Details

Autor(en) / Beteiligte

• Wu, Dong
• Wang, Bing-Han
• Xie, Bing

Titel

Validated predictive modelling of sulfonamide and beta-lactam resistance genes in landfill leachates

Ist Teil von

• Journal of environmental management, 2019-07, Vol.241, p.123-130

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The spread of antimicrobial resistance via landfill leachates jeopardizes millions of people's health, which can be exacerbated due to the unclear quantitative relationships between leachate characteristics and occurrences of antibiotic resistance genes (ARGs). Here, in parallel with sampling raw leachates from a real landfill, we constructed a lab-scale landfill and collected its leachates for 260 days. All leachate samples were analyzed for the abundance of integrons, sulfonamide resistance (sulR; sul1 and sul2) and beta-lactams resistance (blaR; blaOXA, blaCTX-M, and blaTEM) genes. The enrichment of sulR subtypes was closely associated with the integrons' prevalence during the landfilling process (0.65–0.75 log10(copies/mL)), which can be explained by the multiple linear regression that contained intl1, pH, and nitrogen compounds as variables. The predicted abundance of sulR genes (6.06 ± 0.6 log10(copies/mL)) was statistically the same as the observed value in raw leachates (P = 0.73). The abundance of blaR genes decreased from 5.0 to 2.5 log10(copies/mL) during the experiment (P < 0.001); and a locally weighted regression of blaR genes with integrons, COD and total nitrogen accurately predicted blaR genes abundance in raw leachate (Bootstrap = 10,000, P = 0.67). The partial least squares path modelling (PLS-PM) showed that variations of blaR genes in the lab and raw leachates shared an identical pattern (PLS-PM, Bootstrap = 10,000, P > 0.05), which was influenced by integrons and environmental factors with the coefficients of −0.11 and 0.39, respectively. We believe the validated models are highly useful tools to streamline the strategies for monitoring and prediction of ARGs. [Display omitted] •Variation of sulR and blaR genes fitted linear and PLS-PM models, respectively.•sulR genes were influenced by intl1 in a positive way.•blaR genes were influenced by both of integrons and environmental factors.•Predicted results were validated by on-site sampling data.