Biology subjects

MacNamara, A.

Publications and source records attributed to MacNamara, A..

2 recordsLinked to original sources

bioRxiv2024

Data- and knowledge-derived functional landscape of human solute carriers

Research on the understudied solute carrier (SLC) superfamily of membrane transporters would greatly profit from a comprehensive knowledgebase, synthesizing data and knowledge on different aspects of SLC function. We consolidated multi-omics data sets with selected curated information from the public domain, such as structure prediction, substrate annotation, disease association and subcellular localization. This SLC-centric knowledge is made accessible to the scientific community via a web portal, featuring interactive dashboards and a tool for family-wide, tree-based visualization of SLC properties. Making use of the systematically collected and curated data sets, we selected eight feature-dimensions to compute an integrated functional landscape of human SLCs. This landscape represents various functional aspects, harmonizing local and global features of the underlying data sets, as demonstrated by inspecting structural folds and subcellular locations of selected transporters. Based on all available data sets and their integration, we assigned a biochemical/biological function to each SLC, making it one of the largest systematic annotations of human gene function and likely acting as a blueprint for future endeavours.

systems biology↗

bioRxiv2020

Network and pathway expansion of genetic disease associations identifies successful drug targets

It is widely accepted that genetic evidence of disease association acts as a sound basis for the selection of drug targets for complex common diseases and that propagation of genetic evidence through gene or protein interaction networks can accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these beliefs for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naive guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence should be used for drug target identification.

genetics↗