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