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/bioinformatics/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/gkac199.
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/
gkx1001.
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/gkq1019.
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/gkz1021.
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/gkac833.
30. SRA ToolKit v3.0.0. https://github.com/ncbi/sra-tools. Accessed 31 March
2022.
31. FASTX-Toolkit v0.0.13. http://hannonlab.cshl.edu/fastx_toolkit/. 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/bioinformatics/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/bioinformatics/
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/bioinformatics/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/gkx1106.
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/bioinformatics/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/gkaa1083.
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.200324667.
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.1210152.
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/science.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/v14091932.
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/oncotarget.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.abn5164.
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/archinte.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/NEJMra1802337.
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/mvad005.
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/
713608372.
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/10.2174/157340912800492357.
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.
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