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Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map

Abstract

Defining the functional relationships between proteins is critical for understanding virtually all aspects of cell biology. Large-scale identification of protein complexes has provided one important step towards this goal; however, even knowledge of the stoichiometry, affinity and lifetime of every protein–protein interaction would not reveal the functional relationships between and within such complexes. Genetic interactions can provide functional information that is largely invisible to protein–protein interaction data sets. Here we present an epistatic miniarray profile (E-MAP)1 consisting of quantitative pairwise measurements of the genetic interactions between 743 Saccharomyces cerevisiae genes involved in various aspects of chromosome biology (including DNA replication/repair, chromatid segregation and transcriptional regulation). This E-MAP reveals that physical interactions fall into two well-represented classes distinguished by whether or not the individual proteins act coherently to carry out a common function. Thus, genetic interaction data make it possible to dissect functionally multi-protein complexes, including Mediator, and to organize distinct protein complexes into pathways. In one pathway defined here, we show that Rtt109 is the founding member of a novel class of histone acetyltransferases responsible for Asf1-dependent acetylation of histone H3 on lysine 56. This modification, in turn, enables a ubiquitin ligase complex containing the cullin Rtt101 to ensure genomic integrity during DNA replication.

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Figure 1: Hierarchical clustering of genetic interaction patterns.
Figure 2: Relationship between genetic epistasis groups and physical complexes.
Figure 3: Functional dissection of the Mediator complex.
Figure 4: Characterization of the histone H3 K56 acetylation pathway.

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Acknowledgements

We are grateful to K. Tipton and M. Bassik for critically reading the manuscript, S. Gasser, B. Frey and Vincent Cheung for discussion, and G. Narlikar for reagents. We thank N. Datta, T. Punna, N. Thompson, M. Ballantine, N. Gabovic, A. Wind, K. Chin, Y. Xue, A. Chan, Y. Xue, T. Chan, M. Xan, M. Lim, H. Dalgleish, K. Vachon, L. Le, C. Sun, Z. Hassam, J. Rilestone and K. Takhar for technical assistance. We also thank S. Jackson, Z. Zhang, Vanessa Cheung, F. Winston, J. Erkmann and P. Kaufman for communicating results before publication. This research was supported by grants from Genome Canada and the Ontario Genomics Institute (J.F.G., A.E., C.B. and B.A.), the NIH (D.P.T.), the Howard Hughes Medical Institute (J.S.W.) and the Canadian Institute of Health Research (N.J.K., C.J.I. and G.W.B.). S.R.C. was funded by a fellowship from the Burroughs Wellcome Fund. N.J.K. is a Sandler Family Fellow.

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Correspondence to Jonathan S. Weissman or Jack F. Greenblatt.

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Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary Discussion, Supplementary Figures 1-3 with legends and additional references. (PDF 10810 kb)

Supplementary Zip 1

Summary of mutations and genetic profiles.xls” contains a list of all the mutations used in the study, as well as a functional annotation for the corresponding gene, and a list of other mutations with a similar profile of interactions. Summary of genetic data for phys int.doc” contains a summary of genetic interaction data for all pairs of physically interacting proteins analyzed in this study. Chromosome biology EMAP data.txt” contains all of the genetic interaction scores from this study in a tab-delimited form. Chromosome biology genetic interaction scores.xls” contains all of the genetic interaction scores from this study in an Excel format. Chromosome biology correlations.xls” contains all of the pairwise correlations between the genetic interaction profiles created in this study. Chromosome biology COP.xls” contains a COP score computed for every pair of mutations in this study. Clusters” is a folder containing hierarchical clustering of the genetic interaction and COP data. These clusters can be viewed using the Java Treeview program. Instructions for downloading and installing Java Treeview and for viewing the clusters are included in the Clusters folder. (ZIP 17541 kb)

Supplementary Zip 2

This archive contains a folder structure containing (text) data files which include all of the colony size measurements used in this study. This file also contains a summary text file explaining the contents. (ZIP 18374 kb)

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Collins, S., Miller, K., Maas, N. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007). https://doi.org/10.1038/nature05649

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