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Transcriptional robustness and protein interactions are associated in yeast.

Bekaert M, Conant GC - BMC Syst Biol (2011)

Bottom Line: Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes.This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes.We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA. bekaertm@missouri.edu

ABSTRACT

Background: Robustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression.

Results: Here, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears an affinity to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis). Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.

Conclusions: We suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them.

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Correlation between protein-protein interactions and mRNA expression variation. (A) Definition of the three interaction datasets considered. PPI consists of all non-aneuploid chromosome genes coding for a protein with at least one interaction (348 genes out of 413 in the example considered here). Any genes with at least one protein interaction with a protein encoded on the aneuploid chromosome are in AneuPPI (36 here); all other members of PPI are placed in NonAneuPPI (312). (B) Plot of the frequency of the genes with increased or decreased mRNA expression (y-axis; 1.3-fold change, see Methods) coding for a protein interaction with a protein encoded either on an aneuploid chromosome, AneuPPI (green dots), or on a different chromosome, NonAneuPPI (blue dots), against the proportion of genes without interactions that showed changed mRNA expression for each aneuploidy microarray experiment (x-axis). (C) Scatter plot of protein abundance (protein molecules per cell) versus mRNA expression variation. High abundance proteins show low mRNA variation (Spearman rank correlation; P < 10-15). The number of protein-protein interactions is color-coded as illustrated by the legend at right. Proteins with a high degree of interaction are of both low abundance and low mRNA expression variation (Spearman rank correlations, P < 10-15).
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Figure 1: Correlation between protein-protein interactions and mRNA expression variation. (A) Definition of the three interaction datasets considered. PPI consists of all non-aneuploid chromosome genes coding for a protein with at least one interaction (348 genes out of 413 in the example considered here). Any genes with at least one protein interaction with a protein encoded on the aneuploid chromosome are in AneuPPI (36 here); all other members of PPI are placed in NonAneuPPI (312). (B) Plot of the frequency of the genes with increased or decreased mRNA expression (y-axis; 1.3-fold change, see Methods) coding for a protein interaction with a protein encoded either on an aneuploid chromosome, AneuPPI (green dots), or on a different chromosome, NonAneuPPI (blue dots), against the proportion of genes without interactions that showed changed mRNA expression for each aneuploidy microarray experiment (x-axis). (C) Scatter plot of protein abundance (protein molecules per cell) versus mRNA expression variation. High abundance proteins show low mRNA variation (Spearman rank correlation; P < 10-15). The number of protein-protein interactions is color-coded as illustrated by the legend at right. Proteins with a high degree of interaction are of both low abundance and low mRNA expression variation (Spearman rank correlations, P < 10-15).

Mentions: We first determined the set of genes from each aneuploid line that showed either an increase or decrease in expression (i.e., had greater than ± 1.3-fold variation compared to the control strain, Methods). We next defined the set of genes that have at least one protein-protein interaction (hereafter PPI, Figure 1A). The genes in PPI are less likely to show post-aneuploidy expression variation than are non-interacting genes (Figure 1B; Wilcoxon unilateral signed-rank test, P < 10-9). Moreover, those genes coding for a protein possessing an interaction with a protein encoded on the aneuploid chromosome (hereafter AneuPPI, Figure 1A) showed less expression variation than do genes with interactions to proteins encoded on non-aneuploid chromosomes (hereafter NonAneuPPI; Figure 1A &1B; Wilcoxon unilateral signed-rank test, P ≈ 0.0002). We questioned whether these differences between AneuPPI and NonAneuPPI could be due to differences in the average number of protein-protein interactions per gene for the two sets. However, we found that the average number of interactions was very similar: 1.493, and 1.495 for NonAneuPPI and AneuPPI respectively. These distributions are not significantly different. (Kolmogorov-Smirnov test, two sided, P > 0.5). So far, we have lumped over- and under-expression together in these analyses. When we split the data by considering the direction of variation (and in so doing reduce the power of the tests), we again find that genes from the set PPI are under-represented among both the genes with an increase and with a decrease in expression (Wilcoxon unilateral signed-rank test, P < 10-4). Likewise, the AneuPPI set showed fewer under-expressed genes relative to NonAneuPPI than expected (Wilcoxon unilateral signed-rank test, P < 10-5). No significant difference in the proportion of over-expressed genes was seen between AneuPPI and NonAneuPPI (Wilcoxon unilateral signed-rank test, P ≈ 0.13).


Transcriptional robustness and protein interactions are associated in yeast.

Bekaert M, Conant GC - BMC Syst Biol (2011)

Correlation between protein-protein interactions and mRNA expression variation. (A) Definition of the three interaction datasets considered. PPI consists of all non-aneuploid chromosome genes coding for a protein with at least one interaction (348 genes out of 413 in the example considered here). Any genes with at least one protein interaction with a protein encoded on the aneuploid chromosome are in AneuPPI (36 here); all other members of PPI are placed in NonAneuPPI (312). (B) Plot of the frequency of the genes with increased or decreased mRNA expression (y-axis; 1.3-fold change, see Methods) coding for a protein interaction with a protein encoded either on an aneuploid chromosome, AneuPPI (green dots), or on a different chromosome, NonAneuPPI (blue dots), against the proportion of genes without interactions that showed changed mRNA expression for each aneuploidy microarray experiment (x-axis). (C) Scatter plot of protein abundance (protein molecules per cell) versus mRNA expression variation. High abundance proteins show low mRNA variation (Spearman rank correlation; P < 10-15). The number of protein-protein interactions is color-coded as illustrated by the legend at right. Proteins with a high degree of interaction are of both low abundance and low mRNA expression variation (Spearman rank correlations, P < 10-15).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3113729&req=5

Figure 1: Correlation between protein-protein interactions and mRNA expression variation. (A) Definition of the three interaction datasets considered. PPI consists of all non-aneuploid chromosome genes coding for a protein with at least one interaction (348 genes out of 413 in the example considered here). Any genes with at least one protein interaction with a protein encoded on the aneuploid chromosome are in AneuPPI (36 here); all other members of PPI are placed in NonAneuPPI (312). (B) Plot of the frequency of the genes with increased or decreased mRNA expression (y-axis; 1.3-fold change, see Methods) coding for a protein interaction with a protein encoded either on an aneuploid chromosome, AneuPPI (green dots), or on a different chromosome, NonAneuPPI (blue dots), against the proportion of genes without interactions that showed changed mRNA expression for each aneuploidy microarray experiment (x-axis). (C) Scatter plot of protein abundance (protein molecules per cell) versus mRNA expression variation. High abundance proteins show low mRNA variation (Spearman rank correlation; P < 10-15). The number of protein-protein interactions is color-coded as illustrated by the legend at right. Proteins with a high degree of interaction are of both low abundance and low mRNA expression variation (Spearman rank correlations, P < 10-15).
Mentions: We first determined the set of genes from each aneuploid line that showed either an increase or decrease in expression (i.e., had greater than ± 1.3-fold variation compared to the control strain, Methods). We next defined the set of genes that have at least one protein-protein interaction (hereafter PPI, Figure 1A). The genes in PPI are less likely to show post-aneuploidy expression variation than are non-interacting genes (Figure 1B; Wilcoxon unilateral signed-rank test, P < 10-9). Moreover, those genes coding for a protein possessing an interaction with a protein encoded on the aneuploid chromosome (hereafter AneuPPI, Figure 1A) showed less expression variation than do genes with interactions to proteins encoded on non-aneuploid chromosomes (hereafter NonAneuPPI; Figure 1A &1B; Wilcoxon unilateral signed-rank test, P ≈ 0.0002). We questioned whether these differences between AneuPPI and NonAneuPPI could be due to differences in the average number of protein-protein interactions per gene for the two sets. However, we found that the average number of interactions was very similar: 1.493, and 1.495 for NonAneuPPI and AneuPPI respectively. These distributions are not significantly different. (Kolmogorov-Smirnov test, two sided, P > 0.5). So far, we have lumped over- and under-expression together in these analyses. When we split the data by considering the direction of variation (and in so doing reduce the power of the tests), we again find that genes from the set PPI are under-represented among both the genes with an increase and with a decrease in expression (Wilcoxon unilateral signed-rank test, P < 10-4). Likewise, the AneuPPI set showed fewer under-expressed genes relative to NonAneuPPI than expected (Wilcoxon unilateral signed-rank test, P < 10-5). No significant difference in the proportion of over-expressed genes was seen between AneuPPI and NonAneuPPI (Wilcoxon unilateral signed-rank test, P ≈ 0.13).

Bottom Line: Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes.This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes.We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA. bekaertm@missouri.edu

ABSTRACT

Background: Robustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression.

Results: Here, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears an affinity to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis). Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.

Conclusions: We suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them.

Show MeSH