Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

Autoři: Jessica A. Lee aff001;  Siavash Riazi aff002;  Shahla Nemati aff002;  Jannell V. Bazurto aff001;  Andreas E. Vasdekis aff002;  Benjamin J. Ridenhour aff002;  Christopher H. Remien aff002;  Christopher J. Marx aff001
Působiště autorů: Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America aff001;  Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America aff002;  Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America aff003;  Global Viral, San Francisco, California, United States of America aff004;  Bioinformatics and Computational Biology Graduate Program, University of Idaho, Moscow, Idaho, United States of America aff005;  Department of Physics, University of Idaho, Moscow, Idaho, United States of America aff006;  Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota, United States of America aff007;  Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minnesota, United States of America aff008;  Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America aff009
Vyšlo v časopise: Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations. PLoS Genet 15(11): e32767. doi:10.1371/journal.pgen.1008458
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008458


While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Klíčová slova:

Cell death – Formaldehyde – Gene expression – Genomic libraries – Phenotypes – Population dynamics – Population genetics – Protein domains


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