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Published on: February 14, 2019
Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome
Jocelyn R Grunwell1,2, Milad G Rad3, Susan T Stephenson2
1Children's Healthcare of Atlanta, Egleston Hospital, Atlanta, GA.
Insights
Researchers identified key gene networks in the airway immune response of children with pediatric acute respiratory distress syndrome (PARDS). These findings from airway cells and a neutrophil reporter assay offer new insights into PARDS mechanisms.
Area of Science:
- Pediatric critical care medicine
- Molecular biology
- Immunology
Background:
- Pediatric acute respiratory distress syndrome (PARDS) is a critical condition requiring intubation.
- Understanding the molecular mechanisms of PARDS is crucial for developing targeted therapies.
Purpose of the Study:
- To identify differentially expressed genes and networks in airway cells of children with and without PARDS within 72 hours of intubation.
- To evaluate a neutrophil transcription reporter assay for detecting immunogenic responses to airway fluid in PARDS.
Main Methods:
- Prospective cohort study involving 54 children (28 with PARDS) in an academic pediatric intensive care unit.
- Utilized Nanostring transcriptomics on primary airway cells and a neutrophil reporter assay.
- Applied machine learning (ElasticNet, Support Vector Machine) for gene network analysis.
Main Results:
- Identified gene networks differentiating PARDS from non-PARDS cases using transcriptomics and reporter assays.
- The Janus kinase/signal transducer and activator of transcription pathway was highlighted.
- Support Vector Machine achieved an area under the operating curve of 0.75 for airway cells and 0.80 for the reporter assay.
Conclusions:
- Discovered gene networks crucial for the airway immune response in pediatric acute respiratory distress syndrome.
- These identified pathways provide a basis for mechanistic investigations into PARDS.
- Further validation studies are necessary to confirm findings and refine predictive models.
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.
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