Evidence map›Paper›PMID 36698141›Full record

ArticleRespiratory research2023

Airway and parenchyma transcriptomics in a house dust mite model of experimental asthma.

Xiaofan Tu, Henry M Gomez, Richard Y Kim, Alexandra C Brown, Emma de Jong, Izabela Galvao, Alen Faiz, Anthony Bosco, Jay C Horvat, Philip Hansbro and 1 more

Open access · goldAbstract read
In one paragraph

Article in Respiratory research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Xiaofan Tu *Priority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
Henry M Gomez *Priority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
Richard Y KimPriority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
Alexandra C BrownPriority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
Emma de JongCentre for Health Research, Telethon Kids Institute, The University of Western Australia, Nedlands, WA, Australia.
Izabela GalvaoCentre for Inflammation, Faculty of Science, School of Life Sciences, Centenary Institute and University of Technology Sydney, Sydney, NSW, Australia.
Alen FaizFaculty of Science, School of Life Sciences, University of Technology Sydney, Sydney, NSW, Australia.
Anthony BoscoAsthma and Airway Disease Research Center, University of Arizona, Arizona, USA.
Jay C HorvatPriority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.
Philip Hansbro *Priority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia. Philip.Hansbro@uts.edu.au.
Chantal Donovan *Priority Centre for Healthy Lungs, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia. chantal.donovan@uts.edu.au.
University of Technology Sydney · AUHunter Medical Research Institute · AUThe Kids Research Institute Australia · AUUniversity of Arizona · US

Funding

National Health and Medical Research Council 1120152National Health and Medical Research Council 1138402National Health and Medical Research Council 1175134
6 · The paper itself

Abstract

Lung transcriptomics studies in asthma have provided valuable information in the whole lung context, however, deciphering the individual contributions of the airway and parenchyma in disease pathogenesis may expedite the development of novel targeted treatment strategies. In this study, we performed transcriptomics on the airway and parenchyma using a house dust mite (HDM)-induced model of experimental asthma that replicates key features of the human disease. HDM exposure increased the expression of 3,255 genes, of which 212 were uniquely increased in the airways, 856 uniquely increased in the parenchyma, and 2187 commonly increased in both compartments. Further interrogation of these genes using a combination of network and transcription factor enrichment analyses identified several transcription factors that regulate airway and/or parenchymal gene expression, including transcription factor EC (TFEC), transcription factor PU.1 (SPI1), H2.0-like homeobox (HLX), metal response element binding transcription factor-1 (MTF1) and E74-like factor 4 (ets domain transcription factor, ELF4) involved in controlling innate immune responses. We next assessed the effects of inhibiting lung SPI1 responses using commercially available DB1976 and DB2313 on key disease outcomes. We found that both compounds had no protective effects on airway inflammation, however DB2313 (8 mg/kg) decreased mucus secreting cell number, and both DB2313 (1 mg/kg) and DB1976 (2.5 mg/kg and 1 mg/kg) reduced small airway collagen deposition. Significantly, both compounds decreased airway hyperresponsiveness. This study demonstrates that SPI1 is important in HDM-induced experimental asthma and that its pharmacological inhibition reduces HDM-induced airway collagen deposition and hyperresponsiveness.

Indexed as

AsthmaPyroglyphidaeAnimalsCollagenDisease Models, AnimalHumansLungTranscription FactorsTranscriptomeCollagenTranscription FactorsAHRAsthmaHouse dust miteRemodelingSPI1Transcriptomics

Identifiers

PMID36698141
PMCPMC9878882
OpenAlexW4318003480

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.