Essential metabolism for a minimal cell

  1. Marian Breuer
  2. Tyler M Earnest
  3. Chuck Merryman
  4. Kim S Wise
  5. Lijie Sun
  6. Michaela R Lynott
  7. Clyde A Hutchison
  8. Hamilton O Smith
  9. John D Lapek
  10. David J Gonzalez
  11. Valérie de Crécy-Lagard
  12. Drago Haas
  13. Andrew D Hanson
  14. Piyush Labhsetwar
  15. John I Glass
  16. Zaida Luthey-Schulten  Is a corresponding author
  1. University of Illinois at Urbana-Champaign, United States
  2. J Craig Venter Institute, United States
  3. University of California, San Diego, United States
  4. University of Florida, United States

Abstract

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% (68% essential), compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.

Data availability

Proteomics: data were uploaded to MassIVE (massive.ucsd.edu) with dataset identifier MSV000081687 and ProteomeXchange with dataset identifier PXD008159. All other new data are included in the manuscript and supporting files.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Marian Breuer

    Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, United States
    Competing interests
    No competing interests declared.
  2. Tyler M Earnest

    Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1630-0791
  3. Chuck Merryman

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  4. Kim S Wise

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  5. Lijie Sun

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  6. Michaela R Lynott

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  7. Clyde A Hutchison

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    Clyde A Hutchison, is a consultant for Synthetic Genomics, Inc. (SGI), and holds SGI stock and/or stock options.
  8. Hamilton O Smith

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    Hamilton O Smith, is on the Board of Directors and cochief scientific officer of Synthetic Genomics, Inc. (SGI) and holds SGI stock and/or stock options.
  9. John D Lapek

    Department of Pharmacology, University of California, San Diego, La Jolla, United States
    Competing interests
    No competing interests declared.
  10. David J Gonzalez

    Department of Pharmacology, University of California, San Diego, La Jolla, United States
    Competing interests
    No competing interests declared.
  11. Valérie de Crécy-Lagard

    Department of Microbiology and Cell Science, University of Florida, Gainesville, United States
    Competing interests
    No competing interests declared.
  12. Drago Haas

    Department of Microbiology and Cell Science, University of Florida, Gainesville, United States
    Competing interests
    No competing interests declared.
  13. Andrew D Hanson

    Horticultural Sciences Department, University of Florida, Gainesville, United States
    Competing interests
    No competing interests declared.
  14. Piyush Labhsetwar

    Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, United States
    Competing interests
    No competing interests declared.
  15. John I Glass

    Synthetic Biology Group, J Craig Venter Institute, La Jolla, United States
    Competing interests
    No competing interests declared.
  16. Zaida Luthey-Schulten

    Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, United States
    For correspondence
    zan@illinois.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9749-8367

Funding

National Science Foundation (PHY 1430124 Postdoctoral Fellowship)

  • Marian Breuer

National Institutes of Health (K12 GM06852)

  • John D Lapek

University of California (Office of the President)

  • David J Gonzalez

Ray Thomas Edwards Foundation

  • David J Gonzalez

J Craig Venter Institute

  • Chuck Merryman
  • Kim S Wise
  • Clyde A Hutchison
  • Hamilton O Smith
  • John I Glass

National Science Foundation (PHY 1430124)

  • Marian Breuer
  • Tyler M Earnest
  • Zaida Luthey-Schulten

National Science Foundation (MCB-1611711)

  • Valérie de Crécy-Lagard
  • Andrew D Hanson

National Science Foundation (MCB-1244570)

  • Marian Breuer
  • Tyler M Earnest
  • Zaida Luthey-Schulten

Department of Energy (ORNL 4000134575)

  • Piyush Labhsetwar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Zoran Nikoloski, Max Planck Institute of Molecular Plant Physiology, Germany

Version history

  1. Received: March 21, 2018
  2. Accepted: January 17, 2019
  3. Accepted Manuscript published: January 18, 2019 (version 1)
  4. Version of Record published: April 17, 2019 (version 2)
  5. Version of Record updated: February 17, 2021 (version 3)

Copyright

© 2019, Breuer et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 16,500
    views
  • 2,158
    downloads
  • 114
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Marian Breuer
  2. Tyler M Earnest
  3. Chuck Merryman
  4. Kim S Wise
  5. Lijie Sun
  6. Michaela R Lynott
  7. Clyde A Hutchison
  8. Hamilton O Smith
  9. John D Lapek
  10. David J Gonzalez
  11. Valérie de Crécy-Lagard
  12. Drago Haas
  13. Andrew D Hanson
  14. Piyush Labhsetwar
  15. John I Glass
  16. Zaida Luthey-Schulten
(2019)
Essential metabolism for a minimal cell
eLife 8:e36842.
https://doi.org/10.7554/eLife.36842

Share this article

https://doi.org/10.7554/eLife.36842

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Arya Y Nakhe, Prasanna K Dadi ... David A Jacobson
    Research Article

    The gain-of-function mutation in the TALK-1 K+ channel (p.L114P) is associated with maturity-onset diabetes of the young (MODY). TALK-1 is a key regulator of β-cell electrical activity and glucose-stimulated insulin secretion. The KCNK16 gene encoding TALK-1 is the most abundant and β-cell-restricted K+ channel transcript. To investigate the impact of KCNK16 L114P on glucose homeostasis and confirm its association with MODY, a mouse model containing the Kcnk16 L114P mutation was generated. Heterozygous and homozygous Kcnk16 L114P mice exhibit increased neonatal lethality in the C57BL/6J and the CD-1 (ICR) genetic background, respectively. Lethality is likely a result of severe hyperglycemia observed in the homozygous Kcnk16 L114P neonates due to lack of glucose-stimulated insulin secretion and can be reduced with insulin treatment. Kcnk16 L114P increased whole-cell β-cell K+ currents resulting in blunted glucose-stimulated Ca2+ entry and loss of glucose-induced Ca2+ oscillations. Thus, adult Kcnk16 L114P mice have reduced glucose-stimulated insulin secretion and plasma insulin levels, which significantly impairs glucose homeostasis. Taken together, this study shows that the MODY-associated Kcnk16 L114P mutation disrupts glucose homeostasis in adult mice resembling a MODY phenotype and causes neonatal lethality by inhibiting islet insulin secretion during development. These data suggest that TALK-1 is an islet-restricted target for the treatment for diabetes.

    1. Computational and Systems Biology
    David Geller-McGrath, Kishori M Konwar ... Jason E McDermott
    Tools and Resources

    The reconstruction of complete microbial metabolic pathways using ‘omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.