A common variant of IL-6R is associated with elevated IL-6 pathway activity in Alzheimer’s disease brains

Patrick CG Haddick; Jessica L Larson; Nisha Rathore; Tushar R Bhangale; Qui T Phung; Karpagam Srinivasan; David V Hansen; Jennie R Lill; Alzheimer’s Disease Genetic Consortium (ADGC), Alzheimer’s Disease Neuroimaging Initiative (ADNI); Margaret A Pericak-Vance; Jonathan Haines; Lindsay A Farrer; John S Kauwe; Gerard D Schellenberg; Carlos Cruchaga; Alison M Goate; Timothy W Behrens; Ryan J Watts; Robert R Graham; Joshua S Kaminker; Marcel van der Brug

doi:10.3233/JAD-160524

. Author manuscript; available in PMC: 2017 Nov 2.

Published in final edited form as: J Alzheimers Dis. 2017;56(3):1037–1054. doi: 10.3233/JAD-160524

A common variant of IL-6R is associated with elevated IL-6 pathway activity in Alzheimer’s disease brains

Patrick CG Haddick ^a,^*,^‡, Jessica L Larson ^b,^*,^†, Nisha Rathore ^c,^*, Tushar R Bhangale ^c,^*, Qui T Phung ^d, Karpagam Srinivasan ^e, David V Hansen ^e, Jennie R Lill ^d; Alzheimer’s Disease Genetic Consortium (ADGC), Alzheimer’s Disease Neuroimaging Initiative (ADNI), Margaret A Pericak-Vance ^f,^g, Jonathan Haines ^h, Lindsay A Farrer ^i,^j,^k,^l,^m, John S Kauwe ⁿ, Gerard D Schellenberg ^o, Carlos Cruchaga ^p,^q, Alison M Goate ^r,^s, Timothy W Behrens ^c, Ryan J Watts ^e,^‡, Robert R Graham ^c,^§, Joshua S Kaminker ^b,^§, Marcel van der Brug ^a,^§

^aDepartment of Diagnostic Discovery, Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA

^bDepartment of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA

^cDepartment of Human Genetics, Genentech Inc., South San Francisco, CA 94080, USA

^dDepartment of Protein Chemistry, Genentech Inc., South San Francisco, CA 94080, USA

^eDepartment of Neuroscience, Genentech Inc., South San Francisco, CA 94080, USA

^fThe John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33124, USA

^gDr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, FL 33124, USA

^hDepartment of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA

ⁱDepartment of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, MA 02118, USA

^jDepartment of Neurology, Boston University Schools of Medicine and Public Health, Boston, MA 02118, USA

^kDepartment of Ophthalmology, Boston University Schools of Medicine and Public Health, Boston, MA 02118, USA

^lDepartment of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA 02118, USA

^mDepartment of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, MA 02118, USA

ⁿDepartment of Biology, Brigham Young University, Provo, UT 84602, USA

^oDepartment of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA

^pDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA

^qHope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA

^rDepartment of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA

^sRonald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA

^§

To whom correspondence should be addressed: Marcel van der Brug, Biomarker Discovery OMNI, Genentech, Inc., 1 DNA Way, South San Francisco, CA, 94080, USA, +1-650-225-4535, no fax

These authors contributed equally to this work.

^†

Current address: GenePeeks, New York City, NY 10014, USA.

^‡

Current address: Denali Therapeutics, South San Francisco, CA 94080, USA.

PMCID: PMC5667357 NIHMSID: NIHMS890907 PMID: 28106546

Abstract

The common p.D358A variant (rs2228145) in IL-6R is associated with risk for multiple diseases and with increased levels of soluble IL-6R in the periphery and central nervous system (CNS). Here, we show that the p.D358A allele leads to increased proteolysis of membrane bound IL-6R and demonstrate that IL-6R peptides with A358 are more susceptible to cleavage by ADAM10 and ADAM17. IL-6 responsive genes were identified in primary astrocytes and microglia and an IL-6 gene signature was increased in the CNS of late onset Alzheimer’s disease subjects in an IL6R allele dependent manner. We conducted a screen to identify variants associated with the age of AD onset in APOE ε4 carriers. Across 5 datasets, p.D358A had a meta P=3×10⁻⁴ and an odds ratio (OR) = 1.3, 95% confidence interval (CI) 1.12 – 1.48. Our study suggests that a common coding region variant of the IL-6 receptor results in neuroinflammatory changes that may influence the age of onset in AD in APOE ε4 carriers.

Keywords: Alzheimer Disease, IL-6, Metalloproteases, Astrocytes, Microglia

Introduction

IL-6R is a receptor for IL-6, a cytokine that can trigger a range of cellular responses including inflammation [1]. IL-6R is primarily expressed in myeloid lineage cells in both transmembrane and soluble (sIL-6R) versions. On cells expressing transmembrane IL-6R, IL-6 binds to a complex of IL-6R and gp130, inducing cis-signaling. The transmembrane IL6-R can be cleaved and shed as sIL-6R. Unlike many cytokines, sIL-6R is not a decoy receptor. On cells that express gp130 but not transmembrane IL-6R, IL-6 and soluble IL-6R form a complex with membrane bound gp130 to induce trans-signaling[1].

A common variant in IL-6R, p.D358A (rs2228145) has been associated with multiple phenotypes, with A358 increasing risk of asthma[2], higher sIL-6R serum and cerebrospinal fluid levels[3,4], lower serum C-reactive protein levels[5], lower serum fibrinogen levels[6], and reduced risk of coronary heart disease[7] and rheumatoid arthritis[8,9]. Amino acid 358 is within a predicted metalloprotease cleavage site of the IL-6R receptor and the A358 variant is thought to play a role in increased shedding of the extracellular portion of the receptor [10–15].

In the central nervous system (CNS), IL6R is primarily expressed by microglia. We hypothesized that alterations in the ratio of transmembrane to soluble IL-6R, conferred by the D358A variant, may result in detectable gene expression changes in neurodegenerative diseases, including late onset Alzheimer’s disease (LOAD). Neuroinflammation is increasingly recognized as a key pathway in the development and severity of LOAD [16]. LOAD is the most common cause of dementia and is characterized by progressive cognitive decline[17]. Individuals with LOAD have regional neuronal loss, neuroinflammation, plaques containing amyloid β, and neurofibrillary tangles composed of tau[17]. Genome wide association studies (GWAS) in European and African American populations have identified >20 loci associated with LOAD risk[18–25], and several of the risk genes are likely to influence inflammation, including TREM2, CR1, CD33, and INPP5D. The APOE ε4 (APOE4) allele accounts for the largest fraction of the heritable risk of AD (20–40%)[26]. There are three forms of APOE that are defined by haplotypes of 2 coding variants (ε2, ε3, ε4). By age 85, 11% of the general population will develop LOAD, compared to 55% of APOE4 homozygotes and ~25% of APOE4 heterozygotes[27]. Although APOE4 is the strongest common variant associated with LOAD, there is a great deal of heterogeneity in the age of disease onset, even among APOE4 carriers.

We investigated the mechanism by which the IL-6R D358A allele leads to increased sIL6R, and looked for evidence that this functional variant contributes to the pathophysiology of Alzheimer’s disease.

Materials and Methods

Analysis of CSF

CSF data for IL-6R levels and genotype was obtained from the ADNI study and individual data was downloaded from the LONI website.

Detection of soluble and membrane bound IL-6R in cultured human CD4+ T cells

Healthy human volunteers were genotyped for IL-6R SNP rs2228145 by ABI C_16170664_10 assay. PBMC’s were obtained by Ficol gradient from five pairs of homozygous donors (one with each genotype AA/CC) age, gender and ethnicity matched. CD4+T cells were purified from PBMCs by negative selection using EasySep CD4+T cells enrichment kit (STEMCELL Technologies-19052) as recommended by the manufacturer.

CD4+T cells were then cultured for 72 hours in RPMI 1640+10% FBS + 2-Mercaptoethanol treated with and without 100nM PMA for 60 minutes. Cells were harvested soon after the treatment and stained with IL-6R-PE antibody (BD-Cat. No-551850). Membrane bound IL-6R was analyzed by FACS.

Supernatant was also collected after 24, 48 and 72 hours of CD4+T cell culture to measure sIL-6R concentration by ELISA (Human IL-6 R alpha Quantikine ELISA Kit, R&D Systems Cat-No DR600).

In vitro IL-6R shedding in 293T cells

293T cells were transfected with D358 (WT) or A358 (Mut) constructs of IL-6R along with GFP as transfection control. To study the effect over time cells were treated 48 hours after transfection with 100nM PMA for 0, 30, 60 and 120 minutes or to check the effect of MMP inhibitors, cells were treated with/without TAPI-2 (adam 17 Inhibitor) 20uM or with/without GM 6001 (pan MMP inhibitor) 100nM for 60 min prior to treatment with/without 100nM PMA for 60 min. Cell were harvested after the treatment and stained with IL-6R-PE antibody (BD-Cat. No-551850). Membrane bound IL-6R was analyzed by FACS by gating double positive cells for GFP and IL-6R.

Adam 10 and Adam 17 in-vitro enzymatic assay

The effect of recombinant MMP (Adam10 and Adam17) on the IL-6R synthetic peptide was checked by in-vitro enzymatic assay. Synthetic IL-6R peptides with a difference of one amino acid aspartate (D) to alanine (A) were synthesized.

IL-6R D358	-DSANATSLPVQDSSSVPLPTFL
IL-6R A358	-DSANATSLPVQASSSVPLPTFL

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Adam 17 or 10 enzymes (R&D system) at 2.5ug/ml and substrate (peptides) at 10 uM concentration were added in an assay buffer (25mM Tris, 2.5 uM ZnCl2, 0.005 Brij-35 (v/v) pH 9.0) and incubated at 37°C for indicated time. Quantitation of cleaved vs. uncleaved peptide was done by mass-spectrometry.

Quantification of peptides by Mass-spectrometry

Selected reaction monitoring quantitation for the IL-6R reaction mixtures was performed on a triple quadrupole/linear ion trap mass spectrometer, 4000 QTRAP (Applied Biosystems, Foster City, CA) coupled to a Tempo Autosampler (Applied Biosystems), and a 1200 capillary LC system (Agilent, Santa Clara, CA). Samples were loaded via full (2uL) loop injection directly onto a MetaSil AQ 3 C18 150 × 2.0mm column (Varian, Lake Forrest, CA) and separated by reverse phase chromatography at a flow rate of 200 μL/min where solvent A was 98% H₂O/2% Acetonitrile/0.1% formic acid and solvent B was 98% Acetonitrile/2% H₂O/0.1% formic acid. Samples were loaded in 98% water/2% Acetonitrile/0.1% FA and eluted with a gradient of 5–30% solvent B for 1 min, 30–65% solvent B for 1min, and 65–90% solvent B for 4min; with a total run time of 15min. Samples were ionized via a Turbo Ionspray source with the spray voltage set at 5200V, curtain gas of 20 psi, and an interface heating temperature of 550°C. The declustering potential (DP) was 60V, collision energy was 35V, dwell time was 100ms per transition, for a total cycle time of 0.420s. Q1 and Q3 resolution settings were set at low and unit, respectively. The most abundant charge states of each intact and Adam10 or 17 processed peptides were determined empirically and used for SRM transition development. SRM transitions were selected to monitor the fragment ion with the highest intensity and uniqueness to the peptide. The following transitions were monitored for the IL-6R A358 reaction: the [M+3H]³⁺ precursor of DSANATSLPVQASSSVPLPTFL at 802.4 to 477.5 (y₄), the [M+2H]²⁺ precursor of VQASSSVPLPTFL at 673.4 to 477.5 (y₄), the [M+2H] ²⁺ precursor of ASSSVPLPTFL at 559.8 to 477.5 (y₄), and the [M+2H] ²⁺ precursor of PVQASSSVPLPTFL at 721.8 to 477.5 (y₄). The following transitions were monitored for the IL-6R D358 reaction: the [M+3H] ³⁺ precursor of DSANATSLPVQDSSSVPLPTFL at 817.1 to 477.5 (y₄), the [M+2H] ²⁺ precursor of VQDSSSVPLPTFL at 695.4 to 477.5 (y₄), the [M+2H] ²⁺ precursor of DSSSVPLPTFL at 581.8 to 477.5 (y₄), and the [M+2H] ²⁺ precursor of PVQDSSSVPLPTFL at 743.9 to 477.5 (y₄). Transitions corresponding to the N-terminal peptide fragments were not monitored due to the hydrophilicity and suboptimal chromatographic peak shape observed (data not shown). Peak integration was performed using MultiQuant 1.1 software (Applied Biosystems), and area under the curve (AUC) was used to determine the abundance of each peptide fragment relative to intact peptide.

Astrocyte and microglia cell culture

Primary mouse astrocyte and microglia co-culture and RNA extraction was performed as previously described[28]. We determined that 1.25 nM of both IL-6 (R&D 406-ML-005/CF) and sIL-6R was an optimal dose, and treated a new set of co-cultures at these levels for 24 hours prior to RNA extraction. Treated co-cultures were shaken to separate the culture into a microglia-enriched sample of detached cells in the media and an astrocyte-enriched sample adhering to the flask.

Western blotting of IL-6 in astrocytes and microglia

Primary mouse astrocyte and microglia co-culture were treated with 1.25 nM IL-6 or control media for 15 minutes, washed once with cold PBS, and lysed in RIPA buffer (Sigma R0278) with PhosStop phosphatase inhibitors (Roche 04906837001) and cOmplete protease inhibitors (Roche 11836170001) at 4C on a rotator for 30 minutes. The lysate was spun at 20,000g at 4C and the supernatant was transferred to a new tube to remove insoluble material and stored at −80C. Protein lysate concentrations were measured by a BCA protein assay (Thermo Scientific 23227) following the kit provided protocol. 25ug of lysate was loaded per well of a 10% bis-tris gel (Invitrogen WG1202) and immunoblotted using standard procedures using 1:1,000 anti-rabbit pT705 STAT3 (Cell signaling 9131) and 1:20,000 anti-mouse beta-actin (Sigma A3854) antibodies. Proteins on blots were visualized using the LI-COR Odyssey system.

Quantitative PCR and RT-PCR

RNA sample quality was assessed using a RNA 6000 Pico kit (Agilent Technologies 5067-1513) and 2100 Bioanalyzer (Agilent Technologies G2939AA). Technical triplicate quantitative PCR reactions were performed on a Biomark HD System (Fluidigm) using iScript cDNA generated from microglia or astrocyte enriched RNA as a template (Bio-rad 170-8891) and the following Taqman primer sets: Actb (Mm00607939_s1), BC055004 (Mm01290815_m1), Bcl3 (Mm00504306_m1), C4b (Mm00437893_g1), Ccl12 (Mm01617100_m1), Ccl8 (Mm01297183_m1), Cdsn (Mm01275230_m1), Cebpd (Mm00786711_s1), Fn1 (Mm01256744_m1), Gapdh (Mm99999915_g1), Gda (Mm00515820_m1), Hp (Mm00516884_m1), Hprt (Mm01545399_m1), Hspb1 (Mm00834384_g1), Lcn2 (Mm01324470_m1), Saa1 (Mm00656927_g1), Saa3 (Mm00441203_m1), Serpina3n (Mm00776439_m1), Socs3 (Mm00545913_s1), Tgm1 (Mm00498375_m1), Timp1 (Mm00441818_m1) (Life Technologies). Gene expression was normalized to the geometric mean of Gapdh, Actb, and Hprt expression.

Microarray analysis of astrocyte and microglia cultures treated with IL-6 + sIL-6R

Quantity and quality of total RNA samples was determined using ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE) and Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA), respectively. We used Agilent’s method for preparation of Cy-dye labeled cRNA and array hybridization (Santa Clara, CA). Briefly, total RNA was converted to double-stranded cDNA and then to Cy-dye labeled cRNA using Agilent’s Quick Amp Labeling Kit. The labeled cRNA was purified using an RNeasy mini kit (Qiagen, San Diego, CA). cRNA yield and Cy-dye incorporation was determined using an ND-1000 spectrophotometer (Thermo Scientific). 750 ng of the labeled cRNA was fragmented and hybridized to the Agilent’s Whole Mouse Genome 4×44Kv2 arrays as described in the manufacturer’s hybridization kit. All samples were labeled with Cy5 and hybridized against Cy3 labeled universal mouse reference (Stratagene, La Jolla, CA). Following hybridization, the arrays were washed, dried and scanned on Agilent’s microarray scanner. Agilent’s Feature Extraction software 11.0 was used to analyze acquired array images.

Bioinformatics analyses were completed with the Bioconductor (v2.13)[29] software and the R (v3.0.2) programming language. Background correction of acquired images was performed using the normexp function in the limma package using an offset of 50[30]. Within array normalization was performed using the normalizeWithinArrays function with the loess method. Lastly, arrays were normalized with the normalizeBetweenArrays function using the aquantile method. Control probes were removed from the analysis. Data for duplicate probes on the Agilent array were averaged using the avereps function. Prior to comparison between groups, probes were filtered to ensure only a single probe was represented for each gene using the featureFilter function with default parameters. QC was performed using the arrayQualityMetrics package.

Differential expression of genes was determined using the limma package, and p-values reported in the text were corrected for multiple testing using Benjamini and Hochberg’s method. Genes that were not also differentially expressed by PCR were excluded from further analyses.

Creation of IL-6 + sIL-6R responsive gene sets in astrocytes and microglia and pathway analysis

Microarray gene sets of IL-6 + sIL-6R microglia or astrocyte response were determined by selecting the top differentially expressed genes (adjusted p-value < 0.01), ranked by fold change (Table S1). Gene Ontology analysis (Table S2) was performed using the GOstats and ReportingTools packages[31]. We used the conditional setting for the GOstats hypergeometric test to take into account the nesting structure of the GO categories. We considered all ‘Biological Process’ categories with at least 10 gene members.

Public IL-6 responsive gene sets were selected from mSigDB v4.0 and their enrichment in the astrocyte and microglia cultures was determined via the same gene set enrichment methods discussed below. As these publicly available IL-6 responsive gene sets were not generated using CNS cell types they may contain significant numbers of genes that are not expressed in the CNS. A notable example is CRP which is not expressed in our culture system or in our human cortical tissue sets. From this analysis, only a fibroblast enriched gene set from Dasu et al (2004) was significantly enriched in LOAD samples with the risk allele (Table S4)[32].

Analysis of human cortex gene expression and genotype data

The top differentially expressed genes ranked by microarray fold change were explored in human microarray data. Publicly available expression arrays for the temporal region of human cortex samples were downloaded from the Gene Expression Omnibus[33] (GEO, GSE 15222). Raw data was normalized via the variance stabilizing normalization and non-specific filtering was performed with nsFilter to remove duplicate probes. Covariates and genotype data were downloaded from the author’s website (http://labs.med.miami.edu/myers/LFuN/data.html). We discovered a strong batch and sex effect in this data. All subsequent analyses corrected for these covariates. Because our discovered IL6R variant was associated with the age of onset of LOAD patients, we did not adjust for age in our models. We removed samples that were APOE 2/4. After removing SNPs with missing genotype rate > 1% we performed genotype imputation using Shapeit, IMPUTE2, and 1000 Genomes haplotypes as described above.

Gene sets and gene set enrichment analysis

Gene sets generated from IL-6 and sIL-6R stimulated microglia- or astrocyte-enriched cultures were identified as described above. Mouse Entrez IDs were mapped to Human Entrez IDs using the getLDS function of the biomaRt package[34]. Several mouse genes mapped to multiple human homologs; homologene (http://www.ncbi.nlm.nih.gov/homologene) was used to determine a unique human homolog for these genes. Genes that did not map to human or did not map to a probe in the Webster et al. (2009) data set were excluded[35].

To determine the level of enrichment of our IL-6 responsive gene sets in human data, we performed gene set enrichment analysis (GSEA)[36]. GSEA determines the level of the association of a set of related genes with a particular variable of interest. GSEA was performed using the permutation-based JG-Score[37] and rotation-based ROAST[38] methods in corresponding Bioconductor packages (GSEAlm and limma, respectively). The JG-score is a weighted sum of the individual differential expression statistics, and is thus sensitive to outliers. 9,999 permutations were used to calculate the p-value for observed JG-score statistic. Due to the granularity of the permutation method, minimum permutation p-values are 0.0001. For ROAST, the ‘floormean’ method was used; this method is less sensitive than the JG-score to outliers. 9,999 rotations were used to calculate the ROAST p-values. Genes within a set are considered up-regulated if their squared z-statistic for differential expression is greater than 2.

We considered four null hypotheses for the IL-6 responsive gene set in the AD patients: (1) the set is not enriched in A358 carriers versus D358 carriers; (2) the set is not enriched in A358 + APOE4 carriers versus D358 + APOE4 carriers; (3) the set is not enriched in A358 + APOE3 carriers versus D358 + APOE3 carriers; (4) the set is not enriched in A358 + APOE4 carriers versus A358 + APOE3 carriers. For each analysis, we adjusted for sex and batch effects. In the first comparison, we also adjusted for APOE status.

IL-6 score in clinical samples

Expression data from the human AD and control samples was adjusted for batch and sex by fitting the following linear model for the ith gene:

E (Expressio n_{i}) = β_{0 i} + β_{1 i} * Sex + β_{2 i} * Batch .

We then calculated the corresponding residuals for each sample with the lmPerGene and getResidPerGene functions in the GSEAlm package.

We normalized the adjusted expression in each gene (Y_ij) by subtracting its mean $\bar{Y_{i}}$ and dividing by its standard deviation in all samples (s_i):

Z_{i j} = \frac{Y_{i j} - \bar{Y_{i}}}{s_{i}} .

For the jth subject, the IL-6 responsive score is the sum of K normalized adjusted expression values of each gene in the gene set divided by the size of the set:

Scor e_{j} = \frac{\sum_{i = 1}^{K} Z_{i j}}{K} .

Two-sample t-tests for both the AD and control samples were performed to determine whether or not the A358 and D358 samples have the same score.

Subjects and genotyping

For all of the cohorts, we selected individuals of self reported European ancestry that matched the following criteria: APOE ε4 carriers (one or two copies) Alzheimer’s cases with age ≤ 65 yrs, or controls with age ≥ 80 yrs and one copy of ε4, or controls with age ≥ 75 yrs and two copies of ε4 (Table S7).

The primary study included 103 total cases and 109 that were genotyped using Illumina Omni 1M Quad SNP array, which included 1,140,419 SNPs. All samples from this primary study were from NIA ADC cohort (see below for description).

The first US replication cohort included 58 total cases and 275 controls genotyped using Illumina Omni2.5M SNP array (2,379,855 SNPs). The analysis included 215 samples from Cache County[39] and 55 samples from 23andMe.

The second US replication cohort included data obtained from dbGap phs000168.v1.p1 which included 207 cases and 386 controls from the National Institute on Aging – Late Onset Alzheimer’s Disease Study, that were genotyped using Illumina Human610-Quad SNP array (620,901 SNPs).

The third US replication study included 702 cases and 258 controls from the NIA ADC cohort genotyped (see description below) using Illumina HumanExome-12v1 SNP array (247,870 SNPs).

The NIA ADC cohort, assembled by the Alzheimer’s Disease Genetics Consortium (ADGC), included subjects ascertained and evaluated by the clinical and neuropathology cores of the 29 NIA funded ADCs. Data collection is coordinated by the National Alzheimer’s Coordinating Center (NACC). NACC coordinates collection of phenotype data from the 29 ADCs, cleans all data, coordinates implementation of definitions of AD cases and controls, and coordinates collection of samples. The ADC cohort consists of 2,499 autopsy-confirmed and 1,748 clinically confirmed AD cases, 175 cognitively normal elders (CNEs) with complete neuropathology data who were older than 60 years at age of death, and 2,669 living CNEs evaluated using the Uniform data set (UDS) protocol40,41 who were documented to not have mild cognitive impairment (MCI) and were between 60 and 100 years of age at assessment. Among cases, the average age at onset was 71.6 years (±9.0 years), and APOE genotypes were 0.1% e22, 3.5% e23, 31.6% e33, 2.8% e24, 44.3% e34, and 14.7% e44. Among controls, the average age at last exam was 76.6 years (±9.4 years), and APOE genotypes were 0.7% e22, 13.0% e23, 56.8% e33, 2.1% e24, 21.4% e34, and 2.2% e44. Based on the data collected by NACC, the ADGC Neuropathology Core Leaders Subcommittee derived inclusion and exclusion criteria for AD and control samples. All autopsied subjects were age ≥60 years at death. AD cases had dementia according to DSM-IV criteria or Clinical Dementia Rating (CDR) ≥1. Neuropathologic stratification of cases followed NIA/Reagan criteria explicitly or used a similar approach when NIA/Reagan criteria42 were coded as not done, missing, or unknown. Cases were intermediate or high likelihood by NIA/Reagan criteria with moderate to frequent amyloid plaques and neurofibrillary tangle (NFT) Braak stage of III–VI[40,41]. Persons with Down’s syndrome, non-AD tauopathies and synucleinopathies were excluded. All autopsied controls had a clinical evaluation within 2 years of death. Controls did not meet DSM-IV criteria for dementia, did not have a diagnosis of mild cognitive impairment (MCI), and had a CDR of 0, if performed. Controls did not meet or were low-likelihood AD by NIA/Reagan criteria, had sparse or no amyloid plaques, and a Braak NFT stage of 0–II. ADCs sent frozen tissue from autopsied subjects and DNA samples from some autopsied subjects and from living subjects to the ADCs to the National Cell Repository for Alzheimer’s Disease (NCRAD). DNA was prepared by NCRAD and sent either by NCRAD or by the University of Pennsylvania and sent to the North Shore Medical Center for genotyping.

The Icelandic study (Decode Genetics) included whole genome sequencing of 75 cases and 75 controls using Illumina Hi-Seq at an average read coverage of 30× using 100bp paired end reads. The reads were aligned to the reference genome using BWA[42]. QC of the alignment data was performed using GATK[43] and involved removal of PCR duplicates, base quality recalibration, and realignment around short indels. Variant calling was also performed using GATK[43].

Quality control, genotype imputation, and association analysis

In the analysis of the primary study data, we excluded SNPs with >5% missing genotypes, Hardy-Weinberg test p-value < 10⁻⁴, minor allele frequency < 0.01. We performed identity-by-descent analysis to rule out any closely related individuals and PCA to rule out any population outliers and to identify eigenvectors that were significantly associated with case-control status. We then performed logistic regression of the case-control status on the genotype (coded additively 0,1,2) at every SNP, including the above eigenvectors as covariates to account for possible population stratification. All association analysis was performed using SNPTEST[44].

The SNP of interest, rs2228145, was directly genotyped in all but the US replication 2 study. Genotype imputation was performed for those samples using a workflow that included pre-phasing using Shapeit[45] followed by imputation using IMPUTE2[46] and reference haplotypes from the 1000 Genomes project. Meta-analysis of results was performed using METASOFT[47].

Results

A358 IL-6R is associated with increased shedding of membrane bound IL-6R

sIL-6R concentrations have been shown to be higher in A358 IL6R carriers compared to those with the D358 IL6R variant in both serum[2,11,48,49] and the CSF[4]. We confirmed the previous observations that the minor allele (A358) is associated with increased circulating levels of sIL-6R in cerebrospinal fluid (CSF, Fig. S1, each two-way comparison p-value ≤ 5.31 × 10⁻⁵) and serum (p-value = 0.022; Fig. S2A) of Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants

ADAM17 is a metalloprotease that is activated in cells by phorbol 12-myristate 13-acetate (PMA) and generates sIL-6R by cleavage of IL-6R between amino acids 357 and 358[9,50]. In transfected 293T cells, the A358 variant of IL-6R showed 45% lower cell surface levels of IL-6R than p.D358 after 60 minutes of PMA treatment (p-value = 0.0025; Fig. 1A).

Fig. 1 — (A) 293T cells transfected with D358 or A358 IL-6R were treated with 100nM PMA for 0, 30, 60 and 120 minutes. Membrane bound IL-6R was analyzed by FACS. The mean fluorescence intensity relative to the initial time point of three experiments is shown. At each time point, the A358 samples have decreased levels of mIL-6R compared to the D358 samples (p_30minutes=0.0079; p_60minutes=0.0025; p_120minutes=0.050). (B) CD4+ T cells cultured for 72 hours in RPMI 1640+10% FBS + 2-Mercaptoethanol treated without (unstimulated) or with (stimulated) 100nM PMA for 60 minutes. Membrane bound IL-6R was analyzed by FACS. The mean fluorescence intensity from five pairs of genotyped donors is shown. Both the unstimulated (p-value = 0.014) and stimulated (p-value = 0.0030) A358 samples have decreased mIL-6R compared to their D358 counterparts. (C) Supernatant was collected after 24, 48 and 72 hours of CD4+T cell culture and sIL-6R was measured by ELISA (D) ADAM10 and ADAM17 activity on the IL-6R peptides after 6 hours. Quantification of cleaved and uncleaved product by single reaction monitoring mass spectrometry. Area under the curve of transition ion peaks were used to calculate percent cleaved product considering total (cleaved + uncleaved area under the curve) as 100% is shown. P-value <0.05= *, <0.005=**, <0.0005=***.

Steady state and PMA treated CD4+T cells isolated from healthy human donors revealed a similar in-vivo genotype-dependent effect on cleavage showing decreased cell surface IL6R (paired t-test p-value = 0.0030; Fig. 1B) and increased sIL6R (Fig. 1C). These data are consistent with a model where A358 IL6R is associated with an increased rate of proteolytic cleavage of membrane bound IL-6R in A358 carriers at steady state and following PMA stimulation.

ADAM17 and ADAM10 preferentially cleave IL6R A358

We next tested the effect of the probable IL-6R protease ADAM17 and pan-metalloprotease inhibitors on WT (D358), minor allele (A358) and an ADAM cleavage insensitive (Del 353-362) IL-6R constructs in 293T cells. Consistent with the above experiments, A358 showed decreased cell surface staining (p-value = 0.0029) while, Del 353-362 had no increase in shedding upon PMA induction (p-value = 0.580; Fig. S2B). Decreased cell surface staining of IL-6R A358 was completely abrogated by incubating the cells with pan MMP inhibitor (p-value = 0.146), or with an ADAM17 inhibitor prior to PMA treatment (p-value = 0.858; Fig. S2B). Secretion of sIL-6R by ectodomain shedding in CD4+ T cells is most likely mediated by ADAM17[51], though ADAM10 may contribute in other cell types. The cleavage site of ADAM10 on IL-6R has not previously been characterized. ADAM10 and ADAM17 have over 100 known substrates that only partially overlap[52], and no consensus cleavage sequence exists at this time[53]. To determine ADAM10 and ADAM17 activity on IL-6R, we generated synthetic peptides that contained either D358 or A358, and performed an in-vitro cleavage assay. Both ADAM10 and ADAM17 were more active on the A358 compared to D358 peptide (p-value = 4.07 × 10⁻⁹), with ADAM17 having more overall activity after 6 hours (p-value = 1.08 × 10⁻¹⁴; Fig. 1D, Fig. S2C). These data support the conclusion that increased circulating levels of IL-6R in A358 carriers are likely a result of increased proteolytic cleavage of membrane bound IL-6R by ADAM17 and ADAM10.

Identification of astrocyte and microglia IL-6 + sIL-6R responsive gene signatures

We next investigated whether there was evidence for differences in IL-6 signaling in Alzheimer’s disease brains conferred by p.D358A IL-6R. To address this, we first measured transcriptional responses to IL-6 + sIL-6R in co-cultures of mouse astrocytes and microglia. Activation of IL-6 signaling by treatment with IL-6 and sIL-6R was confirmed by detection of STAT3 phosphorylation (Fig. S3A). Treated co-cultures were separated into microglia-enriched and astrocyte-enriched samples and RNA was isolated for microarray analysis (see Methods). In the astrocyte-enriched samples, 68 genes were up-regulated and 246 genes were down-regulated following IL-6 + sIL-6R treatment (adjusted p-value < 0.01 and log₂ fold change > 2; Fig. 2A). In the microglia-enriched cultures, 33 genes were up-regulated and 6 genes were down-regulated after IL-6 + sIL-6R treatment compared to controls (Fig. 2B) using the same significance and fold change filters. Differential expression of the top IL-6 + sIL-6R responsive genes was confirmed by qPCR (Table S1).

Fig. 2 — (A,B) Volcano plot for IL-6 and sIL-6R treated astrocytes and microglia. Average log₂ (fold change) versus −log₁₀ (p-value) for all genes in the (A) astrocyte and (B) microglia samples. The top 25 genes up-regulated by IL-6 and sIL-6R treatment by fold change are noted by color (blue : astrocytes, orange : microglia, and gold : both). (C) IL-6 set enriched in patients with AD and *IL6R* risk variant. Density of the t-statistics is shown for all genes in human AD patients (grey), astrocyte set (blue), and microglia set (orange). Each t-statistic is measuring the relative difference between the average expression in patients who carry A358 and those that do not (after adjusting for APOE, sex and batch). The individual gene statistics of the set are plotted below the density plots. IL-6 responsive gene set densities are shifted to the right, indicating higher expression of these genes in A358 carriers compared to non-carriers. The astrocyte set has the highest level of enrichment among A358 carriers with 40.9% of genes up-regulated in this set (p-value = 0.003) and the microglia gene set is also enriched (p-value = 0.0267).

As expected, genes significantly up-regulated in astrocytes and microglia were enriched in immune related Gene Ontology categories, including “inflammatory response”, “response to stress”, and “acute-phase response” (Table S2). Gene set enrichment analysis[36,54] (GSEA, see Methods) also identified previously identified IL-6 responsive gene sets derived from non-CNS cell types (Table S3). The top 25 up-regulated genes in each cell type were selected for further analysis and were used to generate IL-6 responsive gene sets (Table S1, Fig. S3B,C).

IL-6 responsive genes are enriched in brains of A358 carriers with AD

Matched genotype and gene expression data were obtained from a recent study of the temporal cortex of LOAD patients (n = 94) and normal healthy controls (n = 124)[35]. Combining the genotype and expression data, we analyzed the effect of the A358 and D358 IL6R variants on the expression of genes derived from the astrocyte and microglia experiments. First, we used GSEA as a sensitive method to examine the behavior of the IL-6 responsive gene sets in the LOAD expression data. Astrocyte-specific IL-6 responsive genes were significantly enriched in LOAD patients with the A358 allele (p-value = 0.003; Fig. 2C; Table S4). The microglia-derived gene set was also enriched among A358 carriers though to a lesser degree (p-value = 0.01, Table S4).

To assess the impact of APOE4 status on the IL-6 responsive gene sets in IL6R A358 carriers, LOAD patients were divided into two groups; APOE4 carriers and APOE4 non-carriers. Within each of these groups, IL6R A358 carriers were compared to D358 carriers and the behavior of the IL-6 responsive gene sets were examined in the resulting data using GSEA. In the APOE4 carrier group, the astrocyte gene set remained significantly enriched in IL6R A358 carriers (p-value = 0.019), but the microglia gene set did not (p-value = 0.107) (Fig. S4A; Table S6). The astrocyte IL-6 responsive gene set was also significantly enriched in APOE4 non-carriers with the A358 allele (p-value = 0.040) with the microglia gene sets again not reaching significance (p-value = 0.065) (Fig. S4B; Table S4). These data show that in both APOE4+ and APOE4− AD subjects, astrocyte derived IL-6 responsive genes were significantly enriched in carriers of the IL6R A358 allele. APOE4 carriers were also compared to APOE4 non-carriers regardless of A358 status to rule out enrichment of the IL-6 signature due to APOE mediated inflammatory effects. There was no evidence of enrichment of any of the gene sets in this comparison (Fig. S4C; Table S4), consistent with a model in which differential expression of IL-6R responsive gene sets is driven primarily by the IL-6R genotype.

We next examined individual IL-6 responsive genes in an attempt to increase sensitivity and specificity. We pre-specified genes to combine into a summary response score based on the highest observed summed fold change in the IL-6 treated astrocyte and microglia arrays that also had validation by qRT-PCR. The top pre-specified and validated genes (SERPINA3, TIMP1, TGM1 and FN1) were then tested in LOAD patients with the A358 genotype and compared to those with the D358 genotype. We found significant enrichment of these genes in A358 carrier brains, with SERPINA3 (p-value = 0.0016) and FN1 (p-value = 0.046) driving the association and TIMP1 and TGM1 also showing enrichment but to a lesser extent (Fig. S5A–D).

For each patient, we calculated a score representing the weighted sum of the age-, sex-, and batch-adjusted expression of the IL-6 responsive genes (see Methods). This score is significantly higher among LOAD patients who carry the A358 allele versus those who are homozygous for the D358 allele (two-sided t-test p-value = 0.0080, Fig. 3A). There was no evidence of an association between IL6R genotype and the IL-6 responsive score in the normal healthy control subjects.

Fig. 3 — (A) IL-6 responsive scores for each subject. The score is calculated as the normalized sum of the modified expression (adjusted for batch and sex) of four genes (*TIMP1*, *TGM1*, *FN1* and *SERPINA3*). The score is significantly higher among AD patients with A358 versus D358 (p-value = 0.0080) *IL6R* variant. Whiskers represent interquartile range. (B) Normalized *GFAP* expression in all patients stratified by disease and *IL6R* genotype. Note that *GFAP* expression is higher in AD patients versus controls (p-value < 1 × 10⁻¹⁵), but that this effect is not dependent on *IL6R* genotype. Whiskers represent interquartile range.

As the individual top responsive genes were induced in astrocytes, and the larger astrocyte gene set was consistently enriched, we examined the abundance of IL-6R signaling components in isolated microglia and astrocytes. In our primary cultures, Il6ra (the IL6R mouse homolog) was highly expressed in microglia compared to astrocytes, and Il6st (encoding GP130) was expressed in both astrocytes and microglia. This suggests that increased shedding of IL-6R from the cell surface of microglia may result in reduced IL-6 cis-responsiveness concomitant with an increase in trans-signaling through gp130 on astrocytes.

As the astrocyte derived IL-6 responsive gene set and individual genes in the gene score were enriched in A358 LOAD cases, we examined the expression of GFAP, a marker of astrocytes which can also be elevated under inflammatory conditions. GFAP expression was significantly higher in LOAD versus control subjects (p-value < 1 ×10⁻¹⁵), but there was no association between GFAP expression and IL6R genotype in LOAD subjects (p- value = 0.378, Fig. 3B). This is consistent with a model in which the change in expression of the IL-6 responsive gene signatures is A358 dependent, and not driven by a difference in astrocyte number or activity.

An IL-6R coding region SNP is a candidate modifier of age of onset

In large genome-wide association studies of LOAD, D358A IL-6R has not been associated with the risk of developing Alzheimer’s[18–25]. However, we hypothesized that the changes in neuroinflammatory signaling conferred by D358A IL-6R might be enriched in individuals with an earlier age of onset and reduced in cognitively intact elderly individuals that carry the major LOAD risk allele APO ε4. We performed an initial GWAS (primary study) aimed at identifying common variants that modify the effect of APOE risk by comparing 100 LOAD cases and 102 elderly controls that were homozygous or heterozygous for the ε4 allele and lacked ε2. To augment statistical power, we selected individuals from the extremes of the age distribution and identified cases with early age of onset LOAD (<65 years) and elderly controls (>75 years ε4/ε4, >80 ε3/ε4) with no evidence of cognitive decline. The strongest association was found at rs4474240 (OR = 3.42, p-value = 5.01 × 10⁻⁶) in the IL-6R region (Fig. S6A,B). rs4474240 is on the same haplotype (D′ = 0.88, r² = 0.1) as a missense variant in IL6R (rs2228145, p.D358A) that has been reported to be associated with LOAD in Han Chinese[55]. Amino acid 358 is a site of cleavage of the IL-6R receptor by ADAM17 (Fig. 4A) and the A358 variant is thought to play a role in increased shedding of the extracellular portion of the receptor (as discussed above).

Fig. 4 — (A) The *IL6R* risk allele discovered in our modifier screen changes the 358^th amino acid in IL-6R from D to A. This amino acid lies at the cleavage site for IL-6R. Box = transmembrane spanning residues. (B) Summary of association at rs2228145 in the five datasets. The case and controls frequencies are those of the minor allele “C”.

We examined four additional case/control cohorts (total of 1145 cases and 889 controls) matching the criteria in the primary analysis for APOE4 status and age, for association to rs4474240 and rs2228145 (IL6R p.D358A). rs4474240 showed significant, but modest, association with early onset Alzheimer’s in the replication cohorts (p-value = 0.037). In two of the four replication cohorts, IL6R p.D358A was nominally associated: US replication 1 (OR = 1.4, p-value = 0.086) and US replication 2 (OR = 1.6, p-value = 0.00062), both cohorts are from single site centers. In two other studies, we observed no significant association for IL6R p.D358A: US replication 3 (OR = 1.01, p-value = 0.72) and Icelandic (OR = 1.2, p-value = 0.51). A meta-analysis of IL6R p.D358A in all 5 cohorts resulted in a combined OR= 1.3 and p-value = 0.0003, using fixed effect meta- analysis (Fig. 4B). We conclude that IL6R D358A is a candidate modifier of age of onset in APOE4 carriers, specifically associated with early disease onset.

The analysis was then extended to an Alzheimer’s case/control cohort of totaling 7102 individuals (4121 AD cases and 2981 controls) without the age and APOE status filters used in the modifier screen above. When the whole cohort was examined for an association to IL6R A358, no significant enrichment is observed, with the frequency of IL6R A358 of 41.0% in AD cases and 40.7% in controls (Table S5). In order to test whether IL6R A358 was enriched in APOE4 carriers regardless of age of onset, we stratified the cohort by APOE4 status and observed a modest dose effect, with a 40.2% frequency of IL6R A358 in APOE4− carriers (OR all controls = 0.98), a 41.4% frequency in APOE3/4 carriers (OR relative to controls = 1.03) and a frequency of 42.0% in APOE4/4 cases (OR relative to controls = 1.05, Table S1). The age of onset in APOE3/4 and APOE4/4 carriers was tested for a correlation with IL6R A358 genotype (Table S6). Among AD cases carrying APOE3/4 and APOE4/4, a frequency of 43.1% (OR relative to APOE4+ controls = 1.17) was observed in cases with an age of onset <65 years of age, 41.3% (OR relative to APOE4+ controls = 1.09) in those with an age of onset between 66 and 74 years of age, and 40.6% (OR relative to APOE4+ controls = 1.06) in AD cases with an age of onset >75 years of age. Our data suggests that IL6R A358 is modestly enriched in all AD cases carrying the APOE4 allele, and is further enriched in cases with an age of onset <65 years of age.

Discussion

The 358A variant has been associated with elevated plasma sIL-6R levels in an allelic dose dependent manner[11,12,49] and this effect is also true in Alzheimer’s CSF with genotype being a major driver of sIL-6R concentration[4]. In the CSF, heterozygote carriers of 358A had ~45% higher levels of sIL-6R than non-carriers, however disease status (AD, mild cognitive impairment or control) had no significant effect on sIL-6R levels.

We sought to determine the functional consequences of increased cleavage and elevated levels of sIL-6R in the CNS of AD cases. We hypothesized from prior studies of IL-6 in peripheral cells expressing IL-6R that IL-6 trans-signaling may be enhanced concomitantly with cis-signaling being impaired in carriers of the rs2228145 variant, as evidenced by observations of reduced STAT3 signaling[10,56] and reduced CRP release[57]. By examination of gene expression patterns in primary cells we determined that in the CNS, microglia are the primary source of IL-6R and that astrocytes are competent for IL- 6 trans-signaling through gp130. In addition to our work, other published studies have also observed higher Il6ra (IL6R) expression in microglia compared to astrocytes and oligodendrocytes[58,59]. The expression of Il6st (gp130) however, is consistently higher in astrocytes versus neurons and oligodendrocytes[60]. We then identified IL-6 responsive genes in these cell types by stimulation with IL-6 and sIL-6R. While some gene expression differences were common between enriched cell types, the majority of differentially expressed genes were unique to either astrocytes or microglia. For example, prominent up regulation of SERPINA3 and TIMP1 was observed specifically in astrocytes. Enrichment of these individual genes in A358 carriers was observed in addition to the composite score analysis revealing a clear elevation of signal in A358 IL6R carriers with AD but little effect in control subjects. The possibility of enhanced trans-signaling and reduced cis-signaling would be congruent with our observation of a strong astrocyte derived IL-6 gene signal in LOAD subjects and a weaker IL-6 signal from the microglia derived gene set (Fig. 5).

Fig. 5 — Proposed model for IL-6 cis- and trans-signaling in the CNS.

Given the most prominently altered genes are predominantly expressed by astrocytes, we examined the distribution of GFAP expression as a way to assess the level of astrogliosis across the sample series. As has been previously observed[61,62] there was a significant increase in GFAP expression in AD cases relative to controls, but importantly this occured independently of IL6R genotype. This suggests that the observed increases in astrocyte IL-6 pathway activity are driven by IL6R genotype and not due to unequal astrocyte densities between groups.

Though APOE4 is a major risk factor for developing LOAD, it is incompletely penetrant and individuals display a wide-range of age of onset, suggesting that modifiers of APOE4 risk may exist. We designed a genetic modifier screen that compared APOE4 carriers with an early age of onset of LOAD (<65 years) to elderly cognitively intact APOE4 carriers. Our initial study identified a variant in the IL6R locus as a candidate APOE4 modifier, and subsequent studies supported this finding.

In conclusion, our data indicate that the IL6R D358A polymorphism leads to increased shedding of IL6R from the cell surface, which in AD cases leads to altered IL-6 pathway activity particularly in astrocytes, and may be associated with an earlier age of AD onset in APOE4+ cases. Therapeutics targeting IL-6 signaling for rheumatoid arthritis have been shown to be efficacious thus further work is warranted to consider inhibition of the IL-6 pathway in Alzheimer’s disease.

Supplementary Material

NIHMS890907-supplement-supplement_1.pdf^{(1.4MB, pdf)}

Acknowledgments

The authors would like to thank the patients who generously participated in this study. This work was supported by grants from the National Institutes of Health (R01-NS085419; R01-AG044546, P01-AG003991, U01 AG049508 and R01-AG035083). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

We thank Jason Hackney, Melanie Huntley, Pascal Steiner, and Gerard Manning for their insightful comments in the design and analysis of this project. The authors would also like to thank the Genentech, Inc. microarray lab for their technical assistance with the microarray samples, GAP donation program at Genentech, and Allison Bruce for assistance with the schematic illustration.

Footnotes

Ethics Statement

The Genentech Institutional Animal Care and Use Committee approved all mouse studies.

ADNI

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California – San Francisco. ADNI is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was to recruit 800 subjects but ADNI has been followed by ADNI-GO and ADNI-2. To date these three protocols have recruited over 1500 adults, ages 55 to 90, to participate in the research, consisting of cognitively normal older individuals, people with early or late MCI, and people with early AD. The follow up duration of each group is specified in the protocols for ADNI-1, ADNI-2 and ADNI-GO. Subjects originally recruited for ADNI-1 and ADNI-GO had the option to be followed in ADNI-2. For up-to-date information, see www.adni-info.org.

Author contributions: R.R.G., J.S.K., P.C.G.H., and M.v.d.B. conceived the study and designed the experiments. T.R.B. performed the genetic analysis. N.R. performed all the experiments for functional characterization of the SNP rs2228145. P.C.G.H. implemented the cell culture and PCR experiments. J.L.L. performed the bioinformatics and statistical analyses. Q.T.P. and J.R.L. did the Mass-spec analysis. M.A.P-V, J.H, L.A.F, J.S.K, G.D.S, C.C., and A.M.G contributed genetic data and/or DNA samples. J.L.L., T.R.B, P.C.G.H., N.R, R.R.G, J.S.K., and M.v.d.B. wrote and reviewed the manuscript. All the authors read and approved the manuscript.

Competing interests: P.C.G.H., N.R., T.R.B., Q.T.P., K.S., D.V.H., J.R.L., T.W.B., J.S.K., R.R.G., and M.v.d.B. are paid employees of Genentech Inc.

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PERMALINK

A common variant of IL-6R is associated with elevated IL-6 pathway activity in Alzheimer’s disease brains

Patrick CG Haddick

Jessica L Larson

Nisha Rathore

Tushar R Bhangale

Qui T Phung

Karpagam Srinivasan

David V Hansen

Jennie R Lill

Margaret A Pericak-Vance

Jonathan Haines

Lindsay A Farrer

John S Kauwe

Gerard D Schellenberg

Carlos Cruchaga

Alison M Goate

Timothy W Behrens

Ryan J Watts

Robert R Graham

Joshua S Kaminker

Marcel van der Brug

Abstract

Introduction

Materials and Methods

Analysis of CSF

Detection of soluble and membrane bound IL-6R in cultured human CD4+ T cells

In vitro IL-6R shedding in 293T cells

Adam 10 and Adam 17 in-vitro enzymatic assay

Quantification of peptides by Mass-spectrometry

Astrocyte and microglia cell culture

Western blotting of IL-6 in astrocytes and microglia

Quantitative PCR and RT-PCR

Microarray analysis of astrocyte and microglia cultures treated with IL-6 + sIL-6R

Creation of IL-6 + sIL-6R responsive gene sets in astrocytes and microglia and pathway analysis

Analysis of human cortex gene expression and genotype data

Gene sets and gene set enrichment analysis

IL-6 score in clinical samples

Subjects and genotyping

Quality control, genotype imputation, and association analysis

Results

A358 IL-6R is associated with increased shedding of membrane bound IL-6R

Fig. 1. A358 is associated with increased ectodomain shedding of IL-6R by Adam 17.

ADAM17 and ADAM10 preferentially cleave IL6R A358

Identification of astrocyte and microglia IL-6 + sIL-6R responsive gene signatures

Fig. 2. IL-6 induced gene expression in astrocytes and microglia.

IL-6 responsive genes are enriched in brains of A358 carriers with AD

Fig. 3. Elevated IL-6 responsive score in IL6R A358 carriers.

An IL-6R coding region SNP is a candidate modifier of age of onset

Fig. 4. Association of rs2228145 in IL6R with earlier age of AD onset in APOE4 carriers.

Discussion

Fig. 5.

Supplementary Material

Acknowledgments

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

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