Combination of Metabolomics with Cellular Assays Reveals New Biomarkers and Mechanistic Insights on Xenoestrogenic Exposures in MCF-7 Cells

Potratz, Sarah; Tarnow, Patrick; Jungnickel, Harald; Baumann, Sven; Bergen, Martin von; Tralau, Tewes; Luch, Andreas

doi:10.1021/acs.chemrestox.6b00106

Cited by 24 publications

(15 citation statements)

References 53 publications

(104 reference statements)

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“…To determine the quantity of the extracted metabolites the AbsoluteIDQ p180 Kit (Biocrates, Austria) was used and conducted as described earlier [52]. In brief, metabolite pellets were resolved in 85% EtOH (99.8%, Sigma Aldrich, Germany) / 15% PBS (0.1 M, Sigma Aldrich, Germany).…”

Section: Methodsmentioning

confidence: 99%

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

et al. 2019

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BackgroundNanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO2 NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA).ResultsCluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis.ConclusionsOur multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches.

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Section: Methodsmentioning

confidence: 99%

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

et al. 2019

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“…12 A significant flaw of metabolomics, however, is the need of large amounts of sample material (typically >1x10 6 cells per replicate), making metabolomics mostly incompatible for large screening approaches with the afore mentioned hematopoietic models. [13][14][15][16] Furthermore, profound knowledge of the total cellular metabolism in the different lineages is a prerequisite for interpretation of observed metabolic changes, as it ultimately defines the response of cells towards different stimuli. 17,18 As example, intracellular levels of NADPH define the response towards reactive oxygen species (ROS)-inducing compounds.…”

Section: Introductionmentioning

confidence: 99%

Metabolite patterns in human myeloid hematopoiesis result from lineage-dependent active metabolic pathways

Kaiser

Weinschrott

Quint

et al. 2020

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AbstractAssessment of hematotoxicity from environmental or xenobiotic compounds is of notable interest and is frequently assessed via the colony forming unit (CFU) assay. Identification of the mode of action of single compounds is of further interest, as such often enables transfer of results across different tissues and compounds. Metabolomics displays one promising approach for identifying such, nevertheless, suitability with current protocols is restricted. Here, we combined an HSPC expansion approach with distinct lineage differentiations, resulting in formation of erythrocytes, dendritic cells and neutrophils. We examined the unique combination of fluxes in glycolysis, glutaminolysis, polyamine synthesis, fatty acid oxidation and synthesis, as well as glycerophospholipid and sphingolipid metabolism. We further assessed their interconnections and essentialness for each lineage formation. By this, we provide further insights into metabolic fluxes during differentiation of HSPC into different lineages, enabling profound understanding of possible metabolic changes in each lineage caused by exogenous compounds.

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“…The here presented metabolomics data were published before [80] but re-evaluated in analogy to the proteomics data. In brief, the AbsoluteIDQ p180 Kit (Biocrates, Austria) was used as described previously [11,12,80,81], and samples were analyzed with an API 5500 triple quadrupole mass spectrometer (ABSciex, Germany), coupled to an Agilent 1260 In nity HPLC system (Agilent, USA). Analyst® software and MetIDQ were used for data analysis.…”

Section: Targeted Metabolomics and Untargeted Proteomicsmentioning

confidence: 99%

Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

Karkossa

Bannuscher

Hellack³

et al. 2020

Preprint

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Background The immense variety and constant development of nanomaterials (NMs) raise the demand for a facilitated risk assessment, for which knowledge on NMs mode of actions (MoAs) is required. For this purpose, a comprehensive data basis is of paramountcy that can be obtained using omics. Furthermore, the establishment of suitable in vitro test systems is indispensable to follow the 3R concept and to master the high number of NMs. In the present study, we aimed at comparing NM effects in vitro and in vivo using a multi-omics approach. We applied an integrated data evaluation strategy based on proteomics and metabolomics to four silica NMs and one titanium dioxide-based NM. For in vitro investigations, alveolar epithelial cells and alveolar macrophages were treated with different doses of NMs, and the results were compared to effects on rat lungs after short-term inhalations and instillations at varying doses with and without a recovery period.Results Since the production of reactive oxygen species (ROS) is described to be a critical biological effect of NMs, and enrichment analyses confirmed oxidative stress as a significant effect upon NM treatment in vitro in the present study, we focused on different levels of oxidative stress. Thus, we found opposite changes for proteins and metabolites that are related to the production of reduced glutathione in alveolar epithelial cells and alveolar macrophages, illustrating that NMs MoAs depend on the used model system. Interestingly, in vivo, pathways related to inflammation were affected to a greater extent than oxidative stress responses. Hence, the assignment of the observed effects to the levels of oxidative stress was different in vitro and in vivo as well. However, the overall classification of “active” and “passive” NMs was consistent in vitro and in vivo.Conclusions The consistent classification indicates both tested cell lines to be suitable for NM toxicity assessment even though the induced levels of oxidative stress strongly depend on the used model systems. Thus, the here presented results highlight that model systems need to be carefully revised to decipher the extent to which they can replace in vivo testing.

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Combination of Metabolomics with Cellular Assays Reveals New Biomarkers and Mechanistic Insights on Xenoestrogenic Exposures in MCF-7 Cells

Cited by 24 publications

References 53 publications

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

Metabolite patterns in human myeloid hematopoiesis result from lineage-dependent active metabolic pathways

Nanomaterials induce different levels of oxidative stress, depending on the used model system: Comparison of in vitro and in vivo effects

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