Systematic interaction network filtering identifies CRMP1 as a novel suppressor of huntingtin misfolding and neurotoxicity

Assemblies of huntingtin (HTT) fragments with expanded polyglutamine (polyQ) tracts are a pathological hallmark of Huntington's disease (HD). The molecular mechanisms by which these structures are formed and cause neuronal dysfunction and toxicity are poorly understood. Here, we utilized available gene expression data sets of selected brain regions of HD patients and controls for systematic interaction network filtering in order to predict disease-relevant, brain region-specific HTT interaction partners. Starting from a large protein-protein interaction (PPI) data set, a step-by-step computational filtering strategy facilitated the generation of a focused PPI network that directly or indirectly connects 13 proteins potentially dysregulated in HD with the disease protein HTT. This network enabled the discovery of the neuron-specific protein CRMP1 that targets aggregation-prone, N-terminal HTT fragments and suppresses their spontaneous self-assembly into proteotoxic structures in various models of HD. Experimental validation indicates that our network filtering procedure provides a simple but powerful strategy to identify disease-relevant proteins that influence misfolding and aggregation of polyQ disease proteins.DFG [SFB740, 740/2-11, SFB618, 618/3-09, SFB/TRR43 A7]; BMBF(NGFN-Plus) [01GS08169-73, 01GS08150, 01GS08108]; HDSA Coalition for the Cure; EU (EuroSpin) [Health-F2-2009-241498, HEALTH-F2-2009-242167]; Helmholtz Association (MSBN, HelMA) [HA-215]; FCT [IF/00881/2013]info:eu-repo/semantics/publishedVersio

A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer's disease

BACKGROUND: Alzheimer's disease (AD) is characterized by the intra- and extracellular accumulation of amyloid-β (Aβ) peptides. How Aβ aggregates perturb the proteome in brains of patients and AD transgenic mouse models, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive Aβ aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously. METHODS: We quantified progressive Aβ aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and membrane filter assays. Protein changes in different mouse tissues were analyzed by MS-based proteomics using label-free quantification; resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in cerebrospinal fluid (CSF) from AD patients and controls using ELISAs. RESULTS: Experiments revealed faster accumulation of Aβ42 peptides in hippocampus than in cortex of 5xFAD mice, with more protein abundance changes in hippocampus, indicating that Aβ42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined Aβ aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease-relevant changes in human AD. We detected a positive correlation between Aβ42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular Aβ plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in human AD brain tissue. Arl8b protein levels were significantly increased in CSF of AD patients. CONCLUSIONS: We report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker

The Anti-amyloid Compound DO1 Decreases Plaque Pathology and Neuroinflammation-Related Expression Changes in 5xFAD Transgenic Mice

Self-propagating amyloid-β (Aβ) aggregates or seeds possibly drive pathogenesis of Alzheimer's disease (AD). Small molecules targeting such structures might act therapeutically in vivo. Here, a fluorescence polarization assay was established that enables the detection of compound effects on both seeded and spontaneous Aβ42 aggregation. In a focused screen of anti-amyloid compounds, we identified Disperse Orange 1 (DO1) ([4-((4-nitrophenyl)diazenyl)-N-phenylaniline]), a small molecule that potently delays both seeded and non-seeded Aβ42 polymerization at substoichiometric concentrations. Mechanistic studies revealed that DO1 disrupts preformed fibrillar assemblies of synthetic Aβ42 peptides and decreases the seeding activity of Aβ aggregates from brain extracts of AD transgenic mice. DO1 also reduced the size and abundance of diffuse Aβ plaques and decreased neuroinflammation-related gene expression changes in brains of 5xFAD transgenic mice. Finally, improved nesting behavior was observed upon treatment with the compound. Together, our evidence supports targeting of self-propagating Aβ structures with small molecules as a valid therapeutic strategy

Interactome Mapping Provides a Network of Neurodegenerative Disease Proteins and Uncovers Widespread Protein Aggregation in Affected Brains.

Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP(100) that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer's disease patients, suggesting widespread protein aggregation in NDs

Additional file 3 of A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer’s disease

Additional file 3: Supplementary Excel File 1a. DEPs from 5xFAD versus wild-type tissue comparisons in hippocampus and cortex; DEPs were defined using the “pairwise model”; Supplementary Excel File 1b. Summary of the statistical calculations from Supplementary Excel File 1a; Supplementary Excel File 1c. DEPs in both hippocampal and cortical tissues defined through the “full model”; Supplementary Excel File 2. DEPs that correlate or anticorrelate to Aβ aggregate load in both hippocampal and cortical tissues defined through the “pairwise model”; Supplementary Excel File 3. Identified genes differentially down- or upregulatedin cortex or hippocampus of 5xFAD mice; Supplementary Excel File 4. Results of the Ingenuity Pathway Analyses performed in Figures 3g and 4e; Supplementary Excel File 5a. Proteins with significant abundance changes from Johnson et al. 2020; Supplementary Excel File 5b. Proteins with significant abundance changes from Johnson et al. 2022; Supplementary Excel File 5c. Proteins with significant abundance changes from Johnson et al. 2022; Supplementary Excel File 5d. Proteins with significant abundance changes from Drummond et al. 2022; Supplementary Excel File 6. Numbers of common DEPs in mouse and human datasets; Supplementary Excel File 7. DEPs defined using a “pairwise model” and concomitantly altered both in mouse and human brain tissues from J20, J22 and D22. Supplementary Excel File 8a. The step-by-step selection process to identify the potential AD biomarker Arl8b. Supplementary Excel File 8b. The top 25 Aβ-correlated proteins in mouse hippocampal tissues

Additional file 2 of A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer’s disease

Additional file 2: Fig. S1. Aβ peptide levels and APP and PSEN1 expression in hippocampus and cortex of 5xFAD mice. Fig. S2. Analysis of Aβ aggregate formation using membrane filter assays and sucrose gradient centrifugations. Fig. S3. Analysis of wild-type expression profiles to assess whether the protein abundance changes detected in 5xFAD brains are more frequent among highly expressed mouse proteins. Fig. S4. Functional analysis of dysregulated proteins defined with a pairwise model in brains of 5xFAD mice. Fig. S5. Enrichment analysis of cell-type-specific marker proteins among dysregulated proteins in brains of 5xFAD mice. Fig. S6. IPA and gene ontology enrichment analysis of differentially expressed proteins defined with the full model in cortical and hippocampal tissues of 5xFAD mice. Fig. S7. Ingenuity pathway analysis of Aβ-correlated and anticorrelated DEPs defined by the pairwise model in brains of 5xFAD mice. Fig. S8. Numbers of pairwise common DEPs in the mouse datasets and datasets from human studies. Fig. S9. Strategy to define mouse protein signatures that are concordantly altered also in AD patient brains. Fig. S10. Investigation of the overlap of DEPs in brains of 5xFAD mice with DEPs in asymptomatic AD brains. Fig. S11. Analysis of the correlation in protein effect sizes between 5xFAD mouse and AD patient brains for proteins present in all studies. Fig. S12. Selection of the neuronal lysosome-associated protein Arl8b by step-by-step data filtering. Fig. S13. Immunofluorescence analysis of 5xFAD brain slices. Fig. S14. Analysis of Arl8b protein aggregates using human brain homogenates derived from AD patients and control individuals

Additional file 1 of A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer’s disease

Additional file 1: Table S1. Commercial antibodies used in this study. Table S2. Characteristics of AD and HD patients and corresponding controls. Table S3. Spearman correlation analysis of Arl8b protein level measurements and AD biomarker levels