Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

AnnotSV and knotAnnotSV: a web server for human structural variations annotations, ranking and analysis

With the dramatic increase of pangenomic analysis, Human geneticists have generated large amount of genomic data including millions of small variants (SNV/indel) but also thousands of structural variations (SV) mainly from next-generation sequencing and array-based techniques. While the identification of the complete SV repertoire of a patient is getting possible, the interpretation of each SV remains challenging. To help identifying human pathogenic SV, we have developed a web server dedicated to their annotation and ranking (AnnotSV) as well as their visualization and interpretation (knotAnnotSV) freely available at the following address: https://www.lbgi.fr/AnnotSV/. A large amount of annotations from >20 sources is integrated in our web server including among others genes, haploinsufficiency, triplosensitivity, regulatory elements, known pathogenic or benign genomic regions, phenotypic data. An ACMG/ClinGen compliant prioritization module allows the scoring and the ranking of SV into 5 SV classes from pathogenic to benign. Finally, the visualization interface displays the annotated SV in an interactive way including popups, search fields, filtering options, advanced colouring to highlight pathogenic SV and hyperlinks to the UCSC genome browser or other public databases. This web server is designed for diagnostic and research analysis by providing important resources to the user.

Strict network analysis of evolutionary conserved and brain-expressed genes reveals new putative candidates implicated in Intellectual Disability and in Global Development Delay

OBJECTIVES

Intellectual Disability (ID) and Global Development Delay (GDD) are frequent reasons for referral to genetic services and although they present overlapping phenotypes concerning cognitive, motor, language, or social skills, they are not exactly synonymous. Aiming to better understand independent or shared mechanisms related to these conditions and to identify new candidate genes, we performed a highly stringent protein-protein interaction network based on genes previously related to ID/GDD in the Human Phenotype Ontology portal.

METHODS

ID/GDD genes were searched for reliable interactions through STRING and clustering analysis was applied to detect biological complexes through the MCL algorithm. Six coding hub genes (TP53, CDC42, RAC1, GNB1, APP, and EP300) were recognised by the Cytoscape NetworkAnalyzer plugin, interacting with 1625 proteins not yet associated with ID or GDD. Genes encoding these proteins were explored by gene ontology, associated diseases, evolutionary conservation, and brain expression.

RESULTS

One hundred and seventy-two new putative genes playing a role in enriched processes/pathways previously related to ID and GDD were revealed, some of which were already postulated to be linked to ID/GDD in additional databases.

CONCLUSIONS

Our findings expanded the aetiological genetic landscape of ID/GDD and showed evidence that both conditions are closely related at the molecular and functional levels.

Inborn disorders of the malate aspartate shuttle

Over the last few years, various inborn disorders have been reported in the malate aspartate shuttle (MAS). The MAS consists of four metabolic enzymes and two transporters, one of them having two isoforms that are expressed in different tissues. Together they form a biochemical pathway that shuttles electrons from the cytosol into mitochondria, as the inner mitochondrial membrane is impermeable to the electron carrier NADH. By shuttling NADH across the mitochondrial membrane in the form of a reduced metabolite (malate), the MAS plays an important role in mitochondrial respiration. In addition, the MAS maintains the cytosolic NAD⁺ /NADH redox balance, by using redox reactions for the transfer of electrons. This explains why the MAS is also important in sustaining cytosolic redox-dependent metabolic pathways, such as glycolysis and serine biosynthesis. The current review provides insights into the clinical and biochemical characteristics of MAS deficiencies. To date, five out of seven potential MAS deficiencies have been reported. Most of them present with a clinical phenotype of infantile epileptic encephalopathy. Although not specific, biochemical characteristics include high lactate, high glycerol 3-phosphate, a disturbed redox balance, TCA abnormalities, high ammonia, and low serine, which may be helpful in reaching a diagnosis in patients with an infantile epileptic encephalopathy. Current implications for treatment include a ketogenic diet, as well as serine and vitamin B6 supplementation.

A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities

MOTIVATION

Incorporating the temporal dimension into multimorbidity studies has shown to be crucial for achieving a better understanding of the disease associations. Furthermore, due to the multifactorial nature of human disease, exploring disease associations from different perspectives can provide a holistic view to support the study of their aetiology.

RESULTS

In this work, a temporal systems-medicine approach is proposed for identifying time-dependent multimorbidity patterns from patient disease trajectories, by integrating data from electronic health records with genetic and phenotypic information. Specifically, the disease trajectories are clustered using an unsupervised algorithm based on dynamic time warping and three disease similarity metrics: clinical, genetic and phenotypic. An evaluation method is also presented for quantitatively assessing, in the different disease spaces, both the cluster homogeneity and the respective similarities between the associated diseases within individual trajectories. The latter can facilitate exploring the origin(s) in the identified disease patterns. The proposed integrative methodology can be applied to any longitudinal cohort and disease of interest. In this article, prostate cancer is selected as a use case of medical interest to demonstrate, for the first time, the identification of temporal disease multimorbidities in different disease spaces.

AVAILABILITY AND IMPLEMENTATION

https://gitlab.com/agiannoula/diseasetrajectories.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts and downloads and further parses hundreds of thousands of full-text articles in search of information supporting the causality and associated phenotypes of most published genetic variants. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient's given set of phenotypes. Diagnosis of singleton patients (without relatives' exomes) is the most time-consuming scenario, and AMELIE ranked the causative gene at the very top for 66% of 215 diagnosed singleton Mendelian patients from the Deciphering Developmental Disorders project. Evaluating only the top 11 AMELIE-scored genes of 127 (median) candidate genes per patient resulted in a rapid diagnosis in more than 90% of cases. AMELIE-based evaluation of all cases was 3 to 19 times more efficient than hand-curated database-based approaches. We replicated these results on a retrospective cohort of clinical cases from Stanford Children's Health and the Manton Center for Orphan Disease Research. An analysis web portal with our most recent update, programmatic interface, and code is available at AMELIE.stanford.edu.

Gene Set Knowledge Discovery with Enrichr

Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high-profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2: Searching Enrichr by a single gene or key search term Basic Protocol 3: Preparing raw or processed RNA-seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4: Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5: Using Enrichr in Geneshot Basic Protocol 6: Using Enrichr in ARCHS4 Basic Protocol 7: Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8: Using the Enrichr API Basic Protocol 9: Adding an Enrichr button to a website.

Clinical Bioinformatics in Precise Diagnosis of Mitochondrial Disease

Clinical bioinformatics system is well-established for diagnosing genetic disease based on next-generation sequencing, but requires special considerations when being adapted for the next-generation sequencing-based genetic diagnosis of mitochondrial diseases. Challenges are caused by the involvement of mitochondrial DNA genome in disease etiology. Heteroplasmy and haplogroup are key factors in interpreting mitochondrial DNA variant effects. Data resources and tools for analyzing variant and sequencing data are available at MSeqDR, MitoMap, and HmtDB. Revised specifications of the American College of Medical Genetics/Association of Molecular Pathology standards and guidelines for mitochondrial DNA variant interpretation are proposed by the MSeqDr Consortium and community experts.

Evaluating the clinical utility of early exome sequencing in diverse pediatric outpatient populations in the North Carolina Clinical Genomic Evaluation of Next-generation Exome Sequencing (NCGENES) 2 study: a randomized controlled trial

BACKGROUND

Exome sequencing (ES) has probable utility for shortening the diagnostic odyssey of children with suspected genetic disorders. This report describes the design and methods of a study evaluating the potential of ES as a routine clinical tool for pediatric patients who have suspected genetic conditions and who are in the early stages of the diagnostic odyssey.

METHODS

The North Carolina Clinical Genomic Evaluation by Next-generation Exome Sequencing (NCGENES) 2 study is an interdisciplinary, multi-site Phase III randomized controlled trial of two interventions: educational pre-visit preparation (PVP) and offer of first-line ES. In this full-factorial design, parent-child dyads are randomly assigned to one of four study arms (PVP + usual care, ES + usual care, PVP + ES + usual care, or usual care alone) in equal proportions. Participants are recruited from Pediatric Genetics or Neurology outpatient clinics in three North Carolina healthcare facilities. Eligible pediatric participants are < 16 years old and have a first visit to a participating clinic, a suspected genetic condition, and an eligible parent/guardian to attend the clinic visit and complete study measures. The study oversamples participants from underserved and under-represented populations. Participants assigned to the PVP arms receive an educational booklet and question prompt list before clinical interactions. Randomization to offer of first-line ES is revealed after a child's clinic visit. Parents complete measures at baseline, pre-clinic, post-clinic, and two follow-up timepoints. Study clinicians provide phenotypic data and complete measures after the clinic visit and after returning results. Reportable study-related research ES results are confirmed in a CLIA-certified clinical laboratory. Results are disclosed to the parent by the clinical team. A community consultation team contributed to the development of study materials and study implementation methods and remains engaged in the project.

DISCUSSION

NCGENES 2 will contribute valuable knowledge concerning technical, clinical, psychosocial, and health economic issues associated with using early diagnostic ES to shorten the diagnostic odyssey of pediatric patients with likely genetic conditions. Results will inform efforts to engage diverse populations in genomic medicine research and generate evidence that can inform policy, practice, and future research related to the utility of first-line diagnostic ES in health care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03548779 . Registered on June 07, 2018.

GenomeDiver: a platform for phenotype-guided medical genomic diagnosis

Purpose:

Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process.

Methods:

GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation.

Results:

We show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data.

Conclusions:

GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory-clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

Genomic insights into body size evolution in Carnivora support Peto's paradox

Background

The range of body sizes in Carnivora is unparalleled in any other mammalian order—the heaviest species is 130,000 times heavier than the lightest and the longest species is 50 times longer than the shortest. However, the molecular mechanisms underlying these huge differences in body size have not been explored.

Results

Herein, we performed a comparative genomics analysis of 20 carnivores to explore the evolutionary basis of the order’s great variations in body size. Phylogenetic generalized least squares (PGLS) revealed that 337 genes were significantly related to both head body length and body mass; these genes were defined as body size associated genes (BSAGs). Fourteen positively-related BSAGs were found to be associated with obesity, and three of these were under rapid evolution in the extremely large carnivores, suggesting that these obesity-related BSAGs might have driven the body size expansion in carnivores. Interestingly, 100 BSAGs were statistically significantly enriched in cancer control in carnivores, and 15 of which were found to be under rapid evolution in extremely large carnivores. These results suggested that large carnivores might have evolved an effective mechanism to resist cancer, which could be regarded as molecular evidence to support Peto’s paradox. For small carnivores, we identified 15 rapidly evolving genes and found six genes with fixed amino acid changes that were reported to reduce body size.

Conclusions

This study brings new insights into the molecular mechanisms that drove the diversifying evolution of body size in carnivores, and provides new target genes for exploring the mysteries of body size evolution in mammals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07732-w.

A representation model for biological entities by fusing structured axioms with unstructured texts

MOTIVATION

Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge.

RESULTS

We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation.

AVAILABILITY AND IMPLEMENTATION

The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting candidate genes from phenotypes, functions and anatomical site of expression

Motivation

Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.

Results

We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression.

Availability and implementation

Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cruxome: a powerful tool for annotating, interpreting and reporting genetic variants

Background

Next-generation sequencing (NGS) is an efficient tool used for identifying pathogenic variants that cause Mendelian disorders. However, the lack of bioinformatics training of researchers makes the interpretation of identified variants a challenge in terms of precision and efficiency. In addition, the non-standardized phenotypic description of human diseases also makes it difficult to establish an integrated analysis pathway for variant annotation and interpretation. Solutions to these bottlenecks are urgently needed.

Results

We develop a tool named “Cruxome” to automatically annotate and interpret single nucleotide variants (SNVs) and small insertions and deletions (InDels). Our approach greatly simplifies the current burdensome task of clinical geneticists and scientists to identify the causative pathogenic variants and build personal knowledge reference bases. The integrated architecture of Cruxome offers key advantages such as an interactive and user-friendly interface and the assimilation of electronic health records of the patient. By combining a natural language processing algorithm, Cruxome can efficiently process the clinical description of diseases to HPO standardized vocabularies. By using machine learning, in silico predictive algorithms, integrated multiple databases and supplementary tools, Cruxome can automatically process SNVs and InDels variants (trio-family or proband-only cases) and clinical diagnosis records, then annotate, score, identify and interpret pathogenic variants to finally generate a standardized clinical report following American College of Medical Genetics and Genomics/ Association for Molecular Pathology (ACMG/AMP) guidelines. Cruxome also provides supplementary tools to examine and visualize the genes or variations in historical cases, which can help to better understand the genetic basis of the disease.

Conclusions

Cruxome is an efficient tool for annotation and interpretation of variations and dramatically reduces the workload for clinical geneticists and researchers to interpret NGS results, simplifying their decision-making processes. We present an online version of Cruxome, which is freely available to academics and clinical researchers. The site is accessible at http://114.251.61.49:10024/cruxome/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07728-6.

Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

PURPOSE

Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful.

METHODS

We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols.

RESULTS

We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases.

CONCLUSION

The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Transcriptome profiling of insulin sensitive tissues from GH deficient mice following GH treatment

PURPOSE

Most studies that have examined the transcriptional response to GH have been performed with a single tissue. Thus, the current study performed RNASeq across three insulin-sensitive tissues of GH-treated GH deficient (GHKO) mice.

METHODS

GHKO mice were injected with recombinant human GH (hGH) or vehicle daily for 5 days and adipose, liver, and muscle tissues were collected 4 h after the final injection. RNA was isolated from the tissues and sequenced. Genes that were differentially expressed between GH and vehicle treatments were further analyzed. Enrichment analysis and topology-aware pathway analysis were performed.

RESULTS

GHKO mice treated with hGH had expected phenotypic alterations, with increased body, fat, fluid, liver, and muscle mass, and increased serum IGF-1 and insulin. 55 Genes were differentially expressed in all three tissues, including the canonical GH targets Igf1, Igfals, and Cish. Enrichment analysis confirmed the canonical GH response in select tissues, such as cell proliferation, metabolism, and fibrosis. The JAK/STAT pathway was the only pathway significantly altered in all three tissues.

CONCLUSIONS

As expected, GH caused expression changes of many known target genes, although new candidate GH targets were identified. Liver and muscle appear to be more GH sensitive than adipose tissue due to the larger number of DEG and pathways significantly altered, but adipose still has a characteristic GH response. The diversity of changes uncovered in all three tissues after 5 days of GH treatment highlights the multiplicity of GH's effects in its target tissues.

Transcriptomic analyses of gastrulation-stage mouse embryos with differential susceptibility to alcohol

Genetics are a known contributor to differences in alcohol sensitivity in humans with fetal alcohol spectrum disorders (FASDs) and in animal models. Our study profiled gene expression in gastrulation-stage embryos from two commonly used, genetically similar mouse substrains, C57BL/6J (6J) and C57BL/6NHsd (6N), that differ in alcohol sensitivity. First, we established normal gene expression patterns at three finely resolved time points during gastrulation and developed a web-based interactive tool. Baseline transcriptional differences across strains were associated with immune signaling. Second, we examined the gene networks impacted by alcohol in each strain. Alcohol caused a more pronounced transcriptional effect in the 6J versus 6N mice, matching the increased susceptibility of the 6J mice. The 6J strain exhibited dysregulation of pathways related to cell death, proliferation, morphogenic signaling and craniofacial defects, while the 6N strain showed enrichment of hypoxia and cellular metabolism pathways. These datasets provide insight into the changing transcriptional landscape across mouse gastrulation, establish a valuable resource that enables the discovery of candidate genes that may modify alcohol susceptibility that can be validated in humans, and identify novel pathogenic mechanisms of alcohol. This article has an associated First Person interview with the first author of the paper.

Gordon syndrome: Dental implications and a case report

AIM

This article describes the craniofacial and dental features of an individual with Gordon syndrome. The dental management implications and considerations of treating patients with Gordon Syndrome and similar conditions resulting in limited mouth opening are discussed.

METHODS

A 14-year-old South African male was referred to the Dental Genetics Clinic with the main complaint of carious teeth. His craniofacial characteristics included low set and posteriorly rotated ears, down-slanted palpebral fissures, sloping shoulders, and a broad neck. A prognathic mandible and mild facial asymmetry were noted. He had a significant limitation of mouth opening (2 cm at incisors). Radiographic examination revealed multiple carious teeth, missing mandibular premolars, impacted maxillary premolars, taurodontism of the 44 and 34, and enlarged coronoid processes of the mandible. Dental extractions and restorations have been performed under local anaesthesia.

CONCLUSION

Gordon syndrome and similar conditions, may result in limited oral opening and impaired manual dexterity. The severity of limitation of mouth opening determines management. Dental management should focus on ensuring that the patient is able to maintain good oral hygiene by customising homecare for the individual and regular dental visits.

TSPEAR variants are primarily associated with ectodermal dysplasia and tooth agenesis but not hearing loss: A novel cohort study

Biallelic loss‐of‐function variants in the thrombospondin‐type laminin G domain and epilepsy‐associated repeats (TSPEAR) gene have recently been associated with ectodermal dysplasia and hearing loss. The first reports describing a TSPEAR disease association identified this gene is a cause of nonsyndromic hearing loss, but subsequent reports involving additional affected families have questioned this evidence and suggested a stronger association with ectodermal dysplasia. To clarify genotype–phenotype associations for TSPEAR variants, we characterized 13 individuals with biallelic TSPEAR variants. Individuals underwent either exome sequencing or panel‐based genetic testing. Nearly all of these newly reported individuals (11/13) have phenotypes that include tooth agenesis or ectodermal dysplasia, while three newly reported individuals have hearing loss. Of the individuals displaying hearing loss, all have additional variants in other hearing‐loss‐associated genes, specifically TMPRSS3, GJB2, and GJB6, that present competing candidates for their hearing loss phenotype. When presented alongside previous reports, the overall evidence supports the association of TSPEAR variants with ectodermal dysplasia and tooth agenesis features but creates significant doubt as to whether TSPEAR variants are a monogenic cause of hearing loss. Further functional evidence is needed to evaluate this phenotypic association.

PhenCards: a data resource linking human phenotype information to biomedical knowledge

We present PhenCards ( https://phencards.org ), a database and web server intended as a one-stop shop for previously disconnected biomedical knowledge related to human clinical phenotypes. Users can query human phenotype terms or clinical notes. PhenCards obtains relevant disease/phenotype prevalence and co-occurrence, drug, procedural, pathway, literature, grant, and collaborator data. PhenCards recommends the most probable genetic diseases and candidate genes based on phenotype terms from clinical notes. PhenCards facilitates exploration of phenotype, e.g., which drugs cause or are prescribed for patient symptoms, which genes likely cause specific symptoms, and which comorbidities co-occur with phenotypes.

GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND

Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.

RESULTS

Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions.

CONCLUSION

GWENA is an R package available through Bioconductor ( https://bioconductor.org/packages/release/bioc/html/GWENA.html ) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors—compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

Phenotype-driven variant filtration strategy in exome sequencing toward a high diagnostic yield and identification of 85 novel variants in 400 patients with rare Mendelian disorders

About 6000 to 7000 different rare disorders with suspected genetic etiologies have been described and almost 4500 causative gene(s) have been identified. The advent of next-generation sequencing (NGS) technologies has revolutionized genomic research and diagnostics, representing a major advance in the identification of pathogenic genetic variations. This study presents a 3-year experience from an academic genetics center, where 400 patients were referred for genetic analysis of disorders with unknown etiology. A phenotype-driven proband-only exome sequencing (ES) strategy was applied for the investigation of rare disorders, in the context of optimizing ES diagnostic yield and minimizing costs and time to definitive diagnosis. Overall molecular diagnostic yield reached 53% and characterized 243 pathogenic variants in 210 cases, 85 of which were novel and 148 known, contributing information to the community of disease and variant databases. ES provides an opportunity to resolve the genetic etiology of disorders and support appropriate medical management and genetic counseling. In cases with complex phenotypes, the identification of complex genotypes may contribute to more comprehensive clinical management. In the context of effective multidisciplinary collaboration between clinicians and laboratories, ES provides an efficient and appropriate tool for first-tier genomic analysis.

Clinical, neuroimaging, and molecular spectrum of TECPR2-associated hereditary sensory and autonomic neuropathy with intellectual disability

Bi-allelic TECPR2 variants have been associated with a complex syndrome with features of both a neurodevelopmental and neurodegenerative disorder. Here, we provide a comprehensive clinical description and variant interpretation framework for this genetic locus. Through international collaboration, we identified 17 individuals from 15 families with bi-allelic TECPR2-variants. We systemically reviewed clinical and molecular data from this cohort and 11 cases previously reported. Phenotypes were standardized using Human Phenotype Ontology terms. A cross-sectional analysis revealed global developmental delay/intellectual disability, muscular hypotonia, ataxia, hyporeflexia, respiratory infections, and central/nocturnal hypopnea as core manifestations. A review of brain magnetic resonance imaging scans demonstrated a thin corpus callosum in 52%. We evaluated 17 distinct variants. Missense variants in TECPR2 are predominantly located in the N- and C-terminal regions containing β-propeller repeats. Despite constituting nearly half of disease-associated TECPR2 variants, classifying missense variants as (likely) pathogenic according to ACMG criteria remains challenging. We estimate a pathogenic variant carrier frequency of 1/1221 in the general and 1/155 in the Jewish Ashkenazi populations. Based on clinical, neuroimaging, and genetic data, we provide recommendations for variant reporting, clinical assessment, and surveillance/treatment of individuals with TECPR2-associated disorder. This sets the stage for future prospective natural history studies.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Mapping OMIM Disease-Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes

Human genome resequencing projects provide an unprecedented amount of data about single-nucleotide variations occurring in protein-coding regions and often leading to observable changes in the covalent structure of gene products. For many of these variations, links to Online Mendelian Inheritance in Man (OMIM) genetic diseases are available and are reported in many databases that are collecting human variation data such as Humsavar. However, the current knowledge on the molecular mechanisms that are leading to diseases is, in many cases, still limited. For understanding the complex mechanisms behind disease insurgence, the identification of putative models, when considering the protein structure and chemico-physical features of the variations, can be useful in many contexts, including early diagnosis and prognosis. In this study, we investigate the occurrence and distribution of human disease–related variations in the context of Pfam domains. The aim of this study is the identification and characterization of Pfam domains that are statistically more likely to be associated with disease-related variations. The study takes into consideration 2,513 human protein sequences with 22,763 disease-related variations. We describe patterns of disease-related variation types in biunivocal relation with Pfam domains, which are likely to be possible markers for linking Pfam domains to OMIM diseases. Furthermore, we take advantage of the specific association between disease-related variation types and Pfam domains for clustering diseases according to the Human Disease Ontology, and we establish a relation among variation types, Pfam domains, and disease classes. We find that Pfam models are specific markers of patterns of variation types and that they can serve to bridge genes, diseases, and disease classes. Data are available as Supplementary Material for 1,670 Pfam models, including 22,763 disease-related variations associated to 3,257 OMIM diseases.

Craniofacial features of 3q29 deletion syndrome: Application of next-generation phenotyping technology

3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Detailed dysmorphology exam and 2D facial photos were ascertained from 31 individuals with 3q29del. Photos were used to train the next-generation phenotyping algorithm DeepGestalt (Face2Gene by FDNA, Inc, Boston, MA) to distinguish 3q29del cases from controls and all other recognized syndromes. Area under the curve of receiver operating characteristic curves (AUC-ROC) was used to determine the capacity of Face2Gene to identify 3q29del cases against controls. In this cohort, the most common observed craniofacial features were prominent forehead (48.4%), prominent nose tip (35.5%), and thin upper lip vermillion (25.8%). The FDNA technology showed an ability to distinguish cases from controls with an AUC-ROC value of 0.873 (p = 0.006) and led to the inclusion of 3q29del as one of the supported syndromes. This study found a recognizable facial pattern in 3q29del, as observed by trained clinical geneticists and next-generation phenotyping technology. These results expand the potential application of automated technology such as FDNA in identifying rare genetic syndromes, even when facial dysmorphology is subtle.

Novel variants in TUBA1A cause congenital fibrosis of the extraocular muscles with or without malformations of cortical brain development

Variants in multiple tubulin genes have been implicated in neurodevelopmental disorders, including malformations of cortical development (MCD) and congenital fibrosis of the extraocular muscles (CFEOM). Distinct missense variants in the beta-tubulin encoding genes TUBB3 and TUBB2B cause MCD, CFEOM, or both, suggesting substitution-specific mechanisms. Variants in the alpha tubulin-encoding gene TUBA1A have been associated with MCD, but not with CFEOM. Using exome sequencing (ES) and genome sequencing (GS), we identified 3 unrelated probands with CFEOM who harbored novel heterozygous TUBA1A missense variants c.1216C>G, p.(His406Asp); c.467G>A, p.(Arg156His); and c.1193T>G, p.(Met398Arg). MRI revealed small oculomotor-innervated muscles and asymmetrical caudate heads and lateral ventricles with or without corpus callosal thinning. Two of the three probands had MCD. Mutated amino acid residues localize either to the longitudinal interface at which α and β tubulins heterodimerize (Met398, His406) or to the lateral interface at which tubulin protofilaments interact (Arg156), and His406 interacts with the motor domain of kinesin-1. This series of individuals supports TUBA1A variants as a cause of CFEOM and expands our knowledge of tubulinopathies.

How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system

BACKGROUND

About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain.

RESULTS

To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded.

CONCLUSIONS

With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.

Novel Variant Findings and Challenges Associated With the Clinical Integration of Genomic Testing: An Interim Report of the Genomic Medicine for Ill Neonates and Infants (GEMINI) Study

Importance

A targeted genomic sequencing platform focused on diseases presenting in the first year of life may minimize financial and ethical challenges associated with rapid whole-genomic sequencing.

Objective

To report interim variants and associated interpretations of an ongoing study comparing rapid whole-genomic sequencing with a novel targeted genomic platform composed of 1722 actionable genes targeting disorders presenting in infancy.

Design, Setting, and Participants

The Genomic Medicine in Ill Neonates and Infants (GEMINI) study is a prospective, multicenter clinical trial with projected enrollment of 400 patients. The study is being conducted at 6 US hospitals. Hospitalized infants younger than 1 year of age suspected of having a genetic disorder are eligible. Results of the first 113 patients enrolled are reported here. Patient recruitment began in July 2019, and the interim analysis of enrolled patients occurred from March to June 2020.

Interventions

Patient (proband) and parents (trios, when available) were tested simultaneously on both genomic platforms. Each laboratory performed its own phenotypically driven interpretation and was blinded to other results.

Main Outcomes and Measures

Variants were classified according to the American College of Medical Genetics and Genomics standards of pathogenic (P), likely pathogenic (LP), or variants of unknown significance (VUS). Chromosomal and structural variations were reported by rapid whole-genomic sequencing.

Results

Gestational age of 113 patients ranged from 23 to 40 weeks and postmenstrual age from 27 to 83 weeks. Sixty-seven patients (59%) were male. Diagnostic and/or VUS were returned for 51 patients (45%), while 62 (55%) had negative results. Results were concordant between platforms in 83 patients (73%). Thirty-seven patients (33%) were found to have a P/LP variant by 2 or both platforms and 14 (12%) had a VUS possibly related to phenotype. The median day of life at diagnosis was 22 days (range, 3-313 days). Significant alterations in clinical care occurred in 29 infants (78%) with a P/LP variant. Incidental findings were reported in 7 trios. Of 51 positive cases, 34 (67%) differed in the reported result because of technical limitations of the targeted platform, interpretation of the variant, filtering discrepancies, or multiple causes.

Conclusions and Relevance

As comprehensive genetic testing becomes more routine, these data highlight the critically important variant detection capabilities of existing genomic sequencing technologies and the significant limitations that must be better understood.

FMRP attenuates activity dependent modifications in the mitochondrial proteome

Homeostatic plasticity is necessary for the construction and maintenance of functional neuronal networks, but principal molecular mechanisms required for or modified by homeostatic plasticity are not well understood. We recently reported that homeostatic plasticity induced by activity deprivation is dysregulated in cortical neurons from Fragile X Mental Retardation protein (FMRP) knockout mice (Bulow et al. in Cell Rep 26: 1378-1388 e1373, 2019). These findings led us to hypothesize that identifying proteins sensitive to activity deprivation and/or FMRP expression could reveal pathways required for or modified by homeostatic plasticity. Here, we report an unbiased quantitative mass spectrometry used to quantify steady-state proteome changes following chronic activity deprivation in wild type and Fmr1-/y cortical neurons. Proteome hits responsive to both activity deprivation and the Fmr1-/y genotype were significantly annotated to mitochondria. We found an increased number of mitochondria annotated proteins whose expression was sensitive to activity deprivation in Fmr1-/y cortical neurons as compared to wild type neurons. These findings support a novel role of FMRP in attenuating mitochondrial proteome modifications induced by activity deprivation.

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

Motivation

Gene prioritization at human GWAS loci is challenging due to linkage-disequilibrium and long-range gene regulatory mechanisms. However, identifying the causal gene is crucial to enable identification of potential drug targets and better understanding of molecular mechanisms. Mapping GWAS traits to known phenotypically relevant Mendelian disease genes near a locus is a promising approach to gene prioritization.

Results

We present MendelVar, a comprehensive tool that integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of associated functional annotations such as Human Phenotype Ontology, Disease Ontology and variants from ClinVar. This open web-based platform enables users to strengthen the case for causal importance of phenotypically matched candidate genes at GWAS loci. We demonstrate the use of MendelVar in post-GWAS gene annotation for type 1 diabetes, type 2 diabetes, blood lipids and atopic dermatitis.

Availability and implementation

MendelVar is freely available at https://mendelvar.mrcieu.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.

The Increasing Impact of Translational Research in the Molecular Diagnostics of Neuromuscular Diseases

The diagnosis of neuromuscular diseases (NMDs) has been progressively evolving from the grouping of clinical symptoms and signs towards the molecular definition. Optimal clinical, biochemical, electrophysiological, electrophysiological, and histopathological characterization is very helpful to achieve molecular diagnosis, which is essential for establishing prognosis, treatment and genetic counselling. Currently, the genetic approach includes both the gene-targeted analysis in specific clinically recognizable diseases, as well as genomic analysis based on next-generation sequencing, analyzing either the clinical exome/genome or the whole exome or genome. However, as of today, there are still many patients in whom the causative genetic variant cannot be definitely established and variants of uncertain significance are often found. In this review, we address these drawbacks by incorporating two additional biological omics approaches into the molecular diagnostic process of NMDs. First, functional genomics by introducing experimental cell and molecular biology to analyze and validate the variant for its biological effect in an in-house translational diagnostic program, and second, incorporating a multi-omics approach including RNA-seq, metabolomics, and proteomics in the molecular diagnosis of neuromuscular disease. Both translational diagnostics programs and omics are being implemented as part of the diagnostic process in academic centers and referral hospitals and, therefore, an increase in the proportion of neuromuscular patients with a molecular diagnosis is expected. This improvement in the process and diagnostic performance of patients will allow solving aspects of their health problems in a precise way and will allow them and their families to take a step forward in their lives.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.

An EPIC predictor of gestational age and its application to newborns conceived by assisted reproductive technologies

Background

Gestational age is a useful proxy for assessing developmental maturity, but correct estimation of gestational age is difficult using clinical measures. DNA methylation at birth has proven to be an accurate predictor of gestational age. Previous predictors of epigenetic gestational age were based on DNA methylation data from the Illumina HumanMethylation 27 K or 450 K array, which have subsequently been replaced by the Illumina MethylationEPIC 850 K array (EPIC). Our aims here were to build an epigenetic gestational age clock specific for the EPIC array and to evaluate its precision and accuracy using the embryo transfer date of newborns from the largest EPIC-derived dataset to date on assisted reproductive technologies (ART).

Methods

We built an epigenetic gestational age clock using Lasso regression trained on 755 randomly selected non-ART newborns from the Norwegian Study of Assisted Reproductive Technologies (START)—a substudy of the Norwegian Mother, Father, and Child Cohort Study (MoBa). For the ART-conceived newborns, the START dataset had detailed information on the embryo transfer date and the specific ART procedure used for conception. The predicted gestational age was compared to clinically estimated gestational age in 200 non-ART and 838 ART newborns using MM-type robust regression. The performance of the clock was compared to previously published gestational age clocks in an independent replication sample of 148 newborns from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restrictions (PREDO) study—a prospective pregnancy cohort of Finnish women.

Results

Our new epigenetic gestational age clock showed higher precision and accuracy in predicting gestational age than previous gestational age clocks (R² = 0.724, median absolute deviation (MAD) = 3.14 days). Restricting the analysis to CpGs shared between 450 K and EPIC did not reduce the precision of the clock. Furthermore, validating the clock on ART newborns with known embryo transfer date confirmed that DNA methylation is an accurate predictor of gestational age (R² = 0.767, MAD = 3.7 days).

Conclusions

We present the first EPIC-based predictor of gestational age and demonstrate its robustness and precision in ART and non-ART newborns. As more datasets are being generated on the EPIC platform, this clock will be valuable in studies using gestational age to assess neonatal development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01055-z.

Zebrafish modeling mimics developmental phenotype of patients with RAPGEF1 mutation

RAPGEF1 is a guanine nucleotide exchange factor responsible for transmitting extracellular signals to the Ras family of GTPase located at the inside of membrane. Here, we report for the first time a homozygous mutation of RAPGEF1 in a consanguineous family with two siblings affected by neuropsychiatric disorder. To confirm the correlation of the mutation and the phenotype, we utilized in silico analysis and established a zebrafish model. Survival rate was reduced in the rapgef1a-knockdown model, and the zebrafish showed global morphological abnormalities, particularly of brain and blood vessels. Co-application of human RAPGEF1 wildtype mRNA effectively rescued the abnormal phenotype, while that of RAPGEF1 mRNA carrying the human mutation did not. This work is the first report of a human Mendelian disease associated with RAPGEF1 and the first report of a zebrafish model built for this gene. The phenotype of zebrafish model provides further evidence that defective RAPGEF1 may lead to global developmental delay in human patients.

Towards enhanced understanding of idiopathic ketotic hypoglycemia: a literature review and introduction of the patient organization, Ketotic Hypoglycemia International

BACKGROUND

Idiopathic Ketotic hypoglycemia (IKH) is a diagnosis of exclusion. Although considered as the most frequent cause of hypoglycemia in childhood, little progress has been made to advance the understanding of IKH since the medical term was coined in 1964. We aimed to review the literature on ketotic hypoglycemia (KH) and introduce a novel patient organization, Ketotic Hypoglycemia International (KHI).

RESULTS

IKH may be diagnosed after the exclusion of various metabolic and hormonal diseases with KH. Although often mild and self-limiting, more severe and long-lasting IKH occurs. We therefore divide IKH in physiological KH and pathological KH, the latter defined as recurrent symptomatic, or occasionally symptomatic, episodes with beta-hydroxybutyrate ≥ 1.0 mmol/L and blood glucose < 70 mg/dL (3.9 mol/L), in the absence of prolonged fasting, acute infections and chronic diseases known to cause KH. Pathological KH may represent undiscovered diseases, e.g. glycogen storage disease IXa, Silver-Russel syndrome, and ketone transporter defects, or suggested novel disease entities identified by exome sequencing. The management of KH aims to prevent hypoglycemia, fatty acid oxidation and protein deficiency by supplying adequate amounts of carbohydrates and protein, including nutritional therapy, uncooked cornstarch, and sometimes continuous tube feeding by night. Still, intravenous dextrose may be needed in acute KH episodes. Failure to acknowledge that IKH can be more than normal variation may lead to under-treatment. KHI is a non-profit, patient-centric, global organization established in 2020. The organization was created by adult IKH patients, patient family members, and volunteers. The mission of KHI is to enhance the understanding of IKH while advocating for patients, their families and the continued research into KH.

CONCLUSION

IKH is a heterogeneous disorder including physiological KH and pathological KH. IKH may represent missed diagnoses or novel disease entities, but shares common management principles to prevent fatty acid oxygenation. KHI, a novel patient organization, aims to enhance the understanding of IKH by supporting IKH families and research into IKH.

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.

HPOFiller: identifying missing protein-phenotype associations by graph convolutional network

MOTIVATION

Exploring the relationship between human proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment of diseases. The human phenotype ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human diseases. However, the current HPO annotations of proteins are not complete. Thus, it is important to identify missing protein-phenotype associations.

RESULTS

We propose HPOFiller, a graph convolutional network (GCN)-based approach, for predicting missing HPO annotations. HPOFiller has two key GCN components for capturing embeddings from complex network structures: 1) S-GCN for both protein-protein interaction (PPI) network and HPO semantic similarity network to utilize network weights; 2) Bi-GCN for the protein-phenotype bipartite graph to conduct message passing between proteins and phenotypes. The core idea of HPOFiller is to repeat run these two GCN modules consecutively over the three networks, to refine the embeddings. Empirical results of extremely stringent evaluation avoiding potential information leakage including cross-validation and temporal validation demonstrates that HPOFiller significantly outperforms all other state-of-the-art methods. In particular, the ablation study shows that batch normalization contributes the most to the performance. The further examination offers literature evidence for highly ranked predictions. Finally using known disease-HPO term associations, HPOFiller could suggest promising, unknown disease-gene associations, presenting possible genetic causes of human disorders.

AVAILABILITY

https://github.com/liulizhi1996/HPOFiller.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic testing for unexplained perinatal disorders

PURPOSE OF REVIEW

Perinatal disorders include stillbirth, congenital structural anomalies, and critical illnesses in neonates. The cause of these is often unknown despite a thorough clinical workup. Genetic diseases cause a significant portion of perinatal disorders. The purpose of this review is to describe recent advances in genetic testing of perinatal disorders of unknown cause and to provide a potential diagnostic strategy.

RECENT FINDINGS

Exome and genome sequencing (ES and GS) have demonstrated that significant portions of perinatal disorders are caused by genetic disease. However, estimates of the exact proportion have varied widely across fetal and neonatal cohorts and most of the genetic diagnoses found in recent studies have been unique to individual cases. Having a specific genetic diagnosis provides significant clinical utility, including improved prognostication of the outcome, tailored therapy, directed testing for associated syndromic manifestations, referral to appropriate subspecialists, family planning, and redirection of care.

SUMMARY

Perinatal disorders of unknown cause, with nonspecific presentations, are often caused by genetic diseases best diagnosed by ES or GS. Prompt diagnosis facilitates improved clinical care. Improvements in noninvasive sampling, variant interpretation, and population-level research will further enhance the clinical utility of genetic testing.

VIDEO ABSTRACT

http://links.lww.com/MOP/A61.

Novel bi-allelic variants expand the SPTBN4-related genetic and phenotypic spectrum

Neurodevelopmental disorder with hypotonia, neuropathy, and deafness (NEDHND, OMIM #617519) is an autosomal recessive disease caused by homozygous or compound heterozygous variants in SPTBN4 coding for type 4 βIV-spectrin, a non-erythrocytic member of the β-spectrin family. Variants in SPTBN4 disrupt the cytoskeletal machinery that controls proper localization of ion channels and the function of axonal domains, thereby generating severe neurological dysfunction. We set out to analyze the genetic causes and describe the clinical spectrum of suspected cases of NEDHND. Variant screening was done by whole exome sequencing; clinical phenotypes were described according to the human phenotype ontology, and histochemical analysis was performed with disease-specific antibodies. We report four families with five patients harboring novel homozygous and compound heterozygous SPTBN4 variants, amongst them a multi-exon deletion of SPTBN4. All patients presented with the key features of NEDHND; severe muscular hypotonia, dysphagia, absent speech, gross motor, and mental retardation. Additional symptoms comprised horizontal nystagmus, epileptiform discharges in EEG without manifest seizures, and choreoathetosis. Muscle histology revealed both characteristics of myopathy and of neuropathy. This report expands the SPTBN4 variant spectrum, highlights the spectrum of morphological phenotypes of NEDHND-patients, and reveals clinical similarities between the NEDHND, non-5q SMA, and congenital myopathies.

GPCards: An integrated database of genotype-phenotype correlations in human genetic diseases

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The patient-level genotype-phenotype correlations in GPCards accelerated the development of medical genetics.

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The integrated 62 genomic sources provide comprehensive information for interpreting pathogenicity.

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Analysis function in GPCards would help users to decipher their data of genotype-phenotype correlations.

Genotype–phenotype correlations are the basis of precision medicine of human genetic diseases. However, it remains a challenge for clinicians and researchers to conveniently access detailed individual-level clinical phenotypic features of patients with various genetic variants. To address this urgent need, we manually searched for genetic studies in PubMed and catalogued 8,309 genetic variants in 1,288 genes from 17,738 patients with detailed clinical phenotypic features from 1,855 publications. Based on genotype–phenotype correlations in this dataset, we developed an user-friendly online database called GPCards (http://genemed.tech/gpcards/), which not only provided the association between genetic diseases and disease genes, but also the prevalence of various clinical phenotypes related to disease genes and the patient-level mapping between these clinical phenotypes and genetic variants. To accelerate the interpretation of genetic variants, we integrated 62 well-known variant-level and gene-level genomic data sources, including functional predictions, allele frequencies in different populations, and disease-related information. Furthermore, GPCards enables automatic analyses of users’ own genetic data, comprehensive annotation, prioritization of candidate functional variants, and identification of genotype–phenotype correlations using custom parameters. In conclusion, GPCards is expected to accelerate the interpretation of genotype–phenotype correlations, subtype classification, and candidate gene prioritisation in human genetic diseases.

A review on viral data sources and search systems for perspective mitigation of COVID-19

With the outbreak of the COVID-19 disease, the research community is producing unprecedented efforts dedicated to better understand and mitigate the effects of the pandemic. In this context, we review the data integration efforts required for accessing and searching genome sequences and metadata of SARS-CoV2, the virus responsible for the COVID-19 disease, which have been deposited into the most important repositories of viral sequences. Organizations that were already present in the virus domain are now dedicating special interest to the emergence of COVID-19 pandemics, by emphasizing specific SARS-CoV2 data and services. At the same time, novel organizations and resources were born in this critical period to serve specifically the purposes of COVID-19 mitigation while setting the research ground for contrasting possible future pandemics. Accessibility and integration of viral sequence data, possibly in conjunction with the human host genotype and clinical data, are paramount to better understand the COVID-19 disease and mitigate its effects. Few examples of host-pathogen integrated datasets exist so far, but we expect them to grow together with the knowledge of COVID-19 disease; once such datasets will be available, useful integrative surveillance mechanisms can be put in place by observing how common variants distribute in time and space, relating them to the phenotypic impact evidenced in the literature.

Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights

MOTIVATION

Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behavior for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single <gene, function, cell type> for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks.

RESULTS

We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets <gene, function, cell type> by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes.

AVAILABILITYAND IMPLEMENTATION

The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders

Purpose

Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype–phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed.

Methods

We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the Na_V1.2 protein.

Results

We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance.

Conclusion

Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype–phenotype correlations along a multidimensional spectrum.

Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study

Background

16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient “self-phenotyping” survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome.

Objective

This study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities.

Methods

As part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis.

Results

A total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome.

Conclusions

Several phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their condition. Self-phenotyping may lead to a better understanding of the prevalence of phenotypes in genetic disorders and may identify previously unreported phenotypes.

Improving gene function predictions using independent transcriptional components

The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes

MOTIVATION

Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.

RESULTS

We developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.

AVAILABILITY

Code and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.

De novo variants in neurodevelopmental disorders-experiences from a tertiary care center

Up to 40% of neurodevelopmental disorders (NDDs) such as intellectual disability, developmental delay, autism spectrum disorder, and developmental motor abnormalities have a documented underlying monogenic defect, primarily due to de novo variants. Still, the overall burden of de novo variants as well as novel disease genes in NDDs await discovery. We performed parent-offspring trio exome sequencing in 231 individuals with NDDs. Phenotypes were compiled using human phenotype ontology terms. The overall diagnostic yield was 49.8% (n = 115/231) with de novo variants contributing to more than 80% (n = 93/115) of all solved cases. De novo variants affected 72 different-mostly constrained-genes. In addition, we identified putative pathogenic variants in 16 genes not linked to NDDs to date. Reanalysis performed in 80 initially unsolved cases revealed a definitive diagnosis in two additional cases. Our study consolidates the contribution and genetic heterogeneity of de novo variants in NDDs highlighting trio exome sequencing as effective diagnostic tool for NDDs. Besides, we illustrate the potential of a trio-approach for candidate gene discovery and the power of systematic reanalysis of unsolved cases.