Modeling the Biodegradation of Bacterial Community Assembly

Jul 18, 2016 - Kolpin , D. W.; Furlong , E. T.; Meyer , M. T.; Thurman , E. M.; Zaugg , S. D.; Barber , L. B.; Buxton , H. T. Pharmaceuticals, hormone...
0 downloads 0 Views 2MB Size
Subscriber access provided by La Trobe University Library

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 42

Environmental Science & Technology

1

Modeling the biodegradation of bacterial community assembly-linked antibiotics

2

in river sediment using a deterministic-stochastic combined model

3 4

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

5

Lihua Niu1, Linqiong Wang1, Jing Wang1

6

1 Key Laboratory of Integrated Regulation and Resource Development on Shallow

7

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

8

210098, P.R. China

9

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

10

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

11

China

12 13 14

15

* Corresponding author: Dr. Yi Li

16

College of Environment, Hohai University

17

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

18

Tel: 86-25-83786251

19

Fax: 86-25-83786251

20

Email: [email protected]

1

ACS Paragon Plus Environment

Environmental Science & Technology

21

Abstract

22

To understand the interaction between bacterial community assembly and the

23

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

24

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

25

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

26

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

27

processes driving bacterial population variations played important roles in controlling

28

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

29

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

30

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

31

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

32

promote the acclimation of antibiotic degradation bacteria in microbial community

33

through niche differentiation, which resulted in the specific microbial metabolization

34

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

35

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

36

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

37

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

38

technologies of water environmental protection and remediation.

39 40

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

41

neutral

42 43

2

ACS Paragon Plus Environment

Page 2 of 42

Page 3 of 42

Environmental Science & Technology

44

1. Introduction

45

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

46

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

47

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

48

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

49

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

50

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

51

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

52

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

53

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

54

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

55

Therefore, understanding the attenuation of antibiotics is important for water

56

environmental protection and remediation.

57

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

58

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

59

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

60

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

61

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

62

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

63

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

64

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

65

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

ACS Paragon Plus Environment

Environmental Science & Technology

66

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

67

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

68

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

69

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

70

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

71

biodegradation of antibiotics are accordingly believed to be achieved by the

72

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

73

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

74

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

75

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

76

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

77

biodegradation of antibiotics has not been clear.

78

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

79

framework considering the bacterial community assembly and the assembly-linked

80

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

81

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

82

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

83

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

84

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

85

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

86

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

87

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

ACS Paragon Plus Environment

Page 4 of 42

Page 5 of 42

Environmental Science & Technology

88

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

89

deviations from neutrality would have large repercussions for the predicted patterns

90

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

91

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

92

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

93

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

94

treatment plant by incorporating environmental influences on the reproduction (or

95

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

96

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

97

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

98

differentiation.

99

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

100

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

101

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

102

microbial growth and corresponding biodegradation of hydrocarbons during the

103

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

104

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

105

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

106

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

107

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

108

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

109

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

ACS Paragon Plus Environment

Environmental Science & Technology

110

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

111

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

112

water and soils).

113

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

114

the bacterial community assembly play important roles in controlling the

115

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

116

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

117

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

118

therapeutic concentrations, 2) clarifying the interaction between antibiotic

119

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

120

the biodegradation of bacterial community assembly-linked antibiotics using a

121

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

122

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

123

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

124

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

125

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

126

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

127

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

128

protection and remediation of the water environment.

129

2. Methods

130

2.1 Site and sampling

131

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

ACS Paragon Plus Environment

Page 6 of 42

Page 7 of 42

Environmental Science & Technology

132

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

133

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

134

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

135

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

136

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

137

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

138

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

139

2.2 Experimental procedures

140

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

141

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

142

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

143

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

144

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

145

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

146

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

147

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

148

reagent grade (Sigma-Aldrich).

149

To systematically study the interaction between the bacterial community

150

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

151

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

152

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

153

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

ACS Paragon Plus Environment

Environmental Science & Technology

154

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

155

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

156

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

157

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

158

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

159

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

160

-80 °C in the lab.

161

2.3 Analytical methods

162

2.3.1 Chemical analysis

163

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

164

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

165

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

166

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

167

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

168

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

169

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

170

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

171

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

172

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

173

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

174

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

175

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

ACS Paragon Plus Environment

Page 8 of 42

Page 9 of 42

Environmental Science & Technology

176

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

177

The SMX was analyzed using high performance liquid chromatography

178

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

179

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

180

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

181

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

182

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

183

parameters and implementation process were shown in the Supporting Information.

184

2.3.2 Molecular analysis

185

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

186

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

187

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

188

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

189

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

190

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

191

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

192

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

193

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

194

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

195

were selected randomly for the subsequent sequencing of inserted DNA fragments

196

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

197

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

ACS Paragon Plus Environment

Environmental Science & Technology

198

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

199

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

200

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

201

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

202

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

203

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

204

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

205

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

206

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

207

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

208

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

209

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

210

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

211

study.

212

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

213

biodegradation

214

2.4.1 Model framework development

215

Niche differentiation and neutral theory are accepted as the mechanisms that

216

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

217

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

218

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

219

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

ACS Paragon Plus Environment

Page 10 of 42

Page 11 of 42

Environmental Science & Technology

220

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

221

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

222

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

223

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

224

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

225

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

226

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

227

dX = µ m , L

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

(1)

228

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

229

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

230

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

231

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

232

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

233

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

234

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

235

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

236

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

237

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

238

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

239

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

240

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

241

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

ACS Paragon Plus Environment

Environmental Science & Technology

Page 12 of 42

242

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

243

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

244

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

245

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

246

Therefore,

247

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

248

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

249

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

250

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

the

required

time

period

can

be

calculated

NT − N N × = bn NT NT − 1

as

251

Pr( N + 1/ N ) =

252

Pr( N / N ) =

253

Pr( N − 1/ N ) =

254

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

255

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

257

E ( ∆X ) =

258

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.

12

ACS Paragon Plus Environment

Page 13 of 42

Environmental Science & Technology

261

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

1 2 X (1 − X )dWt a

(7)

262

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

264

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

265

dX = µ m , L

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

266

dC = µ m , L

C X X (1 − ) dt Ks + C X m,L Y

2 X (1 − X ) dWt

(8)

(9)

267

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

268

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

271

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

273

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

274

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

275

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

276

population (as shown in equation (12)).

277

278

RI s =

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

(12)

2.4.2 Estimating parameters 13

ACS Paragon Plus Environment

Environmental Science & Technology

Page 14 of 42

279

The most commonly used methods for estimating the biological parameters in

280

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

281

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

282

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

284

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

285

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

286

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

287

residual errors between the measured and calculated SMX concentration and the

288

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

289

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

299

implementation process of NSGA II is presented in Supporting Information.

300

3. Results and Discussion 14

ACS Paragon Plus Environment

Page 15 of 42

Environmental Science & Technology

301

3.1 SMX degradation

302

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

303

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

ACS Paragon Plus Environment

Environmental Science & Technology

Page 16 of 42

323

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

ACS Paragon Plus Environment

Page 17 of 42

Environmental Science & Technology

345

3.2.1 Bacterial diversity and biomass

346

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

358

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

359

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