Network study of nasal transcriptome profiles reveals master regulator genes of asthma - 04/03/21
Abstract |
Background |
Nasal transcriptomics can provide an accessible window into asthma pathobiology.
Objective |
Our goal was to move beyond gene signatures of asthma to identify master regulator genes that causally regulate genes associated with asthma phenotypes.
Methods |
We recruited 156 children with severe persistent asthma and controls for nasal transcriptome profiling and applied network-based and probabilistic causal methods to identify severe asthma genes and their master regulators. We then took the same approach in an independent cohort of 190 adults with mild/moderate asthma and controls to identify mild/moderate asthma genes and their master regulators. Comparative analysis of the master regulator genes followed by validation testing in independent children with severe asthma (n = 21) and mild/moderate asthma (n = 154) was then performed.
Results |
Nasal gene signatures for severe persistent asthma and for mild/moderate persistent asthma were identified; both were found to be enriched in coexpression network modules for ciliary function and inflammatory response. By applying probabilistic causal methods to these gene signatures and validation testing in independent cohorts, we identified (1) a master regulator gene common to asthma across severity and ages (FOXJ1); (2) master regulator genes of severe persistent asthma in children (LRRC23, TMEM231, CAPS, PTPRC, and FYB); and (3) master regulator genes of mild/moderate persistent asthma in children and adults (C1orf38 and FMNL1). The identified master regulators were statistically inferred to causally regulate the expression of downstream genes that modulate ciliary function and inflammatory response to influence asthma.
Conclusion |
The identified master regulator genes of asthma provide a novel path forward to further uncovering asthma mechanisms and therapy.
Il testo completo di questo articolo è disponibile in PDF.Graphical abstract |
Key words : Asthma, nasal, transcriptome, network, gene expression, severe persistent asthma, mild persistent asthma, moderate persistent asthma, master regulator, probabilistic causal network
Abbreviations used : ARIA, ATOM, FDR, GO, KDA, |log2 FC|, RNA-seq, WGCNA
Mappa
| This work was funded by the US National Institutes of Health NIH R01 AI 118833. |
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| Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest. |
Vol 147 - N° 3
P. 879-893 - Marzo 2021 Ritorno al numero
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