Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. The adjoint correlation mechanism encodes compounds and endpoints synchronously, yielding endpoint-aware and task-focused representations. Comprehensive analyses demonstrate that ToxACoL yields 43%-87% improvements for data-scarce human endpoints, while reducing training data by 70% to 80%. Visualization of the learned top-level representation interprets structural alert mechanisms. Filled-in toxicity values highlight potential for extrapolating animal results to humans. Finally, we deploy ToxACoL as a free web platform for rapid prediction of multi-condition acute toxicities.

Dysregulated intratumoral immune reactions are shaped by complex networks of cytokines, which coordinate with tumor cells to determine tumor progression and aggressiveness. In lung adenocarcinoma (LUAD), the role of intratumoral cytokine gene expression for stratifying prognosis has not been systematically investigated. Using high-dimensional datasets of cancer specimens from clinical patients in The Cancer Genome Atlas (TCGA), we explored the transcript abundance and prognostic impact of 27 clinically evaluable cytokines in 500 LUAD tumor samples according to clinicopathological features and two common driver mutations (EGFR and KRAS). We found that reduced expression of IL12B presented as the single prognostic factor for both poor overall survival (OS) and recurrence free survival (RFS) with high hazard ratios. Moreover, we identified that elevated expression of IL6, CXCL8 and CSF3 were additional independent predictors of poor RFS in LUAD patients. Their prognostic significance was further strengthened by their ability to stratify within clinicopathological factors. Notably, we prioritized high risk cytokines for patients with or without mutations in EGFR and KRAS. Our results provide integrative associations of cytokine gene expression with patient survival and tumor recurrence and demonstrate the necessity and validity of relating clinicopathological and genetic disposition factors for precise and personalized disease prognosis.

High-throughput screening and computational technology has greatly changed the face of microbiology in better understanding pathogen–host interactions. Genome-wide RNA interference (RNAi) screens have given rise to a new class of host genes designated as Essential Host Factors (EHFs), whose knockdown effects significantly influence pathogenic infections. Therefore, we present the first release of a manually-curated bioinformatics database and analysis resource EHFPI (Essential Host Factors for Pathogenic Infection, http://biotech.bmi.ac.cn/ehfpi). EHFPI captures detailed article, screen, pathogen and phenotype annotation information for a total of 4634 EHF genes of 25 clinically important pathogenic species. Notably, EHFPI also provides six powerful and data-integrative analysis tools, i.e. EHF Overlap Analysis, EHF-pathogen Network Analysis, Gene Enrichment Analysis, Pathogen Interacting Proteins (PIPs) Analysis, Drug Target Analysis and GWAS Candidate Gene Analysis, which advance the comprehensive understanding of the biological roles of EHF genes, as in diverse perspectives of protein–protein interaction network, drug targets and diseases/traits. The EHFPI web interface provides appropriate tools that allow efficient query of EHF data and visualization of custom-made analysis results. EHFPI data and tools shall keep available without charge and serve the microbiology, biomedicine and pharmaceutics research communities, to finally facilitate the development of diagnostics, prophylactics and therapeutics for human pathogens.

With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug–virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Genome-wide RNA interference screens have greatly facilitated the identification of essential host factors (EHFs) for viral infections, whose knockdown effects significantly influence virus replication but not host cell viability. However, little has been done to link EHFs with another important host factor type, i.e., virus targeting proteins (VTPs) that viruses directly interact with for intracellular survival, hampering the integrative understanding of virus–host interactions. Using EHFs and VTPs for human immunodeficiency virus type 1 (HIV-1) and influenza A virus (IAV) infections, we found in general that despite limited overlap, EHFs and VTPs are both among the most differentially dysregulated genes in host transcriptional response to HIV and IAV infections, and notably they show consistency in regulation orientation. In the human protein–protein interaction network, both EHFs and VTPs hold topologically important positions at the global center, and importantly their direct interactions are statistically significant. We also identified BRCA1 and TP53 (or SMAD3 and PIK3R1) being the most extensive VTP-interacting EHFs (or EHF-interacting VTPs) for HIV-1 and IAV, which hold great potential in deciphering specific infection features and discovery of host directed antivirals. Further, most EHFs are the upstream regulators of VTPs when mapped in the same signaling pathways, some of which present intensive cross links. Collectively, these results provide insights into functional associations of the identified host gene factors for viral infections and highlight the regulatory significance of EHFs, and the necessity of their selective exploitation in confrontation to viral infections.