=Paper=
{{Paper
|id=Vol-3073/paper8
|storemode=property
|title=Ontology-based Modeling, Representation, and Analysis of Biomarkers in Healthy and Disease Kidney Tissue
|pdfUrl=https://ceur-ws.org/Vol-3073/paper8.pdf
|volume=Vol-3073
|authors=Yingtong Liu,Wenjun Ju,Becky Steck,Sanjay Jain,Matthias Kretzler,Yongqun He
|dblpUrl=https://dblp.org/rec/conf/icbo/LiuJSJKH21
}}
==Ontology-based Modeling, Representation, and Analysis of Biomarkers in Healthy and Disease Kidney Tissue==
https://ceur-ws.org/Vol-3073/paper8.pdf
Ontology-based Modeling, Representation, and Analysis of
Biomarkers in Healthy and Disease Kidney Tissue
Yingtong Liu1,x, Wenjun Ju1, Becky Steck1, Sanjay Jain2, Matthias Kretzler1, and Yongqun
He1, for the Kidney Precision Medicine Project
1
University of Michigan Medical School, Ann Arbor, MI 48109, USA
2
Washington University School of Medicine, St. Louis, MO 63110, USA
x
Corresponding author
Abstract
Biomarker reflects an underlying biological state or identity. Extensive kidney studies have
resulted in the discovery of many kidney cell and disease biomarkers. Our manual literature
annotations have identified 150 cell-specific markers for 73 kidney cell types and 38 diabetic
kidney disease (DKD)-related biomarkers. To systematically study these biomarkers, we first
surveyed and ontologically defined the term biomarker and different types of biomarkers. The
Kidney Tissue Atlas Ontology (KTAO) has been further used as a platform to model and
represent these kidney biomarkers by including the biomarker gene name, cell type, disease,
and axioms linking the biomarkers and other terms. Gene Ontology (GO) analysis revealed 9
shared enriched GO terms in both biomarker sets. A DL-query was performed to demonstrate
the advantages of ontology-based modeling and analysis of kidney biomarkers.
Keywords 1
Kidney biomarker, KTAO, DKD, ontology
1. Introduction
Molecular biomarkers, which are molecules indicating biological identities or states, are crucial in
further understanding of kidney diseases and support the precision kidney medicine. Extensive research
has found various kidney biomarkers associated with various kidney states, either healthy or diseased.
With various kidney biomarkers identified, it is critical to systematically annotate, standardize,
represent, integrate, and analyze these biomarkers and their associated features and conditions.
Ontology is an ideal tool for such study. Basically, an ontology is a human- and computer-
interpretable representation of the entity types, entity properties, and their interrelationships that exist
in a particular domain [2]. Ontologies have emerged to become an important platform for systematical
data and knowledge representation, integration, sharing, and computer-assisted reasoning and analysis.
The Kidney Tissue Atlas Ontology (KTAO) is a community-based open-source biomedical ontology
that systematically represents entities associated with kidney disease, kidney structures, cells, genes etc.
[1]. KTAO is primarily developed by the NIH-supported Kidney Precision Medicine Project (KPMP,
http://kpmp.org), an NIH-funded multi-year project aimed to understand and find ways to treat the
chronic kidney disease (CKD) and acute kidney injury (AKI). Particularly, diabetic kidney disease
(DKD), a subtype of CKD, occurs in people with diabetes.
In this study, we systematically annotated around 180 of biomarkers, mainly gene markers, from the
literature, the HuBMAP and KPMP consortia. These biomarkers are associated with kidney healthy
cells or diseased kidney. We have used KTAO to ontologically classify these biomarkers, their features,
and the relations among these markers and their association with various kidney cell types, biological
processes, and kidney diseases.
International Conference on Biomedical Ontologies 2021, September 16–18, 2021, Bozen-Bolzano, Italy
EMAIL: yingtliu@umich.edu (A. 1); wenjunj@med.umich.edu (A. 2); roesch@med.umich.edu (A. 3); sanjayjain@wustl.edu (A. 4);
kretzler@med.umich.edu (A. 5); yongqunh@med.umich.edu (A. 6)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. Methods
2.1. Data Collection and Annotations
Two types of kidney biomarkers, normal kidney cell lineage biomarkers and diabetic kidney disease
(DKD) were compiled from different resources. The normal cell type lineage biomarkers were obtained
from the literature including our recent KPMP publication [2] and The Human BioMolecular Atlas
Program (HuBMAP) (https://commonfund.nih.gov/hubmap) reference ASCT+B data [3]. A number
of biomarkers were derived from single cell sequencing studies based on statistical cut off’s and in some
cases automated tools such as those in Seurat package (https://satijalab.org/seurat/) to label the cell
types and the most significant markers [4, 5]. DKD protein biomarkers were collected mainly from a
review article [6]. The selection criterion for a non-invasive DKD biomarker is its being reported in at
least one non-high throughput research article using human patients’ samples. Our study focused on the
collection and analysis of DKD protein biomarkers. All the results are based on rigorous experiment
design and statistical analysis [2]. The manual curation confirms and picks the ones seen in multiple
technologies and across species and validations where possible.
2.2. KTAO Biomarker Modeling, Representation and DL Query
Gene and protein markers are modeled. Genes and proteins are presented using the Ontology of
Genes and Genomes (OGG) and Protein Ontology (PR). IDs were retrieved by using Ontobee
(http://ontobee.org). Biomarker-related relations were defined to generate new axioms that semantically
link gene/protein biomarkers and cell types (or diseases). Such axioms were built up using Ontorat [7].
The Description Logic (DL) queries were performed using DL Query plugin of Protégé 5.0 (beta 15,
https://protege.stanford.edu/). After reasoning of the KTAO with the ELK reasoned
(https://www.cs.ox.ac.uk/isg/tools/ELK/), DL queries were performed on biomarkers.
2.3. GSEA Analysis on Gene Markers and Incorporation to KTAO
For functional analysis of our collected gene markers, a gene set enrichment analysis (GSEA) was
performed using the DAVID Bioinformatics Resources (https://david.ncifcrf.gov/tools.jsp). The default
DAVID background was applied for analysis of enriched biological processes, cellular components and
molecular functions as defined by the Gene Ontology (GO). The p-value ≤ 0.05 after FDR adjustment
was used as the cutoff. These GO terms were retrieved by using Ontobee. The axiom relations between
gene markers and GO biological processes and molecular functions are defined using the Relation
Ontology (RO) [8] terms ‘participates in’ and ‘has participant’, and the axiom relation between genes
and GO cellular components are defined using ‘has part’ and ‘part of’. These corresponding axioms in
KTAO were established using Ontorat [7].
3. Results
3.1. Kidney Biomarker Collection and Representation
Our kidney biomarker collection focused on the branches: kidney cell lineage biomarkers and DKD
biomarkers. Our study found a total of 150 genetic biomarkers of 72 kidney cell lineage types using the
method defined in the Methods section. Meanwhile, we collected a total of 38 DKD protein biomarkers.
For each of these biomarkers, we have included at least one peer-reviewed publication. Note that our
non-exhaustive collections are likely incomplete and have not included those markers identified from
high throughput analyses [4, 9]. This focus on this study is primarily on the establishment of ontological
modeling and knowledge representation of these biomarkers.
3.2. Ontological Definitions and Representation of Kidney Biomarkers
There have been many definitions of biomarkers. The NIH Biomarkers and Surrogate Endpoint
Working Group defined a biomarker as “A characteristic that is objectively measured and evaluated as
an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a
therapeutic intervention” [10]. However, the term “characteristic” is vague and difficult to define
ontologically. Mayeux defined biomarker as a type of “alterations in the constituents of tissues or body
fluids” [11]. Biomarkers should be assessable or measurable. In a 2015 paper [12], Ceuters and Smith
proposed three disjoint types of biomarkers: material biomarker, quality biomarker, and process
biomarker. As the bearers of various dispositions, material entities can be measured. However,
processes and qualities do not have assessable dispositions based on the Basic Formal Ontology (BFO).
Therefore, it appears difficult to assess biomarkers as processes and qualities using the BFO framework.
It also appears that the 2015 proposal of the three-type biomarker classification has not been adopted
in the ontology community.
In our study, we define biomarker as a material entity only, and we do not consider quality or process
as a biomarker. Meanwhile, the biomarker material entity has measurable qualities and processes that
can be used as the indicator of some biological state. The biomarker material entity has measurable
dispositions for a specific state/identity, which is the basis of the material entity being the biomarker.
Specifically, we have defined the term ‘biomarker’ in the Ontology of Precision Medicine and
Investigation (OPMI) [1] as:
biomarker = def. A material entity that has a measurable quality or process profile(s), which can be
used as an indicator of an underlying biological state or identity.
A biomarker can be classified into different subtypes. Based on the clinical purpose of the
biomarkers, there are diagnostic, predictive, prognostic, pharmacodynamic biomarkers, etc. [13]. Based
on the types of the material entities, there are gene, protein, RNA, and metabolite biomarkers. Based
on the restricted expression in cell types and tissues, there are cell type- and tissue-specific biomarkers.
Furthermore, there are various morphological biomarkers.
As an example, decreased renal tubular cell expression and reduced urinary excretion of epidermal
growth factor (EGF) has been observed in many human kidney diseases including acute kidney injury
and CKD, and the EGF can serve as a biomarker for progression of these kidney diseases [14, 15]. In
our definition, the urinary human EGF protein (hEGF) is a prognostic biomarker for kidney disease,
and it can be used by measuring its concentration in urinary excretion. This case can be defined
ontologically using the following ontological axiom:
‘urine hEGF’: has_role some (‘prognostic biomarker role’ and (realized_in some ‘DKD process’))
Alternatively, we can make an equivalent axiom with a shortcut relation:
‘urine hEGF’: ‘has prognostic biomarker role in’ some ‘DKD process’
The ontological hierarchical structure of various relevant biomarkers is shown in Figure 1A. The
high level ‘biomarker’ term and several of its subclasses (including disease biomarker, immune
biomarker and cell biomarker) were imported from the OPMI. We further defined many kidney specific
biomarkers under ‘kidney biomarker’ in KTAO. Under ‘kidney disease biomarker’ are ‘AKI biomarker’
and ‘CKD biomarker’, which includes DKD biomarkers. Under ‘kidney cell lineage biomarker’, there
are substructures of the kidney, specific cell types and different types of cell biomarkers. For example,
kidney podocyte biomarker is a biomarker to the kidney podocyte cell lineage (Figure 1A). After
ontology inferencing using a semantic reasoner, 4 biomarkers, NPHS1, NPHS2, PODXL and PTPRQ,
were inferred to be the kidney podocyte biomarkers (Figure 1B).
Biomarker-related relations (or called object properties) were generated for new ontology axiom
generations. For example, the relation ‘is gene marker of cell’ semantically links a gene marker and a
cell type, and the relation ‘protein biomarker of disease’ links a protein biomarker with a disease.
Existing relations in the RO [8] were also used. For example, all enriched GO terms associated with
gene makers are connected by ‘participates in’ defined in RO. For instance, NPHS1 is a kidney cell
marker for podocytes (glomerular visceral epithelial cell) and a kidney disease protein marker for
diabetic nephropathy. This marker is also a component of several GO-defined cellular components such
as extracellular exome (Figure 1C). Each cellular component GO terms related to gene markers are also
well-defined and has axiom ‘has part’ to trace back to gene markers (Figure 1D). All the relations
between biomarkers with disease, with cell type and with GO terms are incorporated into KTAO. In
total, there are 37 new relations between biomarkers and DKD, 107 between biomarkers with cell types,
and over hundreds of new relations between biomarkers and GO terms.
Figure 1. Ontological hierarchy and representation of biomarker axioms in KTAO. A)
Hierarchical structure of different kinds of biomarkers in KTAO. B) After reasoning, the markers that
belong to this classification of biomarkers. C) Example of biomarker representation. D) Example of
associated GO terms representation.
3.3. KTAO-based Analysis and Query of Kidney Biomarkers
Based on our DAVID GO term enrichment analysis, 61 GO terms were enriched in kidney cell
biomarkers, and 41 GO terms were enriched for DKD protein markers. Nine GO terms, including 5
cellular component (CC) terms and 4 biological process (BP) terms, were found to be shared between
these cell biomarkers and DKD biomarkers (Table 1). Our analysis identified large numbers of gene
markers located in extracellular space, extracellular region, extracellular exosome, and cell surface
(Table 1), indicating that the gene markers for both cell types and diabetic kidney diseases are more
likely to involve in extracellular and cell surface structures.
Figure 2. Example of DL-query. This query identifies 3 biomarkers that are podocyte biomarkers, and
also part of extracellular exosome. The DL-query was performed in Protégé-OWL editor. ELK reasoned
was used to perform the reasoning before the query was conducted.
The knowledge represented in KTAO can be interpretable by computers and reasoned with methods
such as DL-query or SPARQL query. For example, we performed a DL query on KTAO to identify
those biomarkers that are the biomarkers for podocytes (i.e., glomerular visceral epithelial cells), and
meanwhile they are located in the extracellular exosome (Figure 2). Three axioms were queried together
in this query example. Genes NPHS1, NPHS2, and PODXL were identified (Figure 2).
Table 1 Common Gene Ontology terms for gene or protein markers of kidney cell types and DKD
GO ID Term Label Type # cell marker # disease # of common
marker genes
GO:0005576 extracellular region CC 34 29 2
GO:0005615 extracellular space CC 37 27 2
GO:0070062 extracellular exosome CC 56 18 3
GO:0006955 immune response BP 17 8 1
GO:0005578 proteinaceous CC 9 6 1
extracellular matrix
GO:0006954 inflammatory response BP 13 7 0
GO:0001666 response to hypoxia BP 8 7 1
GO:0071356 cellular response to BP 6 5 0
tumor necrosis factor
GO:0009986 cell surface CC 21 7 1
4. Discussion
In this paper, we systematically collected and annotated various biomarkers for regular kidney cells
as well as for DKD, and ontologically represented these kidney biomarkers in KTAO. Such ontological
representation provides standardized computer-interpretable knowledge representation and supports
automated semantic queries and data analyses of kidney biomarkers. And the ontological representation
can serve as a knowledge base and be used to compare and analyze the results from high throughput
omics studies, supporting kidney diseases diagnosis, mechanisms study and rational treatment design.
In addition, the KTAO representation can be incorporated into our KPMP web application development
for more advanced browsing, reasoning, and data analysis.
The systematical and logical KTAO representation of the biomarker types, biomarker locations, and
their involving biological processes supports integrative analysis of the biomarkers. We can
systematically find those biomarker-associated cellular components or biological processes and inspect
potential mechanisms of actions of these biomarkers in normal kidney functions or disease formation.
For example, NPHS1 is a gene marker for DKD, and it is a kidney podocyte-specific marker, and it can
be found in extracellular exosomes. Detection of NPHS1 in podocytes may be a potential sign of DKD,
and we can also infer that NPHS1 play important role in cell junctions. Some of the disease markers
may also be potential drug targets for DKD treatment. To facilitate such analysis, we may develop new
models and representation of the drug-biomarker associations in KTAO.
Admittedly, this work has limitation and further study is going on. Due to the time restriction, only
a small number of papers were reviewed and used. Further literature mining is necessary to identify and
annotate more kidney biomarkers. Our future work includes the expansion of the kidney biomarker
presentation to include more inclusive biomarkers for DKD, and additionally for other types of CKD
and AKI. We will also include other types of biomarkers such as RNA and metabolite markers. We
welcome researchers interested in the topic to participate in the community-based KTAO development
and its applications.
5. Conclusion
Kidney biomarkers are critical to the fundamental study of kidney functions and disease
development. In this study, we started with 150 cell gene markers and 38 DKD protein markers,
analyzed and incorporated them into the KTAO. Nine enriched Gene Ontology terms were identified
in these two groups of biomarkers. These DKD gene markers and their associations with different
kidney cell types, cellular components and biological processes were systematically represented in
KTAO. A DL-query demonstrated the KTAO support for computer-assisted reasoning and query.
Overall, our ontological knowledge representation facilitates systematic kidney biomarker
standardization, integration, and analysis.
6. Acknowledgements
This project is supported by the following KPMP grants from the NIDDK: U2C DK114886,
UH3DK114861, UH3DK114866, UH3DK114870, UH3DK114908, UH3DK114915, UH3DK114926,
UH3DK114907, UH3DK114920, UH3DK114923, UH3DK114933, and UH3DK114937, and
HuBMAP consortium grant U54HL145608 funded by the NIH. This work is also partially supported
by George M. O’Brien Michigan Kidney Translational Core Center, funded by NIH/NIDDK grant
2P30-DK-081943, and The Nephrotic Syndrome Rare Disease Clinical Research Network
(NEPTUNE). NEPTUNE is part of the Rare Diseases Clinical Research Network (RDCRN), which is
funded by the National Institutes of Health (NIH) and led by the National Center for Advancing
Translational Sciences (NCATS) through its Office of Rare Diseases Research (ORDR). NEPTUNE is
funded under grant number U54DK083912 as a collaboration between NCATS and the National
Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Additional funding and/or
programmatic support for this project has also been provided by the University of Michigan, NephCure
Kidney International and the Halpin Foundation. All RDCRN consortia are supported by the network’s
Data Management and Coordinating Center (DMCC) (U2CTR002818). Funding support for the DMCC
is provided by NCATS and the National Institute of Neurological Disorders and Stroke (NINDS).
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