vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-24-107vavilov-4421Research ArticleБИОМЕДИЦИНАBIOMEDICINEБаза данных о генах и белках, ассоциированных с нарушениями метаболизма глюкозы (GlucoGenes®): описание и возможности применения в биоинформатических исследованияхGlucoGenes®, a database of genes and proteins associated with glucose metabolism disorders, its description and applications in bioinformatics researchhttps://orcid.org/0000-0002-5407-8722КлимонтовВ. В.KlimontovV. V.

Новосибирск

Novosibirsk

klimontov@mail.ruhttps://orcid.org/0000-0001-5552-9508ШишинК. С.ShishinK. S.

Новосибирск

Novosibirsk

ИвановР. А.IvanovR. A.

Новосибирск

Novosibirsk

https://orcid.org/0000-0003-1663-318XПономаренкоМ. П.PonomarenkoM. P.

Новосибирск

Novosibirsk

https://orcid.org/0009-0006-3933-208XЗолотареваК. А.ZolotarevaK. A.

Новосибирск

Novosibirsk

https://orcid.org/0000-0003-3138-381XЛашинС. А.LashinS. A.

Новосибирск

Novosibirsk

Научно-исследовательский институт клинической и экспериментальной лимфологии – филиал Федерального исследовательского центра Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияResearch Institute of Clinical and Experimental Lymphology – Branch of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian FederationНаучно-исследовательский институт клинической и экспериментальной лимфологии – филиал Федерального исследовательского центра Институт цитологии и генетики Сибирского отделения Российской академии наук; Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияResearch Institute of Clinical and Experimental Lymphology – Branch of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian Federation20242601202528810081017Copyright © Климонтов В.В., Шишин К.С., Иванов Р.А., Пономаренко М.П., Золотарева К.А., Лашин С.А., 20252025Климонтов В.В., Шишин К.С., Иванов Р.А., Пономаренко М.П., Золотарева К.А., Лашин С.А.Klimontov V.V., Shishin K.S., Ivanov R.A., Ponomarenko M.P., Zolotareva K.A., Lashin S.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4421

Данные в области генетики и молекулярной биологии сахарного диабета стремительно накапливаются. Это ставит задачу создания исследовательских инструментов для быстрого поиска, структурирования и анализа информации в этой области. Мы разработали базу данных о генах и белках человека, ассоциированных с высоким уровнем глюкозы (гипергликемией), низким уровнем глюкозы (гипогликемией) и обоими нарушениями. Сведения были собраны с помощью текст-майнинга научных публикаций, проиндексированных в PubMed и PubMed Central, и анализа генных сетей гипергликемии, гипогликемии и вариабельности гликемии, выполненного с помощью биоинформатической системы ANDSystems. Созданный ресурс (GlucoGenes®) доступен по адресу: https://glucogenes.sysbio.ru/genes/main. Ресурс предоставляет информацию о генах и белках, связанных с риском развития гипергликемии и гипогликемии; регуляторных молекулах с гипергликемической и антигипергликемической активностью; генах, экспрессия которых повышается при высоком и/или низком уровне глюкозы; генах, экспрессия которых снижается при высоком и/или низком уровне глюкозы, а также о молекулах, связанных с нарушениями метаболизма глюкозы иным образом. На основе ресурса проведен эволюционный анализ генов, ассоциированных с нарушениями метаболизма глюкозы. Результаты анализа выявили значительное увеличение (до 40 %) доли генов, имеющих филостратиграфический индекс (phylostratigraphy age index, PAI), соответствующий времени происхождения многоклеточных организмов. Анализ консервативности последовательностей белков по индексу дивергенции (divergency index, DI) показал, что большинство соответствующих генов высококонсервативны (DI < 0.6) или консервативны (DI < 1). При анализе однонуклеотидного полиморфизма (SNP) в проксимальных районах промоторов, влияющих на сродство ТАТА-связывающего белка, в базе данных GlucoGenes® найден 181 SNPмаркер, который может снижать (45 SNP-маркеров) или повышать (136 SNР-маркеров) экспрессию 52 генов. Мы полагаем, что разработанный ресурс станет полезным инструментом для дальнейших исследований в области молекулярной биологии диабета.

Data on the genetics and molecular biology of diabetes are accumulating rapidly. This poses the challenge of creating research tools for a rapid search for, structuring and analysis of information in this field. We have developed a web resource, GlucoGenes®, which includes a database and an Internet portal of genes and proteins associated with high glucose (hyperglycemia), low glucose (hypoglycemia), and both metabolic disorders. The data were collected using text mining of the publications indexed in PubMed and PubMed Central and analysis of gene networks associated with hyperglycemia, hypoglycemia and glucose variability performed with ANDSystems, a bioinformatics tool. GlucoGenes® is freely available at: https://glucogenes.sysbio.ru/genes/main. GlucoGenes® enables users to access and download information about genes and proteins associated with the risk of hyperglycemia and hypoglycemia, molecular regulators with hyperglycemic and antihyperglycemic activity, genes up-regulated by high glucose and/or low glucose, genes down-regulated by high glucose and/or low glucose, and molecules otherwise associated with the glucose metabolism disorders. With GlucoGenes®, an evolutionary analysis of genes associated with glucose metabolism disorders was performed. The results of the analysis revealed a significant increase (up to 40 %) in the proportion of genes with phylostratigraphic age index (PAI) values corresponding to the time of origin of multicellular organisms. Analysis of sequence conservation using the divergence index (DI) showed that most of the corresponding genes are highly conserved (DI < 0.6) or conservative (DI < 1). When analyzing single nucleotide polymorphism (SNP) in the proximal regions of promoters affecting the affinity of the TATA-binding protein, 181 SNP markers were found in the GlucoGenes® database, which can reduce (45 SNP markers) or increase (136 SNP markers) the expression of 52 genes. We believe that this resource will be a useful tool for further research in the field of molecular biology of diabetes.

генбелокcахарный диабетгипергликемиягипогликемиявариабельность глюкозыбаза данныхфилостратиграфический индексоднонуклеотидный полиморфизмgeneproteindiabetes mellitushyperglycemiahypoglycemiaglucose variabilitydatabasephylostratigraphic indexsingle nucleotide polymorphismThe sections on evolutionary analysis of genes and SNP analysis were performed using the Bioinformatics Sharing Centre supported by Budget Project No. FWNR-2022-0006. The authors express their sincere gratitude to O.V. Saik (RICEL – branch of ICG SB RAS) for her significant contribution to data collection and valuable advice on the development of the database. We also thank A.M. Mukhin (ICG SB RAS) for technical assistance in creating the web resource.ReferencesCeriello A., Monnier L., Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol. 2019;7(3):221-230. doi 10.1016/S2213-8587(18)30136-0Ceriello A., Monnier L., Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol. 2019;7(3):221-230. doi 10.1016/S2213-8587(18)30136-0Chung W.K., Erion K., Florez J.C., Hattersley A.T., Hivert M.F., Lee C.G., McCarthy M.I., Nolan J.J., Norris J.M., Pearson E.R., Philipson L., McElvaine A.T., Cefalu W.T., Rich S.S., Franks P.W. Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2020;63(9):1671-1693. doi 10.1007/s00125-020-05181-wChung W.K., Erion K., Florez J.C., Hattersley A.T., Hivert M.F., Lee C.G., McCarthy M.I., Nolan J.J., Norris J.M., Pearson E.R., Philipson L., McElvaine A.T., Cefalu W.T., Rich S.S., Franks P.W. Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2020;63(9):1671-1693. doi 10.1007/s00125-020-05181-wDay I.N. dbSNP in the detail and copy number complexities. Hum. Mutat. 2010;31(1):2-4. doi 10.1002/humu.21149Day I.N. dbSNP in the detail and copy number complexities. Hum. Mutat. 2010;31(1):2-4. doi 10.1002/humu.21149Dhawan P., Vasishta S., Balakrishnan A., Joshi M.B. Mechanistic insights into glucose induced vascular epigenetic reprogramming in type 2 diabetes. Life Sci. 2022;298:120490. doi 10.1016/j.lfs.2022.120490Dhawan P., Vasishta S., Balakrishnan A., Joshi M.B. Mechanistic insights into glucose induced vascular epigenetic reprogramming in type 2 diabetes. Life Sci. 2022;298:120490. doi 10.1016/j.lfs.2022.120490Domazet-Lošo T., Tautz D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature. 2010; 468(7325):815-819. doi 10.1038/nature09632Domazet-Lošo T., Tautz D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature. 2010; 468(7325):815-819. doi 10.1038/nature09632Filonov S.V., Podkolodnyy N.L., Podkolodnaya O.A., Tverdokhleb N.N., Ponomarenko P.M., Rasskazov D.A., Bogomolov A.G., Ponomarenko M.P. Human_SNP_TATAdb: a database of SNPs that statistically significantly change the affinity of the TATA-binding protein to human gene promoters: genome-wide analysis and use cases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2023;27(7):728-736. doi 10.18699/VJGB-23-85Filonov S.V., Podkolodnyy N.L., Podkolodnaya O.A., Tverdokhleb N.N., Ponomarenko P.M., Rasskazov D.A., Bogomolov A.G., Ponomarenko M.P. Human_SNP_TATAdb: a database of SNPs that statistically significantly change the affinity of the TATA-binding protein to human gene promoters: genome-wide analysis and use cases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2023;27(7):728-736. doi 10.18699/VJGB-23-85Hall E., Dekker Nitert M., Volkov P., Malmgren S., Mulder H., Bacos K., Ling C. The effects of high glucose exposure on global gene expression and DNA methylation in human pancreatic islets. Mol. Cell. Endocrinol. 2018;472:57-67. doi 10.1016/j.mce.2017.11.019Hall E., Dekker Nitert M., Volkov P., Malmgren S., Mulder H., Bacos K., Ling C. The effects of high glucose exposure on global gene expression and DNA methylation in human pancreatic islets. Mol. Cell. Endocrinol. 2018;472:57-67. doi 10.1016/j.mce.2017.11.019Hanefeld M., Frier B.M., Pistrosch F. Hypoglycemia and cardiovascular risk: is there a major link? Diabetes Care. 2016;39(S.2):S205-S209. doi 10.2337/dcS15-3014Hanefeld M., Frier B.M., Pistrosch F. Hypoglycemia and cardiovascular risk: is there a major link? Diabetes Care. 2016;39(S.2):S205-S209. doi 10.2337/dcS15-3014International Diabetes Federation. IDF Diabetes Atlas, 10th ed. Brussels, 2021International Diabetes Federation. IDF Diabetes Atlas, 10th ed. Brussels, 2021Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst. Biol. 2015;9(S2):S2. doi 10.1186/1752-0509-9-S2-S2Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst. Biol. 2015;9(S2):S2. doi 10.1186/1752-0509-9-S2-S2Ivanisenko V.A., Demenkov P.S., Ivanisenko T.V., Mishchenko E.L., Saik O.V. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019;20(1):34. doi 10.1186/s12859-018-2567-6Ivanisenko V.A., Demenkov P.S., Ivanisenko T.V., Mishchenko E.L., Saik O.V. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019;20(1):34. doi 10.1186/s12859-018-2567-6Kanehisa M., Sato Y., Kawashima M., Furumichi M., Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D457-D462. doi 10.1093/nar/gkv1070Kanehisa M., Sato Y., Kawashima M., Furumichi M., Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D457-D462. doi 10.1093/nar/gkv1070Klimontov V.V., Berikov V.B., Saik O.V. Artificial intelligence in diabetology. Sakharnyi Diabet = Diabetes Mellitus. 2021a;24(2):156-166. doi 10.14341/DM12665 (in Russian)Klimontov V.V., Berikov V.B., Saik O.V. Artificial intelligence in diabetology. Sakharnyi Diabet = Diabetes Mellitus. 2021a;24(2):156-166. doi 10.14341/DM12665 (in Russian)Klimontov V.V., Saik O.V., Korbut A.I. Glucose variability: How does it work? Int. J. Mol. Sci. 2021b;22(15):7783. doi 10.3390/ijms22157783Klimontov V.V., Saik O.V., Korbut A.I. Glucose variability: How does it work? Int. J. Mol. Sci. 2021b;22(15):7783. doi 10.3390/ijms22157783Kolchanov N.A., Ignatieva E.V., Podkolodnaya O.A., Likhoshvai V.A., Matushkin Yu.G. Gene networks. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2): 833-850 (in Russian)Kolchanov N.A., Ignatieva E.V., Podkolodnaya O.A., Likhoshvai V.A., Matushkin Yu.G. Gene networks. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2): 833-850 (in Russian)Landrum M.J., Lee J.M., Riley G.R., Jang W., Rubinstein W.S., Church D.M., Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980-D985. doi 10.1093/nar/gkt1113Landrum M.J., Lee J.M., Riley G.R., Jang W., Rubinstein W.S., Church D.M., Maglott D.R. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980-D985. doi 10.1093/nar/gkt1113Langenberg C., Lotta L.A. Genomic insights into the causes of type 2 diabetes. Lancet. 2018;391(10138):2463-2474. doi 10.1016/S0140-6736(18)31132-2Langenberg C., Lotta L.A. Genomic insights into the causes of type 2 diabetes. Lancet. 2018;391(10138):2463-2474. doi 10.1016/S0140-6736(18)31132-2Li Y., Liang R., Sun M., Li Z., Sheng H., Wang J., Xu P., Liu S., Yang W., Lu B., Zhang S., Shan C. AMPK-dependent phosphorylation of HDAC8 triggers PGM1 expression to promote lung cancer cell survival under glucose starvation. Cancer Lett. 2020;478:82-92. doi 10.1016/j.canlet.2020.03.007Li Y., Liang R., Sun M., Li Z., Sheng H., Wang J., Xu P., Liu S., Yang W., Lu B., Zhang S., Shan C. AMPK-dependent phosphorylation of HDAC8 triggers PGM1 expression to promote lung cancer cell survival under glucose starvation. Cancer Lett. 2020;478:82-92. doi 10.1016/j.canlet.2020.03.007Lyssenko V., Vaag A. Genetics of diabetes-associated microvascular complications. Diabetologia. 2023;66(9):1601-1613. doi 10.1007/s00125-023-05964-xLyssenko V., Vaag A. Genetics of diabetes-associated microvascular complications. Diabetologia. 2023;66(9):1601-1613. doi 10.1007/s00125-023-05964-xMaloof A.C., Porter S.M., Moore J.L., Dudás F.Ö., Bowring S.A., Higgins J.A., Fike D.A., Eddy M.P. The earliest Cambrian record of animals and ocean geochemical change. Geol. Soc. Am. Bull. 2010; 122(11-12):1731-1774. doi 10.1130/B30346.1Maloof A.C., Porter S.M., Moore J.L., Dudás F.Ö., Bowring S.A., Higgins J.A., Fike D.A., Eddy M.P. The earliest Cambrian record of animals and ocean geochemical change. Geol. Soc. Am. Bull. 2010; 122(11-12):1731-1774. doi 10.1130/B30346.1Mustafin Z.S., Lashin S.A., Matushkin Yu.G. Phylostratigraphic analysis of gene networks of human diseases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2021; 25(1):46-56. doi 10.18699/VJ21.006Mustafin Z.S., Lashin S.A., Matushkin Yu.G. Phylostratigraphic analysis of gene networks of human diseases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2021; 25(1):46-56. doi 10.18699/VJ21.006Nielsen J. Systems biology of metabolism: A driver for developing personalized and precision medicine. Cell Metab. 2017:25(3):572-579. doi 10.1016/j.cmet.2017.02.002Nielsen J. Systems biology of metabolism: A driver for developing personalized and precision medicine. Cell Metab. 2017:25(3):572-579. doi 10.1016/j.cmet.2017.02.002Orlov Y.L., Anashkina A.A., Klimontov V.V., Baranova A.V. Medical genetics, genomics and bioinformatics aid in understanding molecular mechanisms of human diseases. Int. J. Mol. Sci. 2021;22(18): 9962. doi 10.3390/ijms22189962Orlov Y.L., Anashkina A.A., Klimontov V.V., Baranova A.V. Medical genetics, genomics and bioinformatics aid in understanding molecular mechanisms of human diseases. Int. J. Mol. Sci. 2021;22(18): 9962. doi 10.3390/ijms22189962Putra S.E.D., Martriano Humardani F., Antonius Y., Jonathan J., Thalia Mulyanata L. Epigenetics of Diabetes: A bioinformatic approach. Clin. Chim. Acta. 2024;557:117856. doi 10.1016/j.cca.2024.117856Putra S.E.D., Martriano Humardani F., Antonius Y., Jonathan J., Thalia Mulyanata L. Epigenetics of Diabetes: A bioinformatic approach. Clin. Chim. Acta. 2024;557:117856. doi 10.1016/j.cca.2024.117856Rehni A.K., Dave K.R. Impact of hypoglycemia on brain metabolism during diabetes. Mol. Neurobiol. 2018;55(12):9075-9088. doi 10.1007/s12035-018-1044-6Rehni A.K., Dave K.R. Impact of hypoglycemia on brain metabolism during diabetes. Mol. Neurobiol. 2018;55(12):9075-9088. doi 10.1007/s12035-018-1044-6Saik O.V., Klimontov V.V. Bioinformatic reconstruction and analysis of gene networks related to glucose variability in diabetes and its complications. Int. J. Mol. Sci. 2020;21(22):8691. doi 10.3390/ijms21228691Saik O.V., Klimontov V.V. Bioinformatic reconstruction and analysis of gene networks related to glucose variability in diabetes and its complications. Int. J. Mol. Sci. 2020;21(22):8691. doi 10.3390/ijms21228691Saik O.V., Klimontov V.V. Hypoglycemia, vascular disease and cognitive dysfunction in diabetes: insights from text mining-based reconstruction and bioinformatics analysis of the gene networks. Int. J. Mol. Sci. 2021;22(22):12419. doi 10.3390/ijms22222212419Saik O.V., Klimontov V.V. Hypoglycemia, vascular disease and cognitive dysfunction in diabetes: insights from text mining-based reconstruction and bioinformatics analysis of the gene networks. Int. J. Mol. Sci. 2021;22(22):12419. doi 10.3390/ijms22222212419Saik O.V., Klimontov V.V. Gene networks of hyperglycemia, diabetic complications, and human proteins targeted by SARS-CoV-2: what is the molecular basis for comorbidity? Int. J. Mol. Sci. 2022;23:7247. doi 10.3390/ijms23137247Saik O.V., Klimontov V.V. Gene networks of hyperglycemia, diabetic complications, and human proteins targeted by SARS-CoV-2: what is the molecular basis for comorbidity? Int. J. Mol. Sci. 2022;23:7247. doi 10.3390/ijms23137247Sasaki T., Kuroko M., Sekine S., Matsui S., Kikuchi O., Susanti V.Y., Kobayashi M., Tanaka Y., Yuasa T., Kitamura T. Overexpression of insulin receptor partially improves obese and diabetic phenotypes in db/db mice. Endocr. J. 2015;62(9):787-796. doi 10.1507/endocrj.ej15-0255Sasaki T., Kuroko M., Sekine S., Matsui S., Kikuchi O., Susanti V.Y., Kobayashi M., Tanaka Y., Yuasa T., Kitamura T. Overexpression of insulin receptor partially improves obese and diabetic phenotypes in db/db mice. Endocr. J. 2015;62(9):787-796. doi 10.1507/endocrj.ej15-0255Shojima N., Yamauchi T. Progress in genetics of type 2 diabetes and diabetic complications. J. Diabetes Investig. 2023;14(4):503-515. doi 10.1111/jdi.13970Shojima N., Yamauchi T. Progress in genetics of type 2 diabetes and diabetic complications. J. Diabetes Investig. 2023;14(4):503-515. doi 10.1111/jdi.13970Vaulont S., Vasseur-Cognet M., Kahn A. Glucose regulation of gene transcription. J. Biol. Chem. 2000;275(41):31555-31558. doi 10.1074/jbc.R000016200Vaulont S., Vasseur-Cognet M., Kahn A. Glucose regulation of gene transcription. J. Biol. Chem. 2000;275(41):31555-31558. doi 10.1074/jbc.R000016200Vega M.E., Finlay J., Vasishtha M., Schwarzbauer J.E. Elevated glucose alters global gene expression and tenascin-C alternative splicing in mesangial cells. Matrix Biol. Plus. 2020;8:100048. doi 10.1016/j.mbplus.2020.100048Vega M.E., Finlay J., Vasishtha M., Schwarzbauer J.E. Elevated glucose alters global gene expression and tenascin-C alternative splicing in mesangial cells. Matrix Biol. Plus. 2020;8:100048. doi 10.1016/j.mbplus.2020.100048Yang Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 2007;24(8):1586-1591. doi 10.1093/molbev/msm088Yang Z. PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 2007;24(8):1586-1591. doi 10.1093/molbev/msm088Wilmot E.G., Choudhary P., Leelarathna L., Baxter M. Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care. Diabetes Obes. Metab. 2019;21(12):2599-2608. doi 10.1111/dom.13842Wilmot E.G., Choudhary P., Leelarathna L., Baxter M. Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care. Diabetes Obes. Metab. 2019;21(12):2599-2608. doi 10.1111/dom.13842Zhang S., Ke Z., Yang C., Zhou P., Jiang H., Chen L., Li Y., Li Q. High glucose causes distinct expression patterns of primary human skin cells by RNA sequencing. Front. Endocrinol. 2021;12:603645. doi 10.3389/fendo.2021.603645Zhang S., Ke Z., Yang C., Zhou P., Jiang H., Chen L., Li Y., Li Q. High glucose causes distinct expression patterns of primary human skin cells by RNA sequencing. Front. Endocrinol. 2021;12:603645. doi 10.3389/fendo.2021.603645Zhang Q., Xiao X., Zheng J., Li M., Yu M., Ping F., Wang T., Wang X. DNA methylation regulates pancreatic gene expression and links maternal high-fat diet to the offspring glucose metabolism. J. Nutr. Biochem. 2024;123:109490. doi 10.1016/j.jnutbio.2023.109490Zhang Q., Xiao X., Zheng J., Li M., Yu M., Ping F., Wang T., Wang X. DNA methylation regulates pancreatic gene expression and links maternal high-fat diet to the offspring glucose metabolism. J. Nutr. Biochem. 2024;123:109490. doi 10.1016/j.jnutbio.2023.109490

The authors declare that there are no conflicts of interest present.