Ping Xie

The tumor necrosis factor receptor (TNF-R)-associated factor (TRAF) family of intracellular proteins were originally identified as signaling adaptors that bind directly to the cytoplasmic regions of receptors of the TNF-R superfamily. The past decade has witnessed rapid expansion of receptor families identified to employ TRAFs for signaling. These include Toll-like receptors (TLRs), NOD-like receptors (NLRs), RIG-I-like receptors (RLRs), T cell receptor, IL-1 receptor family, IL-17 receptors, IFN receptors and TGFβ receptors. In addition to their role as adaptor proteins, most TRAFs also act as E3 ubiquitin ligases to activate downstream signaling events. TRAF-dependent signaling pathways typically lead to the activation of nuclear factor-κBs (NF-κBs), mitogen-activated protein kinases (MAPKs), or interferon-regulatory factors (IRFs). Compelling evidence obtained from germ-line and cell-specific TRAF-deficient mice demonstrates that each TRAF plays indispensable and non-redundant physiological roles, regulating innate and adaptive immunity, embryonic development, tissue homeostasis, stress response, and bone metabolism. Notably, mounting evidence implicates TRAFs in the pathogenesis of human diseases such as cancers and autoimmune diseases, which has sparked new appreciation and interest in TRAF research. This review presents an overview of the current knowledge of TRAFs, with an emphasis on recent findings concerning TRAF molecules in signaling and in human diseases.

The ubiquitination mediated by ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) cascade is crucial to protein degradation, transcription regulation, and cell signaling in eukaryotic cells. The high specificity of ubiquitination is regulated by the interaction between E3 ubiquitin ligases and their target substrates. Unfortunately, the landscape of human E3-substrate network has not been systematically uncovered. Therefore, there is an urgent need to develop a high-throughput and efficient strategy to identify the E3-substrate interaction. To address this challenge, we develop a computational model based on multiple types of heterogeneous biological evidence to investigate the human E3-substrate interactions. Furthermore, we provide UbiBrowser as an integrated bioinformatics platform to predict and present the proteome-wide human E3-substrate interaction network ( http://ubibrowser.ncpsb.org ).Protein stability modulation by E3 ubiquitin ligases is an important layer of functional regulation, but screening for E3 ligase-substrate interactions is time-consuming and costly. Here, the authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.