Biology subjects

Nan, Y.

Publications and source records attributed to Nan, Y..

2 recordsLinked to original sources

bioRxiv2022

DLGAP1-AS2-Mediated Phosphatidic Acid Synthesis Confers Chemoresistance via Activation of YAP Signaling

Squamous cell carcinomas (SCCs) constitute a group of human malignancies that originate from the squamous epithelium. Most SCC patients experience treatment failure and relapse and have a poor prognosis due to de novo and acquired resistance to first-line chemotherapeutic agents. To identify chemoresistance mechanisms and explore novel chemosensitizer targets, we performed whole-transcriptome sequencing of paired resistant/parental SCC cells. We identified DLGAP1 antisense RNA 2 (D-AS2) as a crucial noncoding RNA that contributes to chemoresistance in SCC. Mechanistically, D-AS2 associates with histones to regulate the distal elements of FAM3 metabolism regulating signaling molecule D (FAM3D) and reduces extracellular FAM3D protein secretion. FAM3D interacts with Gi-coupled G protein-coupled receptor (GPCR) formyl peptide receptor (FPR) 1 and FPR2 to suppress phospholipase D (PLD) activity; thus, reduced FAM3D activates PLD signaling. Moreover, activated PLD promotes phosphatidic acid (PA) production and subsequent yes-associated protein (YAP) nuclear translocation. Accordingly, in vivo administration of a D-AS2-targeting antisense oligonucleotide sensitizes SCC to cisplatin treatment. In summary, our study reveals that D-AS2/FAM3D-mediated PLD/PA lipid signaling is essential in SCC chemoresistance and that D-AS2 can be targeted to sensitize SCC to cytotoxic chemotherapeutic agents. SignificanceThis study identifies D-AS2 as a targetable lipid-related lncRNA that activates YAP signaling via PLD/PA axis to trigger chemoresistance in SCC.

cancer biology↗

bioRxiv2021

Combining Multi-Dimensional Molecular Fingerprints to Predict hERG Cardiotoxicity of Compounds

At present, drug toxicity has become a critical problem with heavy medical and economic burdens. acLQTS (acquired Long QT Syndrome) is acquired cardiac ion channel disease caused by drugs blocking the hERG channel. Therefore, it is necessary to avoid cardiotoxicity in the drug design and computer models have been widely used to fix this plight. In this study, we present a molecular fingerprint based on the molecular dynamic simulation and uses it combined with other molecular fingerprints (multi-dimensional molecular fingerprints) to predict hERG cardiotoxicity of compounds. 203 compounds with hERG inhibitory activity (pIC50) were retrieved from a previous study and predicting models were established using four machine learning algorithms based on the single and multi-dimensional molecular fingerprints. Results showed that MDFP has the potential to be an alternative to traditional molecular fingerprints and the combination of MDFP and traditional molecular fingerprints can achieve higher prediction accuracy. Meanwhile, the accuracy of the best model, which was generated by consensus of four algorithms with multi-dimensional molecular fingerprints, was 0.694 (RMSE) in the test dataset. Besides, the number of hydrogen bonds from MDFP has been determined as a critical factor in the predicting models, followed by rgyr and sasa. Our findings provide a new sight of MDFP and multi-dimensional molecular fingerprints in building models of hERG cardiotoxicity prediction.

pharmacology and toxicology↗