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The Spectrum of Drug Susceptibility in Mycobacteria

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  • Authors: Bree B. Aldridge1, Iris Keren2, Sarah M. Fortune3
  • Editors: Graham F. Hatfull4, William R. Jacobs Jr.5
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Molecular Biology and Microbiology, Tufts University, Boston, MA 02111, and Department of Biomedical Engineering, Tufts University, Medford, MA 02155; 2: Antimicrobial Discovery Center and Department of Biology, Northeastern University, Boston, MA 02115; 3: Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115; 4: University of Pittsburgh, Pittsburgh, PA; 5: Howard Hughes Medical Institute, Albert Einstein College of Medicine, Bronx, NY
    • Source: microbiolspec September 2014 vol. 2 no. 5 doi:10.1128/microbiolspec.MGM2-0031-2013
  • Received 30 August 2013 Accepted 04 September 2013 Published 19 September 2014
  • S. M. Fortune, [email protected]
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  • Abstract:

    A major factor complicating efforts to control the tuberculosis epidemic is the long duration of treatment required to successfully clear the infection. One reason that long courses of treatment are required may be the fact that mycobacterial cells arise during the course of infection that are less susceptible to antibiotics. Here we describe the paradigms of phenotypic drug tolerance and resistance as they apply to mycobacteria. We then discuss the mechanisms by which phenotypically drug-tolerant and -resistant cells arise both at a population level and in specialized subpopulations of cells that may be especially important in allowing the bacterium to survive in the face of treatment. These include general mechanisms that have been shown to alter the susceptibility of mycobacteria to antibiotics including growth arrest, efflux pump induction, and biofilm formation. In addition, we discuss emerging data from single-cell studies of mycobacteria that have identified unique ways in which specialized subpopulations of cells arise that vary in their frequency, in their susceptibility to drug, and in their stability over time.

  • Citation: Aldridge B, Keren I, Fortune S. 2014. The Spectrum of Drug Susceptibility in Mycobacteria. Microbiol Spectrum 2(5):MGM2-0031-2013. doi:10.1128/microbiolspec.MGM2-0031-2013.

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2014-09-19
2019-10-14

Abstract:

A major factor complicating efforts to control the tuberculosis epidemic is the long duration of treatment required to successfully clear the infection. One reason that long courses of treatment are required may be the fact that mycobacterial cells arise during the course of infection that are less susceptible to antibiotics. Here we describe the paradigms of phenotypic drug tolerance and resistance as they apply to mycobacteria. We then discuss the mechanisms by which phenotypically drug-tolerant and -resistant cells arise both at a population level and in specialized subpopulations of cells that may be especially important in allowing the bacterium to survive in the face of treatment. These include general mechanisms that have been shown to alter the susceptibility of mycobacteria to antibiotics including growth arrest, efflux pump induction, and biofilm formation. In addition, we discuss emerging data from single-cell studies of mycobacteria that have identified unique ways in which specialized subpopulations of cells arise that vary in their frequency, in their susceptibility to drug, and in their stability over time.

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The Spectrum of Drug Susceptibility in Mycobacteria
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Citation: Aldridge B, Keren I, Fortune S. 2014. The spectrum of drug susceptibility in mycobacteria. microbiolspec 2(5): doi:10.1128/microbiolspec.MGM2-0031-2013
/content/microbiolspec/10.1128/microbiolspec.MGM2-0031-2013.fig1
FIGURE 1
Landscape of drug susceptibility.
microbiolspec/2/5/MGM2-0031-2013-fig1_thmb.gif
microbiolspec/2/5/MGM2-0031-2013-fig1.gif
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FIGURE 1

Landscape of drug susceptibility.

Source: microbiolspec September 2014 vol. 2 no. 5 doi:10.1128/microbiolspec.MGM2-0031-2013
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FIGURE 2
Mycobacterial growth is asymmetric and generates diversity in growth characteristics and drug susceptibility. Time-lapse imaging of pulse-labeled . An amine-active dye (green) stains old cell wall, revealing new growth at old cell poles. Distribution of the difference in absolute elongation rate (averaged over the course of a full division cycle) in paired sister cells. In most pairs, the sister inheriting the old pole elongates faster. Schematic model of mycobacterial growth. Most of the new growth (light green) occurs from the old growing pole (red arrow). Following division, the two sister cells exhibit different growth properties. The cell inheriting the growth pole (called an “accelerator”) elongates faster and is born larger than its sister (called an “alternator”). Distribution of differential drug susceptibility in accelerator and alternator cells in individual microcolonies. Using microfluidics, microcolonies were challenged with MIC levels of antibiotics (meropenem and rifampicin shown here) and scored for survivors by their ability to elongate in recovery media. All figures were adapted from Aldridge et al. ( 16 ).
microbiolspec/2/5/MGM2-0031-2013-fig2_thmb.gif
microbiolspec/2/5/MGM2-0031-2013-fig2.gif
Image of FIGURE 2

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FIGURE 2

Mycobacterial growth is asymmetric and generates diversity in growth characteristics and drug susceptibility. Time-lapse imaging of pulse-labeled . An amine-active dye (green) stains old cell wall, revealing new growth at old cell poles. Distribution of the difference in absolute elongation rate (averaged over the course of a full division cycle) in paired sister cells. In most pairs, the sister inheriting the old pole elongates faster. Schematic model of mycobacterial growth. Most of the new growth (light green) occurs from the old growing pole (red arrow). Following division, the two sister cells exhibit different growth properties. The cell inheriting the growth pole (called an “accelerator”) elongates faster and is born larger than its sister (called an “alternator”). Distribution of differential drug susceptibility in accelerator and alternator cells in individual microcolonies. Using microfluidics, microcolonies were challenged with MIC levels of antibiotics (meropenem and rifampicin shown here) and scored for survivors by their ability to elongate in recovery media. All figures were adapted from Aldridge et al. ( 16 ).

Source: microbiolspec September 2014 vol. 2 no. 5 doi:10.1128/microbiolspec.MGM2-0031-2013
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FIGURE 3
Persister cells in . Schematic representation of persister cells. Persister cell levels in a growing population of . Stationary-phase culture of was diluted 1:100, and growth and persister levels were followed over time. Persister levels were determined after 1 week of challenge with streptomycin (SM) (40 μg/ml) or ciprofloxacin (Cip) (5 µg/ml). Model explaining the need for lengthy antibiotic treatment of infection. Panels B and C were adapted from Keren et al. ( 6 ).
microbiolspec/2/5/MGM2-0031-2013-fig3_thmb.gif
microbiolspec/2/5/MGM2-0031-2013-fig3.gif
Image of FIGURE 3

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FIGURE 3

Persister cells in . Schematic representation of persister cells. Persister cell levels in a growing population of . Stationary-phase culture of was diluted 1:100, and growth and persister levels were followed over time. Persister levels were determined after 1 week of challenge with streptomycin (SM) (40 μg/ml) or ciprofloxacin (Cip) (5 µg/ml). Model explaining the need for lengthy antibiotic treatment of infection. Panels B and C were adapted from Keren et al. ( 6 ).

Source: microbiolspec September 2014 vol. 2 no. 5 doi:10.1128/microbiolspec.MGM2-0031-2013
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