Department of Psychiatry
Cardiovascular Diseases | Genetics and Genomics | Genomics | Medical Genetics | Respiratory Tract Diseases | Therapeutics
BACKGROUND: Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets.
RESULTS: Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.
CONCLUSIONS: The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.
ANDSystem, Apoptosis, Associative gene networks, Asthma, Central nervous system, Comorbidity, Gene prioritization, Hypertension
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© The Author(s). 2018. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
DOI of Published Version
BMC Med Genomics. 2018 Feb 13;11(Suppl 1):15. doi: 10.1186/s12920-018-0331-4. Link to article on publisher's site
BMC medical genomics
Saik, Olga V.; Demenkov, Pavel S.; Ivanisenko, Timofey V.; Bragina, Elena Yu.; Freidin, Maxim B.; Goncharova, Irina A.; Dosenko, Victor E.; Zolotareva, Olga I.; Hofestaedt, Ralf; Lavrik, Inna N.; Rogaev, Evgeny I.; and Ivanisenko, Vladimir A., "Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks" (2018). Open Access Articles. 3395.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.