=Paper=
{{Paper
|id=Vol-2285/ICBO_2018_paper_28
|storemode=property
|title=KTAO: A Kidney Tissue Atlas Ontology to Support Community-Based Kidney Knowledge Base Development and Data Integration
|pdfUrl=https://ceur-ws.org/Vol-2285/ICBO_2018_paper_28.pdf
|volume=Vol-2285
|authors=Yongqun He,Becky Steck,Edison Ong,Laura Mariani,Chrysta Lienczewski,Ulysses Balis,Matthias Kretzler,Jonathan Himmelfarb,John F. Bertram,Evren Azeloglu,Ravi Iyengar,Deborah Hoshizaki,Sean D. Mooney
|dblpUrl=https://dblp.org/rec/conf/icbo/HeSOMLBKHBAIHM18
}}
==KTAO: A Kidney Tissue Atlas Ontology to Support Community-Based Kidney Knowledge Base Development and Data Integration==
https://ceur-ws.org/Vol-2285/ICBO_2018_paper_28.pdf
Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 1
KTAO: A kidney tissue atlas ontology to support
community-based kidney knowledge base
development and data integration
Yongqun He1, Becky Steck1, Edison Ong1, Laura Mariani1, Chrysta Lienczewski1, Ulysses Balis1, Matthias Kretzler1,
Jonathan Himmelfarb2, John F. Bertram3, Evren Azeloglu4, Ravi Iyengar4, Deborah Hoshizaki5, Sean D. Mooney2, for
the KPMP Consortium
1
University of Michigan Medical School, Ann Arbor, MI 48109, USA; 2 University of Washington, Seattle, WA 98195, USA;
3
Monash University, Clayton, Victoria 3800, Australia; 4 Icahn School of Medicine at Mount Sinai, NY 10029, USA; 5 National
Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
Abstract—The human kidney has a complex structure and molecular-level. A huge amount of data will be generated in
diverse interactions among its cells and cell components, both KPMP; one major KPMP challenge is how to systematically
during homeostasis and in its diseased states. To better integrate, store, share, and analyze this large volume of data.
understand the kidney, it is critical to systematically classify,
represent, and integrate kidney gene activities, cell types, cell In the era of big data and precision medicine, ontologies are
states, and interstitial components. Toward this goal, we widely used in biomedical data and metadata standardization,
developed a Kidney Tissue Atlas Ontology (KTAO). KTAO and robustly support data integration, sharing, and analysis. A
reuses and aligns with existing ontologies such as the Cell biomedical ontology is a set of computer- and human-
Ontology, UBERON, and Human Phenotype Ontology. KTAO interpretable terms for entities and relations among the entities
also generates new semantic axioms to logically link terms of in a specific biomedical domain. Hundreds of biomedical
entities in different domains. As a first study, KTAO represents ontologies have been used in the past two decades. Together
over 200 known kidney gene markers and their profiles in they have greatly supported the state-of-the-art biomedical
different cell types in kidney patients. Such a representation research and clinical studies.
supports kidney knowledge base generation, query, and data
integration. By working with the nephrology and ontology communities,
we have developed a community-driven Kidney Tissue Atlas
Keywords—Kidney; atlas; ontology; KTAO; disease; AKI; Ontology (KTAO), with the aim of systematically representing
CKD; gene marker. and integrating different components, cell types, and cell states
of the kidney. Here we report the KTAO development strategy
I. INTRODUCTION and how it can be used to support kidney knowledge base
generation, kidney atlas data standardization and integration,
Kidney diseases pose a major threat to human health. and predictive data analysis to support translational kidney
Human acute kidney injury (AKI) is a sudden and temporary research.
loss of kidney function. Chronic kidney disease (CKD) causes
reduced kidney function over a period of time. CKDs may
develop over many years and lead to end-stage kidney disease. II. METHODS
The prevalence of CKD in the general population is
approximately 14 percent [1]. Almost half of patients with A. KTAO ontology development strategy
CKD also have diabetes and/or self-reported cardiovascular The KTAO development follows the ontology development
disease (CVD) [2]. Most kidney diseases have complex principles (e.g., openness and collaboration) initiated and
pathogenesis involving the interactions among genetic and promoted by the Open Biological and Biomedical Ontologies
environmental factors. While extensive research performed and (OBO) Foundry [6]. Kidney domain experts and ontologists
much progress made, the origins and progression of kidney worked together to generate consensus on KTAO aims,
diseases are not yet fully understood, preventing effective and methods, and content.
rational design of therapeutic measures against many kidney
The KTAO development uses a combination of top-down
diseases [3-5].
and bottom-up methods [7]. Specifically, to avoid reinventing
The Kidney Precision Medicine Project (KPMP) is an NIH- the wheel, the top-down approach is initiated by aligning and
funded precision medicine project aimed at finding new ways extending KTAO from the latest version of existing reliable
to treat human AKI and CKD. The KPMP consortium includes ontologies. The bottom-up strategy is primarily achieved
six recruitment sites to enroll and biopsy human subjects with through specific case applications where new terms and
CKD or AKI, five tissue interrogation sites to perform various relations identified from the use cases are defined by aligning
analyses on the biopsy samples, and one central hub that is them with higher-level ontology classes.
responsible for interoperable data collection, processing,
visualization, and systematic analyses from the tissue- to
ICBO 2018 August 7-10, 2018 1
Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 2
B. Importing kidney-related terms from existing ontologies The KTAO ontology is deposited in the NCBO BioPortal
Existing terms from other ontologies were imported into website: https://bioportal.bioontology.org/ontologies/KTAO,
KTAO using Ontofox (http://ontofox.hegroup.org) [8]. The as well as the Ontobee ontology repository website:
existing ontologies used include the Cell Ontology (CL) [9], http://www.ontobee.org/ontology/KTAO.
Disease Ontology (DOID) [10], Gene Ontology (GO) [11], E. KTAO query and analysis
Human Phenotype Ontology (HPO) [12], Ontology for
The Resource Description Framework (RDF) triples for the
Biomedical Investigations (OBI) [13, 14], Ontology of Genes
KTAO ontology were saved in the Ontobee triple store [18,
and Genomes (OGG) [15], and UBERON ontology [16]. 19], which allows easy KTAO information query using the
C. Application-based KTAO development standard SPARQL query language for RDF
(https://www.w3.org/TR/rdf-sparql-query/). For query
As a first application study, we used KTAO to model and
demonstrations, KTAO was queried from Ontobee’s SPARQL
represent known kidney gene markers collected by our KPMP
query endpoint (http://www.ontobee.org/sparql).
nephrology domain experts. Based on the information
available, we developed an ontology design pattern, utilized
the Ontorat tool [17] to generate new terms and relations, and III. RESULTS
merged the newly generated information into KTAO.
A. KTAO top level design
The Protégé OWL editor (http://protege.stanford.edu/) was
Fig. 1 illustrates the upper level KTAO hierarchical
used for the KTAO visualization, manual new term generation
structure and selected key ontology terms of KTAO. KTAO
and editing, and ontology term merging. KTAO-specific terms
adopts the Basic Formal Ontology (BFO) [20, 21] as its upper
were generated with new identifiers using the prefix
level ontology, which includes the ‘continuant’ and
“KTAO_” followed by auto-generated seven-digit numbers.
‘occurrent’ branches [20]. The continuant branch represents
The Hermit reasoner (http://hermit-reasoner.com/) was used
entities (e.g., ‘material entity’ and quality of material entity)
for consistency checking and inferencing.
which endure through time. The ‘occurrent’ branch represents
D. KTAO format, source code, and deposition time and entities (such as ‘process’) which occur in time. BFO
KTAO is expressed using the W3C standard Web Ontology has been used by over 100 biomedical ontologies. The
Language (OWL2) (http://www.w3.org/TR/owl-guide/). The adoption of BFO allows consistent classification and
KTAO source code is open and freely available at GitHub: integration of KTAO with other ontologies.
https://github.com/KPMP/KTAO.
entity (BFO)
continuant (BFO) occurrent (BFO)
realizable entity quality material entity temporal process (BFO)
(BFO) (BFO) (BFO) region (BFO)
planned biological
disposition (BFO) phenotype gene process (GO)
cell (CL) process
(HP) (OGG)
(OBI) life cycle
(UBERONI)
disease function anatomical cellular
cellular
(DOID) (BFO) entity component
process (GO)
(UBERON) (GO)
Fig. 1. KTAO top level design. All terms are aligned together under the BFO structure.
KTAO imports and semantically links terms from existing ontology development strategy also makes KTAO a basic and
ontologies (Fig. 1). For example, KTAO imports kidney- scalable knowledge environment for standardized KPMP data
specific cell types from the Cell Ontology (CL), anatomic annotation, integration, and analysis.
entities from UBERON, and phenotypes from Human
Phenotype Ontology (HPO). The terms in these different B. KTAO ontology design pattern with example
ontologies often lack linkages. One main task of the KTAO Fig. 2 illustrates the KTAO ontology design pattern that
development is to link these terms together using semantic links different types of entities in the framework of KTAO.
relations. Overall, KTAO aims to systematically classify, Many of the entity types in Fig. 2 represent branches of
represent, and integrate different cell types in the kidney, cell hierarchical terms defined by specific ontologies. For example,
states (healthy, injured, dying, recovering, undergoing hundreds of cells and anatomical entities are defined in the Cell
adaptive/maladaptive repair, etc.), and interstitial components Ontology (CL) and the UBERON anatomical entity ontology,
(collagens, proteoglycans, signaling molecules, etc.). Such an respectively. While the KTAO top level design (Fig. 1) shows
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Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 3
the hierarchical relationships among different terms, Fig. 2 As an example of KTAO development and usage, Fig. 3
shows the relations of terms across different hierarchical illustrates how KTAO links and integrates different terms and
branches of KTAO. Therefore, the combination of Fig. 1 and structures from existing ontologies. First, KTAO imports
Fig. 2 provides a general framework of KTAO ontological kidney-related cell types from CL, anatomic entities from
design. UBERON, human phenotypes from HPO, genes from OGG,
and diseases from DOID. KTAO currently imports 259 human
genes from OGG. These genes, all collected by our
nephrology domain experts, are kidney disease gene markers or
reference genes critical for KPMP research. Based on the
reference gene panel information, we can add relation linkages
(called “axioms”) showing, for example, that the WT1 gene is
up-regulated in podocytes in patients with CKD, and a
podocyte (also named “glomerular visceral epithelial cell”) is
part of the visceral layer of the glomerular capsule (Fig. 3).
Each of these entities is located in hierarchical ontological
structures; for example, podocyte is under the epithelial cell
branch of the Cell Ontology (CL) (Fig. 3).
Fig. 2. KTAO design pattern that links kidney-related entities.
Fig. 3. Demonstration of KTAO linkage of different entities and representation of the WT1 gene marker up-regulated in podocytes in CKD patients.
Fig. 3 also demonstrates how we can provide the synonym epithelial cell branch there are many other epithelial cell types,
information, i.e., ‘podocyte’ being a synonym for ‘glomerular such as ‘epithelial cell of distal tubule’ and ‘epithelial cell of
visceral epithelial cell’. In different KPMP studies, we often proximal tubule’. Such a structure allows many useful queries,
find the representations of the same or similar terms using such as a query of all gene markers located in various types of
different controlled terminologies or ontologies (e.g., ICD- epithelial cells.
9/10, SNOMED). We will map these different representations
with our chosen ontologies using a synonym-like approach so Note that the KTAO relation ‘susceptible to be up-
that software programs can be developed to semantically regulated in CKD in cell’ is generated as a shortcut relation to
understand these representations and the relations among them. directly link the gene, cell, and CKD patient population, and
indicates that a gene marker (e.g., WT1) is susceptible to be
In addition, Fig. 3 shows the hierarchical context of upregulated in a CKD patient’s cells (e.g., podocyte) (Fig. 3).
different entity types. For example, ‘glomerular visceral The CKD in this relation represents chronic kidney disease,
epithelial cell’ is one type of epithelial cell; and under the same one of the two kidney diseases focused on in both the KTAO
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Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 4
and KPMP. The inclusion of CKD in the relation definition type term ‘glomerular visceral epithelial cell’ (e.g., podocyte)
simplifies the axiom representation; this is also a reason why (CL_0000653), including its definition, annotations, class
we call it a “shortcut” relation. Similarly, other new relation hierarchy, and various usages (including the WT1-related
terms are also generated to represent complex knowledge semantic axiom described in the above section). NCBO
between different entities, such as ‘susceptible to be down- BioPortal also includes the KTAO ontology information
regulated in CKD in cell’ and ‘susceptible to be up-regulated (https://bioportal.bioontology.org/ontologies/KTAO) and
in AKI in cell’. provides a user-friendly web query system for KTAO term
browsing and searching.
Fig. 3 also includes a demonstration of the usage of such
new KPMP-specific relations. As shown in the figure, the
WT1-related relations as described above are formally used in TABLE I. SELECTED REUSED ONTOLOGIES IN KTAO
the following axiom: No. of terms
Ontology Content
imported
WT1 gene: ‘susceptible to be up-regulated in CKD in cell’
some ‘glomerular visceral epithelial cells’ BFO Upper level terms 61
CL Cell types 277
This axiom statement indicates that every WT1 is
susceptible to be up-regulated in some “glomerular visceral DOID Diseases 177
epithelial cell” (i.e., podocyte) of CKD patients. The WT1 gene Biological processes, cell
encodes for the Wilm’s tumour protein (WT1), a GO 288
components, molecular functions
transcriptional factor required for podocyte development and HP Phenotypes 264
homeostasis [22]. Our community-generated KPMP kidney
gene panel indicates that the WT1 gene is typically up- OBI Biomedical investigation 51
regulated in podocytes of CKD patients, suggesting that this OGG Genes 277
gene can be used as a gene marker to suggest the presence of
CKD. UBERON Anatomic entities 719
C. KTAO statistics
D. KTAO applications
The latest release of KTAO contains a total of 2,639 terms,
including 2,357 classes, 171 object properties, and 98 The KTAO ontology is being developed with many
annotation properties. Most terms in KTAO were imported applications in mind. First of all, KTAO is being established as
from 31 existing ontologies. Table 1 shows a list of reused a knowledge base and an environmental platform to logically
ontologies in KTAO, including BFO, CL, DO, HPO, GO, OBI, and systematically classify kidney cell types, anatomic entities,
OGG, and UBERON. The usage of these ontologies is phenotypes, diseases, gene markers, and biological processes,
important to the full representation of the kidney atlas as well as the relations among these entities. Existing kidney
information in KTAO. knowledge can be accumulatively added to KTAO; for
example, once we identify new kidney cell types, that
The full ontology statistics of KTAO can be found on information can be added to KTAO. This strategy will
Ontobee at: http://www.ontobee.org/ontostat/KTAO. As continuously improve KTAO and make KTAO a robust
shown on the website, KTAO has many KTAO specific terms, community-based framework for representing continuously
including 13 object property terms such as the term generated and experimentally verified kidney knowledge in a
‘susceptible to be up-regulated in CKD in cell’ tissue atlas.
(KTAO_0000003) (Fig. 3). This term has been used to initiate
9 axioms. Other object properties such as ‘susceptible to be Since the KTAO OWL format is machine-interpretable, the
up-regulated in CKD in anatomic location’ (KTAO_0000009) generation of such a kidney atlas knowledge base will also be
have also been used for axiom generation. These object easily understood by computer programs, supporting various
properties and their usages provide feasible demonstrations on intelligent queries and analyses. For example, since the KTAO
how KTAO can be used to generate new axioms. More work can be stored in an RDF triple store, e.g., the Ontobee triple
is being conducted to add all possible axioms associated with store [19], the KTAO information can be queried using the
kidney gene markers. These KTAO relation terms are critical SPARQL Protocol and RDF Query Language
to link together different components represented in existing (https://www.w3.org/TR/rdf-sparql-protocol/).
ontologies. Fig. 4 demonstrates one SPARQL query over KTAO. As
Since KPMP has been stored in Ontobee and BioPortal, we shown in the figure, a few lines of SPARQL query code were
can also query and visualize specific ontology terms and their able to identify the gene markers known to be upregulated in
annotations and usages in KPMP using the Ontobee or podocytes of CKD patients, which has been represented in the
BioPortal web sites. For example, the Ontobee website KTAO ontology. For more practical usages, various queries
http://www.ontobee.org/ontology/KTAO?iri=http://purl.obolib can be generated with new SPARQL queries. We can also
rary.org/obo/CL_0000653 shows the details about the cell develop other software programs that embed the SPARQL
query code to support additional interactive querying use cases.
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Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 5
Fig. 4. SPARQL query of KTAO looking for all genes upregulated in podocytes of CKD patients. A total of 5 genes were identified in KTAO. This query was
performed using Ontobee SPARQL (http://www.ontobee.org/sparql).
In addition, KTAO is targeted to serve as a resource for directly to KTAO. If newly generated KTAO terms fit well
KPMP data annotation, visualization, and analysis. Given the into another ontology (e.g., UBERON), we will also work with
many types of clinical, pathological, and molecular KPMP the developers of the other ontology to promptly contribute the
experiments, it remains a huge challenge to consistently new terms to that ontology and import back to KTAO. In this
represent and annotate the large amounts of KPMP-generated way, KTAO becomes a buffer ontology that links the KPMP
data. KTAO provides a standardized terminology and domain experts and projects with existing ontologies.
controlled code system for representing kidney-related We are actively collaborating with existing ontology
entities. If all KPMP data use the KTAO terms and codes for communities and efforts. For example, we are working with
annotation (when needed), we can automatically integrate all the developers of the GUDMAP ontology, a high-resolution
the data sets from different KPMP recruitment and ontology that describes the sub-compartments (including
interrogation sites using the same semantic framework. Since histological structures and cell types) of the developing mouse
KTAO logically represents the relations among different genitourinary tract [23]. The GUDMAP ontology previously
entities, KTAO also supports advanced data analysis. KTAO- used the Edinburgh Mouse Atlas Project (EMAP) ontology
based standard visualization tools can also be generated to [24]. Based on our recent discussions with the GUDMAP
take advantage of the standard representation and logic team, GUDMAP intends to transition its use of ontology from
established in KTAO. EMAP to UBERON, which is also used by the KTAO,
benefitting our collaborative development. There are many
IV. DISCUSSION
similarities and differences in mouse and human kidneys. For
In the emerging field of precision medicine and among example, in the context of developmental stages of mouse and
various “atlas” projects, KTAO offers a novel solution that human embryos, the mouse has 28 Theiler stages (or TS) that
reuses and links related entities represented in existing reliable cover 20 days post-conception, while human has 23 Carnegie
ontologies, providing a scalable and reusable knowledge atlas stages (CS) that cover the first 60 days. Humans have ~100
environment to support robust knowledge and data/metadata times more nephrons than mice. The human kidney is multi-
representation, standardization, sharing, integration, and
lobed, forming 8 to 15 renal calyces; however, mouse only has
advanced analysis.
one [25]. We will be working with GUDMAP, UBERON, and
KTAO is developed as an integrative ontology and core other collaborators, and develop and implement community-
platform to support kidney tissue atlas application development. based design patterns for ontologically representing these
Instead of developing everything from scratch, KTAO reuses differences between human and mouse kidneys.
and integrates existing ontologies and adds community-specific To support the community-based ontology development,
terms and annotations with the same semantics and upper-level we will hold a KPMP ontology workshop this summer in
ontologies. Without the integrative KTAO platform, the terms Seattle with the developers of many community-based
that are extracted from existing ontologies and used in KPMP ontologies (such as HPO, UBERON, CL, and OBI) to discuss
may be redundant, do not use the same semantical structure, how we can better collaborate and support community-based
and are difficult to be integrated and utilized. Furthermore, the
ontology development. Such an event will become an
renal disease community has community-specific requirements
influential platform for learning, discussion, and collaboration
and knowledge that is more efficient and appropriate to add
among experts with different backgrounds, and build up
ICBO 2018 August 7-10, 2018 5
Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), Corvallis, Oregon, USA 6
community consensuses on how to effectively develop a novel [12] T. Groza, S. Kohler, D. Moldenhauer, N. Vasilevsky, G. Baynam, T.
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ACKNOWLEDGMENT M. C. Chibucos, et al., "The Ontology for Biomedical Investigations,"
PLoS One, vol. 11, p. e0154556, 2016.
This KPMP project is supported by the NIH National
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