Machine Learning–Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome

• Type of Material: Article
• Authors: Jocelyn Grunwell, Emory University; Milad G. Rad, Georgia Institute of Technology; Susan T. Stephenson, Emory University; Ahmad F. Mohammad, Emory University; Cydney Opolka, Children’s Healthcare of Atlanta; Anne Fitzpatrick, Emory University; Rishikesan Kamaleswaran, Emory University
• Language: English
• Date: 2021-06-01
• Publisher: Wolters Kluwer Health
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1097/CCE.0000000000000431
• Title of Journal or Parent Work: Critical Care Explorations
• Volume: 3
• Issue: 6
• Grant/Funding Information: Supported, in part, by the National Institutes of Health (NIH) grants K12HD072245 (Atlanta Pediatric Scholars Program), K23 HL151897-01 and an Emory University Pediatrics Research Alliance Junior Faculty Focused Pilot award (to Dr. Grunwell). Also supported, in part, by the NIH grant K24 NR018866 (to Dr. Fitzpatrick). Supported, in part, by the Emory Integrated Genomics Core, which is subsidized by the Emory University School of Medicine and is one of the Emory Integrated Core Facilities. Additional support was provided by the Georgia Clinical and Translational Science Alliance of the National Institutes of Health under Award Number UL1TR002378.
• Keywords: machine learning; gene expression profiling; neutrophils; acute respiratory distress syndrome; mechanical ventilation; pediatric
• Research Categories: Biology, Genetics

Abstract

Objectives: To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay to identify immunogenic responses to airway fluid from children with and without pediatric acute respiratory distress syndrome. Design: Prospective cohort study. SETTING: Thirty-six bed academic PICU. PATIENTS: Fifty-four immunocompetent children, 28 with pediatric acute respiratory distress syndrome, who were between 2 days to 18 years old within 72 hours of intubation for acute hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied machine learning methods to a Nanostring transcriptomics on primary airway cells and a neutrophil reporter assay to discover gene networks differentiating pediatric acute respiratory distress syndrome from no pediatric acute respiratory distress syndrome. An analysis of moderate or severe pediatric acute respiratory distress syndrome versus no or mild pediatric acute respiratory distress syndrome was performed. Pathway network visualization was used to map pathways from 62 genes selected by ElasticNet associated with pediatric acute respiratory distress syndrome. The Janus kinase/signal transducer and activator of transcription pathway emerged. Support vector machine performed best for the primary airway cells and the neutrophil reporter assay using a leave-one-out cross-validation with an area under the operating curve and 95% CI of 0.75 (0.63–0.87) and 0.80 (0.70–1.0), respectively. CONCLUSIONS: We identified gene networks important to the pediatric acute respiratory distress syndrome airway immune response using semitargeted transcriptomics from primary airway cells and a neutrophil reporter assay. These pathways will drive mechanistic investigations into pediatric acute respiratory distress syndrome. Further studies are needed to validate our findings and to test our models.