The DOI matches its Crossref record; 107 of the 108 references it cites were found in a registry or an index, and 1 are not checked yet; 28 of the 51 sources its claims cite were checked for support.
Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
Open accessAbstract
Summary Alzheimer's disease (AD) displays a long asymptomatic stage before dementia. We characterize AD stage-associated molecular networks by profiling 14,513 proteins and 34,173 phosphosites in human brain with mass spectrometry, highlighting 173 protein changes in 17 pathways. The altered proteins are validated in two independent cohorts, showing partial RNA dependency. Comparisons of brain tissue and cerebrospinal fluid proteomes reveal biomarker candidates. Combining with 5xFAD mouse analysis, we determine 15 Aβ-correlated proteins (e.g., MDK, NTN1, SMOC1, SLIT2 and HTRA1). 5xFAD shows a proteomic signature similar to symptomatic AD but exhibits activation of autophagy and interferon response, and lacks human-specific deleterious events, such as downregulation of neurotrophic factors and synaptic proteins. Multi-omics integration prioritizes AD-related molecules and pathways, including amyloid cascade, inflammation, complement, WNT signaling, TGFβ/BMP signaling, lipid metabolism, iron homeostasis, and membrane transport. Some Aβ-correlated proteins are colocalized with amyloid plaques. Thus, the multilayer omics approach identifies protein networks during AD progression.
Verified 2026-10-06 · 1 reference not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study uses deep multilayer proteomics of human brain across Alzheimer’s disease stages to map protein and phosphoprotein networks, validate them in independent cohorts and a 5xFAD mouse model, and prioritize candidate disease pathways and biomarkers. It reports widespread stage-specific protein changes, partial RNA independence, Aβ-correlated proteins, and human–mouse differences.
Key findings
- Profiling quantified 14,513 proteins and 34,173 phosphosites, identifying 173 differentially expressed proteins in 17 pathways and 873 differentially expressed phosphopeptides; 163 of the 173 proteins did not change significantly in PSP, suggesting AD specificity.
- Validation in 49 individual Banner Sun samples re-detected 121 of the 173 proteins, with 117 showing similar progression patterns; in 62 Mount Sinai cases, 84 of those 117 were reidentified and 58 changed consistently.
- Aβ-correlated analysis found 28 proteins, of which 15 increased consistently in 5xFAD mice, including MDK, NTN1, SMOC1, SLIT2, and HTRA1; MDK and NTN1 directly bound Aβ40, and several proteins colocalized with plaques.
- CSF profiling of 13 samples detected 49 of the 58 AD-altered brain proteins, with 8 changing consistently, including 4 previously reported candidates and 4 novel ones: SLC39A12, SLC5A3, SLIT2, and STEAP3.
Methods
- Discovery used frontal cortex from 90 selected human cases pooled into 10 groups spanning low-pathology controls, high Aβ pathology without cognitive defects, MCI, late-stage AD, and PSP; validation used 49 Banner Sun cases and 62 Mount Sinai cases, plus 13 CSF samples.
- Proteins and phosphopeptides were measured by TMT labeling and two-dimensional LC/LC-MS/MS, with TiO2 phosphopeptide enrichment and JUMP database searching; analyses included WGCNA clustering, PPI modules, kinase activity inference, and differential expression testing.
- Multi-omics prioritization combined MCI/AD proteome, phosphoproteome, aggregated proteome, GWAS, transcriptome, and interactome data using order statistics and GSEA; 5xFAD and wild-type mice at 3, 6, and 12 months were compared with human disease stages.
Limitations
- The paper states that pooling averages biological variation but can let one poor-quality sample affect an entire pool; it also notes PMI and cell-type composition as confounders, which were addressed by removing PMI-affected proteins, selecting short-PMI samples, and applying cell-type correction.
- The paper describes the CSF analysis as a pilot biomarker study with only 13 samples in the final dataset, and the mouse model as unable to fully mimic AD.
- Evident from the text: the human design is cross-sectional rather than longitudinal, several discovery and validation thresholds allow FDR up to 20%, and the 5xFAD comparison is limited by species-specific biology and a single transgenic model.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
28 of 51 cited sources checked · 17 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In the list, 4 proteins (GPNMB, SMOC1, NPTX2 and VGF) have previously been reported in AD CSF (Dayon et al., 2018; Huttenrauch et al., 2018; Llano et al., 2019; Xiao et al., 2017)”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
What the sources say
- Partly supportedcites Dayon et al., 2018 sourceExperimental — accuracy not yet measured
The Results and Discussion report NPTX2 and SMOC1 as CSF proteins associated with biomarkers of Alzheimer pathology. Our copy does not report GPNMB or VGF, so it supports only two of the four proteins attributed to this citation. (LLM, conf=0.86)
- Supportedcites Huttenrauch et al., 2018 source
The Discussion reports elevated GPNMB in CSF samples from patients with sporadic AD. In this sentence with four citations, Hüttenrauch et al. supports the GPNMB item; the sentence does not specifically assign the other three proteins to this source. (LLM, conf=0.92)
- Not checked — no full text of the sourcecites Llano et al., 2019 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Current targeted therapeutic strategies have failed to slow down cognitive decline in AD (Graham et al., 2017; Huang and Mucke, 2012), raising concerns about these hypotheses and calling for a broad understanding of molecular and cellular components in AD brain, at both asymptomatic and symptomatic stages during the disease progression (De Strooper and Karran, 2016).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Introduction
What the sources say
- Not checked — no full text of the sourcecites Graham et al., 2017 source
- Supportedcites Huang and Mucke, 2012 source
In “Targeting Aβ,” the paper reports that a γ-secretase inhibitor trial was stopped after cognitive impairments worsened and that another targeted drug was ineffective in a phase III trial. The Conclusions and Outlook says the field had not yet translated its insights into more effective AD treatments. The paper notes preliminary promise for some approaches, but does not establish that they slowed cognitive decline. (LLM, conf=0.87)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesEvery source the paper cites for this claim was checked and supports it.
In the paper
“A large number of animal models have been developed to test these hypotheses, but cannot fully recapitulate AD symptoms (LaFerla and Green, 2012; Sasaguri et al., 2017).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Introduction
What the sources say
- Supportedcites LaFerla and Green, 2012 source
The Abstract states that none of the existing animal models fully reproduces the complete spectrum of Alzheimer disease, although they recapitulate important aspects. The Translational Issues section gives a specific limitation: many models do not reproduce the extensive neuronal loss seen in humans. Page numbers are not provided in our copy. (LLM, conf=0.93)
- Supportedcites Sasaguri et al., 2017 source
The Limitations of second-generation mouse models section says APP knock-in mice lack tau pathology and neurodegeneration, and that their Aβ-induced memory failure may not explain all symptoms of AD. The review therefore supports the citing sentence’s broad point that animal models do not fully reproduce AD symptoms, though its detailed assessment focuses on APP mouse models. Page numbers were not provided in our copy. (LLM, conf=0.88)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“The transcriptome, however, is often not an accurate indicator of protein abundance (Liu et al., 2016), especially in complex brain tissue (Sharma et al., 2015).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Introduction
What the sources say
- Not checked — no full text of the sourcecites Liu et al., 2016 source
- Supportedcites Sharma et al., 2015 source
The Discussion says that mRNA–protein correlations in CNS cell types are too weak and variable to reliably predict protein abundance. The Results section, “Assessment of proteome quality and comparison to the transcriptome,” reports correlations of only 0.40–0.45 between corresponding transcriptomes and proteomes, supporting the brain-specific part attributed to Sharma et al. (LLM, conf=0.93)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesEvery source the paper cites for this claim was checked and supports it.
In the paper
“The acquisition of such rich proteomic information is largely attributed to the access to high quality AD brain tissue (Beach et al., 2015) and the strength of the optimized mass spectrometry-based proteomics platform, including multiplex TMT isobaric labeling (Niu et al., 2017), extensive long gradient LC/LC coupled with high resolution MS/MS analysis (Wang et al., 2015), a refined phosphopeptide”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
What the sources say
- Supportedcites Beach et al., 2015 source
The section on studies using proteomic technology (page unavailable) says short post-mortem intervals are crucial for proteomics and reports proteomic analyses of AD white matter. The section on tissue collection (page unavailable) describes the program’s rapid autopsies as meeting the need for tissue suitable for high-quality molecular research. Beach et al. is cited for access to high-quality AD tissue, not for the separately cited mass-spectrometry platform. (LLM, conf=0.87)
- Supportedcites Niu et al., 2017 source
The citation is attached specifically to “multiplex TMT isobaric labeling,” not to the claims about AD brain tissue or the entire platform. The Introduction describes TMT as a multiplexed isobaric-labeling method, and the Results report a 10-plex TMT experiment. (LLM, conf=0.94)
- Supportedcites Wang et al., 2015 source
The Wang citation is attached to the extensive long-gradient LC/LC and high-resolution MS/MS component, not to tissue quality or TMT labeling. The Results and Discussion section (Deep Proteomic Analysis of AD Brain) describes basic-pH LC prefractionation followed by nine-hour LC–MS/MS gradients that acquired high-resolution spectra and identified 10,544 proteins. The Conclusions describe the optimized platform and its deep AD brain proteome coverage. (LLM, conf=0.93)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Collectively, these multilevel proteomic analyses suggest the activation of MAP kinase signaling that is implicated in AD pathogenesis (Kim and Choi, 2010).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
What the sources say
- Supportedcites Kim and Choi, 2010 source
The Conclusion states that MAPK signaling pathways are implicated in AD pathogenesis and that activation of MAPK cascades contributes to disease progression. Section 2.1 also describes mechanisms linking MAPK activation to AD. The multilevel proteomic analyses are the citing authors’ own framing, not a finding attributed to Kim and Choi. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Thus the increased activity of the complement system may accelerate the development of AD symptoms.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Increase of Detrimental Factors and Decrease of Protective Factors at the Symptomatic Stage of AD
What the sources say
- Partly supportedcites Dejanovic et al., 2018 sourceExperimental — accuracy not yet measured
The Discussion says tau-mediated complement activation may be a key mechanism in AD synapse loss and neurodegeneration, and the Results show that blocking C1q rescued synapse density in tauopathy mice. The paper does not show that greater complement activity accelerates the development of AD symptoms in people; it reports that a single antibody injection did not improve LTP in the mice. (LLM, conf=0.84)
- Partly supportedcites Hong et al., 2016 sourceExperimental — accuracy not yet measured
The Abstract reports that complement activity mediates early synapse loss in AD mouse models and that synapse loss correlates with cognitive decline. It does not test whether greater complement activity accelerates the onset or progression of AD symptoms, particularly in people. (LLM, conf=0.82)
- Supportedcites Shi et al., 2017 source
The Introduction and Discussion report that C3 deficiency spared cognitive decline in aged APP/PS1 mice and suggest that C3 signaling can be detrimental to neuronal function. That supports the citing sentence’s hedged inference, though the paper tested an AD mouse model rather than the timing of symptoms in people. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Clinically, there is a long, prodromal stage in AD prior to the irreversibly progressive decline of cognition (Hyman et al., 2012).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Partly supportedcites Hyman et al., 2012 sourceExperimental — accuracy not yet measured
The “Revised Neuropathologic criteria for Alzheimer’s disease” section describes a preclinical phase and says AD processes can be present well before cognitive deficits. It does not establish that this stage is long or that subsequent cognitive decline is irreversible and progressive. (LLM, conf=0.84)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MS-measured Aβ peptide level is largely consistent with the results of plaque pathology in the 5 groups of samples (Figure 1C).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we used a stepwise pipeline (Figure 1E) to define 173 differentially expressed (DE) proteins (FDR < 20%) in 17 pathways”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 64
- Probably not a substantial claim. 22
Every source the paper cites for this claim was checked and supports it.
In the paper
“Notably, PPC2 contains 89 phosphopeptides from Tau (56 phosphosites, Data S5B), and 67 from osteopontin (SPP1), a glycoprotein regulating diverse biological processes (e.g. immune response) (Wang and Denhardt, 2008).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
What the sources say
- Supportedcites Wang and Denhardt, 2008 source
The citation follows the description of osteopontin, rather than the PPC2 peptide counts. The Introduction (§1.1) calls osteopontin a glycosylated phosphoprotein and describes its roles in multiple physiological and pathological processes, including immune responses. (LLM, conf=0.94)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“We performed a comprehensive analysis of the aggregated proteome, detecting the deposition of U1 small nuclear ribonucleoprotein (U1 snRNP) and a deficit of RNA splicing in AD brain (Bai et al., 2013; Lutz and Peng, 2018).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Introduction
What the sources say
- Supportedcites Bai et al., 2013 source
The LC-MS/MS analysis section reports a study of the aggregated, insoluble AD-brain proteome and elevated U1 snRNP components, indicating deposition. The Deep RNA Sequencing section reports widespread splicing defects in AD brains. Together, these support the attributed finding. (LLM, conf=0.94)
- Supportedcites Lutz and Peng, 2018 source
Section 3 reports that comprehensive profiling of the aggregated proteome found U1 snRNP subunits highly increased in AD. Section 4 reports impaired RNA splicing in AD cortical samples. The review therefore supports both findings attributed to it. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For example, lack of neuronal interferon-β signaling in mice was reported to block late-stage autophagy, resulting in sporadic Lewy body accumulation and dementia (Ejlerskov et al., 2015).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Supportedcites Ejlerskov et al., 2015 source
The Results section, “Late-Stage Autophagy Block Causes α-syn Accumulation” (page not available), reports a late-stage autophagy defect and α-synuclein accumulation in neurons lacking IFN-β signaling. The Introduction and Abstract describe spontaneous Lewy body pathology and cognitive impairment in Ifnb-deficient mice, identifying them as a model of sporadic Lewy body dementia. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Midkine is also highly correlated with Aβ in human cases and in the 5xFAD mice and it is proposed to antagonize Aβ cytotoxicity (Herradon and Perez-Garcia, 2014).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Partly supportedcites Herradon and Perez-Garcia, 2014 sourceExperimental — accuracy not yet measured
The Alzheimer's disease discussion (indexed under “Drug addiction”; page unavailable) reports midkine in nearly all amyloid-β plaques examined in eight patients and says it inhibits amyloid-β cytotoxicity. It also describes greater plaque deposition in mice lacking midkine, but does not identify those mice as 5xFAD or report a high correlation in that model. (LLM, conf=0.88)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The results implicate that 5xFAD has the most similar proteome to the late stage of AD, but not the early stage of AD as previously anticipated (Ashe and Zahs, 2010).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Could not verifycites Ashe and Zahs, 2010 sourceExperimental — accuracy not yet measured
The Overall conclusions discuss APP transgenic mice as models of accelerated brain aging or asymptomatic AD, but do not compare their proteomes with stages of human AD. Searches for 5xFAD and proteomic comparisons found no passage addressing whether its proteome most closely resembles late-stage rather than early-stage AD. (LLM, conf=0.76)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Genetically modified mice cannot fully mimic AD and are often viewed as a model of AD asymptomatic stage (LaFerla and Green, 2012).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Partly supportedcites LaFerla and Green, 2012 sourceExperimental — accuracy not yet measured
The Abstract (page unavailable) says no single mouse model recapitulates the full spectrum of AD. The Translational Issues section (page unavailable) adds that many models lack the extensive neuronal loss seen in patients. The paper does not say genetically modified mice are often viewed as models of an asymptomatic stage; it reports cognitive decline in many such mice. (LLM, conf=0.86)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Interestingly, the Netrin-1 receptor UNC5C is an AD risk gene (Wetzel-Smith et al., 2014).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Supportedcites Wetzel-Smith et al., 2014 source
The paper identifies UNC5C as a Netrin receptor and reports that its rare T835M variant is associated with increased risk of late-onset Alzheimer’s disease (Abstract). This supports describing UNC5C as an AD risk gene, while the evidence is specifically for that variant. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Thus, the selected peptide is a surrogate for Aβ, which was also previously used for Aβ quantification by mass spectrometry (Seyfried et al., 2017).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Partly supportedcites Seyfried et al., 2017 sourceExperimental — accuracy not yet measured
The Results section (‘Proteomic analysis of human brain tissues’; page unavailable) reports using mass-spectrometry intensities from two APP peptides as measures of brain Aβ levels. The claim does not identify the selected peptide, so our copy cannot establish that it was one of those two; the paper also quantified Aβ by summing their intensities, not by using a single peptide alone. (LLM, conf=0.82)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Integration of genomic data and gene expression information has proven powerful for determining potential causative pathways in Alzheimer’s disease (Zhang et al., 2013).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
What the sources say
- Supportedcites Zhang et al., 2013 source
The Introduction says the study combined whole-genome gene expression profiling with genotyping data from brain tissues to identify modules relevant to late-onset Alzheimer’s disease and infer their causal regulators. The Experimental Procedures describe using genetic variants to infer directionality in those networks. Together, these passages support the claim about identifying potential causal pathways. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Defective autophagy is usually accompanied by the accumulation of the SQSTM1/p62 protein (Moscat and Diaz-Meco, 2009).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Supportedcites Moscat and Diaz-Meco, 2009 source
The section “p62, a Critical New Player in Cancer” states that impaired autophagy leads to p62 accumulation. “Control of p62 by Autophagy” also reports p62 accumulation in autophagy-deficient mice, supporting the citing sentence’s general paraphrase. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The BioPlex database was developed by the method of affinity purification and mass spectrometry, whereas the STRING and InWeb contain information from various sources.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > QUANTIFICATION AND STATISTICAL ANALYSIS
What the sources say
- Partly supportedcites Szklarczyk et al., 2015 sourceExperimental — accuracy not yet measured
The Introduction states that STRING integrates interactions from multiple sources, including experimental databases, curated pathways, text mining, and computational predictions (page not provided). Our copy does not address how BioPlex was developed or whether InWeb aggregates multiple sources; searches for BioPlex and InWeb found no relevant passages. (LLM, conf=0.88)
- Partly supportedcites Huttlin et al., 2017 sourceExperimental — accuracy not yet measured
The Online Experimental Methods section, “Affinity-Purification Mass Spectrometry” (page unavailable), describes the AP-MS experiments used to build BioPlex. “Comparison with Interaction Databases” names STRING but does not say it aggregates multiple sources; no passage found addresses InWeb or its sources. (LLM, conf=0.88)
- Not matchedcites Li et al., 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Before cell type correction, the AD samples showed a slight increase of microglia and astrocyte, and a slight decrease of neuron and oligodendrocyte, consistent with the previous proteomics analysis (Seyfried et al., 2017).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > QUANTIFICATION AND STATISTICAL ANALYSIS
What the sources say
- Partly supportedcites Seyfried et al., 2017 sourceExperimental — accuracy not yet measured
The Results section (‘Overlap between RNA and protein co-expression networks in AD’; page unavailable) reports increased AD-associated modules enriched for microglial and astrocyte markers and decreased modules enriched for neuronal markers, consistent with the paper’s proteomic findings. The copy does not establish a decrease in oligodendrocytes, describe the changes as slight, or report cell-type abundance before correction; its findings concern protein-network markers rather than measured cell counts. (LLM, conf=0.85)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, among the 173 DE proteins in AD, 163 (94%) proteins show no significant changes in the PSP brain displaying tauopathy but missing amyloid plaques, indicative of a high degree of AD specificity (Data S3A).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 173 DE proteins identified in the pooled analysis, 121 were re-detected in the individual samples and divided into the WPC1-3 clusters.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Three whole proteome clusters (termed WPC) were identified from the analysis (Figure 1F), whereas a small number of proteins (n = 8) did not follow any of these clusters (Data S3A).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The IKAP machine learning algorithm (Mischnik et al., 2016) deduced the activities of 186 kinases, of which 28 are possibly changed, primarily in AD (Figure 3H).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
What the sources say
- Not checked — no full text of the sourcecites Mischnik et al., 2016 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PPC2 (n = 372) is stable in the HPC and MCI samples, and dramatically increases in AD, reminiscent of the pathology of neurofibrillary tangles.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we performed deep proteomics profiling of CSF samples (n = 13, 5 control and 8 AD cases, Figure 2E) and quantified 5,940 proteins (Data S4C).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“We characterize AD stage-associated molecular networks by profiling 14,513 proteins and 34,173 phosphosites in human brain with mass spectrometry, highlighting 173 protein changes in 17 pathways.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Abstract
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In the paper
“WPC2 recapitulates all enriched pathways in WPC1, and contains three additional pathways of cytokine signaling, complement and coagulation, and iron homeostasis (Figure 1G).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“WPC2 proteins (n = 88) are relatively stable in the stages of HPC and MCI, but show a marked increase when progressing to AD, including MAPT (tau) and complement components.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“WPC1 (n = 50) displays an early increase in HPC and MCI and a continuous accumulation in AD, including Aβ, TREM2 and CLU.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“the stepwise computational pipeline was used to identify 873 DE phosphopeptides (FDR < 20%, 398 proteins, Figure 3D) into four major phosphopeptide clusters (PPC1-4, Figure 3E), whereas a small subset of phosphopeptides (n = 22) did not fit into any of these clusters (Data S5A).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
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In the paper
“PPC3 (n = 104) increases slightly in the HPC and MCI cases, and reduces in AD.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
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In the paper
“we integrated the DE proteins in each WPC pattern with the protein-protein interaction network to define 11 functional modules, providing the evidence of protein-protein interaction to support these enrichment pathways (Figure 1H and Data S3B).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“For example, the CDK kinase activity could be computed from its known substrates of MAPT, APP, LMNA, VIM, TLN1 and SQSTM1 (Figure 3I), and the GSK3 kinase activity was similarly inferred (Figure 3J).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
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In the paper
“Only 364 (2.6%) out of 14,007 proteins were changed among different PMIs, whereas 4,736 (16.5%) out of 28,699 phosphopeptides were altered in the comparisons (Data S1A and S1B).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“Among the 117 DE proteins found in the Banner Sun cohort, 84 were reidentified in the Mount Sinai cohort, in which 58 (69%) showed consistent changes in AD (Figures 2C and 2D, Data S4B).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“quantifying 14,513 proteins (12,018 genes, false discovery rate (FDR) < 1%, Data S2B), and 34,173 phosphosites (46,612 phosphopeptides, FDR < 1%, Data S2C)”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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In the paper
“Although the vast majority of kinases (except MAP3K6) do not change at the whole protein levels (Data S2B), some of the MAP kinase cascades exhibit a marked increase in phosphorylation, including four sequentially activated families: MAP4K (e.g. MAP4K4), MAP3K (e.g. ARAF and MAP3K12), MAP2K (e.g. MAP2K6), and MAPK (e.g. MAPK1/ERK2) (Figure 3K) (Keshet and Seger, 2010).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Phosphoproteome-derived Kinase Activities in AD
What the sources say
- Not checked — no full text of the sourcecites Keshet and Seger, 2010 source
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In the paper
“The altered proteins are validated in two independent cohorts, showing partial RNA dependency.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Abstract
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In the paper
“5xFAD shows a proteomic signature similar to symptomatic AD but exhibits activation of autophagy and interferon response, and lacks human-specific deleterious events, such as downregulation of neurotrophic factors and synaptic proteins.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Abstract
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In the paper
“The analysis detected 49 proteins from the 58 proteins changed in AD brain tissue, in which a list of 8 proteins are consistently changed in the CSF samples (p < 0.05, Figure 2F, Data S4D).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Deep Proteomic and Phosphoproteomic Profiling of AD Progression
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- Rests on an assumption: CSF protein changes reflect brain tissue changes in AD.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We then prioritized signaling pathways by GSEA, identifying 16 pathways (FDR < 20%, Figure 6C), collectively classified into 4 major categories of amyloid and Tau pathways, inflammation, growth and development, as well as metabolism and membrane transport.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“For instance, in the top 200 proteins (Data S7B), 42% (n = 83) are secretory proteins; and 20% (n = 40) are cell surface proteins.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“The integrative protein ranking is based on all 7 datasets (Figure 6B, Data S7A), with APP, APOE, and MAPT as the top 3 of the list, consistent with our current understanding of AD pathogenesis.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, we also analyzed complement protein C4B in human cases and identified both amyloidopathy and angiopathy (Figure S6), supporting that the elevation of C4B is unlikely due to blood contamination in human postmortem brain samples.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“The Aβ level is based on its fully tryptic peptide (LVFFAEDVGSNK, Figure 4A), increasing ~7-8-fold from low pathology control cases to AD, which correlates well with the plaque pathology in brain tissue.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“Consistently, these 9 proteins are also demonstrated to increase in the brain of 5xFAD mice by proteome profiling, and their related RNA levels do not display upregulation with statistical significance in the RNA-seq analysis.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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- Rests on an assumption: The 5xFAD mouse model's proteomic changes can be attributed to Aβ accumulation and used to infer cause-effect relationships of Aβ-correlated proteins.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Surprisingly, we identified in WPC1 a large number of development pathway proteins, including WNT5A, WNT5B, RSPO2, GPC5, and SULF2 in WNT signaling, as well as BMP3 and SMOC1 in TGFβ/BMP signaling.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
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In the paper
“In the 5xFAD model, the plaque localization of MDK, NTN1, SMOC1 and ICAM1 is recapitulated in the brain (Figures 7C and 7D), and the accumulation of these proteins is validated by Western blotting (Figures 7E).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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- Rests on an assumption: The 5xFAD mouse model's proteomic changes can be attributed to Aβ accumulation and used to infer cause-effect relationships of Aβ-correlated proteins.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, NTN1/Netrin-1 and NTN3/Netrin-3 are highly correlated with Aβ in human, and are induced in the 5xFAD mice.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
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- Rests on an assumption: The 5xFAD mouse model's proteomic changes can be attributed to Aβ accumulation and used to infer cause-effect relationships of Aβ-correlated proteins.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further examination of the DE proteomes in AD cases and 5xFAD mice reveals 37 human-specific, and 69 5xFAD-specific DE proteins (Data S6D and S6E).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“Further experiments show the plaque localization of MDK, NTN1 and SMOC1 in human and the mouse model, and direct Aβ-binding of MDK and NTN1 (Figure 7).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Out of the 58 DE proteins detected in AD, 35 proteins are linked to corresponding RNA alterations, and 23 proteins appear to be independent of transcriptional regulation (Figure 2D)”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, lysosome/autophagy pathway proteins (e.g. LAMP1 and HEXB) are highly enriched and increased (Figure 5C).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“In the 28 proteins, 23 were identified in all mice, and 15 (65%) are consistently increased in the animals (Figure 4E, Data S6B), including MDK, NTN1, CTHRC1, NTN3, SMOC1, SFRP1, OLFML3, SLIT2, HTRA1, FLT1, SLIT3, CLU, ICAM1, LSP1, and C4B.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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- Rests on an assumption: The 5xFAD mouse model's proteomic changes can be attributed to Aβ accumulation and used to infer cause-effect relationships of Aβ-correlated proteins.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clearly, MDK and NTN1 directly binds to the Aβ40 column, but not to the control column, while the other two proteins (SMOC1 and ICAM1) show no binding to Aβ40 in this assay (Figure 7G).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“Moreover, interferon response is markedly increased in 5xFAD but not in AD (Figure 5C).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“Interestingly, 5xFAD (12 months of age) shares 97, 89, and 169 protein alterations with HPC, MCI and AD, respectively”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“Indeed, p62 is accumulated in AD, but not in 5xFAD.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“While this Aβ-derived peptide has 35 spectral counts, APP has more than 30 tryptic peptides outside the Aβ region, which have only 5 average spectral counts (Data S2B), indicating that Aβ is much more abundant than APP in the samples.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
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In the paper
“When co-stained with Aβ in immunofluorescence analysis, MDK and NTN1 are highly colocalized with Aβ plaques, while SMOC1 and ICAM1 are partially colocalized in the plaque areas (Figure 7B).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“For example, abnormal aggregation of APP-cleaved Aβ peptide and phospho-Tau highlights substantial deregulation of posttranslational events in AD (Taylor et al., 2002).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Introduction
What the sources say
- Not checked — no full text of the sourcecites Taylor et al., 2002 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It has been reported that Netrin-1 overexpression reduces the Aβ level in an AD mouse model (PDAβPPSwe/Ind), and intracerebroventricular injection of Netrin-1 enhances working memory in the mice (Spilman et al., 2016).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Not checked — no full text of the sourcecites Spilman et al., 2016 source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results suggest that the secreted and cell surface subproteomes are particularly perturbed in AD, which might have a strong impact on cell communication.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“Interferons is a class of cytokines with profound neuroprotective or neurotoxic properties, which are dependent on relative interferon levels and cellular context (Schwartz and Deczkowska, 2016).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Not checked — no full text of the sourcecites Schwartz and Deczkowska, 2016 source
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the protein level in AD brain is regulated by both transcriptional and posttranslational mechanisms, which are not mutually exclusive.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As regulatory proteins are central players in cells but often exist at low abundance, the high coverage of proteome has become extremely important to uncover those proteins.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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In the paper
“Interestingly, both BMP3 and SMOC1 are negative regulators of BMP signaling (Bragdon et al., 2011), implying that repression of BMP signaling might play a positive role in maintaining neurogenesis in AD.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Not checked — no full text of the sourcecites Bragdon et al., 2011 source
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In the paper
“Aberrant UNC5C has been reported to activate cell death signaling in cell culture, but netrin-1 may bind to UNC5C on the cell surface to suppress UNC5C-induced cell death (Hashimoto et al., 2016).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Not checked — no full text of the sourcecites Hashimoto et al., 2016 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our deep, multilayer proteomic analyses reveal highly confident lists of altered key proteins and phosphoproteins in both AD and the related 5xFAD model, and prioritize molecular alterations by multi-omics datasets with a comprehensive computational pipeline, providing novel insights into the proteins/pathways associated with AD.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Achievement of Genome-wide Brain Proteomics Data in AD
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In the paper
“Autophagy is essential to clear damaged organelles and aggregated proteins; and enhanced autophagy might reduce AD-related pathology and reverse memory deficit in mice (Nixon, 2013).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Aβ-correlated Protein Alterations and Human-Mouse Comparisons
What the sources say
- Not checked — no full text of the sourcecites Nixon, 2013 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“A compelling concept is that initial Aβ accumulation may induce a compensatory feedback that is protective to attenuate the toxic Aβ insult, whereas a vicious feedforward may require additional toxic factors to override the protection, leading to irreversible degeneration (De Strooper and Karran, 2016).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Compensatory Protective Feedback at the Asymptomatic Stage of AD
What the sources say
- Not checked — no full text of the sourcecites De Strooper and Karran, 2016 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Collectively, these results indicate these Aβ-correlated proteins are colocalized with Aβ in the amyloid plaques, with MDK and NTN1 showing direct Aβ-binding, implicating that they may function together in the AD brain.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Results > Pathway Ranking and Top-ranked Proteins in Amyloid Plaque Pathology
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In the paper
“The pooling strategy averages biological variations, increases statistical power, and reduces workload (Allison et al., 2006).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
What the sources say
- Not checked — no full text of the sourcecites Allison et al., 2006 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Putative PSMs were filtered by mass accuracy and then grouped by precursor ion charge state and filtered by JUMP-based matching scores (Jscore and ΔJn) to reduce FDR below 1% for proteins during the whole proteome analysis or 1% for phosphopeptides during the phosphoproteome analysis.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“FDR in the target database was estimated by the number of decoy matches (nd) and the number of target matches (nt), according to the equation (FDR = nd/nt), assuming mismatches in the target database were the same as in the decoy database.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- The authors state this as an assumption: In FDR estimation, mismatches in the target database are assumed to be the same as in the decoy database.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“to correct individual protein abundance affected by the cell type composition change in different samples by a multivariate linear model”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Enriched pathways with FDR < 20% or lower were considered statistically significant.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“to estimate the final FDR of the accepted DE proteins (p < 10% plus z score > 2), we performed permutation analysis (n = 1,000 permutations) following a reported procedure (Xie et al., 2005) and found that the estimated FDR below 20%.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > QUANTIFICATION AND STATISTICAL ANALYSIS
What the sources say
- Not checked — no full text of the sourcecites Xie et al., 2005 source
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“to validate proteins of interest in individual cases, and in an independent cohort.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“WPC2 also contains numerous cytoskeletal proteins, as exemplified by tau that is hyperphosphorylated and aggregated to contribute to neurodegeneration (Ballatore et al., 2007).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Increase of Detrimental Factors and Decrease of Protective Factors at the Symptomatic Stage of AD
What the sources say
- Not checked — no full text of the sourcecites Ballatore et al., 2007 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The excess iron metabolism may cause the accumulation of lipid peroxidation products and reactive oxygen species, eventually leading to cell death.”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > Discussion > Increase of Detrimental Factors and Decrease of Protective Factors at the Symptomatic Stage of AD
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“JUMP combines pattern scoring with de novo tag scoring, displaying significant improvement in peptide-spectrum match (PSM) scoring (Wang et al., 2014).”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
What the sources say
- Not checked — no full text of the sourcecites Wang et al., 2014 source
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“to examine lysed brain samples with SDS gels to confirm protein integrity prior to pooling (10 out of 100 samples excluded by this step; Figure S1)”
§ Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression > STAR METHODS > METHOD DETAILS
Strength:plain· not yet calibratedn = 100Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
Postmortem human brain proteome measurements reflect antemortem disease processes.
“As clinical proteomic studies are often affected by confounding factors, such as postmortem interval (PMI) (Beach et al., 2015), we performed a survey of mouse brain samples with PMI of 0, 3, and 12 hours.”
The paper relies on this without support.
A lack of significant RNA upregulation for a protein indicates posttranscriptional regulation.
“suggesting that the remaining 45% of increased proteins are regulated by posttranscriptional mechanisms.”
“indicating that most Aβ-correlated proteins in 5xFAD are regulated by posttranscriptional mechanisms.”
The paper relies on this without support.
The 5xFAD mouse model's proteomic changes can be attributed to Aβ accumulation and used to infer cause-effect relationships of Aβ-correlated proteins.
“We then evaluated the cause and effect relationships of the Aβ-correlated proteins in the 5xFAD model (Oakley et al., 2006) by analyzing a total of 16,128 proteins”
The paper relies on this without support.
CSF protein changes reflect brain tissue changes in AD.
“To examine if these protein changes in the AD brain tissue are detectable as potential biomarkers in cerebrospinal fluid (CSF), we performed deep proteomics profiling of CSF samples”
The authors state this as an assumption.
In FDR estimation, mismatches in the target database are assumed to be the same as in the decoy database.
“assuming mismatches in the target database were the same as in the decoy database.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“Mitochondrial changes can co-occur with amyloid deposition early in the brain of asymptomatic cases with amyloid pathology, as well as mild cognitive impairment subjects [18, 33, 75].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“More recently, our group utilized the superior separation capacity of the latest TMT-LC/LC-MS/MS to bypass the depletion step and detect about 5000 proteins in human CSF and serum toward AD biomarker discovery [18, 25].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“All six proteins were reported to be tightly associated with AD pathogenesis [18].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Interestingly, MDK, CTHRC1, and Aβ, which were reported as the most significantly elevated proteins in our AD brain cortex study [18], were not significantly changed in the small cohort of CSF samples (Fig. 3c).”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Remarkably, all of them were reported to be highly linked to AD pathogenesis in brain tissue [18].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Mitochondrial function and energy metabolism are known to be severely compromised processes repeatedly reported in AD [18, 70].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The expression levels of SMOC1, OLFML3, SLIT2, and GPNMB were highly correlated with the Aβ level in AD brain, and these findings were also recapitulated in the 5xFAD mouse model [18].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These proteins have been shown to colocalize with Aβ plaque and are promising biomarkers associated with AD pathology.”
From Unraveling sex differences in Alzheimer's disease and related endophenotypes with brain proteomes (2025) · cites this paper as [51]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These proteins are strongly associated with amyloid plaques and are considered hub proteins in both AsymAD and AD. 43 , 51 , 80”
From Unraveling sex differences in Alzheimer's disease and related endophenotypes with brain proteomes (2025) · cites this paper as [51]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“MS can profile more than 12,000 proteins (> 10,000 genes) from mammalian tissue samples [16–18].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“While, SMOC1 and GPNMB have been reported as putative CSF biomarkers in previous studies [18, 33, 62]”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“Recent breakthroughs, especially in-depth and large-scale omics studies of brain tissue, have dramatically extended our discovery of molecular pathogenesis in AD [18, 49].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
The cited source says what the claim attributes to it.
In the citing paper
“SMOC1 has been shown to accumulate in plaque structures of AD in brain.”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [18]
In the citing paper
“However, for those proteins in this study that overlap with the validated set of differentially expressed proteins in Bai et al. obtained after cell type regression, a majority are also differentially expressed in AD and map to the M4 module.”
In the citing paper
“Indeed, recent studies have identified a number of additional potential biomarkers from M4 and other brain modules by deep mass spectrometry-based discovery on fractionated CSF35,36,54.”
In the citing paper
“Nearly a dozen informative network-based analyses of the AD proteome have been performed in the human brain [62–64, 74, 78, 96, 104–108], including the dorsolateral prefrontal cortex (DLPFC), temporal cortex, and other cortical regions heavily affected in AD.”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [78]
In the citing paper
“In two separate smaller analyses of AD CSF, many of the same AD-associated synaptic and metabolic biomarkers were identified, further supporting the reproducibility these systems-based CSF marker panels [78, 172].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [78]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Percentage of previously reported proteins is misstated
MinorThirteen of 58 is 22.4%, which rounds to 22% rather than the reported 23%.
Reported 23 · recomputed 22.413793
“only 13 (23%) proteins were reported in at least 10 AD-related papers”
Iron-homeostasis count does not match the list
MinorThe text says five proteins were upregulated but names only four after the colon.
“significant upregulation of five proteins in iron homeostasis:”
“LTF (a major iron-binding transport protein), BDH2 (synthesizing siderophore that binds iron), STEAP3 (transferrin-dependent iron uptake), and TSPO (transporting porphyrins and heme)”
Citations
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“178 To directly assess the potential correlation of SMS and polyamine metabolism with AD, we examined the 179 expression level of SMS and other polyamine pathway enzymes (Figure 5A) in the frontal cortex of AD 180 patient brains using a meta-analysis [39] of seven published proteomic datasets based on the TMT181 LC/LC…”
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Can the VTA Come Out to Play? Only When the mPFC’s Predictions Go Astray!
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“10 Most recently, an analysis of the tyrosine, serine, and threonine phosphoproteome in temporal cortex samples from AD patients and age-matched controls identi fi ed co-correlated peptide clusters associated with oligodendrocyte, astrocyte, microglial, neuronal, and phospho-tau pathology.”
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Effects of HSYA on serum and brain cholesterol levels in AD rats based on quantitative proteomics
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Application of proteomics in brain's aging research
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Identification and Analysis of ZIC-Related Genes in Cerebellum of Autism Spectrum Disorders
Li, H., Cui, J. J., Hu, C., Li, H., Luo, X., & Hao, Y. (2024) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s444138 · Cited by 1
“36 The olfactomedin structural domain family protein OLFM3, which is present in neurons, promotes the formation of tight junctions.”
BackgroundIntroducing critical proteins related to liver ischemia/reperfusion injury.
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“Significantly, prior biomarker studies have typically used only male mice, thus ignoring potential sex-related differences (e.g., [2, 3]).”
The role of N6-methyladenosine modification in neurodegenerative diseases
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Deep Crotonylproteomics Analysis of High-Altitude Exposure Hearts in Mice
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“Coupled with 5xFAD mouse model analyses, Aβ-correlated proteins and AD-related molecules pathways were identified (Bai et al. 2020).”
Genetics of age-related macular degeneration
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Broad kinase inhibition mitigates early neuronal dysfunction and cognitive deficits in tauopathyPreprint
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“Aberrant phosphorylation and kinase signaling is a hallmark of AD and other tauopathies 62 (8, 9).”
BackgroundMidbrain dopamine neurons provide teaching signals for goal-directed navigationPreprint
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“The single gene found to be increased in the Western cohort, cyclin dependent kinase 14 ( CDK14 ), regulates the Wnt signaling pathway [65], an important inflammatory modulator [66] implicated in AD progression [67].”
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Learning from Others, but with What Confidence?
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Deciphering cell-type specific signal transduction in the brain: Challenges and promises
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“; https://doi.org/10.1101/2021.11.09.467989 doi: bioRxiv preprint MS/MS) offers front-end separation 38-39 coupled with more efficient fragmentation capability due to multiply-charged ESI produced precursor ions, providing an attractive alternative to MALDI in situ MS/MS. Combining analyte…”
BackgroundInfluentialUT's Project Quest Reaches Deep into Instruction and Research
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Self-citationMulti-omic analysis of the aging mouse brain
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Multi‐omics analysis reveals expression complexity and functional diversity of mouse kinome
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Self-citationBrain cross-omics integration in Alzheimer’s diseasePreprint
Eteleeb, A. M., Novotny, B. C., Soriano‐Tárraga, C., Sohn, C., Dhungel, E., Brase, L., Nallapu, A., Buss, J., Farias, F., Bergmann, K., et al. (2022) · medRxiv · doi:10.1101/2022.12.10.22283295
Using In Vivo Intact Structure for System-wide Quantitative Analysis of Changes in ProteinsPreprint
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Application of Proteomics in Alzheimer’s Disease: A Mini Review
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Shafik, A. M., Zhang, F., Guo, Z., Dai, Q., Pajdzik, K., Li, Y., Kang, Y., Yao, B., Wu, H., He, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13525073
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Sun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557372
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Self-citationSun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557345
Shafik, A. M., Zhang, F., Guo, Z., Dai, Q., Pajdzik, K., Li, Y., Kang, Y., Yao, B., Wu, H., He, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13525061
Self-citationUpadhyay, A., Chhangani, D., Rao, N. R., Kofler, J., Vassar, R. J., Rincón-Limas, D. E., & Savas, J. N. (2023) · Figshare · doi:10.6084/m9.figshare.24144138
Shafik, A. M., Zhang, F., Guo, Z., Dai, Q., Pajdzik, K., Li, Y., Kang, Y., Yao, B., Wu, H., He, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13525070
Self-citationSun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557378
Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P., Yang, K., & Peng, J. (2021) · Figshare · doi:10.6084/m9.figshare.15162179
Self-citationSun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557333
Sun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557360
Shafik, A. M., Zhang, F., Guo, Z., Dai, Q., Pajdzik, K., Li, Y., Kang, Y., Yao, B., Wu, H., He, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13525055
Self-citationSun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557354
Upadhyay, A., Chhangani, D., Rao, N. R., Kofler, J., Vassar, R. J., Rincón-Limas, D. E., & Savas, J. N. (2023) · Figshare · doi:10.6084/m9.figshare.24144147
Sun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557342
Sun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557357
Sun, Y., Zhu, J., Zhou, D., Canchi, S., Wu, C., Cox, N. J., Rissman, R. A., Gamazon, E. R., & Wu, L. (2021) · Figshare · doi:10.6084/m9.figshare.16557375
Metadata
- Authors
- Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., Jiao, Y., Sifford, J. M., Han, J., Wang, M., Tan, H., Shaw, T. I., Cho, J.-H., Zhou, S., Wang, H., Niu, M., Mancieri, A., Messler, K. A., Sun, X., Wu, Z., Pagala, V., High, A. A., Bi, W., Zhang, H., Chi, H., Haroutunian, V., Zhang, B., Beach, T. G., Yu, G., & Peng, J.
- Affiliations
- St. Jude Children's Research Hospital (US)
- The University of Texas Southwestern Medical Center (US)
- James J. Peters VA Medical Center (US)
- Icahn School of Medicine at Mount Sinai (US)
- Banner Sun Health Research Institute (US)
- Corresponding author
- Wang, Xusheng
- Peng, Junmin
- ORCID
- Bai, Bing
- Wang, Xusheng
- Li, Yuxin
- Chen, Ping-Chung
- Yu, Kaiwen
- Yarbro, Jay M.
- Han, Xian
- Rao, Shuquan
- Jiao, Yun
- Han, Jonghee
- Wang, Minghui
- Tan, Haiyan
- Shaw, Timothy I.
- Cho, Ji-Hoon
- Zhou, Suiping
- Wang, Hong
- Niu, Mingming
- Sun, Xiaojun
- Wu, Zhiping
- Pagala, Vishwajeeth
- High, Anthony A.
- Bi, Wenjian
- Zhang, Hui
- Chi, Hongbo
- Haroutunian, Vahram
- Zhang, Bin
- Beach, Thomas G.
- Yu, Gang
- Peng, Junmin
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 105
- Issue
- 6
- Pages
- 975-991.e7
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC7318843
- OpenAlex
- W2998763810
- Semantic Scholar
- 151638d064b3677e739392380ca225b41685040b
- Cited by
- 565
- References
- 108
- Citations by year
- 2026: 73 · 2025: 95 · 2024: 98 · 2023: 83 · 2022: 79 · 2021: 85 · 2020: 50
- FWCI
- 30.75
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-05-20)
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 382 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UND Scholarly Commons (University of North Dakota) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health (P30AG019610, R01AG047928, R01AG053987, R01AG057907, R01AG064909, R01GM114260, R01NS079796, RF1AG057440, U01AG046170, U24NS072026)
- Arizona Department of Health Services (211002)
- Arizona Biomedical Research Commission (0011, 05-901, 1001, 4001)
- American Lebanese Syrian Associated Charities (P30CA021765)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, disease progression, brain proteome, phosphoproteomics, molecular networks, amyloid-beta, cerebrospinal fluid biomarkers, 5xFAD mice, autophagy, interferon response, amyloid plaques, multi-omics integration, neuroinflammation, complement system, Wnt signaling, TGF-beta signaling, BMP signaling, lipid metabolism, iron homeostasis, membrane transport, neurotrophic factors, synaptic proteins
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Animals, Brain, Female, Humans, Male, Mice, Mutant Strains, Middle Aged, Phosphoproteins, Biomarkers, Disease Progression, Proteome, Proteomics, Mice, Metabolic Networks and Pathways
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