Ubiquitin-Proteasome System in the Different Stages of Dominantly Inherited Alzheimer’s Disease

doi:10.21203/rs.3.rs-4202125/v1

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Ubiquitin-Proteasome System in the Different Stages of Dominantly Inherited Alzheimer’s Disease

https://doi.org/10.21203/rs.3.rs-4202125/v1

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This study explored the role of the ubiquitin-proteasome system (UPS) in dominantly inherited Alzheimer’s disease (DIAD) by examining changes in cerebrospinal fluid (CSF) levels of UPS proteins along with disease progression, AD imaging biomarkers (PiB PET, tau PET), neurodegeneration imaging measures (MRI, FDG PET), and Clinical Dementia Rating® (CDR®). Using the SOMAscan assay, we detected subtle increases in specific ubiquitin enzymes associated with proteostasis in mutation carriers (MCs) up to two decades before the estimated symptom onset. This was followed by more pronounced elevations of UPS-activating enzymes, including E2 and E3 proteins, and ubiquitin-related modifiers. Our findings also demonstrated consistent correlations between UPS proteins and CSF biomarkers such as Aβ42/40 ratio, total tau, various phosphorylated tau species to total tau ratios (ptau181/T181, ptauT205/T205, ptauS202/S202, ptauT217/T217), and MTBR-tau243, alongside Neurofilament light chain (NfL) and the CDR®. Notably, a positive association was observed with imaging markers (PiB PET, tau PET) and a negative correlation with markers of neurodegeneration (FDG PET, MRI), highlighting a significant link between UPS dysregulation and neurodegenerative processes. The correlations suggest that the increase in multiple UPS proteins with rising tau levels and tau-tangle associated markers, indicating a potential role for the UPS in relation to misfolded tau/neurofibrillary tangles (NFTs) and symptom onset. These findings indicate that elevated CSF UPS proteins in DIAD MCs could serve as early indicators of disease progression and suggest a link between UPS dysregulation and amyloid plaque, tau tangles formation, implicating the UPS as a potential therapeutic target in AD pathogenesis.

Health sciences/Biomarkers/Predictive markers

Biological sciences/Neuroscience

Alzheimer's disease (AD) is a multifactorial disorder influenced by a variety of genetic and environmental factors.¹ The disease is characterized by the accumulation of misfolded, insoluble protein aggregates, composed primarily of amyloid-β (Aβ) peptide (plaques) and phosphorylated tau protein (forming neurofibrillary tangles (NFT)) in the brain ^2,3, which leads to the insidious onset and gradual disruption of cognitive and behavioral functions^,3,4,5.

Recent studies highlight the role of faulty proteostasis in the progression of neurodegenerative diseases ^{6, 7,8}. Proteostasis encompasses cellular mechanisms that regulate protein synthesis, folding, post-translational modification, and degradation, mechanisms that are disrupted in conditions like AD ^{6, 9,10, 11}. The ubiquitin-proteasome system (UPS) and the autophagy lysosomal pathway work in tandem to preserve proteostasis in cells by preventing the accumulation of non-functional and misfolded proteins ^12,13,14. UPS degrades substrates that are potentially toxic by breaking them down into small peptides to replenish intracellular amino acid pools¹⁵. In humans, the UPS consists of two activating enzymes (E1s), approximately 40 conjugating enzymes (E2s), more than 600 ligase enzymes (E3s), and approximately 100 deubiquitinases (DUBs) ^{16, 17, 18}. Proteostasis defects can lead to neuronal stress, synapse loss, and memory deficits such that impaired proteostasis is considered a main contributor to AD pathogenesis ⁸.

The association between proteasomal dysfunction and AD was first established through histopathological examinations, which highlighted the accumulation of ubiquitin in AD-associated plaques and tangles ^19,20,21. Subsequent Genome-Wide Association Studies (GWAS) and proteomic studies have corroborated this link by identifying key roles for the proteasomal pathway in patients with symptomatic AD and transgenic AD models ^{19, 22,23,24, 25, 26}. These advanced methodologies uncovered significant changes at the proteome level during AD progression, particularly highlighting the dysregulation of the UPS ²⁶. This dysregulation is characterized by changes in the levels of certain ubiquitin-activating and ubiquitin-conjugating enzymes, coupled with the accumulation of a mutant form of ubiquitin known as UBB + 1, due to genetic alterations. These alterations lead to the inhibition of proteasome activity ^27,28. Additionally, the dysregulation of ubiquitin-mediated pathways is associated with alterations in learning and memory ability, Aβ plaque formation, hyperphosphorylation of tau protein, as well as synaptic plasticity and immune function changes in AD mouse models^{19,20, 21}. The potential therapeutic implications of these findings are underscored by the promising effects of small molecules targeting the proteasomal pathway in animal and cellular models of AD ²⁹. To date, most studies of the UPS have been undertaken using animal or cellular models of AD or in brain tissue of symptomatic AD cases. Given the recent evolution of methods for studying AD pathology biomarkers in humans, there is now the opportunity to evaluate the role of the UPS system in the presymptomatic and symptomatic stages of AD.

Studies in Dominantly Inherited Alzheimer's Disease (DIAD) allow the examination of disease-related proteins from the presymptomatic stage to moderately symptomatic stages of AD over three decades of disease progression. Here we analyzed cerebrospinal fluid (CSF) proteomic data from DIAD individuals. Leveraging the high-throughput capabilities of the SOMAscan proteomics platform and data from the Dominantly Inherited Alzheimer Network (DIAN), we explored the changes in expression, stability, and modifications of UPS proteins throughout the disease course. Considering existing evidence that abnormal accumulation of Aβ and tau proteins in the brain in AD begins well before the onset of neurological symptoms, up to 20 years prior, we investigated the early accumulation of both Aβ and tau aggregated protein species in relation to UPS dysregulation in DIAD ^{28, 30, 31, 32,33}. We aimed to explore if dysregulation of UPS proteins impacts the progression of DIAD by assessing the associations with Aβ, and tau pathologies, neuronal loss, and neuroinflammation (all measured using existing established CSF and neuroimaging biomarkers) and clinical symptoms. Our findings could provide important insights into AD initiation and progression and potentially reveal novel biomarkers of disease progression and new therapeutic targets.

2.1 Participants

The DIAN observational study (DIAN Obs) recruited participants from families that carry an autosomal-dominant Alzheimer's disease mutation in one of three genes - APP, PSEN1, or PSEN2. DIAN Obs is a longitudinal, observational study in which participants undergo comprehensive assessments including clinical and neuropsychological testing, brain imaging, and collection of biofluids such as CSF and blood^34–37. This analysis incorporated cross-sectional clinical data and CSF measures in 289 mutation carriers (MCs) and 172 mutation non-carrier participant controls (NC) from data freeze-15, each with at least one CSF measure³⁸. Mutation status was determined using PCR-based amplification of the relevant exon(s) followed by Sanger sequencing ³².

All procedures were approved by the institutional review board at Washington University in St. Louis. Written informed consent was obtained from participants or their caregivers, adhering to the guidelines of their respective local institutional review boards. To ensure participant confidentiality and due to the limited number of individuals at the extreme ends of the timeline, we have not displayed individual participant data for the period before − 30 years and after 10 years of estimated symptom onset.

2.2 CSF Sample Collection and protein measurements by SOMAscan

Cerebrospinal fluid (CSF) samples were collected after an overnight fast and preserved at -80°C for subsequent protein level measurements using the Slow Off-rate Aptamer (SOMAmer)-based capture array, SOMAscan³⁹. Protein measurements reported in relative fluorescence units (RFU) underwent hybridization, median, and iterative adaptive normalization by maximum likelihood (ANML) procedures until convergence. Ensuring data integrity, we performed an in-house quality control, excluding aptamers shared by approximately 70% of participant sample outliers³⁹.

All proteins of interest were analyzed using the SOMAscan assay (v4.1) from SomaLogic³⁹. To identify UPS proteins within our SOMAscan dataset, the UniProt representational state transfer (REST) application programming interface (API) was employed to cross-reference our dataset with the UPS category in UniProt's controlled vocabulary. Further refinement was achieved using the fetching annotations from UniProt and Reactome databases. Discrepancies were manually verified for accuracy. We identified 174 UPS proteins from a SOMAscan pool of approximately 6600 proteins for further analysis.

2.3 Immunoassay

The levels of Aβ42, Aβ40, and total tau (t-tau) in CSF were measured using the Lumipulse platform (Fujirebio, Tokyo, Japan) through immunoassay techniques⁴⁰; Phosphorylated to unphosphorylated ratio of tau at threonine 181, 205, and 217, serine 202 (pT181/T181, pT205/T205, pT217/T217 and pS202/S202) in CSF were measured by mass spectrometry ^41,42. CSF Soluble triggering receptor expressed on myeloid cells 2 (TREM2) immunoassay was performed as described previously ^43,40. Neurofilament light chain (NfL) levels were measured in CSF and serum using enzyme-linked immunosorbent assay(ELISA )^44,45,46.

2.4 Clinical Assessment and DIAN Estimated Year from Symptom Onset

The Clinical Dementia Rating–Sum of Boxes (CDR®-SB) assessment scale was used to assess the stage of dementia in a blinded manner by clinical evaluators (The scale ranges from 0 to 18, with higher scores denoting more significant impairment) ⁴⁷. The participant's estimated years from symptom onset (EYO) were calculated at each visit based on their age and expected age of symptom onset specific to their mutation. If this information was unavailable, the EYO was calculated at the age at which parental cognitive decline began, as determined through a semi-structured interview and historical data ⁴⁸.

2.5 Imaging

Imaging included Magnetic Resonance Imaging (MRI) and positron emission tomography (PET) imaging for volumetric analyses as well as evaluations of amyloid- β (using ¹¹C-Pittsburgh Compound B (PiB) PET) and glucose metabolism (using 18F-FDG-PET) as detailed previously ³⁶. Using FreeSurfer 5.3, we defined cortical and subcortical regions of interest (ROIs). Both PET modalities were partially volume-corrected via a regional spread function technique ^49,50,44. Our study concentrated on the precuneus region for its early and consistent involvement by AD pathology in DIAD ^32,50,51. Tau PET imaging utilized 18F-AV-1451 (flortaucipir), with data from the 80–100 min window converted to Standardized Uptake Value Ratios (SUVRs). To address differences in scanner spatial resolutions, scanner-specific spatial filters were applied, standardizing to a common resolution of 8 mm Region of Interest (ROI) PET data were also converted to SUVRs using the cerebellar grey matter as a reference. Partial volume correction was implemented using a regional spread function for each region, forming a geometric transfer matrix ^52,53,54,55.

2.6 Statistical analysis

In our study, cross-sectional analyses were conducted to examine the descriptive characteristics and baseline biomarker values across distinct clinical groups. These analyses employed chi-square (χ2) tests to assess differences in categorical variables and Analysis of Variance (ANOVA). This approach facilitated a detailed investigation of baseline biomarker discrepancies among the groups. Furthermore, we categorized mutation carrier participants into two distinct cohorts: asymptomatic carriers (those with a baseline CDR® score of 0) and symptomatic carriers (those with a baseline CDR® score greater than 0).

The cross-sectional relationship of different levels of UPS protein between the two mutation groups along the DIAN EYO was evaluated using a linear mixed-effects (LME) model. This model included fixed effects of the mutation group, EYO, and the interaction between mutation groups and EYO, along with random intercepts at the family level. Subsequently, a comparison of the estimated UPS levels between the two groups at each value of DIAN EYO, ranging from − 30 to + 10, was conducted. The EYO point at which the differences became statistically significant was determined by contrasting with specific EYO points. These estimators were then plotted against baseline EYO using local regression (LOESS).

Partial correlation analysis adjusting for age was conducted to assess the correlation between UPS proteins and each biomarker in each mutation group. Then estimated correlation coefficients were compared using Fisher’s Z transformation. Because of the large number of pairwise correlations to be compared, we controlled the False Discovery Rate (FDR) at 5% level ⁵⁶. Additionally, Analysis of Covariance (ANCOVA) for continuous variables were used to assess the differences between the NC and MC groups, taking age, sex, and APOE ε4 status into account as covariates, while also maintaining the FDR control at the 5% level.

Statistical analyses were performed using SAS version 9.4 (SAS Institute) and plots were created with RStudio (version 4.3.1). P values were obtained through two-tailed tests, adopting a significance threshold of p < 0.05 to determine statistical significance.

3.1 Participants Demographics

The cross-sectional cohort study included 179 asymptomatic mutation carriers (MCs) with an average age of 35.6 years (SD = 8.6) and an estimated years to onset (EYO) of -13.4 years (SD = 8.6), 104 symptomatic mutation carriers (MCs) with an average age of 47.4 years (SD = 9.0) and an EYO of 4.1 years (SD = 2.9), and 172 asymptomatic mutation non-carriers (NCs) with an average age of 39.2 years (SD = 11.4). The NCs were, on average, -9.0 years (SD = 12.2) younger relative to the EYO of their MC siblings. Comprehensive demographic details and baseline characteristics of the participants, as well as fluid and imaging biomarkers, are summarized in Table 1.

3.2 UPS Proteins Changes in CSF

Our LME model analysis identified a significant increase in CSF levels of 14 proteins when comparing MC to NC across EYO. These proteins encompassed six E2 enzymes (ubiquitin-conjugating enzymes), one E3 enzyme (ubiquitin ligase), four ubiquitin modifiers, two deubiquitinases, and one proteasome component, all showing statistical significance with FDR p-values less than 0.05 (Fig. 1). Notably, in MCs, the cross-sectional levels of certain proteins within the ubiquitin pathway began to elevate nearly two decades before the EYO. Specifically, between 15 and 20 years prior to the EYO, significant increases were observed in proteins such as ubiquitin-conjugating enzyme E2 H (UBE2H), the E3 ubiquitin ligase SMURF1 (SMURF1), and the small ubiquitin-related modifiers 2, 3, and 4 (SUMO2, SUMO3, and SUMO4).

Between 10 and 15 years prior to symptom onset, multiple proteins within the UPS, particularly E2 ubiquitin-conjugating enzymes, began to show significant increases in MC compared to NC. These increases included ubiquitin-conjugating enzyme E2 Z (UBE2Z), ubiquitin-conjugating enzyme E2 N (UBE2N), the UBE2N/Ubiquitin-conjugating enzyme E2 variant 1A (Uev1a) complex, the UBE2N/Ubiquitin-conjugating enzyme E2 variant 2 (UBE2V2) complex, ubiquitin-fold modifier-conjugating enzyme 1 (UFC1), and the deubiquitinating protein VCIP135. Furthermore, within the 10 to 0-year period leading up to symptom onset, alterations were observed in ubiquitin carboxyl-terminal hydrolase 14 (USP-14), ubiquitin-conjugating enzyme E2 Q1 (UBE2Q1), and the proteasome subunit alpha type-4 (PSMA4). The specific years showing significant differences are depicted in Fig. 1. No modifying effects were observed based on sex, education level, or APOE ε4 status. Of note, nearly all these UPS proteins demonstrated the greatest difference between symptomatic MCs and NCs as symptoms progressed, suggesting a continuing rise with disease progression.

3.3 Partial Spearman's Rank Correlation Analysis of UPS proteins and AD biomarkers

3.3.1 Correlation analysis with amyloid related biomarkers and amyloid PET

3.3.1.1 Correlation with Amyloid PET

After adjusting for age and sex, our analysis indicated that most the above 14 UPS proteins (result 3.2) demonstrated mild to moderate correlations with cortical amyloid PET (PiB PET) SUVR in the MC group, in contrast to the NC group. The correlation coefficients varied from 0.16 to 0.39. Specifically, proteins such as UBE2N, UBE2N/Uev1a, UBE2N/UBE2V2, SMO2, E3 ubiquitin ligase SMURF1, and USP-14 showed significant differences between the MC and NC groups (FDR p < 0.05). For more information, please see Fig. 2 and Table 3.

3.3.2 Correlation with amyloid related biomarkers

The correlations of the 14 UPS proteins were primarily with soluble CSF Aβ. Notably, these correlations were significantly inversely related to the Aβ42/40 ratio in the MC group, with r values ranging from − 0.16 to -0.44. This pattern is largely attributed to a positive association with Aβ40. While NCs also demonstrated several associations with soluble Aβ, proteins such as UBE2N, UBE2N/Uev1a, UBE2N/UBE2V2, and UFC1 positively correlated with Aβ42. In contrast, the associations with Aβ40 were more pronounced in NCs compared to MCs. For additional information, please refer to Fig. 2 and Table 3.

3.3.3 Correlation with tau related biomarkers

3.3.3.1 Correlation with tau PET

We also evaluated the associations between those 14 UPS proteins and flortaucipir uptake in the precuneus for both MC and NC, utilizing Spearman correlation models adjusted for age and sex. Proteins including UBE2H and E3 ubiquitin-protein ligase SMURF1 demonstrated moderate to strong associations with an increasing tau PET signal in the precuneus, with r values ranging from 0.58 to 0.66 in the MC group (p < 0.05, FDR 5%). No significant association was observed in the NC group.

3.3.2.2 Correlation with soluble CSF tau-related biomarkers

We identified significant correlations between CSF total tau in both the MC and NC groups, with each group showing a substantial correlation. These proteins including UBE2N, UBE2N (Ubc13)/Uev1a Complex, UBE2N/UBE2V2 Complex, UBE2H, UBE2Q1, UBE2Z, E3 ubiquitin-protein ligase SMURF1, SUMO2, SUMO3, SUMO4, UFC1, USP14, Deubiquitinating protein VCIP-135 andPSMA4, exhibited stronger correlation coefficients in MC group, ranging from approximately 0.25 to 0.72 (p < 0.05, FDR 5%)). (See Fig. 2 and Table 3 for details.

In the MC group, all of the aforementioned 14 UPS proteins, with the exception of PSMA4, exhibited a positive correlation with pTau181/T181, with r values ranging from 0.22 to 0.49, and with pTau205/T205, where r values ranged from 0.19 to 0.38. Furthermore, these 14 UPS proteins also showed a positive correlation with pTau217/T217, with r values spanning from 0.17 to 0.51. Conversely, 13 out of the 14 UPS proteins, excluding the deubiquitinating protein VCIP-135, demonstrated a negative correlation with pS202/S202, with correlation coefficients ranging from approximately − 0.21 to -0.48.

The most significant correlations were observed with 13 of these 14 proteins, excluding the deubiquitinating protein VCIP-135 and MTBR-tau243, in both MC and NC groups. Their r values varied from approximately 0.3 to 0.75 in the MC group. Although statistically significant correlations were identified in NCs, those in MCs with 3.3–10 times greater based on the model estimated correlations, the β coefficients ranging from 0.000098 to 0.02581 (MC vs NC). Notably, the deubiquitinating protein VCIP-135 was negatively associated with MTBR-tau243, with an r value of -0.41 in the NC group. Table 3 outlines the absolute differences in beta coefficients between MC and NC groups. For further details, refer to Fig. 2 (MC) and Table 3 (MC vs NC).

3.3.3 Correlation analysis with neurodegeneration and clinical state

To investigate the relationship between CSF UPS protein levels and imaging markers of neurodegeneration, as well as clinical stages, we conducted correlation analyses with various imaging parameters. These included FDG Composite and MRI based precuneus cortical thickness, alongside the CDR®-SB. Acknowledging the established correlation between age, sex, AD disease stage, and the age-associated increase in numerous proteostasis peptides, we adjusted the correlations for both age and sex ^48,57,58. Our findings reveal that all 14 UPS proteins exhibited a significant, though mild to moderate, positive correlation with CDR®-SB sin the MC (see Fig. 2). Moreover, all UPS proteins, except PSMA4, demonstrated mild to moderate negative correlations with FDG PET in the precuneus region for the MC group, with r values ranging from − 0.14 to -0.36. Additionally, all 14 UPS proteins displayed a mild negative correlation with MRI findings in the precuneus region (left), with r values ranging from − 0.14 to -0.27.

3.3.4 Correlation with CSF and Serum Neurofilament Light Chain (NfL)

We identified a significant positive correlation between the logarithmic values (log) of CSF and serum NfL. Notably, within the MC impairment group, CSF NfL demonstrated moderate to high positive correlations with 12 of the 14 UPS proteins discussed in section 3.2, excluding UBE2Q1 and PSMA4. The correlation coefficients (r) ranged from 0.3 to 0.68. In the NC group, UBE2N, UBE2N/Uev1a, UBE2N/UBE2V2, UBE2Z, and UFC1 also showed positive correlations with CSF NfL, albeit the associations were more marked in the MC group compared to the NC group. Furthermore, 12 out of these 14 proteins, with the exception of UBE2Q1 and the deubiquitinating protein VCIP-135, exhibited mild to moderate positive correlations with serum NfL in the MC group, with correlation coefficients ranging approximately from 0.18 to 0.36.

3.3.2.4 Correlation with Soluble TREM2

We observed a significant positive correlation between the normalized levels of CSF sTREM2 (normalized using an internal standard, termed relative sTREM2) and a selection of 14 UPS proteins in both MC and NC groups. In the MC group, all proteins except UBE2H, USP-14, and VCIP-135 showed positive correlations with sTREM2, with r-values ranging approximately from 0.21 to 0.55. In the NC group, proteins such as UBE2N, the UBE2N/Uev1a Complex, the UBE2N/UBE2V2 Complex, UBE2Z, E3 ubiquitin-protein ligase SMURF1, SUMO3, and UFC1 also displayed positive correlations with sTREM2, with r values ranging from 0.43 to 0.56, which reached statistical significance (p < 0.05, FDR 5%). However, Fig. 1 illustrates that NCs maintain very normal levels for nearly all these 14 proteins, suggesting that the observed correlations might be driven by much smaller variance rather than a greater range. Furthermore, the model-estimated correlations analysis in Table 3 indicated that the β coefficients showed no significant difference between MC and NC for all 14 UPS proteins. This observation suggests that the associations may not necessarily reflect biological phenomena but could instead be attributed to characteristics of the assay.

In our study, we observed that levels of 14 UPS proteins were elevated in the MC group compared to the NC group across different stages of DIAD. Notably, these differences were especially significant around the time of predicted clinical symptom onset and persisted beyond this point (Fig. 1). The presence of specific DIAD mutations in either the PSEN1, PSEN2, or APP genes. did not significantly affect the extent of the increase in UPS protein levels.

Furthermore, our findings reveal consistently stronger associations with MTBR-tau243, total tau, tau PET, and CSF NfL. Additionally, the correlations between rising levels of UPS-related proteins and markers of neurodegeneration, as evidenced by PiB PET, FDG PET, and MRI, underscore the significant link between UPS dysregulation and neurodegenerative processes. Our results also indicate a consistent correlation between UPS protein levels and several CSF biomarkers, including phosphorylated tau (ptauT181/T181, ptauS202/S202, ptauT205/T205, ptauT217/T217), the Aβ 42/40 ratio, sTREM2, serum NfL, and CDR®-SB. These correlations suggest that amyloid aggregation is accompanied by an increase in these UPS proteins, but the increase is more pronounced when tau levels rise, suggesting the potential involvement of the UPS in the pathogenesis of AD, particularly in relation to misfolded tau/ NFT and critical disease progression points leading to symptom onset.

UPS, Autophagy and AD

The UPS and autophagy represent crucial protein degradation pathways in eukaryotes, targeting distinct substrate types. The UPS primarily processes short-lived, misfolded soluble proteins, while autophagy addresses longer-lived proteins, insoluble aggregates, and organelles. Both pathways utilize ubiquitin for target recognition, underscoring their significance in cellular health and proteostasis maintenance^{59,60,61,62,66}. Notably, the E3 ubiquitin-protein ligase SMURF1 plays a crucial role in autophagy regulation by activating PPP3/calcineurin and TFEB, highlighting the lysosome's significant role in cell signaling. SMURF1 impacts lysosomal biogenesis and, together with PPP3/calcineurin, governs the autolysosome pathway, indirectly aiding in autophagosome maturation through TFEB regulation⁶³. In our study, we noted an upregulation of SMURF1 in DIAD, suggesting it might act as a protective mechanism to enhance protein quality control or could directly contribute to AD pathogenesis through autophagy regulation. The pathophysiological significance of SMURF1 in DIAD, along with its potential interactions with autophagy in AD whether direct or indirect warrants further exploration to elucidate their impact on disease progression. The involvement of the UPS in DIAD and the possible mechanisms are depicted in Fig. 3.

Enzymes E2, E3 Enzymes and AD and Other Neurodegenerative Disease

Alterations in the Ube2 subfamily genes, notably UBE2N, play a significant role in AD and other neurodegenerative disorders^{68, 69}. Changes in the expression and methylation of UBE2N and its complexes suggest their involvement in AD pathology, such as protein aggregation and genomic regulation^{64, 65}.. Recent research using Gene Expression Omnibus (GEO) data identified UBE2N as an immune-related biomarker for AD, linked to T cell and B cell functions and synaptic signaling⁶⁶. Suppressing UBE2N has been shown to alleviate AD pathology by enhancing amyloid-β clearance in mouse models, marking it as a potential therapeutic target⁶⁷. Moreover, heterodimers like UBE2V1-a, involved in atypical polyubiquitination, impact inflammation and proteasomal degradation^67,65,68. Other UBE2 enzymes, such as UBE2I, UBE2Q1, UBE2E1, and UBE2Z, display varied regulatory patterns in neurodegenerative diseases like frontotemporal dementia, suggesting the Ube2 family's extensive influence on neurodegeneration, inflammation, and cellular stress responses⁶⁹.

Our study highlights significant changes in E3 ubiquitin ligases, especially SMURF1, which is associated with aggresome formation in AD, a mechanism to prevent the toxic spread of misfolded proteins⁷⁰. SMURF1's localization in Hirano bodies suggests its involvement in neurodegeneration⁷¹. Elevated levels of UBE2 family proteins and SMURF1, observable years before AD symptoms appear, suggest their early role in AD pathogenesis and potential as biomarkers. Their increased levels post-symptom onset, correlating with neurodegeneration markers like NfL and aggregated-tau, underscore their importance in neurofibrillary tangle development and neurodegeneration^72,70,73,74. This underlines the need for further explore the Ube2 family and SMURF1's roles in AD progression and their therapeutic possibilities.

Ubiquitin Modifiers and AD

Ubiquitin-fold modifier conjugating enzyme 1 (UFC1) is significantly associated with AD, playing a crucial role in protein folding, secretion, and endoplasmic reticulum (ER) stress. Elevated UFC1 levels in the CSF of individuals with mild cognitive impairment in sporadic AD suggest its involvement in AD's pathogenesis ^75,76. Our study supports this finding, showing a strong positive correlation between UFC1 and CSF NfL and a moderate correlation with total tau, marker of later stages of disease⁷⁷.

Additionally, our study enhances understanding of post-translational modifications in AD through the role of SUMOs and SUMOylation ^{74, 78,79, 80}. We observed increased levels of SUMO2, SUMO3, and SUMO4 in individuals with DIAD mutations, implicating SUMOylation in AD pathophysiology. This modification of AD-related proteins, such as APP, affects amyloid-β aggregation, with certain mutations potentially exacerbating the disease. Moreover, SUMOylation's involvement in tau phosphorylation suggests its impact on tau stability and degradation, contributing to AD's characteristic neurofibrillary tangles and neuronal loss ^{74, 79, 80}. The upregulation of SUMO proteins in DIAD MCs before symptom onset suggests SUMOylation's role in early AD pathology, warranting further exploration to understand its mechanisms and aging-related effects. Our findings suggest that modulating SUMOylation processes may offer early detection biomarkers and new therapeutic targets in AD.

Deubiquitinase and AD

In AD, alterations in DUBs underscore their critical role in maintaining ubiquitination balance and their potential involvement in disease progression^{74,79, 81, 82,83}. Dysregulated DUBs are implicated in neuropathy and neurodegeneration; for instance, USP-14 is linked to neuromuscular dysfunctions, while UCHL-1 levels correlate with key AD biomarkers and cognitive scores^{79, 81, 84}. Our study notably identified elevated levels of deubiquitinating protein VCIP-135 and USP-14, which are involved in cellular homeostasis and protein processing, within the group. This elevation might suggest a compensatory mechanism in response to the misfolded proteins characteristic of AD or a role in the proteasomal degradation process^{85,86, 87,88}. However, our findings did not reveal a significant role for UCHL-1 in AD progression, indicating that its involvement may vary across different stages of the disease.

Proteasome and AD

The UPS, with the proteasome as its essential component, is crucial for degrading ubiquitinated proteins. The proteasome is a barrel-shaped 20S complex composed of four types of subunits (α, β, γ, δ), with the β-subunits having peptide-cleaving capabilities⁸². Post-mortem examinations of AD brains have shown reductions in caspase-like and chymotrypsin-like proteasome activities⁸⁹. Protein oxidation, often discussed as a factor in AD progression, remains debated as either a cause or consequence of the pathology. Protein oxidation and excessive phosphorylation could impede the proteasome's key roles in intracellular protein quality control and the processing of Aβ and tau, potentially influencing AD pathology⁹⁰. The proteasome's role in AD remains critically underexplored. Our study highlights the differential expression of PSMA4 between MC and NC groups around symptom onset, with this difference increasing as the disease progresses. This underlines the critical need for more research into the interplay between genetic variations, proteasome function, and neurodegenerative disorders.

Strengths and Limitations

This study represents a foundational investigation into the UPS in AD, leveraging multi-modal data from a detailed cohort of DIAD participants. Despite its strengths, including the use of sensitive proteomics and corroborative imaging, the study faces limitations. It focuses on DIAD, whose genetic predictability differs from the more common sporadic AD, potentially limiting the generalizability of our findings. The cross-sectional design restricts our ability to infer causality or the sequence of UPS changes relative to disease progression, pointing to the need for longitudinal studies. Additionally, our proteomic analysis, limited to proteins detectable by the SOMAscan assay, might not capture all relevant UPS alterations, nor does it clarify the implications of extracellular versus intracellular protein levels. Future research should expand the range of UPS proteins analyzed and compare DIAD to sporadic AD to enhance our understanding of the UPS in AD pathophysiology.

our study underscores UPS dysregulation in DIAD, particularly highlighting the upregulation of the UBE2 family, E3 ligase SMURF1, ubiquitin modifiers like SUMO2, 3, 4, UFC1, deubiquitinase USP-14, VCIP-135, and proteasome component PSMA4. This upregulation, emerging 10 to 15 years before symptom onset and coinciding with increases in Aβ, tau, phospho-tau, and tau PET findings, offers insights into AD mechanisms. It may reflect a protective response or contribute to AD pathogenesis, possibly in reaction to AD-related inflammation⁵¹^, ⁹¹^,⁹²^,⁹³. These findings necessitate further research to explore these proteins' roles in misfolded protein aggregation and their impact on other degradation systems like autophagy⁹⁴. The link between UPS alterations and tau pathology suggests a connection to disease progression and late-stage biomarkers. Our study, highlighting correlations with Aβ, tau load, brain volume, and metabolic changes, opens new research directions for therapeutic strategies targeting UPS to reduce protein aggregation and inflammation in AD. This emphasizes the importance of further exploration into the UPS's involvement in DIAD pathogenesis.

Conflict of Interest:

RJB is the Director of the DIAN-TU and Principal Investigator of DIAN and the DIAN-TU-001 trial. Unrelated to this study, for the DIAN-TU, he receives research support from the NIA, Eli Lilly and Company, F. Hoffman-La Roche, Ltd., Eisai, Alzheimer’s Association, GHR Foundation, Anonymous Organization, DIAN-TU Pharma Consortium (Active Members: Biogen, Eisai, Eli Lilly and Company, Janssen, F. Hoffmann-La Roche, Ltd./Genentech).

JH is a paid consultant/advisor for Parabon Nanolabs, Roche, Prothena, and AlzPath.

GYRH reports no competing interests directly relevant to this work. He has received grants or contracts from CIHR, NIA/NIH, has been a clinical trials investigator supported by Biogen, Cassava, and Lilly, has participated in expert advisory committee supported by Biogen, Roche, and NovoNordisk, and is the current president of C5R (Consortium of Canadian Centres for Clinical Cognitive Research).

EMM receives Grant Funding from NIA; Institutional funding from Eli Lilly, Hoffmann-La Roche, Eisai. He is a DSMB member (paid directly) for Alector; Eli Lilly; a Scientific Advisory Board Member (paid directly to me) for Alzamend, Fondation Alzheimer. He acts as a Consultant/Advisor for Sage Therapeutics, Eli Lilly, Sanofi, AstraZeneca,Hoffmann La-Roche.

CChas received research support from GSK and EISAI. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. CC is a member of the advisory board of Circular Genomics and owns stocks in these companies. DP is an employee of GlaxoSmithKline (GSK) and holds stock in GSK.

CX is supported by National Institute on Aging (NIA) grants R01 AG067505 and R01 AG053550.

All other authors have nothing to disclose.

JCM is the Friedman Distinguished Professor of Neurology, Associate Director, Knight ADRC; Associate Director of DIAN and Founding Principal Investigator of DIAN. He is funded by NIH grants # P30 AG066444; P01AG003991; P01AG026276; and U19 AG024904. Neither he nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company.

TLSB has investigator-initiated research funding from the NIH, the Alzheimer’s Association, the Barnes-Jewish Hospital Foundation and Avid Radiopharmaceuticals. Dr. Benzinger participates as a site investigator in clinical trials sponsored by Avid Radiopharmaceuticals, Eli Lilly and Company, Biogen, Eisai, Jaansen, and F. Hoffmann-La Roche, Ltd. She also serves as an unpaid consultant to Eisai and Siemensand is on the Speaker’s Bureau for Biogen.

AER reports no competing interests. He receives research support for this work from the National Institute on Aging (R01AG053267, U19AG032438)

TI reports no competing interests. He received research support for this work from AMED (JP23dk0207066 and JP23dk0207049).

GSD reports no competing interests directly relevant to this work. His research is supported by NIH (K23AG064029, U01AG057195, U01NS120901, U19AG032438). He serves as a consultant for Parabon Nanolabs Inc and as a Topic Editor (Dementia) for DynaMed (EBSCO). He is the co-Project PI for a clinical trial in anti-NMDAR encephalitis, which receives support from Amgen Pharmaceuticals, and a consultant for Arialys Therapeutics. He has developed educational materials for PeerView Media, Inc, and Continuing Education Inc. He owns stock in ANI pharmaceuticals. Dr. Day’s institution has received support from Eli Lilly for development and participation in an educational event promoting early diagnosis of symptomatic Alzheimer disease, and in-kind contributions of radiotracer precursors for tau-PET neuroimaging in studies of memory and aging (via Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly).

RJP is Neuropathology Core Leader for the DIAN observational study and the DIAN Trials Unit. He receives research support for this work from the National Institute on Aging (U19 AG032438, U19AG032438-09S1, R01AG068319). His laboratory receives cost recovery funding from Biogen for tissue procurement and processing services related to ALS clinical trials. Neither he nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company.

FL has grants not related to this paper from NIH, DIAN, Enroll-HD and BIOGEN.

JL reports speaker fees from Bayer Vital, Biogen, EISAI, TEVA, Zambon, Esteve, Merck and Roche, consulting fees from Axon Neuroscience, EISAI and Biogen, author fees from Thieme medical publishers and W. Kohlhammer GmbH medical publishers and is inventor in a patent “Oral Phenylbutyrate for Treatment of Human 4-Repeat Tauopathies” (EP 23 156 122.6) filed by LMU Munich. In addition, he reports compensation for serving as chief medical officer for MODAG GmbH, is beneficiary of the phantom share program of MODAG GmbH and is inventor in a patent “Pharmaceutical Composition and Methods of Use” (EP 22 159 408.8) filed by MODAG GmbH, all activities outside the submitted work.

SBB receives support from the National Institute on Aging (NIA) and the Michael J Fox Foundation.

All other authors have nothing to disclose.

Acknowledgements/Funds and Supports

Our heartfelt appreciation goes to the participants and their families for their invaluable contribution to this study. We also extend our gratitude to the staff and institutions that played a pivotal role in this research.

Funding for this work was provided by the National Institutes of Health through grants R01AG044546, RF1AG053303, RF1AG058501, U01AG058922, and RF1AG074007, with special acknowledgment to CC and YJS for their efforts. Additional support came from the Chan Zuckerberg Initiative (CZI), the Alzheimer’s Association through the Zenith Fellows Award (ZEN-22-848604, bestowed upon CC), and a generous donation from an anonymous foundation.

This work was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH).

Research reported in this publication was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the NeuroGenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

DIAN Contributions and Data Handling:

This project's data collection and dissemination were supported by The Dominantly Inherited Alzheimer Network (DIAN, U19AG032438), funded by the National Institute on Aging (NIA), along with contributions from the Alzheimer’s Association (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), and other international entities. These include partial funding from Japan's Agency for Medical Research and Development (AMED):JP23dk0207066 and JP23dk0207049), and support from the Korea Dementia Research Project through the Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (HU21C0066), the Spanish Institute of Health Carlos III (ISCIII), the Canadian Institutes of Health Research (CIHR), the Canadian Consortium of Neurodegeneration and Aging, the Brain Canada Foundation, and the Fonds de Recherche du Québec - Santé. The DIAN Study investigators have vetted this manuscript for scientific accuracy and consistency with past publications. We are deeply grateful to our participants and their families for their generosity, as well as to the dedicated DIAN research and support staff across all sites.

DIAN Data Accessibility:

Due to the rarity of dominantly inherited Alzheimer's disease, individual-level data from DIAN cannot be shared publicly, as it would compromise participant anonymity. This limitation has been validated by the Institutional Review Board (IRB) and confirmed with the NIH. Nevertheless, this data remains accessible for qualified researchers upon request. Requests can be submitted through the following link: DIAN Biospecimen Request Form.

DIAN Consortium List:

Randall J. Bateman, James M. Noble, Gregory S. Day, Neill R. Graff-Radford, Jonathan Vöglein, Ricardo Allegri, Patricio Chrem Mendez, Ezequiel Surace, Sarah B. Berman, Snezana Ikonomovic, Neelesh Nadkarni, Francisco Lopera, Laura Ramirez, David Aguillon, Yudy Leon, Claudia Ramos, Diana Alzate, Ana Baena, Natalia Londono, Sonia Moreno, Mathias Jucker, Christoph Laske, Elke Kuder-Buletta, Susanne Graber-Sultan, Oliver Preische, Anna Hofmann, Takeshi Ikeuchi, Kensaku Kasuga, Yoshiki Niimi, Kenji Ishii, Michio Senda, Raquel Sanchez-Valle, Pedro Rosa-Neto, Nick Fox, Dave Cash, Jae-Hong Lee, Jee Hoon Roh, Meghan Riddle, William Menard, Courtney Bodge, Mustafa Surti, Leonel Tadao Takada, Martin Farlow, Jasmeer P. Chhatwal, V. J. Sanchez-Gonzalez, Maribel Orozco-Barajas, Alison Goate, Alan Renton, Bianca Esposito, Celeste M. Karch, Jacob Marsh, Carlos Cruchaga, Victoria Fernandez, Brian A. Gordon, Anne M. Fagan, Gina Jerome, Elizabeth Herries, Jorge Llibre-Guerra, Allan I. Levey, Erik C. B. Johnson, Nicholas T. Seyfried, Peter R. Schofield, William Brooks, Jacob Bechara, Eric McDade, Jason Hassenstab, Richard J. Perrin, Erin Franklin, Tammie L. S. Benzinger, Allison Chen, Charles Chen, Shaney Flores, Nelly Friedrichsen, Nancy Hantler, Russ Hornbeck, Steve Jarman, Sarah Keefe, Deborah Koudelis, Parinaz Massoumzadeh, Austin McCullough, Nicole McKay, Joyce Nicklaus, Christine Pulizos, Qing Wang, Sheetal Mishall, Edita Sabaredzovic, Emily Deng, Madison Candela, Hunter Smith, Diana Hobbs, Jalen Scott, Johannes Levin, Chengjie Xiong, Peter Wang, Xiong Xu, Yan Li, Emily Gremminger, Yinjiao Ma, Ryan Bui, Ruijin Lu, Ralph Martins, Ana Luisa Sosa Ortiz, Alisha Daniels, Laura Courtney, Hiroshi Mori, Charlene Supnet-Bell, Jinbin Xu, John Ringman.

Breijyeh Z, Karaman R. Comprehensive Review on Alzheimer’s Disease: Causes and Treatment. Molecules. 2020;25(24):5789. doi:10.3390/molecules25245789
Cummings JL. Alzheimer’s disease. N Engl J Med. 2004;351(1):56-67. doi:10.1056/NEJMra040223
Rosales-Corral SA, Acuña-Castroviejo D, Coto-Montes A, et al. Alzheimer’s disease: pathological mechanisms and the beneficial role of melatonin. J Pineal Res. 2012;52(2):167-202. doi:10.1111/j.1600-079X.2011.00937.x
Kunkle BW, Vardarajan BN, Naj AC, et al. Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport. JAMA Neurol. 2017;74(9):1113-1122. doi:10.1001/jamaneurol.2017.1518
Reitz C, Rogaeva E, Beecham GW. Late-onset vs nonmendelian early-onset Alzheimer disease: A distinction without a difference? Neurol Genet. 2020;6(5):e512. doi:10.1212/NXG.0000000000000512
Labbadia J, Morimoto RI. The biology of proteostasis in aging and disease. Annu Rev Biochem. 2015;84:435-464. doi:10.1146/annurev-biochem-060614-033955
Douglas PM, Dillin A. Protein homeostasis and aging in neurodegeneration. J Cell Biol. 2010;190(5):719-729. doi:10.1083/jcb.201005144
Höhn A, Tramutola A, Cascella R. Proteostasis Failure in Neurodegenerative Diseases: Focus on Oxidative Stress. Oxid Med Cell Longev. 2020;2020:5497046. doi:10.1155/2020/5497046
Balch WE, Morimoto RI, Dillin A, Kelly JW. Adapting Proteostasis for Disease Intervention. Science. 2008;319(5865):916-919. doi:10.1126/science.1141448
Hartl FU, Bracher A, Hayer-Hartl M. Molecular chaperones in protein folding and proteostasis. Nature. 2011;475(7356):324-332. doi:10.1038/nature10317
Hipp MS, Kasturi P, Hartl FU. The proteostasis network and its decline in ageing. Nat Rev Mol Cell Biol. 2019;20(7):421-435. doi:10.1038/s41580-019-0101-y
Taylor RC, Dillin A. Aging as an event of proteostasis collapse. Cold Spring Harb Perspect Biol. 2011;3(5):a004440. doi:10.1101/cshperspect.a004440
Ji XR, Cheng KC, Chen YR, et al. Dysfunction of different cellular degradation pathways contributes to specific β-amyloid42-induced pathologies. FASEB J Off Publ Fed Am Soc Exp Biol. 2018;32(3):1375-1387. doi:10.1096/fj.201700199RR
Wang C, Wang X. The interplay between autophagy and the ubiquitin-proteasome system in cardiac proteotoxicity. Biochim Biophys Acta. 2015;1852(2):188-194. doi:10.1016/j.bbadis.2014.07.028
Dikic I. Proteasomal and Autophagic Degradation Systems. Annu Rev Biochem. 2017;86:193-224. doi:10.1146/annurev-biochem-061516-044908
Collins GA, Goldberg AL. The Logic of the 26S Proteasome. Cell. 2017;169(5):792-806. doi:10.1016/j.cell.2017.04.023
Zheng N, Shabek N. Ubiquitin Ligases: Structure, Function, and Regulation. Annu Rev Biochem. 2017;86:129-157. doi:10.1146/annurev-biochem-060815-014922
Mevissen TET, Komander D. Mechanisms of Deubiquitinase Specificity and Regulation. Annu Rev Biochem. 2017;86:159-192. doi:10.1146/annurev-biochem-061516-044916
Kuzuhara S, Mori H, Izumiyama N, Yoshimura M, Ihara Y. Lewy bodies are ubiquitinated. A light and electron microscopic immunocytochemical study. Acta Neuropathol (Berl). 1988;75(4):345-353. doi:10.1007/BF00687787
Mori H, Kondo J, Ihara Y. Ubiquitin is a component of paired helical filaments in Alzheimer’s disease. Science. 1987;235(4796):1641-1644. doi:10.1126/science.3029875
Perry G, Friedman R, Shaw G, Chau V. Ubiquitin is detected in neurofibrillary tangles and senile plaque neurites of Alzheimer disease brains. Proc Natl Acad Sci U S A. 1987;84(9):3033-3036. doi:10.1073/pnas.84.9.3033
Jung YJ, Kim YH, Bhalla M, Lee SB, Seo J. Genomics: New Light on Alzheimer’s Disease Research. Int J Mol Sci. 2018;19(12):3771. doi:10.3390/ijms19123771
Elder MK, Erdjument-Bromage H, Oliveira MM, Mamcarz M, Neubert TA, Klann E. Age-dependent shift in the de novo proteome accompanies pathogenesis in an Alzheimer’s disease mouse model. Commun Biol. 2021;4:823. doi:10.1038/s42003-021-02324-6
Shi H, Medway C, Bullock J, Brown K, Kalsheker N, Morgan K. Analysis of Genome-Wide Association Study (GWAS) data looking for replicating signals in Alzheimer’s disease (AD). Int J Mol Epidemiol Genet. 2010;1(1):53-66.
Hu X, Pickering EH, Hall SK, et al. Genome-wide association study identifies multiple novel loci associated with disease progression in subjects with mild cognitive impairment. Transl Psychiatry. 2011;1(11):e54. doi:10.1038/tp.2011.50
Harris LD, Jasem S, Licchesi JDF. The Ubiquitin System in Alzheimer’s Disease. In: Barrio R, Sutherland JD, Rodriguez MS, eds. Proteostasis and Disease : From Basic Mechanisms to Clinics. Advances in Experimental Medicine and Biology. Springer International Publishing; 2020:195-221. doi:10.1007/978-3-030-38266-7_8
Di Domenico F, Lanzillotta C. The disturbance of protein synthesis/degradation homeostasis is a common trait of age-related neurodegenerative disorders. Adv Protein Chem Struct Biol. 2022;132:49-87. doi:10.1016/bs.apcsb.2022.05.008
Tank EMH, True HL. Disease-Associated Mutant Ubiquitin Causes Proteasomal Impairment and Enhances the Toxicity of Protein Aggregates. PLOS Genet. 2009;5(2):e1000382. doi:10.1371/journal.pgen.1000382
George DE, Tepe JJ. Advances in Proteasome Enhancement by Small Molecules. Biomolecules. 2021;11(12):1789. doi:10.3390/biom11121789
Hof PR, Glannakopoulos P, Bouras C. The neuropathological changes associated with normal brain aging. Histol Histopathol. 1996;11(4):1075-1088.
Perl DP. Neuropathology of Alzheimer’s disease. Mt Sinai J Med N Y. 2010;77(1):32-42. doi:10.1002/msj.20157
Bateman RJ, Xiong C, Benzinger TLS, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795-804. doi:10.1056/NEJMoa1202753
Shen Y, Ali M, Timsina J, et al. Systematic proteomics in Autosomal dominant Alzheimer’s disease reveals decades-early changes of CSF proteins in neuronal death, and immune pathways. medRxiv. Published online January 13, 2024:2024.01.12.24301242. doi:10.1101/2024.01.12.24301242
Timsina J, Ali M, Do A, Wang L, Sung YJ, Cruchaga C. Harmonization of CSF and imaging biomarkers for Alzheimer’s disease biomarkers: need and practical applications for genetics studies and preclinical classification. BioRxiv Prepr Serv Biol. Published online May 24, 2023:2023.05.24.542118. doi:10.1101/2023.05.24.542118
O’Connor A, Cash DM, Poole T, et al. Tau accumulation in autosomal dominant Alzheimer’s disease: a longitudinal [18F]flortaucipir study. Alzheimers Res Ther. 2023;15(1):99. doi:10.1186/s13195-023-01234-5
McKay NS, Gordon BA, Hornbeck RC, et al. Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN). Nat Neurosci. 2023;26(8):1449-1460. doi:10.1038/s41593-023-01359-8
Johnson ECB, Bian S, Haque RU, et al. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease. Nat Med. 2023;29(8):1979-1988. doi:10.1038/s41591-023-02476-4
Sung YJ, Yang C, Norton J, et al. Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease. Sci Transl Med. 2023;15(703):eabq5923. doi:10.1126/scitranslmed.abq5923
Timsina J, Gomez-Fonseca D, Wang L, et al. Comparative analysis of Alzheimer’s disease Cerebrospinal fluid biomarkers measurement by multiplex SOMAscan platform and immunoassay-based approach. J Alzheimers Dis JAD. 2022;89(1):193-207. doi:10.3233/JAD-220399
Morenas-Rodríguez E, Li Y, Nuscher B, et al. Soluble TREM2 in CSF and its association with other biomarkers and cognition in autosomal-dominant Alzheimer’s disease: a longitudinal observational study. Lancet Neurol. 2022;21(4):329-341. doi:10.1016/S1474-4422(22)00027-8
Barthélemy NR, Li Y, Joseph-Mathurin N, et al. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease. Nat Med. 2020;26(3):398-407. doi:10.1038/s41591-020-0781-z
Barthélemy NR, Saef B, Li Y, et al. CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer’s disease. Nat Aging. 2023;3(4):391-401. doi:10.1038/s43587-023-00380-7
Suárez-Calvet M, Morenas-Rodríguez E, Kleinberger G, et al. Early increase of CSF sTREM2 in Alzheimer’s disease is associated with tau related-neurodegeneration but not with amyloid-β pathology. Mol Neurodegener. 2019;14(1):1. doi:10.1186/s13024-018-0301-5
Dominantly Inherited Alzheimer Network, Preische O, Schultz SA, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25(2):277-283. doi:10.1038/s41591-018-0304-3
Fagan AM, Mintun MA, Mach RH, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol. 2006;59(3):512-519. doi:10.1002/ana.20730
Henson RL, Doran E, Christian BT, et al. Cerebrospinal fluid biomarkers of Alzheimer’s disease in a cohort of adults with Down syndrome. Alzheimers Dement Amst Neth. 2020;12(1):e12057. doi:10.1002/dad2.12057
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412-2414. doi:10.1212/wnl.43.11.2412-a
Ryman DC, Acosta-Baena N, Aisen PS, et al. Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis. Neurology. 2014;83(3):253-260. doi:10.1212/WNL.0000000000000596
Vlassenko AG, Benzinger TL, Morris JC. PET amyloid-beta imaging in preclinical Alzheimer’s disease. Biochim Biophys Acta BBA-Mol Basis Dis. 2012;1822(3):370-379.
Benzinger TLS, Blazey T, Jack CR, et al. Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci U S A. 2013;110(47):E4502-4509. doi:10.1073/pnas.1317918110
Gordon BA, Blazey TM, Su Y, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol. 2018;17(3):241-250. doi:10.1016/S1474-4422(18)30028-0
Gordon BA, Blazey TM, Christensen J, et al. Tau PET in autosomal dominant Alzheimer’s disease: relationship with cognition, dementia and other biomarkers. Brain. 2019;142(4):1063-1076. doi:10.1093/brain/awz019
Joshi A, Koeppe RA, Fessler JA. Reducing between scanner differences in multi-center PET studies. NeuroImage. 2009;46(1):154-159. doi:10.1016/j.neuroimage.2009.01.057
Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med Off Publ Soc Nucl Med. 1998;39(5):904-911.
Su Y, Blazey TM, Snyder AZ, et al. Partial volume correction in quantitative amyloid imaging. NeuroImage. 2015;107:55-64. doi:10.1016/j.neuroimage.2014.11.058
Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol. 1995;57(1):289-300. doi:10.1111/j.2517-6161.1995.tb02031.x
Morawe T, Hiebel C, Kern A, Behl C. Protein Homeostasis, Aging and Alzheimer’s Disease. Mol Neurobiol. 2012;46(1):41-54. doi:10.1007/s12035-012-8246-0
Castro-Aldrete L, Moser MV, Putignano G, Ferretti MT, Schumacher Dimech A, Santuccione Chadha A. Sex and gender considerations in Alzheimer’s disease: The Women’s Brain Project contribution. Front Aging Neurosci. 2023;15. Accessed February 29, 2024. https://www.frontiersin.org/articles/10.3389/fnagi.2023.1105620
Frontiers | Crosstalk Between Mammalian Autophagy and the Ubiquitin-Proteasome System. Accessed July 11, 2023. https://www.frontiersin.org/articles/10.3389/fcell.2018.00128/full
Wang XJ, Yu J, Wong SH, et al. A novel crosstalk between two major protein degradation systems. Autophagy. 2013;9(10):1500-1508. doi:10.4161/auto.25573
Ji CH, Kwon YT. Crosstalk and Interplay between the Ubiquitin-Proteasome System and Autophagy. Mol Cells. 2017;40(7):441-449. doi:10.14348/molcells.2017.0115
Korolchuk VI, Menzies FM, Rubinsztein DC. Mechanisms of cross-talk between the ubiquitin-proteasome and autophagy-lysosome systems. FEBS Lett. 2010;584(7):1393-1398. doi:10.1016/j.febslet.2009.12.047
Xia Q, Zheng H, Li Y, et al. SMURF1 controls the PPP3/calcineurin complex and TFEB at a regulatory node for lysosomal biogenesis. Autophagy. 2023;0(0):1-17. doi:10.1080/15548627.2023.2267413
Bagyinszky E, Giau VV, An SA. Transcriptomics in Alzheimer’s Disease: Aspects and Challenges. Int J Mol Sci. 2020;21(10):3517. doi:10.3390/ijms21103517
Rosenthal SB, Wang H, Shi D, et al. Mapping the gene network landscape of Alzheimer’s disease through integrating genomics and transcriptomics. PLOS Comput Biol. 2022;18(2):e1009903. doi:10.1371/journal.pcbi.1009903
Feng G, Zhong M, Huang H, et al. Identification of UBE2N as a Biomarker of Alzheimer’s Disease by Combining WGCNA with Machine Learning Algorithms.; 2024. doi:10.21203/rs.3.rs-3904783/v1
Suppressing UBE2N ameliorates Alzheimer’s disease pathology through the clearance of amyloid-β. Published online October 28, 2023. doi:10.6084/m9.figshare.24454177.v1
Song H, Yang J, Yu W. Promoter Hypomethylation of TGFBR3 as a Risk Factor of Alzheimer’s Disease: An Integrated Epigenomic-Transcriptomic Analysis. Front Cell Dev Biol. 2022;9. Accessed November 11, 2023. https://www.frontiersin.org/articles/10.3389/fcell.2021.825729
Xu N, Gulick J, Osinska H, et al. Ube2v1 Positively Regulates Protein Aggregation by Modulating Ubiquitin Proteasome System Performance Partially Through K63 Ubiquitination. Circ Res. 2020;126(7):907-922. doi:10.1161/CIRCRESAHA.119.316444
Dong L, Liu L, Li Y, Li W, Zhou L, Xia Q. E3 ligase Smurf1 protects against misfolded SOD1 in neuronal cells by promoting its K63 ubiquitylation and aggresome formation. Hum Mol Genet. 2022;31(12):2035-2048. doi:10.1093/hmg/ddac008
Makioka K, Yamazaki T, Takatama M, Ikeda M, Okamoto K. Immunolocalization of Smurf1 in Hirano bodies. J Neurol Sci. 2014;336(1-2):24-28. doi:10.1016/j.jns.2013.09.028
Yang Q, Zhao J, Chen D, Wang Y. E3 ubiquitin ligases: styles, structures and functions. Mol Biomed. 2021;2:23. doi:10.1186/s43556-021-00043-2
Kopito RR. Aggresomes, inclusion bodies and protein aggregation. Trends Cell Biol. 2000;10(12):524-530. doi:10.1016/s0962-8924(00)01852-3
Hoppe JB, Salbego CG, Cimarosti H. SUMOylation: Novel Neuroprotective Approach for Alzheimer’s Disease? Aging Dis. 2015;6(5):322-330. doi:10.14336/AD.2014.1205
Gerakis Y, Quintero M, Li H, Hetz C. The UFMylation System in Proteostasis and Beyond. Trends Cell Biol. 2019;29(12):974-986. doi:10.1016/j.tcb.2019.09.005
Witting KF, Mulder MPC. Highly Specialized Ubiquitin-Like Modifications: Shedding Light into the UFM1 Enigma. Biomolecules. 2021;11(2):255. doi:10.3390/biom11020255
Bateman RJ, Barthelemy NR, Benzinger TLS, et al. CSF and blood plasma mass spectrometry measures of Aβ, tau, and NfL species and longitudinal relationship to preclinical and clinical staging of amyloid and tau aggregation and clinical stage of Alzheimer’s disease. Alzheimers Dement. 2021;17(S5):e050711. doi:10.1002/alz.050711
Barbour H, Nkwe NS, Estavoyer B, et al. An inventory of crosstalk between ubiquitination and other post-translational modifications in orchestrating cellular processes. iScience. 2023;26(5):106276. doi:10.1016/j.isci.2023.106276
Lee L, Sakurai M, Matsuzaki S, Arancio O, Fraser P. SUMO and Alzheimer’s Disease. Neuromolecular Med. 2013;15(4):10.1007/s12017-013-8257-7. doi:10.1007/s12017-013-8257-7
Mandel N, Agarwal N. Role of SUMOylation in Neurodegenerative Diseases. Cells. 2022;11(21):3395. doi:10.3390/cells11213395
Kulczynska-Przybik A, Dulewicz M, Mroczko P, et al. The assessment of ubiquitin C-terminal hydrolase-1 (UCH-L1) in patients with Alzheimer’s disease. Alzheimers Dement. 2022;18(S5):e062156. doi:10.1002/alz.062156
Abi Habib J, Lesenfants J, Vigneron N, Van den Eynde BJ. Functional Differences between Proteasome Subtypes. Cells. 2022;11(3):421. doi:10.3390/cells11030421
Fricke B, Heink S, Steffen J, Kloetzel PM, Krüger E. The proteasome maturation protein POMP facilitates major steps of 20S proteasome formation at the endoplasmic reticulum. EMBO Rep. 2007;8(12):1170-1175. doi:10.1038/sj.embor.7401091
Ferrington DA, Gregerson DS. Immunoproteasomes: Structure, Function, and Antigen Presentation. Prog Mol Biol Transl Sci. 2012;109:75-112. doi:10.1016/B978-0-12-397863-9.00003-1
Zhang X, Wang Y. Cell cycle regulation of VCIP135 deubiquitinase activity and function in p97/p47-mediated Golgi reassembly. Mol Biol Cell. 2015;26(12):2242-2251. doi:10.1091/mbc.E15-01-0041
Mevissen TET, Hospenthal MK, Geurink PP, et al. OTU Deubiquitinases Reveal Mechanisms of Linkage Specificity and Enable Ubiquitin Chain Restriction Analysis. Cell. 2013;154(1):169-184. doi:10.1016/j.cell.2013.05.046
Huang J, Zhou Q, Gao M, et al. Tandem Deubiquitination and Acetylation of SPRTN Promotes DNA-Protein Crosslink Repair and Protects against Aging. Mol Cell. 2020;79(5):824-835.e5. doi:10.1016/j.molcel.2020.06.027
Tsai YC, Kotiya A, Kiris E, et al. Deubiquitinating enzyme VCIP135 dictates the duration of botulinum neurotoxin type A intoxication. Proc Natl Acad Sci. 2017;114(26):E5158-E5166. doi:10.1073/pnas.1621076114
Türker F, Bharadwaj RA, Kleinman JE, et al. Orthogonal approaches required to measure proteasome composition and activity in mammalian brain tissue. J Biol Chem. 2023;299(6):104811. doi:10.1016/j.jbc.2023.104811
Bonet-Costa V, Pomatto LCD, Davies KJA. The Proteasome and Oxidative Stress in Alzheimer’s Disease. Antioxid Redox Signal. 2016;25(16):886-901. doi:10.1089/ars.2016.6802
McDade E, Wang G, Gordon BA, et al. Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease. Neurology. 2018;91(14):e1295-e1306. doi:10.1212/WNL.0000000000006277
Ishikawa C, Barreyro L, Sampson A, et al. UBE2N Is a Druggable Target and an Essential Ubiquitin-Conjugating Enzyme in Myeloid Malignancies. Blood. 2022;140(Supplement 1):5830. doi:10.1182/blood-2022-167336
Wang D, Zou Y, Huang X, et al. The role of SMURFs in non-cancerous diseases. FASEB J. 2023;37(8):e23110. doi:10.1096/fj.202300598R
Currais A, Fischer W, Maher P, Schubert D. Intraneuronal protein aggregation as a trigger for inflammation and neurodegeneration in the aging brain. FASEB J. 2017;31(1):5-10. doi:10.1096/fj.201601184

Tables 1 to 3 are available in the Supplementary Files section.

Conflict of Interest:

RJB is the Director of the DIAN-TU and Principal Investigator of DIAN and the DIAN-TU-001 trial. Unrelated to this study, for the DIAN-TU, he receives research support from the NIA, Eli Lilly and Company, F. Hoffman-La Roche, Ltd., Eisai, Alzheimer’s Association, GHR Foundation, Anonymous Organization, DIAN-TU Pharma Consortium (Active Members: Biogen, Eisai, Eli Lilly and Company, Janssen, F. Hoffmann-La Roche, Ltd./Genentech).

JH is a paid consultant/advisor for Parabon Nanolabs, Roche, Prothena, and AlzPath. GYRH reports no competing interests directly relevant to this work. He has received grants or contracts from CIHR, NIA/NIH, has been a clinical trials investigator supported by Biogen, Cassava, and Lilly, has participated in expert advisory committee supported by Biogen, Roche, and NovoNordisk, and is the current president of C5R (Consortium of Canadian Centres for Clinical Cognitive Research).

EMM receives Grant Funding from NIA; Institutional funding from Eli Lilly, Hoffmann-La Roche, Eisai. He is a DSMB member (paid directly) for Alector; Eli Lilly; a Scientific Advisory Board Member (paid directly to me) for Alzamend, Fondation Alzheimer. He acts as a Consultant/Advisor for Sage Therapeutics, Eli Lilly, Sanofi, AstraZeneca,Hoffmann La-Roche.

CC has received research support from GSK and EISAI. The funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. CC is a member of the advisory board of Circular Genomics and owns stocks in these companies. DP is an employee of GlaxoSmithKline (GSK) and holds stock in GSK. CX is supported by National Institute on Aging (NIA) grants R01 AG067505 and R01 AG053550. All other authors have nothing to disclose.

JCM is the Friedman Distinguished Professor of Neurology, Associate Director, Knight ADRC; Associate Director of DIAN and Founding Principal Investigator of DIAN. He is funded by NIH grants # P30 AG066444; P01AG003991; P01AG026276; and U19 AG024904. Neither he nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company.

TLSB has investigator-initiated research funding from the NIH, the Alzheimer’s Association, the Barnes-Jewish Hospital Foundation and Avid Radiopharmaceuticals. Dr. Benzinger participates as a site investigator in clinical trials sponsored by Avid Radiopharmaceuticals, Eli Lilly and Company, Biogen, Eisai, Jaansen, and F. Hoffmann-La Roche, Ltd. She also serves as an unpaid consultant to Eisai and Siemensand is on the Speaker’s Bureau for Biogen.

AER reports no competing interests. He receives research support for this work from the National Institute on Aging (R01AG053267, U19AG032438)

TI reports no competing interests. He received research support for this work from AMED (JP23dk0207066 and JP23dk0207049). GSD reports no competing interests directly relevant to this work. His research is supported by NIH (K23AG064029, U01AG057195, U01NS120901, U19AG032438). He serves as a consultant for Parabon Nanolabs Inc and as a Topic Editor (Dementia) for DynaMed (EBSCO). He is the co-Project PI for a clinical trial in anti-NMDAR encephalitis, which receives support from Amgen Pharmaceuticals, and a consultant for Arialys Therapeutics. He has developed educational materials for PeerView Media, Inc, and Continuing Education Inc. He owns stock in ANI pharmaceuticals. Dr. Day’s institution has received support from Eli Lilly for development and participation in an educational event promoting early diagnosis of symptomatic Alzheimer disease, and in-kind contributions of radiotracer precursors for tau-PET neuroimaging in studies of memory and aging (via Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly). RJP is Neuropathology Core Leader for the DIAN observational study and the DIAN Trials Unit. He receives research support for this work from the National Institute on Aging (U19 AG032438, U19AG032438-09S1, R01AG068319). His laboratory receives cost recovery funding from Biogen for tissue procurement and processing services related to ALS clinical trials. Neither he nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company. FL has grants not related to this paper from NIH, DIAN, Enroll-HD and BIOGEN.

SBB receives support from the National Institute on Aging (NIA) and the Michael J Fox Foundation. All other authors have nothing to disclose. Acknowledgements/Funds and Supports Our heartfelt appreciation goes to the participants and their families for their invaluable contribution to this study. We also extend our gratitude to the staff and institutions that played a pivotal role in this research. Funding for this work was provided by the National Institutes of Health through grants R01AG044546, RF1AG053303, RF1AG058501, U01AG058922, and RF1AG074007, with special acknowledgment to CC and YJS for their efforts. Additional support came from the Chan Zuckerberg Initiative (CZI), the Alzheimer’s Association through the Zenith Fellows Award (ZEN-22-848604, bestowed upon CC), and a generous donation from an anonymous foundation. This work was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). Research reported in this publication was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the NeuroGenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine. DIAN Contributions and Data Handling: This project's data collection and dissemination were supported by The Dominantly Inherited Alzheimer Network (DIAN, U19AG032438), funded by the National Institute on Aging (NIA), along with contributions from the Alzheimer’s Association (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), and other international entities. These include partial funding from Japan's Agency for Medical Research and Development (AMED):JP23dk0207066 and JP23dk0207049), and support from the Korea Dementia Research Project through the Korea Dementia Research Center (KDRC), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (HU21C0066), the Spanish Institute of Health Carlos III (ISCIII), the Canadian Institutes of Health Research (CIHR), the Canadian Consortium of Neurodegeneration and Aging, the Brain Canada Foundation, and the Fonds de Recherche du Québec - Santé. The DIAN Study investigators have vetted this manuscript for scientific accuracy and consistency with past publications. We are deeply grateful to our participants and their families for their generosity, as well as to the dedicated DIAN research and support staff across all sites. DIAN Data Accessibility: Due to the rarity of dominantly inherited Alzheimer's disease, individual-level data from DIAN cannot be shared publicly, as it would compromise participant anonymity. This limitation has been validated by the Institutional Review Board (IRB) and confirmed with the NIH. Nevertheless, this data remains accessible for qualified researchers upon request. Requests can be submitted through the following link: DIAN Biospecimen Request Form. DIAN Consortium List: Randall J. Bateman, James M. Noble, Gregory S. Day, Neill R. Graff-Radford, Jonathan Vöglein, Ricardo Allegri, Patricio Chrem Mendez, Ezequiel Surace, Sarah B. Berman, Snezana Ikonomovic, Neelesh Nadkarni, Francisco Lopera, Laura Ramirez, David Aguillon, Yudy Leon, Claudia Ramos, Diana Alzate, Ana Baena, Natalia Londono, Sonia Moreno, Mathias Jucker, Christoph Laske, Elke Kuder-Buletta, Susanne Graber-Sultan, Oliver Preische, Anna Hofmann, Takeshi Ikeuchi, Kensaku Kasuga, Yoshiki Niimi, Kenji Ishii, Michio Senda, Raquel Sanchez-Valle, Pedro Rosa-Neto, Nick Fox, Dave Cash, Jae-Hong Lee, Jee Hoon Roh, Meghan Riddle, William Menard, Courtney Bodge, Mustafa Surti, Leonel Tadao Takada, Martin Farlow, Jasmeer P. Chhatwal, V. J. Sanchez-Gonzalez, Maribel Orozco-Barajas, Alison Goate, Alan Renton, Bianca Esposito, Celeste M. Karch, Jacob Marsh, Carlos Cruchaga, Victoria Fernandez, Brian A. Gordon, Anne M. Fagan, Gina Jerome, Elizabeth Herries, Jorge Llibre-Guerra, Allan I. Levey, Erik C. B. Johnson, Nicholas T. Seyfried, Peter R. Schofield, William Brooks, Jacob Bechara, Eric McDade, Jason Hassenstab, Richard J. Perrin, Erin Franklin, Tammie L. S. Benzinger, Allison Chen, Charles Chen, Shaney Flores, Nelly Friedrichsen, Nancy Hantler, Russ Hornbeck, Steve Jarman, Sarah Keefe, Deborah Koudelis, Parinaz Massoumzadeh, Austin McCullough, Nicole McKay, Joyce Nicklaus, Christine Pulizos, Qing Wang, Sheetal Mishall, Edita Sabaredzovic, Emily Deng, Madison Candela, Hunter Smith, Diana Hobbs, Jalen Scott, Johannes Levin, Chengjie Xiong, Peter Wang, Xiong Xu, Yan Li, Emily Gremminger, Yinjiao Ma, Ryan Bui, Ruijin Lu, Ralph Martins, Ana Luisa Sosa Ortiz, Alisha Daniels, Laura Courtney, Hiroshi Mori, Charlene Supnet-Bell, Jinbin Xu, John Ringman.

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Ubiquitin-Proteasome System in the Different Stages of Dominantly Inherited Alzheimer’s Disease

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Abstract

Figures

1. Introduction

2. Methods

2.1 Participants

2.2 CSF Sample Collection and protein measurements by SOMAscan

2.3 Immunoassay

2.4 Clinical Assessment and DIAN Estimated Year from Symptom Onset

2.5 Imaging

2.6 Statistical analysis

3. Results

3.1 Participants Demographics

3.2 UPS Proteins Changes in CSF

3.3 Partial Spearman's Rank Correlation Analysis of UPS proteins and AD biomarkers

3.3.1 Correlation analysis with amyloid related biomarkers and amyloid PET

3.3.1.1 Correlation with Amyloid PET

3.3.2 Correlation with amyloid related biomarkers

3.3.3 Correlation with tau related biomarkers

3.3.3.1 Correlation with tau PET

3.3.2.2 Correlation with soluble CSF tau-related biomarkers

3.3.3 Correlation analysis with neurodegeneration and clinical state

3.3.4 Correlation with CSF and Serum Neurofilament Light Chain (NfL)

3.3.2.4 Correlation with Soluble TREM2

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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