A computational framework to find novel therapeutics for autoimmunity
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Abstract
The immune system protects a host from foreign pathogens. In rare cases, the immune system can attack the cells of the host organism causing autoimmune diseases. We outline a computational framework that combines bioinformatics and network analysis with an emerging targets platform. The computational framework presented here can be used to find drug targets for autoimmune diseases. It can also be used to find existing drugs that can be repurposed to treat autoimmune diseases based on networks of interactions or similarities between different diseases. Information on which gene regions are associated with the disease (single nucleotide polymorphisms) can be used in gene therapy when that technique becomes viable. Our analysis also revealed immune cell subtypes that are implicated in these diseases. These immune cell subtypes can be selected for immunotherapy experiments. Finally, our analysis also reveals intra-cellular and protein-protein interaction networks and pathways that can be targeted with small molecule inhibitors. The downstream off-target effects of these inhibitors can also be determined from such a network analysis. In summary, our computational framework can be used to find novel therapeutics for autoimmune diseases and potentially even other dysfunctions.
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2017. A computational framework to find novel therapeutics for autoimmunity. PeerJ Preprints 5:e3217v3 https://doi.org/10.7287/peerj.preprints.3217v3Author comment
Title changed; Tables revised
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Soumya Banerjee conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The research in this article did not generate any data or code (please explain).
Funding
The authors received no funding for this work.
