bioRxiv Science⌕ Search

Biology subjects

Lefaudeux, D.

Publications and source records attributed to Lefaudeux, D..

2 recordsLinked to original sources

A stimulus-contingent positive feedback loop enables IFN-β dose-dependent activation of pro-inflammatory genes

Type I interferons (IFN) induce powerful anti-viral and innate immune responses via the transcription factor, IFN-stimulated gene factor (ISGF3). However, in some pathological contexts type I IFNs are responsible for exacerbating inflammation. Here, we show that a high dose of IFN-{beta} also activates an inflammatory gene expression program in contrast to IFN-{lambda}3, a type III IFN, which elicits only the common anti-viral gene program. We show that the inflammatory gene program depends on a second, potentiated phase in ISGF3 activation. Iterating between mathematical modeling and experimental analysis we show that the ISGF3 activation network may engage a positive feedback loop with its subunits IRF9 and STAT2. This network motif mediates stimulus-specific ISGF3 dynamics that are dependent on ligand, dose, and duration of exposure, and when engaged activates the inflammatory gene expression program. Our results reveal a previously underappreciated dynamical control of the JAK-STAT/IRF signaling network that may produce distinct biological responses, and suggest that studies of type I IFN dysregulation, and in turn therapeutic remedies, may focus on feedback regulators within it. HIGHLIGHTSO_LIHigh dose IFN-{beta} activates a pro-inflammatory gene program in epithelial cells. C_LIO_LIIFN-{beta}, but not IFN-{lambda}3, induces a second, potentiated phase in ISGF3 activity. C_LIO_LIISGF3 induces its subunits to form a stimulus-contingent positive feedback loop. C_LIO_LIThe positive feedback motif is required for the pro-inflammatory gene program. C_LI

immunology↗

A Topological Data Analysis Network Model of Asthma Based on Blood Gene Expression Profiles

Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNAs 3 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

molecular biology↗