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Association between ALS and retroviruses: evidence from bioinformatics analysis

BACKGROUND: Emerging evidence suggests retroviruses play a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Specifically, activation of ancient viral genes embedded in the human genome is theorized to lead to motor neuron degeneration. We explore whether connections exist between...

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Autores principales: Klein, Jon P., Sun, Zhifu, Staff, Nathan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923964/
https://www.ncbi.nlm.nih.gov/pubmed/31861978
http://dx.doi.org/10.1186/s12859-019-3249-8
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author Klein, Jon P.
Sun, Zhifu
Staff, Nathan P.
author_facet Klein, Jon P.
Sun, Zhifu
Staff, Nathan P.
author_sort Klein, Jon P.
collection PubMed
description BACKGROUND: Emerging evidence suggests retroviruses play a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Specifically, activation of ancient viral genes embedded in the human genome is theorized to lead to motor neuron degeneration. We explore whether connections exist between ALS and retroviruses through protein interaction networks (PIN) and pathway analysis, and consider the potential roles in drug target discovery. Protein database and pathway/network analytical software including Ingenuity Pathway BioProfiler, STRING, and CytoScape were utilized to identify overlapping protein interaction networks and extract core cluster (s) of retroviruses and ALS. RESULTS: Topological and statistical analysis of the ALS-PIN and retrovirus-PIN identified a shared, essential protein network and a core cluster with significant connections with both networks. The identified core cluster has three interleukin molecules IL10, Il-6 and IL-1B, a central apoptosis regulator TP53, and several major transcription regulators including MAPK1, ANXA5, SQSTM1, SREBF2, and FADD. Pathway enrichment analysis showed that this core cluster is associated with the glucocorticoid receptor singling and neuroinflammation signaling pathways. For confirmation purposes, we applied the same methodology to the West Nile and Polio virus, which demonstrated trivial connectivity with ALS, supporting the unique connection between ALS and retroviruses. CONCLUSIONS: Bioinformatics analysis provides evidence to support pathological links between ALS and retroviral activation. The neuroinflammation and apoptotic regulation pathways are specifically implicated. The continuation and further analysis of large scale genome studies may prove useful in exploring genes important in retroviral activation and ALS, which may help discover new drug targets.
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spelling pubmed-69239642019-12-30 Association between ALS and retroviruses: evidence from bioinformatics analysis Klein, Jon P. Sun, Zhifu Staff, Nathan P. BMC Bioinformatics Research BACKGROUND: Emerging evidence suggests retroviruses play a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Specifically, activation of ancient viral genes embedded in the human genome is theorized to lead to motor neuron degeneration. We explore whether connections exist between ALS and retroviruses through protein interaction networks (PIN) and pathway analysis, and consider the potential roles in drug target discovery. Protein database and pathway/network analytical software including Ingenuity Pathway BioProfiler, STRING, and CytoScape were utilized to identify overlapping protein interaction networks and extract core cluster (s) of retroviruses and ALS. RESULTS: Topological and statistical analysis of the ALS-PIN and retrovirus-PIN identified a shared, essential protein network and a core cluster with significant connections with both networks. The identified core cluster has three interleukin molecules IL10, Il-6 and IL-1B, a central apoptosis regulator TP53, and several major transcription regulators including MAPK1, ANXA5, SQSTM1, SREBF2, and FADD. Pathway enrichment analysis showed that this core cluster is associated with the glucocorticoid receptor singling and neuroinflammation signaling pathways. For confirmation purposes, we applied the same methodology to the West Nile and Polio virus, which demonstrated trivial connectivity with ALS, supporting the unique connection between ALS and retroviruses. CONCLUSIONS: Bioinformatics analysis provides evidence to support pathological links between ALS and retroviral activation. The neuroinflammation and apoptotic regulation pathways are specifically implicated. The continuation and further analysis of large scale genome studies may prove useful in exploring genes important in retroviral activation and ALS, which may help discover new drug targets. BioMed Central 2019-12-20 /pmc/articles/PMC6923964/ /pubmed/31861978 http://dx.doi.org/10.1186/s12859-019-3249-8 Text en © The Author(s). 2019 Open AccessThis 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.
spellingShingle Research
Klein, Jon P.
Sun, Zhifu
Staff, Nathan P.
Association between ALS and retroviruses: evidence from bioinformatics analysis
title Association between ALS and retroviruses: evidence from bioinformatics analysis
title_full Association between ALS and retroviruses: evidence from bioinformatics analysis
title_fullStr Association between ALS and retroviruses: evidence from bioinformatics analysis
title_full_unstemmed Association between ALS and retroviruses: evidence from bioinformatics analysis
title_short Association between ALS and retroviruses: evidence from bioinformatics analysis
title_sort association between als and retroviruses: evidence from bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923964/
https://www.ncbi.nlm.nih.gov/pubmed/31861978
http://dx.doi.org/10.1186/s12859-019-3249-8
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