Unraveling the Molecular Landscape of Neutrophil Extracellular Traps in Severe Asthma: Identification of Biomarkers and Molecular Clusters

Overview

Journal Mol Biotechnol

Publisher Springer

Specialties Biotechnology
Molecular Biology

Date 2024 May 27

PMID 38801616

Authors

Kunlu Shen

Jiangtao Lin

Affiliations

Soon will be listed here.

Abstract

Neutrophil extracellular traps (NETs) play a central role in chronic airway diseases. However, the precise genetic basis linking NETs to the development of severe asthma remains elusive. This study aims to unravel the molecular characterization of NET-related genes (NRGs) in severe asthma and to reliably identify relevant molecular clusters and biomarkers. We analyzed RNA-seq data from the Gene Expression Omnibus database. Interaction analysis revealed fifty differentially expressed NRGs (DE-NRGs). Subsequently, the non-negative matrix factorization algorithm categorized samples from severe asthma patients. A machine learning algorithm then identified core NRGs that were highly associated with severe asthma. DE-NRGs were correlated and subjected to protein-protein interaction analysis. Unsupervised consensus clustering of the core gene expression profiles delineated two distinct clusters (C1 and C2) characterizing severe asthma. Functional enrichment highlighted immune-related pathways in the C2 cluster. Core gene selection included the Boruta algorithm, support vector machine, and least absolute contraction and selection operator algorithms. Diagnostic performance was assessed by receiver operating characteristic curves. This study addresses the molecular characterization of NRGs in adult severe asthma, revealing distinct clusters based on DE-NRGs. Potential biomarkers (TIMP1 and NFIL3) were identified that may be important for early diagnosis and treatment of severe asthma.

References

Wills-Karp M . Neutrophil ghosts worsen asthma. Sci Immunol. 2018; 3(26). DOI: 10.1126/sciimmunol.aau0112. View

Chung K . Asthma phenotyping: a necessity for improved therapeutic precision and new targeted therapies. J Intern Med. 2015; 279(2):192-204. DOI: 10.1111/joim.12382. View

Froidure A, Mouthuy J, Durham S, Chanez P, Sibille Y, Pilette C . Asthma phenotypes and IgE responses. Eur Respir J. 2015; 47(1):304-19. DOI: 10.1183/13993003.01824-2014. View

Bonnelykke K, Ober C . Leveraging gene-environment interactions and endotypes for asthma gene discovery. J Allergy Clin Immunol. 2016; 137(3):667-79. PMC: 5004762. DOI: 10.1016/j.jaci.2016.01.006. View

DeVries A, Vercelli D . Epigenetic Mechanisms in Asthma. Ann Am Thorac Soc. 2016; 13 Suppl 1:S48-50. PMC: 5015730. DOI: 10.1513/AnnalsATS.201507-420MG. View

Bird L . Asthma exacerbated by neutrophil ghosts. Nat Rev Immunol. 2018; 18(10):602-603. DOI: 10.1038/s41577-018-0059-6. View

Machida K, Aw M, Salter B, Ju X, Mukherjee M, Gauvreau G . The Role of the TL1A/DR3 Axis in the Activation of Group 2 Innate Lymphoid Cells in Subjects with Eosinophilic Asthma. Am J Respir Crit Care Med. 2020; 202(8):1105-1114. DOI: 10.1164/rccm.201909-1722OC. View

Radermecker C, Sabatel C, Vanwinge C, Ruscitti C, Marechal P, Perin F . Locally instructed CXCR4 neutrophils trigger environment-driven allergic asthma through the release of neutrophil extracellular traps. Nat Immunol. 2019; 20(11):1444-1455. PMC: 6859073. DOI: 10.1038/s41590-019-0496-9. View

Papayannopoulos V . Neutrophil extracellular traps in immunity and disease. Nat Rev Immunol. 2017; 18(2):134-147. DOI: 10.1038/nri.2017.105. View

10.

Jorch S, Kubes P . An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med. 2017; 23(3):279-287. DOI: 10.1038/nm.4294. View

11.

Twaddell S, Baines K, Grainge C, Gibson P . The Emerging Role of Neutrophil Extracellular Traps in Respiratory Disease. Chest. 2019; 156(4):774-782. DOI: 10.1016/j.chest.2019.06.012. View

12.

Ritchie M, Phipson B, Wu D, Hu Y, Law C, Shi W . limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7):e47. PMC: 4402510. DOI: 10.1093/nar/gkv007. View

13.

Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D . GeneCards: integrating information about genes, proteins and diseases. Trends Genet. 1997; 13(4):163. DOI: 10.1016/s0168-9525(97)01103-7. View

14.

McKusick V . Mendelian Inheritance in Man and its online version, OMIM. Am J Hum Genet. 2007; 80(4):588-604. PMC: 1852721. DOI: 10.1086/514346. View

15.

Edgar R, Domrachev M, Lash A . Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2001; 30(1):207-10. PMC: 99122. DOI: 10.1093/nar/30.1.207. View

16.

Wu J, Zhang F, Zheng X, Zhang J, Cao P, Sun Z . Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-related genes. Front Immunol. 2022; 13:1047367. PMC: 9752097. DOI: 10.3389/fimmu.2022.1047367. View

17.

Zhang Y, Guo L, Dai Q, Shang B, Xiao T, Di X . A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer. 2022; 10(6). PMC: 9189842. DOI: 10.1136/jitc-2021-004210. View

18.

Yu G, Wang L, Han Y, He Q . clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16(5):284-7. PMC: 3339379. DOI: 10.1089/omi.2011.0118. View

19.

Gaujoux R, Seoighe C . A flexible R package for nonnegative matrix factorization. BMC Bioinformatics. 2010; 11:367. PMC: 2912887. DOI: 10.1186/1471-2105-11-367. View

20.

Hanzelmann S, Castelo R, Guinney J . GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14:7. PMC: 3618321. DOI: 10.1186/1471-2105-14-7. View