Comprehensive Transcriptome Profiling of Peripheral Blood Mononuclear Cells from Patients with Sepsis

• Published in: International journal of medical sciences 2020-01, Vol.17 (14), p.2077-2086
• Main Authors: Wu, Tianzhou, Liang, Xi, Jiang, Yongpo, Chen, Qi, Zhang, Huaping, Zhang, Sheng, Zhang, Chao, Lv, Yuhang, Xin, Jiaojiao, Jiang, Jing, Shi, Dongyan, Chen, Xin, Li, Jun, Xu, Yinghe
• Format: Article
• Language: English
• Subjects: Bacterial infections; Bioinformatics; Biomarkers; Blood; Cytokines; Gene expression; Genomes; Infections; Mortality; Patients; Polymerase chain reaction; Registration; Research Paper; Sepsis; Software; Thermal cycling; Tuberculosis
• ISSN: 1449-1907; 1449-1907
• DOI: 10.7150/ijms.46910

Background: Sepsis, as a clinical emergency, usually causes multiorgan dysfunction and can lead to high mortality. Establishment of specific and sensitive biomarkers for early diagnosis is critical to identify patients who would benefit from targeted therapy. In this study, we investigated this syndrome by analyzing the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with sepsis and identified sepsis-specific biomarkers. Methods: In this study, a total of 87 patients with sepsis and 40 healthy controls from a prospective multicenter cohort were enrolled. Samples from 44 subjects (24 patients with sepsis and 20 healthy controls) were sequenced and the remaining patients were included in the validation group. Using high-throughput sequencing, a gene expression profile of PBMCs from patients with sepsis was generated to elucidate the pathophysiology of sepsis and identify sepsis-specific biomarkers. Results: Principal component analysis (PCA) and unsupervised hierarchical cluster analysis showed that patients with sepsis separated from healthy controls. A total of 1639 differentially expressed genes (DEGs) were identified (|log2 fold change|> 2, adjusted P value < 0.05) between these two groups, with 1278 (78.0%) upregulated and 361 (22.0%) downregulated in patients with sepsis. Gene Ontology (GO) analysis of the upregulated DEGs identified 194 GO terms that were clustered into 27 groups, and analysis of the downregulated DEGs identified 20 GO terms that were clustered into 4 groups. Four unique genes were identified that could be predictive of patients with sepsis. External validation of the four genes using quantitative real-time polymerase chain reaction (qRT-PCR) was consistent with the results of mRNA sequencing, revealing their potential in sepsis diagnosis. Conclusions: The transcriptome characteristics of PBMCs, which were significantly altered in sepsis patients, provide new insights into sepsis pathogenesis. The four identified gene expression changes differentiated patients with sepsis from healthy subjects, which could serve as a convenient tool contributing to sepsis diagnosis.