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.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.

Abstract: A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Host Susceptibility and Clinical Outcomes in Toll-like Receptor 5–Deficient Patients with Typhoid Fever in Vietnam

Abstract: Toll-like receptor 5 (TLR5) mediates innate immune responses to bacterial pathogens by binding to flagellin. A polymorphism in the TLR5 gene introduces a premature stop codon (TLR5(392STOP)) that is associated with susceptibility to legionnaires disease. Here we investigated whether TLR5(392STOP) was associated with typhoid fever. The frequency of TLR5(392STOP) was not significantly different in 565 patients with typhoid fever and 281 ethnically matched control subjects. Furthermore, TLR5 deficiency had no measurable effect on a number of clinical parameters associated with typhoid fever, including fever clearance time, pathogen burden, disease severity, or age at acquisition of disease. TLR5 may not play an important role in TLR-stimulated innate immune responses to human infection with Salmonella enterica serovar Typhi. Initiation of these responses may rely on other TLRs that recognize different bacterial ligands.

A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

Abstract: Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.

Binding specificity of Toll-like receptor cytoplasmic domains.

Abstract: MyD88 participates in signal transduction by binding to the cytoplasmic Toll/IL-1 receptor (TIR) domains of activated Toll-like receptors (TLR). Yeast two-hybrid experiments reveal that the TIR domains of human TLR differ in their ability to associate with MyD88: The TIR of TLR2 binds to MyD88 but the TIR of the closely related TLR1, 6, or 10 do not. Using chimeric TIR domains, we define the critical region responsible for differential MyD88 binding, and use a computational analysis of the critical region to reveal the amino acids that differ between MyD88 binders and non-binders. Remarkably, a single missense mutation created in TLR1 (N672D) confers on it the ability to bind MyD88, without affecting its association with other proteins. Mutations identified as critical for MyD88 binding also affect signaling of TLR pairs in mammalian cells. To investigate the difference between MyD88 binders and non-binders, we identify novel interacting proteins for each cytoplasmic domain of TLR1, 2, 6, and 10. For example, heat shock protein (HSP)60 binds to TLR1 but not to TLR2, and HSP60 and MyD88 appear to bind the same region of the TIR domain. In summary, interactions between the TLR, MyD88, and novel associated proteins have been characterized.