Significant difference of differential expression pyroptosis-related genes and their correlations with infiltrated immune cells in sepsis

Comprehensive modular analyses of scar subtypes illuminate underlying molecular mechanisms and potential therapeutic targets

Pathological scarring resulting from traumas and wounds, such as hypertrophic scars and keloids, pose significant aesthetic, functional and psychological challenges. This study provides a comprehensive transcriptomic analysis of these conditions, aiming to illuminate underlying molecular mechanisms and potential therapeutic targets. We employed a co-expression and module analysis tool to identify significant gene clusters associated with distinct pathophysiological processes and mechanisms, notably lipid metabolism, sebum production, cellular energy metabolism and skin barrier function. This examination yielded critical insights into several skin conditions including folliculitis, skin fibrosis, fibrosarcoma and congenital ichthyosis. Particular attention was paid to Module Cluster (MCluster) 3, encompassing genes like BLK, TRPV1 and GABRD, all displaying high expression and potential implications in immune modulation. Preliminary immunohistochemistry validation supported these findings, showing elevated expression of these genes in non-fibrotic samples rich in immune activity. The complex interplay of different cell types in scar formation, such as fibroblasts, myofibroblasts, keratinocytes and mast cells, was also explored, revealing promising therapeutic strategies. This study underscores the promise of targeted gene therapy for pathological scars, paving the way for more personalised therapeutic approaches. The results necessitate further research to fully ascertain the roles of these identified genes and pathways in skin disease pathogenesis and potential therapeutics. Nonetheless, our work forms a strong foundation for a new era of personalised medicine for patients suffering from pathological scarring.

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

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

CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES

ABSTRACT

Background: Numerous studies have shown that pyroptosis is associated with sepsis progression, which can lead to dysregulated host immune responses and organ dysfunction. Therefore, investigating the potential prognostic and diagnostic values of pyroptosis in patients with sepsis is essential. Methods: We conducted a study using bulk and single-cell RNA sequencing (scRNA-seq) from the Gene Expression Omnibus database to examine the role of pyroptosis in sepsis. Univariate logistic analysis, least absolute shrinkage, and selection operator regression analysis were used to identify pyroptosis-related genes (PRGs), construct a diagnostic risk score model, and evaluate the selected genes' diagnostic value. Consensus clustering analysis was used to identify the PRG-related sepsis subtypes with varying prognoses. Functional and immune infiltration analyses were used to explain the subtypes' distinct prognoses, and scRNA-seq data were used to differentiate immune-infiltrating cells and macrophage subsets and study cell-cell communication. Results: A risk model was established based on 10 key PRGs ( NAIP , ELANE , GSDMB , DHX9 , NLRP3 , CASP8 , GSDMD , CASP4 , APIP , and DPP9 ), of which four ( ELANE , DHX9 , GSDMD , and CASP4 ) were associated with prognosis. Two subtypes with different prognoses were identified based on the key PRG expressions. Functional enrichment analysis revealed diminished nucleotide oligomerization domain-like receptor pathway activity and enhanced neutrophil extracellular trap formation in the subtype with a poor prognosis. Immune infiltration analysis suggested a different immune status between the two sepsis subtypes, with the subtype with a poor prognosis exhibiting stronger immunosuppression. The single-cell analysis identified a macrophage subpopulation characterized by gasdermin D (GSDMD) expression that may be involved in pyroptosis regulation, which was associated with the prognosis of sepsis. Conclusion: We developed and validated a risk score for sepsis identification based on 10 PRGs, four of which also have potential value in the prognosis of sepsis. We identified a subset of gasdermin D macrophages associated with poor prognosis, providing new insights into the role of pyroptosis in sepsis.

Regulatory role of the programmed cell death 1 signaling pathway in sepsis induced immunosuppression

Sepsis is a multiple organ dysfunction syndrome caused by the host's immune response to infection, with extremely high incidence and mortality. Immunosuppression is an essential pathophysiological alteration that influences the clinical treatment and prognosis of sepsis. Recent studies have suggested that the programmed cell death 1 signaling pathway is involved in the formation of immunosuppression in sepsis. In this review, we systematically present the mechanisms of immune dysregulation in sepsis and elucidate the expression and regulatory effects of the programmed cell death 1 signaling pathway on immune cells associated with sepsis. We then specify current research developments and prospects for the application of the programmed cell death 1 signaling pathway in immunomodulatory therapy for sepsis. Several open questions and future research are discussed at the end.

Diagnostic and predictive values of pyroptosis-related genes in sepsis

Background

Sepsis is an organ dysfunction syndrome caused by the body's dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.

Aims

This study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.

Methods

Pediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.

Results

Eight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all P<0.05). SIRT1, STAT3, and TLR2 were identified by the DGIdb database as potentially regulated by multiple drugs. Finally, 7 genes were verified to have significantly different expressions between the sepsis group and the control group (P<0.05).

Conclusion

The pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.