CIDO ontology updates and secondary analysis of host responses to COVID-19 infection based on ImmPort reports and literature

Background With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO. Methods The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports. Results The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients. Conclusion Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection.

Disease ontologies for knowledge graphs

Background Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease clusters and finally build a representation of the chosen disease area. There is a shortage of published resources and tools to facilitate interactive, efficient and flexible cross-referencing and analysis of multiple disease ontologies commonly found in data sources and research. Results Our results are represented as a knowledge graph solution that uses disease ontology cross-references and facilitates switching between ontology hierarchies for data integration and other tasks. Conclusions Grakn core with pre-installed “Disease ontologies for knowledge graphs” facilitates the biomedical knowledge graph build and provides an elegant solution for the multiple disease ontologies problem.

Pleiotropic Variability Score: A Genome Interpretation Metric to Quantify Phenomic Associations of Genomic Variants

A complete understanding of phenomic space is critical for elucidating genome-phenome relationships and assessing disease risk from genome sequencing. We developed a new genome interpretation metric called Pleiotropic Variability Score (PVS) to incorporate phenomic variability into the variant interpretation. PVS uses ontologies of human diseases and medical phenotypes, namely human phenotype ontology (HPO) and disease ontology (DO), to compute the similarities of disease and clinical phenotypes associated with a genetic variant based on semantic reasoning algorithms. We tested 78 unique semantic similarity methods and integrated six robust metrics to define the pleiotropy score of SNPs. We computed PVS for 12, 541 SNPs (10, 021 SNPs mapped to DO phenotype and 8, 569 SNPs mapped to HPO phenotypes) using a repertoire of 382 HPO and 317 DO unique phenotype terms compiled from the genotype-phenotype catalog. We validated the utility of PVS by computing pleiotropy using an electronic health record-linked genomic database (BioME, n=11,210) and generated allele-specific pleiotropy. Further, we demonstrate PVS application in personalized medicine using personalized pleiotropy score reports for individuals with genomic data that could potentially aid in variant interpretation. We further developed a software framework to incorporate PVS into VCF files and consolidate pleiotropy assessment as part of genome interpretation pipelines. As the genome-phenome catalogs are growing, PVS will be a useful metric to assess genetic variation to find SNPs with highly pleiotropic effects. Additionally, genome analysts can prioritize variants with varying degrees of pleiotropy for explorative studies to understand the specific roles of SNPs and pleiotropic hubs in mediating novel phenotypes and drug development.

The Infectious Disease Ontology in the age of COVID-19

Background Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. Results To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents – organisms with an infectious disposition – and infectious structures – acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core’s content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. Conclusions IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

Challenging Disease Ontology by Instances of Atypical PKHD1 and PKD1 Genetics

BackgroundAutosomal polycystic kidney disease is distinguished into dominant (ADPKD) and recessive (ARPKD) inheritance usually caused by either monoallelic (PKD1/PKD2) or biallelic (PKHD1) germline variation. Clinical presentations are genotype-dependent ranging from fetal demise to mild chronic kidney disease (CKD) in adults. Additionally, exemptions from dominant and recessive inheritance have been reported in both disorders resulting in respective phenocopies. Here, we comparatively report three young adults with microcystic-hyperechogenic kidney morphology based on unexpected genetic alterations beyond typical inheritance.MethodsNext-generation sequencing (NGS)-based gene panel analysis and multiplex ligation-dependent probe amplification (MLPA) of PKD-associated genes, familial segregation analysis, and reverse phenotyping.ResultsThree unrelated individuals presented in late adolescence for differential diagnosis of incidental microcystic-hyperechogenic kidneys with preserved kidney and liver function. Upon genetic analysis, we identified a homozygous hypomorphic PKHD1 missense variant causing pseudodominant inheritance in a family, a large monoallelic PKDH1-deletion with atypical transmission, and biallelic PKD1 missense hypomorphs with recessive inheritance.ConclusionBy this report, we illustrate clinical presentations associated with atypical PKD-gene alterations beyond traditional modes of inheritance. Large monoallelic PKHD1-alterations as well as biallelic hypomorphs of both PKD1 and PKHD1 may lead to mild CKD in the absence of prominent macrocyst formation and functional liver impairment. The long-term renal prognosis throughout life, however, remains undetermined. Increased detection of atypical inheritance challenges our current thinking of disease ontology not only in PKD but also in Mendelian disorders in general.

Common DNA methylation dynamics in endometriod adenocarcinoma and glioblastoma suggest universal epigenomic alterations in tumorigenesis

Trends in altered DNA methylation have been defined across human cancers, revealing global loss of methylation (hypomethylation) and focal gain of methylation (hypermethylation) as frequent cancer hallmarks. Although many cancers share these trends, little is known about the specific differences in DNA methylation changes across cancer types, particularly outside of promoters. Here, we present a comprehensive comparison of DNA methylation changes between two distinct cancers, endometrioid adenocarcinoma (EAC) and glioblastoma multiforme (GBM), to elucidate common rules of methylation dysregulation and changes unique to cancers derived from specific cells. Both cancers exhibit significant changes in methylation over regulatory elements. Notably, hypermethylated enhancers within EAC samples contain several transcription factor binding site clusters with enriched disease ontology terms highlighting uterine function, while hypermethylated enhancers in GBM are found to overlap active enhancer marks in adult brain. These findings suggest that loss of original cellular identity may be a shared step in tumorigenesis.

Improved characterisation of clinical text through ontology-based vocabulary expansion

Background Biomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same entities being described by several concepts in the same or similar contexts across several ontologies. While these concepts describe the same entities, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks. Results We develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set. Conclusions Inter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.