Construction and validation of a mitochondria-associated genes prognostic signature and immune microenvironment characteristic of sepsis

Identification and Experimental Validation of Biomarkers Associated With Mitochondria and Macrophage Polarization in Sepsis

Background: Sepsis is a common and serious condition, where mitochondria and macrophage polarization play a crucial role. Therefore, this study aimed to identify and validate biomarkers for sepsis associated with mitochondria-related genes (MCRGs) and macrophage polarization-related genes (MPRGs), providing new targets and strategies for therapeutic intervention. Methods: This study utilized the GSE95233 and GSE28750 datasets. Initially, intersection genes were identified by overlapping MCRGs and the results from differential expression analysis and weighted gene co-expression network analysis (WGCNA). Biomarkers were identified through machine learning and gene expression analysis. A nomogram was developed and evaluated based on these biomarkers. Finally, functional enrichment, immune infiltration, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) analyses were conducted to further elucidate the biological mechanisms underlying sepsis. Results: The study identified YME1L1, ECHDC3, THEM4, and COQ10A as biomarkers for sepsis. Among them, YME1L1, THEM4, and COQ10A showed significantly lower expression levels in sepsis samples, while ECHDC3 exhibited markedly higher expression. Notably, RT-qPCR analysis confirmed that YME1L1, THEM4, and COQ10A exhibited significantly lower expression levels in sepsis samples. A nomogram based on these biomarkers was developed and validated, effectively predicting sepsis risk. Enrichment analysis indicated that the biomarkers were co-enriched in the oxidative phosphorylation pathway. Additionally, 13 significantly different immune cell types were identified between sepsis and control samples. Biomarker association analysis revealed that CD8 T cells had the strongest positive correlation with YME1L1 (cor = 0.84, p < 0.05) and the strongest negative correlation with ECHDC3 (cor = -0.76, p < 0.05), suggesting their potential role in the disease mechanism. Conclusion: In this study, YME1L1, ECHDC3, THEM4, and COQ10A were identified as biomarkers for sepsis, with their expression levels validated in clinical samples. These findings provided a promising theoretical foundation for the development of targeted treatments for sepsis.

Mitochondrial-related genome-wide Mendelian randomization identifies putatively causal genes in the pathogenesis of sepsis

BACKGROUND

The dysfunction of mitochondria has been associated with the development of sepsis, but the specific mitochondrial-related genes and their roles in sepsis have not been fully elucidated. We employed Mendelian randomization and colocalization analysis to investigate the association between mitochondrial-related genes and sepsis by integrating multi-omics data.

METHODS

Summary-level data on mitochondrial gene methylation, expression, and protein abundance levels were obtained from corresponding studies on methylation, expression, and protein quantitative trait loci, respectively. Genetic associations with sepsis were obtained from the genome-wide association studies catalog database. We used the MitoCarta3.0 database, which contains an updated list of 1,136 human mitochondrial genes, to identify mitochondrial genes. To assess the associations between mitochondrial gene-related molecular features and sepsis, we conducted summary data-based Mendelian randomization analysis. In addition, we performed colocalization analysis to determine whether the identified signal pairs shared a causal genetic variant.

RESULTS

After integrating the multi-omics data between methylation quantitative trait loci- expression quantitative trait loci and expression quantitative trait loci-protein quantitative trait loci, we identified FIS1 as having tier 1 evidence for its association with sepsis. Methylation of cg01299997 in FIS1 was found to be associated with lower expression of FIS1, an increased risk of sepsis, and a positive role in cg01299997 methylation. Furthermore, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, and TFAM genes were associated with sepsis risk with tier 2 evidence. Both gene expression and protein abundance levels of NUDT2 were observed to be associated with an increased risk of sepsis. In addition, the ATP5MC1 and VWA8 genes were associated with sepsis risk with tier 3 evidence. Among these tertiary genes, ATP5MC1 gene expression level showed a negative correlation (posterior probability of H4 = 0.9242), whereas the gene expression level of VWA8 exhibited a positive correlation (posterior probability of H4 = 0.7270).

CONCLUSION

We found that the mitochondrial FIS1, NUDT2, IMMP2L, LYRM4, MRPL10, MRPL17, MTIF3, TFAM, ATP5MC1, and VWA8 genes were putatively associated with sepsis risk with evidence from multi-omics levels. This study identified mitochondrial genes in relation to sepsis, which may enhance the understanding of the pathogenic mechanisms of sepsis development.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

Parthenolide improves sepsis-induced coagulopathy by inhibiting mitochondrial-mediated apoptosis in vascular endothelial cells through BRD4/BCL-xL pathway

BACKGROUND

Sepsis is a systemic inflammatory syndrome that can cause coagulation abnormalities, leading to damage in multiple organs. Vascular endothelial cells (VECs) are crucial in the development of sepsis-induced coagulopathy (SIC). The role of Parthenolide (PTL) in regulating SIC by protecting VECs remains unclear.

METHODS

The study utilized septic rats and lipopolysaccharide (LPS)-stimulated VECs to simulate a SIC model and observe the therapeutic effects of PTL. Additionally, nanotechnology was employed to produce Nano-PTL (N-PTL), to observe whether it has advantages over PTL in treating SIC.

RESULTS

PTL has been shown to mitigate lung injury in septic rats, significantly reduce tumor necrosis factor-α (TNF-α) levels, and increase survival rates. PTL treatment also enhances coagulation function, augments vascular endothelial cell (VEC) function, reduces mitochondrial fragmentation, and increases both mitochondrial oxygen consumption rate (OCR) and mitochondrial membrane potential (MMP), while inhibiting reactive oxygen species (ROS) production. By increasing BRD4/BCL-xL levels, PTL can prevent mitochondrial-mediated apoptosis in VECs, improve VEC function, and consequently ameliorate SIC. Additionally, nanotechnology-synthesized N-PTL further enhances the protective effects on VECs and coagulation function.

CONCLUSIONS

This study clarifies the therapeutic effects and mechanisms of PTL on SIC, offering new strategies and directions for the treatment of sepsis.

Identification and verification of disulfidptosis-related genes in sepsis-induced acute lung injury

Background

Sepsis-induced acute lung injury (ALI) is a common and serious complication of sepsis that eventually progresses to life-threatening hypoxemia. Disulfidptosis is a newly discovered type of cell death associated with the pathogenesis of different diseases. This study investigated the potential association between sepsis-induced acute lung injury and disulfidptosis by bioinformatics analysis.

Methods

In order to identify differentially expressed genes (DEGs) linked to sepsis, we screened appropriate data sets from the GEO database and carried out differential analysis. The key genes shared by DEGs and 39 disulfidptosis-related genes were identified: ACSL4 and MYL6 mRNA levels of key genes were detected in different datasets. We then used a series of bioinformatics analysis techniques, such as immune cell infiltration analysis, protein-protein interaction (PPI) network, genetic regulatory network, and receiver operating characteristic (ROC), to investigate the possible relationship between key genes and sepsis. Then, experimental verification was obtained for changes in key genes in sepsis-induced acute lung injury. Finally, to investigate the relationship between genetic variants of MYL6 or ACSL4 and sepsis, Mendelian randomization (MR) analysis was applied.

Results

Two key genes were found in this investigation: myosin light chain 6 (MYL6) and Acyl-CoA synthetase long-chain family member 4 (ACSL4). We verified increased mRNA levels of key genes in training datasets. Immune cell infiltration analysis showed that key genes were associated with multiple immune cell levels. Building the PPI network between MYL6 and ACSL4 allowed us to determine that their related genes had distinct biological functions. The co-expression genes of key genes were involved in different genetic regulatory networks. In addition, both the training and validation datasets confirmed the diagnostic capabilities of key genes by using ROC curves. Additionally, both in vivo and in vitro experiments confirmed that the mRNA levels of ACSL4 and MYL6 in sepsis-induced acute lung injury were consistent with the results of bioinformatics analysis. Finally, MR analysis revealed a causal relationship between MYL6 and sepsis.

Conclusion

We have discovered and confirmed that the key genes ACSL4 and MYL6, which are linked to disulfidptosis in sepsis-induced acute lung injury, may be useful in the diagnosis and management of septic acute lung injury.

Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches

BACKGROUND

Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication.

METHODS

In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types.

RESULTS

Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells.

CONCLUSION

The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.

Prognostic gene landscapes and therapeutic insights in sepsis-induced coagulopathy

BACKGROUND

Sepsis is a common and critical condition encountered in clinical practice that can lead to multi-organ dysfunction. Sepsis-induced coagulopathy (SIC) significantly affects patient outcomes. However, the precise mechanisms remain unclear, making the identification of effective prognostic and therapeutic targets imperative.

METHODS

The analysis of transcriptome data from the whole blood of sepsis patients, facilitated the identification of key genes implicated in coagulation. Then we developed a prognostic model and a nomogram to predict patient survival. Consensus clustering classified sepsis patients into three subgroups for comparative analysis of immune function and immune cell infiltration. Single-cell sequencing elucidated alterations in intercellular communication between platelets and immune cells in sepsis, as well as the role of the coagulation-related gene FYN. Real-time quantitative PCR determined the mRNA levels of critical coagulation genes in septic rats' blood. Finally, administration of a FYN agonist to septic rats was observed for its effects on coagulation functions and survival.

RESULTS

This study identified four pivotal genes-CFD, FYN, ITGAM, and VSIG4-as significant predictors of survival in patients with sepsis. Among them, CFD, FYN, and ITGAM were underexpressed, while VSIG4 was upregulated in patients with sepsis. Moreover, a nomogram that incorporates the coagulation-related genes (CoRGs) risk score with clinical features of patients accurately predicted survival probabilities. Subgroup analysis of CoRGs expression delineated three molecular sepsis subtypes, each with distinct prognoses and immune profiles. Single-cell sequencing shed light on heightened communication between platelets and monocytes, T cells, and plasmacytoid dendritic cells, alongside reduced interactions with neutrophils in sepsis. The collagen signaling pathway was found to be essential in this dynamic. FYN may affect platelet function by modulating factors such as ELF1, PTCRA, and RASGRP2. The administration of the FYN agonist can effectively improve coagulation dysfunction and survival in septic rats.

CONCLUSIONS

The research identifies CoRGs as crucial prognostic markers for sepsis, highlighting the FYN gene's central role in coagulation disorders associated with the condition and suggesting novel therapeutic intervention strategies.