Biology subjects

HUANG, X.

Publications and source records attributed to HUANG, X..

6 recordsLinked to original sources

bioRxiv2026

A brain-tumour axis links chronic stress to gastric cancer progression through NPY signalling

Chronic stress promotes gastric tumour growth, but the central neural pathways remain largely enigmatic. Here, we identify a brain-tumour axis, implicating the central amygdala (CeA) activity in promoting gastric cancer growth through sympathetic neuropeptide Y (NPY) signalling occurring within the tumour. Using clinical datasets, patient samples, pathway tracing, electrophysiology and chemogenetic manipulation, we demonstrate that stress selectively increases NPY Y1 receptor (NPY1R) synthesis in the patients gastric cancer tissue; moreover, the NPY1R density correlates with advanced cancer stage and poor prognosis. In mice, daily constraint chronic stress enhances CeA excitability and promotes sympathetic neurotransmitter norepinephrine release within the tumour microenvironment. Norepinephrine, in turn, increases tumour endogenous NPY/NPY1R production and the activation of downstream MAPK/Erk1/2 signalling pathway, which drives cancer cell proliferation and invasion. Manipulating CeA neuronal activity alone can regulate tumour growth, whereas selectively blocking tumour NPY1R prevents stress-induced tumour progression in vivo and cancer cell migration in vitro. This study identifies a central-to-peripheral circuit linking chronic stress to gastric cancer growth, and highlights NPY1R as a potential therapeutic target.

Cancer Biology↗

bioRxiv2025

Accurate prediction of protein stability changes from single mutations using self-distillation and antisymmetric constraint strategies

Computational approaches for accurately predicting protein stability changes upon residue mutations are crucial for protein engineering and design. Sequence-based methods are easier to apply to large-scale proteins since they do not rely on high-quantity structures. However, existing sequence-based approaches struggle to capture structural changes, resulting in lower performance compared to structure-based methods. In this study, we propose DPStab, a sequence-based deep learning solution that accurately predicts protein stability changes upon single residue mutations. DPStab transfers a protein large language model as a core component and incorporates a cross-attention mechanism to capture the contact changes around mutated positions for {Delta}{Delta}G and {Delta}Tm prediction. To address data imbalance and the antisymmetric nature of mutation effects, DPStab employs a self-distillation inference strategy under the supervision of an antisymmetric constraint. Benchmarking demonstrates that DPStab achieves state-of-the-art performance in both {Delta}{Delta}G and {Delta}Tm prediction. Practical evaluations confirm DPStabs capability in accurately ranking protein stability on large-scale datasets and effectively identifying critical structural contacts impacting stability. More experiments on extensive cDNA display proteolysis data demonstrate the significant contributions of self-distillation and antisymmetric constraint strategies. Significance StatementSingle amino acid mutations significantly influence protein stability, thereby affecting biological function and potential therapeutic uses. Accurately predicting how mutations affect protein stability is fundamental to protein engineering and therapeutic design. However, current sequence-based computational methods fail to capture the structural context changes around mutated residues. To overcome this, we propose DPStab, a sequence-based deep learning approach that combines a protein language model and a cross-attention mechanism with self-distillation and antisymmetric strategies. DPStab effectively captures residue contact changes and predicts stability changes without structural data. Sufficient experiments demonstrate that DPStab significantly outperforms existing methods, providing a fast and practical tool for enhancing protein engineering and biomedical research.

bioinformatics↗

bioRxiv2025

Novel Strategy against AML by Rescuing Loss of Phase Separation of Pathogenic NPM1c

Nucleophosmin (NPM1) is a nucleolar protein that participates in many biological processes through phase separation. In acute myeloid leukemia (AML), nearly one-third of cases harbor NPM1c mutations. Previous studies mainly focused on the contribution of the additional NES in NPM1c mislocalization and AML development; however, the mechanisms underlying the relationship between aberrant localization and phase separation of NPM1c remain poorly understood. In this study, we demonstrate that loss of phase separation (LoPS) with RNA is associated with NPM1c mislocalization, which uncovers a new strategy for therapeutic targeting of NPM1c. Leveraging high-throughput screening, we identify a potent small molecule capable of rescuing NPM1c LoPS in vitro and effectively restoring the nucleolar localization of NPM1c within cells. Remarkably, this compound demonstrates anti-leukemia activity in both AML cell lines and patient-derived blasts. Overall, these findings indicate a promising avenue for developing new AML therapies against NPM1c by reinstating its phase separation capabilities. One-Sentence SummaryRescuing the lost phase separation capacity of NPM1c unveils a promising AML therapy.

cancer biology↗

bioRxiv2024

Chemically Induced Nuclear Pore Complex Protein Degradation via E3 Ligase TRIM21

Despite the exciting progress of the bifunctional degrader molecules, also known as proteolysis-targeting chimeras (PROTACs), the rapidly expanding field is still significantly hampered by the lacking of available E3 ligase ligands. Our research bridges this gap by uncovering a series of small-molecule ligands to the E3 ligase TRIM21 through DNA-Encoded Library (DEL) technology. We confirmed their interaction with TRIM21 using crystallography and demonstrated their anti-proliferative effects across various cancer cell types. Furthermore, proteomic studies identified that the mRNA Export Factor GLE1, and the Nuclear Pore Complex Protein NUP155, were significantly down-regulated upon TRIM21 ligand treatment. This degradation required TRIM21 and was ubiquitin-proteasome-dependent. More specifically, NUP155 was the primary target for the TRIM21 ligands, while GLE1 was considered a passenger target upon the initial degradation of NUP155. Using immunofluorescence techniques, we further demonstrated that the degradation of GLE1 and NUP155 proteins impaired the integrity of the nuclear envelope, leading to cell death. Highlighted by this research, a novel mode of action has been discovered for the TRIM21 E3 ligase ligand, acting as a monovalent degrader that triggers de novo interaction with functional complex proteins and induces their degradation.

cell biology↗

bioRxiv2024

Hist2Cell: Deciphering Fine-grained Cellular Architectures from Histology Images

Histology images, with low cost, are unleashing great power of predicting cellular phenotypes in tissue, thanks to the emerging spatial transcriptomics serving as annotations. Recent efforts aimed to predict individual gene expression, suffering from low accuracy and high variability, while no methods are tailored to predict fine-grained transcriptional cell types -the most critical phenotype. Here, we present Hist2Cell, a Vision Graph-Transformer framework, to accurately resolve fine-grained transcriptional cell types (up to 40 cell types) directly from histology images and further create cellular maps of diverse tissues at a customizable resolution. Specifically, trained on human lung and breast cancer spatial transcriptome datasets, Hist2Cell accurately predicts the abundance of each cell type across space in new patient samples with Pearson Correlation Coefficient of biological informative cell types over 0.80, and effectively capturing their colocalization directly from histology images. Moreover, without re-training, it robustly generalizes to large-scale histology cancer cohorts from TCGA, highlighting recurrent cell co-localization and supporting precise survival prediction by revealing distinct tissue micro-environments and insightful cell type-patient mortality relationship. Therefore, Hist2Cell enables cost-efficient histology analysis for large-scale studies of spatial biology and precise cancer prognosis in real-world clinical diagnostics.

bioengineering↗

bioRxiv2019

The Clinical Significance and Biological Function of DPEP1 in B-cell Acute Lymphoblastic Leukemia

Dehydropeptidase-1 (DPEP1) is a zinc-dependent metalloproteinase abnormally expressed in many cancers. However, its potential role in adults with B-cell acute lymphoblastic leukaemia (ALL) is unknown.\n\nWe found that in adults with common-B-cell ALL high DPEP1 transcript levels at diagnosis was independently-associated with an increased CIR and worse RFS compared with subjects with low transcript levels. We show an increased proliferation and pro-survival role of DPEP1 in B-cell ALL cells via regulation of phosphCREB and p53 which may be the biological basis of the clinical correlation we report. Our data implicate DPEP1 expression in the biology of common B-cell ALL in adults. We report clinical correlates and provide a potential biological basis for these correlations. If confirmed, analyzing DPEP1 transcript levels at diagnosis could help predict therapy-outcomes. Moreover, regulation of DPEP1 expression could be a therapy target in B-cell ALL.

molecular biology↗