ArticleProceedings of the National Academy of Sciences of the United States of America2021
Transfer transcriptomic signatures for infectious diseases.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Antibody therapy reverses biological signatures of COVID-19 progression.Cell reports. Medicine · 2022Trial
- Programmable Nucleic Acid Sensing in Human Cells Using Circularizable ssDNA.Nature communications · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Identification ofAnnals of medicine · 2024Article
- Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease.Journal of translational medicine · 2024Article
- A multi-omics systems vaccinology resource to develop and test computational models of immunity.Cell reports methods · 2024Article
- Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution.International journal of molecular sciences · 2023Article
- Review
- Metabolomics meets systems immunology.EMBO reports · 2023Review
- Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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