African Green Monkeys Respond to Synthetic Aβ Oligomers with Persistent Alzheimer’s-like Activation
Year: 2026
Type: article
Abstract: Supplementary TablesSupplementary Table 1. Protein-level quantification results generated using FragPipe. The table reports protein identifiers (Protein.ID, Entry.Name, Gene), sequence attributes (Protein.Length, Organism, Protein.Existence, Description), probability metrics (Protein.Probability, Top.Peptide.Probability), and spectral evidence (peptide counts, total/unique/combined spectral counts). Spectral counts and intensity values are provided separately for each sample, along with MaxLFQ-normalized intensities. The column Indistinguishable.Proteins lists additional proteins not distinguished from the leading entry.Supplementary Table 2. Enrichment statistics for Gene Ontology (GO) terms identified from proteins detected in FragPipe analysis. Columns report the ratio of genes in the input set versus the background (GeneRatio, BgRatio), enrichment metrics (RichFactor, FoldEnrichment, zScore), and significance measures (p-value, adjusted p-value, q-value). The geneID column lists contributing genes, and Count indicates the number of genes associated with each term.Supplementary Table 3. Protein-level log fold change estimates between AβO and vehicle conditions. Columns include log fold mean and log10 fold changes from two approaches (log10FC mean and log10FC edge), alongside protein identifiers (Protein, Protein.ID, Entry.Name, Gene), sequence attributes (Protein Length, Organism, Protein Existence), and functional descriptions.Supplementary Table 4. Enrichment results for functional categories for differentially abundant proteins. Each row represents a significantly enriched pathway or ontology term (Description). Columns report the ratio of genes in the input set versus the background (GeneRatio, BgRatio), enrichment metrics (RichFactor, FoldEnrichment, zScore), and significance measures (p-value, adjusted p-value, q-value). The geneID column lists contributing genes, and Count indicates the number of genes associated with each term.Supplementary Table 5. Disease Ontology (DO) enrichment analysis for differentially abundant proteins.Significantly enriched disease terms identified from proteins detected in FragPipe analysis. Each term (Description) is shown with enrichment metrics (GeneRatio, BgRatio, RichFactor, FoldEnrichment, zScore) and significance levels (p-value, adjusted p-value, q-value). The geneID column lists proteins associated with each disease term, and Countindicates the number of genes mapped.Supplementary Table 6. Protein-level quantification results generated using FragPipe of CSF samples at concentration 1600 ng. The table reports protein identifiers (Protein.ID, Entry.Name, Gene), sequence attributes (Protein.Length, Organism, Protein.Existence, Description), probability metrics (Protein.Probability, Top.Peptide.Probability), and spectral evidence (peptide counts, total/unique/combined spectral counts). Spectral counts and intensity values are provided separately for each sample, along with MaxLFQ-normalized intensities. The column Indistinguishable.Proteins lists additional proteins not distinguished from the leading entry. Protein overlap and distribution between AβO-treated=2, vehicle-control=2 samples are shown in Figures S3-S4.Code availabilityCode to produce results and main figuresFigure2.RThis code compares proteins detected in CSF and EVps samples from AβO-treated and vehicle-control groups. For Figure 2A, it generates a Venn diagram showing the overlap among detected proteins across CSF and EVps groups. For Figure 2B–E, it performs Gene Ontology cellular component enrichment to summarize the cellular compartments represented in CSF and EVps from vehicle- and AβO-treated samples, then generates the corresponding tree plots.Figure3.RCode generates the analyses and plots shown in Figure 3. For Figure 3A, it compares CSF and EVps protein intensity patterns across compartment and treatment, and uses PCA to visualize how samples cluster by sample type and AβO versus vehicle treatment. For Figure 3B–C, it generates heatmaps of log₁₀-transformed protein intensities for whole CSF and EVps samples, with hierarchical clustering of individual samples and treatment conditions indicated. Proteins were included only if they were detected in at least 3 of 4 CSF samples or 4 of 6 EVps samples to minimize missing data and support robust comparisons. For Figure 3D–E, it performs differential abundance analyses for whole CSF and EVps, plotting log₁₀ fold change based on group means against the fold change between the closest treatment and control values.Figure4.RThis code uses the CSF and EVps fold-change tables generated from Figure3.R. It first filters proteins to retain those where the mean-based fold change and edge-based fold change agree in direction. Proteins are then separated into upregulated and downregulated groups within each compartment. For CSF, the code selects the top 50% of directionally consistent upregulated and downregulated proteins (Figure 4A-B); for EVps, it selects the top 70% (Figure 4C-D). These protein sets are used for Gene Ontology Biological Process enrichment analysis. Figure5.RIdentifies proteins from the CSF and EVps differential abundance results that are annotated to the Alzheimer’s disease Disease Ontology term. Plots their log10 fold changes in CSF and EVps (Figure 5A). Plots treatment level differences protein intensities for representative Alzheimer’s disease-associated proteins from CSF and EVps (Figure 5B-E). All original code for data processing and performing downstream statistics can be found at https://github.com/G2Lab/NHP_Alzheimers_ProteomicsRaw Data The mass spectrometry proteomics data have been deposited in the PRIDE repository under dataset identifier PXD068600.Any additional information required to reanalyze the data reported in this paper is available from authors upon request.
Source: Figshare
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7202210360
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