Nisrine Bissar
Rayan Kassir
Ali Salami2- 1Department of Medical Laboratory Technology, Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
- 2Faculty of Sciences (V), Lebanese University, Nabatieh, Lebanon
- 3Molecular Testing Laboratory, Department of Medical Laboratory Technology, Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
Abstract
Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by progressive cognitive decline. Genetic factors have been implicated in disease susceptibility as its etiology remains multifactorial. The CD33 and the HLA-DRB1 genes, involved in immune responses, have emerged as potential candidates influencing AD risk. In this study, 644 Lebanese individuals, including 127 AD patients and 250 controls, were genotyped, by KASP assay, for six SNPs selected from the largest GWAS study in 2021. Logistic regression analysis assessed the association between SNP genotypes and AD risk, adjusting for potential confounders. Among the six SNPs analyzed, rs1846190G>A in HLA-DRB1 and rs1354106T>G in CD33 showed significant associations with AD risk in the Lebanese population (p < 0.05). Carriers of the AG and AA genotypes of rs1846190 in HLA-DRB1 exhibited a protective effect against AD (AG: OR = 0.042, p = 0.026; AA: OR = 0.052, p = 0.031). The GT genotype of rs1354106T>G in CD33 was also associated with reduced risk (OR = 0.173, p = 0.005). Following Bonferroni correction, a significant correlation of rs1354106T > G with AD risk was established. Our results might highlight the complex interplay between genetic and immunological factors contributing to the development of the disease.
Impact statement
Neuroinflammation and innate immunity have recently emerged as important contributors to AD pathology. GWAS studies pinpointed the association of immunity-related gene SNPs, including, rs1354106T>G in CD33 rs1846190G>A in HLA-DRB1, with AD. However, these studies were limited in the applicability to non-European populations. Our study reports a significant association of rs1354106T>G with AD in a Middle Eastern population, the Lebanese population, for the first time. This further confirms association results and improves the equity of the previously generated genetic information. On the other hand, the importance of our findings lies in providing further genetic support for the role of immunity-related genes and SNPs in AD. Our study establishes the protective role of rs1354106T>G SNP, in CD33, against AD, previously reported in Sherva et al., 2014 [1] and highlights a potential protective effect of rs1846190G>A in HLA-DRB1 against AD. These protective variants could enhance AD risk assessment in asymptomatic individuals and offer potential drug targets.
Introduction
Alzheimer’s disease (AD) is the most common neurodegenerative disorder, leading to memory loss and multiple cognitive impairments, and is the fourth leading cause of death worldwide among the elderly population [2]. There are two main forms of AD: familial and sporadic [3]. Familial AD typically presents as autosomal dominant and early onset (EOAD), in individuals under 65 years of age, accounting for 1–5% of all cases. EOAD has been linked to mutations in three genes, the presenilin 1 gene (PSEN1), which is identified in up to 70% of cases with familial AD cases; the presenilin 2 gene (PSEN2) and the Amyloid precursor protein gene (APP) [4]. Sporadic AD, or late-onset AD (LOAD) occurs in individuals older than 65 years, with age being the primary risk factor [5]. LOAD is a complex disorder with several identified risk factors including female sex, traumatic brain injury, depression, environmental pollution, physical inactivity, social isolation, low academic level, and metabolic syndrome [6]. Genetic susceptibility also plays a significant role, particularly the ε4 allele of apolipoprotein E (APOE) [7]. The heritability of LOAD is estimated to be between 60–80% [8]. AD is associated with the presence of β-amyloid (Aβ)-containing extracellular plaques and tau-containing intracellular neurofibrillary tangles in the brains of patients [9]. However, the utility of Aβ as a biomarker for AD has faced challenges, with its detection in about 30% of cognitively normal elderly individuals and with the absence of significant clinical improvements after removing Aβ from the brains of AD patients [10–12]. Neuroinflammation, triggered by pathological damage in the central and peripheral nervous system, is recognized as a significant contributor to AD pathogenesis [13]. This leads to the release of proinflammatory cytokines, chemokines, complement cytokines, and small molecule messengers like prostaglandins, nitric oxide (NO), and reactive oxygen species (ROS) [14]. In addition, persistently activated microglia produce high levels of proinflammatory cytokines and chemokines, leading to neuronal dysfunction [15]. Furthermore, microglia are implicated in synaptic loss, tau phosphorylation, and cognitive decline [16]. Genome-wide association studies (GWAS) indicate that a large percentage of AD risk genes are associated with innate immunity and inflammation, highlighting the critical role the immune system plays in AD pathology [17–19].
The cluster of differentiation 33 gene, CD33, on chromosome 19p13.3, is one of the top-ranked AD risk genes identified by genome-wide association studies (GWAS) and has been replicated in numerous genetic analyses [20, 21]. CD33 belongs to the sialic acid-binding immunoglobulin (Ig)-like family and is a myeloid cell receptor, exclusively expressed by myeloid cells and microglia. It has several functions in cell adhesion, anti-inflammatory signaling, and endocytosis [22]. Clinical and biochemical evidence implicates CD33 in Aβ-associated pathology by affecting microglia-mediated Aβ clearance [23–25].
CD33 has been implicated in modulating AD susceptibility and the pathology of late-onset Alzheimer’s Disease (LOAD) [25–27]. Higher CD33 expression in the parietal lobe is shown to be associated with more advanced cognitive decline or disease status [24]. Other studies show that reduced expression of CD33 allows more efficient phagocytic clearance of pathogenic Aβ by microglia and thus protects against AD [25].
HLA, located within the major histocompatibility complex (MHC) on chromosome 6p21, consists of several highly polymorphic and tightly linked genes [28]. Numerous association studies have confirmed significant associations between certain HLA gene variants within MHC class I and II regions and AD [29]. The upregulation of HLA class II antigens is widely accepted as a definitive marker of activated microglia, which are implicated in the formation of lesions characteristic of AD [30].
The mechanism by which HLA may contribute to Alzheimer’s disease (AD) involves the recognition and processing of pathological protein deposits, such as Aβ peptides, by microglia. Once engulfed by microglia, these proteins are broken down and presented to T lymphocytes in conjunction with specific HLA class I or II molecules. This process triggers B lymphocytes to produce antibodies against Aβ peptides, while activated T lymphocytes target cells producing excessive Aβ for elimination [31]. While this immune cascade is a natural defense mechanism against harmful protein accumulation, excessive reactions may lead to detrimental effects [32, 33]. Consequently, an immune response’s severity, scope, and duration can vary depending on the expression of HLA molecules. Individuals carrying certain pathogenic HLA alleles are at a higher risk of developing specific immune-mediated diseases compared to those lacking these alleles [34].
A large GWAS study, including 1,126,563 individuals 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls, identified 38 AD risk loci including CD33 and HLA-DRB1 with SNP variants (RS1354106T>G) and (RS1846190G>A) consecutively [20]. In this report, we aimed to investigate the correlation between these SNPs and AD in a sample of 644 Lebanese individuals, including 127 AD patients and 250 controls.
Materials and methods
Study subjects
Blood samples were obtained from 644 Lebanese individuals, out of whom, 127 participants were diagnosed with Alzheimer’s disease (AD) by neurologists after memory and cognitive tests, functional assessment, physical and neurological exams, diagnostic tests, and brain imaging. Subjects with no Alzheimer’s disease were 58 years or older, selected based on the absence of personal or familial psychiatric or cognitive impairment history, and with a Mini-Mental State Examination (MMSE) score above 26 (Table 2). Participants were recruited in accordance with the latest version of the Declaration of Helsinki for Ethical Principles for Medical Research Involving Human Subjects. Ethical approval was obtained from the local IRB Clinical Research Ethics Committee at Beirut Arab University. Each participant underwent a thorough consent process, which included a consent form and questionnaire.
SNP selection
Six SNPs were selected for inclusion in this study based on findings from the largest GWAS study to date conducted by Wightman et al. (2021). This GWAS involved a total of 1,126,563 individuals, comprising 90,338 cases (46,613 proxy) and 1,036,225 controls (318,246 proxy), and identified a total of 38 risk loci, including seven previously unidentified loci.
The SNPs were chosen according to the function and role of their genes in AD pathology. Since this study aims to focus on the role of the immune system in AD, the three SNPs, rs1846190G>A, rs1354106T>G, and rs1582763G>A, were selected based on their respective immunity related genes HLA-DRB1, CD33 and MS4A4A with well documented association with AD [20, 21, 29, 35]. The remaining three SNPs were selected according to a variety of other functions of their respective genes. These are rs2154482G>T in APP gene, a major player of the amyloidogenic pathway of AD pathogenesis [36], rs3935067G>C in EPHA1AS 1 long noncoding RNA gene with significant association with AD [37], rs7912495A>G in ECHDC3, which is responsible for type 2 diabetes Mellitus-related episodic memory impairment [38].
Genotyping
Genomic DNA was extracted from peripheral blood leukocytes using FlexiGene® DNA kit (QIAGEN) according to the manufacturer’s instructions. Genotyping was performed at LGC group (Berlin, Germany) using KASP genotyping assay. KASP is a homogeneous, fluorescence (fluorescence resonance energy transfer) based assay that enables accurate biallelic discrimination of known genetic variations such as SNPs and insertions/deletions as describe previously [39].
Statistical analysis
All analysis was conducted using SPSS software version 24 (SPSS, Inc, Chicago, Illinois). All continuous variables were expressed as mean ± standard deviation. Normality was tested using Shapiro-Wilk test.
Association analysis of the six SNPs with Alzheimer’s disease
A binary multiple logistic regression model was employed to investigate the association between the presence of AD (dependent variable, N = 377) and the genotypes of the six SNPs, while adjusting for potential confounders. Covariates, including age, gender, body mass index, educational level, smoking status, and marital status, were selected based on their established connections with AD and their potential to introduce confounding effects into the SNP-disease association analysis.
Results
The characteristics of all study participants are described in Table 1. The average age is 61, with 37.4% being females. Of 612 participants, 28.1% had normal weight, 32.4% were overweight, and 242 (39.5%) were obese. Education levels varied also as 25.9% had no formal education, 59.0% attended some school, 3.3% completed high school, and 12.0% attended university. Additionally, 38.1% of the participants were smokers. Blood pressure and lipid measurements were also recorded.
Table 1. Characteristics of all study participants.
The characteristics of AD patients and controls are described in Table 2. The mean age of AD patients (80.99 ± 7.94) was significantly greater than the mean age of controls (70.06 ± 8.82) (p < 0.001). Moreover, there were significant differences between AD subjects and controls in terms of marital status, number of smokers.
Table 2. Characteristics of AD patients and controls.
The SNP allele frequencies detected in our study showed minimal variation from the allele frequencies in the Middle Eastern populations (GnomAD) (Table 3). The minor allele frequencies ranged from 0.23 to 0.49, suggesting that these alleles were relatively common in the studied population. The observed genotype frequencies of rs1846190G>A and rs1354106T>G did not show significant deviations from the Hardy-Weinberg equilibrium (HWE). AG and AA carriers of the rs1846190G>A SNP had a decreased risk of AD (OR = 0.042, p = 0.026 and OR = 0.052, p = 0.031 respectively), indicating a much lower likelihood of developing Alzheimer’s disease. Likewise, the rs1354106GT genotype had a lower risk (OR = 0.173, p = 0.005) compared to the TT genotype, indicating a significantly lower risk of Alzheimer’s disease in the studied population.
Table 3. The loci, allele frequencies, and genetic effects of the six SNPs in this study.
Assessment of the association between the six SNPs and the likelihood of developing AD, while adjusting for age, gender, BMI, educational status and smoking showed a significant association with AD for rs1846190G>A (AG; OR = 0.042, P = 0.026 and AA; OR = 0.052, P = 0.031) in HLA-DRB1 and rs1354106T>G (GT; OR = 0.173, P = 0.005) in CD33 (Table 4). When applying Bonferroni correction, only rs1354106T>G in CD33 remained significant thus showing a robust association with AD.
Table 4. Multiple Logistic Regression analysis of risk factors with Alzheimer’s disease.
Discussion
In our study, among the six SNPs analyzed, only rs1846190G>A, a regulatory region variant in HLA-DRB1, and rs1354106T>G, an intron variant in CD33, showed a significant association with AD in the Lebanese population. Following Bonferroni correction, only rs1354106T>G in CD33 remained significant, which highlights the potential importance of this gene in the pathogenesis of AD.
SNPs have the potential to alter CD33’s expression level, structure, and function, altering how microglia clear amyloid β [25, 40, 41]. Two previously reported SNPs in CD33, rs3865444 and rs12459419, have shown a protective effect against AD [42]. The protective allele of the rs3865444, located in the promotor region, plays a role in the reduction of both CD33 expression and insoluble Aβ42 levels in AD brain, especially in the microglial cells [25]. Similarly, rs12459419, located in exon 2, and in linkage disequilibrium with rs3865444, exhibits a protective effect by enhancing exon skipping and promoting the production of a short isoform of CD33, known as human CD33m [43]. Recent studies using cell and animal models have highlighted the functional significance of human CD33m, as a gain-of-function variant that enhances Aβ1–42 phagocytosis in microglia [41].
Conversely, a recent computational analysis investigating the 3D structures of CD33 with rs2455069 A>G SNP suggests a potential increase in the risk of Alzheimer’s disease. The study proposes that over time, the CD33-R69G variant, which binds to sialic acid, could boost CD33’s ability to inhibit the breakdown of amyloid plaques [44].
Our study further explored the association of rs1354106 T>G with AD, revealing a protective effect in Lebanese patients (GT; O. R = 0.173 CI = 0.058–0.586, P = 0.005). This finding notably aligns with the findings from a previous study which utilized a Bayesian longitudinal low-rank regression (L2R2) model to explore the impact of single nucleotide polymorphisms (SNPs). Their results revealed that rs1354106 was associated with a reduced rate of decline in the AD assessment scale cognitive score [1]. Moreover, in the same study, the effect of this SNP on the longitudinal trajectories of the hippocampi was investigated. Results revealed that the minor allele significantly slowed hippocampal atrophy compared to the major allele. This suggests a potential protective effect associated with the minor allele of rs1354106 in patients with Alzheimer’s disease and mild cognitive impairment [45]. This is validated by our findings, which indicated a protective role of the rs1354106 T>G in Lebanese AD patients (GT; O. R = 0.173 CI = 0.051–0.586, P = 0.005).
The association between HLA gene variants and Alzheimer’s disease (AD) risk has been extensively explored across diverse populations. Our study on the Lebanese population, first revealed a protective effect of rs1846190G>A, of HLA-DRB1 but the association did not stand after Bonferroni correction. HLA-DRB1 13:02 protects against age-related neural network deterioration and mitigates the deleterious effects of apoE4 on neural network functioning [46]. Furthermore, a recent study, conducted on the Japanese population, identified a significant association between the HLA-DRB109:01-DQB1*03:03 haplotype and LOAD risk in APOE ε4–negative individuals [47]. Moreover, studies have emphasized the protective function of HLA-DRB1*04 against AD, as its presence is correlated with lower CSF tau levels and fewer neurofibrillary tangles in AD subjects [48]. Conversely, HLA-DRB1*03 was identified as a risk factor for late-onset AD (LOAD) in the German population [31]. Additionally, the SNP rs9271192 in HLA-DRB5–DRB1 region has been found to influence AD risk through large meta-analyses of genome-wide association studies (GWAS) in Caucasian populations [48]. These findings have been replicated successfully in two large-scale studies conducted on the Chinese population [49, 50].
A recent study examined global cortical amyloid PET burden, incorporating the 38 gene variants, from the GWAS study, using PRSice-2, to assess overall phenotypic variance in two cohorts [20]. The analysis revealed a strong association between AD risk variants (such as APOE, PICALM, CR1, and CLU) and amyloid PET levels in both cohorts. Importantly, neither CD33 rs1354106T>A nor HLA-DRB1 rs1846190G>A demonstrated an association with amyloid PET levels in this study [51]. This underscores the alignment of our findings with existing evidence concerning the protective effect of both variants against Alzheimer’s disease risk.
In conclusion, understanding protective variants could refine AD risk assessment in asymptomatic individuals, aiding AD prevention. Furthermore, identifying genetic variants that confer protection via a loss-of-function or gain-of-function offers potential drug targets. Most drug candidates never reach the clinic, but those with the same mechanism as protective variants have a higher success rate. Our current study has provided convincing statistical support for an association between CD33 polymorphisms and LOAD. Specifically, the carriage of GT alleles rs1354106 T>G in CD33 is linked to a protective effect against LOAD in the Lebanese Population. The main limitation of this study is the sample size used, probably affecting the statistical significance of rs1846190 SNP and HLA-DRB1 association with AD after Bonferroni correction. Further investigations involving larger sample sizes and diverse ethnic groups are needed to validate the role of rs1354106 and examine the potential role of rs1846190 in LOAD.
Author contributions
NB designed the study and supervised sample collection, genotyping procedure, statistical analysis and manuscript writing. AS contributed to statistical analysis. SES contributed to genotyping procedure, statistical analysis and reviewed the manuscript. RK contributed to sample collection, statistical analysis and wrote the manuscript. All authors contributed to the article and approved the submitted version.
Data availability
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Beirut Arab University institutional review board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
We would like to express sincere gratitude to the study participants and their families for their time and effort to help create the present study. Gratitude is also extended to Dar-Al Ajaza Al-Islamia Hospital in Beirut, the Social Services Association in North Lebanon, Bayt Al Shaikhookha in Tripoli, Bayt Al Raha Ozanam in Batroun, and Dar Al Inaya in Jbeil for their invaluable assistance in sample collection. We would also like to thank the field investigators for the recruitment and examination of the population involved in this study, namely N. Mohsen, N. Naja and N. Ramadan.
Conflict of interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
1. Sherva, R, Tripodis, Y, Bennett, DA, Chibnik, LB, Crane, PK, de Jager, PL, et al. Genome-wide association study of the rate of cognitive decline in Alzheimer's disease. Alzheimer's and Dementia (2014) 10(1):45–52. doi:10.1016/j.jalz.2013.01.008
2. DeTure, MA, and Dickson, DW. The neuropathological diagnosis of Alzheimer’s disease. Mol neurodegeneration (2019) 14(1):32. doi:10.1186/s13024-019-0333-5
3. Long, JM, and Holtzman, DM. Alzheimer disease: an update on pathobiology and treatment strategies. Cell (2019) 179(2):312–39. doi:10.1016/j.cell.2019.09.001
4. Andrade-Guerrero, J, Santiago-Balmaseda, A, Jeronimo-Aguilar, P, Vargas-Rodríguez, I, Cadena-Suárez, AR, Sánchez-Garibay, C, et al. Alzheimer’s disease: an updated overview of its genetics. Int J Mol Sci (2023) 24(4):3754. doi:10.3390/ijms24043754
5. Silva, MV, Loures, CD, Alves, LC, De Souza, LC, Borges, KB, and Carvalho, MD. Alzheimer’s disease: risk factors and potentially protective measures. J Biomed Sci (2019) 26:33–1. doi:10.1186/s12929-019-0524-y
6. Livingston, G, Sommerlad, A, Orgeta, V, Costafreda, SG, Huntley, J, Ames, D, et al. Dementia prevention, intervention, and care. The Lancet (2017) 390(10113):2673–734. doi:10.1016/s0140-6736(17)31363-6
7. Bezprozvanny, I. Alzheimer's disease–Where do we go from here? Biochem Biophysical Res Commun (2022) 633:72–6. doi:10.1016/j.bbrc.2022.08.075
8. Bellenguez, C, Küçükali, F, Jansen, IE, Kleineidam, L, Moreno-Grau, S, Amin, N, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet (2022) 54(4):412–36. doi:10.1038/s41588-022-01024-z
9. Knopman, DS, Amieva, H, Petersen, RC, Chételat, G, Holtzman, DM, Hyman, BT, et al. Alzheimer disease. Nat Rev Dis primers (2021) 7(1):33. doi:10.1038/s41572-021-00269-y
10. Kruse, N, Persson, S, Alcolea, D, Bahl, JM, Baldeiras, I, Capello, E, et al. Validation of a quantitative cerebrospinal fluid alpha-synuclein assay in a European-wide interlaboratory study. Neurobiol Aging (2015) 36(9):2587–96. doi:10.1016/j.neurobiolaging.2015.05.003
11. Herrup, K. The case for rejecting the amyloid cascade hypothesis. Nat Neurosci (2015) 18(6):794–9. doi:10.1038/nn.4017
12. Villemagne, VL, Pike, KE, Chételat, G, Ellis, KA, Mulligan, RS, Bourgeat, P, et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol (2011) 69(1):181–92. doi:10.1002/ana.22248
13. Wang, C, Zong, S, Cui, X, Wang, X, Wu, S, Wang, L, et al. The effects of microglia-associated neuroinflammation on Alzheimer’s disease. Front Immunol (2023) 14:1117172. doi:10.3389/fimmu.2023.1117172
14. Leng, F, and Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol (2021) 17(3):157–72. doi:10.1038/s41582-020-00435-y
15. Mrak, RE, Griffin, WST, and Griffin, WS. Microglia and neuroinflammation: a pathological perspective. J neuroinflammation (2004) 1:1–4. doi:10.1186/1742-2094-1-1
16. Mander, BA, Dave, A, Lui, KK, Sprecher, KE, Berisha, D, Chappel-Farley, MG, et al. Inflammation, tau pathology, and synaptic integrity associated with sleep spindles and memory prior to β-amyloid positivity. Sleep (2022) 45(9):zsac135. doi:10.1093/sleep/zsac135
17. Shigemizu, D, Mori, T, Akiyama, S, Higaki, S, Watanabe, H, Sakurai, T, et al. Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis. Alzheimer's Res and Ther (2020) 12:87–2. doi:10.1186/s13195-020-00654-x
18. Pimenova, AA, Raj, T, and Goate, AM. Untangling genetic risk for Alzheimer’s disease. Biol Psychiatry (2018) 83(4):300–10. doi:10.1016/j.biopsych.2017.05.014
19. Wes, PD, Sayed, FA, Bard, F, and Gan, L. Targeting microglia for the treatment of Alzheimer's Disease. Glia (2016) 64(10):1710–32. doi:10.1002/glia.22988
20. Wightman, DP, Jansen, IE, Savage, JE, Shadrin, AA, Bahrami, S, Holland, D, et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet (2021) 53(9):1276–82. doi:10.1038/s41588-021-00921-z
21. Zhao, L. CD33 in Alzheimer’s disease–biology, pathogenesis, and therapeutics: a mini-review. Gerontology (2019) 65(4):323–31. doi:10.1159/000492596
22. Wang, WY, Liu, Y, Wang, HF, Tan, L, Sun, FR, Tan, MS, et al. Impacts of CD33 genetic variations on the atrophy rates of Hippocampus and parahippocampal gyrus in normal aging and mild cognitive impairment. Mol Neurobiol (2017) 54:1111–8. doi:10.1007/s12035-016-9718-4
23. Karch, CM, and Goate, AM. Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry (2015) 77(1):43–51. doi:10.1016/j.biopsych.2014.05.006
24. Bradshaw, EM, Chibnik, LB, Keenan, BT, Ottoboni, L, Raj, T, Tang, A, et al. CD33 Alzheimer's disease locus: altered monocyte function and amyloid biology. Nat Neurosci (2013) 16(7):848–50. doi:10.1038/nn.3435
25. Griciuc, A, Serrano-Pozo, A, Parrado, AR, Lesinski, AN, Asselin, CN, Mullin, K, et al. Alzheimer’s disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron (2013) 78(4):631–43. doi:10.1016/j.neuron.2013.04.014
26. Hollingworth, P, Harold, D, Sims, R, Gerrish, A, Lambert, JC, Carrasquillo, MM, et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet (2011) 43(5):429–35. doi:10.1038/ng.803
27. Carrasquillo, MM, Belbin, O, Hunter, TA, Ma, L, Bisceglio, GD, Zou, F, et al. Replication of EPHA1 and CD33 associations with late-onset Alzheimer's disease: a multi-centre case-control study. Mol neurodegeneration (2011) 6:54–9. doi:10.1186/1750-1326-6-54
28. Horton, R, Wilming, L, Rand, V, Lovering, RC, Bruford, EA, Khodiyar, VK, et al. Gene map of the extended human MHC. Nat Rev Genet (2004) 5(12):889–99. doi:10.1038/nrg1489
29. Aliseychik, MP, Andreeva, TV, and Rogaev, EI. Immunogenetic factors of neurodegenerative diseases: the role of HLA class II. Biochemistry (Moscow) (2018) 83:1104–16. doi:10.1134/s0006297918090122
30. McGeer, PL, McGeer, EG, and Yasojima, K. Alzheimer disease and neuroinflammation. Vienna: Springer (2000).
31. Wang, ZX, Wan, Q, and Xing, A. HLA in Alzheimer’s disease: genetic association and possible pathogenic roles. NeuroMolecular Med (2020) 22(4):464–73. doi:10.1007/s12017-020-08612-4
32. Kusdra, L, Rempel, H, Yaffe, K, and Pulliam, L. Elevation of CD69+ monocyte/macrophages in patients with Alzheimer's disease. Immunobiology (2000) 202(1):26–33. doi:10.1016/s0171-2985(00)80049-2
33. Malm, TM, Magga, J, Kuh, GF, Vatanen, T, Koistinaho, M, and Koistinaho, J. Minocycline reduces engraftment and activation of bone marrow-derived cells but sustains their phagocytic activity in a mouse model of Alzheimer's disease. Glia (2008) 56(16):1767–79. doi:10.1002/glia.20726
34. Zota, V, Nemirovsky, A, Baron, R, Fisher, Y, Selkoe, DJ, Altmann, DM, et al. HLA-DR alleles in amyloid β-peptide autoimmunity: a highly immunogenic role for the DRB1*1501 allele. The J Immunol (2009) 183(5):3522–30. doi:10.4049/jimmunol.0900620
35. Sanyal, R, Polyak, MJ, Zuccolo, J, Puri, M, Deng, L, Roberts, L, et al. MS4A4A: a novel cell surface marker for M2 macrophages and plasma cells. Immunol and Cell Biol (2017) 95(7):611–9. doi:10.1038/icb.2017.18
36. Thinakaran, G, and Koo, EH. Amyloid precursor protein trafficking, processing, and function. J Biol Chem (2008) 283(44):29615–9. doi:10.1074/jbc.r800019200
37. De Rojas, I, Moreno-Grau, S, Tesi, N, Grenier-Boley, B, Andrade, V, Jansen, IE, et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nat Commun (2021) 12(1):3417. doi:10.1038/s41467-021-22491-8
38. Zhao, Q, Du, X, Liu, F, Zhang, Y, Qin, W, and Zhang, Q. ECHDC3 variant regulates the right hippocampal microstructural integrity and verbal memory in type 2 diabetes Mellitus. Neuroscience (2024) 538:30–9. doi:10.1016/j.neuroscience.2023.12.003
39. Masri, I, Salami, A, El Shamieh, S, and Bissar-Tadmouri, N. rs3851179G> A in PICALM is protective against Alzheimer’s disease in five different countries surrounding the Mediterranean. Curr Aging Sci (2020) 13(2):162–8. doi:10.2174/1874609812666191019143237
40. Efthymiou, AG, and Goate, AM. Late onset Alzheimer’s disease genetics implicates microglial pathways in disease risk. Mol neurodegeneration (2017) 12:43–2. doi:10.1186/s13024-017-0184-x
41. Bhattacherjee, A, Jung, J, Zia, S, Ho, M, Eskandari-Sedighi, G, St. Laurent, CD, et al. The CD33 short isoform is a gain-of-function variant that enhances Aβ1–42 phagocytosis in microglia. Mol Neurodegeneration (2021) 16:19–22. doi:10.1186/s13024-021-00443-6
42. Jiang, YT, Li, HY, Cao, XP, and Tan, L. Meta-analysis of the association between CD33 and Alzheimer’s disease. Ann translational Med (2018) 6(10):169. doi:10.21037/atm.2018.04.21
43. Malik, M, Simpson, JF, Parikh, I, Wilfred, BR, Fardo, DW, Nelson, PT, et al. CD33 Alzheimer's risk-altering polymorphism, CD33 expression, and exon 2 splicing. The J Neurosci (2013) 33(33):13320–5. doi:10.1523/jneurosci.1224-13.2013
44. Tortora, F, Rendina, A, Angiolillo, A, Di Costanzo, A, Aniello, F, Donizetti, A, et al. CD33 rs2455069 SNP: correlation with Alzheimer’s disease and hypothesis of functional role. Int J Mol Sci (2022) 23(7):3629. doi:10.3390/ijms23073629
45. Apostolova, LG, Green, AE, Babakchanian, S, Hwang, KS, Chou, YY, Toga, AW, et al. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease. Alzheimer Dis and Associated Disord (2012) 26(1):17–27. doi:10.1097/wad.0b013e3182163b62
46. James, LM, and Georgopoulos, AP. Human leukocyte antigen as a key factor in preventing dementia and associated apolipoprotein E4 risk. Front Aging Neurosci (2019) 11:82. doi:10.3389/fnagi.2019.00082
47. Shigemizu, D, Fukunaga, K, Yamakawa, A, Suganuma, M, Fujita, K, Kimura, T, et al. The HLA-DRB1*09:01-DQB1*03:03 haplotype is associated with the risk for late-onset Alzheimer’s disease in APOE $${{\varepsilon }}$$4–negative Japanese adults. npj Aging. (2024) 10(1):3. doi:10.1038/s41514-023-00131-3
48. Lambert, JC, Ibrahim-Verbaas, CA, Harold, D, Naj, AC, Sims, R, Bellenguez, C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet (2013) 45(12):1452–8. doi:10.1038/ng.2802
49. Jiao, B, Liu, X, Zhou, L, Wang, MH, Zhou, Y, Xiao, T, et al. Polygenic analysis of late-onset Alzheimer’s disease from mainland China. PloS one (2015) 10(12):e0144898. doi:10.1371/journal.pone.0144898
50. Lu, RC, Yang, W, Tan, L, Sun, FR, Tan, MS, Zhang, W, et al. Association of HLA-DRB1 polymorphism with Alzheimer's disease: a replication and meta-analysis. Oncotarget (2017) 8(54):93219–26. doi:10.18632/oncotarget.21479
Keywords: Alzheimer’s disease, immunity genes, Lebanese population, CD33, rs1354106
Citation: Bissar N, Kassir R, Salami A and El Shamieh S (2024) Association of immunity-related gene SNPs with Alzheimer’s disease. Exp. Biol. Med. 249:10303. doi: 10.3389/ebm.2024.10303
Received: 26 June 2024; Accepted: 07 November 2024;
Published: 22 November 2024.
Copyright © 2024 Bissar, Kassir, Salami and El Shamieh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nisrine Bissar, bi5iaXNzYXJAYmF1LmVkdS5sYg==