CSB2: Council for Systems Biology in Boston

Center for Cancer Systems Biology

Director: Marc Vidal

Other key investigators: Pascal Braun, Kourosh Salehi-Ashtiani, David Hill

Center for Cancer Systems Biology website

The long-term goal of CCSB is to understand how macromolecular networks control biological processes and how aberrant phenotypes and human disease arise from perturbations in such networks.

We are trying to understand (i) how protein interactions are organized at the scale of the whole cell, (ii) the global principles that organize such complex networks of interactions, and (iii) how such organizational principles are disrupted in human disease.

To achieve these goals, we systematically identify protein-protein interaction networks and investigate the effect of genetic variations on these interactions.

CCSB has grown out of the Vidal lab and now consists of several interdependent research groups working closely together to study network biology.

Network Biology Group

The Network Biology group (Marc Vidal) studies how network properties relate to biology and disease using experimental and theoretical approaches.

Better understanding of genotype-to-phenotype relationships in human disease will require modeling how disease-causing mutations affect systems or interactome properties. Phenotypic variations, including disease, arise from alterations of cellular networks, ranging from the complete loss of a gene product to the specific perturbation of a single molecular interaction. 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. Edgetic network perturbation models reflect upon the dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

Selected achievements

Interactome Group

The Interactome group (Pascal Braun) uses, develops and optimizes high-throughput technologies to map protein-protein interaction networks of Homo sapiens, yeast, Arabidopsis thaliana and other model organisms.

Having high-quality datasets in hand promotes understanding of how global and local properties of protein-protein interaction, or ‘interactome’, networks relate to biological mechanisms. High-quality datasets also guide research on individual proteins.

The Interactome group has developed a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions. This approach, consisting of an interaction tool kit comprised of four complementary, high-throughput protein interaction assays, allows systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks. Additional novel tools are being developed, implemented, and applied so as to increase the sensitivity of Y2H technology, to assure the quality of identified interactions, and to investigate the effects of genetic variation on interactome networks.

Selected achievements

  • First generation proteome-scale map of the human protein-protein interaction network (Rual et al, Nature 2005)
  • A protein domain-based interactome network for C. elegans early embryogenesis (Boxem et al, Cell 2008)
  • High-quality binary protein interaction map of the yeast interactome network (Yu et al, Science 2008)
  • An empirical framework for binary interactome mapping (Venkatesan et al, Nat Methods 2008)
  • An experimentally derived confidence score for binary protein-protein interactions (Braun et al, Nat Methods 2009)
  • First and second large-scale interactome maps of the metazoan C. elegans (Li et al, Science 2004; Simonis et al, Nat Methods 2009)
  • First plant interactome map for Arabidopsis thaliana (ongoing, manuscript in preparation)
  • Second generation human interactome map (ongoing)

ORFeome Group

The ORFeome group (Kourosh Salehi-Ashtiani) experimentally defines open reading frames (ORFs) and splice isoforms for H. sapiens and model organisms.

The advent of systems biology necessitates the cloning of nearly entire sets of protein-encoding open reading frames (ORFs) collected into ORFeome collections, so as to allow functional studies of the corresponding proteomes.

The ORFeome group has learned how to increase the Y2H assay sensitivity by systematic ORF fragmentation, which allows construction of a more complete and better resolved physical interaction network. Such a “fragmentome” approach increases assay sensitivity 2.5- to 3-fold.

Selected achievements

  • Experimental evidence supporting the existence of at least 17,300 genes in C. elegans (Reboul et al, Nat Genet 2001)
  • Cloned and verified ~12,000 C. elegans ORFs, wORFeome 1.1 & 3.1 (Reboul et al, Nat Genet 2003; Lamesch et al, Genome Res 2004)
  • Cloned and verified ~19,000 human ORFs, hORFeome 1.1 & 3.1 (Rual et al, Genome Res 2004; Lamesch et al, Genomics 2007; and unpublished data)
  • Developed a genome-wide 5′ and 3′ RACE approach to experimentally define 2,000 full-length ORFs for C. elegans (Salehi-Ashtiani et al, Genome Res 2009)
  • Initiated an investigation of the extent of alternative splicing for ~900 ORFs (5% of the human ORFeome)

Pathogen Host Interactomes group

The Pathogen Host Interactomes group (David Hill) investigates how proteins of pathogens interact and perturb host interactomes. This project is under the aegis of a Center of Excellence in Genomic Sciences (CEGS) grant, to which eight research groups contribute.

This group endeavors to identify human protein networks that physically interact with viral proteins. The group uses Y2H mapping to systematically identify direct binary contacts among viral proteins, as well as between viral proteins and human proteins available in the human ORFeome v3.1 collection. The product is a map of viral-human protein interactions. Viruses currently under investigation include human papovaviruses, human papillomaviruses, Epstein-Barr virus, influenza virus, hepatitis B virus, hepatitis C virus, Kaposi sarcoma human virus, and human adenoviruses.

Selected achievements

  • Epstein-Barr virus and virus human protein interaction maps (Calderwood et al, Proc Natl Acad Sci USA 2007)
  • Hepatitis C virus infection protein network (de Chassey et al, Mol Syst Biol 2008)
  • VirusMINT: a viral protein interaction database (Chatr-aryamontri et al, Nucleic Acids Res 2009)
  • A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection (Shapira et al, Cell 2009)