Marc Vidal

Harvard Medical School, Boston, Massachusetts, United States

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Publications (218)3280.19 Total impact

  • Cancer Research 12/2009; 69(23 Supplement):A23-A23. DOI:10.1158/0008-5472.FBCR09-A23 · 9.28 Impact Factor
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    ABSTRACT: Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of approximately 50,000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.
    Molecular Systems Biology 11/2009; 5:321. DOI:10.1038/msb.2009.80 · 14.10 Impact Factor
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    Marc Vidal
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    ABSTRACT: The idea that multi-scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental "great ideas" as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems-level understanding of "What is Life" may materialize as one of the major ideas of biology.
    FEBS letters 11/2009; 583(24):3891-4. DOI:10.1016/j.febslet.2009.11.024 · 3.34 Impact Factor
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    ABSTRACT: 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.
    Nature Methods 11/2009; 6(11):843-9. DOI:10.1038/nmeth.1394 · 25.95 Impact Factor
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    ABSTRACT: Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.
    Genome Research 10/2009; 19(12):2334-42. DOI:10.1101/gr.098640.109 · 13.85 Impact Factor
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    ABSTRACT: Autophagy is a highly regulated self-degradative mechanism required at a basal level for intracellular clearance and recycling of cytoplasmic contents. Upon intracellular pathogen invasion, autophagy can be induced as an innate immune mechanism to control infection. Nevertheless, pathogens have developed strategies to avoid or hijack autophagy for their own benefit. The molecular pathways inducing autophagy in response to infection remain poorly documented. We report here that the engagement of CD46, a ubiquitous human surface receptor able to bind several different pathogens, is sufficient to induce autophagy. CD46-Cyt-1, one of the two C-terminal splice variants of CD46, is linked to the autophagosome formation complex VPS34/Beclin1 via its interaction with the scaffold protein GOPC. Measles virus and group A Streptococcus, two CD46-binding pathogens, induce autophagy through a CD46-Cyt-1/GOPC pathway. Thus, upon microorganism recognition, a cell surface pathogen receptor can directly trigger autophagy, a critical step to control infection.
    Cell host & microbe 10/2009; 6(4):354-66. DOI:10.1016/j.chom.2009.09.006 · 12.19 Impact Factor
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    ABSTRACT: SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.
    PLoS Biology 10/2009; 7(10):e1000218. DOI:10.1371/journal.pbio.1000218 · 11.77 Impact Factor
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    ABSTRACT: Unequivocal discrimination between neutral variants and deleterious mutations is crucial for appropriate counseling of individuals with a BRCA1 or BRCA2 sequence change. An increasing number of variants of uncertain significance (VUS) are being identified, the unclassified biological effect of which poses clinical concerns. A multifactorial likelihood-based approach recently suggested disease causality for BRCA1 p.V1688del, a VUS recurrent in Italian breast/ovarian cancer families. Whether and how this single amino acid deletion in the BRCA1 COOH terminus (BRCT) domain affects the function of the mutant protein (DeltaValBRCA1) has not been elucidated. We undertook comprehensive functional characterization of DeltaValBRCA1, comprising comparative structural modeling, analysis of protein stability and associations, and analysis of DNA repair function. Our model predicted BRCT domain destabilization and folding disruption caused by BRCA1 p.V1688del. Consistently, the recombinant DeltaValBRCA1 was less stable than wild-type BRCA1 and, unlike the latter, failed to associate with BRIP1, CtIP, and Rap80 and to relocalize to sites of DNA damage. Yeast two-hybrid analysis revealed a compromised interaction with FHL2 and KPNA2, which is likely responsible for improper subcellular localization of DeltaValBRCA1. In addition, we found four new breast/ovarian cancer families of Italian ancestry who carried this sequence alteration. These results provide the first evidence of the effect of BRCA1 p.V1688del on protein stability and function, supporting the view that it is a deleterious mutation. Multimodal analyses like ours could advance understanding of tumor suppression by BRCA1 and ultimately contribute to developing efficient strategies for screening and characterization of VUS.
    Cancer Research 09/2009; 69(17):7030-7. DOI:10.1158/0008-5472.CAN-09-1440 · 9.28 Impact Factor
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    ABSTRACT: With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.
    Nature Methods 09/2009; 6(8):589-92. DOI:10.1038/nmeth.1348 · 25.95 Impact Factor
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    ABSTRACT: In eukaryotic cells the stability and function of many proteins are regulated by the addition of ubiquitin or ubiquitin-like peptides. This process is dependent upon the sequential action of an E1-activating enzyme, an E2-conjugating enzyme, and an E3 ligase. Different combinations of these proteins confer substrate specificity and the form of protein modification. However, combinatorial preferences within ubiquitination networks remain unclear. In this study, yeast two-hybrid (Y2H) screens were combined with true homology modeling methods to generate a high-density map of human E2/E3-RING interactions. These data include 535 experimentally defined novel E2/E3-RING interactions and >1300 E2/E3-RING pairs with more favorable predicted free-energy values than the canonical UBE2L3-CBL complex. The significance of Y2H predictions was assessed by both mutagenesis and functional assays. Significantly, 74/80 (>92%) of Y2H predicted complexes were disrupted by point mutations that inhibit verified E2/E3-RING interactions, and a approximately 93% correlation was observed between Y2H data and the functional activity of E2/E3-RING complexes in vitro. Analysis of the high-density human E2/E3-RING network reveals complex combinatorial interactions and a strong potential for functional redundancy, especially within E2 families that have undergone evolutionary expansion. Finally, a one-step extended human E2/E3-RING network, containing 2644 proteins and 5087 edges, was assembled to provide a resource for future functional investigations.
    Genome Research 06/2009; 19(10):1905-11. DOI:10.1101/gr.093963.109 · 13.85 Impact Factor
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    ABSTRACT: The idea that genes and their products are the fundamental units of biology has profoundly influenced our scientific thinking during the second half of the past century. Today, this reductionism is challenged by a renaissance of a systems understanding of biology, focusing on the systems formed by interacting gene products rather than on individual gene products. This discipline, based on a complementary and more holistic approach, keeps expanding its scope thanks to biotechnological innovations as well as theoretical modeling. This review aims at showing how and why, since the beginning of the 21st century, in fundamental as well as biomedical research, systems biology is proving a promising paradigm for understanding emerging properties of complex biological systems.
    Medecine sciences: M/S 06/2009; 25(6-7):578-84. DOI:10.1051/medsci/2009256-7578 · 0.52 Impact Factor
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    ABSTRACT: "Smart-pooling," in which test reagents are multiplexed in a highly redundant manner, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. However, previous applications relied on low redundancy designs that do not leverage the full potential of smart-pooling, and more powerful theoretical constructions, such as the Shifted Transversal Design (STD), lack experimental validation. Here we evaluate STD smart-pooling in yeast two-hybrid (Y2H) interactome mapping. We employed two STD designs and two established methods to perform ORFeome-wide Y2H screens with 12 baits. We found that STD pooling achieves similar levels of sensitivity and specificity as one-on-one array-based Y2H, while the costs and workloads are divided by three. The screening-sequencing approach is the most cost- and labor-efficient, yet STD identifies about twofold more interactions. Screening-sequencing remains an appropriate method for quickly producing low-coverage interactomes, while STD pooling appears as the method of choice for obtaining maps with higher coverage.
    Genome Research 06/2009; 19(7):1262-9. DOI:10.1101/gr.090019.108 · 13.85 Impact Factor
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    ABSTRACT: The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5-20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data.Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high-quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects.Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas.A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.
    Molecular oncology 03/2009; 3(1):9-17. DOI:10.1016/j.molonc.2008.11.003 · 5.94 Impact Factor
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    ABSTRACT: The spindle apparatus dictates the plane of cell cleavage, which is critical in the choice between symmetric or asymmetric division. Spindle positioning is controlled by an evolutionarily conserved pathway, which involves LIN-5/GPR-1/2/Galpha in Caenorhabditis elegans, Mud/Pins/Galpha in Drosophila and NuMA/LGN/Galpha in humans. GPR-1/2 and Galpha localize LIN-5 to the cell cortex, which engages dynein and controls the cleavage plane during early mitotic divisions in C. elegans. Here we identify ASPM-1 (abnormal spindle-like, microcephaly-associated) as a novel LIN-5 binding partner. ASPM-1, together with calmodulin (CMD-1), promotes meiotic spindle organization and the accumulation of LIN-5 at meiotic and mitotic spindle poles. Spindle rotation during maternal meiosis is independent of GPR-1/2 and Galpha, yet requires LIN-5, ASPM-1, CMD-1 and dynein. Our data support the existence of two distinct LIN-5 complexes that determine localized dynein function: LIN-5/GPR-1/2/Galpha at the cortex, and LIN-5/ASPM-1/CMD-1 at spindle poles. These functional interactions may be conserved in mammals, with implications for primary microcephaly.
    Nature Cell Biology 03/2009; 11(3):269-77. DOI:10.1038/ncb1834 · 20.06 Impact Factor
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    ABSTRACT: 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
    Nature Methods 02/2009; 6(1):47-54. DOI:10.1038/nmeth.1279 · 25.95 Impact Factor
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    ABSTRACT: 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.
    Nature Methods 02/2009; 6(1):39-46. DOI:10.1038/nmeth.1284 · 25.95 Impact Factor
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    ABSTRACT: Physical interactions between proteins play a key role in probably every cellular process. Efforts to chart the protein interaction networks are ongoing in a number of model organisms using a diversity of approaches. The resulting genome-wide interaction maps will provide a scaffold for further detailed functional analysis. We developed MAPPIT, a mammalian two-hybrid approach that allows identification and analysis of mammalian protein-protein interactions in their native environment. Here, we introduce an efficient MAPPIT assay that permits high-throughput screening of arrayed collections of proteins and complements a previously published cDNA library screening approach. We validated both methods in screens for interaction partners of the Cullin-based E3 ubiquitin ligase subunits SKP1 and Elongin C. In addition to a number of known interactors, novel SKP1 and Elongin C binding proteins were identified. The array assay is an important addition to the MAPPIT suite of technologies that is expected to significantly increase its utility as a toolbox to screen for novel interactors of proteins or small molecules.
    Journal of Proteome Research 02/2009; 8(2):877-86. DOI:10.1021/pr8005167 · 5.00 Impact Factor
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    ABSTRACT: Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.
    Nature Methods 01/2009; 6(1):83-90. DOI:10.1038/nmeth.1280 · 25.95 Impact Factor
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    ABSTRACT: 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.
    Nature Methods 01/2009; 6(1):91-7. DOI:10.1038/nmeth.1281 · 25.95 Impact Factor
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    ABSTRACT: Understanding the consequences on host physiology induced by viral infection requires complete understanding of the perturbations caused by virus proteins on the cellular protein interaction network. The VirusMINT database (http://mint.bio.uniroma2.it/virusmint/) aims at collecting all protein interactions between viral and human proteins reported in the literature. VirusMINT currently stores over 5000 interactions involving more than 490 unique viral proteins from more than 110 different viral strains. The whole data set can be easily queried through the search pages and the results can be displayed with a graphical viewer. The curation effort has focused on manuscripts reporting interactions between human proteins and proteins encoded by some of the most medically relevant viruses: papilloma viruses, human immunodeficiency virus 1, Epstein-Barr virus, hepatitis B virus, hepatitis C virus, herpes viruses and Simian virus 40.
    Nucleic Acids Research 11/2008; 37(Database issue):D669-73. DOI:10.1093/nar/gkn739 · 8.81 Impact Factor

Publication Stats

23k Citations
3,280.19 Total Impact Points

Institutions

  • 2001–2015
    • Harvard Medical School
      • • Department of Genetics
      • • Department of Biological Chemistry and Molecular Pharmacology
      Boston, Massachusetts, United States
  • 2000–2015
    • Dana-Farber Cancer Institute
      • • Center for Cancer Systems Biology
      • • Department of Cancer Biology
      Boston, Massachusetts, United States
  • 1996–2014
    • Harvard University
      Cambridge, Massachusetts, United States
  • 2010
    • Chalmers University of Technology
      • Department of Chemical and Biological Engineering
      Goeteborg, Västra Götaland, Sweden
    • Ecole normale supérieure de Lyon
      • UMR 5239 - Laboratoire de Biologie Moléculaire de la Cellulle (LBMC)
      Lyons, Rhône-Alpes, France
  • 2007
    • Boston Children's Hospital
      Boston, Massachusetts, United States
  • 2006
    • National Human Genome Research Institute
      Maryland, United States
  • 2004
    • Centre for Cancer Biology
      Tarndarnya, South Australia, Australia
    • University of Leeds
      • School of Biology
      Leeds, ENG, United Kingdom
    • Libyan British Medical Center
      Ţarābulus, Tripoli, Libya
  • 2002
    • The Rockefeller University
      New York, New York, United States
    • Enanta Pharmaceuticals
      Watertown, Massachusetts, United States
  • 1992–2001
    • Massachusetts General Hospital
      Boston, Massachusetts, United States