Research highlight by Kazuharu Arakawa and Masaru Tomita, Institute for Advanced Biosciences, Keio University, Japan

Gene duplications and mutations are central driving forces in the evolution of genomes. Genomes must be robust to such changes in order to be evolvable, and many studies have probed genome robustness using systematic gene knockouts or overexpression experiments. In a recent paper, Isalan et al. (2008) took a new approach to test the robustness of Escherichia coli gene circuitry by reconstructing gene duplication events by shuffling the promoter-ORF pairs for about 300 transcription factors and introducing 598 recombined pairs one-by-one into E. coli to rewire its transcriptional network. Surprisingly, ~95% of such additions are robustly tolerated, and some networks even exhibit greater fitness under various selection pressures. Moreover, the study shows that, in contrast to naive expectations, the introduction of positive or negative feedback loops has little effect on the protein expression levels of regulated ORFs.

Since radical rewiring of the gene circuitry appears to have only a limited impact on expression levels, this work suggests that gene regulatory networks are highly dynamic and underscores the potential importance of post-transcriptional mechanisms for the robustness of transcriptional regulation. Moreover, this work illustrates the fundamental robustness and evolvability of gene regulatory networks, which is reassuring news for synthetic biology.

Isalan M, Lemerle C, Michalodimitrakis K, Horn C, Beltrao P, Raineri E, Garriga-Canut M, Serrano L (2008) Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840

Research highlight by Doron Lancet, Crown Human Genome Center, Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

Living cells are typically asymmetric, having tens of thousands different biopolymers (proteins and polynucleotides), but merely <1000 types of small molecules, such as amino acids and lipids. An exception is certain plant cells that harbor members of ~40,000 strong group of low molecular weight terpenoids, often displaying a complex compositional balance essential for plant growth and survival (Aharoni et al, 2005). Understanding the intricacies of biosynthesis and interconversion of such unusual cellular components appears to require the full power of Systems Biology. In a recent paper, Rios-Estepa et al (2008) harness a systems approach, including iterative cycles of mathematical modeling and experimental testing, to help elucidate the metabolic dynamics of the terpenoid universe.

Specifically they ask how plants vary their monoterpene profiles in response to environmental stress – changing levels of illumination. A highlight of their results is that the variation of terpene metabolic fluxes is mediated by specific events in which members of the terpenoid repertoire exert a regulatory effect on terpene biosynthesis enzymes. Rewardingly, this is predicted by a computer simulation and subsequently verified by experiment. The broader conclusion, applicable to all living organisms, is that as the power of computing grows, it will become possible to make increasingly specific and accurate predictions, that will allow both a better global understanding and the successful engineering of cellular networks.

Aharoni A, Jongsma MA, Bouwmeester HJ (2005) Volatile science? Metabolic engineering of terpenoids in plants. Trends Plant Sci. 10:594-602.

Rios-Estepa R, Turner GW, Lee JM, Croteau RB, Lange BM (2008) A systems biology approach identifies the biochemical mechanisms regulating monoterpenoid essential oil composition in peppermint. Proc Natl Acad Sci U S A. 105:2818-2823

Research highlight by Jeongah Yoon and Thomas S. Deisboeck, Massachusetts General Hospital, Charlestown, MA

Targeting receptor tyrosine kinases (RTKs) is currently thought to be a promising anti-cancer strategy (Baselga, 2006). However, clinical trials with RTK inhibitors demonstrated that some solid tumors are sensitive to these drugs while others are not. For instance, only a subset of non small cell lung cancer (NSCLC) tumors with EGFR-activating mutations seems to respond to EGFR inhibitors (Lynch et al, 2004).

The recent study by Guo et al (2008) aims to shed more light on the causes for such selective drug sensitivity by investigating the downstream signaling pathways of several NSCLC cell lines and a gastric cancer cell line. Using a quantitative global proteomic analysis (PhosphoScan-SILAC) they analyzed the EGFR and c-Met networks, treated with the EGFR inhibitor gefitinib and the c-Met inhibitor Su11274, respectively.

The results show a dramatic decrease in EGFR phosphorylation from 5- to 200-fold after gefitinib treatment as well as a reduction of some adaptor proteins (e.g., Her3, Gab1, and Shc1), adhesion and cytoskeletal proteins. Furthermore, a c-Met-driven gastric cancer cell line demonstrated sensitivity to the c-Met inhibitor, Su11274. The authors observed that the inhibited EGFR and c-Met signaling networks share a number of molecular components which underscores that amplified c-Met can drive the activity of (mutated) EGFR and vice versa. In both cases, the targeted kinase is positioned on top of the hierarchical signaling network and thus controls downstream signaling.

In conclusion, this interesting study suggests that there is a common sub-cellular signaling module that processes drug sensitivity and that the effect of an anti-RTK therapeutic compound is maximized when the targeted kinase uniquely controls the downstream signaling networks.

Baselga J (2006) Targeting tyrosine kinases in cancer: the second wave. Science 312:1175-8

Guo A, Villén J, Kornhauser J, Lee KA, Stokes MP, Rikova K, Possemato A, Nardone J, Innocenti G, Wetzel R, Wang Y, MacNeill J, Mitchell J, Gygi SP, Rush J, Polakiewicz RD, Comb MJ (2008) Signaling networks assembled by oncogenic EGFR and c-Met. Proc Natl Acad Sci U S A. 105:692-7

Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 350:2129-39

Research highlight by Frank C.P. Holstege, Department of Physiological Chemistry, University Medical Center Utrecht, the Netherlands.

For eukaryotes, it is widely thought that transcription is primarily regulated through recruitment of the essential machinery to transcription start-sites. Previous hints challenging this paradigm have been confirmed by recent analyses showing that transcription regulation of a large number of genes actually occurs after recruitment. Mechanistically, such studies have gone furthest in Drosophila melanogaster (Muse et al, 2007; Zeitlinger et al, 2007). Here, conservative estimates indicate that more than 10% of genes are regulated through promoter-proximal pausing. On such genes, RNA polymerase II is recruited and initiates transcription, but then pauses around 50 bp downstream of the transcription start-site where it awaits further signals to resume elongation and complete transcription proper. These observations tie in with other observations made in yeast (Radonjic et al, 2005), embryonic stem cells (Bernstein et al, 2006; Lee et al, 2006) and differentiated mammalian cells (Guenther et al, 2007). There are numerous implications to these findings. For example, the widely assumed link between the presence of gene-specific transcription activators and full-length transcription appears to be much looser than expected. These results also underscore the importance of testing established models on a genome-wide scale. Indeed, other such surveys (Birney et al, 2007), indicate that to understand transcription, we may need to take into account even more surprises – such as the presence of ten times more start-sites than protein-coding genes and overlapping transcription units, etc… – than the post-recruitment mechanisms demonstrated in Drosophila.

Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K, et al. (2006) A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125: 315-326

Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE, et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447: 799-816

Guenther MG, Levine SS, Boyer LA, Jaenisch R, and Young RA (2007) A chromatin landmark and transcription initiation at most promoters in human cells. Cell 130: 77-88

Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, Chevalier B, Johnstone SE, Cole MF, Isono K, et al. (2006) Control of developmental regulators by Polycomb in human embryonic stem cells. Cell 125: 301-313

Muse GW, Gilchrist DA, Nechaev S, Shah R, Parker JS, Grissom SF, Zeitlinger J, and Adelman K (2007) RNA polymerase is poised for activation across the genome. Nat Genet 39: 1507-1511

Radonjic M, Andrau JC, Lijnzaad P, Kemmeren P, Kockelkorn TT, van Leenen D, van Berkum NL, and Holstege FC (2005) Genome-wide analyses reveal RNA polymerase II located upstream of genes poised for rapid response upon S. cerevisiae stationary phase exit. Mol Cell 18: 171-183

Zeitlinger J, Stark A, Kellis M, Hong JW, Nechaev S, Adelman K, Levine M, and Young RA (2007) RNA polymerase stalling at developmental control genes in the Drosophila melanogaster embryo. Nat Genet 39: 1512-1516

Research highlight by Charles Auffray, Functional Genomics and Systems Biology for Health, UMR7091, CNRS and Pierre & Marie Curie University—Paris VI, Villejuif, France

The work presented by Helikar et al. (2008) in a paper recently published in the PNAS represents a promising new step in the development of computational cellular physiology in eukaryotes. From curated cellular and biochemical data available in the literature, the authors have assembled a discrete Boolean model of signal transduction comprising 130 nodes, and examined in a systematic and controlled manner how varying combinations of external inputs translate into a range of cellular responses. The qualitative model is not only able to reproduce known input-output relationships representative of major transduction pathways, but it also provides evidence in support of the emergence of information-processing functions from the complex cellular network of molecular interactions. This is strikingly demonstrated by the fact that a large sample of randomly selected input combinations result in a very limited fraction of the possible outputs, which correspond to well-characterized global biological responses, a result which is obtained irrespective of the level of noise introduced in the inputs of the model. Moreover, similar input combinations are neatly clustered by the model into equivalence classes of global outputs, reflecting the ability of the cell to integrate complex environmental signals and translate them into robust specific responses and behaviours through common intracellular pathways. While discrete Boolean modelling makes it possible to highlight emergent properties of transduction networks, overcoming the hurdle of parameter estimation, very much as in classical physiology, it provides only high-order views in the form of black boxes with limited predictive and explanatory power. Integration with continuous models will be essential to unravel and engineer the underlying mechanisms.

Helikar T, Konvalina J, Heidel J, Rogers JA (2008). Emergent decision-making in biological signal transduction networks. PNAS 105, 1913-1918

To raise awareness of important advances in systems and synthetic biology, today we open a new section in this blog: Research Highlights.

Research Highlights will be contributed by members of the Advisory Editorial Board of Molecular Systems Biology and they will cover recent studies on topics related to systems and synthetic biology. These short posts are not intended to represent evaluations of the selected papers. Rather, our hope is that subjective filtering of the literature by our Editorial Board will provide some unique views on the diversity of the field and its progress.

Enjoy!

by George M Church, live from the 9th International Meeting on Human Genome Variation and Complex Genome Analysis, Sep 6-8, 2007 in Barcelona.

Although Jim Watson's genome hasn't been through peer review yet, and Craig Venter’s genome doesn’t have a slick web browser like Jim’s genome yet, we’ve seen enough to ask – what next? Someone at the meeting today got some laughs accidentally when they said that they were comparing Craig’s genome to the human genome. Clearly this is a time requiring great caution. So our first question is: where are we with these first two complete diploid genomes? Well, they’re neither complete nor the first. The Craigome has over 4500 gaps (a bit more than the 341 gaps in the haploid 2004 HGP genome). The first human diploid sequence nod goes to the 269 HapMap genomes published in Oct 2005. Nevertheless we now have the first two non-anonymous personal genomes (hopefully millions someday). Oh, and what is it with press-release that our genomes have higher variation than previously thought? The 0.5% variation observed includes a near-perfect fit to the long-known 0.06% SNP frequency, a 0.08% frequency of smaller indels about twice that seen in 330 genes from Seattle studies, and the remainder being copy-number variants (CNV) 87% of which have been described previously. Just like the number of genes in the genome in 2001, the beauty and the news is in the details not in the summary stats.

We can get from genome variations to systems biology “with focused population association studies, animal models, and functional genomics on the cells from the subjects” (Church 2005). To do genome-wide association studies (GWAS), we must ask where the technology costs are leading? Given the drop in price between the arrivals of the two genomes in the NCBI Personal Genomics directory -- Craig on June 27 at a cost of $70M, and Jim nine days later at a cost of $1M, an over-zealous extrapolationist might be disappointed that the $1K genome did not arrive on July 25. Seriously now, the point is that neither study is inexpensive enough to scale to genome-wide association studies. SNP-chips at $250 each are scaleable, but tend to miss new and/or rare SNPs and small indels. Next generation sequencing and short-read-pairs (Shendure et al 2005) may bring down costs by a factor of 10. Read-pairs seem ripe to become the method of choice for CNVs, smaller indels, and even inversions. Enrichment by hybridization for at least one read to be in an exon or cis-regulatory site might bring costs down another factor of 50. Even if these GWAS studies efficiently get us beyond “linked alleles” to “causative alleles”, they will generate gloriously more hypotheses than they test.

So, back to the other routes to systems biology, animal or cell models could be made to test the 4 million variants per genome (and combinations; oh my!) -- clearly indicating a need for automated homologous recombination methods and/or prioritization of these tests using the third route to systems biology -- “personal functional genomics”. Unlike the HapMap genomes, the Jimome and Craigome are not yet accompanied by extensive phenotypic trait data, nor any cell lines to do so. SNPs and CNVs that affect RNA levels have been elegantly mapped by Spielman et al. 2007 and Stranger et al 2007. Most effects map close to the transcription start sites. Assaying RNA by these standard assays or next-generation sequencing (Kim et al. 2007) from individuals enables comparisons of sum of the two allelic expression levels from the two types of homozygotes (AA & aa) and the heterozygote (Aa) in a variety of different genetic backgrounds and cell-states. In contrast, genome-wide, allele-specific RNA assays would measure the expression from each haplotype in a heterozygote under what is the most ideally identical background state arrangeable. The missing technology is one to gain access to all human tissues (since the list of volunteers for brain biopsies is short). Yet another reason that we will be watching for methods to derive pluripotent stem cells from adult human tissues. Personal functional genomics assays on such personal cell lines are likely to arrive much earlier than (indeed pave the way for) therapeutic applications.

Church GM (2005) The Personal Genome Project. Mol Syst Biol 1:2005.0030

IHGSC et al. (2004) Finishing the euchromatic sequence of the human genome. Nature 431:931-945.

International HapMap Consortium. (2005) A haplotype map of the human genome. Nature 437(7063):1299-320.

Kim JB, Porreca GJ, Gorham JM, Church GM, Seidman CE, Seidman JG (2007) Polony multiplex analysis of gene expression (PMAGE) in a mouse model of hypertrophic cardiomyopathy. Science 316(5830):1481-4.

Levy et al. (2007) The Diploid Genome Sequence of an Individual Human. PLoS 5:e254.

Shendure, J, Porreca, GJ, Reppas, NB, Lin, X, McCutcheon, JP, Rosenbaum, AM, Wang, MD , Zhang, K, Mitra, RD, Church, GM (2005) Accurate Multiplex Polony Sequencing of an Evolved Bacterial Genome. Science 309(5741):1728-32.

Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG. (2007) Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet. 39(2):226-31.

Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, Tyler-Smith C, Carter N, Scherer SW, Tavare S, Deloukas P, Hurles ME, Dermitzakis ET. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007 315(5813):848-53.