Diagnostic and predictive values of pyroptosis-related genes in sepsis

Toll-like receptors in health and disease

Toll-like receptors (TLRs) are inflammatory triggers and belong to a family of pattern recognition receptors (PRRs) that are central to the regulation of host protective adaptive immune responses. Activation of TLRs in innate immune myeloid cells directs lymphocytes to produce the most appropriate effector responses to eliminate infection and maintain homeostasis of the body's internal environment. Inappropriate TLR stimulation can lead to the development of general autoimmune diseases as well as chronic and acute inflammation, and even cancer. Therefore, TLRs are expected to be targets for therapeutic treatment of inflammation-related diseases, autoimmune diseases, microbial infections, and human cancers. This review summarizes the recent discoveries in the molecular and structural biology of TLRs. The role of different TLR signaling pathways in inflammatory diseases, autoimmune diseases such as diabetes, cardiovascular diseases, respiratory diseases, digestive diseases, and even cancers (oral, gastric, breast, colorectal) is highlighted and summarizes new drugs and related clinical treatments in clinical trials, providing an overview of the potential and prospects of TLRs for the treatment of TLR-related diseases.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis

Background

Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis.

Methods

Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples.

Results

Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).

Conclusion

We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

Mitigation of Sepsis-Induced Acute Lung Injury by BMSC-Derived Exosomal miR-125b-5p Through STAT3-Mediated Suppression of Macrophage Pyroptosis

Introduction

Sepsis is a syndrome characterized by high morbidity and mortality rates. One of its most severe complications is acute lung injury, which exhibits a multitude of clinical and biological features, including macrophage pyroptosis. This study investigates the regulatory effects of exosomes derived from Bone Marrow-Derived Mesenchymal Stem Cells (BMSCs) on sepsis-associated acute lung injury (ALI) and explores the potential mechanisms mediated by exosomal miRNAs.

Methods

Exosomes were isolated from primary BMSCs of adult C57BL/6J mice using differential centrifugation. Their uptake and distribution in both in vitro and in vivo contexts were validated. Key sepsis-associated hub gene signal transducer and activator of transcription 3 (STAT3) and its upstream non-coding miR-125b-5p were elucidated through a combination of bioinformatics, machine learning, and miRNA sequencing. Subsequently, the therapeutic potential of BMSC-derived exosomes in alleviating sepsis-induced acute lung injury was substantiated. Moreover, the functionalities of miR-125b-5p and STAT3 were corroborated through miR-125b-5p inhibitor and STAT3 agonist interventions, employing gain and loss-of-function strategies both in vitro and in vivo. Finally, a dual-luciferase reporter assay reaffirmed the interaction between miR-125b-5p and STAT3.

Results

We isolated exosomes from primary BMSCs and confirmed their accumulation in the mouse lung as well as their uptake by macrophages in vitro. This study identified the pivotal sepsis-associated hub gene STAT3 and demonstrated that exosomes derived from BMSCs can target STAT3, thereby inhibiting macrophage pyroptosis. MiR-125b-5p inhibition experiments showed that exosomes mitigate macrophage pyroptosis and lung injury by delivering miR-125b-5p. STAT3 overexpression experiments validated that miR-125b-5p reduces macrophage pyroptosis and lung injury by suppressing STAT3. Furthermore, a dual-luciferase reporter assay confirmed the binding interaction between miR-125b-5p and STAT3.

Conclusion

Exosomes derived from BMSCs, serving as carriers for delivering miR-125b-5p, can downregulate STAT3, thereby inhibiting macrophage pyroptosis and alleviating sepsis-associated ALI. These significant findings provide valuable insights into the potential development of ALI therapies centred around exosomes derived from BMSC.

Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning

Background

There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis.

Methods

Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR).

Results

Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).

Conclusion

A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.