A systems genomics approach uncovers molecular associates of RSV severity
PLoS Computational Biology, ISSN: 1553-7358, Vol: 17, Issue: 12, Page: e1009617
2021
- 4Citations
- 2Usage
- 29Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations4
- Citation Indexes4
- Usage2
- Abstract Views2
- Captures29
- Readers29
- 29
- Mentions1
- News Mentions1
- 1
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Article Description
Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85122587703&origin=inward; http://dx.doi.org/10.1371/journal.pcbi.1009617; http://www.ncbi.nlm.nih.gov/pubmed/34962914; https://dx.plos.org/10.1371/journal.pcbi.1009617; https://scholar.rochesterregional.org/rrhpubs/638; https://scholar.rochesterregional.org/cgi/viewcontent.cgi?article=1638&context=rrhpubs; https://dx.doi.org/10.1371/journal.pcbi.1009617; https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009617
Public Library of Science (PLoS)
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