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Differential Stoichiometry among Core Ribosomal Proteins.

Slavov N, Semrau S, Airoldi E, Budnik B, van Oudenaarden A - Cell Rep (2015)

Bottom Line: Testing such variability requires direct and precise quantification of RPs.Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes.Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, Northeastern University, Boston, MA 02115, USA; Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA. Electronic address: nslavov@alum.mit.edu.

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The Relative Levels of RPs across Monosomes and Polysomes Correlate Significantly to the Fitness of Yeast and Mammalian Cells Lacking the Genes Encoding These RPs(A) The fitness of RP-deleted yeast strains (Qian et al., 2012) is inversely proportional (p value <4 × 10–3) to the relative levels of the corresponding RPs in tetrasomes from yeast growing on ethanol carbon source. The RPs without paralogs are marked with red squares.(B) Extension of the analysis in (A) to all sucrose fractions: correlations between the relative RP levels from Figure 3E and the fitnesses of strains lacking the corresponding RP genes (Qian et al., 2012). The correlations are shown either for all quantified RPs or only for RPs without paralogs.(C) Correlations between the relative levels of the RPs from Figure 3E and the their transcriptional growth rate responses (slopes). The growth-rate slopes were previously computed by regressing (R2> 0.87) the levels of mRNAs in glucose-limited steady-state cultures of yeast against the growth rates of the cultures (Slavov and Botstein, 2011).(D) Distribution of sequence identity between human RPs and their closest mouse orthologs; the sequences and annotations for RPs are from SWISS-PROT.(E) Extension of the analysis for yeast in (A) and (B) to mouse: correlations between the relative levels of mouse RPs from Figure 2 and the fitness of human ESC lacking the corresponding human ortholog (Shalem et al., 2014). The correlations are shown either for all quantified RPs or only for RPs whose sequence identity between mouse and human exceeds 80%. The correlation for monosomes is shown in replicates (1a and 1b).See also Figure S5. All error bars are SD from bootstrapping.
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fig4: The Relative Levels of RPs across Monosomes and Polysomes Correlate Significantly to the Fitness of Yeast and Mammalian Cells Lacking the Genes Encoding These RPs(A) The fitness of RP-deleted yeast strains (Qian et al., 2012) is inversely proportional (p value <4 × 10–3) to the relative levels of the corresponding RPs in tetrasomes from yeast growing on ethanol carbon source. The RPs without paralogs are marked with red squares.(B) Extension of the analysis in (A) to all sucrose fractions: correlations between the relative RP levels from Figure 3E and the fitnesses of strains lacking the corresponding RP genes (Qian et al., 2012). The correlations are shown either for all quantified RPs or only for RPs without paralogs.(C) Correlations between the relative levels of the RPs from Figure 3E and the their transcriptional growth rate responses (slopes). The growth-rate slopes were previously computed by regressing (R2> 0.87) the levels of mRNAs in glucose-limited steady-state cultures of yeast against the growth rates of the cultures (Slavov and Botstein, 2011).(D) Distribution of sequence identity between human RPs and their closest mouse orthologs; the sequences and annotations for RPs are from SWISS-PROT.(E) Extension of the analysis for yeast in (A) and (B) to mouse: correlations between the relative levels of mouse RPs from Figure 2 and the fitness of human ESC lacking the corresponding human ortholog (Shalem et al., 2014). The correlations are shown either for all quantified RPs or only for RPs whose sequence identity between mouse and human exceeds 80%. The correlation for monosomes is shown in replicates (1a and 1b).See also Figure S5. All error bars are SD from bootstrapping.

Mentions: Next, we tested the differential RPs stoichiometry and its phenotypic consequences by independent fitness measurements. Our observation that the RP stoichiometry depends on the number of ribosomes bound per mRNA parallels measurements of higher translational activity of polysomes compared to monosomes (Warner et al., 1963, Goodman and Rich, 1963); some studies have even reported that the translational activity per ribosome increases with the number of ribosomes bound per mRNA (Noll et al., 1963, Wettstein et al., 1963), but this finding has not been widely reproduced. We therefore hypothesized that genetic deletions of RPs enriched in the more active ribosomes—as compared to RPs enriched in less active ribosomes—may result in a larger decrease of the translation rate and thus lower fitness. To test this hypothesis, we computed the correlation (Figure 4A) between the fitness of yeast strains with single RP gene deletions (Qian et al., 2012) and the corresponding relative RP levels measured in the tetra-ribosomal fraction (four ribosomes per mRNA). Consistent with our hypothesis, the fitness of strains lacking RP genes is inversely proportional to the relative levels of the corresponding RPs in the tetra-ribosomes (Figure 4A). Extending this correlation analysis to the RP levels in all sucrose fractions shown in Figure 3E results in a correlation pattern (Figure 4B) that further supports our hypothesis by showing the opposite dependence for fractions with fewer ribosomes per mRNA: the fitness of strains lacking RP genes is proportional to the relative levels of the corresponding RPs in fractions with fewer ribosomes per mRNA (Figure 4B). This correlation pattern holds both for ethanol and for glucose carbon sources. To mitigate possible artifacts in the fitness data due to potential chromosome duplications in the deletion strains, we computed the correlations between the RP levels and the fitness of the corresponding RP deletion strains only for RPs without paralogs (thus unlikely to be affected by chromosome duplication) and found much higher magnitudes of the correlations (Figures 4A and 4B). This result suggests that the differential RP stoichiometry is not limited to paralogous RPs substituting for each other.


Differential Stoichiometry among Core Ribosomal Proteins.

Slavov N, Semrau S, Airoldi E, Budnik B, van Oudenaarden A - Cell Rep (2015)

The Relative Levels of RPs across Monosomes and Polysomes Correlate Significantly to the Fitness of Yeast and Mammalian Cells Lacking the Genes Encoding These RPs(A) The fitness of RP-deleted yeast strains (Qian et al., 2012) is inversely proportional (p value <4 × 10–3) to the relative levels of the corresponding RPs in tetrasomes from yeast growing on ethanol carbon source. The RPs without paralogs are marked with red squares.(B) Extension of the analysis in (A) to all sucrose fractions: correlations between the relative RP levels from Figure 3E and the fitnesses of strains lacking the corresponding RP genes (Qian et al., 2012). The correlations are shown either for all quantified RPs or only for RPs without paralogs.(C) Correlations between the relative levels of the RPs from Figure 3E and the their transcriptional growth rate responses (slopes). The growth-rate slopes were previously computed by regressing (R2> 0.87) the levels of mRNAs in glucose-limited steady-state cultures of yeast against the growth rates of the cultures (Slavov and Botstein, 2011).(D) Distribution of sequence identity between human RPs and their closest mouse orthologs; the sequences and annotations for RPs are from SWISS-PROT.(E) Extension of the analysis for yeast in (A) and (B) to mouse: correlations between the relative levels of mouse RPs from Figure 2 and the fitness of human ESC lacking the corresponding human ortholog (Shalem et al., 2014). The correlations are shown either for all quantified RPs or only for RPs whose sequence identity between mouse and human exceeds 80%. The correlation for monosomes is shown in replicates (1a and 1b).See also Figure S5. All error bars are SD from bootstrapping.
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fig4: The Relative Levels of RPs across Monosomes and Polysomes Correlate Significantly to the Fitness of Yeast and Mammalian Cells Lacking the Genes Encoding These RPs(A) The fitness of RP-deleted yeast strains (Qian et al., 2012) is inversely proportional (p value <4 × 10–3) to the relative levels of the corresponding RPs in tetrasomes from yeast growing on ethanol carbon source. The RPs without paralogs are marked with red squares.(B) Extension of the analysis in (A) to all sucrose fractions: correlations between the relative RP levels from Figure 3E and the fitnesses of strains lacking the corresponding RP genes (Qian et al., 2012). The correlations are shown either for all quantified RPs or only for RPs without paralogs.(C) Correlations between the relative levels of the RPs from Figure 3E and the their transcriptional growth rate responses (slopes). The growth-rate slopes were previously computed by regressing (R2> 0.87) the levels of mRNAs in glucose-limited steady-state cultures of yeast against the growth rates of the cultures (Slavov and Botstein, 2011).(D) Distribution of sequence identity between human RPs and their closest mouse orthologs; the sequences and annotations for RPs are from SWISS-PROT.(E) Extension of the analysis for yeast in (A) and (B) to mouse: correlations between the relative levels of mouse RPs from Figure 2 and the fitness of human ESC lacking the corresponding human ortholog (Shalem et al., 2014). The correlations are shown either for all quantified RPs or only for RPs whose sequence identity between mouse and human exceeds 80%. The correlation for monosomes is shown in replicates (1a and 1b).See also Figure S5. All error bars are SD from bootstrapping.
Mentions: Next, we tested the differential RPs stoichiometry and its phenotypic consequences by independent fitness measurements. Our observation that the RP stoichiometry depends on the number of ribosomes bound per mRNA parallels measurements of higher translational activity of polysomes compared to monosomes (Warner et al., 1963, Goodman and Rich, 1963); some studies have even reported that the translational activity per ribosome increases with the number of ribosomes bound per mRNA (Noll et al., 1963, Wettstein et al., 1963), but this finding has not been widely reproduced. We therefore hypothesized that genetic deletions of RPs enriched in the more active ribosomes—as compared to RPs enriched in less active ribosomes—may result in a larger decrease of the translation rate and thus lower fitness. To test this hypothesis, we computed the correlation (Figure 4A) between the fitness of yeast strains with single RP gene deletions (Qian et al., 2012) and the corresponding relative RP levels measured in the tetra-ribosomal fraction (four ribosomes per mRNA). Consistent with our hypothesis, the fitness of strains lacking RP genes is inversely proportional to the relative levels of the corresponding RPs in the tetra-ribosomes (Figure 4A). Extending this correlation analysis to the RP levels in all sucrose fractions shown in Figure 3E results in a correlation pattern (Figure 4B) that further supports our hypothesis by showing the opposite dependence for fractions with fewer ribosomes per mRNA: the fitness of strains lacking RP genes is proportional to the relative levels of the corresponding RPs in fractions with fewer ribosomes per mRNA (Figure 4B). This correlation pattern holds both for ethanol and for glucose carbon sources. To mitigate possible artifacts in the fitness data due to potential chromosome duplications in the deletion strains, we computed the correlations between the RP levels and the fitness of the corresponding RP deletion strains only for RPs without paralogs (thus unlikely to be affected by chromosome duplication) and found much higher magnitudes of the correlations (Figures 4A and 4B). This result suggests that the differential RP stoichiometry is not limited to paralogous RPs substituting for each other.

Bottom Line: Testing such variability requires direct and precise quantification of RPs.Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes.Together, our findings support the existence of ribosomes with distinct protein composition and physiological function.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, Northeastern University, Boston, MA 02115, USA; Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138, USA. Electronic address: nslavov@alum.mit.edu.

Show MeSH
Related in: MedlinePlus