Главная
16+
Научные результаты биомедицинских исследований → 2021 → Том 7, Выпуск №3, 2021
DOI: 10.18413/2658-6533-2021-7-3-0-2

Integrated in-depth bioinformatic analysis suggests RELCH/KIAA1468, LINC02341, and AKAP11 as candidate genes for ages at menarche and menopause
 

Volodymyr Dvornyk

Aннотация

Background: Polymorphisms of the TNFRSF11A and TNFSF11 genes were reported for their association with age at menarche (AAM) and age at natural menopause (ANM). However, the biological mechanisms underlying this association remain largely unclear. The aim of the study: This study was to determine biological processes backing the observed genetic associations. Materials and methods: Forty-four SNPs were analyzed using in silico approach and ten publicly available online databases and tools. Results: TNFRSF11A and TNFSF11 are highly pleiotropic genes that play a role in many metabolic processes. However, among that variety, lipid metabolism and cell survival and apoptosis seem the most biologically plausible mechanisms, through which these genes contribute to AAM and ANM. The analysis identified several mechanisms underlying the previously determined association of the TNFRSF11A and TNFSF11 genes with AAM and ANM and suggested RELCH/KIAA1468, LINC02341, and AKAP11 as new candidate genes for the traits. Conclusion: The in silico analysis is a powerful approach making it possible to uncover possible metabolic pathways underlying observed genetic associations.



Ключевые слова: bioinformatics, in silico analysis, age at menarche, age at menopause, TNFRSF11A, TNFSF11

Список литературы

  1. Boyce BF, Xing L. Functions of RANKL/RANK/OPG in bone modeling and remodeling. Archives of Biochemistry and Biophysics. 2008;473(2):139-46. DOI: https://doi.org/10.1016/j.abb.2008.03.018
  2. Harper E, Forde H, Davenport C, et al. Vascular calcification in type-2 diabetes and cardiovascular disease: Integrative roles for OPG, RANKL and TRAIL. Vascular Pharmacology. 2016;82:30-40. DOI: https://doi.org/10.1016/j.vph.2016.02.003
  3. Theill LE, Boyle WJ, Penninger JM. RANK-L and RANK: T cells, bone loss, and mammalian evolution. Annual Review of Immunology. 2002;20:795-823. DOI: https://doi.org/10.1146/annurev.immunol.20.100301.064753
  4. Casas-Avila L, Ponce de Leon-Suarez V, Penaloza-Espinosa RI, et al. The RANKL rs12585014 polymorphism is associated with age at menarche in postmenopausal women with hip fracture. Gynecological Endocrinology. 2018;34(12):1031-1034. DOI: https://doi.org/10.1080/09513590.2018.1481943
  5. Duan P, Wang ZM, Liu J, et al. Gene polymorphisms in RANKL/RANK/OPG pathway are associated with ages at menarche and natural menopause in Chinese women. BMC Women's Health. 2015;15:32. DOI: https://doi.org/10.1186/s12905-015-0192-3
  6. Pan R, Liu YZ, Deng HW, et al. Association analyses suggest the effects of RANK and RANKL on age at menarche in Chinese women. Climacteric. 2012;15(1):75-81. DOI: https://doi.org/10.3109/13697137.2011.587556
  7. Lu Y, Liu P, Recker RR, et al. TNFRSF11A and TNFSF11 are associated with age at menarche and natural menopause in white women. Menopause. 2010;17(5):1048-1054. DOI: https://doi.org/10.1097/gme.0b013e3181d5d523
  8. Chen CY, Chang IS, Hsiung CA, et al. On the identification of potential regulatory variants within genome wide association candidate SNP sets. BMC Medical Genomics. 2014;7:34. DOI: https://doi.org/10.1186/1755-8794-7-34
  9. Herman MA, Rosen ED. Making biological sense of GWAS data: lessons from the FTO locus. Cell Metabolism. 2015;22(4):538-9. DOI: https://doi.org/10.1016/j.cmet.2015.09.018
  10. Reshetnikov EA. Study of associations of candidate genes differentially expressing in the placenta with the development of placental insufficiency with fetal growth restriction. Research Results in Biomedicine. 2020;6(3):338-349. Russian. DOI: https://doi.org/10.18413/2658-6533-2020-6-3-0-5
  11. Sim NL, Kumar P, Hu J, et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Research. 2012;40(W1):W452-W457. DOI: https://doi.org/10.1093/nar/gks539
  12. 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(D1):D930-D934. DOI: https://doi.org/10.1093/nar/gkr917
  13. Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Research. 2012;22:1790-1797. DOI: https://doi.org/10.1101/gr.137323.112
  14. Guo L, Du Y, Chang S, et al. rSNPBase: a database for curated regulatory SNPs. Nucleic Acids Research. 2014;42(D1):D1033-D1039. DOI: https://doi.org/10.1093/nar/gkt1167
  15. Xu Z, Taylor JA. SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Research. 2009;37(suppl_2):W600-W605. DOI: https://doi.org/10.1093/nar/gkp290
  16. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Current Protocols in Bioinformatics. 2016;54:1.30.1-1.30.33. DOI: https://doi.org/10.1002/cpbi.5
  17. Westra H-J, Peters MJ, Esko T, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature Genetics. 2013;45(10):1238-1243. DOI: https://doi.org/10.1038/ng.2756
  18. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2019;47(D1):D330-D338. DOI: https://doi.org/10.1093/nar/gky1055
  19. Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research. 2010;38(suppl_2):W214-W220. DOI: https://doi.org/10.1093/nar/gkq537
  20. Ma L, Cao J, Liu L, et al. LncBook: a curated knowledgebase of human long non-coding RNAs. Nucleic Acids Research. 2019;47(D1):D128-D134. DOI: https://doi.org/10.1093/nar/gky960
  21. Fishilevich S, Nudel R, Rappaport N, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017;2017:bax028. DOI: https://doi.org/10.1093/database/bax028
  22. Fagerberg L, Hallstrom BM, Oksvold P, et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Molecular and Cellular Proteomics. 2014;13(2):397-406. DOI: https://doi.org/10.1074/mcp.M113.035600
  23. Liu JZ, van Sommeren S, Huang H, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature Genetics. 2015;47(9):979-986. DOI: https://doi.org/10.1038/ng.3359
  24. Sobajima T, Yoshimura SI, Maeda T, et al. The Rab11-binding protein RELCH/KIAA1468 controls intracellular cholesterol distribution. Journal of Cell Biology. 2018;217(5):1777-1796. DOI: https://doi.org/10.1083/jcb.201709123
  25. Maydan G, Noyman I, Har-Zahav A, et al. Multiple congenital anomalies-hypotonia-seizures syndrome is caused by a mutation in PIGN. Journal of Medical Genetics. 2011;48(6):383-9. DOI: http://dx.doi.org/10.1136/jmg.2010.087114
  26. Tachmazidou I, Suveges D, Min JL, et al. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. American Journal of Human Genetics. 2017;100(6):865-884. DOI: https://doi.org/10.1016/j.ajhg.2017.04.014
  27. Zhang L, Choi HJ, Estrada K, et al. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Human Molecular Genetics. 2014;23(7):1923-33. DOI: https://doi.org/10.1093/hmg/ddt575
  28. Liu PY, Lu Y, Recker RR, et al. ALOX12 gene is associated with the onset of natural menopause in white women. Menopause. 2010;17(1):152-156. DOI: https://doi.org/10.1097/gme.0b013e3181b63c68
  29. Ohba C, Okamoto N, Murakami Y, et al. PIGN mutations cause congenital anomalies, developmental delay, hypotonia, epilepsy, and progressive cerebellar atrophy. Neurogenetics. 2014;15(2):85-92. DOI: https://doi.org/10.1007/s10048-013-0384-7
  30. Infante M, Fabi A, Cognetti F, et al. RANKL/RANK/OPG system beyond bone remodeling: involvement in breast cancer and clinical perspectives. Journal of Experimental and Clinical Cancer Research. 2019;38(1):12. DOI: https://doi.org/10.1186/s13046-018-1001-2
  31. Zhang L, Blackwell K, Shi Z, et al. The RING domain of TRAF2 plays an essential role in the inhibition of TNFalpha-induced cell death but not in the activation of NF-kappaB. Journal of Molecular Biology. 2010;396(3):528-39. DOI: https://doi.org/10.1016/j.jmb.2010.01.008
  32. Biro FM, Khoury P, Morrison JA. Influence of obesity on timing of puberty. International Journal of Andrology. 2006;29(1):272-277. DOI: https://doi.org/10.1111/j.1365-2605.2005.00602.x
  33. Lovejoy JC. The menopause and obesity. Primary Care - Clinics in Office Practice. 2003;30(2):317-325. DOI: https://doi.org/10.1016/S0095-4543(03)00012-5
  34. Zhou S, Zhao L, Yi T, et al. Menopause-induced uterine epithelium atrophy results from arachidonic acid/prostaglandin E2 axis inhibition-mediated autophagic cell death. Scientific Reports. 2016;6:31408. DOI: https://doi.org/10.1038/srep31408
  35. Stark KD, Park EJ, Holub BJ. Fatty acid composition of serum phospholipid of premenopausal women and postmenopausal women receiving and not receiving hormone replacement therapy. Menopause. 2003;10(5):448-55. DOI: https://doi.org/10.1097/01.GME.0000059861.93639.1A
  36. Xu J, Su Z, Ding Q, et al. Inhibition of proliferation by knockdown of transmembrane (TMEM) 168 in glioblastoma cells via suppression of Wnt/beta-catenin pathway. Oncology Research. 2019;27(7):819-826. DOI: https://doi.org/10.3727/096504018X15478559215014
  37. Watanabe K, Stringer S, Frei O, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nature Genetics. 2019;51(9):1339-1348. DOI: https://doi.org/10.1038/s41588-019-0481-0
  38. Dvornyk V, Liu PY, Long JR, et al. Contribution of genotype and ethnicity to bone mineral density variation in Caucasians and Chinese: a test for five candidate genes for bone mass. Chinese Medical Journal. 2005;118(15):1235-1244.
  39. Dvornyk V, Liu XH, Shen H, et al. Differentiation of Caucasians and Chinese at bone mass candidate genes: implication for ethnic difference of bone mass. Ann Hum Genet. 2003;67(Pt 3):216-27. DOI: https://doi.org/10.1046/j.1469-1809.2003.00037.x