Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach

Identification of host immune-related biomarkers in active tuberculosis: A comprehensive analysis of differentially expressed genes

Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB.

Identification and role of differentially expressed genes/proteins between pulmonary tuberculosis patients and controls across lung tissues and blood samples

BACKGROUND

Differentially expressed genes/proteins (DEGs/DEPs) play critical roles in pulmonary tuberculosis (PTB) diagnosis and treatment. However, there is a scarcity of reports on DEGs/DEPs in lung tissues and blood samples in PTB patients.

OBJECTIVE

We aim to identify the DEGs/DEPs in lung tissues and blood samples of PTB patients and investigate their roles in PTB.

MATERIALS AND METHODS

The lung granulomas and normal tissues were collected from PTB patients for proteomic and transcriptomic analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses annotated the functions of DEGs/DEPs. The GSE107994 data set was downloaded to identify the DEGs/DEPs in peripheral blood. The common DEGs and DEPs were identified. A nomogram was established. Pearson correlation analysis was conducted.

RESULTS

Eighty-three DEGs/DEPs were identified. These DEGs/DEPs were mainly enriched in the movement of cell or subcellular components, regulation of cellular component biogenesis, and actin filament-based process as well as in the pathways of inositol phosphate metabolism, adherens junction, phosphatidylinositol signaling system, leukocyte transendothelial migration, regulation of actin cytoskeleton, and tight junction. There were eight common DEGs/DEPs (TYMP, LAP3, ADGRL2, SIL1, LMO7, SULF 1, ANXA3, and PACSIN3) between the lung tissues and blood samples. They were effective in predicting tuberculosis. Moreover, the activated dendritic cells, macrophages, monocytes, neutrophils, and regulatory T cells were significantly positively correlated with TYMP (r > .50), LAP3 (r > .50), SIL1 (r > .50), ANXA3 (r > .5), and PACSIN3 (r < .50), while negatively correlated with LMO7 (r < -0.50) (p < .05). ADGRL2 and SULF1 did not have a significant correlation (p > .05).

LIMITATIONS

The sample size was small.

CONCLUSIONS

Eight common DEGs/DEPs of lung tissues and blood samples were identified. They were correlated with immune cells and demonstrated predictive value for PTB. Our data may facilitate the diagnosis and treatment of PTB.

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.

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis

Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein-protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC-0.993, sensitivity-93.8%, specificity-100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

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.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Reanalysis and validation of the transcriptional pleural fluid signature in pleural tuberculosis

Introduction

Pleural tuberculosis (PlTB), the most common site of extrapulmonary TB, is characterized by a paucibacillary nature and a compartmentalized inflammatory response in the pleural cavity, both of which make diagnosis and management extremely challenging. Although transcriptional signatures for pulmonary TB have already been described, data obtained by using this approach for extrapulmonary tuberculosis and, specifically, for pleural tuberculosis are scarce and heterogeneous. In the present study, a set of candidate genes previously described in pulmonary TB was evaluated to identify and validate a transcriptional signature in clinical samples from a Brazilian cohort of PlTB patients and those with other exudative causes of pleural effusion.

Methods

As a first step, target genes were selected by a random forest algorithm with recursive feature elimination (RFE) from public microarray datasets. Then, peripheral blood (PB) and pleural fluid (PF) samples from recruited patients presenting exudative pleural effusion were collected during the thoracentesis procedure. Transcriptional analysis of the selected top 10 genes was performed by quantitative RT-PCR (RT-qPCR).

Results

Reanalysis of the public datasets identified a set of candidate genes (CARD17, BHLHE40, FCGR1A, BATF2, STAT1, BTN3A1, ANKRD22, C1QB, GBP2, and SEPTIN4) that demonstrated a global accuracy of 89.5% in discriminating pulmonary TB cases from other respiratory diseases. Our validation cohort consisted of PlTB (n = 35) patients and non-TB (n = 34) ones. The gene expressions of CARD17, GBP2, and C1QB in PF at diagnosis were significantly different between the two (PlTB and non-TB) groups (p < 0.0001). It was observed that the gene expressions of CARD17 and GBP2 were higher in PlTB PF than in non-TB patients. C1QB showed the opposite behavior, being higher in the non-TB PF. After anti-TB therapy, however, GBP2 gene expression was significantly reduced in PlTB patients (p < 0.001). Finally, the accuracy of the three above-cited highlighted genes in the PF was analyzed, showing AUCs of 91%, 90%, and 85%, respectively. GBP2 was above 80% (sensitivity = 0.89/specificity = 0.81), and CARD17 showed significant specificity (Se = 0.69/Sp = 0.95) in its capacity to discriminate the groups.

Conclusion

CARD17, GBP2, and C1QB showed promise in discriminating PlTB from other causes of exudative pleural effusion by providing accurate diagnoses, thus accelerating the initiation of anti-TB therapy.

Type I interferons and Mycobacterium tuberculosis whole cell lysate induce distinct transcriptional responses in M. tuberculosis infection

Type I interferon (IFN)-induced genes have the potential for distinguishing active tuberculosis (ATB) from latent TB infection (LTBI) and healthy controls (HC), monitoring treatment, and detection of individuals at risk of progression to active disease. We examined the differential effects of IFN-α, IFN-β and Mycobacterium tuberculosis whole cell lysate (Mtb WCL) stimulation on the expression of selected IFN-stimulated genes in peripheral blood mononuclear cells from individuals with either LTBI, ATB, and healthy controls. Stimulation with IFN-α and IFN-β induced a higher expression of the interrogated genes while Mtb WCL stimulation induced expression similar to that observed at baseline, with the exception of IL-1A and IL-1B genes that were downregulated. The expression of IFN-α-induced FCGR1A gene, IFN-β-induced FCGR1A, FCGR1B, and SOCS3 genes, and Mtb WCL-induced IFI44, IFI44L, IFIT1, and IFITM3 genes differed significantly between LTBI and ATB. These findings suggest stimulation-driven gene expression patterns could potentially discriminate LTBI and ATB. Mechanistic studies are necessary to define the processes through which distinct type I IFNs and downstream ISGs determine infection outcomes and identify potential host-directed therapeutic strategies.

Association of Cytokine Gene Polymorphisms and Their Impact on Active and Latent Tuberculosis in Brazil's Amazon Region

Some genetic variations in cytokine genes can alter their expression and influence the evolution of Mycobacterium tuberculosis (Mtb) infection. This study aimed to investigate the association of polymorphisms in cytokine genes and variability in plasma levels of cytokines with the development of tuberculosis (TB) and latent tuberculosis infection (LTBI). Blood samples from 245 patients with TB, 80 with LTBI, and healthy controls (n = 100) were included. Genotyping of the IFNG +874A/T, IL6 -174G/C, IL4 -590C/T, and IL10 -1082A/G polymorphisms was performed by real-time PCR, and cytokine levels were determined by flow cytometry. Higher frequencies of genotypes AA (IFNG +874A/T), GG (IL6 -174G/C), TT (IL4 -590C/T), and GG (IL10 -1082A/G) were associated with an increased risk of TB compared to that of LTBI (p = 0.0027; p = 0.0557; p = 0.0286; p = 0.0361, respectively) and the control (p = <0.0001, p = 0.0021; p = 0.01655; p = 0.0132, respectively). In combination, the A allele for IFNG +874A/T and the T allele for IL4 -590C/T were associated with a higher chance of TB (p = 0.0080; OR = 2.753 and p < 0.0001; OR = 3.273, respectively). The TB group had lower levels of IFN-γ and higher concentrations of IL-6, IL-4, and IL-10. Cytokine levels were different between the genotypes based on the polymorphisms investigated (p < 0.05). The genotype and wild-type allele for IFNG +874A/T and the genotype and polymorphic allele for IL4 -590C/T appear to be more relevant in the context of Mtb infection, which has been associated with the development of TB among individuals infected by the bacillus and with susceptibility to active infection but not with susceptibility to latent infection.

Identification of Hub Biomarkers and Immune and Inflammation Pathways Contributing to Kawasaki Disease Progression with RT-qPCR Verification

Background. Kawasaki disease (KD) is characterized by a disordered inflammation response of unknown etiology. Immune cells are closely associated with its onset, although the immune-related genes’ expression and possibly involved immune regulatory mechanisms are little known. This study aims to identify KD-implicated significant immune- and inflammation-related biomarkers and pathways and their association with immune cell infiltration. Patients and Methods. Gene microarray data were collected from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find KD hub markers. GSEA was used to assess the infiltration by 28 immune cell types and their connections to essential gene markers. Receiver operating characteristic (ROC) curves were used to examine hub markers’ diagnostic effectiveness. Finally, hub genes’ expressions were validated in Chinese KD patients by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results. One hundred and fifty-one unique genes were found. Among 10 coexpression modules at WGCNA, one hub module exhibited the strongest association with KD. Thirty-six overlapping genes were identified. Six hub genes were potential biomarkers according to LASSO analysis. Immune infiltration revealed connections among activated and effector memory CD4+ T cells, neutrophils, activated dendritic cells, and macrophages. The six hub genes’ diagnostic value was shown by ROC curve analysis. Hub genes were enriched in immunological and inflammatory pathways. RT-qPCR verification results of FCGR1B ( $P<0.001$ ), GPR84 ( $P<0.001$ ), KREMEN1 ( $P<0.001$ ), LRG1 ( $P<0.001$ ), and TDRD9 ( $P<0.001$ ) upregulated expression in Chinese KD patients are consistent with our database analysis. Conclusion. Neutrophils, macrophages, and activated dendritic cells are strongly linked to KD pathophysiology. Through immune-related signaling pathways, hub genes such as FCGR1B, GPR84, KREMEN1, LRG1, and TDRD9 may be implicated in KD advancement.

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.

Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.

Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.

Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

RTP4, a Biomarker Associated with Diagnosing Pulmonary Tuberculosis and Pan-Cancer Analysis

Background

Pulmonary tuberculosis (PTB) is a global epidemic of infectious disease; the purpose of our study was to explore new potential biomarkers for the diagnosis of pulmonary tuberculosis and to use the biomarkers for further pan-cancer analysis.

Methods

Four microarray gene expression sets were downloaded from the GEO public databases and conducted for further analysis. Healthy control (HC) samples and samples of pulmonary tuberculosis (PTB) were calculated with enrichment scores in folate biosynthesis pathways. The scores acted as a new phenotype combined with clinical information (control or PTB) for subsequent analysis. Weight gene coexpression network analysis (WGCNA) was used to seek the modules mostly related to PTB and folate biosynthesis in training sets. Twenty-nine coexistence genes were screened by intersecting the genes in the green-yellow module of GSE28623 and the brown module of GSE83456. We used the protein-protein interaction network analysis to narrow the gene range to search for hub genes. Then, we downloaded the unified and standardized pan-cancer data set from the UCSC database for correlations between biomarkers and prognosis and tumor stage differences.

Results

Eventually, RTP4 was selected as a biomarker. To verify the reliability of this biomarker, an area under the ROC (AUC) was calculated in gene sets (GSE28623, GSE83456, and GSE34608). Lastly, to explore the difference in RTP4 expression before and after antituberculosis treatment, the GSE31348 gene set was enrolled to compare the expressions in weeks 0 and 26. The results showed significant differences between these two time points (p < 0.001). RTP4 was significantly upregulated in the pulmonary tuberculosis group compared to the healthy control group in three gene sets and downregulated after antituberculosis therapy in one gene set. These results suggest that RTP4 can be used as a potential biomarker in diagnosing tuberculosis. The results of pan-cancer analysis showed that high expression of RTP4 in 4 tumor types was positively correlated with poor prognosis and high expression of RTP4 in 6 tumor types was negatively correlated with poor prognosis. We found significant differences in the expression of the RTP4 gene at different stages in 5 types of tumors.

Conclusion

RTP4 might be a new potential biomarker for diagnosing pulmonary tuberculosis.

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation.

Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability.

Results: We identified CD4⁺ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.

Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.