Lu Wang1, Chin-Yi Chu2, Matthew N McCall1, Christopher Slaunwhite2, Jeanne Holden-Wiltse1, Anthony Corbett1, Ann R Falsey3,4, David J Topham5, Mary T Caserta2, Thomas J Mariani6, Edward E Walsh7,8, Xing Qiu9. 1. Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA. 2. Department of Pediatrics, University of Rochester School Medicine, Rochester, NY, USA. 3. Department of Medicine, University of Rochester School Medicine, Rochester, NY, USA. 4. Department of Medicine, Rochester General Hospital, Rochester, NY, USA. 5. Department of Microbiology and Immunology, University of Rochester School Medicine, Rochester, NY, USA. 6. Department of Pediatrics, University of Rochester School Medicine, Rochester, NY, USA. Tom_Mariani@urmc.rochester.edu. 7. Department of Medicine, University of Rochester School Medicine, Rochester, NY, USA. Edward.walsh@rochesterregional.org. 8. Department of Medicine, Rochester General Hospital, Rochester, NY, USA. Edward.walsh@rochesterregional.org. 9. Department of Biostatistics and Computational Biology, University of Rochester School Medicine, Rochester, NY, USA. xing_qiu@urmc.rochester.edu.
Abstract
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.
BACKGROUND: A substantial number of infantsinfected 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 RSVinfected 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.
Authors: Mary T Caserta; Xing Qiu; Brenda Tesini; Lu Wang; Amy Murphy; Anthony Corbett; David J Topham; Ann R Falsey; Jeanne Holden-Wiltse; Edward E Walsh Journal: J Infect Dis Date: 2017-03-01 Impact factor: 5.226
Authors: Ricardo M Fernandes; Amy C Plint; Caroline B Terwee; Cristina Sampaio; Terry P Klassen; Martin Offringa; Johanna H van der Lee Journal: Pediatrics Date: 2015-05-18 Impact factor: 7.124
Authors: Peter F Wright; William C Gruber; Melissa Peters; George Reed; Yuwei Zhu; Frances Robinson; Shanita Coleman-Dockery; Barney S Graham Journal: J Infect Dis Date: 2002-04-01 Impact factor: 5.226
Authors: Wouter A A de Steenhuijsen Piters; Santtu Heinonen; Raiza Hasrat; Eleonora Bunsow; Bennett Smith; Maria-Carmen Suarez-Arrabal; Damien Chaussabel; Daniel M Cohen; Elisabeth A M Sanders; Octavio Ramilo; Debby Bogaert; Asuncion Mejias Journal: Am J Respir Crit Care Med Date: 2016-11-01 Impact factor: 21.405
Authors: Gabrielle B McCallum; Peter S Morris; Clare C Wilson; Lesley A Versteegh; Linda M Ward; Mark D Chatfield; Anne B Chang Journal: Pediatr Pulmonol Date: 2012-09-04
Authors: Matthew N McCall; Chin-Yi Chu; Lu Wang; Lauren Benoodt; Juilee Thakar; Anthony Corbett; Jeanne Holden-Wiltse; Christopher Slaunwhite; Alex Grier; Steven R Gill; Ann R Falsey; David J Topham; Mary T Caserta; Edward E Walsh; Xing Qiu; Thomas J Mariani Journal: PLoS Comput Biol Date: 2021-12-28 Impact factor: 4.779
Authors: Derick R Peterson; Andrea M Baran; Soumyaroop Bhattacharya; Angela R Branche; Daniel P Croft; Anthony M Corbett; Edward E Walsh; Ann R Falsey; Thomas J Mariani Journal: bioRxiv Date: 2021-08-24
Authors: Clarissa M Koch; Andrew D Prigge; Leah Setar; Kishore R Anekalla; Hahn Chi Do-Umehara; Hiam Abdala-Valencia; Yuliya Politanska; Avani Shukla; Jairo Chavez; Grant R Hahn; Bria M Coates Journal: Front Immunol Date: 2022-09-23 Impact factor: 8.786