Тип публикации: статья из журнала

Год издания: 2015

Идентификатор DOI: 10.1186/1752-0509-9-S2-S4

Ключевые слова: ANDSystem, Associative networks, Automated text analysis, Diabetes mellitus, Disease associome, Gestational diabetes, Obesity, Pre-eclampsia, Text-mining

Аннотация: Background: Pre-eclampsia is the most common complication occurring during pregnancy. In the majority of cases, it is concurrent with other pathologies in a comorbid manner (frequent co-occurrences in patients), such as diabetes mellitus, gestational diabetes and obesity. Providing bronchial asthma, pulmonary tuberculosis, certain Показать полностьюneurodegenerative diseases and cancers as examples, we have shown previously that pairs of inversely comorbid pathologies (rare co-occurrences in patients) are more closely related to each other at the molecular genetic level compared with randomly generated pairs of diseases. Data in the literature concerning the causes of pre-eclampsia are abundant. However, the key mechanisms triggering this disease that are initiated by other pathological processes are thus far unknown. The aim of this work was to analyse the characteristic features of genetic networks that describe interactions between comorbid diseases, using pre-eclampsia as a case in point. Results: The use of ANDSystem, Pathway Studio and STRING computer tools based on text-mining and database-mining approaches allowed us to reconstruct associative networks, representing molecular genetic interactions between genes, associated concurrently with comorbid disease pairs, including pre-eclampsia, diabetes mellitus, gestational diabetes and obesity. It was found that these associative networks statistically differed in the number of genes and interactions between them from those built for randomly chosen pairs of diseases. The associative network connecting all four diseases was composed of 16 genes (PLAT, ADIPOQ, ADRB3, LEPR, HP, TGFB1, TNFA, INS, CRP, CSRP1, IGFBP1, MBL2, ACE, ESR1, SHBG, ADA). Such an analysis allowed us to reveal differential gene risk factors for these diseases, and to propose certain, most probable, theoretical mechanisms of pre-eclampsia development in pregnant women. The mechanisms may include the following pathways: [TGFB1 or TNFA]-[IL1B]-[pre-eclampsia]; [TNFA or INS]-[NOS3]-[pre-eclampsia]; [INS]-[HSPA4 or CLU]-[pre-eclampsia]; [ACE]-[MTHFR]-[pre-eclampsia]. Conclusions: For pre-eclampsia, diabetes mellitus, gestational diabetes and obesity, we showed that the size and connectivity of the associative molecular genetic networks, which describe interactions between comorbid diseases, statistically exceeded the size and connectivity of those built for randomly chosen pairs of diseases. Recently, we have shown a similar result for inversely comorbid diseases. This suggests that comorbid and inversely comorbid diseases have common features concerning structural organization of associative molecular genetic networks. © 2015 Glotov et al.; licensee BioMed Central Ltd.

Журнал: BMC Systems Biology

Выпуск журнала: Vol. 9, Is. 2

• Glotov A.S. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation, Saint-Petersburg State University, St. Petersburg, Russian Federation)
• Tiys E.S. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)
• Vashukova E.S. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation)
• Pakin V.S. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation)
• Demenkov P.S. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)
• Saik O.V. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)
• Ivanisenko T.V. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)
• Arzhanova O.N. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation)
• Mozgovaya E.V. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation)
• Zainulina M.S. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation)
• Kolchanov N.A. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)
• Baranov V.S. (Gynecology and Reproductology named after D.O. Ott, Federal State Budget scientific Institution The Research Institute of Obstetrics, St. Petersburg, Russian Federation, Saint-Petersburg State University, St. Petersburg, Russian Federation)
• Ivanisenko V.A. (The Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation, Novosibirsk State University, Novosibirsk, Russian Federation)

• Scopus