Publication in 2022

  • FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis, Briefings in Bioinformatics, 2022, DOI: 10.1093/bib/bbac263, accepted


    Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets,especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

  • A network-based method for drug target discovery of complex diseases and identification of new targets for Alzheimer’s disease, submitted, 2022


    Drug target discovery is the basis of modern drug development. With accumulating disease data, a challenge arises that is how to integrate multiple data into better drug target discovery. In this work, we formulated a new theoretical hypothesis about network perturbation, which describes the disease relevance of network nodes as (1) positively correlated with the number of connected disease genes; and (2) negatively correlated with the network distance to other disease genes. Using this theory, a computational method was proposed to predict drug targets for complex diseases. This method can integrate transcriptomic data, clinical information, genetic findings, and other disease information into network analysis so as to build a disease-specific weighted network. The potential drug targets were predicted by ranking the nodes according to their abilities to regulate the network of disease genes. Applying this method to Alzheimer’s disease (AD) data, we identified two new drug targets, including a general target of HDAC1 and a precise target of ABI2. Bioinformatics analyses and experimental studies confirmed their involvement in AD development, supporting the good performance of computational prediction.

  • Regulatory degeneration contributes to AD development and supports HDAC1 as a novel drug target, submitted, 2022


    Epigenetic drugs are gaining particular interest as a potential therapy to treat Alzheimer’s disease (AD). However, it is less clear how epigenetic drugs affect complex AD causal mechanisms. In this work, we studied the diversity of AD patients by performing regulatory divergence analysis of large-scale AD brain RNA-seq data. We reported a widespread existence of regulatory degeneration among AD brain samples, where transcription factor (TF) mediated regulations were weakened or lost during AD development. Regulatory degeneration leads to detrimental clinical outcomes mainly by affecting protein degradation, neuroinflammation and mitochondria function. Integrative analysis suggested that epigenetic dysregulation may contribute to such a process, and HDAC1 was a potential participator of regulatory degeneration. Overall, our findings revealed a potential mechanism for epigenetic drugs to treat AD, and proposed HDAC1 as a new drug target.