4.7 Article

Time- and Dose-Resolved Proteome of PM2.5-Exposure-Induced Lung Injury and Repair in Rats

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 19, Issue 8, Pages 3162-3175

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.0c00155

Keywords

proteomics; lung injury; PM2.5; inflammatory

Funding

  1. National Key R&D Program of China [2017YFC0908404, 2017YFA0505102]
  2. National Natural Science Foundation of China [31770886, 3197120034]
  3. Chinese Ministry of Science and Technology [2016YFA0502500]
  4. National Program on Key Basic Research Project (973 Program) [2014CBA02000]
  5. National Institute of Health (Illuminating Druggable Genome) [U01MH105026]
  6. Shanghai Municipal Science and Technology Major Project [2017SHZDZX01]
  7. International Science & Technology Cooperation Program [2012DFB30080]

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In recent years, airborne fine particulate matter (PM2.5) is drawing more public attention due to its various physicochemical features and causing pathological harm, as proven by epidemiological and clinical studies. However, the mechanism of PM2.5-exposure-induced lung injury has not been fully characterized. Here, we established a PM2.5-induced rat injury model for both short-term and long-term exposures at different concentrations. We employed the Fast-seq technique to profile 6316 proteins and the catTFRE approach to profile 387 transcription factors (TFs) in the lung tissue. In short-term exposure, we elucidated gradually upregulated proteins enriched in response to oxidative stress, phagosome, and the extracellular matrix (ECM)-receptor inter- action pathway. Long-term exposure mainly showed the immune response pathway to be consisting of increased lymphocytes and cytokines. Intriguingly, we found that immune-related proteins were recoverable during short-term exposure. During the process of PM2.5 exposure, upregulated proteins presented dose-dependence in the lung, including stress response at low dose, minor immune response at middle dose, and severe inflammatory response at high dose. This data set provides a rich resource to facilitate the understanding of PM2.5-induced lung damage and repair mechanism.

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