1. Computational and Systems Biology
  2. Microbiology and Infectious Disease
Download icon

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides

  1. Samuel T Coradetti
  2. Dominic Pinel
  3. Gina Geiselman
  4. Masakazu Ito
  5. Stephen Mondo
  6. Morgann C Reilly
  7. Ya-Fang Cheng
  8. Stefan Bauer
  9. Igor Grigoriev
  10. John M Gladden
  11. Blake A Simmons
  12. Rachel Brem
  13. Adam P Arkin  Is a corresponding author
  14. Jeffrey M Skerker  Is a corresponding author
  1. The Buck Institute for Research on Aging, United States
  2. University of California, Berkeley, United States
  3. United States Department of Energy Joint Genome Institute, United States
  4. Joint BioEnergy Institute, United States
Research Article
  • Cited 40
  • Views 4,441
  • Annotations
Cite this article as: eLife 2018;7:e32110 doi: 10.7554/eLife.32110

Abstract

The basidiomycete yeast Rhodosporidium toruloides (a.k.a. Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted function in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. These results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Samuel T Coradetti

    The Buck Institute for Research on Aging, Novato, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Dominic Pinel

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Gina Geiselman

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Masakazu Ito

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Stephen Mondo

    United States Department of Energy Joint Genome Institute, Walnut Creek, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Morgann C Reilly

    Joint BioEnergy Institute, Emeryville, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Ya-Fang Cheng

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Stefan Bauer

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Igor Grigoriev

    United States Department of Energy Joint Genome Institute, Walnut Creek, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. John M Gladden

    Joint BioEnergy Institute, Emeryville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6985-2485
  11. Blake A Simmons

    Joint BioEnergy Institute, Emeryville, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Rachel Brem

    The Buck Institute for Research on Aging, Novato, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Adam P Arkin

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    For correspondence
    aparkin@lbl.gov
    Competing interests
    The authors declare that no competing interests exist.
  14. Jeffrey M Skerker

    Energy Biosciences Institute, University of California, Berkeley, Berkeley, United States
    For correspondence
    SKERKER@BERKELEY.EDU
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2653-1566

Funding

Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-SC-0012527)

  • Samuel T Coradetti
  • Dominic Pinel
  • Gina Geiselman
  • Masakazu Ito
  • Ya-Fang Cheng
  • Stefan Bauer
  • Rachel Brem
  • Adam P Arkin
  • Jeffrey M Skerker

University of California Berkeley (OO1605)

  • Dominic Pinel
  • Adam P Arkin
  • Jeffrey M Skerker

Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-AC02-05CH11231)

  • Stephen Mondo
  • Igor Grigoriev
  • John M Gladden
  • Blake A Simmons

University of California Berkeley (OO6J01)

  • Dominic Pinel
  • Adam P Arkin
  • Jeffrey M Skerker

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

Reviewing Editor

  1. Joerg Bohlmann, University of British Columbia, Canada

Publication history

  1. Received: September 19, 2017
  2. Accepted: March 5, 2018
  3. Accepted Manuscript published: March 9, 2018 (version 1)
  4. Version of Record published: April 27, 2018 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 4,441
    Page views
  • 690
    Downloads
  • 40
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

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)

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

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

Further reading

    1. Chromosomes and Gene Expression
    2. Computational and Systems Biology
    Lucy Ham et al.
    Research Article Updated

    Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells (‘intrinsic noise’) from variability across the population (‘extrinsic noise’). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that ‘pathway-reporters’ compare favourably to the well-known, but often difficult to implement, dual-reporter method.

    1. Computational and Systems Biology
    2. Neuroscience
    Cathy S Chen et al.
    Research Article

    Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless 2-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get 'stuck' in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses.