Andrew Emili

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Identification of protein–protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein–protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein–protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells.

Devil in the Detail Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533; see the Perspective by Tyagi) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated. Measurement of protein and messenger RNA copy numbers in single Escherichia coli cells gives a system-wide view of stochastic gene expression. Protein and messenger RNA (mRNA) copy numbers vary from cell to cell in isogenic bacterial populations. However, these molecules often exist in low copy numbers and are difficult to detect in single cells. We carried out quantitative system-wide analyses of protein and mRNA expression in individual cells with single-molecule sensitivity using a newly constructed yellow fluorescent protein fusion library for Escherichia coli. We found that almost all protein number distributions can be described by the gamma distribution with two fitting parameters which, at low expression levels, have clear physical interpretations as the transcription rate and protein burst size. At high expression levels, the distributions are dominated by extrinsic noise. We found that a single cell’s protein and mRNA copy numbers for any given gene are uncorrelated.

Interaction network containing conserved and essential protein complexes in Escherichia coli

Proteins often function as components of multi-subunit complexes. Despite its long history as a model organism, no large-scale analysis of protein complexes in Escherichia coli has yet been reported. To this end, we have targeted DNA cassettes into the E. coli chromosome to create carboxy-terminal, affinity-tagged alleles of 1,000 open reading frames (∼ 23% of the genome). A total of 857 proteins, including 198 of the most highly conserved, soluble non-ribosomal proteins essential in at least one bacterial species, were tagged successfully, whereas 648 could be purified to homogeneity and their interacting protein partners identified by mass spectrometry. An interaction network of protein complexes involved in diverse biological processes was uncovered and validated by sequential rounds of tagging and purification. This network includes many new interactions as well as interactions predicted based solely on genomic inference or limited phenotypic data. This study provides insight into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota.

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

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) 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.

Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

Background Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. Principal Findings To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed “Enrichment Map”, a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. Conclusions Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).

Cotranscriptional Set2 Methylation of Histone H3 Lysine 36 Recruits a Repressive Rpd3 Complex

The yeast histone deacetylase Rpd3 can be recruited to promoters to repress transcription initiation. Biochemical, genetic, and gene-expression analyses show that Rpd3 exists in two distinct complexes. The smaller complex, Rpd3C(S), shares Sin3 and Ume1 with Rpd3C(L) but contains the unique subunits Rco1 and Eaf3. Rpd3C(S) mutants exhibit phenotypes remarkably similar to those of Set2, a histone methyltransferase associated with elongating RNA polymerase II. Chromatin immunoprecipitation and biochemical experiments indicate that the chromodomain of Eaf3 recruits Rpd3C(S) to nucleosomes methylated by Set2 on histone H3 lysine 36, leading to deacetylation of transcribed regions. This pathway apparently acts to negatively regulate transcription because deleting the genes for Set2 or Rpd3C(S) bypasses the requirement for the positive elongation factor Bur1/Bur2.

Navigating the Chaperone Network: An Integrative Map of Physical and Genetic Interactions Mediated by the Hsp90 Chaperone

Physical, genetic, and chemical-genetic interactions centered on the conserved chaperone Hsp90 were mapped at high resolution in yeast using systematic proteomic and genomic methods. Physical interactions were identified using genome-wide two hybrid screens combined with large-scale affinity purification of Hsp90-containing protein complexes. Genetic interactions were uncovered using synthetic genetic array technology and by a microarray-based chemical-genetic screen of a set of about 4700 viable yeast gene deletion mutants for hypersensitivity to the Hsp90 inhibitor geldanamycin. An extended network, consisting of 198 putative physical interactions and 451 putative genetic and chemical-genetic interactions, was found to connect Hsp90 to cofactors and substrates involved in a wide range of cellular functions. Two novel Hsp90 cofactors, Tah1 (YCR060W) and Pih1 (YHR034C), were also identified. These cofactors interact physically and functionally with the conserved AAA(+)-type DNA helicases Rvb1/Rvb2, which are key components of several chromatin remodeling factors, thereby linking Hsp90 to epigenetic gene regulation.

Methylation of Histone H3 by Set2 in Saccharomyces cerevisiae Is Linked to Transcriptional Elongation by RNA Polymerase II

ABSTRACT Set2 methylates Lys36 of histone H3. We show here that yeast Set2 copurifies with RNA polymerase II (RNAPII). Chromatin immunoprecipitation analyses demonstrated that Set2 and histone H3 Lys36 methylation are associated with the coding regions of several genes that were tested and correlate with active transcription. Both depend, as well, on the Paf1 elongation factor complex. The C terminus of Set2, which contains a WW domain, is also required for effective Lys36 methylation. Deletion of CTK1, encoding an RNAPII CTD kinase, prevents Lys36 methylation and Set2 recruitment, suggesting that methylation may be triggered by contact of the WW domain or C terminus of Set2 with Ser2-phosphorylated CTD. A set2 deletion results in slight sensitivity to 6-azauracil and much less β-galactosidase produced by a reporter plasmid, resulting from a defect in transcription. In synthetic genetic array (SGA) analysis, synthetic growth defects were obtained when a set2 deletion was combined with deletions of all five components of the Paf1 complex, the chromodomain elongation factor Chd1, the putative elongation factor Soh1, the Bre1 or Lge1 components of the histone H2B ubiquitination complex, or the histone H2A variant Htz1. SET2 also interacts genetically with components of the Set1 and Set3 complexes, suggesting that Set1, Set2, and Set3 similarly affect transcription by RNAPII.

A Snf2 Family ATPase Complex Required for Recruitment of the Histone H2A Variant Htz1

Deletions of three yeast genes, SET2, CDC73, and DST1, involved in transcriptional elongation and/or chromatin metabolism were used in conjunction with genetic array technology to screen approximately 4700 yeast deletions and identify double deletion mutants that produce synthetic growth defects. Of the five deletions interacting genetically with all three starting mutations, one encoded the histone H2A variant Htz1 and three encoded components of a novel 13 protein complex, SWR-C, containing the Snf2 family ATPase, Swr1. The SWR-C also copurified with Htz1 and Bdf1, a TFIID-interacting protein that recognizes acetylated histone tails. Deletions of the genes encoding Htz1 and seven nonessential SWR-C components caused a similar spectrum of synthetic growth defects when combined with deletions of 384 genes involved in transcription, suggesting that Htz1 and SWR-C belong to the same pathway. We show that recruitment of Htz1 to chromatin requires the SWR-C. Moreover, like Htz1 and Bdf1, the SWR-C promotes gene expression near silent heterochromatin.

RNA polymerase II elongation factors of Saccharomyces cerevisiae: a targeted proteomics approach.

ABSTRACT To physically characterize the web of interactions connecting the Saccharomyces cerevisiae proteins suspected to be RNA polymerase II (RNAPII) elongation factors, subunits of Spt4/Spt5 and Spt16/Pob3 (corresponding to human DSIF and FACT), Spt6, TFIIF (Tfg1, -2, and -3), TFIIS, Rtf1, and Elongator (Elp1, -2, -3, -4, -5, and -6) were affinity purified under conditions designed to minimize loss of associated polypeptides and then identified by mass spectrometry. Spt16/Pob3 was discovered to associate with three distinct complexes: histones; Chd1/casein kinase II (CKII); and Rtf1, Paf1, Ctr9, Cdc73, and a previously uncharacterized protein, Leo1. Rtf1 and Chd1 have previously been implicated in the control of elongation, and the sensitivity to 6-azauracil of strains lacking Paf1, Cdc73, or Leo1 suggested that these proteins are involved in elongation by RNAPII as well. Confirmation came from chromatin immunoprecipitation (ChIP) assays demonstrating that all components of this complex, including Leo1, cross-linked to the promoter, coding region, and 3′ end of the ADH1 gene. In contrast, the three subunits of TFIIF cross-linked only to the promoter-containing fragment of ADH1. Spt6 interacted with the uncharacterized, essential protein Iws1 (interacts with Spt6), and Spt5 interacted either with Spt4 or with a truncated form of Spt6. ChIP on Spt6 and the novel protein Iws1 resulted in the cross-linking of both proteins to all three regions of the ADH1 gene, suggesting that Iws1 is likely an Spt6-interacting elongation factor. Spt5, Spt6, and Iws1 are phosphorylated on consensus CKII sites in vivo, conceivably by the Chd1/CKII associated with Spt16/Pob3. All the elongation factors but Elongator copurified with RNAPII.