Gene Set Enrichment Analysis Reveals Individual Variability in Host Responses in Tuberculosis Patients

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

BACKGROUND

Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed.

METHODS

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models.

RESULTS

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. 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 gene sets can distinguish patients with active TB disease from 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.

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Beyond the Clinic: The Activation of Diverse Cellular and Humoral Factors Shapes the Immunological Status of Patients with Active Tuberculosis

Mycobacterium tuberculosis (Mtb), the etiologic agent of tuberculosis (TB), has killed nearly one billion people in the last two centuries. Nowadays, TB remains a major global health problem, ranking among the thirteen leading causes of death worldwide. Human TB infection spans different levels of stages: incipient, subclinical, latent and active TB, all of them with varying symptoms, microbiological characteristics, immune responses and pathologies profiles. After infection, Mtb interacts with diverse cells of both innate and adaptive immune compartments, playing a crucial role in the modulation and development of the pathology. Underlying TB clinical manifestations, individual immunological profiles can be identified in patients with active TB according to the strength of their immune responses to Mtb infection, defining diverse endotypes. Those different endotypes are regulated by a complex interaction of the patient's cellular metabolism, genetic background, epigenetics, and gene transcriptional regulation. Here, we review immunological categorizations of TB patients based on the activation of different cellular populations (both myeloid and lymphocytic subsets) and humoral mediators (such as cytokines and lipid mediators). The analysis of the participating factors that operate during active Mtb infection shaping the immunological status or immune endotypes of TB patients could contribute to the development of Host Directed Therapy.

Reinventing the human tuberculosis (TB) granuloma: Learning from the cancer field

Tuberculosis (TB) remains one of the deadliest infectious diseases in the world and every 20 seconds a person dies from TB. An important attribute of human TB is induction of a granulomatous inflammation that creates a dynamic range of local microenvironments in infected organs, where the immune responses may be considerably different compared to the systemic circulation. New and improved technologies for in situ quantification and multimodal imaging of mRNA transcripts and protein expression at the single-cell level have enabled significantly improved insights into the local TB granuloma microenvironment. Here, we review the most recent data on regulation of immunity in the TB granuloma with an enhanced focus on selected in situ studies that enable spatial mapping of immune cell phenotypes and functions. We take advantage of the conceptual framework of the cancer-immunity cycle to speculate how local T cell responses may be enhanced in the granuloma microenvironment at the site of Mycobacterium tuberculosis infection. This includes an exploratory definition of "hot", immune-inflamed, and "cold", immune-excluded TB granulomas that does not refer to the level of bacterial replication or metabolic activity, but to the relative infiltration of T cells into the infected lesions. Finally, we reflect on the current knowledge and controversy related to reactivation of active TB in cancer patients treated with immune checkpoint inhibitors such as PD-1/PD-L1 and CTLA-4. An understanding of the underlying mechanisms involved in the induction and maintenance or disruption of immunoregulation in the TB granuloma microenvironment may provide new avenues for host-directed therapies that can support standard antibiotic treatment of persistent TB disease.

Blue Skies research is essential for ending the Tuberculosis pandemic and advancing a personalized medicine approach for holistic management of Respiratory Tract infections

OBJECTIVES

Investments into 'Blue Skies' fundamental TB research in low- and middle-income countries (LMICs) have not been forthcoming. We highlight why blue skies research will be essential for achieving global TB control and eradicating TB.

METHODS

We review the historical background to early TB discovery research and give examples of where investments into basic science and fundamental 'blue skies research' are delivering novel data and approaches to advance diagnosis, management and holistic care for patients with active and latent TB infection.

FINDINGS

The COVID-19 pandemic has shown that making available adequate funding for priority investments into 'Blue skies research' to delineate scientific understanding of a new infectious diseases threat to global health security can lead to rapid development and rollout of new diagnostic platforms, treatments, and vaccines. Several advances in new TB diagnostics, new treatments and vaccine development are underpinned by basic science research.

CONCLUSIONS

Blue Skies research is required to pave the way for a personalized medicine approach for management of TB and other Respiratory Tract Infections and preventing long-term functional disability. Transfer of skills and resources by wealthier nations is required to empower researchers in LMICs countries to engage in and lead Blue Skies research.