Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

The molecular landscape of sepsis severity in infants: enhanced coagulation, innate immunity, and T cell repression

Introduction

Sepsis remains a major cause of mortality and morbidity in infants. In recent years, several gene marker strategies for the early identification of sepsis have been proposed but only a few have been independently validated for adult cohorts and applicability to infant sepsis remains unclear. Biomarkers to assess disease severity and risks of shock also represent an important unmet need.

Methods

To elucidate characteristics driving sepsis in infants, we assembled a multi-transcriptomic dataset from public microarray datasets originating from five independent studies pertaining to bacterial sepsis in infant < 6-months of age (total n=335). We utilized a COmbat co-normalization strategy to enable comparative evaluation across multiple studies while preserving the relationship between cases and controls.

Results

We found good concordance with only two out of seven of the published adult sepsis gene signatures (accuracy > 80%), highlighting the narrow utility of adult-derived signatures for infant diagnosis. Pseudotime analysis of individual subjects' gene expression profiles showed a continuum of molecular changes forming tight clusters concurrent with disease progression between healthy controls and septic shock cases. In depth gene expression analyses between bacteremia, septic shock, and healthy controls characterized lymphocyte activity, hemostatic processes, and heightened innate immunity during the molecular transition toward a state of shock.

Discussion

Our analysis revealed the presence of multiple significant transcriptomic perturbations that occur during the progression to septic shock in infants that are characterized by late-stage induction of clotting factors, in parallel with a heightened innate immune response and a suppression of adaptive cell functionality.

Identification of Key Immune-Related Genes in the Progression of Septic Shock

Objective: Septic shock is the severe complication of sepsis, with a high mortality. The inflammatory response regulates the immune status and mediates the progression of septic shock. In this study, we aim to identify the key immune-related genes (IRGs) of septic shock and explore their potential mechanism. Methods: Gene expression profiles of septic shock blood samples and normal whole blood samples were retrieved from the Gene Expression Omnibus (GEO) and Genotype-Tissue Expression Portal (GTEx). The differential expression genes (DEGs) and septic shock-specific immune-related genes (SSSIRGs) were evaluated and identified, along with the immune components by "cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT, version x)" algorithm. Additionally, in order to explore the key regulatory network, the relationship among SSSIRGs, upstream transcription factors (TFs), and downstream signaling pathways were also identified by Gene Set Variation Analysis (GSVA) and co-expression analysis. Moreover, the Connectivity Map (CMap) analysis was applied to find bioactive small molecules against the members of regulation network while Chromatin Immunoprecipitation sequencing (ChIP-seq) and Assay for Targeting Accessible-Chromatin with high-throughput sequencing (ATAC-seq) data were used to validate the regulation mechanism of the network. Results: A total of 14,843 DEGs were found between 63 septic shock blood samples and 337 normal whole blood samples. Then, we identified septic shock-specific 839 IRGs as the intersection of DEGs and IRGs. Moreover, we uncovered the regulatory networks based on co-expression analysis and found 28 co-expression interaction pairs. In the regulation network, protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA) may regulate late estrogen response, glycolysis and TNFα signaling via NFκB and HLA; Kirsten rat sarcoma viral oncogene homolog (KRAS) may be related to late estrogen response and HLA; and Toll-like receptor 8 (TLR8) may be associated with TNFα signaling via NFκB. And the regulation mechanisms between TFs and IRGs (TLR8, PPP3CA, and KRAS) were validated by ChIP-seq and ATAC-seq. Conclusion: Our data identify three SSSIRGs (TLR8, PPP3CA, and KRAS) as candidate therapeutic targets for septic shock and provide constructed regulatory networks in septic shock to explore its potential mechanism.

MicroRNA-877-5p alleviates ARDS via enhancing PI3K/Akt path by targeting CDKN1B both in vivo and in vitro

Acute respiratory distress syndrome (ARDS) is a public health problem with high morbidity and mortality worldwide due to lacking known characteristic biomarkers and timely intervention. Pulmonary edema caused by inflammation and pulmonary microvascular endothelial cell disfunction is the main pathophysiological change of ARDS. Circulating microRNAs (miRNAs) are differentially expressed between subjects who did and did not develop ARDS. Many miRNAs have been exemplified to be involved in ARDS and could represent the novel therapeutic targets, but the role of microRNA-877-5p (miR-877-5p) in ARDS and its regulatory mechanisms are still unknown. Herein, we explore the underlying function of miR-877-5p toward anesis of ARDS and addressed that miRNA-877 can reduce the release of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6 thus attenuating the damage of pulmonary microvascular endothelial cells (HPMECs). Have further evaluated the protein expression, we detected that miR-877-5p contributed to the relief of ARDS by suppressing Cyclin-dependent kinase inhibitor 1B (CDKN1B), which serves as a regulator of endothelial cell polarization and migration through phosphatidylinositol-3-kinase and AKT (PI3K/Akt) signaling pathway. Besides, we noticed that CDKN1B restrains cell differentiation by inhibiting Cdk2 (cyclin-dependent kinase 2), instead of Cdk4 (cyclin-dependent kinase 4), during which the nuclear translocation of CDKN1B may participate. Together, our works testified that miR-877-5p might suppress inflammatory responses and promote HPMECs regeneration via targeting CDKN1B by modulation of Cdk2 and PI3K/Akt path. These molecules likely modulating ARDS progression may inform biomarkers and therapeutic development.

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Objective

Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock.

Methods

The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults).

Results

The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation.

Conclusion

This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.

Gene correlation network analysis to identify regulatory factors in sepsis

BACKGROUND AND OBJECTIVES

Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments.

METHODS

The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA.

RESULTS

A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018).

CONCLUSIONS

The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

Identification of Key mRNAs and lncRNAs in Neonatal Sepsis by Gene Expression Profiling

Neonatal sepsis is one of the most prevalent causes of death of the neonates. However, the mechanisms underlying neonatal sepsis remained unclear. The present study identified a total of 1128 upregulated mRNAs and 1008 downregulated mRNAs, 28 upregulated lncRNAs, and 61 downregulated lncRNAs in neonatal sepsis. Then, we constructed PPI networks to identify key regulators in neonatal sepsis, including ITGAM, ITGAX, TLR4, ITGB2, SRC, ELANE, RPLP0, RPS28, RPL26, and RPL27. lncRNA coexpression analysis showed HS.294603, LOC391811, C12ORF47, LOC729021, HS.546375, HNRPA1L-2, LOC158345, and HS.495041 played important roles in the progression of neonatal sepsis. Bioinformatics analysis showed DEGs were involved in the regulation cellular extravasation, acute inflammatory response, macrophage activation of NF-kappa B signaling pathway, TNF signaling pathway, HIF-1 signaling pathway, Toll-like receptor signaling pathway, and ribosome, RNA transport, and spliceosome. lncRNAs were involved in regulating ribosome, T cell receptor signaling pathway, RNA degradation, insulin resistance, ribosome biogenesis in eukaryotes, and hematopoietic cell lineage. We thought this study provided useful information for identifying novel therapeutic markers for neonatal sepsis.