IRF1 as a potential biomarker in Mycobacterium tuberculosis infection

Cost-Effective Whole Transcriptome Sequencing Landscape and Diagnostic Potential Biomarkers in Active Tuberculosis

Tuberculosis (TB) is a prevalent and severe infectious disease that poses a significant threat to human health. However, it is frequently disregarded as there are not enough quick and accurate ways to diagnose tuberculosis. Here, we develop a strategy for tuberculosis detection to address the challenges, including an experimental strategy, namely, Double Adapter Directional Capture sequencing (DADCSeq), an easily operated and low-cost whole transcriptome sequencing method, and a computational method to identify hub differentially expressed genes as well as the diagnosis of TB based on whole transcriptome data using DADCSeq on peripheral blood mononuclear cells (PBMCs) from active TB and latent TB or healthy control. Applying our approach to create a robust and stable TB multi-mRNA risk probability model (TBMMRP) that can accurately distinguish active and latent TB patients, including active TB and healthy controls in clinical cohorts, this diagnostic biomarker was successfully validated by several independent cross-platform cohorts with favorable performance in differentiating active TB from latent TB or active TB from healthy controls and further demonstrated superior or similar diagnostic accuracy compared to previous diagnostic markers. Overall, we develop a low-cost and effective strategy for tuberculosis diagnosis; as the clinical cohort increases, we can expand to different disease kinds and learn new features through our disease diagnosis strategy.

Identification of hub genes and establishment of a diagnostic model in tuberculosis infection

Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance.

Transcriptional analysis of human peripheral blood mononuclear cells stimulated by Mycobacterium tuberculosis antigen

Background

Mycobacterium tuberculosis antigen (Mtb-Ag) is a polypeptide component with a molecular weight of 10-14 kDa that is obtained from the supernatant of the H37Ra strain after heat treatment. It stimulates the activation and proliferation of γδT cells in the blood to produce an immune response against tuberculosis. Mtb-Ag is therefore crucial for classifying and detecting the central genes and key pathways involved in TB initiation and progression.

Methods

In this study, we performed high-throughput RNA sequencing of peripheral blood mononuclear cells (PBMC) from Mtb-Ag-stimulated and control samples to identify differentially expressed genes and used them for gene ontology (GO) and a Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Meanwhile, we used PPI protein interaction network and Cytoscape analysis to identify key genes and qRT-PCR to verify differential gene expression. Single-gene enrichment analysis (GSEA) was used further to elucidate the potential biological functions of key genes. Analysis of immune cell infiltration and correlation of key genes with immune cells after Mtb-Ag-stimulated using R language.

Results

We identified 597 differentially expressed genes in Mtb-Ag stimulated PBMCs. KEGG and GSEA enrichment analyzed the cellular pathways related to immune function, and DEGs were found to be primarily involved in the TNF signaling pathway, the IL-17 signaling pathway, the JAK-STAT signaling pathway, cytokine-cytokine receptor interactions, and the NF-κB signaling pathway. Wayne analysis using GSEA, KEGG, and the protein-protein interaction (PPI) network showed that 34 genes, including PTGS2, IL-1β, IL-6, TNF and IFN-γ et al., were co-expressed in the five pathways and all were up-regulated by Mtb-Ag stimulation. Twenty-four DEGs were identified using qRT-PCR, including fourteen up-regulated genes (SERPINB7, IL20, IFNG, CSF2, PTGS2, TNF-α, IL36G, IL6, IL10, IL1A, CXCL1, CXCL8, IL4, and CXCL3) and ten down-regulated genes (RTN1, CSF1R CD14, C5AR1, CXCL16, PLXNB2, OLIG1, EEPD1, ENG, and CCR1). These findings were consistent with the RNA-Seq results.

Conclusion

The transcriptomic features associated with Mtb-Ag provide the scientific basis for exploring the intracellular immune mechanisms against Mtb. However, more studies on these DEGs in pathways associated with Mtb-Ag stimulation are needed to elucidate the underlying pathologic mechanisms of Mtb-Ag during Mtb infection.

IRF1 as a potential biomarker in Mycobacterium tuberculosis infection

Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell-cell adhesion and regulation of T-cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine-cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein-protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up-regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis.