4.8 Article

Next-Generation Sequencing Reveals Low-Dose Effects of Cationic Dendrimers in Primary Human Bronchial Epithelial Cells

期刊

ACS NANO
卷 9, 期 1, 页码 146-163

出版社

AMER CHEMICAL SOC
DOI: 10.1021/nn5061783

关键词

poly(amidoamine) dendrimers; next-generation sequencing; systems biology; nanotoxicology

资金

  1. Swedish Cancer and Allergy Foundation
  2. Swedish Research Council
  3. Seventh Framework Programme of the European Commission (FP7-NANOMMUNE) [214281]
  4. Seventh Framework Programme of the European Commission (FP7-NANOSOLUTIONS) [309329]

向作者/读者索取更多资源

Gene expression profiling has developed rapidly in recent years with the advent of deep sequencing technologies such as RNA sequencing (RNA Seq) and could be harnessed to predict and define mechanisms of toxicity of chemicals and nanomaterials. However, the full potential of these technologies in (nano)toxicology is yet to be realized. Here, we show that systems biology approaches can uncover mechanisms underlying cellular responses to nanomaterials. Using RNA Seq and computational approaches, we found that cationic poly(amidoamine) dendrimers (PAMAM-NH2) are capable of triggering down-regulation of cell-cycle-related genes in primary human bronchial epithelial cells at doses that do not elicit acute cytotoxicity, as demonstrated using conventional cell viability assays, while gene transcription was not affected by neutral PAMAM-OH dendrimers. The PAMAMs were internalized in an active manner by lung cells and localized mainly in lysosomes; amine-terminated dendrimers were internalized more efficiently when compared to the hydroxyl-terminated dendrimers. Upstream regulator analysis implicated NF-kappa B as a putative transcriptional regulator, and subsequent cell-based assays confirmed that PAMAM-NH2 caused NF-kappa B-dependent cell cycle arrest. However, PAMAM-NH2 did not affect cell cycle progression in the human A549 adenocarcinoma cell line. These results demonstrate the feasibility of applying systems biology approaches to predict cellular responses to nanomaterials and highlight the importance of using relevant (primary) cell models.

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