Biomics

Long non-coding RNA locus PVT1 produces multiple regulatory RNA molecules via alternative splicing, and therefore is a promising object for the development of targeted therapies of different disorders. Performing association analysis of genetic variants rs4410871 and rs759648 at the PVT1 locus with multiple sclerosis (MS) in the ethnic group of Russians, Tatars, and Bashkirs from the Volga-Ural region of Russia, we established an association between rs759648*С and MS in the group of Tatars (OR=1.42, PBonf0.046), confirmed by the results of meta-analysis in the three ethnic groups (OR=1.28, P = 0.015). Haplotypic analysis has revealed an association of the rs759648*С/rs4410871*С haplotype with MS in the total study group (χ 2 =6.083, Рperm =0.012), while rs4410871*C/C + rs759648* C/C combination conferred an increased risk of MS (OR = 2.71, Pperm = 0.027) in this group according to the data yielded by the multilocus analysis. The results of our analysis indicate the significance of the rs759648 polymorphism located near the DNA sequence producing miR-1208. Further studies will help to elucidate the molecular mechanisms of the involvement of PVT1 gene in the etiopathogenesis of MS.

multiple sclerosis, long non-coding RNA, PVT1, gene polymorphism, association study

1. Baranzini SE, Oksenberg JR. The Genetics of Multiple Sclerosis: From 0 to 200 in 50 Years. Trends Genet. 2017. V.33(12). P.960-970. doi: 10.1016/j.tig.2017.09.004

2. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps //
Bioinformatics. 2005. V. 21(2). P. 263-5. doi: 10.1093/bioinformatics/bth457

3. Boyko A, Melnikov M. Prevalence and Incidence of Multiple Sclerosis in Russian Federation: 30 Years of Studies. Brain Sciences. 2020. V.10(5). P.305. doi: 10.3390/brainsci10050305

4. Favorov AV, Andreewski TV, Sudomoina MA, Favorova OO, Parmigiani G, Ochs MF. A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans. Genetics. 2005. V.171(4). P.2113-2121. doi: 10.1534/genetics.105.048090

5. Olsson T, Barcellos LF, Alfredsson L. Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nature Reviews Neurology. 2017. V.13(1). P.25-36. doi: 10.1038/nrneurol.2016.187

6. He F, Song Z, Chen H, Chen Z, Yang P, Li W, Yang Z, Zhang T, Wang F, Wei J, Wei F, Wang Q, Cao J. Long noncoding RNA PVT1-214 promotes proliferation and invasion of colorectal cancer by stabilizing Lin28 and interacting with miR-128. Oncogene. 2019. V.38(2). P.164-179. doi: 10.1038/s41388-018-0432-8

7. Huang M, Li T, Wang Q, Li C, Zhou H, Deng S, Lv Z, He Y, Hou B, Zhu G. Silencing circPVT1 enhances radiosensitivity in non-small cell lung cancer by sponging microRNA-1208. Cancer Biomarkers. 2021. V.31. P.263-279. doi: 10.3233/CBM-203252

8. International Multiple Sclerosis Genetics Consortium &The Wellcome Trust Case Control Consortium, Sawcer S, Hellenthal G, Pirinen, M., <...> McVean, G. Donnelly, P. Compston, A. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011. V.476(7359). P.214-219. doi: 10.1038/nature10251

9. International Multiple Sclerosis Genetics Consortium (IMSGC), Beecham AH, Patsopoulos NA, Xifara, D. K. <...> De Jager, P., Sawcer, S. J., McCauley, J. L. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet. 2013. V.45(11). P.1353-1360. doi: 10.1038/ng.2770

10. International Multiple Sclerosis Genetics Consortium, Patsopoulos NA, Baranzini SE, Santaniello, A. <...> Ivinson, A. J., Olsson, T., De Jager, P. L. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science. 2019. V.365(6460). P.1417-1427. doi: 10.1126/science.aav7188

11. Kim E-A, Jang J-H, Sung E-G, Song I-H, Kim JY, Lee T-J. MiR-1208 Increases the Sensitivity to Cisplatin by Targeting TBCK in Renal Cancer Cells. International Journal of Molecular Sciences. 2019. V.20(14). P. 3540. doi: 10.3390/ijms20143540

12. Kozin M, Kulakova O, Kiselev I, Baulina N, Boyko A, Favorova O. Mitonuclear interactions influence multiple sclerosis risk. Gene. 2020. V.758. P.144962. doi: 10.1016/j.gene.2020.144962

13. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015. V.31(21). P.3555-3557. doi: 10.1093/bioinformatics/btv402

14. Olsson T, Barcellos LF, Alfredsson L. Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nature Reviews Neurology. 2017. V.13(1). P.25-36. doi: 10.1038/nrneurol.2016.187

15. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC. PLINK: A Tool Set for WholeGenome Association and Population-Based Linkage Analyses // The American Journal of Human Genetics. 2007. V. 81(3). P. 559-75. doi: 10.1086/519795

16. Traversa D, Simonetti G, Tolomeo D, Visci G, Macchia G, Ghetti M, Martinelli G, Kristensen LS, Storlazzi CT. Unravelling similarities and differences in the role of circular and linear PVT1 in cancer and human disease. British Journal of Cancer. 2022. V.126(6). P.835-850. doi: 10.1038/s41416-021-01584-7

17. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, Robertson N, La Rocca N, Uitdehaag B, van der Mei I. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS. Multiple Sclerosis Journal. 2020. V.26(14). P.1816-1821. doi: 10.1177/1352458520970841