Biology subjects

Wu, W. K. K.

Publications and source records attributed to Wu, W. K. K..

3 recordsLinked to original sources

bioRxiv2024

Functional and multi-omic aging rejuvenation with GLP-1R agonism

Identifying readily implementable methods that can effectively counteract aging is urgently needed for tackling age-related degenerative disorders. Here, we conducted functional assessments and deep molecular phenotyping in the aging mouse to demonstrate that glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment attenuates body-wide age-related changes. Apart from improvements in physical and cognitive performance, the age-counteracting effects are prominently evident at multiple omic levels. These span the transcriptomes and DNA methylomes of various tissues, organs and circulating white blood cells, as well as the plasma metabolome. Importantly, the beneficial effects are specific to aged mice, not young adults, and are achieved with a low dosage of GLP-1RA which has a negligible impact on food consumption and body weight. The molecular rejuvenation effects exhibit organ-specific characteristics, which are generally heavily dependent on hypothalamic GLP-1R. We benchmarked the GLP-1RA age-counteracting effects against those of mTOR inhibition, a well-established anti-aging intervention, observing a strong resemblance across the two strategies. Our findings have broad implications for understanding the mechanistic basis of the clinically observed pleiotropic effects of GLP-1RAs, the design of intervention trials for age-related diseases, and the development of anti-aging-based therapeutics.

systems biology↗

bioRxiv2024

BioXNet: a biologically inspired neural network for deciphering anti-cancer drug response in precision medicine

Accurate prediction of anti-cancer drug responses in preclinical and clinical studies is crucial for drug discovery and personalized medicine. While machine learning models have demonstrated promising prediction accuracy in this task, their translational value in cancer therapy is constrained by the lack of model interpretability and insufficient patients data with genomic profiles to calibrate models. The rich cell line data has the potential to supplement patients data, but the difference between the drug response mechanisms in cell lines and human body needs to be characterized quantitatively. To address these challenges, we proposed the BioXNet, which captures drug response mechanisms by seamlessly integrating drug target information with genomic profiles (genetic and epigenetic modifications) into a single biologically inspired neural network. BioXNet exhibited superior performance in drug response prediction tasks in both preclinical and clinical settings. An analysis of BioXNets interpretability revealed its ability to identify significant differences in drug response mechanisms between cell lines and the human body. Notably, the key factor of drug response is the drug targeting genes in cell lines but methylation modifications in the human body. Furthermore, we developed an online human-readable interface of BioXNet for drug response exploration by medical professionals and laymen. BioXNet represents a step further towards unifying drug, cell line and patients data under a holistic interpretable machine learning framework for precision medicine in cancer therapy.

cancer biology↗

bioRxiv2022

A Prism Vote Framework for Individualized Risk Prediction of Traits in Genome-wide Sequencing Data of Multiple Populations

Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is represented by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as the sample size, genetic heterogeneity, and population diversity increase. In two real genome-wide association data consists of multiple populations, we showed that the framework enhanced prediction accuracy of the linear mixed model by up to 12.1% in five-group cross validations. The proposed framework offers a new aspect to analyze individuals disease risk and improve accuracy for predicting complex traits in genome data.

genetics↗