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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 coarse-grained proteome sectorsA–F Coarse-grained responses of the C-, A-, R-, U-, S-, and O-sectors to the three modes of growth limitation. As indicated in (A), the red symbols in each panel are for C-limitation, the blue for A-limitation, and the green for R-limitation. The error bars indicate the standard deviation of triplicate mass spectrometry runs. Error bars smaller than the corresponding symbols are not shown (see Supplementary Fig S10 on the different degrees of variability associated with different sectors.) On each plot, the number in the title indicates the number of proteins in that sector, and colored lines are best linear fits of the data represented by symbols of the same colors; see Supplementary Table S3 for the data on proteome fraction and Supplementary Table S4 for parameters of the fitted lines.
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fig03: The coarse-grained proteome sectorsA–F Coarse-grained responses of the C-, A-, R-, U-, S-, and O-sectors to the three modes of growth limitation. As indicated in (A), the red symbols in each panel are for C-limitation, the blue for A-limitation, and the green for R-limitation. The error bars indicate the standard deviation of triplicate mass spectrometry runs. Error bars smaller than the corresponding symbols are not shown (see Supplementary Fig S10 on the different degrees of variability associated with different sectors.) On each plot, the number in the title indicates the number of proteins in that sector, and colored lines are best linear fits of the data represented by symbols of the same colors; see Supplementary Table S3 for the data on proteome fraction and Supplementary Table S4 for parameters of the fitted lines.

Mentions: The collective behavior of a protein group can be approximated by coarse graining, effectively summing the absolute protein abundance of proteins in the same group. Among the methods for quantifying absolute protein abundance from proteomic mass spectrometry data (Beynon et al, 2005; Ishihama et al, 2005, 2008; Lu et al, 2006; Silva et al, 2006; Vogel & Marcotte, 2008; Schmidt et al, 2011; Muntel et al, 2014), the method of spectral counting takes the number of peptides recorded for each protein as proxy for the absolute abundance of the protein (Malmström et al, 2009). While spectral counting provides a crude estimate of the absolute protein abundance for individual proteins (Bantscheff et al, 2007), it gives a much more reliable approximation for groups of proteins. For a protein group comprising more than ∽5% of the total proteome, spectral counting produces estimates with < 20% error (Supplementary Fig S9A). The comparison of spectral counting data for ribosomal proteins with estimates based on biochemical measurements and the ribosome profiling results (Li et al, 2014) is in good agreement (Supplementary Fig S9B). By applying the spectral counting method, the proteome fractions for the nine protein groups defined in Supplementary Table S2 were determined for each of the three series of growth limitations (Supplementary Fig S10). It is clear from Supplementary Fig S10 that some groups occupy significant fractions of the proteome while others are minor constituents. Ranked by the extent the fraction varies (indicated by the difference between the maximal and minimal intercepts on the y-axis), the top three groups are C↑A↓R↓, C↓A↑R↓, and C↓A↓R↑. These consist of proteins that only respond upward to the C-, A-, and R-limitation and are referred to as the C-, A-, and R-sector, respectively (FigA–C). The C↓A↓R↓ group includes proteins that are uninduced by any of the three applied limitations, and is referred to as the U-sector (Fig3D). Another significant protein sector is the C↑A↑R↓ group, which is composed of proteins that have upward response to both the A- and C-limitations, and referred to as the S-sector for general starvation; see Fig3E. The three remaining groups (i.e., C↑A↑R↑, C↑A↓R↑, and C↓A↑R↑ groups) are small, with most of the data at or below 5% of the proteome, below the accuracy of the spectral counting method (Supplementary Fig S9A). The three small groups were placed together into the O-sector (FigF). In summary, the proteome is coarse-grained into 6 ‘sectors’: C-, A-, R-, U-, S-, and O-sectors with distinct growth rate dependences as shown in Fig3, with complete data for all fractions shown in Supplementary Table S3. In contrast, the results obtained for randomly shuffled expression matrices do not show significant growth rate dependence (Supplementary Fig S11).


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 coarse-grained proteome sectorsA–F Coarse-grained responses of the C-, A-, R-, U-, S-, and O-sectors to the three modes of growth limitation. As indicated in (A), the red symbols in each panel are for C-limitation, the blue for A-limitation, and the green for R-limitation. The error bars indicate the standard deviation of triplicate mass spectrometry runs. Error bars smaller than the corresponding symbols are not shown (see Supplementary Fig S10 on the different degrees of variability associated with different sectors.) On each plot, the number in the title indicates the number of proteins in that sector, and colored lines are best linear fits of the data represented by symbols of the same colors; see Supplementary Table S3 for the data on proteome fraction and Supplementary Table S4 for parameters of the fitted lines.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig03: The coarse-grained proteome sectorsA–F Coarse-grained responses of the C-, A-, R-, U-, S-, and O-sectors to the three modes of growth limitation. As indicated in (A), the red symbols in each panel are for C-limitation, the blue for A-limitation, and the green for R-limitation. The error bars indicate the standard deviation of triplicate mass spectrometry runs. Error bars smaller than the corresponding symbols are not shown (see Supplementary Fig S10 on the different degrees of variability associated with different sectors.) On each plot, the number in the title indicates the number of proteins in that sector, and colored lines are best linear fits of the data represented by symbols of the same colors; see Supplementary Table S3 for the data on proteome fraction and Supplementary Table S4 for parameters of the fitted lines.
Mentions: The collective behavior of a protein group can be approximated by coarse graining, effectively summing the absolute protein abundance of proteins in the same group. Among the methods for quantifying absolute protein abundance from proteomic mass spectrometry data (Beynon et al, 2005; Ishihama et al, 2005, 2008; Lu et al, 2006; Silva et al, 2006; Vogel & Marcotte, 2008; Schmidt et al, 2011; Muntel et al, 2014), the method of spectral counting takes the number of peptides recorded for each protein as proxy for the absolute abundance of the protein (Malmström et al, 2009). While spectral counting provides a crude estimate of the absolute protein abundance for individual proteins (Bantscheff et al, 2007), it gives a much more reliable approximation for groups of proteins. For a protein group comprising more than ∽5% of the total proteome, spectral counting produces estimates with < 20% error (Supplementary Fig S9A). The comparison of spectral counting data for ribosomal proteins with estimates based on biochemical measurements and the ribosome profiling results (Li et al, 2014) is in good agreement (Supplementary Fig S9B). By applying the spectral counting method, the proteome fractions for the nine protein groups defined in Supplementary Table S2 were determined for each of the three series of growth limitations (Supplementary Fig S10). It is clear from Supplementary Fig S10 that some groups occupy significant fractions of the proteome while others are minor constituents. Ranked by the extent the fraction varies (indicated by the difference between the maximal and minimal intercepts on the y-axis), the top three groups are C↑A↓R↓, C↓A↑R↓, and C↓A↓R↑. These consist of proteins that only respond upward to the C-, A-, and R-limitation and are referred to as the C-, A-, and R-sector, respectively (FigA–C). The C↓A↓R↓ group includes proteins that are uninduced by any of the three applied limitations, and is referred to as the U-sector (Fig3D). Another significant protein sector is the C↑A↑R↓ group, which is composed of proteins that have upward response to both the A- and C-limitations, and referred to as the S-sector for general starvation; see Fig3E. The three remaining groups (i.e., C↑A↑R↑, C↑A↓R↑, and C↓A↑R↑ groups) are small, with most of the data at or below 5% of the proteome, below the accuracy of the spectral counting method (Supplementary Fig S9A). The three small groups were placed together into the O-sector (FigF). In summary, the proteome is coarse-grained into 6 ‘sectors’: C-, A-, R-, U-, S-, and O-sectors with distinct growth rate dependences as shown in Fig3, with complete data for all fractions shown in Supplementary Table S3. In contrast, the results obtained for randomly shuffled expression matrices do not show significant growth rate dependence (Supplementary Fig S11).

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