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

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 distancesmore » 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. As a result, 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.« less

Authors:
 [1];  [2];  [1];  [3]; ORCiD logo [1];  [1];  [1];  [1];  [1];  [1];  [2];  [2];  [4];  [3];  [5];  [6]
  1. New York Univ. Abu Dhabi Institute, Abu Dhabi (United Arab Emirates)
  2. Harvard Medical School, Boston, MA (United States)
  3. Cornell Univ., Ithaca, NY (United States)
  4. Univ. of Virginia, Charlottesville, VA (United States)
  5. New York Univ. Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Harvard Medical School, Boston, MA (United States); MRC Lab. of Molecular Biology, Cambridge (United Kingdom)
  6. New York Univ. Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Harvard Medical School, Boston, MA (United States)
Publication Date:
Research Org.:
Harvard Medical School, Boston, MA (United States). Dana-Farber Cancer Institute, Inc.
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1434909
Grant/Contract Number:  
FG02-07ER64496
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Molecular BioSystems
Additional Journal Information:
Journal Volume: 12; Journal Issue: 8; Journal ID: ISSN 1742-206X
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Chaiboonchoe, Amphun, Ghamsari, Lila, Dohai, Bushra, Ng, Patrick, Khraiwesh, Basel, Jaiswal, Ashish, Jijakli, Kenan, Koussa, Joseph, Nelson, David R., Cai, Hong, Yang, Xinping, Chang, Roger L., Papin, Jason, Yu, Haiyuan, Balaji, Santhanam, and Salehi-Ashtiani, Kourosh. Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. United States: N. p., 2016. Web. doi:10.1039/c6mb00237d.
Chaiboonchoe, Amphun, Ghamsari, Lila, Dohai, Bushra, Ng, Patrick, Khraiwesh, Basel, Jaiswal, Ashish, Jijakli, Kenan, Koussa, Joseph, Nelson, David R., 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. United States. doi:10.1039/c6mb00237d.
Chaiboonchoe, Amphun, Ghamsari, Lila, Dohai, Bushra, Ng, Patrick, Khraiwesh, Basel, Jaiswal, Ashish, Jijakli, Kenan, Koussa, Joseph, Nelson, David R., Cai, Hong, Yang, Xinping, Chang, Roger L., Papin, Jason, Yu, Haiyuan, Balaji, Santhanam, and Salehi-Ashtiani, Kourosh. Tue . "Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation". United States. doi:10.1039/c6mb00237d. https://www.osti.gov/servlets/purl/1434909.
@article{osti_1434909,
title = {Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation},
author = {Chaiboonchoe, Amphun and Ghamsari, Lila and Dohai, Bushra and Ng, Patrick and Khraiwesh, Basel and Jaiswal, Ashish and Jijakli, Kenan and Koussa, Joseph and Nelson, David R. and Cai, Hong and Yang, Xinping and Chang, Roger L. and Papin, Jason and Yu, Haiyuan and Balaji, Santhanam and Salehi-Ashtiani, Kourosh},
abstractNote = {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. As a result, 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.},
doi = {10.1039/c6mb00237d},
journal = {Molecular BioSystems},
number = 8,
volume = 12,
place = {United States},
year = {Tue Jun 14 00:00:00 EDT 2016},
month = {Tue Jun 14 00:00:00 EDT 2016}
}

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