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Research article
Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants
Thomas J. Mariani
University of Rochester Medical Center
Corresponding Author
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Background
A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.
Method
We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).
Results
NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.
Conclusion
Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
Keywords
respiratory syncytial virus, respiratory severity score, gene expression, RNA-seq, classification
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Sep, 2020
No community comments so far
Review #2 received
Received 10 Oct, 2020
Editorial decision: Major revision
On 10 Oct, 2020
Review #1 received
Received 27 Sep, 2020
Reviewer #1 agreed
On 19 Sep, 2020
Reviewer #2 agreed
On 19 Sep, 2020
Reviewers invited
Invitations sent on 18 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 12 Sep, 2020
Editor invited
On 11 Sep, 2020
First submitted
On 20 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biostatistics
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This preprint is under consideration at BMC Medical Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants
Thomas J. Mariani
University of Rochester Medical Center
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Sep, 2020
No community comments so far
Review #2 received
Received 10 Oct, 2020
Editorial decision: Major revision
On 10 Oct, 2020
Review #1 received
Received 27 Sep, 2020
Reviewer #1 agreed
On 19 Sep, 2020
Reviewer #2 agreed
On 19 Sep, 2020
Reviewers invited
Invitations sent on 18 Sep, 2020
Editor assigned
On 17 Sep, 2020
Submission checks complete
On 12 Sep, 2020
Editor invited
On 11 Sep, 2020
First submitted
On 20 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biostatistics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness.
Method
We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2).
Results
NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%.
Conclusion
Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.
Figure 1
Figure 2
Figure 3
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.