Evidence map›Paper›PMID 33303696›Full record

ArticleThorax2021

Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency.

Jen-Hwa Chu, Wenlan Zang, Milica Vukmirovic, Xiting Yan, Taylor Adams, Giuseppe DeIuliis, Buqu Hu, Antun Mihaljinec, Jonas C Schupp, Michael J Becich and 16 more

Open access · hybridAbstract readMulticenter Study
In one paragraph

Article in Thorax, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 48% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

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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

26 authors at 7 institutions in 3 countries.

Jen-Hwa Chu *Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA jen-hwa.chu@yale.edu.ORCID 0000-0001-7179-9428
Wenlan Zang *Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Milica Vukmirovic *Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Xiting YanDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Taylor AdamsDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Giuseppe DeIuliisDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0003-0841-8156
Buqu HuDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Antun MihaljinecDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Jonas C SchuppDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Michael J BecichDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Harry HochheiserDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Kevin F GibsonDivision of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Edward S ChenDepartment of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Alison MorrisDivision of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Joseph K LeaderDepartment of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Stephen R WisniewskiDepartment of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA.
Yingze ZhangDivision of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.ORCID 0000-0001-6947-2901
Frank C SciurbaDivision of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Ronald G CollmanDepartment of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Robert SandhausDepartment of Medicine, National Jewish Health, Denver, Colorado, USA.
Erica L HerzogDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Karen C PattersonDepartment of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Maor SaulerDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Charlie StrangeMedical University of South Carolina, Charleston, South Carolina, USA.
Naftali KaminskiDepartment of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0001-5917-4601
GRADS Investigators
Yale University · USUniversity of Pittsburgh · USBrighton and Sussex Medical School · GBJohns Hopkins University · USMedical University of South Carolina · USNational Jewish Health · USUniversity of Pennsylvania · US

Funding

Sarcoidosis and A1AT Genomics & Informatics CenterU01HL112707 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BECICH, MICHAEL JOHN, KAMINSKI, NAFTALI · 2012 to 2015
$8.5M
Role of MIF and CD74 in the pathogenesis of emphysemaR01HL155948 · NHLBI · YALE UNIVERSITY · PI BUCALA, RICHARD J, SAULER, MAOR · 2021 to 2025
$3.4M
MIF and DNA repair in the emphysematous lungK08HL135402 · NHLBI · YALE UNIVERSITY · PI SAULER, MAOR · 2017 to 2021
$842k
Causal Inference of Disease-Relevant Regulatory NetworksR00HL114651 · NHLBI · YALE UNIVERSITY · PI CHU, JEN-HWA · 2016 to 2018
$741k
NHLBI NIH HHS R00 HL114651NHLBI NIH HHS R01 HL155948NHLBI NIH HHS U01 HL112707NIOSH CDC HHS U24 OH009077
6 · The paper itself

Abstract

backgroundAlpha-1 antitrypsin deficiency (AATD) is a genetic condition that causes early onset pulmonary emphysema and airways obstruction. The complete mechanisms via which AATD causes lung disease are not fully understood. To improve our understanding of the pathogenesis of AATD, we investigated gene expression profiles of bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMCs) in AATD individuals.

methodsWe performed RNA-Seq on RNA extracted from matched BAL and PBMC samples isolated from 89 subjects enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Subjects were stratified by genotype and augmentation therapy. Supervised and unsupervised differential gene expression analyses were performed using Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene profiles associated with subjects' clinical variables. The genes in the most significant WGCNA module were used to cluster AATD individuals. Gene validation was performed by NanoString nCounter Gene Expression Assay.

resultWe observed modest effects of AATD genotype and augmentation therapy on gene expression. When WGCNA was applied to BAL transcriptome, one gene module, ME31 (2312 genes), correlated with the highest number of clinical variables and was functionally enriched with numerous immune T-lymphocyte related pathways. This gene module identified two distinct clusters of AATD individuals with different disease severity and distinct PBMC gene expression patterns.

conclusionsWe successfully identified novel clusters of AATD individuals where severity correlated with increased immune response independent of individuals' genotype and augmentation therapy. These findings may suggest the presence of previously unrecognised disease endotypes in AATD that associate with T-lymphocyte immunity and disease severity.

Indexed as

Gene Regulatory NetworksAdultalpha 1-Antitrypsin DeficiencyBronchoalveolar Lavage FluidFemaleGene Expression ProfilingGenotypeHumansMaleMiddle AgedNeutrophilsProspective StudiesPulmonary Disease, Chronic ObstructiveTranscriptomealpha1 antitrypsin deficiencyCOPD mechanisms

Identifiers

PMID33303696
PMCPMC10794043
OpenAlexW2911101733

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Registered trials

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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.