Evidence map›Paper›PMID 34717611›Full record

ArticleBMC medical genomics2021

Analysis of multiple gene co-expression networks to discover interactions favoring CFTR biogenesis and ΔF508-CFTR rescue.

Matthew D Strub, Long Gao, Kai Tan, Paul B McCray

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 3 institutions in 1 country.

Matthew D StrubDepartment of Pediatrics, University of Iowa, 6320 PBDB, 169 Newton Road, Iowa City, IA, 52242, USA.
Long GaoDepartment of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Kai TanCenter for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Paul B McCrayDepartment of Pediatrics, University of Iowa, 6320 PBDB, 169 Newton Road, Iowa City, IA, 52242, USA. paul-mccray@uiowa.edu.ORCID 0000-0002-4067-577X
University of Iowa · USChildren's Hospital of Philadelphia · USUniversity of Pennsylvania · US

Funding

Respiratory virus infection in early cystic fibrosis lung diseaseP01HL091842 · NHLBI · UNIVERSITY OF IOWA · PI ZABNER, JOSEPH · 2008 to 2022
$34.2M
Vector Core-Core 2P30DK054759 · NIDDK · UNIVERSITY OF IOWA · PI Alejandro Antonio Pezzulo · 1998 to 2026
$30.5M
PREDOCTORAL TRAINING PROGRAM IN GENETICST32GM008629 · NIGMS · UNIVERSITY OF IOWA · PI EBERL, DANIEL F · 1997 to 2021
$4.1M
Mining a microRNA Regulated Gene Network to Rescue CFTR-DeltaF508 FunctionR01HL118000 · NHLBI · UNIVERSITY OF IOWA · PI MCCRAY, PAUL B · 2014 to 2017
$1.9M
NHLBI NIH HHS P01 HL091842NHLBI NIH HHS R01 HL118000NIDDK NIH HHS P30 DK054759NIGMS NIH HHS T32 GM008629NIH HHS P30 DK054759NIH HHS R01 HL118000NIH HHS T32 GM008629
6 · The paper itself

Abstract

backgroundWe previously reported that expression of a miR-138 mimic or knockdown of SIN3A in primary cultures of cystic fibrosis (CF) airway epithelia increased ΔF508-CFTR mRNA and protein levels, and partially restored CFTR-dependent chloride transport. Global mRNA transcript profiling in ΔF508-CFBE cells treated with miR-138 mimic or SIN3A siRNA identified two genes, SYVN1 and NEDD8, whose inhibition significantly increased ΔF508-CFTR trafficking, maturation, and function. Little is known regarding the dynamic changes in the CFTR gene network during such rescue events. We hypothesized that analysis of condition-specific gene networks from transcriptomic data characterizing ΔF508-CFTR rescue could help identify dynamic gene modules associated with CFTR biogenesis.

methodsWe applied a computational method, termed M-module, to analyze multiple gene networks, each of which exhibited differential activity compared to a baseline condition. In doing so, we identified both unique and shared gene pathways across multiple differential networks. To construct differential networks, gene expression data from CFBE cells were divided into three groups: (1) siRNA inhibition of NEDD8 and SYVN1; (2) miR-138 mimic and SIN3A siRNA; and (3) temperature (27 °C for 24 h, 40 °C for 24 h, and 27 °C for 24 h followed by 40 °C for 24 h).

resultsInterrogation of individual networks (e.g., NEDD8/SYVN1 network), combinations of two networks (e.g., NEDD8/SYVN1 + temperature networks), and all three networks yielded sets of 1-modules, 2-modules, and 3-modules, respectively. Gene ontology analysis revealed significant enrichment of dynamic modules in pathways including translation, protein metabolic/catabolic processes, protein complex assembly, and endocytosis. Candidate CFTR effectors identified in the analysis included CHURC1, GZF1, and RPL15, and siRNA-mediated knockdown of these genes partially restored CFTR-dependent transepithelial chloride current to ΔF508-CFBE cells.

conclusionsThe ability of the M-module to identify dynamic modules involved in ΔF508 rescue provides a novel approach for studying CFTR biogenesis and identifying candidate suppressors of ΔF508.

Indexed as

Gene Expression RegulationGene Regulatory NetworksCystic Fibrosis Transmembrane Conductance RegulatorHumansMutationNEDD8 ProteinProtein TransportSin3 Histone Deacetylase and Corepressor ComplexTranscriptomeUbiquitin-Protein LigasesCFTR protein, humanCystic Fibrosis Transmembrane Conductance RegulatorNEDD8 ProteinNEDD8 protein, humanSIN3A transcription factorSin3 Histone Deacetylase and Corepressor ComplexSYVN1 protein, humanUbiquitin-Protein LigasesCFTRCystic fibrosisGene modulesGene ontologyM-moduleNetwork biologyTranscriptional profiling

Identifiers

PMID34717611
PMCPMC8557508
OpenAlexW3209435096

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.