Institution
Institute for Systems Biology
Nonprofit•Seattle, Washington, United States•
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.
Topics: Population, Proteomics, Gene, Proteome, Systems biology
Papers published on a yearly basis
Papers
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TL;DR: The authors showed that activation of caspase-1 clears intracellular bacteria in vivo independently of IL-1β and IL-18 and establishes pyroptosis as an efficient mechanism of bacterial clearance by the innate immune system.
Abstract: Macrophages mediate crucial innate immune responses via caspase-1-dependent processing and secretion of interleukin 1β (IL-1β) and IL-18. Although infection with wild-type Salmonella typhimurium is lethal to mice, we show here that a strain that persistently expresses flagellin was cleared by the cytosolic flagellin-detection pathway through the activation of caspase-1 by the NLRC4 inflammasome; however, this clearance was independent of IL-1β and IL-18. Instead, caspase-1-induced pyroptotic cell death released bacteria from macrophages and exposed the bacteria to uptake and killing by reactive oxygen species in neutrophils. Similarly, activation of caspase-1 cleared unmanipulated Legionella pneumophila and Burkholderia thailandensis by cytokine-independent mechanisms. This demonstrates that activation of caspase-1 clears intracellular bacteria in vivo independently of IL-1β and IL-18 and establishes pyroptosis as an efficient mechanism of bacterial clearance by the innate immune system.
1,022 citations
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TL;DR: This article used a system biology approach to identify early gene signatures that predicted immune responses in humans vaccinated with the yellow fever vaccine YF-17D, with up to 90% accuracy in an independent, blinded trial.
Abstract: A major challenge in vaccinology is to prospectively determine vaccine efficacy. Here we have used a systems biology approach to identify early gene 'signatures' that predicted immune responses in humans vaccinated with yellow fever vaccine YF-17D. Vaccination induced genes that regulate virus innate sensing and type I interferon production. Computational analyses identified a gene signature, including complement protein C1qB and eukaryotic translation initiation factor 2 alpha kinase 4-an orchestrator of the integrated stress response-that correlated with and predicted YF-17D CD8(+) T cell responses with up to 90% accuracy in an independent, blinded trial. A distinct signature, including B cell growth factor TNFRS17, predicted the neutralizing antibody response with up to 100% accuracy. These data highlight the utility of systems biology approaches in predicting vaccine efficacy.
1,004 citations
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University of Iceland1, University of Manchester2, Charité3, University of California, San Diego4, University of Amsterdam5, Netherlands Bioinformatics Centre6, Chalmers University of Technology7, University of Virginia8, University of Sheffield9, Central Manchester University Hospitals NHS Foundation Trust10, University of Vienna11, University of North Texas12, California Institute of Technology13, European Bioinformatics Institute14, Babraham Institute15, University of Warwick16, University of Edinburgh17, Institute for Systems Biology18, University of Luxembourg19, Jacobs University Bremen20, Russian Academy of Sciences21, VU University Amsterdam22, Virginia Bioinformatics Institute23
TL;DR: Recon 2, a community-driven, consensus 'metabolic reconstruction', is described, which is the most comprehensive representation of human metabolism that is applicable to computational modeling and has improved topological and functional features.
Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
1,002 citations
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TL;DR: A browser for the first new human genome reference assembly in 4 years in December 2013, a watershed comparative genomics annotation (100-species multiple alignment and conservation), and a novel distribution mechanism for the browser are among the highlights of the past year.
Abstract: Launched in 2001 to showcase the draft human genome assembly, the UCSC Genome Browser database (http://genome.ucsc.edu) and associated tools continue to grow, providing a comprehensive resource of genome assemblies and annotations to scientists and students worldwide. Highlights of the past year include the release of a browser for the first new human genome reference assembly in 4 years in December 2013 (GRCh38, UCSC hg38), a watershed comparative genomics annotation (100-species multiple alignment and conservation) and a novel distribution mechanism for the browser (GBiB: Genome Browser in a Box). We created browsers for new species (Chinese hamster, elephant shark, minke whale), 'mined the web' for DNA sequences and expanded the browser display with stacked color graphs and region highlighting. As our user community increasingly adopts the UCSC track hub and assembly hub representations for sharing large-scale genomic annotation data sets and genome sequencing projects, our menu of public data hubs has tripled.
984 citations
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TL;DR: The difficulties of interpreting shotgun proteomic data are illustrated and the need for common nomenclature and transparent informatic approaches are discussed and related issues such as the state of protein sequence databases and their role in shotgun proteomics analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, and the integration of proteomic and transcriptional data are discussed.
983 citations
Authors
Showing all 1292 results
Name | H-index | Papers | Citations |
---|---|---|---|
Younan Xia | 216 | 943 | 175757 |
Ruedi Aebersold | 182 | 879 | 141881 |
David Haussler | 172 | 488 | 224960 |
Steven P. Gygi | 172 | 704 | 129173 |
Nahum Sonenberg | 167 | 647 | 104053 |
Leroy Hood | 158 | 853 | 128452 |
Mark H. Ellisman | 117 | 637 | 55289 |
Wei Zhang | 112 | 1189 | 93641 |
John Ralph | 109 | 442 | 39238 |
Eric H. Davidson | 106 | 454 | 47058 |
James R. Heath | 103 | 425 | 58548 |
Alan Aderem | 99 | 246 | 46682 |
Anne-Claude Gingras | 97 | 336 | 40714 |
Trey Ideker | 97 | 306 | 72276 |
Michael H. Gelb | 94 | 506 | 34714 |