Distinguishing septic shock from non-septic shock in postsurgical patients using gene expression

A shared inflammatory signature across severe malaria syndromes manifested by transcriptomic, proteomic and metabolomic analyses

Factors governing the clinical trajectory of Plasmodium falciparum infection remain an important area of investigation. Here we present transcriptomic, proteomic and metabolomic analyses comparing clinical subtypes of severe Plasmodium falciparum malaria to matched controls with uncomplicated disease in 79 children from Mali. MMP8, IL1R2, and ARG1 transcription is higher across cerebral malaria, severe malarial anemia, and concurrent cerebral malaria and severe malarial anemia, indicating a shared inflammatory signature. Tissue inhibitor of metalloproteinases 1 is the most upregulated protein in cerebral malaria, which along with elevated MMP8 and MMP9 transcription, underscores the importance of the metalloproteinase pathway in central nervous system pathophysiology. L-arginine metabolites are decreased in cerebral malaria, which coupled with increased ARG1 transcription suggests a putative mechanism impairing cerebral vasodilation. Using multi-omics approaches, we thus describe the inflammatory cascade in severe malaria syndromes, and identify potential therapeutic targets and biological markers.

From Gene to Intervention: NLRC4 and WIPI1 Regulate Septic Acute Lung Injury Through Autophagy

Background

Septic Acute Lung Injury (SALI)-induced severe respiratory dysfunction has been established to significantly increase patient mortality rates and socioeconomic costs. To mitigate cellular damage, autophagy -a conserved biological process in organisms -degrades damaged cellular components, such as proteins and organelles. Although autophagy is crucially involved in the inflammatory response, its precise molecular mechanisms in SALI remain unclear, forming the basis of this study.

Methods

Herein, two microarray datasets (GSE33118 and GSE131761) and three single-cell sequencing datasets (SCP43, SCP548, and SCP2156) derived from human samples were used to ascertain the interrelationship between Differentially Expressed Autophagy-Related Genes (DEARGs) and SALI. The relationship between key DEARGs and SALI was validated both in vitro and in vivo using various techniques, including flow cytometry, Immunofluorescence (IF), Quantitative Polymerase Chain Reaction (qPCR), Western Blotting (WB), and small interfering RNA (siRNA).

Results

Herein, we found that autophagy activation attenuated SALI, with NLRC4 and WIPI1 as the two key DEARGs involved. Specifically, NLRC4 and WIPI1 downregulation mitigated SALI via autophagy activation. Compared to NLRC4, WIPI1 was more closely associated with noncanonical autophagic flux in SALI. Furthermore, immune infiltration analysis and single-cell data showed a close relationship between NLRC4, WIPI1, and immune cells.

Conclusion

Our findings revealed that SALI correlated strongly with autophagy, with the downregulation of the two key DEARGs, NLRC4 and WIPI1, attenuating sepsis lung injury via autophagy regulation, highlighting their therapeutic significance in SALI.

The novel role of LCK and other PcDEGs in the diagnosis and prognosis of sepsis: Insights from bioinformatic identification and experimental validation

BACKGROUND

Programmed cell death (PCD) has emerged as a pivotal progress in pathogenesis of sepsis, but its role in identification of sepsis has not been fully understood.

METHODS

Differentially expressed genes (DEGs) were identified from the GEO database. PCD-related genes were intersected with DEGs, and key PcDEGs were identified through the protein-protein interaction (PPI) network. To pinpoint hub PcDEGs in sepsis, we applied Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Generalized Linear Model (GLM) algorithms. Additionally, the expression levels of five hub PcDEGs were validated in single cell RNA sequencing of sepsis patients and peripheral blood mononuclear cells (PBMCs) from a clinical cohort by quantitative real-time PCR (qRT-PCR). LCK expression was further determined by ELISA, and its diagnostic and prognostic value was evaluated using ROC analysis. LCK levels in the cecal ligation and puncture (CLP)-induced sepsis mouse model were assessed by Western blot and Immunofluorescence (IF). Finally, we assessed the regulatory role of LCK in cell apoptosis using flow cytometry and Western blot analysis.

RESULTS

70 PcDEGs were identified by intersecting 690 DEGs and 1254 PCD-related genes. PPI analysis identified top 15 genes based on Degree algorithm. We then identified five hub PcDEGs (LCK, IL10RA, CD3E, CD5 and ITGAM) that could serve as biomarkers through machine learning. As the expressions of LCK, IL10RA, CD3E and CD5 decreased and ITGAM expression was upregulated in septic patients. Consistently, Serum LCK concentration was reduced in septic patients, and the area under the ROC curve (AUC) of LCK was 0.753. Importantly, LCK displayed more pronounced reduction in non-survivors and those with septic shock than survivors and non-shock patients. The AUC for LCK was 0.726 in predicting mortality of septic patients. Moreover, we observed a decrease expression of LCK in the vital organs (liver, lung, spleen, thymus and PBMC) of septic mice model which mirrored observations in septic patients. Finally, we found that inhibiting LCK promoted apoptosis in Jurkat cells.

CONCLUSIONS

Our study reveals that PcDEGs are dysregulated in sepsis, and closely related to disease pathology. Our finding provides new insights into clinical identification and outcome prediction of sepsis. Of note, LCK is a new biomarker for diagnosis and prognosis, which might be a potential therapeutic target for the treatment of sepsis.

Study on the diagnostic role of exosome-derived miRNAs in postoperative septic shock and non-septic shock patients

BACKGROUND

Diagnosing septic shock promptly is essential but challenging, especially due to its clinical similarity to non-septic shock. Extracellular vesicle-derived miRNAs may serve as biomarkers to distinguish septic shock from non-septic shock, providing a more accurate diagnostic tool for postsurgical patients. This study aims to identify extracellular vesicle-derived miRNA signatures that differentiate septic shock from non-septic shock in postsurgical patients, potentially improving diagnostic accuracy and clinical decision-making.

METHODS

A multicentre, prospective study was conducted on miRNA profiles in shock patients. Two cohorts were recruited from the Intensive Care Units of two Spanish hospitals: a discovery cohort with 109 patients and a validation cohort with 52 patients. Plasma samples were collected within 24 h of shock diagnosis and subjected to miRNA sequencing. High-throughput sequencing data from the discovery cohort were analysed to identify differentially expressed miRNAs. These findings were validated via qPCR in the validation cohort.

RESULTS

Thirty miRNAs were identified as significantly differentially expressed between septic and non-septic shock patients. Among these, six miRNAs-miR-100-5p, miR-484, miR-10a-5p, miR-148a-3p, miR-342-3p, and miR-451a-demonstrated strong diagnostic capabilities for septic shock. A combination of miR-100-5p, miR-148a-3p, and miR-451a achieved an area under the curve of 0.894, with qPCR validation in the validation cohort yielding an area under the curve of 0.960.

CONCLUSIONS

This study highlights extracellular vesicle-derived miRNAs as promising biomarkers for differentiating septic from non-septic shock. The identified three-miRNA signature has significant potential to enhance septic shock diagnosis, thereby aiding in timely and appropriate treatment for postsurgical patients.

Integrative bioinformatics analysis reveals CGAS as a ferroptosis-related signature gene in sepsis and screens the potential natural inhibitors of CGAS

Sepsis is a fatal organ dysfunction characterized by the simultaneous hyperinflammation and immunosuppression. Nowadays, the early precision intervention of sepsis is challenging. Ferroptosis is involved in the development of sepsis. The current study aimed to find out the signature genes of sepsis with network topology analysis and machine learning, and further provide the potential natural compounds for sepsis with virtual screening and in vitro validation. In this study, five genes namely CGAS, DPP4, MAPK14, PPARG and TXN were identified as ferroptosis-related signature genes for sepsis by network topological analysis, machine learning algorithms, and external datasets verification. The results of immune infiltration analysis confirmed these genes were significantly associated with the infiltration abundance of some immune cells including neutrophil, macrophage, plasmacytoid dendritic cell and activated dendritic cell. Moreover, coniferin, 5-O-caffeoylshikimic acid, and psoralenoside were initially identified as the natural inhibitors of CGAS by virtual screening. However, further in vitro study on macrophages revealed coniferin and psoralenoside had better inhibitory activities on CGAS. In summary, the present study pointed out the importance of CGAS in sepsis, and discovered novel natural inhibitors of CGAS.

Protective role of olfactomedin 4 gene polymorphisms in preterm neonates with sepsis

BACKGROUND

Olfactomedin 4 (OLFM4) gene polymorphisms have been associated with variations in inflammatory responses and the severity of infections. This study aimed to investigate the association between OLFM4 single nucleotide polymorphisms (SNPs) rs17552047 and rs1891944 and severe outcomes in preterm neonatal sepsis.

METHODS

A prospective observational cohort study was conducted from April 2023 to April 2024, involving all preterm infants diagnosed with neonatal sepsis. Genotyping was performed using real-time polymerase chain reaction, and the associations with severe outcomes were analyzed using genetic models (dominant, recessive, and additive) through multivariate logistic regression and survival analysis.

RESULTS

Among the 174 preterm newborns included in the study, 39 experienced severe outcomes. The AA/AG genotypes of SNP rs17552047 and TT/TC genotypes of rs1891944 were associated with a reduced risk of severe outcomes (adjusted hazard ratio: 0.271, 95 % confidence interval [CI]: 0.115-0.641, p = 0.003, and adjusted hazard ratio: 0.349, 95 % CI: 0.175-0.698, p = 0.003, respectively). The odds of severe outcomes decreased by 65 % for each additional A allele (95 % CI: 0.15-0.78, p = 0.01). The model incorporating both SNPs and clinical variables demonstrated good predictive capability (area under the receiver operating characteristic curve: 0.826, 95 % CI: 0.748-0.903, p = 0.03).

CONCLUSIONS

The OLFM4 rs17552047 AA/AG and rs1891944 TT/TC genotypes have been linked to favorable outcomes in neonatal sepsis. These SNPs hold promise for predicting severe outcomes in neonatal sepsis.

The Diagnostic Utility of Host RNA Biosignatures in Adult Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVES

Sepsis is a life-threatening medical emergency, with a profound healthcare burden globally. Its pathophysiology is complex, heterogeneous and temporally dynamic, making diagnosis challenging. Medical management is predicated on early diagnosis and timely intervention. Transcriptomics is one of the novel "-omics" technologies being evaluated for recognition of sepsis. Our objective was to evaluate the performance of host gene expression biosignatures for the diagnosis of all-cause sepsis in adults.

DATA SOURCES

PubMed/Ovid Medline, Ovid Embase, and Cochrane databases from inception to June 2023.

STUDY SELECTION

We included studies evaluating the performance of host gene expression biosignatures in adults who were diagnosed with sepsis using existing clinical definitions. Controls where applicable were patients without clinical sepsis.

DATA EXTRACTION

Data including population demographics, sample size, study design, tissue specimen, type of transcriptome, health status of comparator group, and performance of transcriptomic biomarkers were independently extracted by at least two reviewers.

DATA SYNTHESIS

Meta-analysis to describe the performance of host gene expression biosignatures for the diagnosis of sepsis in adult patients was performed using the random-effects model. Risk of bias was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 117 studies (n = 17,469), comprising 132 separate patient datasets, were included in our final analysis. Performance of transcriptomics for the diagnosis of sepsis against pooled controls showed area under the receiver operating characteristic curve (AUC, 0.86; 95% CI, 0.84-0.88). Studies using healthy controls showed AUC 0.87 (95% CI, 0.84-0.89), while studies using controls with systemic inflammatory response syndrome (SIRS) had AUC 0.84 (95% CI, 0.78-0.90). Transcripts with excellent discrimination against SIRS controls include UrSepsisModel, a 210 differentially expressed genes biosignature, microRNA-143, and Septicyte laboratory.

CONCLUSIONS

Transcriptomics is a promising approach for the accurate diagnosis of sepsis in adults and demonstrates good discriminatory ability against both healthy and SIRS control subjects.

Systematic analysis based on bioinformatics and experimental validation identifies Alox5 as a novel therapeutic target of quercetin for sepsis

PURPOSE

This study investigated the molecular mechanism of quercetin in the treatment of sepsis using network pharmacological prediction and experimentation.

METHODS

Hub genes were identified by intersecting the differentially expressed genes (DEGs) of the GSE131761 and GSE9960 databases with genes from the hub modules of Weighted Gene Co-Expression Network Analysis (WGCNA), targets of quercetin, and ferroptosis. Subsequently, in order to determine the functional characteristics and molecular link of hub gene obtained above, we redetermined the hub-DEGs in GSE131761 according to high or low hub gene expression. Afterward, the main pathways of enrichment analysis were validated using these hub-DEGs. Finally, an experiment was conducted to validate the findings.

RESULTS

By intersecting 1415 DEGs in GSE131761, 543 DEGs in GSE9960, 5784 key modular genes, 470 ferroptosis-related genes, and 154 quercetin-related genes, we obtained one quercetin-related gene, Alox5. Subsequently, 340 hub-DEGs were further validated according to high or low Alox5 expression. The results of the enrichment analysis revealed that hub-DEGs were mainly associated with inflammation and the immune response. Immune infiltration analysis showed that higher expression of Alox5 was related to macrophage infiltration and could be a predictor of diagnosis in patients with sepsis. The expression pattern of Alox5 was then depicted and the upregulation of Alox5 in the vital organs of septic mice was further demonstrated. In vitro and in vivo experiments showed that upregulation of Alox5 and inflammation-related cytokines induced by sepsis could be inhibited by quercetin (p < 0.05).

CONCLUSIONS

Alox5 may be involved in the occurrence and development of multi-organ functional disturbances in sepsis and is a reliable target of quercetin against sepsis.

Gene Expression Dysregulation in Whole Blood of Patients with Clostridioides difficile Infection

Clostridioides difficile (C. difficile) is a global threat and has significant implications for individuals and health care systems. Little is known about host molecular mechanisms and transcriptional changes in peripheral immune cells. This is the first gene expression study in whole blood from patients with C. difficile infection. We took blood and stool samples from patients with toxigenic C. difficile infection (CDI), non-toxigenic C. difficile infection (GDH), inflammatory bowel disease (IBD), diarrhea from other causes (DC), and healthy controls (HC). We performed transcriptome-wide RNA profiling on peripheral blood to identify diarrhea common and CDI unique gene sets. Diarrhea groups upregulated innate immune responses with neutrophils at the epicenter. The common signature associated with diarrhea was non-specific and shared by various other inflammatory conditions. CDI had a unique 45 gene set reflecting the downregulation of humoral and T cell memory functions. Dysregulation of immunometabolic genes was also abundant and linked to immune cell fate during differentiation. Whole transcriptome analysis of white cells in blood from patients with toxigenic C. difficile infection showed that there is an impairment of adaptive immunity and immunometabolism.

Upregulation of CRISP3 and its clinical values in adult sepsis: a comprehensive analysis based on microarrays and a two-retrospective-cohort study

Background

Current lines of evidence indicate that cysteine-rich secretory protein 3 (CRISP3) is an immunoregulatory factor. Nevertheless, no study has explored the relationships between the values of CRISP3 and sepsis.

Methods

We conducted a comprehensive literature search and meta-analysis from the Gene Expression Omnibus (GEO) and ArrayExpress to determine the expression of CRISP3 in sepsis patients. Then, we explored whether plasma CRISP3 could serve as a potential biomarker to predict the risk of sepsis via two retrospective trauma cohorts. We evaluated the prediction power using the area under the curve (AUC).

Results

A total of 23 datasets were recruited for the comprehensive meta-analysis, and the combined standardized mean difference (SMD) of CRISP3 was 0.90 (0.50-1.30) (p < 0.001), suggesting that CRISP3 was overexpressed in sepsis patients. Meanwhile, sepsis patients had higher CRISP3 concentrations than non-sepsis patients in 54 trauma patients (p < 0.001). Plasma CRISP3 on admission was significantly associated with the incidence of sepsis [OR = 1.004 (1.002-1.006), p < 0.001]. As a predictive biomarker, CRISP3 obtained a better AUC [0.811 (0.681-0.905)] than C-reactive protein (CRP) [0.605 (0.463-0.735)], procalcitonin (PCT) [0.554 (0.412-0.689)], and Sequential Organ Failure Assessment (SOFA) [0.754 (0.618-0.861)]. Additionally, the clinical relationships between plasma CRISP3 and sepsis were verified in another trauma cohort with 166 patients [OR = 1.002 (1.001-1.003), p < 0.001]. The AUC of CRISP3 was 0.772 (0.701-0.834), which was better than that of CRP [0.521 (0.442-0.599)] and PCT [0.531 (0.452-0.609)], but not SOFA [0.791 (0.717-0.853)].

Conclusion

Our study indicated and validated that CRISP3 was highly expressed in sepsis. More importantly, CRISP3 may serve as a latent biomarker to predict the risk of sepsis.

Spleen tyrosine kinase: a novel pharmacological target for sepsis-induced cardiac dysfunction and multi-organ failure

Sepsis is a systemic condition caused by a dysregulated host response to infection and often associated with excessive release of proinflammatory cytokines resulting in multi-organ failure (MOF), including cardiac dysfunction. Despite a number of effective supportive treatments (e.g. ventilation, dialysis), there are no specific interventions that prevent or reduce MOF in patients with sepsis. To identify possible intervention targets, we re-analyzed the publicly accessible Gene Expression Omnibus accession GSE131761 dataset, which revealed an increased expression of spleen tyrosine kinase (SYK) in the whole blood of septic patients compared to healthy volunteers. This result suggests a potential involvement of SYK in the pathophysiology of sepsis. Thus, we investigated the effects of the highly selective SYK inhibitor PRT062607 (15mg/kg; i.p.) on sepsis-induced cardiac dysfunction and MOF in a clinically-relevant, murine model of sepsis. PRT062607 or vehicle (saline) was administered to 10-weeks-old C57BL/6 mice at 1h after the onset of sepsis induced by cecal ligation and puncture (CLP). Antibiotics (imipenem/cilastatin; 2mg/kg; s.c.) and analgesic (buprenorphine; 0.05mg/kg; i.p.) were administered at 6h and 18h post-CLP. After 24h, cardiac function was assessed in vivo by echocardiography and, after termination of the experiments, serum and cardiac samples were collected to evaluate the effects of SYK inhibition on the systemic release of inflammatory mediators and the degree of organ injury and dysfunction. Our results show that treatment of CLP-mice with PRT062607 significantly reduces systolic and diastolic cardiac dysfunction, renal dysfunction and liver injury compared to CLP-mice treated with vehicle. In addition, the sepsis-induced systemic inflammation (measured as an increase in inflammatory cytokines and chemokines in the serum) and the cardiac activation of NF-kB (IKK) and the NLRP3 inflammasome were significantly reduced in CLP-mice treated with PRT062607. These results demonstrate, for the first time, that SYK inhibition 1h after the onset of sepsis reduces the systemic inflammation, cardiac dysfunction and MOF, suggesting a potential role of the activation of SYK in the pathophysiology of sepsis. Novel therapeutic strategies that inhibit SYK activity may be of benefit in patients with diseases associated with local or systemic inflammation including sepsis.

Signature Proteins in Small Extracellular Vesicles of Granulocytes and CD4+ T-Cell Subpopulations Identified by Comparative Proteomic Analysis

Several studies have described the proteomic profile of different immune cell types, but only a few have also analysed the content of their delivered small extracellular vesicles (sEVs). The aim of the present study was to compare the protein signature of sEVs delivered from granulocytes (i.e., neutrophils and eosinophils) and CD4+ T cells (i.e., TH1, TH2, and TH17) to identify potential biomarkers of the inflammatory profile in chronic inflammatory diseases. Qualitative (DDA) and quantitative (DIA-SWATH) analyses of in vitro-produced sEVs revealed proteome variations depending on the cell source. The main differences were found between granulocyte- and TH cell-derived sEVs, with a higher abundance of antimicrobial proteins (e.g., LCN2, LTF, MPO) in granulocyte-derived sEVs and an enrichment of ribosomal proteins (RPL and RPS proteins) in TH-derived sEVs. Additionally, we found differentially abundant proteins between neutrophil and eosinophil sEVs (e.g., ILF2, LTF, LCN2) and between sEVs from different TH subsets (e.g., ISG15, ITGA4, ITGB2, or NAMPT). A "proof-of-concept" assay was also performed, with TH2 biomarkers ITGA4 and ITGB2 displaying a differential abundance in sEVs from T2high and T2low asthma patients. Thus, our findings highlight the potential use of these sEVs as a source of biomarkers for diseases where the different immune cell subsets studied participate, particularly chronic inflammatory pathologies such as asthma or chronic obstructive pulmonary disease (COPD).

Unveiling the glycolysis in sepsis: Integrated bioinformatics and machine learning analysis identifies crucial roles for IER3, DSC2, and PPARG in disease pathogenesis

Sepsis, a multifaceted syndrome driven by an imbalanced host response to infection, remains a significant medical challenge. At its core lies the pivotal role of glycolysis, orchestrating immune responses especially in severe sepsis. The intertwined dynamics between glycolysis, sepsis, and immunity, however, have gaps in knowledge with several Crucial genes still shrouded in ambiguity. We harvested transcriptomic profiles from the peripheral blood of 107 septic patients juxtaposed against 29 healthy controls. Delving into this dataset, differential expression analysis shed light on genes distinctly linked to glycolysis in both cohorts. Harnessing the prowess of LASSO regression and SVM-RFE, we isolated Crucial genes, paving the way for a sepsis risk prediction model, subsequently vetted via Calibration and decision curve analysis. Using the CIBERSORT algorithm, we further mapped 22 immune cell subtypes within the septic samples, establishing potential interactions with the delineated Crucial genes. Our efforts unveiled 21 genes intricately tied to glycolysis that exhibited differential expression patterns. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses offered insights, spotlighting pathways predominantly associated with oxidative phosphorylation, PPAR signaling pathway, Glycolysis/Gluconeogenesis and HIF-1 signaling pathway. Among the myriad genes, IER3, DSC2, and PPARG emerged as linchpins, their prominence in sepsis further validated through ROC analytics. These sentinel genes demonstrated profound affiliations with various immune cell facets, bridging the complex terrain of glycolysis, sepsis, and immune responses. In line with our endeavor to "unveil the glycolysis in sepsis," the discovery of IER3, DSC2, and PPARG reinforces their cardinal roles in sepsis pathogenesis. These revelations accentuate the intricate dance between glycolysis and immunological shifts in septic conditions, offering novel avenues for therapeutic interventions.

The Value of Serum Procalcitonin, Thromboelastography Combined with Platelet Count in Predicting the Short-Term Progression of Septic Shock in the Intensive Care Unit

Objective

By evaluating the level of serum procalcitonin (PCT), thromboelastography (TEG) and platelet count (PLT) of patients with septic shock in intensive care unit (ICU), the predictive value of the combination of the three indicators on the short-term progression was discussed, which provided a new basis for early clinical diagnosis and disease evaluation.

Methods

The clinical data of 130 patients with septic shock admitted to the IUC of our hospital from December 2021 to December 2023 were analyzed retrospectively. These subjects were divided into good prognosis group (n=78) and poor prognosis group (n=52) according to the 28 d deaths. The influencing factors were explored using the Multivariate logistic regression analysis. The value of single or combined PCT, PLT and TEG in predicting poor short-term prognosis was assessed using the receiver operating characteristic (ROC) curve.

Results

The patients in poor prognosis group had higher Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, serum PCT level, coagulation reaction time (R value) and coagulation formation time (K value), but lower PLT levels, final strength of coagulation (MA value) and coagulation formation rate (α angle) than those in good prognosis group (P<0.001). PCT, R value and K value were risk factors (P<0.001), while PLT, MA value and α angle were protective factors (P<0.001). The area under the curve (AUC) of PCT, PLT and TEG predicting poor short-term progression was 0.813, 0.658 and 0.752, respectively. The AUC of combined three indicators was 0.905, which had the highest predictive value.

Conclusion

Serum levels of PCT, PLT and TEG had certain value in predicting poor short-term progression of septic shock patients, and their combined diagnostic value was higher. Therefore, regular monitoring of these three indicators could provide certain guiding significance for the prevention and treatment of poor short-term prognosis in patients with septic shock.

Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis

Background

Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.

Methods

Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes.

Results

By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes.

Conclusion

This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.

Identification of key regulatory genes in the pathogenesis of COVID-19 and sepsis: An observational study

Patients with severe COVID-19 and those with sepsis have similar clinical manifestations. We used bioinformatics methods to identify the common hub genes in these 2 diseases. Two RNA-seq datasets from the Gene Expression Omnibus were used to identify common differentially expressed genes (DEGs) in COVID-19 and sepsis. These common genes were used for analysis of functional enrichment; pathway analysis; identification of associated transcription factors, metabolites, and miRNAs; and mapping of protein-protein interaction networks. The major hub genes of COVID-19 and sepsis were identified, and validation datasets were used to assess the value of these hub genes using receiver operating characteristic (ROC) curves. Analysis of the 800 common DEGs for COVID-19 and sepsis, as well as common transcription factors, miRNAs, and metabolites, demonstrated that the immune response had a key role in both diseases. DLGAP5, BUB1, CDK1, CCNB1, and BUB1B were the most important common hub genes. Analysis of a validation cohort indicated these 5 genes had significantly higher expression in COVID-19 patients and sepsis patients than in corresponding controls, and the area under the ROC curves ranged from 0.832 to 0.981 for COVID-19 and 0.840 to 0.930 for sepsis. We used bioinformatics tools to identify common DEGs, miRNAs, and transcription factors for COVID-19 and sepsis. The 5 identified hub genes had higher expression in validation cohorts of COVID-19 and sepsis. These genes had good or excellent diagnostic performance based on ROC analysis, and therefore have potential use as novel markers or therapeutic targets.

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.

Unlocking the potential of senescence-related gene signature as a diagnostic and prognostic biomarker in sepsis: insights from meta-analyses, single-cell RNA sequencing, and in vitro experiments

Cellular senescence is closely associated with the pathogenesis of sepsis. However, the diagnostic and prognostic value of senescence-related genes remain unclear. In this study, 866 senescence-related genes were collected from CellAge. The training cohort, GSE65682, which included 42 control and 760 sepsis samples, was obtained from the Gene Expression Omnibus (GEO). Feature selection was performed using gene expression difference detection, LASSO analysis, random forest, and Cox regression. TGFBI and MAD1L1 were ultimately selected for inclusion in the multivariate Cox regression model. Clustering based on the expressions of TGFBI and MAD1L1 was significantly associated with sepsis characteristics and prognoses (all P < 0.05). The risk signature served as a reliable prognostic predictor across the GSE65682, GSE95233, and GSE4607 cohorts (pooled hazard ratio = 4.27; 95% confidence interval [CI] = 1.63-11.17). Furthermore, it also served as a robust classifier to distinguish sepsis samples from control cases across 14 cohorts (pooled odds ratio = 5.88; 95% CI = 3.54-9.77). Single-cell RNA sequencing analyses from five healthy controls and four sepsis subjects indicated that the risk signature could reflect the senescence statuses of monocytes and B cells; this finding was then experimentally validated in THP-1 and IM-9 cells in vitro (both P < 0.05). In all, a senescence-related gene signature was developed as a prognostic and diagnostic biomarker for sepsis, providing cut-in points to uncover underlying mechanisms and a promising clinical tool to support precision medicine.

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.

Methods

Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.

Results

Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).

Discussion

The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

BIOINFORMATICS APPLICATIONS UNDER CONDITION CONTROL: HIGH DIAGNOSTIC VALUE OF DDX47 IN REAL MEDICAL SETTINGS

ABSTRACT

Sepsis is an organ dysfunction caused by a dysregulated host response to infection and remains an ongoing threat to human health worldwide. Septic shock is the most severe subset of sepsis as characterized by abnormalities in cells, circulation, and metabolism. As a time-dependent condition, early recognition allowing appropriate therapeutic measures to be started in a timely manner becomes the most effective way to improve prognosis. However, because of the lack of a criterion standard, most diagnoses merely rely on medical history, empirical diagnosis, and blood culture results. Gene expression profiles have specific diagnostic value, as they reflect a subjective host response to pathogens. We propose a method, Condition Control based on Real-life Medical Scenarios, to control for factors in realistic medical scenarios. Restricted variables are used as much as possible to identify unique differential genes and progressively test their diagnostic value by relaxing restrictions. In total, three data sets were included in the study; the first two data sets were from the Gene Expression Omnibus database, and the third involved patients who were diagnosed with sepsis or septic shock within 7 days of admission to the intensive care unit at West China Hospital of Sichuan University from 2020 to 2021. DDX47 showed preferable diagnostic value in various scenarios, especially in patients with common infections or sepsis and septic shock. Here we also show that hub genes may regulate immune function and immune cell counts through the interaction of different apoptotic pathways and immune checkpoints based on the high correlation. DDX47 is closely associated with B cells according to single-cell sequencing results.

Comparative analysis of peripheral blood immunoinflammatory landscapes in patients with acute cholangitis and its secondary septic shock using single-cell RNA sequencing

BACKGROUND

Acute cholangitis (AC) is a key pathogeny of septic shock, which has a high mortality rate. AC has significant clinical heterogeneity, but no study has analyzed the discrepancies in immunoresponsiveness between AC and its secondary septic shock. The immune inflammatory responses play a critical role in the development of septic shock.

METHODS

We performed single-cell RNA sequencing (scRNA-seq) to analyze the differences of immunocytes in immunoresponse and inflammation between the early stages of AC (A1, A2, and A3) and its secondary septic shock (B1, B2, and B3).

RESULTS

This study has identified seven cell types, including T cells, B cells, plasma cells, neutrophils, monocytes, platelets and erythrocytes. We mainly focused on neutrophils, monocytes, and T cells. Neutrophil subpopulation analysis indicated that neutrophil progenitors (proNeus) were identified in neutrophil subsets. Compared with patients suffering from AC, the gene phenotypes of proNeus (ELANE, AZU1, MPO, and PRTN3) were significantly upregulated in septic shock. The differentiation direction of neutrophil subsets in peripheral blood mononuclear cells (PBMCs) was determined; Moreover, the proNeus in septic shock presented a state of "expansion", with upregulation of neutrophil degranulation and downregulation of monocyte and T cell proliferation. Neutrophils-7 (CCL5, RPL23A, RPL13, RPS19 and RPS18) were mainly involved in the regulation of cellular functions. The neutrophils-7 subpopulation in septic shock were in a state of "exhaustion", and its biological functions showed the characteristics of weakening neutrophil migration and phagocytosis, etc., which maked infection difficult to control and aggravated the development of septic shock. Analysis of monocyte and T cell subpopulations showed that the expression genes and biological functions of subpopulations were closely related to immunoinflammatory regulation. In addition, CCL3 - CCR1, CXCL1 - CXCR2 and other ligand-receptors were highly expressed in neutrophils and monocytes, enhancing interactions between immune cells.

CONCLUSION

ScRNA-seq revealed significant differences in immune cells between AC and its secondary septic shock, which were primarily manifested in the cellular numbers, differentially expressed genes, functions of cellular subsets, differentiation trajectories, cell-cell interactions and so on. We identified many subsets of neutrophil, T cell and monocyte were associated with inflammation and immunosuppression induced by septic shock. These provided a reference for accurately evaluating the pathological severity of patients with AC and discovering the targets for therapy.

Baricitinib protects mice from sepsis-induced cardiac dysfunction and multiple-organ failure

Sepsis is one of the major complications of surgery resulting in high morbidity and mortality, but there are no specific therapies for sepsis-induced organ dysfunction. Data obtained under Gene Expression Omnibus accession GSE131761 were re-analyzed and showed an increased gene expression of Janus Kinase 2 (JAK2) and Signal Transducer and Activator of Transcription 3 (STAT3) in the whole blood of post-operative septic patients. Based on these results, we hypothesized that JAK/STAT activation may contribute to the pathophysiology of septic shock and, hence, investigated the effects of baricitinib (JAK1/JAK2 inhibitor) on sepsis-induced cardiac dysfunction and multiple-organ failure (MOF). In a mouse model of post-trauma sepsis induced by midline laparotomy and cecal ligation and puncture (CLP), 10-week-old male (n=32) and female (n=32) C57BL/6 mice received baricitinib (1mg/kg; i.p.) or vehicle at 1h or 3h post-surgery. Cardiac function was assessed at 24h post-CLP by echocardiography in vivo, and the degree of MOF was analyzed by determination of biomarkers in the serum. The potential mechanism underlying both the cardiac dysfunction and the effect of baricitinib was analyzed by western blot analysis in the heart. Trauma and subsequent sepsis significantly depressed the cardiac function and induced multiple-organ failure, associated with an increase in the activation of JAK2/STAT3, NLRP3 inflammasome and NF- κβ pathways in the heart of both male and female animals. These pathways were inhibited by the administration of baricitinib post the onset of sepsis. Moreover, treatment with baricitinib at 1h or 3h post-CLP protected mice from sepsis-induced cardiac injury and multiple-organ failure. Thus, baricitinib may be repurposed for trauma-associated sepsis.

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.

Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study

Background

Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency.

Methods

The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in septic shock was determined using receiver operating characteristic (ROC) analysis and verified using quantitative PCR (qPCR) and Western blotting in our hospital patients with septic shock.

Results

A total of 975 DEGs in the GSE33118 and GSE26440 databases were obtained, of which 30 DEGs were remarkably upregulated. With the use of Lasso regression and Boruta feature selection algorithm, six hub genes (CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4) with expression differences in septic shock were screened as potential diagnostic markers for septic shock among the significant DEGs and were further validated in the GSE9692 dataset. WGCNA was used to identify the co-expression modules and module-trait correlation. Enrichment analysis showed significant enrichment in the reactive oxygen species pathway, hypoxia, phosphatidylinositol 3-kinases (PI3K)/Protein Kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling, nuclear factor-κβ/tumor necrosis factor alpha (NF-κβ/TNF-α), and interleukin-6 (IL-6)/Janus Kinase (JAK)/Signal Transducers and Activators of Transcription 3 (STAT3) signaling pathways. The receiver operating characteristic curve (ROC) of these signature genes was 0.938, 0.914, 0.939, 0.956, 0.932, and 0.914, respectively. In the immune cell infiltration analysis, the infiltration of M0 macrophages, activated mast cells, neutrophils, CD8 T cells, and naive B cells was more significant in the septic shock group. In addition, higher expression levels of CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 messenger RNA (mRNA) were observed in peripheral blood mononuclear cells (PBMCs) isolated from septic shock patients than from healthy donors. Higher expression levels of CD177 and MMP8 proteins were also observed in the PBMCs isolated from septic shock patients than from control participants.

Conclusions

CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 were identified as hub genes, which were of considerable value in the early diagnosis of septic shock patients. These preliminary findings are of great significance for studying immune cell infiltration in the pathogenesis of septic shock, which should be further validated in clinical studies and basic studies.

Co-expression and interaction network analysis reveals dysregulated neutrophil and T-cell activation as the core mechanism associated with septic shock

Septic shock as a subset of sepsis, has a much higher mortality, while the mechanism is still elusive. This study was aimed at identifying core mechanisms associated with septic shock and its high mortality by investigating transcriptome data. We screened 72 septic-shock-associated genes (SSAGs) with differential expression between septic shock and sepsis in the discovery dataset. Further gene set enrichment analysis identified upregulated neutrophil activation and impaired T-cell activation in septic shock. Co-expression analysis revealed nine co-expressed gene modules. In addition, we determined twenty-one prognostic SSAGs using cox regression analysis in an independent dataset. Moreover, protein-protein interaction (PPI) network revealed two clusters. Among these neutrophil activation was enriched in the most positively-related modules and the cluster2 PPI network, while T-cell activation was enriched in both the most negatively-related module and one of the most positively-related modules as well as the cluster1 PPI network. ELANE, LCN2 and IFI44 were identified as hub genes with CytoHubba methods and semantic similarity analysis. Notably, ELANE was the only prognostic gene and was further validated in an external dataset. Blood neutrophil count was demonstrated to increase in septic shock and be a risky factor of prognosis based on clinical data. In conclusions, septic shock is associated with upregulated neutrophil activation and dysregulated T-cell activation. Three hub genes might have potentials as sensitive markers for the further translational research and ELANE could be a robust prognostic biomarker and effective therapeutic target.

An immune-related gene signature predicts the 28-day mortality in patients with sepsis

Introduction

Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated.

Methods

Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR.

Results

We established a prognostic IRG signature comprising three gene members (LTB4R, HLA-DMB and IL4R). The IRG signature demonstrated good predictive performance for 28-day mortality on the internal and external validation datasets. The immune infiltration and cytokine analyses revealed that the IRG signature was significantly associated with multiple immune cells and cytokines. The molecular pathway analysis uncovered ontology enrichment in myeloid cell differentiation and iron ion homeostasis, providing clues regarding the underlying biological mechanisms of the IRG signature. Finally, qPCR detection verified the differential expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls.

Discussion

This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients' immune state.

Single-cell transcriptome profiling reveals heterogeneous neutrophils with prognostic values in sepsis

Neutrophils constitute the largest proportion of nucleated peripheral blood cells, and neutrophils have substantial heterogeneity. We profiled nearly 300,000 human peripheral blood cells in this study using single-cell RNA sequencing. A large proportion (>50%) of these cells were annotated as neutrophils. Neutrophils were further clustered into four subtypes, including Neu1, Neu2, Neu3, and Neu4. Neu1 is characterized by high expression of MMP9, HP, and RGL4. Neu1 was associated with septic shock and significantly correlated with the sequential organ failure assessment (SOFA) score. A gene expression module in Neu1 named Neu1_C (characterized by expression of NFKBIA, CXCL8, G0S2, and FTH1) was highly predictive of septic shock with an area under the curve of 0.81. The results were extensively validated in external bulk datasets by using single-cell deconvolution methods. In summary, our study establishes a general framework for studying neutrophil-related mechanisms, prognostic biomarkers, and potential therapeutic targets for septic shock.

Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis

Background

Septic cardiomyopathy is widespread during sepsis and has adverse effects on mortality. Diagnosis of septic cardiomyopathy now mainly depends on transthoracic echocardiogram. Although some laboratory tests such as troponin T and atrial brain natriuretic peptide play a role in the diagnosis, specific blood biochemistry biomarkers are still lacking. Objective and Methods. In our study, we sought to find potential biological markers from genes and pathways that are covariant in the blood and myocardium of septic patients. Bioinformatics and machine learning methods were applied to achieve our goal. Datasets of myocardium and peripheral blood of patients with sepsis were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected and received functional enrichment analysis. Unsupervised hierarchical clustering analysis was performed to identify the subtypes of sepsis. Random forest, lasso regression, and logistic regression were used for variable screening and model construction. Internal and external validation sets were applied to verify the efficiency of the model in classifying disease and predicting mortality.

Results

By defining significance for genes using Student's t-test, we obtained 1,049 genes commonly changed in both myocardium and blood of patients with sepsis. The upregulated genes (LogFC >0) were related to inflammation pathways, and downregulated (LogFC <0) genes were related to mitochondrial and aerobic metabolism. We divided 468 sepsis patients into two groups with different clinical result based on the mortality-related commonly changed genes (104 genes), using unsupervised hierarchical clustering analysis. In our validation datasets, a six-gene model (SMU1, CLIC3, SP100, ARHGAP25, DECR1, and TNS3) was obtained and proven to perform well in classifying groups and predicting mortality.

Conclusion

We have identified genes that have the potential to become biomarkers for septic cardiomyopathy. Additionally, the pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis.

Elevated serum level of human epididymal protein 4 (HE4) predicts poor prognosis in the critically ill with sepsis: a prospective observational cohort study

OBJECTIVE

Human epididymal protein 4 (HE4) has been widely used as an important clinical tumor biomarker for epithelial ovarian cancer. HE4 has recently been suggested to be an inflammatory biomarker and we hypothesized that the serum HE4 level upon intensive care unit (ICU) admission might predict prognosis in septic patients. We hypothesized that serum HE4 level upon intensive care unit (ICU) admission could predict prognosis in septic patients.

METHODS

Serum levels of HE4, procalcitonin (PCT), C-reactive protein (CRP), IL-6 and IL-8 were quantified, and sequential organ failure assessment (SOFA) scores were recorded on day one of admission to ICU. The area under the receiver operating characteristic (ROC) curve (AUC) analysis of HE4, IL-6, PCT and SOFA at ICU admission for 28-day mortality was used to evaluate the ability of HE4 in predicting 28-day mortality of sepsis. Multivariate regression analysis was used to identify the independent risk factors for 28-day mortality.

RESULTS

A total of 1289 patients were recruited, and 117 patients were included for final analysis. On day of ICU admission, septic patients had significantly higher levels of serum HE4 than those with infection without sepsis, those with ovarian cancer, or healthy controls. Compared with septic survivors, septic non-survivors presented with significantly higher serum HE4 concentrations. Serum levels of HE4 correlated with disease severity scores and cytokine levels (IL-6 and IL-8). Upon ICU admission, the AUC for HE4 level association with 28-day mortality was 0.881, higher than the AUC for SOFA (0.713), IL-6 (0.589), and PCT (0.567). A regression analysis showed that HE4 was an independent mortality predictor.

CONCLUSION

HE4 can predict poor prognosis in septic patients, which may help to identify a group of septic patients at high risk of death.

Key Signature Genes of Early Terminal Granulocytic Differentiation Distinguish Sepsis From Systemic Inflammatory Response Syndrome on Intensive Care Unit Admission

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15+ cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.

Olfactomedin 4 Is a Biomarker for the Severity of Infectious Diseases

Biomarkers of infectious diseases are essential tools for patient monitoring, diagnostics, and prognostics. Here we review recent advances in our understanding of olfactomedin 4 (OLFM4) in neutrophil biology and of OLFM4 as a new biomarker for certain infectious diseases. OLFM4 is a neutrophil-specific granule protein that is expressed in a subset of human and mouse neutrophils. OLFM4 expression is upregulated in many viral and bacterial infections, as well as in malaria. OLFM4 appears to play an important role in regulating host innate immunity against bacterial infection. Further, higher expression of OLFM4 is associated with severity of disease for dengue virus, respiratory syncytial virus, and malaria infections. In addition, higher expression of OLFM4 or a higher percentage of OLFM4 + neutrophils is associated with poorer outcomes in septic patients. OLFM4 is a promising biomarker and potential therapeutic target in certain infectious diseases.

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.

Lactylated Histone H3K18 as a Potential Biomarker for the Diagnosis and Predicting the Severity of Septic Shock

Objective

To date, there are no studies regarding the lactylation profile and its role in critically ill patients. Thus, we aimed to examine expression of histone H3 lysine 18 (H3K18) lactylation and its role in patients with septic shock.

Methods

Thirteen healthy volunteers and 35 critically ill patients from the Department of Surgical Intensive Care Medicine, Beijing Hospital were enrolled in our study. Baseline information and clinical outcomes were obtained prospectively. Lactylation levels of all proteins and H3K18 from peripheral blood mononuclear (PBMC) were determined by western blotting and serum levels of inflammatory cytokines by flow cytometry. Arginase-1 (Arg1) and Krüppel-like factor-4 (Klf4) mRNA expression was evaluated by quantitative real-time PCR (qRT-PCR).

Results

Lactylation was found to be an all-protein post-translational modification and was detected in PBMCs from both healthy volunteers and critically ill patients, with a significantly higher relative density in shock patients (t=2.172, P=0.045). H3K18la was expressed in all subjects, including healthy volunteers, with the highest level in septic shock patients (compared with non-septic shock patients, critically ill without shock patients and healthy volunteers P=0.033, 0.000 and 0.000, respectively). Furthermore, H3K18la protein expression correlated positively with APACHE II scores, SOFA scores on day 1, ICU stay, mechanical ventilation time and serum lactate (ρ=0.42, 0.63, 0.39, 0.51 and 0.48, respectively, ρ=0.012, 0.000, 0.019, 0.003 and 0.003, respectively). When we matched patients with septic shock and with non-septic shock according to severity, we found higher H3K18la levels in the former group (t=-2.208, P =0.040). Moreover, H3K18la exhibited a close correlation with procalcitonin levels (ρ=0.71, P=0.010). Patients with high H3K18la expression showed higher IL-2, IL-5, IL-6, IL-8, IL-10, IL-17, IFN-α levels (ρ=0.33, 0.37, 0.62, 0.55, 0.65, 0.49 and 0.374 respectively, P=0.024, 0.011, 0.000, 0.000, 0.000 and 0.000 respectively). H3K18la expression also displayed a positive correlation with the level of Arg1 mRNA (ρ=0.561, P=0.005).

Conclusions

Lactylation is an all-protein post-translational modification occurring in both healthy subjects and critically ill patients. H3K18la may reflect the severity of critical illness and the presence of infection. H3K18la might mediate inflammatory cytokine expression and Arg1 overexpression and stimulate the anti-inflammatory function of macrophages in sepsis.

Construction and validation of a robust prognostic model based on immune features in sepsis

Purpose

Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.

Materials and methods

The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.

Results

A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).

Conclusions

Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.