Nextcast: A software suite to analyse and model toxicogenomics data

Serra, Angela; Saarimäki, Laura Aliisa; Pavel, Alisa; Giudice, Giusy del; Fratello, Michele; Cattelani, Luca; Federico, Antonio; Laurino, Omar; Marwah, Veer Singh; Fortino, Vittorio; Scala, Giovanni; Kinaret, Pia Anneli Sofia; Greco, Dario

doi:10.1016/j.csbj.2022.03.014

Cited by 5 publications

(3 citation statements)

References 101 publications

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“…To compare the KNeMAP method, to a commonly used gene-based method ( Scala et al 2018 , Kinaret et al 2020a , Saarimäki et al 2020 , Kinaret et al 2021 , Serra et al 2022a ), a binary gene vector (BDG) for each data instance was created. To create this vector, the same 200 genes for each instance, as used in the KNeMAP feature vector, were selected.…”

Section: Methodsmentioning

confidence: 99%

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles

et al. 2023

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Motivation Transcriptomic data can be used to describe the mechanism of action (MOA) of a chemical compound. However, omics data tend to be complex and prone to noise, making the comparison of different datasets challenging. Often, transcriptomic profiles are compared at the level of individual gene expression values, or sets of differentially expressed genes. Such approaches can suffer from underlying technical and biological variance, such as the biological system exposed on or the machine/method used to measure gene expression data, technical errors and further neglect the relationships between the genes. We propose a network mapping approach for knowledge-driven comparison of transcriptomic profiles (KNeMAP), which combines genes into similarity groups based on multiple levels of prior information, hence adding a higher level view onto the individual gene view. When comparing KNeMAP with fold change (expression) based and deregulated gene set based methods, KNeMAP was able to group compounds with higher accuracy with respect to prior information as well as is less prone to noise corrupted data. Result We applied KNeMAP to analyze the Connectivity Map dataset, where the gene expression changes of three cell lines were analyzed after treatment with 676 drugs as well as the Fortino et al. dataset where two cell lines with 31 nanomaterials were analyzed. Although the expression profiles across the biological systems are highly different, KNeMAP was able to identify sets of compounds that induce similar molecular responses when exposed on the same biological system. Availability Relevant data and the KNeMAP function is available at: https://github.com/fhaive/KNeMAP and 10.5281/zenodo.7334711. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles

et al. 2023

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“…Toxicogenomics provides a complementary approach to the traditional observation of phenotypic effects of chemical exposures by focusing on the mechanism of action (MOA) of chemicals using omics technologies. This further enables an array of data-driven and computational approaches, including chemical grouping, read-across, and predictive models, and helps to explain why and how an exposure induces its effects 8 . This way, toxicogenomics can also inform the development of novel AOPs and support the application of AOP-based knowledge in the development of NAMs 9 – 14 .…”

Section: Background and Summarymentioning

confidence: 99%

A curated gene and biological system annotation of adverse outcome pathways related to human health

et al. 2023

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Adverse outcome pathways (AOPs) are emerging as a central framework in modern toxicology and other fields in biomedicine. They serve as an extension of pathway-based concepts by depicting biological mechanisms as causally linked sequences of key events (KEs) from a molecular initiating event (MIE) to an adverse outcome. AOPs guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation. While AOPs model the systemic mechanisms at various levels of biological organisation, toxicogenomics provides the means to study the molecular mechanisms of chemical exposures. Systematic integration of these two concepts would improve the application of AOP-based knowledge while also supporting the interpretation of complex omics data. Hence, we established this link through rigorous curation of molecular annotations for the KEs of human relevant AOPs. We further expanded and consolidated the annotations of the biological context of KEs. These curated annotations pave the way to embed AOPs in molecular data interpretation, facilitating the emergence of new knowledge in biomedicine.

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“…The introduction of mechanistic toxicology and toxicogenomics (TGx) has resulted in an increased understanding of how chemical substances induce their effects in biological systems (Serra et al, 2020a;Federico et al, 2020;Halappanavar et al, 2020;Kinaret et al, 2020;Serra et al, 2022a). Characterizing the mechanism of action of chemical exposures through TGx can support the identification of early molecular effects and predictive biomarkers for chemical safety assessment (Fortino et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction

del Giudice,

Migliaccio,

D’Alessandro

et al. 2023

Front. Toxicol.

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Assessing chemical safety is essential to evaluate the potential risks of chemical exposure to human health and the environment. Traditional methods relying on animal testing are being replaced by 3R (reduction, refinement, and replacement) principle-based alternatives, mainly depending on in vitro test methods and the Adverse Outcome Pathway framework. However, these approaches often focus on the properties of the compound, missing the broader chemical-biological interaction perspective. Currently, the lack of comprehensive molecular characterization of the in vitro test system results in limited real-world representation and contextualization of the toxicological effect under study. Leveraging omics data strengthens the understanding of the responses of different biological systems, emphasizing holistic chemical-biological interactions when developing in vitro methods. Here, we discuss the relevance of meticulous test system characterization on two safety assessment relevant scenarios and how omics-based, data-driven approaches can improve the future generation of alternative methods.

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Nextcast: A software suite to analyse and model toxicogenomics data

Cited by 5 publications

References 101 publications

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles

A curated gene and biological system annotation of adverse outcome pathways related to human health

Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction

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