ApoJ/Clusterin concentrations are determinants of cerebrospinal fluid cholesterol efflux capacity and reduced levels are associated with Alzheimer’s disease

CSF and plasma samples were obtained from the Biosample Repository of the Center for Neurodegenerative Disease Research (CNDR) at the Perelman School of Medicine (PSOM), University of Pennsylvania (Philadelphia, PA, USA). This case-control study examined participants enrolled in a prospective longitudinal study under the auspices of the National Institute on Aging (NIA)-funded Alzheimer’s Disease Clinical Center (ADCC) at PSOM. The participants were recruited at the Penn Memory Center, PSOM, and the Maria de los Santos Health Center (Philadelphia, PA, USA), following written informed consent under approval of the University of Pennsylvania Institutional Review Board.

Cases were classified as AD or MCI based on standard diagnostic criteria according to procedures established for ADCCs and the National Alzheimer’s Coordinating Center (NACC) [18‐21]. From this cohort, we identified a subset of 127 CSF and plasma samples obtained on the same day from 112 unique participants and other CSF biomarker data (AD = 42, MCI = 20, and CN = 50). Four subjects were further removed due to major disruption of the blood brain barrier as adjudged by albumin and ApoB serum to CSF ratio (2 AD and 2 MCI samples, AD = 40, MCI = 18, and CN = 50). The total number of subjects used in this study is 108.

Subjects were selected from the Integrated Neurodegenerative Diseases Database (INDD) of the CNDR, using the web-based query tool INQUERY developed at CNDR. Neuropsychological testing was conducted including the Mini-Mental State Examination (MMSE) and/or tests of frontal executive function, memory, language, praxis, visuospatial construction, motor performance, mood, and function, based on the NACC Uniform Data Set (UDS) [21‐23]. Details for CSF samples collection and for standardized Luminex assay for amyloid-beta (Aβ_1-42), total tau (TTau), and phosphorylated tau (pTau) at the threonine 181 are available in previous publications [24‐26].

Plasma and CSF aliquots (0.5 mL) were retrieved from the UPenn biorepository, coded to allow assays to be performed without knowledge of subject characteristics, and transported or shipped on dry ice to the respective laboratories for biochemical analysis.

The criteria for selecting CSF and plasma samples from the biobank were as follows: (1) subject evaluated at Penn Memory Center; (2) diagnosis of AD, MCI, or CN; and (3) availability of CSF and plasma from the same date.

Plasma cholesterol (Roche), ApoA-I (Roche), ApoB (Roche), and ApoE (Kamiya) concentrations were assayed on a Cobas C311 (Roche) and plasma and CSF human serum albumin by ELISA (E88-129; Bethyl Laboratories, Inc.). CSF cholesterol and triglyceride phospholipid were determined by fluorescence assays (A12216 (Invitrogen) and MAK122 (Sigma-Aldrich), respectively). CSF ApoA-I (DAPA10 (R&D Systems, Inc.)), ApoB (ab108807 (Abcam)), ApoE (ab108813 (Abcam)), and ApoJ (DCLU00 (R&D Systems, Inc.)) were determined by ELISA.

The CSF CEC assay using N9 microglial cells and SH-SY5Y neuroblastoma cells has been described previously [17]. Briefly, SH-SY5Y cells were maintained in DMEM/4.5 g/L D-glucose/4mM L-glutamine/110 mg/L sodium pyruvate, and N9 were maintained in RPMI 1640/2mM L-glutamine (both media from Life Technologies), supplemented with 10% FBS. In the CSF CEC assay, cells were seeded in 96 well plates and labeled with 2 μCi/mL [1,2-³H(N)] cholesterol overnight. On day 2, SH-SY5Y were treated with 2 μM T0901317, and N9 and J774 cells were treated with 0.3 mM 8-CPT-cAMP to upregulate ABCA1. Cells were exposed to 33 μl of individual CSF and cholesterol efflux was allowed to proceed for 2.5 h. Cell medium was counted upon removal of floating cells and cell lipids were extracted with isopropanol read in a scintillation counter. Cell cholesterol efflux was quantified as the percent of [³H]-cholesterol counts in the medium relative to the total counts in medium and cells. A reference CSF sample was included on each plate to decrease inter-assay variability, and CEC activity was calculated as a unitless ratio of cholesterol efflux to sample CSF normalized to the reference CSF.

A similar assay with J774 mouse macrophages was used to determine plasma CEC using 1% ApoB depleted plasma in place of CSF and is described in Khera et al. [15] and Horiuchi et al. [27].

For demographics, categorical variables are in percentages, and continuous variables as means with standard deviations or medians with range. Demographic statistics were computed using ANOVA and chi-square test for the continuous variable and categorical variables, respectively. For the post hoc multiple comparisons, we used the Tukey honestly significant difference (HSD) test. The correlation of efflux capacity between different cell lines was assessed with Spearman’s correlation coefficients. Correlations with P < 0.05 were considered significant. CEC values were log-transformed, and the N9 and SH-SY5Y CEC were not significant for the Shapiro-Wilk normality test (P = 0.58 for N9 and P = 0.66 for SH-SY5Y). For the association analysis, linear regression models were used to predict CEC, including lipoproteins and CSF biomarkers. Age, sex, race, and CSF (or plasma) storage time were incorporated as covariates in all analyses. We assessed the contribution of predictors in multinomial logistic regression, and calculated β, the logistic regression coefficient that shows the direction and size of the relationship between predictor and diagnosis. Odds ratio (OR) is the ratio of the odds that are calculated as the exponent of β with 95% confidence interval. The association between plasma CEC and biomarkers were analyzed using the same model. All analysis was carried out in the R application (v 3.6.0).

The characteristics of the study cohort are shown in Table 1. Among the 108 participants, 40 (37.0%) were diagnosed as AD, 18 (16.7%) as MCI, and 50 (46.3%) were CN. Mean age at lumbar puncture was 69.9 years. Fifty-three percent were female, and 9% were African American. There were no significant differences among the three groups except for the MMSE as expected. The prevalence of the ApoE ε4 allele was highest in the AD group (65%) and lowest in the CN group (24%) as expected. AD biomarker CSF Aβ_1-42 levels were significantly reduced in AD, whereas total Tau (TTau) and phosphorylated Tau (pTau) were significantly increased in AD. MCI subjects were intermediate between CN and AD in all cases (Supplemental Figure 1; Table 2).

CSF CEC was measured using both N9 microglial cells and SH-SY5Y neuroblastoma cells. The correlation of CSF CEC between the two cell types was moderate (correlation coefficient = 0.54, P-value = 1.3e−09) (Supplemental Figure 2A). Importantly, there was no association between CSF CEC and plasma CEC (Supplemental Figure 2B-C). CSF CEC was significantly lower in MCI subjects in SH-SY5Y neuroblastoma cells (F = 3.212, P = 0.0442). We further investigated the relationship between groups using multinomial logistic regression and observed a significant difference between MCI and CN for CECs from both cell types (Fig. 1 and Table 2). CSF cholesterol was significantly associated with CSF CEC from SH-SY5Y neuroblastoma cells (B = 26.0, t = 2.289, P = 0.0243) and from N9 microglial cells (B = 48.605, t = 2.883, P = 4.9e−03) (Supplemental Figure 3). CSF phospholipids were not significantly associated with CSF CEC in either cell type. There was no difference in CSF phospholipid, CSF cholesterol, plasma CEC, or cholesterol by diagnosis (Table 2 and Supplemental Table 1).

CSF ApoJ/Clusterin and ApoA-I were significantly associated with CEC and were lower in MCI compared to CN. ApoA-I, ApoE, and ApoJ were measured in CSF samples and ApoA-I and ApoE were measured in plasma samples (Table 2 and Supplemental Table 1). CSF ApoA-I and CSF ApoJ were each strongly correlated with both measures of CSF CEC, but ApoE was not (Fig. 2). Although we observed that CSF ApoJ and ApoA-I are closely correlated (Fig. 3), CSF ApoA-I and ApoJ were not significantly correlated with each other when we adjust for age, sex, and race (Supplemental Figure 4) CSF ApoA-I and ApoJ/Clusterin were both significantly lower in MCI than CN but not in AD, whereas CSF ApoE was significantly lower in AD subjects than in CN controls but not in MCI (Fig. 4 and Table 2). CSF ApoE levels were significantly lower in APOE ε4 allele carriers, and the AD group had significantly more ε4 allele carriers (Supplemental Figure 5). CSF ApoA-I had no correlation with plasma ApoA-I, and plasma ApoA-I was not different among CN, MCI, and AD subjects (Supplemental Table 1). Similarly, CSF and plasma ApoE levels were not correlated, and in contrast to CSF ApoE, plasma ApoE levels did not differ by diagnosis (Supplemental Table 1).

We measured Aβ_1-42, total Tau (TTau), and phosphorylated-Tau (pTau) in the same CSF samples. Aβ_1-42 had a modestly significant association with CSF CEC in SH-SY5Y cells but not in N9 cells (Supplemental Figure 6). None of the other AD CSF biomarkers had an association with CSF CEC (Supplemental Figure 6). There was a strong and highly significant positive association between CSF Aβ_1-42 and CSF ApoE and a strong negative association between pTau/Aβ_1-42 ratio and CSF ApoE (Table 3; Supplemental Figure 6). Neither ApoA-I nor ApoJ had significant correlations with the AD biomarkers. We performed a pair-wise correlation analysis for all measurements made in the CSF (Fig. 3). ApoA-I, ApoJ, and the two CEC measurements clearly clustered together and were negatively associated with Tau and pTau. Aβ_1-42 was positively correlated with ApoE and negatively with Tau and pTau.