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.

Key questions for modelling COVID-19 exit strategies.

Abstract: Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Aspirin-triggered proresolving mediators stimulate resolution in cancer

Abstract: Inflammation in the tumor microenvironment is a strong promoter of tumor growth. Substantial epidemiologic evidence suggests that aspirin, which suppresses inflammation, reduces the risk of cancer. The mechanism by which aspirin inhibits cancer has remained unclear, and toxicity has limited its clinical use. Aspirin not only blocks the biosynthesis of prostaglandins, but also stimulates the endogenous production of anti-inflammatory and proresolving mediators termed aspirin-triggered specialized proresolving mediators (AT-SPMs), such as aspirin-triggered resolvins (AT-RvDs) and lipoxins (AT-LXs). Using genetic and pharmacologic manipulation of a proresolving receptor, we demonstrate that AT-RvDs mediate the antitumor activity of aspirin. Moreover, treatment of mice with AT-RvDs (e.g., AT-RvD1 and AT-RvD3) or AT-LXA4 inhibited primary tumor growth by enhancing macrophage phagocytosis of tumor cell debris and counter-regulating macrophage-secreted proinflammatory cytokines, including migration inhibitory factor, plasminogen activator inhibitor-1, and C-C motif chemokine ligand 2/monocyte chemoattractant protein 1. Thus, the pro-resolution activity of AT-resolvins and AT-lipoxins may explain some of aspirin's broad anticancer activity. These AT-SPMs are active at considerably lower concentrations than aspirin, and thus may provide a nontoxic approach to harnessing aspirin's anticancer activity.

Pathway discovery using transcriptomic profiles in adult-onset severe asthma

Abstract: Background Adult-onset severe asthma is characterized by highly symptomatic disease despite high-intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease is needed for the development of targeted treatments. Gene set variation analysis is a statistical technique used to identify gene profiles in heterogeneous samples. Objective We sought to identify gene profiles associated with adult-onset severe asthma. Methods This was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at age ≥18 years) as compared with childhood-onset severe asthma ( Results Significant differentially enriched gene signatures in patients with adult-onset as compared with childhood-onset severe asthma were identified in nasal brushings (5 signatures), sputum (3 signatures), and endobronchial brushings (6 signatures). Signatures associated with eosinophilic airway inflammation, mast cells, and group 3 innate lymphoid cells were more enriched in adult-onset severe asthma, whereas signatures associated with induced lung injury were less enriched in adult-onset severe asthma. Conclusions Adult-onset severe asthma is characterized by inflammatory pathways involving eosinophils, mast cells, and group 3 innate lymphoid cells. These pathways could represent useful targets for the treatment of adult-onset severe asthma.

Comparison of Pancreas Juice Proteins from Cancer Versus Pancreatitis Using Quantitative Proteomic Analysis

Abstract: Pancreatic cancer is a highly lethal disease.1,2 The death rate nearly matches the incidence because the diagnosis usually occurs late, after metastases have occurred, and the only chance for a cure (ie, surgical excision) has been eliminated. The problem of early diagnosis is complicated by the obscure location of the pancreas, the absence of reliable symptoms, and the insensitivity and expense of current tests. Better methods of detecting early stages of cancer or precancerous lesions are needed. In the efforts to develop biomarkers for the early detection of pancreatic cancer, one of the problems is the false-positive involvement of pancreatitis patients. Pancreatitis is an inflammatory condition of the pancreas that shares many molecular features with pancreatic cancer. Thus, biomarkers present in the setting of pancreatic cancer frequently occur in pancreatitis, providing an unacceptably low level of specificity for screening. It is therefore important to understand the proteins that underlie pancreatitis, as they could be a source of false-positive biomarkers for pancreatic cancer. Moreover, chronic pancreatitis is risk factor for eventual neoplastic progression; thus, understanding the proteins involved in both diseases may yield some insights into the mechanisms that link these events. Recently, there has been substantial interest in applying proteomic methods for the discovery of new targets for therapeutics and new biomarkers for diagnosis and early detection.3 In particular, quantitative proteomics has enabled researchers to use a combination of biochemistry, biology, and bioinformatics to detect proteins that are differentially expressed in cancer. In pancreatic cancer, recent studies using proteomics approach have focused on pancreatic cancer tissues.4–6 However, from a biomarker’s standpoint, pancreatic juice is an excellent starting specimen for the identification of protein biomarkers. Pancreatic juice is a rich source for cancer-specific proteins because the highly proliferative cancer cells are shed into the juice, as they undergo cellular turnover and degradation.7 Pancreatic juice was extensively studied in late 1970s and 1980s, primarily by early 2-dimensional electrophoresis analyses, which led to the discovery and description of several pancreatic enzymes.8–12 Recently, Gronborg and colleagues13 used a mass spectrometry-based proteomic approach for the analysis of pancreatic juice which used 1-dimensional electrophoresis and liquid chromatography (LC) tandem mass spectrometry (MS/MS). We previously used an isotope-coded affinity tag (ICAT)–based quantitative proteomic approach to identify and characterize potential biomarkers from pancreatic cancer juice.14 A total of 30 proteins were identified that exhibited greater than 2-fold abundance change in pancreatic cancer juice compared with normal pancreatic juice. Given the false-positive role of pancreatitis in pancreatic cancer, it is important to discover possible pancreatitis specific proteins that can be used to differentiate pancreatic cancer and pancreatitis. In addition, discovery of the proteins in pancreatitis could help identify proteins that might contribute to false-positive findings of pancreatic cancer. Isotope-coded affinity tag (ICAT) technology provides a comprehensive approach for quantitative proteomic analysis.15,16 This methodology demonstrates a significant improvement over gel-based methods in identifying low-abundant proteins, and it minimizes problems associated with solubility and extremes of pH.17 In this study, we used ICAT technology to perform comprehensive quantitative protein profiling of the pancreatitis juice. We performed the analyses by comparing pooled normal pancreatic juice with pancreatic juice from a chronic pancreatitis patient. Identification and quantification of the proteins from pancreatic juice were accomplished by differentially labeling the target proteins (pancreatitis) with heavy ICAT reagents and the normal comparator proteins with light ICAT reagents. The isotopically labeled proteins were then combined, purified, and followed by LC MS/MS analysis. Protein identification and quantification were then accomplished by using a suite of bioinformatics software. The proteomic analysis of pancreatitis juice was then compared with the analysis of pancreatic cancer juice.