Bistability, Epigenetics, and Bet-Hedging in Bacteria

Veening, Jan‐Willem; Smits, Wiep Klaas; Kuipers, Oscar P.

doi:10.1146/annurev.micro.62.081307.163002

Cited by 942 publications

(921 citation statements)

References 106 publications

(108 reference statements)

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“…The power of metabolomics can be exploited in population-level measurements, but as cell populations are not necessarily homogeneous, it will be even more informative to measure at the single cell level. In addition to the abovementioned sources for heterogeneity, stochasticity-induced phenotypic heterogeneity was identified a few years ago as an additional source of cell-to-cell variability [18,19]. Because of low copy numbers of specific biomolecules, certain processes at the gene and protein expression level are inherently stochastic and can cause random fluctuations in the abundance of biomolecules.…”

Section: Why Single Cell Metabolomics?mentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity. IntroductionQuantitative metabolomics, the technology for large-scale quantification of intracellular metabolite concentrations, is a powerful tool in systems biology research that has recently led to a series of interesting findings (e.g. [1][2][3][4]). Because of the metabolome's chemical diversity, mass spectrometry (MS) is the analytical method of choice [5]. In addition to analytical challenges, quantitative metabolomics as required for addressing (systems) biology questions poses significant challenges in sample processing. One important challenge is the need to preserve the original metabolome during sample processing, which is often difficult because of the presence of enzymes in the sample and the fast metabolic turnover rates.For sensitivity reasons, current metabolomics methods require samples that contain a large number of cells. However, cell populations are not necessarily homogeneous. Besides genetic differences, several other sources for population heterogeneity exist, of which several are also known to cause metabolic differences. Today, methods capable of resolving differences in metabolite levels on the single cell level are provided, within limits, by molecular sensors such as FRET sensors [6,7] or aptamer-based technology [8 ,9]. Both types of molecular sensors, however, are difficult to develop, are limited to specific analytes, and quantitative analyses (e.g. in terms of mol/L) are hardly possible with them. Laserinduced fluorescence, as introduced by Dovichi for single cell proteomics, is limited to fluorescent compounds or labelled species [10,11]. In addition, all these existing methods share the limitation that they can never be extended to the ''-omics'' level, that is to measuring a large number of metabolites at the same time. They will thus not be applicable to discovery type research and research that requires a large number of metabolites to be measured in the same cell.Because of the success of mass spectrometry in population-level metabolome analyses, the analytical community has recently made great strides towards single cell level metabolite analyses (for a review, see [12]). So far, however, hardly any new biological insight has been generated from these endeavours. In this Current Opinion paper, we will thus not only review the current status of MS-based single cell metabolomics but also discuss which of the differ...

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Section: Why Single Cell Metabolomics?mentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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“…motility of Bacillus subtilis (Veening et al 2008;Piggot 2010). The paramutation phenomenon, reported in corn in 1958 by Alexander Brink, was another piece of evidence pointing to the occurrence of epigenetics (Brink 1959).…”

Section: From Waddington's Concept and Nanney's Epigenetics To The Prmentioning

confidence: 97%

Epigenetics: from the past to the present

Villota-Salazar

Mendoza‐Mendoza

González-Prieto

2016

Frontiers in Life Science

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The definition of epigenetics is still under intense debate; however, its concept has evolved since it was originally introduced in 1939 by Conrad Hal Waddington as a way to reconcile antagonistic views between the school of preformationism and the school of epigenesis. The characterization of a large number of phenomena that diverge from the dogmas of classical genetics, and the discovery of the molecular mechanisms through which these phenomena occur, has given rise to a new area of study with important implications for biological sciences. Interactions between the environment and the DNA through modifications on the chromatin are not only responsible for the expression of a normal phenotype, these may be involved in the development of various pathologies. The epigenome, as the bridge between the genome and the phenotype, is no doubt one of the most interesting current ideas in genetics and is so revolutionary that it may change our present notions about inheritance and evolution. In this review, we made a compilation of the most important events in the history of epigenetics, its implications and some perspectives to the future.

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“…In bacterial phenotypic variation, transcriptional and translational noise among individual cells can be amplified by positive-or negative-feedback loops, causing the bacterial population to bifurcate into two inheritable and reversible phenotypes, a phenomenon called bistability (72). In X. nematophila, Lrp negatively regulates its own promoter, raising the possibility that Lrp bistable expression is controlled by a single autoregulatory negative-feedback loop (68).…”

Section: Phenotypic Variation In X Nematophilamentioning

confidence: 99%

Ready or Not: Microbial Adaptive Responses in Dynamic Symbiosis Environments

Cao

Goodrich‐Blair

2017

J Bacteriol

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In mutually beneficial and pathogenic symbiotic associations, microbes must adapt to the host environment for optimal fitness. Both within an individual host and during transmission between hosts, microbes are exposed to temporal and spatial variation in environmental conditions. The phenomenon of phenotypic variation, in which different subpopulations of cells express distinctive and potentially adaptive characteristics, can contribute to microbial adaptation to a lifestyle that includes rapidly changing environments. The environments experienced by a symbiotic microbe during its life history can be erratic or predictable, and each can impact the evolution of adaptive responses. In particular, the predictability of a rhythmic or cyclical series of environments may promote the evolution of signal transduction cascades that allow preadaptive responses to environments that are likely to be encountered in the future, a phenomenon known as adaptive prediction. In this review, we summarize environmental variations known to occur in some well-studied models of symbiosis and how these may contribute to the evolution of microbial population heterogeneity and anticipatory behavior. We provide details about the symbiosis between Xenorhabdus bacteria and Steinernema nematodes as a model to investigate the concept of environmental adaptation and adaptive prediction in a microbial symbiosis.

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Bistability, Epigenetics, and Bet-Hedging in Bacteria

Cited by 942 publications

References 106 publications

Single cell metabolomics

Single cell metabolomics

Epigenetics: from the past to the present

Ready or Not: Microbial Adaptive Responses in Dynamic Symbiosis Environments

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