Detection of omic markers of the nervous system adverse effects in children with a combined exposure to airborne chemicals and conditions of educational environment

Introduction: The rationale of prognostic and predictive molecular biomarkers of malfunctioning of homeostatic control mechanisms is important for solving the tasks of early diagnosis and prevention of priority noncom-municable diseases. Our objective was to detect omic markers of adverse effects of a combined exposure to airborne contaminants and factors of educational environment on the nervous system of children. Materials and methods: We studied school outdoor and indoor concentrations of certain air pollutants, the intensity of the educational process, and plasma proteins characterizing nervous system adverse effects in children aged 7-10 with a combined exposure to various factors of educational environment in the primary school with various types of educational programs and hygienic conditions. Results: We established that blood manganese, nickel, lead, chromium, benzene, xylene, and phenol levels among the schoolchildren of the study group were 1.2-2.4 times higher than those in the control group. The phenol concentration in blood is a proven marker of the inhalation exposure. We also identified such violations of the educational process as uneven distribution of study loads, an increase in the maximum permissible load, a 1.2-fold increase in intellectual loads, shortening of the break between basic and optional classes, and a 1.5-fold increase in intensity of the training mode. We obtained mass spectra of the peptides reflecting changes in homeostasis on the molecular level. As a result of establishing a direct causal relationship between the increase in the relative mass of a Kazal-type 5 serine protease inhibitor, the increased blood phenol level, effects of intellectual loads, routine and distribution of the training load, the Kazal-type 5 serine protease inhibitor was proved to be an omic marker of the combined exposure to ambient phenol and the factors of educational environment. Conclusions: An increase in the relative mass of the Kazal-type 5 serine protease inhibitor following the combined exposure to airborne phenol and educational factors is a molecular indicator of its prognostically unfavorable involvement into the pathogenesis of functional disorders of the nervous system in the form of vegetative-vascular dystonia.

сочетанное воздействие, аэрогенный химический фактор, факторы образовательного процесса, школьники начальных классов, протеомный профиль, омик-маркеры, негативные эффекты, combined exposure, airborne chemicals, educational process factors, primary school children, proteomic profile, omic markers, adverse effects

1. Кучма В.Р. Охрана здоровья детей и подросток в национальной стратегии действий в интересах детей на 2012-2017 // Гигиена и санитария. 2013. № 6. С. 26-30.

2. Назаров В.А. Сравнительный анализ уровня адаптации и психосоматического здоровья школьников Москвы и Подмосковья при сочетанном воздействии факторов окружающей среды // Вестник РУДН, Серия Экология и безопасность жизнедеятельности. 2012. № 2. С. 57-62.

3. Акарачкова Е.С., Вершинина С.В. Синдром вегетативной дистонии у современных детей и подростков // Педиатрия. 2011. Т. 90, № 6. С. 131-136.

4. Вялков А.И., Мартынчик С.А. , Полесский В.А., Ковров Г.В. Концепция персонализированной медицины в предметной области «нейромедицина» на технологической платформе «Медицина здоровья» // Здравоохранение Российской Федерации. 2014. Т. 58, № 6. С. 4-9.

5. Дон Е.С., Тарасов А.В., Эпштейн О.И., Тарасов С.А. Биомаркеры в медицине: поиск, выбор, изучение и валидация // Клиническая лабораторная диагностика. 2017. Т. 62, № 1. С. 52-59.

6. Мошковский С.А. Омикс-биомаркеры и ранняя диагностика // Биомедицинская химия. 2017. Т. 63, № 5. С. 369-372.

7. Ткачук Е.А., Филиппов Е.С., Жданова-Заплесвичко И.Г. Состояние здоровья школьников в условиях реформирования образования // Сибирский медицинский журнал. 2012. № 3. С. 14-17.

8. Гланц С. Медико-биологическая статистика. М.: Практика. 1998. 459 с.

9. Guidelines for Neurotoxicity Risk Assessment. Risk Assessment Forum. U.S. Environmental Protection Agency Washington, DC EPA/630/R-95/001F April 1998. Available at: www. epa.gov.neuro_tox.pdf. Accessed: 25 Jan 2020

10. Craft GE, Chen A, Nairn AC. Recent advances in quantitative neuroproteomics. Methods. 2013; 61(3):186-218. DOI: https://doi.org/10.1016/j.ymeth.2013.04.008

11. Golubnitschaja O, Costigliola V. General Report & Recommendations in Predictive, Preventive and Personalised Medicine 2012: White Paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA Journal. 2012; 3(1):1-53. (In Russian). DOI: https://doi. org/10.1186/1878-5085-3-14

12. Pamirsky IE, Borodin EA, Shtarberg MA. Regulation of proteolysis of plant and animal inhibitors. Publisher: Lambert Academic Publishing GmbH & Co, 2012. 96 p.

13. Almonte AG, Sweatt JD. Serine proteases, serine protease inhibitors, and protease-activated receptors: roles in synaptic function and behavior. Brain Res. 2011; 1407:107-122. DOI: https://doi.org/10.1016/j.brainres.2011.06.042

14. Tripathi LP, Sowdhamini R. Genome-wide survey of prokaryotic serine proteases: Analysis of distribution and domain architectures of five serine protease families in prokaryotes. BMC Genomics. 2008; 9:549. DOI: https:// doi.org/10.1186/1471-2164-9-549

15. Dalva MB, McClelland AC, Kayser MS. Cell adhesion molecules: signalling functions at the synapse. Nat Rev Neurosci. 2007; 8:206-220. DOI: https://doi.org/10.1038/ nrn2075

16. Lee TW, Tsang VW, Birch NP. Synaptic plasticity-associated proteases and protease inhibitors in the brain linked to the processing of extracellular matrix and cell adhesion molecules. Neuron Glia Biol. 2008; 4(3):223-34. DOI: https://doi.org/10.1017/s1740925X09990172