Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

Saarimäki, Laura Aliisa; Federico, Antonio; Lynch, Iseult; Papadiamantis, Anastasios G.; Tsoumanis, Andreas; Melagraki, Georgia; Afantitis, Antreas; Serra, Angela; Greco, Dario

doi:10.1038/s41597-021-00808-y

Cited by 25 publications

(19 citation statements)

References 100 publications

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“…These cell lines include differentiated THP-1 cells as a model of macrophages, A549 representing alveolar basal epithelial cells, BEAS-2B as bronchial epithelial cells, and MRC as a model of lung fibroblasts. Differentially expressed genes (DEGs) from all experimental conditions in vivo and each cell line in vitro were obtained from Saarimäki et al (67) and merged into a single mechanism of action in vivo and in vitro , respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Transcriptomics data: In vivo and in vitro transcriptomics data from MWCNT (Mitsui-7) exposures were selected from a previously published collection by Saarimäki et al (67). The original data sets are available under GEO accession number GSE29042 (in vivo) and ArrayExpress entry EMTAB6396 (in vitro), while the preprocessed data is available on Zenodo (https://doi.org/10.5281/zenodo.6425445).…”

Section: Characterisation Of the Aop-fingerprintsmentioning

confidence: 99%

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Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Saarimäki

Morikka

Pavel

et al. 2022

Preprint

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Mechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established. Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derived in vitro biomarkers for pulmonary fibrosis.

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Section: Resultsmentioning

confidence: 99%

Section: Characterisation Of the Aop-fingerprintsmentioning

confidence: 99%

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Saarimäki

Morikka

Pavel

et al. 2022

Preprint

Self Cite

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“…Currently, a large amount of toxicogenomics data is available to the scientific community [103] , [104] , [25] . This data is used to answer different questions such as mechanism of action reconstruction, biomarker selection, evaluation of dose dependent alteration, inference of molecular co-alteration, which require complex and specific analytical strategies.…”

Section: Discussionmentioning

confidence: 99%

“…The suggested strategy has been successfully utilised in a wide range of applications ranging from the study of nickel-induced allergic contact dermatitis [29] , copper oxide nanoparticles induced asthma [24] , and the characterisation of the effects of ten carbon nanomaterials in three cell lines [76] . Moreover, the eUTOPIA pipeline has been widely applied to create harmonised transcriptomics data collections [28] , [25] . FunMappOne, on the other hand, has proven to be an effective tool for comparing the pathway enrichment of different experimental conditions in multiple studies [37] , [7] .…”

Section: Use Of the Nextcast Componentsmentioning

confidence: 99%

Nextcast: A software suite to analyse and model toxicogenomics data

Serra

Saarimäki

Pavel

et al. 2022

Computational and Structural Biotechnology Journal

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“…However, when studying the properties, biological effect relations are complicated because of the difficulty in creating identical nanomaterials from different batches and/or sources due to the varying physicochemical properties of nanomaterials [15]. Nevertheless, manual curation of transcriptomics datasets regarding nanomaterials is performed to enhance their degree of fairness to aid nanomaterial safety assessment [16].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts

Winckers

Evelo

Willighagen

et al. 2021

IJMS

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Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms (apoptosis, inflammation, DNA damage, and oxidative stress) to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms.

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Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

Cited by 25 publications

References 100 publications

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Nextcast: A software suite to analyse and model toxicogenomics data

Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts

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