Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis

Assessment of hemostatic profile in neonates with necrotizing enterocolitis using Rotational Thromboelastometry (ROTEM)

BACKGROUND

This study aimed to explore the hemostatic profile of neonates with necrotizing enterocolitis (NEC) using Rotational Thromboelastometry (ROTEM) and to investigate if ROTEM parameters have the capacity to play a role in the differentiation of NEC from sepsis at the disease onset.

METHODS

This observational study included 62 neonates (mean gestational age 31.6 weeks and mean birth weight 1620g) hospitalized in a neonatal intensive care unit. The neonates were categorized in three groups: neonates with NEC (Bell stage II and above), neonates with sepsis and healthy neonates and they were matched 1:1:1 with regards to gestational age, delivery mode, and sex. Clinical, laboratory data as well as measurements of ROTEM parameters at disease onset were recorded.

RESULTS

ROTEM parameters differed between neonates with NEC and neonates with sepsis, indicating that NEC results in accelerated clot formation and higher clot strength compared to sepsis. The EXTEM CFT and A10 parameters demonstrated the highest diagnostic performance for NEC in terms of discrimination between NEC and sepsis (AUC, 0.997; 95% CI: 0.991-1.000 and 0.973; 95% CI: 0.932-1.000, respectively).

CONCLUSIONS

Neonates with NEC manifested accelerated clot formation and higher clot strength compared to septic and healthy neonates, as these were expressed by ROTEM parameters.

IMPACT

This work reports data on the hemostatic profile of neonates with necrotizing enterocolitis (NEC) using Rotational Thromboelastometry (ROTEM) and the capacity of ROTEM parameters in differentiating of NEC from sepsis at the disease onset. Neonates with NEC present acceleration of coagulation and exhibit a hypercoagulable profile, as this is expressed by ROTEM parameters, in comparison to septic and healthy neonates. ROTEM parameters demonstrated a good diagnostic capacity in differentiating NEC from sepsis at the disease onset.

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

Objective

Sepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis.

Method

The study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR.

Results

Among the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls.

Conclusion

These three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.

The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells

Introduction

Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness.

Methods

Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering.

Results

We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome.

Discussion

The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.

Multilevel omics for the discovery of biomarkers in pediatric sepsis

Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi-omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi-levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.

Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis

BACKGROUND

Sepsis is a high mortality disease which seriously threatens human life and health, for which the pathogenetic mechanism still unclear. There is increasing evidence showed that immune and inflammation responses are key players in the development of sepsis pathology. LncRNAs, which act as ceRNAs, have critical roles in various diseases. However, the regulatory roles of ceRNA in the immunopathogenesis of sepsis have not yet been elucidated.

RESULTS

In this study, we aimed to identify immune biomarkers associated with sepsis. We first generated a global immune-associated ceRNA (IMCE) network based on data describing interactions pairs of gene-miRNA and miRNA-lncRNA. Afterward, we excavated a dysregulated sepsis immune-associated ceRNA (SPIMC) network from the global IMCE network by means of a multi-step computational approach. Functional enrichment indicated that lncRNAs in SPIMC network have pivotal roles in the immune mechanism underlying sepsis. Subsequently, we identified module and hub genes (CD4 and STAT4) via construction of a sepsis immune-related PPI network. Then, we identified hub genes based on the modular structure of PPI network and generated a ceRNA subnetwork to analyze key lncRNAs associated with sepsis. Finally, 6 lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) that identified as immune biomarkers of sepsis. Moreover, the CIBERSORT algorithm and the infiltration of circulating immune cells types were performed to identify the inflammatory state of sepsis. Correlation analyses between immune cells and sepsis immune biomarkers showed that the LINC00265 was strongly positive correlated with the macrophages M2 (r = 0.77).

CONCLUSION

Collectively, these results may suggest that these lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) played important roles in the immune pathogenesis of sepsis and provide potential therapeutic targets for further researches on immune therapy treatment in patients with sepsis.

Potential involvement of polycystins in the pathogenesis of ameloblastomas: Analysis based on bioinformatics and immunohistochemistry

OBJECTIVE

To perform an integrated analysis in identifying novel hub genes that could facilitate the diagnosis and targeted therapy of ameloblastoma.

DESIGN

The expression profiling dataset, GSE38494, was obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified through GEO2R online tool and characterised via Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The protein-protein interaction network and hub genes were screened using the STRING database and Cytoscape software. Subsequently, an upregulated gene was selected for further validation using the GSE132472 dataset. Further, immunohistochemistry was performed to assess the expression of the selected gene in ameloblastomas, odontogenic keratocysts, dentigerous cysts, and gingival tissues. The diagnostic and therapeutic utility of the selected hub genes were further verified by receiver operating characteristic analysis and the DGIdb database.

RESULTS

We identified six hub genes in ameloblastoma, among which the upregulated gene PKD2 and its related gene PKD1 were further validated. GO functional annotation revealed that PKD2 is involved in cell-cell junction, extracellular exosome, cytoplasm, endoplasmic reticulum, and calcium ion transport. The immunohistochemical analysis showed that the expression of polycystin-1 and polycystin-2, encoded by the PKD1 and PKD2 genes, respectively, was upregulated in ameloblastoma. PKD1 and PKD2 had a high diagnostic utility for ameloblastoma, and allopurinol interacted with the PKD2 gene.

CONCLUSION

Our research indicates that polycystins are highly expressed in ameloblastoma and might be involved in the oncogenesis of ameloblastoma, thus offering a new perspective on the molecular mechanisms and targeted therapies on ameloblastoma.

ADAM10-a "multitasker" in sepsis: focus on its posttranslational target

BACKGROUND

Sepsis has a complex pathogenesis in which the uncontrolled systemic inflammatory response triggered by infection leads to vascular barrier disruption, microcirculation dysfunction and multiple organ dysfunction syndrome. Numerous recent studies reveal that a disintegrin and metalloproteinase 10 (ADAM10) acts as a "molecular scissor" playing a pivotal role in the inflammatory response during sepsis by regulating proteolysis by cleaving various membrane protein substrates, including proinflammatory cytokines, cadherins and Notch, which are involved in intercellular communication. ADAM10 can also act as the cellular receptor for Staphylococcus aureus α-toxin, leading to lethal sepsis. However, its substrate-specific modulation and precise targets in sepsis have not yet to be elucidated.

METHODS

We performed a computer-based online search using PubMed and Google Scholar for published articles concerning ADAM10 and sepsis.

CONCLUSIONS

In this review, we focus on the functions of ADAM10 in sepsis-related complex endothelium-immune cell interactions and microcirculation dysfunction through the diversity of its substrates and its enzymatic activity. In addition, we highlight the posttranslational mechanisms of ADAM10 at specific subcellular sites, or in multimolecular complexes, which will provide the insight to intervene in the pathophysiological process of sepsis caused by ADAM10 dysregulation.

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

Background

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

Methods

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

Results

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

Conclusions

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

Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection

Neonatal early-onset sepsis (EOS) has unfortunately been the third leading cause of neonatal death worldwide. The current study is aimed at discovering reliable biomarkers for the diagnosis of neonatal EOS through transcriptomic analysis of publicly available datasets. Whole blood mRNA expression profiling of neonatal EOS patients in the GSE25504 dataset was downloaded and analyzed. The binomial LASSO model was constructed to select genes that most accurately predicted neonatal EOS. Then, ROC curves were generated to assess the performance of the predictive features in differentiating between neonatal EOS and normal infants. Finally, the miRNA-mRNA network was established to explore the potential biological mechanisms of genes within the model. Four genes (CST7, CD3G, CD247, and ANKRD22) were identified that most accurately predicted neonatal EOS and were subsequently used to construct a diagnostic model. ROC analysis revealed that this diagnostic model performed well in differentiating between neonatal EOS and normal infants in both the GSE25504 dataset and our clinical cohort. Finally, the miRNA-mRNA network consisting of the four genes and potential target miRNAs was constructed. Through bioinformatics analysis, a diagnostic four-gene model that can accurately distinguish neonatal EOS in newborns with bacterial infection was constructed, which can be used as an auxiliary test for diagnosing neonatal EOS with bacterial infection in the future.

CONCLUSION

In the current study, we analyzed gene expression profiles of neonatal EOS patients from public databases to develop a genetic model for predicting sepsis, which could provide insight into early molecular changes and biological mechanisms of neonatal EOS.

WHAT IS KNOWN

• Infants with suspected EOS usually receive empiric antibiotic therapy directly after birth. • When blood cultures are negative after 48 to 72 hours, empirical antibiotic treatment is often halted. Needless to say, this is not a short time. Additionally, because of the concern for inadequate clinical sepsis production and the limited sensitivity of blood cultures, the duration of antibiotic therapy for the kid is typically extended.

WHAT IS NEW

• We established a 4-gene diagnostic model of neonatal EOS with bacterial infection by bioinformatics analysis method. The model has better diagnostic performance compared with conventional inflammatory indicators such as CRP, Hb, NEU%, and PCT.

Bioinformatics Analysis Identifies TNFRSF1A as a Biomarker of Liver Injury in Sepsis TNFRSF1A is a Biomarker for Septic Liver Injury

Sepsis is a severe disease with high mortality, and liver injury is an independent risk factor for sepsis morbidity and mortality. We analyzed co-differentially expressed genes (co-DEGs) to explore potential biomarkers and therapeutic targets for sepsis-related liver injury. Three gene expression datasets (GSE60088, GSE23767, and GSE71530) were downloaded from the Gene Expression Omnibus (GEO). DEGs were screened between sepsis and control samples using GEO2R. The association of these DEGs with infection and liver disease was analyzed by using the CTD database. GO functional analysis, KEGG pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate the potential molecular mechanism of DEGs. DEGs of different tissues in GSE60088 were analyzed again to obtain specific markers of septic liver injury. Mouse model of sepsis was also established by cecal ligation and puncture (CLP), and the expression of specific markers in liver, lung, and kidney tissues was analyzed using Western blot. Here, we identified 21 DEGs in three datasets with 8 hub genes, all of which showed higher inference scores in liver diseases than bacterial infections. Among them, only TNFRSF1A had a liver-specific differential expression. TNFRSF1A was also confirmed to be specifically reduced in septic liver tissues in mice. Therefore, TNFRSF1A may serve as a potential biomarker for septic liver injury.

Interferon gamma as an immune modulating adjunct therapy for invasive mucormycosis after severe burn – A case report

Background

Mucormycosis is a deadly fungal infection that mainly affects severely immunocompromised patients. We report herein the case of a previously immunocompetent adult woman who developed invasive cutaneous mucormycosis after severe burn injuries. Interferon-gamma (IFN-γ) treatment was added after failure of conventional treatment and confirmation of a sustained profound immunodepression. The diagnosis was based on a reduced expression of HLA-DR on monocytes (mHLA-DR), NK lymphopenia and a high proportion of immature neutrophils. The immune-related alterations were longitudinally monitored using panels of immune-related biomarkers.

Results

Initiation of IFN-γ was associated with a rapid clinical improvement and a subsequent healing of mucormycosis infection, with no residual fungi at the surgical wound repair. The serial immunological assessment showed sharp improvements of immune parameters: a rapid recovery of mHLA-DR and of transcriptomic markers for T-cell proliferation. The patient survived and was later discharged from the ICU.

Conclusion

The treatment with recombinant IFN-γ participated to the resolution of a progressively invasive mucormycosis infection, with rapid improvement in immune parameters. In the era of precision medicine in the ICU, availability of comprehensive immune monitoring tools could help guiding management of refractory infections and provide rationale for immune stimulation strategies in these high risk patients.

Identification of a Prognostic Model Based on Fatty Acid Metabolism-Related Genes of Head and Neck Squamous Cell Carcinoma

The fatty acid metabolism (FAM) is known to impact tumorigenesis, tumor progression and treatment resistance via enhancing lipid synthesis, storage and catabolism. However, the role of FAM in head and neck squamous cell carcinoma (HNSCC) has remained elusive. In the present study, we obtained a total of 69 differentially expressed FAM-related genes between 502 HNSCC samples and 44 normal samples from The Cancer Genome Atlas (TCGA) database. The HNSCC samples were divided into 2 clusters according to 69 differentially expressed genes (DEGs) via cluster analysis. Then DEGs in the two clusters were found, and 137 prognostic DEGs were identified by univariate analysis. Subsequently, combined with the clinical information of 546 HNSCC patients from TCGA database, a 12-gene prognostic risk model was established (FEPHX3, SPINK7, FCRLA, MASP1, ZNF541, CD5, BEST2 and ZAP70 were down-regulation, ADPRHL1, DYNC1I1, KCNG1 and LINC00460 were up-regulation) using multivariate Cox regression and LASSO regression analysis. The risk scores of 546 HNSCC samples were calculated. According to the median risk score, 546 HNSCC patients were divided into the high- and low-risk (high- and low score) groups. The Kaplan-Meier survival analysis showed that the survival time of HNSCC patients was significantly shorter in the high-risk group than that in the low-risk group (p < 0.001). The same conclusion was obtained in the Gene Expression Omnibus (GEO) dataset. After that, the multivariate Cox regression analysis indicated that the risk score was an independent factor for patients with HNSCC in the TCGA cohort. In addition, single-sample gene set enrichment analysis (ssGSEA) indicated that the level of infiltrating immune cells was relatively low in the high-risk group compared with the low-risk group. In summary, FAM-related gene expression-based risk signature could predict the prognosis of HNSCC independently.

Leucine rich alpha-2-glycoprotein 1 (Lrg1) silencing protects against sepsis-mediated brain injury by inhibiting transforming growth factor beta1 (TGFβ1)/SMAD signaling pathway

Sepsis-associated encephalopathy (SAE) is key manifestation of sepsis which is responsible for increased morbidity and mortality. Leucine rich alpha-2-glycoprotein 1 (Lrg1) is a secreted protein implicated in a variety of diseases. We aimed to explore the effects and potential mechanism of Lrg1 on sepsis-mediated brain injury. A sepsis-induced brain damage mice model was established. Then, ELISA was utilized to detect the levels of inflammatory factors in brain tissues. Behavioral performance, spatial learning and memory of mice were evaluated by open field test and Morris water maze test. The number of neurons was tested by H&E staining. Lrg1 expression was evaluated by RT-qPCR and western blot. In vitro, mouse hippocampal neuronal cell line (HT22) was stimulated by lipopolysaccharide (LPS). After Lrg1 silencing, cell viability was determined using CCK-8 and cell apoptosis was assessed by TUNEL. The levels of inflammatory factors were detected by ELISA. Moreover, western blot was applied to analyze the expression of proteins in transforming growth factor beta1 (TGFβ1)/SMAD signaling. Results revealed that mice in the model group showed obvious behavioral changes. Lrg1 was highly expressed in the brain tissues of model mice. Besides, Lrg1 knockdown suppressed the inflammation and apoptosis of LPS-induced HT22 cells. Moreover, Lrg1 silencing caused the inactivation of TGFβ1/SMAD signaling. Rescue assays confirmed that TGFβ1 overexpression reversed the impacts of Lrg1 deletion on the inflammation and apoptosis in LPS-induced HT22 cells. Collectively, Lrg1 silencing alleviates brain injury in SAE via inhibiting TGFβ1/SMAD signaling, implying that Lrg1 might serve as a promising target for SAE treatment.

Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis

Aim

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

Methods

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

Results

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

Conclusion

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

Research Progress on Leucine-Rich Alpha-2 Glycoprotein 1: A Review

Leucine-rich alpha⁃2 glycoprotein 1 (LRG1) is an important member of the leucine-rich repetitive sequence protein family. LRG1 was mainly involved in normal physiological activities of the nervous system, such as synapse formation, synapse growth, the development of nerve processes, neurotransmitter transfer and release, and cell adhesion molecules or ligand-binding proteins. Also, LRG1 affected the development of respiratory diseases, hematological diseases, endocrine diseases, tumor diseases, eye diseases, cardiovascular diseases, rheumatic immune diseases, infectious diseases, etc. LRG1 was a newly discovered important upstream signaling molecule of transforming growth factor⁃β (TGF⁃β) that affected various pathological processes through the TGF⁃β signaling pathway. However, research on LRG1 and its involvement in the occurrence and development of diseases was still in its infancy and the current studies were mainly focused on proteomic detection and basic animal experimental reports. We could reasonably predict that LRG1 might act as a new direction and strategy for the treatment of many diseases.

OLFM4 Regulates Lung Epithelial Cell Function in Sepsis-Associated ARDS/ALI via LDHA-Mediated NF-κB Signaling

Background

Acute respiratory distress syndrome (ARDS) is one of the leading causes of death in patients with sepsis. As such, early and accurate identification of sepsis-related ARDS is critical.

Methods

Bioinformatic analysis was used to explore the GEO datasets. ELISA method was used to detect the plasma or cellular supernatant of relevant proteins. Quantitative real-time PCR was used for mRNA measurements and Western blot was applied for protein measurements. Immunohistochemistry staining and Immunofluorescence staining were used to identify the localization of OLFM4. Cecal ligation and puncture (CLP) model was used to establish sepsis model.

Results

The bioinformatic analysis results identified ten genes (CAMP, LTF, RETN, LCN2, ELANE, PGLYRP1, BPI, DEFA4, MPO, and OLFM4) as critical in sepsis and sepsis-related ARDS. OLFM4, LCN2, and BPI were further demonstrated to have diagnostic values in sepsis-related ARDS. Plasma expression of OLFM4 and LCN2 was also upregulated in sepsis-related ARDS patients compared to septic patients alone. OLFM4 expression was significantly increased in the lung tissues of septic mice and was co-localized with Ly6G+ neutrophils, F4/80+ macrophages and pro-surfactant C+ lung epithelial cells. In vitro data showed that OLFM4 expression in lung epithelial cells was downregulated upon LPS stimulation, whereas neutrophil media induced OLFM4 expression in lung epithelial cells. Overexpression of OLFM4 and treatment with recombinant OLFM4 effectively suppressed LPS-induced pro-inflammatory responses in lung epithelial cells. Furthermore, the increased levels of LDHA phosphorylation and the downstream NF-κB activation induced by LPS in epithelial cells were effectively diminished by OLFM4 overexpression and recombinant OLFM4 treatment via a reduction in ROS production and HIF1α expression.

Conclusion

OLFM4 may regulate the pro-inflammatory response of lung epithelial cells in sepsis-related ARDS by modulating metabolic disorders; this result could provide new insights into the treatment of sepsis-induced ARDS.

Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

This study illustrated that lipoprotein and lipid metabolism might play a significant role in patients with sepsis and that complement activation was significantly enriched in patients with sepsis-associated encephalopathy.

Sepsis is defined as an organ dysfunction syndrome and it has high mortality worldwide. This study analysed the proteome of serum from patients with sepsis to characterize the pathological mechanism and pathways involved in sepsis. A total of 59 patients with sepsis were enrolled for quantitative proteomic analysis. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network specific to sepsis. Key regulatory modules that were detected were highly correlated with sepsis patients and related to multiple functional groups, including plasma lipoprotein particle remodeling, inflammatory response, and wound healing. Complement activation was significantly associated with sepsis-associated encephalopathy. Triglyceride/cholesterol homeostasis was found to be related to sepsis-associated acute kidney injury. Twelve hub proteins were identified, which might be predictive biomarkers of sepsis. External validation of the hub proteins showed their significantly differential expression in sepsis patients. This study identified that plasma lipoprotein processes played a crucial role in sepsis patients, that complement activation contributed to sepsis-associated encephalopathy, and that triglyceride/cholesterol homeostasis was associated with sepsis-associated acute kidney injury.

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

Objective

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

Methods

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

Results

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

Conclusion

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