Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Amphun Chaiboonchoe, Lila Ghamsari, Bushra Dohai, Patrick Ng, Basel Khraiwesh, Ashish Jaiswal, Kenan Jijakli, Joseph Koussa, David Nelson, Hong Cai, Xinping Yang, Roger L. Chang, Jason Papin, Haiyuan Yu, Santhanam Balaji, Kourosh Salehi-Ashtiani

Research output: Contribution to journalArticle

Abstract

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Original languageEnglish (US)
Pages (from-to)2394-2407
Number of pages14
JournalMolecular BioSystems
Volume12
Issue number8
DOIs
StatePublished - Jan 1 2016

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Chlamydomonas reinhardtii
Metabolic Networks and Pathways
Genes
Synthetic Biology
Metabolic Engineering
Plant Genes
Chlorophyta
Biofuels

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology

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Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. / Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David; Cai, Hong; Yang, Xinping; Chang, Roger L.; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh.

In: Molecular BioSystems, Vol. 12, No. 8, 01.01.2016, p. 2394-2407.

Research output: Contribution to journalArticle

Chaiboonchoe, A, Ghamsari, L, Dohai, B, Ng, P, Khraiwesh, B, Jaiswal, A, Jijakli, K, Koussa, J, Nelson, D, Cai, H, Yang, X, Chang, RL, Papin, J, Yu, H, Balaji, S & Salehi-Ashtiani, K 2016, 'Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation', Molecular BioSystems, vol. 12, no. 8, pp. 2394-2407. https://doi.org/10.1039/c6mb00237d
Chaiboonchoe, Amphun ; Ghamsari, Lila ; Dohai, Bushra ; Ng, Patrick ; Khraiwesh, Basel ; Jaiswal, Ashish ; Jijakli, Kenan ; Koussa, Joseph ; Nelson, David ; Cai, Hong ; Yang, Xinping ; Chang, Roger L. ; Papin, Jason ; Yu, Haiyuan ; Balaji, Santhanam ; Salehi-Ashtiani, Kourosh. / Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. In: Molecular BioSystems. 2016 ; Vol. 12, No. 8. pp. 2394-2407.
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