Evidence mapPaperPMID 39363208Full record

ArticleBMC infectious diseases2024

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells.

Zhe Liu, Olutomilayo Olayemi Petinrin, Nanjun Chen, Muhammad Toseef, Fang Liu, Zhongxu Zhu, Furong Qi, Ka-Chun Wong

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
field-weighted citation impact
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

8 citing papers in PubMed.

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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

8 authors.

Zhe LiuInstitute for Hepatology, The Second Affiliated Hospital, School of Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong Province, 518112, China.
Olutomilayo Olayemi PetinrinDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Nanjun ChenDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Muhammad ToseefDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Fang LiuRocgene (Beijing) Technology Co., Ltd, Beijing, Beijing, 102200, China.
Zhongxu ZhuHIM-BGI Omics Center, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. zhuzhongxu@him.cas.cn.
Furong QiInstitute for Hepatology, The Second Affiliated Hospital, School of Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong Province, 518112, China. qifurong2012@gmail.com.
Ka-Chun WongDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China. kc.w@cityu.edu.hk.

Funding

City University of Hong Kong 11203221Innovation and Technology Commission ITB/FBL/9037/22/SNational Natural Science Foundation of China 32170654Research Grants Council of the Hong Kong Special Administrative Region 11203723Strategic Interdisciplinary Research Grant of City University of Hong Kong 2021SIRG036
6 · The paper itself

Abstract

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Indexed as

COVID-19Plasma CellsSARS-CoV-2Antiviral AgentsCOVID-19 Drug TreatmentGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MapsAntiviral AgentsCOVID-19Hub genesPlasma cellsScRNA-seqWGCNA

Identifiers

PMID39363208
PMCPMC11451256

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.