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Improved functional overview of protein complexes using inferred epistatic relationships.

Ryan C, Greene D, Guénolé A, van Attikum H, Krogan NJ, Cunningham P, Cagney G - BMC Syst Biol (2011)

Bottom Line: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions.We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links.We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Ireland. colm.ryan@ucd.ie.

ABSTRACT

Background: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.

Results: We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

Conclusion: Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Show MeSH
Novel inter-complex edges generated by newly-inferred epistatic interactions: Nodes represent protein complexes (as cataloged by Pu et al [57]) while edges represent strong net positive or net negative genetic interactions between complexes. Grey edges represent interactions which are unaffected by our predicted interactions, violet edges represent interactions which have been given additional links by our predicted interactions, and red edges represent previously unreported interactions between complexes, established using our method. Edges are only drawn if the median genetic interaction is significantly more positive or negative than one would expect by chance (P < 0.001)
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Figure 3: Novel inter-complex edges generated by newly-inferred epistatic interactions: Nodes represent protein complexes (as cataloged by Pu et al [57]) while edges represent strong net positive or net negative genetic interactions between complexes. Grey edges represent interactions which are unaffected by our predicted interactions, violet edges represent interactions which have been given additional links by our predicted interactions, and red edges represent previously unreported interactions between complexes, established using our method. Edges are only drawn if the median genetic interaction is significantly more positive or negative than one would expect by chance (P < 0.001)

Mentions: Our inferred interactions have two main uses in this context. First, they provide additional evidence for previously proposed connections among protein complexes, and second, they establish new connections. By comparing the resulting network of linked complexes before and after the addition of predicted interactions, we can see which links are a direct result of our predicted interactions. In total 105 'inter complex' links were significantly 'monochromatic' after the addition of our predictions, in other words a set of previously unknown inter-complex links identified with the help of inference. In contrast, the statistical significance of only one 'intra complex' link increased after including our predictions. This apparent discrepancy chiefly arises due to the composition of the E-MAPs published to date, where complexes tend to be represented in only a single E-MAP. Interactions between complexes therefore frequently correspond to links between E-MAPs (Table 5 and Figure 3).


Improved functional overview of protein complexes using inferred epistatic relationships.

Ryan C, Greene D, Guénolé A, van Attikum H, Krogan NJ, Cunningham P, Cagney G - BMC Syst Biol (2011)

Novel inter-complex edges generated by newly-inferred epistatic interactions: Nodes represent protein complexes (as cataloged by Pu et al [57]) while edges represent strong net positive or net negative genetic interactions between complexes. Grey edges represent interactions which are unaffected by our predicted interactions, violet edges represent interactions which have been given additional links by our predicted interactions, and red edges represent previously unreported interactions between complexes, established using our method. Edges are only drawn if the median genetic interaction is significantly more positive or negative than one would expect by chance (P < 0.001)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117733&req=5

Figure 3: Novel inter-complex edges generated by newly-inferred epistatic interactions: Nodes represent protein complexes (as cataloged by Pu et al [57]) while edges represent strong net positive or net negative genetic interactions between complexes. Grey edges represent interactions which are unaffected by our predicted interactions, violet edges represent interactions which have been given additional links by our predicted interactions, and red edges represent previously unreported interactions between complexes, established using our method. Edges are only drawn if the median genetic interaction is significantly more positive or negative than one would expect by chance (P < 0.001)
Mentions: Our inferred interactions have two main uses in this context. First, they provide additional evidence for previously proposed connections among protein complexes, and second, they establish new connections. By comparing the resulting network of linked complexes before and after the addition of predicted interactions, we can see which links are a direct result of our predicted interactions. In total 105 'inter complex' links were significantly 'monochromatic' after the addition of our predictions, in other words a set of previously unknown inter-complex links identified with the help of inference. In contrast, the statistical significance of only one 'intra complex' link increased after including our predictions. This apparent discrepancy chiefly arises due to the composition of the E-MAPs published to date, where complexes tend to be represented in only a single E-MAP. Interactions between complexes therefore frequently correspond to links between E-MAPs (Table 5 and Figure 3).

Bottom Line: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions.We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links.We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Informatics, University College Dublin, Ireland. colm.ryan@ucd.ie.

ABSTRACT

Background: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.

Results: We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.

Conclusion: Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Show MeSH