Identification of Potential Transcriptional Biomarkers Differently Expressed in Both S. aureus- and E. coli-Induced Sepsis via Integrated Analysis

Identification and validation of diagnostic biomarkers and immune cell abundance characteristics in Staphylococcus aureus bloodstream infection by integrative bioinformatics analysis

Staphylococcus aureus (S. aureus) is an opportunistic pathogen that could cause life-threatening bloodstream infections. The objective of this study was to identify potential diagnostic biomarkers of S. aureus bloodstream infection. Gene expression dataset GSE33341 was optimized as the discovery dataset, which contained samples from human and mice. GSE65088 dataset was utilized as a validation dataset. First, after overlapping the differentially expressed genes (DEGs) in S. aureus infection samples from GSE33341-human and GSE33341-mice samples, we detected 63 overlapping genes. Subsequently, the hub genes including DRAM1, PSTPIP2, and UPP1 were identified via three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. Additionally, the receiver operating characteristic curve was leveraged to verify the efficacy of the hub genes. DRAM1 (AUC=1), PSTPIP2 (AUC=1), and UPP1 (AUC=1) were investigated and demonstrated significant expression differences (all P < 0.05) and diagnostic efficacy in the training and validation datasets. Furthermore, the relationship between the diagnostic markers and the abundance of immune cells was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). These three diagnostic indicators also correlated with multiple immune cells to varying degrees. The expression of DRAM1 was significantly positively correlated with B cell naive and mast cell activation, and negatively correlated with NK cells and CD4/CD8+ T cells. The expression of PSTPIP2 was significantly positively correlated with macrophage M0, macrophage M1, B cell naive, and dendritic cell activation, while the expression of PSTPIP2 was negatively correlated with NK cells and CD4/CD8+ T cells. Significant negative correlations between UPP1 expression and T cell CD4 memory rest and neutrophils were also observed. Finally, we established a mouse model of S. aureus bloodstream infection and collected the blood samples for RNA-Seq analysis and RT-qPCR experiments. The analysis results in RNA-Seq and RT-qPCR experiments further confirmed the significant expression differences (all P < 0.05) of these three genes. Overall, three candidate hub genes (DRAM1, PSTPIP2, and UPP1) were identified initially for S. aureus bloodstream infection diagnosis. Our study could provide potential diagnostic biomarkers for S. aureus bloodstream infection patients.

Evaluating the Causal Effects of Circulating Proteome on the Risk of Sepsis and Related Outcomes

The current investigation deployed Mendelian randomization (MR) to elucidate the causal relationship between circulating proteins and sepsis. A rigorous two-sample MR analysis evaluated the effect of plasma proteins on the sepsis susceptibility. To affirm the integrity of MR findings, a suite of supplementary analyses, including Bayesian colocalization, Steiger filtering, the assessment of protein-altering polymorphisms, and the correlation between expression quantitative trait loci and protein quantitative trait loci (pQTLs), was employed. The study further integrated the examination of protein-protein interactions and pathway enrichment, along with the identification of pharmacologically actionable targets, to advance our comprehension and outline potential sepsis therapies. Subsequent analyses leveraging cis-pQTLs within MR studies unveiled noteworthy relationships: 94 specific proteins exhibited significant links with sepsis-related 28 day mortality, while 96 distinct proteins correlated with survival outcomes in sepsis. Furthermore, incorporating both cis- and trans-pQTLs in MR investigations revealed more comprehensive findings, associating 201 unique proteins with sepsis-related 28 day mortality and 199 distinct proteins with survival outcomes in sepsis. Markedly, colocalization analyses confirmed that eight of these proteins exhibited prominent evidence for colocalization, emphasizing their potential criticality in sepsis pathophysiology. Further in silico analyses were conducted to delineate putative regulatory networks and to highlight prospective drug targets among these proteins. Employing the MR methodology has shed light on plasma proteins implicated in the etiopathogenesis of sepsis. This novel approach unveiled numerous biomarkers and targets, providing a scientific rationale for the development of new therapeutic strategies and prophylactic measures against sepsis.

Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood.

METHODS

Multiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds.

RESULTS

We elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis.

CONCLUSION

Our study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

Insights Into the Impact of Small RNA SprC on the Metabolism and Virulence of Staphylococcus aureus

Aim

Our previous proteomic analysis showed that small RNA SprC (one of the small pathogenicity island RNAs) of Staphylococcus aureus possesses the ability to regulate the expression of multiple bacterial proteins. In this study, our objective was to further provide insights into the regulatory role of SprC in gene transcription and metabolism of S. aureus.

Methods

Gene expression profiles were obtained from S. aureus N315 wild-type and its sprC deletion mutant strains by RNA-sequencing (RNA-seq), and differentially expressed genes (DEGs) were screened by R language with a |log2(fold change)| ≥1 and a false discovery rate (FDR) ≤ 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out to understand the significance of the DEGs. The quality of RNA-seq was further verified by quantitative real-time PCR (qRT-PCR), mRNA target prediction, metabolomics analysis and transcript-level expression analysis of genes of sprC complementation strain.

Results

A total of 2497 transcripts were identified, of which 60 transcripts expressions in sprC knockout strain were significantly different (37 up-regulated and 23 down-regulated DEGs). GO analysis showed that the functions of these DEGs were mainly concentrated in the biological process and molecular function related to metabolism and pathogenesis, and a higher number of genes were involved in the oxidation-reduction process, catalytic activity and binding. KEGG pathways enrichment analysis demonstrated that metabolism and pathogenesis were the most affected pathways, such as metabolic pathways, biosynthesis of secondary metabolites, purine metabolism, fructose and mannose metabolism and S. aureus infection. The qRT-PCR results of the DEGs with defined functions in the sprC deletion and complementation strains were in general agreement with those obtained by RNA-seq. Metabolomics analysis revealed 77 specific pathways involving metabolic pathways. Among them, many, such as metabolic pathways, biosynthesis of secondary metabolites and purine metabolism, were consistent with those enriched in the RNA-seq analysis.

Conclusion

This study offered valuable and reliable information about the regulatory roles of SprC in S. aureus biology through transcriptomics and metabolomics analysis. These results may provide clues for new potential targets for anti-virulence adjuvant therapy on S. aureus infection.

Role of the F-BAR Family Member PSTPIP2 in Autoinflammatory Diseases

Proline-serine-threonine-phosphatase-interacting protein 2 (PSTPIP2) belongs to the Fes/CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain family. It exhibits lipid-binding, membrane deformation, and F-actin binding activity, suggesting broader roles at the membrane–cytoskeleton interface. PSTPIP2 is known to participate in macrophage activation, neutrophil migration, cytokine production, and osteoclast differentiation. In recent years, it has been observed to play important roles in innate immune diseases and autoinflammatory diseases (AIDs). Current research indicates that the protein tyrosine phosphatase PTP-PEST, Src homology domain-containing inositol 5’-phosphatase 1 (SHIP1), and C‐terminal Src kinase (CSK) can bind to PSTPIP2 and inhibit the development of AIDs. However, the mechanisms underlying the function of PSTPIP2 have not been fully elucidated. This article reviews the research progress and mechanisms of PSTPIP2 in AIDs. PSTPIP2 also provides a new therapeutic target for the treatment of AIDs.

Comprehensive Analysis of Common Different Gene Expression Signatures in the Neutrophils of Sepsis

The central component of sepsis pathogenesis is inflammatory disorder, which is related to dysfunction of the immune system. However, the specific molecular mechanism of sepsis has not yet been fully elucidated. The aim of our study was to identify genes that are significantly changed during sepsis development, for the identification of potential pathogenic factors. Differentially expressed genes (DEGs) were identified in 88 control and 214 septic patient samples. Gene ontology (GO) and pathway enrichment analyses were performed using David. A protein-protein interaction (PPI) network was established using STRING and Cytoscape. Further validation was performed using real-time polymerase chain reaction (RT-PCR). We identified 37 common DEGs. GO and pathway enrichment indicated that enzymes and transcription factors accounted for a large proportion of DEGs; immune system and inflammation signaling demonstrated the most significant changes. Furthermore, eight hub genes were identified via PPI analysis. Interestingly, four of the top five upregulated and all downregulated DEGs were involved in immune and inflammation signaling. In addition, the most intensive hub gene AKT1 and the top DEGs in human clinical samples were validated using RT-PCR. This study explored the possible molecular mechanisms underpinning the inflammatory, immune, and PI3K/AKT pathways related to sepsis development.

Transcriptomic Profiling Identifies Neutrophil-Specific Upregulation of Cystatin F as a Marker of Acute Inflammation in Humans

Cystatin F encoded by CST7 is a cysteine peptidase inhibitor known to be expressed in natural killer (NK) and CD8⁺ T cells during steady-state conditions. However, little is known about its expression during inflammatory disease states in humans. We have developed an analytic approach capable of not only identifying previously poorly characterized disease-associated genes but also defining regulatory mechanisms controlling their expression. By exploring multiple cohorts of public transcriptome data comprising 43 individual datasets, we showed that CST7 is upregulated in the blood during a diverse set of infectious and non-infectious inflammatory conditions. Interestingly, this upregulation of CST7 was neutrophil-specific, as its expression was unchanged in NK and CD8⁺ T cells during sepsis. Further analysis demonstrated that known microbial products or cytokines commonly associated with inflammation failed to increase CST7 expression, suggesting that its expression in neutrophils is induced by an endogenous serum factor commonly present in human inflammatory conditions. Overall, through the identification of CST7 upregulation as a marker of acute inflammation in humans, our study demonstrates the value of publicly available transcriptome data in knowledge generation and potential biomarker discovery.

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.

Identification of the Diagnostic Signature of Sepsis Based on Bioinformatic Analysis of Gene Expression and Machine Learning

BACKGROUND

Sepsis is a life-threatening disease caused by the dysregulated host response to the infection and the major cause of death of patients in the intensive care unit (ICU).

OBJECTIVE

Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other factors. Hence, the analysis of mass data by bioinformatics tools and machine learning is a promising method for exploring early diagnosis.

METHODS

We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO) and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment analysis based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI) network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type.

RESULTS

Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub genes was used to assess GSE95233 and GSE95233 datasets, and the area under the curve (AUC) of ROC was 0.900 and 0.7988, respectively, which confirmed the efficacy of this model.

CONCLUSION

The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study may provide promising diagnostic methods for sepsis.

Annexin A3 in sepsis: novel perspectives from an exploration of public transcriptome data

According to publicly available transcriptome datasets, the abundance of Annexin A3 (ANXA3) is robustly increased during the course of sepsis; however, no studies have examined the biological significance or clinical relevance of ANXA3 in this pathology. Here we explored this interpretation gap and identified possible directions for future research. Based on reference transcriptome datasets, we found that ANXA3 expression is restricted to neutrophils, is upregulated in vitro after exposure to plasma obtained from septic patients, and is associated with adverse clinical outcomes. Secondly, an increase in ANXA3 transcript abundance was also observed in vivo, in the blood of septic patients in multiple independent studies. ANXA3 is known to mediate calcium-dependent granules-phagosome fusion in support of microbicidal activity in neutrophils. More recent work has also shown that ANXA3 enhances proliferation and survival of tumour cells via a Caspase-3-dependent mechanism. And this same molecule is also known to play a critical role in regulation of apoptotic events in neutrophils. Thus, we posit that during sepsis ANXA3 might either play a beneficial role, by facilitating microbial clearance and resolution of the infection; or a detrimental role, by prolonging neutrophil survival, which is known to contribute to sepsis-mediated organ damage.

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.

Bioinformatics Analysis of Gene Expression Profiles for Risk Prediction in Patients with Septic Shock

Background

Septic shock occurs when sepsis is associated with critically low blood pressure, and has a high mortality rate. This study aimed to undertake a bioinformatics analysis of gene expression profiles for risk prediction in septic shock.

Material/Methods

Two good quality datasets associated with septic shock were downloaded from the Gene Expression Omnibus (GEO) database, GSE64457 and GSE57065. Patients with septic shock had both sepsis and hypotension, and a normal control group was included. The differentially expressed genes (DEGs) were identified using OmicShare tools based on R. Functional enrichment of DEGs was analyzed using DAVID. The protein-protein interaction (PPI) network was established using STRING. Survival curves of key genes were constructed using GraphPad Prism version 7.0. Each putative central gene was analyzed by receiver operating characteristic (ROC) curves using MedCalc statistical software.

Results

GSE64457 and GSE57065 included 130 RNA samples derived from whole blood from 97 patients with septic shock and 33 healthy volunteers to obtain 975 DEGs, 455 of which were significantly down-regulated and 520 were significantly upregulated (P<0.05). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified significantly enriched DEGs in four signaling pathways, MAPK, TNF, HIF-1, and insulin. Six genes, WDR82, ASH1L, NCOA1, TPR, SF1, and CREBBP in the center of the PPI network were associated with septic shock, according to survival curve and ROC analysis.

Conclusions

Bioinformatics analysis of gene expression profiles identified four signaling pathways and six genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in septic shock.