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1.  Electrical activity can impose time-of-day on the circadian transcriptome of pacemaker neurons 
Current biology : CB  2012;22(20):1871-1880.
Summary
Background
Circadian (~24hr) rhythms offer one of the best examples of how gene expression is tied to behavior. Circadian pacemaker neurons contain molecular clocks that control ~24hr rhythms in gene expression that in turn regulate electrical activity rhythms to control behavior.
Results
Here we demonstrate the inverse relationship: there are broad transcriptional changes in Drosophila clock neurons (LNvs) in response to altered electrical activity, including a large set of circadian genes. Hyperexciting LNvs creates a morning-like expression profile for many circadian genes while hyperpolarization leads to an evening-like transcriptional state. The electrical effects robustly persist in per0 mutant LNvs but not in cyc0 mutant LNvs suggesting that neuronal activity interacts with the transcriptional activators of the core circadian clock. Bioinformatic and immunocytochemical analyses suggest that CREB family transcription factors link LNv electrical state to circadian gene expression.
Conclusions
The electrical state of a clock neuron can impose time-of-day to its transcriptional program. We propose that this acts as an internal zeitgeber to add robustness and precision to circadian behavioral rhythms.
doi:10.1016/j.cub.2012.07.070
PMCID: PMC3562355  PMID: 22940468
2.  On Crowd-verification of Biological Networks 
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks.
Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified.
The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.
doi:10.4137/BBI.S12932
PMCID: PMC3798292  PMID: 24151423
community curation; biological network models; reputation system; Biological Expression Language
3.  Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project 
Gerstein, Mark B. | Lu, Zhi John | Van Nostrand, Eric L. | Cheng, Chao | Arshinoff, Bradley I. | Liu, Tao | Yip, Kevin Y. | Robilotto, Rebecca | Rechtsteiner, Andreas | Ikegami, Kohta | Alves, Pedro | Chateigner, Aurelien | Perry, Marc | Morris, Mitzi | Auerbach, Raymond K. | Feng, Xin | Leng, Jing | Vielle, Anne | Niu, Wei | Rhrissorrakrai, Kahn | Agarwal, Ashish | Alexander, Roger P. | Barber, Galt | Brdlik, Cathleen M. | Brennan, Jennifer | Brouillet, Jeremy Jean | Carr, Adrian | Cheung, Ming-Sin | Clawson, Hiram | Contrino, Sergio | Dannenberg, Luke O. | Dernburg, Abby F. | Desai, Arshad | Dick, Lindsay | Dosé, Andréa C. | Du, Jiang | Egelhofer, Thea | Ercan, Sevinc | Euskirchen, Ghia | Ewing, Brent | Feingold, Elise A. | Gassmann, Reto | Good, Peter J. | Green, Phil | Gullier, Francois | Gutwein, Michelle | Guyer, Mark S. | Habegger, Lukas | Han, Ting | Henikoff, Jorja G. | Henz, Stefan R. | Hinrichs, Angie | Holster, Heather | Hyman, Tony | Iniguez, A. Leo | Janette, Judith | Jensen, Morten | Kato, Masaomi | Kent, W. James | Kephart, Ellen | Khivansara, Vishal | Khurana, Ekta | Kim, John K. | Kolasinska-Zwierz, Paulina | Lai, Eric C. | Latorre, Isabel | Leahey, Amber | Lewis, Suzanna | Lloyd, Paul | Lochovsky, Lucas | Lowdon, Rebecca F. | Lubling, Yaniv | Lyne, Rachel | MacCoss, Michael | Mackowiak, Sebastian D. | Mangone, Marco | McKay, Sheldon | Mecenas, Desirea | Merrihew, Gennifer | Miller, David M. | Muroyama, Andrew | Murray, John I. | Ooi, Siew-Loon | Pham, Hoang | Phippen, Taryn | Preston, Elicia A. | Rajewsky, Nikolaus | Rätsch, Gunnar | Rosenbaum, Heidi | Rozowsky, Joel | Rutherford, Kim | Ruzanov, Peter | Sarov, Mihail | Sasidharan, Rajkumar | Sboner, Andrea | Scheid, Paul | Segal, Eran | Shin, Hyunjin | Shou, Chong | Slack, Frank J. | Slightam, Cindie | Smith, Richard | Spencer, William C. | Stinson, E. O. | Taing, Scott | Takasaki, Teruaki | Vafeados, Dionne | Voronina, Ksenia | Wang, Guilin | Washington, Nicole L. | Whittle, Christina M. | Wu, Beijing | Yan, Koon-Kiu | Zeller, Georg | Zha, Zheng | Zhong, Mei | Zhou, Xingliang | Ahringer, Julie | Strome, Susan | Gunsalus, Kristin C. | Micklem, Gos | Liu, X. Shirley | Reinke, Valerie | Kim, Stuart K. | Hillier, LaDeana W. | Henikoff, Steven | Piano, Fabio | Snyder, Michael | Stein, Lincoln | Lieb, Jason D. | Waterston, Robert H.
Science (New York, N.Y.)  2010;330(6012):1775-1787.
We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
doi:10.1126/science.1196914
PMCID: PMC3142569  PMID: 21177976
4.  Benzo[a]pyrene diol epoxide stimulates an inflammatory response in normal human lung fibroblasts through a p53 and JNK mediated pathway 
Carcinogenesis  2010;31(6):1149-1157.
Cellular responses to carcinogens are typically studied in transformed cell lines, which do not reflect the physiological status of normal tissues. To address this question, we have characterized the transcriptional program and cellular responses of human lung WI-38 fibroblasts upon exposure to the ultimate carcinogen benzo[a]pyrene diol epoxide (BPDE). In contrast to observations in cell lines, we find that BPDE treatment induces a strong inflammatory response in these normal fibroblasts. Whole-genome microarrays show induction of numerous inflammatory factors, including genes that encode interleukins (ILs), growth factors and enzymes related to prostaglandin synthesis and signaling. Real-time reverse transcription–polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) revealed a time- and dose-dependent-induced expression and production of cyclooxygenase 2, prostglandin E2 and IL1B, IL6 and IL8. In parallel, cell cycle progression and DNA repair processes were repressed, but DNA damage signaling was increased via p53-Ser15 phosphorylation and induced expression levels of GADD45A, CDKN1A, BTG2 and SESN1. Network analysis suggested that activator protein 1 transcription factors may link the cell cycle response and DNA damage signaling with the inflammatory stress–response in these cells. We confirmed this hypothesis by showing that p53-dependent signaling through c-jun N-terminal kinase (JNK) led to increased cJun-Ser63 phosphorylation and that inhibition of JNK-mediated cJun activation using p53- or JNK-specific inhibitors significantly reduced IL gene expression and subsequent production of IL8. This is the first demonstration that a strong inflammatory response is triggered in normal fibroblasts by BPDE and that this occurs through coordinated regulation with other cellular processes.
doi:10.1093/carcin/bgq073
PMCID: PMC2878364  PMID: 20382639
5.  MINE: Module Identification in Networks 
BMC Bioinformatics  2011;12:192.
Background
Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.
Results
MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.
Conclusions
MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans.
doi:10.1186/1471-2105-12-192
PMCID: PMC3123237  PMID: 21605434
6.  A protein domain-based interactome network for C. elegans early embryogenesis 
Cell  2008;134(3):534-545.
Summary
Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or “interactome” networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed new insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.
doi:10.1016/j.cell.2008.07.009
PMCID: PMC2596478  PMID: 18692475

Results 1-6 (6)