Biomedical subjects

Feixiong Cheng

Publications and source records attributed to Feixiong Cheng.

3 recordsLinked to original sources

PubMed2026

CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons.

Mitochondrial dysfunction and chromatin dysregulation are interconnected contributors to neuronal vulnerability in Alzheimer's disease (AD), yet the molecular mechanisms linking these processes remain poorly understood. CHCHD10, a mitochondrial intermembrane space protein, has been implicated in neurodegenerative disorders, but its role in AD has not been defined. Here, we identify CHCHD10 as a previously unrecognized modulator of neuronal epigenomic stability in AD. Using direct fibroblast-to-neuron reprogramming, which preserves patient-specific epigenetic signatures, we show that AD neurons recapitulate genome-wide hypomethylation patterns observed in postmortem AD cortex. CHCHD10 expression is significantly reduced in AD neurons and across multiple human brain datasets, including single-cell and bulk RNA sequencing, proteomics, and human cortical tissue analyses. Restoration of CHCHD10 in AD neurons reduces amyloid-β and insoluble tau accumulation while reversing AD-associated differentially methylated regions across CpG islands, promoters, and regulatory elements. CHCHD10-responsive methylation changes overlap with those observed in human AD brain regions and colocalize with significant AD loci and cortex-specific eQTL loci, including MAPT and ABCA7. Finally, we identify KATNAL2 as a CHCHD10-responsive effector whose loss enhances tau phosphorylation and seeding, whereas its restoration mitigates tau pathology. Together, these findings support a CHCHD10-associated neuroprotective pathway linking mitochondrial dysfunction, epigenomic instability, and tau pathology in AD.

Humans↗

PubMed2026

Development and characterization of triazole-based WDR5 inhibitors for the treatment of glioblastoma.

Glioblastoma (GBM) cancer stem cells (CSCs) contribute to tumor recurrence, treatment resistance, and dismal clinical outcomes. Genetic and pharmacological evidence suggests that the nuclear scaffolding protein WD-repeat containing protein 5 (WDR5) is a therapeutic vulnerability of the CSC population. However, previously reported WDR5 inhibitors display low permeability and are unable to penetrate the blood-brain barrier (BBB), limiting their utility in GBM. Herein, we report the structure-guided development of a series of triazole-based WDR5 WIN-site inhibitors designed to increase passive brain penetration. We identified triazole-based WDR5 inhibitors that are potent, passively permeable, and in some cases more brain penetrant than other scaffolds. We phenotypically assessed our WDR5 inhibitors in a panel of patient-derived CSC models and uncovered unique WDR5-regulated metabolic genes in GBM. We also evaluated their antiproliferative activity against CSCs both in vitro and in vivo. Finally, to identify potential combination opportunities, we screened a 2,100-compound chemical probe library and identified that the ATAD2 inhibitor BAY-850 synergizes with WDR5 inhibitors to enhance CSC killing. Our work diversifies the chemical matter targeting WDR5, clarifies the in vitro consequences of WIN-site inhibition in CSCs, and encourages the future development of next-generation WDR5 inhibitors with the potential to achieve in vivo efficacy in the brain.

Humans↗

PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗