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Phenotypic Heterogeneity in

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  • Authors: Neeraj Dhar1, John McKinney2, Giulia Manina3
  • Editors: William R. Jacobs Jr.4, Helen McShane5, Valerie Mizrahi6, Ian M. Orme7
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Global Health Institute, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 2: Global Health Institute, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 3: Microbial Individuality and Infection Group, Institut Pasteur, 75015 Paris, France; 4: Howard Hughes Medical Institute, Albert Einstein School of Medicine, Bronx, NY 10461; 5: University of Oxford, Oxford OX3 7DQ, United Kingdom; 6: University of Cape Town, Rondebosch 7701, South Africa; 7: Colorado State University, Fort Collins, CO 80523
  • Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
  • Received 16 June 2016 Accepted 21 July 2016 Published 11 November 2016
  • G. Manina, giulia.manina@pasteur.fr
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  • Abstract:

    The interaction between the host and the pathogen is extremely complex and is affected by anatomical, physiological, and immunological diversity in the microenvironments, leading to phenotypic diversity of the pathogen. Phenotypic heterogeneity, defined as nongenetic variation observed in individual members of a clonal population, can have beneficial consequences especially in fluctuating stressful environmental conditions. This is all the more relevant in infections caused by wherein the pathogen is able to survive and often establish a lifelong persistent infection in the host. Recent studies in tuberculosis patients and in animal models have documented the heterogeneous and diverging trajectories of individual lesions within a single host. Since the fate of the individual lesions appears to be determined by the local tissue environment rather than systemic response of the host, studying this heterogeneity is very relevant to ensure better control and complete eradication of the pathogen from individual lesions. The heterogeneous microenvironments greatly enhance heterogeneity influencing the growth rates, metabolic potential, stress responses, drug susceptibility, and eventual lesion resolution. Single-cell approaches such as time-lapse microscopy using microfluidic devices allow us to address cell-to-cell variations that are often lost in population-average measurements. In this review, we focus on some of the factors that could be considered as drivers of phenotypic heterogeneity in as well as highlight some of the techniques that are useful in addressing this issue.

  • Citation: Dhar N, McKinney J, Manina G. 2016. Phenotypic Heterogeneity in . Microbiol Spectrum 4(6):TBTB2-0021-2016. doi:10.1128/microbiolspec.TBTB2-0021-2016.

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/content/journal/microbiolspec/10.1128/microbiolspec.TBTB2-0021-2016
2016-11-11
2017-09-22

Abstract:

The interaction between the host and the pathogen is extremely complex and is affected by anatomical, physiological, and immunological diversity in the microenvironments, leading to phenotypic diversity of the pathogen. Phenotypic heterogeneity, defined as nongenetic variation observed in individual members of a clonal population, can have beneficial consequences especially in fluctuating stressful environmental conditions. This is all the more relevant in infections caused by wherein the pathogen is able to survive and often establish a lifelong persistent infection in the host. Recent studies in tuberculosis patients and in animal models have documented the heterogeneous and diverging trajectories of individual lesions within a single host. Since the fate of the individual lesions appears to be determined by the local tissue environment rather than systemic response of the host, studying this heterogeneity is very relevant to ensure better control and complete eradication of the pathogen from individual lesions. The heterogeneous microenvironments greatly enhance heterogeneity influencing the growth rates, metabolic potential, stress responses, drug susceptibility, and eventual lesion resolution. Single-cell approaches such as time-lapse microscopy using microfluidic devices allow us to address cell-to-cell variations that are often lost in population-average measurements. In this review, we focus on some of the factors that could be considered as drivers of phenotypic heterogeneity in as well as highlight some of the techniques that are useful in addressing this issue.

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Figures

Image of FIGURE 1
FIGURE 1

Causes and consequences of phenotypic heterogeneity. Bacterial isogenic populations arising from a single progenitor cell are usually expected to be homogeneous (left snapshot). However, single-cell analysis unveils significant cell-to-cell heterogeneity (right snapshot). Some of the causal factors leading to this heterogeneity are variations in growth rate, growth continuity, interdivision time, division symmetry, gene expression, protein distribution, and cell age generated either through deterministic or stochastic mechanisms.

Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
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Image of FIGURE 2
FIGURE 2

Stress conditions enhance phenotypic heterogeneity. Upper panel, single-cell rRNA-GFP fluorescence of isolated from different environments: Exp (exponential phase), Stat (stationary phase), Drug (treated with isoniazid), Mɸ (grown in macrophages), and Mouse (explanted from mouse lungs during the acute phase of infection). Each circle represents a single cell and the mean fluorescence ± SD is indicated (n = 200 per time point). Asterisks indicate significance difference of each data set in comparison with the control group, Exp ( < 0.0001), according to ANOVA followed by the Kruskal-Wallis test. The numbers shown on top are the coefficient of variation (CV) for each data set. Lower panel, representative snapshots from the corresponding conditions are shown. Green (rRNA-GFP) and red (constitutive dsRed) fluorescence channels are merged. Macrophages are also shown in phase contrast. Scale bars, 5 μm. Figure adapted from Manina et al. ( 35 ).

Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
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Image of FIGURE 3
FIGURE 3

Identification of NGMA bacteria by single-cell techniques. A schematic of the fluorescence recovery after photobleaching (FRAP) method is shown on the top. expressing cytoplasmic rRNA-GFP were subjected to photobleaching using a laser, followed by staining with a dye that penetrates only cells with a compromised membrane. Metabolically active cells (green), bleached or metabolically inactive cells (gray), and dead cells (blue) are depicted. Representative snapshots of stationary-phase cells that were exposed to fresh 7H9 medium for 1 week. Top row, a nongrowing cell that does not recover fluorescence after photobleaching and stains positive for dead-cell stain (negative control). Middle row, a nongrowing cell that recovers fluorescence after photobleaching and stains negative for dead-cell stain and is therefore identified as nongrowing but metabolically active (NGMA). Bottom row, a growing cell that recovers fluorescence after photobleaching, stains negative for dead-cell stain, and continues to grow postbleaching.

Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
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Image of FIGURE 4
FIGURE 4

Host and pathogen heterogeneity contributes to TB diversity. Schematic of the increasingly heterogeneous environments resides in. Disease heterogeneity initiates in the major site of infection where host immunity gives rise to the typical granulomatous lesion (magnified from the lung parenchyma). This assembly of host cells consists of different types of macrophages, dendritic cells, neutrophils, lymphocytes, fibroblasts, and a necrotic caseous core. Bacilli can reside in discrete niches both intracellularly (magnified from the granuloma) and extracellularly, where they are subjected to a plethora of host immune effectors (red arrow) and antibiotics (blue arrow). Diverse environmental cues found within each microniche contribute to enhance the intrinsic phenotypic diversity of (right snapshot).

Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
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Image of FIGURE 5
FIGURE 5

Cellular dynamic processes and single-cell techniques. The different biological processes that occur during cell growth and that often determine cell fate and some of the techniques that are used to track these processes at the single-cell level are depicted. Fluorescent approaches involve the use of fluorescent protein fusions or fluorescent-tagged molecules. Figure adapted from Spiller et al. ( 227 ).

Source: microbiolspec November 2016 vol. 4 no. 6 doi:10.1128/microbiolspec.TBTB2-0021-2016
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