MétaCan
Menu
← Retour à la cohorte
Enregistrement W3018493338 · doi:10.1002/etc.4735

A Dynamic Shift in Soil Metal Risk Assessment, It is Time to Shift from Toxicokinetics to Toxicodynamics

2020· article· en· W3018493338 sur OpenAlexafffund
Mathieu Renaud, José Paulo Sousa, Steven D. Siciliano

Notice bibliographique

RevueEnvironmental Toxicology and Chemistry · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental Toxicology and Ecotoxicology
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesFundação para a Ciência e a TecnologiaNatural Sciences and Engineering Research Council of Canada
Mots-clésToxicodynamicsEnvironmental chemistryToxicokineticsSoil waterBioaccumulationBioavailabilityToxicityEarthwormMetal toxicityOrganismSpringtailInvertebrateEnvironmental scienceSoil PollutantsTrophic levelEcotoxicologySoil contaminationChemistryEcologyBiologySoil scienceHeavy metalsPharmacology

Résumé

récupéré en direct d'OpenAlex

Most ecotoxicological research of the effect of metals on soil organisms focuses on the toxicokinetics of these elements to predict their toxicity to soil invertebrates or microorganisms. Typically, it is thought that free ions cross the cell membrane, are distributed to the site of toxic action, and cause deleterious effects (Peijnenburg and Jager 2003). In this paradigm, metal toxicity is dependent on the availability of metals present in soil porewater. In turn, metal porewater availability is linked to the partitioning of metals between soil and water, the availability of porewater itself (soil moisture), as well as organism traits that influence organism exposure to it. Despite considerable research on this topic, there is still no clear and consistent correlation between metal availability, soil properties, and toxicity. For instance, soil pH is a good predictor of metal availability across soils but not of toxicity for many metals (Smolders et al. 2009). The mismatch between availability and toxicity may arise if porewater ingestion or dermal exposure is not the dominant exposure pathway. It is possible that for invertebrates soil ingestion and subsequent changes of metal availability in the gut explain why metal bioavailability is not always linked to toxicity. Annelids, including both earthworms and enchytraeids, actively ingest soil as a result of their burrowing behavior; but for other invertebrates (oribatid mites and collembolans) it is unclear if or how much soil is ingested. For earthworms only one experiment has tried to distinguish dermal from oral exposure to metals, but more explorations under different conditions/metals/soils/species are needed (Vijver et al. 2003). In this Points of Reference, we argue that the paradigm where toxicokinetics is the sole driver of metal toxicity to soil organisms should be abandoned and that we should look also at toxicodynamic drivers. Toxicodynamics is the dynamic interaction of a toxicant with a site of toxic action and its subsequent biological effects. Typically for metals, toxicodynamic research focuses solely on the site of toxic action, such as reactive oxygen species generation or calcium homeostasis disruption. In the soil environment there are multiple factors that influence soil organism health, such as texture, organic matter composition, pH, and cation exchange capacity. In the past, these parameters have largely been viewed through the lens of toxicokinetics. For example, studies on cation exchange capacity or organic matter focus on how these factors influence metal bioavailability. We argue that we need a different view, in which we explicitly recognize that soil factors not only are key for toxicokinetics but in fact drive toxicodynamic behavior in the organism (Figure 1). Energy is one example by which soil properties drive toxicodynamics of metals within an organism. For example, Oppia nitens has an increased tolerance to metals in high–habitat quality soils compared to those with low habitat quality (Jegede et al. 2019), despite similar Zn bioavailability. An organism's ecological strategies can also affect its responses to metal contamination; for example, some species could reduce reproductive output as a strategy to increase energy allocation for metal resistance and survival (Van Gestel and Hoogerwerf 2001). Although this hypothesis has been suggested (Van Gestel and Hoogerwerf 2001), it has not been experimentally demonstrated, and the mechanisms behind these strategies are not well known. Finally, climate, which has been a major focus in recent years as a result of climate change, is known to affect the response of organisms to contamination through temperature and is expected to function as an added stressor to the organism's biology. Although experimentation has been performed at different temperatures, few studies, if any, report the toxicodynamic mechanisms of how temperature affects organism response to metals. The current risk assessment of metals in soil is broken. Typically, field observations and laboratory toxicity tests show no correspondence. Further, in the real world, metals exist as mixtures; and our existing methods to account for this are theoretically inadequate and empirically not predictive. Yet, field practitioners recognize that increasing the quality of the environment, via amendments or eco-restorative practices, can dramatically increase the abundance of organisms at impacted sites. Our current risk-assessment framework is unable to account for this because it can only account for toxicokinetic changes in metals. A new risk-assessment framework needs a different platform of ecotoxicological research, one that focuses on the key toxicodynamic questions that determine organism response to pollutants. We can build off of the existing literature for how metals interact at the site of toxic action, but additional research is needed on how habitat quality, organism behavior, and organism traits interact during metal impacts. Once built, we can readily adapt existing risk-assessment frameworks to modify predicted risk based on toxicodynamic modifiers. In doing so, we can combine the best of toxicokinetic research with toxicodynamic research and thereby better predict the real risk in our environment. The authors acknowledge the Natural Sciences and Engineering Research Council for funding the Strategic Grant to S.D. Siciliano and the Portuguese Institution Fundação para a Ciência e a Tecnologia for funding the PhD grant of M. Renaud (SFRH/BD/130442/2017). The authors also declare that they have no conflict of interest in the publication of this research. Address correspondence to M. Renaud (jeanmathieubr@gmail.com).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,100

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,016
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0010,007
Communication savante0,0080,014
Science ouverte0,0030,005
Intégrité de la recherche0,0050,010
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,216
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueEnvironmental Toxicology and Chemistry→Même sujetEnvironmental Toxicology and Ecotoxicology→Travaux en français237 207→