Identification of Potential Early Diagnostic Biomarkers of Sepsis

USF2 activates RhoB/ROCK pathway by transcriptional inhibition of miR-206 to promote pyroptosis in septic cardiomyocytes

Septic cardiomyopathy (SCM) is one of the most serious complications of sepsis. The present study investigated the role and mechanism of upstream stimulatory factor 2 (USF2) in SCM. Serum samples were extracted from SCM patients and healthy individuals. A murine model of sepsis was induced by caecal ligation and puncture (CLP) surgery. Myocardial injury was examined by echocardiography and HE staining. ELISA assay evaluated myocardial markers (CK-MB, cTnI) and inflammatory cytokines (TNF-α, IL-1β, IL-18). Primary mouse cardiomyocytes were treated with lipopolysaccharide (LPS) to simulate sepsis in vitro. RT-qPCR and Western blot were used for analyzing gene and protein levels. CCK-8 assay assessed cell viability. NLRP3 was detected by immunofluorescence. ChIP, RIP and dual luciferase reporter assays were conducted to validate the molecular associations. USF2 was increased in serum from SCM patients, septic mice and primary cardiomyocytes. USF2 silencing improved the survival of septic mice and attenuated sepsis-induced myocardial pyroptosis and inflammation in vitro and in vivo. Mechanistically, USF2 could directly bind to the promoter of miR-206 to transcriptionally inhibit its expression. Moreover, RhoB was confirmed as a target of miR-206 and could promote ROCK activation and NLRP3 inflammasome formation. Moreover, overexpression of RhoB remarkably reversed the protection against LPS-induced inflammation and pyroptosis mediated by USF2 deletion or miR-206 overexpression in cardiomyocytes. The above findings elucidated that USF2 knockdown exerted a cardioprotective effect on sepsis by decreasing pyroptosis and inflammation via miR-206/RhoB/ROCK pathway, suggesting that USF2 may be a novel drug target in SCM.

Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication

Background

Multiple sclerosis (MS) causes chronic inflammation and demyelination of the central nervous system and comprises a class of neurodegenerative diseases in which interactions between multiple immune cell types mediate the involvement of MS development. However, the early diagnosis and treatment of MS remain challenging.

Methods

Gene expression profiles of MS patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses were performed to identify candidate gene sets. Predictive models were constructed using LASSO regression. Relationships between genes and immune cells were analyzed by single sample gene set enrichment analysis (ssGSEA). The molecular mechanisms of key genes were explored using gene enrichment analysis. An miRNA network was constructed to search for target miRNAs related to key genes, and related transcription factors were searched by transcriptional regulation analysis. We utilized the GeneCard database to detect the correlations between disease-regulated genes and key genes. We verified the mRNA expression of 4 key genes by reverse transcription-quantitative PCR (RT‒qPCR).

Results

Monocyte marker genes were selected as candidate gene sets. CD3D, IL2RG, MS4A6A, and NCF2 were found to be the key genes by LASSO regression. We constructed a prediction model with AUC values of 0.7569 and 0.719. The key genes were closely related to immune factors and immune cells. We explored the signaling pathways and molecular mechanisms involving the key genes by gene enrichment analysis. We obtained and visualized the miRNAs associated with the key genes using the miRcode database. We also predicted the transcription factors involved. We used validated key genes in MS patients, several of which were confirmed by RT‒qPCR.

Conclusion

The prediction model constructed with the CD3D, IL2RG, MS4A6A, and NCF2 genes has good diagnostic efficacy and provides new ideas for the diagnosis and treatment of MS.

Identification of key biomarkers of the glomerulus in focal segmental glomerulosclerosis and their relationship with immune cell infiltration based on WGCNA and the LASSO algorithm

OBJECTIVE

The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.

METHODS

The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.

RESULTS

A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.

CONCLUSION

TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

Identification and Development of Synovial B-Cell-Related Genes Diagnostic Signature for Rheumatoid Arthritis

Background

The aim of the study was to investigate the landscape of B-cell-related gene expression profiling in rheumatoid arthritis (RA) synovium and explore the biological and clinical significance of these genes in RA.

Methods

Expression profiling of synovial biopsies from subjects with 152 RA patients, 22 osteoarthritis (OA) patients, and 28 healthy controls was downloaded from the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to evaluate the abundance of infiltrated immune cells, and the results were validated using immunohistochemical staining. GSEA was employed to decipher differences in B-cell-related biological pathways. B-cell-related differential expression genes (BRDEGs) were screened, and BRDEGs-based model was developed by machine learning algorithms and evaluated by an external validation set and clinical RA cohort, then biological functions were further analyzed.

Results

High levels of immune cell infiltration and B-cell-related pathway activation were revealed in RA synovium. BRDEGs were screened, and three key molecular markers consisting of FAS, GPR183, and TFRC were identified. The diagnosis model was established, and these gene markers have good discriminative ability for RA. Molecular pathological evaluation confirmed RA patients with high-risk scores presented higher levels of B-cell activation and RA characteristics. In addition, a competitive endogenous RNA network was established to elucidate the molecular mechanisms of the posttranscriptional network.

Conclusions

We described the B-cell-related molecular landscape of RA synovium and constructed a molecular diagnostic model in RA. The three genes FAS, GPR183, and TFRC may be potential targets for clinical diagnosis and immunoregulatory therapy of RA.

Characterization and function of Japanese flounder (Paralichthys olivaceus) slc2a6 in response to lymphocystis disease virus infection

Slc2a6 is a member of the slc2 family (solute carrier 2 family) and previous reports have indicated its involvement in the inflammatory response. Slc2a6 is regulated by the NF-ĸB signaling pathway. This study investigated the differential expression of slc2a6 in the early embryonic development of Japanese flounder, revealing that the early gastrula stage had the highest level of slc2a6 expression. Moreover, slc2a6 expression was increased in vitro after stimulation by lymphocystis disease virus (LCDV), and in vivo experiments also showed significantly elevated levels in the spleen and muscle tissues following LCDV stimulation. Subcellular localization revealed that Slc2a6 was expressed in both the nucleus and cytoplasm of cells. The pcDNA3.1-slc2a6 overexpression plasmid was successfully constructed; the si-slc2a6 interfering strand was screened and samples were collected. The expression of NF-ĸB signaling pathway-related genes il-1β, il-6, nf-ĸb, and tnf-α was evaluated in overexpressed, silenced, and LCDV-stimulated samples. The results showed that slc2a6 is involved in viral regulation in Japanese flounder by regulating innate immune responses.

Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting

BACKGROUND

Sepsis is the third leading cause of neonatal death in low and middle-income countries, accounting for one third of all deaths in Ethiopia. A concerning issue is the increasing number of multidrug-resistant microorganisms facilitated by suboptimal antibiotic stewardship. The study aims to identify clusters of newborns switching antibiotic lines for sepsis in a neonatal intensive care unit (NICU) in Ethiopia, and to explore their potential association with sepsis outcomes.

METHODS

A retrospective cohort study was conducted including all newborns discharged with a diagnosis of probable neonatal sepsis from the St. Luke Catholic Hospital NICU between April and July 2021. The antibiotic management protocol included two lines according to WHO guidelines and a third line based on internal hospital guidelines. In the cluster analysis, the Gower distance was estimated based on the antibiotics employed in the different lines and the duration of each line. Mortality and respiratory distress (RD) were the response variables.

RESULTS

In the study period, 456 newborns were admitted to the NICU and 196 (42.8%) had probable neonatal sepsis. Four antibiotic management clusters were identified. Cluster 1 (n = 145, 74.4%) had no antibiotic switches, using only the first line. Cluster 2 (n = 26, 13.3%) had one switch from the first to the second line. Cluster 4 (n = 9, 4.6%) had two switches: from first to second and then to third line. In cluster 3 (n = 15, 7.7%), newborns were switched from ceftriaxone/cloxacillin as second line to off-protocol antibiotics. There were no differences in sex, age, weight on admission or crude mortality between clusters. Cluster 3 included a higher frequency of infants who did not breathe at birth (53.3%, p = 0.011) and that necessitated bag ventilation (46.7%, p = 0.039) compared to the other clusters.

CONCLUSIONS

The first antibiotic line failed in one out of four newborns with probable sepsis while third-generation cephalosporins were insufficient in one in ten patients. Cluster analysis can provide valuable insights into antibiotic treatment patterns and their potential implications. This approach may support antibiotic stewardship and aid in contrasting antimicrobial resistance in limited resource settings.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

ABSTRACT

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.

Classification of subtypes and identification of dysregulated genes in sepsis

Background

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.

Methods

The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.

Results

A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.

Conclusion

This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Single-cell RNA-seq and bulk-seq identify RAB17 as a potential regulator of angiogenesis by human dermal microvascular endothelial cells in diabetic foot ulcers

Background

Angiogenesis is crucial in diabetic wound healing and is often impaired in diabetic foot ulcers (DFUs). Human dermal microvascular endothelial cells (HDMECs) are vital components in dermal angiogenesis; however, their functional and transcriptomic characteristics in DFU patients are not well understood. This study aimed to comprehensively analyse HDMECs from DFU patients and healthy controls and find the potential regulator of angiogenesis in DFUs.

Methods

HDMECs were isolated from skin specimens of DFU patients and healthy controls via magnetic-activated cell sorting. The proliferation, migration and tube-formation abilities of the cells were then compared between the experimental groups. Both bulk RNA sequencing (bulk-seq) and single-cell RNA-seq (scRNA-seq) were used to identify RAB17 as a potential marker of angiogenesis, which was further confirmed via weighted gene co-expression network analysis (WGCNA) and least absolute shrink and selection operator (LASSO) regression. The role of RAB17 in angiogenesis was examined through in vitro and in vivo experiments.

Results

The isolated HDMECs displayed typical markers of endothelial cells. HDMECs isolated from DFU patients showed considerably impaired tube formation, rather than proliferation or migration, compared to those from healthy controls. Gene set enrichment analysis (GSEA), fGSEA, and gene set variation analysis (GSVA) of bulk-seq and scRNA-seq indicated that angiogenesis was downregulated in DFU-HDMECs. LASSO regression identified two genes, RAB17 and CD200, as characteristic of DFU-HDMECs; additionally, the expression of RAB17 was found to be significantly reduced in DFU-HDMECs compared to that in the HDMECs of healthy controls. Overexpression of RAB17 was found to enhance angiogenesis, the expression of hypoxia inducible factor-1α and vascular endothelial growth factor A, and diabetic wound healing, partially through the mitogen-activated protein kinase/extracellular signal-regulated kinase signalling pathway.

Conclusions

Our findings suggest that the impaired angiogenic capacity in DFUs may be related to the dysregulated expression of RAB17 in HDMECs. The identification of RAB17 as a potential molecular target provides a potential avenue for the treatment of impaired angiogenesis in DFUs.

Urinary microRNAs in sepsis function as a novel prognostic marker

Renal dysfunction is a common complication of sepsis. Early diagnosis and prompt treatment of sepsis with renal insufficiency are crucial for improving patient outcomes. Diagnostic markers can help identify patients at risk for sepsis and AKI, allowing for early intervention and potentially preventing the development of severe complications. The aim of the present study was to investigate the expression difference of urinary microRNAs (miRNAs/miRs) in elderly patients with sepsis and secondary renal insufficiency, and to evaluate their diagnostic value in these patients. In the present study, RNA was extracted from urine samples of elderly sepsis-related acute renal damage patients and the expression profiles of several miRNAs were analyzed. In order to evaluate the expression profile of several miRNAs, urine samples from elderly patients with acute renal damage brought on by sepsis were obtained. RNA extraction and sequencing were then performed on the samples. Furthermore, multiple bioinformatics methods were used to analyze miRNA profiles, including differential expression analysis, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of different miRNA target genes, to further explore miRNAs that are suitable for utilization as biomarkers. A total of four miRNAs, including hsa-miR-31-5p, hsa-miR-151a-3p, hsa-miR-142-5p and hsa-miR-16-5p, were identified as potential biological markers and were further confirmed in sepsis using reverse transcription-quantitative PCR. The results of the present study demonstrated that the four urinary miRNAs were differentially expressed and may serve as specific markers for prediction of secondary acute kidney injury in elderly patients with sepsis.

Liang-Ge-San: a classic traditional Chinese medicine formula, attenuates acute inflammation via targeting GSK3β

Sepsis is a serious life-threatening health disorder with high morbidity and mortality rates that burden the world, but there is still a lack of more effective and reliable drug treatment. Liang-Ge-San (LGS) has been shown to have anti-inflammatory effects and is a promising candidate for the treatment of sepsis. However, the anti-sepsis mechanism of LGS has still not been elucidated. In this study, a set of genes related to inflammatory chemotaxis pathways was downloaded from Encyclopedia of Genes and Genomes (KEGG) and integrated with sepsis patient information from the Gene Expression Omnibus (GEO) database to perform differential gene expression analysis. Glycogen synthase kinase-3β (GSK-3β) was found to be the feature gene after these important genes were examined using the three algorithms Random Forest, support vector machine recursive feature elimination (SVM-REF), and least absolute shrinkage and selection operator (LASSO), and then intersected with possible treatment targets of LGS found through the search. Upon evaluation, the receiver operating characteristic (ROC) curve of GSK-3β indicated an important role in the pathogenesis of sepsis. Immune cell infiltration analysis suggested that GSK-3β expression was associated with a variety of immune cells, including neutrophils and monocytes. Next, lipopolysaccharide (LPS)-induced zebrafish inflammation model and macrophage inflammation model was used to validate the mechanism of LGS. We found that LGS could protect zebrafish against a lethal challenge with LPS by down-regulating GSK-3β mRNA expression in a dose-dependent manner, as indicated by a decreased neutrophils infiltration and reduction of inflammatory damage. The upregulated mRNA expression of GSK-3β in LPS-induced stimulated RAW 264.7 cells also showed the same tendency of depression by LGS. Critically, LGS could induce M1 macrophage polarization to M2 through promoting GSK-3β inactivation of phosphorylation. Taken together, we initially showed that anti-septic effects of LGS is related to the inhibition on GSK-3β, both in vitro and in vivo.

Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis

: Although early recognition of sepsis is essential for timely treatment and can improve sepsis outcomes, no marker has demonstrated sufficient discriminatory power to diagnose sepsis. This study aimed to compare gene expression profiles between patients with sepsis and healthy volunteers to determine the accuracy of these profiles in diagnosing sepsis and to predict sepsis outcomes by combining bioinformatics data with molecular experiments and clinical information. We identified 422 differentially expressed genes (DEGs) between the sepsis and control groups, of which 93 immune-related DEGs were considered for further studies due to immune-related pathways being the most highly enriched. Key genes upregulated during sepsis, including S100A8, S100A9, and CR1, are responsible for cell cycle regulation and immune responses. Key downregulated genes, including CD79A, HLA-DQB2, PLD4, and CCR7, are responsible for immune responses. Furthermore, the key upregulated genes showed excellent to fair accuracy in diagnosing sepsis (area under the curve 0.747-0.931) and predicting in-hospital mortality (0.863-0.966) of patients with sepsis. In contrast, the key downregulated genes showed excellent accuracy in predicting mortality of patients with sepsis (0.918-0.961) but failed to effectively diagnosis sepsis.In conclusion, bioinformatics analysis identified key genes that may serve as biomarkers for diagnosing sepsis and predicting outcomes among patients with sepsis.

Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for Sepsis, PBMC single cell RNA sequencing data for Sepsis and microarray data for MetS were downloaded from the GEO database. Limma differential analysis identified 122 upregulated genes and 90 downregulated genes in Sepsis and MetS. WGCNA identified brown co-expression modules as Sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely, STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in Sepsis and MetS. The immune infiltration results show that Hub genes were expressed at high levels in all immune cells. After performing Seurat analysis on PBMC from normal and Sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway.

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.

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

BACKGROUND

Kawasaki disease (KD) is a febrile systemic vasculitis involvingchildren younger than five years old. However, the specific biomarkers and precise mechanisms of this disease are not fully understood, which can delay the best treatment time, hence, this study aimed to detect the potential biomarkers and pathophysiological process of KD through bioinformatic analysis.

METHODS

The Gene Expression Omnibus database (GEO) was the source of the RNA sequencing data from KD patients. Differential expressed genes (DEGs) were screened between KD patients and healthy controls (HCs) with the "limma" R package. Weighted gene correlation network analysis (WGCNA) was performed to discover the most corresponding module and hub genes of KD. The node genes were obtained by the combination of the least absolute shrinkage and selection operator (LASSO) regression model with the top 5 genes from five algorithms in CytoHubba, which were further validated with the receiver operating characteristic curve (ROC curve). CIBERSORTx was employed to discover the constitution of immune cells in KDs and HCs. Functional enrichment analysis was performed to understand the biological implications of the modular genes. Finally, competing endogenous RNAs (ceRNA) networks of node genes were predicted using online databases.

RESULTS

A total of 267 DEGs were analyzed between 153 KD patients and 92 HCs in the training set, spanning two modules according to WGCNA. The turquoise module was identified as the hub module, which was mainly enriched in cell activation involved in immune response, myeloid leukocyte activation, myeloid leukocyte mediated immunity, secretion and leukocyte mediated immunity biological processes; included type II diabetes mellitus, nicotinate and nicotinamide metabolism, O-glycan biosynthesis, glycerolipid and glutathione metabolism pathways. The node genes included ADM, ALPL, HK3, MMP9 and S100A12, and there was good performance in the validation studies. Immune cell infiltration analysis revealed that gamma delta T cells, monocytes, M0 macrophage, activated dendritic cells, activated mast cells and neutrophils were elevated in KD patients. Regarding the ceRNA networks, three intact networks were constructed: NEAT1/NORAD/XIST-hsa-miR-524-5p-ADM, NEAT1/NORAD/XIST-hsa-miR-204-5p-ALPL, NEAT1/NORAD/XIST-hsa-miR-524-5p/hsa-miR-204-5p-MMP9.

CONCLUSION

To conclude, the five-gene signature and three ceRNA networks constructed in our study are of great value in the early diagnosis of KD and might help to elucidate our understanding of KD at the RNA regulatory level.

A machine learning-derived gene signature for assessing rupture risk and circulatory immunopathologic landscape in patients with intracranial aneurysms

Background

Intracranial aneurysm (IA) is an uncommon but severe subtype of cerebrovascular disease, with high mortality after aneurysm rupture. Current risk assessments are mainly based on clinical and imaging data. This study aimed to develop a molecular assay tool for optimizing the IA risk monitoring system.

Methods

Peripheral blood gene expression datasets obtained from the Gene Expression Omnibus were integrated into a discovery cohort. Weighted gene co-expression network analysis (WGCNA) and machine learning integrative approaches were utilized to construct a risk signature. QRT-PCR assay was performed to validate the model in an in-house cohort. Immunopathological features were estimated using bioinformatics methods.

Results

A four-gene machine learning-derived gene signature (MLDGS) was constructed for identifying patients with IA rupture. The AUC of MLDGS was 1.00 and 0.88 in discovery and validation cohorts, respectively. Calibration curve and decision curve analysis also confirmed the good performance of the MLDGS model. MLDGS was remarkably correlated with the circulating immunopathologic landscape. Higher MLDGS scores may represent higher abundance of innate immune cells, lower abundance of adaptive immune cells, and worse vascular stability.

Conclusions

The MLDGS provides a promising molecular assay panel for identifying patients with adverse immunopathological features and high risk of aneurysm rupture, contributing to advances in IA precision medicine.

Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis

Introduction

The aim of this study is to establish a prognostic risk model based on ferroptosis to prognosticate the severity of Alzheimer's disease (AD) through gene expression changes.

Methods

The GSE138260 dataset was initially downloaded from the Gene expression Omnibus database. The ssGSEA algorithm was used to evaluate the immune infiltration of 28 kinds of immune cells in 36 samples. The up-regulated immune cells were divided into Cluster 1 group and Cluster 2 group, and the differences were analyzed. The LASSO regression analysis was used to establish the optimal scoring model. Cell Counting Kit-8 and Real Time Quantitative PCR were used to verify the effect of different concentrations of Aβ_1-42 on the expression profile of representative genes in vitro.

Results

Based on the differential expression analysis, there were 14 up-regulated genes and 18 down-regulated genes between the control group and Cluster 1 group. Cluster 1 and Cluster 2 groups were differentially analyzed, and 50 up-regulated genes and 101 down-regulated genes were obtained. Finally, nine common differential genes were selected to establish the optimal scoring model. In vitro, CCK-8 experiments showed that the survival rate of cells decreased significantly with the increase of Aβ_1-42 concentration compared with the control group. Moreover, RT-qPCR showed that with the increase of Aβ_1-42 concentration, the expression of POR decreased first and then increased; RUFY3 was firstly increased and then decreased.

Discussion

The establishment of this research model can help clinicians make decisions on the severity of AD, thus providing better guidance for the clinical treatment of Alzheimer's disease.

Identification of a novel sepsis prognosis model and analysis of possible drug application prospects: Based on scRNA-seq and RNA-seq data

Sepsis is a disease with a high morbidity and mortality rate. At present, there is a lack of ideal biomarker prognostic models for sepsis and promising studies using prognostic models to predict and guide the clinical use of medications. In this study, 71 differentially expressed genes (DEGs) were obtained by analyzing single-cell RNA sequencing (scRNA-seq) and transcriptome RNA-seq data, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses were performed on these genes. Then, a prognosis model with CCL5, HBD, IFR2BP2, LTB, and WFDC1 as prognostic signatures was successfully constructed after univariate LASSO regression analysis and multivariate Cox regression analysis. Kaplan-Meier (K-M) survival analysis, receiver operating characteristic (ROC) time curve analysis, internal validation, and principal component analysis (PCA) further validated the model for its high stability and predictive power. Furthermore, based on a risk prediction model, gene set enrichment analysis (GSEA) showed that multiple cellular functions and immune function signaling pathways were significantly different between the high- and low-risk groups. In-depth analysis of the distribution of immune cells in healthy individuals and sepsis patients using scRNA-seq data revealed immunosuppression in sepsis patients and differences in the abundance of immune cells between the high- and low-risk groups. Finally, the genetic targets of immunosuppression-related drugs were used to accurately predict the potential use of clinical agents in high-risk patients with sepsis.

Identification of cross-talk pathways and ferroptosis-related genes in periodontitis and type 2 diabetes mellitus by bioinformatics analysis and experimental validation

Purpose

There is a bidirectional relationship between periodontitis and type 2 diabetes mellitus (T2DM). The aim of this study was to further explore the pathogenesis of this comorbidity, screen out ferroptosis-related genes involved in the pathological process, and predict potential drug targets to develop new therapeutic strategies.

Methods

Common cross-talk genes were identified from periodontitis datasets (GSE16134, GSE10334 and GSE106090) and T2DM databases (DisGeNET and GeneCard). Then, GO and KEGG enrichment analyses, PPI network analysis and hub gene identification were performed. The association between ferroptosis and periodontitis with T2DM was investigated by Pearson correlation analysis. Core ferroptosis-related cross-talk genes were identified and verified by qRT-PCR. Potential drugs targeting these core genes were predicted via DGIDB.

Results

In total, 67 cross-talk genes and two main signalling pathways (immuno-inflammatory pathway and AGE-RAGE signalling pathway) were identified. Pearson correlation analysis indicated that ferroptosis served as a crucial target in the pathological mechanism and treatment of periodontitis with T2DM. IL-1β, IL-6, NFE2L2 and ALOX5 were identified as core ferroptosis-related genes and the qRT-PCR detection results were statistically different. In total, 13 potential drugs were screened out, among which, Echinacea and Ibudilast should be developed first.

Conclusions

This study contributes to a deeper understanding of the common pathogenesis of periodontitis and T2DM and provides new insights into the role of ferroptosis in this comorbidity. In addition, two drugs with potential clinical application value were identified. The potential utility of these drugs requires further experimental investigation.

Immunological risk factors for sepsis-associated delirium and mortality in ICU patients

Background

A major challenge in intervention of critical patients, especially sepsis-associated delirium (SAD) intervention, is the lack of predictive risk factors. As sepsis and SAD are heavily entangled with inflammatory and immunological processes, to identify the risk factors of SAD and mortality in the intensive care unit (ICU) and determine the underlying molecular mechanisms, the peripheral immune profiles of patients in the ICU were characterized.

Methods

This study contains a cohort of 52 critical patients who were admitted to the ICU of the First Affiliated Hospital of Jinan University. Comorbidity, including sepsis and SAD, of this cohort was diagnosed and recorded. Furthermore, peripheral blood samples were collected on days 1, 3, and 5 of admission for peripheral immune profiling with blood routine examination, flow cytometry, ELISA, RNA-seq, and qPCR.

Results

The patients with SAD had higher mortality during ICU admission and within 28 days of discharge. Compared with survivors, nonsurvivors had higher neutrophilic granulocyte percentage, higher CRP concentration, lower monocyte count, lower monocyte percentage, lower C3 complement level, higher CD14^loCD16⁺ monocytes percentage, and higher levels of IL-6 and TNFα. The CD14^hiCD16^- monocyte percentage manifested favorable prediction values for the occurrence of SAD. Differentially expressed genes between the nonsurvival and survival groups were mainly associated with immune response and metabolism process. The longitudinal expression pattern of SLC2A1 and STIMATE were different between nonsurvivors and survivors, which were validated by qPCR.

Conclusions

Nonsurvival critical patients have a distinct immune profile when compared with survival patients. CD14^hiCD16^- monocyte prevalence and expression levels of SLC2A1 and STIMATE may be predictors of SAD and 28-day mortality in ICU patients.

Screening of potential key ferroptosis-related genes in sepsis

Background

Sepsis leads to multiple organ dysfunction caused by a dysregulated host response to infection with a high incidence and mortality. The effect of ferroptosis on the development of sepsis remains unclear. In this study, we aimed to identify the key ferroptosis-related genes involved in sepsis and further explore the potential biological functions of these ferroptosis-related genes in sepsis using bioinformatics analysis.

Methods

The GSE13904 (from children) and GSE28750 (from adults) datasets were downloaded from the Gene Expression Omnibus (GEO). The ferroptosis-related genes were obtained from the FerrDb database. The ferroptosis-related differentially expressed genes (DEGs) were screened by the limma R package. The DAVID online database or clusterProfiler R package was used for the functional enrichment analysis. Then, the STRING database was used to predict the interactions of proteins, and the CytoHubba plugin of Cytoscape was used to confirm key clustering modules. Then, the miRNAs and lncRNAs associated with the key clustering modules were predicted by miRWalk 2.0 and LncBase v.2 respectively. Finally, we generated a cecal ligation and puncture (CLP) polymicrobial sepsis model in C57 male mice and examined the expression of the mRNAs and noncoding RNAs of interest in peripheral blood leukocytes by PCR during the acute inflammation phase.

Results

In total, 34 ferroptosis-related DEGs were identified in both adult and pediatric septic patients. These ferroptosis-related DEGs were mainly enriched in inflammatory pathways. Then, a significant clustering module containing eight genes was identified. Among them, the following five genes were closely associated with the MAPK signaling pathway: MAPK14, MAPK8, DUSP1, MAP3K5 and MAPK1. Then, crucial miRNAs and lncRNAs associated with biomarker MAPK-related genes were also identified. In particular, let-7b-5p and NEAT1 were selected as noncoding RNAs of interest because of their correlation with ferroptosis in previous studies. Finally, we examined the mRNAs, miRNAs and lncRNAs of interest using CLP-induced sepsis in peripheral blood leukocytes of mice. The results showed that MAPK14, MAPK8, MAP3K5, MAPK1 and NEAT1 were upregulated, while DUSP1 and let-7b-5p were downregulated in the CLP group compared with the sham group.

Conclusions

The MAPK signaling pathway may play a key role in regulating ferroptosis during sepsis. This study provides a valuable resource for future studies investigating the mechanism of MAPK-related ferroptosis in sepsis.

Six macrophage-associated genes in synovium constitute a novel diagnostic signature for osteoarthritis

Background

Synovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA.

Methods

The OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein–protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models.

Results

OA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms.

Conclusions

We developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy.

Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease

In countries with a high incidence of tuberculosis, the typical clinical features of Crohn's disease (CD) may be covered up after tuberculosis infection, and the identification of atypical Crohn's disease and intestinal tuberculosis (ITB) is still a dilemma for clinicians. Least absolute shrinkage and selection operator (LASSO) regression has been applied to select variables in disease diagnosis. However, its value in discriminating ITB and atypical Crohn's disease remains unknown. A total of 400 patients were enrolled from January 2014 to January 2019 in second Xiangya hospital Central South University.Among them, 57 indicators including clinical manifestations, laboratory results, endoscopic findings, computed tomography enterography features were collected for further analysis. R software version 3.6.1 (glmnet package) was used to perform the LASSO logistic regression analysis. SPSS 20.0 was used to perform Pearson chi-square test and binary logistic regression analysis. In the variable selection step, LASSO regression and Pearson chi-square test were applied to select the most valuable variables as candidates for further logistic regression analysis. Secondly, variables identified from step 1 were applied to construct binary logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed on these models to assess the ability and the optimal cutoff value for diagnosis. The area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy rate, together with their 95% confidence and intervals (CIs) were calculated. MedCalc software (Version 16.8) was applied to analyze the ROC curves of models. 332 patients were eventually enrolled to build a binary logistic regression model to discriminate CD (including comprehensive CD and tuberculosis infected CD) and ITB. However, we did not get a satisfactory diagnostic value via applying the binary logistic regression model of comprehensive CD and ITB to predict tuberculosis infected CD and ITB (accuracy rate:79.2%VS 65.1%). Therefore, we further established a binary logistic regression model to discriminate atypical CD from ITB, based on Pearsonchi-square test (model1) and LASSO regression (model 2). Model 1 showed 89.9% specificity, 65.9% sensitivity, 88.5% PPV, 68.9% NPV, 76.9% diagnostic accuracy, and an AUC value of 0.811, and model 2 showed 80.6% specificity, 84.4% sensitivity, 82.3% PPV, 82.9% NPV, 82.6% diagnostic accuracy, and an AUC value of 0.887. The comparison of AUCs between model1 and model2 was statistically different (P < 0.05). Tuberculosis infection increases the difficulty of discriminating CD from ITB. LASSO regression showed a more efficient ability than Pearson chi-square test based logistic regression on differential diagnosing atypical CD and ITB.

Transcriptome-Based Dissection of Intracranial Aneurysms Unveils an “Immuno-Thermal” Microenvironment and Defines a Pathological Feature-Derived Gene Signature for Risk Estimation

Immune inflammation plays an essential role in the formation and rupture of intracranial aneurysm (IA). However, the current limited knowledge of alterations in the immune microenvironment of IA has hampered the mastery of pathological mechanisms and technological advances, such as molecular diagnostic and coated stent-based molecular therapy. In this study, seven IA datasets were enrolled from the GEO database to decode the immune microenvironment and relevant biometric alterations. The ssGSEA algorithm was employed for immune infiltration assessment. IAs displayed abundant immune cell infiltration, activated immune-related pathways, and high expression of immune-related genes. Several immunosuppression cells and genes were also coordinately upregulated in IAs. Five immune-related hub genes, including CXCL10, IL6, IL10, STAT1, and VEGFA, were identified from the protein-protein interaction network and further detected at the protein level. CeRNA networks and latent drugs targeting the hub genes were predicted for targeted therapy reference. Two gene modules recognized via WCGNA were functionally associated with contractile smooth muscle loss and extracellular matrix metabolism, respectively. In blood datasets, a pathological feature-derived gene signature (PFDGS) for IA diagnosis and rupture risk prediction was established using machine learning. Patients with high PFDGS scores may possess adverse biological alterations and present with a high risk of morbidity or IA rupture, requiring more vigilance or prompt intervention. Overall, we systematically unveiled an “immuno-thermal” microenvironment characterized by co-enhanced immune activation and immunosuppression in IA, which provides a novel insight into molecular pathology. The PFDGS is a promising signature for optimizing risk surveillance and clinical decision-making in IA patients.

Identification of an Alveolar Macrophage-Related Core Gene Set in Acute Respiratory Distress Syndrome

Purpose

Acute respiratory distress syndrome (ARDS) is a rapidly progressive diffuse lung injury that is characterized by high mortality and acute onset. The pathological mechanisms of ARDS are still unclear. But alveolar macrophages have been shown to play an important role in inflammatory responses during ARDS. We aimed to find the biomarkers for ARDS for early diagnosis, to give ARDS patients timely treatment.

Methods

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) and screened for differentially expressed genes (DEGs). The common upregulated genes in all the datasets were defined as circulating ARDS alveolar macrophage-related genes (cARDSAMGs). We performed a functional enrichment analysis to explore potential biological functions of cARDSAMGs, and we built protein–protein interaction networks. Gene set variation analysis (GSVA) was used to calculate the core gene set variation analysis (CGSVA) score for individual samples. Receiver operating characteristic (ROC) curve analysis was applied on the CGSVA score to evaluate its ability for diagnosis of ARDS.

Results

A total of 60 genes were upregulated in all ARDS datasets and were therefore denominated as cARDSAMGs. The cARDSAMGs were significantly involved in multiple inflammation-, immunity- and phagocytosis-related biological processes and pathways. In the protein–protein interaction network associated with host responses to ADRS, eight genes were identified as a core gene set: PTCRA, JAG1, C1QB, ADAM17, C1QA, MMP9, VSIG4 and TNFAIP3. ROC curve analysis showed that the CGSVA score may be considered as a biomarker for ARDS: it was significantly higher in patients with ARDS than those in healthy in both alveolar lavage fluid and whole blood.

Conclusion

The ARDS alveolar macrophage-related CGSVA score may be useful as a biomarker for ARDS.