Systematic proteomic approach to characterize the impacts of chemical interactions on protein and cytotoxicity responses to metal mixture exposures.

Clicks: 562
ID: 51430
2015
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #3 of 51 articles by views in journal of proteome research

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Chemical interactions have posed a big challenge in toxicity characterization and human health risk assessment of environmental mixtures. To characterize the impacts of chemical interactions on protein and cytotoxicity responses to environmental mixtures, we established a systems biology approach integrating proteomics, bioinformatics, statistics, and computational toxicology to measure expression or phosphorylation levels of 21 critical toxicity pathway regulators and 445 downstream proteins in human BEAS-2B cells treated with 4 concentrations of nickel, 2 concentrations each of cadmium and chromium, as well as 12 defined binary and 8 defined ternary mixtures of these metals in vitro. Multivariate statistical analysis and mathematical modeling of the metal-mediated proteomic response patterns showed a high correlation between changes in protein expression or phosphorylation and cellular toxic responses to both individual metals and metal mixtures. Of the identified correlated proteins, only a small set of proteins including HIF-1α is likely to be responsible for selective cytotoxic responses to different metals and metals mixtures. Furthermore, support vector machine learning was utilized to computationally predict protein responses to uncharacterized metal mixtures using experimentally generated protein response profiles corresponding to known metal mixtures. This study provides a novel proteomic approach for characterization and prediction of toxicities of metal and other chemical mixtures.
Reference Key
ge2015systematicjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ge, Yue;Bruno, Maribel;Wallace, Kathleen;Leavitt, Sharon;Andrews, Debora;Spassova, Maria A;Xi, Mingyu;Roy, Anindya;Haykal-Coates, Najwa;Lefew, William;Swank, Adam;Winnik, Witold M;Chen, Chao;Woodard, Jonne;Farraj, Aimen;Teichman, Kevin Y;Ross, Jeffrey A;
Journal journal of proteome research
Year 2015
DOI
10.1021/pr500795d
URL
Keywords
Cytotoxicity Proteomics systems biology metals risk assessment chemical mixtures chemical interactions dose response mixture toxicity

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.