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Meta-analysis and interpretable machine learning characterize ARG and MGE co-enrichment under microplastic exposure during anaerobic digestion.

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Τίτλος: Meta-analysis and interpretable machine learning characterize ARG and MGE co-enrichment under microplastic exposure during anaerobic digestion.
Συγγραφείς: Dong Y; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China., Meng S; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China., Yan L; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China., Tang X; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China., Li Q; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China. Electronic address: qian.li@xauat.edu.cn.
Πηγή: Journal of environmental management [J Environ Manage] 2026 Sep 15; Vol. 417, pp. 130998. Date of Electronic Publication: 2026 Sep 21.
Τύπος έκδοσης: Journal Article; Meta-Analysis
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE
Imprint Name(s): Original Publication: London ; New York, Academic Press.
Ιατρικοί όροι (MeSH): Drug Resistance, Microbial*/genetics , Machine Learning* , Microplastics* , Interspersed Repetitive Sequences*, Anaerobiosis ; Sewage ; Boosting Machine Learning Algorithms ; Bioreactors
Περίληψη: Waste activated sludge is an important sink for microplastics (MPs), and anaerobic digestion (AD), the mainstream process for sludge treatment, may provide conditions that facilitate MP-associated enrichment and dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), posing potential resistance-related risks. However, how MP physical attributes and AD operating conditions are jointly associated with ARG and MGE dynamics remains unclear. Here, we integrated evidence using dependency-aware three-level random-effects meta-analysis and XGBoost-SHAP association analysis, with reactor mode tested as a moderator and generalizability assessed by study-grouped and leave-one-study-out validation. MP exposure was associated with 57.5% and 35.0% increases in ARGs and MGEs, respectively, while responses were broadly consistent across batch and semi-continuous reactors. Tetracycline, β-lactam, and sulfonamide ARGs showed enrichment, whereas MGE responses were concentrated in integron-related elements, particularly intI1. SHAP analysis associated MP shape and aging status more strongly with ARG enrichment and particle size more closely with MGE responses, while incubation duration acted as a shared factor. Joint interaction analysis identified shape- and aging-centred combinations as predominantly ARG-associated, duration-temperature interactions as predominantly MGE-associated, and particle size and duration as links between the two response structures. The design separates pooled-effect estimation and reactor-related moderation from nonlinear association mapping while accounting for dependent observations across studies. Together, the findings support a staged associative framework linking plastisphere establishment, ARG host enrichment, and MGE-associated mobilization, providing a basis for coordinated monitoring and management of MP-related resistance risks in AD systems.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Anaerobic digestion; Antibiotic resistance genes; Machine learning; Meta-analysis; Microplastics; Mobile genetic elements
Substance Nomenclature: 0 (Microplastics)
0 (Sewage)
Entry Date(s): Date Created: 20260921 Date Completed: 20260929 Latest Revision: 20261005
Update Code: 20261006
DOI: 10.1016/j.jenvman.2026.130998
PMID: 42767154
Βάση Δεδομένων: MEDLINE
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  Label: Title
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  Data: Meta-analysis and interpretable machine learning characterize ARG and MGE co-enrichment under microplastic exposure during anaerobic digestion.
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  Data: <searchLink fieldCode="AU" term="%22Dong+Y%22">Dong Y</searchLink>; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China.<br /><searchLink fieldCode="AU" term="%22Meng+S%22">Meng S</searchLink>; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China.<br /><searchLink fieldCode="AU" term="%22Yan+L%22">Yan L</searchLink>; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China.<br /><searchLink fieldCode="AU" term="%22Tang+X%22">Tang X</searchLink>; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China.<br /><searchLink fieldCode="AU" term="%22Li+Q%22">Li Q</searchLink>; School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, PR China. Electronic address: qian.li@xauat.edu.cn.
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  Data: <searchLink fieldCode="JN" term="%220401664%22">Journal of environmental management</searchLink> [J Environ Manage] 2026 Sep 15; Vol. 417, pp. 130998. <i>Date of Electronic Publication: </i>2026 Sep 21.
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  Data: Journal Article; Meta-Analysis
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  Data: English
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Academic+Press%22">Academic Press </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>0401664 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1095-8630 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2203014797%22">03014797 </searchLink><i>NLM ISO Abbreviation: </i>J Environ Manage <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: London ; New York, Academic Press.
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  Data: <searchLink fieldCode="MM" term="%22Drug+Resistance%2C+Microbial%22">Drug Resistance, Microbial*</searchLink>/<searchLink fieldCode="MM" term="%22Drug+Resistance%2C+Microbial+genetics%22">genetics</searchLink> <br /><searchLink fieldCode="MM" term="%22Machine+Learning%22">Machine Learning*</searchLink> <br /><searchLink fieldCode="MM" term="%22Microplastics%22">Microplastics*</searchLink> <br /><searchLink fieldCode="MM" term="%22Interspersed+Repetitive+Sequences%22">Interspersed Repetitive Sequences*</searchLink><br /><searchLink fieldCode="MH" term="%22Anaerobiosis%22">Anaerobiosis</searchLink> ; <searchLink fieldCode="MH" term="%22Sewage%22">Sewage</searchLink> ; <searchLink fieldCode="MH" term="%22Boosting+Machine+Learning+Algorithms%22">Boosting Machine Learning Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Bioreactors%22">Bioreactors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Waste activated sludge is an important sink for microplastics (MPs), and anaerobic digestion (AD), the mainstream process for sludge treatment, may provide conditions that facilitate MP-associated enrichment and dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), posing potential resistance-related risks. However, how MP physical attributes and AD operating conditions are jointly associated with ARG and MGE dynamics remains unclear. Here, we integrated evidence using dependency-aware three-level random-effects meta-analysis and XGBoost-SHAP association analysis, with reactor mode tested as a moderator and generalizability assessed by study-grouped and leave-one-study-out validation. MP exposure was associated with 57.5% and 35.0% increases in ARGs and MGEs, respectively, while responses were broadly consistent across batch and semi-continuous reactors. Tetracycline, β-lactam, and sulfonamide ARGs showed enrichment, whereas MGE responses were concentrated in integron-related elements, particularly intI1. SHAP analysis associated MP shape and aging status more strongly with ARG enrichment and particle size more closely with MGE responses, while incubation duration acted as a shared factor. Joint interaction analysis identified shape- and aging-centred combinations as predominantly ARG-associated, duration-temperature interactions as predominantly MGE-associated, and particle size and duration as links between the two response structures. The design separates pooled-effect estimation and reactor-related moderation from nonlinear association mapping while accounting for dependent observations across studies. Together, the findings support a staged associative framework linking plastisphere establishment, ARG host enrichment, and MGE-associated mobilization, providing a basis for coordinated monitoring and management of MP-related resistance risks in AD systems.<br /> (Copyright © 2026 Elsevier Ltd. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Anaerobic digestion; Antibiotic resistance genes; Machine learning; Meta-analysis; Microplastics; Mobile genetic elements
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  Data: 0 (Microplastics)<br />0 (Sewage)
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      – SubjectFull: Boosting Machine Learning Algorithms
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      – TitleFull: Meta-analysis and interpretable machine learning characterize ARG and MGE co-enrichment under microplastic exposure during anaerobic digestion.
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