High throughput toxicity screening and intracellular detection of nanomaterials

With the growing numbers of nanomaterials (NMs), there is a great demand for rapid and reliable ways of testing NM safety—preferably using in vitro approaches, to avoid the ethical dilemmas associated with animal research. Data are needed for developing intelligent testing strategies for risk assessment of NMs, based on grouping and read‐across approaches. The adoption of high throughput screening (HTS) and high content analysis (HCA) for NM toxicity testing allows the testing of numerous materials at different concentrations and on different types of cells, reduces the effect of inter‐experimental variation, and makes substantial savings in time and cost. HTS/HCA approaches facilitate the classification of key biological indicators of NM‐cell interactions. Validation of in vitroHTS tests is required, taking account of relevance to in vivo results. HTS/HCA approaches are needed to assess dose‐ and time‐dependent toxicity, allowing prediction of in vivo adverse effects. Several HTS/HCA methods are being validated and applied for NM testing in the FP7 project NANoREG, including Label‐free cellular screening of NM uptake, HCA, High throughput flow cytometry, Impedance‐based monitoring, Multiplex analysis of secreted products, and genotoxicity methods—namely High throughput comet assay, High throughput in vitro micronucleus assay, and γH2AX assay. There are several technical challenges with HTS/HCA for NM testing, as toxicity screening needs to be coupled with characterization of NMs in exposure medium prior to the test; possible interference of NMs with HTS/HCA techniques is another concern. Advantages and challenges of HTS/HCA approaches in NM safety are discussed. WIREs Nanomed Nanobiotechnol 2017, 9:e1413. doi: 10.1002/wnan.1413

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Specific uptake and genotoxicity induced by polystyrene nanobeads with distinct surface chemistry on human lung epithelial cells and macrophages

Nanoparticle surface chemistry is known to play a crucial role in interactions with cells and their related cytotoxic effects. As inhalation is a major route of exposure to nanoparticles, we studied specific uptake and damages of well-characterized fluorescent 50 nm polystyrene (PS) nanobeads harboring different functionalized surfaces (non-functionalized, carboxylated and aminated) on pulmonary epithelial cells and macrophages (Calu-3 and THP-1 cell lines respectively). Cytotoxicity of in mass dye-labeled functionalized PS nanobeads was assessed by xCELLigence system and alamarBlue viability assay. Nanobeads-cells interactions were studied by video-microscopy, flow cytometry and also confocal microscopy. Finally ROS generation was assessed by glutathione depletion dosages and genotoxicity was assessed by γ-H2Ax foci detection, which is considered as the most sensitive technique for studying DNA double strand breaks. The uptake kinetic was different for each cell line. All nanobeads were partly adsorbed and internalized, then released by Calu-3 cells, while THP-1 macrophages quickly incorporated all nanobeads which were located in the cytoplasm rather than in the nuclei. In parallel, the genotoxicity study reported that only aminated nanobeads significantly increased DNA damages in association with a strong depletion of reduced glutathione in both cell lines. We showed that for similar nanoparticle concentrations and sizes, aminated polystyrene nanobeads were more cytotoxic and genotoxic than unmodified and carboxylated ones on both cell lines. Interestingly, aminated polystyrene nanobeads induced similar cytotoxic and genotoxic effects on Calu-3 epithelial cells and THP-1 macrophages, for all levels of intracellular nanoparticles tested. Our results strongly support the primordial role of nanoparticles surface chemistry on cellular uptake and related biological effects. Moreover our data clearly show that nanoparticle internalization and observed adverse effects are not necessarily associated.

In vitro biocompatibility of mesoporous metal (III; Fe, Al, Cr) trimesate MOF nanocarriers

The high porosity and versatile composition of the benchmarked mesoporous metal (Fe, Al, Cr) trimesate metal–organic frameworks (MIL-100(Fe, Al, Cr)) make them very promising solids in different strategic industrial and societal domains (separation, catalysis, biomedicine, etc.).

Carboxylated nanodiamonds are neither cytotoxic nor genotoxic on liver, kidney, intestine and lung human cell lines

Although nanodiamonds (NDs) appear as one of the most promising nanocarbon materials available so far for biomedical applications, their risk for human health remains unknown. Our work was aimed at defining the cytotoxicity and genotoxicity of two sets of commercial carboxylated NDs with diameters below 20 and 100 nm, on six human cell lines chosen as representative of potential target organs: HepG2 and Hep3B (liver), Caki-1 and Hek-293 (kidney), HT29 (intestine) and A549 (lung). Cytotoxicity of NDs was assessed by measuring cell impedance (xCELLigence® system) and cell survival/death by flow cytometry while genotoxicity was assessed by γ-H2Ax foci detection, which is considered the most sensitive technique for studying DNA double-strand breaks. To validate and check the sensitivity of the techniques, aminated polystyrene nanobeads were used as positive control in all assays. Cell incorporation of NDs was also studied by flow cytometry and luminescent N-V center photoluminescence (confirmed by Raman microscopy), to ensure that nanoparticles entered the cells. Overall, we show that NDs effectively entered the cells but NDs do not induce any significant cytotoxic or genotoxic effects on the six cell lines up to an exposure dose of 250 µg/mL. Taken together these results strongly support the huge potential of NDs for human nanomedicine but also their potential as negative control in nanotoxicology studies.

A new phosphorylated form of Ku70 identified in resistant leukemic cells confers fast but unfaithful DNA repair in cancer cell lines

Ku70-dependent canonical nonhomologous end-joining (c-NHEJ) DNA repair system is fundamental to the genome maintenance and B-cell lineage. c-NHEJ is upregulated and error-prone in incurable forms of chronic lymphocytic leukemia which also displays telomere dysfunction, multiple chromosomal aberrations and the resistance to DNA damage-induced apoptosis. We identify in these cells a novel DNA damage inducible form of phospho-Ku70. In vitro in different cancer cell lines, Ku70 phosphorylation occurs in a heterodimer Ku70/Ku80 complex within minutes of genotoxic stress, necessitating its interaction with DNA damage-induced kinase pS2056-DNA-PKcs and/or pS1981-ATM. The mutagenic effects of phospho-Ku70 are documented by a defective S/G2 checkpoint, accelerated disappearance of γ-H2AX foci and kinetics of DNA repair resulting in an increased level of genotoxic stress-induced chromosomal aberrations. Together, these data unveil an involvement of phospho-Ku70 in fast but inaccurate DNA repair; a new paradigm linked to both the deregulation of c-NHEJ and the resistance of malignant cells.

Phospho-Ku70 induced by DNA damage interacts with RNA Pol II and promotes the formation of phospho-53BP1 foci to ensure optimal cNHEJ

Canonical non-homologous end-joining (cNHEJ) is the prominent mammalian DNA double-strand breaks (DSBs) repair pathway operative throughout the cell cycle. Phosphorylation of Ku70 at ser27-ser33 (pKu70) is induced by DNA DSBs and has been shown to regulate cNHEJ activity, but the underlying mechanism remained unknown. Here, we established that following DNA damage induction, Ku70 moves from nucleoli to the sites of damage, and once linked to DNA, it is phosphorylated. Notably, the novel emanating functions of pKu70 are evidenced through the recruitment of RNA Pol II and concomitant formation of phospho-53BP1 foci. Phosphorylation is also a prerequisite for the dynamic release of Ku70 from the repair complex through neddylation-dependent ubiquitylation. Although the non-phosphorylable ala-Ku70 form does not compromise the formation of the NHEJ core complex per se, cells expressing this form displayed constitutive and stress-inducible chromosomal instability. Consistently, upon targeted induction of DSBs by the I-SceI meganuclease into an intrachromosomal reporter substrate, cells expressing pKu70, rather than ala-Ku70, are protected against the joining of distal DNA ends. Collectively, our results underpin the essential role of pKu70 in the orchestration of DNA repair execution in living cells and substantiated the way it paves the maintenance of genome stability.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BACKGROUND

The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.

RESULTS

To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.

CONCLUSION

The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).