The modulation of the transcriptome is among the earliest responses to infection. However, defining the transcriptomic signatures of disease is challenging because logistic, technical, and cost factors limit the size and representativeness of samples in clinical studies. These limitations lead to a poor performance of signatures when applied to new datasets. Although the study focuses on infection, the central hypothesis of the work is the generalization of sets of signatures across diseases. We use a machine learning approach to identify common elements in datasets and then test empirically whether they are informative about a second dataset from a disease or process distinct from the original dataset. We identify sets of genes, which we name transfer signatures, that are predictive across diverse datasets and/or species (e.g., rhesus to humans). We demonstrate the usefulness of transfer signatures in two use cases: the progression of latent to active tuberculosis and the severity of COVID-19 and influenza A H1N1 infection. This indicates that transfer signatures can be deployed in settings that lack disease-specific biomarkers. The broad significance of our work lies in the concept that a small set of archetypal human immunophenotypes, captured by transfer signatures, can explain a larger set of responses to diverse diseases.
Keywords: immunophenotype; infection; transcriptomics; transfer learning; vaccination.