Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysis

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation

BACKGROUND

Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

METHODS

We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

RESULTS

We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

CONCLUSION

Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

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.

Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis

Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.

Inhibition of host PARP1 contributes to the anti-inflammatory and antitubercular activity of pyrazinamide

The antibiotic pyrazinamide (PZA) is a cornerstone of tuberculosis (TB) therapy that shortens treatment durations by several months despite being only weakly bactericidal. Intriguingly, PZA is also an anti-inflammatory molecule shown to specifically reduce inflammatory cytokine signaling and lesion activity in TB patients. However, the target and clinical importance of PZA's host-directed activity during TB therapy remain unclear. Here, we identify the host enzyme Poly(ADP-ribose) Polymerase 1 (PARP1), a pro-inflammatory master regulator strongly activated in TB, as a functionally relevant host target of PZA. We show that PZA inhibits PARP1 enzymatic activity in macrophages and in mice where it reverses TB-induced PARP1 activity in lungs to uninfected levels. Utilizing a PZA-resistant mutant, we demonstrate that PZA's immune-modulatory effects are PARP1-dependent but independent of its bactericidal activity. Importantly, PZA's bactericidal efficacy is impaired in PARP1-deficient mice, suggesting that immune modulation may be an integral component of PZA's antitubercular activity. In addition, adjunctive PARP1 inhibition dramatically reduces inflammation and lesion size in mice and may be a means to reduce lung damage and shorten TB treatment duration. Together, these findings provide insight into PZA's mechanism of action and the therapeutic potential of PARP1 inhibition in the treatment of TB.

Integrated bioinformatics analysis of dendritic cells hub genes reveal potential early tuberculosis diagnostic markers

BACKGROUND

Dendritic cells (DCs) are most potent antigen-processing cells and play key roles in host defense against Mycobacterium tuberculosis (MTB) infection. In this study, hub genes in DCs during MTB infection were first investigated using bioinformatics approaches and further validated in Monocyte-derived DCs.

METHODS

Microarray datasets were obtained from Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) and immune infiltration analysis were performed to select suitable samples for further analysis. Differential analysis and functional enrichment analysis were conducted on DC samples, comparing live MTB-infected and non-infected (NI) groups. The CytoHubba plugin in Cytoscape was used to identify hub genes from the differentially expressed genes (DEGs). The expression of the hub genes was validated using two datasets and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in human monocyte-derived DCs. Enzyme-linked immunosorbent assay (ELISA) was used to validate interferon (IFN) secretion. Transcription factors (TFs) and microRNAs (miRNAs) that interact with the hub genes were predicted using prediction databases. The diagnostic value of the hub genes was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.

RESULTS

A total of 1835 common DEGs among three comparison groups (18 h, 48 h, 72 h after MTB infection) were identified. Six DEGs (IFIT1, IFIT2, IFIT3, ISG15, MX1, and RSAD2) were determined as hub genes. Functions enrichment analysis revealed that all hub genes all related to IFN response. RT-qPCR showed that the expression levels of six hub genes were significantly increased after DC stimulated by live MTB. According to the results of ELISA, the secretion of IFN-γ, but not IFN-α/β, was upregulated in MTB-stimulated DCs. AUC values of six hub genes ranged from 84 to 94% and AUC values of 5 joint indicators of two hub genes were higher than the two hub genes alone.

CONCLUSION

The study identified 6 hub genes associated with IFN response pathway. These genes may serve as potential diagnostic biomarkers in tuberculosis (TB). The findings provide insights into the molecular mechanisms involved in the host immune response to MTB infection and highlight the diagnostic potential of these hub genes in TB.

Identification of immune biomarkers in recent active pulmonary tuberculosis

Tuberculosis (TB) has remained an unsolved problem and a major public health issue, particularly in developing countries. Pakistan is one of the countries with the highest tuberculosis infection rates globally. However, methods or biomarkers to detect early signs of TB infection are limited. Here, we characterized the mRNA profiles of immune responses in unstimulated Peripheral blood mononuclear cells obtained from treatment naïve patients with early signs of active pulmonary tuberculosis without previous history of clinical TB. We identified a unique mRNA profile in active TB compared to uninfected controls, including cytokines such as IL-27, IL-15, IL-2RA, IL-24, and TGFβ, transcription factors such as STAT1 and NFATC1 and immune markers/receptors such as TLR4, IRF1, CD80, CD28, and PTGDR2 from an overall 84 different transcripts analyzed. Among 12 significant differentially expressed transcripts, we identified five gene signatures which included three upregulated IL-27, STAT1, TLR4 and two downregulated IL-24 and CD80 that best discriminate between active pulmonary TB and uninfected controls with AUC ranging from 0.9 to 1. Our data identified a molecular immune signature associated with the early stages of active pulmonary tuberculosis and it could be further investigated as a potential biomarker of pulmonary TB.