ArticleNature communications2018
A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Explainable AI multiomics analysis reveals shared and divergent host responses in COVID-19 and influenza.NPJ digital medicine · 2026Article
- Predictive signatures of immune response to vaccination and implications of the immune setpoint remodeling.mSphere · 2025Review
- Article
- Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.Frontiers in immunology · 2022Article
- Early prognosis of respiratory virus shedding in humans.Scientific reports · 2021Article
- Causal Effects of Genetically Predicted Iron Status on Sepsis: A Two-Sample Bidirectional Mendelian Randomization Study.Frontiers in nutrition · 2021Article
- mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus-May the Data Be with You.mSphere · 2020Article
- Machine Learning SNP Based Prediction for Precision Medicine.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
27 authors at 20 institutions in 18 countries.
Funding
Abstract
The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.