Skip to main content
NCBI home page
International Journal of General Medicine logoLink to International Journal of General Medicine
. 2022 Jul 6;15:6055–6071. doi: 10.2147/IJGM.S368782

Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation

Xu Huang 1,1, Jixiang Tan 1, Xiaoying Chen 1,1, Lin Zhao 1,✉
PMCID: PMC9271908  PMID: 35832399

Abstract

Purpose

Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improve the survival of patients with sepsis.

Methods

We downloaded 7 datasets from Gene Expression Omnibus database and screened the immune-related differential genes (IRDEGs). The related functions of IRDEGs were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CIBERSORT was used to evaluate the infiltration of the immune cells, and Pearson algorithm of R software was used to calculate the correlation between the immune cell content and gene expression to screen the genes most related to immune cells in sepsis group, which were intersected with IRDEGs to obtain common genes. Key genes were identified from common genes based on the area under the receiver operating characteristic curve (AUC) greater than 0.8 in the 6 datasets. We then analyzed the predictive value of key genes in sepsis survival. Finally, we verified the expression of key genes in patients with sepsis by PCR analysis.

Results

A total of 164 IRDEGs were obtained, which were associated mainly with inflammatory and immunometabolic responses. Ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were identified with high diagnostic efficacy. Logistic regression analysis revealed that six of the key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) may affect the survival prognosis of sepsis. PCR analysis confirmed that the expression of seven key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was consistent with microarray outcome.

Conclusion

This study explored the immune and metabolic response mechanisms associated with sepsis, and identified ten potential diagnostic and six prognostic genes.

Keywords: sepsis, diagnostic biomarker, prognostic biomarkers

Introduction

Sepsis is a common clinical emergency and critical illness, and is regarded as one of the life-threatening infectious diseases.1 There are approximately 48.9 million septic patients and 11 million septic patients die worldwide each year, accounting for 19.7% of all the global deaths.2

From the perspective of immunology, the essence of sepsis is life-threatening multiple organ dysfunction caused by immune response and regulation disorder caused by severe infection.3 In other terms, sepsis progresses from excessive immune activation to widespread immunosuppression.4 What is more, the main cause of sepsis death is important organ dysfunction, which is characterized by impaired vascular barrier function, disordered immune regulation, excessive coagulation, impaired vascular regulation and ischemia, which further leads to multiple organ failure and death.3 Growing evidence supports immunosuppression as a major cause of sepsis-related deaths,5 and the main reason of immunosuppression is the death of immune cells, especially the apoptosis of T and B lymphocytes.6–8

Sepsis is considered a medical emergency because it can rapidly develop into organ dysfunction and death even if active medical treatment is taken immediately.9 Although our understanding of the pathogenesis of sepsis has progressed, it is still a worldwide health challenge due to high morbidity and high mortality. In the last few years, many technologies have been developed to diagnose sepsis reliably and quickly, but none of them is widely used in clinical practice.10,11

Bioinformatics analysis helps people to explore the molecular mechanisms underlying disease development and provides a new and effective method for identifying potential diagnostic biomarkers, assessing prognosis and redirecting treatment. Therefore, to better improve early diagnosis techniques and optimise treatment of sepsis, we use bioinformatics to identify diagnostic and prognostic relevant biomarkers and potential therapeutic targets for sepsis.

Materials and Methods

Microarray Data

We downloaded data from the Gene Expression Omnibus (GEO) database, the largest and most complete public gene expression data resource in the world today.12 The array-based gene expression profile dataset GSE69528 included whole blood samples from 83 septic patients and 55 uninfected controls. The array-based gene expression profile dataset GSE13904 included whole blood samples from 52 septic children, 106 septic shock children and 18 normal children. The array-based gene expression profile dataset GSE25504 included whole blood samples from 25 sepsis-infected human neonates and 37 controls. The array-based gene expression profile dataset GSE26440 included whole blood samples from 98 septic shock children and 32 controls. The array-based gene expression profile dataset GSE28750 included whole blood samples from 10 septic patients and 20 controls. The array-based gene expression profile dataset GSE145227 included peripheral blood mononuclear cell samples from 10 septic children and 12 controls. The array-based gene expression profile dataset GSE54514 included whole blood samples from 26 septic survivors, 9 sepsis non-survivors and 18 controls.

Screening for Differentially Expressed Genes (DEGs) from Blood Microarray Datasets of Sepsis Patients

DEGs between sepsis patients and control individuals in GSE69528 were screened by the “limma” package13 and volcano plots of DEGs were plotted using the “ggplot2” package14 to show the differential expression. P < 0.05 and | log2FC |> 1 were considered statistically significant.

Screening of Immune-Related DEGs

From the Immunology Database and Analysis Portal (IMMPORT) database (https://www.immport.org/home), we downloaded totaling 2483 immune-related genes (IRGs). The overlapping genes between IRGs and DEG were identified as immune-related differential DEGs (IRDEGs) by Venn diagram analysis, and were analysed by R language and cluster analysis packages for subsequent Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis.

Estimate the 22 Immune Cell Types in Sepsis and Screen the Genes Most Related to Immune Cells in the Sepsis Group

Cibersort R package was used for cellular immune infiltration analysis to obtain the immune cell content matrix.15 The Pearson algorithm of R software was applied to calculate the correlation between the cell content and gene expression in the sepsis group samples, and the genes with a correlation coefficient greater than 0.7 were further intersected with IRDEGs to obtain common genes.

Screening Key Genes According to Receiver Operating Characteristic Curve Analysis

We calculated the area under the receiver operating characteristic curve (AUC) for common genes by using the pROC package in R16 in the six datasets, and visualized the results by using ggplot2. We selected genes with an AUC greater than 0.8 in all six datasets as the key genes for subsequent analyses.

Expression of Key Genes

We used R software and the ComplexHeatmap package to calculate the expression difference of key genes between septic patients and controls in 6 datasets. Further, we used Spearman analysis and Circlize package in R to calculate the correlation between key genes and then visualized the results.

Correlation Analysis of Key Genes and Immune Infiltrating Cells

We performed Spearman correlation analysis of key genes and immune infiltrating cells using R software and the ggstatsplot package, and visualised the results using the ggplot2 package.

TF-miRNA-mRNA Interaction Analysis

We obtained miRNA-key gene interactions from TarBase17 and mirtarbase18 to identify regulatory miRNAs that affect key genes at the post-transcriptional level. Meanwhile, in order to determine the regulatory TF controlling miRNA, TF-miRNA interaction was obtained using Jaspar database.19 All of the above use NetworkAnalyst to analyze topology parameters (ie, degree and medium centrality).20 We further used Cytoscape software21 to integrate the miRNA-target gene network and the TF-miRNA network to construct a TF-miRNA-target network to discover major regulatory genes.

Associations Between Key Genes and Diseases

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public database describing gene–disease interactions and providing information on gene–disease interactions.22 Ten key genes are input into the gene search box of CTD database one by one, and the diseases associated with each key gene can be obtained respectively. We therefore use these data to identify and analyse associations between key genes and diseases, and to understand the diseases in which key genes primarily act.

Functional Enrichment in Key Gene

The AmiGO database (v2.0; http://AmiGO.geneontology.org/AmiGO/) is a web application that is primarily used to query, browse, visualize and download gene ontologies and annotations.23 We used this database to confirm the enrichment of key sepsis-related genes that have been identified in this study.

Predictive Value of Key Genes in Sepsis Death

Using the data of the dataset GSE54514, the selected 10 key genes, gender, age, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and neutral percentage were used to perform multivariate and univariate regression analysis on the prognosis of death. APACHE II is one of the ICU scoring systems, higher scores suggest an increased severity.

Patient Enrollment and Sample Collection

From March 23, 2022 to March 30, 2022, patients diagnosed with sepsis in the Department of critical medicine of the First Affiliated Hospital of Chongqing Medical University were included in the study. We collected samples at the first time of admission (generally within 1 hour after admission) of patients with sepsis before the use of antibiotics. The follow-up treatment was carried out according to the routine treatment scheme. This part of the study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University (ethic reference number: 2022-02). Written informed consent was obtained from all donors in accordance with the Declaration of Helsinki and under Institutional Review Board–approved protocols. The inclusion criteria were: (1) the patient met the diagnostic criteria of sepsis 3.0,24 (2) the patient was aged between 18 and 90 years, and (3) the patient was hospitalized for more than 24 hours. The following patients were excluded: (1) using drugs that affect immunity, (2) having malignant tumors, and (3) pregnancy or lactation.

Real-Time PCR Analysis

Total RNA was extracted from whole blood using trlzogen reagent (Tiangen biochemical technology (Beijing) Co., Ltd). We use high-capacity cDNA reverse transcription kit (TaKaRa TB Green Premix Ex Taq II, RR820A) and first-strand cDNA synthesis kit (TaKaRa PrimeScript RT reagent Kit, RR0474) for RNA reverse transcription and first-strand DNA synthesis. Specific PCR primer pairs are listed in Table 1. GAPDH was used for internal control, and 2–ΔΔCT method was used to calculate relative mRNA expression level.

Table 1.

Specific PCR Primer Pairs

PCR Primer Primer Sequence Product Length (bp)
h-IL1R2-F GCGGGAGTTCAGGCTGGAAG 184
h-IL1R2-R TGGCAGAAGCCACAGAGCAC
h-LTB4R-F GCCCAAGGCACCTGGAGTTT 162
h-LTB4R-R CGCCTTGGTGCGTAGCTTCT
h-S100A11-F GACTGAGCGGTGCATCGAGT 162
h-S100A11-R ATGCGGTCAAGGACACCAGG
h-S100A12-F CGGAAGGGGCATTTTGACACC 187
h-S100A12-R TCAGCGCAATGGCTACCAGG
h-SORT1-F GGCTCCTGCAAAGCTGACCT 200
h-SORT1-R TGGGCCATGCTCCATGTGTC
h-RASGRP1-F GGCCTGGGCTTTCCTCACAA 188
h-RASGRP1-R ACTGGGTTCTTGGCTCGCTT
h-CD3G-F GGAGTTCGCCAGTCGAGAGC 185
h-CD3G-R AGGAGGAGAACACCTGGACTAC
h-CD40LG-F GCAACCAGGTGCTTCGGTGT 168
h-CD40LG-R GTGGGCTTAACCGCTGTGCT
h-CD8A-F GGAGGCACCCATGCCATCTC 190
h-CD8A-R TGAGACAGGGGCCTCGGAAA
h-PPP3CC-F ACACTGTCCGAGGGTGCTCT 144
h-PPP3CC-R AGCCTGTGGCTTGGCTCTTC
h-GAPDH-F GACCTGACCTGCCGTCTA 83
h-GAPDH-R AGGAGTGGGTGTCGCTGT
Open in a new tab

Abbreviation: PCR, polymerase chain reaction.

Statistical Analysis

Cox regression analysis was conducted at both univariate and multivariate levels to analyze independent factors affecting prognosis by using R software and Survival package. Statistical analysis and visualisation were performed using R3.6.3 and ggplot2. Statistical significance was calculated by Student’s t-test, Mann–Whitney U-test, chi-square test or Fisher’s exact test (as appropriate). Statistical tests were bilateral tests, and P values less than 0.05 were considered statistically significant.

Results

Identification of DEGs

The flow chart of this study is shown in Figure 1. In the dataset GSE69528, 1572 DEGs were screened between sepsis patients and controls in total, of which 755 were up-regulated genes and 807 were down-regulated genes (Figure 2A). There are 164 overlapping genes (IRDEGs) between IRGs and DEGs. The Venn diagram is shown in Figure 2B.

Figure 1.

Figure 1

Open in a new tab

Flow chart of this study.

Figure 2.

Figure 2

Open in a new tab

Identification and functional analysis of differentially expressed genes (A) volcano map, (B) Venn of GSE69528 and IMMPORT database, (C) BP, CC, MF analysis, (D) KEGG analysis).

GO and KEGG Pathway Enrichment Analyses of IRDEGs

IRDEGs obtained in the previous step are used for GO and KEGG analysis. BP associated with GO terms were mainly enriched in T cell activation, neutrophil degranulation, neutrophil activation, lymphocyte differentiation. CC were primarily associated with vesicle lumen, external side of plasma membrane, cytoplasmic vesicle lumen, secretory granule lumen. MF were mainly involved in receptor-ligand activity, cytokine receptor activity, MHC protein complex binding (Figure 2C).

KEGG pathway enrichment analysis of IRDEGs was mainly enriched in T cell receptor signaling pathway, cytokine–cytokine receptor interaction, Th17 cell differentiation, natural killer cell mediated cytotoxicity, Th1 and Th2 cell differentiation and Primary immunodeficiency (Figure 2D).

Immune Infiltration Analysis

To estimate 22 immune cell types, we used the Cibersort R software package to reveal the infiltration of 22 immune cell subsets in sepsis by analyzing the GSE69528 dataset. The box plot of the immune cell difference showed that, compared with the control sample, innate immune cells (such as mast cells, macrophages and neutrophils) infiltrated more, while T cells (CD4, CD8), NK cells infiltrated less, as shown in Figure 3A. Figure 3B shows the patterns of 22 subpopulations of immune cells’ proportion in sepsis and control group. The heat map of immune cell infiltration between the septic group and the control group is shown in Figure 3C.

Figure 3.

Figure 3

Open in a new tab

Immune infiltration analysis. (A) Proportional box plot of 22 kinds of infiltrating immune cells in the sample. (B) The proportion and distribution of infiltrating immune cells in different samples. (C) Proportional heat map of 22 kinds of infiltrating immune cells in the sample.

To screen the most relevant genes of immune cells in sepsis group, the Pearson algorithm of R software was used to calculate the correlation between the content of immune cells and gene expression in the sepsis group samples. A total of 441 genes with a correlation coefficient greater than 0.7 were obtained, and a total of 27 common genes were obtained by further intersecting with IRGs and DEGs (Figure 4A).

Figure 4.

Figure 4

Open in a new tab

Identification of potential diagnostic genes for sepsis. (A) Venn diagram of GSE69528, IMMPORT database and immune infiltrating cell related genes. (B) AUC value histogram of 10 key genes in 6 datasets. (C–H) ROC values of up-regulated genes (IL1R2, LTB4R, S100A11, S100A12, SORT1). (I–N) ROC values of down regulated genes (RASGRP1, CD3G, CD40LG, CD8A, PPP3CC).

Screening Key Genes According to the Diagnostic Value

To further screen the key genes with high diagnostic efficiency, we calculated the AUC values of 27 common genes in six datasets (GSE13904, GSE26440, GSE28750, GSE25504, GSE69528 and GSE145227). Finally, we identified ten genes with AUC values greater than 0.8 in six datasets and defined them as key genes (Figure 4B). Figure 4C–H shows the ROC curve of up-regulated genes (IL1R2, LTB4R, S100A11, S100A12 and SORT1); Figure 4I–N shows the ROC curve of down-regulated genes (RASGRP1, CD3G, CD40LG, CD8A and PPP3CC).

Subsequent Analysis of Key Genes: Expression, Regulation and Correlation with Immune Cells

In order to explore the expression in other data sets, upstream regulation mechanism and correlation with immune cells of these key genes, we made further analysis with R software. Among key genes, IL1R2, LTB4R, S100A11, S100A12 and SORT1 were up-regulated in sepsis, while RASGRP1, CD3G, CD40LG, CD8A and PPP3CC were down-regulated. Importantly, the differential expression patterns of these key genes were consistent in the above 6 datasets (Figure 5A). We studied TF-miRNA-mRNA interactions (Figure 5B) and detected central regulatory biomolecules (TFs and miRNAs) using topological parameters. Correlation analysis results show that there is a positive correlation between genes that are regulated in the same direction, and there is a significant negative correlation between up-regulated genes and down-regulated genes (Figure 5C). Correlation between key genes and immune infiltrating cells showed that the up-regulated genes were significantly positively correlated with macrophages, neutrophils and memory B cells, and negatively correlated with T cells (CD4/CD8) and NK cells, the results of down-regulated genes are just opposite to those of up-regulated genes (Figure 5D).

Figure 5.

Figure 5

Open in a new tab

Expression and correlation analysis of 10 key genes. (A) Expression of 10 key genes in 6 datasets. (B) TF-miRNA-mRNA network construction (triangle: TF, square: miRNA, circle: mRNA). (C) Correlation analysis of 10 key genes. (D) Correlation between key genes and immune infiltrating cells, based on Pearson correlation analysis. Red nodes represent positive correlations, blue nodes represent negative correlations. *P < 0.05, **P < 0.01.

Functional Enrichment in Key Genes

We used AmiGO database to confirm GO term enrichment for identified key genes involved in sepsis (Table 2). To analyze the association between key genes and disease and GO term enrichment for key genes, we used CTD and amigo database for further research. The CTD databases show that key genes are mainly targeted in inflammatory and immune-related diseases, including sepsis, acute kidney injury, acute lung injury and multiple organ dysfunction (Table 3).

Table 2.

Use the Amigo Database to Analyze the GO Terms of Keygenes

Gene GO Class (Direct) Evidence Reference
IL1R2 Interleukin-1 receptor activity IBA PMID:21873635
LTB4R2 Neuropeptide signaling pathway IBA PMID:21873635
Leukotriene B4 receptor activity IBA PMID:21873635
G protein-coupled peptide receptor activity IBA PMID:21873635
Integral component of plasma membrane IBA PMID:21873635
Inflammatory response IBA PMID:21873635
S100A11 Calcium-dependent protein binding IBA PMID:21873635
Extracellular space IBA PMID:21873635
S100 protein binding IBA PMID:21873635
Calcium ion binding IBA PMID:21873635
Cytoplasm IBA PMID:21873635
Calcium ion binding IBA ZFIN:ZDB-PUB-020724-1
Transition metal ion binding IBA ZFIN:ZDB-PUB-020724-1
Extracellular space IBA PMID:21873635
Calcium-dependent protein binding IBA PMID:21873635
Perinuclear region of cytoplasm IBA PMID:21873635
S100A12 Calcium ion binding IEA GO_REF:0000002
RAGE receptor binding IEA GO_REF:0000107
Xenobiotic metabolic process IEA GO_REF:0000107
Killing of cells of other organism IEA GO_REF:0000107
Positive regulation of I-kappaB kinase/NF-kappaB signaling IEA GO_REF:0000107
Defense response to fungus IEA GO_REF:0000107
Antimicrobial humoral immune response mediated by antimicrobial peptide IEA GO_REF:0000107
Nucleus IEA GO_REF:0000107
Cytoplasm IEA GO_REF:0000107
Protein binding IPI PMID:10399917
SORT1 Golgi apparatus IBA PMID:21873635
Post-Golgi vesicle-mediated transport IBA PMID:21873635
Integral component of membrane IBA PMID:21873635
Vesicle organization IBA PMID:21873635
Endocytosis IBA PMID:21873635
Golgi to endosome transport IBA PMID:21873635
Golgi to endosome transport IBA PMID:21873635
Integral component of membrane IBA PMID:21873635
Post-Golgi vesicle-mediated transport IBA PMID:21873635
Vesicle organization IBA PMID:21873635
RASGRP1 Guanyl-nucleotide exchange factor activity IMP PMID:23908768
Calcium ion binding IMP PMID:23908768
Zinc ion binding IMP PMID:23908768
Lipid binding TAS PMID:9582122
Diacylglycerol binding IMP PMID:23908768
Phosphatidylcholine binding IMP PMID:23908768
Identical protein binding IPI PMID:23908768
CD3G Alpha-beta T cell receptor complex IBA PMID:21873635
Positive thymic T cell selection IBA PMID:21873635
Cell surface receptor signaling pathway IBA PMID:21873635
External side of plasma membrane IBA PMID:21873635
Transmembrane signaling receptor activity IBA PMID:21873635
Positive thymic T cell selection IBA PMID:21873635
Alpha-beta T cell receptor complex IBA PMID:21873635
Transmembrane signaling receptor activity IBA PMID:21873635
External side of plasma membrane IBA PMID:21873635
Cell surface receptor signaling pathway IBA PMID:21873635
CD40LG Membrane IEA ZFIN:ZDB-PUB-020724-1
Cytokine activity IEA ZFIN:ZDB-PUB-020723-1
Tumor necrosis factor receptor binding IEA ZFIN:ZDB-PUB-020724-1
Immune response IEA ZFIN:ZDB-PUB-020724-1
Integral component of membrane IEA ZFIN:ZDB-PUB-020723-1
Membrane IEA ZFIN:ZDB-PUB-020723-1
Extracellular space IEA ZFIN:ZDB-PUB-020723-1
Positive regulation of immunoglobulin production IDA PMID:19494299
CD40 receptor binding IDA PMID:19494299
External side of plasma membrane IDA PMID:19494299
CD8A Plasma membrane IEA ZFIN:ZDB-PUB-120306-4
Integral component of membrane IEA ZFIN:ZDB-PUB-020723-1
Membrane IEA ZFIN:ZDB-PUB-020723-1
Molecular function ND ZFIN:ZDB-PUB-031118-1
MHC class I protein complex binding IEA GO_REF:0000107
External side of plasma membrane IEA GO_REF:0000107
Integral component of membrane IEA GO_REF:0000043
Plasma membrane raft IEA GO_REF:0000107
Integral component of membrane IEA GO_REF:0000043
MHC class I protein complex binding IEA GO_REF:0000107
PPP3CC Cytoplasm IBA PMID:21873635
Calcineurin-mediated signaling IBA PMID:21873635
Calmodulin binding IBA PMID:21873635
Calmodulin-dependent protein phosphatase activity IBA PMID:21873635
Calcineurin complex IBA PMID:21873635
Calmodulin binding IEA ZFIN:ZDB-PUB-020723-1
Phosphoprotein phosphatase activity IEA ZFIN:ZDB-PUB-020723-1
Hydrolase activity IEA ZFIN:ZDB-PUB-020723-1
Calmodulin-dependent protein phosphatase activity IEA ZFIN:ZDB-PUB-020723-1
Hydrolase activity IEA ZFIN:ZDB-PUB-020723-1
Open in a new tab

Abbreviation: GO, Gene Ontology.

Table 3.

Diseases Related to Key Gene Regulation

Gene Disease Name Disease ID Inference Score Reference Count
IL1R2 Inflammation MESH:D007249 143.71 259
Immune System Diseases MESH:D007154 57.41 23
Sepsis MESH:D018805 14.49 18
Immunologic Deficiency Syndromes MESH:D007153 36.92 6
Autoimmune Diseases MESH:D001327 32.27 12
RASGRP1 Acute Kidney Injury MESH:D058186 97.07 391
Acute Lung Injury MESH:D055371 52.73 68
Sepsis MESH:D018805 27.87 30
Immune System Diseases MESH:D007154 41.24 17
Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis MESH:D056648 12.88 51
CD3G Inflammation MESH:D007249 127.93 209
Acute Kidney Injury MESH:D058186 63.38 183
Lung Injury MESH:D055370 74.6 77
Sepsis MESH:D018805 15.26 17
Autoimmune Diseases MESH:D001327 26.01 8
CD8A Acute Kidney Injury MESH:D058186 60.12 269
Inflammation MESH:D007249 122.47 265
Acute Lung Injury MESH:D055371 55.32 39
Sepsis MESH:D018805 33.58 21
Immune System Diseases MESH:D007154 37.71 15
CD40LG Inflammation MESH:D007249 176.78 321
Acute Kidney Injury MESH:D058186 101.01 193
Acute Lung Injury MESH:D055371 55.62 70
Sepsis MESH:D018805 43.35 37
Immune System Diseases MESH:D007154 40.23 13
LTB4R Inflammation MESH:D007249 66.35 105
Acute Kidney Injury MESH:D058186 27.17 120
Liver Failure, Acute MESH:D017114 19.26 160
Acute Lung Injury MESH:D055371 16.25 8
Sepsis MESH:D018805 2.6 2
PPP3CC Acute Kidney Injury MESH:D058186 44.08 235
Liver Failure, Acute MESH:D017114 29.48 167
Inflammation MESH:D007249 91.18 136
Immune System Diseases MESH:D007154 29.59 15
Sepsis MESH:D018805 15.16 9
S100A11 Acute Kidney Injury MESH:D058186 150.56 420
Inflammation MESH:D007249 237.55 337
Acute Lung Injury MESH:D055371 55.04 101
Sepsis MESH:D018805 39.53 38
Immune System Diseases MESH:D007154 46.9 20
S100A12 Inflammation MESH:D007249 58.08 173
Acute Kidney Injury MESH:D058186 20.05 106
Acute Lung Injury MESH:D055371 20.15 44
Shock, Septic MESH:D012772 3.32 15
Sepsis MESH:D018805 6.66 13
SORT1 Inflammation MESH:D007249 173.13 229
Acute Kidney Injury MESH:D058186 91.31 214
Acute Lung Injury MESH:D055371 46.1 70
Sepsis MESH:D018805 26.53 17
Multiple Organ Failure MESH:D009102 33.14 8
Open in a new tab

Predictive Value of Key Genes in Sepsis Survival

To determine the predictive value of key genes for sepsis survival, we performed univariate and multivariate logistic regression analysis. Among key genes, univariate regression analysis showed that 6 genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) expression were significantly associated with sepsis survival. Multivariate regression analysis showed that S100A11 was an independent prognostic factor for sepsis survival (Table 4).

Table 4.

Univariate and Multiple Regressive Analysis of Death for Sepsis

Characteristics Total (N) Univariate Analysis Multivariate Analysis
Hazard Ratio (95% CI) P value Hazard Ratio (95% CI) P value
Sex 127
Female 75 Reference
Male 52 2.376 (1.092–5.167) 0.029 3.766 (1.473–9.625) 0.006
APACHE II score 127 1.121 (1.047–1.201) 0.001 1.098 (1.023–1.179) 0.009
Neutrophil proportion 126 1.350 (0.055–33.301) 0.854
IL1R2 127 0.704 (0.208–2.389) 0.574
LTB4R 127 0.376 (0.187–0.759) 0.006 1.639 (0.460–5.830) 0.446
S100A11 127 0.384 (0.217–0.678) <0.001 0.329 (0.120–0.898) 0.030
S100A12 127 0.723 (0.473–1.105) 0.134
SORT1 127 0.408 (0.223–0.749) 0.004 0.668 (0.300–1.489) 0.324
RASGRP1 127 3.361 (1.107–10.199) 0.032 1.114 (0.117–10.569) 0.925
CD3G 127 6.421 (2.150–19.180) <0.001 2.487 (0.521–11.876) 0.254
CD40LG 127 0.410 (0.035–4.817) 0.478
CD8A 127 1.606 (1.076–2.397) 0.020 0.982 (0.444–2.175) 0.965
PPP3CC 127 2.186 (0.476–10.035) 0.315
Open in a new tab

Notes: APACHE II score: Acute Physiology and Chronic Health Evaluation II score. All P values less than 0.05 are shown in bold font.

In the comparison of survival and non-survival groups of sepsis, the expression of down-regulated genes (RASGRP1, CD3G and CD8A) were significantly decreased in the survival group; the expression of up-regulated genes (LTB4R, S100A11, SORT1) increased significantly in the survival group (Figure 6).

Figure 6.

Figure 6

Open in a new tab

(A-F) Expression of RASGRP1, CD8A, CD3G, SORT1, LTB4R, S100A11, respectively. *P < 0.05, **P < 0.01, ***P < 0.001.

Our results revealed that the 6 key genes expression levels are significantly associated with survival in sepsis. Most importantly, multivariate analysis showed that the S100A11 was an independent and favorable marker of prognosis in patients with sepsis. Therefore, the expression of S100A11 has the potential to predict the prognosis of patients with sepsis.

PCR Validation of Key Gene Expression Profiles

The expression of 10 key genes in sepsis patients (n = 6) and healthy controls (n = 6) was compared by real-time quantitative PCR. The results showed that compared with the control group, the expression of genes (RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was significantly down-regulated and genes (IL1R2, S100A12) was significantly up-regulated in patients with sepsis (P < 0.05; Figure 7). The expression results of these 7 key genes were consistent with the trend observed in microarray analysis.

Figure 7.

Figure 7

Open in a new tab

(A-G) Expression of CD40LG, PPP3CC, RASGRP1, CD8A, CD3G, S100A12 and IL1R2, respectively.

Notes: *P < 0.05, **P < 0.01, ***P < 0.001.

Discussion

Sepsis accounts for one-fifth of deaths worldwide, and the number of deaths from sepsis has doubled compared with estimates in previous years. Early diagnosis, prognostic evaluation and timely treatment of sepsis play an important role in reducing mortality. In essence, sepsis is a disorder of immune response caused by severe infection, but the specific mechanism is still not very clear. Despite years of research, the diagnosis and prediction of sepsis and septic shock still mainly rely on clinical criteria. Many biomarkers have been studied, none of which have the accuracy required for clinical practice.25 In this study, bioinformatic analysis of sepsis-related gene expression data was performed to explore the function of immune cell infiltration in sepsis and to identify molecules involved in causing immune dysregulation in sepsis to assist in the early diagnosis of sepsis and to predict sepsis prognosis. We found that in patients with sepsis, there was an increased infiltration of intrinsic immune cells and a significant decrease in adaptive immune cell infiltration. We identified 10 key genes with high diagnostic efficacy associated with the immune infiltration characteristics of sepsis, and six of these may be key genes affecting the prognosis of sepsis.

By intersecting immune-related and differentially expressed genes, we identified immune-related differential genes associated with sepsis. Enrichment analysis revealed that these immune-associated differential genes are mainly involved in inflammatory and immunometabolic responses. In our analysis, immune cell infiltration in septic patients showed significantly higher levels of infiltration of certain intrinsic immune cells (eg, macrophages, neutrophils, mast cells) and reduced levels of infiltration of T cells (CD4, CD8) and NK cells. This is similar to the results of previous studies.25 In the sepsis host, macrophages generate pro-inflammatory cytokines and chemokines which upregulate the inflammatory response and fight pathogens by generating a systemic inflammatory response in innate immune cells.26 Neutrophils can migrate rapidly from the bone marrow in early sepsis, and within a few hours, the number of neutrophils can be 10 times higher than normal.27 The number of circulating NK cells is significantly reduced in patients with sepsis, possibly due to increased apoptosis, and the results are associated with increased mortality.28 It has been found that reduced and dysfunctional NK cells are closely associated with sepsis-induced immunosuppression, leading to a greater susceptibility to infection and a poorer prognosis for patients.29,30 It is evident that these intrinsic immune cells play an important role in the regulation of inflammation in sepsis. On the other hand, sepsis increases apoptosis, which may lead to a decrease in the number of CD4 T cells,31 and significantly reduces the proliferative capacity of CD4 T cells.32 Also, sepsis-induced apoptosis reduces the number of naive and memory CD8 T cells.33,34 Condotta et al observed increased expression of co-suppressor molecules (eg, PD-1, LAG-3 and 2B4) and reduced release of effector cytokines (eg, IFN-γ, TNF-α and IL-2), suggesting that CD8 T cells are depleted in mice and sepsis patients.35–37 Overall, our findings are largely consistent with previous reports that sepsis leads to a significant reduction in CD4 and CD8 T cells, which is a major driver of impaired immune responses and reduced host antimicrobial capacity.38–40

We further identified 10 key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) with high diagnostic efficacy by AUC analysis, all of which had AUC values greater than 0.8 for the diagnosis of sepsis in six datasets, and therefore they may be early diagnostic markers for sepsis. Analysis of the correlation between these 10 key genes and immune infiltrating cells revealed that their differential expression was mainly associated with high levels of infiltration of macrophages and neutrophils and low levels of infiltration of T cells (CD4, CD8) and NK cells significantly. We speculate that upregulation of the key genes IL1R2, LTB4R, S100A11, S100A12, SORT1 and downregulation of the key genes RASGRP1, CD3G, CD40LG, CD8A and PPP3CC may play an important role in the enhancement of intrinsic immunity and suppression of adaptive immunity during the development of sepsis. Specifically, our PCR results showed that the expression levels of 7 key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were consistent with the results of microarray analysis, while other three genes (LTB4R, S100A11, SORT1) were not significantly different. This inconsistency may be related to different assay methods, sample size, patient heterogeneity and disease duration. Later experiments with larger sample sizes are needed for validation.

The high mortality rate of sepsis has long plagued the world. Immunosuppression is now largely recognised as a major cause of sepsis-related death, with T-cell depletion being one of the most serious factors.5,41 Significant reductions in NK-cell numbers have also been associated with increased mortality.28 However, the exact mechanisms are not well understood. To further understand the association between key genes and the survival prognosis of sepsis, we used logistic regression analysis to identify 10 key genes, and the results suggested that six key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) might affect the survival prognosis of sepsis, among which S100A11 might be an independent prognostic factor for survival. These six key genes were significantly differentially expressed in the survival group compared to the non-survival group of sepsis, further suggesting that they may be prognostic markers of sepsis.

SA10012 is mainly expressed and secreted by neutrophils and is an important pro-inflammatory factor that plays an important role in microbial resistance, regulation of apoptosis and involvement in inflammatory and immune responses.42 It has been shown that S100A12 expression levels are significantly upregulated in neonatal septic rats and that it has a positive pro-inflammatory role in the inflammatory response, a role that is important in the development and progression of sepsis.42 Whereas S100A11 was mainly involved in the process of tumor development in previous studies, it is not clear whether it is associated with disease regulation in sepsis. We found that S100A11 expression was upregulated in septic patients and independently affected the prognosis of septic patients, the mechanism of which needs further investigation. RASGRP1 is a key signaling molecule essential for lymphocyte function. Immunologically, RASGRP1 deficiency is characterized by reduced CD4 and CD8 T-cell lymphocytes and impaired proliferation, as well as impaired B-cell development and proliferation, and abnormal NK cytotoxic function.43,44 RASGRP1-deficient mice have shown significant T-cell lymphopenia and immunodeficiency45 and are more susceptible than wild-type mice to sepsis infections caused by cecum ligation and puncture.46 Similarly, we found reduced levels of RASGRP1 expression in sepsis patients, suggesting that impaired immunity in sepsis patients may be associated with low RASGRP1 expression. Therefore, we speculate that RASGRP1 may play a very critical role in the development of sepsis. LTB4R is the receptor for LTB 4, which induces the recruitment of CD4 and CD8 effector T cells as well as helper T cells to inflammatory tissues,47,48 and the recruited immune cells will promote inflammation by secreting cytokines and chemokines. Sort1 may participate in innate immunity by regulating inflammation and phagocytosis, as well as participating in adaptive immunity by controlling the maturation of immune cells and regulating the activation of T cells and NK cells.49 CD3G is involved in the formation of T cell receptor CD3 complex and is an important regulator of T cell development and differentiation.50 Its defects are related to T cell immunodeficiency.51 CD8A is primarily involved in cell-mediated immune defence and T cell development.52,53 The role of these key genes in sepsis is rarely studied, but the results of our analysis suggest that they may contribute to the early diagnosis and prognostic assessment of sepsis.

Our research also has some limitations. First of all, because our research is based on multiple public datasets, the course of sepsis may be inconsistent. Secondly, due to the small sample size and short study time, there is a lack of patient data, such as blood culture results and mortality. We will increase sample size, extend research time, and add more patient data in subsequent research.

Conclusions

In conclusion, we identified ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) as potential diagnostic biomarkers of sepsis. Six key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) might affect the survival prognosis of sepsis, among which S100A11 might be an independent prognostic biomarker for survival. These genes may aid in the early diagnosis and assessment of sepsis, and could serve as interesting targets for further in-depth research.

Funding Statement

This study was supported by the Basic Science and Cutting-edge Technology Research Projects of Chongqing Science and Technology Commission (N.O. cstc2019jcyj-msxmX0848 and N.O. cstc2020jcyj-msxmX0336).

Disclosure

The authors report no conflicts of interest in this work.

References

  • 1.Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR, Vincent JL. Sepsis and septic shock. Nat Rev Dis Primers. 2016;2:16045. doi: 10.1038/nrdp.2016.45 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–211. doi: 10.1016/S0140-6736(19)32989-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–810. doi: 10.1001/jama.2016.0287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.van der PT, van de VFL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 2017;17(7):407–420. doi: 10.1038/nri.2017.36 [DOI] [PubMed] [Google Scholar]
  • 5.Patil NK, Bohannon JK, Sherwood ER. Immunotherapy: a promising approach to reverse sepsis-induced immunosuppression. Pharmacol Res. 2016;111:688–702. doi: 10.1016/j.phrs.2016.07.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Venet F, Bohe J, Debard AL, Bienvenu J, Lepape A, Monneret G. Both percentage of γδ T lymphocytes and CD3 expression are reduced during septic shock. Crit Care Med. 2005;33(12):2836–2840. doi: 10.1097/01.CCM.0000189745.66585.AE [DOI] [PubMed] [Google Scholar]
  • 7.Hotchkiss RS, Swanson PE, Freeman BD, et al. Apoptotic cell death in patients with sepsis, shock, and multiple organ dysfunction. Crit Care Med. 1999;27(7):1230–1251. doi: 10.1097/00003246-199907000-00002 [DOI] [PubMed] [Google Scholar]
  • 8.Voll RE, Herrmann M, Roth EA, Stach C, Kalden JR, Girkontaite I. Immunosuppressive effects of apoptotic cells. Nature. 1997;390(6658):350–351. doi: 10.1038/37022 [DOI] [PubMed] [Google Scholar]
  • 9.Coburn B, Morris AM, Tomlinson G, Detsky AS. Does this adult patient with suspected bacteremia require blood cultures? JAMA. 2012;308(5):502–511. doi: 10.1001/jama.2012.8262 [DOI] [PubMed] [Google Scholar]
  • 10.Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI. Emerging technologies for molecular diagnosis of sepsis. Clin Microbiol Rev. 2018;31(2):e00089–17. doi: 10.1128/CMR.00089-17 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Miller RR 3rd, Lopansri BK, Burke JP, et al. Validation of a host response assay, septicyte lab, for discriminating sepsis from systemic inflammatory response syndrome in the ICU. Am J Respir Crit Care Med. 2018;198(7):903–913. doi: 10.1164/rccm.201712-2472OC [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(D1):D991–5. doi: 10.1093/nar/gks1193 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ginestet C. ggplot2: elegant graphics for data analysis. J R Stat Soc Ser A Stat Soc. 2011;174(1):245. doi: 10.1111/j.1467-985X.2010.00676_9.x [DOI] [Google Scholar]
  • 15.Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods. 2015;12(5):453–457. doi: 10.1038/nmeth.3337 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12(1):77. doi: 10.1186/1471-2105-12-77 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: a comprehensive database of experimentally supported animal microRNA targets TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA. 2006;12(2):192–197. doi: 10.1261/rna.2239606 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Da Hsu S, Lin FM, Wu WY, et al. MiRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011;39(suppl_1):163–169. doi: 10.1093/nar/gkq1107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Khan A, Fornes O, Stigliani A, et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 2018;46(D1):D260–D266. doi: 10.1093/nar/gkx1126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Xia J, Gill EE, Hancock REW. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–844. doi: 10.1038/nprot.2015.052 [DOI] [PubMed] [Google Scholar]
  • 21.Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol. 2011;696:291–303. [DOI] [PubMed] [Google Scholar]
  • 22.Davis AP, Grondin CJ, Johnson RJ, et al. The comparative toxicogenomics database: update 2017. Nucleic Acids Res. 2017;45(D1):D972–8. doi: 10.1093/nar/gkw838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009;25(2):288–289. doi: 10.1093/bioinformatics/btn615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Fernando SM, Rochwerg B, Seely AJ. Clinical implications of the third international consensus definitions for sepsis and septic shock (Sepsis-3). CMAJ. 2018;190(36):E1058–9. doi: 10.1503/cmaj.170149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Coopersmith CM, De Backer D, Deutschman CS, et al. Surviving sepsis campaign: research priorities for sepsis and septic shock. Intensive Care Med. 2018;44(9):1400–1426. doi: 10.1007/s00134-018-5175-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Vachharajani V, McCall CE. Epigenetic and metabolic programming of innate immunity in sepsis. Innate Immun. 2019;25(5):267–279. doi: 10.1177/1753425919842320 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Manz MG, Boettcher S. Emergency granulopoiesis. Nat Rev Immunol. 2014;14(5):302. doi: 10.1038/nri3660 [DOI] [PubMed] [Google Scholar]
  • 28.Jensen IJ, Winborn CS, Fosdick MG, et al. Polymicrobial sepsis influences NK-cell-mediated immunity by diminishing NK-cell-intrinsic receptor-mediated effector responses to viral ligands or infections. PLoS Pathog. 2018;14(10):e1007405. doi: 10.1371/journal.ppat.1007405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Demirkol D, Kavgaci U, Babaoglu B, Tanju S, Oflaz Sozmen B, Tekin S. Cytomegalovirus reactivation in a critically ill patient: a case report. J Med Case Rep. 2018;12(1):163. doi: 10.1186/s13256-018-1681-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cook CH, Trgovcich J. Cytomegalovirus reactivation in critically ill immunocompetent hosts: a decade of progress and remaining challenges. Antiviral Res. 2011;90(3):151–159. doi: 10.1016/j.antiviral.2011.03.179 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Boomer JS, To K, Chang KC, et al. Immunosuppression in patients who die of sepsis and multiple organ failure. JAMA. 2011;306(23):2594–2605. doi: 10.1001/jama.2011.1829 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Cabrera-Perez J, Condotta SA, James BR, et al. Alterations in antigen-specific naive CD4 T cell precursors after sepsis impairs their responsiveness to pathogen challenge. J Immunol. 2015;194(4):1609–1620. doi: 10.4049/jimmunol.1401711 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hotchkiss RS, Osmon SB, Chang KC, Wagner TH, Coopersmith CM, Karl IE. Accelerated lymphocyte death in sepsis occurs by both the death receptor and mitochondrial pathways. J Immunol. 2005;174(8):5110–5118. doi: 10.4049/jimmunol.174.8.5110 [DOI] [PubMed] [Google Scholar]
  • 34.Weber SU, Schewe JC, Lehmann LE, et al. Induction of Bim and Bid gene expression during accelerated apoptosis in severe sepsis. Crit Care. 2008;12(5):R128. doi: 10.1186/cc7088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Condotta SA, Khan SH, Rai D, Griffith TS, Badovinac VP. Polymicrobial sepsis increases susceptibility to chronic viral infection and exacerbates CD8 +T cell exhaustion. J Immunol. 2015;195(1):116–125. doi: 10.4049/jimmunol.1402473 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Xie J, Crepeau RL, Chen CW, et al. Sepsis erodes CD8(+) memory T cell-protective immunity against an EBV homolog in a 2B4-dependent manner. J Leukoc Biol. 2019;105:565–575. doi: 10.1002/JLB.4A0718-292R [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Condotta SA, Rai D, James BR, Griffith TS, Badovinac VP. Sustained and incomplete recovery of naive CD8+ T cell precursors after sepsis contributes to impaired CD8+ T cell responses to infection. J Immunol. 2013;190:1991–2000. doi: 10.4049/jimmunol.1202379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hotchkiss RS, Tinsley KW, Swanson PE, et al. Sepsis-induced apoptosis causes progressive profound depletion of B and CD4+ T lymphocytes in humans. J Immunol. 2001;166(11):6952–6963. doi: 10.4049/jimmunol.166.11.6952 [DOI] [PubMed] [Google Scholar]
  • 39.Unsinger J, McDonough JS, Shultz LD, Ferguson TA, Hotchkiss RS. Sepsis-induced human lymphocyte apoptosis and cytokine production in“humanized”. Mice J Leukoc Biol. 2009;86(2):219–227. doi: 10.1189/jlb.1008615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Hotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach. Lancet Infect Dis. 2013;13(3):260–268. doi: 10.1016/S1473-3099(13)70001-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Jensen IJ, Sjaastad FV, Griffith TS, Badovinac VP. Sepsis-induced T cell immunoparalysis: the ins and outs of impaired T cell immunity. J Immunol. 2018;200(5):1543–1553. doi: 10.4049/jimmunol.1701618 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Enerback C, Shubbar E, Petersson S. Expression of S100 proteins in the progression of melanocytic lesions[J]. Eur J Cancer Suppl. 2008;6(9):133. doi: 10.1016/S1359-6349(08)71686-0 [DOI] [Google Scholar]
  • 43.Salzer E, Cagdas D, Hons M, et al. RASGRP1 deficiency causes immunodeficiency with impaired cytoskeletal dynamics. Nat Immunol. 2016;17(12):1352–1360. doi: 10.1038/ni.3575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Platt CD, Fried AJ, Hoyos-Bachiloglu R, et al. Combined immunodeficiency with EBV positive B cell lymphoma and epidermodysplasia verruciformis due to a novel homozygous mutation in RASGRP1. Clin Immunol. 2017;183:142–144. doi: 10.1016/j.clim.2017.08.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Dower NA, Stang SL, Bottorff DA, et al. RasGRP is essential for mouse thymocyte differentiation and TCR signaling. Nat Immunol. 2000;1(4):317–321. doi: 10.1038/79766 [DOI] [PubMed] [Google Scholar]
  • 46.Guo B, Ludlow AV, Brightwell AS, et al. Impairment of PD-L2 positive b1a cells enhances susceptibility to sepsis in RasGRP1-deficient mice. Cell Immunol. 2019;346:103993. doi: 10.1016/j.cellimm.2019.103993 [DOI] [PubMed] [Google Scholar]
  • 47.Goodarzi K, Goodarzi M, Tager AM, Luster AD, von Andrian UH. Leukotriene B4 and BLT1 control cytotoxic effector T cell recruitment to inflamed tissues. Nat Immunol. 2003;4(10):965–973. doi: 10.1038/ni972 [DOI] [PubMed] [Google Scholar]
  • 48.Tager AM, Bromley SK, Medoff BD, et al. Leukotriene B4 receptor BLT1 mediates early effector T cell recruitment. Nat Immunol. 2003;4(10):982–990. doi: 10.1038/ni970 [DOI] [PubMed] [Google Scholar]
  • 49.Talbot H, Saada S, Naves T, et al. Regulatory roles of Sortilin and SorLA in immune-related processes[J]. Front Pharmacol;2019. 9. doi: 10.3389/fphar.2019.00009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Flanagan BF, Wotton D, Tuck-Wah S, et al. DNase hypersensitivity and methylation of the human CD3G and D genes during T-cell development. Immunogenetics. 1990;31(1):13–20. doi: 10.1007/BF00702484 [DOI] [PubMed] [Google Scholar]
  • 51.Alain F, Geneviève DSB, Franoise LD. CD3 Deficiencies. Curr Opin Allergy Clin Immunol. 2005;5(6):491–495. doi: 10.1097/01.all.0000191886.12645.79 [DOI] [PubMed] [Google Scholar]
  • 52.Tregaskes CA, Kong FK, Paramithiotis E, et al. Identification and analysis of the expression of CD8 alpha beta and CD8 alpha alpha isoforms in chickens reveals a major TCR-gamma delta CD8 alpha beta subset of intestinal intraepithelial lymphocytes. J Immunol. 1995;154(9):4485–4494. [PubMed] [Google Scholar]
  • 53.Xu Q, Chen Y, Zhao WM, et al. DNA methylation and regulation of the CD8A after duck hepatitis virus type 1 infection. PLoS One. 2014;9(2):e88023. doi: 10.1371/journal.pone.0088023 [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from International Journal of General Medicine are provided here courtesy of Dove Press

RESOURCES