Biology subjects

Hu, P.-W.

Publications and source records attributed to Hu, P.-W..

4 recordsLinked to original sources

bioRxiv2022

A geometric deep learning framework for drug repositioning over heterogeneous information networks

The effectiveness of computational drug repositioning techniques has been further improved due to the development of artificial intelligence technology. However, most of the existing approaches fall short of taking into account the non-Euclidean nature of biomedical data. To overcome this problem, we propose a geometric deep learning (GDL) framework, namely DDAGDL, to predict drug-disease associations (DDAs) on heterogeneous information networks (HINs). DDAGDL can take advantage of complicated biological information to learn the feature representations of drugs and diseases by ingeniously projecting drugs and diseases including geometric prior knowledge of network structure in a non-Euclidean domain onto a latent feature space. Experimental results show that DDAGDL is able to identify high-quality candidates for Alzheimers disease (AD) and Breast neoplasms (BN) that have already been reported by previously published studies, and some of them are not even identified by comparing models.

bioinformatics↗

bioRxiv2022

An improved graph representation learning method for drug-target interaction prediction over heterogeneous biological information graph

The prediction task of the relationships between drugs and targets plays a significant role in the process of new drug discovery. Computational-based strategies for predicting drug-target interactions (DTIs) are regarded as a high-efficiency way. Moreover, recent studies adopted a graph neural network (GNN) to discover underlying DTIs and achieved better performance. Although these inductive methods can straightway learn biomolecules latent representations, they have an over-smoothing phenomenon in the course of obtaining the rich neighborhood information of each node in the biological information network, which further leads to a consistent feature representation of each node. To address the above issues, a novel model, called iGRLDTI, is proposed to precisely identify new DTIs based on an improved graph representation learning strategy. Specifically, iGRLDTI first constructs a biological information graph (BIG) by calculating the biological knowledge of drugs and targets with the relationships between them. Then, an improved graph representation learning strategy is designed to capture the enriched feature representations of drugs and targets. Finally, the Gradient Boosting Decision Tree classifier is applied to predict potential DTIs. Experimental results demonstrate that iGRLDTI yields better performance by comparing it with other state-of-the-art models on the benchmark dataset. Besides, our case studies denote that iGRLDTI can successfully identify unknown DTIs according to the improved feature representations of drugs and targets.

bioinformatics↗

bioRxiv2021

Lysosome Lipid Signaling from the Periphery to Neurons Regulates Longevity

Lysosomes are key cellular organelles that metabolize extra- and intracellular substrates. Alterations in lysosomal metabolism are implicated in aging-associated metabolic and neurodegenerative diseases. However, how lysosomal metabolism actively coordinates the metabolic and nervous systems to regulate aging remains unclear. Here, we report a fat-to-neuron lipid signaling pathway induced by lysosomal metabolism and its longevity promoting role in Caenorhabditis elegans. We discovered that lysosomal lipolysis in peripheral fat storage tissue up-regulates the neuropeptide signaling pathway in the nervous system to promote longevity. This cell-non-autonomous regulation requires the secretion from the fat storage tissue of a lipid chaperone protein LBP-3 and polyunsaturated fatty acids (PUFAs). LBP-3 binds to specific PUFAs, and acts through a nuclear hormone receptor NHR-49 and neuropeptide NLP-11 in neurons to extend lifespan. Together, these results reveal lysosomes as a signaling hub to coordinate metabolism and aging, and a lysosomal signaling mechanism that mediates intertissue communication to promote longevity.

cell biology↗

bioRxiv2020

Lysosomal nucleotide metabolism regulates ER proteostasis through mTOR signaling

Organelles in eukaryotic cells are compartmentalized to carry out different functions, and require specific mechanisms governing their coordination. Two of these organelles, the lysosome and the endoplasmic reticulum (ER), play crucial roles in regulating cellular homeostasis and organismal health. The lysosome contains various enzymes devoted to the hydrolysis of specific substrates, and dysfunctions of these hydrolases and their related metabolic processes are implicated in many diseases 1. On the other hand, ER is essential for protein synthesis and utilizes quality control mechanisms to maintain proteostasis. The metabolic status of the lysosome is now known to actively influence nuclear transcription and mitochondrial signaling 2-4. However, whether and how mechanistically lysosomal metabolic activities regulate ER proteostasis remain unclear. Here, we reported that RSH-1, the Caenorhabditis elegans RelA/SpoT Homolog (RSH) protein, carries a lysosomal NADPH phosphatase activity and acts through the mammalian/mechanistic target of rapamycin (mTOR) signaling to regulate ER proteostasis. We discovered that RSH-1 is localized to the lysosome. Its mutation reduces NADPH hydrolysis by the lysosome, leading to a protection against ER stress-induced lethality. We further revealed that this ER stress tolerance requires lysosomal vacuolar-type H+-ATPase (v-ATPase), and Rag GTPases and ribosomal protein S6 kinase (S6K) in the mTOR signaling pathway. Through transcriptome analysis, we discovered that the S6K activation by the RSH-1 mutation increases the basal expression of X-box binding protein 1 (XBP-1) and peptidyl-prolyl cis-trans isomerases, which is necessary for improving ER proteostasis. Moreover, we demonstrated the lysosomal localization of RSH-1 mammalian homolog and its conserved role in regulating mTOR signaling. Together, our findings reveal the key role of a specific lysosomal nucleotide hydrolase in regulating organellar coordination and also signaling mechanisms underlying this active regulation, and suggest that diseases resulted from deficiency in lysosomal hydrolases may be attributed to the distortion of signal transduction and organelle homeostasis.

cell biology↗