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Single-cell real-time visualization and quantification of perylene bioaccumulation in microorganisms Xin Jin, Xuejun Guo, Deshu Xu, Yanna Zhao, Xinghui Xia, and Fan Bai Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 17 May 2017 Downloaded from http://pubs.acs.org on May 19, 2017
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Environmental Science & Technology
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Single-cell real-time visualization and quantification of perylene bioaccumulation in microorganisms
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Xin Jin†, Xuejun Guo†*, Deshu Xu†, Yanna Zhao‡, Xinghui Xia and Fan Bai‡*
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†
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Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
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‡
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5 Yiheyuan Road, 100871, China
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*Corresponding author
1 2
State Key Laboratory of Environment Simulation, School of Environment, Beijing
Biodynamic Optical Imaging Center, School of Life Sciences, Peking University, No.
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Guo, X. Email:
[email protected]; Phone: 86-10-5880-7808 Fax: 86-10-5880-7808;
11
Bai, F. Email:
[email protected]; Phone: 86-10-6275-6164 Fax: 86-10-6275-6164.
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ABSTRACT
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Bioaccumulation of perylene in Escherichia coli (E. coli) and Staphylococcus
14
aureus (S. aureus) was visualized and quantified in real time with high sensitivity at
15
high temporal resolution. For the first time, single-molecule fluorescence microscopy
16
(SMFM) with a microfluidic flow chamber and temperature control has enabled us to
17
record the dynamic process of perylene bioaccumulation in single bacterial cells and
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examine the cell-to-cell heterogeneity. Although with identical genomes, individual E.
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coli cells exhibited a high degree of heterogeneity in perylene accumulation dynamics,
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as shown by the high coefficient of variation (C.V=1.40). This remarkable
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heterogeneity was exhibited only in live E. coli cells. However, the bioaccumulation
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of perylene in live and dead S. aureus cells showed similar patterns with a low degree 1
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of heterogeneity (C.V=0.36). We found that the efflux systems associated with Tol C
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played an essential role in perylene bioaccumulation in E. coli, which caused a
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significantly lower accumulation and a high cell-to-cell heterogeneity. In comparison
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with E. coli, the Gram-positive bacteria S. aureus lacked an efficient efflux system
27
against perylene. Therefore, perylene bioaccumulation in S. aureus was simply a
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passive diffusion process across the cell membrane.
29 30
TOC
31 32 33
INTRODUCTION
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Hydrophobic organic chemicals (HOCs) are of special ecotoxicological concern
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among numerous anthropogenic chemicals because of their capacity to directly
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incorporate into the tissue of living organisms.1,
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bio-concentrated if a living organism is under continuous exposure.3 HOC
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bioaccumulation may occur in predators and humans through biomagnification along
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the food chain because of the ingestion of contaminated foodstuffs.4, 5
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Many HOCs can be
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Microorganisms, which comprise the most diversified, fundamental component of
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the biosphere, significantly affect the mobility and bioavailability of HOCs in the 2
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environment via bioaccumulation and biotransformation.6-10 Because of convection
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and active processes in the cellular cytoplasm, microorganisms can accumulate a
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higher concentration of HOCs from water than particulates.11 Previous studies have
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shown that microorganisms can act as microbial carriers that enhance the mass
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transfer of HOCs by up to a 100-fold through diffusive boundary layers.6 Furthermore,
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because it is efficient and economical, biodegradation has been the primary method to
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remove HOCs from the environment.10, 12-14
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The transmembrane transport of HOCs is the first step in the bioprocesses (e.g.,
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bioaccumulation, and biodegradation) between HOCs and microorganisms. The
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uptake of HOCs commonly determines the efficiency of bioaccumulation and
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biodegradation.7,
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transmembrane transport can significantly promote the degradation efficiency of
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HOCs.16 Nevertheless, the mechanisms underlying the entry of HOCs into
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microorganisms are unclear and are notably controversial. There are three hypotheses
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for the cross-membrane traffic of hydrocarbons in microorganisms: 1) passive
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diffusion; 19 2) facilitated diffusion; 20 and 3) energy-dependent transport.21, 22 The
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detailed molecular processes in microbial transmembrane transport of hydrocarbons
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remain poorly studied, mainly because there is no sensitive detection method to
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analyze the transmembrane process in vivo. In general, chromatography and mass
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spectrometry are used to analyze HOCs in microorganisms and other media. However,
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these ensemble measurements of cellular HOCs require a large number of samples
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and a series of complicated pretreatment procedures (e.g., extraction and
8, 15-18
It has been demonstrated that optimizing and enhancing
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concentration). These complex steps introduce errors and destroy biological samples;
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thus, it is difficult to determine the microscopic distribution and dynamic changes of
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cellular HOCs in situ. Moreover, these methods only measure the ensemble HOC
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behavior in a microbial population, which inevitably ignores the cell-to-cell
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heterogeneity. In fact, the phenotypic heterogeneity in a population of cells with an
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identical genome can be remarkable because of epigenetic modifications, stochastic
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gene expression, variable mRNA stabilities and protein activities.23-25 For example,
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the protein abundance in single cells may vary by more than 10-fold in an isogenic E.
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coli population, which causes a significant metabolic heterogeneity.26 In the case of
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free-fatty-acid (FFA) production, the FFA yield increases 5 times by selecting
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high-performance E. coli cells from an isogenic population.27 Therefore, we
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hypothesize that the capacity and kinetics of single bacterial cells in HOC
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bioaccumulation may also significantly vary among individuals. Real-time
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visualization and quantification of HOC accumulation at the single-cell level can
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reveal the heterogeneity of transmembrane transport of HOCs, which is required for
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our research purpose.
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In this paper, by implementing single-molecule fluorescence microscopy (SMFM)
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with a microfluidic device and temperature control, we monitored, in real time, the
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bioaccumulation and efflux of HOCs in microorganisms at the single-cell level. The
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Gram-negative bacteria Escherichia coli (E. coli) and Gram-positive bacteria
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Staphylococcus aureus (S. aureus) were selected as model organisms. These two types
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of bacteria have distinct membrane structures, which may lead to different features in 4
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bioaccumulation. We selected perylene (log KOW=5.82), which is a five-ring
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polycyclic aromatic hydrocarbon (PAH), as the research target. Perylene is an
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important and widely distributed HOC in the environment,28-32 and its high quantum
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yield (0.94) and excitation spectra in the visible-light region make it an ideal probe for
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fluorescence microscopy. The high sensitivity of SMFM enabled us to detect low
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copy number changes of perylene molecules in live cells with a high temporal
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resolution.33
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MATERIALS AND METHODS
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Chemicals and Reagents
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Perylene,
phosphate
buffer
solution
(PBS),
carbonyl
cyanide
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3-chlorophenylhydrazone (CCCP) and poly-L-lysine were purchased from Sigma
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Aldrich Ltc. LB broth (Sangon Biotech, China) and LB agar (Sangon Biotech, China)
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were used for routine bacterial cultivation. M9 glucose media supplemented with
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amino acids (Thermo Fisher, USA) and vitamins (Thermo Fisher, USA) were used in
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the imaging process to reduce the fluorescence background from the culture medium.
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Propidium iodide (PI) from the LIVE/DEAD staining kit (Invitrogen, USA) was used
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to differentiate dead cells from live cells. A stock solution of 0.5 mM perylene was
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prepared using ethanol and stored at 4 °C in the dark. Before use, the perylene
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solution was prepared by diluting the stock solution to 1:500. Ultrapure water was
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used throughout this work.
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Emission spectra of the perylene solution
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The fluorescence spectra of 1 nM perylene in ethanol and water were recorded 5
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using a fluorescence spectrophotometer (Hitachi, F-4600, Japan). The solution (1.0
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mL) in a 4-mL tube was excited at 405 nm. The emission spectra at 438-486 nm were
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recorded.
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Bacterial strains and culture conditions
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The wild-type E. coli strain and its TolC knockout strain (∆tolC) were laboratory
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stocks (Biodynamic Optical Imaging Center, Peking University, Beijing, China). S.
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aureus was purchased from China Center of Industrial Culture Collection. Both
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bacterial strains were cultured in LB broth in a 37 °C shaker overnight. Before the
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experiments, these overnight cultures of bacteria were re-inoculated (1:200) into fresh
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LB and subsequently returned to the 37℃ shaker until OD600 reached 0.4.
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Microscopy
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All imaging work was performed on an inverted microscope (Axio Observer,
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Zeiss, Germany). Perylene was excited by a 405-nm laser (OBIS 405, Coherent, USA)
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and monitored with a filter set (dichroic mirror 405 nm, emission 435-485 nm). The
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fluorescence emission was collected using a 100×oil-immersion objective with a
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numerical aperture of 1.46 and imaged with an electron multiplying charge coupled
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device camera (Evolve 512, Photometrics, USA). The exposure time and gain value of
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the camera were set as 100 ms and 260, respectively, and maintained constant. The
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microscope was mounted with a microfluidic flow chamber with temperature control
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(Bioptech, FCS2).
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Visualization of the accumulation and efflux process of perylene by E. coli and S.
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aureus 6
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The LB-cultured cells were washed twice using fresh M9 media and re-suspended
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at a 2-fold dilution. Then, 200 µL of cells was added onto a coverslip, which was
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coated with poly-L-lysine, for 15 min at room temperature for stable resting.
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Subsequently, the coverslip was placed on top of a Micro-aqueduct slide (Bioptechs,
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FCS2) to sandwich the cells in between. The sample chamber was maintained at
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37 °C during the experiments. After the temperature was stabilized, M9 medium
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containing 1 µM perylene was injected into the chamber though the Perfusion Tubes,
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which were connected to the chamber. We began time-lapse epifluorescence imaging
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as soon as the focal plane became stable. Bright-field and fluorescence images were
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taken every 1 min for 30 min. Then, 0.85% NaCl was injected into the chamber to
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wash away the M9 medium. PI in 0.85% NaCl was subsequently injected into the
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chamber and incubated for 15 min to identify dead cells. Fresh M9 medium was
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added to the chamber again. Then, we captured the fluorescence images of the PI dye.
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Figure 1(A) schematically shows the experimental procedure to measure perylene
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accumulation in bacteria.
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To monitor the efflux process of E. coli, CCCP (50 µM) in PBS was injected into
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the chamber for 10 min to dissipate the proton motive force after the chamber
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temperature became stable. Then, PBS containing 1 µM perylene and CCCP was
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injected into the chamber. Bright-field and fluorescence images were taken every 1
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min for 30 min. Thereafter, fresh M9 medium was injected into the chamber.
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Bright-field and fluorescence images were taken every 1 min for 30 min. Then, PI
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was used to distinguish dead cells. 7
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Image processing
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Image analysis was performed using the ImageJ software (Fiji). The cell contour
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was constructed from bright-field images. The background intensity and cell
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auto-fluorescence intensity were subtracted from the fluorescence intensity of each
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cell. The integrated fluorescence intensity over the entire cell area was obtained for
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each cell and normalized by the cell area.
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RESULTS AND DISCUSSION
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Calibration of the fluorescence intensity with the perylene concentration
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161 162
Figure 1 (A) Schematically experimental procedures for measuring perylene
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accumulation at single cell level using SMFM; (B) Calibration curve of perylene
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concentration and fluorescence intensity (R2=0.997). The illustration is an enlarged 8
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view of the low concentration area.; (C) Emission spectra of 1.0 nM perylene in water
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and ethanol (EX=405 nm).
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Before the precise measurement of the dynamic transmembrane process of
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perylene in microorganisms, we established the relationship between the fluorescence
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intensity and perylene concentration. The fluorescence intensity of perylene in ethanol
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at different concentrations (0 nM, 10 nM, 50 nM, 100 nM, 250 nM, 500 nM, 1 µM, 2
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µM, 3 µM, 4 µM and 5 µM) was measured. As shown in Figure 1(B), the relationship
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between the fluorescence intensity (I, AU·pixel-2) and perylene concentration (C, µM)
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can be well fitted by a linear function as follows
ܫா௧ைு = 609.20 + 927.96ܥா௧ைு
175
(1)
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Because the microorganism was in a water environment in our experiment, factor
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α was introduced to convert the fluorescence intensity of perylene in ethanol to that in
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water. The emission spectra of 1 nM perylene in ethanol and water were separately
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measured (Figure 1(C)). We hypothesize that the conversion factor α can be calculated
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as
181
ಹమ ೀ
α=
=0.89 (2)
ಶೀಹ
182
where AH2O and AEtOH are the integration of the area (AU×nm) under emission
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spectra from 438 nm to 486 nm, which is the wave range of the emission filter in the
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microscope, in water and ethanol, respectively. Therefore, the relationship between
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the fluorescence intensity and perylene concentration in water is calculated as follows:
ܫுమ ை = ߙܫா௧ைு 9
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= 542.19 + 825.88( ܥ3) C = 0.00121 × ܪܫ2 ܱ − 0.66 (4)
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The effect of the biota on the perylene fluorescence property, i.e., the quantum
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yield and excitation spectra, is not considered here. Perylene is insensitive to solvent
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effect because it is a nonpolar aromatic hydrocarbon.34 In addition, most cellular
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perylene molecules are in an aqueous environment (the water content of live cells is
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more than 80%). Thereby, the effect of the biota on the perylene fluorescence property,
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i.e., the quantum yield and excitation spectra, can be ignored and not considered here.
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After the deduction of the background fluorescence intensity of microorganisms, the
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cellular concentration of perylene was calculated based on Equation (4).
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Heterogeneity in perylene accumulation by individual E. coli and S. aureus cells
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The dynamic accumulation of perylene by these two strains is presented in
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movies 1-2 (Supporting Information). Figure 2(A) shows representative images of
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these processes. We calculated the cellular perylene concentration of the two strains
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after a 30 min incubation with perylene (N=1000). Histograms of the cellular perylene
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concentration are shown in Figure 2(B). Unlike the ensemble methods, which only
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provide the overall mean value, SMFM can clearly display cell-to-cell heterogeneity.
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In Figure 2(B), the perylene concentration in E. coli was notably heterogeneous. The
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coefficient of variation (C.V) was calculated to evaluate the heterogeneity.
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The C.V of cellular perylene accumulation in 1000 E. coli cells was up to 1.40.
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There was a small fraction of E. coli cells that presented much higher (20-fold)
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fluorescence than the rest of the population. 10
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SMFM offered another advantage of differentiating the sub-population with
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different features using specific molecular markers. The fluorescent dye PI was
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employed here to differentiate live and dead cells.35,
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fluorescence intensity than the threshold value are identified as dead cells. As shown
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in Figure 2(C), it was obvious that all dead E. coli cells accumulated to a higher
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degree with perylene than the average level. The C.V value of perylene
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bioaccumulation in dead cells was 0.28, which was ~1/5 of that of live cells. It was
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interesting to observe that not all cells with high perylene accumulation were dead
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cells. These C.V values indicated a considerable heterogeneity in HOC
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bioaccumulation in live E. coli cells, but homogeneity in dead cells.
36
Cells with a higher PI
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This high heterogeneity of perylene bioaccumulation by live E. coli cells was due
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to biological processes because the dead cells showed a much lower heterogeneity. It
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must be attributed to some phenotypic difference within the isogenic population of E.
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coli. Even though the cells had identical genomes, there can be often remarkable
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cell-to-cell heterogeneity among a total microbial population because of epigenetic
223
modifications, stochastic gene expression and variable mRNA stabilities and protein
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activities.23-25 Because neither E. coli nor S. aureus we used here has perylene
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degradation genes, the biodegradation pathway was excluded.37 We proposed that it
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was the E. coli membrane-efflux system that not only led to significantly lower
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perylene bioaccumulation in live E. coli cells than dead cells but also induced a high
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heterogeneity of pollutant bioaccumulation in live E. coli. The important role of the
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membrane-efflux system and related genes are presented in detail in later sections. 11
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On the contrary, perylene bioaccumulation in S. aureus was not correlated with
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their survival status (Figure 2(D)). The C.V value and averaged perylene
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accumulation by live cells vs. dead cells were comparable: 0.35 vs. 0.31 and 3.06 µM
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vs. 3.29 µM, respectively. This result indicates that bioaccumulation of perylene by
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live and dead S. aureus cells is simply an energy-independent passive process.
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Figure 2 (A) Representative merged images of perylene accumulation by E.coli and S.
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aureus at different times; (B) The distributed histogram of cellular perylene
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accumulation after 30 min incubation (N=1000 for each strain); (C-D) Identification 13
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of dead bacteria from total population using fluorescence dye PI; (E-F) Kinetics of
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perylene accumulation in live cells of E.coli and S.aureus (N=100). Symbols: original
241
data from single cells. Lines: the mean cellular perylene concentration of the 100 cells.
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Shaded areas: standard deviation; (G-H) Kinetics of perylene accumulation in dead
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cells of E.coli and S.aureus (N=10).
244 245
Single-cell kinetics of perylene accumulation in E. coli and S. aureus
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In our SMFM method, the accumulation kinetics of single cells was recorded in
247
real time with high temporal resolution and high sensitivity. The single-cell kinetics of
248
perylene accumulation by E. coli and S. aureus are shown in Figures 2(E-H).
249
Consistent with the above results, Figures 2(E) and 2(G) show that the kinetics of
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live E. coli cells were much more heterogeneous than those of dead E. coli cells. In
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contrast, the kinetics of live and dead S. aureus cell are also alike (Figures 2(F) and
252
2(H)). The kinetics curves of perylene in dead E. coli cells were actually similar to
253
those of both live and dead S. aureus cells. More careful observation found that
254
perylene accumulation in live E. coli cells attained steady state much faster than the
255
latter three (dead E. coli cells, live and dead S. aureus cells). Most live E. coli cells
256
attained steady state after 10 min, but the latter three did not attain steady state over
257
the entire duration of perylene incubation (30 min). The phenomena of slow kinetics
258
and un-reached steady state even after a relatively long duration indicated that
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perylene accumulation by the latter three obeys an identical mechanism: passive
260
trans-membrane diffusion. However, the membrane-efflux system in live E. coli cells 14
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can effectively resist perylene, which results in low perylene accumulation and a
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rapidly steady state. In a later section, we show that knocking out the key genes of the
263
E. coli membrane-efflux system results in a completely contrary result.
264 265 266
The accumulation kinetics of perylene in individual cells was fitted using pseudo-first-order kinetic rate equation. The pseudo-first-order rate kinetics equation is: ௗ
267 268
ௗ௧
271
(5)
After integration, we have, ܥ௧ = ܥ ሺ1 − ݁ ିభ ௧ ሻ
269 270
= ݇ଵ ሺܥ − ܥ௧ ሻ
(6)
Ce and Ct (µM) are the perylene accumulation at equilibrium and time t, respectively; k1 (min-1) is the rate constant of the pseudo-first order.
272 273
Figure 3 (A) Distributed histograms correlation coefficient of pseudo first kinetics
274
model; (B) Distributed histograms of initial perylene uptake rate over the first 5
275
minutes in E.coli and S.aureus.
276
Therefore, we plotted histogram distributions of the correlation coefficient (R2,
277
Figures 3A). For live E. coli cells, the resultant R2 from the two equations was 15
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distributed in a wide range from 0.60 to 0.95, with a few points with R2 < 0.6 (Figure
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3A). This high dispersion of R2 not only demonstrated the high heterogeneity of live E.
280
coli cells, but also indicated that simple kinetic processes were inadequate to fit the
281
perylene accumulation dynamics in live E coli cells. However, for live S. aureus, the
282
resultant R2 was distributed in a concentrated zone of 0.92~1.0 (Figure 3B), which
283
indicated that perylene accumulation dynamics in live S. aureus were well fitted by
284
pseudo-first- and pseudo-second-order kinetic rate equations. This significant
285
difference of R2 in live E. coli and S. aureus was consistent with our previous
286
inference that the perylene accumulation in live E. coli was complex transport
287
processes which involved active process, while the uptake of perylene in live S.
288
aureus was simple passive diffusion. To compare the rate of perylene accumulation in
289
different cases, the initial perylene uptake rate within the first 5 minutes was
290
calculated (Figure 3B). The mean initial rate of live S. aureus cells was 0.22 µM/min,
291
which was 7.29 times higher than that of E. coli (0.03 µM/min). We also calculated
292
the mean initial accumulation rates of dead E. coli and S. aureus cells. The initial
293
perylene uptake rate was 0.31 µM/min for dead E. coli cells and 0.25 µM/min for
294
dead S. aureus cells. The initial rate in dead E. coli was ~10 times of that in live E.
295
coli. However, the initial rate of live and dead S. aureus were similar.
296
Dead E. coli cells (Figure 2G), live S. aureus cells (Figure 2F) and dead S. aureus
297
cells (Figure 2H) show a much stronger perylene accumulation ability than live E. coli
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cells (Figure 2E). Previous studies have shown that the presence of polar
299
lipopolysaccharide (LPS) in the outer membrane of Gram-negative bacteria provides 16
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an efficient barrier for the passage of hydrophobic molecules.38 If this were the entire
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truth, the perylene accumulation in live E. coli cells should reach the steady state
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much more slowly, as shown in dead E. coli cells. However, our results for live E. coli
303
cells were notably the opposite and reached the steady state rapidly (Figure 2E)
304
despite a much lower rate of initial uptake (Figure 3B) . Thus, additional mechanisms
305
are required to explain this intrinsic resistance. It has been demonstrated that the
306
multidrug transporters of E. coli have multi-specificity for many relatively lipophilic,
307
planar molecules. 18, 39 Thus, the multidrug efflux pumps may play an important role
308
in the intrinsic resistance of E. coli to perylene.
309
An essential role of the E. coli efflux system in pumping out cellular perylene
310
To demonstrate whether a multidrug efflux system plays a role in perylene
311
accumulation in E. coli, a Tol C efflux pump knock out strain (∆tol C) and the proton
312
motive force (PMF) inhibitor CCCP were used. Tol C is a common channel protein of
313
both major and minor efflux systems, which enables interactions with many
314
translocase complexes.40 Deletion of the tol C gene in E. coli largely abolishes the
315
efflux activity.41 PMF is the driving force of multidrug efflux pumps in E. coli. 42 The
316
use of CCCP can dissipate PMF and block the efflux system. 43 This dissipation of
317
PMF is reversible by removing CCCP and supplying a carbon source.
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Figure 4 (A) The distributed histogram of cellular perylene accumulation in E.coli,
320
∆tol C and CCCP pre-incubated E.coli (N=1000 for each strain); (B-C) Kinetics of
321
perylene accumulation in live cells ofΔtol C and CCCP pre-incubated E.coli; (D)
322
Kinetics of perylene efflux in E.coli after elimination of CCCP inhibition.
323 324
Figure 4(A) shows histograms of cellular perylene accumulation of wild-type E.
325
coli, ∆tol C and CCCP pre-incubated E. coli after 30 min of incubation (N=1000). The
326
cellular perylene concentration of ∆tol C and CCCP-pre-incubated E. coli was
327
significantly higher than that shown in wild-type E. coli, which demonstrates that the
328
efflux system played an important role in excluding perylene. The cellular perylene
329
concentration of ∆tol C (0.42) had a much lower C.V than that of E. coli (1.40). This
330
situation may occur because the accumulation of perylene is a passive process after
331
the blocking the efflux system. The C.V of cellular perylene accumulation in 18
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CCCP-pre-incubated E. coli (0.90) is higher than that of ∆tol C, which was probably
333
because of the heterogeneous CCCP sensitivity of live E. coli cells.
334
The dynamic perylene accumulation process by ∆tol C and CCCP-treated E. coli
335
is shown in movies 3-4 (Supporting Information). The kinetics of perylene
336
accumulation by the two strain is shown in Figures 4 (B-C) (live cells, N=100). In
337
contrast to wild-type E. coli, the kinetics of ∆tol C and CCCP-treated E. coli showed a
338
linear relationship with time. Neither ∆tol C nor CCCP-treated E. coli attained steady
339
state after 30 min of incubation. The accumulation kinetics of ∆tol C were fitted to a
340
zero-order rate kinetic equation. The mathematical representations of the models are
341
shown in Eqs. (7). ܥ௧ = ݇௱௧ ݐ
342 343 344
(7)
Ct (µM) is the cellular perylene concentration at time t; k∆tolC (µM /min) is the rate constant of kinetics of ∆tol C.
345
The accumulation of perylene by CCCP-treated E. coli lagged by a few minutes
346
after injection of the perylene solution, which might result from the incomplete
347
dissipation of PMF in the first few minutes. Thus, we added a new parameter, tlag, to
348
the zero-order rate kinetic equation. The equation is
349 350 351
ܥ௧ = ݇ ሺ ݐ− ݐ ሻ
(8)
kCCCP (µM/min) is the rate constant of CCCP-pre-incubated E. coli; tlag is the lag time of bioaccumulation of perylene in CCCP-pre-incubated E. coli.
352
The parameters obtained from the two kinetic models are shown in Figure 5. As a
353
result of the heterogeneous sensitivity of live E. coli cells to CCCP, the fitting 19
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parameters of CCCP-treated E. coli have a higher C.V than those of ∆tol C. The
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higher values of k∆tol C (0.060 µM/min) than kCCCP (0.043 µM/min) indicate a faster
356
perylene accumulation rate in ∆tol C than in CCCP-treated E. coli.
357
Unlike dead E. coli cells, the rate of perylene accumulation in ∆tol C and
358
CCCP-treated E. coli is constant during the entire 30 min of incubation. We also
359
found that the perylene accumulation rate of both ∆tol C (0.060 µM/min) and
360
CCCP-treated E. coli (0.043 µM/min) was much lower than that of dead E. coli cells
361
(0.31 µM/min). This can be explained by the different membrane structure of live and
362
dead cells. The cytoplasmic membranes of dead bacteria are generally considered to
363
be partly damaged,35,
364
difference in the accumulation velocity between efflux-blocked live cells and dead
365
cells was likely the result of the different membrane structures of live and dead cells.
366
Although the active efflux of perylene was intercepted in ∆tol C and CCCP-treated E.
367
coli, the integrated membrane structure of live cells (e.g., LPS) could still
368
significantly slow the accumulation of perylene molecules.
36, 44
whereas live cells have integrated membranes. This
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Figure 5 (A-E) Distributed histograms of fitted kinetics parameters for perylene
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accumulation inΔtol C and CCCP pre-incubated E.coli. (F-H) Distributed histograms
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of fitted kinetics parameters for perylene efflux by E.coli after elimination of CCCP
374
inhibition.
375 376
After CCCP-pre-incubated E. coli with perylene were incubated for 30 min,
377
CCCP and the perylene solution were removed by injecting fresh M9 medium. Then,
378
monitored efflux of perylene by E. coli. The dynamic process of perylene efflux by E.
379
coli after eliminating the effects of CCCP is shown in movie 5 (Supporting
380
Information). Figure 4(D) shows the efflux kinetics of perylene (N=100). Intracellular
381
perylene was rapidly pumped out of bacteria within 10 min. A small fraction of
382
cellular perylene remained in cells after 30 min, which could be irreversibly 21
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sequestered in the hydrophobic region of the cellular proteins and lipophilic plasma
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membrane.
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The kinetics of perylene efflux was fitted to the following equation ܥ௧ = ܥ × ݁ ିೠೣ ௧
386 387 388
(9)
where Ct (µM) is the cellular perylene concentration at time t; kCCCP (µM /min) is the rate constant of efflux; and C0 is the initial cellular perylene concentration.
389
The parameters obtained from this kinetics models are shown in Figures 5 (F, G,
390
H). The R2 of the efflux model had a wide distribution of 0.04-0.99, with a C.V of
391
0.22. The rate constant of efflux (kCCCP) varied in the range of 0.08-0.23 min-1, with a
392
C.V. of 0.55. The initial perylene concentration (C0) also showed a broad distribution
393
from 0.28 µM to 3.88 µM, which resulted in a C.V. of 0.72. The high heterogeneity of
394
the initial perylene accumulation (when pre-incubated with CCCP) and subsequent
395
efflux process demonstrated the phenotypic difference in the isogenic population of E.
396
coli. More comprehensively, this high heterogeneity can be attributed to the combined
397
phenotypic differences in cellular sensitivity to the CCCP treatment, individual
398
capability of recovering from CCCP and individual membrane-efflux activity.
399
Environmental Implications
400
In this study, we visualized the accumulation and efflux processes of perylene in
401
Gram-negative E. coli and Gram-positive S. aureus at the single-cellular level in real
402
time. The accumulation of perylene by live S. aureus cells is significantly higher than
403
that of live E. coli cells. We wondered whether this distinct pattern of HOC
404
accumulation was ubiquitously present in Gram-negative and Gram-positive bacteria. 22
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We also visualized the perylene accumulation by some other Gram-positive and
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Gram-negative bacteria (B. subtilis (G+), V. alginolyticus (G-) and P. vulgaris (G-))
407
(data not shown). Gram-positive B. subtilis showed similarity to S. aureus for
408
perylene accumulation. Gram-negative V. alginolyticus and P. vulgaris also efficiently
409
excluded perylene. Stringfellow et al. investigated phenanthrene accumulation by nine
410
bacteria strains. Except for one strain, Micrococcus luteus (G+), they found that all
411
Gram-positive bacteria had better accumulation than Gram-negative bacteria
412
Thereby, our observation of significantly higher HOC accumulation in Gram-positive
413
bacteria than Gram-negative bacteria is consistent with the ensemble observation from
414
the previous study 14.
14
.
415
The difference in the amount of HOC accumulation between Gram-positive
416
bacteria and Gram-negative bacteria was inaccurately interpreted to be due to the
417
different membrane structure and components between them. In this study, we
418
demonstrated that a multidrug efflux system associated with Tol C played a primary
419
role in the accumulation of perylene in E. coli. Driven by the PMF, the multidrug
420
efflux system of E. coli can efficiently expel perylene molecules as soon as they enter
421
cells. By contrast, perylene accumulation in Gram-positive bacteria S. aureus is from
422
simple passive diffusion, which depends less on cellular phenotypic differences. If the
423
E. coli efflux system is blocked by knocking out the involved genes or eliminating the
424
PMF, the kinetics of perylene accumulation become comparable to that of S. aureus.
425
However, Gram-positive bacteria also have numerous membrane transporters that
426
promote the efflux of various drugs from the cells to the outer medium.45 Questions 23
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arise regarding the free diffusion of a typical HOC into a Gram-positive bacterium
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(visualized here). Why did they exhibit no significant ability to expel perylene, even
429
though it has been proven to be a growth inhibitor in most bacteria ?46
430
In this study, dead E. coli cells were distinguished from living cells according to
431
their significantly higher perylene accumulation. Ensemble methods cannot determine
432
the mortality of a microbial population, so they always ignore the effect of the
433
metabolic status of cells. In fact, mortality is considerable in practical applications
434
(e.g., bacteria in the stationary phases or active sludge). The ensemble measurement
435
of only the average change of cellular HOCs will false results regarding the real
436
exposure and bioaccumulation of pollutants in a living cell. Our single-cellular
437
approach reveals a detailed heterogeneity of pollutant exposure and bioaccumulation
438
for each individual cell, which offers a better understanding of the transmembrane
439
transport of HOCs.
440
As demonstrated above, microorganisms are the fundamental component in
441
biosphere, acting as the microbial carriers which enhance the mass transfer of HOCs
442
by two orders of magnitude. The bioaccumulation and biotransformation via
443
microorganisms significantly affect the mobility and bioavailability of HOCs in the
444
environment 6-10. We imaged the transmembrane transport (both the uptake and efflux)
445
of perylene in microorganisms with a high temporal resolution, which revealed a high
446
degree of heterogeneity in both the uptake and efflux of this HOC pollutant by live E.
447
coli cells. Similar to the bacterial resistance to the antibiotics, the heterogeneity in
448
phenotype expression of efflux systems (TolC observed here) would ultimately affect 24
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the adaptive process of E. coli to this HOC polluted environment. However, some
450
unclear pathways to reduce intracellular perylene toxicity in S. aureus (gram-positive
451
bacteria) warrants further investigation.
452
Our observations here also have potential implications in environmental
453
remediation. The results of this study, such as the efflux-system-mediated HOCs
454
resistance in Gram-negative bacteria, difference between live/dead cells, and notable
455
heterogeneity in live cells, will provide important guidance for the selection and
456
optimization of bio-sorption and biodegradation systems. Through the genetic
457
modification that disables the efflux associated gene, one might enhance the
458
microbial-availability of a targeted pollutant, and hence potentially improve the
459
efficiency of HOC’s biodegradation.
460
To the best of our knowledge, this study for the first time visualized and
461
quantified accumulation of a typical HOC pollutant in microorganisms at the
462
single-cell level by implementing SMFM. Overcoming the disadvantage of general
463
ensemble based methods, such as chromatography and mass spectrometry, SMFM
464
presented a sensitive detection method to visualize the transmembrane process in vivo,
465
which enabled us to achieve a real-time visualization of molecular processes in
466
microbial transmembrane transport of hydrocarbons. Although the accuracy of SMFM
467
quantification cannot be as good as traditional chromatographic analysis and mass
468
spectrometry. But bio-imaging approach used here provided a film-projection mode to
469
real-time visualize and quantify the accumulation of pollutants in cells at the
470
single-cell level, which also cannot be done by traditional methods. Taking the 25
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advantage of SMFM, many environment-induced biological processes can be detected,
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sorted, and quantitatively compared at individually cellular level. The real-time
473
visualization of pollutants at the single-cellular level will add a vital extra dimension
474
to further investigate the mutual interactions among various pollutants and even
475
multicellular organisms.
476
SUPPORTING INFORMATION AVAILABLE
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Movies of perylene bioaccumulation in E. coli, S. aureus, ∆tolC and
478
CCCP-pre-incubated E. coli; and the movie of perylene efflux in CCCP-pre-incubated
479
E. coli; table of initial perylene uptake rates of different strains; emission spectra of
480
perylene in ethanol and in ethanol supplemented with 1 mM palmitic acid. These
481
materials are available free of charge via the Internet at http://pubs.acs.org.
482
ACKNOWLEDGMENTS
483
This work was supported by the National Natural Science Foundation of China
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(grant no. 41371440, no. 91547207, no. 31370847, no. 31327901) and the National
485
Key Basic Research Program of China (2013CB430406). F.B. also acknowledges
486
financial support from the Recruitment Program of Global Youth Experts.
487
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