindbrain, but there is no degeneration in the midbrain, cortex or cerebellum and female NeuN-positive cells are unaffected in all of the tested brain regions (Camargo et al., 2021). Microglia and astrocyte numbers are also not affected at these ages in

either females or males (Camargo et al., 2021). However, there is some evidence that astrocytes accumulate tau from 10 months, at least in spinal cord (Lin et al., 2005).

Despite the relative paucity of neurodegeneration in this model, the MAPT P301L mice do display some cognitive deficits, albeit with some differences between studies (Sahara and Yanai 2023). Recognition memory impairments in the NOR test appear around 6–9 months but not as early as 4 months in male mice (Maurin et al., 2014, Camargo et al., 2021, Xiong et al., 2022), although at least one study has seen that the MAPT P301L mice out-performed their wildtype counterparts in the NOR test at 2 months of age (Boekhoorn et al., 2006). Female mice had, paradoxically, significant impairments in NOR at 4 months and 8 months but not at 2 and 6 months of age (Camargo et al., 2021). Equally inconsistent findings have been shown for the passive avoidance test, where cognitive deficits are evident at 5 months and 7 months of age, but not at 2, 3 or 9 months (Maurin et al., 2014).

## 3.4. The 3xTg-AD mouse model

Mice of this model have both the Swedish (APP K670N and M671L) and the PSN1 M146V mutations, contributing to accumulation of Aβ, as well as the MAPT P301L mutation contributing to tau pathology (Oddo et al., 2003). The 3xTg-AD mouse has been used in hundreds of studies because it develops behavioural impairments, as well as both NFT and Aβ-positive senile plaques, in an age-dependent manner (Vandal et al., 2015). While plaques have been reported at 6 months in the prefrontal cortex, AD-like pathology becomes well-established in the hippocampus and other key AD-related regions at 12 months (Belfiore et al., 2019). Similarities between NFT observed in the 3xTg-AD mouse and the human AD brain are notable. This model is also characterized by a strong sexual dimorphism, with females exhibiting more prominent Aβ pathology than males (Clinton et al., 2007, Hirata-Fukae et al., 2008, Bories et al., 2012, Dennison et al., 2021). 3xTg-AD mice do not develop frank neuron loss, however, which makes them rather a model of early AD. While the development of neuropathology may be slow compared to other models, the main advantage of the 3xTg-AD model is that it displays both canonical diagnostic markers of AD (Aβ plaques and NFT) in an age-related manner while being easy to breed in animal facilities (Javonillo et al., 2022).

There appears to be an age-dependent reduction in catecholamine neurons in the locus coeruleus in at least one study, but this study compared transgenic mice at different ages without assessing them relative to wildtype mice, leaving the possibility that this was a simple effect of aging (Manaye et al., 2013). There is no neuron loss in the CA1 region at 8–15 months in the 3xTg-AD mice (Manaye et al., 2013), but microglia and astrocyte coverage is greater in the CA1 (7–24 months) (Rodríguez et al., 2010, Caruso et al., 2013) and more astrocytes are observed in dentate gyrus (18 months) (Rodríguez et al., 2013) compared with wildtypes.

Cognitive deficits have been extensively investigated by several groups in this model, with learning and memory defects sometimes appearing at 6 months but being more clearly observable after 12 months (Sterniczuk et al., 2010, Stover et al., 2015). For instance, learning impairments in the MWM are reported from 6 months (Clinton et al., 2007, Belfiore et al., 2019) or 12 months (Pietropaolo et al., 2008), and females perform worse than males at this time (Clinton et al., 2007). In contrast, 3xTg-AD male mice made more errors than the females in the Barnes maze test at 6 months (Stover et al., 2015). Y maze deficits are also seen in 3xTg-AD mice older than 12 months (Biyong et al., 2021). Cognitive deficits in the passive avoidance test were observed at 6 and 15 months old by Clinton and colleagues (Clinton et al., 2007), but this deficit was not replicated at 6 or 12 months by Pairojana et al., who saw deficits at 9 months but not younger or older (Pairojana et al., 2021). Recognition memory impairments in the NOR test have also been reported at 9 months of age (Clinton et al., 2007) and older (St-Amour et al., 2014), but not at younger ages (Clinton et al.,
2007, Stover et al., 2015).

### 3.5. The okadaic acid model

Okadaic acid (OKA) directly targets the tau pathway, and inhibits dephosphorylation of tau, so although it has additional effects on Aβ and neuroinflammation, we have included it in the tau hypothesis section. OKA is a polyether toxin extracted from the *Halichondria okadai* species that has been reported to cause AD-like symptoms in rodent models. OKA acts as an antagonist for phosphatases like protein phosphatase 2 A (PP2A), which play a role in dephosphorylation of tau (Kamat et al., 2010). Therefore, OKA infusion causes increased tau hyper-phosphorylation. pThr205 levels and pThr123 levels become elevated (Zhang and Simpkins 2010, Xu et al., 2018) and there are NFT-like silver-positive accumulations in the cortex and hippocampus (Zhang and Simpkins 2010) as well as aggregated insoluble tau (Arendt et al., 1995). Furthermore, chronic infusion of OKA into the lateral ventricles causes plaque-like deposits of Aβ to form in the striatum after two weeks. Aβ deposits are also found in the neocortex after 4-6 weeks (Arendt et al., 1995).

Kamat and colleagues (2011) have observed neurodegeneration in the hippocampus and periventricular cortex in Sprague-Dawley rats treated with icv OKA (Arias et al., 1998, Kamat et al., 2011). Nissl staining has also revealed a reduction in the thickness of the CA1 (Arias et al., 1998). OKA-treated animals may also have increased levels of reactive oxygen species (ROS) in hippocampus, cortex and striatum (Kamat et al., 2010) as well as astrogliosis in the hippocampus (Costa et al., 2012).

Intra-hippocampal injection of OKA in Wistar rats and icv OKA in adult Sprague-Dawley rats causes memory deficits (Kamat et al., 2010, Costa et al., 2012). For instance, there are notable learning and memory impairments in the MWM and reference and working memory impairments in the radial arm maze (Arendt et al., 1995, Kamat et al., 2010, Zhang and Simpkins 2010, Costa et al., 2012).

## 4. Knock-in murine models incorporating Aβ and tau pathology

Most transgenic models have been based on random integration of the transgenic construct into the genome. This strategy has successfully led to models developing Aβ plaques, but also NFTs as discussed, metabolic defects, and replicable cognitive impairment, as exemplified by the 3xTg-AD mouse (Oddo et al., 2003, Bories et al., 2012, Vandal et al., 2015, Tournissac et al., 2019, Javonillo et al., 2022). However, these over-expression models are not without significant drawbacks, such as random location, multiple copies of transgenes, and the need for the use of an exogenous promoter (Sasaguri et al., 2017, Scearce-Levie et al., 2020). Therefore, new mouse models are being generated using KI strategies to insert mutant or wildtype forms of AD-related genes without over-expression (Oblak et al., 2020, Scearce-Levie et al., 2020). While these models typically display less neuropathology and behavioural defects, they are thought to better recapitulate the underlying molecular pathways leading to late-onset sporadic AD (Saito et al., 2014, Mehla et al., 2019, Oblak et al., 2020, Vitek et al., 2020, Baglietto-Vargas et al., 2021, Trujillo-Estrada et al., 2021). Many of these models target a single gene, that can help to uncover molecular mechanisms and pathological significance of single gene mutations or humanized genes associated with AD, for example, APP-KI (Pang et al., 2022), humanized MAPT-KI (Saito et al., 2019), APOEε4-KI (Sepulveda et al., 2022).

### 4.1. The APP<sup>NL-G-F</sup> model

In the APP<sup>NL-G-F</sup> mouse model (Swedish, KM670/671NL; Arctic, E693G; and Iberian, I716F mutations) Aβ plaques are observed around 3-4 months of age (Saito et al., 2014, Wang et al., 2022), with possible zygosity affecting plaque accumulation age. However, the thioflavin-S

positive plaques are smaller in size and fewer in number than in the 5xFAD and APP/PS1 transgenics (Locci et al., 2021). In comparison, in the rat KI model, the Aβ pathology is aggressive and observed at 1 month of age in homozygous rats and at 3 months in heterozygotes (Pang et al., 2022, Tang et al., 2024). Phosphorylated tau markers are increased in the KI mouse model at 6 months and 24 months, with a significant increase in the phosphorylated tau load with age (Hirota et al., 2022). However, this result was in contrast to the original study on this line in which they were not able to observe tau pathology (Saito et al., 2014). AT8-positive phosphorylated tau is detected by the age of 6 months in homozygous rats with a significant (30 %) loss of NeuN-positive neurons in hippocampus by 12 months and a 50 % loss by 22 months (Pang et al., 2022).

KI mice exhibit activated microglia and astrocytes surrounding the plaques from the 3 months of age with progressively increasing immune reactivity for microglia and astrocytes with age (Wang et al., 2022, Tang et al., 2024). Protein markers also indicate stimulated microglia by 6 months of age and astroglia by 9 months in the rat KI model (Pang et al., 2022).

Cognitive deficits in the Barnes maze are observed by as early as 3 months (Bellio et al., 2024) and the Y maze by 9 months (Saito et al., 2014). Spatial learning deficits in the MWM and spatial memory deficits in the NOR have been found in homozygous APP<sup>NL-G-F</sup> mice after 10 months (Sacher et al., 2019, Locci et al., 2021, Wang et al., 2022). Interestingly, in the fear conditioning test 6 month-old mice exhibited deficits in learning but not memory, but 12 month-old mice exhibited both fear-induced learning and memory deficits (Wang et al., 2022).

### 4.2. Knock-ins *x* familial AD mouse models

Some other studies have targeted multiple genes or have introduced KI modifications to FAD models that have APP-associated or other established FAD mutations, for example, the APOEε4-KI mouse model crossed with the 5xFAD model (Montagne et al., 2021) and the TREM2-KI crossed with the 5xFAD mouse model (Joshi et al., 2021). More recently, double APP<sup>NL-G-F</sup> × MAPT-KI and APP<sup>NL-G-F</sup>/MAPT<sup>P301S</sup> models have been produced to generate both Aβ and tau neuropathologies (Jiang et al., 2024). Hyperphosphorylation of tau, misfolding of tau, and tau oligomers are present and neurodegeneration is much higher in these KI mice compared to the MAPT<sup>P310S</sup> mice, highlighting the exacerbation of tau pathology and neurodegenerative pathology in the presence of Aβ pathology (Jiang et al., 2024). Even though the Aβ accumulation is observed in an age dependent manner, plaque accumulation is relatively low compared to the APP<sup>NL-G-F</sup> model (Jiang et al., 2024). These models may open new doors to understanding the effect of human genes that are associated with AD and their complex relationships (Sasaguri et al., 2017, Zhong et al., 2024).

## 5. The inflammatory hypothesis

Neuroinflammation, or inflammation of the CNS, is a key histo-pathological feature observed in AD and in other neurodegenerative diseases (Kwon and Koh 2020). Controlled neuroinflammation can be beneficial to the host as it is essential for tissue repair, neuroplasticity, and neuroprotection. However, prolonged and/or uncontrolled neuro-inflammation can lead to neuron damage, reduced neuronal plasticity and cognitive deficits (DiSabato et al., 2016, McManus and Heneka 2017, Heneka et al., 2025). Aging, obesity, traumatic brain injury, and hypercholesteremia, for example, are known to promote inflammation and these conditions are also known to be risk factors for AD (Nazem et al., 2015, Silva et al., 2019, Madore et al., 2020). The inflammation hypothesis in sporadic AD suggests that chronic neuroinflammation may lead to a sequence of events resulting in Aβ pathology, including APP production, impaired clearance and tau pathology, subsequently causing neuron damage and loss and ultimately resulting in accelerated cognitive decline (Krstic and Knuesel 2013, Nazem et al., 2015).
Inflammation is mediated through acute phase proteins, cytokines, chemokines, and ROS. In the CNS these can be produced by glial cells, and microglia are thought to play a vital role in regulating neuroinflammation (DiSabato et al., 2016). Postmortem studies on AD brains provide undeniable evidence for the increase of microglia-associated markers such as cluster differentiation 68 (CD68) and MHC-II, and mixed results on Iba-1, a microglial marker commonly used in animal models to examine these cells (Minett et al., 2016, Hopperton et al., 2018, Knezevic and Mizrahi 2018). Microglial changes are also a signature of many animal models of AD. Microglia play an important role as phagocytes to clear out pathogens and debris, while also assisting in the repair of damaged neurons, neuroplasticity and regulation of inflammation (Michell-Robinson et al., 2015, Colonna and Butovsky 2017). With aging, microglia become more sensitive to immunogenic stimuli, including to misfolded Aβ protein, in a phenomenon known as microglial priming, which can lead to uncontrolled or exaggerated inflammatory response to immunogens (Heneka et al., 2015, Niraula et al., 2017, Woodburn et al., 2021). In association with AD, microglia play a vital role in clearing out Aβ from the brain and are thus generally considered to be neuroprotective (Richter et al., 2020). Microglial hyperactivity or priming in AD may cause inefficient Aβ clearance, further exacerbating neuroinflammation and neurodegeneration.

Transcriptomic studies have provided a detailed complex insight into microglia as a protective as well as a deleterious agent on AD symptoms. Microglial phenotypes including, but not limited to, disease associated microglia (DAM), similar to neurodegenerative microglia, lipid droplet accumulating microglia, activated response microglia, and proliferative regions-associated microglia have been observed in murine models of AD (Keren-Shaul et al., 2017; Li et al., 2019; Marschallinger et al., 2020; Sala Frigerio et al., 2019; Yin et al., 2023). While it is generally considered that a DAM profile is protective in AD (Krasemann et al., 2017, Deczkowska et al., 2018), the presence of a multitude of different phenotypes speaks to the complexity of the neuroinflammatory contribution. During early stages of the disease, homeostatic microglial subpopulations may decrease, and distinct lipid-associated microglial subpopulations increase. Then, specific subpopulations of astrocytes and oligodendrocytes associated with high energy demand and removal of damaged mitochondria through autophagy (mitophagy) appear to increase (Green et al., 2023).

Activation pathways and the molecular environment necessary for phenotypic changes of these microglia may also differ. For instance, the DAM phenotype is activated sequentially, though a TREM2-independent downregulation of microglia checkpoints and TREM2-dependent activation of phagocytotic and lipid metabolism pathways (Keren-Shaul et al., 2017). Moreover, microgliosis during early Aβ accumulation is TREM2 dependent (Zhou et al., 2020). In the hTau model, TauPS2APP, and PS2APP model, knocking out TREM2 exacerbated neurodegenerative symptoms, while in P301S and APPPS1 model, knocking out the gene had a neuroprotective effect (Jay et al., 2015, Bemiller et al., 2017, Leyns et al., 2017, Meilandt et al., 2020, Lee et al., 2021). This evidence highlights the differential role played by subpopulations of microglia that have unique transcriptomic signatures and are “activated” (shifted to disease-associated phenotypes) through different pathways under different molecular environments in regulating the AD symptoms (Depp et al., 2023).

Different variants of the TREM2 protein may also affect the risk of AD. The *R47H* variant of *TREM2* seems to increase AD risk in European populations (Pottier et al., 2013, Slattery et al., 2014, Gratuze et al., 2018), but is not associated with increased AD risk in Chinese (Gratuze et al., 2018, Wang et al., 2018) and African American populations (Jin et al., 2015, Gratuze et al., 2018). Other variants of *TREM2* may be associated with AD risk, however, such as the *p.L211P* variant in African Americans (Jin et al., 2015) and the *p.H157Y* variant in Chinese populations (Gan et al., 2020). AD patients with the *R62H* or *R47H* mutation have decreased expression of microglia-associated genes, such as *IRF8* and *AIF-1* (*R47H*), *HLA-DRA* (*R47H* and *R62H*) (Zhou et al., 2020).

Astrocytes are another important glial cell type involved in immunomodulation, including neuroprotection and neurotransmitter recycling. Much like microglia, astrocytes can have pro-inflammatory and neurotoxic effects but can also be involved in releasing neurotropic factors, and clearing debris, having an overall protective role. Similar to microglia, the transcriptomic signature reveals a complex role of astrocytes in AD. For instance, Qian and colleagues have identified eight subclusters of astrocytes including astrocytes with homeostatic transcriptomic signatures, pan-reactive astrocytes, astrocytes with inflammatory transcriptomic signatures, etc. (Qian et al., 2023). There is evidence for increased expression of inflammatory astrocytes with apoptotic, senescent or functionally impaired phenotypes with AD (Kim et al., 2024). Astrocytes are also involved in Aβ clearance. While the astrocyte during early AD may function as intended by ingesting Aβ, the excess Aβ ingestion can cause accumulation of Aβ in the cell, resulting in endosomal and lysosomal defects impairing functionality, including their neuroprotective functions. Moreover, astrocytes secrete macrovesicles containing N terminus truncated Aβ1-42, that is neurotoxic and can induce apoptosis (Sollvander et al., 2016). With all this evidence in mind, animal models that recapitulate the neuroinflammatory aspects of AD can be useful for investigating risk and revealing likely treatment options (Table 1). We discuss the major neuroinflammatory animal models of AD below.

### 5.1. The lipopolysaccharide model

Lipopolysaccharide (LPS) is a component of the Gram-negative bacterial cell wall. It induces neuroinflammation by acting as a ligand for toll-like receptor (TLR)-4 and TLR-2 on microglia. LPS administration is therefore a common neuroinflammatory model that can be used to study neuroinflammation-associated diseases such as AD (Batista et al., 2019). Notably, LPS has been found in erythrocytes present in people with AD and LPS is three-fold higher in AD blood compared to control blood in humans (Zhan et al., 2018). There has been no causative role identified for LPS in AD. Therefore, the LPS model is not AD-specific. It is useful as an inflammation model to study the contribution of neuroinflammation to neurodegenerative disease. However, it is important to note that LPS has caused amyloid and tau pathology in rodent models (Kitazawa et al., 2005, Lee et al., 2008, Carret-Rebillat et al., 2015, Wang et al., 2018, Zhao et al., 2019). Furthermore, APOEε4 carriers are more sensitive to LPS treatment (Gale et al., 2014). LPS is also associated with the endotoxin hypothesis of AD, which describes the possibility of endotoxins (including LPS) produced by bacteria in gut, lungs and gum causing increased Aβ, tau and an inflammatory pathology to exacerbate AD (Brown and Heneka 2024). Reducing the gut microbial diversity with broad spectrum antibiotics not only reduces microglial activity but reduces the plaque load, tau pathology and neurodegeneration in different AD murine models, although the anti-inflammatory properties of antibiotics, independent of their anti-microbial action should be considered when interpreting the data (Seo et al., Zimmermann et al., 2009, Minter et al., 2016, Pradhan et al., 2016, Dodiya et al., 2019).

The inflammatory response to LPS is dose-dependent (Skrzypczak-Wiercioch and Salat 2022) and age-dependent (Bardou et al., 2014), with comparisons between studies confounded by differences in LPS type and even serotype (Skrzypczak-Wiercioch and Salat 2022). In mice, 250, 500, or 1000 µg/kg/day intraperitoneal (i.p.) LPS for 3-7 days is sufficient to cause increased beta-site amyloid precursor protein cleaving enzyme (Bace)I expression, increased Aβ42 protein and decreased Aβ40, as well as the accumulation of extracellular Aβ42, although there is no change in App expression (Lee et al., 2008, Carret-Rebillat et al., 2015, Zhao et al., 2019). A single very high dose (10 mg/kg) of LPS can cause progressive deposition of immunoreactive amyloid plaques and increased Thr181-positive phosphorylated tau levels in young Sprague Dawley rats (Wang et al., 2018). Similar results were replicated for tau pathology in a chronic LPS study (500 µg/kg/day
x 12 doses within 6 weeks) in both non-transgenic and 3xTg mice with increased AT8-positive protein expression (Kitazawa et al., 2005). Neurodegenerative pathology is observed in the LPS treated mice as indicated by TUNEL staining (DNA damage) (Lee et al., 2008) and Map2 staining (Zhao et al., 2019).

To induce chronic neuroinflammation with an acute LPS administration a very high-dose (>5 mg/kg) may be required (Wang et al., 2018; Zhao et al., 2020). For instance, in the substantia nigra, a significant increase in Iba-1 immunoreactivity is observed at 6 h after 1 mg/kg and 5 mg/kg LPS, indicating acute inflammation, but sustained Cd11b immunoreactivity is only seen in the 5 mg/kg group at 10 months post-injection (Zhao et al., 2020). Similarly, IL-6 levels in the brain are elevated in 7-9 days after 10 mg/kg LPS injection (Wang et al., 2018). In SWR/J mice, lasting recognition memory deficits in the NOR test are seen with prior 750 µg/kg LPS but not with lower doses (Sadraie et al., 2019, Alzahrani et al., 2022), although post-training low dose (250 µg/kg) LPS can cause memory deficits in this test (Frühauf et al., 2015). LPS can also cause spatial impairments in the MWM (Shaw et al., 2001, Lee et al., 2008, Abareshi et al., 2016, Azzam et al., 2023) and fear-based memory impairments in the passive avoidance test (Azmand and Rajaei 2021)

These studies indicate that LPS-induced inflammation can cause increased accumulation of Aβ42 and phosphorylation of tau, coupled with cognitive deficits, highlighting the importance of the inflammatory pathway and microglia in sporadic AD. However, we were not able to locate studies that have looked at congophilic plaques or argyrophilic tangles in chronic LPS models. Aβ and tau pathology seem to be associated with either acute very high doses of LPS or chronic low to medium doses of LPS, with both models likely sustaining a chronic neuroinflammatory pathology.

## 5.2. The aluminium model

Since 1921, many studies have provided evidence for cognitive impairments in humans caused by chronic exposure to aluminium (Spofforth, 1921, Kawahara and Kato-Negishi 2011). Recent studies further indicate an association between the intake of high levels of AlCl₃ and the incidence of and mortality from AD (Kawahara and Kato-Negishi 2011). In animal models, Aβ plaque-like structures are aggregated in the hippocampus of rats treated with chronic AlCl₃ (Borai et al., 2017, Singh et al., 2018, ELBini-Dhouib et al., 2021) and Aβ plaque markers such as Aβ1-42, acetylcholinesterase (AChE) and App are increased (Wisniewski et al., 1980, Singh et al., 2018). NFT may occur in the hippocampus and spinal cord (Benuck et al., 1985). More recent studies show that AlCl₃-treated rats exhibit higher levels of phosphorylated tau than controls (Justin-Thenmozhi et al., 2018).

Evaluating gene expression has revealed a dose dependent increase in Aif-1, Mac-1, and Gfap levels in Wistar rats indicating possible microgliosis and astrogliosis in the hippocampus (Zhang et al., 2018). These results are replicated when the protein levels are evaluated in the cortex and hippocampus, along with increased caspase-3 levels indicative of apoptotic pathology (Justin-Thenmozhi et al., 2018). Neurodegenerative pathology in this model is further supported by reduced neuron numbers in CA1 and CA3 regions (Ravi et al., 2018), DNA damage (Borai et al., 2017; Zhang et al., 2023) and oxidative stress markers (Sharma et al., 2007).

Rats administered with AlCl₃ exhibit impaired learning ability in the MWM, radial arm maze, and passive avoidance tests and also exhibit memory deficits in the NOR (Zhang et al., 2003, Abdel-Aal et al., 2011a, b, Nampoothiri et al., 2015, Borai et al., 2017, Ravi et al., 2018). However, a dose escalation study showed that aluminium salt (300 mg/kg/day) in drinking water caused significant peripheral toxicity without producing any AD-relevant signs including lack of cognitive deficits in the MWM test and T maze test, and lack of observable differences in the inflammatory and neurodegenerative pathology in the brain (Poirier et al., 2011). The aluminium model's ability

to induce a suite of AD-like symptoms make it an attractive model for sporadic AD but concerns over high peripheral toxicity should be considered. Doses given to animals are different across studies, and peripheral toxicity observed with different doses varies from study to study, with possible strain/sub-strain-specific thresholds (Llobet et al., 1987, Xu et al., 2017, Lim et al., 2020). It is also important to note that many aluminium studies have used nonspecific histological staining techniques such as hematoxylin and eosin to infer the presence of plaques and NFT in rats, although in some cases these results are presented along with more accepted markers of Aβ and tau pathology (Table 1). As such, this model may provide a useful addition to the suite of tools to investigate AD if peripheral toxicity implications are handled carefully.

## 6. Limitations

The understanding of AD has come a long way from attributing all the symptoms to plaques and tangles to the field now showing a broad appreciation of Aβ pathology, tau pathology, neuroinflammation, oxidative stress, and a range of other neurophysiological and biochemical features (Morris et al., 2014, Alzheimer's Association, 2021). However, the causality of the disease and the interplay between these pathologies is still debated, with many different interpretations of the currently available data (Morris et al., 2014, Drummond and Wisniewski 2017, Morris et al., 2018). Most studies do not provide an explanation for choosing their animal model (Drummond and Wisniewski 2017) and different studies may have different requirements, making certain animal models suitable and disqualifying others (Golde, 2022).

As has been suggested, face validity, construct validity and predictive validity can be used to assess the quality of an animal model (Sams-Dodd, 2006). The face validity is impressive in APP based models, but less impressive in tau-based models, which tend to cause aggressive motor deficits not commonly seen in human AD until the end-stages. The construct validity of both genetic and induced models is questionable, due to our current poor understanding of the aetiology of AD. However, transgenic models may have the upper hand due to the presence of these mutations, genes or specific polymorphisms in AD patients. APP models often exhibit Aβ pathology and inflammation. While models such as APP23 and J20 have region-specific evidence for localized neurodegeneration, and increased phosphorylated tau levels are observed in models such as APP23 and PDAPP, APP models in general are not good at replicating neurodegeneration and tau pathology. Tau models on the other hand do not replicate the Aβ pathology. In comparison, induced models such as AlCl₃, and inflammatory models involve biochemical pathways to induce AD-like symptoms that are not well understood. Current research acknowledges inflammation as a risk factor rather than a causative factor in AD (Heneka et al., 2025). A comparative study conducted by Veening-Griffoen and colleagues correctly points out the poor predictive validity of genetic and induced models alike (Veening-Griffoen et al., 2019). It should be considered, however, that some human diseases and their symptoms are just too complex to be easily and fully replicated in an animal model (like human cognition), meaning animal models may be more useful for addressing specific endpoints if not treating the full disease.

### 6.1. Limitations in improving face and construct validity of AD models

The objective of most of the animal models is to recreate clinical and pathological signs observed in humans by creating genetic models with multiple human transgenes or introducing agents that are not present in sporadic forms or to which humans are rarely exposed naturally. Therefore, the aetiology of the disruptions and their progression in both sporadic AD and FAD animal models may differ from the human AD condition. Beyond their technical limitations, a problem with all "AD models" is that they are often used as models of AD (i.e. the human disease), while they are actually just models of certain specific aspects of AD neuropathology. And, indeed, FAD, on which most models are based,
accounts for only a very small percentage of AD cases (Zhu et al., 2015). For example, APP/PS1 mice are a very good model for studying the generation and removal of Aβ plaques, but the mice do not form NFT, and they do not mimic the full expression of the disease (Table 1). On the other hand, KI mixed models with both APP and MAPT mutations and KI and AAV mixed models that target other AD risk genes, may provide a method to mimic the protein expression so that it is more similar to that observed in human disease. At the end of the day, Aβ and tau are ca- nonical diagnostic markers, but their true causal role in AD patho- physiology remains unknown, at least in most sporadic cases. Despite the clinical significance of using plaques and tangles for diagnosis, the difficulty of producing these naturally in rodents should be taken into consideration when choosing the animal models.

## 6.2. Limitations due to genetic background of the animals

A further limitation for research in this field is the variables associ- ated with the genetic background of the animals upon which the various models are based. For example, the Tg2576 mice with the B6.SJL background have a Pde6brd1 mutation that causes blindness, and this can severely impact the behavioural outcomes. Some studies have used animals of the 129 background (129S6, 129Svj etc.). These mice reportedly have high occurrence of callosal hyperplasia, disc-1 deletion (a gene associated with schizophrenia in humans), and poor behavioural performances compared to other inbred mouse strains (Balogh et al., 1999, Clapcote and Roder 2006, Dahoun et al., 2017). Thus, pre-existing neurological or behavioural deficits in the animal model may lead to poor controls and misleading conclusions. Zygosity of the transgene(s) has also caused different progression and impairments observed in an- imal models of AD (Dodart et al., 1999, Oddo et al., 2003, Oakley et al., 2006, Leon et al., 2010). Background strain also has an effect on microglial heterogeneity. For example, APP/PS1 mice bred on a B6 background have more DAM-associated gene enrichment, and those from the PWK background have the lowest enrichment for homeostatic genes when the APP/PS1 mice of B6, PWK, CAST and WSB backgrounds are compared (Yang et al., 2021).

## 6.3. Aging, the major AD risk factor ignored in animal models

Another major issue in the field is age. The overwhelming main risk factor for AD in humans is old age (Guerreiro and Bras 2015, Shue et al., 2024). There are concerns over whether changes associated with human aging that are essential to AD pathogenesis cannot be replicated within the lifespan of a mouse. For instance, transcriptomic signatures of the senesce of microglia over the human lifespan have differences to those for murine microglial senescence (Streit et al., 2014, Galatro et al., 2017, Virgili et al., 2018). In addition, aging is not merely chronological - biological age, assessed by molecular or transcriptomic clocks, and brain age inferred from neuroimaging, may better reflect vulnerability to AD (Liang et al., 2022). As such, over-aggressive models with disease-associated mutations may quickly generate plaques and cogni- tive decline but are probably far from the sporadic age-related human disease, such as in the case of 5xFAD Tg6799 line, where the plaques are observed at early adulthood of the mice (2-4 months). Likewise, inducible models, particularly single-dose Aβ seeding, fail to mimic the chronic nature of the human disease and other confounding factors such as age-related obesity, diabetes, changes in lipidomic and metabolic profiles in animal models are frequently not considered.

## 6.4. Limitations associated with sexual dimorphism

Potential sexual dimorphism is also an important factor to consider in animal models of AD. Some transgenic models have notable sexual dimorphism in the disease progression, including differences in the Aβ pathology, tau pathology and inflammatory pathology along with the prominent behavioural differences (Vegeto et al., 2006, Carroll et al.,

2007, Sun et al., 2020, Dennison et al., 2021). However, sexual dimor- phism is also found within wildtype animals of specific backgrounds, confounding interpretations relating to disease (Kelly et al., 2003, Jonasson, 2005, Safari et al., 2021). Indeed, some studies pool both male and female animals together into a group, while others assess them separately, potentially affecting whether a significant pathological feature is observed. A primary analysis of pooled data may fail to reveal crucial sex differences in the impact of the model (Ankeny et al., 2023). Furthermore, the sex of the parent carrying the transgene may also play a role in differential outcomes.

## 6.5. Limitations in studying genetic and protein biomarkers

Histological assessments are also often made using different pro- tocols or different analyses to claim the presence or absence of a path- ological feature, complicating comparison between studies. For example Mac-1 (CD11b), Iba-1, F4/80 (Oakley et al., 2006), and CD68 (Wright et al., 2013) are all used to identify microglia. Likewise, studies variably conduct assessment of microglial number, microglial clusters (Fu et al., 2014), percentage area covered with microglia (Ameen-Ali et al., 2019), soma area or soma diameter (Hanzel et al., 2014), shape or structure (Games et al., 1995), the intensity of the stain, and quantitative analysis of Iba-1 protein or gene expression of Aif-1. Similarly, different anti- bodies used for detection, mode of extraction of the target protein, or the brain subregion tested at a specific age, may influence the results ob- tained for Aβ and tau pathology. All these parameters are insightful but different conclusions can be made based on the parameter tested. Therefore, comparing different studies and models can be challenging.

## 6.6. Limitations in behavioural studies, reproducibility and interpretation

One of the limitations in comparing different studies is that meth- odological approaches used by different teams to declare the presence or absence of a specific pathological feature are not consistent, especially in the case of behavioural studies. For example, to test spatial learning and spatial memory, the MWM test, radial arm maze test and Barnes maze test are commonly used. In the MWM, water acts as an aversive stimulus, which requires adequate thermogenic capacity. In the Barnes maze, open area alone or combined with bright light or sound are used as aversive stimuli, and in the radial arm maze, a reward is used as a positive stimulus. These can all impact anxiety levels and the motivation of the animals to complete a given task. These differences can impact the sensitivity of the test and ultimately influence whether a statistically significantly different result is identified between two groups of animals. Both MWM and Barnes maze cause significant increases in plasma corticosterone levels in C57BL/6 J mice, approximately a four-fold in- crease with the Barnes maze and a five-fold increase after the MWM (Harrison et al., 2009). These corticosterone levels are correlated with outcomes tested with the MWM, including escape latency and path length, but are notably not correlated with outcomes in the Barnes maze (Harrison et al., 2009). Therefore, results of different behavioural as- sessments that test the same cognitive index may not be comparable to each other. Tg2576 mice are vulnerable to food restriction-induced hypoglycaemia and death compared to wildtype mice with healthy glucose homeostasis, and restraint-induced stress in these transgenic mice causes prolonged stress, elevated blood glucocorticoids and a reduction in blood glucose compared to wildtypes. Therefore, it is imperative to consider impairments in stress response, glucocorticoid signalling and regulation of blood glucose in interpretation of behav- ioural data from past and future studies of APP transgenic mice (Pedersen et al., 1999). Potential locomotor deficiencies in the animal models are also important to consider.

There are also different experimenter approaches to the same testing paradigm. In the NOR test, the time given for the animals to explore the objects and the time interval between the training phase and trial phase are different between studies. Similarly, the number of trials per day and
the number of days animals are tested varies in longer duration tests across different studies (Dodart et al., 1999; King et al., 1999; Huitrón-Reséndiz et al., 2002; Heneka et al., 2006; Prut et al., 2007). An initiative to standardize these tests, for specific strains, sex and age could resolve discrepancies between studies.

Another important between-group difference is how the data are interpreted and what outcomes from a specific assay are used to support a conclusion. Importantly, the presence or absence of a statistically significant difference in one parameter does not guarantee a similar statistical outcome in another parameter. In the case of NOR, some investigators use the discrimination ratio to determine the presence or absence of memory deficits (i.e., the excess time spent with the novel object as a ratio of the total time spent with both novel and familiar objects); some investigators use the percentage discrimination (i.e., the percentage of time spent with each object). Statistical analysis used to determine memory loss is also different in different studies, where some studies have compared the discrimination ratios between genotypes or treatment groups and some have looked at whether the discrimination ratio with same objects versus that with different objects are statistically significant within a given group (Heneka et al., 2006). While many of these parameters are likely useful, even with the same dataset two studies may arrive at contradictory conclusions based on the type of analysis used.

## 7. Conclusion

The clinical expression of AD comprises a plethora of cognitive and behavioural symptoms that may not be fully replicable in a mouse or rat. Therefore, relying on behavioural outcomes in a mouse to predict translation into the clinical setting might be too optimistic. Together, these limitations suggest urgent changes are needed in the field so that we may better translate findings from the laboratory to the clinic. We suggest that it is time the scientific community embraced a more collaborative way of working where multiple models are compared within the same study using the same (multiple) end-point measures and time points. Standardised end-point measures and reporting guidelines, such as the Stroke Therapy Academic Industry Roundtable (STAIR) guidelines for stroke, should also be adopted. For instance, the MODEL-AD consortium has initiated projects to compare different models of different backgrounds, with comparisons of the same pathology with different biomarkers, which can function as a foundation for future research in AD (Oblak et al., 2020; Benzow et al., 2024). In this way, the weaknesses of each of the models outlined here will be mitigated and strengths and commonalities amplified, allowing the development of new treatment options that will be successful irrespective of ultimate disease causes. An AD drug development pipeline has been outlined by Cummings et al., discussing relevant treatment options that are on the market or currently in development (Cummings et al., 2024). Limitations aside, most of the models discussed here can be reliably used to determine the impact of biological factors or a therapeutic intervention on molecular diagnostic markers of AD when assessed with appropriate techniques, as with anti-amyloid therapies (Morgan and Lamb 2024). They can also be very informative on the pathological consequences of the accumulation of such markers in a living brain. We suggest a standardised approach to AD research is timely to effect translation of impactful therapeutics to the clinic.

### CRediT authorship contribution statement

**Luba Sominsky:** Writing – review & editing, Writing – original draft, Conceptualization.
**Carsten Culmsee:** Writing – review & editing, Writing – original draft, Conceptualization.
**Mary Slayo:** Writing – review & editing, Writing – original draft, Conceptualization.
**Calon Frederic:** Writing – review & editing, Writing – original draft, Conceptualization.
**Christoph Rummel:** Writing – review & editing, Writing – original draft, Conceptualization.
**Hoeksema Emmy:** Writing

– review & editing, Writing – original draft, Conceptualization.
**Jorine Geertsema:** Writing – review & editing, Writing – original draft, Conceptualization.
**Martin Feldotto:** Writing – review & editing, Writing – original draft, Conceptualization.
**Spencer Sarah:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
**Jean-Christophe Delpech:** Writing – review & editing, Writing – original draft, Conceptualization.
**de Rooij Susanne:** Writing – review & editing, Writing – original draft, Conceptualization.
**Sin-ghaarachchi Pasindu:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Conceptualization.
**Jonathan McQualter:** Writing – review & editing, Writing – original draft, Conceptualization.
**Sophie Layé:** Writing – review & editing, Writing – original draft, Conceptualization.
**Aniko Korosi:** Writing – review & editing, Writing – original draft, Conceptualization.
**Peter Antal:** Writing – review & editing, Writing – original draft, Conceptualization.

### Funding

This project was supported by funding from the European Union (EU) Joint Program on Neurodegenerative Disease (JPND) Grant: (SOLID JPND2021–650–233) to PA, FC (Canadian Institutes of Health Research (CIHR), ENG 179033), AK, SL (ANR-21-JPW2–0004–05), CR, LS (National Health and Medical Research Council (NHMRC)-2014629), SJS (National Health and Medical Research Council (NHMRC)-2014629). The team is also supported by an Australian Government Research Training Program Scholarships to PHS; the Chaire d'excellence Région Nouvelle Aquitaine ExoMarquAge (13059720–13062120) and GPR Brain_2030 NeuroDegen to JCD and NeuroFood to SL; an ANR to JCD (ANR-23-CE14–0056); a Bundesministerium für Bildung und Forschung (BMBF), grant to CR (01ED2207); an RMIT University Vice Chancellor's Ph.D Scholarship to MS; an NHMRC Ideas Grant (NHMRC-2019196) and an Australian Research Council Discovery Project (ARC; DP230101331) to SJS.

### Declaration of Competing Interest

The authors have no conflicts of interest to declare.

### Data availability

No data was used for the research described in the article.

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