Biology subjects

Zhang, Y. D.

Publications and source records attributed to Zhang, Y. D..

5 recordsLinked to original sources

bioRxiv2026

Leveraging Twin Information for Inference of Gene Regulatory Networks

Gene regulatory networks (GRNs) underlie maintenance of cellular phenotypes and responses to stimuli. Modern single-cell profiling methods offer high-throughput datasets to infer GRNs en masse but do not capture dynamic information. Cell-state heterogeneity further confounds correlation-based inference approaches. We addressed these challenges with TwINFER, a conceptual framework leveraging information from recently divided sister cells, or "twins," identifiable via recently developed barcoding techniques. We show that twin information discriminates regulatory from non-regulatory correlations and resolves interaction direction and type (activation/repression). We performed a diverse set of simulations, covering common network motifs and large-scale networks, where TwINFER outperforms state-of-the-art inference capabilities. Crucially, TwINFER resolved the commonly observed false positives in fan-out and feed-forward loop motifs where most methods perform poorly. Lastly, we applied TwINFER to a lineage-barcoded hematopoiesis dataset which refined the network inference, flagged multi-state genes, and determined causal relations. Our work exploits cellular twins as untapped information, readily complementing existing inference approaches.

systems biology↗

bioRxiv2025

Butyrate Rescues Cardiac Metabolic Dysfunction in Hypertensive Heart Failure with Preserved Ejection Fraction

Diastolic dysfunction in heart failure with preserved ejection fraction (HFpEF) is characterized by metabolic inflexibility. Unlike systolic heart failure, where ketone bodies support energy homeostasis, the failing heart in HFpEF lacks well-characterized alternative fuels to meet its high ATP demand. Here, we show that butyrate, a microbiota-derived short-chain fatty acid, serves as an ancillary energy source and improves diastolic function. Although cardiac power was preserved in rats with HFpEF, both experimental and human HFpEF hearts exhibited an impaired expression of proteins in mitochondrial electron transport chain and oxidative phosphorylation. Additionally, accumulation of 3-hydroxy-butyrate (BOH) in rat and also human HFpEF indicated that ketones do not rescue the cardiac energetic deficit. In HFpEF patients from the UK Biobank, higher BOH levels were associated with increased mortality, particularly those with hypertension. Applying 13C-butyrate to isolated perfused hearts with and without HFpEF resulted in isotope incorporation in butyryl-CoA and downstream TCA intermediates and thus proving its active metabolization. Butyrate was efficiently oxidized by cardiomyocytes and was overtaking BOH and amino acids in supporting respiration. Finaly, chronic butyrate supplementation improved survival, enhanced diastolic function, and reduced fibrosis and inflammation in HFpEF rats despite persistent hypertension. These findings identify butyrate as a compensatory fuel and a promising therapeutic candidate in energetically compromised HFpEF.

biochemistry↗

bioRxiv2025

A-TWAS: An aggregated transcriptome-wide association study model incorporating multiple Bayesian priors

MotivationTranscriptome-wide association study (TWAS) is a significant methodology utilized for identifying associations between genes and diseases by integrating transcriptome and genome-wide association studies (GWAS) data. The approach has been successful in pinpointing risk genes for various diseases, including Alzheimers disease, schizophrenia, and different types of cancers. TWAS typically involves two key steps: imputation and association analysis. In the imputation step, the original PrediXcan employs the elastic-net model, while subsequent research endeavors have delved into more intricate models such as FUSION and TIGAR. Despite these advancements, the existing individual methods may not capture the intricate genotype-expression relationships in a comprehensive way. Given the complexity of genetic contributions of genotypes to gene expression, sophisticated modeling techniques are imperative. In response to this, we have introduced Aggregated-TWAS (A-TWAS), a comprehensive tool that amalgamates multiple imputation models to accommodate diverse genetic architectures. A-TWAS utilizes several continuous shrinkage priors, including Laplace, Horseshoe, and Horseshoe+, to ensure precise imputation results while incorporating the benefits of Bayesian variable selection methods. In the association phase of TWAS, we employ the aggregated Cauchy association test (ACAT) to obtain an omnibus p-value. ResultWe demonstrate the effectiveness of A-TWAS through comprehensive simulations and real data analyses. Simulation studies highlight that A-TWAS produces substantial improvements in predictive R2 and statistical power, while maintaining the type error I at a low level. Furthermore, TWASs are conducted on schizophrenia and obsessive-compulsive symptoms datasets. In comparison to standard methods, the Bayesian imputation model demonstrates superior accuracy, and A-TWAS, by integrating multiple Bayesian priors, identifies a notably greater number of disease-relevant genes. Availability and ImplementationThe source code of A-TWAS is available at https://github.com/Yilan-Liang/A-TWAS.

bioinformatics↗

bioRxiv2022

Variable Selection via Grace-AKO with Applications in Genomics

MotivationVariable selection is a common statistical approach to identifying genes associated with clinical outcomes of scientific interest. There are thousands of genes in genomic studies, while only a limited number of individual samples are available. Therefore, it is important to develop a method to identify genes associated with outcomes of interest that can control finite-sample false discovery rate (FDR) in high-dimensional data settings. ResultsThis article proposes a novel method named Grace-AKO for graph-constrained estimation (Grace), which incorporates aggregation of multiple knockoffs (AKO) with the network-constrained penalty. Grace-AKO can control FDR in finite-sample settings and improve model stability simultaneously. Simulation studies show that Grace-AKO has better performance in finite-sample FDR control than the original Grace model. We apply Grace-AKO to the prostate cancer data in The Cancer Genome Atlas (TCGA) program by incorporating prostate-specific antigen (PSA) pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) as the prior information. Grace-AKO finally identifies 47 candidate genes associated with PSA level, and more than 75% of the detected genes can be validated. Availability and implementationWe developed an R package for Grace-AKO available at: https://github.com/mxxptian/GraceAKO Contactdoraz@hku.hk or zl2509@cumc.columbia.edu

bioinformatics↗

bioRxiv2022

Multiethnic Polygenic Risk Prediction in Diverse Populations through Transfer Learning

Polygenic risk scores (PRS) leverage the genetic contribution of an individuals genotype to a complex trait by estimating disease risk. Traditional PRS prediction methods are predominantly for European population. The accuracy of PRS prediction in non-European populations is diminished due to much smaller sample size of genome-wide association studies (GWAS). In this article, we introduced a novel method to construct PRS for non-European populations, abbreviated as TL-Multi, by conducting transfer learning framework to learn useful knowledge from European population to correct the bias for non-European populations. We considered non-European GWAS data as the target data and European GWAS data as the informative auxiliary data. TL-Multi borrows useful information from the auxiliary data to improve the learning accuracy of the target data while preserving the efficiency and accuracy. To demonstrate the practical applicability of the proposed method, we applied TL-Multi to predict the risk of systemic lupus erythematosus (SLE) in Asian population and the risk of asthma in Indian population by borrowing information from European population. TL-Multi achieved better prediction accuracy than the competing methods including Lassosum and meta-analysis in both simulations and real applications.

genetics↗