Gene Co-Expression Analysis Identified Preserved and Survival-Related Modules in Severe Blunt Trauma, Burns, Sepsis, and Systemic Inflammatory Response Syndrome

A gene network database for the identification of key genes for diagnosis, prognosis, and treatment in sepsis

Sepsis and sepsis-related diseases cause a high rate of mortality worldwide. The molecular and cellular mechanisms of sepsis are still unclear. We aim to identify key genes in sepsis and reveal potential disease mechanisms. Six sepsis-related blood transcriptome datasets were collected and analyzed by weighted gene co-expression network analysis (WGCNA). Functional annotation was performed in the gProfiler tool. DSigDB was used for drug signature enrichment analysis. The proportion of immune cells was estimated by the CIBERSORT tool. The relationships between modules, immune cells, and survival were identified by correlation analysis and survival analysis. A total of 37 stable co-expressed gene modules were identified. These modules were associated with the critical biology process in sepsis. Four modules can independently separate patients with long and short survival. Three modules can recurrently separate sepsis and normal patients with high accuracy. Some modules can separate bacterial pneumonia, influenza pneumonia, mixed bacterial and influenza A pneumonia, and non-infective systemic inflammatory response syndrome (SIRS). Drug signature analysis identified drugs associated with sepsis, such as testosterone, phytoestrogens, ibuprofen, urea, dichlorvos, potassium persulfate, and vitamin B12. Finally, a gene co-expression network database was constructed ( https://liqs.shinyapps.io/sepsis/ ). The recurrent modules in sepsis may facilitate disease diagnosis, prognosis, and treatment.

Comprehensive analysis of immune cell landscapes revealed that immune cell ratio eosinophil/B.cell.memory is predictive of survival in sepsis

BACKGROUND

Immune dysregulation is a feature of sepsis. However, a comprehensive analysis of the immune landscapes in septic patients has not been conducted.

OBJECTIVES

This study aims to explore the abundance ratios of immune cells in sepsis and investigate their clinical value.

METHODS

Sepsis transcriptome data sets were downloaded from the NCBI GEO database. The immunedeconv R package was employed to analyze the abundance of immune cells in sepsis patients and calculate the ratios of different immune cell types. Differential analysis of immune cell ratios was performed using the t test. The Spearman rank correlation coefficient was utilized to find the relationships between immune cell abundance and pathways. The prognostic significance of immune cell ratios for patient survival probability was assessed using the log-rank test. In addition, differential gene expression was performed using the limma package, and gene co-expression analysis was executed using the WGCNA package.

RESULTS

We found significant changes in immune cell ratios between sepsis patients and healthy controls. Some of these ratios were associated with 28-day survival. Certain pathways showed significant correlations with immune cell ratios. Notably, six immune cell ratios demonstrated discriminative ability for patients with systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis, with an Area Under the Curve (AUC) larger than 0.84. Patients with a high eosinophil/B.cell.memory ratio exhibited poor survival outcomes. A total of 774 differential genes were identified in sepsis patients with a high eosinophil/B.cell.memory ratio compared to those with a low ratio. These genes were organized into seven co-expression modules associated with relevant pathways, including interferon signaling, T-cell receptor signaling, and specific granule pathways.

CONCLUSIONS

Immune cell ratios eosinophil/B.cell.memory and NK.cell.activated/NK.cell.resting in sepsis patients can be utilized for disease subtyping, prognosis, and diagnosis. The proposed cell ratios may have higher prognostic values than the neutrophil-to-lymphocyte ratio (NLR).

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

Background

Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.

Methods

Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.

Results

Eighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.

Conclusion

Sepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening.

Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning

Background

To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis.

Methods

The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting.

Results

Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all P < 0.05). Moreover, YOD1, GADD45A and IL1R2 were upregulated, and BCL11B was downregulated in the heart, liver, lungs, and kidneys of the septic mice model.

Conclusions

We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

The Network of miRNA-mRNA Interactions in Circulating T Cells of Patients Following Major Trauma - A Pilot Study

Purpose

Following major trauma, genes involved in adaptive immunity are downregulated, which accompanies the upregulation of genes involved in systemic inflammatory responses. This study investigated microRNA (miRNA)-mRNA interactome dysregulation in circulating T cells of patients with major trauma.

Patients and Methods

This study included adult trauma patients who had an injury severity score ≥16 and required ventilator support for more than 48 h in the intensive care unit. Next-generation sequencing was used to profile the miRNAs and mRNAs expressed in CD3+ T cells isolated from patient blood samples collected during the injury and recovery stages.

Results

In the 26 studied patients, 9 miRNAs (hsa-miR-16-2-3p, hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-192-5p, hsa-miR-197-3p, hsa-miR-23a-3p, hsa-miR-26b-5p, hsa-miR-223-3p, and hsa-miR-485-5p) were significantly upregulated, while 58 mRNAs were significantly downregulated in T cells following major trauma. A network consisting of 8 miRNAs and 22 mRNAs interactions was revealed by miRWalk, with three miRNAs (hsa-miR-185-5p, hsa-miR-197-3p, and hsa-miR-485-5p) acting as hub genes that regulate the network. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that “chemokine signaling pathway” was the predominant pathway.

Conclusion

The study revealed a miRNA-mRNA interactome consisting of 8 miRNAs and 22 mRNAs that are predominantly involved in chemokine signaling in circulating T cells of patients following major trauma.

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

BACKGROUND

Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE

Use of reduced vector to represent transcriptome data independent of platforms.

METHODS

Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS

Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS

TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .