Identification of potential genes and miRNAs associated with sepsis based on microarray analysis

Genome-wide identification and functional analysis of dysregulated alternative splicing profiles in sepsis

BACKGROUND

An increasing body of evidence now shows that the long-term mortality of patients with sepsis are associated with various sepsis-related immune cell defects. Alternative splicing (AS), as a sepsis-related immune cell defect, is considered as a potential immunomodulatory therapy target to improve patient outcomes. However, our understanding of the role AS plays in sepsis is currently insufficient.

AIM

This study investigated possible associations between AS and the gene regulatory networks affecting immune cells. We also investigated apoptosis and AS functionality in sepsis pathophysiology.

METHODS

In this study, we assessed publicly available mRNA-seq data that was obtained from the NCBI GEO dataset (GSE154918), which included a healthy group (HLTY), a mild infection group (INF1), asepsis group (Seps), and a septic shock group (Shock). A total of 79 samples (excluding significant outliers) were identified by a poly-A capture method to generate RNA-seq data. The variable splicing events and highly correlated RNA binding protein (RBP) genes in each group were then systematically analyzed.

RESULTS

For the first time, we used systematic RNA-seq analysis of sepsis-related AS and identified 1505 variable AS events that differed significantly (p <= 0.01) across the four groups. In the sepsis group, the genes related to significant AS events, such as, SHISA5 and IFI27, were mostly enriched in the cell apoptosis pathway. Furthermore, we identified differential splicing patterns within each of the four groups. Significant differences in the expression of RNA Binding Protein(RBP) genes were observed between the control group and the sepsis group. RBP gene expression was highly correlated with variant splicing events in sepsis, as determined by co-expression analysis; The expression of DDX24, CBFA2T2, NOP, ILF3, DNMT1, FTO, PPRC1, NOLC1 RBPs were significant reduced in sepsis compared to the healthy group. Finally, we constructed an RBP-AS functional network.

CONCLUSION

Analysis indicated that the RBP-AS functional network serves as a critical post-transcriptional mechanism that regulates the development of sepsis. AS dysregulation is associated with alterations in the regulatory gene expression network that is involved in sepsis. Therefore, the RBP-AS expression network could be useful in refining biomarker predictions in the development of new therapeutic targets for the pathogenesis of sepsis.

Identification and validation of key biomarkers based on RNA methylation genes in sepsis

Background

RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis.

Methods

Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis.

Results

Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively.

Conclusions

Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.

Liver proteomic analysis reveals the key proteins involved in host immune response to sepsis

Background

Sepsis is a serious infection-induced response in the host, which can result in life-threatening organ dysfunction. It is of great importance to unravel the relationship between sepsis and host immune response and its mechanisms of action. Liver is one of the most vulnerable organs in sepsis, however, the specific pathogenesis of septic liver injury has not been well understood at the protein level.

Methods

A total of 12 healthy Sprague-Dawley (SD) male rats aged from 6 to 8 weeks were adaptively housed in individual cages in the specific pathogen free animal room. These lab rats were grouped into two groups: treatment (N = 9) and control (N = 3) groups; only three mice from the treatment group survived and were used for subsequent experiments. A TMT-based proteomic analysis for liver tissue was performed in the septic rat model.

Results

A total of 37,012 unique peptides were identified, and then 6,166 proteins were determined, among which 5,701 were quantifiable. Compared to the healthy control group, the septic rat group exhibited 162 upregulated and 103 downregulated differentially expressed proteins (DEPs). The upregulated and downregulated DEPs were the most significantly enriched into the complement and coagulation cascades and metabolic pathways. Protein-protein interaction (PPI) analysis further revealed that the upregulated and downregulated DEPs each clustered in a PPI network. Several highly connected upregulated and downregulated DEPs were also enriched into the complement and coagulation cascades pathways and metabolic pathways, respectively. The parallel reaction monitoring (PRM) results of the selected DEPs were consistent with the results of the TMT analysis, supporting the proteomic data.

Conclusion

Our findings highlight the roles of complement and coagulation cascades and metabolic pathways that may play vital roles in the host immune response. The DEPs may serve as clinically potential treatment targets for septic liver injury.

DDX10 and BYSL as the potential targets of chondrosarcoma and glioma

To provide reliable molecular markers and effective therapeutic targets for chondrosarcoma and glioma.Gene Set Enrichment (GSE) 29745 and GSE48420 were downloaded from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) were identified by the GEO2R. We annotated the function of common DEGs through Digital Audio/Video Interactive Decoder (DAVID) and Metascape. Protein-protein interaction network construction was performed through STRING. Hub genes were identified by the two different algorithms (MCC, EPC). DDX10 and BYSL were key factors in embryo implantation and development, and plays a role in a variety of cancers. The role of the DDX10 and BYSL on the glioma derived from the chondrosarcoma would be explored by the clinical samples.A total of 1442 DEGs were identified. The variations in DEGs were mainly enriched in vasculature development, cell motion, blood vessel development, cell migration, regulation of cell proliferation, regulation of cell proliferation, wound healing, biological adhesion, growth factor binding, identical pathways in cancer, and p53 signaling pathway. Dead-box helicase 10 (DDX10), Bystin-like (BYSL), and WD repeat domain 12 (WDR12) were identified as the hub genes, and the three hub genes were up-regulated in the chondrosarcoma. Chondrosarcoma patients with high expression levels of DDX10 (Logrank P = .0052; HR (high) = 1.8; n (high) = 131, 50%), and BYSL (P = 6.5e-05; HR (high) = 2.3; n (high) = 131, 50%) had poorer overall survival times than those with low expression levels.DDX10 and BYSL genes may provide reliable molecular markers and effective therapeutic targets for chondrosarcoma and glioma.

The lncRNA CCAT2 Rs6983267 G Variant Contributes to Increased Sepsis Susceptibility in a Southern Chinese Population

PURPOSE

Accumulating evidence demonstrates that genetic susceptibility genes can be used as biomarkers to assess sepsis susceptibility, and genetic variation is associated with susceptibility and clinical outcomes in patients with sepsis and inflammatory disease. Although studies have shown that the lncRNA CCAT2 is involved in inflammatory diseases, it remains unclear whether CCAT2 gene polymorphisms are associated with susceptibility to inflammatory diseases, such as sepsis, in children.

METHODS

We genotyped the rs6983267 CCAT2 polymorphism in 474 cases (pediatric sepsis) and 678 controls using TaqMan methods, and odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the strength of associations.

RESULTS

Our results indicate that the rs6983267 T > G polymorphism is significantly associated with an increased risk of sepsis in children (TG and TT: adjusted OR = 1.311, 95% CI = 1.016-1.743, GG and TT: adjusted OR = 1.444, 95% CI = 1.025-2.034 dominant model: GG/TG vs TT adjusted OR = 1.362, 95% CI = 1.055-1.756). Furthermore, the risk effect was more pronounced in children younger than 60 months who were male and who had sepsis.

CONCLUSION

We found that the CCAT2 gene polymorphism rs6983267 T > G may be associated with an increased risk of pediatric sepsis in southern China. A larger multicenter study should be performed to confirm these results.

MYC Participates in Lipopolysaccharide-Induced Sepsis via Promoting Cell Proliferation and Inhibiting Apoptosis

OBJECTIVE

This study aimed to explore the potential mechanism of MYC proto-oncogene, BHLH Transcription Factor (MYC) gene, on sepsis.

MATERIALS AND METHODS

In this experimental study, rat-derived H9C2 cardiomyocyte cells were cultured in vitro, followed by lipopolysaccharide (LPS) treatment with different concentration gradients. The cholecystokinin octapeptide (CCK-8) assay, enzyme-linked immunoassay (ELISA) assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), cell transfection, Western blot and flow cytometry were used to observe the cellular apoptosis and proliferation of cells in both treated LPS groups and normal control group.

RESULTS

The result of CCK-8 assay showed that silencing MYC inhibited cellular proliferation of sepsis in absence or presence of LPS treatment. ELISA assay showed that the expressions of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) were decreased in MYC silenced group, but they were increased after LPS treatment. Moreover, Flow cytometry assay showed that MYC silencing contributed to the apoptosis of sepsis cells. Furthermore, the expression of inflammatory factors showed that MYC silencing elevated the expression of inflammation factors.

CONCLUSION

MYC might take part in the process of LPS induced sepsis through suppressing apoptosis and inducing cell proliferation. Moreover, MYC might reduce inflammation during the progression of LPS induced sepsis.

Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis

Background

This work aimed to screen key biomarkers related to sepsis progression by bioinformatics analyses.

Material/Methods

The microarray datasets of blood and neutrophils from patients with sepsis or septic shock were downloaded from Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) from 4 groups (sepsis versus normal blood samples; septic shock versus normal blood samples; sepsis neutrophils versus normal controls and septic shock neutrophils versus controls) were respectively identified followed by functional analyses. Subsequently, protein–protein network was constructed, and key functional sub-modules were extracted. Finally, receiver operating characteristic analysis was conducted to evaluate diagnostic values of key genes.

Results

There were 2082 DEGs between blood samples of sepsis patients and controls, 2079 DEGs between blood samples of septic shock patients and healthy individuals, 6590 DEGs between neutrophils from sepsis and controls, and 1056 DEGs between neutrophils from septic shock patients and normal controls. Functional analysis showed that numerous DEGs were significantly enriched in ribosome-related pathway, cell cycle, and neutrophil activation involved in immune response. In addition, TRIM25 and MYC acted as hub genes in protein–protein interaction (PPI) analyses of DEGs from microarray datasets of blood samples. Moreover, MYC (AUC=0.912) and TRIM25 (AUC=0.843) had great diagnostic values for discriminating septic shock blood samples and normal controls. RNF4 was a hub gene from PPI analyses based on datasets from neutrophils and RNF4 (AUC=0.909) was capable of distinguishing neutrophil samples from septic shock samples and controls.

Conclusions

Our findings identified several key genes and pathways related to sepsis development.