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      Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

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          Abstract

          Background

          There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a P aO 2/F iO 2 < 200 to those with a P aO 2/F iO 2 ≥ 200.

          Results

          We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a P aO 2/F iO 2 < 200 vs. P aO 2/F iO 2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

          Conclusions

          Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

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          Most cited references45

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                Journal
                Front Pediatr
                Front Pediatr
                Front. Pediatr.
                Frontiers in Pediatrics
                Frontiers Media S.A.
                2296-2360
                17 March 2023
                2023
                : 11
                : 1159473
                Affiliations
                [ 1 ]Division of Critical Care Medicine, Children’s Healthcare of Atlanta , Atlanta, GA, United States
                [ 2 ]Department of Pediatrics, Emory University School of Medicine , Atlanta, GA, United States
                [ 3 ]Department of Electrical and Computer Engineering, Georgia Institute of Technology , Atlanta, GA, United States
                [ 4 ]Department of Anesthesiology and Critical Care Medicine, University of Pennsylvania , Philadelphia, PA, United States
                [ 5 ]Division of Pediatric Intensive Care Medicine, Children’s Hospital of Philadelphia , Philadelphia, PA, United States
                [ 6 ]Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH, United States
                [ 7 ]Department of Biomedical Informatics, Emory University School of Medicine , Atlanta, GA, United States
                [ 8 ]Department of Biomedical Engineering, Georgia Institute of Technology , Atlanta, GA, United States
                Author notes

                Edited by: Reinout A. Bem, Amsterdam University Medical Center, Netherlands

                Reviewed by: Jerry John Zimmerman, Seattle Children’s Hospital, United States Mary E. Hartman, Washington University in St. Louis, United States

                [* ] Correspondence: Jocelyn R. Grunwell jgrunwe@ 123456emory.edu
                [ † ]

                These authors have contributed equally to this work and share first authorship

                [ ‡ ]

                Deceased

                Specialty Section: This article was submitted to Pediatric Critical Care, a section of the journal Frontiers in Pediatrics

                Abbreviations AHRF, acute hypoxemic respiratory failure; ARDS, acute respiratory distress syndrome; AUPRC, area under the precision recall curve; AUROC, area under the receive operating characteristic curve; eIF2, eukaryotic initiation of factor 2; GEO, Gene Expression Omnibus; HDAC, histone deacetylase; IPA, Ingenuity Pathway Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; PANEV, Pathway Network Visualizer; PEEP, positive end-expiratory pressure; PICU pediatric intensive care unit; TCA, tricarboxylic acid.

                Article
                10.3389/fped.2023.1159473
                10063855
                37009294
                e271cf57-2cc6-48fc-a5ac-6fbf1e5bb27d
                © 2023 Grunwell, Rad, Ripple, Yehya, Wong and Kamaleswaran.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 February 2023
                : 01 March 2023
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 47, Pages: 0, Words: 0
                Funding
                Funded by: NIH, doi 10.13039/100000002;
                Award ID: K23 HL151897 JRG, K23-HL136688
                Funded by: National Institutes of Health, doi 10.13039/100000002;
                Award ID: R01GM139967, UL1TR002378
                Funded by: NIH, doi 10.13039/100000002;
                Award ID: R35GM126943
                Funding was provided by NIH grants K23 HL151897 JRG and K23-HL136688 to NY. RK was supported by the National Institutes of Health under Award Numbers R01GM139967 and UL1TR002378. HRW was supported by NIH R35GM126943.
                Categories
                Pediatrics
                Original Research

                pediatric,acute respiratory distress syndrome,mechanical ventilation,gene expression profiling,machine learning,transcriptomics

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