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Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain

BACKGROUND: An important issue in the target identification for the drug design is the tissue-specific effect of inhibition of target genes. The task of assessing the tissue-specific effect in suppressing gene activity is especially relevant in the studies of the brain, because a significant variabi...

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Autores principales: Petrovskiy, Evgeny D, Saik, Olga V, Tiys, Evgeny S, Lavrik, Inna N, Kolchanov, Nikolay A, Ivanisenko, Vladimir A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686796/
https://www.ncbi.nlm.nih.gov/pubmed/26693857
http://dx.doi.org/10.1186/1471-2164-16-S13-S3
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author Petrovskiy, Evgeny D
Saik, Olga V
Tiys, Evgeny S
Lavrik, Inna N
Kolchanov, Nikolay A
Ivanisenko, Vladimir A
author_facet Petrovskiy, Evgeny D
Saik, Olga V
Tiys, Evgeny S
Lavrik, Inna N
Kolchanov, Nikolay A
Ivanisenko, Vladimir A
author_sort Petrovskiy, Evgeny D
collection PubMed
description BACKGROUND: An important issue in the target identification for the drug design is the tissue-specific effect of inhibition of target genes. The task of assessing the tissue-specific effect in suppressing gene activity is especially relevant in the studies of the brain, because a significant variability in gene expression levels among different areas of the brain was well documented. RESULTS: A method is proposed for constructing statistical models to predict the potential effect of the knockout of target genes on the expression of genes involved in the regulation of apoptosis in various brain regions. The model connects the expression of the objective group of genes with expression of the target gene by means of machine learning models trained on available expression data. Information about the interactions between target and objective genes is determined by reconstruction of target-centric gene network. STRING and ANDSystem databases are used for the reconstruction of gene networks. The developed models have been used to analyse gene knockout effects of more than 7,500 target genes on the expression of 1,900 objective genes associated with the Gene Ontology category "apoptotic process". The tissue-specific effect was calculated for 12 main anatomical structures of the human brain. Initial values of gene expression in these anatomical structures were taken from the Allen Brain Atlas database. The results of the predictions of the effect of suppressing the activity of target genes on apoptosis, calculated on average for all brain structures, were in good agreement with experimental data on siRNA-inhibition. CONCLUSIONS: This theoretical paper presents an approach that can be used to assess tissue-specific gene knockout effect on gene expression of the studied biological process in various structures of the brain. Genes that, according to the predictions of the model, have the highest values of tissue-specific effects on the apoptosis network can be considered as potential pharmacological targets for the development of drugs that would potentially have strong effect on the specific area of the brain and a much weaker effect on other brain structures. Further experiments should be provided in order to confirm the potential findings of the method.
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spelling pubmed-46867962015-12-31 Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain Petrovskiy, Evgeny D Saik, Olga V Tiys, Evgeny S Lavrik, Inna N Kolchanov, Nikolay A Ivanisenko, Vladimir A BMC Genomics Research BACKGROUND: An important issue in the target identification for the drug design is the tissue-specific effect of inhibition of target genes. The task of assessing the tissue-specific effect in suppressing gene activity is especially relevant in the studies of the brain, because a significant variability in gene expression levels among different areas of the brain was well documented. RESULTS: A method is proposed for constructing statistical models to predict the potential effect of the knockout of target genes on the expression of genes involved in the regulation of apoptosis in various brain regions. The model connects the expression of the objective group of genes with expression of the target gene by means of machine learning models trained on available expression data. Information about the interactions between target and objective genes is determined by reconstruction of target-centric gene network. STRING and ANDSystem databases are used for the reconstruction of gene networks. The developed models have been used to analyse gene knockout effects of more than 7,500 target genes on the expression of 1,900 objective genes associated with the Gene Ontology category "apoptotic process". The tissue-specific effect was calculated for 12 main anatomical structures of the human brain. Initial values of gene expression in these anatomical structures were taken from the Allen Brain Atlas database. The results of the predictions of the effect of suppressing the activity of target genes on apoptosis, calculated on average for all brain structures, were in good agreement with experimental data on siRNA-inhibition. CONCLUSIONS: This theoretical paper presents an approach that can be used to assess tissue-specific gene knockout effect on gene expression of the studied biological process in various structures of the brain. Genes that, according to the predictions of the model, have the highest values of tissue-specific effects on the apoptosis network can be considered as potential pharmacological targets for the development of drugs that would potentially have strong effect on the specific area of the brain and a much weaker effect on other brain structures. Further experiments should be provided in order to confirm the potential findings of the method. BioMed Central 2015-12-16 /pmc/articles/PMC4686796/ /pubmed/26693857 http://dx.doi.org/10.1186/1471-2164-16-S13-S3 Text en Copyright © 2015 Petrovskiy et al. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
spellingShingle Research
Petrovskiy, Evgeny D
Saik, Olga V
Tiys, Evgeny S
Lavrik, Inna N
Kolchanov, Nikolay A
Ivanisenko, Vladimir A
Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title_full Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title_fullStr Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title_full_unstemmed Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title_short Prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
title_sort prediction of tissue-specific effects of gene knockout on apoptosis in different anatomical structures of human brain
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686796/
https://www.ncbi.nlm.nih.gov/pubmed/26693857
http://dx.doi.org/10.1186/1471-2164-16-S13-S3
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