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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

The quantitative protein mass spectrometryA Observed values versus real values for ratios of 15N ribosomal proteins to 14N ribosomal proteins. Black dots are the mean values, with error bars representing the range of the values for all ribosomal proteins. The dashed line represents perfect agreement between the observed values and real values.B Observed values versus real values for ratios of 15N proteins to 14N proteins from whole-cell lysates. Black dots are the median values for more than 600 proteins. The error bar for each median value indicates the quartiles. The dashed line represents perfect agreement between the observed values and real values. Additional characterizations are shown in Supplementary Figs S3 and S4.C The expression matrix and clustering results. The matrix is composed of 1,053 proteins (rows) and 14 conditions (columns); see Supplementary Table S2. The first five columns are for C-limitation, the next five columns for A-limitation, and the last four columns for R-limitation. For each mode of growth limitation, the growth rate increases from left to right. The matrix is log2-transformed, with expression values at the standard condition as zero (see Materials and Methods), represented as black color. Red color indicates negative values, green color positive values, and gray color missing entries. A dendrogram generated by clustering analysis is shown on the left of the expression matrix (see Materials and Methods), with the five major clusters shown on the right side of the matrix. The data are estimated to cover ˜80% of the proteome; see Supplementary Fig S5.
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fig02: The quantitative protein mass spectrometryA Observed values versus real values for ratios of 15N ribosomal proteins to 14N ribosomal proteins. Black dots are the mean values, with error bars representing the range of the values for all ribosomal proteins. The dashed line represents perfect agreement between the observed values and real values.B Observed values versus real values for ratios of 15N proteins to 14N proteins from whole-cell lysates. Black dots are the median values for more than 600 proteins. The error bar for each median value indicates the quartiles. The dashed line represents perfect agreement between the observed values and real values. Additional characterizations are shown in Supplementary Figs S3 and S4.C The expression matrix and clustering results. The matrix is composed of 1,053 proteins (rows) and 14 conditions (columns); see Supplementary Table S2. The first five columns are for C-limitation, the next five columns for A-limitation, and the last four columns for R-limitation. For each mode of growth limitation, the growth rate increases from left to right. The matrix is log2-transformed, with expression values at the standard condition as zero (see Materials and Methods), represented as black color. Red color indicates negative values, green color positive values, and gray color missing entries. A dendrogram generated by clustering analysis is shown on the left of the expression matrix (see Materials and Methods), with the five major clusters shown on the right side of the matrix. The data are estimated to cover ˜80% of the proteome; see Supplementary Fig S5.

Mentions: The accuracy and precision of quantifying relative protein expression levels was determined from a standard curve using samples of unlabeled and 15N-labeled purified ribosomes. The observed relative levels, measured by ratios of the labeled to the unlabeled ribosomal proteins (or 15N/14N), agree extremely well with the expected values over a range of about two orders of magnitude (Fig2A). To assess the accuracy and precision for a whole-cell lysate with a much more complex proteome, labeled and unlabeled cells were mixed in fixed ratios and measured with quantitative mass spectrometry. The relative changes in protein levels can be precisely determined over the range of ratios from 0.1 to 10, as shown in Fig2B. The effective precision of relative protein quantification is ±18%, based on analysis of the 1:1 sample (Supplementary Fig S3). Thus, subtle changes in proteome composition that are much < 2-fold can be precisely determined. Furthermore, the relative quantitation using quantitative mass spectrometry agrees extremely well with a traditional biochemical measurement of ribosome content (Supplementary Fig S4A) and also with quantitation of LacZ using a β-galactosidase assay (Supplementary Fig S4B).


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)

The quantitative protein mass spectrometryA Observed values versus real values for ratios of 15N ribosomal proteins to 14N ribosomal proteins. Black dots are the mean values, with error bars representing the range of the values for all ribosomal proteins. The dashed line represents perfect agreement between the observed values and real values.B Observed values versus real values for ratios of 15N proteins to 14N proteins from whole-cell lysates. Black dots are the median values for more than 600 proteins. The error bar for each median value indicates the quartiles. The dashed line represents perfect agreement between the observed values and real values. Additional characterizations are shown in Supplementary Figs S3 and S4.C The expression matrix and clustering results. The matrix is composed of 1,053 proteins (rows) and 14 conditions (columns); see Supplementary Table S2. The first five columns are for C-limitation, the next five columns for A-limitation, and the last four columns for R-limitation. For each mode of growth limitation, the growth rate increases from left to right. The matrix is log2-transformed, with expression values at the standard condition as zero (see Materials and Methods), represented as black color. Red color indicates negative values, green color positive values, and gray color missing entries. A dendrogram generated by clustering analysis is shown on the left of the expression matrix (see Materials and Methods), with the five major clusters shown on the right side of the matrix. The data are estimated to cover ˜80% of the proteome; see Supplementary Fig S5.
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Related In: Results  -  Collection

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fig02: The quantitative protein mass spectrometryA Observed values versus real values for ratios of 15N ribosomal proteins to 14N ribosomal proteins. Black dots are the mean values, with error bars representing the range of the values for all ribosomal proteins. The dashed line represents perfect agreement between the observed values and real values.B Observed values versus real values for ratios of 15N proteins to 14N proteins from whole-cell lysates. Black dots are the median values for more than 600 proteins. The error bar for each median value indicates the quartiles. The dashed line represents perfect agreement between the observed values and real values. Additional characterizations are shown in Supplementary Figs S3 and S4.C The expression matrix and clustering results. The matrix is composed of 1,053 proteins (rows) and 14 conditions (columns); see Supplementary Table S2. The first five columns are for C-limitation, the next five columns for A-limitation, and the last four columns for R-limitation. For each mode of growth limitation, the growth rate increases from left to right. The matrix is log2-transformed, with expression values at the standard condition as zero (see Materials and Methods), represented as black color. Red color indicates negative values, green color positive values, and gray color missing entries. A dendrogram generated by clustering analysis is shown on the left of the expression matrix (see Materials and Methods), with the five major clusters shown on the right side of the matrix. The data are estimated to cover ˜80% of the proteome; see Supplementary Fig S5.
Mentions: The accuracy and precision of quantifying relative protein expression levels was determined from a standard curve using samples of unlabeled and 15N-labeled purified ribosomes. The observed relative levels, measured by ratios of the labeled to the unlabeled ribosomal proteins (or 15N/14N), agree extremely well with the expected values over a range of about two orders of magnitude (Fig2A). To assess the accuracy and precision for a whole-cell lysate with a much more complex proteome, labeled and unlabeled cells were mixed in fixed ratios and measured with quantitative mass spectrometry. The relative changes in protein levels can be precisely determined over the range of ratios from 0.1 to 10, as shown in Fig2B. The effective precision of relative protein quantification is ±18%, based on analysis of the 1:1 sample (Supplementary Fig S3). Thus, subtle changes in proteome composition that are much < 2-fold can be precisely determined. Furthermore, the relative quantitation using quantitative mass spectrometry agrees extremely well with a traditional biochemical measurement of ribosome content (Supplementary Fig S4A) and also with quantitation of LacZ using a β-galactosidase assay (Supplementary Fig S4B).

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