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. 2022 Jan 12;23(1):37.
doi: 10.1186/s12859-021-04530-9.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Jeremy J Yang  1   2   3 , Christopher R Gessner  1   4 , Joel L Duerksen  2 , Daniel Biber  2 , Jessica L Binder  3 , Murat Ozturk  1   2 , Brian Foote  2 , Robin McEntire  2 , Kyle Stirling  1   2 , Ying Ding  1   2   5 , David J Wild  6   7
Affiliations

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Jeremy J Yang et al. BMC Bioinformatics. .

Abstract

Background: LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.

Results: Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.

Conclusions: The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Keywords: Drug discovery; Drug target; Druggable genome; Graph analytics; Knowledge graph; Parkinson's disease; Systems biology.

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Conflict of interest statement

JY, JD, BF, DB, KS, YD, and DW are founders, employees or contractors of Data2Discovery, a private company spun off from Indiana University to develop and commercialize knowledge graph technologies.

Figures

Fig. 1
Fig. 1
Schematic of overall logic, that strong knowledge of approved drugs and cell lines associate diseases via LINCS expression signatures to differentially expressed genes, for IDG filtering and druggability evaluation
Fig. 2
Fig. 2
a–d ROC curves with AUC for degree-only and z-score weighted evidence path graph analytics, validated against DrugCentral PD targets and known-with-MoA targets
Fig. 3
Fig. 3
a and b TIN-X scatterplot of genes for Parkinson's disease, DOID:14330, showing pop-up details for SYNGR3, Synaptogyrin-3, and publication details view for PD associated gene SYNGR3
Fig. 4
Fig. 4
Evidence paths generated from Cypher queries on our Neo4j graph database, for case study SYNGR3, showing associated expression signatures and drugs
Fig. 5
Fig. 5
Neo4j meta graph. Both nodes and relationships have properties which can be used in query filtering and analysis
Fig. 6
Fig. 6
Composite image combining command line executing KGAP for "Parkinson", output data table, and example ROC plot
See this image and copyright information in PMC

References

    1. National Institutes of Health, U.S. Department of Health and Human Services. National Institutes of Health Common Fund [Internet]. NIH Office of Strategic Coordination—The Common Fund. [cited 2020 Dec 21]. https://commonfund.nih.gov/.
    1. Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E, Torre D, et al. The library of integrated network-based cellular signatures NIH program: System-level cataloging of human cells response to perturbations. Cell Syst. 2018;6(1):13–24. doi: 10.1016/j.cels.2017.11.001. - DOI - PMC - PubMed
    1. Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, et al. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov. 2018;17(5):377. doi: 10.1038/nrd.2018.52. - DOI - PubMed
    1. Sheils T, Mathias SL, Siramshetty VB, Bocci G, Bologa CG, Yang JJ, et al. How to illuminate the druggable genome using pharos. Curr Protoc Bioinform. 2020;69(1):92. doi: 10.1002/cpbi.92. - DOI - PMC - PubMed
    1. Avram S, Bologa CG, Holmes J, Bocci G, Wilson TB, Nguyen D-T, DrugCentral, , et al. supports drug discovery and repositioning. Nucleic Acids Res. 2021 doi: 10.1093/nar/gkaa997. - DOI - PMC - PubMed