Mycobacterial Diseases
Open Access

Gene Expression and its Impact on Tuberculosis Treatment Pathways

Lobina Torino^{* *}Correspondence: Lobina Torino, Department of Microbiology and Parasitology, Warmadewa University, Bali, Indonesia, Email:

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Description

Recent advancements in transcriptomics, the study of RNA expression patterns, have opened new avenues for understanding these variations. Blood transcriptomic signatures have emerged as powerful tools for predicting treatment outcomes, offering insights into patient responses and guiding more personalized therapeutic approaches. Transcriptomic signatures refer to specific patterns of gene expression that can be detected in a patient's blood. These patterns provide a snapshot of the biological processes occurring within the body, including immune responses to TB infection and treatment. Unlike traditional diagnostic tools that focus on detecting the pathogen, transcriptomic analysis highlights the host’s response, offering a complementary perspective. By analysing the host’s gene expression profiles, transcriptomic approaches reveal dynamic changes throughout the course of treatment, aiding in monitoring disease progression or recovery. This method offers the potential for earlier intervention by detecting subtle shifts in immune activity before clinical symptoms appear. Furthermore, integrating transcriptomic data with other diagnostic tools can improve the accuracy of TB management, paving the way for more effective and tailored healthcare solutions.

Role of blood transcriptomics in tuberculosis

In TB, blood transcriptomic signatures have shown potential in predicting various aspects of the disease and its treatment, including, specific gene expression profiles can indicate the severity of TB, distinguishing between active disease and latent infection. Transcriptomic signatures can identify early markers of treatment success or failure, allowing clinicians to adjust therapies as needed. Certain gene expression patterns are associated with a higher likelihood of relapse after completing treatment, helping identify patients who may require closer monitoring or extended therapy. Studies have identified several blood transcriptomic signatures that correlate with treatment outcomes in drug-susceptible pulmonary TB. For example, genes involved in interferon signalling pathways are often upregulated in patients who respond well to treatment, reflecting robust immune activation. Non-responders to standard TB treatment exhibit distinct transcriptomic profiles compared to responders.

These profiles often include dysregulation of immune pathways, suggesting underlying challenges in mounting an effective immune response. Monitoring transcriptomic signatures over the course of treatment reveals dynamic changes in gene expression. Successful treatment is typically accompanied by a normalization of immune-related gene activity, while persistent abnormalities may signal ongoing infection or inflammation. The ability to predict treatment outcomes through blood transcriptomic signatures has several potential benefits for TB management, such as identifying patients who are unlikely to respond to standard therapies, clinicians can tailor treatment regimens to individual needs, improving overall outcomes. Predictive signatures enable early identification of patients at risk of treatment failure or relapse, allowing for timely interventions such as intensified therapy or adjunctive treatments. In resource-limited settings, transcriptomic tools can help prioritize patients for additional diagnostic tests or follow-up care, ensuring efficient use of healthcare resources.

Challenges and future directions

While the potential of blood transcriptomic signatures in TB treatment is undeniable, several challenges must be addressed to translate these findings into clinical practice of transcriptomic technologies vary widely in terms of platforms and methodologies. Developing standardized assays will be essential for ensuring consistency and comparability across studies and settings. Transcriptomic analysis is currently expensive and requires specialized equipment and expertise. Efforts to develop cost-effective and user-friendly platforms are essential for widespread adoption, particularly in low- and middle-income countries where TB burden is highest. Incorporating transcriptomic tools into existing TB diagnostic and treatment workflows will require training for healthcare providers, as well as the development of guidelines for interpretation and action based on test results.

The host’s immune response to TB is influenced by numerous factors, including comorbidities, coinfections, and genetic diversity. Further research is needed to refine transcriptomic signatures and account for this complexity. The field of TB transcriptomics is rapidly evolving, with several promising avenues for future research and application, such as simplified, portable platforms for transcriptomic analysis could bring these tools closer to patients, enabling rapid decision-making in clinical and community settings. Combining transcriptomic data with other biomarkers, such as proteomic or metabolomic profiles, could enhance predictive accuracy and provide a more comprehensive understanding of treatment responses. While most research has focused on drug-susceptible TB, similar approaches could be applied to drug-resistant forms of the disease, where treatment outcomes are even more variable. Large-scale, long-term studies are needed to validate the utility of transcriptomic signatures across diverse populations and healthcare settings.

Conclusion

Blood transcriptomic signatures represent an innovative tool for predicting treatment outcomes in drug-susceptible pulmonary TB. By providing a window into the host’s immune response, these signatures offer valuable insights that can inform personalized treatment strategies and improve patient care. While challenges remain, ongoing advancements in technology and research potential to bring these tools closer to routine clinical use, contributing to the global fight against TB.