Evidence map›Paper›PMID 34504502›Full record

ArticleFrontiers in immunology2021

The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis.

Miao Zou, Xiaoyun Su, Luoying Wang, Xingcheng Yi, Yue Qiu, Xirui Yin, Xuan Zhou, Xinhui Niu, Liuli Wang, Manman Su

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.4field-weighted citation impact, top 4% 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

18 citing papers in PubMed, 30 citations in OpenAlex.

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  15. What Is Currently Known about the Role of CXCL10 in SARS-CoV-2 Infection?International journal of molecular sciences · 2022
    Review
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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

10 authors at 1 institution in 1 country.

Miao ZouDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Xiaoyun SuDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Luoying WangDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Xingcheng YiDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Yue QiuDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Xirui YinDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Xuan ZhouDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Xinhui NiuDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Liuli WangDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Manman SuDepartment of Regenerative Medicine, School of Pharmaceutical Sciences, Jilin University, ChangChun, China.
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as

Indexed as

Antiviral AgentsBrainCOVID-19COVID-19 Drug TreatmentGene Expression ProfilingGene OntologyHumansLungMultiple Organ FailureOlfactory BulbProtein Interaction MapsSARS-CoV-2TracheaTranscriptomeAntiviral AgentsBFS algorithmCOVID-19cytokine stormmultiple organ dysfunctionmultiple organ heterogeneitytargeted interventionTO-GCN

Identifiers

PMID34504502
PMCPMC8421734
OpenAlexW3193873895

What OpenQuestion holds

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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.