Institute for Systems Biology

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.

Expression and regulation of IL-22 in the IL-17-producing CD4+ T lymphocytes.

Abstract: IL-22 is a novel cytokine in the IL-10 family that functions to promote innate immunity of tissues against infection. Although CD4+ helper T lymphocytes (TH) were found as a source of IL-22, the regulation of this cytokine has been poorly understood. Here, we show that IL-22 is expressed at both mRNA and protein levels by a novel subset of TH cells that also makes IL-17. IL-22 and IL-17 were found to be coordinately regulated by TGFbeta and IL-6 during TH differentiation by real-time PCR as well as ELISA analysis. However, IL-22 does not regulate TH differentiation; exogenous IL-22 or an IL-22 antagonist had no effect on TH differentiation. These data demonstrate a novel cytokine expressed by IL-17-producing T cells, and suggest interaction and synergy of IL-22 and IL-17 signaling pathways in tissue inflammation and autoimmune diseases.

Toll-like receptors: networking for success.

Abstract: The innate immune system is essential for host defense and is responsible for early detection of potentially pathogenic microorganisms. Upon recognition of microbes by innate immune cells such as macrophages and dendritic cells, diverse signaling pathways are activated that combine to define inflammatory responses that direct sterilization of the threat and/or orchestrate development of the adaptive immune response. Innate immune signaling must be carefully controlled, and regulation comes in part from interactions between activating and inhibiting signaling receptors. Toll-like receptors (TLR) have recently emerged as key receptors responsible for recognizing specific conserved components of microbes including lipopolysaccharides from Gram-negative bacteria, CpG DNA, and flagellin. Full activation of inflammatory responses by TLR may require the assembly of receptor signaling complexes including other transmembrane proteins that may influence signal transduction. In addition to TLR, many additional receptors participate in innate recognition of microbes, and recent studies demonstrate strong interactions between signaling through these receptors and signaling through TLR. Useful models for these interacting signaling pathways are now emerging and should pave the way for understanding the molecular mechanisms that drive the rich diversity of inflammatory responses.

Dfam: a database of repetitive DNA based on profile hidden Markov models

Abstract: We present a database of repetitive DNA elements, called Dfam (http://dfam.janelia.org). Many genomes contain a large fraction of repetitive DNA, much of which is made up of remnants of transposable elements (TEs). Accurate annotation of TEs enables research into their biology and can shed light on the evolutionary processes that shape genomes. Identification and masking of TEs can also greatly simplify many downstream genome annotation and sequence analysis tasks. The commonly used TE annotation tools RepeatMasker and Censor depend on sequence homology search tools such as cross_match and BLAST variants, as well as Repbase, a collection of known TE families each represented by a single consensus sequence. Dfam contains entries corresponding to all Repbase TE entries for which instances have been found in the human genome. Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated using RepeatMasker and Repbase. When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9% increase in coverage over consensus sequence search methods on a large human benchmark, while maintaining low false discovery rates, and coverage of the full human genome is 54.5%. The website provides a collection of tools and data views to support improved TE curation and annotation efforts. Dfam is also available for download in flat file format or in the form of MySQL table dumps.

ProbID: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data

Abstract: With the recent quick expansion of DNA and protein sequence databases, intensive efforts are underway to interpret the linear genetic information of DNA in terms of function, structure, and control of biological processes. The systematic identification and quantification of expressed proteins has proven particularly powerful in this regard. Large-scale protein identification is usually achieved by automated liquid chromatography-tandem mass spectrometry of complex peptide mixtures and sequence database searching of the resulting spectra [Aebersold and Goodlett, Chem. Rev. 2001, 101, 269-295]. As generating large numbers of sequence-specific mass spectra (collision-induced dissociation/CID) spectra has become a routine operation, research has shifted from the generation of sequence database search results to their validation. Here we describe in detail a novel probabilistic model and score function that ranks the quality of the match between tandem mass spectral data and a peptide sequence in a database. We document the performance of the algorithm on a reference data set and in comparison with another sequence database search tool. The software is publicly available for use and evaluation at http://www.systemsbiology.org/research/ software/proteomics/ProbID.

Systems medicine and integrated care to combat chronic noncommunicable diseases

Abstract: We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.