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8 - Inferring genetic architecture from systems genetics studies

Published online by Cambridge University Press:  05 July 2015

By
Xiaoyun Sun ,
Stephanie Mohr ,
Arunachalam Vinayagam ,
Pengyu Hong  and
Norbert Perrimon
Edited by
Florian Markowetz  and
Michael Boutros
Xiaoyun Sun
Affiliation:
Brandeis University
Stephanie Mohr
Affiliation:
Harvard Medical School
Arunachalam Vinayagam
Affiliation:
Harvard Medical School
Pengyu Hong
Affiliation:
Brandeis University
Norbert Perrimon
Affiliation:
Harvard Medical School
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Michael Boutros
Affiliation:
German Cancer Research Center, Heidelberg
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Summary

In recent years many efforts have been invested in comprehensively evaluating the behavior and relationships of all genes/proteins in a particular biological system and at a particular state. Here, we review how genome-wide RNAi screens together with mass spectrometry can be integrated to generate high-confidence functional interac- tome networks. Next we review the mathematical modeling methods available today that allow the computational reconstruction of such networks. Network modeling will play an important role in generating hypotheses, driving further experimentation and thus novel insights into network structure and behavior.

Introduction

Most biologists study a specific biological problem by investigating the activities of a limited number of genes or proteins involved in a particular biological process. This traditional approach is critical and has proven to be extremely successful to reveal the detailed molecular functions of individual genes and proteins. For example, genetic studies of embryonic patterning in Drosophila identified about 40 genes with striking segmentation defects that fell into distinct phenotypic classes: gap genes, pair rule genes, segment polarity genes, and homeotic genes (Nusslein-Volhard & Wieschaus 1980). Detailed analyses of the mutant phenotypes and functions of even this relatively small set of genes led to a comprehensive molecular framework of the process of embryonic patterning (St Johnston & Nusslein-Volhard 1992). Reductionist approaches, however, are not sufficient for generating the big picture of how a biological system, including multiple levels of many different gene products and the interactions among them, works at different physiological states or developmental stages (Friedman & Perrimon 2007). Thus, as our knowledge of individual genes and proteins accumulates, there is a need to comprehensively evaluate the behavior and relationships of all genes/proteins in a particular biological system and at a particular state. In recent years, progress has been made in multi cellular organisms towards this goal mostly in tissue culture, a platform that allows a sufficient amount of homogeneous material to be easily obtained.

Type
Chapter
Information
Systems Genetics
Linking Genotypes and Phenotypes
 , pp. 139 - 160
Publisher: Cambridge University Press
Print publication year: 2015

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References

Bakal, C. (2011), ‘Drosophila RNAi screening in a postgenomic world’, Briefings in Functional Genomics 10(4), 197–205.CrossRefGoogle Scholar
Bakal, C. & Perrimon, N. (2010), ‘Realizing the promise of RNAi high throughput screening’, Developmental Cell 18(4), 506–7.CrossRefGoogle Scholar
Bakal, C., Aach, J., Church, G. & Perrimon, N. (2007), ‘Quantitative morphological signatures define local signaling networks regulating cell morphology’, Science 316(5832), 1753–6.CrossRefGoogle Scholar
Bakal, C., Linding, R., Llense, F., Heffern, E., Martin-Blanco, E. et al. (2008), ‘Phosphorylation networks regulating JNK activity in diverse genetic backgrounds’, Science 322(5900), 453–6.CrossRefGoogle Scholar
Bonneau, R., Reiss, D. J., Shannon, P., Facciotti, M., Hood, L. et al. (2006), ‘The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo’, Genome Biology 7(5), R36.CrossRefGoogle Scholar
Booker, M., Samsonova, A. A., Kwon, Y., Flockhart, I., Mohr, S. E. et al. (2011), ‘False negative rates in Drosophila cell-based RNAi screens: a case study’, BMC Genomics 12, 50.CrossRefGoogle Scholar
Boutros, M., Kiger, A. A., Armknecht, S., Kerr, K., Hild, M. et al. (2004), ‘Genome-wide RNAi analysis of growth and viability in Drosophila cells’, Science 303(5659), 832–5.CrossRefGoogle Scholar
Choi, H., Larsen, B., Lin, Z.-Y., Breitkreutz, A., Mellacheruvu, D. et al. (2011), ‘SAINT: probabilistic scoring of affinity purification-mass spectrometry data’, Nature Methods 8(1), 70–3.CrossRefGoogle Scholar
Churchill, G. A. (1989), ‘Stochastic models for heterogeneous DNA sequences’, Bulletin of Mathematical Biology 51(1), 79–94.CrossRefGoogle Scholar
Collinet, C., Stoter, M., Bradshaw, C. R., Samusik, N., Rink, J. C. et al. (2010), ‘Systems survey of endocytosis by multiparametric image analysis’, Nature 464(7286), 243–9.CrossRefGoogle Scholar
DasGupta, R. & Gonsalves, F. C. (2008), ‘High-throughput RNAi screen in Drosophila’, Methods in Molecular Biology 469, 163–84.Google Scholar
DasGupta, R., Nybakken, K., Booker, M., Mathey-Prevot, B., Gonsalves, F. et al. (2007), ‘A case study of the reproducibility of transcriptional reporter cell-based RNAi screens in Drosophila’, Genome Biology 8(9), R203.CrossRefGoogle Scholar
Falschlehner, C., Steinbrink, S., Erdmann, G. & Boutros, M. (2010), ‘High-throughput RNAi screening to dissect cellular pathways: a how-to guide’, Biotechnology Journal 5(4), 368–76.CrossRefGoogle Scholar
Fliri, A. F., Loging, W. T. & Volkmann, R. A. (2009), ‘Drug effects viewed from a signal transduction network perspective’, Journal of Medicinal Chemistry 52(24), 8038–46.CrossRefGoogle Scholar
Flockhart, I., Booker, M., Kiger, A., Boutros, M., Armknecht, S. et al. (2006), ‘FlyRNAi: the Drosophila RNAi screening center database’, Nucleic Acids Research 34 (Database issue), D489–94.CrossRefGoogle Scholar
Friedman, A. & Perrimon, N. (2006), ‘High-throughput approaches to dissecting MAPK signaling pathways’, Methods 40(3), 262–71.CrossRefGoogle Scholar
Friedman, A. & Perrimon, N. (2007), ‘Genetic screening for signal transduction in the era of network biology’, Cell 128(2), 225–31.CrossRefGoogle Scholar
Friedman, A. A., Tucker, G., Singh, R., Yan, E., Vinayagam, A. et al. (2011), ‘Proteomic and functional genomic landscape of receptor tyrosine kinase and Ras/ERK signaling’, Science Signaling 4(196), rs10.CrossRefGoogle Scholar
Gavin, A.-C., Aloy, P., Grandi, P., Krause, R., Boesche, M. et al. (2006), ‘Proteome survey reveals modularity of the yeast cell machinery’, Nature 440(7084), 631–6.CrossRefGoogle Scholar
Gilsdorf, M., Horn, T., Arziman, Z., Pelz, O., Kiner, E. et al. (2010), ‘GenomeRNAi: a database for cell-based RNAi phenotypes – 2009 update’, Nucleic Acids Research 38(Database issue), D448–52.CrossRefGoogle Scholar
Gunsalus, K. C., Ge, H., Schetter, A. J., Goldberg, D. S., Han, J.-D. J. et al. (2005), ‘Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis’, Nature 436(7052), 861–5.CrossRefGoogle Scholar
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E. & Guthke, R. (2009), ‘Gene regulatory network inference: data integration in dynamic models – a review’, Biosystems 96(1), 86–103.CrossRefGoogle Scholar
Huang, S. S. & Fraenkel, E. (2009), ‘Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks’, Science Signaling 2(81), ra40.CrossRefGoogle Scholar
Hughey, J. J., Lee, T. K. & Covert, M. W. (2010), ‘Computational modeling of mammalian signaling networks’, Wiley Interdisciplinary Reviews. Systems Biology and Medicine 2(2), 194–209.CrossRefGoogle Scholar
Hwang, W. C., Zhang, A. & Ramanathan, M. (2008), ‘Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery’, Clinical Pharmacology and Therapeutics 84(5), 563–72.CrossRefGoogle Scholar
Jeronimo, C., Forget, D., Bouchard, A., Li, Q., Chua, G. et al. (2007), ‘Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme’, Molecular Cell 27(2), 262–4.CrossRefGoogle Scholar
Kaderali, L., Dazert, E., Zeuge, U., Frese, M. & Bartenschlager, R. (2009), ‘Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks’, Bioinformatics 25(17), 2229–35.CrossRefGoogle Scholar
Kaplow, I. M., Singh, R., Friedman, A., Bakal, C., Perrimon, N. et al. (2009), ‘RNAiCut: automated detection of significant genes from functional genomic screens’, Nature Methods 6(7), 476–7.CrossRefGoogle Scholar
Kauffman, S. A. (1969), ‘Metabolic stability and epigenesis in randomly constructed genetic nets’, Journal of Theoretical Biology 22(3), 437–67.CrossRefGoogle Scholar
Kitano, H. (2007), ‘Biological robustness in complex host-pathogen systems’, Progress in Drug Research 64, 239, 241–63.Google Scholar
Kockel, L., Kerr, K. S., Melnick, M., Bruckner, K., Hebrok, M. et al. (2010), ‘Dynamic switch of negative feedback regulation in Drosophila Akt-TOR signaling’, PLoS Genetics 6(6), e1000990.CrossRefGoogle Scholar
Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X. et al. (2006), ‘Global landscape of protein complexes in the yeast Saccharomyces cerevisiae’, Nature 440 (7084), 637–43.CrossRefGoogle Scholar
Kummel, A., Gubler, H., Gehin, P., Beibel, M., Gabriel, D. et al. (2010), ‘Integration of multiple readouts into the Z-factor for assay quality assessment’, Journal of Biomolecular Screening 15(1), 95–101.CrossRefGoogle Scholar
Liu, Y. Y., Slotine, J. J. & Barabasi, A. L. (2011), ‘Controllability of complex networks’, Nature 473(7346), 167–73.CrossRefGoogle Scholar
Ljosa, V. & Carpenter, A. E. (2009), ‘Introduction to the quantitative analysis of two-dimensional fluorescence microscopy images for cell-based screening’, PLoS Computational Biology 5(12), e1000603.CrossRefGoogle Scholar
Lorenz, D. R., Cantor, C. R. & Collins, J. J. (2009), ‘A network biology approach to aging in yeast’, Proceedings of the National Academy of Sciences of the United States of America 106(4), 1145–50.CrossRefGoogle Scholar
Ma'ayan, A., Jenkins, S. L., Neves, S., Hasseldine, A., Grace, E. et al. (2005), ‘Formation of regulatory patterns during signal propagation in a mammalian cellular network’, Science 309(5737), 1078–83.CrossRefGoogle Scholar
Martin, S., Zhang, Z., Martino, A. & Faulon, J.-L. (2007), ‘Boolean dynamics of genetic regulatory networks inferred from microarray time series data’, Bioinformatics 23(7), 866–74.CrossRefGoogle Scholar
Mohr, S., Bakal, C. & Perrimon, N. (2010), ‘Genomic screening with RNAi: results and challenges’, Annual Review of Biochemistry 79, 37–64.CrossRefGoogle Scholar
Niederlein, A., Meyenhofer, F., White, D. & Bickle, M. (2009), ‘Image analysis in high-content screening’, Combinatorial Chemistry & High Throughput Screening 12(9), 899–907.CrossRefGoogle Scholar
Nusslein-Volhard, C. & Wieschaus, E. (1980), ‘Mutations affecting segment number and polarity in Drosophila’, Nature 287(5785), 795–801.CrossRefGoogle Scholar
Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F. et al. (2004), ‘Multidimensional drug profiling by automated microscopy’, Science 306(5699), 1194–8.CrossRefGoogle Scholar
Rabiner, L. R. (1989), ‘A tutorial on hidden Markov models and selected applications in speech recognition’, Proceedings of the IEEE, 77(2), 257–86.CrossRefGoogle Scholar
Sardiu, M. E., Cai, Y., Jin, J., Swanson, S. K., Conaway, R. C. et al. (2008), ‘Probabilistic assembly of human protein interaction networks from label-free quantitative proteomicsy’, Proceedings of the National Academy of Sciences of the United States of America 105(5), 1454–9.CrossRefGoogle Scholar
Schadt, E. E., Friend, S. H. & Shaywitz, D. A. (2009), ‘A network view of disease and compound screening’, Nature Reviews Drug Discovery 8(4), 286–95.CrossRefGoogle Scholar
Seinen, E., Burgerhof, J. G., Jansen, R. C. & Sibon, O. C. (2011), ‘RNAi-induced off-target effects in Drosophila melanogaster: frequencies and solutions’, Briefings in Functional Genomics 10(4), 206–14.CrossRefGoogle Scholar
Shumate, C. & Hoffman, A. F. (2009), ‘Instrumental considerations in high content screening’, Combinatorial Chemistry & High Throughput Screening 12(9), 888–98.CrossRefGoogle Scholar
Sims, D., Bursteinas, B., Gao, Q., Zvelebil, M. & Baum, B. (2006), ‘FLIGHT: database and tools for the integration and cross-correlation of large-scale RNAi phenotypic datasets’, Nucleic Acids Research 34(Database issue), D479–83.CrossRefGoogle Scholar
Sowa, M. E., Bennett, E. J., Gygi, S. P. & Harper, J. W. (2009), ‘Defining the human deubiquitinating enzyme interaction landscape’, Cell 138(2), 389–403.CrossRefGoogle Scholar
St Johnston, D. & Nusslein-Volhard, C. (1992), ‘The origin of pattern and polarity in the Drosophila embryo’, Cell 68(2), 201–19.CrossRefGoogle Scholar
Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. (2003), ‘A gene-coexpression network for global discovery of conserved genetic modules’, Science 302(5643), 249–55.CrossRefGoogle Scholar
Sun, X. & Hong, P. (2007), ‘Computational modeling of Caenorhabditis elegans vulval induction’, Bioinformatics 23(13), i499–507.CrossRefGoogle Scholar
Sun, X. & Hong, P. (2009), ‘Automatic inference of multicellular regulatory networks using informative priors’, International Journal of Computational Biology and Drug Design 2(2), 115–33.CrossRefGoogle Scholar
van Someren, E. P., Wessels, L. F. A., Backer, E. & Reinders, M. J. T. (2002), ‘Genetic network modeling’, Pharmacogenomics 3(4), 507–25.Google Scholar
Vinayagam, A., Stelzl, U., Foulle, R., Plassmann, S., Zenkner, M. et al. (2011), ‘A directed protein interaction network for investigating intracellular signal transduction’, Science Signaling 4(189), rs8.CrossRefGoogle Scholar
Walhout, A. J. M., Reboul, J., Shtanko, O., Bertin, N., Vaglio, P. etal. (2002), ‘Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline’, Current Biology 12(22), 1952–8.CrossRefGoogle Scholar
Werhli, A. V. & Husmeier, D. (2007), ‘Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge’, Statistical Applications in Genetics and Molecular Biology 6, Article 15.CrossRefGoogle Scholar
Yeger-Lotem, E., Riva, L., Su, L. J., Gitler, A. D., Cashikar, A. G. et al. (2009), ‘Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity’, Nature Genetics 41(3), 316–23.CrossRefGoogle Scholar
Zanella, F., Lorens, J. B. & Link, W. (2010), ‘High content screening: seeing is believing’, Trends in Biotechnology 28(5), 237–45.CrossRefGoogle Scholar
Zhong, W. & Sternberg, P. W. (2007), ‘Automated data integration for developmental biological research’, Development 134(18), 3227–38.CrossRefGoogle Scholar
Zhu, X., Gerstein, M. & Snyder, M. (2007), ‘Getting connected: analysis and principles of biological networks’, Genes & Development 21(9), 1010–24.CrossRefGoogle Scholar

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