Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled

Jun 25, 2019 - Glenn A. Fried .... in the MGC, figures showing dynamic spatial distribution of biomass across the well array in replicate experiments ...
1 downloads 0 Views 3MB Size
Download PDF
Article pubs.acs.org/est

Cite This: Environ. Sci. Technol. 2019, 53, 7996−8005

Adaptive Evolution of Escherichia coli to Ciprofloxacin in Controlled Stress Environments: Contrasting Patterns of Resistance in Spatially Varying versus Uniformly Mixed Concentration Conditions Jinzi Deng,† Lang Zhou,‡ Robert A. Sanford,§ Lauren A. Shechtman,∥,⊥ Yiran Dong,†,# Reinaldo E. Alcalde,‡ Mayandi Sivaguru,† Glenn A. Fried,† Charles J. Werth,*,‡ and Bruce W. Fouke†,§,¶ Downloaded via KEAN UNIV on July 18, 2019 at 07:14:41 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

Carl R. Woese Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801 United States Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78705 United States § Department of Geology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801 United States ∥ Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801 United States ⊥ Department of Integrative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801 United States # School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan, 430074, China ¶ Department of Microbiology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801 United States ‡

S Supporting Information *

ABSTRACT: A microfluidic gradient chamber (MGC) and a homogeneous batch culturing system were used to evaluate whether spatial concentration gradients of the antibiotic ciprofloxacin allow development of greater antibiotic resistance in Escherichia coli strain 307 (E. coli 307) compared to exclusively temporal concentration gradients, as indicated in an earlier study. A linear spatial gradient of ciprofloxacin and Luria− Bertani broth (LB) medium was established and maintained by diffusion over 5 days across a well array in the MGC, with relative concentrations along the gradient of 1.7−7.7× the original minimum inhibitory concentration (MICoriginal). The E. coli biomass increased in wells with lower ciprofloxacin concentrations, and only a low level of resistance to ciprofloxacin was detected in the recovered cells (∼2× MICoriginal). Homogeneous batch culture experiments were performed with the same temporal exposure history to ciprofloxacin concentration, the same and higher initial cell densities, and the same and higher nutrient (i.e., LB) concentrations as in the MGC. In all batch experiments, E. coli 307 developed higher ciprofloxacin resistance after exposure, ranging from 4 to 24× MICoriginal in all replicates. Hence, these results suggest that the presence of spatial gradients appears to reduce the driving force for E. coli 307 adaptation to ciprofloxacin, which suggests that results from batch experiments may over predict the development of antibiotic resistance in natural environments.

■

INTRODUCTION

and sulfide gradients across smoker vents on the ocean floor.11,12 In spatial gradients, microorganisms are challenged to a more complicated and dynamic environment and hence respond and evolve dynamically. An important example of adaptive evolution is the rapid emergence of antibiotic resistance in both engineered and natural settings.13−23 The past 25 years have seen tremendous progress in understanding the cause and occurrence of antibiotic resistance in micro-

Bacteria are some of the most ancient and diverse forms of life on our planet as a result of more than four billion years of evolution.1 This process has taken place in response to stresses caused in large part by changing environmental conditions (i.e., temperature, pH, and nutrient availability) and interactions with other microorganisms.2,3 Evolutionarily successful bacteria are those that adapt to the presence of these stressors and respond accordingly. Stressors in natural environments are commonly conceptualized in the form of spatial gradients. Examples include potentially toxic solute gradients in soil aggregates4−7 and at groundwater plume margins,8,9 extreme temperature and pH gradients in Yellowstone hot springs,10 © 2019 American Chemical Society

Received: Revised: Accepted: Published: 7996

February 11, 2019 June 12, 2019 June 25, 2019 June 25, 2019 DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology

Figure 1. Photographs of the microfluidic gradient chamber (MGC) (a) immediately and (b) 15 min after blue dye was introduced to both boundary channels and the well array (time is required for dye to diffuse through the hydrogel). (c) Fluorescent image of E. coli 307 cells in one hexagonal well of the array. Yellow boxes indicate areas of hydrogel. The presence of blue color in the yellow boxes in part b but not in part a indicates that dye diffused through the hydrogel. Elongated cells in part c indicate that ciprofloxacin is inhibiting cell division.

each time the cells reached a preset optical density level in this continuously stirred reactor system. These authors observed an increase in antibiotic resistance by 870, 10, and 1680 fold, respectively, for each of the tested antibiotics. However, it is not clear how antibiotic exposure histories developed from well-mixed culture systems apply to more complex settings where concentration gradients develop over space and time. The objective of the present study is to determine the effects of spatially varying versus temporally varying ciprofloxacin exposure histories on the development of antibiotic resistance in E. coli strain 307 (E. coli 307). We note that strain E. coli 307 has a number of genes that have been shown to undergo change in association with increased antibiotic resistance, including gyrA, ompF, acrA, marR, marA, and parC.35−37 Therefore, the development of antibiotic resistance is expected in suitable environments. On the basis of work by Zhang et al. (2011)32 and Baym et al. (2016),33 it is hypothesized that cells will adapt to elevated concentrations of this antibiotic more quickly along a spatial concentration gradient than in uniformly mixed conditions. To test this hypothesis, a spatial gradient of ciprofloxacin concentration was created across an array of 224 interconnected hexagonal wells in an MGC, and the physiological response and distribution of E. coli 307 along the ciprofloxacin gradient was monitored over 5 days. After this period, cells were extracted from the entire MGC and evaluated for antibiotic resistance. Complementary batch culture experiments were performed by exposing E. coli 307 cells to ciprofloxacin concentrations similar to that of the region in the MGC with lowest concentrations occurring in the MGC, incubating for 5 days, and measuring the new antibiotic resistance.

organisms as well as increasingly aggressive treatment strategies to combat bacterial infections.13−16,24−31 Although the importance of spatial gradients of stress on microorganisms has been widely considered, few studies have probed its impact on adaptive microbial evolution due to the experimental challenge of creating and controlling spatial gradients. An exception is the work by Zhang et al. (2011),32 who exposed E. coli K-12 to steep concentration gradients of ciprofloxacin in a microfluidic gradient chamber (MGC), with the maximum exposure concentration close to 200× MIC. They reported accelerated resistance to ciprofloxacin after 5 h at a location in the MGC characterized by the steepest gradient of nutrient substrates and ciprofloxacin. By the end of 30 h, resistant cells occupied the entire MGC and reached high cell density, even in the regions adjacent to the boundary flow channel containing extremely high ciprofloxacin concentrations (200× MIC). Cells extracted from the MGC exhibited a very slight increase to OD600 from 0.06 to 0.1 when exposed to ciprofloxacin concentrations of up to 20× MICoriginal for 12 h (although the actual resistance (MICfinal) was not reported). Another exception is the work by Baym et al. (2016)33 who exposed E. coli to a spatial gradient of antibiotics (i.e., ciprofloxacin or trimethoprim) across multiple interconnected agar sections on a rectangular plate, with each sequential section having a different, uniform, and higher concentration of antibiotic. They found that bacteria grew on the entire area of one section and depleted its nutrients before growing on to the next section with a fresh source of nutrients and a step increase in antibiotic concentration. By the end of their experiment, bacteria on the rectangular plate with step increases of ciprofloxacin acquired a resistance of at least 20000× the MICoriginal. Results from this study suggest that accelerated development of antibiotic resistance is possible along spatially varying gradients of antibiotic concentrations. The majority of the studies on development of antibiotic resistance, however, have been done using batch cultures, where the test compounds are homogeneously mixed with bacteria. Toprak et al. (2012)34 repeatedly dosed E. coli to incrementally increasing concentrations of the antibiotics chloramphenicol, doxycycline, and trimethoprim in parallel experiments over a period of ∼20 days using a “morbidostat”. With this approach, the antibiotic concentration was increased

■

MATERIALS AND METHODS Bacterial Strains, Media and Inoculation Culture of the MGC. Escherichia coli strain 307 was used in all experiments. The strain was constructed by inserting plasmid pBG307 into wild-type E. coli DH5α and was first reported by Chen et al. (2006).38 The genome of DH5α has been published by Chen et al. (2018).39 The plasmid pBG307 was modified from pBC(gfp)Pmip by changing a CG to AT in its RNA II promoter, which increases replication efficiency.38 The gfp gene is promoted by the mip gene. The plasmid also has a 7997

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology chloramphenicol-resistant gene to ensure its selection.40 E. coli 307 emits intense green fluorescence due to the presence of the green fluorescence protein (GFP) when excited at 495 nm and does not require an inducer, which facilitates fluorescence imaging when the cells are in the MGC. All experiments started from inoculation of a frozen stock of E. coli 307 into LB medium and incubation overnight before the culture was streaked onto an LB agar plate. A single colony from the plate was then inoculated into 3 mL of LB medium, and the mixture was incubated at 37 °C overnight on a shaker. All culturing tubes were covered but not sealed during the incubation, so the bacteria had access to air. An inoculation culture for the MGC was prepared by diluting the fresh overnight culture to an optical density (OD600) of 0.05 in sterilized minimal 9 (M9) medium, instead of LB medium, to avoid excessive growth and rapid oxygen depletion in the MGC before a ciprofloxacin gradient was established. All media contained 10 mg/L chloramphenicol. We note that all overnight cultures grown from a single colony could not tolerate ciprofloxacin in excess of the MICoriginal, so any increase in MICs after exposure in either MGC or batch experiments is due to adaptation. MGC Design, Construction, and Fabrication. The microfluidic gradient chamber (MGC) pattern was cast into PDMS (polydimethylsiloxane) from a reverse mold,41 which was patterned on a silicon wafer.42 As shown in Figure 1a, the MGC consists of two parallel flow channels bounding an array of 224 hexagonal wells interconnected by narrow throats. The lengths of the sides of the hexagonal wells are 250 μm each, and the interconnecting throats are 50 μm wide × 200 μm long. A glass slide was plasma bonded to the PDMS. Inlet and outlet ports were attached to each of the two flow channels. Tubing and fittings (Figure S1) were connected to the inlet and outlet ports, and the bonded MGC was purged with DI water to maintain hydrophilic conditions43 before the start of the experiment. To restrict water flow to the two boundary channels and prevent it from crossing into the well array, a boundary of solidified poly(ethylene glycol) diacrylate (PEGDA) was placed in the three wells adjacent to and along the side of each flow channel. PEGDA is a hydrogel that solidifies when both a cross-link initiator and UV light are present. It is biocompatible, impermeable to water flow and bacteria, and allows diffusion of solutes.44 Food coloring was loaded into the boundary channels and the well array to confirm the integrity of the hydrogel (Figure 1a,b). Further details are presented in Supporting Information. MGC Experiment with Spatial Gradients of Ciprofloxacin. The MGC was initially saturated with nanopure water. Next, LB medium containing a ciprofloxacin concentration equivalent to 10× MICoriginal (0.5 μg/mL) and 10 mg/L chloramphenicol was continuously infused into one boundary channel of the MGC, while M9 medium containing neither a carbon source nor ciprofloxacin but containing 10 mg/L chloramphenicol was infused into the other flow channel. The flow in each channel was in the same direction and was maintained at 0.4 mL/h. Next, the well array of the MGC was sterilized with 70% ethanol, washed with sterilized nanopure water, and then purged with the inoculation culture from a syringe for around 300 pore volumes to obtain an initial cell density of approximately 50 cells per well. The MGC was then placed inside a custom-made temperature control chamber at 33 °C, which was mounted on the microscope stage. This

marked the start of an experiment and the time from which ciprofloxacin plus LB medium began to diffuse across the well array containing E. coli cells. A linear concentration gradient of these solutes is expected to form across the well array in the absence of degradation, with the concentrations at each end determined by the flow channel conditions (i.e., C/C0 = 1 in one flow channel, and C/C0 = 0 in the other flow channel). The MGC experiment and a replicate were each run for 3 to 5 days, and fluorescent images of E. coli in the well arrays were recorded (details of the microscopy and image processing are in the Supporting Information). At the end of each experiment, the entire well array of the MGC was purged with 1 mL of LB medium to displace and capture the E. coli population in a sterilized syringe. A third MGC experiment (the control) was performed that contained no ciprofloxacin in the MGC but had LB media delivered to both boundary channels; E. coli were also extracted from the entire well array of this MGC after 5 days. A 500 μL portion of the captured effluent of each MGC was inoculated into 3 mL of LB medium containing no ciprofloxacin, and this was followed with aerobic incubation at 37 °C overnight. Then the new MIC was measured by spreading 100 μL of the revived overnight culture onto agar plates containing ciprofloxacin. Plating was used instead of measuring in liquid culture because the number of cells acquiring resistance in the revived culture could be counted and due to concern that the extracted cells would be below the detection limit using optical density. Each plate contained ciprofloxacin at concentrations equivalent to 0×, 1×, 2×, 3×, 5×, 10×, 30×, or 50× MICoriginal. The plates were incubated for up to 3 days at 37 °C. Since each MGC-extracted culture contained different subpopulations, we expected growth on plates of only those that adapted to at least the corresponding ciprofloxacin concentration. We note that in recent work, we measured the MIC of cells using several different methods (e.g., plating, liquid culture) with comparable results.45 Modeling of Ciprofloxacin Mass Transport. A fluorescence dye tracer experiment was performed in the MGC to confirm that diffusion controls mass transport in the well array as well as to determine if diffusion is hindered in the hydrogel. COMSOL Multiphysics 5.1 was used to simulate the dye concentration profiles and then extended to predict ciprofloxacin concentrations in the well array over time. Constant concentrations of dye or ciprofloxacin (e.g., C0,cipro side= 0.5 μg/mL, equivalent to 10× MICoriginal, and C0,M9 side= 0) were assumed at the interface between hydrogel and flow channels containing either LB medium plus ciprofloxacin or M9 medium, respectively. Details of the tracer experiments and modeling efforts are presented in the Supporting Information and Figure S4. Batch Culture Experiments with Conditions Similar to the Lower Region of the MGC. Batch experiments were performed using conditions that replicated cell, ciprofloxacin, and LB concentrations in the lower region of the MGC, which is the region closest to the hydrogel and adjacent to the boundary channel containing M9 medium with the lowest ciprofloxacin and nutrient concentrations. Briefly, a single colony of E. coli 307 was inoculated in 3 mL of LB media containing 10 mg/L chloramphenicol at 37 °C overnight. An inoculation culture was then prepared by diluting the fresh overnight culture to an optical density (OD600) of 0.05 in sterilized minimal 9 (M9) medium. Then, 29 μL of the inoculation culture was added into 3 mL of 0.1 LB media 7998

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology

Figure 2. (a) Photograph of the MGC with blue dye loaded into the flow channels and the well array. Yellow boxes indicate area of hydrogel. (b) COMSOL Multiphysics modeling of ciprofloxacin diffusion across the MGC well array at 120 h. Yellow boxes indicate the area of hydrogel. (c) COMSOL modeled concentration profiles (in × MICoriginal) of the 11 regions across the well array at 6, 12, 18, 24, 48, 72, 96, and 120 h after inoculation. (d) The well array with color coded regions delineated into 11 regions transverse to the ciprofloxacin gradient. (e) Biomass spatial distribution across the well array at 120 h. (f) Dynamic spatial distribution of the fluorescent biomass area across the 11 regions from 0 to 120 h after inoculation.

overnight before determination of new MIC values is consistent with that for cells extracted from MGC after 5 days. Serial Passage Batch Experiments with Varying Ciprofloxacin and LB Exposure Histories. Additional batch experiments were performed to evaluate the effect of different ciprofloxacin and LB concentration histories on microbial adaptation. The latter allowed us to evaluate the effects of nutrient availability (0.1 LB versus 1 LB) on development of antibiotic resistance. Conventional serial passage experiments46,47 were performed by transferring 10% of a culture every 8 h into new media. Briefly, three single colonies of E. coli 307 were picked from a fresh LB plate and inoculated into 3 mL of 1 LB, 0.5 LB, or 0.1 LB media. All media contained 10 mg/L chloramphenicol. After incubation at 37 °C for 24 h, the MICoriginal of each culture was measured. Next, each growth culture was used to start three replicate experiments by transferring 500 μL of the culture into 4.5 mL of fresh media of 1 LB, 0.5 LB, or 0.1 LB, respectively. This marked the start of

containing 10 mg/L chloramphenicol. The volume of inoculated culture was selected to match initial cell numbers in the MGC, including the hexagonal wells and the inoculation reservoirs. The batch culture was then placed on a shaker at 37 °C. This marked the start of the experiments. For the first 8 h, ciprofloxacin stock solution was added every hour into the culture so that the exposure history matched that in the lower region of the MGC. From 8 to 16 h, when the rate of ciprofloxacin concentration increase in the MGC slowed, the antibiotic was added every 2 h to the batch cultures. From 16 h onward, ciprofloxacin was added only every 4 h until the steady state concentration was matched to that in the lower region of the MGC. Triplicate batch cultures were performed, and by the end of 5 days, the final optical density of each replicate was measured. At that point, 1 mL of each culture was inoculated into 2 mL of LB media, and the mixture was incubated on a shaker at 37 °C overnight, followed by MIC measurement. Details of the MIC measurements are in Supporting Information. We note that the inoculation in LB media 7999

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology

closer to the boundary channel containing LB media and ciprofloxacin, indicating that elongation was due to replication inhibition by ciprofloxacin and not nutrient limitations. The elongation of cells is consistent with previously published work from Diver and Wise et al. (1986)54 in which E. coli cells elongated or extensively filamented upon exposure to ciprofloxacin concentrations above the MIC. The elongation of cells slowly progressed toward regions closer to the M9 side. At 24 h, cells in region 6 become elongated, with ciprofloxacin concentrations near 4.3× MICoriginal. After exposure to ciprofloxacin, there appeared to be a lag phase before cells become elongated. At all times, elongated E. coli and normal sized E. coli coexisted in the well array. The progression of cell elongation toward the M9 side of the well array is shown in Figure S3. No elongation of cells occurred in the control experiment without ciprofloxacin. Spatial Distribution of E. coli in the MGC. The spatial distribution of E. coli cells within the well array is represented by the total fluorescent area in each of the previously defined 11 regions of the MGC (Figure 2f). The same color coding was used to correlate ciprofloxacin concentrations with biomass area. Immediately after inoculating the MGC with E. coli, the biomass distribution was relatively homogeneous. No obvious growth occurred during the first 12 h. This lag in growth of E. coli in the MGC may have been due to insufficient nutrient concentrations in regions with ciprofloxacin lower than 1× MICoriginal. From 12 to 24 h, the biomass across the well array remained low, with slightly more detected in region 1 with higher nutrient and ciprofloxacin concentrations. The higher biomass is unlikely due to cell growth because of the high ciprofloxacin concentration present at 24 h (7.6 × MICoriginal). Instead, it is likely due to a chemotactic response of the E. coli cells in the well array toward the higher nutrient concentration at this boundary. Starting from 24 h, biomass in all regions increased, even though the minimum ciprofloxacin concentration was equivalent to 1.6 × MICoriginal at 24 h. The most significant increase of biomass was found in regions 7−11 and stayed high thereafter until the end of the 5 day experiment. The local ciprofloxacin concentration in regions 7−11 ranged from 1.7 to 3.9 × MICoriginal. In regions 1−6, the biomass area showed slight increases from 24 to 50 h, and then fluctuated before stabilizing at lower values at 90 h. The timing of biomass changes in regions 2−6 corresponds to changes in regions 7−11, and extent of biomass increase in regions 2−6 corresponds to distance from the ciprofloxacin boundary channel. By the end of 120 h, ciprofloxacin concentrations in regions 1−6 ranged from 4.5 to 7.7 × MICoriginal. The similar fluctuations of biomass in regions 7−11 and regions 2−6 provide some evidence of cell migration from the former to the latter regions to obtain nutrients. We note that regular-sized (i.e., nonelongated) E. coli cells in our reactor were motile. We measured their swimming speed in wells adjacent to the boundary channel containing ciprofloxacin and nutrients, and a rate of approximately 10− 25 μm/s was obtained. This is consistent with previously published motility rates of E. coli cells. For example, Kaya et al. (2012)55 reported that E. coli can migrate rapidly upstream at speeds higher than 20 μm/s. Results of the replicate MGC experiment are presented in Supporting Information (Figure S5). General trends in both were the same (more growth near the M9 boundary), with differences (magnitude of growth away from the M9 boundary) attributed to variations in MGC fabrication (small

the experiments, and initial OD600 values were 0.18, 0.16, and 0.023, respectively. Every 8 h thereafter, 10% of the 0.1 LB and 1 LB cultures were transferred into 4.5 mL of fresh media containing ciprofloxacin concentrations that matched the transient increases in the lower region of the MGC, while 0.5 LB cultures were transferred the same way into 0.5 LB media containing ciprofloxacin concentrations that matched the transient increases in the middle region of the MGC. After 5 days, maximum ciprofloxacin exposure concentrations of 4.5× and 1.7× MICoriginal were obtained, and new MICs of the exposed cultures were measured directly after the exposure. A control test showed that MIC values obtained directly after ciprofloxacin exposure or by first incubating overnight in LB showed no significant difference.

■

RESULTS Spatial Gradient of Ciprofloxacin in the MGC. Diffusion of the tracer appeared similar in the hydrogel relative to the media-filled well array; therefore, the same diffusivity was assigned to both hydrogel and water. The molecular diffusion coefficient of fluorescein at 33 °C is Dmol,fluor = 6.65 × 10−10 m2/s, based on the Stokes−Einstein equation. The simulated concentration profile and the tracer diffusion experimental data are similar using this value. The molecular diffusion coefficient for ciprofloxacin is within 6% of that for fluorescein. Therefore, the same value was used to simulate ciprofloxacin diffusion in the MGC (Figure S4). We note that ciprofloxacin does not sorb to the microfluidic gradient chamber (MGC) reactor components or batch bottles. These are made from glass or glass coated silicon (i.e., silicon dioxide). Previous studies also reported that ciprofloxacin is not readily biodegradable (Baginska et al. 2015,48 Girardi et al. 2011,49 Kummerer et al. 2000,50 Li and Zhang 2010,51 Wu et al. 200952), and only one bacterial strain has been shown to degrade ciprofloxacin, i.e., Labrys portucalensis.53 Therefore, when modeling ciprofloxacin diffusion in the MGC, we assume that the concentration added is equivalent to the highest bioavailable concentration and that ciprofloxacin does not degrade. Simulated ciprofloxacin concentrations in the well array of the MGC are shown in Figures 2b,c. The boundary areas, shown as yellow boxes in Figures 2a,b, are filled with hydrogel. All other areas in the well array contain E. coli cells. Figure 2c shows the predicted ciprofloxacin concentration profiles of the area containing E. coli cells, while those at steady state are delineated into 11 regions perpendicular to the ciprofloxacin gradient (Figure 2d). Region 1 represents the area closest to the ciprofloxacin boundary channel, and region 11 represents the area closest to the M9 boundary channel. At time zero, there is no ciprofloxacin in the well array. However, after only 12 h, the concentration increased markedly near the ciprofloxacin boundary in region 1 to 7.1× MICoriginal, while the concentration was 1.1× MICoriginal in region 11. After 24 h, the ciprofloxacin concentration in the well array approached steady state with the concentration ranging from 1.7 to 7.7 × MICoriginal by the end of 120 h (Figure 2c). Cell Morphology in the MGC. A fluorescence image of the E. coli spatial distribution in the MGC well array at 120 h is shown in Figure 2e. Elongation of E. coli cells were observed in some wells of region 1−4 starting from 18 h. The majority of the cells in region 1 were elongated at this time (Figure 1c.) The ciprofloxacin concentration in region 1 was 7.4× MICoriginal at 18 h. Elongation was first observed in regions 8000

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology variations in pore sizes and hydrogel cross-linking affect nutrient and ciprofloxacin diffusion rates). In the control experiment, where both boundary channels were infused with LB media only (i.e., no ciprofloxacin), cell numbers in the well array increased over time and preferentially near each of the boundary channels where nutrient concentrations were the highest. At some locations in wells directly adjacent to the hydrogel boundary, cells aggregated and appeared to form biofilms (Figure S2). No elongation of E. coli cells was observed in the control experiment, again indicating that nutrient limitations did not contribute to this phenomenon. Antibiotic Resistance of E. coli Cells Extracted from the MGC. Aliquots of MGC well array fluid plated onto ciprofloxacin containing agar medium revealed 10−30 colonies on each of 1× and 2× MICoriginal agar plates. Colonies appeared on 1× MICoriginal agar plates after 36 h and on 2× MICoriginal agar plates after 3 days. The replicate MGC experiment revealed similar resistance, although colonies appeared on 2× MICoriginal agar plates after 2 days of incubation. Plates containing higher ciprofloxacin concentrations did not show colonies. No growth was observed on agar plates with 1× MICoriginal or higher concentration for cells extracted from the control MGC experiment containing no ciprofloxacin. The results indicate that E. coli cells exposed to a spatial gradient of ciprofloxacin for 5 days in the MGC acquired a small level of resistance, i.e., up to 2× MICoriginal. This was slightly above the minimum exposure concentration of 1.7× MICoriginal but well below the maximum exposure concentration of 7.7× MICoriginal in the MGC. Batch Culture Experiments with Conditions Similar to the Lower Region of the MGC. During the 5 day incubation, the cultures of all replicates showed very low turbidity. By the end of 5 days, the OD600 of the three replicates were all lower than 0.07, indicating very little cell growth. The starting nutrient concentration was hence not significantly altered over time, and the cells were exposed to a constant flux of nutrient. We note that this is consistent with the cells in the MGC, which were exposed to a continuous flux of nutrient via diffusion from the boundary channel. After inoculation into LB media containing no ciprofloxacin, all replicate cultures were able to grow to high cell density, indicating there were still viable cells after the ciprofloxacin exposure. The revived culture of two replicates showed new MIC values of 6−10× MICoriginal (OD600 equals to 0.25 and 0.41 at 6 × MICoriginal, respectively), while the third replicate showed a new MIC of 4−6× MICoriginal (OD600 equals to 0.19 at 4 × MICoriginal), higher than new MIC values for cells extracted from the MGC experiments. Serial Passage Batch Experiments with Varying Ciprofloxacin and LB Exposure Histories. Average OD600 values of E. coli batch cultures subject to serial passage every 8 h, with a ciprofloxacin concentration history comparable to the lower region of the MGC, are shown in Figure 3. Replicates amended with either 0.1 LB or 1 LB were evaluated. During the first 24 h, when the ciprofloxacin concentrations initially exceeded 1× MICoriginal, the OD600 dropped sharply in all replicates. However, at 32 h, the OD600 rebounded significantly followed by a gradual increase as the ciprofloxacin concentration leveled off. This occurred for experiments with either 0.1 LB or 1 LB, but the increase in cell density was greater for the latter. After 5 days of exposure, new MICs for the 0.1 LB and 1 LB cultures were 18−24 and 6−24× MICoriginal, respectively (Figure 4). The higher resistance level compared

Figure 3. OD600 in E. coli batch cultures subject to increasing exposure concentrations of ciprofloxacin every 8 h that represent the lower wells (closest to the M9 boundary channel) of the MGC in either 0.1 LB (red) or 1 LB (blue) solution. Error bars (1 standard deviation) are shown for all data points and are not visible when exceeded by the symbol size.

to the maximum exposure concentration is consistent with the literature. For example, Gilbert et al. (2001)56 reported that the MIC90 for E. coli increased from 0.016 to 0.25 μg/mL, a 15.6-fold increase, after a 48 h incubation with a ciprofloxacin concentration that was 4× MICoriginal. Friedman et al. (2006)57 observed a greater than 140-fold increase in ciprofloxacin resistance after serial passage of S. aureus, with measurement of new MIC after each passage, and subsequent dosing at the new MIC in the next passage. D’Lima et al. (2012)58 used the same method and showed that E. coli cells acquired a 256-fold increase in MIC for ciprofloxacin after 25 serial passages. Pollard et al. (2012)59 reported a > 300-fold increase in the MIC for ciprofloxacin (i.e., 0.3 mg/L to >100 mg/L) after 18 serial passages of S. aureus cultures, also with a similar method. Only one of three E. coli cultures, exposed to the higher ciprofloxacin concentration increases corresponding to the middle region of the MGC, showed any growth during the 5 day exposure, indicating that this 8 h incremental exposure history is toxic. The newly measured MIC value of this one replicate was 30−36 × MICoriginal.

■

DISCUSSION The results from this work do not appear to support our initial hypothesis that cells will adapt to elevated concentrations of ciprofloxacin more quickly along a spatial concentration gradient than in batch culture. For example, the gradient of ciprofloxacin reached steady state in the MGC after 24 h. Therefore, for the majority of the 5 day experiment, E. coli cells were exposed to a spatial gradient of ciprofloxacin concentration ranging from 1.7 to 7.7× MICoriginal. E. coli cells appeared to grow in regions of the MGC with lower ciprofloxacin concentrations, and extracted cells were able to grow at ciprofloxacin concentrations up to only 2× MICoriginal. In contrast, when the same total number of E. coli cells in batch were inoculated into 0.1 LB media and dosed with ciprofloxacin over time so that the increase matched the lower region (i.e., adjacent to M9 boundary) of the MGC, the antibiotic resistance after 5 days reached 6−10 × MICoriginal, significantly higher than that of the MGC (t test, p-value = 0.015). 8001

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology

Figure 4. Average new MICs of samples after 5 days of exposure to the spatial gradient of the MGC and batch experiments with homogeneous ciprofloxacin concentrations matching the lower region in the MGC in 1 LB and 0.1 LB media, with same inoculation size and 8 h serial passage. Error bars show the range of the data.

Our results also appear to be at odds with those of Baym et al. (2016),33 who observed rapid adaptation to ciprofloxacin by E. coli exposed to a spatial gradient of this antibiotic across multiple interconnected agar sections on a rectangular plate, with each section having a different and sequentially higher ciprofloxacin concentration. However, in that experiment, the bacteria either adapted to higher ciprofloxacin concentrations consecutively to gain access to nutrients or died from lack of nutrients. Whereas in our work, bacteria along the entire gradient were exposed to the whole range of ciprofloxacin concentrations and had access to a continuous flux of nutrients via diffusion from the boundary channel. Therefore, the conditions in the experimental setup by Baym et al. appear more analogous to our serial-passage experiments, where bacteria are transferred from one bottle to the next over time, each with a successively higher antibiotic concentration. There are several possible reasons that cells in the MGC acquire lower antibiotic resistance than that in cells in serialpassage experiments and in the experiments by Baym et al. (2016).33 Cells in those experiments face an “adapt or die” situation when exposed to increasingly greater ciprofloxacin concentrations. The same is not true in the MGC. Hence, one possible reason for lower adaptation along a spatial gradient of ciprofloxacin in the MGC is that the majority of cells can continuously grow in regions with lower ciprofloxacin concentrations and avoid high ciprofloxacin concentrations. This behavior would potentially make adaptation up-gradient not favorable. Another possible reason is that E. coli may temporarily access greater LB concentrations in regions of the MGC with elevated ciprofloxacin concentrations that inhibit replication and then move back down gradient to replicate; this would alleviate the need to adapt, and similar cell number fluctuation patterns near and far from the ciprofloxacin boundary indicate that there is movement of cells between different regions of the MGC. In support of this mechanism, El Meouche et al. (2016)62 showed that the stochastic expression of MarA leads to transient tolerance in E. coli when exposed to

We supplied LB broth and ciprofloxacin together at only one boundary of the MGC with the goal of creating pressure for cells to adapt up-gradient toward higher ciprofloxacin concentrations. We were concerned that if LB broth were applied at both boundaries, the cells would preferentially grow away from the boundary with ciprofloxacin and not have sufficient pressure to adapt up-gradient. A potential drawback with our approach is that cells would not grow at all because of the co-occurrence of elevated ciprofloxacin and LB broth, but this was not the outcome observed in our experiments. We note that work with yeast cultures has indicated that competition for limiting resources can be a driving force for adaptation.60,61 Our results indicate that nutrient concentration changes from 0.1 to 1 LB are not a key factor in determining differences in antibiotic resistance between MGC and batch experiments. This was indicated by serial passage batch experiments with both 0.1 and 1 LB and with ciprofloxacin concentration increases matching the lower region of the MGC. The new MIC values of the replicates of the 0.1 LB cultures and 1 LB cultures were 18−24× MICoriginal and 6−24× MICoriginal, respectively, both well above the maximum exposure concentration of 1.7× MICoriginal and not significantly different from each other (p-value = 0.42). Our results appear at odds with those of Zhang et al. (2011).32 These authors observed an increase in cell number in their MGC in as little as 5 h at a location they called the “goldilocks” point; it was characterized by the steepest ciprofloxacin gradient in their reactor. No such goldilocks point was observed in any of our MGC experiments within that time frame, and cells were at their lowest numbers in regions with the highest ciprofloxacin concentrations. It is possible that in that previous work, advective flow from the peripheral channels entered the MGC well array and the location where growth occurred actually had low ciprofloxacin concentrations. However, it was not possible to probe actual water flow paths or ciprofloxacin concentrations in that system. 8002

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology ORCID

a lethal concentration of antibiotic carbonicillin. After removing the antibiotic, the stressed and filamented E. coli cells returned to normal growth and did not show a change in susceptibility,62 indicating that the cells were only tolerating the stressful enviroment instead of developing resistance. Also in support of this mechanism, Alcalde et al. (2019)45 presented results supporting the ability of Shewanella oneidensis MR-1 to access lactate and nitrate in regions with inhibitory ciprofloxacin concentrations without acquiring comparable antibiotic resistance. Experiments with a nonmotile mutant of E. coli strain 307 would help clarify the effects of motility on cell adaptation in the MGC and are needed in future work. Regardless, it is clear from our results that researchers may be overestimating the development of antibiotic resistance in natural systems when inferring from results of batch cultures.

Jinzi Deng: 0000-0002-1727-6156 Charles J. Werth: 0000-0002-8492-5523 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS This work was supported by the National Aeronautics and Space Administration (NASA) through the NASA Astrobiology Institute under Cooperative Agreement No. NNA13AA91A issued through the Science Mission Directorate. The authors want to thank Dr. Rajveer Singh for his assistance on microscopy, Dr. Thanh H. (Helen) Nguyen for providing E. coli strain 307 used in all the experiments, and Dr. Glennys Mensing for technical support on the MGC fabrication. Conclusions in this study are those of the authors and do not necessarily reflect those of the funding or permitting agencies.

■

ENVIRONMENTAL IMPLICATIONS Adaptive evolution of microorganisms in response to stress is common in engineered and natural environments. In natural systems, stress often comes from toxic solutes that vary spatially, such as antibiotics. Common examples include antibiotic concentration gradients in soil aggregates and along the transverse margins of groundwater plumes. Understanding how microorganisms respond and adapt dynamically to spatial gradients of antibiotics can lead to strategies to mitigate the emergence of antibiotic resistance in microorganisms. Nutrient availability may be a key factor that affects adaptation to antibiotics. Comparison of results from our work to those from Baym et al. (2016)33 indicates that when cells face nutrient limitations, they are likely to adapt to higher antibiotic concentrations to acquire additional nutrients, whereas in our study, the continuous flux of nutrients may have mitigated this response. For example, we supplied full strength LB (i.e., 1 LB) media at a boundary channel, so greater than 0.1 LB was potentially available throughout the well array. It is possible that more adaptation would arise if a lower LB boundary concentration was used. This is important since widely varying nutrient conditions are expected in natural systems. In closing, our findings indicate that batch culturing systems, widely used in laboratories to understand the extent and rate of emergence of antibiotic resistance, can over predict adaptation in natural and engineered systems where solute concentration gradients are established.

■

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b00881.

■

REFERENCES

(1) Winker, S.; Woese, C. R. A Definition of the Domains Archaea, Bacteria and Eucarya in Terms of Small Subunit Ribosomal-Rna Characteristics. Syst. Appl. Microbiol. 1991, 14 (4), 305−310. (2) Knoll, A. H.; Canfield, D. E.; Konhauser, K. Fundamentals of geobiology; John Wiley & Sons: Chichester, 2012; pp 1 online resource (xii, 24, 443 pages). (3) Lane, N. The Vital Question − Energy, Evolution, and the Origins of Complex Life; W. W. Norton & Company: 2015; p 368. (4) Almas, A. R.; Mulder, J.; Bakken, L. R. Trace metal exposure of soil bacteria depends on their position in the soil matrix. Environ. Sci. Technol. 2005, 39 (16), 5927−32. (5) Mandaliev, P. N.; Mikutta, C.; Barmettler, K.; Kotsev, T.; Kretzschmar, R. Arsenic species formed from arsenopyrite weathering along a contamination gradient in Circumneutral river floodplain soils. Environ. Sci. Technol. 2014, 48 (1), 208−17. (6) Guzzella, L.; Poma, G.; De Paolis, A.; Roscioli, C.; Viviano, G. Organic persistent toxic substances in soils, waters and sediments along an altitudinal gradient at Mt. Sagarmatha, Himalayas, Nepal. Environ. Pollut. 2011, 159 (10), 2552−64. (7) Rath, K. M.; Maheshwari, A.; Bengtson, P.; Rousk, J. Comparative Toxicities of Salts on Microbial Processes in Soil. Appl. Environ. Microbiol. 2016, 82 (7), 2012−2020. (8) Dvorski, S. E.; Gonsior, M.; Hertkorn, N.; Uhl, J.; Muller, H.; Griebler, C.; Schmitt-Kopplin, P. Geochemistry of Dissolved Organic Matter in a Spatially Highly Resolved Groundwater Petroleum Hydrocarbon Plume Cross-Section. Environ. Sci. Technol. 2016, 50 (11), 5536−46. (9) Patterson, B. M.; Cohen, E.; Prommer, H.; Thomas, D. G.; Rhodes, S.; McKinley, A. J. Origin of a mixed brominated ethene groundwater plume: contaminant degradation pathways and reactions. Environ. Sci. Technol. 2007, 41 (4), 1352−8. (10) Fouke, B. W.; Farmer, J. D.; Des Marais, D. J.; Pratt, L.; Sturchio, N. C.; Burns, P. C.; Discipulo, M. K. Depositional facies and aqueous-solid geochemistry of travertine-depositing hot springs (Angel Terrace, Mammoth Hot Springs, Yellowstone National Park, U.S.A.). J. Sediment. Res. 2000, 70 (3), 565−85. (11) Martin, W.; Baross, J.; Kelley, D.; Russell, M. J. Hydrothermal vents and the origin of life. Nat. Rev. Microbiol. 2008, 6 (11), 805−14. (12) Baross, J.; Hoffman, S. E. Submarine Hydrothermal Vents and Associated Gradient Environments as Sites for the Origin and Evolution of Life. Origins Life Evol. Biospheres 1985, 15, 327−345. (13) Hou, Z.; An, Y.; Hjort, K.; Hjort, K.; Sandegren, L.; Wu, Z. Time lapse investigation of antibiotic susceptibility using a microfluidic linear gradient 3D culture device. Lab Chip 2014, 14 (17), 3409−18.

Test information, figures showing MGC experimental system, demonstration of the control MGC experiment, figures showing progression of cell elongation along the ciprofloxacin gradient in the MGC, demonstration of fluorescein diffusion in the MGC, figures showing dynamic spatial distribution of biomass across the well array in replicate experiments (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. 8003

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology (14) Levin-Reisman, I.; Ronin, I.; Gefen, O.; Braniss, I.; Shoresh, N.; Balaban, N. Q. Antibiotic tolerance facilitates the evolution of resistance. Science 2017, 355 (6327), 826−830. (15) Li, D.; Zeng, S. Y.; He, M.; Gu, A. Z. Water Disinfection Byproducts Induce Antibiotic Resistance-Role of Environmental Pollutants in Resistance Phenomena. Environ. Sci. Technol. 2016, 50 (6), 3193−3201. (16) Knapp, C. W.; Dolfing, J.; Ehlert, P. A. I.; Graham, D. W. Evidence of Increasing Antibiotic Resistance Gene Abundances in Archived Soils since 1940. Environ. Sci. Technol. 2010, 44 (2), 580− 587. (17) Bougnom, B. P.; Piddock, L. J. V. Wastewater for Urban Agriculture: A Significant Factor in Dissemination of Antibiotic Resistance. Environ. Sci. Technol. 2017, 51 (11), 5863−5864. (18) Hu, H. W.; Wang, J. T.; Li, J.; Shi, X. Z.; Ma, Y. B.; Chen, D.; He, J. Z. Long-Term Nickel Contamination Increases the Occurrence of Antibiotic Resistance Genes in Agricultural Soils. Environ. Sci. Technol. 2017, 51 (2), 790−800. (19) Vikesland, P. J.; Pruden, A.; Alvarez, P. J. J.; Aga, D.; Burgmann, H.; Li, X. D.; Manaia, C. M.; Nambi, I.; Wigginton, K.; Zhang, T.; Zhu, Y. G. Toward a Comprehensive Strategy to Mitigate Dissemination of Environmental Sources of Antibiotic Resistance. Environ. Sci. Technol. 2017, 51 (22), 13061−13069. (20) Negreanu, Y.; Pasternak, Z.; Jurkevitch, E.; Cytryn, E. Impact of treated wastewater irrigation on antibiotic resistance in agricultural soils. Environ. Sci. Technol. 2012, 46 (9), 4800−8. (21) Marton, A.; Gulyas, M.; Munoz, R.; Tomasz, A. Extremely high incidence of antibiotic resistance in clinical isolates of Streptococcus pneumoniae in Hungary. J. Infect. Dis. 1991, 163 (3), 542−8. (22) French, G. L. Clinical impact and relevance of antibiotic resistance. Adv. Drug Delivery Rev. 2005, 57 (10), 1514−27. (23) Chow, J. W.; Fine, M. J.; Shlaes, D. M.; Quinn, J. P.; Hooper, D. C.; Johnson, M. P.; Ramphal, R.; Wagener, M. M.; Miyashiro, D. K.; Yu, V. L. Enterobacter bacteremia: clinical features and emergence of antibiotic resistance during therapy. Ann. Intern. Med. 1991, 115 (8), 585−90. (24) Oberle, K.; Capdeville, M. J.; Berthe, T.; Budzinski, H.; Petit, F. Evidence for a Complex Relationship between Antibiotics and Antibiotic-Resistant Escherichia Coli: From Medical Center Patients to a Receiving Environment. Environ. Sci. Technol. 2012, 46 (3), 1859−1868. (25) Sharma, V. K.; Johnson, N.; Cizmas, L.; McDonald, T. J.; Kim, H. A review of the influence of treatment strategies on antibiotic resistant bacteria and antibiotic resistance genes. Chemosphere 2016, 150, 702−714. (26) Andersson, D. I.; Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 2010, 8 (4), 260−71. (27) Zaman, S. B.; Hussain, M. A.; Nye, R.; Mehta, V.; Mamun, K. T.; Hossain, N. A Review on Antibiotic Resistance: Alarm Bells are Ringing. Cureus 2017, 9 (6), No. e1403. (28) Frederiksen, B.; Koch, C.; Hoiby, N. Antibiotic treatment of initial colonization with Pseudomonas aeruginosa postpones chronic infection and prevents deterioration of pulmonary function in cystic fibrosis. Pediatr Pulm 1997, 23 (5), 330−335. (29) Davies, J.; Davies, D. Origins and evolution of antibiotic resistance. Microbiol Mol. Biol. Rev. 2010, 74 (3), 417−33. (30) Blair, J. M.; Webber, M. A.; Baylay, A. J.; Ogbolu, D. O.; Piddock, L. J. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 2015, 13 (1), 42−51. (31) Gould, I. M. A review of the role of antibiotic policies in the control of antibiotic resistance. J. Antimicrob. Chemother. 1999, 43 (4), 459−65. (32) Zhang, Q.; Lambert, G.; Liao, D.; Kim, H.; Robin, K.; Tung, C. K.; Pourmand, N.; Austin, R. H. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 2011, 333 (6050), 1764−7.

(33) Baym, M.; Lieberman, T. D.; Kelsic, E. D.; Chait, R.; Gross, R.; Yelin, I.; Kishony, R. Spatiotemporal microbial evolution on antibiotic landscapes. Science 2016, 353 (6304), 1147−1151. (34) Toprak, E.; Veres, A.; Michel, J. B.; Chait, R.; Hartl, D. L.; Kishony, R. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 2012, 44 (1), 101−U140. (35) Vila, J.; Ruiz, J.; Marco, F.; Barcelo, A.; Goni, P.; Giralt, E.; Jimenez de Anta, T. Association between double mutation in gyrA gene of ciprofloxacin-resistant clinical isolates of Escherichia coli and MICs. Antimicrob. Agents Chemother. 1994, 38 (10), 2477−9. (36) Karczmarczyk, M.; Martins, M.; Quinn, T.; Leonard, N.; Fanning, S. Mechanisms of fluoroquinolone resistance in Escherichia coli isolates from food-producing animals. Appl. Environ. Microbiol. 2011, 77 (20), 7113−20. (37) Vinue, L.; Corcoran, M. A.; Hooper, D. C.; Jacoby, G. A. Mutations That Enhance the Ciprofloxacin Resistance of Escherichia coli with qnrA1. Antimicrob. Agents Chemother. 2016, 60 (3), 1537− 45. (38) Chen, D. Q.; Zheng, X. C.; Lu, Y. J. Identification and characterization of novel ColE1-type, high-copy number plasmid mutants in Legionella pneumophila. Plasmid 2006, 56 (3), 167−78. (39) Chen, J.; Li, Y.; Zhang, K.; Wang, H. Whole-Genome Sequence of Phage-Resistant Strain Escherichia coli DH5alpha. Genome Announc 2018, 6 (10), 97-18 DOI: 10.1128/genomeA.00097-18. (40) Kohler, R.; Bubert, A.; Goebel, W.; Steinert, M.; Hacker, J.; Bubert, B. Expression and use of the green fluorescent protein as a reporter system in Legionella pneumophila. Mol. Gen. Genet. 2000, 262 (6), 1060−9. (41) Deng, J. Z.; Dhummakupt, A.; Samson, P. C.; Wikswo, J. P.; Shor, L. M. Dynamic Dosing Assay Relating Real-Time Respiration Responses of Staphylococcus aureus Biofilms to Changing Microchemical Conditions. Anal. Chem. 2013, 85 (11), 5411−5419. (42) Deng, J. Z.; Orner, E. P.; Chau, J. F.; Anderson, E. M.; Kadilak, A. L.; Rubinstein, R. L.; Bouchillon, G. M.; Goodwin, R. A.; Gage, D. J.; Shor, L. M. Synergistic effects of soil microstructure and bacterial EPS on drying rate in emulated soil micromodels. Soil Biol. Biochem. 2015, 83, 116−124. (43) Tan, S. H.; Nguyen, N. T.; Chua, Y. C.; Kang, T. G., Oxygen plasma treatment for reducing hydrophobicity of a sealed polydimethylsiloxane microchannel. Biomicrofluidics 2010, 4, 3. (44) Kadilak, A. L.; Rehaag, J. C.; Harrington, C. A.; Shor, L. M., A 3D-printed microbial cell culture platform with in situ PEGDA hydrogel barriers for differential substrate delivery. Biomicrofluidics 2017, 11, 5. (45) Alcalde, R. E.; Michelson, K.; Zhou, L.; Schmitz, E.; Deng, J.; Sanford, R. A.; Fouke, B. W.; Werth, C. J., Motility of Shewanella oneidensis MR-1 Allows for Nitrate Reduction in the Toxic Region of a Ciprofloxacin Concentration Gradient in a Microfluidic Reactor. Environ. Sci. Technol. 2019.532778 (46) Blount, Z. D.; Borland, C. Z.; Lenski, R. E. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (23), 7899−906. (47) Kram, K. E.; Geiger, C.; Ismail, W. M.; Lee, H.; Tang, H.; Foster, P. L.; Finkel, S. E., Adaptation of Escherichia coli to LongTerm Serial Passage in Complex Medium: Evidence of Parallel Evolution. MSystems 2017, 2, 2. DOI: 10.1128/mSystems.00192-16 (48) Baginska, E.; Haiss, A.; Kummerer, K. Biodegradation screening of chemicals in an artificial matrix simulating the water-sediment interface. Chemosphere 2015, 119, 1240−1246. (49) Girardi, C.; Greve, J.; Lamshoft, M.; Fetzer, I.; Miltner, A.; Schaffer, A.; Kastner, M. Biodegradation of ciprofloxacin in water and soil and its effects on the microbial communities. J. Hazard. Mater. 2011, 198, 22−30. (50) Kummerer, K.; Al-Ahmad, A.; Mersch-Sundermann, V. Biodegradability of some antibiotics, elimination of the genotoxicity and affection of wastewater bacteria in a simple test. Chemosphere 2000, 40 (7), 701−10. 8004

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005

Article

Environmental Science & Technology (51) Li, B.; Zhang, T. Biodegradation and adsorption of antibiotics in the activated sludge process. Environ. Sci. Technol. 2010, 44 (9), 3468−73. (52) Wu, C.; Spongberg, A. L.; Witter, J. D. Sorption and biodegradation of selected antibiotics in biosolids. J. Environ. Sci. Health, Part A: Toxic/Hazard. Subst. Environ. Eng. 2009, 44 (5), 454− 61. (53) Amorim, C. L.; Moreira, I. S.; Maia, A. S.; Tiritan, M. E.; Castro, P. M. Biodegradation of ofloxacin, norfloxacin, and ciprofloxacin as single and mixed substrates by Labrys portucalensis F11. Appl. Microbiol. Biotechnol. 2014, 98 (7), 3181−90. (54) Diver, J. M.; Wise, R. Morphological and Biochemical-Changes in Escherichia-Coli after Exposure to Ciprofloxacin. J. Antimicrob. Chemother. 1986, 18, 31−41. (55) Kaya, T.; Koser, H. Direct upstream motility in Escherichia coli. Biophys. J. 2012, 102 (7), 1514−23. (56) Gilbert, D. N.; Kohlhepp, S. J.; Slama, K. A.; Grunkemeier, G.; Lewis, G.; Dworkin, R. J.; Slaughter, S. E.; Leggett, J. E. Phenotypic resistance of Staphylococcus aureus, selected Enterobacteriaceae, and Pseudomonas aeruginosa after single and multiple in vitro exposures to ciprofloxacin, levofloxacin, and trovafloxacin. Antimicrob. Agents Chemother. 2001, 45 (3), 883−92. (57) Friedman, L.; Alder, J. D.; Silverman, J. A. Genetic changes that correlate with reduced susceptibility to daptomycin in Staphylococcus aureus. Antimicrob. Agents Chemother. 2006, 50 (6), 2137−45. (58) D’Lima, L.; Friedman, L.; Wang, L.; Xu, P.; Anderson, M.; Debabov, D. No decrease in susceptibility to NVC-422 in multiplepassage studies with methicillin-resistant Staphylococcus aureus, S. aureus, Pseudomonas aeruginosa, and Escherichia coli. Antimicrob. Agents Chemother. 2012, 56 (5), 2753−5. (59) Pollard, J. E.; Snarr, J.; Chaudhary, V.; Jennings, J. D.; Shaw, H.; Christiansen, B.; Wright, J.; Jia, W.; Bishop, R. E.; Savage, P. B. In vitro evaluation of the potential for resistance development to ceragenin CSA-13. J. Antimicrob. Chemother. 2012, 67 (11), 2665−72. (60) Ferea, T. L.; Botstein, D.; Brown, P. O.; Rosenzweig, R. F. Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc. Natl. Acad. Sci. U. S. A. 1999, 96 (17), 9721− 6. (61) Hong, J.; Gresham, D. Molecular specificity, convergence and constraint shape adaptive evolution in nutrient-poor environments. PLoS Genet. 2014, 10 (1), No. e1004041. (62) El Meouche, I.; Siu, Y.; Dunlop, M. J. Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Sci. Rep-Uk 2016, 6, 6.

8005

DOI: 10.1021/acs.est.9b00881 Environ. Sci. Technol. 2019, 53, 7996−8005