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Research Article

Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica

Eduard J. Kerkhoven, Young-Mo Kim, Siwei Wei, Carrie D. Nicora, Thomas L. Fillmore, Samuel O. Purvine, Bobbie-Jo Webb-Robertson, Richard D. Smith, Scott E. Baker, Thomas O. Metz, Jens Nielsen
Sang Yup Lee, Editor
Eduard J. Kerkhoven
a Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
b Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Göteborg, Sweden
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  • ORCID record for Eduard J. Kerkhoven
Young-Mo Kim
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Siwei Wei
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Carrie D. Nicora
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Thomas L. Fillmore
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Samuel O. Purvine
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Bobbie-Jo Webb-Robertson
d National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Richard D. Smith
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Scott E. Baker
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Thomas O. Metz
c Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
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Jens Nielsen
a Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
b Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Göteborg, Sweden
e Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
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Sang Yup Lee
Korea Advanced Institute of Science and Technology
Roles: Editor
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Hal Alper
University of Texas at Austin
Roles: Solicited external reviewer
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Jean-Marc Nicaud
INRA
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DOI: 10.1128/mBio.00857-17
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  • FIG 1 
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    FIG 1 

    Consensus gene-set analysis using differential RNA expression according to a general linear model. (A) Overview of number of genes whose expression values were significantly (adjusted P < 0.05) influenced by DGA1 overexpression (DGA1), nitrogen limitation (N-lim), or the DGA1 × N-lim interaction. (B) GO term enrichment analysis. For each significantly changed GO term (rank score of ≤5), the direction and significance of the changes in RNA levels of their constitutive genes are shown, together with the total number of genes within each GO term. ox-red, oxidation-reduction; polII, polymerase II; TF, transcription factor; transp. act., transporter activity. (C) Expression profiles of ILV3, involved in leucine biosynthesis, and ATG8, involved in autophagy, exemplifying the factorial effects. acyl-CoA, acyl-coenzyme A.

  • FIG 2 
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    FIG 2 

    Enrichments of GO terms and transcription-factor binding motifs from genes with similar regulatory responses. (A) For each differentially expressed gene (adjusted P < 0.01), the relative contributions of the three factors (D, DGA1 overexpression; N, nitrogen limitation; D×N, interaction of DGA1 overexpression and nitrogen limitation) were ranked. The genes with the same order of factor contributions were subsequently clustered together (the number of genes per cluster is indicated). For example, for genes in group 4 (e.g., ILV3; see Fig. 1C), nitrogen limitation positively affects transcript levels, while D×N has a negative effect. DGA1 overexpression by itself has an effect that is between those of the other two factors. (B) For each cluster, (i) the GO term enrichment (redundant terms removed by manual curation) and (ii) the motifs found by DREME (15), their E value, and S. cerevisiae transcription factors with similar binding motifs, as identified with Tomtom (31), are indicated.

  • FIG 3 
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    FIG 3 

    Correlation of log-normalized RNAseq read counts with log-normalized protein counts. (A) All measured RNA and protein counts combined in one comparison, with poor correlation observed. (B) Density plot of Pearson’s r for all RNA-protein pairs (dotted line) and for only those RNA-protein pairs that showed differential expression at the level of RNA (adjusted P < 0.01; solid line). (C) Representation of the correlations of both the genes that were not significantly changed (in red box) and the genes whose transcripts were found to have changed in comparisons of all chemostats (adjusted P < 0.01). (D) Network plot of enriched GO terms in the highly correlated gene pairs (P < 0.01). The size of the node represents the number of genes within the GO term. The node’s color represents the adjusted P value. The thickness of the edge indicates the number of genes overlapping two GO terms. m.p., metabolic process; b.p., biosynthetic process.

  • FIG 4 
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    FIG 4 

    Regulation of Y. lipolytica metabolism. (A) Transcriptional downregulation of leucine biosynthesis. The boxes indicate z scores, incorporating both fold change and significance values. Regulation scores indicate correlations between all three levels and are calculated as detailed in Materials and Methods. (B) Relative intensity levels of two metabolites as measured by exometabolomics (also represented in Fig. S3 in the supplemental material). While most carbohydrates (e.g., d-mannitol) are excreted during nitrogen limitation in both WT and DGA1 strains, 2-isopropylmalate (IPM) is no longer excreted when nitrogen limitation co-occurs with DGA1 overexpression.

  • FIG 5 
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    FIG 5 

    Schematic overview of responses to either nitrogen limitation or DGA1 × N-lim interaction. Potential regulatory networks are drawn, guided by changes in motif enrichments and in metabolite and expression levels. Leucine activates TORC1 only as a response to DGA1 × N-lim interaction, as leucine levels are not increased in the presence of nitrogen limitation alone. Gln3 and autophagy are seemingly not repressed by TORC1 as a response to DGA1 × N-lim interaction. Leu3 either represses or activates expression of leucine biosynthesis, depending on IPM levels. While TORC1 is repressed upon nitrogen limitation, its activity is seemingly modulated upon simultaneous DGA1 overexpression, likely due to the sensing of increased leucine levels, mediated by leucyl-tRNA synthetase.

Tables

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  • TABLE 1 

    Physiological parameters

    StrainValue(s)
    WTDGA1
    RestrictionCarbonNitrogenCarbonNitrogen
    Specific growth ratea (h−1)0.048 (± 0.001)0.049 (± 0.005)0.047 (± 0.004)0.048 (± 0.002)
    Maximum growth rateb (h−1)0.191 (± 0.0045)0.180 (± 0.0025)0.222 (± 0.0085)0.220 (± 0.0032)
    Biomass (g liter−1)1.98 (± 0.06)2.01 (± 0.18)2.14 (± 0.10)2.66 (± 0.46)
    Nonlipid biomassc (g liter−1)1.89 (± 0.06)1.84 (± 0.22)2.05 (± 0.10)2.14 (± 0.42)
    Extracellular glucose level (g liter−1)018.9 (± 0.4)017.9 (± 0.8)
    r Gluc (mmol gDW−1 h−1)0.65 (± 0.01)0.72 (± 0.07)0.61 (± 0.06)0.64 (± 0.06)
    r O2 (mmol gDW−1 h−1)1.7 (± 0.2)2.1 (± 0.2)1.3 (± 0.3)2.1 (± 0.3)
    r CO2 (mmol gDW−1 h−1)1.67 (± 0.06)2.1 (± 0.2)1.5 (± 0.1)2.2 (± 0.3)
    RQ0.97 (± 0.12)0.97 (± 0.05)1.15 (± 0.20)1.0 (± 0.1)
    Y sx (gDW g glucose−1)0.41 (± 0.01)0.37 (± 0.01)0.43 (± 0.02)0.42 (± 0.01)
    • ↵a Data represent dilution rates during the chemostat stage.

    • ↵b Data were determined from the exponential phase during the batch stage.

    • ↵c Data were obtained by subtracting the measured lipid concentration as depicted in Fig. S1 in the supplemental material. Data are means (SD) of results from three independent chemostats. rGluc (mmol gDW−1 h−1), glucose uptake rate in millimoles per gram dry weight per hour; rO2, oxygen uptake rate; rCO2, CO2 excretion rate; RQ, respiratory quotient; Ysx, biomass yield.

Supplemental Material

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  • FIG S1 

    Effect of genetic and environmental factors on Y. lipolytica lipid levels. For comparisons, measurements from the DGA1-overexpressing strain were taken from reference 11. (A and B) Total lipid levels (A) and lipid levels in each class (B). SE, sterol esters; TAG, triacylglycerols; FFA, free fatty acids; ES, ergosterol; PA, phosphatidic acid; CL, cardiolipin; PE, phosphatidylethanolamine; PC, phosphatidylcholine; PS, phosphatidylserine; PI, phosphatidylinositol. The rationale for the increases in the phospholipid levels upon DGA1 overexpression is unclear, as discussed in reference 11, but they are unlikely to be due solely to the increased size of lipid droplets upon TAG accumulation. Furthermore, phospholipids do not increase upon TAG accumulation during DGA2 overexpression (10), supporting that TAG accumulation and phospholipid accumulation are not as tightly linked as suggested here. (C) Ratio of total fatty acid composition, after whole-cell hydrolysis. Significance levels of results of comparisons of samples were calculated with the homoscedastic t test. Download FIG S1, PDF file, 3.4 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • DATA SET S1 

    Differentially expressed genes. An Excel file with differentially expressed genes (adjusted P < 0.05), as identified from the general linear model, listed per factor, is shown. For each gene, predicted GO terms are indicated, as obtained from reference 11. Download DATA SET S1, XLSX file, 0.2 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S2 

    Transcript levels of differentially expressed cellular amino acid metabolism genes. Of the 34 genes corresponding to the GO term “cellular amino acid metabolism”, 13 were differentially expressed between the conditions (general linear model; adjusted P < 0.01). The normalized and log-transformed RNA read counts are shown for each of the four conditions. While nitrogen limitation in the control strain increases RNA levels, the combination of DGA1 overexpression and nitrogen limitation represses the expression of most genes. Download FIG S2, PDF file, 3.4 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • DATA SET S2 

    Correlation between RNA and protein levels. An Excel file with differentially expressed genes (adjusted P < 0.05), as identified from the general linear model, that has data from both RNAseq and proteomics is shown. Correlation is indicated by Pearson’s r and the associated P value. For each gene, predicted GO terms are indicated, as obtained from reference 11. Download DATA SET S2, XLSX file, 0.04 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • TABLE S1 

    Correlations between flux, protein, and RNA. Download TABLE S1, DOCX file, 0.02 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S3 

    Contributions of different factors to levels of intracellular and extracellular metabolites. Whether each of the three factors from the general linear model significantly influenced its level is indicated per metabolite (directional adjusted P values). The surprising decrease in the 2-isopropylmalate level seen under conditions in which nitrogen limitation co-occurred with DGA1 overexpression is noteworthy (see also Fig. 4C). 2-Isopropylmalate and leucine are highlighted. Download FIG S3, PDF file, 3.4 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S4 

    Exometabolomics signal intensities at all conditions. For each metabolite, its relative log-normalized signal intensities from GC-MS analysis are indicated for each of the four conditions. Download FIG S4, PDF file, 0.1 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S5 

    Endometabolomics signal intensities at all conditions. For each metabolite, its relative log-normalized signal intensities from GC-MS analysis is indicated for each of the four conditions. Download FIG S5, PDF file, 0.1 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S6 

    Expression levels of BAT1 and BAT2. The two branched-chain amino acid transaminases show different transcriptional responses upon nitrogen limitation in a DGA1-overexpressing strain. Download FIG S6, PDF file, 3.4 MB.

    Copyright © 2017 Kerkhoven et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

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Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica
Eduard J. Kerkhoven, Young-Mo Kim, Siwei Wei, Carrie D. Nicora, Thomas L. Fillmore, Samuel O. Purvine, Bobbie-Jo Webb-Robertson, Richard D. Smith, Scott E. Baker, Thomas O. Metz, Jens Nielsen
mBio Jun 2017, 8 (3) e00857-17; DOI: 10.1128/mBio.00857-17

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Leucine Biosynthesis Is Involved in Regulating High Lipid Accumulation in Yarrowia lipolytica
Eduard J. Kerkhoven, Young-Mo Kim, Siwei Wei, Carrie D. Nicora, Thomas L. Fillmore, Samuel O. Purvine, Bobbie-Jo Webb-Robertson, Richard D. Smith, Scott E. Baker, Thomas O. Metz, Jens Nielsen
mBio Jun 2017, 8 (3) e00857-17; DOI: 10.1128/mBio.00857-17
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KEYWORDS

Gene Expression Regulation, Fungal
Leucine
lipogenesis
Yarrowia
biofuels
biotechnology
metabolic engineering
systems biology
yeast

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