Biology subjects

Chang, Y.-S.

Publications and source records attributed to Chang, Y.-S..

3 recordsLinked to original sources

bioRxiv2026

HSPB8 regulates CTP synthase filaments to couple nucleotide metabolism and autophagy in tumors

Metabolic adaptation and proteostasis are essential for tumor survival under nutrient stress, yet how these processes are mechanistically integrated remains unclear. Here, we identify CTP synthase (CTPS) filament dynamics as a regulatory nexus linking nucleotide metabolism to autophagic flux. Under glutamine deprivation--a hallmark of poorly vascularized solid tumors--CTPS undergoes polymerization into filamentous assemblies that exhibit reduced enzymatic activity. We demonstrate that filament formation is driven by intracellular asparagine availability and stabilized by the accumulation of misfolded proteins. Using APEX2-based proximity labeling and proteomics, we identify the small heat shock protein HSPB8 as a filament-associated regulator. HSPB8, acting within the chaperone-assisted selective autophagy (CASA) pathway, antagonizes CTPS polymerization by promoting clearance of misfolded proteins, thereby restoring soluble, catalytically active CTPS. Filament disassembly increases CTP production and enhances synthesis of autophagy-related phospholipids, including phosphatidylinositol and phosphatidylethanolamine, resulting in accelerated autophagic flux. Cells expressing filament-deficient CTPS mutants display elevated autophagosome formation and increased LC3-II accumulation upon lysosomal blockade, confirming enhanced flux. In vivo, disruption of CTPS filament assembly impairs xenograft tumor growth and is associated with excessive autophagy. Analysis of TCGA datasets further reveals that high CTPS and low HSPB8 expression correlate with poor patient survival across multiple cancers. Collectively, our findings establish CTPS filament dynamics as a proteostasis-sensitive metabolic switch that coordinates nucleotide biosynthesis with autophagy, revealing a previously unrecognized vulnerability in tumors adapting to nutrient limitation.

cell biology↗

bioRxiv2023

De novo fatty-acid synthesis protects invariant NKT cells from cell death, thereby promoting their homeostasis and pathogenic roles in airway hyperresponsiveness

AbstractInvariant natural-killer T (iNKT) cells play pathogenic roles in allergic asthma in murine models and possibly also humans. While many studies show that the development and functions of innate and adaptive immune cells depend on their metabolic state, the evidence for this in iNKT cells is very limited. It is also not clear whether such metabolic regulation of iNKT cells could participate in their pathogenic activities in asthma. Here, we showed that acetyl-coA-carboxylase 1 (ACC1)-mediated de novo fatty-acid synthesis is required for the survival of iNKT cells and their deleterious functions in allergic asthma. ACC1, which is a key fatty-acid synthesis enzyme, was highly expressed by lung iNKT cells from WT mice that were developing asthma. Cd4-CreAcc1fl/fl mice failed to develop OVA-induced and HDM-induced asthma. Moreover, iNKT cell-deficient mice that were reconstituted with ACC1-deficient iNKT cells failed to develop asthma, unlike when WT iNKT cells were transferred. ACC1 deficiency in iNKT cells associated with reduced expression of fatty acid-binding proteins (FABPs) and peroxisome proliferator-activated receptor (PPAR){gamma}, but increased glycolytic capacity that promoted iNKT-cell death. Furthermore, circulating iNKT cells from allergic-asthma patients expressed higher ACC1 and PPARG levels than the corresponding cells from non-allergic-asthma patients and healthy individuals. Thus, de novo fatty-acid synthesis prevents iNKT-cell death via an ACC1-FABP-PPAR{gamma} axis, which contributes to their homeostasis and their pathogenic roles in allergic asthma.

immunology↗

bioRxiv2020

Forecasting symptomatic influenza A infection based on pre-exposure gene expression using a Deep Learning approach

Background and ObjectiveNot everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. MethodTo examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. ResultsThe results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu. ConclusionsDeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

bioinformatics↗