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Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug’s mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA’s accuracy. We find that network topology predominantly determines the accuracy of ProTINA’s predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

Aluminum precipitates have long been used as adjuvants for human vaccines, but there is a clear need for safer and more effective adjuvants. Here we report in a mouse model that the psoriasis drug Oxarol ointment is a highly effective vaccine adjuvant. By applying Oxarol ointment onto skin, humoral responses and germinal center (GC) reactions were augmented, and the treated mice were protected from death caused by influenza virus infection. Keratinocyte-specific vitamin D3 receptor (Vdr) gene expression was required for these responses through induction of the thymic stromal lymphopoietin (Tslp) gene. Experiments involving administration of recombinant TSLP or, conversely, anti-TSLP antibody demonstrated that TSLP plays a key role in the GC reactions. Furthermore, cell-type-specific Tslpr gene deletion or diphtheria toxin-mediated deletion of specific cell types revealed that CD11c+ cells excluding Langerhans cells were responsible for the Oxarol-mediated GC reactions. These results indicate that active vitamin D3 is able to enhance the humoral response via Tslp induction in the skin and serves as a new vaccine adjuvant.

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.; We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in the enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error, NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

Immune cell dynamics are critical to disease pathology. Deconvolution algorithms attempt to predict cell quantities based on gene expression profiles derived from diseased tissue and are evaluated using artificial or simple samples. We tested their capability of estimating cell counts given data from influenza-infected lung sampled over time post infection. Our findings suggest that current methods must be improved in order to characterize disease-associated immune cell dynamics.