Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition

Journal name:
Nature Protocols
Volume:
8,
Pages:
555–567
Year published:
DOI:
doi:10.1038/nprot.2013.021
Published online

Abstract

We present a protocol for building and operating an automated fluidic system for continuous culture that we call the 'morbidostat'. The morbidostat is used to follow the evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug-induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within 2–3 weeks by biologists with some electronics experience or engineers familiar with basic microbiology.

At a glance

Figures

  1. The morbidostat is an automated continuous-culture device that maintains a constant level of growth inhibition on evolving bacterial populations.
    Figure 1: The morbidostat is an automated continuous-culture device that maintains a constant level of growth inhibition on evolving bacterial populations.

    (a) The working algorithm of the morbidostat. (b) Representative bacterial growth in the morbidostat. OD values are shown with gray dots. Growth rates (r) of bacterial populations are periodically calculated by fitting exponential growth functions (black lines). Markers with magenta and green colors represent dilutions with drug solution and fresh medium, respectively. (c) Final OD values at the end of each growth cycle. (d) Growth rates (blue) and drug concentrations (magenta) between two consecutive drug solution injections. (e) The drug concentration that inhibits growth by 50% (IC50) is calculated by analyzing growth rates and corresponding drug concentrations. TMP, trimethoprim.

  2. Construction steps for morbidostat vial assembly.
    Figure 2: Construction steps for morbidostat vial assembly.

    (a) PEEK tubing pieces. (b) Assembled silicone tubing and Luer connector. (c) Teflon insert assembled with PEEK tubing pieces. (d) Assembled morbidostat vial. The height of the longer PEEK tubing is adjusted such that it just touches the top of the culture in the vial, the volume of which is 12 ml. (e) High temperature–resistant silicone is applied to the entire upper face of the Teflon insert.

  3. Construction steps for tube holder assembly.
    Figure 3: Construction steps for tube holder assembly.

    (a) Commercial LED holder. (b) The LED and the photodetectors are inserted into the LED holder. (c) Legs of the LED are soldered to wires. (d) Wire connections are insulated with heat-shrink insulators. (e) Custom-machined LED housing is attached to the LED holder. (f) LED and photodetector assemblies are inserted into the custom-machined Delrin tube holders.

  4. Circuit diagrams for the OD detection system.
    Figure 4: Circuit diagrams for the OD detection system.

    (a) Each LED is connected to a 68-Ω resistor in series. All LEDs are connected in parallel. (b) Each photodetector is connected to a resistor in series. All photodetectors are connected in parallel.

  5. Construction steps for pump array and medium reservoir assemblies.
    Figure 5: Construction steps for pump array and medium reservoir assemblies.

    (a) A peristaltic pump is attached to the pump stand (Supplementary Fig. 4). (b) Two pieces of silicone tubing are attached to the pumps. (c) An assembled peristaltic pump with Luer connectors and silicone tubing. (d) Silicone tubing pieces are inserted through holes on a GL45 screw cap. High temperature–resistant silicone is applied to the entire upper face of the screw cap. (e) The assembled medium reservoir.

  6. Calibration of the detectors.
    Figure 6: Calibration of the detectors.

    (a) The voltages (gray circles) created by cultures with known OD values are recorded. A line (red line) is fitted for finding the calibration factor. (b) An E. coli culture is grown overnight and OD values are recorded (gray line) every second. Red line shows the range in which the growth is exponential.

References

  1. Alekshun, M.N. & Levy, S.B. Molecular mechanisms of antibacterial multidrug resistance. Cell 128, 10371050 (2007).
  2. Taubes, G. The bacteria fight back. Science 321, 356361 (2008).
  3. Levy, S.B. & Marshall, B. Antibacterial resistance worldwide: causes, challenges and responses. Nat. Med. 10, S122S129 (2004).
  4. Lipsitch, M., Bergstrom, C.T. & Levin, B.R. The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc. Natl. Acad. Sci. USA 97, 19381943 (2000).
  5. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101105 (2012).
  6. Lee, H.H., Molla, M.N., Cantor, C.R. & Collins, J.J. Bacterial charity work leads to population-wide resistance. Nature 467, 8285 (2010).
  7. Bryson, V. & Szybalski, W. Microbial selection. Science 116, 4551 (1952).
  8. Hegreness, M., Shoresh, N., Damian, D., Hartl, D. & Kishony, R. Accelerated evolution of resistance in multidrug environments. Proc. Natl. Acad. Sci. USA 105, 1397713981 (2008).
  9. Michel, J.B., Yeh, P.J., Chait, R., Moellering, R.C. Jr. & Kishony, R. Drug interactions modulate the potential for evolution of resistance. Proc. Natl. Acad. Sci. USA (2008).
  10. Bauer, A.W., Kirby, W.M., Sherris, J.C. & Turck, M. Antibiotic susceptibility testing by a standardized single disk method. Am. J. Clin. Pathol. 45, 493496 (1966).
  11. Weinreich, D.M., Delaney, N.F., Depristo, M.A. & Hartl, D.L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111114 (2006).

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Author information

Affiliations

  1. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.

    • Erdal Toprak,
    • Remy Chait,
    • Johan Paulsson &
    • Roy Kishony
  2. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.

    • Erdal Toprak &
    • Sadik Yildiz
  3. Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts, USA.

    • Adrian Veres
  4. Department of Physics, Universidad de los Andes, Bogotá, Colombia.

    • Juan M Pedraza
  5. School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.

    • Johan Paulsson &
    • Roy Kishony

Contributions

E.T., A.V., S.Y., R.C., J.M.P., J.P. and R.K. contributed to the design of the setup. E.T., A.V. and R.K. developed the assay and the algorithm for the morbidostat. E.T., A.V. and R.K. performed the experiments and analyzed the data. A.V. and S.Y. wrote the MATLAB code. E.T., A.V., S.Y., R.C., J.M.P., J.P. and R.K. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

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Author details

Supplementary information

PDF files

  1. Supplementary Figure 1 (417 KB)

    Technical drawings for machining the custom components of tube holder array. Tube holders are machined from black Delrin or polycarbonate material. The adaptor plate and the cover plate are made from clear Plexiglass. All dimensions are in inches.

  2. Supplementary Figure 2 (5.14 MB)

    Solid model of the tube holder array. A detailed drawing of the assembled tube holder array is shown. All components that are used in the tube holder array are numbered. Components required for the assembly, their quantities, and vendors are listed in the table.

  3. Supplementary Figure 3 (574 KB)

    Technical drawing of the pump holder array. The body of the pump holder rack is made from aluminum sigma profile (40mm x 40mm thickness) and commercially available assembly brackets. Threaded holes are opened on the sigma profiles for mounting the peristaltic pumps.

  4. Supplementary Figure 4 (1.09 MB)

    Algorithm of the morbidostat software. Morbidostat algorithm runs three parallel timers. Using these timers OD values are acquired, data is periodically saved, and the dilution decisions are made and executed. Using the existing settings, morbidostat can be used also as a chemostat or turbidostat

  5. Supplementary Figure 5 (649 KB)

    Calibration algorithm of the morbidostat software illustrated. Calibrator function can be used directly from the GUI (gui.m). A glass vial filled with cell culture is used for converting voltage values read by the computer into OD values. OD of the sample is first measured with a standard OD reading device such as spectrophotometers. Culture vial is then placed into the tube holders and the median voltage on across the detector is recorded. This procedure is repeated for all tube holders using several dilutions of the cell culture. Typically 8-10 samples that are distributed between the 0.03-0.75 OD is enough for an accurate calibration. Use of different cell types or different media may need extra calibrations.

  6. Supplementary Figure 6 (451 KB)

    User interface of the pump control software. All of the injection pumps and suction pump can be controlled using the interface shown. This feature is particularly useful for sterilization of the tubings.

Zip files

  1. Supplementary Data (90.7 KB)

    Morbidostat control software and necessary files for executing the code. Supplementary Data contains several Matlab files and file folders that are necessary to execute the code. The folder named as “Numbers” includes the audio files used in calibrator.m. The folder named as “Images” includes images used for the control interface. The folder named as “background data” includes a sample data set that we use for correcting optical density readings by subtracting the background voltage values. The folder named as “calibration data” includes a sample data set we use to convert voltage readings to optical density values.

    Morbidostat control software is written in Matlab. Following Matlab files are provided: Calibrator.m, cleanMatlab.m, DataMonitor.m, DataReceiver.m, ExperimentController.m, gui.fig, gui.m, PumpController.m, and Simulator.m. ExperimentController.m, PumpController.m, DataReceiver.m, DataMonitor.m, Calibrator.m, and Simulator.m are Matlab classes that can be independently instantiated as objects. ExperimentController.m is the main class file that controls all timers and objects for running the morbidostat. PumpController.m is the class file that controls the pumps via relay boxes according to orders that it takes from the parent ExperimentController.m object or the manual commands given from MATLAB command window. DataReceiever.m is the class file that receives analog input from the data acquisition devices and converts the voltage readings to optical density values according to the calibration parameters. DataMonitor.m is the class file that monitors the real time data to the user as the experiment is running on.

Additional data