DOI: 10.5061/dryad.0000000dn
title: Inhibition of amyloid beta oligomer accumulation by NU-9: A unifying mechanism for the treatment of neurodegenerative diseases data set

description: Protein aggregation is a hallmark of neurodegenerative diseases, which
 connects these neuropathologies by a common phenotype. Various proteins
 and peptides form aggregates that are poorly degraded, and their ensuing
 pathological accumulation underlies these neurodegenerative diseases.
 Similarities may exist in the mechanisms responsible for the buildup of
 these aggregates. Therefore, therapeutics designed to treat one
 neurodegenerative disease may be beneficial to others. In ALS models, the
 compound NU-9 was previously shown to block neurodegeneration produced by
 aggregation-inducing mutations of SOD-1 and TDP-43 [B. Genç et al., Clin.
 Transl. Med. 11, e336 (2021)]. Here, we report that NU-9 also prevents the
 accumulation of amyloid beta oligomers (AβOs), small peptide aggregates
 that are instigators of Alzheimer’s disease neurodegeneration [M. Tolar et
 al., Int. J. Mol. Sci. 22, 6355 (2021)). AβO buildup was measured by
 immunofluorescence imaging of cultured hippocampal neurons exposed to
 exogenous monomeric Aβ. In this model, AβO buildup occurs via cathepsin L-
 and dynamin-dependent trafficking. This is prevented by NU-9 through a
 cellular mechanism that is cathepsin B- and lysosome-dependent, suggesting
 that NU-9 enhances the ability of endolysosomal trafficking to protect
 against AβO buildup. This possibility is strongly supported by a
 quantitative assay for autophagosomes that shows robust stimulation by
 NU-9. These results contribute additional understanding to the mechanisms
 of protein aggregation and suggest that multiple neurodegenerative
 diseases might be treatable by targeting common pathogenic mechanisms
 responsible for protein aggregation.
description: # Inhibition of amyloid beta oligomer accumulation by NU-9: A unifying
 mechanism for the treatment of neurodegenerative diseases data set
 [https://doi.org/10.5061/dryad.0000000dn](https://doi.org/10.5061/dryad.0000000dn) ## Description of the data and file structure We have submitted our raw data with file names corresponding to the Figure number and subset as well as descriptive titles. The data has been uploaded in (.zip) files, one for each figure of the manuscript. The (.zip) files include (.png) and (.tif) image files and GraphPad Prism files (.pzfx) This data set contains the raw confocal fluorescence images used for Figures 1-6. Images were collected on a Leica SP5 or Leica SP8 confocal microscope using the Leica software. Images to be presented in the various figures were extracted as (.tif) files. Graph files, and some fluorescence images were saved as (.png) files. All files were combined using Photoshop software into the final figures found in the associated manuscript. Image names correspond to the experimental conditions and date of data collection. This data set also contains raw dot-blot data images for Figure 2. This data was collected using a Sapphire Imager. Data are presented as un-processed (.tif) files in the Figure 2 dataset (Figure 2 Raw data collection.zip). ### Figure 1 raw data files: (Figure 1 raw data collection.zip) ### Figure 2 raw data files: (Figure 2 Raw data collection.zip) ### Figure 3 raw data files: (Figure 3 raw data collection.zip) ### Figure 4 raw data files: (Figure 4 raw data collection.zip) ### Figure 5 raw data files: (Figure 5 collection of raw data.zip) ### Figure 6 raw data files: (Figure 6 - collection of raw data.zip) ### Figure 7 raw data file: (Figure 7-Proposed mechanism of action for NU-9.zip) ## Code/software Data was analyzed using ImageJ software (free from NIH). Data collected from ImageJ analysis was graphed for publication using Excel (Microsoft Corp) and/or Prism (GraphPad). Immunofluorescence images were collected using Leica Software (LAS X freeware available from Leica Microsystems). Selected images were saved as TIFF files using the LAS X software.