Proteomic Analysis of Chlorella vulgaris: Potential Targets for Enhanced Lipid Accumulation

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Abstract

Oleaginous microalgae are capable of producing large quantities of fatty acids and triacylglycerides. As such, they are promising feedstocks for the production of biofuels and bioproducts. Genetic strain-engineering strategies offer a means to accelerate the commercialization of algal biofuels by improving the rate and total accumulation of microalgal lipids. However, the industrial potential of these organisms remains to be met, largely due to the incomplete knowledgebase surrounding the mechanisms governing the induction of algal lipid biosynthesis. Such strategies require further elucidation of genes and gene products controlling algal lipid accumulation. In this study, we have set out to examine these mechanisms and identify novel strain-engineering targets in the oleaginous microalga, Chlorella vulgaris. Comparative shotgun proteomic analyses have identified a number of novel targets, including previously unidentified transcription factors and proteins involved in cell signaling and cell cycle regulation. These results lay the foundation for strain-improvement strategies and demonstrate the power of translational proteomic analysis. Biological significance: We have applied label-free, comparative shotgun proteomic analyses, via a transcriptome-to-proteome pipeline, in order to examine the nitrogen deprivation response in the oleaginous microalga, C. vulgaris. Herein, we identify potential targets for strain-engineering strategies targeting enhanced lipid accumulation for algal biofuels applications. Among the identified targets are proteins involved in transcriptional regulation, lipid biosynthesis, cell signaling and cell cycle progression. This article is part of a Special Issue entitled: Translational Plant Proteomics.

Original languageAmerican English
Pages (from-to)245-253
Number of pages9
JournalJournal of Proteomics
Volume93
DOIs
StatePublished - 20 Nov 2013

NREL Publication Number

  • NREL/JA-5100-58983

Keywords

  • Biofuels
  • Cell cycle
  • Lipid
  • Metabolic engineering
  • Microalgae
  • Proteomics

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