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.

Mobile DNA in Old World Monkeys: A Glimpse Through the Rhesus Macaque Genome

Abstract: The completion of the draft sequence of the rhesus macaque genome allowed us to study the genomic composition and evolution of transposable elements in this representative of the Old World monkey lineage, a group of diverse primates closely related to humans. The L1 family of long interspersed elements appears to have evolved as a single lineage, and Alu elements have evolved into four currently active lineages. We also found evidence of elevated horizontal transmissions of retroviruses and the absence of DNA transposon activity in the Old World monkey lineage. In addition, approximately 100 precursors of composite SVA (short interspersed element, variable number of tandem repeat, and Alu) elements were identified, with the majority being shared by the common ancestor of humans and rhesus macaques. Mobile elements compose roughly 50% of primate genomes, and our findings illustrate their diversity and strong influence on genome evolution between closely related species.

Increased quantitative proteome coverage with 13C/12C‐based, acid‐cleavable isotope‐coded affinity tag reagent and modified data acquisition scheme

Abstract: Quantitative protein profiling using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS) enables the pair-wise comparison of protein expression levels in biological samples. A new version of the ICAT reagent with an acid-cleavable bond, which allows removal of the biotin moiety prior to MS and which utilizes (13)C substitution for (12)C in the heavy-ICAT reagent rather than (2)H (for (1)H) as in the original reagent, was investigated. We developed and validated an MS data acquisition strategy using this new reagent that results in an increased number of protein identifications per experiment, without losing the accuracy of protein quantification. This was achieved by following a single survey (precursor) ion scan and serial collision induced dissociations (CIDs) of four different precursor ions observed in the prior survey scan. This strategy is common to many high-performance liquid chromatography-electrospray ionization (HPLC-ESI)-MS shotgun proteomic strategies, but heretofore not to ICAT experiments. This advance is possible because the new ICAT reagent uses (13)C as the "heavy" element rather than (2)H, thus, eliminating the slight delay in retention time of ICAT-labeled "light" peptides on a C18-based HPLC separation that occurs with (2)H and (1)H. Analyses using this new scheme of an ICAT-labeled trypsin-digested six protein mixture as well as a tryptic digest of a total yeast lysate, indicated that about two times more proteins were identified in a single analysis, and that there was no loss in accuracy of quantification.

Mass spectrometry-based quantitative proteomic profiling.

Abstract: Quantitative proteomics involves the identification and quantitation of protein components in various biological systems. Stable isotope labelling technology, by both metabolic and chemical methods, has been the most commonly used approach for global proteome-wide profiling. Recently, its capability has been extended from labelled pairs to multiple labels, allowing for the simultaneous quantification of multiplex samples. The ion intensity-based quantitative approach has progressively gained more popularity as mass spectrometry performance has improved significantly. Although some success has been reported, it remains difficult comprehensively to characterise the global proteome, due to its enormous complexity and dynamic range. The use of sub-proteome fractionation techniques permits a simplification of the proteome and provides a practical step towards the ultimate dissection of the entire proteome. Further development of the technology for targeting sub-proteomes on a functional basis - such as selecting proteins with differential expression profiles from mass spectrometric analyses, for further mass spectrometric sequencing in an intelligent manner - is expected in the near future.

Participatory medicine: a driving force for revolutionizing healthcare.

Abstract: Healthcare is undergoing a profound revolution as a consequence of three contemporary thrusts: systems medicine [1-4], big data and patient involvement in their own health through social networks. This convergence is leading to a medicine that is predictive, preventive, personalized and participatory (P4) [4-7]. The first three Ps, predictive, preventive and personalized, were delineated in the early 2000s [1,2], whereas the fourth P, participatory, was added later. To achieve a participatory healthcare system, major technical and societal challenges will need to be overcome, and this will require close integration with systems medicine and big data. Before commenting further on participatory medicine, let us first delineate the essence of systems medicine and big data and their implications for P4 medicine. Systems medicine, the application of systems biology approaches to disease [3,4,6,8,9], is already changing how healthcare is practiced (Table 1). We predict that in 5 to 10 years each patient will be surrounded by a virtual cloud of billions of data points - molecular, clinical chemistries, cellular, organ, phenotypic, imaging, social networks, and so on. We also predict that we will have the analytical tools to reduce this big data cloud to simple models that will guide improvements in health and minimize disease. This requires addressing two grand challenges: the signal-to-noise issues intrinsic to big data and integration of multi-scale data into predictive models. Table 1 How systems medicine is transforming healthcare In addition, for each individual patient we foresee that we will be able to integrate an individual’s genetic, molecular, cellular, organ and social networks into an overall 'network of networks’ [10]. Disease leads to the perturbation of the network of networks and this alters the information the network manages. The availability of a personal data cloud will allow one to characterize an individual’s network of networks in its normal or disease-perturbed states. This altered, disease-perturbed information will provide deep insights into disease mechanisms, new approaches to diagnostics and therapeutics, and a platform for participatory medicine.

Quantitative proteomics identifies a Dab2/integrin module regulating cell migration

Abstract: Clathrin-associated endocytic adapters recruit cargoes to coated pits as a first step in endocytosis. We developed an unbiased quantitative proteomics approach to identify and quantify glycoprotein cargoes for an endocytic adapter, Dab2. Surface levels of integrins β1, α1, α2, and α3 but not α5 or αv chains were specifically increased on Dab2-deficient HeLa cells. Dab2 colocalizes with integrin β1 in coated pits that are dispersed over the cell surface, suggesting that it regulates bulk endocytosis of inactive integrins. Depletion of Dab2 inhibits cell migration and polarized movement of integrin β1 and vinculin to the leading edge. By manipulating intracellular and surface integrin β1 levels, we show that migration speed correlates with the intracellular integrin pool but not the surface level. Together, these results suggest that Dab2 internalizes integrins freely diffusing on the cell surface and that Dab2 regulates migration, perhaps by maintaining an internal pool of integrins that can be recycled to create new adhesions at the leading edge.