リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data

Zou, Zhaonan Yoshimura, Yuka Yamanishi, Yoshihiro Oki, Shinya 京都大学 DOI:10.1186/s13072-023-00510-w

2023.09.25

概要

[Background] Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed “DAR-ChIPEA, ” to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants. [Methods] Large-scale public ChIP-Seq data (human, n = 15, 155; mouse, n = 13, 156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant–TF matrices were then cross-referenced to a repository of TF–disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants. [Results] The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 × 10⁻⁴²; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms. [Conclusions] Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.

この論文で使われている画像

参考文献

1. Lee SW, Yon DK, James CC, Lee S, Koh HY, Sheen YH, et al. Short-term

effects of multiple outdoor environmental factors on risk of asthma

exacerbations: age-stratified time-series analysis. J Allergy Clin Immunol.

2019;144(6):1542–50. https://​doi.​org/​10.​1016/j.​jaci.​2019.​08.​037.

2. Kaufman JD, Adar SD, Barr RG, Budoff M, Burke GL, Curl CL, et al.

Association between air pollution and coronary artery calcification

within six metropolitan areas in the USA (the Multi-Ethnic Study of

Atherosclerosis and Air Pollution): a longitudinal cohort study. Lancet.

2016;388(10045):696–704. https://​doi.​org/​10.​1016/​S0140-​6736(16)​

00378-0.

3. Yang BY, Qian Z, Howard SW, Vaughn MG, Fan SJ, Liu KK, et al. Global association between ambient air pollution and blood pressure: a systematic

review and meta-analysis. Environ Pollut. 2018;235:576–88. https://​doi.​

org/​10.​1016/j.​envpol.​2018.​01.​001.

4. Zhu RX, Nie XH, Chen YH, Chen J, Wu SW, Zhao LH. Relationship between

particulate matter (PM(2.5)) and hospitalizations and mortality of chronic

obstructive pulmonary disease patients: a meta-analysis. Am J Med Sci.

2020;359(6):354–64. https://​doi.​org/​10.​1016/j.​amjms.​2020.​03.​016.

5. Wong CM, Tsang H, Lai HK, Thomas GN, Lam KB, Chan KP, et al. Cancer

mortality risks from long-term exposure to ambient fine particle. Cancer

Epidemiol Biomarkers Prev. 2016;25(5):839–45. https://​doi.​org/​10.​1158/​

1055-​9965.​EPI-​15-​0626.

6. Gianicolo EA, Bruni A, Rosati E, Sabina S, Guarino R, Padolecchia G, et al.

Congenital anomalies among live births in a polluted area. A ten-year

retrospective study. BMC Pregnancy Childbirth. 2012;12:165. https://​doi.​

org/​10.​1186/​1471-​2393-​12-​165.

7. Mahalingaiah S, Hart JE, Laden F, Farland LV, Hewlett MM, Chavarro J,

et al. Adult air pollution exposure and risk of infertility in the Nurses’

Health Study II. Hum Reprod. 2016;31(3):638–47. https://​doi.​org/​10.​1093/​

humrep/​dev330.

8. Bosc N, Atkinson F, Felix E, Gaulton A, Hersey A, Leach AR. Large scale

comparison of QSAR and conformal prediction methods and their applications in drug discovery. J Cheminform. 2019;11(1):4. https://​doi.​org/​10.​

1186/​s13321-​018-​0325-4.

9. Jacob L, Vert JP. Protein–ligand interaction prediction: an improved

chemogenomics approach. Bioinformatics. 2008;24(19):2149–56. https://​

doi.​org/​10.​1093/​bioin​forma​tics/​btn409.

10. Kolb P, Ferreira RS, Irwin JJ, Shoichet BK. Docking and chemoinformatic screens for new ligands and targets. Curr Opin Biotechnol.

2009;20(4):429–36. https://​doi.​org/​10.​1016/j.​copbio.​2009.​08.​003.

11. Pham TH, Qiu Y, Liu J, Zimmer S, O’Neill E, Xie L, et al. Chemical-induced

gene expression ranking and its application to pancreatic cancer drug

repurposing. Patterns (N Y). 2022;3(4):100441. https://​doi.​org/​10.​1016/j.​

patter.​2022.​100441.

12. Pilarczyk M, Fazel-Najafabadi M, Kouril M, Shamsaei B, Vasiliauskas J, Niu

W, et al. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat Commun. 2022;13(1):4678. https://​doi.​org/​10.​

1038/​s41467-​022-​32205-3.

13. Lee CW, Kim SM, Sa S, Hong M, Nam SM, Han HW. Relationship between

drug targets and drug-signature networks: a network-based genomewide landscape. BMC Med Genomics. 2023;16(1):17. https://​doi.​org/​10.​

1186/​s12920-​023-​01444-8.

14. DALYs GBD, Collaborators H. Global, regional, and national disabilityadjusted life-years (DALYs) for 359 diseases and injuries and healthy

life expectancy (HALE) for 195 countries and territories, 1990–2017: a

systematic analysis for the Global Burden of Disease Study 2017. Lancet.

2018;392(10159):1859–922. https://​doi.​org/​10.​1016/​S0140-​6736(18)​

32335-3.

Zou et al. Epigenetics & Chromatin

(2023) 16:34

15. Quenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD,

Fisher J, et al. Miscarriage matters: the epidemiological, physical,

psychological, and economic costs of early pregnancy loss. Lancet.

2021;397(10285):1658–67. https://​doi.​org/​10.​1016/​S0140-​6736(21)​

00682-6.

16. Go S, Kurita H, Matsumoto K, Hatano M, Inden M, Hozumi I. Methylmercury causes epigenetic suppression of the tyrosine hydroxylase gene in

an in vitro neuronal differentiation model. Biochem Biophys Res Commun. 2018;502(4):435–41. https://​doi.​org/​10.​1016/j.​bbrc.​2018.​05.​162.

17. Huang D, Zhang Y, Qi Y, Chen C, Ji W. Global DNA hypomethylation, rather

than reactive oxygen species (ROS), a potential facilitator of cadmiumstimulated K562 cell proliferation. Toxicol Lett. 2008;179(1):43–7. https://​

doi.​org/​10.​1016/j.​toxlet.​2008.​03.​018.

18. van Tilburg CM, Milde T, Witt R, Ecker J, Hielscher T, Seitz A, et al. Phase I/II

intra-patient dose escalation study of vorinostat in children with relapsed

solid tumor, lymphoma, or leukemia. Clin Epigenet. 2019;11(1):188.

https://​doi.​org/​10.​1186/​s13148-​019-​0775-1.

19. Bardia A, Kaklamani V, Wilks S, Weise A, Richards D, Harb W, et al. Phase

I study of elacestrant (RAD1901), a novel selective estrogen receptor

degrader, in ER-positive, HER2-negative advanced breast cancer. J Clin

Oncol. 2021;39(12):1360–70. https://​doi.​org/​10.​1200/​JCO.​20.​02272.

20. Duttke SH, Chang MW, Heinz S, Benner C. Identification and dynamic

quantification of regulatory elements using total RNA. Genome Res.

2019;29(11):1836–46. https://​doi.​org/​10.​1101/​gr.​253492.​119.

21. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME suite. Nucleic Acids

Res. 2015;43(W1):W39-49. https://​doi.​org/​10.​1093/​nar/​gkv416.

22. Zou Z, Iwata M, Yamanishi Y, Oki S. Epigenetic landscape of drug

responses revealed through large-scale ChIP-seq data analyses. BMC

Bioinform. 2022;23(1):51. https://​doi.​org/​10.​1186/​s12859-​022-​04571-8.

23. Zou Z, Ohta T, Miura F, Oki S. ChIP-Atlas 2021 update: a data-mining suite

for exploring epigenomic landscapes by fully integrating ChIP-seq, ATACseq and Bisulfite-seq data. Nucleic Acids Res. 2022;50(W1):W175–82.

https://​doi.​org/​10.​1093/​nar/​gkac1​99.

24. Suzuki A, Kawano S, Mitsuyama T, Suyama M, Kanai Y, Shirahige K, et al.

DBTSS/DBKERO for integrated analysis of transcriptional regulation.

Nucleic Acids Res. 2018;46(D1):D229–38. https://​doi.​org/​10.​1093/​nar/​

gkx10​01.

25. Wang T, Pehrsson EC, Purushotham D, Li D, Zhuo X, Zhang B, et al. The

NIEHS TaRGET II Consortium and environmental epigenomics. Nat Biotechnol. 2018;36(3):225–7. https://​doi.​org/​10.​1038/​nbt.​4099.

26. Leinonen R, Sugawara H, Shumway M, International Nucleotide

Sequence Database C. The sequence read archive. Nucleic Acids Res.

2011;39(Database issue):D19-21. https://​doi.​org/​10.​1093/​nar/​gkq10​19.

27. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al.

Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137.

https://​doi.​org/​10.​1186/​GB-​2008-9-​9-​R137.

28. Pinero J, Ramirez-Anguita JM, Sauch-Pitarch J, Ronzano F, Centeno E, Sanz

F, et al. The DisGeNET knowledge platform for disease genomics: 2019

update. Nucleic Acids Res. 2020;48(D1):D845-855. https://​doi.​org/​10.​

1093/​nar/​gkz10​21.

29. Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res.

2023;51(D1):D1257–62. https://​doi.​org/​10.​1093/​nar/​gkac8​33.

30. SRA ToolKit v3.0.0. https://​github.​com/​ncbi/​sra-​tools. Accessed 31 March

2022.

31. FASTX-Toolkit v0.0.13. http://​hanno​nlab.​cshl.​edu/​fastx_​toolk​it/. Accessed

31 March 2022.

32. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome

alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15. https://​doi.​org/​10.​1038/​s41587-​019-​0201-4.

33. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose

program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. https://​doi.​org/​10.​1093/​bioin​forma​tics/​btt656.

34. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package

for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. https://​doi.​org/​10.​1093/​bioin​forma​tics/​

btp616.

35. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing

genomic features. Bioinformatics. 2010;26(6):841–2. https://​doi.​org/​10.​

1093/​bioin​forma​tics/​btq033.

Page 14 of 15

36. Oki S, Ohta T, Shioi G, Hatanaka H, Ogasawara O, Okuda Y, et al. ChIP-Atlas:

a data-mining suite powered by full integration of public ChIP-seq data.

EMBO Rep. 2018;19(12):e46255. https://​doi.​org/​10.​15252/​embr.​20184​

6255.

37. Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD,

Rumynskiy EI, et al. HOCOMOCO: towards a complete collection of

transcription factor binding models for human and mouse via large-scale

ChIP-Seq analysis. Nucleic Acids Res. 2018;46(D1):D252–9. https://​doi.​org/​

10.​1093/​nar/​gkx11​06.

38. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier

performance in R. Bioinformatics. 2005;21(20):3940–3941. https://​doi.​org/​

10.​1093/​bioin​forma​tics/​bti623.

39. McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al. GREAT

improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010;28(5):495–501. https://​doi.​org/​10.​1038/​nbt.​1630.

40. Blake JA, Baldarelli R, Kadin JA, Richardson JE, Smith CL, Bult CJ, et al.

Mouse Genome Database (MGD): knowledgebase for mouse-human

comparative biology. Nucleic Acids Res. 2021;49(D1):D981–7. https://​doi.​

org/​10.​1093/​nar/​gkaa1​083.

41. Xu J, Ou K, Chen C, Li B, Guo J, Zuo Z, et al. Tributyltin exposure disturbs

hepatic glucose metabolism in male mice. Toxicology. 2019;425:152242.

https://​doi.​org/​10.​1016/j.​tox.​2019.​152242.

42. Chamorro-Garcia R, Diaz-Castillo C, Shoucri BM, Kach H, Leavitt R, Shioda

T, et al. Ancestral perinatal obesogen exposure results in a transgenerational thrifty phenotype in mice. Nat Commun. 2017;8(1):2012. https://​

doi.​org/​10.​1038/​s41467-​017-​01944-z.

43. Zuo Z, Chen S, Wu T, Zhang J, Su Y, Chen Y, et al. Tributyltin causes obesity

and hepatic steatosis in male mice. Environ Toxicol. 2011;26(1):79–85.

https://​doi.​org/​10.​1002/​tox.​20531.

44. Mendes ABA, Motta NAV, Lima GF, Autran LJ, Brazao SC, Magliano

DC, et al. Evaluation of the effects produced by subacute tributyltin

administration on vascular reactivity of male wistar rats. Toxicology.

2022;465:153067. https://​doi.​org/​10.​1016/j.​tox.​2021.​153067.

45. Lehrke M, Lazar MA. The many faces of PPARgamma. Cell.

2005;123(6):993–9. https://​doi.​org/​10.​1016/j.​cell.​2005.​11.​026.

46. Li X, Ycaza J, Blumberg B. The environmental obesogen tributyltin chloride acts via peroxisome proliferator activated receptor gamma to induce

adipogenesis in murine 3T3-L1 preadipocytes. J Steroid Biochem Mol

Biol. 2011;127(1–2):9–15. https://​doi.​org/​10.​1016/j.​jsbmb.​2011.​03.​012.

47. Ueno S, Kashimoto T, Susa N, Asai T, Kawaguchi S, Takeda-Homma S, et al.

Reduction in peripheral lymphocytes and thymus atrophy induced by

organotin compounds in vivo. J Vet Med Sci. 2009;71(8):1041–8. https://​

doi.​org/​10.​1292/​jvms.​71.​1041.

48. Zhang Y, Liang J, Sun L, Guo Z, Xu L. Inhibition of PP2A and the consequent activation of JNK/c-Jun are involved in tributyltin-induced apoptosis in human amnionic cells. Environ Toxicol. 2013;28(7):390–400. https://​

doi.​org/​10.​1002/​tox.​20730.

49. Tada-Oikawa S, Murata M, Kato T. Preferential induction of apoptosis in

regulatory T cells by tributyltin: possible involvement in the exacerbation

of allergic diseases. Nihon Eiseigaku Zasshi. 2010;65(4):530–5. https://​doi.​

org/​10.​1265/​jjh.​65.​530.

50. Kato T, Uchikawa R, Yamada M, Arizono N, Oikawa S, Kawanishi S, et al.

Environmental pollutant tributyltin promotes Th2 polarization and exacerbates airway inflammation. Eur J Immunol. 2004;34(5):1312–21. https://​

doi.​org/​10.​1002/​eji.​20032​4667.

51. Grabarczyk P, Przybylski GK, Depke M, Volker U, Bahr J, Assmus K, et al.

Inhibition of BCL11B expression leads to apoptosis of malignant but not

normal mature T cells. Oncogene. 2007;26(26):3797–810. https://​doi.​org/​

10.​1038/​sj.​onc.​12101​52.

52. Ikawa T, Hirose S, Masuda K, Kakugawa K, Satoh R, Shibano-Satoh A, et al.

An essential developmental checkpoint for production of the T cell lineage. Science. 2010;329(5987):93–6. https://​doi.​org/​10.​1126/​scien​ce.​11889​

95.

53. Szabo SJ, Kim ST, Costa GL, Zhang X, Fathman CG, Glimcher LH. A

novel transcription factor, T-bet, directs Th1 lineage commitment. Cell.

2000;100(6):655–69. https://​doi.​org/​10.​1016/​s0092-​8674(00)​80702-3.

54. Zhu J, Jankovic D, Oler AJ, Wei G, Sharma S, Hu G, et al. The transcription factor T-bet is induced by multiple pathways and prevents an

endogenous Th2 cell program during Th1 cell responses. Immunity.

2012;37(4):660–73. https://​doi.​org/​10.​1016/j.​immuni.​2012.​09.​007.

Zou et al. Epigenetics & Chromatin

(2023) 16:34

55. Kim Y, Park EH, Ng CFS, Chung Y, Hashimoto K, Tashiro K, et al. Respiratory function declines in children with asthma associated with chemical

species of fine particulate matter (PM(2.5)) in Nagasaki, Japan. Environ

Health. 2021;20(1):110. https://​doi.​org/​10.​1186/​s12940-​021-​00796-x.

56. Chen PC, Mou CH, Chen CW, Hsieh DPH, Tsai SP, Wei CC, et al. Roles of

ambient temperature and PM(2.5) on childhood acute bronchitis and

bronchiolitis from viral infection. Viruses. 2022;14(9):1932. https://​doi.​org/​

10.​3390/​v1409​1932.

57. Shi W, Liu C, Annesi-Maesano I, Norback D, Deng Q, Huang C, et al. Ambient PM(2.5) and its chemical constituents on lifetime-ever pneumonia

in Chinese children: a multi-center study. Environ Int. 2021;146:106176.

https://​doi.​org/​10.​1016/j.​envint.​2020.​106176.

58. He F, Yanosky JD, Fernandez-Mendoza J, Chinchilli VM, Al-Shaar L, Vgontzas AN, et al. Acute impact of fine particulate air pollution on cardiac

arrhythmias in a population-based sample of adolescents: the Penn State

Child Cohort. J Am Heart Assoc. 2022;11(18):e026370. https://​doi.​org/​10.​

1161/​JAHA.​122.​026370.

59. Chaulin AM, Sergeev AK. The role of fine particles (PM 2.5) in the genesis

of atherosclerosis and myocardial damage: emphasis on clinical and

epidemiological data, and pathophysiological mechanisms. Cardiol Res.

2022;13(5):268–82. https://​doi.​org/​10.​14740/​cr1366.

60. Huang F, Pan B, Wu J, Chen E, Chen L. Relationship between exposure to

PM2.5 and lung cancer incidence and mortality: a meta-analysis. Oncotarget. 2017;8(26):43322–31. https://​doi.​org/​10.​18632/​oncot​arget.​17313.

61. Chen YW, Huang MZ, Chen CL, Kuo CY, Yang CY, Chiang-Ni C, et al.

PM(2.5) impairs macrophage functions to exacerbate pneumococcusinduced pulmonary pathogenesis. Part Fibre Toxicol. 2020;17(1):37.

https://​doi.​org/​10.​1186/​s12989-​020-​00362-2.

62. Ge J, Yang H, Lu X, Wang S, Zhao Y, Huang J, et al. Combined exposure to

formaldehyde and PM(2.5): hematopoietic toxicity and molecular mechanism in mice. Environ Int. 2020;144:106050. https://​doi.​org/​10.​1016/j.​

envint.​2020.​106050.

63. Yamanaka R, Barlow C, Lekstrom-Himes J, Castilla LH, Liu PP, Eckhaus

M, et al. Impaired granulopoiesis, myelodysplasia, and early lethality in

CCAAT/enhancer binding protein epsilon-deficient mice. Proc Natl Acad

Sci U S A. 1997;94(24):13187–92. https://​doi.​org/​10.​1073/​pnas.​94.​24.​

13187.

64. Tanaka T, Akira S, Yoshida K, Umemoto M, Yoneda Y, Shirafuji N, et al. Targeted disruption of the NF-IL6 gene discloses its essential role in bacteria

killing and tumor cytotoxicity by macrophages. Cell. 1995;80(2):353–61.

https://​doi.​org/​10.​1016/​0092-​8674(95)​90418-2.

65. Reckzeh K, Bereshchenko O, Mead A, Rehn M, Kharazi S, Jacobsen SE,

et al. Molecular and cellular effects of oncogene cooperation in a genetically accurate AML mouse model. Leukemia. 2012;26(7):1527–36. https://​

doi.​org/​10.​1038/​leu.​2012.​37.

66. Grossmann M, Metcalf D, Merryfull J, Beg A, Baltimore D, Gerondakis

S. The combined absence of the transcription factors Rel and RelA

leads to multiple hemopoietic cell defects. Proc Natl Acad Sci U S A.

1999;96(21):11848–53. https://​doi.​org/​10.​1073/​pnas.​96.​21.​11848.

67. McKercher SR, Torbett BE, Anderson KL, Henkel GW, Vestal DJ, Baribault H,

et al. Targeted disruption of the PU.1 gene results in multiple hematopoietic abnormalities. EMBO J. 1996;15(20):5647–58.

68. Lee H, Lee MW, Warren JR, Ferrie J. Childhood lead exposure is

associated with lower cognitive functioning at older ages. Sci Adv.

2022;8(45):eabn5164. https://​doi.​org/​10.​1126/​sciadv.​abn51​64.

69. Rubens O, Logina I, Kravale I, Eglite M, Donaghy M. Peripheral neuropathy

in chronic occupational inorganic lead exposure: a clinical and electrophysiological study. J Neurol Neurosurg Psychiatry. 2001;71(2):200–4.

https://​doi.​org/​10.​1136/​jnnp.​71.2.​200.

70. Lin JL, Tan DT, Hsu KH, Yu CC. Environmental lead exposure and progressive renal insufficiency. Arch Intern Med. 2001;161(2):264–71. https://​doi.​

org/​10.​1001/​archi​nte.​161.2.​264.

71. Wan H, Wang Y, Zhang H, Zhang K, Chen Y, Chen C, et al. Chronic lead

exposure induces fatty liver disease associated with the variations of gut

microbiota. Ecotoxicol Environ Saf. 2022;232:113257. https://​doi.​org/​10.​

1016/j.​ecoenv.​2022.​113257.

72. Allada R, Bass J. Circadian mechanisms in medicine. N Engl J Med.

2021;384(6):550–61. https://​doi.​org/​10.​1056/​NEJMr​a1802​337.

73. Oishi K, Shirai H, Ishida N. CLOCK is involved in the circadian transactivation of peroxisome-proliferator-activated receptor alpha (PPARalpha) in

Page 15 of 15

74. 75. 76. 77. 78. 79. 80. mice. Biochem J. 2005;386(Pt 3):575–81. https://​doi.​org/​10.​1042/​BJ200​

41150.

Inoue I, Shinoda Y, Ikeda M, Hayashi K, Kanazawa K, Nomura M, et al.

CLOCK/BMAL1 is involved in lipid metabolism via transactivation of the

peroxisome proliferator-activated receptor (PPAR) response element. J

Atheroscler Thromb. 2005;12(3):169–74. https://​doi.​org/​10.​5551/​jat.​12.​

169.

Kasano-Camones CI, Takizawa M, Ohshima N, Saito C, Iwasaki W, Nakagawa Y, et al. PPARalpha activation partially drives NAFLD development in

liver-specific Hnf4a-null mice. J Biochem. 2023. https://​doi.​org/​10.​1093/​

jb/​mvad0​05.

Klaunig JE, Babich MA, Baetcke KP, Cook JC, Corton JC, David RM, et al.

PPARalpha agonist-induced rodent tumors: modes of action and human

relevance. Crit Rev Toxicol. 2003;33(6):655–780. https://​doi.​org/​10.​1080/​

71360​8372.

Stanton DT. QSAR and QSPR model interpretation using partial least

squares (PLS) analysis. Curr Comput Aided Drug Des. 2012;8(2):107–27.

https://​doi.​org/​10.​2174/​15734​09128​00492​357.

Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition

of native chromatin for fast and sensitive epigenomic profiling of open

chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10(12):1213–8. https://​doi.​org/​10.​1038/​NMETH.​2688.

Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, et al.

Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids

Res. 2015;43(Database issue):D921–7. https://​doi.​org/​10.​1093/​nar/​

gku955.

Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A

next generation connectivity map: L1000 platform and the first 1,000,000

profiles. Cell. 2017;171(6):1437–52. https://​doi.​org/​10.​1016/j.​cell.​2017.​10.​

049.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research ? Choose BMC and benefit from:

• fast, convenient online submission

• thorough peer review by experienced researchers in your field

• rapid publication on acceptance

• support for research data, including large and complex data types

• gold Open Access which fosters wider collaboration and increased citations

• maximum visibility for your research: over 100M website views per year

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

...

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る