A multidimensional network-based approach to elucidate the molecular mechanism underlying tuberculosis and HIV coinfection

Tuberculosis (TB) and human immunodeficiency virus (HIV) coinfection is a severe global health challenge with high morbidity and mortality rates. In this study, we employed a novel bioinformatics approach to gain a comprehensive understanding of the state of TB/HIV coinfection. Using a multidimensional graph-based clustering methodology, we identified several key pathways associated with infectious and autoimmune diseases, immune and inflammatory responses, cardiovascular dysfunctions, and metabolic processes. We identified therapeutic biomarkers regulated by established therapeutic chemicals and developed robust machine learning-based quantitative structure-activity relationship (ML-QSAR) models to identify effective drug candidates. Our models successfully identified S5105 proanthocyanidin as a promising modulator of the key inflammatory biomarkers TNF, IL1B, and IFNG. Furthermore, we analyzed the influence of environmental factors, such as arsenic, air pollutants, and carbon monoxide, on the progression and occurrence of TB/HIV coinfection. Our findings revealed that these toxicants can trigger a cascade of inflammatory responses, leading to lung fibrosis and a cytokine storm that exacerbates immune dysregulation in coinfected individuals. Additionally, the impact of air pollutants on cardiovascular health and neurological complications, such as AIDS-Dementia Complex, adds to the complexity of managing TB/HIV coinfection. Again, with the findings of multidimensional graphs, we tried to elucidate the correlation between the air quality indices and the occurrence of TB/HIV coinfection. The models highlighted particulate matter (PM)10 and PM2.5 concentrations as critical predictors, indicating that poor air quality is positively correlated with higher rates of HIV testing among TB patients and an increased percentage of HIV-positive TB patients on antiretroviral therapy (ART). This suggests regions with worse air quality may have more comprehensive health monitoring. By identifying these associations, we can better understand how arsenic and air pollutants might contribute to immune deterioration and disease progression in TB and HIV patients. Thus, a multidimensional methodology can significantly enhance drug discovery and environmental toxicological efforts by integrating diverse data sources and analytical techniques to uncover complex interactions, identify potential therapeutic targets, and assess the impact of environmental toxins on health with greater precision.