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题目:: Differential Transcriptional Changes in Mice Exposed to Chemically Distinct Diesel Samples
ID:
状态:: 发布时间July 10, 2010 , 更新时间 June 10, 2011
物种:: Mus musculus
摘要:: Epidemiological studies have linked exposure to ambient particulate matter (PM) with increased asthmatic symptoms. Diesel exhaust particles (DEP) are a predominant source of vehicle derived ambient PM, and experimental studies have demonstrated that they may have adjuvant potential when given with an antigen. We previously compared 3 DEP samples: N-DEP, A-DEP, and C-DEP in a murine ovalbumin (OVA) mucosal sensitization model and reported the adjuvant activity to be: C-DEP ≈ A-DEP > N-DEP. The present study analyzed gene expression changes from the lungs of these mice. Transcription profiling demonstrated that all the DEP samples altered cytokine and toll-like receptor pathways regardless of type, with or without antigen sensitization. Further analysis of DEP exposure with OVA showed that all DEP treatments altered networks involved in immune and inflammatory responses. The A- and C-DEP/OVA treatments induced differential expression of apoptosis pathways in association with stronger adjuvant responses, while expression of cell cycle control and DNA damage pathways were also altered in the C-DEP/OVA treatment. This comprehensive approach using gene expression analysis to examine changes at a pathway level provides detailed information on events occurring in the lung after DEP exposure, and confirms that the most bioactive sample induced many more individual genes and changes in immuno-regulatory and homeostatic pathways. Female BALB/C mice (8-10 weeks old) were randomly divided into 8 treatment groups containing 3 mice each and exposed to saline, 20 ug ovalbumin, 150 ug diesel exhaust particles (either C-DEP, A-DEP, or N-DEP), or diesel exhaust particles + ovalbumin by intranasal instillation on Days 0 and 13 and necropsied 18 hrs latter.
实验种类:: transcription profiling by array
样本量:: 24
实验设计:
: 无设计数据
数据号:: E-GEOD-22357, GSE22357
数据状态:: 无法自动分析，您可以尝试手动分析数据。

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