Principles of proteome allocation are revealed using proteomic data and genome-scale models

Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.; Ebrahim, Ali; Saunders, Michael A.; Palsson, Bernhard O.

doi:10.1038/srep36734

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Journal Article · Thu Nov 17 23:00:00 EST 2016 · Scientific Reports

DOI:https://doi.org/10.1038/srep36734· OSTI ID:1373286

Yang, Laurence ^[1]; Yurkovich, James T. ^[1]; Lloyd, Colton J. ^[1]; Ebrahim, Ali ^[1]; Saunders, Michael A. ^[2]; Palsson, Bernhard O. ^[3]

Univ. of California, San Diego, La Jolla, CA (United States)
Stanford Univ., Stanford, CA (United States)
Univ. of California, San Diego, La Jolla, CA (United States); The Technical Univ. of Denmark, Horsholm (Denmark)

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. Furthermore, this flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0008701

OSTI ID:: 1373286

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 6; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (36)

Current knowledge of the Escherichia coli phosphoenolpyruvate–carbohydrate phosphotransferase system: peculiarities of regulation and impact on growth and product formation Escalante, Adelfo; Salinas Cervantes, Ania; Gosset, Guillermo Applied Microbiology and Biotechnology, Vol. 94, Issue 6 https://doi.org/10.1007/s00253-012-4101-5	journal	May 2012
Genome-wide Reconstruction of OxyR and SoxRS Transcriptional Regulatory Networks under Oxidative Stress in Escherichia coli K-12 MG1655 Seo, Sang Woo; Kim, Donghyuk; Szubin, Richard Cell Reports, Vol. 12, Issue 8 https://doi.org/10.1016/j.celrep.2015.07.043	journal	August 2015
Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution Utrilla, Jose; O’Brien, Edward J.; Chen, Ke Cell Systems, Vol. 2, Issue 4 https://doi.org/10.1016/j.cels.2016.04.003	journal	April 2016
Computing the functional proteome: recent progress and future prospects for genome-scale models O’Brien, Edward J.; Palsson, Bernhard O. Current Opinion in Biotechnology, Vol. 34 https://doi.org/10.1016/j.copbio.2014.12.017	journal	August 2015
Methods for integration of transcriptomic data in genome-scale metabolic models Kim, Min Kyung; Lun, Desmond S. Computational and Structural Biotechnology Journal, Vol. 11, Issue 18 https://doi.org/10.1016/j.csbj.2014.08.009	journal	August 2014
Large‐scale ¹³ C‐flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli Haverkorn van Rijsewijk, Bart R. B.; Nanchen, Annik; Nallet, Sophie Molecular Systems Biology, Vol. 7, Issue 1 https://doi.org/10.1038/msb.2011.9	journal	January 2011
Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth Ibarra, Rafael U.; Edwards, Jeremy S.; Palsson, Bernhard O. Nature, Vol. 420, Issue 6912 https://doi.org/10.1038/nature01149	journal	November 2002
Network-based prediction of human tissue-specific metabolism Shlomi, Tomer; Cabili, Moran N.; Herrgård, Markus J. Nature Biotechnology, Vol. 26, Issue 9 https://doi.org/10.1038/nbt.1487	journal	August 2008
Expanded microbial genome coverage and improved protein family annotation in the COG database Galperin, Michael Y.; Makarova, Kira S.; Wolf, Yuri I. Nucleic Acids Research, Vol. 43, Issue D1 https://doi.org/10.1093/nar/gku1223	journal	November 2014
A Comparison of the Costs and Benefits of Bacterial Gene Expression Price, Morgan N.; Wetmore, Kelly M.; Deutschbauer, Adam M. PLOS ONE https://doi.org/10.1101/038851	journal	July 2016
Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium LaCroix, Ryan A.; Sandberg, Troy E.; O'Brien, Edward J. Applied and Environmental Microbiology, Vol. 81, Issue 1 https://doi.org/10.1128/aem.02246-14	journal	October 2014
Robust flux balance analysis of multiscale biochemical reaction networks Sun, Yuekai; Fleming, Ronan MT; Thiele, Ines BMC Bioinformatics, Vol. 14, Issue 1 https://doi.org/10.1186/1471-2105-14-240	journal	July 2013
Hierarchy of non-glucose sugars in Escherichia coli Aidelberg, Guy; Towbin, Benjamin D.; Rothschild, Daphna BMC Systems Biology, Vol. 8, Issue 1 https://doi.org/10.1186/s12918-014-0133-z	journal	December 2014
Shrinking the Metabolic Solution Space Using Experimental Datasets Reed, Jennifer L. PLoS Computational Biology, Vol. 8, Issue 8 https://doi.org/10.1371/journal.pcbi.1002662	journal	August 2012
Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments O’Brien, Edward J.; Utrilla, Jose; Palsson, Bernhard O. PLOS Computational Biology, Vol. 12, Issue 6 https://doi.org/10.1371/journal.pcbi.1004998	journal	June 2016
Multiscale Modeling of Metabolism and Macromolecular Synthesis in E. coli and Its Application to the Evolution of Codon Usage Thiele, Ines; Fleming, Ronan M. T.; Que, Richard PLoS ONE, Vol. 7, Issue 9 https://doi.org/10.1371/journal.pone.0045635	journal	September 2012
Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria Hui, Sheng; Silverman, Josh M.; Chen, Stephen S. Molecular Systems Biology, Vol. 11, Issue 2 https://doi.org/10.15252/msb.20145697	journal	February 2015
Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor: Transcriptomics-based Strain Optimization Tool Kim, Minsuk; Yi, Jeong Sang; Lakshmanan, Meiyappan Biotechnology and Bioengineering, Vol. 113, Issue 3 https://doi.org/10.1002/bit.25830	journal	September 2015
Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data Gerosa, Luca; Haverkorn van Rijsewijk, Bart R. B.; Christodoulou, Dimitris Cell Systems, Vol. 1, Issue 4 https://doi.org/10.1016/j.cels.2015.09.008	journal	October 2015
Bacterial growth: global effects on gene expression, growth feedback and proteome partition Klumpp, Stefan; Hwa, Terence Current Opinion in Biotechnology, Vol. 28 https://doi.org/10.1016/j.copbio.2014.01.001	journal	August 2014
Optimizing genome-scale network reconstructions Monk, Jonathan; Nogales, Juan; Palsson, Bernhard O. Nature Biotechnology, Vol. 32, Issue 5 https://doi.org/10.1038/nbt.2870	journal	May 2014
The quantitative and condition-dependent Escherichia coli proteome Schmidt, Alexander; Kochanowski, Karl; Vedelaar, Silke Nature Biotechnology, Vol. 34, Issue 1 https://doi.org/10.1038/nbt.3418	journal	December 2015
Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol Yim, Harry; Haselbeck, Robert; Niu, Wei Nature Chemical Biology, Vol. 7, Issue 7 https://doi.org/10.1038/nchembio.580	journal	May 2011
In silico method for modelling metabolism and gene product expression at genome scale Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem Nature Communications, Vol. 3, Issue 1 https://doi.org/10.1038/ncomms1928	journal	January 2012
Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem Nature Communications, Vol. 5, Issue 1 https://doi.org/10.1038/ncomms5910	journal	September 2014
Decoding genome-wide GadEWX-transcriptional regulatory networks reveals multifaceted cellular responses to acid stress in Escherichia coli Seo, Sang Woo; Kim, Donghyuk; O’Brien, Edward J. Nature Communications, Vol. 6, Issue 1 https://doi.org/10.1038/ncomms8970	journal	August 2015
Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods Lewis, Nathan E.; Nagarajan, Harish; Palsson, Bernhard O. Nature Reviews Microbiology, Vol. 10, Issue 4 https://doi.org/10.1038/nrmicro2737	journal	February 2012
Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression Ma, Ding; Yang, Laurence; Fleming, Ronan M. T. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/srep40863	journal	January 2017
Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS: Metabolic flux profiling in E.��coli Fischer, Eliane; Sauer, Uwe European Journal of Biochemistry, Vol. 270, Issue 5 https://doi.org/10.1046/j.1432-1033.2003.03448.x	journal	March 2003
Global Transcriptional Programs Reveal a Carbon Source Foraging Strategy by Escherichia coli Liu, Mingzhu; Durfee, Tim; Cabrera, Julio E. Journal of Biological Chemistry, Vol. 280, Issue 16 https://doi.org/10.1074/jbc.M414050200	journal	February 2005
Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium LaCroix, Ryan A.; Sandberg, Troy E.; O'Brien, Edward J. Applied and Environmental Microbiology, Vol. 81, Issue 1 https://doi.org/10.1128/AEM.02246-14	journal	October 2014
solveME: fast and reliable solution of nonlinear ME models Yang, Laurence; Ma, Ding; Ebrahim, Ali BMC Bioinformatics, Vol. 17, Issue 1 https://doi.org/10.1186/s12859-016-1240-1	journal	September 2016
Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale Liu, Joanne K.; O’Brien, Edward J.; Lerman, Joshua A. BMC Systems Biology, Vol. 8, Issue 1 https://doi.org/10.1186/s12918-014-0110-6	journal	September 2014
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism Machado, Daniel; Herrgård, Markus PLoS Computational Biology, Vol. 10, Issue 4 https://doi.org/10.1371/journal.pcbi.1003580	journal	April 2014
Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling Volkmer, Benjamin; Heinemann, Matthias PLoS ONE, Vol. 6, Issue 7 https://doi.org/10.1371/journal.pone.0023126	journal	July 2011
A Comparison of the Costs and Benefits of Bacterial Gene Expression Price, Morgan N.; Wetmore, Kelly M.; Deutschbauer, Adam M. PLOS ONE, Vol. 11, Issue 10 https://doi.org/10.1371/journal.pone.0164314	journal	October 2016

Cited By (8)

Machine and deep learning meet genome-scale metabolic modeling Zampieri, Guido; Vijayakumar, Supreeta; Yaneske, Elisabeth PLOS Computational Biology, Vol. 15, Issue 7 https://doi.org/10.1371/journal.pcbi.1007084	journal	July 2019
Genome-scale model of metabolism and gene expression provides a multi-scale description of acid stress responses in Escherichia coli Du, Bin; Yang, Laurence; Lloyd, Colton J. PLOS Computational Biology, Vol. 15, Issue 12 https://doi.org/10.1371/journal.pcbi.1007525	journal	December 2019
Genome-scale metabolic models applied to human health and disease: Genome-scale metabolic models Cook, Daniel J.; Nielsen, Jens Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 9, Issue 6 https://doi.org/10.1002/wsbm.1393	journal	June 2017
A quantitative method for proteome reallocation using minimal regulatory interventions Lastiri-Pancardo, Gustavo; Mercado-Hernández, Jonathan S.; Kim, Juhyun Nature Chemical Biology, Vol. 16, Issue 9 https://doi.org/10.1038/s41589-020-0593-y	journal	July 2020
Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth Reimers, Alexandra-M.; Knoop, Henning; Bockmayr, Alexander Proceedings of the National Academy of Sciences, Vol. 114, Issue 31 https://doi.org/10.1073/pnas.1617508114	journal	July 2017
Formation of Dominant Mode by Evolution in Biological Systems Furusawa, Chikara; kaneko, Kunihiko Physical Review E https://doi.org/10.1101/125278	journal	October 2017
Formation of dominant mode by evolution in biological systems Furusawa, Chikara; Kaneko, Kunihiko Physical Review E, Vol. 97, Issue 4 https://doi.org/10.1103/physreve.97.042410	journal	April 2018
DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression Yang, Laurence; Ebrahim, Ali; Lloyd, Colton J. BMC Systems Biology, Vol. 13, Issue 1 https://doi.org/10.1186/s12918-018-0675-6	journal	January 2019

Similar Records

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Journal Article · Wed Jun 23 00:00:00 EDT 2021 · PLoS Computational Biology (Online) · OSTI ID:1805318

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
60 APPLIED LIFE SCIENCES
biochemical networks
computer modelling

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Citation Formats

References (36)

Cited By (8)

Similar Records

Related Subjects