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DOI: 10.18413/2658-6533-2022-8-3-0-2
C9orf16 (BBLN) gene, encoding a member of Hero proteins, is a novel marker in ischemic stroke risk
Ksenia A. Kobzeva1 , Irina V. Shilenok2 , Andrey E. Belykh1,3 , Denis E. Gurtovoy1 , Lyubov A. Bobyleva1 , Anna B. Krapiva1 , Tatiana A. Stetskaya1 , Marina A. Bykanova1 , Anastasiya A. Mezhenskaya4 , Ekaterina A. Lysikova5 , Maxim B. Freidin6'7 , Olga Y. Bushueva1
1 Kursk State Medical University, 3 Karl Marx St., Kursk, 305041, Russia 2 Kursk City Clinical Emergency Hospital, 14 Pirogova St., Kursk, 305035, Russia 3 Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 3 Pasteur St., Warsaw, 02-093, Poland 4 Alekseevskaya Central District Hospital, 2 Nikolskaya St., Alekseevka, 309850, Russia 5 Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilov St., Moscow, 119334, Russia 6 Queen Mary University of London, Mile End Road, London, E1 4DQ, United Kingdom ^Research Institute of Medical Genetics, Tomsk National Research Medical Center of the Russian Academy of Sciences, 10 Nabereznaya Ushaiki street, Tomsk, 634050, Russia Corresponding author: Olga Y. Bushueva ([email protected])
Abstract
Background: Ischemic stroke (IS) is the leading cause of death and disability worldwide. Chaperone proteins protect brain cells from the ischemic damage by restoring the structures of damaged proteins. Chaperone C9orf16 (also known as BBLN) belongs to the class of heat-resistant obscure (HERO) proteins, characterized by the ability to stabilize various proteins, suppress neurotoxicity and reduce proteotoxic stress. In this regard, it may play a potentially significant role in the risk of development and clinical manifestations of IS. The aim of the study: To investigate an association between a single nucleotide polymorphism rs2900262 in the gene encoding C9orf16 and predisposition to IS. Materials and methods: A total of 897 patients with IS and 1140 healthy controls were recruited for the study. Genotyping was done using a probe-based genotyping assay. Multiple logistic regression analysis was performed to evaluate the associations of the rs2900262 genotypes with the risk of IS and ischemic events. Dominant, recessive and additive models of associations of genotypes were analyzed. Adjustment for sex, age, and smoking was done throughout. Benjamini-Hogberg false-discovery rate was used to correct for multiple comparisons. Results: The rs2900262*T allele was found to be associated with the increased risk of IS exclusively in females (dominant model:
OR=1.74, 95% CI=1.07-2.82, PFDR=0.042; additive model: OR=1.69, 95% CI=1.06-2.71, pFDR=0.042). Additional analysis showed that the rs2900262*T is associated with the increased risk of IS in smokers only (dominant model: OR=1.92, 95% CI=1.09-3.37, PFDR=0.042; additive model: OR=1.79, 95%CI=1.04-3.08, PFDR=0.042). Also, we demonstrated that C/T-T/T genotype carriers exhibit an earlier manifestation of IS (59.53±1.12 years) compared to the C/C genotype carriers (61.63±0.4 years); mean difference=-1.98; 95% CI=-3.61 - -0.36; PFDR=0.026. Conclusion: This study is the first in the world to demonstrate the possible contribution of the rs2900262 C9orf16 gene polymorphism to the risk of ischemic stroke.
Keywords: ischemic stroke; Hero; heat-resistant obscure; chaperones; rs2900262; C9orf16; BBLN
For citation: Kobzeva KA, Shilenok IV, Belykh AE, et al. C9orf16 (BBLN) gene, encoding a member of Hero proteins, is a novel marker in ischemic stroke risk. Research Results in Biomedicine. 2022;8(3):278-292. DOI: 10.18413/2658-6533-2022-8-3-0-2
Background. Despite the significant efforts made by the world's medical community in the field of prevention and treatment of stroke, this disease remains the second leading cause of death worldwide, and a leading cause of adult disability [1], cognitive decline and dementia [2]. Ischemic stroke (IS) comprises up to 85% of all cases [3]. The risk of IS is determined by a complex interplay of genetic and environmental factors partly acting through modifiable risk factors such as high blood pressure [4, 5].
The previous studies have already revealed genetic markers associated with the development of IS [6, 7, 8], as well as related phenotypes/risk factors, such as hypertension [9, 10, 11], atherosclerosis [12, 13], and thrombosis [14]. However, despite the large number of studies conducted in the world, the molecular mechanisms of IS development remain poorly understood.
One of the most promising fields in this area is the study of chaperone proteins. A critical decrease in perfusion in IS leads to hypoxic damage to brain tissues [15]. Cascades of ischemic and reperfusion damage to neurons and glia include hundreds of individual links that lead to cell death. It is the critical accumulation of damaged enzymes and structural proteins that causes irreversible changes that lead to necrosis or programmed cell death [16]. The main function of chaperones is to restore the native tertiary and quaternary structure of damaged proteins. There is already enough information that confirms the neuroprotective role of chaperones in IS, for example, the HSP class of chaperones ("heat shock" proteins) [17].
Recently, a new class of chaperones, "Hero proteins", has been discovered, which main mechanism of action is the physical shielding of other proteins. Such a function is provided by a flexible structure and a high negative or positive charge of molecules [18]. Moreover, Hero proteins can suppress some forms of pathogenic protein aggregates in cells and in animal models of neurodegenerative diseases in vivo. The described properties of Hero let us suggest their wide involvement in the physiological and pathological processes occurring in cells with ischemic damage.
The aim of this study was to analyze association of the tagger SNP rs2900262 in gene C9orf16 encoding the member of Hero proteins with the risk and clinical manifestations of ischemic stroke.
Material and methods. A total of 1929 unrelated Russians (861 patients with IS and 1068 healthy individuals) from Central Russia were recruited into the study. The Ethical Review Committee of Kursk State Medical University approved the study protocol. All the participants gave written informed consent before the enrollment in this study, subject to the following inclusion criteria: self-declared Russian descent, a birthplace inside of Central Russia.
Baseline and clinical characteristics of the study population are listed in Table 1. The patients were enrolled in to the study in two periods: from the Regional Vascular Center of Kursk Regional Clinical Hospital between 2015 and 2017 [19, 20] and Neurology Clinics of Kursk Emergency Medicine Hospital Kursk
between 2010 and 2012 [21, 22]. All the patients were examined by qualified neurologists. The diagnosis of IS was made in the acute phase of stroke, according to the results of the neurological examination and computed tomography and/or magnetic resonance imaging of the brain. The patients were recruited consecutively. The IS patients were enrolled under the following exclusion criteria: hepatic or renal failure or endocrine, autoimmune, oncological, or other diseases that can cause an acute cerebrovascular event; intracerebral hemorrhage, hemodynamic or dissection-related stroke, traumatic brain injury. All the patients with IS had a history of hypertension and received antihypertensive therapy.
Baseline and clinical char
The control group was compiled from healthy volunteers with no clinical evidence of cerebrovascular, cardiovascular, or other chronic diseases and with normal blood pressure without antihypertensive therapy. Healthy individuals were included in control group if they had a systolic blood pressure less than 130 mm Hg and/or a diastolic blood pressure less than 85 mm Hg on at least 3 separate measurements. Control subjects were enrolled from Kursk hospitals during periodic medical examinations at public institutions and industrial enterprises of Kursk region. This group was recruited from the same population and during the same period [23, 24].
Table 1
istics of the studied groups
Baseline and clinical characteristics IS patients (n=897) Controls (n=1140) praw
Age, Me [Q1; Q31 63 [55; 70] 57 [53; 64] <0.001
Gender Males, N(%) 498 (55.5%) 508 (44.6%) <0.001
Females, N(%) 399 (44.5%) 632 (55.4%)
Smoking Yes, N (%) 442 (49.3%) 308 (27%) <0.001
No, N (%) 455 (50.7%) 832 (73%)
Coronary artery disease Yes, N (%) 113 (13.2%) -
No, N (%) 746 (86.8%) -
Type 2 diabetes mellitus Yes, N (%) 103 (12.1%) -
No, N (%) 747 (87.9%) -
Family history of cerebrovascular diseases Yes, N (%) 306 (35.9%) ND
No, N (%) 546 (64.1%) ND
Age at onset of stroke, Ме [Q1; Q3] 61 [54; 70], n=869 -
Number of strokes including event in question 1, N (%) 774 (89.1%) -
2, N (%) 82 (9.4%) -
3, N (%) 13 (1.5%) -
Stroke localization Right/left middle cerebral artery basin, N (%) 723 (83.2%) -
Vertebrobasilar basin, N (%) 146 (16.8%) -
Area of lesion in stroke, mm2, Me [Q1; Q3] 104 [30; 468], n=850 -
Total cholesterol, mmol/L, Me [Q1; Q31 5.2 [4.4; 5.9], n=583 ND
Triglycerides, mmol/L, Me [Q1; Q3] 1.3 [1.1; 1.8], n=575 ND
Glucose level, mmol/L, Me [Q1; Q3] 4.8 [4.3; 5.5], n=861 ND
Prothrombin time, seconds, Me [Q1; Q3] 10.79 [10.14; 11.7], n=845 ND
International normalized ratio, Me [Q1; Q3] 1 [0.93; 1.09], n=566 ND
Activated partial thromboplastin time, seconds, Me [Q1; Q3] 32.6 [29; 37], n=569 ND
Note: statistically significant differences between groups are indicated in bold; ND - no data.
The following criteria were used in the selection of SNPs: the SNP must be tagging, have a minor allele frequency of at least 0.05 in the European population and be characterized by a high regulatory potential. According to the bioinformatic tool GenePipe (https://snpinfo.niehs.nih.gov/snpinfo/sele-gene.html), which was used to select SNPs based on the reference haplotypic structure of the Caucasian population (CEU) of the project HapMap, the only tag SNP in the gene C9orf16 (also known as BBLN, ID:79095) is rs2900262. This genetic variant is localized in the intron. Several bioinformatic resources were used to assess the regulatory potential of this SNP. Bioinformatic resource HaploReg (v4.1) established a connection of rs2900262 C9orf16 with modifications of histones marking promoters in 15 different tissues; it established the localization of this SNP in the regions of hypersensitivity to deoxyribonuclease in 7 issues, regions of 4 regulatory motifs (https://pubs.broadinstitute.org/mammals/hap-loreg/haploreg.php). According to rSNPBase (http://rsnp.psych.ac.cn/index.do), it is characterized by distal transcriptional regulation, the regulation mediated by RNA-binding proteins and regulation mediated by microRNA. The instrument RegulomeDB allowed to reveal that rs2900262 C9orf16 is characterized by a regulatory coefficient of 1b (eQTL + TF binding + any motif + DNase Footprint + DNase peak) (http://regulome.stanford.edu/). According to the date presented in Ensembl genome browser, this genetic variant is characterized by an average frequency of the minor T allele in European populations of 0.057 (https://www.ensembl.org/). Thus, rs2900262 C9orf16 was selected for this study, which meets the necessary criteria for inclusion in the study.
Genetic Analysis. DNA analysis was carried out at the Laboratory of Genomic Research of Research Institute for Genetic and Molecular Epidemiology of Kursk State Med-
ical University (Kursk, Russia). Approximately 5 mL of venous blood was collected from the cubital vein of each participant into EDTA-coated tubes and maintained at -20°C until processed. Genomic DNA was extracted from thawed blood samples by the standard procedure of phenol/chloroform extraction and ethanol precipitation. Genotyping of the SNP was done using Taq-Man-based real-time pol-ymerase chain reaction assays according to the protocol designed in the Laboratory of Ge-nomic Research, Research Institute for Genetic and Molecular Epidemiology. Primers and probes were designed (http://primer3.ut.ee/) using the Primer3 online program [25], and then synthesized by the Syntol company (Moscow, Russia). The 2 flanking primers such as forward 5'- ATATACCATGAGGGGGTT-GGA -3' and reverse 5'- GAGGAGCTGGAG-GAGCGTAT-3' and also allele-specific probes T-5'-FAM-CCTTTCCCAGAACACA-RTQ1-3' and 5'-ROX-CCTTTCCTA-GAACACA-BHQ2-3' were used for genotyping of the polymorphism. A real-time PCR was done in a 25-mL reaction mixture containing 1.5 U of Hot Start Taq DNA polymerase (Bi-olabmix, Novosibirsk, Russia), about 1 p,g of DNA, 0.25 ^M each primer, 250 ^M dNTPs, 3.0 mM MgCl2, 1xPCR buffer [67 mM Tris-HCl, pH 8.8, 16.6 mM (NH4)2SO4, 0.01% Tween-20]. The amplification reaction consisted of an initial denaturation for 10 minutes at 95°C, followed by 39 cycles of 92°C for 30 seconds and 58°C for 1 minute. Figure 1 shows allelic discrimination plot for SNP rs2900262 C9orf16 assay designed for this study. The plot shows clear separation between the signals derived from allele 1 (rs2900262-C, FAM fluorescent dye) or allele 2 rs2900262-T, ROX fluorescent dye). Genotypes CC, CT, and TT are shown as circles, triangles and squares, respectively. Primer sequences used for genotyping are available upon request. To ensure quality control, 10% of DNA samples were genotyped in duplicates blinded to the case-control status. The concordance rate was >99%.
Allelic Discrimination
0 200 400 600 800 1000
RFU for Allele 1 - FAM
O Allele 1 □ Alleles <:■ None A Heterozygote_
Fig. 1. Allelic discrimination plot for SNP rs2900262 C9orf16 assay designed for this study.
Allele and genotype frequencies in the case and control groups were counted and compared by the Fisher exact test to identify significant departures from Hardy-Weinberg equilibrium. Genotype frequencies in the study groups and their associations with disease risk were analyzed using the SNPStats software (https://www.snpstats.net/start.htm) [26]. For the analysis of associations of genotypes, dominant, recessive, and additive models were considered; taking into account the number of models of associations, a correction for multiple testing was applied using Benjamini-Hog-berg false-discovery rate approach.
Multiple logistic regression analysis was performed to evaluate the associations of rs2900262 C9orf16 genotypes with the risk of ischemic events. Continuous variables were analyzed for normal distribution by the Kol-mogorov-Smirnov test. Linear regression analysis was used to evaluate the association between rs4644832 and age of ischemic events, which was previously transformed to a normal variable through the procedure of inverse transformation of ranks. All associations were adjusted for age, gender and smoking status.
The following bioinformatics recourses were used for analysis of regulatory potential of rs2900262 C9orf16:
- The rSNPBase tool (http://rsnp.psych.ac.cn/index.do) was used to evaluate the effect of SNPs on proximal tran-scriptional regulation, distal transcriptional regulation, RNA-binding protein-mediated regulation, and miRNA-mediated regulation (accessed on 7 May 2022) [27].
- RegulomeDB (Version 1.1) (http://reg-ulome.stanford.edu/) was used to estimate the regulatory coefficient of SNP (accessed on 7 May 2022) [28].
- GTExportal (http://www.gtexportal. org/) was used to analyze the expression levels of the studied genes in brain, whole blood, and blood vessels, as well as to analyze the binding of SNPs to quantitative expression trait loci (eQTLs) (accessed on 7 May 2022) [29].
- Additionally, to analyze the binding of rs2900262 C9orf16 to quantitative expression trait loci (eQTL) in peripheral blood, the eQTLGen resource was used (https://www.eqtlgen.org/) (accessed on 7 May 2022) [30].
- Bioinformatics resource HaploReg (v4.1) (http://archive.broadinstitute.org/mam-mals/haploreg/haploreg.php) was used to assess the association of rs2900262 C9orf16 with histone modifications marking promoters and enhancers: mono-methylation at the 4th lysine residue of the histone H3 protein (H3K4me1),
tri-methylation at the 4th lysine residue of the histone H3 protein (H3K4me3), the acetylation at the 9th lysine residues of the histone H3 protein (H3K9ac), acetylation of the lysine residues at N-terminal position 27 of the histone H3 protein (H3K27ac). This resource has also been used to analyze the localization of SNPs in regions of DNase hypersensitivity, regions of regulatory motifs, and sites binding with regulatory proteins (accessed on 7 May 2022) [31].
- To assess the effect of rs2900262 C9orf16 on the binding of transcription factors (TFs) to DNA, depending on the carriage of the reference/alternative alleles, the atSNP Function Prediction online tool was used (http://atsnp.biostat.wisc.edu/search; accessed on 14 May 2022) [32]. TFs were included in the analysis only under the condition of a high and very high degree of influence of SNP on the interaction of TFs with DNA, calculated on the basis of a positional weight matrix. The subsequent analysis of the possible joint participation of TFs associated with the refer-ence/SNP alleles in common biological processes directly related to the pathogenesis of IS was carried out using the Enrichr online tool (https://maayanlab.cloud/Enrichr/) [33]. Biological functions controlled by transcription factors associated with SNP rs2900262 C9orf16 were used as functional groups (accessed on 14 May 2022). The level of significance of gene ontologies, taking into account
multiple tests, was assessed by the Benjamini-Hochberg method (False Discovery Rate) [34].
- For bioinformatics analysis of interactions between the C9orf16 gene and cigarette smoke components, the Comparative Toxi-cogenomics Database resource (CTD) available online, was used (http://ctdbase.org) (accessed on 16 May 2022) [35]. CTD provides the ability to analyze specific interactions between genes and chemicals in vertebrates and invertebrates based on data obtained from published scientific studies worldwide. This tool was used to analyze binary interactions involving one chemical and one gene/protein.
The Cerebrovascular Disease Knowledge Portal (CDKP) (https://cd.huge-amp.org/) was used for bioinformatic analyze of the associations of rs2900262 C9orf16 with atherosclerosis-associated diseases, intermediate phenotypes, and risk factors for ischemic stroke (such as blood pressure, the level of triglycerides, the level of low density lipopro-teins, etc.) (accessed on 18 May 2022) [36].
Results and discussion. The allele frequencies in the study patients according to the data published in the 1000 Genomes Project, Phase 3 (http://www.ensembl .org (accessed on 30 May 2022)) were comparable with those reported in following European populations: British in England and Scotland (GBR), Iberian populations in Spain (IBS), Toscani in Italy (TSI) (Table 2).
Table 2
Comparative analysis of allele frequencies between the population investigated
in the present study and other Euro ean populations
Population Minor allele Present study (control) European populations г2 P-level
CEU T 0.038 0.071 4.94 0.03
FIN T 0.038 0.076 6.53 0.01
GBR T 0.038 0.060 2.19 0.14
IBS T 0.038 0.047 0.38 0.54
TSI T 0.038 0.033 0.16 0.69
Note: Data on the frequency of the minor allele in European populations were obtained using the Ensembl genome browser (https://www.ensembl.org/index.html); statistically significant differences in the frequency of the minor allele are indicated in bold
The genotype frequencies of rs2900262 C9orf16 in study groups are presented in Table 3. The distribution of genotypes frequencies corresponded to the Hardy-Weinberg equilibrium
both in the group of patients and in the control group (P>0.05).
Analysis of the general group of patients/controls did not show an association of rs2900262 C9orf16 with the risk of IS (Table 3).
Table 3
Genetic mode s of associations of rs2900262 C9orf16 with the risk of ischemic stroke
M1 Genotypes Controls (N=1140) Patients (N=897) corOR (95% CI)2 Padj3 pFDR
D C/C 1054 (92.5%) 817 (91.1%) 1.00 0.33 0.44
C/T-T/T 86 (7.5%) 80 (8.9%) 1.18 (0.84-1.66)
R C/C-C/T 1139 (99.9%) 895 (99.8%) 1.00 0.44 0.44
T/T 1 (0.1%) 2 (0.2%) 2.52 (0.22-28.50)
A - — — 1.19 (0.86-1.65) 0.29 0.44
Note: 1 - genetic models of associations of genotypes: D - dominant; R - recessive; A-additive; 2 - odds ratio and 95% confidence interval adjusted by sex, age, smoking; 3-P - level adjusted by sex, age, smoking; PFDR - P-level, after the correction on the number of models (calculated by the Benjamini-Hochberg method); All calculations were performed relative to the minor allele T; Statistically significant differences are marked in bold.
Due to a possible sex dimorphism in the associations of genetic variants with the risk of IS, an analysis of the relationship of rs2900262 C9orf16 with the development of IS separately in males and females was carried out. It turned out that this genetic variant does not affect the risk of the disease in males. As shown in Table 4, SNP rs2900262 C9orf16 was found to be associated with a increased risk of IS in females.
Sex-specific associations of rs2900262 C9orf16
In particular, according to the dominant model, genotypes C/T-T/T was associated with an increased risk of IS (corOR=1.74, 95%CI 1.072.82, Padj=0.025, PFDR=0.042). According to the additive model, the rs2900262 genotypes were also associated with an increased risk of IS (corOR=1.69, 95%CI 1.06-2.71, Padj=0.028, PFDR=0.042).
Table 4
Males
M1 Genotypes Controls (N=508) Patients (N=498) corOR (95% CI)2 Padj3 PFDR4
D C/C 461 (90.8%) 457 (91.8%) 1.00 0.57 0.65
C/T-T/T 47 (9.2%) 41 (8.2%) 0.88 (0.57-1.36)
R C/C-C/T 508 (100%) 497 (99.8%) 1.00 0.24 0.65
T/T 0 (0%) 1 (0.2%) NA (0.00-NA)
A - - - 0.91 (0.59-1.39) 0.65 0.65
Females
M1 Genotypes Controls (N=632) Patients (N=399) corOR (95% CI)2 P3 PFDR
D C/C 593 (93.8%) 360 (90.2%) 1.00 0.025 0.042
C/T-T/T 39 (6.2%) 39 (9.8%) 1.74 (1.07-2.82)
R C/C-C/T 631 (99.8%) 398 (99.8%) 1.00 0.83 0.83
T/T 1 (0.2%) 1 (0.2%) 1.40 (0.06-32.79)
A - - - 1.69 (1.06-2.71) 0.028 0.042
Note: 1 - genetic models of associations of genotypes: D - dominant; R - recessive; A-additive; 2 - odds ratio and 95% confidence interval adjusted by age, smoking; 3-P - level adjusted by age, smoking; PFDR - P-level, after the correction on the number of models (calculated by the Benjamini-Hochberg method); All calculations were performed relative to the minor allele T; Statistically significant differences are marked in bold.
The analysis of associations of rs2900262 C9orf16 with of IS risk depending on the smoking status revealed that this genetic variant is associated with an increased risk of IS exclusively in smoking individuals according to the dominant model (OR=1.92, 95%CI
1.09-3.37, Padj=0.019, PFDR=0.042). Analysis of the log-additive model also revealed the association of rs2900262 C9ORF16 with the increased risk of IS in smokers (OR=1.79, 95%CI 1.04-3.08, Padj=0.028, PFDR=0.042) (Table 5).
Table 5
The effect of smoking on the association of rs2900262 C9orf16 with the risk of ischemic stroke
Non-smokers
M1 Genotypes Controls (N=832) Patients (N=455) OR (95% CI)2 Padj3 PFDR
D C/C 764 (91.8%) 422 (92.8%) 1.00 0.56 0.66
C/T-T/T 68 (8.2%) 33 (7.2%) 0.88 (0.57-1.35)
R C/C-C/T 832 (100%) 454 (99.8%) 1.00 0.15 0.45
T/T 0 (0%) 1 (0.2%) NA (0.00-NA)
A - - - 0.91 (0.59-1.39) 0.66 0.66
Smokers
M1 Genotypes Controls (N=308) Patients (N=442) OR (95% CI)2 P3 PFDR
D C/C 290 (94.2%) 395 (89.4%) 1.00 0.019 0.042
C/T-T/T 18 (5.8%) 47 (10.6%) 1.92 (1.09-3.37)
R C/C-C/T 307 (99.7%) 441 (99.8%) 1.00 0.8 0.8
T/T 1 (0.3%) 1 (0.2%) 0.70 (0.04-11.17)
A - - - 1.79 (1.04-3.08) 0.028 0.042
Note: 1 - genetic models of associations of genotypes: D - dominant; R - recessive; A-additive; 2 - odds ratio and 95% confidence interval adjusted by sex, age; 3-P - level adjusted by sex, age; PFDR - P-level, after the correction on the number of models (calculated by the Benjamini-Hochberg method); All calculations were performed relative to the minor allele T; Statistically significant differences are marked in bold.
Analysis of the effects of rs2900262 C9orf16 on clinical manifestations of IS revealed that in carriers of genotypes C/T-T/T, ischemic stroke manifests on average 2 years earlier than in those of genotype C/C (Recessive model: Padj=0.017; Difference= -1.98,
Analysis of the impact of rs2900262 C9oi
95% CI=-3.61 - -0.36, Padj= 0.017, PFDR=0.026). According to the additive model, rs2900262 C9ORF16 is also associated with a decrease in the age of manifestation of IS (Dif-ference= -1.82, 95% CI=-3.38 - -0.26, Padj= 0.015, PFDR=0.026) (Table 6).
Table 6
on the age of ischemic stroke manifestation
M1 Genotypes N Response mean (s.e.) Difference (95% CI) Padj PFDR
D C/C 792 61.63 (0.4) 0.00 0.017 0.026
C/T-T/T 78 59.53 (1.12) -1.98 (-3.61 - -0.36)
R C/C-C/T 868 61.45 (0.37) 0.00 0.9 0.9
T/T 2 60 (19) 0.59 (-9.11 - 10.29)
A - — — -1.82 (-3.38 - -0.26) 0.015 0.026
Note: 1 - genetic models of associations of genotypes: D - dominant; R - recessive; A - additive; Padj - P-level adjusted by sex, age, smoking; PFDR - P-level, after the correction on the number of models (calculated by the Benjamini-Hochberg method); All calculations were performed relative to the minor allele T; Statistically significant differences are marked in bold.
The C9orf16 gene is expressed in brain from 50.20 to 172.2; in blood vessels - from tissues, blood vessels, and whole blood 64.62 to 79.12; in whole blood MeTPM=50.32 (Figure 2). In brain tissues, MeTPM varies (Fig. 2).
2. Level of the expression of the C9orf16 gene in brain, whole blood, and vessels (https://gtexportal.org)
C9orf16 (also known as BBLN), a gene with unknown functions, has not been investigated for the risk of IS. However, in a previous study, a change in the expression of this gene in the blood of patients with Parkinson's Disease was found, which indicates a possible significant role of this gene in neuroprotection [37].
Due to the fact that the use of PubMed and Google Scholar search resources did not allow us to detect studies aimed at studying the associations of the studied Tag SNP rs2900262 C9orf16 with the development of IS, we used the bioinformatic resource Cardiovascular Dis-
ease Knowledge Portal to interpret the pheno-typic effects of this genetic variant (CVDKP), (https://cvd.hugeamp.org), which combine and analyze the results of genetic associations of the largest consortia for the study of cardio-and cerebrovascular diseases, as well as their intermediate phenotypes. It was found that the protective C allele is associated with a decrease in BMI, Body fat percentage, Systolic blood pressure, Diastolic blood pressure, Mean arterial pressure, Pulse pressure, Creatine kinase. At the same time, this allele is associated with an increase in levels of HDL cholesterol u AF-SNP hypertension interaction (Table 7).
Table 7
Results of aggregated analyzes of associations between Tag SNP rs2900262 C9orf16 cardiovas-
N SNP Phenotype P-Value Beta (OR) Sample Size
1. BMI 6.38x10-5 Beta^-0.0122 3 133 750
2. Systolic blood pressure 5.44x10-4 Beta^-0.0102 1 269 390
3. Pulse pressure 0.001 Beta^-0.0097 858 587
4. rs2900262 C9orf16 (T/C) Creatine kinase 0.008 Beta^-0.0122 111 424
5. HDL cholesterol 0.02 BetaA0.0052 1 726 890
6. Diastolic blood pressure 0.02 Beta^-0.0079 1 282 710
7. Mean arterial pressure 0.03 Beta^-0.0086 136 482
8. AF-SNP hypertension interaction 0.04 OR ▲ 1.2460 20 050
9. Body fat percentage 0.047 Beta^-0.0800 26 639
Note: Data obtained using the bioinformatic resource Cardiovascular Disease Knowledge Portal (https://cvd.huge-amp.org/); All presented calculations were performed with respect to the C allele.
Thus, this genetic variant may play an important role in the regulation of blood pressure, lipid metabolism, and may also influence the association of SNPs with the development of atrial fibrillation. Taking into account that atrial fibrillation acts as a significant risk factor for ischemic stroke of cardioembolic type, the influence of rs2900262 C9orf16 on the risk of IS may include a range of molecular mechanisms involved in various links in the pathogenesis of the disease.
Bioinformatic analysis was additionally performed to interpret the functional effects of rs2900262 C9orf16. The use of the GTEx Portal
resource and the eQTL Calculator search query showed that the C allele (with a protective effect on the risk of IS development established in our study) affects an increase in gene expression in blood vessels. At the same time, the C allele is characterized by multidirectional effects relative to the effect on the level of expression in brain tissues: carriers of the C allele showed an increase in the expression of the C9orf16 gene in Brain - Caudate (basal ganglia) and Brain - Cerebellum and, conversely, a decrease in gene expression in Brain - Cortex u Brain - Frontal Cortex (BA9) (Table 8).
Table 8
Impact of rs2900262 on C9orf16 expression by cis-eQ ^TL-effects (GTEx Portal data)
Tissue P-Value NES T-statistic
Brain - Caudate (basal ganglia) 0.024 0.17 2.03
Brain - Cerebellum 0.0062 0.22 2.04
Brain - Cortex 0.018 -0.19 -2.4
Brain - Frontal Cortex (BA9) 0.037 -0.18 -2.1
Artery - Tibial 0.00059 0.13 3.05
Note: effective allele - C
Resource eQTLGen Consortium (https://www.eqtlgen.org/cis-eqtls.html) showed that the risk allele T affects the decrease in expression of gene URM1 (Z-score = -10.6884, P =1.15x10-26) and DNM1 (Z-score = -4.67, P = 2.9*10-6), as well as an increase in gene expression SH3GLB2 (Z-score = 4.44, P = 8.8x10-6) u RP11-395P17.3 (Z-score = 5.78, P = 7.41x10-9) through cis-eQTL effects in blood cells. Gene URM1 acts as a ubiquitin-like protein (UBL) that is covalently conjugated via an isopeptide bond to lysine residues of target proteins such as MOCS3, ATPBD3, CTU2, USP15 and CAS. The thiocarboxylated form serves as substrate for conjugation and oxidative stress specifically induces the formation of UBL-protein conjugates (https://www.genecards.org/cgi-bin/carddisp.pl?gene=URM1&key-words=URM1). DNM1 gene encodes a member of the dynamin subfamily of GTP-binding proteins. The encoded protein possesses unique mechanochemical properties used to tubulate and sever membranes, and is involved in clathrin-mediated endocytosis and other vesicular trafficking processes. Actin and other cytoskeletal proteins act as binding partners for the encoded protein, which can also self-assemble leading to stimulation of GTPase
activity (https://www.genecards.org/cgi-bin/carddisp.pl?gene=DNM1&key-words=DNM1). SH3GLB2 is a Protein Coding gene. Enables identical protein binding activity (https://www.genecards.org/cgi-bin/carddisp .pl?gene=SH3GLB2&key-words=SH3GLB2).
Subsequent analysis revealed a significant effect of rs2900262 C9orf16 on histone modifications. According to the HaploReg database (v4.1), this genetic variant is located in the region of DNA binding to histone H3, characterized by mono-methylation at the 4th lysine residue of the histone H3 protein (H3K4me1) and marking enhancers in all brain tissues represented by HaploReg (v4.1), peripheral blood cells, as well as tri-methylation at the 4th lysine residue of the histone H3 protein (H3K4me3) and marking promoters in the brain tissues and blood. The effect of this histone mark is enhanced by acetylation of the lysine residues at N-terminal position 27 of the histone H3 protein (H3K27ac), marking enhancers in peripheral blood cells and all brain tissues, as well as the acetylation at the 9th lysine residues of the histone H3 protein (H3K9ac), marking promoters in blood cells and all brain tissues, except Brain Hippocampus Middle (Table 9).
Table 9
Tissue-specific effects of rs2900262 C9orf16 on histone modifications (https://pubs.broadinsti-
tute.org/mammals/haploreg/haploreg.php)__
—---Histone marks Tissues ——--- H3K4me1 H3K4me3 H3K27ac H3K9ac
Brain Hippocampus Middle Enh Pro Enh -
Brain Substantia Nigra Enh - Enh Pro
Brain Anterior Caudate Enh - Enh Pro
Brain Cingulate Gyrus Enh Pro Enh Pro
Brain Inferior Temporal Lobe Enh Pro Enh Pro
Brain Angular Gyrus Enh Pro Enh Pro
Brain_Dorsolateral_Prefrontal_Cortex Enh Pro Enh Pro
Cells from peripheral blood (any) Enh Pro Enh Pro
Note: H3K4me1 - mono-methylation at the 4th lysine residue of the histone H3 protein; H3K4me3 - tri-methylation at the 4th lysine residue of the histone H3 protein; H3K9ac - the acetylation at the 9th lysine residues of the histone H3 protein; H3K27ac - acetylation of the lysine residues at N-terminal position 27 of the histone H3 protein; Enh - histone modification in the enhancer region; Pro - histone modification at the promoter region.
Table 10
Analysis of the impact of rs2900262 C9ORF16 on the binding of transcription factors with DNA__
Ref/SNP allele1 TF2 GAIN /LOSS3 Motif4 P-Value SNP impact5 P-Value Ref6 P-Value SNP7
T/C NFKB GAIN NFKB disc2 <1x10"5 4.71x10-6 0.18
T/C IKZF2 GAIN IKZF2 2 2.09X10"3 1.11x10-2 0.40
T/C RAD21 GAIN RAD21 disc10 2.28x10"3 1.99x10-2 0.28
T/C SMC3 GAIN SMC3 disc3 2.53 x10"3 3.10x10-2 0.35
T/C NR3C1 GAIN NR3C1 disc6 3.27 x10-3 1.22x10-2 0.14
T/C HAND1 GAIN HAND1 2 5.87x10-3 1.99x10-2 0.48
T/C BCL GAIN BCL disc9 7.63 x10-3 4.01x10-2 0.30
T/C MGA GAIN MGA 3 9.46 x10-3 3.52x10-2 0.30
T/C NANOG GAIN NANOG 1 1.00x10-2 2.40x10-2 0.71
T/C ZNF410 GAIN ZNF410 1 1.45x10-2 2.67x10-2 0.29
T/C MZF1 5-13 GAIN MA0057.1 1.50x10-2 4.93x10-2 0.46
T/C THAP1 GAIN MA0597.1 1.53x10-2 2.31x10-2 0.35
T/C RFX1 GAIN RFX1 4 1.84 x10-2 3.83x10-2 0.40
T/C SOX2 GAIN SOX2 1 2.02 x10-2 9.14x10-3 6.18x10-2
T/C NR2C2 GAIN MA0504.1 4.40x10-2 4.67x10-2 0.23
T/C NR1H LOSS NR1H 3 2.37x10-5 0.15 1.09x10-4
T/C IKZF2 LOSS IKZF2 3 3.91 x10-3 0.38 1.10x10-2
T/C ETS1 LOSS ETS1 2 6.90x10-3 4.20 4.23x10-2
T8 -
C9 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994; Padj=0.02); regulation of vasculature development (GO:1901342; Padj=0.02); regulation of blood vessel endothelial cell migration (GO:0043535; Padj=0.02); positive regulation of defense response (GO:0031349; Padj=0.03); regulation of angiogenesis (GO:0045765; Padj=0.045); regulation of inflammatory response (GO:0050727; Padj=0.045)
Note: 1 - reference (Ref) / alternative (SNP) allele; 2 - TF - transcription factor; 3 - TF binding to the reference (GAIN) / alternative (LOSS) allele; 4 - binding sites with high affinity for TF; 5 - p value, statistically confirming the potential increase or loss of function of the genomic region with SNP in terms of transcription factor binding; 6 - p-value for assessing the binding of TF to the Ref allele; 7 - p-value for assessing the binding of TF to the SNP allele; 8 - biological processes in which TFs that bind to the reference allele are jointly involved (data from the Enrichr resource; https://maayanlab.cloud/Enrichr/); 9 - biological processes in which TFs that bind to an alternative allele are jointly involved (data from the Enrichr resource; https://maayanlab.cloud/Enrichr/)
The analysis of TFs using atSNP source revealed that TFs binding to the protective allele C, are involved in joint regulation of leukocyte adhesion to vascular endothelial cell (G0:1904994; Padj=0.02), regulation of vasculature development (G0:1901342; Padj=0.02), regulation of blood vessel endothelial cell migration (G0:0043535; Padj=0.02), positive regulation of defense response (G0:0031349; Padj=0.03), regulation of angiogenesis (G0:0045765; Padj=0.045), regulation of inflammatory response (G0:0050727; Padj=0.045) (Table 10).
It is noteworthy that the association of rs2900262 C9orf16 with the risk of IS was found in women and was not found in men. Previously, Smith et al. showed that C9orf16
expression may be negatively regulated by es-tradiol [38]. Thereby decreased expression of C9orf16 in T allele carriers may manifest fe-male-specifically being more impeded in women.
Similarly, in contrast to non-smokers, smoking patients were characterized by more strong association between mutant allele and the risk of ischemic stroke. This finding may have the same ground since some tobacco components apparently may decrease C9orf16 expression. Using bioinformatic resourse Comparative Toxicogenomics Database we revealed that tobacco smoke and its components are able to down-regulate expression of C9orf16 as well as up-regulate methylation of C9orf16 promoter (Table 11).
Table 11
Proven mechanisms of interaction between the gene C9orf16 and cigarette smoke
(data obtained using a bioinformatics resource Comparative Toxicogenomics D database)
N Interacting chemical Interactiong References
1. Tobacco Smoke Pollution Tobacco Smoke Pollution results in increased methylation of BBLN promoter [39]
2. Tobacco tar Tobacco tar results in decreased expression of BBLN mRNA [40]
3. Benzo(a)pyrene Benzo(a)pyrene results in decreased expression of BBLN mRNA [41]
4. Benzo(a)pyrene Benzo(a)pyrene results in increased methylation of BBLN promoter [42]
Considering that an increase in methyl-ation leads to a decrease in gene expression, studies conducted in this direction provide additional evidence of the effect of smoking on a decrease in the expression of the C9orf16 gene and an increase in "risk" effects regarding the development of IS in carriers of variant genotypes C/C-C/T.
Conclusion. To the best of our knowledge, this is the first study that showed the association of rs2900262 C9orf16 with the risk of IS, sex-specific effect in this association, and the trigger effect of smoking on the association of this genetic variant with the development of the disease. Bioinformatic analysis revealed a high regulatory potential of rs2900262 C9orf16, particular cis-eQTL-me-diated effects on the regulation of gene expression in blood, blood vessels and brain, binding to transcription factors involved in biological
processes with significant role in the pathogenesis of IS, as well as epigenetic regulation of this SNP. Future studies have yet to accumulate the data disclosing detailed networks of its probable implication in ischemic stroke risk and course.
Financial support
This research was funded by Russian Science Foundation (No. 22-15-00288,
https://rscf.ru/en/project/22-15-00288/).
Conflict of interests
The authors have no conflict of interest to declare.
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Received 11 April 2022 Revised 19 May 2022 Accepted 25 May 2022
Information about the authors Ksenia A. Kobzeva, Student, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https://or-cid.org/0000-0002-0652-2219. Irina V. Shilenok, Neurologist, Kursk City Clinical Emergency Hospital, Kursk, Russia, E-mail: [email protected], ORCID: https:// orcid.org/0000-0001-7894-8385. Andrey E. Belykh, Cand. Sci. (Medicine), Associate Professor at the Department of Pathophysiology, Kursk State Medical University, Kursk, Russia, Postdoctoral Researcher at the Dioscuri Centre for Metabolic Diseases, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland, E-mail: [email protected], ORCID: https://orcid.org/0000-0001-9766-2104.
Denis E. Gurtovoy, Student, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https://orcid.org/0000-0001-8632-4708.
Lyubov A. Bobyleva, Student, Kursk State Medical University, Kursk, Russia, E-mail:
[email protected], ORCID: https:// orcid.org/0000-0002-2014-5827. Anna B. Krapiva, Student, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https:// orcid.org/0000-0001-5163-575X. Tatiana A. Stetskaya, Cand. Sci. (Medicine), Junior Researcher at the Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https:// orcid.org/0000-0002-4729-4654. Marina A. Bykanova, Researcher at the Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https://or-cid.org/0000-0001-5420-3557. Anastasiya A. Mezhenskaya, General Practitioner, Alekseevskaya Central District Hospital, Alekseevska, Russia, E-mail: [email protected], ORCID: https://
orcid.org/0000-0002-5811-2537. Ekaterina A. Lysikova, Research Scientist at the Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia, E-mail: [email protected], ORCID: https://orcid.org/0000-0001-6521-3793. Maxim B. Freidin, Doct. Sci. (Biology), Lecturer at the Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, London, United Kingdom, Senior Researcher at the Laboratory of Population Genetics, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia, E-mail: [email protected], ORCID: https://orcid.org/0000-0002-1439-6259. Olga Y. Bushueva, Cand. Sci. (Medicine), Associate Professor at the Department of Biology, Medical Genetics and Ecology, Head of the Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russia, E-mail: [email protected], ORCID: https:// orcid.org/0000-0003-3333-0623.
