Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

Overexpression of LAG-3: a potential indicator of low immune function in tuberculosis

Background

Tuberculosis (TB) persists as a global health challenge, with its treatment hampered by the side effects of long-term combination drug therapies and the growing issue of drug resistance. Therefore, the development of novel therapeutic strategies is critical. This study focuses on the role of immune checkpoint molecules (ICs) and functions of CD8+ T cells in the search for new potential targets against TB.

Methods

We conducted differential expression genes analysis and CD8+ T cell functional gene analysis on 92 TB samples and 61 healthy individual (HI) samples from TB database GSE83456, which contains data on 34,603 genes. The GSE54992 dataset was used to validated the findings. Additionally, a cluster analysis on single-cell data from primates infected with mycobacterium tuberculosis and those vaccinated with BCG was performed.

Results

The overexpression of LAG-3 gene was found as a potentially important characteristic of both pulmonary TB (PTB) and extrapulmonary TB (EPTB). Further correlation analysis showed that LAG-3 gene was correlated with GZMB, perforin, IL-2 and IL-12. A significant temporal and spatial variation in LAG-3 expression was observed in T cells and macrophages during TB infection and after BCG vaccination.

Conclusion

LAG-3 was overexpressed in TB samples. Targeting LAG-3 may represent a potential therapeutic target for tuberculosis.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations

BACKGROUND

Cerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development.

METHODS

Bulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs.

RESULTS

Our study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis.

CONCLUSION

This study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes-PFDN4, CEMIP, and BTBD10-may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an in vitro model

Venous thromboembolism (VTE) is characterized by a high recurrence rate and adverse consequences, including high mortality. Damage to vascular endothelial cells (VECs) serves a key role in VTE and lactate (LA) metabolism is associated with VEC damage. However, the pathogenesis of VTE and the role of lactate metabolism-related molecules (LMRMs) remain unclear. Based on the GSE48000 dataset, the present study identified differentially expressed (DE-)LMRMs between healthy individuals and those with VTE. Thereafter, LMRMs were used to establish four machine learning models, namely, the random forest, support vector machine and generalized linear model (GLM) and eXtreme gradient boosting. To verify disease prediction efficiency of the models, nomograms, calibration curves, decision curve analyses and external datasets were used. The optimal machine learning model was used to predict genes involved in disease and an in vitro oxygen-glucose deprivation (OGD) model was used to detect the survival rate, LA levels and LMRM expression levels of VECs. A total of four DE-LMRMs, solute carrier family 16 member 1 (SLC16A1), SLC16A7, SLC16A8 and SLC5A12 were obtained and GLM was identified as the best performing model based on its ability to predict differential expression of the embigin, lactate dehydrogenase B, SLC16A1, SLC5A12 and SLC16A8 genes. Additionally, SLC16A1, SLC16A7 and SLC16A8 served key roles in VTE and the OGD model demonstrated a significant decrease in VEC survival rate as well as a significant increase and decrease in intracellular LA and SLC16A1 expression levels in VECs, respectively. Thus, LMRMs may be involved in VTE pathogenesis and be used to build accurate VTE prediction models. Further, it was hypothesized that the observed increase in intracellular LA levels in VECS was associated with the decrease in SLC16A1 expression. Therefore, SLC16A1 expression may be an essential target for VTE treatment.