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Mechanistic Insights into the Cytotoxicity of Graphene Oxide Derivatives in Mammalian Cells.

Phillip LuAlireza Zehtab YazdiXiao Xia HanKhalsa Al HusainiJessica HaimeNaomi WayePu Chen
Published in: Chemical research in toxicology (2020)
Graphene oxide derivatives (GODs) have superb physical/chemical properties with promise for applications in biomedicine. Shape, size, and chemistry of the GODs are identified as the key parameters that impact any biological system. In this work, the GODs with a wide range of shapes (sheets, helical/longitudinal ribbons, caps, dots), sizes (10 nm to 20 μm), and chemistry (partially to fully oxidized) are synthesized, and their cytotoxicity in normal cells (NIH3T3) and colon cancer cells (HCT116) are evaluated. The mechanisms by which the GODs induce cytotoxicity are comprehensively investigated, and the toxic effects of the GODs on the NIH3T3 and the HCT116 cells are compared. While the GODs show no toxicity under the size of 50 nm, they impose moderate toxic effects at the sizes of 100 nm to 20 μm (max viability >57%). For the GODs with the similar size (100-200 nm), the helical ribbon-like structure is found to be much less toxic than the longitudinal ribbon structure (max viability 83% vs 18%) and the tubular structure (0% viability for the oxidized carbon nanotubes). It is also evident that the level of oxidation of the GOD is inversely related to the toxicity. Although the extent of GOD-induced cytotoxicity (reduction of cell viability) to the two cell lines is similar, their toxicity mechanisms are interestingly found to be substantially different. In the HCT116 cancer cells, cell membrane leakage leads to DNA damage followed by cell death, whereas in the NIH3T3 normal cells, increases in oxidative stress and physical interference between the GODs and the cells are identified as the main toxicity sources.
Keyphrases
  • cell cycle arrest
  • induced apoptosis
  • oxidative stress
  • cell death
  • dna damage
  • photodynamic therapy
  • carbon nanotubes
  • signaling pathway
  • machine learning
  • single molecule
  • atomic force microscopy
  • high resolution
  • big data