VDAC2 malonylation participates in sepsis-induced myocardial dysfunction via mitochondrial-related ferroptosis

Nanodrug Delivery Systems for Myasthenia Gravis: Advances and Perspectives

Myasthenia gravis (MG) is a rare chronic autoimmune disease caused by the production of autoantibodies against the postsynaptic membrane receptors present at the neuromuscular junction. This condition is characterized by fatigue and muscle weakness, including diplopia, ptosis, and systemic impairment. Emerging evidence suggests that in addition to immune dysregulation, the pathogenesis of MG may involve mitochondrial damage and ferroptosis. Mitochondria are the primary site of energy production, and the reactive oxygen species (ROS) generated due to mitochondrial dysfunction can induce ferroptosis. Nanomedicines have been extensively employed to treat various disorders due to their modifiability and good biocompatibility, but their application in MG management has been rather limited. Nevertheless, nanodrug delivery systems that carry immunomodulatory agents, anti-oxidants, or ferroptosis inhibitors could be effective for the treatment of MG. Therefore, this review focuses on various nanoplatforms aimed at attenuating immune dysregulation, restoring mitochondrial function, and inhibiting ferroptosis that could potentially serve as promising agents for targeted MG therapy.

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.

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation

Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis.

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.

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

BACKGROUND

Sepsis is a common critical condition seen in clinical settings, with mitochondrial dysfunction playing an important role in the progression of sepsis. However, a mitochondrial prognosis model related to sepsis has not been established yet, and the relationship between the sepsis immune microenvironment and mitochondria remains unclear.

METHODS

Sepsis prognostic mitochondria-associated genes (MiAGs) were screened by univariate Cox, multivariate Cox, and LASSO analysis from the GEO dataset. Consensus Cluster was used to analyze MiAGs-based molecular subtypes for sepsis. The ESTIMATE and ssGSEA algorithms were used to analyze the situation of sepsis immune cell infiltration and its relation to MiAGs. Further, MiAGs score was calculated to construct a sepsis prognosis risk model to predict the prognosis of sepsis patients. Clinical blood samples were used to investigate the expression level of selected MiAGs in sepsis. Single-cell sequencing analysis, mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and ATP detection were used to verify the influence of MiAGs on mitochondrial dysfunction in sepsis.

RESULTS

A total of 5 MiAGs of sepsis were screened. Based on MiAGs, sepsis MiAGs subtypes were analyzed, where Cluster A had a better prognosis than Cluster B, and there were significant differences in immune infiltration between the two clusters. The sepsis mitochondrial prognosis model suggested that the high MiAG score group had a shorter survival time compared to the low MiAG score group. Significant differences were also observed in the immune microenvironment between the high and low MiAG score groups. Prognostic analysis and the Nomogram indicated that the MiAG score is an independent prognostic factor in sepsis. Single-cell sequencing analysis exhibited the possible influence of MiAGs on the mitochondrial function of monocytes. Finally, the downregulation of the COX7B could effectively improve mitochondrial function in the LPS-stimulated sepsis model.

CONCLUSION

Our findings suggest that MiAGs can be used to predict the prognosis of sepsis and that regulating the mitochondrial prognostic gene COX7B can effectively improve the mitochondrial function of immune cells in sepsis.

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).

Mechanistic engineering of celastrol liposomes induces ferroptosis and apoptosis by directly targeting VDAC2 in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of most common and deadliest malignancies. Celastrol (Cel), a natural product derived from the Tripterygium wilfordii plant, has been extensively researched for its potential effectiveness in fighting cancer. However, its clinical application has been hindered by the unclear mechanism of action. Here, we used chemical proteomics to identify the direct targets of Cel and enhanced its targetability and anti-tumor capacity by developing a Cel-based liposomes in HCC. We demonstrated that Cel selectively targets the voltage-dependent anion channel 2 (VDAC2). Cel directly binds to the cysteine residues of VDAC2, and induces cytochrome C release via dysregulating VDAC2-mediated mitochondrial permeability transition pore (mPTP) function. We further found that Cel induces ROS-mediated ferroptosis and apoptosis in HCC cells. Moreover, coencapsulation of Cel into alkyl glucoside-modified liposomes (AGCL) improved its antitumor efficacy and minimized its side effects. AGCL has been shown to effectively suppress the proliferation of tumor cells. In a xenograft nude mice experiment, AGCL significantly inhibited tumor growth and promoted apoptosis. Our findings reveal that Cel directly targets VDAC2 to induce mitochondria-dependent cell death, while the Cel liposomes enhance its targetability and reduces side effects. Overall, Cel shows promise as a therapeutic agent for HCC.