Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseasesCitation formats

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Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases. / Martin, Paul; Ding, James; Duffus, Kate; Gaddi, Vasanthipriyadarshini; Mcgovern, Amanda; Ray-Jones, Helen; Yarwood, Annie; Worthington, Jane; Barton, Anne; Orozco, Gisela.

In: Annals Of Rheumatic Diseases, Vol. 78, 15.05.2019, p. 1127-1134.

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@article{f545b7a31d9844c3acffaabb42ef00bc,
title = "Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases",
abstract = "ObjectivesThere is a need to identify effective treatments for rheumatic diseases and whilst genetic studies have been successful, it is unclear which genes contribute to disease. Using our existing Capture Hi-C data on 3 rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined utilising Ingenuity Pathway Analysis to identify enriched pathways and therefore, further treatments, relevant to disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential re-positioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic datasets are generated, this prospect will improve further.",
keywords = "GWAS, drug repositioning, functional genomics, rheumatic diseases",
author = "Paul Martin and James Ding and Kate Duffus and Vasanthipriyadarshini Gaddi and Amanda Mcgovern and Helen Ray-Jones and Annie Yarwood and Jane Worthington and Anne Barton and Gisela Orozco",
year = "2019",
month = may,
day = "15",
doi = "10.1136/annrheumdis-2018-214649",
language = "English",
volume = "78",
pages = "1127--1134",
journal = "Annals of the rheumatic diseases",
issn = "0003-4967",
publisher = "BMJ ",

}

RIS

TY - JOUR

T1 - Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

AU - Martin, Paul

AU - Ding, James

AU - Duffus, Kate

AU - Gaddi, Vasanthipriyadarshini

AU - Mcgovern, Amanda

AU - Ray-Jones, Helen

AU - Yarwood, Annie

AU - Worthington, Jane

AU - Barton, Anne

AU - Orozco, Gisela

PY - 2019/5/15

Y1 - 2019/5/15

N2 - ObjectivesThere is a need to identify effective treatments for rheumatic diseases and whilst genetic studies have been successful, it is unclear which genes contribute to disease. Using our existing Capture Hi-C data on 3 rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined utilising Ingenuity Pathway Analysis to identify enriched pathways and therefore, further treatments, relevant to disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential re-positioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic datasets are generated, this prospect will improve further.

AB - ObjectivesThere is a need to identify effective treatments for rheumatic diseases and whilst genetic studies have been successful, it is unclear which genes contribute to disease. Using our existing Capture Hi-C data on 3 rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined utilising Ingenuity Pathway Analysis to identify enriched pathways and therefore, further treatments, relevant to disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential re-positioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic datasets are generated, this prospect will improve further.

KW - GWAS

KW - drug repositioning

KW - functional genomics

KW - rheumatic diseases

U2 - 10.1136/annrheumdis-2018-214649

DO - 10.1136/annrheumdis-2018-214649

M3 - Article

VL - 78

SP - 1127

EP - 1134

JO - Annals of the rheumatic diseases

JF - Annals of the rheumatic diseases

SN - 0003-4967

ER -