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Leveraging transcriptomics to develop bronchopulmonary dysplasia endotypes: a concept paper.

Alvaro G Moreira1, Tanima Arora2, Shreyas Arya3

  • 1Department of Pediatrics, Division of Neonatology, University of Texas Health San Antonio, San Antonio, TX, USA. MoreiraA@uthscsa.edu.

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|November 16, 2023
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Summary
This summary is machine-generated.

Machine learning identified distinct subtypes (endotypes) of bronchopulmonary dysplasia (BPD) from gene expression data. T helper 17 cell differentiation pathways were key in differentiating these BPD endotypes.

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Area of Science:

  • Neonatal Medicine
  • Genomics
  • Computational Biology

Background:

  • Bronchopulmonary dysplasia (BPD) is a frequent complication in extremely premature infants.
  • Identifying distinct BPD endotypes from peripheral blood transcriptomes may reveal underlying disease mechanisms.

Purpose of the Study:

  • To discover unbiased BPD endotypes using unsupervised machine learning on genome-wide expression data.
  • To associate identified endotypes with clinical characteristics and outcomes.

Main Methods:

  • Applied agglomerative hierarchical clustering to genome-wide expression profiling of 62 infants at day of life five.
  • Utilized linear models with empirical Bayes statistics to identify differentially expressed genes across endotypes.

Main Results:

  • Identified four BPD endotypes (A, B, C, D) based on 7,319 differentially expressed genes.
  • Endotype A had higher gestational age and birthweight; Endotypes B-D were associated with extreme prematurity.
  • Endotype D showed a potentially protective role against BPD compared to Endotypes B and C.

Conclusions:

  • Unsupervised machine learning effectively identified distinct BPD endotypes.
  • These endotypes correlate with clinical definitions and may represent different disease pathways, notably involving T helper 17 cell differentiation.