Patrick Kemmeren

Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.

An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas

Metastasis is the process by which cancers spread to distinct sites in the body. It is the principal cause of death in individuals suffering from cancer. For some types of cancer, early detection of metastasis at lymph nodes close to the site of the primary tumor is pivotal for appropriate treatment. Because it can be difficult to detect lymph node metastases reliably, many individuals currently receive inappropriate treatment. We show here that DNA microarray gene-expression profiling can detect lymph node metastases for primary head and neck squamous cell carcinomas that arise in the oral cavity and oropharynx. The predictor, established with an 82-tumor training set, outperforms current clinical diagnosis when independently validated. The 102 predictor genes offer unique insights into the processes underlying metastasis. The results show that the metastatic state can be deciphered from the primary tumor gene-expression pattern and that treatment can be substantially improved.

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.

Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

Monitoring global messenger RNA changes in externally controlled microarray experiments

Expression profiling is a universal tool, with a range of applications that benefit from the accurate determination of differential gene expression. To allow normalization using endogenous transcript levels, current microarray analyses assume that relatively few transcripts vary, or that any changes that occur are balanced. When normalization using endogenous genes is carried out, changes in expression levels are calculated relative to the behaviour of most of the transcripts. This does not reflect absolute changes if global shifts in messenger RNA populations occur. Using external RNA controls, we have set up microarray experiments to monitor global changes. The levels of most mRNAs were found to change during yeast stationary phase and human heat shock when external controls were included. Even small global changes had a significant effect on the number of genes reported as being differentially expressed. This suggests that global mRNA changes occur more frequently than is assumed at present, and shows that monitoring such effects may be important for the accurate determination of changes in gene expression.

The specificity and topology of chromatin interaction pathways in yeast

Packaging of DNA into chromatin has a profound impact on gene expression. To understand how changes in chromatin influence transcription, we analyzed 165 mutants of chromatin machinery components in Saccharomyces cerevisiae. mRNA expression patterns change in 80% of mutants, always with specific effects, even for loss of widespread histone marks. The data are assembled into a network of chromatin interaction pathways. The network is function based, has a branched, interconnected topology, and lacks strict one-to-one relationships between complexes. Chromatin pathways are not separate entities for different gene sets, but share many components. The study evaluates which interactions are important for which genes and predicts additional interactions, for example between Paf1C and Set3C, as well as a role for Mediator in subtelomeric silencing. The results indicate the presence of gene-dependent effects that go beyond context-dependent binding of chromatin factors and provide a framework for understanding how specificity is achieved through regulating chromatin.

Expression Profiler: next generation—an online platform for analysis of microarray data

Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.

Functional Overlap and Regulatory Links Shape Genetic Interactions between Signaling Pathways

To understand relationships between phosphorylation-based signaling pathways, we analyzed 150 deletion mutants of protein kinases and phosphatases in S. cerevisiae using DNA microarrays. Downstream changes in gene expression were treated as a phenotypic readout. Double mutants with synthetic genetic interactions were included to investigate genetic buffering relationships such as redundancy. Three types of genetic buffering relationships are identified: mixed epistasis, complete redundancy, and quantitative redundancy. In mixed epistasis, the most common buffering relationship, different gene sets respond in different epistatic ways. Mixed epistasis arises from pairs of regulators that have only partial overlap in function and that are coupled by additional regulatory links such as repression of one by the other. Such regulatory modules confer the ability to control different combinations of processes depending on condition or context. These properties likely contribute to the evolutionary maintenance of paralogs and indicate a way in which signaling pathways connect for multiprocess control.

A comprehensive framework of E2–RING E3 interactions of the human ubiquitin–proteasome system

Covalent attachment of ubiquitin to substrates is crucial to protein degradation, transcription regulation and cell signalling. Highly specific interactions between ubiquitin‐conjugating enzymes (E2) and ubiquitin protein E3 ligases fulfil essential roles in this process. We performed a global yeast‐two hybrid screen to study the specificity of interactions between catalytic domains of the 35 human E2s with 250 RING‐type E3s. Our analysis showed over 300 high‐quality interactions, uncovering a large fraction of new E2–E3 pairs. Both within the E2 and the E3 cohorts, several members were identified that are more versatile in their interaction behaviour than others. We also found that the physical interactions of our screen compare well with reported functional E2–E3 pairs in in vitro ubiquitination experiments. For validation we confirmed the interaction of several versatile E2s with E3s in in vitro protein interaction assays and we used mutagenesis to alter the E3 interactions of the E2 specific for K63 linkages, UBE2N(Ubc13), towards the K48‐specific UBE2D2(UbcH5B). Our data provide a detailed, genome‐wide overview of binary E2–E3 interactions of the human ubiquitination system.

Large-Scale Genetic Perturbations Reveal Regulatory Networks and an Abundance of Gene-Specific Repressors

To understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.