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Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project

Abstract

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patients with kidney disease. Ontologies are powerful tools that facilitate these efforts by enabling researchers to organize and make sense of different data elements and the relationships between them. Ontologies are critical to support the types of big data analysis necessary for kidney precision medicine, where heterogeneous clinical, imaging and biopsy data from diverse sources must be combined to define a patient’s phenotype. The development of two new ontologies — the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation — will support the creation of the Kidney Tissue Atlas, which aims to provide a comprehensive molecular, cellular and anatomical map of the kidney. These ontologies will improve the annotation of kidney-relevant data, and eventually lead to new definitions of kidney disease in support of precision medicine.

Key points

  • Ontologies are powerful tools for organizing, integrating and linking heterogeneous data types, especially in the biomedical sciences.

  • Significant additions to biomedical ontologies are necessary to better define kidney molecular and histopathological phenotypes, which is critical for kidney precision medicine.

  • The Kidney Precision Medicine Project is creating a community-based Kidney Tissue Atlas to integrate molecular, cellular and anatomical knowledge of the kidney.

  • The development of the Kidney Tissue Atlas Ontology and the Ontology of Precision Medicine and Investigation will facilitate data collection, harmonization and analysis in support of kidney precision medicine.

  • The Kidney Precision Medicine Project has extensively adopted, reused and extended community-based reference ontologies to support the annotation of kidney data.

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Fig. 1: Overview of KPMP centres and the flow of KPMP data from different provenances.
Fig. 2: The KPMP ontology framework for supporting data representation, integration and analysis.
Fig. 3: Using the Kidney Tissue Atlas Ontology to support molecular and histopathological extensions to kidney disease diagnosis.

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Acknowledgements

The KPMP project is supported by the NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) U2C project 1U2CDK114886. The authors appreciate the discussion with, editing by and support from Deborah Hoshizaki from the NIDDK and the KPMP consortium.

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Contributions

This review was coordinated by the KPMP, with S.M. and Y.H. providing conceptual guidance. E.O., J.S., J.F.O., B.S., F.D., L.B., S.J., C.P., S.M. and Y.H. contributed to the design, development, coordination and application of the KPMP ontologies. E.O., L.L.W., J.S., J.F.O., S.M. and Y.H. wrote the manuscript with support from B.S., S.J., I.H.D., S.S.W., D.C.C., C.S., C.W., A.D.D., C.J.M. and M.K. J.S., J.F.O., S.J., I.H.D., M.T.V., S.S.W., J. Himmelfarb and M.K. provided guidance on clinical nephrology, A.Z.R. and L.B. provided guidance on pathology, B.S. and F.D. provided guidance on data systems, D.C.C., T.A., C.R.A., C.S. and R.I. provided guidance on omics analyses and J. Hansen, C.W., A.D.D., C.M., M.H. and P.N.R. provided guidance on reference ontologies. All authors reviewed and approved the manuscript.

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Correspondence to Sean Mooney or Yongqun He.

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Nature Reviews Nephrology thanks B. Smith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Allen Brain Atlas: https://portal.brain-map.org/

Encyclopedia of DNA Elements (ENCODE) project: https://www.encodeproject.org/

Human BioMolecular Atlas Program: https://commonfund.nih.gov/hubmap

Human Cell Atlas: https://www.humancellatlas.org/

International Classification of Diseases (ICD): https://www.who.int/classifications/icd/en/

Kidney Precision Medicine Project: https://kpmp.org/

Kidney Tissue Atlas data portal: https://atlas.kpmp.org/

Library of Integrated Network-Based Cellular Signatures: http://www.lincsproject.org/

Ontology of General Medical Science: https://github.com/OGMS/ogms

Ontology of Genes and Genomes: http://www.obofoundry.org/ontology/ogg.html

Open Biological and Biomedical Ontology (OBO) Foundry: http://www.obofoundry.org/

Systematized Nomenclature of Medicine (SNOMED): https://www.snomed.org/snomed-ct/

The Cancer Genome Atlas: https://www.cancer.gov/tcga

Supplementary information

Glossary

Natural language

A language that has evolved naturally in humans and is used in speech or writing, as opposed to a constructed or formal language.

Logical expressions

A programmatic construct that expresses logical operations over mathematical terms or entities, which allows a computer to reason over the entities in the expression.

Controlled vocabularies

A way to organize knowledge for retrieval; comprises a set of selected terms used for document indexing and information retrieval.

Taxonomies

Controlled vocabularies that have a hierarchical structure indicating subclass relationships between entities.

Fuzzy matching

A technique that identifies the correspondence among phenotypic profiles that may be less than 100% perfect.

Causal reasoning

The process used to identify the causality (cause and effect) between two entities.

Case report forms

A document (paper or electronic) containing a questionnaire used for clinical research or other purposes.

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Ong, E., Wang, L.L., Schaub, J. et al. Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project. Nat Rev Nephrol 16, 686–696 (2020). https://doi.org/10.1038/s41581-020-00335-w

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