Deep Phenotypic Analysis for Transposition of the Great Arteries and Prognosis Implication

Background Transposition of the great arteries (TGA) consists of about 3% of all congenital heart diseases and 20% of cyanotic congenital heart diseases. It is always accompanied by a series of other cardiac malformations that affect the surgical intervention strategy as well as prognosis. In this study, we comprehensively analyzed the phenotypes of the patients who had TGA with concordant atrioventricular and discordant ventriculoarterial connections and explored their association with prognosis. Methods and Results We retrospectively reviewed 666 patients with a diagnosis of TGA with concordant atrioventricular and discordant ventriculoarterial connections in Fuwai Hospital from 1997 to 2019. Under the guidance of the Human Phenotype Ontology database, patients were classified into 3 clusters. The Kaplan‐Meier method was used to analyze the prognosis, and the Cox proportional regression model was used to investigate the risk factors. In this 666‐patient TGA cohort, the overall 5‐year survival rate was 94.70% (92.95%–96.49%). Three clusters with distinct phenotypes were obtained by the Human Phenotype Ontology database. Kaplan‐Meier analysis revealed a significant difference in freedom from reintervention among 3 clusters ( P <0.001). To eliminate the effect of surgeries, we analyzed patients who only received an arterial switch operation and still found a significant difference in reintervention ( P =0.019). Conclusions We delineated a big cardiovascular phenotypic profile of an unprecedentedly large TGA cohort and successfully risk stratified them to reveal prognostic significance. Also, we reported the outcomes of a large TGA population in China.

Identification of HOX Signatures Contributing to Oral Cancer Phenotype

Purpose: Evolutionarily conserved homeobox-containing HOX genes as transcriptional regulators in the developmental specification of organisms is well known. The contribution of HOX genes involvement in oral cancer phenotype has yet to be fully ascertained.Methods: GEO datasets (GSE72627, GSE30784, GSE37991) were accessed and analyzed using GEO2R. TCGA-HNSC HTSeq-counts and clinical data were retrieved from the GDC portal for oral cavity neoplasms. Differential HOX gene expression was profiled using the DESeq2 R package with a log2 fold change cut-off (-1 and +1) and Benjamini-Hochberg p-adjusted value at <0.01. Gene set over-representation analysis and semantic analysis associated with the disease ontology were performed using ClusterProfiler R package and pathway over-representation analysis was performed using IMPaLa. HOX protein interaction network was constructed using the Pathfind R package. HOX phenotype associations were performed using Mammalian Phenotype Ontology, Human Phenotype Ontology, PhenGenI associations, Jensen tissues, and OMIM entries. Drug connectivity mapping was carried out with Dr. Insight R Package.Results: HOXB2 and HOXA5 genes were upregulated in oral dysplasia but silenced during tumor progression. Loss of HOXB2 expression was consistent through potentially malignant dysplastic oral lesions (PMOL) to primary tumor formation. HOXA10, HOXB7, HOXC6, HOXC10 and HOXD10 showed consistent upregulation from premalignancy to malignancy and were notably associated with risk factors. Overrepresentation analysis suggested HOXA10 was involved in the transcriptional misregulation leading to oral cancer phenotype. HOX subnetwork analysis showed crucial interactions with cell cycle regulators, growth responsive elements, and proto-oncogenes.Conclusion: Phenotype associations specific to the oral region involving HOX genes provide intrinsic cues to tumor development. The 5’ HOX genes were aberrantly deregulated which reflects their posterior prevalence during oral carcinogenesis.

The Xenopus Phenotype Ontology: bridging model organism phenotype data to human health and development.

Background: Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease. Results: Here we present the Xenopus Phenotype Ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated. Conclusions: The XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype-phenotype data that can be directly related to other uPheno compliant resources.

Social Media Validation for Pre-Screened Ontologies As a Driver for Biomolecular Interaction Significance in COVID-19 and Hematological Malignancies

With the rise of social media use during the COVID-19 pandemic, impressions from online content can affect behavioral changes resulting in exacerbating disparities in care. Thus, there exists a need to utilize social media platforms, like Twitter, to help augment preparedness, especially at the intersection between oncology and COVID-19, where tweets could help hint at potential biomolecular interactions. To address this, a study was developed to assess relationship and ontologies on the interaction between hematological malignancies and COVID-19 on Twitter. Ontologies are groupings of terms and related identifiers, such as genes, for general search terms, such as "Blood Cancer", were found utilizing the Human Phenotype Ontology. These were combined with the term "COVID-19" and used as search terms for Twitter's Standard Search API. The resulting tweets were cross-checked to assess if they included any of the other terms or genes related to the starting ontologies to then determine how many terms or genes each tweet was associated with. Once the most associated tweets to the ontologies were found, the genes related to those ontologies were utilized to find biological structures within the AlphaFold EMBL database, before being used in binding using HEX Docking software's shape based binding tool in 3D. Finally, Root Mean Square (RMS) Deviations were performed between the top 2000 conformations for each bound structure to determine if the binding was statistically significant. Results showed strong clustering of top tweets around keyword combinations. In the case of the starting entry, "Blood COVID-19", the ontologies that were found were linked to 45 terms that each had 100 or more tweets linked to them (Figure 1a). One such term of significance was Acute Myeloid Leukemia, which was linked to the gene BRCA1. The biological significance of the molecular interaction between BRCA1 and SARS CoV-2 was determined using the predicted protein structure from the AlphaFold-EMBL database for BRCA1 and the RCSB Protein Bank structure for the SARS CoV-2 spike (PDB# 6VSB), which can be found in Figure 1b. This interaction was found to be significant based on the average RMS Deviation of 82.97 Angstroms that ranged across the top 2000 conformation. Each model had an average RMS of 85.05 Angstroms between BRCA1 and the COVID-19 spike, with binding occurring on the spike's carbohydrate recognition domain within its S1 segment that is typically used for cell entry. Thus, human phenotype ontology was effective in classifying tweets to specific biomolecular interactions. Therefore, this approach could be utilized to proactively influence treatment designs for blood cancer patients infected with COVID-19, as well as in other areas where medical illnesses are already well defined by ontologies or other literature data. Forward looking, future studies will help to ensure that terms that are not well characterized by ontologies can still be utilized in this type of analysis by employing de novo ontology production methods. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Proposed criteria for nevoid basal cell carcinoma syndrome in children assessed using statistical optimization

Nevoid basal cell carcinoma syndrome (NBCCS) is a tumor predisposition condition, the cardinal features of which emerge in adolescence or adulthood. Using statistical optimization, this study proposes NBCCS criteria with improved sensitivity in children less than 18 years of age. Earlier detection may lead to improved surveillance and prevention of sequelae. A survey eliciting medical history was completed by, or on behalf of, individuals with NBCCS. Based on these findings, criteria for suspicion of NBCCS in children were suggested using information from a Bernoulli naïve Bayes classifier relying on the human phenotype ontology. The sensitivity and specificity of the existing and proposed diagnostic criteria were also assessed. Participants (n = 48) reported their first signs of NBCCS appeared at a median age of 8 months, but by our retrospective analysis, they did not fulfill the current diagnostic criteria until a median age of 7 years. This study delineates the early-onset features of NBCCS and proposes criteria that should prompt consideration of NBCCS. Additionally, we demonstrate a method for quantitatively assessing the utility of diagnostic criteria for genetic disorders.

ACO2 clinicobiological dataset with extensive phenotype ontology annotation

Pathogenic variants of the aconitase 2 gene (ACO2) are responsible for a broad clinical spectrum involving optic nerve degeneration, ranging from isolated optic neuropathy with recessive or dominant inheritance, to complex neurodegenerative syndromes with recessive transmission. We created the first public locus-specific database (LSDB) dedicated to ACO2 within the “Global Variome shared LOVD” using exclusively the Human Phenotype Ontology (HPO), a standard vocabulary for describing phenotypic abnormalities. All the variants and clinical cases listed in the literature were incorporated into the database, from which we produced a dataset. We followed a rational and comprehensive approach based on the HPO thesaurus, demonstrating that ACO2 patients should not be classified separately between isolated and syndromic cases. Our data highlight that certain syndromic patients do not have optic neuropathy and provide support for the classification of the recurrent pathogenic variants c.220C>G and c.336C>G as likely pathogenic. Overall, our data records demonstrate that the clinical spectrum of ACO2 should be considered as a continuum of symptoms and refines the classification of some common variants.

Pathogenic Mutations and Putative Phenotype-Affecting Variants in Polish Myofibrillar Myopathy Patients

Myofibrillar myopathies (MFM) are heterogeneous hereditary muscle diseases with characteristic myopathological features of Z-disk dissolution and aggregates of its degradation products. The onset and progression of the disease are variable, with an elusive genetic background, and around half of the cases lacking molecular diagnosis. Here, we attempted to establish possible genetic foundations of MFM by performing whole exome sequencing (WES) in eleven unrelated families of 13 patients clinically diagnosed as MFM spectrum. A filtering strategy aimed at identification of variants related to the disease was used and included integrative analysis of WES data and human phenotype ontology (HPO) terms, analysis of muscle-expressed genes, and analysis of the disease-associated interactome. Genetic diagnosis was possible in eight out of eleven cases. Putative causative mutations were found in the DES (two cases), CRYAB, TPM3, and SELENON (four cases) genes, the latter typically presenting with a rigid spine syndrome. Moreover, a variety of additional, possibly phenotype-affecting variants were found. These findings indicate a markedly heterogeneous genetic background of MFM and show the usefulness of next generation sequencing in the identification of disease-associated mutations. Finally, we discuss the emerging concept of variant load as the basis of phenotypic heterogeneity.