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del Giudice G, Serra A, Pavel A, Torres Maia M, Saarimäki LA, Fratello M, Federico A, Alenius H, Fadeel B, Greco D. A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400389. [PMID: 38923832 PMCID: PMC11348149 DOI: 10.1002/advs.202400389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/10/2024] [Indexed: 06/28/2024]
Abstract
Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.
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Affiliation(s)
- Giusy del Giudice
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
| | - Alisa Pavel
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Marcella Torres Maia
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Laura Aliisa Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Michele Fratello
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Antonio Federico
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
| | - Harri Alenius
- Human Microbiome Research Program (HUMI)University of HelsinkiHelsinki00014Finland
- Institute of Environmental MedicineKarolinska InstitutetStockholm171 77Sweden
| | - Bengt Fadeel
- Institute of Environmental MedicineKarolinska InstitutetStockholm171 77Sweden
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
- Institute of BiotechnologyUniversity of HelsinkiHelsinki00790Finland
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Bahl A, Halappanavar S, Wohlleben W, Nymark P, Kohonen P, Wallin H, Vogel U, Haase A. Bioinformatics and machine learning to support nanomaterial grouping. Nanotoxicology 2024; 18:373-400. [PMID: 38949108 DOI: 10.1080/17435390.2024.2368005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 05/22/2024] [Accepted: 06/11/2024] [Indexed: 07/02/2024]
Abstract
Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.
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Affiliation(s)
- Aileen Bahl
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Department of Biological Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Canada
| | - Wendel Wohlleben
- BASF SE, Department Analytical and Material Science and Department Experimental Toxicology and Ecology, Ludwigshafen, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Håkan Wallin
- Department of Chemical and Biological Risk Factors, National Institute of Occupational Health, Oslo, Norway
- Department of Public Health, Copenhagen University, Copenhagen, Denmark
| | - Ulla Vogel
- National Research Centre for the Working Environment, Copenhagen, Denmark
| | - Andrea Haase
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
- Freie Universität Berlin, Institute of Pharmacy, Berlin, Germany
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Costa E, Johnson KJ, Walker CA, O’Brien JM. Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches. Front Genet 2024; 15:1374791. [PMID: 38784034 PMCID: PMC11112360 DOI: 10.3389/fgene.2024.1374791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/02/2024] [Indexed: 05/25/2024] Open
Abstract
A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.
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Affiliation(s)
| | | | | | - Jason M. O’Brien
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Ottawa, ON, Canada
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del Giudice G, Migliaccio G, D’Alessandro N, Saarimäki LA, Torres Maia M, Annala ME, Leppänen J, Mӧbus L, Pavel A, Vaani M, Vallius A, Ylä‐Outinen L, Greco D, Serra A. Advancing chemical safety assessment through an omics-based characterization of the test system-chemical interaction. FRONTIERS IN TOXICOLOGY 2023; 5:1294780. [PMID: 38026842 PMCID: PMC10673692 DOI: 10.3389/ftox.2023.1294780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
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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Affiliation(s)
- Giusy del Giudice
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Giorgia Migliaccio
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Nicoletta D’Alessandro
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Laura Aliisa Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Marcella Torres Maia
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Maria Emilia Annala
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Jenni Leppänen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Lena Mӧbus
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Maaret Vaani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Anna Vallius
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Laura Ylä‐Outinen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- BioMediTech Unit, Tampere University, Tampere, Finland
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
- Tampere Institute for Advanced Study, Tampere University, Tampere, Finland
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5
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Ji C, Shao K. The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics. Chem Res Toxicol 2023; 36:1345-1354. [PMID: 37494567 PMCID: PMC10462436 DOI: 10.1021/acs.chemrestox.3c00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.
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Affiliation(s)
- Chao Ji
- Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States
| | - Kan Shao
- Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States
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6
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Del Giudice G, Serra A, Saarimäki LA, Kotsis K, Rouse I, Colibaba SA, Jagiello K, Mikolajczyk A, Fratello M, Papadiamantis AG, Sanabria N, Annala ME, Morikka J, Kinaret PAS, Voyiatzis E, Melagraki G, Afantitis A, Tämm K, Puzyn T, Gulumian M, Lobaskin V, Lynch I, Federico A, Greco D. An ancestral molecular response to nanomaterial particulates. NATURE NANOTECHNOLOGY 2023; 18:957-966. [PMID: 37157020 PMCID: PMC10427433 DOI: 10.1038/s41565-023-01393-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 03/31/2023] [Indexed: 05/10/2023]
Abstract
The varied transcriptomic response to nanoparticles has hampered the understanding of the mechanism of action. Here, by performing a meta-analysis of a large collection of transcriptomics data from various engineered nanoparticle exposure studies, we identify common patterns of gene regulation that impact the transcriptomic response. Analysis identifies deregulation of immune functions as a prominent response across different exposure studies. Looking at the promoter regions of these genes, a set of binding sites for zinc finger transcription factors C2H2, involved in cell stress responses, protein misfolding and chromatin remodelling and immunomodulation, is identified. The model can be used to explain the outcomes of mechanism of action and is observed across a range of species indicating this is a conserved part of the innate immune system.
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Affiliation(s)
- G Del Giudice
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - A Serra
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tampere Institute for Advanced Study, Tampere, Finland
| | - L A Saarimäki
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - K Kotsis
- School of Physics, University College Dublin, Dublin, Ireland
| | - I Rouse
- School of Physics, University College Dublin, Dublin, Ireland
| | - S A Colibaba
- School of Physics, University College Dublin, Dublin, Ireland
| | - K Jagiello
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - A Mikolajczyk
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - M Fratello
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - A G Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Novamechanics Ltd, Nicosia, Cyprus
| | - N Sanabria
- National Institute for Occupational Health, National Health Laboratory Services, Johannesburg, South Africa
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - M E Annala
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - J Morikka
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - P A S Kinaret
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLife), University of Helsinki, Helsinki, Finland
| | | | - G Melagraki
- Division of Physical Sciences and Applications, Hellenic Military Academy, Vari, Greece
| | | | - K Tämm
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - T Puzyn
- Group of Environmental Chemoinformatics, Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- QSAR Lab Ltd, Gdańsk, Poland
| | - M Gulumian
- National Institute for Occupational Health, National Health Laboratory Services, Johannesburg, South Africa
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
- Water Research Group, Unit for Environmental Sciences and Management, North West University, Potchefstroom, South Africa
| | - V Lobaskin
- School of Physics, University College Dublin, Dublin, Ireland
| | - I Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - A Federico
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Tampere Institute for Advanced Study, Tampere, Finland
| | - D Greco
- FHAIVE, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Institute of Biotechnology, Helsinki Institute of Life Sciences (HiLife), University of Helsinki, Helsinki, Finland.
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland.
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7
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Hautanen V, Morikka J, Saarimäki LA, Bisenberger J, Toimela T, Serra A, Greco D. The in vitro immunomodulatory effect of multi-walled carbon nanotubes by multilayer analysis. NANOIMPACT 2023; 31:100476. [PMID: 37437691 DOI: 10.1016/j.impact.2023.100476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/17/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
The study of multi-walled carbon nanotube (MWCNT) induced immunotoxicity is crucial for determining hazards posed to human health. MWCNT exposure most commonly occurs via the airways, where macrophages are first line responders. Here we exploit an in vitro assay, measuring dose-dependent secretion of a wide panel of cytokines, as a measure of immunotoxicity following the non-lethal, multi-dose exposure (IC5, IC10 and IC20) to 7 MWCNTs with different intrinsic properties. We find that a tangled structure, and small aspect ratio are key properties predicting MWCNT induced immunotoxicity, mediated predominantly by IL1B cytokine secretion. To assess the mechanism of action giving rise to MWCNT immunotoxicity, transcriptomics analysis was linked to cytokine secretion in a multilayer model established through correlation analysis across exposure concentrations. This reinforced the finding that tangled MWCNTs have greater immunomodulatory potency, displaying enrichment of immune system, signal transduction and pattern recognition associated pathways. Together our results further elucidate how structure, length and aspect ratio, critical intrinsic properties of MWCNTs, are tied to immunotoxicity.
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Affiliation(s)
- Veera Hautanen
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland; Institute of Biotechnology, University of Helsinki, P.O.Box 56, Helsinki, Uusimaa 00014, Finland
| | - Jack Morikka
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Laura Aliisa Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Jan Bisenberger
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Tarja Toimela
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland; Tampere Institute for Advanced Study, Tampere University, Kalevantie 4, Tampere 33100, Finland
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland; Institute of Biotechnology, University of Helsinki, P.O.Box 56, Helsinki, Uusimaa 00014, Finland; Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Finland.
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8
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Saarimäki LA, Morikka J, Pavel A, Korpilähde S, del Giudice G, Federico A, Fratello M, Serra A, Greco D. Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2203984. [PMID: 36479815 PMCID: PMC9839874 DOI: 10.1002/advs.202203984] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/09/2022] [Indexed: 05/25/2023]
Abstract
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.
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Affiliation(s)
- Laura Aliisa Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Jack Morikka
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Alisa Pavel
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Seela Korpilähde
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Giusy del Giudice
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Antonio Federico
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Michele Fratello
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
- Tampere Institute for Advanced StudyTampere UniversityKalevantie 4Tampere33100Finland
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
- Institute of BiotechnologyUniversity of HelsinkiP.O.Box 56HelsinkiUusimaa00014Finland
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9
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Pavel A, Saarimäki LA, Möbus L, Federico A, Serra A, Greco D. The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design. Comput Struct Biotechnol J 2022; 20:4837-4849. [PMID: 36147662 PMCID: PMC9464643 DOI: 10.1016/j.csbj.2022.08.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/20/2022] Open
Abstract
Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.
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Affiliation(s)
- Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Laura A Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Lena Möbus
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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10
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Serra A, del Giudice G, Kinaret PAS, Saarimäki LA, Poulsen SS, Fortino V, Halappanavar S, Vogel U, Greco D. Characterization of ENM Dynamic Dose-Dependent MOA in Lung with Respect to Immune Cells Infiltration. NANOMATERIALS 2022; 12:nano12122031. [PMID: 35745370 PMCID: PMC9228743 DOI: 10.3390/nano12122031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 02/01/2023]
Abstract
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.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (G.d.G.); (L.A.S.)
- BioMediTech Institute, Tampere University, 33520 Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), 33520 Tampere, Finland
| | - Giusy del Giudice
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (G.d.G.); (L.A.S.)
- BioMediTech Institute, Tampere University, 33520 Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), 33520 Tampere, Finland
| | | | - Laura Aliisa Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (G.d.G.); (L.A.S.)
- BioMediTech Institute, Tampere University, 33520 Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), 33520 Tampere, Finland
| | - Sarah Søs Poulsen
- National Research Centre for the Working Environment, 2100 Copenhagen, Denmark; (S.S.P.); (U.V.)
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, 70211 Kuopio, Finland;
| | - Sabina Halappanavar
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada;
| | - Ulla Vogel
- National Research Centre for the Working Environment, 2100 Copenhagen, Denmark; (S.S.P.); (U.V.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (G.d.G.); (L.A.S.)
- BioMediTech Institute, Tampere University, 33520 Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), 33520 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland;
- Correspondence:
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11
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Serra A, Saarimäki LA, Pavel A, del Giudice G, Fratello M, Cattelani L, Federico A, Laurino O, Marwah VS, Fortino V, Scala G, Sofia Kinaret PA, Greco D. Nextcast: A software suite to analyse and model toxicogenomics data. Comput Struct Biotechnol J 2022; 20:1413-1426. [PMID: 35386103 PMCID: PMC8956870 DOI: 10.1016/j.csbj.2022.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 03/16/2022] [Accepted: 03/16/2022] [Indexed: 11/28/2022] Open
Abstract
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).
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Laura Aliisa Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Giusy del Giudice
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | | | - Veer Singh Marwah
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Giovanni Scala
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Department of Biology, University of Naples Federico II, Naples, Italy
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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12
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Ji C, Weissmann A, Shao K. A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. ENVIRONMENT INTERNATIONAL 2022; 161:107135. [PMID: 35151117 PMCID: PMC8934139 DOI: 10.1016/j.envint.2022.107135] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/16/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision. OBJECTIVE By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose-response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress. METHODS The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties. RESULTS This system was tested and validated versus 24 previously published in-vivo microarray dose-response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78-0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established. DISCUSSION Overall, this study demonstrates that dose-response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose-response data analysis with outstanding functionalities to quantify uncertainty from various sources.
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Affiliation(s)
- Chao Ji
- Department of Environmental and Occupational Health, School of Public Health, Indiana University - Bloomington, Bloomington, IN 47405, USA
| | | | - Kan Shao
- Department of Environmental and Occupational Health, School of Public Health, Indiana University - Bloomington, Bloomington, IN 47405, USA.
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13
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Serra A, Cattelani L, Fratello M, Fortino V, Kinaret PAS, Greco D. Supervised Methods for Biomarker Detection from Microarray Experiments. Methods Mol Biol 2022; 2401:101-120. [PMID: 34902125 DOI: 10.1007/978-1-0716-1839-4_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), University of Tampere, Tampere, Finland.
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
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14
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Serra A, Fratello M, Federico A, Ojha R, Provenzani R, Tasnadi E, Cattelani L, Del Giudice G, Kinaret PAS, Saarimäki LA, Pavel A, Kuivanen S, Cerullo V, Vapalahti O, Horvath P, Lieto AD, Yli-Kauhaluoma J, Balistreri G, Greco D. Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation. Brief Bioinform 2021; 23:6484515. [PMID: 34962256 PMCID: PMC8769897 DOI: 10.1093/bib/bbab507] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/03/2021] [Accepted: 11/04/2021] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Ravi Ojha
- Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Riccardo Provenzani
- Drug Research Program, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Ervin Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre, Eotvos Lorand Research Network, Szeged, Hungary
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Giusy Del Giudice
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Pia A S Kinaret
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Laura A Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Alisa Pavel
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland
| | - Suvi Kuivanen
- Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Vincenzo Cerullo
- Drug Research Program, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Olli Vapalahti
- Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland.,Department of Virology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Peter Horvath
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.,Synthetic and Systems Biology Unit, Biological Research Centre, Eotvos Lorand Research Network, Szeged, Hungary
| | - Antonio Di Lieto
- Department of Forensic Psychiatry, Aarhus University, Aarhus, Denmark
| | - Jari Yli-Kauhaluoma
- Drug Research Program, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Giuseppe Balistreri
- Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,BioMediTech Institute, Tampere University, Tampere, Finland.,Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Tampere, Finland.,Institute of Biotechnology, University of Helsinki, Helsinki, Finland
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15
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Saarimäki LA, Federico A, Lynch I, Papadiamantis AG, Tsoumanis A, Melagraki G, Afantitis A, Serra A, Greco D. Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials. Sci Data 2021; 8:49. [PMID: 33558569 PMCID: PMC7870661 DOI: 10.1038/s41597-021-00808-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/16/2020] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Laura Aliisa Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, United Kingdom
| | - Anastasios G Papadiamantis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, B15 2TT, Birmingham, United Kingdom
- NovaMechanics Ltd, P.O Box 26014 1666, Nicosia, Cyprus
| | | | | | | | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- BioMediTech Institute, Tampere University, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- BioMediTech Institute, Tampere University, Tampere, Finland.
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
- Finnish Centre for Alternative Methods (FICAM), Faculty of Medicine and Heath Technology, Tampere University, Tampere, Finland.
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16
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FeSTwo, a two-step feature selection algorithm based on feature engineering and sampling for the chronological age regression problem. Comput Biol Med 2020; 125:104008. [DOI: 10.1016/j.compbiomed.2020.104008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 08/26/2020] [Accepted: 08/26/2020] [Indexed: 02/02/2023]
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17
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Serra A, Fratello M, del Giudice G, Saarimäki LA, Paci M, Federico A, Greco D. TinderMIX: Time-dose integrated modelling of toxicogenomics data. Gigascience 2020; 9:giaa055. [PMID: 32449777 PMCID: PMC7247400 DOI: 10.1093/gigascience/giaa055] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/22/2020] [Accepted: 05/05/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Omics technologies have been widely applied in toxicology studies to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow the study of the molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis cannot identify dynamic (time-dependent) events of dose responsiveness. RESULTS We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify whether the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure. CONCLUSIONS To showcase the TinderMIX method, we analysed 2 drugs from the Open TG-GATEs dataset, namely, cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action of each drug and compared them. Our analysis highlights that different time- and dose-integrated point of departure recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent mechanism of action.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Giusy del Giudice
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Laura Aliisa Saarimäki
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Michelangelo Paci
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- BioMediTech Institute, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland
- Institute of Biotechnology, University of Helsinki, Viikinkaari 5, 00014, Helsinki, Finland
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Serra A, Fratello M, Cattelani L, Liampa I, Melagraki G, Kohonen P, Nymark P, Federico A, Kinaret PAS, Jagiello K, Ha MK, Choi JS, Sanabria N, Gulumian M, Puzyn T, Yoon TH, Sarimveis H, Grafström R, Afantitis A, Greco D. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E708. [PMID: 32276469 PMCID: PMC7221955 DOI: 10.3390/nano10040708] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/30/2022]
Abstract
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - My Kieu Ha
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Jang-Sik Choi
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Natasha Sanabria
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
| | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tae-Hyun Yoon
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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