USP7/HAUSP is a key regulator of p53 and Mdm2 and is targeted by the Epstein-Barr nuclear antigen 1 (EBNA1) protein of Epstein-Barr virus (EBV). We have determined the crystal structure of the p53 binding domain of USP7 alone and bound to an EBNA1 peptide. This domain is an eight-stranded beta sandwich similar to the TRAF-C domains of TNF-receptor associated factors, although the mode of peptide binding differs significantly from previously observed TRAF-peptide interactions in the sequence (DPGEGPS) and the conformation of the bound peptide. NMR chemical shift analyses of USP7 bound by EBNA1 and p53 indicated that p53 binds the same pocket as EBNA1 but makes less extensive contacts with USP7. Functional studies indicated that EBNA1 binding to USP7 can protect cells from apoptotic challenge by lowering p53 levels. The data provide a structural and conceptual framework for understanding how EBNA1 might contribute to the survival of Epstein-Barr virus-infected cells.
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
BackgroundMany high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.ResultsThis article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.ConclusionOverall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1790-4) contains supplementary material, which is available to authorized users.
USP7 or HAUSP is a ubiquitin-specific protease in human cells that regulates the turnover of p53 and is bound by at least two viral proteins, the ICP0 protein of herpes simplex type 1 and the EBNA1 protein of EpsteinBarr virus. We have overexpressed and purified USP7 and shown that the purified protein is monomeric and is active for cleaving both a linear ubiquitin substrate and conjugated ubiquitin on EBNA1. Using partial proteolysis of USP7 coupled with matrix-assisted laser desorption ionization time-of-flight mass spectrometry, we showed that USP7 comprises four structural domains; an N-terminal domain known to bind p53, a catalytic domain, and two C-terminal domains. By passing a mixture of USP7 domains over EBNA1 and ICP0 affinity columns, we showed that the N-terminal p53 binding domain was also responsible for the EBNA1 interaction, while the ICP0 binding domain mapped to a C-terminal domain between amino acids 599 -801. Tryptophan fluorescence assays showed that an EBNA1 peptide mapping to residues 395-450 was sufficient to bind the USP7 N-terminal domain and did so with a dissociation constant of 0.9 -2 M, whereas p53 peptides spanning the USP7-binding region gave dissociation constants of 9 -17 M in the same assay. In keeping with these relative affinities, gel filtration analyses of the complexes showed that the EBNA1 peptide efficiently competed with the p53 peptide for USP7 binding, suggesting that EBNA1 could affect p53 function in vivo by competing for USP7.
Advances in high-throughput technologies allow for measurements of many types of omics data, yet the meaningful integration of several different data types remains a significant challenge. Another important and difficult problem is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. Here we present a novel approach, called erturbation clustering for datategration and disease ubtyping (PINS), which is able to address both challenges. The framework has been validated on thousands of cancer samples, using gene expression, DNA methylation, noncoding microRNA, and copy number variation data available from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. This simultaneous subtyping approach accurately identifies known cancer subtypes and novel subgroups of patients with significantly different survival profiles. The results were obtained from genome-scale molecular data without any other type of prior knowledge. The approach is sufficiently general to replace existing unsupervised clustering approaches outside the scope of bio-medical research, with the additional ability to integrate multiple types of data.
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