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Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in Daphnia magna Toxicogenomics
Örebro University, School of Science and Technology. (The Life Science Center-Biology)ORCID iD: 0000-0002-2299-5024
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology. (The Life Science Center-Biology)ORCID iD: 0000-0001-7957-0310
Örebro University, School of Science and Technology. (The Life Science Center-Biology)ORCID iD: 0000-0001-7336-6335
2023 (English)In: Biology, E-ISSN 2079-7737, Vol. 12, no 5, article id 692Article in journal (Refereed) Published
Abstract [en]

Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 12, no 5, article id 692
Keywords [en]
Zn, bioavailability, biomarker, machine learning, water hardness
National Category
Water Engineering
Identifiers
URN: urn:nbn:se:oru:diva-106096DOI: 10.3390/biology12050692ISI: 000995573200001PubMedID: 37237504Scopus ID: 2-s2.0-85160308477OAI: oai:DiVA.org:oru-106096DiVA, id: diva2:1760024
Funder
Knowledge Foundation, 20180027Örebro University, 1214-NT3060Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2024-01-02Bibliographically approved
In thesis
1. New Approaches for Daphnia magna Toxicity Assessment
Open this publication in new window or tab >>New Approaches for Daphnia magna Toxicity Assessment
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Risk assessment plays a crucial role in evaluating and managing the potential hazards and health effects associated with exposure to substances. In recent decades, the field of risk assessment has undergone significant expansion, embracing innovative approaches and methodologies. Despite these advancements, it's noteworthy that the standards governing experimental setups have remained largely unchanged. This thesis focuses on enhancing environmental risk assessment methodologies, particularly in the context of exposure protocols, by incorporating toxicogenomics and machine learning approaches as well as suggestions for improved toxicity testing setup. The main objectives of the Paper I was to assess the sensitivity differences and shared responses of different animal models to exposure settings. Seven different organisms were tested with varying metal concentrations. Paper II investigated the effects of altering exposure media parameters, particularly water hardness. Paper III utilized computational advancements in toxicogenomics for gene ranking and exposure prediction. Paper IV investigated a larger number of genes by utilizing transcriptomics to discover novel biomarkers and molecular functions affected by metal exposures at the boundaries of Zn and Cu homeostasis. The research findings revealed that traditional toxicity assessment setups may not fully provide a base to capture the complexity of occurring toxicity. Therefore, the study proposes deviations from standard test protocols and emphasizes the need for holistic models that consider multiple factors to accurately assess toxicity risks in aquatic environments.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2023. p. 74
Series
Örebro Studies in Life Science, ISSN 1653-3100 ; 20
Keywords
Risk assessment, toxicogenomics, zinc, copper, machine learning
National Category
Other Biological Topics
Identifiers
urn:nbn:se:oru:diva-106168 (URN)9789175295183 (ISBN)9789175295190 (ISBN)
Public defence
2023-09-15, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 09:15 (English)
Opponent
Supervisors
Available from: 2023-06-01 Created: 2023-06-01 Last updated: 2023-09-06Bibliographically approved

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Paylar, BerkayLängkvist, MartinJass, JanaOlsson, Per-Erik

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