IFN-I secretion provides a rapid host defense against infection with RNA viruses. Within the host cell, viral RNA triggers the activation of the RIG-I signaling pathway, leading to the production of IFN-I. Because an exaggerated IFN-I response causes severe tissue damage, RIG-I signaling is tightly regulated. One of the factors that control the IFN-I response is the ubiquitin-like modifier FAT10, which is induced by TNF and IFNγ and targets covalently FAT10-linked proteins for proteasomal degradation. However, the mechanism of how FAT10 modulates IFN-I secretion remains to be fully elucidated. Here, we provide strong evidence that FAT10 is phosphorylated by IκB kinase β (IKKβ) upon TNF stimulation and during influenza A virus infection on several serine and threonine residues. FAT10 phosphorylation increases the binding of FAT10 to the TRAF3-deubiquitylase OTUB1 and its FAT10-mediated activation. Consequently, FAT10 phosphorylation results in a low ubiquitylation state of TRAF3, which is unable to maintain interferon regulatory factor 3 phosphorylation and downstream induction of IFN-I. Taken together, we reveal a mechanism of how phosphorylation of FAT10 limits the production of tissue-destructive IFN-I in inflammation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631552PMC
http://dx.doi.org/10.26508/lsa.202101282DOI Listing

Publication Analysis

Top Keywords

fat10
8
fat10 phosphorylated
8
ifn-i secretion
8
rig-i signaling
8
ifn-i response
8
fat10 phosphorylation
8
ifn-i
7
phosphorylated ikkβ
4
ikkβ inhibit
4
inhibit antiviral
4

Similar Publications

: FAT10 is a member of the ubiquitin-like modifier family. Similar to ubiquitin, FAT10 has a distinct enzyme cascade consisting of E1-activating, E2-conjugating, and possibly several E3-ligating enzymes, which will covalently link FAT10 to substrate proteins in order to target them directly for proteasomal degradation. FAT10 was reported to be phosphorylated by IKKβ during infection with influenza A virus.

View Article and Find Full Text PDF
Article Synopsis
  • Deep learning has emerged as a critical tool for analyzing medical images, but issues like limited data, high costs, and privacy concerns hinder progress; generative models offer solutions by creating new images.
  • This study focuses on enhancing detection of incidental vertebral compression fractures in chest CT scans from AUBMC, aiming to improve automated identification and ultimately patient outcomes.
  • Through the use of various generative models, particularly the VAE-GAN, and transfer learning, the research demonstrates that augmenting real datasets with synthetic images significantly boosts classification accuracy in identifying fractures.
View Article and Find Full Text PDF

Background: Defective ribosomal products (DRiPs) are non-functional proteins rapidly degraded during or after translation being an essential source for MHC class I ligands. DRiPs are characterized to derive from a substantial subset of nascent gene products that degrade more rapidly than their corresponding native retiree pool. So far, mass spectrometry analysis revealed that a large number of HLA class I peptides derive from DRiPs.

View Article and Find Full Text PDF

Objectives: Dysregulation of proteolysis underlies diseases like cancer. Protease inhibitors (PIs) regulate many biological functions and hence have potential anticancer properties. With this background, the current study aimed to identify the PI from natural sources such as plants and microbes against trypsin (a protease), which was assayed against casein, using an ultraviolet spectrophotometer-based methodology.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!