Biology subjects

Fraser, I. D. C.

Publications and source records attributed to Fraser, I. D. C..

5 recordsLinked to original sources

bioRxiv2026

Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint

Toll-like receptor (TLR) signaling must be activated rapidly and then terminated to support host defense without sustained inflammation. We developed a rule-based model of mouse macrophage TLR4 signaling at the molecular-interaction level using measured protein copy numbers, RNA-seq-based abundance estimates, literature- and structure-informed reaction rates, and 979 dynamic experimental constraints. The trained model reproduced much of the TLR4-induced NF-{kappa}B and MAP kinase response but consistently failed to capture deactivation of MyD88, TRAF6-associated species, and IKK/{beta}. The recurrent model failure conveyed important biological information, localizing missing regulation to the proximal MyD88-IRAK-TRAF6 module and guiding experimental evaluation of IKK{varepsilon} and its scaffold TANK. Loss of IKK{varepsilon} enhanced transcriptional, cytokine, MAP kinase, and NF-{kappa}B responses to MyD88-specific TLR ligands. TANK deficiency produced a similar cellular phenotype and abolished stimulus-induced IKK{varepsilon} phosphorylation. Deficiency of either protein increased IRAK1 and TRAF6 ubiquitination without increasing MyD88 ubiquitination, placing the inhibitory checkpoint at or immediately downstream of the IRAK1-TRAF6 ubiquitin-signaling node. Overlapping but non-identical in vivo phenotypes further supported a shared regulatory axis with additional protein-specific functions. Our study presents a model-experiment discovery cycle where quantitative pathway discordance identifies missing biology and reveals a TANK-dependent IKK{varepsilon} checkpoint that restrains MyD88-driven inflammation.

systems biology↗

bioRxiv2020

High-throughput imaging of mRNA at the single-cell level in human primary immune cells

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

genomics↗

bioRxiv2020

TRIAGE: A web-based iterative analysis platform integrating pathway and network databases optimizes hit selection from high- throughput assays.

Comprehensive and efficient gene hit selection from high throughput assays remains a critical bottleneck in realizing the potential of genome-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs, and are often combined in an ad hoc manner. The specific limitations of these individual approaches, the lack of a systematic way by which to integrate their rankings, and the inaccessibility of complex computational approaches to many researchers, has contributed to unexpected variability and limited overlap in the reported results from comparable genome-wide studies. Using a set of three highly studied genome-wide datasets for HIV host factors that have been broadly cited for their limited number of shared candidates, we characterize the specific complementary contributions of commonly used analysis approaches and find an optimal framework by which to integrate these methods. We describe Throughput Ranking by Iterative Analysis of Genomic Enrichment (TRIAGE), an integrated, iterative approach which uses pathway and network statistical methods and publicly available databases to optimize gene prioritization. TRIAGE is accessible as a secure, rapid, user-friendly web-based application (https://triage.niaid.nih.gov). O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/204917v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@7150f8org.highwire.dtl.DTLVardef@17af411org.highwire.dtl.DTLVardef@12d8f01org.highwire.dtl.DTLVardef@78c48_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioinformatics↗

bioRxiv2019

IRAK1-mediated coincidence detection of microbial signals licenses inflammasome activation

The innate immune system signals through various higher order signaling complexes called supramolecular organizing centers (SMOCs), which typically organize components of a single pathway. While innate immune signaling pathways have been largely characterized using single receptor stimuli, responses to pathogens require the coordinated engagement of multiple pathways. Here, we report an IRAK1-containing SMOC formed specifically when multiple receptors are activated, which recruits select components of the TLR, MAPK and inflammasome pathways. This allows for signal flux redistribution from TLRs to inflammasomes and facilitates inflammasome licensing through an MKK7-JNK axis, which is defective in Irak1-/- mice. Furthermore, this defect in Irak1-/- mice manifests in increased susceptibility to inflammasome-sensitive pathogens and diminished IL1 production from inflammasomes after co-TLR priming. Thus, IRAK1 SMOCs form a multi-pathway coordinating hub for coincidence detection of microbial signals, which may be employed by innate immune cells as a threat assessment and thresholding mechanism for inflammasome activation.

immunology↗

bioRxiv2019

A sustained small increase in NOD1 expression promotes ligand-independent oncogenic activity

Small genetically-determined differences in transcription (eQTLs) are implicated in complex disease but the mechanisms by which small changes in gene expression impact complex disease are unknown. Here we show that a persistent small increase in expression of the innate sensor NOD1 precipitates large cancer-promoting changes in cell state. A ~1.2-1.4 fold increase in NOD1 protein concentration by loss of miR-15b/16 regulation sensitizes cells to ligand-induced inflammation, with an additional slight increase leading to ligand-independent NOD1 activation that is linked to poor prognosis in gastric cancer. Our data show that tight expression regulation of NOD1 prevents this sensor from exceeding a physiological switching checkpoint that promotes persistent inflammation and oncogene expression and reveal the impact of a single small quantitative change in cell state on cancer.\n\nOne Sentence SummaryA small change in NOD1 expression has a large cancer-promoting impact on cell state.

systems biology↗