Modulation of LILRB2 protein and mRNA expressions in septic shock patients and after ex vivo lipopolysaccharide stimulation

ANGPTL2 knockdown induces autophagy to relieve alveolar macrophage pyroptosis by reducing LILRB2-mediated inhibition of TREM2

Acute lung injury (ALI) is featured with a robust inflammatory response. Angiopoietin-like protein 2 (ANGPTL2), a pro-inflammatory protein, is complicated with various disorders. However, the role of ANGPTL2 in ALI remains to be further explored. The mice and MH-S cells were administrated with lipopolysaccharide (LPS) to evoke the lung injury in vivo and in vitro. The role and mechanism of ANGPTL was investigated by haematoxylin-eosin, measurement of wet/dry ratio, cell count, terminal deoxynucleotidyl transferase deoxyuridine triphosphate (dUTP) nick end labeling, reverse transcription quantitative polymerase chain reaction, immunofluorescence, enzyme-linked immunosorbent assay, detection of autophagic flux and western blot assays. The level of ANGPTL2 was upregulated in lung injury. Knockout of ANGPTL2 alleviated LPS-induced pathological symptoms, reduced pulmonary wet/dry weight ratio, the numbers of total cells and neutrophils in BALF, apoptosis rate and the release of pro-inflammatory mediators, and modulated polarization of alveolar macrophages in mice. Knockdown of ANGPTL2 downregulated the level of pyroptosis indicators, and elevated the level of autophagy in LPS-induced MH-S cells. Besides, downregulation of ANGPTL2 reversed the LPS-induced the expression of leukocyte immunoglobulin (Ig)-like receptor B2 (LILRB2) and triggering receptor expressed on myeloid cells 2 (TREM2), which was reversed by the overexpression of LILRB2. Importantly, knockdown of TREM2 reversed the levels of autophagy- and pyroptosis-involved proteins, and the contents of pro-inflammatory factors in LPS-induced MH-S cells transfected with si ANGPTL2, which was further inverted with the treatment of rapamycin. Therefore, ANGPTL2 silencing enhanced autophagy to alleviate alveolar macrophage pyroptosis via reducing LILRB2-mediated inhibition of TREM2.

C3AR1 is a regulatory factor associated with coagulation cascade and inflammation in sepsis

Sepsis is a leading cause of mortality in intensive care units. Sepsis is associated with activation of the coagulation cascade and inflammation. The aim of this study was to identify coagulation-related genes in sepsis that may provide translational potential therapeutic targets. The datasets GSE28750, GSE95233, and GSE65682 were downloaded from the gene expression omnibus database. Consensus-weighted gene co-expression network analysis (WGCNA) was used to identify sepsis modules. Gene set enrichment analysis was used to identify genes enriched in the coagulation cascade. The value of hub-gene in immunological analysis was tested in the validation sets (GSE95233). The value of hub-gene in clinical prognosis was tested in the validation sets (GSE65582). One thousand one hundred seventy-six genes with high connectivity in the clinically significant module were identified as hub genes. Ten genes were found to be enriched in coagulation-related signaling pathways. C3AR1 was selected for further analysis. The immune infiltration analysis showed that lower expression of C3AR1 was associated with immune response in sepsis and could be an independent predictor of survival status in sepsis patients. Meanwhile, univariate and multivariate Cox analysis showed that C3AR1 had a significant correlation with survival. C3AR1 may become an effective biomarker for worse outcomes in sepsis patients associated with immune and coagulation cascade.

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.

Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.

ATF4 knockdown in macrophage impairs glycolysis and mediates immune tolerance by targeting HK2 and HIF-1α ubiquitination in sepsis

Strengthened glycolysis is crucial for the macrophage pro-inflammatory response during sepsis. Activating transcription factor 4 (ATF4) plays an important role in regulating glucose and lipid metabolic homeostasis in hepatocytes and adipocytes. However, its immunometabolic role in macrophage during sepsis remains largely unknown. In the present study, we found that the expression of ATF4 in peripheral blood mononuclear cells (PBMCs) was increased and associated with glucose metabolism in septic patients. Atf4 knockdown specifically decreased LPS-induced spleen macrophages and serum pro-inflammatory cytokines levels in mice. Moreover, Atf4 knockdown partially blocked LPS-induced pro-inflammatory cytokines, lactate accumulation and glycolytic capacity in RAW264.7. Mechanically, ATF4 binds to the promoter region of hexokinase II (HK2), and interacts with hypoxia inducible factor-1α (HIF-1α) and stabilizes HIF-1α through ubiquitination modification in response to LPS. Furthermore, ATF4-HIF-1α-HK2-glycolysis axis launches pro-inflammatory response in macrophage depending on the activation of mammalian target of rapamycin (mTOR). Importantly, Atf4 overexpression improves the decreased level of pro-inflammatory cytokines and lactate secretion and HK2 expression in LPS-induced tolerant macrophages. In conclusion, we propose a novel function of ATF4 as a crucial glycolytic activator contributing to pro-inflammatory response and improving immune tolerant in macrophage involved in sepsis. So, ATF4 could be a potential new target for immunotherapy of sepsis.

Mechanisms of Panax Ginseng on Treating Sepsis by RNA-Seq Technology

Purpose

To explore the potential active targets and mechanisms of Panax Ginseng in the treatment of sepsis using network pharmacology and RNA-seq technology.

Patients and Methods

Patients with sepsis and healthy volunteers were collected according to SEPSIS 3.0, and their peripheral blood was used for RNA-seq analysis. The active ingredients and targets of Panax Ginseng were obtained using the TCMSP database, PPI and GO analysis were performed for disease-drug intersection targets. Then, we used Meta-analysis to screen core genes. Finally, single-cell RNA-seq was used to perform cell localization analysis on core genes.

Results

RNA-seq analysis collected 4521 sepsis-related genes, TCMSP database obtained 86 Panax Ginseng active ingredients and their 294 active targets. PPI and GO analysis showed intersection targets were closely linked, and mainly involved in cellular response to chemical stress, response to drug and molecule of bacterial origin, etc. Then, core targets, IL1B, ALOX5, BCL2 and IL4R, were sorted by Meta-analysis, and all four genes have high expression in the sepsis survivor group compared to the sepsis non-survivor group; single-cell RNA-seq revealed that IL1B was mainly localized in macrophages, ALOX5 was mainly localized in macrophages and B cells, BCL2 was mainly localized in natural killer cells, T cells and B cells, IL4R was widely distributed in immune cells. Finally, according to the correspondence between the active ingredients and targets of Panax Ginseng in TCMSP database, we found that Ginsenoside rh2 regulates the expression of IL1B, Ginsenoside rf regulates the expression of IL1B and IL4R, Kaempferol regulates the expression of ALOX5 and BCL2, and β-sitosterol regulates the expression of BCL2.

Conclusion

Ginsenoside rh2, Ginsenoside rf, Kaempferol and β-sitosterol may produce anti-sepsis effects by regulating the expression of IL1B, ALOX5, BCL2 and IL4R, thus improving the survival rate of sepsis patients.

Development and validation of novel inflammatory response-related gene signature for sepsis prognosis

Due to the low specificity and sensitivity of biomarkers in sepsis diagnostics, the prognosis of sepsis patient outcomes still relies on the assessment of clinical symptoms. Inflammatory response is crucial to sepsis onset and progression; however, the significance of inflammatory response-related genes (IRRGs) in sepsis prognosis is uncertain. This study developed an IRRG-based signature for sepsis prognosis and immunological function. The Gene Expression Omnibus (GEO) database was retrieved for two sepsis microarray datasets, GSE64457 and GSE69528, followed by gene set enrichment analysis (GSEA) comparing sepsis and healthy samples. A predictive signature for IRRGs was created using least absolute shrinkage and selection operator (LASSO). To confirm the efficacy and reliability of the new prognostic signature, Cox regression, Kaplan-Meier (K-M) survival, and receiver operating characteristic (ROC) curve analyses were performed. Subsequently, we employed the GSE95233 dataset to independently validate the prognostic signature. A single-sample GSEA (ssGSEA) was conducted to quantify the immune cell enrichment score and immune-related pathway activity. We found that more gene sets were enriched in the inflammatory response in sepsis patient samples than in healthy patient samples, as determined by GSEA. The signature of nine IRRGs permitted the patients to be classified into two risk categories. Patients in the low-risk group showed significantly better 28-d survival than those in the high-risk group. ROC curve analysis corroborated the predictive capacity of the signature, with the area under the curve (AUC) for 28-d survival reaching 0.866. Meanwhile, the ssGSEA showed that the two risk groups had different immune states. The validation set and external dataset showed that the signature was clinically predictive. In conclusion, a signature consisting of nine IRRGs can be utilized to predict prognosis and influence the immunological status of sepsis patients. Thus, intervention based on these IRRGs may become a therapeutic option in the future.

Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Purpose

To screen biomarkers in the serum of patients with sepsis by proteomics combined with RNA sequencing technology, and to find new diagnostic and therapeutic targets for sepsis.

Patients and Methods

Blood samples of 22 sepsis patients (sepsis group) and 10 healthy volunteers (normal group) were collected from January 2019 to December 2020. Data-independent acquisition (DIA) method was employed for protein profiling, RNA sequencing was employed for gene sequencing. Subsequently, quality control and differential analysis (FC≥2; FDR<0.05) of DIA data and RNA sequencing data were performed. Then we identified expression trend-consistent divergence factors by nine-quadrant analysis; subsequent protein-protein interaction (PPI) and gene ontology (GO) functional enrichment analysis of intersection factors was performed, and meta-analysis of targets at transcriptome level was implemented using public datasets. Finally, five Peripheral blood mononuclear cell (PBMC) samples (NC=2; SIRS=1; SEPSIS =2) were collected, and cell localization analysis of core genes was performed by 10× single-cell RNA sequencing (scRNA-seq).

Results

Compared with the normal group, there were 4681 differentially expressed genes and 202 differentially expressed proteins in the sepsis group. Among them, 25 factors were expressed in both proteome and transcriptome, and the analysis of PPI and GO found that they were mainly involved in biological processes such as white blood cell and neutrophil response, inflammatory and immune response. Four core genes GSTO1, C1QA, RETN, and GRN were screened by meta-analysis, all of which were highly expressed in the sepsis group compared with the normal group (P<0.05); scRNA-seq showed the core genes were mainly localized in macrophage cell lines.

Conclusion

The core genes GSTO1, C1QA, RETN and GRN are mainly expressed in macrophages, widely involved in inflammation and immune responses, and are highly expressed in plasma in the sepsis, suggesting that they may become potential research targets for sepsis.

UBE2D1 and COX7C as Potential Biomarkers of Diabetes-Related Sepsis

Patients with diabetes are physiologically frail and more likely to suffer from infections and even life-threatening sepsis. This study aimed to identify and verify potential biomarkers of diabetes-related sepsis (DRS). Datasets GSE7014, GSE57065, and GSE95233 from the Gene Expression Omnibus were used to explore diabetes- and sepsis-related differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and functional analyses were performed to explore potential functions and pathways associated with sepsis and diabetes. Weighted gene co-expression network analysis (WGCNA) was performed to identify diabetes- and sepsis-related modules. Functional enrichment analysis was performed to determine the characteristics and pathways of key modules. Intersecting DEGs that were also present in key modules were considered as common DEGs. Protein-protein interaction (PPI) network and key genes were analyzed to screen hub genes involved in DRS development. A mouse C57 BL/6J-DRS model and a neural network prediction model were constructed to verify the relationship between hub genes and DRS. In total, 7457 diabetes-related DEGs and 2606 sepsis-related DEGs were identified. GSEA indicated that gene datasets associated with diabetes and sepsis were mainly enriched in metabolic processes linked to inflammatory responses and reactive oxygen species, respectively. WGCNA indicated that grey60 and brown modules were diabetes- and sepsis-related key modules, respectively. Functional analysis showed that grey60 module genes were mainly enriched in cell morphogenesis, heart development, and the PI3K-Akt signaling pathway, whereas genes from the brown module were mainly enriched in organelle inner membrane, mitochondrion organization, and oxidative phosphorylation. UBE2D1, IDH1, DLD, ATP5C1, COX6C, and COX7C were identified as hub genes in the PPI network. Animal DRS and neural network prediction models indicated that the expression levels of UBE2D1 and COX7C in DRS models and samples were higher than control mice. UBE2D1 and COX7C were identified as potential biomarkers of DRS. These findings may help develop treatment strategies for DRS.

Quantitative Proteomics of Polarised Macrophages Derived from Induced Pluripotent Stem Cells

Macrophages (M_Φ) are highly heterogenous and versatile innate immune cells involved in homeostatic and immune responses. Activated M_Φ can exist in two extreme phenotypes: pro-inflammatory (M1) M_Φ and anti-inflammatory (M2) M_Φ. These phenotypes can be recapitulated in vitro by using ligands of toll-like receptors (TLRs) and cytokines such as IFNγ and IL-4. In recent years, human induced pluripotent stem cells (iPSC)-derived M_Φ have gained major attention, as they are functionally similar to human monocyte-derived M_Φ and are receptive to genome editing. In this study, we polarised iPSC-derived M_Φ to M1 or M2 and analysed their proteome and secretome profiles using quantitative proteomics. These comprehensive proteomic data sets provide new insights into functions of polarised M_Φ.

A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Gasdermin D inhibition prevents multiple organ dysfunction during sepsis by blocking NET formation

Multiple organ dysfunction is the most severe outcome of sepsis progression and is highly correlated with a worse prognosis. Excessive neutrophil extracellular traps (NETs) are critical players in the development of organ failure during sepsis. Therefore, interventions targeting NET release would likely effectively prevent NET-based organ injury associated with this disease. Herein, we demonstrate that the pore-forming protein gasdermin D (GSDMD) is active in neutrophils from septic humans and mice and plays a crucial role in NET release. Inhibition of GSDMD with disulfiram or genic deletion abrogated NET formation, reducing multiple organ dysfunction and sepsis lethality. Mechanistically, we demonstrate that during sepsis, activation of the caspase-11/GSDMD pathway controls NET release by neutrophils during sepsis. In summary, our findings uncover a novel therapeutic use for disulfiram and suggest that GSDMD is a therapeutic target to improve sepsis treatment.

Androgen Receptor Pathway Activity Assay for Sepsis Diagnosis and Prediction of Favorable Prognosis

Introduction: Sepsis is a life-threatening complication of a bacterial infection. It is hard to predict which patients with a bacterial infection will develop sepsis, and accurate and timely diagnosis as well as assessment of prognosis is difficult. Aside from antibiotics-based treatment of the causative infection and supportive measures, treatment options have remained limited. Better understanding of the immuno-pathophysiology of sepsis is expected to lead to improved diagnostic and therapeutic solutions. Functional activity of the innate (inflammatory) and adaptive immune response is controlled by a dedicated set of cellular signal transduction pathways, that are active in the various immune cell types. To develop an immune response-based diagnostic assay for sepsis and provide novel therapeutic targets, signal transduction pathway activities have been analyzed in whole blood samples from patients with sepsis. Methods: A validated and previously published set of signal transduction pathway (STP) assays, enabling determination of immune cell function, was used to analyze public Affymetrix expression microarray data from clinical studies containing data from pediatric and adult patients with sepsis. STP assays enable quantitative measurement of STP activity on individual patient sample data, and were used to calculate activity of androgen receptor (AR), estrogen receptor (ER), JAK-STAT1/2, JAK-STAT3, Notch, Hedgehog, TGFβ, FOXO-PI3K, MAPK-AP1, and NFκB signal transduction pathways. Results: Activity of AR and TGFβ pathways was increased in children and adults with sepsis. Using the mean plus two standard deviations of normal pathway activity (in healthy individuals) as threshold for abnormal STP activity, diagnostic assay parameters were determined. For diagnosis of pediatric sepsis, the AR pathway assay showed high sensitivity (77%) and specificity (97%), with a positive prediction value (PPV) of 99% and negative prediction value (NPV) of 50%. For prediction of favorable prognosis (survival), PPV was 95%, NPV was 21%. The TGFβ pathway activity assay performed slightly less for diagnosing sepsis, with a sensitivity of 64% and specificity of 98% (PPV 99%, NPV 39%). Conclusion: The AR and TGFβ pathways have an immunosuppressive role, suggesting a causal relation between increased pathway activity and sepsis immunopathology. STP assays have been converted to qPCR assays for further evaluation of clinical utility for sepsis diagnosis and prediction of prognosis, as well as for prediction of risk at developing sepsis in patients with a bacterial infection. STPs may present novel therapeutic targets in sepsis.

Leukocyte Immunoglobulin-Like Receptors in Regulating the Immune Response in Infectious Diseases: A Window of Opportunity to Pathogen Persistence and a Sound Target in Therapeutics

Immunoregulatory receptors are essential for orchestrating an immune response as well as appropriate inflammation in infectious and non-communicable diseases. Among them, leukocyte immunoglobulin-like receptors (LILRs) consist of activating and inhibitory receptors that play an important role in regulating immune responses modulating the course of disease progression. On the one hand, inhibitory LILRs constitute a safe-guard system that mitigates the inflammatory response, allowing a prompt return to immune homeostasis. On the other hand, because of their unique capacity to attenuate immune responses, pathogens use inhibitory LILRs to evade immune recognition, thus facilitating their persistence within the host. Conversely, the engagement of activating LILRs triggers immune responses and the production of inflammatory mediators to fight microbes. However, their heightened activation could lead to an exacerbated immune response and persistent inflammation with major consequences on disease outcome and autoimmune disorders. Here, we review the genetic organisation, structure and ligands of LILRs as well as their role in regulating the immune response and inflammation. We also discuss the LILR-based strategies that pathogens use to evade immune responses. A better understanding of the contribution of LILRs to host-pathogen interactions is essential to define appropriate treatments to counteract the severity and/or persistence of pathogens in acute and chronic infectious diseases lacking efficient treatments.

Detection of Immune Checkpoint Receptors - A Current Challenge in Clinical Flow Cytometry

Immunological therapy principles are increasingly determining modern medicine. They are used to treat diseases of the immune system, for tumors, but also for infections, neurological diseases, and many others. Most of these therapies base on antibodies, but small molecules, soluble receptors or cells and modified cells are also used. The development of immune checkpoint inhibitors is amazingly fast. T-cell directed antibody therapies against PD-1 or CTLA-4 are already firmly established in the clinic. Further targets are constantly being added and it is becoming increasingly clear that their expression is not only relevant on T cells. Furthermore, we do not yet have any experience with the long-term systemic effects of the treatment. Flow cytometry can be used for diagnosis, monitoring, and detection of side effects. In this review, we focus on checkpoint molecules as target molecules and functional markers of cells of the innate and acquired immune system. However, for most of the interesting and potentially relevant parameters, there are still no test kits suitable for routine use. Here we give an overview of the detection of checkpoint molecules on immune cells in the peripheral blood and show examples of a possible design of antibody panels.

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

Long non-coding RNA pairs to assist in diagnosing sepsis

Background

Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.

Results

Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA_i > lncRNA_j in sepsis patients and lncRNA_i < lncRNA_j in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.

Conclusion

Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07576-4.

Immune gene expression networks in sepsis: A network biology approach

To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.

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.

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Background

Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited.

Methods

We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules.

Results

Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs.

Conclusion

The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.

Leukocyte Immunoglobulin-Like Receptors (LILRs) on Human Neutrophils: Modulators of Infection and Immunity

Neutrophils have a crucial role in defense against microbes. Immune receptors allow neutrophils to sense their environment, with many receptors functioning to recognize signs of infection and to promote antimicrobial effector functions. However, the neutrophil response must be tightly regulated to prevent excessive inflammation and tissue damage, and regulation is achieved by expression of inhibitory receptors that can raise activation thresholds. The leukocyte immunoglobulin-like receptor (LILR) family contain activating and inhibitory members that can up- or down-regulate immune cell activity. New ligands and functions for LILR continue to emerge. Understanding the role of LILR in neutrophil biology is of general interest as they can activate and suppress antimicrobial responses of neutrophils and because several human pathogens exploit these receptors for immune evasion. This review focuses on the role of LILR in neutrophil biology. We focus on the current knowledge of LILR expression on neutrophils, the known functions of LILR on neutrophils, and how these receptors may contribute to shaping neutrophil responses during infection.

Activated peripheral blood mononuclear cell mediators trigger astrocyte reactivity

Sepsis is characterized by a severe and disseminated inflammation. In the central nervous system, sepsis promotes synaptic dysfunction and permanent cognitive impairment. Besides sepsis-induced neuronal dysfunction, glial cell response has been gaining considerable attention with microglial activation as a key player. By contrast, astrocytes' role during acute sepsis is still underexplored. Astrocytes are specialized immunocompetent cells involved in brain surveillance. In this context, the potential communication between the peripheral immune system and astrocytes during acute sepsis still remains unclear. We hypothesized that peripheral blood mononuclear cell (PBMC) mediators are able to affect the brain during an episode of acute sepsis. With this in mind, we first performed a data-driven transcriptome analysis of blood from septic patients to identify common features among independent clinical studies. Our findings evidenced pronounced impairment in energy-related signaling pathways in the blood of septic patients. Since astrocytes are key for brain energy homeostasis, we decided to investigate the communication between PBMC mediators and astrocytes in a rat model of acute sepsis, induced by cecal ligation and perforation (CLP). In the CLP animals, we identified widespread in vivo brain glucose hypometabolism. Ex vivo analyses demonstrated astrocyte reactivity along with reduced glutamate uptake capacity during sepsis. Also, by exposing cultured astrocytes to mediators released by PBMCs from CLP animals, we reproduced the energetic failure observed in vivo. Finally, by pharmacologically inhibiting phosphoinositide 3-kinase (PI3K), a central metabolic pathway downregulated in the blood of septic patients and reduced in the CLP rat brain, we mimicked the PBMC mediators effect on glutamate uptake but not on glucose metabolism. These results suggest that PBMC mediators are capable of directly mediating astrocyte reactivity and contribute to the brain energetic failure observed in acute sepsis. Moreover, the evidence of PI3K participation in this process indicates a potential target for therapeutic modulation.

Endogenous Retroviruses Transcriptional Modulation After Severe Infection, Trauma and Burn

Although human endogenous retroviruses (HERVs) expression is a growing subject of interest, no study focused before on specific endogenous retroviruses loci activation in severely injured patients. Yet, HERV reactivation is observed in immunity compromised settings like some cancers and auto-immune diseases. Our objective was to assess the transcriptional modulation of HERVs in burn, trauma and septic shock patients. We analyzed HERV transcriptome with microarray data from whole blood samples of a burn cohort (n = 30), a trauma cohort (n = 105) and 2 septic shock cohorts (n = 28, n = 51), and healthy volunteers (HV, n = 60). We described expression of the 337 probesets targeting HERV from U133 plus 2.0 microarray in each dataset and then we compared HERVs transcriptional modulation of patients compared to healthy volunteers. Although all 4 cohorts contained critically ill patients, the majority of the 337 HERVs was not expressed (around 74% in mean). Each cohort had differentially expressed probesets in patients compared to HV (from 19 to 46). Strikingly, 5 HERVs were in common in all types of severely injured patients, with 4 being up-modulated in patients. We highlighted co-expressed profiles between HERV and nearby CD55 and CD300LF genes as well as autonomous HERV expression. We suggest an inflammatory-specific HERV transcriptional response, and importantly, we introduce that the HERVs close to immunity-related genes might have a role on its expression.