Precision medicine — networks to the rescue. (June 2020)
- Record Type:
- Journal Article
- Title:
- Precision medicine — networks to the rescue. (June 2020)
- Main Title:
- Precision medicine — networks to the rescue
- Authors:
- Yadav, Anupama
Vidal, Marc
Luck, Katja - Abstract:
- Graphical abstract: Highlights: One gene-one disease paradigm rarely explains incomplete penetrance. Cellular systems of interacting molecules mediate genotype-to-phenotype relationships. Protein–protein interactions are being systematically mapped at proteome-scale. Protein–protein interaction networks help identify molecular disease mechanisms. Edgotyping emerges as a tool to characterize variants of uncertain significance. Abstract : Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of humanGraphical abstract: Highlights: One gene-one disease paradigm rarely explains incomplete penetrance. Cellular systems of interacting molecules mediate genotype-to-phenotype relationships. Protein–protein interactions are being systematically mapped at proteome-scale. Protein–protein interaction networks help identify molecular disease mechanisms. Edgotyping emerges as a tool to characterize variants of uncertain significance. Abstract : Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine. … (more)
- Is Part Of:
- Current opinion in biotechnology. Volume 63(2020)
- Journal:
- Current opinion in biotechnology
- Issue:
- Volume 63(2020)
- Issue Display:
- Volume 63, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 63
- Issue:
- 2020
- Issue Sort Value:
- 2020-0063-2020-0000
- Page Start:
- 177
- Page End:
- 189
- Publication Date:
- 2020-06
- Subjects:
- Biotechnology -- Periodicals
660.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09581669 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.copbio.2020.02.005 ↗
- Languages:
- English
- ISSNs:
- 0958-1669
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3500.772500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13511.xml