Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis

Schisandrin a Ameliorates Cardiac Injury and Dysfunction Induced by Hemorrhagic Shock via Activating the Nrf2 Signaling Pathway

Hemorrhagic shock (HS) is a critical condition with high mortality caused by acute blood loss. Cardiac injury and dysfunction induced by HS is a major factor associated with the poor prognosis of affected patients. Schisandrin A (Sch A), a dibenzocyclooctadiene lignan extracted from Fructus schisandrae, exhibits multiple biological activities, including anti-inflammatory, and antioxidant effects. However, the effect of Sch A on HS-caused cardiac injury and its underlying mechanism still lack research. In this study, we established an HS rat model through blood loss from the femoral artery and monitoring mean arterial pressure (MAP) followed by fluid resuscitation. Our findings suggested that cardiac dysfunction and pathological injury were induced by HS and attenuated by Sch A treatment in a dose-dependent manner. Apoptosis in cardiac tissue was promoted by HS, but suppressed after administration of Sch A by decreasing the protein expressions of cleaved-caspase-3 and -9. Moreover, excessive ROS production induced by HS was mitigated by Sch A, and the levels of oxidative stress indicators were improved by Sch A. Additionally, HS triggered the reduction of mitochondrial membrane potential (MMP), and led to mitochondrial dysfunction. Sch A reversed this effect of HS on mitochondria. The transformation of cytochrome c (Cyto c) induced by HS was also restored by Sch A. Importantly, the activation of the Nrf2 signaling pathway mediated the protective effects of Sch A against cardiac injury induced by HS. In conclusion, it was found that Sch A ameliorated HS-induced cardiac injury and dysfunction through suppressing apoptosis and oxidative stress, as well as alleviating mitochondrial dysfunction via the Nrf2 signaling pathway.

Deciphering ferroptosis in critical care: mechanisms, consequences, and therapeutic opportunities

Ischemia-reperfusion injuries (IRI) across various organs and tissues, along with sepsis, significantly contribute to the progression of critical illnesses. These conditions disrupt the balance of inflammatory mediators and signaling pathways, resulting in impaired physiological functions in human tissues and organs. Ferroptosis, a distinct form of programmed cell death, plays a pivotal role in regulating tissue damage and modulating inflammatory responses, thereby influencing the onset and progression of severe illnesses. Recent studies highlight that pharmacological agents targeting ferroptosis-related proteins can effectively mitigate oxidative stress caused by IRI in multiple organs, alleviating associated symptoms. This manuscript delves into the mechanisms and signaling pathways underlying ferroptosis, its role in critical illnesses, and its therapeutic potential in mitigating disease progression. We aim to offer a novel perspective for advancing clinical treatments for critical illnesses.

Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning

PURPOSE

Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.

METHODS

The GSE57065 dataset was acquired from the Gene Expression Omnibus (GEO) database and filtered by limma and the weighted correlation network analysis (WGCNA) to identify mitochondrial-related differentially expressed genes (MitoDEGs) in septic shock. The function of MitoDEGs was analyzed using the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), respectively. The Protein-Protein Interaction (PPI) network composed of MitoDEGs was established using Cytoscape. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify diagnostic MitoDEGs, which were validated using receiver operating characteristic (ROC) analysis and Quantitative Real-time Reverse Transcription Polymerase Chain Reaction (qRT-PCR). Furthermore, the infiltration of immunocytes was analyzed using CIBERSORT, and the correlation between diagnostic MitoDEGs and immunocytes was explored using Spearman.

RESULTS

A total of 44 MitoDEGs were filtered, and functional enrichment analysis showed they were associated with mitochondrial function, and the PPI network had 457 nodes and 547 edges. Four diagnostic genes, MitoDEGs, PGS1, C6orf136, THEM4, and EPHX2, were identified by three machine learning algorithms, and qRT-PCR results obtained similar expression levels as bioinformatics analysis. Furthermore, the diagnostic model constructed by the diagnostic genes had fine diagnostic efficacy. Immunocyte infiltration analysis showed that activated immunocytes were abundant and correlated with hub genes, with neutrophils accounting for the largest proportion in septic shock.

CONCLUSIONS

In this study, we recognized four immune-mitochondrial key genes (PGS1, C6orf136, THEM4, and EPHX2) in septic shock and designed a novel gene diagnosis model that provided a new and meaningful way for the diagnosis of septic shock.

Single-Cell Lipidomics: An Automated and Accessible Microfluidic Workflow Validated by Capillary Sampling

We report the first demonstration of a microfluidics-based approach to measure lipids in single living cells using widely available liquid chromatography mass spectrometry (LC-MS) instrumentation. The method enables the rapid sorting of live cells into liquid chambers formed on standard Petri dishes and their subsequent dispensing into vials for analysis using LC-MS. This approach facilitates automated sampling, data acquisition, and analysis and carries the additional advantage of chromatographic separation, aimed at reducing matrix effects present in shotgun lipidomics approaches. We demonstrate that our method detects comparable numbers of features at around 200 lipids in populations of single cells versus established live single-cell capillary sampling methods and with greater throughput, albeit with the loss of spatial resolution. We also show the importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, and demonstrate a 75% increase in the number of lipids identified. This work opens up a novel, accessible, and high-throughput way to obtain single-cell lipid profiles and also serves as an important validation of single-cell lipidomics through the use of different sampling methods.

The New Nano-Resuscitation Solution (TPP-MR) Attenuated Myocardial Injury in Hemorrhagic Shock Rats by Inhibiting Ferroptosis

Background

Hemorrhagic shock was a leading cause of death worldwide, with myocardial injury being a primary affected organ. As commonly used solutions in fluid resuscitation, acetated Ringer's (AR) and Lactate Ringer's solution (LR) were far from perfect for their adverse reactions such as lactic acidosis and electrolyte imbalances. In previous studies, TPP@PAMAM-MR (TPP-MR), a novel nanocrystal resuscitation fluid has been found to protect against myocardial injury in septic rats. However, its role in myocardial injury in rats with hemorrhagic shock and underlying mechanism is unclear.

Methods

The hemorrhagic shock rats and hypoxia-treated cardiomyocytes (H9C2) were utilized to investigate the impact of TPP-MR on cardiac function, mitochondrial function, and lipid peroxidation. The expressions of ferritin-related proteins glutathione peroxidase 4 (GPX4), Acyl CoA Synthase Long Chain Family Member 4 (ACSL4), and Cyclooxygenase-2(COX2) were analyzed through Western blotting to explore the mechanism of TPP-MR on hemorrhagic myocardial injury.

Results

TPP-MR, a novel nanocrystalline resuscitation fluid, was synthesized using TPP@PAMAM@MA as a substitute for L-malic acid. We found that TPP-MR resuscitation significantly reduced myocardial injury reflected by enhancing cardiac output, elevating mean arterial pressure (MAP), and improving perfusion. Moreover, TPP-MR substantially prolonged hemorrhagic shock rats' survival time and survival rate. Further investigations indicated that TPP-MR improved the mitochondrial function of myocardial cells, mitigated the production of oxidative stress agents (ROS) and increased the glutathione (GSH) content. Additionally, TPP-MR inhibited the expression of the ferroptosis-associated GPX4 protein, ACSL4 and COX2, thereby enhancing the antioxidant capacity.

Conclusion

The results showed that TPP-MR had a protective effect on myocardial injury in rats with hemorrhagic shock, and its mechanism might be related to improving the mitochondrial function of myocardial cells and inhibiting the process of ferroptosis.

Cuproptosis-Related Genes as Prognostic Biomarkers for Sepsis: Insights into Immune Function and Personalized Immunotherapy

Background

This study aimed to discover diagnostic and prognostic biomarkers for sepsis immunotherapy through analyzing the novel cellular death process, cuproptosis.

Methods

We used transcriptome data from sepsis patients to identify key cuproptosis-related genes (CuRGs). We created a predictive model and used the CIBERSORT algorithm to observe the link between these genes and the septic immune microenvironment. We segregated sepsis patients into three subgroups, comparing immune function, immune cell infiltration, and differential analysis. Single-cell sequencing and real-time quantitative PCR were used to view the regulatory effect of CuRGs on the immune microenvironment and compare the mRNA levels of these genes in sepsis patients and healthy controls. We established a sepsis forecast model adapted to heart rate, body temperature, white blood cell count, and cuproptosis key genes. This was followed by a drug sensitivity analysis of cuproptosis key genes.

Results

Our results filtered three key genes (LIAS, PDHB, PDHA1) that impact sepsis prognosis. We noticed that the high-risk group had poorer immune cell function and lesser immune cell infiltration. We also discovered a significant connection between CuRGs and immune cell infiltration in sepsis. Through consensus clustering, sepsis patients were classified into three subgroups. The best immune functionality and prognosis was observed in subgroup B. Single-cell sequencing exposed that the key genes manage the immune microenvironment by affecting T cell activation. The qPCR results highlighted substantial mRNA level reduction of the three key genes in the SP compared to the HC. The prediction model, which combines CuRGs and traditional diagnostic indicators, performed better in accuracy than the other markers. The drug sensitivity analysis listed bisphenol A as highly sensitive to all the key genes.

Conclusion

Our study suggests these CuRGs may offer substantial potential for sepsis prognosis prediction and personalized immunotherapy.

Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection

We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.

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.

Development of macrophage-associated genes prognostic signature predicts clinical outcome and immune infiltration for sepsis

Sepsis is a major global health problem, causing a significant burden of disease and death worldwide. Risk stratification of sepsis patients, identification of severe patients and timely initiation of treatment can effectively improve the prognosis of sepsis patients. We procured gene expression datasets for sepsis (GSE54514, GSE65682, GSE95233) from the Gene Expression Omnibus and performed normalization to mitigate batch effects. Subsequently, we applied weighted gene co-expression network analysis to categorize genes into modules that exhibit correlation with macrophage activity. To pinpoint macrophage-associated genes (MAAGs), we executed differential expression analysis and single sample gene set enrichment analysis. We then established a prognostic model derived from four MAAGs that were significantly differentially expressed. Functional enrichment analysis and immune infiltration assessments were instrumental in deciphering the biological mechanisms involved. Furthermore, we employed principal component analysis and conducted survival outcome analyses to delineate molecular subgroups within sepsis. Four novel MAAGs-CD160, CX3CR1, DENND2D, and FAM43A-were validated and used to create a prognostic model. Subgroup classification revealed distinct molecular profiles and a correlation with 28-day survival outcomes. The MAAGs risk score was developed through univariate Cox, LASSO, and multivariate Cox analyses to predict patient prognosis. Validation of the risk score upheld its prognostic significance. Functional enrichment implicated ribonucleoprotein complex biogenesis, mitochondrial matrix, and transcription coregulator activity in sepsis, with an immune infiltration analysis indicating an association between MAAGs risk score and immune cell populations. The four MAAGs exhibited strong diagnostic capabilities for sepsis. The research successfully developed a MAAG-based prognostic model for sepsis, demonstrating that such genes can significantly stratify risk and reflect immune status. Although in-depth mechanistic studies are needed, these findings propose novel targets for therapy and provide a foundation for future precise clinical sepsis management.

Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis

BACKGROUND

Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis.

METHODS

Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis.

RESULTS

A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis.

CONCLUSIONS

This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis.

TRIAL REGISTRATION

This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021-179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).