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Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR - Mol. Syst. Biol. (2015)

Bottom Line: The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters.The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell.Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of California at San Diego, La Jolla, CA, USA.

No MeSH data available.


Related in: MedlinePlus

Abundance-based GO analysisA Composition of proteome sectors. Each bar graph shows the results of the abundance-based Gene Ontology analysis for each of the six sectors. Each bar indicates the mass fraction the corresponding GO term accounts for within a sector. The empty bar in each graph indicates the remaining fraction of the sector not accounted for by the GO terms listed. The results were calculated based on triplicate runs of all samples. Each bar height indicates the mean result and the standard deviation is shown as the error bar. See Supplementary Table S9 for the list of proteins represented by each bar in each sector, and Supplementary Text S3 for details of the method.B Association between metabolic fluxes and proteome sectors. There are four fluxes JC, JA, JU, and JR, represented by the arrows, replenishing the pools of carbon precursors, amino acids, other building blocks, and macromolecules, respectively. The ϕs on top of the fluxes represents the corresponding proteome fractions that carry the fluxes. Note that the S-sector proteins contribute to both JC and JA.
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fig04: Abundance-based GO analysisA Composition of proteome sectors. Each bar graph shows the results of the abundance-based Gene Ontology analysis for each of the six sectors. Each bar indicates the mass fraction the corresponding GO term accounts for within a sector. The empty bar in each graph indicates the remaining fraction of the sector not accounted for by the GO terms listed. The results were calculated based on triplicate runs of all samples. Each bar height indicates the mean result and the standard deviation is shown as the error bar. See Supplementary Table S9 for the list of proteins represented by each bar in each sector, and Supplementary Text S3 for details of the method.B Association between metabolic fluxes and proteome sectors. There are four fluxes JC, JA, JU, and JR, represented by the arrows, replenishing the pools of carbon precursors, amino acids, other building blocks, and macromolecules, respectively. The ϕs on top of the fluxes represents the corresponding proteome fractions that carry the fluxes. Note that the S-sector proteins contribute to both JC and JA.

Mentions: The results of the analysis are summarized in Fig4A, with each bar graph describing the major proteome composition for each sector. Sixty percent of the mass fraction of each sector could be accounted for by at most three terms, providing a simple interpretation of the functional significance of the sectors. For example, a single GO term, ‘translation’, describes more than 70% of the proteins in the R-sector. Since the R-limitation inhibits translation rate, the term suggests a strategy by which the cell specifically counteracts the applied growth limitation by increasing the abundance of ‘translational’ proteins (Scott et al, 2010).


Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR - Mol. Syst. Biol. (2015)

Abundance-based GO analysisA Composition of proteome sectors. Each bar graph shows the results of the abundance-based Gene Ontology analysis for each of the six sectors. Each bar indicates the mass fraction the corresponding GO term accounts for within a sector. The empty bar in each graph indicates the remaining fraction of the sector not accounted for by the GO terms listed. The results were calculated based on triplicate runs of all samples. Each bar height indicates the mean result and the standard deviation is shown as the error bar. See Supplementary Table S9 for the list of proteins represented by each bar in each sector, and Supplementary Text S3 for details of the method.B Association between metabolic fluxes and proteome sectors. There are four fluxes JC, JA, JU, and JR, represented by the arrows, replenishing the pools of carbon precursors, amino acids, other building blocks, and macromolecules, respectively. The ϕs on top of the fluxes represents the corresponding proteome fractions that carry the fluxes. Note that the S-sector proteins contribute to both JC and JA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358657&req=5

fig04: Abundance-based GO analysisA Composition of proteome sectors. Each bar graph shows the results of the abundance-based Gene Ontology analysis for each of the six sectors. Each bar indicates the mass fraction the corresponding GO term accounts for within a sector. The empty bar in each graph indicates the remaining fraction of the sector not accounted for by the GO terms listed. The results were calculated based on triplicate runs of all samples. Each bar height indicates the mean result and the standard deviation is shown as the error bar. See Supplementary Table S9 for the list of proteins represented by each bar in each sector, and Supplementary Text S3 for details of the method.B Association between metabolic fluxes and proteome sectors. There are four fluxes JC, JA, JU, and JR, represented by the arrows, replenishing the pools of carbon precursors, amino acids, other building blocks, and macromolecules, respectively. The ϕs on top of the fluxes represents the corresponding proteome fractions that carry the fluxes. Note that the S-sector proteins contribute to both JC and JA.
Mentions: The results of the analysis are summarized in Fig4A, with each bar graph describing the major proteome composition for each sector. Sixty percent of the mass fraction of each sector could be accounted for by at most three terms, providing a simple interpretation of the functional significance of the sectors. For example, a single GO term, ‘translation’, describes more than 70% of the proteins in the R-sector. Since the R-limitation inhibits translation rate, the term suggests a strategy by which the cell specifically counteracts the applied growth limitation by increasing the abundance of ‘translational’ proteins (Scott et al, 2010).

Bottom Line: The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters.The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell.Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, University of California at San Diego, La Jolla, CA, USA.

No MeSH data available.


Related in: MedlinePlus