A cellular model of amyloid precursor protein processing and amyloid-β peptide production

Document Type

Article

Abstract

Background: A hallmark pathologic feature of Alzheimer's disease (AD) is accumulation of neuritic senile plaques in the brain parenchyma. Neurotoxic plaque cores are composed predominantly of amyloid-β (Aβ) peptides of 40 and 42 amino acids in length, formed by sequential cleavage of amyloid precursor protein (APP) by β-, and γ-secretases. There is a great interest in approaches to modulate Aβ peptide production and develop therapeutic interventions to reduce Aβ levels to halt or slow the progression of neurodegeneration. New method: We characterized and present the BE(2)-M17 human neuroblastoma cell line as a novel in vitro model of the APP-cleavage cascade to support future (1) functional studies of molecular regulators in Aβ production, and (2) high-throughput screening assays of new pharmacotherapeutics. Results: In BE(2)-M17 cells, both RNA (i.e., RT-PCR, RNA sequencing) and protein analyses (i.e., Western blots, ELISA), show endogenous expression of critical components of the amyloidogenic pathway, APP-cleavage intermediates CTF83 and CTF99, and final cleavage products Aβ40 and Aβ42. We further report effects of retinoic acid-mediated differentiation on morphology and gene expression in this cell line. Comparison with existing method(s): In contrast to primary isolates or other cell lines reported in current literature, BE(2)-M17 not only sustains baseline expression of the full contingent of APP-processing components, but also remains stably adherent during culture, facilitating experimental manipulations. Conclusions: Our evidence supports the use of BE(2)-M17 as a novel, human, cell-based model of the APP processing pathway that offers a potential streamlined approach to dissect molecular functions of endogenous regulatory pathways, and perform mechanistic studies to identify modulators of Aβ production. © 2013 Elsevier B.V.

Publication Date

2-15-2014

Publication Title

Journal of Neuroscience Methods

ISSN

01650270

E-ISSN

1872678X

Volume

223

First Page

114

Last Page

122

PubMed ID

24333289

Digital Object Identifier (DOI)

10.1016/j.jneumeth.2013.11.024

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