Modeling the Biodegradation of Bacterial Community Assembly

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Modeling the biodegradation of bacterial community assembly-linked antibiotics in river sediment using a deterministic-stochastic combined model Wenlong Zhang, Yi Li, Chao Wang, Peifang Wang, Jun Hou, Zhongbo Yu, Lihua Niu, Linqiong Wang, and Jing Wang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b01573 • Publication Date (Web): 18 Jul 2016 Downloaded from http://pubs.acs.org on July 24, 2016

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Environmental Science & Technology

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Modeling the biodegradation of bacterial community assembly-linked antibiotics

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in river sediment using a deterministic-stochastic combined model

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Wenlong Zhang1, Yi Li*1, Chao Wang1, Peifang Wang1, Jun Hou1, Zhongbo Yu2,

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Lihua Niu1, Linqiong Wang1, Jing Wang1

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1 Key Laboratory of Integrated Regulation and Resource Development on Shallow

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Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing

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210098, P.R. China

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2 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,

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Center for Global Change and Water Cycle, Hohai University, Nanjing 210098, P.R.

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China

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* Corresponding author: Dr. Yi Li

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College of Environment, Hohai University

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Xikang Road #1, Nanjing, 210098, P.R.China

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Tel: 86-25-83786251

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Fax: 86-25-83786251

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Email: [email protected]

1

ACS Paragon Plus Environment


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Abstract

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To understand the interaction between bacterial community assembly and the

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assembly-linked antibiotics biodegradation, a unique model framework containing a

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Monod kinetic, logistic kinetic and a stochastic item was established to describe the

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biodegradation of bacterial community assembly-linked sulfamethoxazole (SMX) in

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river sediment. According to the modeling results, both deterministic and stochastic

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processes driving bacterial population variations played important roles in controlling

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SMX biodegradation, and the relative importance depended on the in-situ

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concentration of SMX. A threshold concentration of SMX which was biodegraded in

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the experimental river sediment depending on different processes was obtained (i.e.,

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20 µg/kg). The higher introduced concentration of SMX (> 20 µg/kg) was found to

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promote the acclimation of antibiotic degradation bacteria in microbial community

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through niche differentiation, which resulted in the specific microbial metabolization

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of SMX. In contrast, the lower introduced concentration of SMX (< 20 µg/kg) was not

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able to lead to a significant increase of deterministic processes and resulted in the

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biodegradation of SMX through co-metabolism by the coexisting microorganisms.

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The developed model can be considered to be a useful tool for improving the

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technologies of water environmental protection and remediation.

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Keywords: sulfamethoxazole, biodegradation, bacterial community, modeling, niche,

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neutral

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1. Introduction

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Since the advent of penicillin in 1929, antibiotics have become a boon for improving

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human and animal health. Today, the estimated consumption of antibiotics worldwide

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ranges from 100,000 to 200,000 tons annually (1, 2). However, due to extensive

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consumption, excretion and disposal, different levels of antibiotics have been detected

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in water environmental compartments, such as hospital wastewaters (from µg/L to

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mg/L) (3), wastewater treatment plant effluents (from ng/L to µg/L) (3), surface

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waters (from ng/L to µg/L) (3), groundwaters (ng/L) (4), and drinking water (ng/L)

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(5), leading to adverse effects on the integrity of microbial community (6, 7) and then

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disrupting the key bacterial cycles/processes critical to aquatic ecology (e.g.,

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nitrification/denitrification) and animal production (e.g., rudimentary processes) (6).

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Therefore, understanding the attenuation of antibiotics is important for water

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environmental protection and remediation.

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As soon as the antibiotics are introduced into natural water, they may undergo

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several physico-chemical reactions, such as photolysis, hydrolysis, adsorption to

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sediment and biodegradation (8). Among these reactions, bacterial biodegradation has

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long been known to contribute to the natural attenuation of antibiotics in rivers (9-11).

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Jiang et al. (9) suggested that abiotic hydrolysis and direct photolysis were the

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primary processes for the elimination of the cephalosporins in the surface water of the

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lake, whereas biodegradation was responsible for the elimination of cephalosporins in

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the lake sediment. Radke et al. (10) indicated that both specific microbial

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metabolization and cometabolic degradation played important roles in the process of 3



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SMX biodegradation. Xu et al. (11) found that the isolated Bacillus firmus and

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Bacillus cereus from the river water-sediment system achieved the removal of SMX

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with the rate ranging from 40% to 90%. However, bacteria in river sediment are not

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present in the form of individuals, but coexist in a community according to certain

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ecological criteria (i.e., niche-based and neutral mechanisms) (12, 13). The

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biodegradation of antibiotics are accordingly believed to be achieved by the

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cooperative efforts of various bacteria in a bacterial community. Moreover, due to the

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inhibitory effects on bacteria, the introduced antibiotics can significantly change the

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structure of the bacterial community in the river sediment, which may increase niche

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differentiation and in turn affect the biodegradation processes of antibiotics. However,

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until now, the interaction between the bacterial community assembly and the

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biodegradation of antibiotics has not been clear.

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To characterize the biodegradation of antibiotics in river sediment, a model

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framework considering the bacterial community assembly and the assembly-linked

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biodegradation of antibiotics is required. Recently, the microbial community was

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thought to be shaped by mainly two types of processes, i.e., deterministic processes

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and stochastic processes (13-15). The former, such as competition and niche

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differentiation, came from the assumption of traditional niche-based theory (16).

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However, such theories struggle to explain very diverse environments where many

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rare taxa can coexist (17). The later was proposed according to a neutral theory, which

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considers birth, death, dispersal, and speciation and disregards the differences

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between species at the same trophic level (18). However, the mechanisms of neutral 4


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models are just “too simple” to represent biological reality. Moreover, small

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deviations from neutrality would have large repercussions for the predicted patterns

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(19). It is now more generally accepted that deterministic and stochastic processes

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occur simultaneously during the assembly of biofilm communities (20, 21). According

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to the theory, models, including both deterministic and stochastic elements, were

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established. Ofiţeru et al. (20) examined the microbial communities in a wastewater

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treatment plant by incorporating environmental influences on the reproduction (or

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birth) rate of the individual taxa. Li et al. (12) described the effects of hydrodynamics

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on the assembly of the microbial community within the fluvial biofilm through a two

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dimensional model considering the mechanisms of immigration, dispersal, and niche

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differentiation.

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However, the related research on modeling the bacterial community assembly and

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the assembly-linked micro-pollutants biodegradation is very limited. Song et al. (22,

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23) developed a model combining Monod and logistic kinetics to represent the

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microbial growth and corresponding biodegradation of hydrocarbons during the

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natural attenuation process in unsaturated subsurface soil. Liu et al. (24) developed a

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model framework based on Monod kinetics to describe the growth-linked

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biodegradation of trace-level pollutants in the presence of coincidental carbon

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substrates and microbes. Both of the models were established based on the Monod

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kinetic, which was proposed according to the assumption of traditional niche-based

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theory because it takes into account that the substrate concentration as the limiting

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factor of microbial growth (22). Although the stochastic process was also reported to 5



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play an important role in the assembly of the bacterial community, it has not yet been

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considered in modeling the biodegradation of pollutants in the environment (e.g.,

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water and soils).

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Therefore, our hypothesis is that both deterministic and stochastic processes of

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the bacterial community assembly play important roles in controlling the

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biodegradation of antibiotics in river sediment, and their relative importance is

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time-dependent. To test the hypothesis, this study was conducted in the following

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three steps: 1) studying the biodegradation of antibiotics at environmental and

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therapeutic concentrations, 2) clarifying the interaction between antibiotic

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biodegradation and bacterial community change in river sediment, and 3) modeling

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the biodegradation of bacterial community assembly-linked antibiotics using a

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deterministic-stochastic combined model. SMX was selected as the target antibiotic

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due to its common use in most countries and highly frequent detection in water

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systems (11, 25). According to reconnaissance of the USGS, SMX was categorized as

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a persistent antibiotic due to its mobile physico-chemical characteristics and was

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predicted to result in greater negative effects on the water environment than other

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antibiotics (26). The obtained results would not only be helpful for understanding the

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biodegradation of antibiotics in river sediment but also play important roles in

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protection and remediation of the water environment.

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2. Methods

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2.1 Site and sampling

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Water and sediment samples were collected from the upstream portion of the 6


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Qinhuai River at Nanjing, China, where the concentration of SMX was relatively low

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(< 10 ng/kg in the sediment). The water and sediment samples were kept in the dark at

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4 °C during the sampling events and immediately transported to the lab and stored in

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the dark at 4 °C until pretreatment within 24 h. The sediment samples were

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homogenized and wet sieved to less than 2 mm. The combined water and sediment

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were stored at a volume ratio of 3:1 at 4 °C in the dark before the experiment. The

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physico-chemical properties of the water samples are shown in Table S1.

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2.2 Experimental procedures

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Continuous stirring cylindrical bioreactors (140 cm height, 80 cm diameter) were

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used in this work because the kinetic parameters in the completely mixed system are

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easy to calculate and model. Sediment and water samples were put into the

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bioreactors at a volume ratio of 3:1. Fresh mineral mediums containing K2HPO4 (43.8

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mg/L), Na2HPO4 (62.4 mg/L), MgSO4 (45.0 mg/L), FeCl3-6H2O (0.5 mg/L), NH4Cl

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(5.4 mg/L), and CaCl2 (55 mg/L) was prepared and added to the experimental

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bioreactors every 5 day to provide suitable nutrient and buffering capacity for

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biological growth (27). All of the salts used to prepare the mineral medium were

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reagent grade (Sigma-Aldrich).

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To systematically study the interaction between the bacterial community

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assembly and the biodegradation of antibiotics, the sterile and non-sterile experiments

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were run with spiking of SMX at the concentration of 2 mg/L (therapeutic

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concentration level) and 20 µg/L (environmental concentration level). For the sterile

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system, both the water and the sediment were sterilized at 121 °C and 2.16 bar for 1.5 7



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h, and then 1 ‰ NaN3 was added to the water to inhibit its biological activities. All of

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the experiments were run in triplicates for a period of 120 days at 20 ± 3 °C in the

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laboratory in the dark to minimize the photodegradation of SMX and to prevent

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photosynthesis in the sediment. Sediment (4 g dry weight) samples were collected

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every 24 hours for SMX detection and the bacterial community analysis. After

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sampling, the samples were transferred into sealable plastic bags and later stored at

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-80 °C in the lab.

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2.3 Analytical methods

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2.3.1 Chemical analysis

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Pretreatment processes were conducted to determine the SMX in sediments. The

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lyophilized sediment samples were firstly extracted with 15 mL of methanol, 5 mL of

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Na2EDTA (0.1 M), and 10 mL of citrate buffer (pH 4) for three times. After vortexing,

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the supernatant of the mixture was collected. Then, the supernatants were blended and

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diluted with purified water to a final volume of 500 mL. Solid phase extraction (SPE)

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method was applied to concentrate the compounds from the supernatants using Oasis

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hydrophilic-lipophilic balance (HLB) cartridges (Waters, Watford, UK) previously

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washed with 5 mL of methanol and 5 mL of pure water. The supernatant were then

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passed through the cartridges at a loading rate of approximately 5 mL/min. After

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washing the cartridges with 5 mL pure water, they were air-dried for 10 min and

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eluted with 5 mL of methanol. The final eluate was collected and evaporated in a

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gentle nitrogen stream to 0.1 mL. The initial mobile-phase acetonitrile and purified

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water containing 0.3% formic acid (v/v) (approximately 0.7 mL) were used to bring 8


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the final sample volume up to 1 mL for further analysis.

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The SMX was analyzed using high performance liquid chromatography

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electrospray ionization tandem mass spectrometry (HPLC-MS/MS), which consists of

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an Alliance 2695 HPLC (Waters, Manchester, UK) and a Waters Micromass Quattro

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Micro™ detector with electrospray ionization (ESI). The quantitative analysis was

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performed using LC-ESI-MS/MS in the multiple reaction monitoring (MRM) mode,

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using the two highest characteristic precursor ion/product ion transitions. The detailed

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parameters and implementation process were shown in the Supporting Information.

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2.3.2 Molecular analysis

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The bacterial community was detected using the T-RFLP method, which has been

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reported in our previous studies (28). The genomic DNA of the samples was extracted

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using an E.Z.N.A® soil DNA kit (Omega Bio-Tek Inc., USA). The 16S rRNA genes of

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the bacteria were amplified from DNA extract using the primer pair 27F and 1492R.

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The PCR products were digested in duplicates using Hae III and Hinf I restriction

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endonucleases (TaKaRa, Japan) at 37 °C for 3 h. The fluorescently labeled terminal

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restriction fragments (T-RFs) were run on an automated DNA sequencer (ABI Prism

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TM 3730). The T-RF sizes and peak areas were measured using GeneMarker.

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For the identity of the bacterial phylogenetic affiliation, a clone library of 16S

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rRNA genes from the pooled samples was constructed. A total of 100 positive clones

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were selected randomly for the subsequent sequencing of inserted DNA fragments

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with the bacterial library. The phylogenetic affiliation of these 16S rRNA gene

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sequences were determined by the Ribosomal Database Project and BALSTN online. 9



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Given the potential discrepancy between in silico-determined T-RF length and the

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actual T-RF length determined by the sequencing, the origins of the T-RFs were

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identified according to the T-RFLP profiles of the cloned 16S rRNA genes (29). The

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T-RFLP analysis of the cloned 16S rRNA genes was the same as above. The

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phylogenetic affiliation of each peak was determined by the cloned 16S rRNA gene

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sequences with the same T-RF size. The T-RFLP profiles of the same sample digested

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by Hae III and Hinf I, separately, presented a good agreement in the microbial

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community compositions. However, the T-RFLP profiles corresponding to Hae III

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generated more detailed T-RFLP profiles and were used for further analysis. All of

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T-RFLP profiles digested by Hae III were pooled and standardized into a T-RFLP

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abundance matrix for the following analysis. The diversity indices (Gini-Simpson

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coefficient and evenness) based on the T-RFLP abundance matrix were calculated by

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PAST 4.0. Each T-RF size was defined as an operational taxonomic unit (OTU) in this

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study.

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2.4 Modeling the bacterial community assembly and the assembly-linked SMX

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biodegradation

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2.4.1 Model framework development

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Niche differentiation and neutral theory are accepted as the mechanisms that

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shape the microbial community (20, 21). As soon as SMX was introduced into the

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sediment of river, the bacteria would make responses with the representations of dose

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dependent adaptation (i.e., niche differentiation) and unaffected (i.e., neutral process).

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The dose dependent adaptation process could be divided into two scenarios, i.e., 10


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growth-promotion and inactivation. The growth-promotion process could be

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described by the kinetic growth model based on the classical Monod kinetics and

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logostic model, which is used to express the limitation of population growth due to

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available substrates and other factors in natural environment (22). The inactivation

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kinetics is typically dependent on substrate (i.e., SMX) concentration (22). Therefore,

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the niche-based process for bacterial population variation under the stress of SMX in

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the river sediments can be described in equation (1).

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dX = µ m , L

C X (1 − ) Xdt − k d , L C Ks + C X m,L

(1)

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where X is the bacterial biomass concentration (TRFLP peak areas/kg dry sediment), t

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is time (day), µm,L is the maximum specific growth rate system (day-1), C is SMX

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concentration (mg/kg dry sediment), Ks is the half-saturation constant for bacterial

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growth (mg/kg dry sediment), Xm,L is the peak bacterial biomass concentration of the

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system (TRFLP peak areas/kg dry sediment), and kd,L is the cell decay rate (day-1).

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The neutral process was described by a stochastic differential term that considers

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birth, death, dispersal, and speciation and disregards the differences between species

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at the same trophic level (20). In the completely mixed experimental system saturated

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with NT individuals, an individual must die for the assemblage to change. According

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to the theory, the dead individual would be replaced by an immigrant from a source

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community with the probability m, or by reproduction by a member of the local

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community with probability (1-m) (20). In this study, all the experiments were carried

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out in the completely mixed system, which meant that the probability of the dead

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individual replaced by an immigrant from a source community was zero. Therefore, 11



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the neutral processes mentioned in this manuscript indicate stochastic processes of the

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dead individual replaced by reproduction of a member in the local community. Let the

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mean frequency for replacement of an individual be a , and then the scaled time

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representing the mean time of an individual replaced once can be defined as τ = t / a .

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Therefore,

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∆τ = (1/ NT ) × (1/ NT ) = 1/ NT2 , when the process of one replacement of an

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individual in the community is considered as a whole. For the ith species comprising

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N individuals, the probability of an increase by one, no change, and a decrease by one

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individual are described by the equations (2) to (4).

the

required

time

period

can

be

calculated

NT − N N × = bn NT NT − 1

as

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Pr( N + 1/ N ) =

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Pr( N / N ) =

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Pr( N − 1/ N ) =

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The expected changes in the abundance (E(△X)) and the corresponding squared

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difference (E(△X2)) are given in formulas (5) and (6), where O(1/NT3) is the residual

256

error.

(2)

N ( N − 1) + ( NT − N )( NT − N − 1) NT ( NT − 1) N NT − N × = dn NT NT − 1

(4)

1 (bn − d n ) + 0 = 0 NT

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E ( ∆X ) =

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E (∆X 2 ) =

(3)

(5)

1 1 1 (bn + d n ) + 0 ≈ 2 [2 X (1 − X )] + Ο( 3 ) ≈ ∆τ [2 X (1 − X )] 2 NT NT NT

(6)

259

Then, the equivalent stochastic differential for equations (2), (3), and (4) can be given

260

in formulas (7), where Wτ is standard Brownian motion and a > 0.

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dX = 2 X (1 − X )dWτ = 2 X (1 − X )dWt / a =

1 2 X (1 − X )dWt a

(7)

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Therefore, the variation of the bacterial population can be determined by

263

integrating equation (1) and (7) (as shown in equation (8)) and the corresponding

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kinetics for SMX biodegradation can be determined in equation (9).

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dX = µ m , L

C X 1 (1 − ) Xdt − k d , L C + Ks + C X m, L a

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dC = µ m , L

C X X (1 − ) dt Ks + C X m,L Y

2 X (1 − X ) dWt

(8)

(9)

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where Y is the yield coefficient of the bacterial cells. Then, the solutions for X and C

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can be expressed as equations (10) and (11), respectively. kd ,L Y − + µ m , L C (t ) µm,LC kd ,L k exp[ (1 − )t + α ] + X m , L (1 − d , L ) K s + C (t ) Ks + C Y Y 1−

269

X (t ) = X m , L

1 2 (sin( w(t ) + β ) + 1) 2a a

270

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C − K s ln( K s + C ) = µ m , L (1 −

X X ) t +γ X m,L Y

(10)

(11)

272

where α, β and γ are the constants depending on the initial conditions. The variable

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w(t ) follows a Gaussian distribution. The relative importance of the stochastic

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process (RIs) during the assembly of the bacterial community can be expressed by the

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ratio of the stochastic processes generated bacterial population to the total bacterial

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population (as shown in equation (12)).

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RI s =

1 2 (sin( w(t ) + β ) + 1) 2a a X (t )

(12)

2.4.2 Estimating parameters 13



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The most commonly used methods for estimating the biological parameters in

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nonlinear equations are the fitting of the available measured data to the calculated

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results. However, these methods may fail to obtain reasonable estimated parameters

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because the fitting degree here is required to be simultaneously tested by two

283

correlation coefficients (i.e., fittings of bacterial population and SMX concentration in

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river sediment). To overcome the practical difficulty, a multi-objective algorithm

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based on a non-dominated sorting genetic algorithm (NSGA II) was developed to

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estimate the parameters in equations (10) and (11) (30, 31). The minimizations of the

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residual errors between the measured and calculated SMX concentration and the

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bacterial biomass concentration were used as objective functions (as shown in

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equation (12) and (13)).

290

F ( X ) = min ∑ ( X cal (ti ) − X obs (ti )) 2

(12)

i

291

F (C ) = min ∑ (Ccal (ti ) − Cobs (ti )) 2

(13)

i

292

where Xcal(ti) and Xobs(ti) are the calculated and measured bacterial biomass

293

concentration, and Ccal(ti) and Cobs(ti) are the corresponding calculated and measured

294

concentrations of residual SMX in sediments, respectively. Based on a series of

295

measurements of residuals concentration and bacterial biomass concentration, the

296

parameters in equations (10) and (11) can be estimated. The flowchart of the solution

297

methodology is presented in Figure S1. In NSGA II, the concept of Pareto-dominance

298

is used to rank the individuals (control strategies) of a population. The detailed

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implementation process of NSGA II is presented in Supporting Information.

300

3. Results and Discussion 14


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3.1 SMX degradation

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The removal of SMX at different concentrations in the sterile and non-sterile

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systems is shown in Figure 1. Approximately 3.9 ± 0.3% and 94.5 ± 0.2% of SMX at

304

the therapeutic concentration were removed within 120 days in the sterile and

305

non-sterile systems, respectively (Figure 1a). This indicates that microbial

306

biodegradation was the dominant process for SMX removal in the experimental

307

systems. Although the observed lag phase was longer in the SMX biodegradation

308

process at a therapeutic concentration than that at an environmental concentration (i.e.,

309

16 days vs. 4 days), the biodegradation rate of SMX was significantly higher at the

310

therapeutic concentration than that at the environmental concentration. Therefore, it

311

can be deduced that the dominated biodegradation mechanisms of SMX were

312

different at the different concentrations. The biodegradation of SMX in most water

313

environments, including the bioreactors in this study, proceeded with the presence of

314

other coexisting dissolved organic carbon substrates and microbes. It is now generally

315

accepted that cometabolic and specific degradation are the two most important

316

mechanisms for SMX biodegradation in sediment, and the biodegradation rate is

317

much higher through specific microbial metabolization than the co-metabolism

318

process (11, 12). Therefore, it can be deduced that specific microbial metabolization

319

may play a much more important role in the degradation of SMX at a therapeutic

320

concentration, and cometabolic degradation is dominant in the degradation of SMX at

321

an environmental concentration.

322

According to our initial hypothesis, the deduction could be understood as follows. 15



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Unlike conventional carbon substrates, the introduced SMX can significantly change

324

the structure of the bacterial community in sediments, which in turn affects its

325

biodegradation process. The introduced therapeutic concentration of SMX resulted in

326

an enhanced niche selection (deterministic process) in this study, which is helpful for

327

the acclimation of antibiotic resistant bacteria and degradation bacteria. Therefore,

328

specific microbial metabolization was expected to play a much more important role in

329

the biodegradation of the therapeutic concentration of SMX in river sediments. In

330

contrast, the introduced environmental concentration of SMX was not sufficient to

331

destroy the integrity of the biological community in river sediment, and it was thus

332

proposed to be

333

microorganisms in the microbial community. Thus, it seems likely that cometabolic

334

degradation is the dominant process for the microbial degradation of the

335

environmental concentration of SMX. Our proposed theoretical framework can also

336

provide a solution to understand the controversy from Al-Ahmad et al. (32), Letzel et

337

al. (33), and Radke et al. (11) who observed different lengths of lag phases during

338

SMX biodegradation through Organization for Economic Co-operation and

339

Development (OECD) tests spiked with SMX at different concentrations (i.e., 3.8

340

mg/L, 0.7 mg/L and 20 µg/L).

341

3.2 The bacterial community associated with SMX biodegradation

biodegraded by the synergistic metabolisms of

various

342

To verify the proposed explanation of SMX biodegradation, the shifts in bacterial

343

community diversity, biomass and composition during SMX biodegradation were

344

investigated. 16


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3.2.1 Bacterial diversity and biomass

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The variations of bacterial diversity and biomass during SMX biodegradation are

347

shown in Figure 2. As expected, the variations of bacterial diversity and biomass were

348

closely related to the concentration of SMX in the river sediment. In the non-sterile

349

systems spiked with the therapeutic concentration of SMX, the number of OTUs was

350

first found to decrease from 97 ± 2 to 85 ± 1, and then increased to 96 ± 1 with the

351

degradation of SMX. In contrast, in the non-sterile systems spiked with an

352

environmental concentration of SMX, only irregular fluctuations in small scales were

353

observed, similarly as the variation observed in control experiments (Figure 2a).

354

Computed as the OTUs richness, the corresponding variation trends of Shannon_H

355

diversity indexes were found to be in accordance with the change of the OTUs (Figure

356

2b). Moreover, bacterial abundance, which could easily be affected by the introduced

357

SMX, was used to represent the biomass of the bacterial community (34). In all

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non-sterile systems, the bacterial abundances were first found to decrease with the

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spiking of SMX, and the falling ranges were significantly positively correlated (P

Modeling the Biodegradation of Bacterial Community Assembly

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