Source: http://www.asmscience.org/content/book/10.1128/9781555815509.ch13
Timestamp: 2019-04-24 16:01:02+00:00

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This chapter focuses on heterogeneity phenomena among single-celled organisms. It has generally been considered that phenotypic heterogeneity provides a dynamic source of diversity in addition to that derived from genotypic changes such as genome rearrangements and mutation (i.e., genotypic heterogeneity). Recent studies highlighted in this chapter have substantiated this view. This chapter addresses examples of cell-individualized phenotypes in which the benefits of heterogeneity to population fitness can be readily envisaged. Moreover, the most recent studies have risen to the challenge of demonstrating fitness benefits of heterogeneity experimentally, and these studies are discussed in this chapter. The chapter integrates recent studies to describe the molecular bases and consequences of heterogeneity for several of the key phenotypes characterized by variability in clonal microbial populations. Much of the recent focus of research relevant to the field of cell individuality has been to uncover the drivers of diversity operating at the molecular level, in particular the contributions of stochasticity or noise to the processes of gene transcription and translation. The chapter presents a study which describes that the heterogeneity-specific advantage was particularly striking considering that it was prevalent in mutant populations that are conventionally considered to be disadvantaged. Recent research efforts in the area of phenotypic heterogeneity have served to advance significantly the understanding of its nature. The potential benefits of this form of heterogeneity to microbial cell populations are now widely acknowledged and have recently been demonstrated experimentally.
Hierarchy of timescales over which key drivers of cellular heterogeneity operate. Temporal information on the stability of heterogeneous phenotypes is generally lacking. However, the timescales of such stability (e.g., for how long is a particular phenotype maintained by individual cells?) can facilitate prediction of the source of the heterogeneity. The figure is intended to reflect the hierarchical nature of the timescales involved. The timescale values indicated are approximations and are generally organism and condition dependent. Note that while epigenetically regulated phenotypes may be sustained over many generations of cells, it could be stochastic events operating at the opposite end of the timescale that may with time trigger a switch spontaneously to an alternative epigenetic state. Adapted from Avery (2006) with permission.
Cell cycle-dependent copper resistance in S. cerevisiae requires Sod1p. Wild-type (A) and sod1 (B) cells were exposed to Cu(NO3)2 for 10 min at concentrations that gave ~50% killing. With the aid of fluorescent viability dyes ( Sumner et al., 2003 ), cells were sorted into Cu-resistant and -sensitive subpopulations. Unsorted cells (left panels) and the sorted subpopulations (right panels) were then stained for DNA content and reanalyzed with flow cytometry. Data were analyzed using SIMfit software to fit individual 1C (first peak) and 2C (second peak) DNA content plots. Cu-resistant and -sensitive wild-type subpopulations had markedly different cell cycle (1C and 2C DNA) distributions, whereas those of the sod1 subpopulations were similar. Adapted from Sumner et al. (2003) with permission.
Release of phenotypic heterogeneity and enhanced survival under extreme stress in specific yeast mutants. Wild-type (○) or mutant (●) cultures of S. cerevisiaewere plated onto YPD supplemented or not with either Ni(NO3)2, Cu(NO3)2, menadione, or paraquat, as indicated, or buffered to the specified pH values. Viability (colony formation) was determined after several days” incubation at 30°C, and converted to percentages by reference to growth in unsupplemented control medium. The gradients of the slopes reflect the degree of heterogeneity ( Sumner et al., 2003 ). Data are shown for the deletion strains vma3Δ, ctr1Δ, and sod1Δ, as indicated. The points represent means from three replicate determinations. Typical results from one of at least two independent experiments are shown. Adapted from Bishop et al. (2007) with permission.
Lytic or lysogenic pathways in bacteriophage lambda. The figure summarizes some of the principal regulatory inputs that determine the “decision”’by bacteriophage lambda whether to take the lytic (replication and release) or lysogenic (integration into host DNA) life cycle pathways. Please see the main text for accompanying explanation. Adapted from Avery (2006) with permission.
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