Biology subjects

zeng, y.

Publications and source records attributed to zeng, y..

4 recordsLinked to original sources

bioRxiv2024

Ligand-independent c-Met activation by HHLA2 drives hepatocellular carcinoma and predicts c-MET inhibitor efficacy

The HGF/c-Met signaling pathway facilitates the initiation, progression, and metastasis of hepatocellular carcinoma (HCC). c-Met activation, however, is complex and not solely dependent on HGF, hindering targeted therapy development. This study identifies a critical oncogenic role for HHLA2, a B7 family member, in HCC and highlights its potential as a therapeutic target. We demonstrate that HHLA2 directly interacts with and activates c-Met through N-glycosylation, triggering sustained signaling and promoting aggressive HCC features. Mechanistically, we identified the pro-tumorigenic role of HHLA2 required downstream upregulation of MMP9 and VEGFA, both implicated in tumor progression. In multiple mouse models, HHLA2 overexpression accelerated tumor progression, metastasis, and reduced liver NK cell infiltration, all of which were reversed by c-Met inhibition. In a cohort of 176 HCC patients, HHLA2 expression strongly correlated with c-Met phosphorylation, advanced tumor stage, and poor prognosis. Importantly, HHLA2 expression predicted sensitivity to c-Met inhibitors in cell lines and patient-derived organoids and could be detected in patient serum, suggesting its potential as a prognostic biomarker. Collectively, our findings reveal an HHLA2-mediated mechanism of c-Met activation and provide a strong rationale for targeting the HHLA2-c-Met axis as a novel therapeutic strategy, with HHLA2 serving as a potential prognostic biomarker. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/622557v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@8b10dforg.highwire.dtl.DTLVardef@b43dorg.highwire.dtl.DTLVardef@394c4aorg.highwire.dtl.DTLVardef@1bfbc8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

A spatiotemporal steroidogenic regulatory network in human fetal adrenal glands and gonads

Human fetal adrenal glands produce substantial amounts of dehydroepiandrosterone (DHEA), which is one of the most important precursors of sex hormones. However, the underlying biological mechanism remains largely unknown. Herein, we sequenced human fetal adrenal glands and gonads from 7 to 14 GW via the 10x Genomics single-cell transcriptome techniques and reconstructed their location information by Spatial Transcriptome, conducted COOL-seq for the MC2R+ inner zone steroidogenic cells during the time window of sex differentiation (8-12GW). We found that relative to gonads, adrenal glands begin to synthesize steroids early. The coordination among steroidogenic cells and multiple nonsteroidogenic cells promotes adrenal cortex construction and steroid synthesis. Notably, during the time window of sex differentiation (8-12 GW), key enzyme gene expression shifts to accelerate DHEA synthesis in males and cortisol synthesis in females. Furthermore, high SST+ expressions in the adrenal gland and testis amplify androgen synthesis in males. Our research highlights the robustness of the action of fetal adrenal glands on gonads to modify the process of sexual differentiation.

systems biology↗

bioRxiv2020

Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Single-cell RNA sequencing (scRNA-seq) technologies promise to characterize the transcriptome of genes at cellular resolution, which shed light on unfolding cell heterogeneity and diversity. Fast-growing scRNA-seq profiles require efficient clustering algorithms to identify the same type of cells. Although many methods have been developed for cell clustering, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed GraphSCC, a robust graph artificial intelligence model to cluster single cells by accounting for structural relations between cells. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. The experimental results indicate that GraphSCC model outperforms state-of-the-art methods in terms of various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability.

bioinformatics↗

bioRxiv2020

Selective Germline Genome Edited Pig Meninges Grafts for the Abdominal Wall Closure in Damage Control Surgery

Reconstruction of abdominal wall defects is still a big challenge in surgery, especially where there is insufficient fascia muscular or excessive tension of the defects in emergency and life-threatening scenarios. Indeed, the concept of damage control surgery has been advanced in the management of both traumatic and nontraumatic surgical settings. The strategy requires abridged surgery and quick back to intensive care units (ICU) for aggressive resuscitation. In the damage control laparotomy, patients are left with open abdomen or provisional closure of the abdomen with a planned return to the operating room for definitive surgery. So far, various techniques have been utilized to achieve early temporary abdominal closure, but there is no clear consensus on the ideal method or material for abdominal wall reconstruction. We recently successfully created the selective germline genome-edited pig (SGGEP) and here we aimed to explore the feasibility of in vivo reconstruction of the abdominal wall in a rabbit model with SGGEP meninges grafts (SGGEP-MGs). Our result showed that the SGGEP-MGs could restore the integrity of the defect very well. After 7 weeks of engraftment, there was no sign of herniation observed, the grafts were re-vascularized, and the defects were well repaired. Histologically, the boundary between the graft and the host was very well integrated and there was no strong inflammatory response. Therefore, this kind of closure could help restore the fluid and electrolyte balance and to dampen systemic inflammatory response in damge control surgery while ADM graft failed to establish re-vascularization as the same as the SGGEP-MG. It is concluded that the meninges of SGGEP could serve as a high-quality alternative for restoring the integrity of the abdominal wall, especially for damage control surgery.

synthetic biology↗