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Cited by 234Algal biology and biofuel productionLipid metabolism and biosynthesisMicrobial Metabolic Engineering and Bioproduction

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Examination of Triacylglycerol Biosynthetic Pathways via De Novo Transcriptomic and Proteomic Analyses in an Unsequenced Microalga

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Guarnieri, Michael T., Nag, Ambarish, Smolinski, Sharon L., Darzins, Al, Seibert, Michael, Pienkos, Philip T.·2011·PLoS ONE
DOI 10.1371/journal.pone.0025851PMC PMC3197185
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Abstract

Biofuels derived from algal lipids represent an opportunity to dramatically impact the global energy demand for transportation fuels. Systems biology analyses of oleaginous algae could greatly accelerate the commercialization of algal-derived biofuels by elucidating the key components involved in lipid productivity and leading to the initiation of hypothesis-driven strain-improvement strategies. However, higher-level systems biology analyses, such as transcriptomics and proteomics, are highly dependent upon available genomic sequence data, and the lack of these data has hindered the pursuit of such analyses for many oleaginous microalgae. In order to examine the triacylglycerol biosynthetic pathway in the unsequenced oleaginous microalga, Chlorella vulgaris, we have established a strategy with which to bypass the necessity for genomic sequence information by using the transcriptome as a guide. Our results indicate an upregulation of both fatty acid and triacylglycerol biosynthetic machinery under oil-accumulating conditions, and demonstrate the utility of a de novo assembled transcriptome as a search model for proteomic analysis of an unsequenced microalga.

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Figures

Figure 1 Growth and lipid accumulation properties of C. vulgaris under nitrogen limitation. (A) Representative growth curves for C. vulgaris cultured photoautotrophically under nitrogen-replete (blue) and nitrogen-deplete (red) conditions. Harvest points utilized for all comparative analyses and images are circled. Inset: cultures grown under nitrogen-replete (green) and nitrogen-deplete (yellow) conditions. (B) Fatty acid methyl ester (FAME) analysis of C. vulgaris under nitrogen-replete and nitrogen-deplete conditions. (C) Average fatty acid composition (%, w/w) for C. vulgaris under nitrogen-replete and nitrogen-deplete conditions. (D) Epifluorescent microscopy images of BODIPY-stained C. vulgaris in nitrogen-replete (top panel) and nitrogen-deplete (bottom panel) medium. Red fluorescence is due to chlorophyll autofluorescence and green fluorescence is due to BODIPY-neutral lipid interaction. Magnification bar (white) equals 5 µm.
Figure 1 Growth and lipid accumulation properties of C. vulgaris under nitrogen limitation. (A) Representative growth curves for C. vulgaris cultured photoautotrophically under nitrogen-replete (blue) and nitrogen-deplete (red) conditions. Harvest points utilized for all comparative analyses and images are circled. Inset: cultures grown under nitrogen-replete (green) and nitrogen-deplete (yellow) conditions. (B) Fatty acid methyl ester (FAME) analysis of C. vulgaris under nitrogen-replete and nitrogen-deplete conditions. (C) Average fatty acid composition (%, w/w) for C. vulgaris under nitrogen-replete and nitrogen-deplete conditions. (D) Epifluorescent microscopy images of BODIPY-stained C. vulgaris in nitrogen-replete (top panel) and nitrogen-deplete (bottom panel) medium. Red fluorescence is due to chlorophyll autofluorescence and green fluorescence is due to BODIPY-neutral lipid interaction. Magnification bar (white) equals 5 µm.CC BY · PubMed Central
Figure 2 Workflow for de novo transcriptome assembly and comparative proteomic analyses.
Figure 2 Workflow for de novo transcriptome assembly and comparative proteomic analyses.CC BY · PubMed Central
Figure 3 Proteomic Analysis of C. vulgaris. (A) One dimensional SDS-PAGE of C. vulgaris soluble fraction utilized for comparative proteomic analysis. M, Marker; N+, Nitrogen-replete; N−, Nitrogen-deplete. (B) Mass spectral analysis data for the C. vulgaris proteome searched against all available Chlorophyta genome databases (left) and the C. vulgaris Transcriptome (right).
Figure 3 Proteomic Analysis of C. vulgaris. (A) One dimensional SDS-PAGE of C. vulgaris soluble fraction utilized for comparative proteomic analysis. M, Marker; N+, Nitrogen-replete; N−, Nitrogen-deplete. (B) Mass spectral analysis data for the C. vulgaris proteome searched against all available Chlorophyta genome databases (left) and the C. vulgaris Transcriptome (right).CC BY · PubMed Central
Figure 4 Gene Ontology enrichment analysis. Functional distribution of the complete annotated C. vulgaris transcriptome (grey bars) and the 2,949 transcripts corresponding to proteins identified in sub-proteomic soluble fraction via MS/MS analysis (black bars). Distribution is represented as percent of total transcripts in respective fractions.
Figure 4 Gene Ontology enrichment analysis. Functional distribution of the complete annotated C. vulgaris transcriptome (grey bars) and the 2,949 transcripts corresponding to proteins identified in sub-proteomic soluble fraction via MS/MS analysis (black bars). Distribution is represented as percent of total transcripts in respective fractions.CC BY · PubMed Central
Figure 5 Multiple sequence alignment for acetyl-coA acyltransferase (ACAT) peptides identified via MS/MS analysis. Despite high sequence identity (Expect values<1e-124), ACAT was not identified using Chlorophyta sequence databases as search models in MS/MS analysis. Notably, underlined peptides differed by just a single amino acid substitution between C. vulgaris and C. variabilis, preventing positive identification. Using the C. vulgaris transcriptome as a sequence search database yielded 7 peptide identifications at a confidence interval >95%.
Figure 5 Multiple sequence alignment for acetyl-coA acyltransferase (ACAT) peptides identified via MS/MS analysis. Despite high sequence identity (Expect values<1e-124), ACAT was not identified using Chlorophyta sequence databases as search models in MS/MS analysis. Notably, underlined peptides differed by just a single amino acid substitution between C. vulgaris and C. variabilis, preventing positive identification. Using the C. vulgaris transcriptome as a sequence search database yielded 7 peptide identifications at a confidence interval >95%.CC BY · PubMed Central
Figure 6 Improved pathway identification using a de novo assembled transcriptome database and changes in protein abundance under nitrogen depletion. (A) Critical components of the fatty acid and triacylglycerol (TAG) biosynthetic pathways were absent from initial MS/MS searches against all available Chlorophyta databases. Proteins highlighted in yellow were amongst the proteins absent from initial analyses, yet positively identified when searching against the C. vulgaris transcriptome database. Proteins in green (not highlighted) were identified using both Chlorophyta and C. vulgaris transcriptome databases. Numbers below proteins represent NSAF values (105) for nitrogen replete and nitrogen deplete conditions, respectively. ACCase, acetyl-CoA carboxylase; ACP, acyl carrier protein; AMPK, AMP-activated kinase; DAGK, diacylglycerol kinase; DGAT, diacylglycerol acyltransferase; DHAP, dihydroxyacetone phosphate; ENR, enoyl-ACP reductase; FATP, fatty acyl-ACP thioesterase (putative); G3PDH, glycerol-3-phosphate dehydrogenase; GPAT, glycerol-3-phosphate acyltransferase; HD, 3-hydroxyacyl-ACP dehydratase; KAR, 3-ketoacyl-ACP reductase; KAS, 3-ketoacyl-ACP synthase; LPAAT, lyso-phosphatidic acid acyltransferase; LPAT, lyso-phosphatidylcholine acyltransferase; MAT, malonyl-CoA:ACP transacylase; PAP, phosphatidic acid phosphatase. Adapted from Radakovits et al., 2010 and Hu et al., 2008. (B) Corresponding spectral count fold-changes for components of the FA (left panel) and TAG (right panel) biosynthetic components.
Figure 6 Improved pathway identification using a de novo assembled transcriptome database and changes in protein abundance under nitrogen depletion. (A) Critical components of the fatty acid and triacylglycerol (TAG) biosynthetic pathways were absent from initial MS/MS searches against all available Chlorophyta databases. Proteins highlighted in yellow were amongst the proteins absent from initial analyses, yet positively identified when searching against the C. vulgaris transcriptome database. Proteins in green (not highlighted) were identified using both Chlorophyta and C. vulgaris transcriptome databases. Numbers below proteins represent NSAF values (105) for nitrogen replete and nitrogen deplete conditions, respectively. ACCase, acetyl-CoA carboxylase; ACP, acyl carrier protein; AMPK, AMP-activated kinase; DAGK, diacylglycerol kinase; DGAT, diacylglycerol acyltransferase; DHAP, dihydroxyacetone phosphate; ENR, enoyl-ACP reductase; FATP, fatty acyl-ACP thioesterase (putative); G3PDH, glycerol-3-phosphate dehydrogenase; GPAT, glycerol-3-phosphate acyltransferase; HD, 3-hydroxyacyl-ACP dehydratase; KAR, 3-ketoacyl-ACP reductase; KAS, 3-ketoacyl-ACP synthase; LPAAT, lyso-phosphatidic acid acyltransferase; LPAT, lyso-phosphatidylcholine acyltransferase; MAT, malonyl-CoA:ACP transacylase; PAP, phosphatidic acid phosphatase. Adapted from Radakovits et al., 2010 and Hu et al., 2008. (B) Corresponding spectral count fold-changes for components of the FA (left panel) and TAG (right panel) biosynthetic components.CC BY · PubMed Central

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  1. [47]Ratledge C 1988 An overview of microbial lipids. RCaW SG New York Academic Press

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Metadata

Authors
Guarnieri, M. T., Nag, A., Smolinski, S. L., Darzins, A., Seibert, M., & Pienkos, P. T.
Affiliations
National Laboratory of the Rockies (US)
Corresponding author
Guarnieri, Michael T.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-10-17
Volume
6
Issue
10
Pages
e25851
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0025851
PMC
PMC3197185
OpenAlex
W2028421881
Cited by
234
References
55
Citations by year
2026: 6 · 2025: 11 · 2024: 5 · 2023: 1 · 2022: 11 · 2021: 9 · 2020: 17 · 2019: 24 · 2018: 16 · 2017: 18 · 2016: 34 · 2015: 19 · 2014: 23 · 2013: 23 · 2012: 17
FWCI
8.37
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Energy (DE -AC36-08GO28308, No. DE-AC36-08GO28308, 08GO28308, Contract No. DE-AC36-08GO28308, AC36‐08GO28308)
  • University of Nebraska-Lincoln
  • Office of Energy Efficiency and Renewable Energy (Contract No. DE-AC36-08GO28308, DE-AC36-08-GO28308., AC36-08-GO28308, No. DE-AC36-08GO28308)
  • Laboratory Directed Research and Development (DE-AC36-08GO28308)
  • Office of Energy Efficiency
  • National Renewable Energy Laboratory (Contract No. DE-AC36-08GO28308, DE-AC36-08-GO28308, AC36-08GO28308)
Topics
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biochemistry › Lipid metabolism and biosynthesis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
triglyceride synthesis, oleaginous microalgae, Chlorella vulgaris, de novo transcriptome assembly, fatty acid synthesis, lipid accumulation, algal biofuels
MeSH terms
Oils, Triglycerides, Up-Regulation, Gene Expression Profiling, Proteomics, Biosynthetic Pathways, Biofuels, Microalgae

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