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Functional maps of protein complexes from quantitative genetic interaction data.

Bandyopadhyay S, Kelley R, Krogan NJ, Ideker T - PLoS Comput. Biol. (2008)

Bottom Line: Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits.Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism.These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

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Pathway models identify novel functional associations among cellular machinery.Each panel represents complexes and between-complex links taken from Figure 2. Physical interactions with PE>1 are shown and strong genetic interactions (/S/>2.5) are shown with increased thicknesses corresponding to stronger genetic interactions. (A) Histone acetyltransferase complex RTT109 – VPS75 showing strong alleviating interactions with the Elongator transcription elongation factor complex. (B) Between-complex model highlighting alleviating interactions between the LRP1 – RRP6 nuclear exosome complex and an mRNA degradation complex. (C) Complexes associated with the RAD6-C histone ubiquitination complex (BRE1/LGE1).
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pcbi-1000065-g005: Pathway models identify novel functional associations among cellular machinery.Each panel represents complexes and between-complex links taken from Figure 2. Physical interactions with PE>1 are shown and strong genetic interactions (/S/>2.5) are shown with increased thicknesses corresponding to stronger genetic interactions. (A) Histone acetyltransferase complex RTT109 – VPS75 showing strong alleviating interactions with the Elongator transcription elongation factor complex. (B) Between-complex model highlighting alleviating interactions between the LRP1 – RRP6 nuclear exosome complex and an mRNA degradation complex. (C) Complexes associated with the RAD6-C histone ubiquitination complex (BRE1/LGE1).

Mentions: Figure 5 presents detailed diagrams of example functional relationships elucidated by our module mapping method. Figure 5A shows the alleviating relationship between the RTT109-VPS75 histone acetyltransferase complex [6],[21],[22] and Elongator, a complex that is associated with RNA Polymerase II and is involved in transcriptional elongation [23]. Since several subunits both of Elongator and RTT109/VPS75 have been shown to be involved in histone acetylation levels [22],[24], these two complexes may operate together to effectively clear histones from actively transcribed regions. To identify further mechanisms of their cooperation, future studies may search for specific residues of histone H3 whose acetylation levels are modulated by both complexes. This example highlights the utility of an integrated approach, since although RTT109 and VPS75 are known to form a complex their genetic interaction profiles are not congruent (correlation of profiles of −0.1) and had been missed by hierarchical clustering. Figure 5B highlights non-essential components (LRP1 and RRP6) of the exosome, which contributes to the quality-control system that retains and degrades aberrant mRNAs in the nucleus [25]. These components have alleviating interactions with a complex composed of Lsm proteins involved in mRNA decay.


Functional maps of protein complexes from quantitative genetic interaction data.

Bandyopadhyay S, Kelley R, Krogan NJ, Ideker T - PLoS Comput. Biol. (2008)

Pathway models identify novel functional associations among cellular machinery.Each panel represents complexes and between-complex links taken from Figure 2. Physical interactions with PE>1 are shown and strong genetic interactions (/S/>2.5) are shown with increased thicknesses corresponding to stronger genetic interactions. (A) Histone acetyltransferase complex RTT109 – VPS75 showing strong alleviating interactions with the Elongator transcription elongation factor complex. (B) Between-complex model highlighting alleviating interactions between the LRP1 – RRP6 nuclear exosome complex and an mRNA degradation complex. (C) Complexes associated with the RAD6-C histone ubiquitination complex (BRE1/LGE1).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2289880&req=5

pcbi-1000065-g005: Pathway models identify novel functional associations among cellular machinery.Each panel represents complexes and between-complex links taken from Figure 2. Physical interactions with PE>1 are shown and strong genetic interactions (/S/>2.5) are shown with increased thicknesses corresponding to stronger genetic interactions. (A) Histone acetyltransferase complex RTT109 – VPS75 showing strong alleviating interactions with the Elongator transcription elongation factor complex. (B) Between-complex model highlighting alleviating interactions between the LRP1 – RRP6 nuclear exosome complex and an mRNA degradation complex. (C) Complexes associated with the RAD6-C histone ubiquitination complex (BRE1/LGE1).
Mentions: Figure 5 presents detailed diagrams of example functional relationships elucidated by our module mapping method. Figure 5A shows the alleviating relationship between the RTT109-VPS75 histone acetyltransferase complex [6],[21],[22] and Elongator, a complex that is associated with RNA Polymerase II and is involved in transcriptional elongation [23]. Since several subunits both of Elongator and RTT109/VPS75 have been shown to be involved in histone acetylation levels [22],[24], these two complexes may operate together to effectively clear histones from actively transcribed regions. To identify further mechanisms of their cooperation, future studies may search for specific residues of histone H3 whose acetylation levels are modulated by both complexes. This example highlights the utility of an integrated approach, since although RTT109 and VPS75 are known to form a complex their genetic interaction profiles are not congruent (correlation of profiles of −0.1) and had been missed by hierarchical clustering. Figure 5B highlights non-essential components (LRP1 and RRP6) of the exosome, which contributes to the quality-control system that retains and degrades aberrant mRNAs in the nucleus [25]. These components have alleviating interactions with a complex composed of Lsm proteins involved in mRNA decay.

Bottom Line: Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits.Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism.These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics, University of California San Diego, La Jolla, California, United States of America.

ABSTRACT
Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

Show MeSH