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通过生物信息学分析鉴定念珠菌感染中的差异表达基因和信号通路。

Identification of differentially expressed genes and signaling pathways with Candida infection by bioinformatics analysis.

作者信息

Zhu Guo-Dong, Xie Li-Min, Su Jian-Wen, Cao Xun-Jie, Yin Xin, Li Ya-Ping, Gao Yuan-Mei, Guo Xu-Guang

机构信息

Department of Oncology, Guangzhou Geriatric Hospital, Guangzhou, 510180, China.

Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.

出版信息

Eur J Med Res. 2022 Mar 21;27(1):43. doi: 10.1186/s40001-022-00651-w.

DOI:10.1186/s40001-022-00651-w
PMID:35314002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8935812/
Abstract

BACKGROUND

Opportunistic Candida species causes severe infections when the human immune system is weakened, leading to high mortality.

METHODS

In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candida species. And the hub genes were obtained by statistical analysis.

RESULTS

A total of 547, 422, 415 and 405 differentially expressed genes (DEGs) of Candida albicans, Candida glabrata, Candida parapsilosis and Candida tropicalis groups were obtained, respectively. A total of 216 DEGs were obtained after taking intersections of DEGs from the four groups. A protein-protein interaction (PPI) network was established using these 216 genes. The top 10 hub genes (FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3) were acquired through calculation by the cytoHubba plug-in in Cytoscape software. Validated by the sequencing data of peripheral blood, JUNB, ATF3 and EGR2 genes were  significant statistical significance.

CONCLUSIONS

In conclusion, our study demonstrated the potential pathogenic genes in Candida species and their underlying mechanisms by bioinformatic analysis methods. Further, after statistical validation, JUNB, ATF3 and EGR2 genes were attained, which may be used as potential biomarkers with Candida species infection.

摘要

背景

当人类免疫系统减弱时，机会性念珠菌会引发严重感染，导致高死亡率。

方法

在我们的研究中，运用生物信息学分析来研究感染四种念珠菌的样本的高通量测序数据。并通过统计分析获得枢纽基因。

结果

分别获得白色念珠菌、光滑念珠菌、近平滑念珠菌和热带念珠菌组的差异表达基因（DEG）547个、422个、415个和405个。对四组的DEG取交集后共获得216个DEG。利用这216个基因构建了蛋白质-蛋白质相互作用（PPI）网络。通过Cytoscape软件中的cytoHubba插件计算获得前10个枢纽基因（FOSB、EGR1、JUNB、ATF3、EGR2、NR4A1、NR4A2、DUSP1、BTG2和EGR3）。经外周血测序数据验证，JUNB、ATF3和EGR2基因具有显著统计学意义。

结论

总之，我们的研究通过生物信息学分析方法展示了念珠菌中的潜在致病基因及其潜在机制。此外，经统计验证后获得了JUNB、ATF3和EGR2基因，它们可能作为念珠菌感染的潜在生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/32fe877f4cac/40001_2022_651_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/c63feaecfc1f/40001_2022_651_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/32fe877f4cac/40001_2022_651_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/c63feaecfc1f/40001_2022_651_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/f6458906555b/40001_2022_651_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe7/8935812/13a3fe23aadb/40001_2022_651_Fig3_HTML.jpg
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