Preview

Vavilov Journal of Genetics and Breeding

Advanced search

MiceDEGdb: a knowledge base on differentially expressed mouse genes as a model object in biomedical research

https://doi.org/10.18699/vjgb-25-18

Abstract

   The fundamental understanding of many biological processes that unfold in a human body has become possible due to experimental studies on animal models. The backbone of modern biomedical research is the use of mouse models for studying important pathophysiological mechanisms, assessing new therapeutic approaches and making decisions on acceptance or rejection of new candidate medicines in preclinical trials. The use of mice is advantageous because they have small size, are easy to keep and to genetically modify. Mice make up more than 90 % of the rodents used for pharmaceutical research. We present the pilot version of MiceDEGdb, a knowledge base on the genes that are differentially expressed in the mouse used as a model object in biomedical researc h. MiceDEGdb is a collection of published data on gene expression in mouse strains used for studying age-related diseases, such as hypertension, pe rio dontal disease, bone fragility, renal fibrosis, smooth muscle remodeling, heart failure and circadian rhythm disorder. The pilot release of MiceDEGdb contains 21,754 DEGs representing 9,769 unique Mus musculus genes the transcription levels whereof were found as being changed in 25 RNA-seq experiments involving eight tissues – gum, bone, kidney, right ventricle, aortic arch, hippocampus, skeletal muscle and uterus – in six genetic mouse strains (C57BL/6J, Ren1cCre|ZsGreen, B6.129S7(Cg)-Polgtm1Prol/J, BPN/3J, BPH/2J and Kunming) used as models of eight human diseases – all these data were based on information in 10 original articles. MiceDEGdb is novel in that it features a curated annotation of changes in the expression levels of mouse DEGs using independent biomedical publications about same-direction changes in the expression levels of human homologs in patients with one disease or the other. In its pilot release, MiceDEGdb documented 85,092 such annotations for 318 human genes in 895 diseases, as suggest to 912 scientific articles referenced by their PubMed ID. The information contained in MiceDEGdb may be of interest to geneticists, molecular biologists, bioinformatics scientists, clinicians, pharmacologists and genetic advisors in personalized medicine. MiceDEGdb is freely available at https://www.sysbio.ru/MiceDEGdb.

About the Authors

O. A. Podkolodnaya
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



I. V. Chadaeva
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



S. V. Filonov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



N. L. Podkolodnyy
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



D. A. Rasskazov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



N. N. Tverdokhleb
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



K. A. Zolotareva
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



A. G. Bogomolov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University
Russian Federation

Novosibirsk



E. Yu. Kondratyuk
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Siberian Federal Scientific Centre of Agro-BioTechnologies of the Russian Academy of Sciences
Russian Federation

Novosibirsk; Novosibirsk region; Krasnoobsk



D. Yu. Oshchepkov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State University
Russian Federation

Novosibirsk



M. P. Ponomarenko
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



References

1. Amaladoss A., Chen Q., Liu M., Dummler S.K., Dao M., Suresh S., Chen J., Preiser P.R. De novo generated human red blood cells in humanized mice support Plasmodium falciparum infection. PLoS One. 2015;10(6):e0129825. doi: 10.1371/journal.pone.0129825

2. Bruter A.V., Varlamova E.A., Okulova Y.D., Tatarskiy V.V., Si­laeva Y.Y., Filatov M.A. Genetically modified mice as a tool for the study of human diseases. Mol Biol Rep. 2024;51(1):135. doi: 10.1007/s11033­023­09066­0

3. Chadaeva I.V., Filonov S.V., Zolotareva K.A., Khandaev B.M., Ershov N.I., Podkolodnyy N.L., Kozhemyakina R.V., Rasskazov D.A., Bogomolov A.G., Kondratyuk E.Yu., Klimova N.V., Shikhevich S.G., Ryazanova M.A., Fedoseeva L.A., Redina О.Е., Kozhevnikova O.S., Stefanova N.A., Kolosova N.G., Markel A.L., Ponomarenko M.P., Oshchepkov D.Yu. RatDEGdb: a knowledge base of differentially expressed genes in the rat as a model object in biomedical research. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2023;27(7):794­806. doi: 10.18699/VJGB­23­92

4. Chen G., Tang Q., Yu S., Xie Y., Sun J., Li S., Chen L. The biological function of BMAL1 in skeleton development and disorders. Life Sci. 2020;253:117636. doi: 10.1016/j.lfs.2020.117636

5. Chen Z., Huang Z., Zhao X., Zhou Y., Zhang P., Li Y. Transcriptome analysis of differentially expressed genes involved in the inflam­mageing status of gingiva in aged mice. Oral Dis. 2023;29(4):1757-1769. doi: 10.1111/odi.14222

6. Chuang D.M., Chen R.W., Chalecka­Franaszek E., Ren M., Hashimoto R., Senatorov V., Kanai H., Hough C., Hiroi T., Leeds P. Neuroprotective effects of lithium in cultured cells and animal models of diseases. Bipolar Disord. 2002;4(2):129­136. doi: 10.1034/j.13995618.2002.01179.x

7. Chuprin J., Buettner H., Seedhom M.O., Greiner D.L., Keck J.G., Ishikawa F., Shultz L.D., Brehm M.A. Humanized mouse models for immuno­oncology research. Nat Rev Clin Oncol. 2023;20(3): 192­206. doi: 10.1038/s41571­022­00721­2

8. Conti L., Reitano E., Cattaneo E. Neural stem cell systems: diversities and properties after transplantation in animal models of diseases. Brain Pathol. 2006;16(2):143­154. doi: 10.1111/j.1750­3639.2006.00009.x

9. Ding H., Liu S., Yuan Y., Lin Q., Chan P., Cai Y. Decreased expression of Bmal2 in patients with Parkinson’s disease. Neurosci Lett. 2011;499(3):186­188. doi: 10.1016/j.neulet.2011.05.058

10. Elshazley M., Sato M., Hase T., Yamashita R., Yoshida K., Toyokuni S., Ishiguro F., Osada H., Sekido Y., Yokoi K., Usami N., Shames D.S., Kondo M., Gazdar A.F., Minna J.D., Hasegawa Y. The circadian clock gene BMAL1 is a novel therapeutic target for malignant pleural mesothelioma. Int J Cancer. 2012;131(12):2820­2831. doi: 10.1002/ijc.27598

11. Fang K., Liu D., Pathak S.S., Yang B., Li J., Karthikeyan R., Chao O.Y., Yang Y.M., Jin V.X., Cao R. Disruption of circadian rhythms by ambient light during neurodevelopment leads to autistic­like molecular and behavioral alterations in adult mice. Cells. 2021;10(12):3314. doi: 10.3390/cells10123314

12. Filonov 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: genome­wide analysis and use cases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2023;27(7):728­736. doi: 10.18699/VJGB­23­85

13. Frias­Staheli N., Dorner M., Marukian S., Billerbeck E., Labitt R.N., Rice C.M., Ploss A. Utility of humanized BLT mice for analysis of dengue virus infection and antiviral drug testing. J Virol. 2014; 88(4):2205­2218. doi: 10.1128/JVI.03085­13

14. Giebfried J., Lorentz A. Relationship between the biological clock and inflammatory bowel disease. Clocks Sleep. 2023;5(2):260­275. doi: 10.3390/clockssleep5020021

15. Girard C.A., Wunderlich F.T., Shimomura K., Collins S., Kaizik S., Proks P., Abdulkader F., Clark A., Ball V., Zubcevic L., Bentley L., Clark R., Church C., Hugill A., Galvanovskis J., Cox R., Rorsman P., Bruning J.C., Ashcroft F.M. Expression of an activating mutation in the gene encoding the KATP channel subunit Kir6.2 in mouse pancreatic beta cells recapitulates neonatal diabetes. J Clin Invest. 2009;119(1):80­90. doi: 10.1172/jci35772

16. Gorr M.W., Francois A., Marcho L.M., Saldana T., McGrail E., Sun N., Stratton M.S. Molecular signature of cardiac remodeling associated with Polymerase Gamma mutation. Life Sci. 2022;298:120469. doi: 10.1016/j.lfs.2022.120469

17. Gryksa K., Schmidtner A.K., Masis­Calvo M., Rodriguez­Villagra O.A., Havasi A., Wirobski G., Maloumby R., Jagle H., Bosch O.J., Slattery D.A., Neumann I.D. Selective breeding of rats for high (HAB) and low (LAB) anxiety­related behaviour: A unique model for comorbid depression and social dysfunctions. Neurosci Biobehav Rev. 2023;152:105292. doi: 10.1016/j.neubiorev.2023.105292

18. Gunawan M., Her Z., Liu M., Tan S.Y., Chan X.Y., Tan W.W.S., Dhar­maraaja S., Fan Y., Ong C.B., Loh E., Chang K.T.E., Tan T.C., Chan J.K.Y., Chen Q. A novel human systemic lupus erythematosus model in humanised mice. Sci Rep. 2017;7(1):16642. doi: 10.1038/s41598­017­16999­7

19. Hild B., Dreier M.S., Oh J.H., McCulloch J.A., Badger J.H., Guo J., Thefaine C.E., Umarova R., Hall K.D., Gavrilova O., Rosshart S.P., Trinchieri G., Rehermann B. Neonatal exposure to a wild­derived microbiome protects mice against diet­induced obesity. Nat Metab. 2021;3(8):1042­1057. doi: 10.1038/s42255­021­00439­y

20. Jacenik D., Cygankiewicz A.I., Mokrowiecka A., Malecka-­Panas E., Fichna J., Krajewska W.M. Sex­ and age­related estrogen signaling alteration in inflammatory bowel diseases: modulatory role of estrogen receptors. Int J Mol Sci. 2019;20(13):3175. doi: 10.3390/ijms20133175

21. Kaya S., Schurman C.A., Dole N.S., Evans D.S., Alliston T. Prioritization of genes relevant to bone fragility through the unbiased integration of aging mouse bone transcriptomics and human GWAS analy­ses. J Bone Miner Res. 2022;37(4):804­817. doi: 10.1002/jbmr.4516

22. Keng C.T., Sze C.W., Zheng D., Zheng Z., Yong K.S., Tan S.Q., Ong J.J., Tan S.Y., Loh E., Upadya M.H., Kuick C.H., Hotta H., Lim S.G., Tan T.C., Chang K.T., Hong W., Chen J., Tan Y.J., Chen Q. Characterisation of liver pathogenesis, human immune responses and drug testing in a humanised mouse model of HCV infection. Gut. 2016; 65(10):1744­1753. doi: 10.1136/gutjnl­2014­307856

23. Kikyo N. Circadian regulation of bone remodeling. Int J Mol Sci. 2024;25(9):4717. doi: 10.3390/ijms25094717

24. Kim J.K., Forger D.B. A mechanism for robust circadian timekeeping via stoichiometric balance. Mol Syst Biol. 2012;8:630. doi: 10.1038/msb.2012.62

25. Kiss T., Nyul­Toth A., Gulej R., Tarantini S., Csipo T., Mukli P., Ungvari A., Balasubramanian P., Yabluchanskiy A., Benyo Z., Conley S.M., Wren J.D., Garman L., Huffman D.M., Csiszar A., Ungvari Z. Old blood from heterochronic parabionts accelerates vascular aging in young mice: transcriptomic signature of pathologic smooth muscle remodeling. GeroScience. 2022;44(2):953­981. doi: 10.1007/s11357­022­00519­1

26. Krause C., Suwada K., Blomme E.A.G., Kowalkowski K., Liguori M.J., Mahalingaiah P.K., Mittelstadt S., Peterson R., Rendino L., Vo A., Van Vleet T.R. Preclinical species gene expression database: development and meta­analysis. Front Genet. 2023;13:1078050. doi: 10.3389/fgene.2022.1078050

27. Li J., Gao F., Wei L., Chen L., Qu N., Zeng L., Luo Y., Huang X., Jiang H. Predict the role of lncRNA in kidney aging based on RNA sequencing. BMC Genomics. 2022;23(1):254. doi: 10.1186/s12864022­08479­8

28. Li L., Zhang M., Zhao C., Cheng Y., Liu C., Shi M. Circadian clock gene Clock­Bmal1 regulates cellular senescence in Chronic obstructive pulmonary disease. BMC Pulm Med. 2022;22(1):435. doi: 10.1186/s12890­022­02237­y

29. Li Z., Zhang Z., Ren Y., Wang Y., Fang J., Yue H., Ma S., Guan F. Aging and age­related diseases: from mechanisms to therapeutic stra tegies. Biogerontology. 2021;22(2):165­187. doi: 10.1007/s10522­021­09910­5

30. Liu B., Cui D., Liu J., Shi J.S. Transcriptome analysis of the aged SAMP8 mouse model of Alzheimer’s disease reveals novel molecular targets of formononetin protection. Front Pharmacol. 2024;15: 1440515. doi: 10.3389/fphar.2024.1440515

31. Liu L., van Schaik T.A., Chen K.S., Rossignoli F., Borges P., Vrbanac V., Wakimoto H., Shah K. Establishment and immune phenotyping of patient­derived glioblastoma models in humanized mice. Front Immunol. 2024;14:1324618. doi: 10.3389/fimmu.2023.1324618

32. Lu Z. PubMed and beyond: a survey of web tools for searching biomedical literature. Database (Oxford). 2011;2011:baq036. doi: 10.1093/database/baq036

33. Lukacs N.W., Strieter R.M., Standiford T.J., Kunkel S.L. Characterization of chemokine function in animal models of diseases. Methods. 1996;10(1):158­165. doi: 10.1006/meth.1996.0090

34. Monteiro C.J., Heery D.M., Whitchurch J.B. Modern approaches to mouse genome editing using the CRISPR­Cas toolbox and their applications in functional genomics and translational research. Adv Exp Med Biol. 2023;1429:13­40. doi: 10.1007/978­3­03133325­5_2

35. Myers M.J., Shaik F., Shaik F., Alway S.E., Mohamed J.S. Skeletal muscle gene expression profile in response to caloric restriction and aging: a role for Sirt1. Genes (Basel). 2021;12(5):691. doi: 10.3390/genes12050691

36. Neba Ambe G.N.N., Breda C., Bhambra A.S., Arroo R.R.J. Effect of the citrus flavone nobiletin on circadian rhythms and metabolic syndrome. Molecules. 2022;27(22):7727. doi: 10.3390/molecules27227727

37. Oishi K., Ohkura N., Amagai N., Ishida N. Involvement of circadian clock gene Clock in diabetes­induced circadian augmentation of plasminogen activator inhibitor­1 (PAI-1) expression in the mouse heart. FEBS Lett. 2005;579(17):3555­3559. doi: 10.1016/j.febslet.2005.05.027

38. Petrova D.D., Dolgova E.V., Proskurina A.S., Ritter G.S., Ruzanova V.S., Efremov Y.R., Potter E.A., Kirikovich S.S., Levites E.V., Taranov O.S., Ostanin A.A., Chernykh E.R., Kolchanov N.A., Bogachev S.S. The new general biological property of stem­like tumor cells. Part II: surface molecules, which belongs to distinctive groups with particular functions, form a unique pattern charac­teristic of a certain type of tumor stem­like cells. Int J Mol Sci. 2022;23(24):15800. doi: 10.3390/ijms232415800

39. Podkolodnyy N.L., Tverdokhleb N.N., Podkolodnaya O.A. Computa­tional model for mammalian circadian oscillator: interacting with NAD+/SIRT1 pathway and age­related changes in gene expres­sion of circadian oscillator. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2016;20(6):848­856. doi: 10.18699/VJ16.201

40. Puig O., Wang I.M., Cheng P., Zhou P., Roy S., Cully D., Peters M., Benita Y., Thompson J., Cai T.Q. Transcriptome profiling and net­work analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension. Physiol Genomics. 2010; 42A(1):24­32. doi: 10.1152/physiolgenomics.00010.2010

41. Roybal K., Theobold D., Graham A., DiNieri J.A., Russo S.J., Krishnan V., Chakravarty S., Peevey J., Oehrlein N., Birnbaum S., Vitaterna M.H., Orsulak P., Takahashi J.S., Nestler E.J., Carle­zon W.A. Jr., McClung C.A. Mania­like behavior induced by disruption of CLOCK. Proc Natl Acad Sci USA. 2007;104(15):6406-6411. doi: 10.1073/pnas.0609625104

42. Sarsani V.K., Raghupathy N., Fiddes I.T., Armstrong J., Thibaud-­Nissen F., Zinder O., Bolisetty M., Howe K., Hinerfeld D., Ruan X., Rowe L., Barter M., Ananda G., Paten B., Weinstock G.M., Churchill G.A., Wiles M.V., Schneider V.A., Srivastava A., Reinholdt L.G. The genome of C57BL/6J “Eve”, the mother of the laboratory mouse genome reference strain. G3 (Bethesda). 2019;9(6):1795­1805. doi: 10.1534/g3.119.400071

43. Segalat L. Invertebrate animal models of diseases as screening tools in drug discovery. ACS Chem Biol. 2007;2(4):231­236. doi: 10.1021/cb700009m

44. Siniscalchi C., Nouvenne A., Cerundolo N., Meschi T., Ticinesi A.; on behalf of the Parma Post­Graduate Specialization School in Emergency­Urgency Medicine Interest Group on Thoracic Ultrasound. Diaphragm ultrasound in different clinical scenarios : a review with a focus on older patients. Geriatrics (Basel). 2024;9(3):70. doi: 10.3390/geriatrics9030070

45. Stelzer G., Rosen N., Plaschkes I., Zimmerman S., Twik M., Fishilevich S., Stein T.I., Nudel R., Lieder I., Mazor Y., Kaplan S., Dahary D., Warshawsky D., Guan­Golan Y., Kohn A., Rappaport N., Safran M., Lancet D. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1­1.30.33. doi: 10.1002/cpbi.5

46. Sun S., Wang Y., Maslov A.Y., Dong X., Vijg J. SomaMutDB: a data-base of somatic mutations in normal human tissues. Nucleic Acids Res. 2022;50(D1):D1100­D1108. doi: 10.1093/nar/gkab914

47. Swanson C.M., Kohrt W.M., Buxton O.M., Everson C.A., Wright K.P. Jr., Orwoll E.S., Shea S.A. The importance of the circadian system & sleep for bone health. Metabolism. 2018;84:28­43. doi: 10.1016/j.metabol.2017.12.002

48. Swindell W.R., Johnston A., Sun L., Xing X., Fisher G.J., Bulyk M.L., Elder J.T., Gudjonsson J.E. Meta-profiles of gene expression during aging: limited similarities between mouse and human and an un­-expectedly decreased inflammatory signature. PLoS One. 2012; 7(3):e33204. doi: 10.1371/journal.pone.0033204

49. Vandamme T.F. Use of rodents as models of human diseases. J Pharm Bioallied Sci. 2014;6(1):2­9. doi: 10.4103/0975­7406.124301

50. Viehmann Milam A.A., Maher S.E., Gibson J.A., Lebastchi J., Wen L., Ruddle N.H., Herold K.C., Bothwell A.L. A humanized mouse mo­del of autoimmune insulitis. Diabetes. 2014;63(5):1712­1724. doi: 10.2337/db13­1141

51. Wang Y., Eng D.G., Pippin J.W., Gharib S.A., McClelland A., Gross K.W., Shankland S.J. Sex differences in transcriptomic profiles in aged kidney cells of renin lineage. Aging (Albany NY ). 2018;10(4):606­621. doi: 10.18632/aging.101416

52. White P.L., Wiederhold N.P., Loeffler J., Najvar L.K., Melchers W., Herrera M., Bretagne S., Wickes B., Kirkpatrick W.R., Barnes R.A., Donnelly J.P., Patterson T.F. Comparison of nonculture blood­based tests for diagnosing invasive aspergillosis in an animal model. J Clin Microbiol. 2016;54(4):960­966. doi: 10.1128/jcm.03233­15

53. Yajima M., Imadome K., Nakagawa A., Watanabe S., Terashima K., Nakamura H., Ito M., Shimizu N., Honda M., Yamamoto N., Fuji­wara S. A new humanized mouse model of Epstein–Barr virus infection that reproduces persistent infection, lymphoproliferative disorder, and cell­mediated and humoral immune responses. J Infect Dis. 2008;198(5):673­682. doi: 10.1086/590502

54. Yong K.S.M., Her Z., Chen Q. Humanized mice as unique tools for human-specific studies. Arch Immunol Ther Exp (Warsz). 2018;66(4): 245­266. doi: 10.1007/s00005­018­0506­x

55. Yuan G., Hua B., Yang Y., Xu L., Cai T., Sun N., Yan Z., Lu C., Qian R. The circadian gene Clock regulates bone formation via PDIA3. J Bone Miner Res. 2017;32(4):861­871. doi: 10.1002/jbmr.3046

56. Zayoud M., El Malki K., Frauenknecht K., Trinschek B., Kloos L., Karram K., Wanke F., Georgescu J., Hartwig U.F., Sommer C., Jonuleit H., Waisman A., Kurschus F.C. Subclinical CNS inflammation as response to a myelin antigen in humanized mice. J Neuroimmune Pharmacol. 2013; 8(4):1037­1047. doi: 10.1007/s11481­013­9466­4

57. Zhou X., Zhang X.X., Mahmmod Y.S., Hernandez J.A., Li G.F., Huang W.Y., Wang Y.P., Zheng Y.X., Li X.M., Yuan Z.G. A transcriptome analysis: various reasons of adverse pregnancy outcomes caused by acute Toxoplasma gondii infection. Front Physiol. 2020; 11:115. doi: 10.3389/fphys.2020.00115


Review

Views: 431


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2500-3259 (Online)