Network Biology editorial 2013

Recent advances in high throughput methods make it possible to study the behaviour of biomolecules that make up entire biological systems. This recent interest in whole systems comes from the belief that they have functions that none of the entities of the systems have, and that “the total is more than the sum of its parts”. The rules that govern the behaviour of biological systems are currently the focus of intense research in the field of “Systems Biology”. The resulting models are expected to be predictive of different healthy and pathological conditions. They might provide synthetic biologists with the general principles for the (re)engineering of biological systems for particular purposes. In this blooming field, many groups have pioneered the development and use of biochemical methods, coupled to quantitative mass-spectrometry with the aim of systematically linking dynamic protein interaction networks to various phenotypes in model organisms, human cell lines and human pathogens. Our long term goals are to advance network biology & medicine through integration of quantitative biochemistry/proteomics, genetics and whenever possible structural data. We intend to define system-wide mechanistic models explaining complex phenotypes and human diseases. Our effort will ultimately contribute new strategies for targeting human pathologies and simultaneously provide insight into the fundamental principles and rules guiding biomolecular recognition.

The availability of the growing number of sequenced genomes from diverse organisms has fundamentally changed the way we address biological questions. This paradigm shift motivated the development of various follow-ups, accompanying technologies for global interrogation of gene activity and function. This so called “genomics revolution” broadly influences life science research; scientists from all fields now routinely measure, characterize and localize an ever-growing number of molecular players at the level of entire biological systems. While these “omics” approaches are still in full expansion, increasingly contributing to the editing of systems-level networks, we still poorly understand how discrete biological activities are organized in space and time, and integrated within entire systems, producing coherent phenotypes. The way biological systems organize themselves in dynamic, functional assemblies with varying levels of complexity, such as protein complexes, molecular circuits, pathways, organelles, etc., remains largely elusive. Deciphering the molecular mechanisms of cell function – or dysfunction – relies to a large extent on tracing the multitude of physical interactions between the numerous components of living cells. Decades of single-protein studies, and more recent efforts devoted to the large-scale charting of physical interaction networks, contributed to the characterization of a variety of modular binding domains with specificity for distinct linear sequence motifs or for different metabolites. These constitute the basic syntax principles for a still largely elusive biomolecular assembly code. In the absence of systematic and comprehensive analyses we still frequently miss the mechanistic or structural determinants responsible for the specificity and the precision of biomolecular recognition.

The remarkable functional relevance of biomolecular interactions is particularly evident from the major phenotypic effects caused by their disruptions. In humans, among the >3000 human monogenic syndromes with a known molecular basis,¹ mutations that affect biomolecular interactions are not uncommon. For instance, immunodeficiency, centromeric instability, facial anomalies syndrome, are caused by defects in DNMT3B, a DNA methyltransferase. The missense mutations have been mapped not only within the catalytic site, but also affect an N-terminal PWWP domain of DNMT3B, involved in protein–protein interactions.² Mutations have been characterised that prevent the assembly of functional multiprotein complexes. A good example is a RFXANK gene mutant that fails to assemble the regulatory factor X complex (an obligate transcription factor required for the expression of MHC class II genes), leading to the bare lymphocyte syndrome.³ Further mutations have been characterized that prevent an interaction between protein and metabolites. The Bannayan–Riley–Ruvalcaba syndrome, characterized by macrocephaly, multiple lipomas and hemangiomas, is caused by mutation of the phosphatase PTEN.⁴ Different mutations map in a protein kinase C conserved region 2 (C2) domain that has broad specificities for phospholipids. It is worth noting that even discrete changes in the affinities between two interacting biomolecules can have devastating consequences. For instance, mutations in the Fibroblast Growth Factor Receptor 2 (FGFR2) that selectively increase the affinity for FGF2⁵ are responsible for the Apert syndrome, characterized by skull malformation, syndactyly and mental deficiency. These examples do not represent a comprehensive inventory. They illustrate that the spatial and temporal orchestration of the many cellular components’ activities through extensive and highly regulated biomolecular interaction networks bears remarkable functional relevance. Mutational lesions or environmental factors damaging these networks lead to pathologies. Additionally, recent successes in the development of small molecules targeting disease-relevant interactions, either directly or through the binding of an allosteric site, demonstrate that biomolecular interactions may represent new points for therapeutic intervention. Promising successes include – among others – FTY720 (fingolimod; 2-amino-2[2-(4-octylphenyl)ethyl]-1,3-propanediol, Novartis) a sphingosine-one phosphate (S1P) analogue that binds four of the S1P receptors,⁶ disruptors of the interaction between p53 and murine double minute 2 (MDM2),⁷ compounds that interfere with the interaction between Bcl-2 and Bak⁸ and small molecule inhibitors of SH3-mediated interactions.⁹

The importance of biomolecular interactions in biology and physiology motivated the development of a number of methods designed to the charting of protein–protein, protein–DNA, and protein–RNA interactions on systems-wide scales. The impacts of these new strategies are spectacular. They contributed detailed cartographies of many pathways or biological processes relevant to human health or diseases.^10–13 The resulting molecular maps guide the identification of drug targets, and help in understanding the mechanism-of-action and side effects of therapeutic compounds. In human pathogens large-scale interaction screens have contributed new hypotheses on how viruses and bacteria use the cellular machinery to their own purpose;^14–17 new opportunities for small molecule inhibitors may emerge. Most importantly, interaction networks have also been used as a molecular frame for the explanation of genetic traits and to prioritize positional candidate disease genes identified by linkage or association studies.^18–20

We are therefore extremely pleased to present the 26 papers in this particular themed issue as they address the various aspects of networks described above and describe the cutting-edge of this subject area. We sincerely hope that you enjoy reading these papers and use them as a reference source for your own future work, or just to have as a comprehensive record of the subject so far.

References

1. J. Amberger, et al., McKusick's Online Mendelian Inheritance in Man (OMIM), Nucleic Acids Res., 2009, 37, D793.
2. T. Chen, et al., The PWWP domain of Dnmt3a and Dnmt3b is required for directing DNA methylation to the major satellite repeats at pericentric heterochromatin, Mol. Cell. Biol., 2004, 24, 9048.
3. W. Wiszniewski, et al., Novel mutations in the RFXANK gene: RFX complex containing in vitro-generated RFXANK mutant binds the promoter without transactivating MHC II, Immunogenetics, 2003, 54, 747.
4. C. Eng, PTEN: one gene, many syndromes, Hum. Mutat., 2003, 22, 183.
5. J. Anderson, et al., Apert syndrome mutations in fibroblast growth factor receptor 2 exhibit increased affinity for FGF ligand, Hum. Mol. Genet., 1998, 7, 1475.
6. Y. Sonoda, et al., FTY720, a novel immunosuppressive agent, induces apoptosis in human glioma cells, Biochem. Biophys. Res. Commun., 2001, 281, 282.
7. L. T. Vassilev, et al., In vivo activation of the p53 pathway by small-molecule antagonists of MDM2, Science, 2004, 303, 844.
8. T. Oltersdorf, et al., An inhibitor of Bcl-2 family proteins induces regression of solid tumours, Nature, 2005, 435, 677.
9. S. R. Inglis, et al., Identification and specificity studies of small-molecule ligands for SH3 protein domains, J. Med. Chem., 2004, 47, 5405.
10. M. Vidal, et al., Interactome networks and human disease, Cell, 2011, 144, 986.
11. J. Lim, et al., A protein–protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration, Cell, 2006, 125, 801.
12. K. Lage, et al., A human phenome-interactome network of protein complexes implicated in genetic disorders, Nat. Biotechnol., 2007, 25, 309.
13. T. Bouwmeester, et al., A physical and functional map of the human TNF-alpha/NF-kappa B signal transduction pathway, Nat. Cell Biol., 2004, 6, 97.
14. P. Uetz, et al., Herpesviral protein networks and their interaction with the human proteome, Science, 2006, 311, 239.
15. M. A. Calderwood, et al., Epstein-Barr virus and virus human protein interaction maps, Proc. Natl. Acad. Sci. U. S. A., 2007, 104, 7606.
16. S. Kuhner, et al., Proteome organization in a genome-reduced bacterium, Science, 2009, 326, 1235.
17. S. Jager, et al., Global landscape of HIV-human protein complexes, Nature, 2012, 481, 365.
18. S. Aerts, et al., Gene prioritization through genomic data fusion, Nat. Biotechnol., 2006, 24, 537.
19. L. Franke, et al., Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes, Am. J. Hum. Genet., 2006, 78, 1011.
20. M. Oti, et al., Predicting disease genes using protein–protein interactions, J. Med. Genet., 2006, 43, 691.

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