ArticleAnnals of the rheumatic diseases2019
Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases.
Article in Annals of the rheumatic diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Discovery of therapeutic targets in cardiovascular diseases using high-throughput chromosome conformation capture (Hi-C).Frontiers in genetics · 2025Review
- Identification of Mechanisms by Which Genetic Susceptibility Loci Influence Systemic Sclerosis Risk Using Functional Genomics in Primary T Cells and Monocytes.Arthritis & rheumatology (Hoboken, N.J.) · 2023Article
- 3D genome organization and epigenetic regulation in autoimmune diseases.Frontiers in immunology · 2023Review
- Fine mapping with epigenetic information and 3D structure.Seminars in immunopathology · 2022Review
- Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools.Nature protocols · 2021Article
- Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability.Genome biology · 2021Article
- Chromatin Looping Links Target Genes with Genetic Risk Loci for Dermatological Traits.The Journal of investigative dermatology · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Article
- Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques.Therapeutic advances in musculoskeletal disease · 2021Article
- Approaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics.Genes · 2020Review
- Using functional genomics to advance the understanding of psoriatic arthritis.Rheumatology (Oxford, England) · 2020Review
- Functional genomics in autoimmune diseases.Human molecular genetics · 2020Review
- Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis.Nature communications · 2020Article
- Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.Nature reviews. Rheumatology · 2020Review
- Towards a Better Classification and Novel Therapies Based on the Genetics of Systemic Sclerosis.Current rheumatology reports · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
objectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three 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 cells 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 using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the 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 repositioning.
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 data sets are generated, this prospect will improve further.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.