Functional profiling of nucleotide Excision repair in breast cancer.

Homologous recombination deficiency conferred by alterations in BRCA1 or BRCA2 are common in breast tumors and can drive sensitivity to platinum chemotherapy and PARP inhibitors. Alterations in nucleotide excision repair (NER) activity can also impact sensitivity to DNA damaging agents, but NER activity in breast cancer has been poorly characterized. Here, we apply a novel immunofluorescence-based cellular NER assay to screen a large panel of breast epithelial and cancer cell lines. Although the majority of breast cancer models are NER proficient, we identify an example of a breast cancer cell line with profound NER deficiency. We show that NER deficiency in this model is driven by epigenetic silencing of the ERCC4 gene, leading to lack of expression of the NER nuclease XPF, and that ERCC4 methylation is also strongly correlated with ERCC4 mRNA and XPF protein expression in primary breast tumors. Re-expression of XPF in the ERCC4-deficient breast cancer rescues NER deficiency and cisplatin sensitivity, but does not impact PARP inhibitor sensitivity. These findings demonstrate the potential to use functional assays to identify novel mechanisms of DNA repair deficiency and nominate NER deficiency as a platinum sensitivity biomarker in breast cancer.

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

Monitoring repair of UV-induced 6-4-photoproducts with a purified DDB2 protein complex.

Because cells are constantly subjected to DNA damaging insults, DNA repair pathways are critical for genome integrity [1]. DNA damage recognition protein complexes (DRCs) recognize DNA damage and initiate DNA repair. The DNA-Damage Binding protein 2 (DDB2) complex is a DRC that initiates nucleotide excision repair (NER) of DNA damage caused by ultraviolet light (UV) [2]-[4]. Using a purified DDB2 DRC, we created a probe ("DDB2 proteo-probe") that hybridizes to nuclei of cells irradiated with UV and not to cells exposed to other genotoxins. The DDB2 proteo-probe recognized UV-irradiated DNA in classical laboratory assays, including cyto- and histo-chemistry, flow cytometry, and slot-blotting. When immobilized, the proteo-probe also bound soluble UV-irradiated DNA in ELISA-like and DNA pull-down assays. In vitro, the DDB2 proteo-probe preferentially bound 6-4-photoproducts [(6-4)PPs] rather than cyclobutane pyrimidine dimers (CPDs). We followed UV-damage repair by cyto-chemistry in cells fixed at different time after UV irradiation, using either the DDB2 proteo-probe or antibodies against CPDs, or (6-4)PPs. The signals obtained with the DDB2 proteo-probe and with the antibody against (6-4)PPs decreased in a nearly identical manner. Since (6-4)PPs are repaired only by nucleotide excision repair (NER), our results strongly suggest the DDB2 proteo-probe hybridizes to DNA containing (6-4)PPs and allows monitoring of their removal during NER. We discuss the general use of purified DRCs as probes, in lieu of antibodies, to recognize and monitor DNA damage and repair.

Protein-protein interactions and networks: forward and reverse edgetics.

Phenotypic variations of an organism may arise from alterations of cellular networks, ranging from the complete loss of a gene product to the specific perturbation of a single molecular interaction. In interactome networks that are modeled as nodes (macromolecules) connected by edges (interactions), these alterations can be thought of as node removal and edge-specific or "edgetic" perturbations, respectively. Here we present two complementary strategies, forward and reverse edgetics, to investigate the phenotypic outcomes of edgetic perturbations of binary protein-protein interaction networks. Both approaches are based on the yeast two-hybrid system (Y2H). The first allows the determination of the interaction profile of proteins encoded by alleles with known phenotypes to identify edgetic alleles. The second is used to directly isolate edgetic alleles for subsequent in vivo characterization.

Independently evolved virulence effectors converge onto hubs in a plant immune system network.

Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of Arabidopsis immune system proteins, and ~8000 other Arabidopsis proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.

High-quality binary interactome mapping.

Physical interactions mediated by proteins are critical for most cellular functions and altogether form a complex macromolecular "interactome" network. Systematic mapping of protein-protein, protein-DNA, protein-RNA, and protein-metabolite interactions at the scale of the whole proteome can advance understanding of interactome networks with applications ranging from single protein functional characterization to discoveries on local and global systems properties. Since the early efforts at mapping protein-protein interactome networks a decade ago, the field has progressed rapidly giving rise to a growing number of interactome maps produced using high-throughput implementations of either binary protein-protein interaction assays or co-complex protein association methods. Although high-throughput methods are often thought to necessarily produce lower quality information than low-throughput experiments, we have recently demonstrated that proteome-scale interactome datasets can be produced with equal or superior quality than that observed in literature-curated datasets derived from large numbers of small-scale experiments. In addition to performing all experimental steps thoroughly and including all necessary controls and quality standards, careful verification of all interacting pairs and validation tests using independent, orthogonal assays are crucial to ensure the release of interactome maps of the highest possible quality. This chapter describes a high-quality, high-throughput binary protein-protein interactome mapping pipeline that includes these features.

'Edgetic' perturbation of a C. elegans BCL2 ortholog.

Genes and gene products do not function in isolation but within highly interconnected 'interactome' networks, modeled as graphs of nodes and edges representing macromolecules and interactions between them, respectively. We propose to investigate genotype-phenotype associations by methodical use of alleles that lack single interactions, while retaining all others, in contrast to genetic approaches designed to eliminate gene products completely. We describe an integrated strategy based on the reverse yeast two-hybrid system to isolate and characterize such edge-specific, or 'edgetic', alleles. We established a proof of concept with CED-9, a Caenorhabditis elegans BCL2 ortholog. Using ced-9 edgetic alleles, we uncovered a new potential functional link between apoptosis and a centrosomal protein. This approach is amenable to higher throughput and is particularly applicable to interactome network analysis in organisms for which transgenesis is straightforward.

Literature-curated protein interaction datasets.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

An experimentally derived confidence score for binary protein-protein interactions.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

Towards a proteome-scale map of the human protein-protein interaction network.

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.