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

Jessica A. Lee, Siavash Riazi, Shahla Nemati, Jannell V. Bazurto, Andreas E. Vasdekis, Benjamin J. Ridenhour, Christopher H. Remien, Christopher J. Marx

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere1008458
JournalPLoS genetics
Volume15
Issue number11
DOIs
StatePublished - 2019

Bibliographical note

Funding Information:
Funding for this work came from an Army Research Office MURI sub-award to CJM (W911NF-12-1-0390), a CMCI Pilot Grant to CJM, CHR, and AEV (parent NIH award P20GM104420), and a grant from the John Templeton Foundation to Global Viral (Grant ID 60973). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to Eric Bruger, Sergey Stolyar, Nicholas Shevalier, and Joshua Wirtz for assistance in conducting M. extorquens experiments; to Dipti Nayak for advice and resources facilitating those experiments; to Craig Miller for advice on analyzing colony counts; to William Harcombe and Jeremy Chac?n for guidance on time-lapse imaging of colony growth using flatbed scanners; to Mark Lamourine for help with software and Dan Schneider for contributions to hardware for running the scanners; and to P?l Johnsen and Alex Bradley for helpful comments on the manuscript. Flow cytometry was conducted at the IBEST Optical Imaging Core at the University of Idaho under the guidance of Ann Norton. Genomic DNA sample preparation and sequencing were managed by the IBEST Genomics Resources Core at the University of Idaho by Dan New, Samuel Hunter, and Matt Fagnan. Juan E. Abrahante of the University of Minnesota Informatics Institute (UMII) assisted with RNA-Seq data analysis. We dedicate this paper to the memory of Paul J. Joyce and his passion to bring math and biology together.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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