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Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network

Al-Anzi, Bader and Arpp, Patrick and Gerges, Sherif and Ormerod, Christopher and Olsman, Noah and Zinn, Kai (2015) Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network. PLOS Computational Biology, 11 (5). Art. No. e1004264. ISSN 1553-7358. PMCID PMC4447291. https://resolver.caltech.edu/CaltechAUTHORS:20150529-105953426

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[img] Image (TIFF) (S2 Fig. Synergy between the Fus3 and Kss1 MAPKs, and the effects of TORC1 mutations and rapamycin in yeast) - Supplemental Material
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[img] Image (JPEG) (S3 Fig. Histograms of the relationships between fat storage regulation network and all yeast GO Biological Process categories) - Supplemental Material
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[img] Image (TIFF) (S4 Fig. Internode communication deduced from drug-mutant interactions) - Supplemental Material
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[img] MS Excel (S1 Table. TLC quantification of fat levels in yeast mutants) - Supplemental Material
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[img] MS Excel (S2 Table. Connection matrix of nodes and edges in the network) - Supplemental Material
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[img] MS Excel (S3 Table. GO term classifications that are significantly enriched for network proteins) - Supplemental Material
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[img] MS Excel (S4 Table. Mathematical analysis and computational modeling of the different network topological parameters shown in Fig 2) - Supplemental Material
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[img] MS Excel (S5 Table. Mathematical analysis and computational modeling of the LD morphology subnetworks shown in Fig 3) - Supplemental Material
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[img] MS Excel (S6 Table. Mathematical analysis and computational modeling of the carbon source utilization subnetworks shown in Fig 4) - Supplemental Material
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[img] MS Excel (S7 Table. Mathematical analysis and computational modeling of subnetworks of proteins for which mutations affect conversion of glucose or aspartic acid to fat as shown in Fig 5) - Supplemental Material
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[img] MS Excel (S8 Table. Quantification of the increase in fat levels produced by drugs in mutants vs. wild-type, as shown in Fig 6) - Supplemental Material
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[img] MS Excel (S9 Table. Mathematical analysis and computational modeling of the “same pathway” subnetworks in Fig 6) - Supplemental Material
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Abstract

An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates “small-world” networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein’s position within a module and to the module’s relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.


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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4447291/PubMed CentralArticle
ORCID:
AuthorORCID
Olsman, Noah0000-0002-4351-3880
Zinn, Kai0000-0002-6706-5605
Additional Information:© 2015 Al-Anzi 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. Received: October 23, 2014; Accepted: April 2, 2015; Published: May 28, 2015. We thank Robert Oania, Christopher Frick, Mark Budde, Erich Schwarz, and Eric Forgoston for helpful discussions and comments on the manuscript. Author Contributions: Conceived and designed the experiments: BAA KZ. Performed the experiments: BAA PA CO SG NO. Analyzed the data: BAA KZ CO SG NO. Contributed reagents/materials/analysis tools: BAA CO SG NO. Wrote the paper: BAA KZ CO NO. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This work was funded by a National Institutes of Health R21 grant to KZ, NS083874. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.
Funders:
Funding AgencyGrant Number
NIHR21 NS083874
Issue or Number:5
PubMed Central ID:PMC4447291
Record Number:CaltechAUTHORS:20150529-105953426
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150529-105953426
Official Citation:Al-Anzi B, Arpp P, Gerges S, Ormerod C, Olsman N, Zinn K (2015) Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network. PLoS Comput Biol 11(5): e1004264. doi:10.1371/journal.pcbi.1004264
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:57902
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:29 May 2015 18:28
Last Modified:09 Mar 2020 13:19

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