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SUPPORTING INFORMATION FOR:
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The activity level of a microbial community function can be predicted from
its metatranscriptome
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Damian E. Helbling1, Martin Ackermann1,2, Kathrin Fenner1,2, Hans-Peter K. Kohler1, David R.
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Johnson1,2,*
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Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
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Department of Environmental Sciences (D-UWIS), ETH Zurich, 8092 Zurich, Switzerland
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Corresponding author: david.johnson@eawag.ch, phone: +41 58 765 55 20, fax: +41 58 765 55 47
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S1
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Methods
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used for all experiments. The pACYC184 plasmid is a derivative of the pMD4 plasmid and has the
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atzA gene cloned downstream of a constitutive promoter (De Souza et al., 1996). The atzA gene
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encodes the atrazine chlorohydrolase enzyme that catalyzes the degradation of atrazine to the
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metabolite 2-hydroxy atrazine (De Souza et al., 1995; De Souza et al., 1996;. The pACYC184
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plasmid also confers resistance to chloramphenicol. For all experiments, cells were pre-grown to
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stationary phase in 50 mL of LB liquid media containing chloramphenicol (30 µg/mL) for 15 hours
Bacterial culture. E.coli strain DH5 containing plasmid pACYC184 (De Souza et al., 1996) was
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at 30°C. The grown cells were then transferred to a 50 mL Falcon tube and collected by
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centrifugation at 4000g for 5 minutes. The supernatant was then discarded and cells were
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resuspended in filtered (0.70 µm) effluent from a pilot-scale wastewater treatment plant prior to
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addition into bioreactors.
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Degradation experiment. Five micrograms of atrazine dissolved in 100% methanol were added to
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100 mL amber glass Schott bottles. After evaporation of the methanol, activated sludge from a
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pilot-scale wastewater treatment plant and resuspended E. coli cells were added to the reactors in the
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following volumetric ratios 0:50, 25:25, 45:5, 49:1, 49.5:0.5, 49.95:0.05, and 50:0 (all in mL) to
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produce final atrazine concentrations of 0.46 µM (100 µg/L); the corresponding percentage of E.
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coli cells in the overall population were 100%, 35%, 6%, 1%, 0.5%, and 0.05%, respectively. We
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calculated the percentages of E. coli cells added to the reactors by quantifying the numbers of E. coli
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cells on LB agar plates and quantifying the total suspended solids of the activated sludge using
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Standard Method 2540B (Clesceri, 1998) and estimating cell density from the dry weight (Neidhardt
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et al., 1990). Cell numbers were determined only at the beginning of the experiments; no growth of
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E. coli cells was expected during the 8 h experiment. For metatranscriptome and RT-qPCR analysis,
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a single 1.5 mL sample was taken from each reactor (except for the 0% reactor into which no E. coli
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cells were spiked and no 2-hydroxy atrazine formation was observed) at t=4 h for nucleic acid
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extraction and preparation (see Figure S1 for time course of 2-hydroxy atrazine formation). For
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transformation measurements, 1.5 mL samples were removed from each reactor and the
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concentrations of atrazine and 2-hydroxy-atrazine were quantified at time points t=0 (x3), 1h, 2h, 4h,
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and 8h (x3).
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Estimation of Formation Rate. Formation rate constants for 2-hydroxy-atrazine were estimated
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assuming first-order kinetics at the low substrate concentrations used in this study (see Equations 1
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and 2 for the differential equations).
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dc A
A
 kdeg
 c A (t)
dt
Eq.1
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dc HA
A
HA
   kdeg
 c A (t)  kdeg
 c HA (t)
dt
Eq. 2
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with c A and c HA being the molar concentrations of atrazine and 2-hydroxy-atrazine, respectively,
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A
HA
k deg
and k deg
their respective first-order degradation rate constants, and  the fraction of atrazine
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A
transformed into 2-hydroxy-atrazine. The product of  and k deg
corresponds to the formation rate
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constant of 2-hydroxy-atrazine.
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Based on the mass balances where we observed complete transformation of atrazine to 2-hydroxy
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HA
atrazine with no subsequent degradation of the transformation product,  was set to 1 and k deg
was
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set to 0. A traditional non-linear regression approach could be employed to solve the differential
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A
equations to directly obtain the value of k deg
for each experiment by setting the initial concentrations
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of c HA to zero and c A to 0.46 µM (100 µg/L). However, due to some uncertainty in the measured
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A
values of the initial c A concentration (see Table S2), distributions of k deg
and the starting
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concentration c0A were estimated with a Monte Carlo Markov Chain (MCMC) procedure (Vahteristo
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et al., 2009; Görlitz et al., 2011). MCMC allows estimating parameter uncertainties and cross-
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correlations from non-linear models without assuming distribution functions for the individual
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parameters. Markov Chain sampling (n = 50.000) was based on the Metropolis-Hastings random
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walk algorithm (Metropolis et al., 1953) and a likelihood function for normally distributed squared
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residuals. The parameter traces in the chains were visually checked against (a) initial burn-ins, (b)
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non-convergent parameter distributions, and (c) long lags in the autocorrelation within each chain.
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c0A was constrained to lie between 0.32 and 0.42 µM (70 and 90 µg/L), which corresponds to the
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average  2 standard deviations uncertainty interval of measured starting concentrations in the
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reactors with 6%, 1%, 0.5% and 0.05% E. coli (see section labeled Raw Degradation Data in the
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following for further discussion). k deg
was constrained to be positive and smaller than 20 h-1 based
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on a visual inspection of the concentration time series. Finally, the 2-hydroxy-atrazine formation
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A
rate constants, k deg
, in each reactor and their uncertainty intervals as given in Figure 1 of the
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manuscript were derived as median, 5th and 95th quantiles of density estimates of the MCMC
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parameter distributions, respectively. Calculations were conducted in the R statistical environment
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(R Development Core Team, 2009) with the mcmc (Geyer, 2010) and coda (Plummer et al., 2006)
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packages.
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Mass Spectrometry. We quantified the concentrations of atrazine and 2-hydroxy atrazine by means
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of linear ion trap-orbitrap mass spectrometry (Orbitrap, Thermo, Waltham, MA) as previously
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detailed (Helbling et al., 2010). Both atrazine and 2-hydroxy atrazine were identified with authentic
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standards and quantification was facilitated with a matrix-matched external calibration. Limits of
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detection and quantification were less than 0.46 nM (1.0 µg/L), which was the lowest concentration
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measured during calibration within the background matrix.
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RNA extraction and purification. We used a conventional acid phenol protocol to lyse the cells
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and isolate total RNA from 1.5 ml culture samples as previously described (Johnson et al., 2005).
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Residual DNA was digested with the commercially available TURBO DNA-free kit (Applied
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Biosystems). Total RNA was purified and concentrated with the commercially available RNeasy
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MinElute Cleanup columns (Qiagen). mRNA was enriched from total RNA by selectively removing
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bacterial 16S and 23S rRNA molecules from total RNA with the commercially available
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MICROBExpress Bacterial mRNA Enrichment Kit (Ambion). RNA purity (A260/A280 and A260/A230)
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and mass concentrations were measured with a NanoDrop ND-1000 spectrophotometer (NanoDrop
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Technologies). All RNA samples had A260/A280 and A260/A230 values greater than 1.94. The same
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RNA pools were used for both reverse transcription-quantitative PCR and metatranscriptome
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sequencing. A summary of extraction masses following each extraction and purification step is
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provided in Table S1.
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Reverse transcription-quantitative PCR (RT-qPCR). We synthesized cDNA from purified
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mRNA (2 µL) with the SuperScript III First Strand Synthesis SuperMix (Invitrogen) and 0.5 µM of
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the targeted atzA reverse primer (ATGATCGAGCACAGTTCGAC) (Microsynth). qPCR reactions
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were performed with the 7500 Fast Real-Time PCR System (Applied Biosystems). Each 25-µl
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reaction contained 12.5 µl of the TaqMan Universal PCR Master Mix (Applied Biosystems), 2 µl of
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sample RNA, 0.2 µM of atzA probe (6-carboxyfluorescein-AGCGAGCCTTCAAGGCGTCC-6-
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carboxytetramethylrhodamine) (Microsynth), 0.7 µM of atzA forward primer
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(CATGTTCTTTGATCGGATGG), and 0.7 µM of atzA reverse primer. The probe and primers were
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designed using the OligoPerfect Designer software (Invitrogen) with default parameters. Thermal
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cycling conditions were as follows: 2 min at 50C, 10 min at 95C, and 40 cycles of 15 s at 95C
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and 1 min at 60C.
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Plasmid pACYC184 was used as a standard to calculate the number of atzA transcripts in
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each sample. The plasmid was isolated from stationary phase cultures using the Wizard Plus SV
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Miniprep kit (Promega). The mass of the plasmid per volume was measured with a NanoDrop ND-
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1000 spectrophotometer and converted into copies of atzA sequences per volume using a plasmid
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size of 108,845 kb and an average molecular mass of 660 Da per nucleotide pair. The standard was
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serially diluted and analyzed by qPCR in parallel with the samples. For both standards and samples,
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a single fluorescence value (R) was identified that corresponded with exponential amplification in
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every sample. The threshold cycle [CT(R)] values associated with fluorescence measurements were
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then plotted against the initial number of cDNA molecules in each reaction. The efficiency in the RT
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step was not assessed when measuring the number of atzA transcripts, but was shown to be greater
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than 85% when using a similar methodology (Johnson et al., 2005).
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Metatranscriptome sequencing. RNA-seq libraries were prepared from one µg of enriched mRNA
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using the TruSeq RNA Sample Prep Kit (Illumina) according to the protocol supplied with the
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reagents and selection of an insert size of 150 nucleotides. Multiplexed sequencing of the libraries
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was performed on the Illumina Genome Analyzer IIx using the TruSeq SR Cluster Kit v5 (Illumina)
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and TruSeq SBS Kit v5 (Illumina). Sequencing data were processed using the Illumina software
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packages RTA (v2.8) and CASAVA (v1.7). The total number of sequence reads obtained per
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community dataset ranged between 6.05 million and 7.39 million. The individual reads were 32 bp
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and were not trimmed. We wrote a custom Perl script to count the number of sequence reads within
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each community dataset that were identical over their entire length to the positive or negative strand
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of the atzA gene. Figure S3 shows the relationship between atzA copies measured by qPCR and by
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metatranscriptome sequencing. The data shows that an order of magnitude increase in qPCR copies
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equates to an order of magnitude increase in copies measured by metatranscriptome sequencing.
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Test for Linearity. We tested the linearity between the observed formation rate of 2-hydroxy
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atrazine and transcript abundances measured by qPCR and in the measured transcriptome with
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several methods. To check for proportionality, we tested the hypothesis that the data plotted on a
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linear scale (as shown in Figure 1 of the manuscript) had an intercept that was not significantly
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different than zero. Here, we observed that the 95% confidence interval for the intercept was not
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significantly different than zero for both qPCR and metatranscriptome data, respectively. Because
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the experiment was designed in an exponential manner to obtain a dataset over four orders of
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magnitude, the data points are not equally distributed over the range of atzA copies per community.
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Therefore, the data set was log-transformed and replotted in Figure S2 prior to linear regression
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analysis. Correlation coefficients were determined to be 0.98 and 0.93 for the qPCR and
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metatranscriptome data, respectively, indicating linearity. Finally, a residual analysis of the log-
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transformed data shows no systematic bias; a normal quantile plot was used to test the hypothesis
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that the residuals were normally distributed. The hypothesis could not be rejected at a significance
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level of 0.10.
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Raw Degradation Data
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The measured concentrations of atrazine and 2-hydroxy-atrazine in the six reactors are given in
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Table S2. Mass balances relative to the spiked concentration indicate a systematic deviation of –
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196% of the actually measured concentrations when compared to the spiked concentrations, which
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is most likely due to incomplete dissolution following sludge addition or a lag phase for diffusion of
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2-hydroxy atrazine out of the cell following rapid uptake and transformation. Relative to the average
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measured concentration at t=0, mass balances were complete with <10% deviation for the reactors
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with 6%, 1%, 0.5% and 0.05% E. coli, indicating that atrazine was quantitatively transformed into 2-
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hydroxy-atrazine and that 2-hydroxy-atrazine was not further degraded. For the reactors with 35%
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and 100% E. coli, the total mass seemed to increase between t=0 and 1-4 hrs, most likely because
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some atrazine had already been taken up by the cells and partially transformed at t=0, but 2-hydroxy
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atrazine had not yet diffused out of the cells. Figure S1 shows the time course of 2-hydroxy atrazine
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formation for each of the six reactors amended with E. coli cells.
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