Toxicogenomics Data for Chemical Safety Assessment : From Intrinsic Characteristics to Functional Potential

Nykyaikainen maailma ympäröi meitä valtavalla määrällä kemikaaleja, josta suurin osa on edelleen arvioimatta niiden mahdollisten terveys- ja ympäristövaikutusten osalta. Samalla uusien kemikaalien nopea kehittäminen ja käyttöönotto vaatii taidokasta tasapainottelua innovaation ja turvallisuuden välillä, muodostaen yhden nykypäivän keskeisistä haasteista. Kemikaalien turvallisuusarviointi on pitkään keskittynyt kalliisiin ja työläisiin eläinkokeisiin, joiden eettisyys ja relevanssi usein kyseenalaistetaan. Lisäksi perinteinen lähestymistapa kemikaalien turvallisuusarviointiin keskittyy havaittaviin fenotyyppimuutoksiin, tarjoten rajallisen käsityksen lopputulokseen johtavista mekanismeista ja molekyylitason vasteista. Tämä puolestaan rajoittaa kehitystä kohti uusia kemikaaleja, jotka suunnitellaan turvallisiksi ja kestäviksi alusta alkaen (safe and sustainable by design, SSbD). Vaikka merkittäviä ponnisteluja on tehty niin in vitro - kuin in silico -vaihtoehtojen edistämiseksi kemikaalien turvallisuusarvioinnissa, näiden eläinkokeita korvaavien menetelmien ensimmäinen sukupolvi kohtaa samankaltaisia haasteita kuin niiden eläinperusteiset vastineet. Tämä on johtanut merkittäviin globaaleihin aloitteisiin siirtää painopiste kohti mekanistista toksikologiaa, joka mahdollistaa syvällisemmän ymmärryksen kemikaalien mahdollisista haitoista. Mekanistinen toksikologia perustuu perusteelliseen ymmärrykseen kemiallisten altistusten aiheuttamista mekanismeista ja vasteista. Molekyylitasolla näitä mekanismeja tutkitaan toksikogenomiikan keinoin ja laaja-alaisemmin mekanismit voidaan kuvata käyttäen apuna haittavaikutusreittejä (adverse outcome pathways, AOPs). Vaikka tämän mekanistisen lähestymistavan potentiaali tunnustetaan laajasti, toksigenomiikkaan perustuvaa näyttöä ei vielä käytetä systemaattisesti osana kemikaalien turvallisuusarviointia. Tämän väitöskirjan lähtökohta piileekin niissä monitahoisissa haasteissa, jotka vaikuttavat toksigenomiikan käyttöön kemiakaalien turvallisuusarvioinnissa. Tässä väitöskirjassa näitä haasteita tutkittiin toksikogenomiikkaan ja sen dataan liittyvillä kolmella kriittisellä osa-alueella: sen sisäiset ominaisuudet, toiminnalliset ominaispiirteet sekä sen translationaalinen potentiaali. Datan sisäisiin ominaisuuksiin määriteltiin kuuluvaksi sen FAIR-luonteisuus (lyhenne sanoista Findability, Accessibility, Interoperability ja Reusability), käytettävyys ja laatu. Näitä ominaisuuksia tutkittiin ja käsiteltiin systemaattisella datan kuratoinnilla ja merkinnällä, mikä mahdollisti myös datan nykytilanteen kattavan tarkastelun. Tuloksena syntyi kokoelma, jolla on parannellut FAIR-ominaisuudet, luoden vankan pohjan seuraaville analyyttisille pyrkimyksille. Lisäksi tämä pyrkimys loi systemaattisen yhteyden toksigenomiikan ja olemassa olevien haittavaikutusreittien välille, vahvistaen molempien datatyyppien toiminnallisia ominaisuuksia. Innovatiiviset menetelmät ja lähestymistavat datan analysointiin muodostavat toiminnallisten ominaisuuksien perustan. Näiden tavoitteena on löytää merkityksellisiä oivalluksia monimutkaisesta ja moniulotteisesta datasta, jota tuotetaan toksikogenomiikan keinoin. Tässä väitöskirjassa näitä toiminnallisia ominaisuuksia tutkittiin hyödyntämällä edistyneitä laskennallisia menetelmiä sekä haittavaikutusreittien ja toksikogenomisen datan välille luotua yhteyttä pyrkimyksenä luonnehtimaan ja mallintamaan kemikaalien ja biologisten järjestelmien monimutkaisia vuorovaikutuksia. Esimerkkinä tästä syntyi dynaamisen vaikutusmekanismin malli, jonka avulla voitiin luonnehtia lyhyen in vitro -altistuksen avulla niitä mekanismeja, jotka johtavat keuhkofibroosiin pitkällä aikavälillä moniseinäisille hiilinanoputkille altistuttaessa. Viimeinen osa-alue keskittyi toksigenomiikasta johdetun näytön kääntämiseen biologisesti merkityksellisiksi tapahtumiksi, jotka ovat ymmärrettäviä laajemmalle yleisölle. Toksikogenomiikan datan translationaalinen potentiaali piilee keinoissa kuroa umpeen kuilu raakadatan ja käytännön oivallusten välillä. Tässä väitöskirjassa pyrittiin luomaan tämä konkreettinen linkki molekulaaristen vasteiden ja niiden mahdollisten terveysvaikutusten välille kääntämällä toksikogenomiikan keinoin saatu näyttö mekanistisiksi uuden sukupolven menetelmiksi (new approach methodologies, NAMs). Tuloksissa korostui datan sisäisten ja toiminnallisten ominaisuuksien merkitys translationaaliselle potentiaalille. Kokonaisuutena tämä väitöskirjatutkimus pyrkii edistämään kemikaalien turvallisuusarvioinnin alaa tutkimalla toksigenomisen datan sisäisiä ominaisuuksia, toiminnallisia ominaispiirteitä sekä translationaalista potentiaalia. Hyödyntämällä datan kuratointia, edistyneitä analyyttisiä menetelmiä ja uusia lähestymistapoja, tämä väitöskirja pyrkii parantamaan toksigenomiikan soveltamista kemikaalien turvallisuusarviointiin laaja-alaisesti, edistäen matkaa kohti terveellisempää ja turvallisempaa tulevaisuutta.The modern world surrounds us with a vast sea of chemicals of which a large majority remains uncharted in terms of their potential hazard for human health and the environment. At the same time, the rapid introduction of new chemicals necessitates a delicate balance between innovation and safety, presenting one of the key challenges of the 21st century. Chemical safety assessment has been long focused on resource-intensive and ethically challenged animal experiments. Furthermore, the traditional approach focuses on phenotypic endpoints, providing limited insight into toxicity mechanisms which impedes the development of chemicals that are safe and sustainable by design (SSbD). Despite major efforts to advance in vitro and in silico alternatives for chemical safety assessment, the first generation of these non-animal approaches present similar challenges as their in vivo counterparts. This has resulted in major initiatives to shift the focus towards mechanistic toxicology, enabling a deeper understanding of chemical hazards. This mechanistic approach is fuelled by the introduction of adverse outcome pathways (AOPs) and toxicogenomics, offering unprecedented insights into the molecular underpinnings of chemical-induced toxicity. While the value of this mechanistic venture is broadly recognised, toxicogenomics-based evidence is not systematically integrated into chemical safety assessment. Hence, the foundational premise of this dissertation lies in the recognition of the multifaceted challenges that surround the utilisation of toxicogenomics data in chemical safety assessment. These challenges were characterised through three critical aspects of toxicogenomics data: its intrinsic characteristics, functional properties, and translational potential. The intrinsic characteristics, defined as the FAIRness (Findability, Accessibility, Interoperability and Reusability) and quality of data, were investigated, and addressed through systematic data curation and annotation. This enabled a comprehensive review of the current state of toxicogenomics data, resulting in a resource with improved FAIRness and a robust foundation for subsequent analytical endeavours. Similarly, this effort established a systematic link between toxicogenomics-based evidence and the AOP framework, empowering the functional properties of both data types. Innovative methodologies and approaches to data analysis form the cornerstone of the functional properties of data, aiming at the extraction of meaningful insights from complex, high-dimensional toxicogenomics datasets. By harnessing advanced computational techniques and the link established between AOPs and toxicogenomics, this dissertation further sought to distinguish subtle molecular signatures and discern the intricate interplay between chemicals and biological systems. This was exemplified by a model of a dynamic dose-dependent mechanism of action that revealed crucial mechanisms related known long-term adverse effects of multi-walled carbon nanotube exposure in a short-term in vitro exposure. Finally, a pivotal facet of this research lies in the translation of toxicogenomics- derived evidence into biologically meaningful events that are comprehensible to a broader audience. Bridging the gap between raw data and actionable insights, this dissertation endeavored to provide a tangible link between molecular alterations and their potential implications for human health through the translation of toxicogenomics-based evidence into mechanistic new approach methodologies (NAMs). This dissertation highlighted how the intrinsic characteristics and functional properties of toxicogenomics data enable its translational potential, resulting in the AOP fingerprint and in vitro biomarkers for the evaluation of profibrotic potential of chemicals. Ultimately, the results of this research have the potential to propel the field of chemical safety assessment forward by elucidating the intrinsic characteristics, functional properties, and translational potential of toxicogenomics data. By synergistically employing data curation, advanced analytical methodologies, and translational approaches, this dissertation endeavours to enhance the applicability of toxicogenomics in the broader context of chemical safety evaluation, thus contributing to the safeguarding of public health and the environment

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.Peer reviewe

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies : An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.Peer reviewe

Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.Peer reviewe

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

The molecular effects of exposures to engineered nanomaterials (ENMs) are still largely unknown. In classical inhalation toxicology, cell composition of bronchoalveolar lavage (BAL) is a toxicity indicator at the lung tissue level that can aid in interpreting pulmonary histological changes. Toxicogenomic approaches help characterize the mechanism of action (MOA) of ENMs by investigating the differentially expressed genes (DEG). However, dissecting which molecular mechanisms and events are directly induced by the exposure is not straightforward. It is now generally accepted that direct effects follow a monotonic dose-dependent pattern. Here, we applied an integrated modeling approach to study the MOA of four ENMs by retrieving the DEGs that also show a dynamic dose-dependent profile (dddtMOA). We further combined the information of the dddtMOA with the dose dependency of four immune cell populations derived from BAL counts. The dddtMOA analysis highlighted the specific adaptation pattern to each ENM. Furthermore, it revealed the distinct effect of the ENM physicochemical properties on the induced immune response. Finally, we report three genes dose-dependent in all the exposures and correlated with immune deregulation in the lung. The characterization of dddtMOA for ENM exposures, both for apical endpoints and molecular responses, can further promote toxicogenomic approaches in a regulatory context.Peer reviewe

Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration

The molecular effects of exposures to engineered nanomaterials (ENMs) are still largely unknown. In classical inhalation toxicology, cell composition of bronchoalveolar lavage (BAL) is a toxicity indicator at the lung tissue level that can aid in interpreting pulmonary histological changes. Toxicogenomic approaches help characterize the mechanism of action (MOA) of ENMs by investigating the differentially expressed genes (DEG). However, dissecting which molecular mechanisms and events are directly induced by the exposure is not straightforward. It is now generally accepted that direct effects follow a monotonic dose-dependent pattern. Here, we applied an integrated modeling approach to study the MOA of four ENMs by retrieving the DEGs that also show a dynamic dose-dependent profile (dddtMOA). We further combined the information of the dddtMOA with the dose dependency of four immune cell populations derived from BAL counts. The dddtMOA analysis highlighted the specific adaptation pattern to each ENM. Furthermore, it revealed the distinct effect of the ENM physicochemical properties on the induced immune response. Finally, we report three genes dose-dependent in all the exposures and correlated with immune deregulation in the lung. The characterization of dddtMOA for ENM exposures, both for apical endpoints and molecular responses, can further promote toxicogenomic approaches in a regulatory context

Nextcast : A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.Peer reviewe

the NEMESIS project's quest for novel biomarkers, evidence on adverse effects, and efficient methodologies

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Expanding adverse outcome pathways towards one health models for nanosafety

The ever-growing production of nano-enabled products has generated the need for dedicated risk assessment strategies that ensure safety for humans and the environment. Transdisciplinary approaches are needed to support the development of new technologies while respecting environmental limits, as also highlighted by the EU Green Deal Chemicals Strategy for Sustainability and its safe and sustainable by design (SSbD) framework. The One Health concept offers a holistic multiscale approach for the assessment of nanosafety. However, toxicology is not yet capable of explaining the interaction between chemicals and biological systems at the multiscale level and in the context of the One Health framework. Furthermore, there is a disconnect between chemical safety assessment, epidemiology, and other fields of biology that, if unified, would enable the adoption of the One Health model. The development of mechanistic toxicology and the generation of omics data has provided important biological knowledge of the response of individual biological systems to nanomaterials (NMs). On the other hand, epigenetic data have the potential to inform on interspecies mechanisms of adaptation. These data types, however, need to be linked to concepts that support their intuitive interpretation. Adverse Outcome Pathways (AOPs) represent an evolving framework to anchor existing knowledge to chemical risk assessment. In this perspective, we discuss the possibility of integrating multi-level toxicogenomics data, including toxicoepigenetic insights, into the AOP framework. We anticipate that this new direction of toxicogenomics can support the development of One Health models applicable to groups of chemicals and to multiple species in the tree of life.Peer reviewe