CSF 14-3-3ζ is associated with tau pathology and elevated across the Alzheimer’s disease continuum

doi:10.21203/rs.3.rs-2229567/v1

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CSF 14-3-3ζ is associated with tau pathology and elevated across the Alzheimer’s disease continuum

https://doi.org/10.21203/rs.3.rs-2229567/v1

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14-3-3 is a family of conserved proteins that consist of seven isoforms which are highly expressed in the brain, and 14-3-3ζ is one of the isoforms encoded by the YWHAZ gene. Previous studies demonstrated that 14-3-3ζ is deposited in the neurofibrillary tangles of Alzheimer’s disease (AD) brains, and that 14-3-3ζ interacts with tau from the purified neurofibrillary tangles of AD brain extract. The present study examined the cerebrospinal fluid (CSF) 14-3-3ζ levels of 719 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including cognitively normal (CN) participants, patients with mild cognitive impairment (MCI) and patients with AD dementia. CSF 14-3-3ζ levels were increased in prodromal AD, and further elevated in AD dementia stage. CSF 14-3-3ζ levels were associated with CSF phosphorylated tau 181(p-tau) and plasma p-tau, which are fluid biomarkers of tau pathology, and could predict tau positive status with high accuracy (area under the receiver operating characteristic curve [AUC], 0.891). CSF 14-3-3ζ levels were also associated with cognitive measurements and neuroimaging findings both at baseline and during follow up, and high CSF 14-3-3ζ levels were associated with subsequent progression to AD dementia in patients with MCI. In conclusion, this study suggests that CSF 14-3-3ζ is a potential diagnostic and prognostic biomarker of AD that may be useful in clinical practice.

Alzheimer’s disease

14-3-3ζ

tau pathology

CSF

dementia

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia worldwide. The characteristic neuropathological features of AD include the extracellular aggregation of amyloid β (Aβ) plaques and the accumulation of phosphorylated tau protein as paired helical filaments within intraneuronal neurofibrillary tangles, as well as progressive neurodegeneration [26]. Progress has been made in identifying these neuropathological changes through cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers, which classify patients using the amyloid/tau/neurodegeneration (AT(N)) biomarker system [16]. Three core CSF biomarkers including Aβ42, phosphorylated tau (p-tau) and total tau (t-tau), are believed to reflect amyloid pathology, tau pathology and neurodegeneration in AD [1, 5, 35]. However, these core CSF biomarkers represent just a fraction of the intricate pathophysiology underlying the disease, with AD also being characterized by dysfunction of multiple biological processes, such as synaptic transmission, mitochondrial metabolism and neuroinflammation [9]. There is thus urgent need for the development of a diverse range of AD biomarkers to reflect these underlying pathological changes.

14-3-3 proteins are a family of conserved proteins that are ubiquitously expressed in various types of mammalian tissues, with the highest concentration of 14-3-3 proteins in brain tissue. There are seven known 14-3-3 isoforms, and 14-3-3ζ is one of the isoforms encoded by the gene YWHAZ, which is located on the 8th chromosome in humans [2, 10]. An earlier study reported that 14-3-3ζ is present in neurofibrillary tangles of autopsied AD brains [40], and within the purified neurofibrillary tangles from AD brain extracts, 14-3-3ζ interacts with tau protein [32]. Several studies have shown that 14-3-3ζ binds to tau in the brain and promotes tau phosphorylation, mediates tau aggregation and causes synaptic pathology [14, 22, 23, 31]. A recent study utilized integrative proteomics to explore new CSF biomarkers related to AD identified hundreds of proteins that were significantly increased or decreased in AD patients. Among those altered proteins in the CSF samples of AD patients, CSF 14-3-3ζ levels were increased [15].

The present study involved 719 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with available CSF 14-3-3ζ measurements, who were cognitively normal (CN), or clinically diagnosed with mild cognitive impairment (MCI) or AD dementia. In this study we aimed to identify whether CSF 14-3-3ζ is elevated across the AD continuum – from preclinical to prodromal and dementia stages, whether CSF 14-3-3ζ is associated with other CSF and blood biomarkers in AD, and the potential of CSF 14-3-3ζ in predicting disease progression, or predicting future development of AD in MCI patients.

ADNI database

Funded as a public-private partnership project, the Alzheimer’s disease Neuroimaging Initiative (ADNI) was launched in 2003 under the leadership of principal investigator Michael W. Weiner, MD. The primary goal of the ADNI project is to assess whether serial neuroimaging, and various clinical, biochemical, and genetic biomarkers can be combined to monitor progression of AD dementia. Data used in the preparation of this article were downloaded from the ADNI database (http://adni.loni.usc.edu/) in November 2021. This multi-centered research project was approved by regional ethical committees of all participating institutions, and all study procedures were performed in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all study participants. From the ADNI, we included 719 participants with available CSF 14-3-3ζ measurements. The study population was composed of participants with clinically diagnosed CN, MCI and AD dementia based on cognitive assessments. The inclusion and exclusion criteria, and the specific ADNI diagnostic criteria for distinguishing CN, MCI and AD participants have been described previously [29].

CSF 14-3-3ζ Measurements

CSF 14-3-3ζ levels were measured by mass spectrometry using targeted proteomics assay at the Department of Neurology, Emory University School of Medicine. Crude CSF samples (50 µl) from participants and quality control samples were reduced and alkylated by adding 5 mM TCEP, 40 mM chloroacetamide and 1% sodium deoxycholate in 100 mM triethylammonium bicarbonate buffer, then heated at 95°C for 10 min followed by a 10 min cool down at room temperature. Digest solution (Lys-C and trypsin) was added to the CSF sample and left overnight at 37°C. After digestion, isotopically labeled peptide standards for relative quantification were added to the peptide solution. The samples were desalted with 30-mg C18 HLB 96-well plates (Waters) according to the manufacturer’s protocol, and eluates were dried using a vacuum.

For mass spectrometry analysis, samples were analyzed on a TSQ Altis Triple Quadrupole mass spectrometer (Thermo Fisher Scientific) fitted with an AdvanceBio Peptide analytical column and coupled to an Agilent 1290 Infinity II liquid chromatography system. The mass spectrometer was set to collect data in positive-ion mode using single reaction monitoring acquisition, then data were uploaded into Skyline for analysis. Total area ratios were reported for relative quantification of the targeted peptide related to 14-3-3ζ protein. Details of the targeted proteomics assay of CSF 14-3-3ζ can be downloaded from the ADNI database (http://adni.loni.usc.edu/).

Cognitive Assessments

The general cognition level of participants in the cohort was evaluated by the Mini-Mental State Examination (MMSE) and the CDR Scale Sum of Boxes (CDR-SB). Co-calibration of memory function, executive function and language function composite scores were generated using modern psychometric approaches across four cohorts: Alzheimer’s Disease Neuroimaging Initiative (ADNI), Adult Changes in Thought (ACT), the Religious Orders Study and the Memory and Aging Project (ROS/MAP), and the National Alzheimer's Coordinating Center (NACC). The co-calibrated composite scores related to test results of memory function, executive function and language function are standardized on the same metric, which facilitate the composite scores in being comparable across different studies. All composite cognitive scores were downloaded from the dataset “ADSP Phenotype Harmonization Consortium (PHC)-Composite Cognitive Scores”, and the detailed methods document applicable to this dataset is available from the ADNI database (http://adni.loni.usc.edu/).

CSF And Plasma Markers Measurements

CSF samples were collected by lumbar puncture, and the method of lumbar puncture was described in the ADNI procedures manual (http://www.adni-info.org/). Levels of CSF Aβ42, CSF total tau (t-tau), and CSF phosphorylated tau 181 (p-tau) were measured at the ADNI Biomarker Core laboratory at the University of Pennsylvania Medical Center using a fully automated Elecsys cobas e 601 instrument. CSF samples were analyzed using the electrochemiluminescence immunoassays (ECLIA) Elecsys β-amyloid (1–42) CSF, phosphor-Tau (181P) CSF, and Total-Tau CSF according to the Roche Study Protocol and the kit manufacturer’s instructions as previously described [3, 12]. According to the ATN framework [16], the published cutoff values (CSF Aβ42 < 977pg/mL, CSF p-tau > 27pg/mL, CSF t-tau > 300pg/mL) were used to define amyloid pathology (A), tau pathology (T) and neurodegeneration (N) respectively [6]. CSF growth-associated protein 43 (GAP-43) levels were analyzed at the Clinical Neurochemistry Lab, University of Gothenburg, Sweden, by an in-house enzyme-linked immunosorbent assay (ELISA) method as previously described [33]. The ELISA was performed by combining mouse monoclonal GAP-43 antibody NM4 (coating antibody) (Fujirebio, Ghent, Belgium) and a polyclonal GAP-43 antibody (detector antibody) (ABB-135, Nordic Biosite, Sweden) that recognized the C-terminal of GAP-43.

Measurements of CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) were analyzed with a Meso Scale Discovery (MSD) platform-based assay as previously reported [38], at Ludwig-Maximilians-Universität München, Munich, Germany. The MSD ELISA was conducted by combining the biotinylated polyclonal goat anti-human TREM2 antibody (capture antibody) (R&D Systems) that recognized aminoacids 19–174 of human TREM2 and the monoclonal mouse anti-human TREM2 antibody (detection antibody) (Santa Cruz Biotechnology) that recognized aminoacids 1-160 of human TREM2.

Plasma Aβ42 and Aβ40 levels were analyzed at Bateman lab, Washington University School of Medicine. Anti-Aβ mid-domain antibody (HJ5.1) was used to immunoprecipitate Aβ isoforms from plasma samples using a KingFisher (Thermo) automated immunoprecipitation platform, eluted Aβ species were subsequently digested with Lys-N protease then analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) as previously described [27]. Plasma p-tau181 and neurofilament light (NfL) levels were measured at the Clinical Neurochemistry Lab, University of Gothenburg, Sweden, using the Single Molecule array (Simoa) technique. The assay of plasma p-tau181 utilized a combination of two monoclonal antibodies (Tau12 and AT270) and measured N-terminal to mid-domain forms of p-tau181 [18]. Plasma NfL levels were measured using an NfL kit on a Simoa platform [24].

Neuroimaging

Structural MRI imaging in this study was performed using 3.0 T MRI scanners. Regional volume estimates were processed using FreeSurfer version 5.1 according to the 2010 Desikan-Killany atlas [17], and the input was T1 weighted MRI images in the NiFTI format. Hippocampus and medial temporal lobe data were used in our analyses, and intracranial volume was used to adjust for head size variation.

FDG-PET data were acquired according to the standardized protocol on the USC Laboratory of Neuroimaging (LONI) website (https://adni.loni.usc.edu/), and FDG-PET image data was processed by the Helen Wills Neuroscience Institute, University of California Berkeley and Lawrence Berkeley National Laboratory. For FDG-PET assessment, a composite ROI (region of interest) was generated by averaging across the left angular gyrus, right angular gyrus, bilateral posterior cingulate gyrus, left inferior temporal gyrus, right inferior temporal gyrus relative to the pons and cerebellar vermis reference regions [20].

Statistical analysis

Differences in baseline characteristics across the diagnostic groups were compared using tests appropriate for the distribution of each variable, including ANOVA or Kruskal-Wallis tests for continuous variables and chi-square tests for categorical variables. According to the Shapiro-Wilk Test and visual inspection of the histogram of the data, the CSF 14-3-3ζ data were not normally distributed, thus the CSF 14-3-3ζ data were log transformed to produce a normally distributed dataset. The differences of the log transformed CSF 14-3-3ζ biomarker between two groups were compared by two sample t-tests, and differences across multiple groups were examined with one-way analysis of variance (ANOVA) and the Tukey post hoc test. Receiver operating characteristic (ROC) analyses were used to evaluate the diagnostic accuracy of CSF 14-3-3ζ, the specificity and sensitivity of CSF 14-3-3ζ were determined based on area under the curve (AUC) analyses, and comparisons of the AUC values between different biomarkers were performed using DeLong’s test. The plot to predict probability of tau positive status based on CSF 14-3-3ζ levels was created using marginsplot code in Stata software. The predictive ability of CSF 14-3-3ζ was also evaluated by decision curve analysis (DCA), and DCA was performed using the DCA function code in Stata software. Associations between the other biofluid markers with CSF 14-3-3ζ were tested by linear regression models with adjustment for age, sex, years of education and APOE ε4 status. Longitudinal analysis of the changes of cognition scores and neuroimaging findings over time in different CSF 14-3-3ζ level groups (stratified into low, intermediate and high levels) were conducted using linear mixed-effect models (LME). All LME models included interaction between time and group with random intercepts and slopes, and unstructured covariance structure for random effects. All LME models were adjusted for age, sex, years of education and APOE ε4 status, and models involving MRI imaging of brain structures were also adjusted for intracranial volume. Unadjusted Kaplan-Meier curves were constructed to demonstrate the differences in progression to AD among participants with MCI in CSF 14-3-3ζ low level, intermediate level, and high level groups. Cox proportional hazards models were used to assess the relative risk of progression to AD in patients with MCI, and the analyses were performed with and without adjustment for age, sex, years of education and APOE ε4 status. We assessed the proportional hazards assumption using Schoenfeld residuals. All statistical analyses were performed using Stata version 16 (College Station, TX) statistical software, and two-sided P < 0.05 was considered statistically significant.

We studied 719 participants in the cohort with available baseline CSF 14-3-3ζ measurements. The mean (SD) age of all the included participants was 72.25 (7.36) years old, the mean (SD) education years were 16.27 (2.60) years, 52.16% of the participants were men, and 45.06% had at least one APOE ε4 allele (Online Resource Table 1). Of the 719 participants included in this study, 225 were CN controls, 381 had MCI, and 113 had AD dementia, further demographic characteristics according to clinical diagnosis are shown in Table 1.

Table 1

Baseline demographic characteristics of the study population Abbreviations: CSF, cerebrospinal fluid; p-tau, phosphorylated tau 181; t-tau, total tau; FDG-PET, fluorodeoxyglucose-positron emission tomography; MEM, memory function; EXF, executive function; LAN, language function. Continuous variables are reported as means (standard deviations), and categorical variables are reported as numbers and percentages. ^**MRI imaging measurements are not adjusted by total intracranial volume.*
Variable	CN (n = 225)	MCI (n = 381)	AD (n = 113)	P-value
Age at baseline, years	73.06 (6.06)	71.17 (7.58)	74.27 (8.34)	< 0.001
Male sex, N (%)	99 (44.00%)	208 (54.59%)	68 (60.18%)	0.007
Education level, years	16.64 (2.51)	16.24 (2.62)	15.74 (2.70)	0.011
APOE ε4 status, N (%)				< 0.001
APOE ε4-/-, N (%)	161 (71.56%)	196 (51.44%)	38 (33.63%)
APOE ε4+/-, N (%)	57 (25.33%)	145 (38.06%)	50 (44.25%)
APOE ε4+/+, N (%)	7 (3.11%)	40 (10.50%)	25 (22.12%)
CSF biomarkers
Aβ42 level, pg/mL	1033.23 (377.47)	896.32 (335.18)	648.68 (259.78)	< 0.001
p-tau level, pg/mL	21.73 (9.53)	26.10 (14.10)	37.48 (16.15)	< 0.001
t-tau level, pg/mL	236.43 (93.60)	272.38 (125.99)	378.29 (153.65)	< 0.001
14-3-3ζ, total_area_ratio	0.023 (0.009)	0.026 (0.011)	0.033 (0.011)	< 0.001
Neuroimaging ^*
Hippocampus, mm³	7530.92 (881.47)	7072.44 (1120.39)	5957.63 (916.30)	< 0.001
Medial temporal lobe, mm³	20747.76 (2517.69)	20391.78 (2717.59)	17807.44 (3245.54)	< 0.001
FDG-PET composite	1.32 (0.11)	1.26 (0.14)	1.06 (0.15)	< 0.001
Cognitive score
MEM composite	0.89 (0.47)	0.27 (0.54)	-0.78 (0.33)	< 0.001
EXF composite	0.74 (0.48)	0.42 (0.54)	-0.38 (0.68)	< 0.001
LAN composite	0.79 (0.48)	0.46 (0.49)	-0.19 (0.56)	< 0.001

Figure 1a illustrates the CSF 14-3-3ζ measured in the CN, MCI and AD dementia groups. CSF 14-3-3ζ levels were increased in the AD dementia group compared with the other clinical diagnostic groups (P < 0.001). CSF 14-3-3ζ concentrations in participants with MCI were higher than CN controls (P = 0.02). We also compared CSF 14-3-3ζ levels across the Alzheimer’s disease clinical continuum, the lowest concentrations were found in the CN controls Aβ pathology negative group (CN-), increased concentrations were found in MCI Aβ pathology positive groups (MCI+), and the highest concentrations were found in the Aβ pathology positive AD dementia group (AD+) (Fig. 1b). These results indicate that CSF 14-3-3ζ levels gradually increase across the Alzheimer’s disease clinical continuum. When comparing the groups’ CSF 14-3-3ζ levels according to the participants’ CSF A/T/N profiles, the A + T + N + group had the highest CSF 14-3-3ζ levels above all other groups. CSF 14-3-3ζ levels were higher in the A + T + N- group than the A-T-N- and A + T-N- groups, and there were no statistically significant differences between the A-T-N- and A + T-N- groups (Fig. 1c). Groups were further stratified according to participant characteristics including clinical diagnosis and CSF tau status, and the tau positive clinical diagnostic groups had higher CSF 14-3-3ζ levels then their counterpart tau negative groups (P < 0.001) (Fig. 1d).

We evaluated the association of CSF 14-3-3ζ levels with tau pathology as measured by CSF p-tau and plasma p-tau, and CSF 14-3-3ζ levels were positively associated with CSF p-tau (r = 0.741, P < 0.001) (Fig. 2a) and plasma p-tau (r = 0.293, P < 0.001) (Fig. 2b). The participants were further stratified by clinical diagnostic group, and CSF 14-3-3ζ levels were positively associated with CSF p-tau in all diagnostic groups (Online Resource Fig. 1). We also assessed the association of CSF 14-3-3ζ levels with fluid biomarkers of neurodegeneration, and CSF 14-3-3ζ levels were positively associated with CSF t-tau (r = 0.755, P < 0.001) (Fig. 2c) and plasma NfL (r = 0.280, P < 0.001) (Fig. 2d). CSF 14-3-3ζ levels were also positively associated with the synaptic biomarker CSF GAP-43 (r = 0.609, P < 0.001) (Online Resource Fig. 2a) and neuroinflammatory biomarker CSF sTREM-2 (r = 0.507, P < 0.001) (Online Resource Fig. 2b). There were no association of CSF 14-3-3ζ levels with CSF Aβ42, or plasma Aβ42/40 ratio (Table 2). The diagnostic performances of CSF 14-3-3ζ, CSF p-tau, plasma p-tau, CSF t-tau and plasma NfL in distinguishing CN- from AD + participants were then compared using DeLong’s test (Online Resource Fig. 3). The accuracy of CSF 14-3-3ζ (AUC = 0.814) was significantly lower than CSF p-tau (AUC = 0.878; DeLong P = 0.003), but not significantly different from the accuracy of CSF t-tau (AUC = 0.848; DeLong P = 0.111) and plasma p-tau (AUC = 0.822; DeLong P = 0.834), and significantly higher than plasma NfL (AUC = 0.726; DeLong P = 0.036).

Table 2

Associations between CSF 14-3-3ζ and other fluid biomarkers
	CSF 14-3-3ζ
Biomarker	β-coefficient		P-value		P_adj value
CSF Aβ42	-0.055	0.223		0.260
CSF p-tau	0.690	< 0.001		< 0.001
CSF t-tau	0.704	< 0.001		< 0.001
CSF GAP-43	0.484	< 0.001		< 0.001
CSF sTREM2	0.443	< 0.001		< 0.001
Plasma Aβ42/40	-0.049	0.442		0.442
Plasma p-tau	0.119	0.001		0.002
Plasma NfL	0.111	0.005		0.007
Linear regression models were used to examine associations between CSF 14-3-3ζ and other fluid biomarkers (all fluid biomarkers were standardized to z scores), adjusted for age, sex, education years, and APOE ε4 genotype. P_adj represents P-values that were corrected for multiple comparisons with the Benjamini-Hochberg method. Abbreviations: CSF, cerebrospinal fluid; p-tau, phosphorylated tau 181; t-tau, total tau; NfL, neurofilament light; GAP-43, growth-associated protein 43; sTREM2, soluble triggering receptor expressed on myeloid cells 2.

As illustrated in Fig. 3a, CSF 14-3-3ζ levels were higher in tau positive participants compared with tau negative participants (P < 0.0001), tau statuses were defined by CSF p-tau. We next investigated how CSF 14-3-3ζ levels discriminate tau status by using ROC analysis, and CSF 14-3-3ζ as a biomarker could accurately discriminate tau positive participants from tau negative participants, with an area under the curve (AUC) of 0.891 (Fig. 3b). A plot was constructed to estimate the probability of positive tau status based on a participant’s CSF 14-3-3ζ levels, which indicated that CSF 14-3-3ζ levels are a strong predictor for tau status (Fig. 3c). Decision curve analysis showed that CSF 14-3-3ζ as a biomarker is better at predicting positive tau status then either the all-participants tau positive scenario or the no-participants tau positive scenario. The line corresponding to the logistic regression model included adjustment for CSF 14-3-3ζ, age, sex, education years and APOE ε4 status, and had higher net benefit than CSF 14-3-3ζ alone (Fig. 3d).

We further utilized the CSF biomarker based AT classification, which classified the participants into three groups: Aβ- and tau- (A-T-), Aβ + but tau- (A + T-) and Aβ + and tau+ (A + T+). CSF 14-3-3ζ levels were significantly higher in the A + T + group than the other two groups (P < 0.001), and there were no statistically significant differences between the A-T- and A + T- groups (Fig. 4a). We conducted ROC analysis to determine the ability of CSF 14-3-3ζ to discriminate between different AT groups. CSF 14-3-3ζ distinguished the A-T- group from the A + T + group (AUC = 0.921, Fig. 4c), but not from the A + T- group (AUC = 0.527, Fig. 4b). CSF 14-3-3ζ also distinguished the A + T- group from the A + T + group (AUC = 0.890, Fig. 4d).

Based on tertiles of CSF 14-3-3ζ levels distribution, we subdivided the participants into low level, intermediate level and high level groups. Figure 5 shows associations of baseline and longitudinal cognition and neuroimaging findings with CSF 14-3-3ζ levels (Online Resource Table 2–7 listed coefficients and P-values). At baseline, high and intermediate levels of CSF 14-3-3ζ were associated with worse memory and language function. High levels of CSF 14-3-3ζ were associated with reductions in executive function, lower FDG-PET values, and smaller hippocampus and medial temporal lobe volumes. Longitudinally, high and intermediate CSF 14-3-3ζ levels were associated with more rapid change of memory, executive and language function, accelerated reduction in hippocampus volume and FDG-PET values. High CSF 14-3-3ζ levels were associated with a faster reduction in medial temporal lobe volume.

Figure 6 presents the Kaplan-Meier curve with the log-rank test for progression from MCI to AD dementia across three groups (low level, intermediate level and high level CSF 14-3-3ζ). The survival curve demonstrates the differences of progression to AD dementia among the three groups of MCI participants (log-rank P < 0.0001). Compared to the low level group, intermediate and high level CSF 14-3-3ζ groups demonstrated a faster decline of the proportion of participants without AD progression (Fig. 6a). In the adjusted Cox proportional-hazards model with the low level CSF 14-3-3ζ group as a reference, MCI participants with high level CSF 14-3-3ζ presented with increased progression risks to AD dementia (HR = 3.20 [1.77–5.78], P < 0.001) (Fig. 6b and Online Resource Table 8).

In this study we demonstrated that CSF 14-3-3ζ (1) was increased across the AD clinical continuum, from prodromal to dementia stages, (2) was correlated to fluid biomarkers of tau pathology (3) had good diagnostic accuracy for distinguishing AD dementia from CN (4) was associated with cognitive deficits and neuroimaging findings both at baseline and during follow up, (5) predicted longitudinal conversion from MCI to AD dementia with good precision.

This study measured CSF 14-3-3ζ levels by targeted proteomics assay. Targeted proteomics technique is a powerful mass spectrometry-based protein quantification tool for biomedical and clinical research, which is far more accurate compared to traditional protein quantification methods such as western blot and does not require any antibodies. Targeted proteomics can detect and quantify targeted proteins of interest with high sensitivity and accuracy. In contrast with integrative proteomics, in which thousands of proteins can be screened and quantified without any previous knowledge about their expression, targeted proteomics is a method to test specific hypotheses for a subset of proteins of interest [7]. A recent study using integrative proteomics to explore new CSF biomarkers in AD identified hundreds of altered proteins. Among those altered proteins in CSF samples, CSF 14-3-3ζ levels were increased in AD patients [15]. The present study further focused on CSF 14-3-3ζ, and quantified CSF 14-3-3ζ levels using targeted proteomics by mass spectrometry.

14-3-3 proteins are a family of highly conserved proteins, which comprise 1% of total soluble brain protein. There are seven known 14-3-3 isoforms: beta (β), gamma (γ), epsilon (ε), zeta (ζ), eta (η), theta (θ), and sigma (σ) [2]. AD is a neurodegenerative disorder, and the neuropathology of AD is characterized by amyloid plaques and neurofibrillary tangles. A postmortem study first identified that 14-3-3 proteins are components of neurofibrillary tangles in hippocampal sections of AD brains [21]. Later, another study demonstrated that the 14-3-3ζ isoform is the major isoform deposited in neurofibrillary tangles of AD brains [40]. Solie et al examined gene expression in AD brain samples, and found that the expression of 14-3-3ζ was upregulated significantly among the 236 genes analyzed. In particular, 14-3-3ζ expression was strongly up-regulated in brain areas affected by neurofibrillary tangles [36]. In accordance with these neuropathology findings in AD, our study demonstrated that CSF 14-3-3ζ was elevated across the AD clinical continuum, from prodromal to dementia stages, with the highest CSF 14-3-3ζ levels observed in the AD dementia group. Since CSF 14-3-3ζ levels increased across the AD clinical continuum, the elevation of CSF 14-3-3ζ levels might be a result of dying neurons, or the presence of neurofibrillary tangles in the AD brain.

14-3-3 proteins are involved in a wide range of physiological processes via interacting with diverse signaling proteins, including kinases, phosphatases and transmembrane receptors. In the central nervous system, 14-3-3 proteins play important roles in regulating intracellular signaling, cell division and differentiation, apoptosis, and neuronal development [8]. A previous study reported that 14-3-3ζ proteins colocalized with neurofibrillary tangles of autopsied AD brains [40], and that the main component of neurofibrillary tangles are aggregates of hyperphosphorylated tau protein. Several studies implicated that 14-3-3ζ protein interacted with tau from the purified neurofibrillary tangles of AD brain extract, and that 14-3-3ζ interacted with both phosphorylated and nonphosphorylated tau proteins [14, 32]. Since hyperphosphorylated tau protein and 14-3-3ζ are components of neurofibrillary tangles, there might also be a correlation between 14-3-3ζ released in CSF and the extent of neurofibrillary tangle pathology in the brain. Our study demonstrated that CSF 14-3-3ζ levels were correlated to CSF p-tau and plasma p-tau levels. Since CSF p-tau and plasma p-tau are believed to reflect tau pathology in AD [16, 18], our findings suggest that CSF 14-3-3ζ levels were associated with tau pathology. Furthermore, CSF 14-3-3ζ was particularly pronounced in tau pathology positive participants compared to negative participants, and CSF 14-3-3ζ could efficiently distinguish tau pathology status (AUC = 0.891). In this study, A + T- indicated a status of amyloid pathological change, and A + T + represented an advanced pathological stage with both amyloid and tau pathologies present. CSF 14-3-3ζ could effectively discriminate between A + T- and A + T + stages (AUC = 0.890), indicating that CSF 14-3-3ζ as a biomarker was associated with tau pathology and had good performance in identifying an advanced AD pathological stage. In this study CSF 14-3-3ζ was also correlated with CSF t-tau and plasma NfL, which are fluid biomarkers of neurodegeneration [4].

Several studies have shown that 14-3-3ζ protein bind to tau in the brain and promote tau phosphorylation, mediate tau aggregation and cause synaptic pathology [14, 23, 31, 41]. In this study we observed that CSF 14-3-3ζ levels were associated with CSF GAP-43 and CSF sTREM2. GAP-43 is a protein located on the cytoplasmic side of the presynaptic membrane [11], and studies have indicated that CSF GAP-43 is a biomarker of synaptic dysfunction in AD [30, 33, 39]. The correlation between CSF 14-3-3ζ and CSF GAP-43 may suggest that increased CSF 14-3-3ζ concentration in AD is linked with the degeneration of axons or presynaptic terminals. In the central nervous system, TREM2 is an immune receptor located in the plasma membrane of microglia, microglial cell surface TREM2 can be shed by proteases, which then releases soluble TREM2 (sTREM2) into biological fluid such as the CSF and blood [19, 34]. sTREM2 in CSF can be considered as a microglial and neuroinflammatory biomarker of AD [37]. Together with tau pathology biomarkers, this study demonstrated that CSF 14-3-3ζ was correlated with the CSF microglial biomarker sTREM2. Microglial activation progresses along with the deposition of tau aggregates across the different Braak stages of AD [28], and the correlation between CSF 14-3-3ζ and CSF sTREM2 suggests the combination of tau deposition, microglial activation and 14-3-3ζ pathological changes in AD.

Another finding of this study was that CSF 14-3-3ζ levels were associated with several cognition tests and neuroimaging measures at baseline and over time. In terms of cognition tests, CSF 14-3-3ζ levels were significantly associated with declined memory, language, and executive functions longitudinally. For neuroimaging measures, longitudinal associations between CSF 14-3-3ζ levels and decreased FDG metabolism, hippocampus and medial temporal volumes were observed. An increase in CSF 14-3-3ζ was associated with worsening cognition and brain atrophy over time, suggests that baseline CSF 14-3-3ζ levels could be used as a biomarker to predict cognition and neuroimaging progression in AD patients.

Prediction of progression to AD in individuals with MCI is a key issue for the diagnostic work-up and management of patients with MCI, and for the design of clinical trials in incipient AD patients. This study indicates that high level CSF 14-3-3ζ is associated with increased risks of progression from MCI to AD, with adjusted hazard ratios around 3.2 fold in comparison to the low level CSF 14-3-3ζ group. This suggests that high CSF 14-3-3ζ levels are a biomarker for risk of AD development in patients with MCI. With a previous study reporting that CSF concentrations of Aβ42, t-tau and p-tau are associated with future progression to AD in patients with MCI [13], our present study indicates that CSF 14-3-3ζ levels may also have a similar utility in predicting future AD dementia development.

The present study has several limitations. First, this study lacked inclusion of participants with other neurodegenerative diseases besides AD, which limits our ability to investigate whether CSF 14-3-3ζ can distinguish AD from other neurodegenerative disorders, and whether CSF 14-3-3ζ is associated with the neuropathological biomarkers of those disorders, such as α-synuclein in Parkinson’s disease and TDP-43 in amyotrophic lateral sclerosis. Second, another limitation is that this study lack tau-PET measurements, in this study CSF p-tau and plasma p-tau were used as proxies in the assessment of tau pathology, however a previous study demonstrated that tau-PET imaging was associated with tau pathology more closely compared to fluid biomarkers [25]. Third, our study lacked longitudinal CSF 14-3-3ζ data, and analyzing longitudinal changes of CSF 14-3-3ζ levels could better delineate the characteristics of CSF 14-3-3ζ in AD.

In conclusion, our results suggest that CSF 14-3-3ζ is a novel biomarker for AD. CSF 14-3-3ζ levels increased across the AD clinical continuum, and were associated with biomarkers of tau pathology, as well as biomarkers of neurodegeneration, synaptic dysfunction, and neuroinflammation in AD. As a biomarker, CSF 14-3-3ζ could be used to predict future development of AD in MCI patients and may also be useful in predicting longitudinal disease progression and therapeutic response in AD clinical trials.

Acknowledgements

The authors express their appreciation to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Data utilized in the preparation of this manuscript were obtained from the ADNI database (http://adni.loni.usc.edu/), data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol- Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Author contributions

QQ and HA conceived this study, QQ analyzed the data and wrote the manuscript. HA, LS, TT and WW contributed to revision of this manuscript for intellectual content.

Conflict of interest

The authors declare that there are no conflicts of interest.

Ashton NJ, Scholl M, Heurling K, Gkanatsiou E, Portelius E, Hoglund K, Brinkmalm G, Hye A, Blennow K, Zetterberg H (2018) Update on biomarkers for amyloid pathology in Alzheimer's disease. Biomark Med 12:799–812. Doi 10.2217/bmm-2017-0433
Berg D, Holzmann C, Riess O (2003) 14-3-3 proteins in the nervous system. Nat Rev Neurosci 4:752–762. Doi 10.1038/nrn1197
Bittner T, Zetterberg H, Teunissen CE, Ostlund RE Jr, Militello M, Andreasson U, Hubeek I, Gibson D, Chu DC, Eichenlaub Uet al et al (2016) Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of beta-amyloid (1–42) in human cerebrospinal fluid. Alzheimers Dement 12:517–526. Doi 10.1016/j.jalz.2015.09.009
Blennow K (2021) Phenotyping Alzheimer's disease with blood tests. Science 373:626–628. Doi 10.1126/science.abi5208
Blennow K, Hampel H, Weiner M, Zetterberg H (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6:131–144. Doi 10.1038/nrneurol.2010.4
Blennow K, Shaw LM, Stomrud E, Mattsson N, Toledo JB, Buck K, Wahl S, Eichenlaub U, Lifke V, Simon Met al et al (2019) Predicting clinical decline and conversion to Alzheimer's disease or dementia using novel Elecsys Abeta(1–42), pTau and tTau CSF immunoassays. Sci Rep 9:19024. Doi 10.1038/s41598-019-54204-z
Borras E, Sabido E (2017) What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics 17. Doi 10.1002/pmic.201700180
Cornell B, Toyo-Oka K (2017) 14-3-3 Proteins in Brain Development: Neurogenesis, Neuronal Migration and Neuromorphogenesis. Front Mol Neurosci 10:318. Doi 10.3389/fnmol.2017.00318
De Strooper B, Karran E (2016) The Cellular Phase of Alzheimer's Disease. Cell 164:603–615. Doi 10.1016/j.cell.2015.12.056
Foote M, Zhou Y (2012) 14-3-3 proteins in neurological disorders. Int J Biochem Mol Biol 3:152–164
Gorgels TG, Van Lookeren Campagne M, Oestreicher AB, Gribnau AA, Gispen WH (1989) B-50/GAP43 is localized at the cytoplasmic side of the plasma membrane in developing and adult rat pyramidal tract. J Neurosci 9:3861–3869
Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, Lifke V, Corradini V, Eichenlaub U, Batrla R al (2018) CSF biomarkers of Alzheimer's disease concord with amyloid-beta PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement 14:1470–1481. Doi 10.1016/j.jalz.2018.01.010
Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L (2006) Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5:228–234. Doi 10.1016/S1474-4422(06)70355-6
Hashiguchi M, Sobue K, Paudel HK (2000) 14-3-3zeta is an effector of tau protein phosphorylation. J Biol Chem 275:25247–25254. Doi 10.1074/jbc.M003738200
Higginbotham L, Ping L, Dammer EB, Duong DM, Zhou M, Gearing M, Hurst C, Glass JD, Factor SA, Johnson ECBet al (2020) Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer's disease. Sci Adv 6. Doi 10.1126/sciadv.aaz9360
Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W, Jessen F, Karlawish J et al (2018) NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement 14: 535–562 Doi 10.1016/j.jalz.2018.02.018
Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Ward Cet al et al (2008) The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27:685–691. Doi 10.1002/jmri.21049
Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL, Chamoun M, Savard M, Kang MS, Therriault Jet al et al (2020) Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol 19:422–433. Doi 10.1016/S1474-4422(20)30071-5
Kleinberger G, Yamanishi Y, Suarez-Calvet M, Czirr E, Lohmann E, Cuyvers E, Struyfs H, Pettkus N, Wenninger-Weinzierl A, Mazaheri F et al (2014) TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci Transl Med 6: 243ra286 Doi 10.1126/scitranslmed.3009093
Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, Weiner MW, Jagust WJ, Alzheimer's Disease Neuroimaging I (2011) Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 32:1207–1218. Doi 10.1016/j.neurobiolaging.2009.07.002
Layfield R, Fergusson J, Aitken A, Lowe J, Landon M, Mayer RJ (1996) Neurofibrillary tangles of Alzheimer's disease brains contain 14-3-3 proteins. Neurosci Lett 209:57–60 Doi 10.1016/0304–3940(96)12598-2
Li T, Paudel HK (2007) 14-3-3zeta facilitates GSK3beta-catalyzed tau phosphorylation in HEK-293 cells by a mechanism that requires phosphorylation of GSK3beta on Ser9. Neurosci Lett 414:203–208. Doi 10.1016/j.neulet.2006.11.073
Li T, Paudel HK (2016) 14-3-3zeta Mediates Tau Aggregation in Human Neuroblastoma M17 Cells. PLoS ONE 11:e0160635. Doi 10.1371/journal.pone.0160635
Mattsson N, Andreasson U, Zetterberg H, Blennow K, Alzheimer's Disease Neuroimaging I (2017) Association of Plasma Neurofilament Light With Neurodegeneration in Patients With Alzheimer Disease. JAMA Neurol 74:557–566. Doi 10.1001/jamaneurol.2016.6117
Mattsson N, Scholl M, Strandberg O, Smith R, Palmqvist S, Insel PS, Hagerstrom D, Ohlsson T, Zetterberg H, Jogi J et al (2017) (18)F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer's disease. EMBO Mol Med 9: 1212–1223 Doi 10.15252/emmm.201707809
Montine TJ, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SSet al et al (2012) National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease: a practical approach. Acta Neuropathol 123:1–11. Doi 10.1007/s00401-011-0910-3
Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, Sullivan M, Paumier K, Holtzman DM, Morris JC et al (2017) Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement 13: 841–849 Doi 10.1016/j.jalz.2017.06.2266
Pascoal TA, Benedet AL, Ashton NJ, Kang MS, Therriault J, Chamoun M, Savard M, Lussier FZ, Tissot C, Karikari TKet al et al (2021) Microglial activation and tau propagate jointly across Braak stages. Nat Med 27:1592–1599. Doi 10.1038/s41591-021-01456-w
Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM Toga AW (2010) Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization.Neurology74:201–209Doi 10.1212/WNL.0b013e3181cb3e25
Qiang Q, Skudder-Hill L, Toyota T, Wei W, Adachi H (2022) CSF GAP-43 as a biomarker of synaptic dysfunction is associated with tau pathology in Alzheimer's disease. Sci Rep 12:17392. Doi 10.1038/s41598-022-20324-2
Qureshi HY, Han D, MacDonald R, Paudel HK (2013) Overexpression of 14-3-3z promotes tau phosphorylation at Ser262 and accelerates proteosomal degradation of synaptophysin in rat primary hippocampal neurons. PLoS ONE 8:e84615. Doi 10.1371/journal.pone.0084615
Qureshi HY, Li T, MacDonald R, Cho CM, Leclerc N, Paudel HK (2013) Interaction of 14-3-3zeta with microtubule-associated protein tau within Alzheimer's disease neurofibrillary tangles. Biochemistry 52:6445–6455. Doi 10.1021/bi400442d
Sandelius A, Portelius E, Kallen A, Zetterberg H, Rot U, Olsson B, Toledo JB, Shaw LM, Lee VMY, Irwin DJet al (2019) Elevated CSF GAP-43 is Alzheimer's disease specific and associated with tau and amyloid pathology. Alzheimers Dement 15:55–64. Doi 10.1016/j.jalz.2018.08.006
Schlepckow K, Kleinberger G, Fukumori A, Feederle R, Lichtenthaler SF, Steiner H, Haass C (2017) An Alzheimer-associated TREM2 variant occurs at the ADAM cleavage site and affects shedding and phagocytic function. EMBO Mol Med 9:1356–1365. Doi 10.15252/emmm.201707672
Scholl M, Maass A, Mattsson N, Ashton NJ, Blennow K, Zetterberg H, Jagust W (2019) Biomarkers for tau pathology. Mol Cell Neurosci 97:18–33. Doi 10.1016/j.mcn.2018.12.001
Soulie C, Nicole A, Delacourte A, Ceballos-Picot I (2004) Examination of stress-related genes in human temporal versus occipital cortex in the course of neurodegeneration: involvement of 14-3-3 zeta in this dynamic process. Neurosci Lett 365:1–5. Doi 10.1016/j.neulet.2004.03.090
Suarez-Calvet M, Kleinberger G, Araque Caballero MA, Brendel M, Rominger A, Alcolea D, Fortea J, Lleo A, Blesa R, Gispert JD et al (2016) sTREM2 cerebrospinal fluid levels are a potential biomarker for microglia activity in early-stage Alzheimer's disease and associate with neuronal injury markers. EMBO Mol Med 8: 466–476 Doi 10.15252/emmm.201506123
Suarez-Calvet M, Morenas-Rodriguez E, Kleinberger G, Schlepckow K, Araque Caballero MA, Franzmeier N, Capell A, Fellerer K, Nuscher B, Eren E et al (2019) Early increase of CSF sTREM2 in Alzheimer's disease is associated with tau related-neurodegeneration but not with amyloid-beta pathology. Mol Neurodegener 14: 1 Doi 10.1186/s13024-018-0301-5
Tible M, Sandelius A, Hoglund K, Brinkmalm A, Cognat E, Dumurgier J, Zetterberg H, Hugon J, Paquet C, Blennow K (2020) Dissection of synaptic pathways through the CSF biomarkers for predicting Alzheimer disease. Neurology 95:e953–e961. Doi 10.1212/WNL.0000000000010131
Umahara T, Uchihara T, Tsuchiya K, Nakamura A, Iwamoto T, Ikeda K, Takasaki M (2004) 14-3-3 proteins and zeta isoform containing neurofibrillary tangles in patients with Alzheimer's disease. Acta Neuropathol 108:279–286. Doi 10.1007/s00401-004-0885-4
Zhang J, Zhou Y (2018) 14-3-3 Proteins in Glutamatergic Synapses. Neural Plast 2018: 8407609 Doi 10.1155/2018/8407609

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CSF 14-3-3ζ is associated with tau pathology and elevated across the Alzheimer’s disease continuum

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Abstract

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Introduction

Materials And Methods

ADNI database

CSF 14-3-3ζ Measurements

Cognitive Assessments

CSF And Plasma Markers Measurements

Neuroimaging

Statistical analysis

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