Distinct effects of typical sludge pretreatment approaches on the antibiotic resistance genes variations, associated bacterial community dynamics and metabolic activities during anaerobic fermentation process

Anaerobic fermentation is effective for waste activated sludge (WAS) disposal to realize resource generation and pollutants reduction, and various pretreatments were commonly applied to improve the performance. This work mainly investigated the effects of typical WAS pretreatment approaches on the antibiotic resistance genes (ARGs, as emerging contaminants) removal during anaerobic fermentation processes and unveiled the underlying mechanisms. The results indicated that all the pretreatment strategies exhibited evident effects on the overall ARGs removal with the order of Fe2+ activated persulfate (PS/Fe2+) > pH 10 > Ultrasonication > Heat, and showed selective removal tendency for the specific ARGs (namely easily removed (aadA1 and sul1) and persistent ARGs). Mechanistic analysis demonstrated that the pretreatments disrupted the extracellular polymeric substances (EPS) and rose the cell membrane permeability (particularly for PS/Fe2+ and Heat). Then the increased ARGs release benefitted the subsequent reduction of mobile genetic elements (MGEs) and extracellular ARGs (especially for PS/Fe2+ and pH10), resulting the ARGs attenuation. Pretreatments significantly shifted the microbial community structure and the abundances of potential ARGs hosts (i.e., Sulfuritalea, and Denitratisoma). Also, the different pretreatments exhibited distinct effects on the microbial metabolic traits related with ARGs proliferation (i.e., ABC transporters, two-component system and bacterial secretion systems), which also contributed to the ARGs attenuations during WAS fermentation. The partial least-squares path modeling (PLS-PM) analysis indicated that the bacterial community (total effects = 0.968) was key factor determining ARGs fates.

» Author: Xiaoshi Cheng, Le Zhang, Zhicheng Wei, Gang Zhao, Jun Tai, Wei Du, Feng Wang, Qian Feng, Jiashun Cao, Yinglong Su, Jingyang Luo

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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