Malnutrition leads to increased inflammation and expression of tuberculosis risk signatures in recently exposed household contacts of pulmonary tuberculosis

New genetic biomarkers from transcriptome RNA-sequencing for Mycobacterium tuberculosis complex and Mycobacterium avium complex infections by bioinformatics analysis

The study aims to accurately identify differentially expressed genes (DEGs) and biological pathways in mycobacterial infections through bioinformatics for deeper disease understanding. Differentially expressed genes (DEGs) was explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Unique DEGs were submitted on least absolute shrinkage and selection operator (LASSO) regression analysis. 1,057 DEGs from two GSE datasets were identified, which were closely connected with NTM/ latent TB infection (LTBI)/active TB disease (ATB). It was demonstrated that these DEGs are mainly associated with detoxification processes, and virus and bacterial infections. Moreover, the METTL7B gene was the most informative marker for distinguishing LTBI and ATB with an area under the curve (AUC) of 0.983 (95%CI: 0.964 to 1). The significantly upregulated HBA1/2 genes were the most informative marker for distinguishing between individuals of IGRA-HC/NTM and LTBI (P < 0.001). Moreover, the upregulated HBD gene was also differ between IGRA-HC/NTM and ATB (P < 0.001). We have identified gene signatures associated with Mycobacterium infection in whole blood, which could be significant for understanding the molecular mechanisms and diagnosis of NTM, LTBI, or ATB.

Undernourished Household Contacts Are at Increased Risk of Tuberculosis (TB) Disease, but not TB Infection-a Multicenter Prospective Cohort Analysis

Undernutrition is the leading risk factor for tuberculosis (TB) globally and in India. This multicenter prospective cohort analysis from India suggests that undernutrition is associated with increased risk of TB disease but not TB infection among household contacts of persons with TB.

Food Insecurity and Undernutrition Are Associated With Distinct Immunologic Profiles in People With Tuberculosis and Advanced HIV Starting Antiretroviral Therapy

BACKGROUND

Food insecurity and undernutrition are related but distinct concepts contributing to poor HIV and tuberculosis outcomes. Pathways linking them with immunologic profile, which may relate to clinical outcomes, remain understudied.

METHODS

We analyzed data from a cohort study of 165 antiretroviral therapy (ART)-naïve adults with advanced HIV and newly diagnosed tuberculosis in Botswana from 2009 to 2013. Twenty-nine plasma biomarkers were measured pre-ART and 4 weeks post-ART initiation. We used principal components analysis (PCA) and multivariable linear regression models to assess relationships between immunological profiles and food insecurity (based on the Household Food Insecurity Access Scale), undernutrition (body mass index <18.5 kg/m 2 ), and clinical outcomes.

RESULTS

PCA identified 5 principal components with eigenvalues >1. After adjustment, food insecurity was associated with PC3 pre-ART (0.19 per increased category of severity, 95% CI: 0.02 to 0.36) and post-ART (0.24, 95% CI: 0.07 to 0.41). PC3 was driven by higher levels of IFN-α, IFN-γ, interleukin (IL)-12p40, vascular endothelial growth factor, IL-1α, and IL-8 and decreased concentrations of IL-3. Undernutrition was associated with PC5 post-ART (0.49, 95% CI: 0.16 to 0.82). PC5 was driven by higher levels of IL-8, MIP-1α, IL-6, and IL-10 and decreased concentrations in IP-10 and IFN-α. Post-ART PC3 (4.3 percentage point increased risk per increased score of 1, 95% CI: 0.3 to 8.9) and post-ART PC5 (4.8, 95% CI: 0.6 to 8.9) were associated with death in adjusted models.

DISCUSSION

We identified 2 distinct immunologic profiles associated with food insecurity, undernutrition, and clinical outcomes in patients with advanced HIV and tuberculosis. Different pathophysiologic processes may link food insecurity and undernutrition with poor outcomes in this vulnerable patient population. Future studies should assess the impact of improving food access and intake on immune function and clinical outcomes.

Assessing the risk of concurrent mycoplasma pneumoniae pneumonia in children with tracheobronchial tuberculosis: retrospective study

Objective

This study aimed to create a predictive model based on machine learning to identify the risk for tracheobronchial tuberculosis (TBTB) occurring alongside Mycoplasma pneumoniae pneumonia in pediatric patients.

Methods

Clinical data from 212 pediatric patients were examined in this retrospective analysis. This cohort included 42 individuals diagnosed with TBTB and Mycoplasma pneumoniae pneumonia (combined group) and 170 patients diagnosed with lobar pneumonia alone (pneumonia group). Three predictive models, namely XGBoost, decision tree, and logistic regression, were constructed, and their performances were assessed using the receiver's operating characteristic (ROC) curve, precision-recall curve (PR), and decision curve analysis (DCA). The dataset was divided into a 7:3 ratio to test the first and second groups, utilizing them to validate the XGBoost model and to construct the nomogram model.

Results

The XGBoost highlighted eight significant signatures, while the decision tree and logistic regression models identified six and five signatures, respectively. The ROC analysis revealed an area under the curve (AUC) of 0.996 for XGBoost, significantly outperforming the other models (p < 0.05). Similarly, the PR curve demonstrated the superior predictive capability of XGBoost. DCA further confirmed that XGBoost offered the highest AIC (43.226), the highest average net benefit (0.764), and the best model fit. Validation efforts confirmed the robustness of the findings, with the validation groups 1 and 2 showing ROC and PR curves with AUC of 0.997, indicating a high net benefit. The nomogram model was shown to possess significant clinical value.

Conclusion

Compared to machine learning approaches, the XGBoost model demonstrated superior predictive efficacy in identifying pediatric patients at risk of concurrent TBTB and Mycoplasma pneumoniae pneumonia. The model's identification of critical signatures provides valuable insights into the pathogenesis of these conditions.

Incidence and Risk Factors of Active Tuberculosis Among Hospitalized Patients with Latent Tuberculosis Infection in China: A Cohort Study

Background

The population with latent tuberculosis infection (LTBI) represents a potential pool of patients with active tuberculosis (ATB). T-SPOT.TB is an important test tool for screening LTBI. Owing to the large population of LTBI patients in China, it is necessary to identify a high-risk group for LTBI and enlarge tuberculosis preventive treatment (TPT) to reduce the incidence of ATB.

Methods

Hospitalized patients with positive T-SPOT.TB results were recruited from January 2013 to December 2016. Patients with ATB were excluded. Basic information was collected and the development of ATBs was examined during follow-up. The life-table method was used to calculate cumulative incidence rates. Potential risk factors were analyzed through Cox regression analysis.

Results

A total of 1680 patients with LTBI were recruited in the follow-up cohort, and 377 (22.44%) patients dropped out. With a median follow-up time of 81 months [interquartile range (IQR):61-93], 19 of 1303 patients with LTBI developed ATB. The 1-year incidence of ATB was 614 per 100,000 individuals [95%confidence interval (95% CI):584-644]. Over 5-year period, the cumulative incidence of ATB was 1496 per 100,000 [95% CI:1430-1570], and the incidence density was 240 per 100,000 person-years[95% CI:144-375]. In the Cox regression model, exposure of pulmonary tuberculosis (PTB) [adjusted hazard ratio (aHR)=10.557, 95% CI:2.273-49.031], maximum daily dosage of glucocorticoids (GCs)≥ 50 mg/d (aHR=2.948, 95% CI:1.122-7.748), leflunomide (LEF) treatment (aHR=8.572, 95% CI:2.222 -33.070), anemia (aHR=2.565, 95% CI:1.015-6.479) and T-SPOT.TB level≥300SFCs/106 PBMCs (aHR=4.195, 95% CI:1.365-12.892) were independent risk factors for ATB development in LTBI patients.

Conclusion

The incidence of ATB is significantly higher in hospitalized patients with LTBI than in the general population. The exposure history of PTB, maximum daily dosage of GCs≥ 50 mg/day, LEF treatment, anemia, and T-SPOT.TB level≥300SFCs/106PBMCs, were the risk factors of tuberculosis reactivation. Hospitalized LTBI patients with the above factors may need TPT.

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

Latent Tuberculosis: Challenges in Diagnosis and Treatment, Perspectives, and the Crucial Role of Biomarkers

Tuberculosis (TB) is the major cause of morbidity and mortality globally, which is caused by a single infectious agent Mycobacterium tuberculosis. For years, many TB control programmes are established for effective diagnosis and treatment of active TB cases, but these approaches alone are insufficient for TB eradication. This review aims to discourse on the crucial management of latent tuberculosis infection. This review will first summarize the current status, and methods for diagnosing latent tuberculosis then describes the challenges involved in the diagnosis and treatment of latent tuberculosis, and finally encounters the purpose of biomarkers as predicting tool in latent tuberculosis.

COVID-19 and Tuberculosis: Unveiling the Dual Threat and Shared Solutions Perspective

The year 2020 will likely be remembered as the year dominated by COVID-19, or coronavirus disease. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for this pandemic, can be traced back to late 2019 in China. The COVID-19 pandemic has significantly impacted the tuberculosis (TB) care system, reducing TB testing and reporting. This can be attributed to the disruption of TB services and restrictions on patient movement, consequently increasing TB-related deaths. This perspective review aims to highlight the intersection between COVID-19 and TB, highlighting their dual threat and identifying shared solutions to address these two infectious diseases effectively. There are several shared commonalities between COVID-19 and tuberculosis, particularly the transmission of their causative agents, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Mycobacterium tuberculosis. Both pathogens are transmitted via respiratory tract secretions. TB and COVID-19 are diseases that can be transmitted through droplets and airborne particles, and their primary target is typically the lungs. Regarding COVID-19 diagnostics, several methods are available for rapid and accurate detection. These include RT-PCR, which can provide results within two hours, and rapid antigen test kits that offer results in just a few minutes. The availability of point-of-care self-testing further enhances convenience. On the other hand, various approaches are employed for TB diagnostics to swiftly identify active TB. These include sputum microscopy, sputum for reverse transcription polymerase chain reaction (RT-PCR), and chest X-rays. These methods enable the rapid detection of active TB on the same day, while culture-based testing may take significantly longer, ranging from 2 to 8 weeks. The utilization of diverse diagnostic tools helps ensure the timely identification and management of COVID-19 and TB cases. The quality of life of patients affected by COVID-19 and tuberculosis (TB) can be significantly impacted due to the nature of these diseases and their associated challenges. In conclusion, it is crucial to emphasize the urgent need to address the dual threat of COVID-19 and TB. Both diseases have devastated global health, and their convergence poses an even greater challenge. Collaborative efforts, research investments, and policy reforms are essential to tackle this dual threat effectively.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures

Rationale

Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed.

Objectives

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use.

Methods

We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models.

Measurement and Main Results

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

Conclusion

Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.