ACS Environmental Au最新文献

{"title":"Machine Learning Reveals Signatures of Promiscuous Microbial Amidases for Micropollutant Biotransformations.","authors":"Thierry D Marti, Diana Schweizer, Yaochun Yu, Milo R Schärer, Silke I Probst, Serina L Robinson","doi":"10.1021/acsenvironau.4c00066","DOIUrl":"10.1021/acsenvironau.4c00066","url":null,"abstract":"<p><p>Organic micropollutants, including pharmaceuticals, personal care products, pesticides, and food additives, are widespread in the environment, causing potentially toxic effects. Human waste is a direct source of micropollutants, with the majority of pharmaceuticals being excreted through urine. Urine contains its own microbiota with the potential to catalyze micropollutant biotransformations. Amidase signature (AS) enzymes are known for their promiscuous activity in micropollutant biotransformations, but the potential for AS enzymes from the urinary microbiota to transform micropollutants is not known. Moreover, the characterization of AS enzymes to identify key chemical and enzymatic features associated with biotransformation profiles is critical for developing benign-by-design chemicals and micropollutant removal strategies. Here, to uncover the signatures of AS enzyme-substrate specificity, we tested 17 structurally diverse compounds against a targeted enzyme library consisting of 40 AS enzyme homologues from diverse urine microbial isolates. The most promiscuous enzymes were active on nine different substrates, while 16 enzymes had activity on at least one substrate and exhibited diverse substrate specificities. Using an interpretable gradient boosting machine learning model, we identified chemical and amino acid features associated with AS enzyme biotransformations. Key chemical features from our substrates included the molecular weight of the amide carbonyl substituent and the number of formal charges in the molecule. Four of the identified amino acid features were located in close proximity to the substrate tunnel entrance. Overall, this work highlights the understudied potential of urine-derived microbial AS enzymes for micropollutant biotransformation and offers insights into substrate and protein features associated with micropollutant biotransformations for future environmental applications.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"114-127"},"PeriodicalIF":6.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Reveals Signatures of Promiscuous Microbial Amidases for Micropollutant Biotransformations","authors":"Thierry D. Marti,&nbsp;Diana Schweizer,&nbsp;Yaochun Yu,&nbsp;Milo R. Schärer,&nbsp;Silke I. Probst and Serina L. Robinson*,&nbsp;","doi":"10.1021/acsenvironau.4c0006610.1021/acsenvironau.4c00066","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00066https://doi.org/10.1021/acsenvironau.4c00066","url":null,"abstract":"<p >Organic micropollutants, including pharmaceuticals, personal care products, pesticides, and food additives, are widespread in the environment, causing potentially toxic effects. Human waste is a direct source of micropollutants, with the majority of pharmaceuticals being excreted through urine. Urine contains its own microbiota with the potential to catalyze micropollutant biotransformations. Amidase signature (AS) enzymes are known for their promiscuous activity in micropollutant biotransformations, but the potential for AS enzymes from the urinary microbiota to transform micropollutants is not known. Moreover, the characterization of AS enzymes to identify key chemical and enzymatic features associated with biotransformation profiles is critical for developing benign-by-design chemicals and micropollutant removal strategies. Here, to uncover the signatures of AS enzyme–substrate specificity, we tested 17 structurally diverse compounds against a targeted enzyme library consisting of 40 AS enzyme homologues from diverse urine microbial isolates. The most promiscuous enzymes were active on nine different substrates, while 16 enzymes had activity on at least one substrate and exhibited diverse substrate specificities. Using an interpretable gradient boosting machine learning model, we identified chemical and amino acid features associated with AS enzyme biotransformations. Key chemical features from our substrates included the molecular weight of the amide carbonyl substituent and the number of formal charges in the molecule. Four of the identified amino acid features were located in close proximity to the substrate tunnel entrance. Overall, this work highlights the understudied potential of urine-derived microbial AS enzymes for micropollutant biotransformation and offers insights into substrate and protein features associated with micropollutant biotransformations for future environmental applications.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"114–127 114–127"},"PeriodicalIF":6.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsenvironau.4c00066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143086876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Haloacetonitriles Induce Structure-Related Cellular Toxicity Through Distinct Proteome Thiol Reaction Mechanisms.","authors":"Kirsten Yeung, Linna Xie, Pranav Nair, Hui Peng","doi":"10.1021/acsenvironau.4c00068","DOIUrl":"10.1021/acsenvironau.4c00068","url":null,"abstract":"<p><p>Haloacetonitriles (HANs) are a class of toxic drinking water disinfection byproducts (DBPs). However, the toxicity mechanisms of HANs remain unclear. We herein investigated the structure-related in vitro toxicity of 6 representative HANs by utilizing complementary bioanalytical approaches. Dibromoacetonitrile (DBAN) displayed strong cytotoxicity and Nrf2 oxidative stress responses, followed by monohalogenated HANs (monoHANs) while other polyhalogenated HANs (polyHANs) exhibited little toxicity. Activity based protein profiling (ABPP) revealed that toxic HANs adduct to human proteome thiols, supporting thiol reactivity as the primary toxicity mechanism for HANs. By using glutathione (GSH) as a thiol surrogate, monoHANs reacted with GSH via S_N2 while polyHANs reacted through ultrafast addition reactions. In contrast, DBAN generated an unexpected fully debrominated product and glutathione disulfide (GSSG). The unique reaction of DBAN with GSH was found to be mediated by radicals which was supported by electron paramagnetic resonance (EPR) spectroscopy and by radical trapping reagent reaction quenching. Shotgun proteomics further revealed that monoHANs and DBAN adducted to proteome thiols in live cells forming dehalogenated adducts. Multiple antioxidant proteins, SOD1, CSTB, and GAPDH, were adducted by toxic HANs at specific cysteine residues. This study highlights the structurally selective toxicity of HANs in human cells, which are attributed to their distinct reactions with proteome thiols.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"101-113"},"PeriodicalIF":6.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Haloacetonitriles Induce Structure-Related Cellular Toxicity Through Distinct Proteome Thiol Reaction Mechanisms","authors":"Kirsten Yeung,&nbsp;Linna Xie,&nbsp;Pranav Nair and Hui Peng*,&nbsp;","doi":"10.1021/acsenvironau.4c0006810.1021/acsenvironau.4c00068","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00068https://doi.org/10.1021/acsenvironau.4c00068","url":null,"abstract":"<p >Haloacetonitriles (HANs) are a class of toxic drinking water disinfection byproducts (DBPs). However, the toxicity mechanisms of HANs remain unclear. We herein investigated the structure-related in vitro toxicity of 6 representative HANs by utilizing complementary bioanalytical approaches. Dibromoacetonitrile (DBAN) displayed strong cytotoxicity and Nrf2 oxidative stress responses, followed by monohalogenated HANs (monoHANs) while other polyhalogenated HANs (polyHANs) exhibited little toxicity. Activity based protein profiling (ABPP) revealed that toxic HANs adduct to human proteome thiols, supporting thiol reactivity as the primary toxicity mechanism for HANs. By using glutathione (GSH) as a thiol surrogate, monoHANs reacted with GSH via S_N2 while polyHANs reacted through ultrafast addition reactions. In contrast, DBAN generated an unexpected fully debrominated product and glutathione disulfide (GSSG). The unique reaction of DBAN with GSH was found to be mediated by radicals which was supported by electron paramagnetic resonance (EPR) spectroscopy and by radical trapping reagent reaction quenching. Shotgun proteomics further revealed that monoHANs and DBAN adducted to proteome thiols in live cells forming dehalogenated adducts. Multiple antioxidant proteins, SOD1, CSTB, and GAPDH, were adducted by toxic HANs at specific cysteine residues. This study highlights the structurally selective toxicity of HANs in human cells, which are attributed to their distinct reactions with proteome thiols.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"101–113 101–113"},"PeriodicalIF":6.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsenvironau.4c00068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143087428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating GPT Models for Automated Literature Screening in Wastewater-Based Epidemiology","authors":"Kaseba Chibwe,&nbsp;David Mantilla-Calderon and Fangqiong Ling*,&nbsp;","doi":"10.1021/acsenvironau.4c0004210.1021/acsenvironau.4c00042","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00042https://doi.org/10.1021/acsenvironau.4c00042","url":null,"abstract":"<p >Methods to quantitatively synthesize findings across multiple studies is an emerging need in wastewater-based epidemiology (WBE), where disease tracking through wastewater analysis is performed at broad geographical locations using various techniques to facilitate public health responses. Meta-analysis provides a rigorous statistical procedure for research synthesis, yet the manual process of screening large volumes of literature remains a hurdle for its application in timely evidence-based public health responses. Here, we evaluated the performance of GPT-3, GPT-3.5, and GPT-4 models in automated screening of publications for meta-analysis in the WBE literature. We show that the chat completion model in GPT-4 accurately differentiates papers that contain original data from those that did not with texts of the Abstract as the input at a Precision of 0.96 and Recall of 1.00, exceeding current quality standards for manual screening (Recall = 0.95) while costing less than $0.01 per paper. GPT models performed less accurately in detecting studies reporting relevant sampling location, highlighting the value of maintaining human intervention in AI-assisted literature screening. Importantly, we show that certain formulation and model choices generated nonsensical answers to the screening tasks, while others did not, urging the attention to robustness when employing AI-assisted literature screening. This study provided novel performance evaluation data on GPT models for document screening as a step in meta-analysis, suggesting AI-assisted literature screening a useful complementary technique to speed up research synthesis in WBE.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"61–68 61–68"},"PeriodicalIF":6.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsenvironau.4c00042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143087361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating GPT Models for Automated Literature Screening in Wastewater-Based Epidemiology.","authors":"Kaseba Chibwe, David Mantilla-Calderon, Fangqiong Ling","doi":"10.1021/acsenvironau.4c00042","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00042","url":null,"abstract":"<p><p>Methods to quantitatively synthesize findings across multiple studies is an emerging need in wastewater-based epidemiology (WBE), where disease tracking through wastewater analysis is performed at broad geographical locations using various techniques to facilitate public health responses. Meta-analysis provides a rigorous statistical procedure for research synthesis, yet the manual process of screening large volumes of literature remains a hurdle for its application in timely evidence-based public health responses. Here, we evaluated the performance of GPT-3, GPT-3.5, and GPT-4 models in automated screening of publications for meta-analysis in the WBE literature. We show that the chat completion model in GPT-4 accurately differentiates papers that contain original data from those that did not with texts of the Abstract as the input at a Precision of 0.96 and Recall of 1.00, exceeding current quality standards for manual screening (Recall = 0.95) while costing less than $0.01 per paper. GPT models performed less accurately in detecting studies reporting relevant sampling location, highlighting the value of maintaining human intervention in AI-assisted literature screening. Importantly, we show that certain formulation and model choices generated nonsensical answers to the screening tasks, while others did not, urging the attention to robustness when employing AI-assisted literature screening. This study provided novel performance evaluation data on GPT models for document screening as a step in meta-analysis, suggesting AI-assisted literature screening a useful complementary technique to speed up research synthesis in WBE.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"61-68"},"PeriodicalIF":6.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective Nutrient Management of Surface Waters in the United States Requires Expanded Water Quality Monitoring in Agriculturally Intensive Areas","authors":"Christopher Oates,&nbsp;Hector Fajardo,&nbsp;Khara Grieger,&nbsp;Daniel Obenour,&nbsp;Rebecca L. Muenich and Natalie G. Nelson*,&nbsp;","doi":"10.1021/acsenvironau.4c0006010.1021/acsenvironau.4c00060","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00060https://doi.org/10.1021/acsenvironau.4c00060","url":null,"abstract":"<p >The U.S. Clean Water Act is believed to have driven widespread decreases in pollutants from point sources and developed areas, but has not substantially affected nutrient pollution from agriculture. Today, the highest nutrient concentrations in surface waters are often associated with agricultural production. In this Perspective, we explore whether challenges stemming from the Clean Water Act’s inability to mitigate agricultural nutrient pollution are also exacerbated by coarse nutrient monitoring. We evaluate the current state of nutrient monitoring in surface waters of the contiguous U.S. relative to agricultural nutrient inputs to assess how monitoring effort varies across agriculturally intensive areas. The locations of nutrient monitoring stations with approximately seasonal sampling frequency (4 samples per year, on average) from 2012 to 2021 were compiled from the U.S. Water Quality Portal. Monitoring station locations were then compared to watershed-scale (HUC-8) nutrient inventory estimates for agricultural fertilizer and livestock manure inputs. From this assessment, we found that many, but not all, of the nation’s most agriculturally intensive areas are under-monitored, and often unmonitored. While it is well-known that the Midwest is the epicenter of agricultural production in the U.S., our results reveal it is poorly monitored relative to its agricultural nutrient inputs. Other regions, like the California Central Valley and parts of the southeastern Coastal Plain were also coarsely monitored relative to nutrient inputs. Conversely, some agriculturally intensive watersheds were moderately-to-well monitored (e.g., western Lake Erie basin, eastern North Carolina, and the Delmarva Peninsula), with these basins largely having established Total Maximum Daily Loads and discharging to prominent waterways. In closing, we argue that sparse monitoring across many of the nation’s most agriculturally intensive areas motivate a need to re-envision nutrient monitoring networks, and that increased resources and advanced technologies are likely required to enable effective nutrient source identification throughout the nation.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"1–11 1–11"},"PeriodicalIF":6.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsenvironau.4c00060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective Nutrient Management of Surface Waters in the United States Requires Expanded Water Quality Monitoring in Agriculturally Intensive Areas.","authors":"Christopher Oates, Hector Fajardo, Khara Grieger, Daniel Obenour, Rebecca L Muenich, Natalie G Nelson","doi":"10.1021/acsenvironau.4c00060","DOIUrl":"10.1021/acsenvironau.4c00060","url":null,"abstract":"<p><p>The U.S. Clean Water Act is believed to have driven widespread decreases in pollutants from point sources and developed areas, but has not substantially affected nutrient pollution from agriculture. Today, the highest nutrient concentrations in surface waters are often associated with agricultural production. In this Perspective, we explore whether challenges stemming from the Clean Water Act's inability to mitigate agricultural nutrient pollution are also exacerbated by coarse nutrient monitoring. We evaluate the current state of nutrient monitoring in surface waters of the contiguous U.S. relative to agricultural nutrient inputs to assess how monitoring effort varies across agriculturally intensive areas. The locations of nutrient monitoring stations with approximately seasonal sampling frequency (4 samples per year, on average) from 2012 to 2021 were compiled from the U.S. Water Quality Portal. Monitoring station locations were then compared to watershed-scale (HUC-8) nutrient inventory estimates for agricultural fertilizer and livestock manure inputs. From this assessment, we found that many, but not all, of the nation's most agriculturally intensive areas are under-monitored, and often unmonitored. While it is well-known that the Midwest is the epicenter of agricultural production in the U.S., our results reveal it is poorly monitored relative to its agricultural nutrient inputs. Other regions, like the California Central Valley and parts of the southeastern Coastal Plain were also coarsely monitored relative to nutrient inputs. Conversely, some agriculturally intensive watersheds were moderately-to-well monitored (e.g., western Lake Erie basin, eastern North Carolina, and the Delmarva Peninsula), with these basins largely having established Total Maximum Daily Loads and discharging to prominent waterways. In closing, we argue that sparse monitoring across many of the nation's most agriculturally intensive areas motivate a need to re-envision nutrient monitoring networks, and that increased resources and advanced technologies are likely required to enable effective nutrient source identification throughout the nation.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"1-11"},"PeriodicalIF":6.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11740920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Slow-Release Pharmaceutical Implants in Ecotoxicology: Validating Functionality across Exposure Scenarios.","authors":"Michael G Bertram, Jack A Brand, Eli S J Thoré, Daniel Cerveny, Erin S McCallum, Marcus Michelangeli, Jake M Martin, Jerker Fick, Tomas Brodin","doi":"10.1021/acsenvironau.4c00056","DOIUrl":"10.1021/acsenvironau.4c00056","url":null,"abstract":"<p><p>Pharmaceutical contaminants have spread in natural environments across the globe, endangering biodiversity, ecosystem functioning, and public health. Research on the environmental impacts of pharmaceuticals is growing rapidly, although a majority of studies are still conducted under controlled laboratory conditions. As such, there is an urgent need to understand the impacts of pharmaceutical exposures on wildlife in complex, real-world scenarios. Here, we validate the performance of slow-release pharmaceutical implants-a recently developed tool in field-based ecotoxicology that allows for the controlled chemical dosing of free-roaming aquatic species-in terms of the accumulation and distribution of pharmaceuticals of interest in tissues. Across two years, we directly exposed 256 Atlantic salmon (<i>Salmo salar</i>) smolts to one of four pharmaceutical treatments: clobazam (50 μg g^-1 of implant), tramadol (50 μg g^-1), clobazam and tramadol (50 μg g^-1 of each), and control (0 μg g^-1). Fish dosed with slow-release implants containing clobazam or tramadol, or their mixture, accumulated these pharmaceuticals in all of the sampled tissues: brain, liver, and muscle. Concentrations of both pharmaceuticals peaked in all tissues at 1 day post-implantation, before reaching relatively stable, slowly declining concentrations for the remainder of the 30-day sampling period. Generally, the highest concentrations of clobazam and tramadol were detected in the liver, followed by the brain and then muscle, with observed concentrations of each pharmaceutical being higher in the single-exposure treatments relative to the mixture exposure. Taken together, our findings underscore the utility of slow-release implants as a tool in field-based ecotoxicology, which is an urgent research priority given the current lack of knowledge on the real-world impacts of pharmaceuticals on wildlife.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"69-75"},"PeriodicalIF":6.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143012795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Slow-Release Pharmaceutical Implants in Ecotoxicology: Validating Functionality across Exposure Scenarios","authors":"Michael G. Bertram*,&nbsp;Jack A. Brand,&nbsp;Eli S. J. Thoré,&nbsp;Daniel Cerveny,&nbsp;Erin S. McCallum,&nbsp;Marcus Michelangeli,&nbsp;Jake M. Martin,&nbsp;Jerker Fick and Tomas Brodin,&nbsp;","doi":"10.1021/acsenvironau.4c0005610.1021/acsenvironau.4c00056","DOIUrl":"https://doi.org/10.1021/acsenvironau.4c00056https://doi.org/10.1021/acsenvironau.4c00056","url":null,"abstract":"<p >Pharmaceutical contaminants have spread in natural environments across the globe, endangering biodiversity, ecosystem functioning, and public health. Research on the environmental impacts of pharmaceuticals is growing rapidly, although a majority of studies are still conducted under controlled laboratory conditions. As such, there is an urgent need to understand the impacts of pharmaceutical exposures on wildlife in complex, real-world scenarios. Here, we validate the performance of slow-release pharmaceutical implants─a recently developed tool in field-based ecotoxicology that allows for the controlled chemical dosing of free-roaming aquatic species─in terms of the accumulation and distribution of pharmaceuticals of interest in tissues. Across two years, we directly exposed 256 Atlantic salmon (<i>Salmo salar</i>) smolts to one of four pharmaceutical treatments: clobazam (50 μg g^–1 of implant), tramadol (50 μg g^–1), clobazam and tramadol (50 μg g^–1 of each), and control (0 μg g^–1). Fish dosed with slow-release implants containing clobazam or tramadol, or their mixture, accumulated these pharmaceuticals in all of the sampled tissues: brain, liver, and muscle. Concentrations of both pharmaceuticals peaked in all tissues at 1 day post-implantation, before reaching relatively stable, slowly declining concentrations for the remainder of the 30-day sampling period. Generally, the highest concentrations of clobazam and tramadol were detected in the liver, followed by the brain and then muscle, with observed concentrations of each pharmaceutical being higher in the single-exposure treatments relative to the mixture exposure. Taken together, our findings underscore the utility of slow-release implants as a tool in field-based ecotoxicology, which is an urgent research priority given the current lack of knowledge on the real-world impacts of pharmaceuticals on wildlife.</p>","PeriodicalId":29801,"journal":{"name":"ACS Environmental Au","volume":"5 1","pages":"69–75 69–75"},"PeriodicalIF":6.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsenvironau.4c00056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}