Background
Alzheimer’s disease (AD) is the most common form of dementia worldwide and has been recently reconceptualized as a dynamic and progressive process in which pathological changes start decades prior to the onset of clinical symptoms [
1,
2]. According to the amyloid cascade hypothesis [
3], the accumulation of brain amyloid β (Aβ) sets a cascade of progressive neurodegenerative changes—including the formation of intracellular inclusion of neurofibrillary tangles (NFTs)—resulting in cognitive impairment and, ultimately, dementia. Imaging and cerebrospinal fluid (CSF) biomarkers have successfully advanced our knowledge in terms of the evolution of AD [
1]. However, the most recent hypothetical model of AD biomarkers [
4] has not explored the role of neuroinflammation, a phenomenon implicated in the pathogenesis of AD by several lines of evidence [
5‐
7].
It is becoming a common theme the high likelihood that neuroinflammation in AD is dependent on several genetic factors and is affected by environmental interactions that happen during an individual’s lifetime (for review, see [
8]). Previous studies have shown important interactions between immune responses and brain amyloidosis [
9], with both in vitro and in vivo studies demonstrating altered cytokine expression in AD. In addition, neuroinflammation secondary to systemic infections, traumatic brain injuries, or other neurologic conditions has been shown to increase the risk of sporadic AD [
10,
11].
Currently, it is widely accepted that Aβ is associated with innate immunity pathways—as well as molecular mediators such as cytokines, chemokines, and complement molecules—leading to neuroinflammation and disturbance in brain homeostasis. However, findings linking immune-related genes with AD have raised the possibility that inflammation is the cause of brain amyloid load. In fact, the activation of the immune response by damage-associated factors is able to increase Aβ production (for review, see [
12]). Thus, it has been hypothesized that impaired immune response either fails to clear Aβ from the brain or drives an overreaction against this protein, resulting in chronic inflammation, which effects could be either harmful or protective in nature.
Endophenotypes associated with variations in immune-related genes, particularly related to AD neuropathological features, remain elusive. Genome-wide association studies (GWAS) and meta-analysis have found immunogenetic variants associated with AD, namely
CR1,
CLU,
TREM2,
PICALM,
CD33, and
MEF2C, reasserting the role of the immune system in AD pathophysiology [
13‐
17]. However, recent investigations did not reveal a link between brain amyloidosis and immunologic genetic variants [
18,
19], suggesting that some endophenotypes might be affected by gene-to-gene interactions or epistasis.
In multifactorial diseases such as AD, the power to detect isolated genetic variants can be reduced due to epistatic effects, which occur when one locus masks or alters the effect of another [
20‐
22]. In this respect, approaches moving beyond single-marker outcomes may better capture heritability links [
23].
In this study, we aimed to investigate the interactions between immune-related genes—primarily molecular mediators of inflammation—and the accumulation of Aβ in vivo, as quantitated by amyloid imaging with positron emission tomography (PET). We hypothesize that differential amyloid burden is associated with the deregulation of innate immunity response, which could be evidenced by epistasis analysis of genes that encode for immune proteins reported to be related to AD.
Discussion
In the present study, two interactions between two immune-related genes, C9 and IL6r, were found to be associated with [18F]florbetapir SUVRs. This result suggests that Aβ burden in the brain may be differentially affected depending on the allelic combination of the cited variants.
The SNP rs261752 is an intronic variation of the
C9 gene, with no previously reported association to any phenotypic feature or neurodegenerative endophenotype. However, it has been associated with age-related macular degeneration, a highly frequent disorder among AD patients [
46,
47]. Moreover, several studies have described increased immunoreactivity of classical complement molecules, including C9, in the vicinity of brain Aβ aggregates [
25,
48,
49]. C9 protein is also a component of the MAC, which is responsible for disrupting cellular homeostasis, causing cell death following activation of the complement pathway [
50]. Indeed, it is well known that extracellular Aβ triggers the complement cascade, leading to MAC formation [
26,
48,
51]. Since MAC requires a lipid bilayer structure to act upon, it binds to the surrounding neurites [
26,
52], leading to neurodegeneration and cell death. Furthermore, the protein clusterin, encoded by the AD-related CLU gene, has been shown to play an important role in reducing inappropriate MAC activity tied to physical interaction with the C9 protein [
53].
The two SNPs from the
IL6r gene are more than 1800 bp apart from each other (
r
2 = 0.69) and, despite not being in high linkage disequilibrium, might reflect the same signal. The SNP rs4240872 is an intronic variant of the
IL6r gene while the variant rs7514452 is located in the 3’-untranslated region (3’-UTR), an important sequence at the end of the messenger RNA (mRNA) known to affect post-translational regulation and subsequent protein expression [
54]. A previous study suggested a possible association between 3’-UTR markers and diabetes mellitus type 2 [
55], an association of possible relevance owing to evidence showing that insulin signaling is down-regulated in AD (for review, see [
56]). Additionally, Walston et al. [
57] reported that some
IL6r SNPs are associated with plasmatic levels of interleukin 6 (IL6), a cytokine that plays an important role in the regulation of neuroimmune responses, promoting both pro-inflammatory and anti-inflammatory effects [
58‐
60]. Similar results were reported here showing that CSF levels of IL6r are associated with one
IL6r SNP while plasmatic levels are associated with both SNPs (rs7514452 and rs4240872) in ADNI-1 subsample, reflecting a genotype-phenotype effect. The IL6r protein is either a part of the ligand-binding receptor of IL6 or a soluble form (s-IL6r), which binds to IL6 to enhance its activity [
61,
62]. Deregulation of immune response signaling in AD is evidenced by altered protein expression in the brain [
63,
64]. Differences in CSF and serum levels of both IL6 and s-IL6r are also evident when comparing AD patients to CN [
65‐
67].
Voxel-based findings revealed by this study further corroborate global increases of amyloid load in regions typically affected by AD pathophysiology. Homozygous subjects for minor alleles of both
IL6r and
C9 genes show higher levels of amyloid in brain areas that correspond to regions impaired in AD [
68]. Interestingly, amyloid plaques depicted by amyloid imaging agents are typically surrounded by neuroinflammatory changes such as astrocytosis and microglial activation (for review, see [
69]), reinforcing a link between amyloidosis and immune response. Additionally, one could claim that a reduction in the IL6r levels causes decreases in the IL6 activity, contributing to Aβ accumulation through different possible mechanisms.
In agreement with [
18F]florbetapir findings, the interactions between
C9 and
IL6r genes were also associated with the CSF Aβ
1-42/p-tau ratio. This finding based on an independent measurement of brain amyloidosis provides additional evidence that
C9 and
IL6r interactions affect brain accumulation of neuritic plaques in a disease-specific manner [
70]. However, it is important to take the reduced sample size present in the CSF population into consideration.
Based on our results, it seems plausible that a combination of gene polymorphisms in complement factors and interleukins plays a synergic role in determining amyloid burden in the brain. Specifically, a particular combination of genotypes that up-regulate both C9 and IL6r may exert an additive effect via neuroinflammatory processes. Besides the supposition of how these SNPs may jointly affect amyloid accumulation in the brain, no relationship between these two genes or proteins has been reported to date with respect to amyloid metabolism. However, it has been shown that the protein IL6 is able to stimulate C9 mRNA expression in post-mortem human astrocytes and neuroblastoma cells [
71,
72], showing a metabolic link between the two proteins in the cells of the nervous system.
In order to overcome the well-known limitations of association studies, several assumptions need to be addressed. For example, although all the cited proteins are related to the immune system, their roles in Aβ accumulation remain unclear. Presently, the functions of the reported SNPs remain elusive due to the lack of relevant literature. Regarding the association found between IL6r levels and
IL6r SNPs, linking the genotype with the phenotype, it is important to mention that [
73] (1) protein levels were measured on average 55 months prior to [
18F]florbetapir image acquisition; (2) there was no association between the use of anti-inflammatory drugs and IL6r levels in this sample; and (3) beyond the effect that
IL6r SNPs can have at the protein level, it is very important to know the effect of the
C9 genotypes on C9 protein to better understand how they jointly impact the immune response.
Among the limitations of the study, the ADNI is a cohort mostly composed of non-Hispanic Caucasians, limiting the extrapolation of the present findings to other population groups. A wider range of subjects varying in terms of ethnicity, family history, and disease progression should be considered for future replication of this study. It is also important to acknowledge that, despite postulated that amnestic MCI have high probability to convert to dementia due to AD, a considerable proportion of these individuals remain stable or convert to non-AD dementias [
74], being a methodological limitation to the study of the AD spectrum. Moreover, currently, it is thought that the Aβ oligomers (soluble forms) are the most synaptotoxic (for review, see [
75]) and the most chased by the immune system; however, it is not possible to detect these forms in vivo using brain imaging; [
18F]florbetapir is only able to bind to amyloid plaques. Recently, MRI probes for targeting Aβ oligomers have been developed and will likely provide further information regarding the association between Aβ oligomers and the immune system [
76]. In fact, more studies are needed to address the biological mechanisms in which gene interactions may affect the phenotype, using both amyloid plaque and Aβ oligomer quantifications.
It is also important to mention that statistical analyses between genetic factors do not define their biological interactions or interferences [
21], necessitating more investigation. It should be noted that age showed negligible or no effect in our analyses and does not alter the conclusions if added in the model. Though ADNI-1 data was used to confirm significant associations, the reduced sample size could have been a limiting factor with respect to the achievement of statistical significance. Based on the effect size of the interactions found in the first analysis with ADNI-GO/2 data (data not shown), the sample size required to reach 95 % of power and a type I error of 0.05 is 497 subjects. For this reason, a less strict FDR correction was adopted in the first step of the analysis. Sample size requirements might also explain why it was not possible to fully replicate the results using data from ADNI-1, while results were replicated in the combined sample—the
p values obtained for the interactions in the combined sample would still be significant at 0.05 level if FDR correction had to be applied. Additionally, the highly significant
p values obtained with the combined sample indicate a high likelihood that the initial results obtained using ADNI 2 data were not a consequence of a type II error.
Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (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: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; 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.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. 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 Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
This work was also supported by the Canadian Institutes of Health Research (CIHR) (MOP-11-51-31), the Alan Tiffin Foundation, the Alzheimer’s Association (NIRG-08-92090), the Fonds de la recherche en santé du Québec (chercheur boursier,PRN), and by the CAPES Foundation [0327/13-1]. SG and PR are members of the CIHR Canadian Consortium of Neurodegeneration in Aging.
Competing interests
The authors declare no competing financial interests.
Authors’ contributions
AB, ALa, JP, and PR participated in the design of this study. ALa and PR supervised the study. AB and ALa carried out the statistical analysis. PL, AD, and CP provided support in the genetic analysis. AB, SMa, SMo, MS, and TB performed the imaging processing and quality control. SMa and TP provided support in the imaging analysis. AB wrote the paper. ERZ, ALe, SG, and GR contributed to the revision of the paper. All authors read and approved the final version of the manuscript.