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In-silico identification and prioritization of therapeutic targets of asthma
Asthma is a “common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asth...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514284/ https://www.ncbi.nlm.nih.gov/pubmed/37735578 http://dx.doi.org/10.1038/s41598-023-42803-w |
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author | Mallick, Ishita Panchal, Pradnya Kadam, Smita Mohite, Priyanka Scheele, Jürgen Seiz, Werner Agarwal, Amit Sharma, Om Prakash |
author_facet | Mallick, Ishita Panchal, Pradnya Kadam, Smita Mohite, Priyanka Scheele, Jürgen Seiz, Werner Agarwal, Amit Sharma, Om Prakash |
author_sort | Mallick, Ishita |
collection | PubMed |
description | Asthma is a “common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asthma and the response to treatment" [cited from: National Heart, Lung, and Blood Institute. Expert Panel 3 Report. Guidelines for the Diagnosis and Management of Asthma 2007 (EPR-3). Available at: https://www.ncbi.nlm.nih.gov/books/NBK7232/ (accessed on January 3, 2023)]. As per the WHO, 262 million people were affected by asthma in 2019 that leads to 455,000 deaths (https://www.who.int/news-room/fact-sheets/detail/asthma). In this current study, our aim was to evaluate thousands of scientific documents and asthma associated omics datasets to identify the most crucial therapeutic target for experimental validation. We leveraged the proprietary tool Ontosight(®) Discover to annotate asthma associated genes and proteins. Additionally, we also collected and evaluated asthma related patient datasets through bioinformatics and machine learning based approaches to identify most suitable targets. Identified targets were further evaluated based on the various biological parameters to scrutinize their candidature for the ideal therapeutic target. We identified 7237 molecular targets from published scientific documents, 2932 targets from genomic structured databases and 7690 dysregulated genes from the transcriptomics and 560 targets from genomics mutational analysis. In total, 18,419 targets from all the desperate sources were analyzed and evaluated though our approach to identify most promising targets in asthma. Our study revealed IL-13 as one of the most important targets for asthma with approved drugs on the market currently. TNF, VEGFA and IL-18 were the other top targets identified to be explored for therapeutic benefit in asthma but need further clinical testing. HMOX1, ITGAM, DDX58, SFTPD and ADAM17 were the top novel targets identified for asthma which needs to be validated experimentally. |
format | Online Article Text |
id | pubmed-10514284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105142842023-09-23 In-silico identification and prioritization of therapeutic targets of asthma Mallick, Ishita Panchal, Pradnya Kadam, Smita Mohite, Priyanka Scheele, Jürgen Seiz, Werner Agarwal, Amit Sharma, Om Prakash Sci Rep Article Asthma is a “common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asthma and the response to treatment" [cited from: National Heart, Lung, and Blood Institute. Expert Panel 3 Report. Guidelines for the Diagnosis and Management of Asthma 2007 (EPR-3). Available at: https://www.ncbi.nlm.nih.gov/books/NBK7232/ (accessed on January 3, 2023)]. As per the WHO, 262 million people were affected by asthma in 2019 that leads to 455,000 deaths (https://www.who.int/news-room/fact-sheets/detail/asthma). In this current study, our aim was to evaluate thousands of scientific documents and asthma associated omics datasets to identify the most crucial therapeutic target for experimental validation. We leveraged the proprietary tool Ontosight(®) Discover to annotate asthma associated genes and proteins. Additionally, we also collected and evaluated asthma related patient datasets through bioinformatics and machine learning based approaches to identify most suitable targets. Identified targets were further evaluated based on the various biological parameters to scrutinize their candidature for the ideal therapeutic target. We identified 7237 molecular targets from published scientific documents, 2932 targets from genomic structured databases and 7690 dysregulated genes from the transcriptomics and 560 targets from genomics mutational analysis. In total, 18,419 targets from all the desperate sources were analyzed and evaluated though our approach to identify most promising targets in asthma. Our study revealed IL-13 as one of the most important targets for asthma with approved drugs on the market currently. TNF, VEGFA and IL-18 were the other top targets identified to be explored for therapeutic benefit in asthma but need further clinical testing. HMOX1, ITGAM, DDX58, SFTPD and ADAM17 were the top novel targets identified for asthma which needs to be validated experimentally. Nature Publishing Group UK 2023-09-21 /pmc/articles/PMC10514284/ /pubmed/37735578 http://dx.doi.org/10.1038/s41598-023-42803-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mallick, Ishita Panchal, Pradnya Kadam, Smita Mohite, Priyanka Scheele, Jürgen Seiz, Werner Agarwal, Amit Sharma, Om Prakash In-silico identification and prioritization of therapeutic targets of asthma |
title | In-silico identification and prioritization of therapeutic targets of asthma |
title_full | In-silico identification and prioritization of therapeutic targets of asthma |
title_fullStr | In-silico identification and prioritization of therapeutic targets of asthma |
title_full_unstemmed | In-silico identification and prioritization of therapeutic targets of asthma |
title_short | In-silico identification and prioritization of therapeutic targets of asthma |
title_sort | in-silico identification and prioritization of therapeutic targets of asthma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514284/ https://www.ncbi.nlm.nih.gov/pubmed/37735578 http://dx.doi.org/10.1038/s41598-023-42803-w |
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