Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Genetic Analysis
2.2. Statistical and Bioinformatic Analysis
- The expression quantitative trait loci (eQTLs) in the brain, whole blood, and blood vessels have been evaluated using the bioinformatic tool QTLbase (http://www.mulinlab.org/qtlbase/index.html (accessed on 21 February 2023)) [35].
- The STRING database’s bioinformatic tools were utilised to analyse the main functional partners of HSPA8 (https://string-db.org/ (accessed on 21 February 2023)) [36]. Additionally, the STRING database was used to assess biological processes and molecular functions data describing interactions between HSPA8 and its functionally significant partner proteins. For the interpretation of interactions only experimentally confirmed data was used.
- The effect of HSPA8 SNPs on the binding of transcription factors (TFs) to DNA was assessed using the atSNP Function Prediction online tool (http://atsnp.biostat.wisc.edu/search (accessed on 21 February 2023)) [37]. Based on a positional weight matrix-based calculation of the impact of SNPs on how well TFs interact with DNA, certain TFs were added.
- The online Gene Ontology tool was used to conduct the subsequent study of the potential joint involvement of TFs linked with the reference and SNP alleles in overrepresented biological processes that are related to the mechanisms of IS (http://geneontology.org/ (accessed on 21 February 2023)) [38]. As functional groups, we employed biological processes governed by transcription factors connected to HSPA8 SNPs.
- HaploReg (v4.1), a bioinformatics tool (http://archive.broadinstitute.org/mammals/haploreg/haploreg.php (accessed on 20 February 2023)) was used to evaluate the relationships between HSPA8 SNPs and the following histone modifications that mark promoters and enhancers: acetylation of the lysine residues at positions 27 and 9 of the histone H3 protein, as well as mono-methylation at position 4 of the histone H3 protein (H3K4me1) and tri-methylation at position 4 of the histone H3 protein (H3K4me3). Additionally, this tool has been employed to examine the localization of SNPs in DNase hypersensitive areas, regulatory motif regions, and locations that bind to regulatory proteins [32].
- The interpretation of environment-associated correlates of HSPA8 polymorphism was done using the Comparative Toxicogenomics Database (CTD) resource at http://ctdbase.org (accessed on 24 February 2023) [39]. Based on data gathered from internationally published scientific studies, CTD offers the capability to investigate particular interactions between genes and chemicals in vertebrates and invertebrates. Using this method, bidirectional interactions comprising a single chemical and a single gene or protein were examined.
- The Cerebrovascular Disease Knowledge Portal (CDKP) is available at https://cd.hugeamp.org/ (accessed on 24 February 2023) was employed for a bioinformatic investigation of the relationships between HSPA8 SNPs and stroke-related traits, intermediate phenotypes, and risk factors for IS (such as blood pressure, heart rate, etc.) [40].
3. Results
3.1. Bioinformatic Analysis of the HSPA8 Gene
Protein–Protein Interactions
3.2. HSPA8 SNPs and the Ischemic Stroke Risk: An Analysis of Associations
3.3. Functional Annotation of HSPA8 SNPs
3.3.1. QTL-Effects
3.3.2. Histone Modifications
3.3.3. Analysis of Transcription Factors
3.3.4. Bioinformatic Analysis of the Associations of HSPA8 SNPs with IS-Related Phenotypes
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Baseline and Clinical Characteristics | IS Patients (N = 888) | Controls (N = 1251) | p-Value | |
---|---|---|---|---|
Age, Me [Q1; Q3] | 62 [55; 69] | 58 [53; 66] | <0.001 | |
Gender | Males, N (%) | 481 (54.2%) | 577 (46.1%) | <0.001 |
Females, N (%) | 407 (45.8%) | 674 (53.9%) | ||
Smoking | Yes, N (%) | 425 (47.9%) | 331 (26.5%) | <0.001 |
No, N (%) | 463 (52.1%) | 920 (73.5%) | ||
Hypodynamia | Yes, N (%) | 332 (39.34%) | ND | |
No, N (%) | 512 (60.66%) | |||
Low fruit/vegetable consumption | Yes, N (%) | 449 (53.20%) | ND | |
No, N (%) | 395 (46.80%) | |||
Type 2 diabetes mellitus | Yes, N (%) | 103 (11,6%) | - | |
No, N (%) | 740 (83,3%) | - | ||
ND, N (%) | 45 (5,1%) | - | ||
Body mass index, Me [Q1; Q3] | 23 [22; 26] (N = 567) | - | ||
Family history of cerebrovascular diseases | Yes, N (%) | 296 (35.20%) | ND | |
No, N (%) | 545 (64.80%) | ND | ||
Age at onset of stroke, Me [Q1; Q3] | 61 [54; 69] (N = 862) | - | ||
Number of strokes including event in question | 1, N (%) | 766 (88.86%) | - | |
2, N (%) | 85 (9.86%) | - | ||
3, N (%) | 11 (1.28%) | - | ||
Stroke localization | Right/left middle cerebral artery basin, N (%) | 720 (83.82%) | - | |
Vertebrobasilar basin, N (%) | 139 (16.18%) | - | ||
Area of lesion in stroke, mm2, Me [Q1; Q3] | 105.00 [28; 468] (N = 841) | - | ||
Total cholesterol, mmol/L, Me [Q1; Q3] | 5.2 [4.4; 5.8] (N = 583) | ND | ||
Triglycerides, mmol/L, Me [Q1; Q3] | 1.3 [1.1; 1.8] (N = 577) | ND | ||
Glucose level, mmol/L, Me [Q1; Q3] | 4.7 [4.3; 5.5] (N = 849) | ND | ||
Prothrombin time, seconds, Me [Q1; Q3] | 10.79 [10.14; 11.70] (N = 839) | ND | ||
International normalized ratio, Me [Q1; Q3] | 1 [0.94; 1.09] (N = 573) | ND | ||
Activated partial thromboplastin time, seconds, Me [Q1; Q3] | 32.7 [29; 37] (N = 576) | ND |
Genetic Variant | Effect Allele | Other Allele | N | OR [95% CI] 1 | p2 (Pbonf) |
---|---|---|---|---|---|
Entire group | |||||
rs1461496 | A | G | 2132 | 1.00 [0.88–1.15] | 0.95 |
rs10892958 | G | G | 2138 | 1.16 [0.99–1.35] | 0.06 |
rs1136141 | A | G | 2024 | 1.09 [0.90–1.30] | 0.38 |
Males | |||||
rs1461496 | A | G | 1065 | 1.01 [0.85; 1.21] | 0.9 |
rs10892958 | G | G | 1057 | 1.30 [1.05; 1.61] | 0.01 |
rs1136141 | A | G | 999 | 1.08 [0.84; 1.40] | 0.55 |
Females | |||||
rs1461496 | A | G | 1076 | 1.05 [0.87; 1.26] | 0.63 |
rs10892958 | G | G | 1081 | 1.08 [0.87; 1.33] | 0.49 |
rs1136141 | A | G | 1025 | 1.16 [0.91; 1.47] | 0.24 |
Nonsmokers (f−) | |||||
rs1461496 | A | G | 1379 | 1.10 [0.93; 1.31] | 0.24 |
rs10892958 | G | G | 1383 | 1.07 [0.88; 1.30] | 0.51 |
rs1136141 | A | G | 1306 | 0.87 [0.68; 1.10] | 0.23 |
Smokers (f+) | |||||
rs1461496 | A | G | 753 | 0.86 [0.70; 1.07] | 0.18 |
rs10892958 | G | G | 755 | 1.37 [1.07; 1.77] | 0.01 |
rs1136141 | A | G | 718 | 1.68 [1.23; 2.28] | 7.0 × 10−4 |
Normal fruit and vegetable intake (f−) | |||||
rs1461496 | A | G | 1639 | 1.02 [0.86; 1.20] | 0.86 (bonf1.0) |
rs10892958 | G | G | 1645 | 1.03 [0.84; 1.25] | 0.78 (bonf1.0) |
rs1136141 | A | G | 1559 | 0.91 [0.72; 1.16] | 0.45 (bonf 0.9) |
Low fruit and vegetable intake (f+) | |||||
rs1461496 | A | G | 1694 | 1.07 [0.91; 1.25] | 0.42 (bonf0.84) |
rs10892958 | G | G | 1699 | 1.36 [1.14; 1.63] | 9.0 × 10−4 (bonf0.002) |
rs1136141 | A | G | 1608 | 1.29 [1.05; 1.60] | 0.02 (bonf0.04) |
SNP | Trait | Effect Allele | Tissue | Effect Size (Beta) | PVAL | FDR |
---|---|---|---|---|---|---|
rs10892958 | HSPA8 | G | Brain-Hippocampus | −0.44 | 1.9 × 10−7 | 5.8 × 10−5 |
rs1136141 | HSPA8 | A | Brain-Hippocampus | −0.12 | 3.8 × 10−5 | 0.006 |
CLMP | A | Brain-Hippocampus | 0.13 | 4.8 × 10−5 | 0.008 |
SNP (Ref/Alt Allele) | Tissues Marks | Brain | Blood | ||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | ||
rs10892958 (C/G) | H3K4me1 | No | No | No | E | No | No | E | E |
H3K4me3 | P | P | P | P | P | P | P | P | |
H3K27ac | E | E | E | E | E | E | E | E | |
H3K9ac | No | P | P | P | P | P | P | P | |
DNase | No | No | No | No | No | No | No | DNase | |
rs1136141 (G/A) | H3K4me1 | No | No | No | No | No | No | No | E |
H3K4me3 | P | P | P | P | P | P | P | P | |
H3K27ac | E | E | E | E | E | E | E | E | |
H3K9ac | No | P | P | P | P | P | P | P | |
DNase | No | No | No | No | No | No | No | DNase |
No. | SNP | Phenotype | p-Value | Beta (OR) | Sample Size |
---|---|---|---|---|---|
1. | rs1136141 (G/A) | Systolic blood pressure | 0.008 | Beta▲0.0056 | 1,325,890 |
2. | Heart rate | 0.01 | Beta▲0.007 | 484,178 | |
3. | Peripheral artery disease in ever-smokers | 0.04 | OR▲1.0639 | 28,235 | |
4. | TOAST, other-determined | 0.03 | OR▲2.4103 | 9277 |
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Kobzeva, K.A.; Soldatova, M.O.; Stetskaya, T.A.; Soldatov, V.O.; Deykin, A.V.; Freidin, M.B.; Bykanova, M.A.; Churnosov, M.I.; Polonikov, A.V.; Bushueva, O.Y. Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis. Genes 2023, 14, 1171. https://doi.org/10.3390/genes14061171
Kobzeva KA, Soldatova MO, Stetskaya TA, Soldatov VO, Deykin AV, Freidin MB, Bykanova MA, Churnosov MI, Polonikov AV, Bushueva OY. Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis. Genes. 2023; 14(6):1171. https://doi.org/10.3390/genes14061171
Chicago/Turabian StyleKobzeva, Ksenia A., Maria O. Soldatova, Tatiana A. Stetskaya, Vladislav O. Soldatov, Alexey V. Deykin, Maxim B. Freidin, Marina A. Bykanova, Mikhail I. Churnosov, Alexey V. Polonikov, and Olga Y. Bushueva. 2023. "Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis" Genes 14, no. 6: 1171. https://doi.org/10.3390/genes14061171
APA StyleKobzeva, K. A., Soldatova, M. O., Stetskaya, T. A., Soldatov, V. O., Deykin, A. V., Freidin, M. B., Bykanova, M. A., Churnosov, M. I., Polonikov, A. V., & Bushueva, O. Y. (2023). Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis. Genes, 14(6), 1171. https://doi.org/10.3390/genes14061171