Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom

Jennifer Levering, Jared Broddrick, Christopher L. Dupont, Graham Peers, Karen Beeri, Joshua Mayers, Alessandra A. Gallina, Andrew E. Allen, Bernhard O. Palsson, Karsten Zengler

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)

Abstract

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027-lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

Original languageEnglish
Article numbere0155038
JournalPLoS ONE
Volume11
Issue number5
DOIs
Publication statusPublished - 1 May 2016

Bibliographical note

Publisher Copyright:
© 2016 Levering et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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