Host-directed antivirals offer a promising strategy for addressing the challenge of viral resistance. Virus-host interactions often trigger stage-specific metabolic reprogramming in the host, and the causal links between these interactions and virus-induced metabolic changes provide valuable insights for identifying host targets. In this study, we present a workflow for repurposing host-directed antivirals using virus-induced protein networks. These networks capture the dynamic progression of viral infection by integrating host proteins directly interacting with the virus and enzymes associated with significantly altered metabolic fluxes, identified through dual-species genome-scale metabolic models. This approach reveals numerous hub nodes as potential host targets. As a case study, 50 approved drugs with potential anti-influenza virus A (IVA) activity were identified through eight stage-specific IVA-induced protein networks, each comprising 699-899 hub nodes. Lisinopril, saxagliptin, and gliclazide were further validated for anti-IVA efficacy in vitro through assays measuring the inhibition of cytopathic effects and viral titers in A549 cells infected with IVA PR8. This workflow paves the way for the rapid repurposing of host-directed antivirals.

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS: We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/hzauzqy/TransGEM.

Targeting at small satellite applications, we propose a lightweight and configurable circuit design for synchronization and demodulation, which are the key processing steps of PDSCH receiver, on FPGA. We design an elaborately pipelined circuit with balanced speed and area, by analysing the operation complexity and sharing resources among the operations. The circuit design is configurable such that the circuit can switch state at run-time to support multiple data rates and modulation schemes. Also, a parametric truncation module is realized to implement the circuit with fixed-point numbers. Experiments prove that the lightweight circuit can perform real-time processing of received data with good demodulation performance.

Due to the wide spread of multidrug-resistant tuberculosis, Mycobacterium tuberculosis (MTB) has once again become a serious public health threat. However, drug resistance mechanisms and the many loci related to drug resistance in MTB currently remain unclear. Similar to genome-wide association studies (GWASs), phenome-wide association scans (PheWAS) is a method for evaluating the association between whole phenotypes and a significant SNP. In this study, we identified S315N, S315T and R463L SNP sites within the katG gene that are related to INH-resistance as well as W191R and G169S SNPs within the katG gene related to ETH-resistance in MTB using the PheWAS method with the available data of Zhang et al. Compared with the GWAS results reported by Zhang et al., R463L and S315N were new sites of the INH prodrug identified by the PheWAS method. Moreover, S266R on gene ethA and A187V variation on mshA are also new sites that were found to be significantly associated with the resistance of ETH by our approach. The loci found by the PheWAS analysis method in this study had been experimentally estimated to be associated with drug resistance in MTB. These findings lend further credence to our PheWAS analysis method in SNP analysis and demonstrate the power of PheWAS in genetic disease loci identification.

Mycobacterium tuberculosis (MTB) and Staphylococcus aureus (STA) are very common and complicated diseases that infect both humans and animals. The emergence of multidrug-resistant bacterium represents an urgent need to identify new drug targets and therapeutic drugs. In this study, we propose a systems chemical biology method to identify targets of anti-bacterial agents. This method integrates gene expression profiles of MTB upon chemical treatment and prior knowledge of protein-protein interactions (PPI) to predict antibacterial targets. We first validate the method by examining the targets of two approved anti-MTB drugs. Then, we predict the targets for Chelerythrine and Rhein, an anti-MTB natural product extracted from Chelidonium majus and a natural anti-bacterial agent for STA respectively. The identified targets are further evaluated visually by molecular docking and molecular dynamics simulation. The results show that our method is applicable for predicting the potential targets of anti-bacterial agents and has stable performance among multiple datasets across different species. Our strategy is expected to be used in target identification for other antibacterial.