journal article·2017-09Open access

The Genetic Heterogeneity among Different Mouse Strains Impacts the Lung Injury Potential of Multiwalled Carbon Nanotubes

Xiang Wang, Yu‐Pei Liao, Donatello Telesca, Chong Hyun Chang, Tian Xia, André E. Nel

Small· Volume 13· Issue 33

Abstract

Genetic variation constitutes an important variable impacting the susceptibility to inhalable toxic substances and air pollutants, as reflected by epidemiological studies in humans and differences among animal strains. While multiwalled carbon nanotubes (MWCNTs) are capable of causing lung fibrosis in rodents, it is unclear to what extent the genetic variation in different mouse strains influence the outcome. Four inbred mouse strains, including C57Bl/6, Balb/c, NOD/ShiLtJ, and A/J, to test the pro‐fibrogenic effects of a library of MWCNTs in vitro and in vivo are chosen. Ex vivo analysis of IL‐1β production in bone marrow‐derived macrophages (BMDMs) as molecular initiating event (MIE) is performed. The order of cytokine production (Balb/c > A/J > C57Bl/6 > NOD/ShiLtJ) in BMDMs is also duplicated during assessment of IL‐1β production in the bronchoalveolar lavage fluid of the same mouse strains 40 h after oropharyngeal instillation of a representative MWCNT. Animal test after 21 d also confirms a similar hierarchy in TGF‐β1 production and collagen deposition in the lung. Statistical analysis confirms a correlation between IL‐1β production in BMDM and the lung fibrosis. All considered, these data demonstrate that genetic background indeed plays a major role in determining the pro‐fibrogenic response to MWCNTs in the lung.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Air Quality and Health Impacts · Occupational and environmental lung diseases · Occupational exposure and asthma

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Division of NanoMedicine University of California Los Angeles CA 90095 USA

California NanoSystems Institute University of California Los Angeles CA 90095 USA

Department of Biostatistics University of California Los Angeles CA 90095 USA

Funding

National Institute of Environmental Health Sciences · National Institutes of Health

References (60)

Show source-provided references
1. 10.1126/science.122245310.1126/science.1222453 ↗
2. 10.1038/nmat244210.1038/nmat2442 ↗
3. 10.1021/nl300895y10.1021/nl300895y ↗
4. 10.1002/smll.20140285910.1002/smll.201402859 ↗
5. 10.1021/nn501275410.1021/nn5012754 ↗
6. 10.1513/pats.200907-061RM10.1513/pats.200907-061RM ↗
7. 10.1016/j.taap.2012.03.02310.1016/j.taap.2012.03.023 ↗
8. 10.1021/acsnano.6b0156010.1021/acsnano.6b01560 ↗
9. 10.1021/nn507243w10.1021/nn507243w ↗
10. 10.1080/1528739090348655010.1080/15287390903486550 ↗
11. 10.1152/ajplung.00084.200510.1152/ajplung.00084.2005 ↗
12. 10.1152/ajplung.90287.200810.1152/ajplung.90287.2008 ↗
13. 10.1165/rcmb.2008-0276OC10.1165/rcmb.2008-0276OC ↗
14. 10.1021/nl904248310.1021/nl9042483 ↗
15. Murthy P. B. · 2011
16. 10.1007/s11051-008-9369-010.1007/s11051-008-9369-0 ↗
17. 10.1021/nn305567s10.1021/nn305567s ↗
18. 10.1186/1743-8977-6-3510.1186/1743-8977-6-35 ↗
19. 10.1126/science.115699510.1126/science.1156995 ↗
20. 10.1016/j.toxlet.2014.02.00410.1016/j.toxlet.2014.02.004 ↗
21. 10.1289/ehp.120569310.1289/ehp.1205693 ↗
22. 10.1152/ajplung.00002.201510.1152/ajplung.00002.2015 ↗
23. 10.1016/S0140-6736(14)60617-610.1016/S0140-6736(14)60617-6 ↗
24. 10.1016/j.taap.2015.09.01910.1016/j.taap.2015.09.019 ↗
25. 10.1039/C4PP90033B10.1039/C4PP90033B ↗
26. 10.1007/s40291-013-0051-610.1007/s40291-013-0051-6 ↗
27. 10.1038/tpj.2011.4410.1038/tpj.2011.44 ↗
28. Du Y. X. · J. Thorac. Dis. · 2016 · 8 · E8
29. 10.1038/nrg157810.1038/nrg1578 ↗
30. 10.1038/sj.jes.750053110.1038/sj.jes.7500531 ↗
31. 10.1021/acsnano.5b0094110.1021/acsnano.5b00941 ↗
32. 10.1021/ar300022h10.1021/ar300022h ↗
33. 10.1001/jama.2016.203710.1001/jama.2016.2037 ↗
34. Wilson J. · Environment · 2017 · 59 · 3010.1080/00139157.2017.1252606 ↗
35. 10.1021/nn403792710.1021/nn4037927 ↗
36. 10.1021/nn102112b10.1021/nn102112b ↗
37. 10.1021/nn203305510.1021/nn2033055 ↗
38. 10.1002/smll.20120196210.1002/smll.201201962 ↗
39. 10.1186/1755-1536-3-1510.1186/1755-1536-3-15 ↗
40. 10.1186/s12989-016-0123-y10.1186/s12989-016-0123-y ↗
41. 10.1038/nature1075910.1038/nature10759 ↗
42. 10.1126/science.118400310.1126/science.1184003 ↗
43. 10.1016/j.cell.2010.01.04010.1016/j.cell.2010.01.040 ↗
44. 10.1534/g3.115.01704610.1534/g3.115.017046 ↗
45. 10.1021/nn102848210.1021/nn1028482 ↗
46. 10.1080/0895837080194223810.1080/08958370801942238 ↗
47. 10.1002/smll.20130159710.1002/smll.201301597 ↗
48. Han J. · 2012
49. Wakefield J. · 2012
50. 10.1128/IAI.00094-0710.1128/IAI.00094-07 ↗
51. 10.1016/j.mvr.2011.08.01110.1016/j.mvr.2011.08.011 ↗
52. 10.1128/IAI.00711-0910.1128/IAI.00711-09 ↗
53. 10.1177/019262331667732710.1177/0192623316677327 ↗
54. Bozkurt Y. · Comput. Math. Method · 2015 · 13 · O43
55. 10.1513/pats.201203-023AW10.1513/pats.201203-023AW ↗
56. 10.1016/j.tox.2013.08.01110.1016/j.tox.2013.08.011 ↗
57. 10.1002/jbio.20160012510.1002/jbio.201600125 ↗
58. 10.12703/P6-6910.12703/P6-69 ↗
59. 10.1016/S0027-5107(01)00292-510.1016/S0027-5107(01)00292-5 ↗
60. 10.1186/1743-8977-8-1710.1186/1743-8977-8-17 ↗