Biomedical subjects

Weiwei Chen

Publications and source records attributed to Weiwei Chen.

4 recordsLinked to original sources

PubMed2026

DipTRANS: an improved method for in planta transformation and genome engineering in Nicotiana benthamiana.

Plant transformation remains constrained by labor-intensive tissue culture. Our previous work showed that direct delivery of developmental regulators (DRs) can induce de novo meristems on plants, offering a promising transformation approach. In this resource article, we introduced DipTRANS (Direct in planta Transformation), an optimized, soil-based heritable transformation platform for Nicotiana benthamiana that bypasses sterile culture entirely. DipTRANS is built on DR-induced de novo meristem formation. After optimizing parameters, including regulator combinations, Agrobacterium strain, and infiltration density, DipTRANS yielded transformation efficiencies to 46.7%. Developmental abnormalities associated with regulator expression are resolved through cutting-based propagation and virus-induced transgene excision, enabling recovery of fertile, transgenic progeny. Furthermore, DipTRANS supports tissue culture-free, transgene-free iterative genome modification via virus-induced genome editing. Overall, DipTRANS enables the generation of transgenic plants within 30 days and engineered progeny within 90 days. This methodology provides a rapid, versatile platform and a blueprint for extending direct in planta transformation to other plant species.

DRs↗

PubMed2026

Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1+ migratory dendritic cell axis in breast cancer.

BACKGROUND: Tertiary lymphoid structures (TLS) are spatially organized immune niches associated with therapeutic response and favorable outcomes in breast cancer (BC). However, TLS assessment currently relies on invasive tissue-based analyses, and the biological mechanisms underlying imaging-based TLS prediction remain poorly understood. METHODS: We developed and validated a spatial heterogeneity-based radiomic TLS signature (shTLS) using dynamic contrast-enhanced MRI to non-invasively predict TLS status across multicenter BC cohorts. Spatial habitat radiomics were used to capture intratumoral and peritumoral immune-related heterogeneity. Integrated multi-omics analyses, including transcriptomics, pathomics, genomics, single-cell RNA sequencing, immunohistochemistry, and multiplex immunofluorescence, were performed to biologically interpret shTLS-defined subgroups. Functional drug-sensitivity assays were conducted to assess therapeutic implications. RESULTS: The shTLS model achieved robust predictive performance across independent cohorts and molecular subtypes. High shTLS scores were associated with immune-inflamed tumors characterized by spatially clustered activated T cells and dendritic cells (DCs). In contrast, shTLS-low tumors exhibited an immunosuppressive spatial niche with peripheral accumulation of CD4+ PD-1+ T cells and plasma cells, increased immune-tumor separation, and enhanced inflammatory and immunoregulatory signaling. An indoleamine 2,3-dioxygenase 1 (IDO1)-associated immunoregulatory program was observed in the shTLS-low tumors, which appeared to be preferentially expressed by LAMP3+CCR7+ migratory DCs. Pharmacologic inhibition of IDO1 enhanced chemotherapy and CDK4/6 inhibitor sensitivity in vitro. CONCLUSION: This study establishes spatial radiomics as a non-invasive approach to decode TLS-associated immune ecosystems and supports the presence of an IDO1-associated immunosuppressive phenotype, providing biological insight and translational rationale for patient stratification and future combination strategies.

Humans↗

PubMed2026

Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.

BACKGROUND: Over the past few years, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)- a compound from grilled or processed meats-has emerged as a major player in cancer development, especially colorectal cancer (CRC). This work dives into its potential links to CRC and uncovers the key genes that bridge this connection. METHODS: We tapped into various databases to pinpoint target genes tied to PhIP and CRC, then ran protein-protein interaction (PPI) analyses for visualization. Next, we explored underlying mechanisms through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. To nail down predictions, we tested 107 machine learning pipelines and picked the best one, validating its accuracy and the core genes' prognostic value across datasets. Next, molecular docking and dynamics simulations probed the interactions between these genes and PhIP. Finally, cell proliferation was assessed using Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays, and polymerase chain reaction (PCR) was performed to validate the expression levels of the hub genes. RESULTS: Our analysis identified 39 overlapping genes, from which a machine learning model (glmBoost + Enet) identified six candidate targets: CDK4, CEBPB, COMT, SOX9, TIMP1, and TOP2A. To prioritize these, a hierarchical screening framework was applied. Molecular docking and dynamics simulations identified CDK4, COMT, and TIMP1 as the most stable interactors with PhIP. Functional assays confirmed that PhIP treatment significantly enhanced the proliferation of CRC cells. Crucially, quantitative PCR (qPCR) validation in multiple CRC cell lines identified TIMP1 as the primary target, showing the most consistent and significant upregulation upon PhIP exposure. CONCLUSIONS: In essence, these genes drive PhIP is role in CRC, offering novel insights into its molecular pathways. This could reshape how we tackle food-related pollutants, paving the way for better prevention and targeted therapies.

Colorectal cancer (CRC)↗

PubMed2026

A Phosphoproteomic Platform Identifies Erythrocyte Membrane Protein Band 4.1-Like 3-Mediated Lipid Droplet Remodeling Linked to Liver Cancer Invasion and Migration.

Aberrant lipid metabolism is a hallmark of hepatocellular carcinoma (HCC), yet the regulatory mechanisms governing lipid droplet (LD) dynamics and their contribution to tumor progression remain poorly understood. Here, we developed an ultrasensitive phosphoproteomic platform using high-affinity HPDA@Ti4+ nanospheres to map LD-associated phosphorylation events across six HCC cell lines. By correlating phosphoproteomic signatures with LD morphology, we identified distinct regulatory signatures associated with LD size and abundance. Functional perturbation screens identified two distinct phosphoprotein modules controlling LD size: silencing SH3KBP1, SLK, EHD2, EPB41L3, and NEXN reduced LD size in Huh1 cells, whereas silencing CPD, BET1, UFL1, RRP1B, OGFR, and CD2BP2 enlarged LDs in Huh7 cells. Notably, we identified EPB41L3 as a critical metabolic-metastatic link; its loss decreased LD size and accelerated HCC migration and invasion, correlating with poor clinical prognosis. Crucially, we identified five key phosphorylation sites on EPB41L3 essential for its function; substituting these with alanine completely abolished its regulatory control over both LD size and HCC metastatic potential. Together, these findings delineate a phosphorylation-based regulatory network controlling the LD architecture and metastatic potential in HCC. Our study not only identifies potential therapeutic targets but also establishes a generalizable phosphoproteomic framework for interrogating lipid signaling in cancer metabolism.

Humans↗