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Additional file 2: Table 2. contains dataset for serum Aβ42, Aβ40 and Aβ42/Aβ40 ratio of F1 and F2 generation transgenic rats (see Fig. 3).
Additional file 1: Table 1. contains dataset for Northern blot analysis (see Fig. 2). PS1 mRNA level, serum Aβ42 and tissue are reported in the table.
Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data 1) and FC (Data 2), Cnp in the FC (Data 3), and Plp1 in the CBM (Data 4). For these differential expression analyses, we mapped RNAseq reads... more
Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data 1) and FC (Data 2), Cnp in the FC (Data 3), and Plp1 in the CBM (Data 4). For these differential expression analyses, we mapped RNAseq reads using TopHat, converted to count space using HTSeq, used voom to transform the read space data to log2 counts per million, and used limma for differential expression analysis. We also used the Ensembl database to identify the human gene with the highest homology percentage based on protein coding region DNA divergence, and report this homology percentage for each gene. Note that the differential expression signatures of Cnp in the CBM and Plp1 in the FC were not found not have any differentially expressed genes at FDR
Summary of read mapping from the three knockout mouse RNAseq experiments generated by TopHat. (XLSX 52Â kb)
Module membership file for the Mount Sinai Brain Bank (MSBB) proteomic coexpression network. The module label, a randomly chosen color name, is in the 1st column, while the protein name is in the 2nd column. (TSV 34Â kb)
Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the protein co-expression network constructed from the proteomics data from the autopsied brains in the MSBB cohort, along with the dendrogram... more
Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the protein co-expression network constructed from the proteomics data from the autopsied brains in the MSBB cohort, along with the dendrogram showing the tree cutting process used to define modules (above). Figure S2. Confirmation that the key driver knockouts abrogate gene expression of the key driver in the RNA-seq experiments. For each of the key driver knockouts whose genome-wide gene expression was profiled in this study using RNA-seq, we plotted the log10 counts overlapping that gene in both the wildtype (WT) and knockout (KO) samples. Notably, one of the matched samples from Cnp was detected as an outlier in both the CBM and FC brain regions (red), due to suspected mislabeling. These samples were removed prior to downstream differential expression analysis. (DOCX 161Â kb)
Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD mouse microglia. (XLSX 20 kb)
Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p<0.05) in either WT-LPS vs WT-control and 5xFAD vs WT comparisons. Figure S2. Pre-incubation of anti-Apoe antibody with fibrillar AĂ does... more
Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p<0.05) in either WT-LPS vs WT-control and 5xFAD vs WT comparisons. Figure S2. Pre-incubation of anti-Apoe antibody with fibrillar AĂ does not abolish plaque-like immunostaining for Apoe. (DOCX 761 kb)
Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator of future CVD, combining the influence of shared genetic, environmental and behavioral risk factors. Though CVD risk factors have been... more
Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator of future CVD, combining the influence of shared genetic, environmental and behavioral risk factors. Though CVD risk factors have been associated with an increased risk of cognitive impairment and decline, less is known about the association between family history of CVD and cognitive function. Evaluating this association may further elucidate the role of cardiovascular health in cognitive health. Methods: The Emory Healthy Aging Study isan ongoing prospective cohort study aimed at identifying predictors of healthy aging and age-related diseases. Participants are primarily residents of the Atlanta area, at least 18 years old, who completed an online baseline health survey. Multiple recruitment forums were used, including clinic waiting rooms, informational letters and emails, community events and online recruitment. Baseline information about demographic (age, race, gender), socioeconomi...
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Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown in Figure S1. Comparison of ShK-F6CA labeling of Kv1.3 channels in splenic and brain-infiltrating macrophages is shown in Figure S2.... more
Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown in Figure S1. Comparison of ShK-F6CA labeling of Kv1.3 channels in splenic and brain-infiltrating macrophages is shown in Figure S2. Morphological changes induced by LPS, ShK-223, and LPS+ShK-223 treatment conditions are shown in Figure S3. Distribution of missing data in the proteomic data set is shown in Figure S4. Quantitative RT-PCR data showing validation of pro-inflammatory activation of BV2 microglia by LPS are shown in Figure S5. EHD1 upregulation by LPS and inhibition of EHD1 upregulation by ShK-223 is shown in Figure S6. (DOCX 1262 kb)
Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-Tau/Aβ42 < 0.39 in the ADNI and Emory cohorts. Table S2. Main effects from mixed linear modeling of CSF FH and C3 levels in ADNI. In each... more
Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-Tau/Aβ42 < 0.39 in the ADNI and Emory cohorts. Table S2. Main effects from mixed linear modeling of CSF FH and C3 levels in ADNI. In each model, FH or C3 was entered as the dependent variable; age, gender, presence of APOE ε4 allele, diagnosis, Aβ42, t-Tau, p-Tau181, gender X age, presence of APOE ε4 allelle X age, and diagnosis X age were entered as fixed factors; and age was also entered as a random factor. Factors with main effect p > 0.10 were removed in a step-wise fashion to arrive at final model. See text and Fig. 2 for effects from different diagnostic categories. Table S3. Mixed linear modeling of PAD-based diagnostic classification and time (in months) on longitudinal memory and executive functions in the Emory validation cohort. A) Among patients initially classified as MCI with longitudinal follow-up (n=44), reclassification using PAD showed differences in absolute execu...
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Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable proteins at a given threshold of missing values in the 47 brain samples from the BLSA cohort for TMT-LysC and... more
Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable proteins at a given threshold of missing values in the 47 brain samples from the BLSA cohort for TMT-LysC and LFQ-trypsin analyses is shown. The point at 23 samples and 6533 proteins represents the threshold used for the TMT-LysC analysis pipeline in this study. This point falls slightly below the TMT curve because 11 MCI samples were included in the TMT analysis workflow, for a total of 58 samples, but were later dropped from the analysis (see Methods). The increased number of samples when including the 11 MCI cases slightly reduced the number of quantifiable proteins at the ~ 50% missing value threshold. (PDF 79 kb)
Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homogenates from cases shown in Table S1 were analyzed by SDS-PAGE to assess sample integrity prior to TMT labeling and mass spectrometry... more
Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homogenates from cases shown in Table S1 were analyzed by SDS-PAGE to assess sample integrity prior to TMT labeling and mass spectrometry analysis. Gels were stained with Coomassie Blue to visualize protein. AD, Alzheimer's disease; AS, asymptomatic Alzheimer's disease; CT, control; MCI, mild cognitive impairment. (PDF 22000 kb)
Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty proteins identified as risk factors for AD by GWAS at genome-wide significance [12], five had alt-EEjxn peptides that were observed and... more
Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty proteins identified as risk factors for AD by GWAS at genome-wide significance [12], five had alt-EEjxn peptides that were observed and quantifiable in the BLSA-TMT analysis (observed). The number of observed and quantifiable alt-EEjxn peptides for each of these five proteins was a subset of the total number of alt-EEjxn peptides predicted to exist after LysC digestion (peptide database). This number was a further subset of the total number of alt-EEjxns observed for each of the five proteins from RNAseq data (transcript level). For details on generation of the peptide database and transcript level numbers, see Methods. (DOCX 28Â kb)
Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsin Approaches. The number of alt-EEjxn peptides identified by matching to the listed databases (Swiss-Prot, Trembl, or RNAseq) is shown, along... more
Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsin Approaches. The number of alt-EEjxn peptides identified by matching to the listed databases (Swiss-Prot, Trembl, or RNAseq) is shown, along with the number of quantifiable alt-EEjxn peptides. A peptide was considered quantifiable in this analysis if it had a minimum of 2 measurements in at least 2 different case groups. RNAseq data from control and AD patient brains (n = 6) were used to generate the RNAseq alt-EEjxn peptide database, as described in Methods. (DOCX 28 kb)
Table S4. List of Biological Terms for GO Network in Figure S8. GO, gene ontology; UP, UniProt; KEGG, Kyoto Encyclopedia of Genes and Genomes; SMART, Simple Modular Architecture Research Tool; FDR, false discovery rate. (DOCX 35Â kb)
Table S2. Case Characteristics. Values shown are means ± SD. AD, Alzheimer's disease; AsymAD, asymptomatic Alzheimer's disease; MCI, mild cognitive impairment; CERAD, Consortium to Establish a Registry for Alzheimer's Disease... more
Table S2. Case Characteristics. Values shown are means ± SD. AD, Alzheimer's disease; AsymAD, asymptomatic Alzheimer's disease; MCI, mild cognitive impairment; CERAD, Consortium to Establish a Registry for Alzheimer's Disease amyloid-β plaque load score; Braak, Braak stage for tau tangle burden; PMI, post-mortem interval; ApoE, apolipoprotein E isoform genotype. (DOCX 30 kb)
Figure S15. Enrichment of RNA Binding Proteins in TMT Network Module 18. Graphical representation of the correlation relationships among proteins for lightgreen module M18, with proteins centrally located representing those most highly... more
Figure S15. Enrichment of RNA Binding Proteins in TMT Network Module 18. Graphical representation of the correlation relationships among proteins for lightgreen module M18, with proteins centrally located representing those most highly correlated with other proteins in the module. Proteins annotated as RNA binding proteins in geneontology.org are highlighted in yellow. (PDF 177Â kb)
Figure S13. GO Analysis of Alternative Exon-Exon Junction Peptides Unique to the RNAseq Database. Alternative exon-exon junction (alt-EEjxn) peptides that were identified by LFQ-trypsin or TMT-LysC approaches from the RNAseq data only... more
Figure S13. GO Analysis of Alternative Exon-Exon Junction Peptides Unique to the RNAseq Database. Alternative exon-exon junction (alt-EEjxn) peptides that were identified by LFQ-trypsin or TMT-LysC approaches from the RNAseq data only were analyzed by gene ontology (GO), which showed that the alternatively spliced proteins identified by the two approaches in the RNAseq data were largely unique. (PDF 660Â kb)
Figure S12. Correlation Between Alternative Exon-Exon Junctions Quantified by TMT-LysC and LFQ-trypsin Analyses. Alternative exon-exon junctions (alt-EEjxns) that were identified and quantified in both TMT-LysC and LFQ-trypsin analyses... more
Figure S12. Correlation Between Alternative Exon-Exon Junctions Quantified by TMT-LysC and LFQ-trypsin Analyses. Alternative exon-exon junctions (alt-EEjxns) that were identified and quantified in both TMT-LysC and LFQ-trypsin analyses (n = 1202 alt-EEjxns) and which had no missing values across the 47 BLSA cases were matched case-to-case, and the log(2) normalized intensity measurements for each alt-EEjxn were correlated between the two quantification approaches. Note that the peptide containing the alt-EEjxn is not necessarily identical between TMT-LysC and LFQ-trypsin analyses. When the correlation is restricted to identical alt-EEjxn peptides (n = 728), the strength of correlation increases only slightly (r = 0.6) (data not shown). (PDF 15000 kb)
Figure S11. Alternative Exon-Exon Junction Peptide Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable alternative exon-exon junction (alt-EEjxn) peptides at a given threshold of missing... more
Figure S11. Alternative Exon-Exon Junction Peptide Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable alternative exon-exon junction (alt-EEjxn) peptides at a given threshold of missing values in the 47 brain samples from the BLSA cohort for TMT-LysC and LFQ-trypsin analyses is shown, without regard to case group. Also shown is the number of alt-EEjxn peptides quantified by TMT-LysC that had a LFQ-trypsin cognate peptide, as well as the number of alt-EEjxn peptides quantified by LFQ-trypsin that had a cognate TMT-LysC peptide. The point at 23 samples represents the 50% missingness threshold. (PDF 89Â kb)
Figure S9. GO Network Analysis for Differential Protein Abundance Between AD and AsymAD. Proteins with significant differences in abundance between asymptomatic AD and AD after cell type deconvolution were analyzed by gene ontology (GO)... more
Figure S9. GO Network Analysis for Differential Protein Abundance Between AD and AsymAD. Proteins with significant differences in abundance between asymptomatic AD and AD after cell type deconvolution were analyzed by gene ontology (GO) network analysis. A complete list of biological terms that correspond to each node in the network, along with the source for the term and the false discovery rate (FDR) Q value statistic, is given in Table S4. (PDF 244Â kb)
Table S1. Mouse microglial transcriptomic modules identified by Weighted Correlation Network Analysis (WGCNA). (XLSX 5372Â kb)
3D viSNE representation of microglial modules: DAM (Red) and Homeostatic (Black) genes identified by single-cell RNAseq mapped to WGCNA modules. (HTML 3818 kb)
3D viSNE representation of microglial modules. Color is indicative of the respective module color. (HTML 3835 kb)
Figure S1, related to Figure 1. Expression of microglial transcriptomic modules identified by WGCNA. Figure S2, related to Figure 1. Gene ontology analysis reveals distinct cellular localization and functional profiles of microglial... more
Figure S1, related to Figure 1. Expression of microglial transcriptomic modules identified by WGCNA. Figure S2, related to Figure 1. Gene ontology analysis reveals distinct cellular localization and functional profiles of microglial networks in AD. Figure S3, related to Figure 1. Identification of transcriptional regulators of AD-associated microglial modules. Figure S4, related to Figure 2. A transcriptomic landscape of microglial activation states in AD. Figure S5, related to Figure 2. Magenta and Yellow modules likely emerge as distinct subtypes from a common microglial precursor state (Additional file 7: Table S7). Figure S6, related to Figure 2. Pro- and anti-inflammatory DAM networks emerge downstream of the Trem2-mediated immune checkpoint in AD. Figure S7. ShK-223 promotes compartmentalization of Aβ in mature phagolysosomes. (DOCX 2159 kb)
Table S6. Top AD risk genes in Blue, Magenta and Yellow AD-associated microglial modules. (XLSX 22Â kb)
Table S5. Enrichment analysis of GWAS-identified human AD-risk genes in mouse microglial modules. (XLSX 22 kb)
Table S2. Gene Ontology (GO) analyses of Blue, Magenta, Yellow and Midnightblue mouse microglial modules. (XLSX 110 kb)
Figure S5. TMT Protein Network Modules Enriched for AD Risk Factors. Graphical representation of the correlation relationships among TMT network module proteins for the four modules identified to contain enrichment of AD risk factors from... more
Figure S5. TMT Protein Network Modules Enriched for AD Risk Factors. Graphical representation of the correlation relationships among TMT network module proteins for the four modules identified to contain enrichment of AD risk factors from GWAS, along with the relationship of each module to case status, neuritic amyloid plaque load (CERAD score), and tau tangle burden (Braak stage). Proteins identified by GWAS as AD risk factors are highlighted in red. Only the top 100 proteins by kME value are shown for the M4 yellow (257 total proteins) and M7 black (162 total proteins) modules. (PDF 462Â kb)
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Retrieval of the Alzheimer’s amyloid precursor protein from the endosome to the TGN is S655 phosphorylation state-dependent and retromer-mediated
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Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue. However, the long... more
Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue. However, the long acquisition time required by conventional 3D high-resolution $T_2^*$ mapping method causes discomfort to patients and introduces motion artifacts to reconstructed images, which limits its wider applicability. In this paper we address this issue by performing $T_2^*$ mapping from undersampled data using compressive sensing (CS). We formulate the reconstruction as a nonconvex problem that can be decomposed into two subproblems. They can be solved either separately via the standard approach or jointly via the alternating direction method of multipliers (ADMM). Compared to previous CS-based approaches that only apply sparse regularization on the spin density $\boldsymbol X_0$ and the relaxation rate $\boldsymbol R_2^*$, our formulation enforces addition...
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Raw LFQ expression data from proteomic experiments are provided in Supplemental Tables S1 and S2. Table S3 lists LPS-regulated proteins in our analysis, cross-referenced with previously published microglia proteomics and transcriptomics... more
Raw LFQ expression data from proteomic experiments are provided in Supplemental Tables S1 and S2. Table S3 lists LPS-regulated proteins in our analysis, cross-referenced with previously published microglia proteomics and transcriptomics data. Table S4 contains lists of all Kv1.3-dependent proteins identified in this study. Table S5 contains results from the canonical pathway analysis of Kv1.3-dependent proteins. Raw proteomic datasets have been deposited at http://www.proteomexchange.org/. (XLSX 1848 kb)
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Elevated expression of β-amyloid (Aβ1-42) and tau are considered risk-factors for Alzheimer's disease in healthy older adults. We investigated the effect of aging and cerebrospinal fluid levels of Aβ1-42 and tau on 1) frontal... more
Elevated expression of β-amyloid (Aβ1-42) and tau are considered risk-factors for Alzheimer's disease in healthy older adults. We investigated the effect of aging and cerebrospinal fluid levels of Aβ1-42 and tau on 1) frontal metabolites measured with proton magnetic resonance spectroscopy (MRS) and 2) cognition in cognitively normal older adults (n = 144; age range 50-85). Levels of frontal gamma aminobutyric acid (GABA+) and myo-inositol relative to creatine (mI/tCr) were predicted by age. Levels of GABA+ predicted cognitive performance better than mI/tCr. Additionally, we found that frontal levels of n-acetylaspartate relative to creatine (tNAA/tCr) were predicted by levels of t-tau. In cognitively normal older adults, levels of frontal GABA+ and mI/tCr are predicted by aging, with levels of GABA+ decreasing with age and the opposite for mI/tCr. These results suggest that age- and biomarker-related changes in brain metabolites are not only located in the posterior cortex as suggested by previous studies and further demonstrate that MRS is a viable tool in the study of aging and biomarkers associated with pathological aging and Alzheimer's disease.
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Inflammation and immune mechanisms are believed to play important roles in Alzheimer's disease pathogenesis. Research supports the link between poor oral health and Alzheimer's disease. Periodontal disease and dental caries... more
Inflammation and immune mechanisms are believed to play important roles in Alzheimer's disease pathogenesis. Research supports the link between poor oral health and Alzheimer's disease. Periodontal disease and dental caries represent the two most common infections of the oral cavity. This study focused on a precursor to Alzheimer's disease, mild cognitive impairment (MCI). Using 16S rRNA sequencing, we characterized and compared the oral microbiome of 68 older adults who met the criteria for MCI or were cognitively normal, then explored relationships between the oral microbiome, diagnostic markers of MCI, and blood markers of systemic inflammation. Two taxa, Pasteurellacae and Lautropia mirabilis were identified to be differentially abundant in this cohort. Although systemic inflammatory markers did not differentiate the two groups, differences in five cerebrospinal fluid inflammatory mediators were identified and had significant associations with MCI. Because inflammatory markers may reflect CNS changes, pursuing this line of research could provide opportunities for new diagnostic tools and illuminate mechanisms for prevention and mitigation of Alzheimer's disease.
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ABSTRACTBackgroundProteomic characterization of microglia has been limited by low yield and contamination by non-microglial proteins in magnetic-activated cell sorting (MACS) enrichment strategies. To determine whether a... more
ABSTRACTBackgroundProteomic characterization of microglia has been limited by low yield and contamination by non-microglial proteins in magnetic-activated cell sorting (MACS) enrichment strategies. To determine whether a fluorescence-activated cell sorting (FACS)-based strategy overcomes these limitations, we compared microglial proteomes of MACS and FACS-isolated CD11b+ microglia in order to identify core sets of microglial proteins in adult mouse brain tissue.ResultsQuantitative multiplexed proteomics by tandem mass tag mass spectrometry (TMT-MS) of MACS-enriched (N = 5) and FACS-isolated (N = 5) adult wild-type CD11b+ microglia identified 1,791 proteins, of which 953 were differentially abundant, indicating significant differences between both approaches. While the FACS-isolated microglia proteome was enriched with cytosolic, endoplasmic reticulum and ribosomal proteins involved in protein metabolism and immune system functions, the MACS-enriched microglia proteome was enriched w...
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Our understanding of the biological changes in the brain associated with Alzheimer’s disease (AD) pathology and cognitive impairment remains incomplete. To increase our understanding of these changes, we analyzed dorsolateral prefrontal... more
Our understanding of the biological changes in the brain associated with Alzheimer’s disease (AD) pathology and cognitive impairment remains incomplete. To increase our understanding of these changes, we analyzed dorsolateral prefrontal cortex of control, asymptomatic AD, and AD brains from four different centers by label-free quantitative mass spectrometry and weighted protein co-expression analysis to obtain a consensus protein co-expression network of AD brain. This network consisted of 13 protein co-expression modules. Six of these modules correlated with amyloid-β plaque burden, tau neurofibrillary tangle burden, cognitive function, and clinical functional status, and were altered in asymptomatic AD, AD, or in both disease states. These six modules reflected synaptic, mitochondrial, sugar metabolism, extracellular matrix, cytoskeletal, and RNA binding/splicing biological functions. The identified protein network modules were preserved in a community-based cohort analyzed by a d...
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The complicated cellular and biochemical changes that occur in brain during Alzheimer's disease are poorly understood. In a previous study we used an unbiased label-free quantitative mass spectrometry-based proteomic approach to... more
The complicated cellular and biochemical changes that occur in brain during Alzheimer's disease are poorly understood. In a previous study we used an unbiased label-free quantitative mass spectrometry-based proteomic approach to analyze these changes at a systems level in post-mortem cortical tissue from patients with Alzheimer's disease (AD), asymptomatic Alzheimer's disease (AsymAD), and controls. We found modules of co-expressed proteins that correlated with AD phenotypes, some of which were enriched in proteins identified as risk factors for AD by genetic studies. The amount of information that can be obtained from such systems-level proteomic analyses is critically dependent upon the number of proteins that can be quantified across a cohort. We report here a new proteomic systems-level analysis of AD brain based on 6,533 proteins measured across AD, AsymAD, and controls using an analysis pipeline consisting of isobaric tandem mass tag (TMT) mass spectrometry and off...
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Several neurodegenerative diseases including Alzheimer's Disease (AD) are characterized by ubiquitin-positive pathological protein aggregates. Here, an immunoaffinity approach is utilized to enrich ubiquitylated isopeptides after... more
Several neurodegenerative diseases including Alzheimer's Disease (AD) are characterized by ubiquitin-positive pathological protein aggregates. Here, an immunoaffinity approach is utilized to enrich ubiquitylated isopeptides after trypsin digestion from five AD and five age-matched control postmortem brain tissues. Label-free MS-based proteomic analysis identifies 4291 unique ubiquitylation sites mapping to 1682 unique proteins. Differential enrichment analysis shows that over 800 ubiquitylation sites are significantly altered between AD and control cases. Of these, ≈80% are increased in AD, including seven poly ubiquitin linkages, which is consistent with proteolytic stress and high burden of ubiquitylated pathological aggregates in AD. The microtubule associated protein Tau, the core component of neurofibrillary tangles, has the highest number of increased sites of ubiquitylation per any protein in AD. Tau poly ubiquitylation from AD brain homogenates is confirmed by reciprocal...