Alzheimer Disease Cell Models for Research

Alzheimer's disease (AD) is the most common cause of dementia, affecting over 55 million people worldwide, with nearly 10 million new cases each year (WHO, 2023). The global cost of dementia was estimated at $1.3 trillion in 2019 and is projected to double by 2030. Age is the greatest risk factor, with the incidence rising sharply after 65 years. The disease is characterized by progressive cognitive decline, memory loss, and behavioral changes, leading to complete dependence and death typically 3-9 years after diagnosis. There is no cure, and current treatments only manage symptoms. The urgent need for disease-modifying therapies drives extensive research into the molecular mechanisms of AD.

AD is a complex, multifactorial disease with both genetic and sporadic forms. The genetic forms (familial AD) are caused by mutations in APP, PSEN1, and PSEN2, providing clear targets for mechanistic studies. Sporadic AD is associated with risk genes such as APOE4, TREM2, and SORL1. The disease involves multiple pathological processes, including amyloid-beta aggregation, tau hyperphosphorylation, neuroinflammation, and synaptic dysfunction. This complexity makes AD an ideal model for studying gene-environment interactions and for developing targeted therapies. Public datasets like the AD Knowledge Portal and GEO provide extensive omics data, enabling integrative analyses. Open questions include the precise role of APOE4 in neurodegeneration and the interplay between amyloid and tau pathologies.

The pathogenesis of AD involves several interconnected pathways:

1. Amyloid hypothesis: Sequential cleavage of amyloid precursor protein (APP) by beta-secretase (BACE1) and gamma-secretase (a complex containing presenilin) generates amyloid-beta (Abeta) peptides. The accumulation of neurotoxic Abeta42 oligomers and plaques is a key trigger.

2. Tau hypothesis: Hyperphosphorylation of tau protein leads to the formation of neurofibrillary tangles, disrupting microtubule stability and axonal transport.

3. Neuroinflammation: Activated microglia and astrocytes release pro-inflammatory cytokines, contributing to neuronal damage.

4. Oxidative stress and mitochondrial dysfunction: Increased reactive oxygen species and impaired mitochondrial function lead to neuronal apoptosis.

5. Cholinergic deficit: Loss of cholinergic neurons in the basal forebrain results in cognitive decline.

Data from NCBI Gene, ClinVar, and AD databases.

Key signaling networks implicated in AD:

• • Amyloidogenic processing: APP cleavage by BACE1 and gamma-secretase. Key nodes: APP, BACE1, PSEN1, PSEN2, NCSTN, APH1B.
• • Tau phosphorylation: Kinases such as GSK3B, CDK5, and MAPK. Phosphatases like PP2A are downregulated.
• • Neuroinflammation: TREM2-DAP12 signaling, TLR4, NF-kB pathway. Key nodes: TREM2, TYROBP, IL1B, TNF, NFKB1.
• • Lipid metabolism and cholesterol: APOE and ABCA1. APOE4 impairs lipid transport and promotes Abeta aggregation.
• • Autophagy and proteostasis: mTOR pathway, beclin-1. Impaired autophagy leads to accumulation of protein aggregates.
• • Synaptic plasticity: BDNF, NMDAR, and calcium signaling. Disruption leads to synaptic loss.

Organoids: 3D cerebral organoids derived from iPSCs recapitulate aspects of AD pathology, including Abeta aggregation and tau phosphorylation. They are useful for studying cell-cell interactions and drug testing.

Animal models for AD:

• • Transgenic mice overexpressing mutant APP (e.g., APP/PS1, 5xFAD) or tau (e.g., P301S) are widely used.
• • Knock-in mice with humanized APP or PSEN1 mutations (e.g., AppNL-G-F) better mimic sporadic AD.
• • APOE4 targeted replacement mice model the major genetic risk factor.
• • Rat models: TgF344-AD rats exhibit both amyloid and tau pathology.
• • Non-human primate models are being developed but are costly.
• • PDX (patient-derived xenografts) are not applicable for AD as it is not a cancer; instead, patient-derived iPSC models are used.

CRISPR-Cas9 gene editing enables the generation of isogenic cell lines with precise genetic modifications. For AD research, common models include:

• • APP knockout cell lines: Eliminate APP expression to study its physiological function and the effects of Abeta depletion.
• • PSEN1 knockout cell lines: Ablate gamma-secretase activity, affecting Notch signaling and APP processing.
• • APOE4 knock-in cell lines: Replace the common APOE3 allele with APOE4 to study its impact on Abeta clearance and lipid metabolism.
• • TREM2 knockout cell lines: Investigate microglial function and neuroinflammation.
• • Isogenic pairs: A wild-type and a gene-edited line derived from the same parental clone, ensuring that phenotypic differences are due to the specific genetic alteration. These models are commercially available and sequence-verified, providing reliable tools for drug discovery and functional genomics.

Gene-edited cell lines are essential for validating the role of genes in AD pathways. For example:

• • APP knockout in SH-SY5Y cells reduces Abeta production, confirming the role of APP in amyloidogenesis.
• • PSEN1 knockout in HEK293 cells abolishes gamma-secretase activity, leading to accumulation of APP C-terminal fragments and altered Notch signaling.
• • APOE4 knock-in in iPSC-derived neurons impairs synaptic function and increases Abeta aggregation compared to APOE3.
• • TREM2 knockout in microglial cell lines reduces phagocytosis of Abeta, linking TREM2 to neuroinflammation.

Isogenic cell line pairs are powerful tools for drug screening. For example:

• • A BACE1 inhibitor can be tested on APP-overexpressing cells with and without a PSEN1 mutation to assess efficacy and resistance.
• • APOE4 vs APOE3 isogenic lines can be used to screen compounds that modulate APOE4-related phenotypes.
• • Resistance mechanisms can be studied by exposing cells to drugs and selecting for resistant clones, then identifying genetic changes via sequencing.

CRISPR screens can identify genes that, when knocked out, alter Abeta production or tau phosphorylation. For example:

• • A genome-wide CRISPR knockout screen in iPSC-derived neurons can identify novel regulators of APP processing.
• • Synthetic lethality screens: In cells with a specific AD mutation, knocking out a second gene may lead to cell death, revealing potential therapeutic targets.
• • Secreted biomarkers can be measured in conditioned media from gene-edited cells to identify novel diagnostic or prognostic markers.