Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis

Background

Postmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers.

Methods

The GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models.

Results

Using machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated.

Conclusion

JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Integrative gene expression analysis and animal model reveal immune- and autophagy-related biomarkers in osteomyelitis

BACKGROUND

Osteomyelitis (OM) is recognized as a significant challenge in orthopedics due to its complex immune and inflammatory responses. The prognosis heavily depends on timely diagnosis, accurate classification, and assessment of severity. Thus, the identification of diagnostic and classification-related genes from an immunological standpoint is crucial for the early detection and tailored treatment of OM.

METHODS

Transcriptomic data for OM was sourced from the Gene Expression Omnibus (GEO) database, leading to the identification of autophagy- and immune-related differentially expressed genes (AIR-DEGs) through differential expression analysis. Diagnostic and classification models were subsequently developed. The CIBERSORT algorithm was utilized to examine immune cell infiltration in OM, and the relationship between OM clusters and various immune cells was explored. Key AIR-DEGs were further validated through the creation of OM animal models.

RESULTS

Analysis of the transcriptomic data revealed three AIR-DEGs that played a significant role in immune responses and pathways. Nomogram and receiver operating characteristic curve analyses were performed, demonstrating excellent diagnostic capability for differentiating between OM patients and healthy individuals, with an area under the curve of 0.814. An unsupervised clustering analysis discerned two unique patterns of autophagy- and immune-related genes, as well as gene patterns. Further exploration into immune infiltration exhibited notable variances across different subtypes, especially between OM cluster 1 and gene cluster A, highlighting their potential role in mitigating inflammatory responses by regulating immune activities. Moreover, the mRNA and protein expression levels of three AIR-DEGs in the animal model were aligned with those in the training and validation data sets.

CONCLUSIONS

From an immunological perspective, a diagnostic model was successfully developed, and two distinct clustering patterns were identified. These contributions offer a significant resource for the early detection and personalized immunotherapy of patients with OM.

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.

N6 methyladenosine eraser FTO suppresses Staphylococcus aureus-induced ferroptosis of bone marrow mesenchymal stem cells to ameliorate osteomyelitis through regulating the MDM2/TLR4/SLC7A11 signaling pathway

Osteomyelitis is a bone destructive inflammatory disease caused by infection. Ferroptosis is closely related to multiple inflammatory diseases, but the role of ferroptosis in Staphylococcus aureus (SA)-induced osteomyelitis remains unknown. In the present study, we found that SA treatment promoted the accumulation of iron, Fe2+ , lipid peroxide, and malondialdehyde, increased TFRC and reduced FTH1 and GPX4 to trigger ferroptosis in rat bone marrow mesenchymal stem cells (BMSCs). Interestingly, increased level of N6 methyl adenosine (m6A) modification along with decreased expression level of m6A eraser FTO were observed in SA-induced BMSCs, while upregulating FTO alleviated SA-triggered ferroptosis and protected cell viability in BMSCs. Mechanistically, MDM2 was identified as a target of FTO-mediated m6A demethylation, and FTO upregulation promoted MDM2 instability to downregulated TLR4 signal and elevate the expression of SLC7A11 and GPX4 in SA-induced BMSCs. Functional recovery experiments verified that overexpressing MDM2 or TLR4 reversed the inhibiting effect of FTO upregulation on ferroptosis in SA-treated BMSCs. Additionally, FTO upregulation restrained ferroptosis and pathological damage to bone tissue in SA-induced osteomyelitis model rats. Altogether, m6A eraser FTO alleviates SA-induced ferroptosis in osteomyelitis models partly through inhibiting the MDM2-TLR4 axis.