Michael Shales

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.

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.

Extensive homology among the largest subunits of eukaryotic and prokaryotic RNA polymerases

We have determined the nucleotide sequence of two yeast RNA polymerase genes, RPO21 and RPO31, which encode the largest subunits of RNA polymerases II and III, respectively. The RPO21 and RPO31 sequences are homologous to each other, to the sequence of the largest subunit of E. coli RNA polymerase, and to sequences in the putative DNA-binding domain of E. coli DNA polymerase I. RPO21 has an unusual heptapeptide sequence tandemly repeated 26 times at its C-terminus; this sequence is conserved in the RNA polymerase II of higher eukaryotes and may play an important role in polymerase II-mediated transcription. Since eukaryotic and prokaryotic RNA polymerases appear to have evolved from a common ancestral polymerase, other features of the transcription process may also be evolutionarily conserved.

Global landscape of HIV-human protein complexes

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host’s cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV–human protein–protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Phenotypic landscape of a bacterial cell.

The explosion of sequence information in bacteria makes developing high-throughput, cost-effective approaches to matching genes with phenotypes imperative. Using E. coli as proof of principle, we show that combining large-scale chemical genomics with quantitative fitness measurements provides a high-quality data set rich in discovery. Probing growth profiles of a mutant library in hundreds of conditions in parallel yielded > 10,000 phenotypes that allowed us to study gene essentiality, discover leads for gene function and drug action, and understand higher-order organization of the bacterial chromosome. We highlight new information derived from the study, including insights into a gene involved in multiple antibiotic resistance and the synergy between a broadly used combinatory antibiotic therapy, trimethoprim and sulfonamides. This data set, publicly available at http://ecoliwiki.net/tools/chemgen/, is a valuable resource for both the microbiological and bioinformatic communities, as it provides high-confidence associations between hundreds of annotated and uncharacterized genes as well as inferences about the mode of action of several poorly understood drugs.

Rewiring of Genetic Networks in Response to DNA Damage

DNA Damage Pathways Revealed Despite the dynamic nature of cellular responses, the genetic networks that govern these responses have been mapped primarily as static snapshots. Bandyopadhyay et al. (p. 1385; see the Perspective by Friedman and Schuldiner) report a comparison of large genetic interactomes measured among all yeast kinases, phosphatases, and transcription factors, as the cell responded to DNA damage. The interactomes revealed were highly dynamic structures that changed dramatically with changing conditions. These dynamic interactions reveal genetic relationships that can be more effective than classical “static” interactions (for example, synthetic lethals and epistasis maps) in identifying pathways of interest. A network comparison of genetic interactions mapped at two conditions reveals genetic responses to DNA damage in yeast. Although cellular behaviors are dynamic, the networks that govern these behaviors have been mapped primarily as static snapshots. Using an approach called differential epistasis mapping, we have discovered widespread changes in genetic interaction among yeast kinases, phosphatases, and transcription factors as the cell responds to DNA damage. Differential interactions uncover many gene functions that go undetected in static conditions. They are very effective at identifying DNA repair pathways, highlighting new damage-dependent roles for the Slt2 kinase, Pph3 phosphatase, and histone variant Htz1. The data also reveal that protein complexes are generally stable in response to perturbation, but the functional relations between these complexes are substantially reorganized. Differential networks chart a new type of genetic landscape that is invaluable for mapping cellular responses to stimuli.

Conservation and Rewiring of Functional Modules Revealed by an Epistasis Map in Fission Yeast

An epistasis map (E-MAP) was constructed in the fission yeast, Schizosaccharomyces pombe, by systematically measuring the phenotypes associated with pairs of mutations. This high-density, quantitative genetic interaction map focused on various aspects of chromosome function, including transcription regulation and DNA repair/replication. The E-MAP uncovered a previously unidentified component of the RNA interference (RNAi) machinery (rsh1) and linked the RNAi pathway to several other biological processes. Comparison of the S. pombe E-MAP to an analogous genetic map from the budding yeast revealed that, whereas negative interactions were conserved between genes involved in similar biological processes, positive interactions and overall genetic profiles between pairs of genes coding for physically associated proteins were even more conserved. Hence, conservation occurs at the level of the functional module (protein complex), but the genetic cross talk between modules can differ substantially.

Functional Organization of the S. cerevisiae Phosphorylation Network

Reversible protein phosphorylation is a signaling mechanism involved in all cellular processes. To create a systems view of the signaling apparatus in budding yeast, we generated an epistatic miniarray profile (E-MAP) comprised of 100,000 pairwise, quantitative genetic interactions, including virtually all protein and small-molecule kinases and phosphatases as well as key cellular regulators. Quantitative genetic interaction mapping reveals factors working in compensatory pathways (negative genetic interactions) or those operating in linear pathways (positive genetic interactions). We found an enrichment of positive genetic interactions between kinases, phosphatases, and their substrates. In addition, we assembled a higher-order map from sets of three genes that display strong interactions with one another: triplets enriched for functional connectivity. The resulting network view provides insights into signaling pathway regulation and reveals a link between the cell-cycle kinase, Cak1, the Fus3 MAP kinase, and a pathway that regulates chromatin integrity during transcription by RNA polymerase II.

A Genetic Interaction Map of RNA Processing Factors Reveals Links Between Sem1/Dss1-Containing Complexes and mRNA Export and Splicing

We used a quantitative, high-density genetic interaction map, or E-MAP (Epistatic MiniArray Profile), to interrogate the relationships within and between RNA-processing pathways. Due to their complexity and the essential roles of many of the components, these pathways have been difficult to functionally dissect. Here, we report the results for 107,155 individual interactions involving 552 mutations, 166 of which are hypomorphic alleles of essential genes. Our data enabled the discovery of links between components of the mRNA export and splicing machineries and Sem1/Dss1, a component of the 19S proteasome. In particular, we demonstrate that Sem1 has a proteasome-independent role in mRNA export as a functional component of the Sac3-Thp1 complex. Sem1 also interacts with Csn12, a component of the COP9 signalosome. Finally, we show that Csn12 plays a role in pre-mRNA splicing, which is independent of other signalosome components. Thus, Sem1 is involved in three separate and functionally distinct complexes.

Meta- and Orthogonal Integration of Influenza “OMICs” Data Defines a Role for UBR4 in Virus Budding

Several systems-level datasets designed to dissect host-pathogen interactions during influenza A infection have been reported. However, apparent discordance among these data has hampered their full utility toward advancing mechanistic and therapeutic knowledge. To collectively reconcile these datasets, we performed a meta-analysis of data from eight published RNAi screens and integrated these data with three protein interaction datasets, including one generated within the context of this study. Further integration of these data with global virus-host interaction analyses revealed a functionally validated biochemical landscape of the influenza-host interface, which can be queried through a simplified and customizable web portal (http://www.metascape.org/IAV). Follow-up studies revealed that the putative ubiquitin ligase UBR4 associates with the viral M2 protein and promotes apical transport of viral proteins. Taken together, the integrative analysis of influenza OMICs datasets illuminates a viral-host network of high-confidence human proteins that are essential for influenza A virus replication.