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Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug r...

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Autores principales: Pain, Oliver, Jones, Ashley, Al Khleifat, Ahmad, Agarwal, Devika, Hramyka, Dzmitry, Karoui, Hajer, Kubica, Jędrzej, Llewellyn, David J., Ranson, Janice M., Yao, Zhi, Iacoangeli, Alfredo, Al-Chalabi, Ammar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901068/
https://www.ncbi.nlm.nih.gov/pubmed/36747854
http://dx.doi.org/10.1101/2023.01.18.23284589
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author Pain, Oliver
Jones, Ashley
Al Khleifat, Ahmad
Agarwal, Devika
Hramyka, Dzmitry
Karoui, Hajer
Kubica, Jędrzej
Llewellyn, David J.
Ranson, Janice M.
Yao, Zhi
Iacoangeli, Alfredo
Al-Chalabi, Ammar
author_facet Pain, Oliver
Jones, Ashley
Al Khleifat, Ahmad
Agarwal, Devika
Hramyka, Dzmitry
Karoui, Hajer
Kubica, Jędrzej
Llewellyn, David J.
Ranson, Janice M.
Yao, Zhi
Iacoangeli, Alfredo
Al-Chalabi, Ammar
author_sort Pain, Oliver
collection PubMed
description INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R(2) = 4%; p-value = 2.1×10(−21)). CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.
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spelling pubmed-99010682023-02-07 Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction Pain, Oliver Jones, Ashley Al Khleifat, Ahmad Agarwal, Devika Hramyka, Dzmitry Karoui, Hajer Kubica, Jędrzej Llewellyn, David J. Ranson, Janice M. Yao, Zhi Iacoangeli, Alfredo Al-Chalabi, Ammar medRxiv Article INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. METHODS: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. RESULTS: SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R(2) = 4%; p-value = 2.1×10(−21)). CONCLUSIONS: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk. Cold Spring Harbor Laboratory 2023-01-24 /pmc/articles/PMC9901068/ /pubmed/36747854 http://dx.doi.org/10.1101/2023.01.18.23284589 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Pain, Oliver
Jones, Ashley
Al Khleifat, Ahmad
Agarwal, Devika
Hramyka, Dzmitry
Karoui, Hajer
Kubica, Jędrzej
Llewellyn, David J.
Ranson, Janice M.
Yao, Zhi
Iacoangeli, Alfredo
Al-Chalabi, Ammar
Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title_full Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title_fullStr Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title_full_unstemmed Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title_short Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction
title_sort harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901068/
https://www.ncbi.nlm.nih.gov/pubmed/36747854
http://dx.doi.org/10.1101/2023.01.18.23284589
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