Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system
[Display omitted] •Spring poses the highest ARG abundance and risk in full-scale AD plant.•Risk scores are not always lower after AD due to the increased Rank I ARG ratio.•Dynamics of ARGs is much more chaotic (nonlinear) than microbial community.•EDM exhibits good potential in predicting the dynami...
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Veröffentlicht in: | Bioresource technology 2024-02, Vol.394, p.130267-130267, Article 130267 |
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creator | Liu, Wei Sun, Chenxiang Ren, Zhengran Hao, Shan Chen, Zhan Li, Tianle Wen, Xianghua |
description | [Display omitted]
•Spring poses the highest ARG abundance and risk in full-scale AD plant.•Risk scores are not always lower after AD due to the increased Rank I ARG ratio.•Dynamics of ARGs is much more chaotic (nonlinear) than microbial community.•EDM exhibits good potential in predicting the dynamics of ARGs and microbes.
The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs. |
doi_str_mv | 10.1016/j.biortech.2023.130267 |
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•Spring poses the highest ARG abundance and risk in full-scale AD plant.•Risk scores are not always lower after AD due to the increased Rank I ARG ratio.•Dynamics of ARGs is much more chaotic (nonlinear) than microbial community.•EDM exhibits good potential in predicting the dynamics of ARGs and microbes.
The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.</description><identifier>ISSN: 0960-8524</identifier><identifier>EISSN: 1873-2976</identifier><identifier>DOI: 10.1016/j.biortech.2023.130267</identifier><identifier>PMID: 38154733</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>anaerobic digestion ; antibiotic resistance ; Empirical dynamic modeling ; Long-term monitoring ; metagenomics ; microbial communities ; nonlinear models ; risk ; Risk assessment ; Seasonal dynamics ; sludge ; spring</subject><ispartof>Bioresource technology, 2024-02, Vol.394, p.130267-130267, Article 130267</ispartof><rights>2023</rights><rights>Copyright © 2023. Published by Elsevier Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-35a5e6e1926baf7cd1aa7d8bf91c925853a705c33c9f0919d61caeed99c520c23</cites><orcidid>0000-0002-3686-0315 ; 0000-0002-9792-8678</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.biortech.2023.130267$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38154733$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Sun, Chenxiang</creatorcontrib><creatorcontrib>Ren, Zhengran</creatorcontrib><creatorcontrib>Hao, Shan</creatorcontrib><creatorcontrib>Chen, Zhan</creatorcontrib><creatorcontrib>Li, Tianle</creatorcontrib><creatorcontrib>Wen, Xianghua</creatorcontrib><title>Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system</title><title>Bioresource technology</title><addtitle>Bioresour Technol</addtitle><description>[Display omitted]
•Spring poses the highest ARG abundance and risk in full-scale AD plant.•Risk scores are not always lower after AD due to the increased Rank I ARG ratio.•Dynamics of ARGs is much more chaotic (nonlinear) than microbial community.•EDM exhibits good potential in predicting the dynamics of ARGs and microbes.
The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.</description><subject>anaerobic digestion</subject><subject>antibiotic resistance</subject><subject>Empirical dynamic modeling</subject><subject>Long-term monitoring</subject><subject>metagenomics</subject><subject>microbial communities</subject><subject>nonlinear models</subject><subject>risk</subject><subject>Risk assessment</subject><subject>Seasonal dynamics</subject><subject>sludge</subject><subject>spring</subject><issn>0960-8524</issn><issn>1873-2976</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkc9u1DAQxi0EokvhFaocOZDF41kn8Q1UlT_SSlzgbDnjydbLJi52gtQH4L1xtC3i1tOM9P2-mdF8QlyB3IKE5v1x24eYZqbbrZIKt4BSNe0zsYGuxVqZtnkuNtI0su602l2IVzkfpZQIrXopLrADvWsRN-LPPk6HeuY0VpFoSYkn4ndV4hzy7EpfpZB_Vm7yFd26OAdaa3JULIUIlKs4FHkO5Z5V_c954IlzFaYqnxZ_4EI5TrEvkA8HLuZYpPs88_havBjcKfObh3opfny6-X79pd5_-_z1-uO-Jtx1c43aaW4YjGp6N7TkwbnWd_1ggIzSnUbXSk2IZAZpwPgGyDF7Y0grSQovxdvz3LsUfy3lBDuGTHw6uYnjki2CRlgftnsSVUZ2oBBwndqcUUox58SDvUthdOnegrRrWvZoH9Oya1r2nFYxXj3sWPqR_T_bYzwF-HAGuDzld-BkM4U1IR8S02x9DE_t-AvRjaxA</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Liu, Wei</creator><creator>Sun, Chenxiang</creator><creator>Ren, Zhengran</creator><creator>Hao, Shan</creator><creator>Chen, Zhan</creator><creator>Li, Tianle</creator><creator>Wen, Xianghua</creator><general>Elsevier Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-3686-0315</orcidid><orcidid>https://orcid.org/0000-0002-9792-8678</orcidid></search><sort><creationdate>20240201</creationdate><title>Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system</title><author>Liu, Wei ; Sun, Chenxiang ; Ren, Zhengran ; Hao, Shan ; Chen, Zhan ; Li, Tianle ; Wen, Xianghua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-35a5e6e1926baf7cd1aa7d8bf91c925853a705c33c9f0919d61caeed99c520c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>anaerobic digestion</topic><topic>antibiotic resistance</topic><topic>Empirical dynamic modeling</topic><topic>Long-term monitoring</topic><topic>metagenomics</topic><topic>microbial communities</topic><topic>nonlinear models</topic><topic>risk</topic><topic>Risk assessment</topic><topic>Seasonal dynamics</topic><topic>sludge</topic><topic>spring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Sun, Chenxiang</creatorcontrib><creatorcontrib>Ren, Zhengran</creatorcontrib><creatorcontrib>Hao, Shan</creatorcontrib><creatorcontrib>Chen, Zhan</creatorcontrib><creatorcontrib>Li, Tianle</creatorcontrib><creatorcontrib>Wen, Xianghua</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Bioresource technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Wei</au><au>Sun, Chenxiang</au><au>Ren, Zhengran</au><au>Hao, Shan</au><au>Chen, Zhan</au><au>Li, Tianle</au><au>Wen, Xianghua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system</atitle><jtitle>Bioresource technology</jtitle><addtitle>Bioresour Technol</addtitle><date>2024-02-01</date><risdate>2024</risdate><volume>394</volume><spage>130267</spage><epage>130267</epage><pages>130267-130267</pages><artnum>130267</artnum><issn>0960-8524</issn><eissn>1873-2976</eissn><abstract>[Display omitted]
•Spring poses the highest ARG abundance and risk in full-scale AD plant.•Risk scores are not always lower after AD due to the increased Rank I ARG ratio.•Dynamics of ARGs is much more chaotic (nonlinear) than microbial community.•EDM exhibits good potential in predicting the dynamics of ARGs and microbes.
The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38154733</pmid><doi>10.1016/j.biortech.2023.130267</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-3686-0315</orcidid><orcidid>https://orcid.org/0000-0002-9792-8678</orcidid></addata></record> |
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subjects | anaerobic digestion antibiotic resistance Empirical dynamic modeling Long-term monitoring metagenomics microbial communities nonlinear models risk Risk assessment Seasonal dynamics sludge spring |
title | Long-term occurrence, resistance risk and chaotic characteristics of antibiotic resistance genes in sludge anaerobic digestion system |
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