Creating bioinformatics matrix of molecular markers to predict risk-associated health disorders

Abstract

Long-term or permanent chemical ambient air pollution in residential areas is among priority factors that cause medical and demographic losses. It is necessary to achieve greater precision when assessing risks of changes in homeostasis at their early reversible stage (molecular level). These changes are highly likely to transform into pathological processes at an older age in case the exposure persists. Our research goal was to create a bioinformatics matrix of molecular markers to predict risk-associated health disorders (exemplified by a marker of exposure). We introduced a stepwise research algorithm that involved using the proteome technology to identify expressed proteins and cause-effect relations between them and influencing factors; revealing molecular-cellular and functional relationships within the “exposure factor – gene – protein – negative outcome” system to predict risk-associated health disorders. The algorithm was implemented to examine the proteomic blood plasma profile of children aged 3–6 years living under long-term aerogenic exposure to fluoride-containing compounds. We established certain changes in the proteomic profiles of the exposed children in comparison with non-exposed ones as per 27 identified proteins. A bioinformatics matrix was created on the example of cathepsin L1; we established that changes in the level of this protein had a cause-effect relationship with fluoride ion concentrations in urine. Qualitative synthesis of molecular-cellular localization, functional and tissue belonging showed that cathepsin L1 expression caused by elevated fluoride ion levels in urine could affect extracellular matrix remodeling, degradation and post-translation modification of proteins in cells of the lungs, large intestine, and pancreas, in cardiomyocytes and in glomerular podocytes. It also mediated proteolysis of the subunits of the SARS-CoV-2 S1 protein necessary for the virus penetration into a cell and its replication. This created bioinformatics matrix exemplified by cathepsin L1 made it possible to predict risk-associated negative outcomes in exposed people including cardiomyopathy, colitis, glomerulonephritis, diabetes mellitus, atherosclerosis, and coronavirus infection. These predictive estimates raise effectiveness of early detection and development of preventive measures aimed at minimizing possible negative outcomes.