Dihydrolipoamide dehydrogenase, pyruvate oxidation, and acetylation-dependent mechanisms intersecting drug iatrogenesis

Comprehensive Gene Analysis Reveals Cuproptosis-Related Gene Signature Associated with M2 Macrophage in Staphylococcus aureus-Infected Osteomyelitis

Objective

Osteomyelitis is a challenging disease in the field of bone infections, with its immune and molecular regulatory mechanisms still poorly understood. The aim of this study is to explore the value and potential mechanisms of cuproptosis-related genes (CRGs) in Staphylococcus aureus (S. aureus)-infected osteomyelitis from an immunological perspective.

Methods

Initially, three transcriptomic datasets from public databases were integrated and analyzed, and consistent expression of CRGs in S. aureus-infected osteomyelitis was identified. Subsequently, immune infiltration analysis was performed, and M2 macrophage-related CRGs (M2R-CRGs) were further identified. Their potential molecular mechanisms were evaluated using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Finally, distinct osteomyelitis subtypes and diagnostic models based on characteristic M2R-CRGs were constructed.

Results

Through correlation analysis with immune cell infiltration, three characteristic M2R-CRGs (SLC31A1, DLD, and MTF1) were identified. Further analysis using unsupervised clustering and immune microenvironment analysis indicated that cluster 1 might activate pro-inflammatory responses, while cluster 2 was shown to exhibit anti-inflammatory effects in osteomyelitis. Compared to Cluster A, Cluster B demonstrated higher levels and a greater diversity of immune cell infiltrations in CRG-related molecular patterns, suggesting a potential anti-inflammatory role in osteomyelitis. A diagnostic model for S. aureus-infected osteomyelitis, based on the three M2R-CRGs, was constructed, exhibiting excellent diagnostic performance and validated with an independent dataset. Significant upregulation in mRNA and protein expression levels of the three M2R-CRGs was observed in rat models of S. aureus-infected osteomyelitis, aligning with bioinformatic results.

Conclusion

The M2R-CRGs (SLC31A1, DLD, and MTF1) may be considered characteristic genes for early diagnosis and personalized immune therapy in patients with S. aureus-infected osteomyelitis.

A cuproptosis-based prognostic model for predicting survival in low-grade glioma

BACKGROUND

It is unknown what variables contribute to the formation and multiplication of low-grade gliomas (LGG). An emerging process of cell death is called cuproptosis. Our research aims to increase therapeutic options and gain a better understanding of the role that cuproptosis-related genes play in the physical characteristics of low-grade gliomas.

METHODS

The TCGA database was utilized to find cuproptosis genes that may be used to develop LGG risk model. Cox analysis in three different formats: univariate, multivariate, and LASSO. The gene signature's independent predictive ability was assessed using ROC curves and Cox regression analysis based on overall survival. Use of CGGA data and nomogram model for external validation Immunohistochemistry, gene mutation, and functional enrichment analysis are also employed to clarify risk models' involvement. Next, we analyzed changes in the immunological microenvironment in the risk model and forecasted possible chemotherapeutic drugs to target each group. Finally, we validated the protein expression levels of cuproptosis-related genes using LGG and adjacent normal tissues in a small self-case-control study.

RESULTS

This study developed a glioma predictive model based on five cuproptosis-associated genes. Compared to the high-risk group, the low-risk group's OS was significantly longer. The ROC curves showed high genetic signature performance in both groups. The signature-based categorisation was also linked to clinical characteristics and molecular subgroups. The prognosis of individuals with grade 2 or 3 glioma is also influenced by our risk model. Immunological testing revealed that the high-risk group had more immune cells and immunological function. The risk model also predicted immunotherapy and chemotherapy medication results. Also, this study confirmed that the expression of cuproptosis-related genes by Western blot.

CONCLUSION

We developed a prediction model for LGG patients using genes associated with cuproptosis. With acceptable prediction performance, this risk model may effectively stratify the prognosis of glioma patients.

Mitochondrial targets in hyperammonemia: Addressing urea cycle function to improve drug therapies

The urea cycle (UC) is a critically important metabolic process for the disposal of nitrogen (ammonia) produced by amino acids catabolism. The impairment of this liver-specific pathway induced either by primary genetic defects or by secondary causes, namely those associated with hepatic disease or drug administration, may result in serious clinical consequences. Urea cycle disorders (UCD) and certain organic acidurias are the major groups of inherited rare diseases manifested with hyperammonemia (HA) with UC dysregulation. Importantly, several commonly prescribed drugs, including antiepileptics in monotherapy or polytherapy from carbamazepine to valproic acid or specific antineoplastic agents such as asparaginase or 5-fluorouracil may be associated with HA by mechanisms not fully elucidated. HA, disclosing an imbalance between ammoniagenesis and ammonia disposal via the UC, can evolve to encephalopathy which may lead to significant morbidity and central nervous system damage. This review will focus on biochemical mechanisms related with HA emphasizing some poorly understood perspectives behind the disruption of the UC and mitochondrial energy metabolism, namely: i) changes in acetyl-CoA or NAD+ levels in subcellular compartments; ii) post-translational modifications of key UC-related enzymes, namely acetylation, potentially affecting their catalytic activity; iii) the mitochondrial sirtuins-mediated role in ureagenesis. Moreover, the main UCD associated with HA will be summarized to highlight the relevance of investigating possible genetic mutations to account for unexpected HA during certain pharmacological therapies. The ammonia-induced effects should be avoided or overcome as part of safer therapeutic strategies to protect patients under treatment with drugs that may be potentially associated with HA.

Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis

Background

Multiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear.

Method

We screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC).

Results

In sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793-0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues.

Conclusions

Cuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.

Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

Background and aims

Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD).

Method

The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes.

Results

Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples.

Conclusion

Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.

Oncogenic role of copper‑induced cell death‑associated protein DLD in human cancer: A pan‑cancer analysis and experimental verification

Copper ions can bind directly to lipoylated components of the tricarboxylic acid (TCA) cycle, triggering the aggregation of mitochondrial lipoylated proteins and the destabilization of Fe-S cluster proteins, resulting in copper-dependent cell death. Dihydrolipoamide dehydrogenase (DLD) is a key protein of the TCA cycle and constitutes the E3 component of the α-ketoglutarate dehydrogenase complex, which is deeply interconnected with the mitochondrial electron transfer chain in the TCA cycle. Tumor cells demonstrate dependency on glutaminolysis fuelling to carry out the TCA cycle and essential biosynthetic processes supporting tumor growth. Therefore, DLD plays an important role in the tumor biological process. However, to the best of our knowledge, no pan-cancer analysis is currently available for DLD. Therefore, the present study first explored the DLD expression profile in 33 tumors in publicly available datasets, including TIMER2, GEPIA2, UALCAN, cBioPortal and STRING. TIMER2, GEPIA2 and UALCAN were used for exploring gene expression; survival prognosis was detected by GEPIA2; genetic alteration was analysed by cBioPortal; immune infiltration data was obtained from TIMER2; interacting proteins of DLD were detected by STRING. DLD was found to be highly expressed in colon, liver, lung, stomach, renal, corpus uteri endometrial and ovarian cancers compared with normal tissues, and its high expression was associated with poorer prognosis in ovarian cancer. To the best of our knowledge, the present study provided the first comprehensive pan-cancer analysis of the oncogenic role of DLD across different tumors types. As the expression of DLD in ovarian cancer was high, and high expression is associated with poor prognosis, experimental verification of DLD in ovarian cancer was conducted. In the present study, DLD expression was found to be high in the ovarian cancer OC3 cell line, compared with the normal ovarian epithelial IOSE80 cell line by reverse transcription-quantitative PCR analysis. After knockdown of DLD expression, it was found that DLD regulated metabolic pathways by suppressing the intracellular NAD⁺/NADH ratio, which then in turn suppressed tumor cell proliferation detected by MTT assay. In conclusion, the present pan-cancer analysis of DLD demonstrated that DLD expression was associated with the clinical prognosis, immune infiltration and tumor mutational burden in 33 tumor types, and experimental verification in ovarian cancer was conducted. These results may contribute to the understanding of the role of DLD in tumorigenesis.

Comprehensive analysis of the cuproptosis-related gene DLD across cancers: A potential prognostic and immunotherapeutic target

DLD is a key gene involved in “cuproptosis,” but its roles in tumor progression and immunity remain unclear. Exploring the potential mechanisms and biological roles of DLD may provide new insights for therapeutic strategies for tumors. In the present study, we analyzed the role of DLD in a variety of tumors by using several bioinformatic tools. The results showed that compared with normal tissues, tumor tissues representing multiple cancers showed significant differential expression of DLD. High DLD expression was associated with a good prognosis in BRCA, KICH, and LUAD. Conversely, high expression levels of DLD were detrimental to patient prognosis in many other tumors, such as COAD, KIRC, and KIRP. In addition, the associations of DLD with infiltrating immune cells, genetic alterations and methylation levels across cancers were assessed. Aberrant expression of DLD was positively correlated with most infiltrating immune cells, especially neutrophils. The DLD methylation level was significantly decreased in COAD, LIHC, and LUSC but significantly increased in BRCA. DLD had the highest mutation rate (6.04%) in ESCA. In LUSC, patients with genetic alterations in DLD showed a poorer prognosis. At the single-cell level, the roles of DLD in regulating cancer-associated biological functions, such as metastasis, inflammation, and differentiation, were explored. Afterward, we further investigated whether several disease-associated genes could be correlated with DLD. GO enrichment analysis indicated that DLD-related genes were mainly associated with mitochondria-related cellular components, aerobic respiration and the tricarboxylic acid cycle. Finally, the correlations between DLD expression and immunomodulatory genes, immune checkpoints, and sensitivity to some antitumor drugs were investigated. It is worth noting that DLD expression was positively correlated with immune checkpoint genes and immunomodulatory genes in most cancers. In conclusion, this study comprehensively analyzed the differential expression, prognostic value and immune cell infiltration-related function of DLD across cancers. Our results suggest that DLD has great potential to serve as a candidate marker for pancancer prognosis and immunotherapy and may provide a new direction for cancer treatment development.

A novel prognostic signature of cuproptosis-related genes and the prognostic value of FDX1 in gliomas

Background: Gliomas are the most common malignant tumors of the central nervous system, with extremely bad prognoses. Cuproptosis is a novel form of regulated cell death. The impact of cuproptosis-related genes on glioma development has not been reported. Methods: The TCGA, GTEx, and CGGA databases were used to retrieve transcriptomic expression data. We employed Cox's regressions to determine the associations between clinical factors and cuproptosis-related gene expression. Overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were evaluated using the Kaplan-Meier method. We also used the least absolute shrinkage and selection operator (LASSO) regression technique. Results: The expression levels of all 10 CRGs varied considerably between glioma tumors and healthy tissues. In glioma patients, the levels of CDKN2A, FDX1, DLD, DLAT, LIAS, LIPT1, and PDHA1 were significantly associated with the OS, disease-specific survival, and progression-free interval. We used LASSO Cox's regression to create a prognostic model; the risk score was (0.882340) *FDX1 expression + (0.141089) *DLD expression + (-0.333875) *LIAS expression + (0.356469) *LIPT1 expression + (-0.123851) *PDHA1 expression. A high-risk score/signature was associated with poor OS (hazard ratio = 3.50, 95% confidence interval 2, -4.55, log-rank p < 0.001). Cox's regression revealed that the FDX1 level independently predicted prognosis; FDX1 may control immune cell infiltration of the tumor microenvironment. Conclusion: The CRG signature may be prognostic in glioma patients, and the FDX1 level may independently predict glioma prognosis. These data may afford new insights into treatment.