Leroy Hood

Asparagine-linked glycosylation of the scrapie and cellular prion proteins☆

Post-translational modification of the scrapie prion protein (PrP) is thought to account for the unusual features of this protein. Molecular cloning of a PrP cDNA identified two potential Asn-linked glycosylation sites. Both the scrapie (PrPSc) and cellular (PrPC) isoforms were susceptible to digestion by peptide N-glycosidase F (PNGase F) but resistant to endoglycosidase H as measured by migration in sodium dodecyl sulfate-polyacrylamide gel electrophoresis. PNGase F digestion of PrPC yielded two proteins of Mr26K and 28K; however, the 26-k species was only a minor component. In contrast, PNGase F digestion of PrPSc yielded equimolar amounts of two proteins of Mr26K and 28K. The significance of this altered stoichiometry between the 26- and 28-kDa deglycosylated forms of PrP during scrapie infection remains to be established. Both isoforms as well as PrP 27-30, which is produced by limited proteolysis of PrPSc, exhibited a reduced number of charge isomers after PNGase F digestion. The molecular weight of PrP 27-30 was reduced from 27K-30K by PNGase F digestion to 20K-22K while anhydrous hydrogen fluoride or trifluoromethanesulfonic acid treatment reduced the molecular weight to 19K-21K and 20K-22K, respectively. Denatured PrP 27-30 was radioiodinated and then assessed for its binding to lectin columns. PrP 27-30 was bound to wheat germ agglutinin (WGA) or lentil lectins and eluted with N-acetylglucosamine or alpha-methyl-mannoside, respectively. Digestion of PrP 27-30 with sialidase prevented its binding to WGA but enhanced its binding to Ricinus communis lectin. These findings argue that PrP 27-30 probably possesses Asn-linked, complex oligosaccharides with terminal sialic acids, penultimate galactoses, and fucose residues attached to the innermost N-acetyl-glucosamine. Whether differences in Asn-linked oligosaccharide structure between PrPC and PrPSc exist and are responsible for the distinct properties displayed by these two isoforms remain to be established.

The Prion Disease Database: a comprehensive transcriptome resource for systems biology research in prion diseases

Prion diseases reflect conformational conversion of benign isoforms of prion protein (PrPC) to malignant PrPSc isoforms. Networks perturbed by PrPSc accumulation and their ties to pathological events are poorly understood. Time-course transcriptomic and phenotypic data in animal models are critical for understanding prion-perturbed networks in systems biology studies. Here, we present the Prion Disease Database (PDDB), the most comprehensive data resource on mouse prion diseases to date. The PDDB contains: (i) time-course mRNA measurements spanning the interval from prion inoculation through appearance of clinical signs in eight mouse strain-prion strain combinations and (ii) histoblots showing temporal PrPSc accumulation patterns in brains from each mouse–prion combination. To facilitate prion research, the PDDB also provides a suite of analytical tools for reconstructing dynamic networks via integration of temporal mRNA and interaction data and for analyzing these networks to generate hypotheses. Database URL: http://prion.systemsbiology.net

Bridging Systems Medicine and Patient Needs

While there is widespread consensus on the need both to change the prevailing research and development (R&D) paradigm and provide the community with an efficient way to personalize medicine, ecosystem stakeholders grapple with divergent conceptions about which quantitative approach should be preferred. The primary purpose of this position paper is to contrast these approaches. The second objective is to introduce a framework to bridge simulation outputs and patient outcomes, thus empowering the implementation of systems medicine.

Prequips—an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data

SUMMARY We describe an integrative software platform, Prequips, for comparative proteomics-based systems biology analysis that: (i) integrates all information generated from mass spectrometry (MS)-based proteomics as well as from basic proteomics data analysis tools, (ii) visualizes such information for various proteomic analyses via graphical interfaces and (iii) links peptide and protein abundances to external tools often used in systems biology studies. AVAILABILITY http://prequips.sourceforge.net

The Hallmarks of Cancer Revisited Through Systems Biology and Network Modelling

Since 10 years ago, when the seven hallmarks of cancer were first defined by Hanahan and Weinberg, after decades of molecular, cellular and clinical investigations, new systems-based approaches have provided a wide range of improved investigative methods. These approaches integrate various global data types into mathematical and computational models of molecular and cellular pathways and networks that become dysregulated in cancer, since the models are now able to take into account the large-scale properties of complex biological networks. Genome variation and instability have been revisited through study of genetic and genomic networks; while transcription and protein interaction networks are revealing cancer biomarkers of modular change. Growth, proliferation and apoptosis are being more fully described by signalling network modelling. Sustained angiogenesis and metastasis are being addressed via multiscale modelling. Enhanced understanding of the initial hallmarks of cancer, extended to the control of metabolism and stress, is opening novel avenues for cancer diagnosis and treatment. It is fully expected that further progress will take place through iterative cycles of experimentation and modelling, typical of systems biology. All of this will require advances in molecular data acquisition, multiscale integration of data scales and types, new approaches to data analysis and improved modelling. Success in all these endeavours cannot be achieved without better cross-disciplinary interactions among researchers and technologists.

Erratum to: On Different Aspects of Network Analysis in Systems Biology

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein– protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of An erratum to this chapter is available at 10.1007/978-94-007-6803-1_19 A. Chaiboonchoe J. Pellet S. Ballereau L. Meyniel H. Ahmed V. Volpert V. Lotteau C. Auffray (&) European Institute for Systems Biology and Medicine, CNRS-UCBL-ENS, Université de Lyon, 50 Avenue Tony Garnier, 69366 Lyon cedex 07, France e-mail: cauffray@eisbm.org, achaiboonchoe@eisbm.org, jpellet@eisbm.org, sballereau@eisbm.org, laurene.meyniel@inserm.fr, hahmed@eisbm.org, vvolpert@eisbm.org, vlotteau@eisbm.org W. Jurkowski E. Glaab A. Kolodkin A. Raussel A. Le Béchec I. Crespo A. d. Sol R. Balling Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg e-mail: wiktor.jurkowski@uni.lu, enrico.glaab@uni.lu, alexey.kolodkin@uni.lu, antonio.raussel@uni.lu, anthony.lebechec@uni.lu, isaac.crespo@uni.lu, antonio.delsol@uni.lu, rudi.balling@uni.lu A. Kolodkin N. Baliga L. Hood Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109-5234, USA e-mail: nbaliga@systemsbiology.org, lhood@systemsbiology.org A. Prokop and B. Csukás (eds.), Systems Biology, DOI: 10.1007/978-94-007-6803-1_6, Springer Science+Business Media Dordrecht 2013 181 accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type.

Chapter 11 – Systems Biology and Systems Medicine

Publisher Summary A systems approach to medicine argues that disease arises from disease-perturbed biological networks and the dynamically changing, altered patterns of gene expression controlled by these perturbed networks give rise to the disease manifestations. This chapter presents a systems view of biology and disease, and recent advances in state-of-the-art in vitro and in vivo diagnostics technologies. As these technologies mature, they will move towards a future of predictive, personalized, preventive, and participatory medicine. Two primary domains of biological information lend themselves readily to systems-level analysis: the static, digital information of the genome, and the dynamic information arising from environmental interactions with the subcellular, cellular, and tissue levels of organization. Digital genome information encodes two types of biological networks–protein interactions and gene regulatory networks. Protein networks transmit biological information for development, physiology, and metabolism. Other RNAs interacting with one another receive information from signal-transduction networks, integrate and modulate it, and convey the processed information to networks of genes or molecular machines that execute developmental and physiological functions.