Evidence map›Paper›PMID 29929510›Full record

ArticleRespiratory research2018

Landscape of transcription and long non-coding RNAs reveals new insights into the inflammatory and fibrotic response following ventilator-induced lung injury.

Lu Wang, Nannan Zhang, Yi Zhang, Jingen Xia, Qingyuan Zhan, Chen Wang

Open access · goldAbstract read
In one paragraph

Article in Respiratory research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.5field-weighted citation impact, top 42% 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

9 citing papers in PubMed, 16 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

6 authors at 3 institutions in 1 country.

Lu WangBeijing University of Chinese Medicine, No 11, East Bei San Huan Road, Chaoyang District, Beijing, 100029, China.
Nannan ZhangCenter for Respiratory Diseases, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China.
Yi ZhangCenter for Respiratory Diseases, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China.
Jingen XiaCenter for Respiratory Diseases, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China.
Qingyuan ZhanCenter for Respiratory Diseases, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China. zhanqy0915@163.com.
Chen WangCenter for Respiratory Diseases, China-Japan Friendship Hospital, No 2, East Yinghua Road, Chaoyang District, Beijing, 100029, China. wangchen66366@163.com.
National Clinical Research Center for Digestive Diseases · CNBeijing University of Chinese Medicine · CNChina-Japan Friendship Hospital · CN

Funding

National Natural Science Foundation of China 8140080765National Natural Science Foundation of China 81470270The National Key Research and Development Program of China 2016YFC1304300
6 · The paper itself

Abstract

backgroundMechanical ventilation can cause ventilator-induced lung injury (VILI) and lung fibrosis; however, the underlying mechanisms are still not fully understood. RNA sequencing is a powerful means for detecting vitally important protein-coding transcripts and long non-coding RNAs (lncRNAs) on a genome-wide scale, which may be helpful for reducing this knowledge gap.

methodsNinety C57BL/6 mice were subjected to either high tidal volume ventilation or sham operation, and then mice with ventilation were randomly allocated to periods of recovery for 0, 1, 3, 5, 7, 14, 21, or 28 days. Lung histopathology, wet-to-dry weight ratio, hydroxyproline concentration, and transforming growth factor beta 1 (TGF-β1) levels were determined to evaluate the progression of inflammation and fibrosis. To compare sham-operated lungs, and 0- and 7-day post-ventilated lungs, RNA sequencing was used to elucidate the expression patterns, biological processes, and functional pathways involved in inflammation and fibrosis.

resultsA well-defined fibrotic response was most pronounced on day 7 post-ventilation. Pairwise comparisons among the sham and VILI groups showed a total of 1297 differentially expressed transcripts (DETs). Gene Ontology analysis determined that the stimulus response and immune response were the most important factors involved in inflammation and fibrosis, respectively. Kyoto Encyclopedia of Genes and Genomes analysis revealed that mechanistic target of rapamycin (mTOR), Janus kinase-signal transducer and activator of transcription (JAK/STAT), and cyclic adenosine monophosphate (cAMP) signaling were implicated in early inflammation; whereas TGF-β, hypoxia inducible factor-1 (HIF-1), Toll-like receptor (TLR), and kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathways were significantly involved in subsequent fibrosis. Additionally, 332 DE lncRNAs were identified and enriched in the processes of cellular and biological regulation. These lncRNAs may potentially regulate fibrosis through signaling pathways such as wingless/integrase-1 (Wnt), HIF-1, and TLR.

conclusionsThis is the first transcriptome study to reveal all of the transcript expression patterns and critical pathways involved in the VILI fibrotic process based on the early inflammatory state, and to show the important DE lncRNAs regulated in inflammation and fibrosis. Together, the results of this study provide novel perspectives into the potential molecular mechanisms underlying VILI and subsequent fibrosis.

Indexed as

AnimalsFibrosisGene Regulatory NetworksInflammationMaleMiceMice, Inbred C57BLRandom AllocationRespiration, ArtificialRNA, Long NoncodingTranscription, GeneticVentilator-Induced Lung InjuryRNA, Long NoncodingLncRNAsLung fibrosisRNA-seqVentilator-induced lung injury

Identifiers

PMID29929510
PMCPMC6013938
OpenAlexW2809335665

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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.