Reply to Comment on Josef H. Finsterer, et al. “Polymorphism in Genes Encoding HSP40 Family Proteins is Associated With Ischemic Stroke Risk and Brain Infarct Size: A Pilot Study. Journal of Integrative Neuroscience. 2024;23(12):211”

Thank you very much for your interest in our research and for the comments.

Indeed, our study established an association between polymorphisms of individual genes encoding members of the heat shock protein (HSP) family 40 family and the risk of developing, and the clinical manifestations of, ischemic stroke (IS) [1]. Our study was a pilot project in which a genetic epidemiological study in a case-control design was used to analyze associations of polymorphic loci. That was followed by a functional annotation of genetic variants using a range of bioinformatics tools that are widely used to interpret the functional role of single nucleotide polymorphisms (SNPs) and study their potential involvement in the molecular mechanisms of the disease [2, 3, 4, 5, 6, 7].

We would like to point out that our work was an association study and did not conclude causal effects of HSP40 SNPs in IS. The nature of the discovered associations has yet to be elucidated, and further experimental studies will certainly be able to shed light on the causal relationship between HSP40 family proteins and IS. As is standard in genetic epidemiology, association studies represent the first step in hypothesis generation that can guide functional follow-up in vitro or in vivo studies. This was clearly acknowledged in our discussion section.

In our work, we found associations of SNPs in HSP40 genes in a large sample of patients and controls. That sample was described in detail in our previous articles [8, 9, 10, 11, 12, 13]; it is one of the largest research samples in Central Russia and included more than 2500 people, of whom 1306 are patients with IS. That sample size ensured the high power for genetic calculations necessary to obtain unbiased representative results in the field of association studies of complex human diseases [14]. In this regard, the associations we found do not raise any doubts. Furthermore, to account for biological heterogeneity in the cohort (such as age, sex, smoking), we included those variables as covariates in the logistic regression model, which complied with the current methodological standards for genetic-epidemiological research and ensured that our results were not confounded by known non-genetic risk factors.

Moreover, the associations we obtained were replicated (confirmed) using the Cerebrovascular Disease Knowledge Portal (CDKP [15]), which combines data (summary statistics) from association studies conducted around the world. Specifically, two independent genome-wide association studies (GWAS) reported associations between rs7189628 DnaJ heat shock protein family (Hsp40) member A2 (DNAJA2) and stroke [16, 17]; as for rs2034598 DNAJA2, it was found to be associated with the white matter hyperintensities in cerebral small vessel disease by Persyn et al. [18]. In addition, rs6500605 DNAJA3 was found to be associated with post-stroke functional outcomes (Modified Rankin Scale 0–1 vs 2–6) in a separate study [19].

Currently, the GWAS catalog contains information on more than 600 IS risk loci identified by genome-wide association studies [20]. Hundreds of IS risk loci have also been identified by the candidate approach [21, 22, 23, 24, 25, 26, 27, 28, 29, 30]. However, the use of a candidate approach in the association analysis does not require the inclusion of SNPs identified in previous association studies as potential risk factors. The candidate approach remains a widely accepted strategy for investigating biologically plausible genes (such as those involved in proteostasis and stress response) that may not reach genome-wide significance thresholds due to limited sample sizes or effect sizes below detection power in GWAS meta-analyses.

The functional effects of SNPs are not limited to the direct influence of genotypes on the expression level of the corresponding genes. (The expression level, by the way, is highly tissue-specific and cannot always be measured in patients in tissues of high pathogenetic significance for the disease under study.) However, SNPs can actualize their diverse functional role, for example, by binding to transcription factors [4, 31, 32, 33], influencing histone modifications [7, 34], and influencing the expression level of other genes through binding to quantitative trait loci [3, 6, 35].

Finally, as indicated in Supplementary Table 1 [1], there were no individuals with diabetes in the patient or control groups. Hyperlipidemia and atrial fibrillation are phenotypes that may also be determined by the genes we studied. Since only independent risk factors can be included in the regression model for association analysis (stroke and hyperlipidemia, and stroke and atrial fibrillation are phenotypes that may be codetermined by the genes we studied), we included sex, age, and smoking as covariates—independent risk factors for IS.

We hope this detailed response clarifies our study design, findings, and rationale. Ultimately, the aim of our work was to open new directions in understanding IS susceptibility.

References

[1] Kobzeva KA, Gurtovoy DE, Polonikov AV, Pokrovsky VM, Patrakhanov EA, Bushueva OY. Polymorphism in Genes Encoding HSP40 Family Proteins is Associated with Ischemic Stroke Risk and Brain Infarct Size: A Pilot Study. Journal of Integrative Neuroscience. 2024; 23: 211. https://doi.org/10.31083/j.jin2312211.

[2] The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2019; 47: D330–D338. https://doi.org/10.1093/nar/gky1055.

[3] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science (New York, N.Y.). 2020; 369: 1318–1330. https://doi.org/10.1126/science.aaz1776.

[4] Shin S, Hudson R, Harrison C, Craven M, Keleş S. atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding. Bioinformatics (Oxford, England). 2019; 35: 2657–2659. https://doi.org/10.1093/bioinformatics/bty1010.

[5] von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, et al. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Research. 2005; 33: D433–7. https://doi.org/10.1093/nar/gki005.

[6] Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nature Genetics. 2021; 53: 1300–1310. https://doi.org/10.1038/s41588-021-00913-z.

[7] Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Research. 2012; 40: D930–4. https://doi.org/10.1093/nar/gkr917.

[8] Belykh AE, Soldatov VO, Stetskaya TA, Kobzeva KA, Soldatova MO, Polonikov AV, et al. Polymorphism of SERF2, the gene encoding a heat-resistant obscure (Hero) protein with chaperone activity, is a novel link in ischemic stroke. IBRO Neuroscience Reports. 2023; 14: 453–461. https://doi.org/10.1016/j.ibneur.2023.05.004.

[9] Kobzeva K, Ivenkov M, Gromov R, Bushueva O. HSP90 Family Members, Their Regulators and Ischemic Stroke Risk: A Comprehensive Molecular-Genetics and Bioinformatics Analysis. Frontiers in Bioscience (Scholar Edition). 2024; 16: 19. https://doi.org/10.31083/j.fbs1604019.

[10] Kobzeva KA, Soldatova MO, Stetskaya TA, Soldatov VO, Deykin AV, Freidin MB, et al. 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.

[11] Shilenok I, Kobzeva K, Soldatov V, Deykin A, Bushueva O. C11orf58 (Hero20) Gene Polymorphism: Contribution to Ischemic Stroke Risk and Interactions with Other Heat-Resistant Obscure Chaperones. Biomedicines. 2024; 12: 2603. https://doi.org/10.3390/biomedicines12112603.

[12] Shilenok I, Kobzeva K, Deykin A, Pokrovsky V, Patrakhanov E, Bushueva O. Obesity and Environmental Risk Factors Significantly Modify the Association between Ischemic Stroke and the Hero Chaperone C19orf53. Life (Basel, Switzerland). 2024; 14: 1158. https://doi.org/10.3390/life14091158.

[13] Shilenok I, Kobzeva K, Stetskaya T, Freidin M, Soldatova M, Deykin A, et al. SERPINE1 mRNA Binding Protein 1 Is Associated with Ischemic Stroke Risk: A Comprehensive Molecular-Genetic and Bioinformatics Analysis of SERBP1 SNPs. International Journal of Molecular Sciences. 2023; 24: 8716. https://doi.org/10.3390/ijms24108716.

[14] Hong EP, Park JW. Sample size and statistical power calculation in genetic association studies. Genomics & Informatics. 2012; 10: 117–122. https://doi.org/10.5808/GI.2012.10.2.117.

[15] Crawford KM, Gallego-Fabrega C, Kourkoulis C, Miyares L, Marini S, Flannick J, et al. Cerebrovascular Disease Knowledge Portal: An Open-Access Data Resource to Accelerate Genomic Discoveries in Stroke. Stroke. 2018; 49: 470–475. https://doi.org/10.1161/STROKEAHA.117.018922.

[16] Aldridge CM, Armstrong ND, Sunmonu NA, Becker C, Palakshappa D, Lindgren AG, et al. Diversity in genetic risk of recurrent stroke: a genome-wide association study meta-analysis. Frontiers in Stroke. 2024; 3. https://doi.org/10.3389/fstro.2024.1338636.

[17] Mishra A, Malik R, Hachiya T, Jürgenson T, Namba S, Posner DC, et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature. 2022; 611: 115–123. https://doi.org/10.1038/s41586-022-05165-3.

[18] Persyn E, Hanscombe KB, Howson JMM, Lewis CM, Traylor M, Markus HS. Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants. Nature Communications. 2020; 11: 2175. https://doi.org/10.1038/s41467-020-15932-3.

[19] Söderholm M, Pedersen A, Lorentzen E, Stanne TM, Bevan S, Olsson M, et al. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology. 2019; 92: e1271–e1283. https://doi.org/10.1212/WNL.0000000000007138.

[20] Cerezo M, Sollis E, Ji Y, Lewis E, Abid A, Bircan KO, et al. The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity. Nucleic Acids Research. 2025; 53: D998–D1005. https://doi.org/10.1093/nar/gkae1070.

[21] Akinyemi R, Tiwari HK, Arnett DK, Ovbiagele B, Irvin MR, Wahab K, et al. APOL1, CDKN2A/CDKN2B, and HDAC9 polymorphisms and small vessel ischemic stroke. Acta Neurologica Scandinavica. 2018; 137: 133–141. https://doi.org/10.1111/ane.12847.

[22] Giusti B, Saracini C, Bolli P, Magi A, Martinelli I, Peyvandi F, et al. Early-onset ischaemic stroke: analysis of 58 polymorphisms in 17 genes involved in methionine metabolism. Thrombosis and Haemostasis. 2010; 104: 231–242. https://doi.org/10.1160/TH09-11-0748.

[23] Hanson E, Jood K, Nilsson S, Blomstrand C, Jern C. Association between genetic variation at the ADAMTS13 locus and ischemic stroke. Journal of Thrombosis and Haemostasis: JTH. 2009; 7: 2147–2148. https://doi.org/10.1111/j.1538-7836.2009.03617.x.

[24] Kobzeva KA, Shilenok IV, Belykh AE, Gurtovoy DE, Bobyleva LA. C9orf16 (BBLN) gene, encoding a member of Hero proteins, is a novel marker in ischemic stroke risk. Research Results in Biomedicine. 2022; 8: 278–292. https://doi.org/10.18413/2658-6533-2022-8-3-0-2.

[25] Li BH, Zhang LL, Yin YW, Pi Y, Guo L, Yang QW, et al. Association between 12p13 SNPs rs11833579/rs12425791 near NINJ2 gene and ischemic stroke in East Asian population: evidence from a meta-analysis. Journal of the Neurological Sciences. 2012; 316: 116–121. https://doi.org/10.1016/j.jns.2012.01.010.

[26] Matsushita T, Ashikawa K, Yonemoto K, Hirakawa Y, Hata J, Amitani H, et al. Functional SNP of ARHGEF10 confers risk of atherothrombotic stroke. Human Molecular Genetics. 2010; 19: 1137–1146. https://doi.org/10.1093/hmg/ddp582.

[27] Park HK, Kim DH, Yun DH, Ban JY. Association between IL10, IL10RA, and IL10RB SNPs and ischemic stroke with hypertension in Korean population. Molecular Biology Reports. 2013; 40: 1785–1790. https://doi.org/10.1007/s11033-012-2232-5.

[28] Tuttolomondo A, Di Raimondo D, Forte GI, Casuccio A, Vaccarino L, Scola L, et al. Single nucleotide polymorphisms (SNPs) of pro-inflammatory/anti-inflammatory and thrombotic/fibrinolytic genes in patients with acute ischemic stroke in relation to TOAST subtype. Cytokine. 2012; 58: 398–405. https://doi.org/10.1016/j.cyto.2012.02.012.

[29] Wu L, Shen Y, Liu X, Ma X, Xi B, Mi J, et al. The 1425G/A SNP in PRKCH is associated with ischemic stroke and cerebral hemorrhage in a Chinese population. Stroke. 2009; 40: 2973–2976. https://doi.org/10.1161/STROKEAHA.109.551747.

[30] Zhang L, Sui R. Effect of SNP polymorphisms of EDN1, EDNRA, and EDNRB gene on ischemic stroke. Cell Biochemistry and Biophysics. 2014; 70: 233–239. https://doi.org/10.1007/s12013-014-9887-6.

[31] Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y. The functional consequences of variation in transcription factor binding. PLoS Genetics. 2014; 10: e1004226. https://doi.org/10.1371/journal.pgen.1004226.

[32] Oksuz O, Henninger JE, Warneford-Thomson R, Zheng MM, Erb H, Vancura A, et al. Transcription factors interact with RNA to regulate genes. Molecular Cell. 2023; 83: 2449–2463.e13. https://doi.org/10.1016/j.molcel.2023.06.012.

[33] Weidemüller P, Kholmatov M, Petsalaki E, Zaugg JB. Transcription factors: Bridge between cell signaling and gene regulation. Proteomics. 2021; 21: e2000034. https://doi.org/10.1002/pmic.202000034.

[34] Kang HG, Lee YH, Lee SY, Choi JE, Do SK, Hong MJ, et al. Genetic variants in histone modification regions are associated with the prognosis of lung adenocarcinoma. Scientific Reports. 2021; 11: 21520. https://doi.org/10.1038/s41598-021-00909-z.

[35] Lee B, Yao X, Shen L, Alzheimer’s Disease Neuroimaging Initiative. Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer’s disease. BMC Genomics. 2022; 23: 414. https://doi.org/10.1186/s12864-022-08584-8.