Browse Articles

C-terminal amides mark proteins for degradation via SCF–FBXO31

Abstract During normal cellular homeostasis, unfolded and mislocalized proteins are recognized and removed, preventing the build-up of toxic byproducts 1 . When protein homeostasis is perturbed during ageing, neurodegeneration or cellular stress, proteins can accumulate several forms of chemical damage through reactive metabolites 2,3 . Such modifications have been proposed to trigger the selective removal of chemically marked proteins 3–6 ; however, identifying modifications that are sufficient to induce protein degradation has remained challenging. Here, using a semi-synthetic chemical biology approach coupled to cellular assays, we found that C-terminal amide-bearing proteins (CTAPs) are rapidly cleared from human cells. A CRISPR screen identified FBXO31 as a reader of C-terminal amides. FBXO31 is a substrate receptor for the SKP1–CUL1–F-box protein (SCF) ubiquitin ligase SCF–FBXO31, which ubiquitylates CTAPs for subsequent proteasomal degradation. A conserved binding pocket enables FBXO31 to bind to almost any C-terminal peptide bearing an amide while retaining exquisite selectivity over non-modified clients. This mechanism facilitates binding and turnover of endogenous CTAPs that are formed after oxidative stress. A dominant human mutation found in neurodevelopmental disorders reverses CTAP recognition, such that non-amidated neosubstrates are now degraded and FBXO31 becomes markedly toxic. We propose that CTAPs may represent the vanguard of a largely unexplored class of modified amino acid degrons that could provide a general strategy for selective yet broad surveillance of chemically damaged proteins.

Signs of damage that drive protein degradation

NEJM at AHA — Routine Spironolactone in Acute Myocardial Infarction

Sotorasib plus Panitumumab in Refractory Colorectal Cancer with Mutated <i>KRAS</i> G12C

Maternal Anti-PF4 Antibodies as Cause of Neonatal Stroke

Microsatellite-Instability–High Metastatic Colorectal Cancer

Bat genomes illuminate adaptations to viral tolerance and disease resistance

Transcatheter Repair versus Mitral-Valve Surgery for Secondary Mitral Regurgitation

Colchicine in Acute Myocardial Infarction

Routine Spironolactone in Acute Myocardial Infarction

NEJM at ESMO — Phase 3 Trial of Cabozantinib in Advanced Neuroendocrine Tumors

Transcatheter Valve Repair in Heart Failure with Moderate to Severe Mitral Regurgitation

Tumoral Melanosis

My Mother’s Choices

Phase 3 Trial of Cabozantinib to Treat Advanced Neuroendocrine Tumors

Tirzepatide for Congenital Generalized Lipodystrophy

Marburg Virus Disease in Rwanda — Centering Both Evidence and Equity

Lung Transplantation

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT

Purpose This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver. Materials and methods The study retrospectively enrolled 840 individuals who underwent non-contrast abdominal CT and quantitative CT (QCT) examinations at the First Affiliated Hospital of Zhengzhou University from July 2022 to May 2023. Subsequently, these participants were divided into a training set (n = 539) and a testing set (n = 301) in a 9:5 ratio. The liver fat content measured by experienced radiologists using QCT technology served as the reference standard. The liver images from the non-contrast abdominal CT scans were then segmented as regions of interest (ROI) from which radiomics features were extracted. Two-dimensional (2D) and three-dimensional (3D) radiomics models, as well as 2D and 3D deep learning models, were developed, and machine learning models based on clinical data were constructed for the four-category diagnosis of fatty liver. The characteristic curves for each model were plotted, and area under the receiver operating characteristic curve (AUC) were calculated to assess their efficacy in the classification and diagnosis of fatty liver. Results A total of 840 participants were included (mean age 49.1 years ± 11.5 years [SD]; 581 males), of whom 610 (73%) had fatty liver. Among the patients with fatty liver, there were 302 with mild fatty liver (CT fat fraction of 5%–14%), 155 with moderate fatty liver (CT fat fraction of 14%–28%), and 153 with severe fatty liver (CT fat fraction &gt;28%). Among all models used for diagnosing fatty liver, the 2D radiomics model based on the random forest algorithm achieved the highest AUC (0.973), while the 2D radiomics model based on the Bagging decision tree algorithm showed the highest sensitivity (0.873), specificity (0.939), accuracy (0.864), precision (0.880), and F1 score (0.876). Conclusion A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. It was found that the 2D radiomics models based on the random forest and Bagging decision tree algorithms show high consistency with the QCT-based classification diagnosis of fatty liver used by experienced radiologists.

Pleural Paragonimiasis