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1.  Identification of Genes Critical for Resistance to Infection by West Nile Virus Using RNA-Seq Analysis 
Viruses  2013;5(7):1664-1681.
The West Nile virus (WNV) is an emerging infection of biodefense concern and there are no available treatments or vaccines. Here we used a high-throughput method based on a novel gene expression analysis, RNA-Seq, to give a global picture of differential gene expression by primary human macrophages of 10 healthy donors infected in vitro with WNV. From a total of 28 million reads per sample, we identified 1,514 transcripts that were differentially expressed after infection. Both predicted and novel gene changes were detected, as were gene isoforms, and while many of the genes were expressed by all donors, some were unique. Knock-down of genes not previously known to be associated with WNV resistance identified their critical role in control of viral infection. Our study distinguishes both common gene pathways as well as novel cellular responses. Such analyses will be valuable for translational studies of susceptible and resistant individuals—and for targeting therapeutics—in multiple biological settings.
doi:10.3390/v5071664
PMCID: PMC3738954  PMID: 23881275
anti-viral gene expression; immune response; macrophage; RNA-Seq; West Nile virus
2.  CheV: CheW-like coupling proteins at the core of the chemotaxis signaling network 
Trends in microbiology  2010;18(11):494-503.
Microbes have chemotactic signaling systems that enable them to detect and follow chemical gradients in their environments. The core of these sensory systems consists of chemoreceptor proteins coupled to the CheA kinase via the scaffold or coupler protein CheW. Some bacterial chemotaxis systems replace or augment CheW with a related protein CheV, which is less well understood. CheV consists of a CheW domain fused to a phosphorylatable receiver domain. Our review of the literature, as well as comparisons of CheV and CheW sequence and structure, suggest that CheV proteins conserve CheW residues that are crucial for coupling. Phosphorylation of the CheV receiver domain might adjust the efficiency of its coupling, and thus allow the system to modulate the response to chemical stimuli in an adaptation process.
doi:10.1016/j.tim.2010.07.004
PMCID: PMC2975053  PMID: 20832320
3.  Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data 
PLoS ONE  2010;5(1):e8121.
We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.
doi:10.1371/journal.pone.0008121
PMCID: PMC2811182  PMID: 20126643
4.  Comparative Genomic and Protein Sequence Analyses of a Complex System Controlling Bacterial Chemotaxis 
Methods in enzymology  2007;422:1-31.
Molecular machinery governing bacterial chemotaxis consists of the CheA-CheY two-component system, an array of specialized chemoreceptors, and several auxiliary proteins. It has been studied extensively in Escherichia coli and, to a significantly lesser extent, in several other microbial species. Emerging evidence suggests that homologous signal transduction pathways regulate not only chemotaxis, but several other cellular functions in various bacterial species. The availability of genome sequence data for hundreds of organisms enables productive study of this system using comparative genomics and protein sequence analysis. This chapter describes advances in genomics of the chemotaxis signal transduction system, provides information on relevant bioinformatics tools and resources, and outlines approaches toward developing computational framework for predicting important biological functions from raw genomic data based on available experimental evidence.
doi:10.1016/S0076-6879(06)22001-9
PMCID: PMC2754700  PMID: 17628132
5.  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

Results 1-5 (5)