Proteome response of Phaeodactylum tricornutum, during lipid accumulation induced by nitrogen depletion

Joseph Longworth, Danying Wu, María Huete-Ortega, Phillip C. Wright, Seetharaman Vaidyanathan*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

93 Citations (Scopus)


Nitrogen stress is a common strategy employed to stimulate lipid accumulation in microalgae, a biofuel feedstock of topical interest. Although widely investigated, the underlying mechanism of this strategy is still poorly understood. We examined the proteome response of lipid accumulation in the model diatom, Phaeodactylum tricornutum (CCAP 1055/1), at an earlier stage of exposure to selective nitrogen exclusion than previously investigated, and at a time point when changes would reflect lipid accumulation more than carbohydrate accumulation. In total 1043 proteins were confidently identified (≥. 2 unique peptides) with 645 significant (p < 0.05) changes observed, in the LC-MS/MS based iTRAQ investigation. Analysis of significant changes in KEGG pathways and individual proteins showed that under nitrogen starvation P. tricornutum reorganizes its proteome in favour of nitrogen scavenging and reduced lipid degradation whilst rearranging the central energy metabolism that deprioritizes photosynthetic pathways. By doing this, this species appears to increase nitrogen availability inside the cell and limit its use to the pathways where it is needed most. Compared to previously published proteomic analysis of nitrogen starvation in Chlamydomonas reinhardtii, central energy metabolism and photosynthesis appear to be affected more in the diatom, whilst the green algae appears to invest its energy in reorganizing respiration and the cellular organization pathways.

Original languageEnglish
Pages (from-to)213-224
Number of pages12
JournalAlgal Research
Publication statusPublished - 1 Sept 2016
Externally publishedYes


  • Biofuels
  • Chlamydomonas reinhardtii
  • Lipid production
  • Microalgae
  • Quantitative proteomics


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