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Генетика, 2023, T. 59, № 11, стр. 1253-1269

Генетическая оценка прогнозируемого остаточного потребления корма и экспрессия значимых генов-кандидатов свиней породы дюрок и товарных помесей второго поколения

А. А. Белоус 1, *, А. А. Сермягин 1, Н. А. Зиновьева 1

1 Федеральный исследовательский центр животноводства – ВИЖ им. академика Л.К. Эрнста
142132 пос. Дубровицы, Московская обл., Россия

* E-mail: belousa663@gmail.com

Поступила в редакцию 27.04.2023
После доработки 29.05.2023
Принята к публикации 02.06.2023

Аннотация

Остаточное потребление корма (RFI) – одна из основных и сложных кормовых характеристик, которая экономически важна для животноводства. Однако генетические и биологические механизмы, регулирующие данный признак, у свиней в значительной степени неизвестны. Таким образом, настоящее исследование было направлено на выявление полногеномных однонуклеотидных полиморфизмов (SNP), генов-кандидатов, участвующих в регуляции RFI, их биологических путей и кластеризации, с использованием полногеномного анализа ассоциации (GWAS). Исследование проводилось на свиньях породы дюрок (n = 783) и их товарных гибридах второго поколения (n = 250), проходящих тестовый откорм на автоматических кормовых станциях индивидуального учета. В результате были получены значимые по онтологии биологических функций и по экспрессии в тканях и органах гены, имеющие связь с RFI. К таким генам-кандидатам отнесены гены, кодирующие адгезию–рецептор, связанный с белком G6 (ADGRG6), центромерный белок S (APITD1), карбоксипептидазу E (CPE), трансмембранный кальций-связывающий белок (SYTL2), молекулу клеточной адгезии 1 (CADM1), протоонкоген Fli-1, фактор транскрипции ETS (FLI1), трансмембранный белок 3 теневрина (TENM3), простагландин Е4 (PTGER4) и член 2 подсемейства D калиевых потенциал-зависимых каналов (KCND2). Также анализ полученных данных по кластеризации показал разделение на биологическую, функциональную и молекулярную библиотеки и данные, опубликованные в PubMed. Объединяя полученную информацию, можно сказать, что генетическая составляющая показателя прогнозируемого остаточного потребления корма важна, о чем было указано в предыдущих исследованиях. В связи с чем возникает необходимость создания молекулярных диагностик и разработки расчетов геномной оценки, в совокупности с конверсией корма, что позволит улучшить показатели продуктивности в племенных стадах свиней и улучшить качество производимой продукции.

Ключевые слова: эффективность использования корма, прогнозируемое остаточное потребление корма, геномная оценка, полногеномное ассоциативное исследование, экспрессия генов, хрячки породы дюрок, помеси второго поколения, свиноводство.

Список литературы

  1. Koch R.M., Swiger L.A., Chambers D., Gregory K.E. Efficiency of feed use in beef cattle // J. Anim. Sci. 1963. V. 22. № 2. P. 486–494. https://doi.org/10.2527/jas1963.222486x

  2. Vigors S., Sweeney T., O’Shea C.J. et al. Pigs that are divergent in feed efficiency, differ in intestinal enzyme and nutrient transporter gene expression, nutrient digestibility and microbial activity // Animal. 2016. V. 10. № 11. P. 1848–1855. https://doi.org/10.1017/S1751731116000847

  3. Patience J.F., Rossoni-Serao M.C., Gutierrez N.A. A review of feed efficiency in swine: biology and application // J. Anim. Sci. Biotechnol. 2015. V. 6. № 1. P. 33. https://doi.org/10.1186/s40104-015-0031-2

  4. Rakhshandeh A., Dekkers J.C., Kerr B.J. et al. Effect of immune system stimulation and divergent selection for residual feed intake on digestive capacity of the small intestine in growing pigs // J. Anim. Sci. 2012. V. 90. № 4. P. 233–255. https://doi.org/10.2527/jas.53976

  5. Grubbs J.K., Huff-Lonergan E., Gabler N.K. et al. Liver and skeletal muscle mitochondria proteomes are altered in pigs divergently selected for residual feed intake // J. Anim. Sci. 2014. V. 92. № 5. P. 1995–2007. https://doi.org/10.2527/jas.2013-7391

  6. Fu L., Xu Y., Hou Y. et al. Proteomic analysis indicates that mitochondrial energy metabolism in skeletal muscle tissue is negatively correlated with feed efficiency in pigs // Sci. Rep. 2017. № 7. https://doi.org/10.1038/srep45291

  7. Jing L., Hou Y., Wu H. et al. Transcriptome analysis of mRNA and miRNA in skeletal muscle indicates an important network for differential residual feed intake in pigs // Sci. Rep. 2015. № 5. https://doi.org/10.1038/srep11953

  8. Vigors S., O’Doherty J.V., Kelly A.K. et al. The effect of divergence in feed efficiency on the intestinal microbiota and the intestinal immune response in both unchallenged and lipopolysaccharide challenged Ileal and colonic explants // PLoS One. 2016. V. 11. № 2. https://doi.org/10.1371/journal.pone.0148145

  9. Mani V., Harris A.J., Keating A.F. et al. Intestinal integrity, endotoxin transport and detoxification in pigs divergently selected for residual feed intake // J. Anim. Sci. 2013. V. 91. № 5. P. 2141–2150. https://doi.org/10.2527/jas.2012-6053

  10. Hayes B.J., Lewin H.A., Goddard M.E. The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation // Trends Genet. 2013. V. 29. № 4. P. 206–214. https://doi.org/10.1016/j.tig.2012.11.009

  11. Saintilan R., Mérour I., Brossard L. et al. Genetics of residual feed intake in growing pigs: Relationships with production traits, and nitrogen and phosphorus excretion traits // J. Anim. Sci. 2013. V. 91. № 6. P. 2542–2554.

  12. Zhang C., Kemp R.A., Stothard P. et al. Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants // Genet. Sel. Evol. 2018. V. 50. № 1. https://doi.org/10.1186/s12711-018-0387-9

  13. Onteru S.K., Gorbach D.M., Young J.M. et al. Whole genome association studies of residual feed intake and related traits in the pig // PLoS One. 2013. V. 8. № 6. https://doi.org/10.1371/journal.pone.0061756

  14. Do D.N., Ostersen T., Strathe A.B. et al. Genome-wide association and systems genetic analyses of residual feed intake, daily feed consumption, backfat and weight gain in pigs // BMC Genet. 2014. V. 15. № 27. https://doi.org/10.1186/1471-2156-15-27

  15. Reyer H., Oster M., Magowan E. et al. Strategies towards improved feed efficiency in pigs comprise molecular shifts in hepatic lipid and carbohydrate metabolism // Int. J. Mol. Sci. 2017. V. 18. № 8. https://doi.org/10.3390/ijms18081674

  16. Белоус А.А., Требунских Е.А., Сермягин А.А., Зиновьева Н.А. Методические рекомендации по расчету и использованию в селекции свиней показателя прогнозируемого остаточного потребления корма (RFI). Дубровицы: ФГБНУ ФИЦ ВИЖ им. Л.К. Эрнста, 2022. 32 с.

  17. An M., Zhou G., Li Y. et al. Multi-breed genetic parameters and genome-wide association studies for mortality rate at birth in pigs // Res. Square. 2021. https://doi.org/10.21203/rs.3.rs-146253/v1

  18. Mignon G.L., Iannuccelli N., Robic A. et al. Fine mapping of quantitative trait loci for androstenone and skatole levels in pig // Res. Gate. 2011. https://hal.inrae.fr/hal-02816807

  19. Turnbull J., Tiberia E., Striano P. et al. Lafora disease // Epileptic Disord. 2016. V. 18. № S2. P. 38–62. https://doi.org/10.1684/epd.2016.0842

  20. Yang X., Sun J., Zhao G. et al. Identification of major loci and candidate genes for meat production-related traits in broilers // Front. Genet. 2021. V. 12. https://doi.org/10.3389/fgene.2021.645107

  21. Traini M., Quinn C.M., Sandoval C. et al. Sphingomyelin phosphodiesterase acid-like 3A (SMPDL3A) is a novel nucleotide phosphodiesterase regulated by cholesterol in human macrophages // J. Biol. Chem. 2014. V. 21. № 289. P. 32895–32913. https://doi.org/10.1074/jbc.M114.612341

  22. Ramdas M., Harel C., Armoni M., Karnieli E. AHNAK KO mice are protected from diet-induced obesity but are glucose intolerant // Horm. Metab. Res. 2015. V. 47. P. 265–272. https://doi.org/10.1055/s-0034-1387736

  23. Nakanishi N., Takahashi T., Ogata T. et al. PARM-1 promotes cardiomyogenic differentiation through regulating the BMP/Smad signaling pathway // Biochem. Biophys. Res. Commun. 2012. V. 428. P. 500–505. https://doi.org/10.1016/j.bbrc.2012.10.078

  24. Zhu Y., Wang D., Wang F. et al. A comprehensive analysis of GATA-1-regulated miRNAs reveals miR-23a to be a positive modulator of erythropoiesis // Nucl. Acids Res. 2013. V. 41. № 7. P. 4129–4143. https://doi.org/10.1093/nar/gkt093

  25. Alvarez J.I., Kébir H., Cheslow L. et al. JAML mediates monocyte and CD8 T-cell migration across the brain endothelium // Ann. Clin. Transl. Neurol. 2015. V. 2. № 11. P. 1032–1037. https://doi.org/10.1002/acn3.255

  26. Forbes M.K., Wright A.G.C., Markon K.E., Krueger R.F. Evidence that psychopathology symptom networks have limited replicability // J. Abnormal Psychol. 2017. V. 126. № 7. P. 969–988. https://doi.org/10.1037/abn0000276

  27. Tarekegn A.A., Mengistu M.Y., Mirach T.H. Health professionals’ willingness to pay and associated factors for cervical cancer screening program at College of Medicine and Health Sciences, University of Gondar, Northwest Ethiopia // PLoS One. 2019. V. 14. № 4. https://doi.org/10.1371/journal.pone.0215904

  28. Zhang Y., Wildsoet C.F. RPE and choroid mechanisms underlying ocular growth and myopia // Prog. Mol. Biol. Transl. Sci. 2015. V. 134. P. 221–240. https://doi.org/10.1016/bs.pmbts.2015.06.014

  29. Zitouni S., Nabais C., Jana S.C. et al. Polo-like kinases: Structural variations lead to multiple functions // Nat. Rev. Mol. Cell. Biol. 2014. V. 15. P. 433–452. https://doi.org/10.1038/nrm3819

  30. Liu Z., Sun Q., Wang X. PLK1, A potential target for cancer therapy // Transl. Oncol. 2017. V. 10. P. 22–32. https://doi.org/10.1016/j.tranon.2016.10.003

  31. Strebhardt K., Ullrich A. Targeting polo-like kinase 1 for cancer therapy // Nat. Rev. Cancer. 2006. V. 6. P. 321–330. https://doi.org/10.1038/nrc1841

  32. Gheghiani L., Wang L., Zhang Y. et al. PLK1 induces chromosomal instability and overrides cell-cycle checkpoints to drive tumorigenesis // Cancer Res. 2021. V. 81. № 5. P. 1293–1307. https://doi.org/10.1158/0008-5472.CAN-20-1377

  33. Qiong J., Beihua K., Xingsheng Y. et al. Overexpression of CHP2 enhances tumor cell growth, invasion and metastasis in ovarian cancer // In Vivo. 2007. V. 21. № 4. P. 593–598.

  34. Machuka E.M., Juma J., Muigai A.W.T. et al. Transcriptome profile of spleen tissues from locally-adapted Kenyan pigs (Sus scrofa) experimentally infected with three varying doses of a highly virulent African swine fever virus genotype IX isolate: Ken12/busia.1 (ken-1033) // BMC Genomics. 2022. V. 23. № 522. https://doi.org/10.1186/s12864-022-08754-8

  35. Zhou X., Padanad M.S., Evers B.M. et al. Modulation of mutant krasg12d -driven lung tumorigenesis in vivo by gain or loss of PCDH7 function // Mol. Cancer Res. 2019. V. 17. № 2. P. 594–603. https://doi.org/10.1158/1541-7786.MCR-18-0739

  36. Eckstrum K., Bany B.M. Tumor necrosis factor receptor subfamily 9 (Tnfrsf9) gene is expressed in distinct cell populations in mouse uterus and conceptus during implantation period of pregnancy // Cell Tissue Res. 2011. № 344. P. 567–576. https://doi.org/10.1007/s00441-011-1171-0

  37. Karki R., Malireddi R.K.S., Zhu Q., Kanneganti T.D. NLRC3 regulates cellular proliferation and apoptosis to attenuate the development of colorectal cancer // Cell Cycle. 2017. V. 16. № 13. P. 1243–1251. https://doi.org/10.1080/15384101.2017.1317414

  38. Andrews N.C. Iron homeostasis: Insights from genetics and animal models // Nat. Rev. Genet. 2000. № 3. P. 208–217. https://doi.org/10.1038/35042073

  39. Fleming M.D., Campagna D.R., Haslett J.N. et al. A mutation in a mitochondrial transmembrane protein is responsible for the pleiotropic hematological and skeletal phenotype of flexed-tail (f/f) mice // Genes Dev. 2001. V. 15. № 6. P. 652–657. https://doi.org/10.1101/gad.873001

  40. Núñez Y., Radović Č., Savić R. et al. Muscle transcriptome analysis reveals molecular pathways related to oxidative phosphorylation, antioxidant defense, fatness and growth in mangalitsa and moravka pigs // Animals. 2021. V. 11. https://doi.org/10.3390/ani11030844

  41. Ji-Youn K., Hwang H., Hak-Jae C. et al. Identification and functional analysis of pig β-1,4-N-Acetylglucosaminyltransferase A (MGAT4A) // J. Life Sci. 2016. № 26(3). P. 275–281. https://doi.org/10.5352/JLS.2016.26.3.275

  42. Chen Y., Yang L., Lin X. et al. Effects of genetic variation of the sorting nexin 29 (SNX29) gene on growth traits of xiangdong black goat // Animals. 2022. V. 12. https://doi.org/10.3390/ani12243461

  43. de Baaij J., Arjona F., van den Brand M. et al. Identification of SLC41A3 as a novel player in magnesium homeostasis // Sci. Rep. 2016. № 6. https://doi.org/10.1038/srep28565

  44. Zhang W., Li J., Guo Y. et al. Multi-strategy genome-wide association studies identify the DCAF16-NCAPG region as a susceptibility locus for average daily gain in cattle // Sci. Rep. 2016. № 6. https://doi.org/10.1038/srep38073

  45. Li J., Geraldo L.H., Dubrac A. et al. Slit2-robo signaling promotes glomerular vascularization and nephron development // J. Am. Soc. Nephrol. 2021. V. 32. № 9. P. 2255–2272. https://doi.org/10.1681/ASN.2020111640

  46. Xu D., Li C. Gene 33/Mig6/ERRFI1, an adapter protein with complex functions in cell biology and human diseases // Cells. 2021. V. 10. № 1574. https://doi.org/10.3390/cells10071574

  47. Messad F., Louveau I., Koffi B. et al. Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs // BMC Genomics. 2019. V. 20. № 659. https://doi.org/10.1186/s12864-019-6010-9

  48. Andersen O.M., Bøgh N., Landau A.M. et al. A genetically modified minipig model for Alzheimer’s disease with SORL1 haploinsufficiency // Cell Rep. Med. 2022. V. 3. № 9. https://doi.org/10.1016/j.xcrm.2022.100740

  49. Sironen A., Uimari P., Venhoranta H. et al. An exonic insertion within Tex14 gene causes spermatogenic arrest in pigs // BMC Genomics. 2011. V. 12. https://doi.org/10.1186/1471-2164-12-591

  50. Xue Y., Li C., Duan D. et al. Genome-wide association studies for growth-related traits in a crossbreed pig population // Anim. Genet. 2021. V. 52. № 2. P. 217–222. https://doi.org/10.1111/age.13032

  51. Jaing C., Rowland R.R., Allen J.E. et al. Gene expression analysis of whole blood RNA from pigs infected with low and high pathogenic African swine fever viruses // Sci. Rep. 2017. V. 7. № 1. https://doi.org/10.1038/s41598-017-10186-4

  52. Kapetanovic R., Fairbairn L., Downing A. et al. The impact of breed and tissue compartment on the response of pig macrophages to lipopolysaccharide // BMC Genomics. 2013. V. 14. https://doi.org/10.1186/1471-2164-14-581

  53. Zhang L., Huang Y., Wang M. et al. Development and genome sequencing of a laboratory-inbred miniature pig facilitates study of human diabetic disease // Science. 2019. V. 19. P. 162–176. https://doi.org/10.1016/j.isci.2019.07.025

  54. Ran X., Hu F., Mao N. et al. Differences in gene expression and variable splicing events of ovaries between large and small litter size in Chinese Xiang pigs // Porc. Health Manag. 2021. V. 7. № 52. https://doi.org/10.1186/s40813-021-00226-x

  55. Diao S., Huang S., Chen Z. et al. Genome-wide signatures of selection detection in three south china indigenous pigs // Genes. 2019. V. 10. https://doi.org/10.3390/genes10050346

  56. Liu X., Zhang J., Xiong X. et al. An integrative analysis of transcriptome and GWAS data to identify potential candidate genes influencing meat quality traits in pigs // Front. Genet. 2021. V. 12. https://doi.org/10.3389/fgene.2021.748070

  57. Scarl R.T., Lawrence C.M., Gordon H.M., Nunemaker C.S. STEAP4: Its emerging role in metabolism and homeostasis of cellular iron and copper // J. Endocrinol. 2017. V. 234. № 3. P. R123–R134. https://doi.org/10.1530/JOE-16-0594

  58. Henzi A., Senatore A., Lakkaraju A.K. et al. Soluble dimeric prion protein ligand activates Adgrg6 receptor but does not rescue early signs of demyelination in PrP-deficient mice // PLoS One. 2020. V. 15. № 11. https://doi.org/10.1371/journal.pone.0242137

  59. Torregrosa-Carrión R., Piñeiro-Sabarís R., Siguero-Álvarez M. et al. Adhesion G protein-coupled receptor Gpr126/Adgrg6 is essential for placental development // Sci. Adv. 2021. V. 7. № 46. https://doi.org/10.1126/sciadv.abj5445

  60. Hong C., Moorefield K.S., Jun P. et al. Epigenome scans and cancer genome sequencing converge on WNK2, a kinase-independent suppressor of cell growth // Proc. Natl Acad. Sci. USA. 2007. V. 104. № 26. https://doi.org/10.1073/pnas.0700683104

  61. Krona C., Ejeskär K., Carén H. et al. A novel 1p36.2 located gene, APITD1, with tumour-suppressive properties and a putative p53-binding domain, shows low expression in neuroblastoma tumours // Br. J. Cancer. 2004. V. 91. P. 1119–1130. https://doi.org/10.1038/sj.bjc.6602083

  62. Shin S.C., Chung E.R. Association of SNP marker in the leptin gene with carcass and meat quality traits in Korean cattle // Asian-Aust. J. Anim. Sci. 2007. V. 20. P. 1–6. https://doi.org/10.5713/ajas.2007.1

  63. Cawley N.X., Wetsel W.C., Murthy S.R. et al. New roles of carboxypeptidase E in endocrine and neuronal funciton and cancer // Endocr. Rev. 2012. V. 33. P. 216–253. https://doi.org/10.1210/er.2011-1039

  64. Wang J., Zhang Y., Yang Z. et al. Association of human carboxypeptidase E exon5 gene polymorphisms with angiographical characteristics of coronary atherosclerosis in a Chinese population // Acta Pharmacol. Sin. 2008. № 29. P. 736–744. https://doi.org/10.1111/j.1745-7254.2008.00798.x

  65. Koshimizu H., Senatorov V., Loh Y.P., Gozes I. Neuroprotective protein and carboxypeptidase E // J. Mol. Neurosci. 2009. № 39(1–2). P. 1–8. https://doi.org/10.1007/s12031-008-9164-5

  66. Valente T.S., Baldi F., Sant’Anna A.C. et al. Genome-wide association study between single nucleotide polymorphisms and flight speed in nellore cattle // PLoS One. 2016. V. 11. № 6. https://doi.org/10.1371/journal.pone.0156956

  67. Lafage-Pochitaloff M., Gerby B., Baccini V. et al. The CADM1 tumor suppressor gene is a major candidate gene in MDS with deletion of the long arm of chromosome 11 // Blood Adv. 2022. V. 6. № 2. P. 386–398. https://doi.org/10.1182/bloodadvances.2021005311

  68. Machiela M.J., Grünewald T.G.P., Surdez D. et al. Genome-wide association study identifies multiple new loci associated with Ewing sarcoma susceptibility // Nat. Commun. 2018. V. 9. № 1. https://doi.org/10.1038/s41467-018-05537-2

  69. Terao C., Momozawa Y., Ishigaki K. et al. GWAS of mosaic loss of chromosome Y highlights genetic effects on blood cell differentiation // Nat. Commun. 2019. V. 10. № 1. https://doi.org/10.1038/s41467-019-12705-5

  70. Dawood M., Kramer L.M., Shabbir M.I., Reecy J.M. Genome-wide association study for fatty acid composition in american angus cattle // Animals (Basel). 2021. V. 11. № 8. https://doi.org/10.3390/ani11082424

  71. Ben-Zur T., Feige E., Motro B., Wides R. The mammalian odz gene family: Homologs of a drosophila pair-rule gene with expression implying distinct yet overlapping developmental roles // Dev. Biol. 2000. V. 217. № 1. P. 107–120. https://doi.org/10.1006/dbio.1999.9532

  72. Berns D.S., DeNardo L.A., Pederick D.T., Luo L. Teneurin-3 controls topographic circuit assembly in the hippocampus // Nature. 2018. V. 554. № 7692. P. 328–333. https://doi.org/10.1038/nature25463

  73. Takano I., Takeshita N., Yoshida M. et al. Ten-m/Odz3 regulates migration and differentiation of chondrogenic ATDC5 cells via RhoA-mediated actin reorganization // J. Cell Physiol. 2021. V. 236. № 4. P. 2906–2919. https://doi.org/10.1002/jcp.30058

  74. Carr O.P., Glendining K.A., Leamey C.A., Marotte L.R. Retinal overexpression of ten-m3 alters ipsilateral retinogeniculate projections in the wallaby (Macropus eugenii) // Neurosci. Lett. 2014. № 566. P. 167–171. https://doi.org/10.1016/j.neulet.2014.02.048

  75. Glendining K.A., Liu S.C., Nguyen M. et al. Downstream mediators of ten-m3 signalling in the developing visual pathway // BMC Neurosci. 2017. V. 18. № 1. https://doi.org/10.1186/s12868-017-0397-5

  76. Young T.R., Bourke M., Zhou X. et al. Ten-m2 is required for the generation of binocular visual circuits // J. Neurosci. 2013. V. 33. № 30. P. 12490–12509. https://doi.org/10.1523/JNEUROSCI.4708-12.2013

  77. Aldahmesh M.A., Mohammed J.Y., Al-Hazzaa S., Alkuraya F.S. Homozygous null mutation in ODZ3 causes microphthalmia in humans // Genet. Med. 2012. V. 14. № 11. P. 900–904. https://doi.org/10.1038/gim.2012.718

  78. Lu F., Xu X., Zheng B. et al. Case report: Expansion of phenotypic and genotypic data in TENM3-related syndrome: Report of two cases // Front. Pediatr. 2023. № 11. https://doi.org/10.3389/fped.2023.1111771

  79. Rodriguez-Rodriguez L., Ivorra-Cortes J., Carmona F.D. et al. PTGER4 gene variant rs76523431 is a candidate risk factor for radiological joint damage in rheumatoid arthritis patients: a genetic study of six cohorts // Arthritis Res. Ther. 2015. V. 17. № 306. https://doi.org/10.1186/s13075-015-0830-z

  80. Losonczy A., Makara J.K., Magee J.C. Compartmentalized dendritic plasticity and input feature storage in neurons // Nature. 2008. № 452. P. 436–441. https://doi.org/10.1038/nature06725

  81. Aceto G., Colussi C., Leone L. et al. Chronic mild stress alters synaptic plasticity in the nucleus accumbens through GSK3beta-dependent modulation of Kv4.2 channels // Proc. Natl Acad. Sci. USA. 2020. № 117. P. 8143–8153. https://doi.org/10.1073/pnas.1917423117

  82. Lin M.A., Cannon S.C., Papazian D.M. Kv4.2 autism and epilepsy mutation enhances inactivation of closed channels but impairs access to inactivated state after opening // Proc. Natl Acad. Sci. USA. 2018. № 115. P. E3559–E3568. https://doi.org/10.1073/pnas.1717082115

  83. Lee H., Lin M.C., Kornblum H.I. et al. Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation // Hum. Mol. Genet. 2014. № 23. P. 3481–3489. https://doi.org/10.1093/hmg/ddu056

  84. Liu M., Yu C., Zhang Z. et al. Whole-genome sequencing reveals the genetic mechanisms of domestication in classical inbred mice // Genome Biol. 2022. V. 23. № 1. https://doi.org/10.1186/s13059-022-02772-1

  85. Казанцева А.В., Еникеева Р.Ф., Романова А.Р. и др. Гены семейства нейрексинов (CNTNAP2 и NRXN1): их роль в развитии математической тревожности // Мед. генетика. 2016. Т. 15. № 11(173). С. 17–23.

  86. Iijima T., Wu K., Witte H. et al. SAM68 regulates neuronal activity-dependent alternative splicing of neurexin-1 // Cell. 2011. V. 147. № 7. P. 1601–1614. https://doi.org/10.1016/j.cell.2011.11.028

  87. Lyu Y.L., Lin C.P., Azarova A.M. et al. Role of topoisomerase IIbeta in the expression of developmentally regulated genes // Mol. Cell Biol. 2006. V. 26. № 21. P. 7929–7941. https://doi.org/10.1128/MCB.00617-06

  88. Colland F., Jacq X., Trouplin V. et al. Functional proteomics mapping of a human signaling pathway // Genome Res. 2004. V. 14. № 7. P. 1324–1332. https://doi.org/10.1101/gr.2334104

  89. Zhao Q., Wang F., Chen Y.X. et al. Comprehensive profiling of 1015 patients’ exomes reveals genomic-clinical associations in colorectal cancer // Nat. Commun. 2022. V. 13. № 1. https://doi.org/10.1038/s41467-022-30062-8

  90. Sung H.Y., Han J., Ju W., Ahn J.H. Synaptotagmin-like protein 2 gene promotes the metastatic potential in ovarian cancer // Oncol. Rep. 2016. V. 36. № 1. P. 535–541. https://doi.org/10.3892/or.2016.4835

  91. Zhou X., Li J., Teng J. et al. Long noncoding RNA BSN-AS2 induced by E2F1 promotes spinal osteosarcoma progression by targeting miR-654-3p/SYTL2 axis // Cancer Cell Int. 2020. V. 20. № 133. https://doi.org/10.1186/s12935-020-01205-y

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