mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGen.

Mechanisms of Severe Mortality-Associated Bacterial Co-infections Following Influenza Virus Infection.

Influenza virus infection remains one of the largest disease burdens on humans. Influenza-associated bacterial co-infections contribute to severe disease and mortality during pandemic and seasonal influenza episodes. The mechanisms of severe morbidity following influenza-bacteria co-infections mainly include failure of an antibacterial immune response and pathogen synergy. Moreover, failure to resume function and tolerance might be one of the main reasons for excessive mortality. In this review, recent advances in the study of mechanisms of severe disease, caused by bacterial co-infections following influenza virus pathogenesis, are summarized. Therefore, understanding the synergy between viruses and bacteria will facilitate the design of novel therapeutic approaches to prevent mortality associated with bacterial co-infections.