The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

Mining functional gene modules by multi-view NMF of phenome-genome association

BACKGROUND

Mining functional gene modules from genomic data is an important step to detect gene members of pathways or other relations such as protein-protein interactions. This work explores the plausibility of detecting functional gene modules by factorizing gene-phenotype association matrix from the phenotype ontology data rather than the conventionally used gene expression data. Recently, the hierarchical structure of phenotype ontologies has not been sufficiently utilized in gene clustering while functionally related genes are consistently associated with phenotypes on the same path in phenotype ontologies.

RESULTS

This work demonstrates a hierarchical Nonnegative Matrix Factorization (NMF) framework, called Consistent Multi-view Nonnegative Matrix Factorization (CMNMF), which factorizes genome-phenome association matrix at consecutive levels of the hierarchical structure in phenotype ontology to mine functional gene modules. CMNMF constrains the gene clusters from the association matrices at two consecutive levels to be consistent since the genes are annotated with both the child-level phenotypes and the parent-level phenotypes in two levels. CMNMF also restricts the identified gene clusters to be densely connected in the phenotype ontology hierarchy. In the experiments on mining functionally related genes from mouse phenotype ontology and human phenotype ontology, CMNMF effectively improves clustering performance over the baseline methods. Gene ontology enrichment analysis is also conducted to verify its practical effectiveness to reveal meaningful gene modules.

CONCLUSIONS

Utilizing the information in the hierarchical structure of phenotype ontology, CMNMF can identify functional gene modules with more biological significance than conventional methods. CMNMF can also be a better tool for predicting members of gene pathways and protein-protein interactions.

Leveraging clinical intuition to improve accuracy of phenotype-driven prioritization

PURPOSE

Clinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability.

METHODS

The LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely.

RESULTS

The average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors.

CONCLUSION

ClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

Further evidence of biallelic NAV3 variants associated with recessive neurodevelopmental disorder with dysmorphism, developmental delay, intellectual disability, and behavioral abnormalities

Neuron navigators (NAVs) are cytoskeleton-associated proteins well known for their role in axonal guidance, neuronal migration, and neurite growth necessary for neurodevelopment. Neuron navigator 3 (NAV3) is one of the three NAV proteins highly expressed in the embryonic and adult brain. However, the role of the NAV3 gene in human disease is not well-studied. Recently, five bi-allelic and three mono-allelic variants in NAV3 were reported in 12 individuals from eight unrelated families with neurodevelopmental disorder (NDD). Here, we report five patients from three unrelated consanguineous families segregating autosomal recessive NDD. Patients have symptoms of dysmorphism, intellectual disability, developmental delay, and behavioral abnormalities. Exome sequencing (ES) was performed on two affected individuals from one large family, and one affected individual from each of the other two families. ES revealed two homozygous nonsense c.6325C > T; p.(Gln2109Ter) and c.6577C > T; p.(Arg2193Ter) and a homozygous splice site (c.243 + 1G > T) variants in the NAV3 (NM_001024383.2). Analysis of single-cell sequencing datasets from embryonic and young adult human brains revealed that NAV3 is highly expressed in the excitatory neurons, inhibitory neurons, and microglia, consistent with its role in neurodevelopment. In conclusion, in this study, we further validate biallelic protein truncating variants in NAV3 as a cause of NDD, expanding the spectrum of pathogenic variants in this newly discovered NDD gene.

Arterial endothelial deletion of hereditary hemorrhagic telangiectasia 2/ Alk1 causes epistaxis and cerebral microhemorrhage with aberrant arteries and defective smooth muscle coverage

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant vascular disorder with manifestations including severe nose bleeding and microhemorrhage in brains. Despite being the second most common inherited bleeding disorder, the pathophysiological mechanism underlying HHT-associated hemorrhage is poorly understood. HHT pathogenesis is thought to follow a Knudsonian two-hit model, requiring a second somatic mutation for lesion formation. Mutations in activin receptor-like kinase 1 ( ALK1 ) gene cause HHT type 2. We hypothesize that somatic mutation of Alk1 in arterial endothelial cells (AECs) leads to arterial defects and hemorrhage. Here, we mutated Alk1 in AECs in postnatal mice using Bmx(PAC)-Cre ERT2 and found that somatic arterial endothelial mutation of Alk1 was sufficient to induce spontaneous epistaxis and multifocal cerebral microhemorrhage. This bleeding occurred in the presence of tortuous and enlarged blood vessels, loss of arterial molecular marker Efnb2 , disorganization of vascular smooth muscle, and impaired vasoregulation. Our data suggest that arterial endothelial deletion of Alk1 leading to reduced arterial identity and disrupted vascular smooth muscle cell coverage is a plausible molecular mechanism for HHT-associated severe epistaxis. This work provides the first evidence that somatic Alk1 mutation in AECs can cause hemorrhagic vascular lesions, offering a novel preclinical model critically needed for studying HHT-associated epistaxis, and delineating an arterial mechanism to HHT pathophysiology.

Examining the role of common variants in rare neurodevelopmental conditions

Although rare neurodevelopmental conditions have a large Mendelian component1, common genetic variants also contribute to risk2,3. However, little is known about how this polygenic risk is distributed among patients with these conditions and their parents nor its interplay with rare variants. It is also unclear whether polygenic background affects risk directly through alleles transmitted from parents to children, or whether indirect genetic effects mediated through the family environment4 also play a role. Here we addressed these questions using genetic data from 11,573 patients with rare neurodevelopmental conditions, 9,128 of their parents and 26,869 controls. Common variants explained around 10% of variance in risk. Patients with a monogenic diagnosis had significantly less polygenic risk than those without, supporting a liability threshold model5. A polygenic score for neurodevelopmental conditions showed only a direct genetic effect. By contrast, polygenic scores for educational attainment and cognitive performance showed no direct genetic effect, but the non-transmitted alleles in the parents were correlated with the child's risk, potentially due to indirect genetic effects and/or parental assortment for these traits4. Indeed, as expected under parental assortment, we show that common variant predisposition for neurodevelopmental conditions is correlated with the rare variant component of risk. These findings indicate that future studies should investigate the possible role and nature of indirect genetic effects on rare neurodevelopmental conditions, and consider the contribution of common and rare variants simultaneously when studying cognition-related phenotypes.

Phenotypic spectrum of dual diagnoses in developmental disorders

As more patients receive genome-wide sequencing, the number of individuals diagnosed with multiple monogenic conditions is increasing. We sought to investigate the relative phenotypic contribution of dual diagnoses using both manual curation and computational approaches. First, we computed 1,003,236 semantic similarity scores for all possible pairs of 1,417 genes in the Developmental Disorder Gene2Phenotype (DDG2P) database using Human Phenotype Ontology terms. Next, for 62 probands with two molecular diagnoses in the Deciphering Developmental Disorders study, we computed semantic similarity scores between the probands' phenotypes and DDG2P phenotypes associated with the two disorders and compared the results with manual attribution of proband phenotypes to none, one, or both of the genes. We found a spectrum of phenotypic similarity for dual diagnoses, both across all DDG2P genes and within dual diagnosed probands, from phenotypically distinct through blended to indistinguishable conditions. Pairwise semantic similarity scores between two DDG2P genes were a good predictor of the extent of phenotypic blending observed in probands. Dual diagnoses involving genes linked with synergistic phenotypes can result in more extreme presentations while those involving antagonistic phenotypes have spuriously high pairwise semantic similarity scores despite a potentially milder atypical presentation. We suggest that the phenotypic contribution of two molecular diagnoses may contain discrete, synergistic, or antagonistic elements. Conceptual recognition of this phenotypic spectrum is important for making a final clinico-molecular diagnosis and providing accurate genetic counseling.

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

BACKGROUND

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.

RESULTS

We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.

CONCLUSIONS

These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ~75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

Large-scale evaluation of outcomes after a genetic diagnosis in children with severe developmental disorders

Purpose

We sought to evaluate outcomes for clinical management after a genetic diagnosis from the Deciphering Developmental Disorders study.

Methods

Individuals in the Deciphering Developmental Disorders study who had a pathogenic/likely pathogenic genotype in the DECIPHER database were selected for inclusion (n = 5010). Clinical notes from regional clinical genetics services notes were reviewed to assess predefined clinical outcomes relating to interventions, prenatal choices, and information provision.

Results

Outcomes were recorded for 4237 diagnosed probands (85% of those eligible) from all 24 recruiting centers across the United Kingdom and Ireland. Clinical management was reported to have changed in 28% of affected individuals. Where individual-level interventions were recorded, additional diagnostic or screening tests were started in 903 (21%) probands through referral to a range of different clinical specialties, and stopped or avoided in a further 26 (0.6%). Disease-specific treatment was started in 85 (2%) probands, including seizure-control medications and dietary supplements, and contra-indicated medications were stopped or avoided in a further 20 (0.5%). The option of prenatal/preimplantation genetic testing was discussed with 1204 (28%) families, despite the relatively advanced age of the parents at the time of diagnosis. Importantly, condition-specific information or literature was given to 3214 (76%) families, and 880 (21%) were involved in family support groups. In the most common condition (KBG syndrome; 79 [2%] probands), clinical interventions only partially reflected the temporal development of phenotypes, highlighting the importance of consensus management guidelines and patient support groups.

Conclusion

Our results underscore the importance of achieving a clinico-molecular diagnosis to ensure timely onward referral of patients, enabling appropriate care and anticipatory surveillance, and for accessing relevant patient support groups.

Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease

Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.

Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis

Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant that explained the patient's clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared with a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77 of 82) of the family-based exomes and in first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28 of 35) of cases. The SAP score, which is independent of detected variants, demonstrates comparable performance with Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be used in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.

Prioritization of oligogenic variant combinations in whole exomes

MOTIVATION

Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion.

RESULTS

We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores.

AVAILABILITY AND IMPLEMENTATION

Hop is available at https://github.com/oligogenic/HOP.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

The impact of the Turkish population variome on the genomic architecture of rare disease traits

Purpose

The variome of the Turkish (TK) population, a population with a considerable history of admixture and consanguinity, has not been deeply investigated for insights on the genomic architecture of disease.

Methods

We generated and analyzed a database of variants derived from exome sequencing data of 773 TK unrelated, clinically affected individuals with various suspected Mendelian disease traits and 643 unaffected relatives.

Results

Using uniform manifold approximation and projection, we showed that the TK genomes are more similar to those of Europeans and consist of 2 main subpopulations: clusters 1 and 2 (N = 235 and 1181, respectively), which differ in admixture proportion and variome (https://turkishvariomedb.shinyapps.io/tvdb/). Furthermore, the higher inbreeding coefficient values observed in the TK affected compared with unaffected individuals correlated with a larger median span of long-sized (>2.64 Mb) runs of homozygosity (ROH) regions (P value = 2.09e-18). We show that long-sized ROHs are more likely to be formed on recently configured haplotypes enriched for rare homozygous deleterious variants in the TK affected compared with TK unaffected individuals (P value = 3.35e-11). Analysis of genotype-phenotype correlations reveals that genes with rare homozygous deleterious variants in long-sized ROHs provide the most comprehensive set of molecular diagnoses for the observed disease traits with a systematic quantitative analysis of Human Phenotype Ontology terms.

Conclusion

Our findings support the notion that novel rare variants on newly configured haplotypes arising within the recent past generations of a family or clan contribute significantly to recessive disease traits in the TK population.

Improved phenotypic classification of male infertility to promote discovery of genetic causes

An increasing number of genes are being described in the context of non-syndromic male infertility. Linking the underlying genetic causes of non-syndromic male infertility with clinical data from patients is important to establish new genotype-phenotype correlations. This process can be facilitated by using universal nomenclature, but no standardized vocabulary is available in the field of non-syndromic male infertility. The International Male Infertility Genomics Consortium aimed at filling this gap, providing a standardized vocabulary containing nomenclature based on the Human Phenotype Ontology (HPO). The "HPO tree" was substantially revised compared with the previous version and is based on the clinical work-up of infertile men, including physical examination and hormonal assessment. Some causes of male infertility can already be suspected based on the patient's clinical history, whereas in other instances, a testicular biopsy is needed for diagnosis. We assembled 49 HPO terms that are linked in a logical hierarchy and showed examples of morphological features of spermatozoa and testicular histology of infertile men with identified genetic diagnoses to describe the phenotypes. This work will help to record patients' phenotypes systematically and facilitate communication between geneticists and andrologists. Collaboration across institutions will improve the identification of patients with the same phenotypes, which will promote the discovery of novel genetic causes for non-syndromic male infertility.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

MSeqDR Quick-Mitome (QM): Combining Phenotype-Guided Variant Interpretation and Machine Learning Classifiers to Aid Primary Mitochondrial Disease Genetic Diagnosis

The international Mitochondrial Disease Sequence Data Resource Consortium (MSeqDR) Quick-Mitome (QM) is a web-based platform enabling automated variant interpretation of whole-exome sequencing (WES) datasets for the genetic diagnosis of primary mitochondrial diseases (PMD). Designed specifically to address the unique dual genome nature of PMD etiologies, QM includes features for both nuclear and mitochondrial DNA (mtDNA) genome analysis. QM requires VCF variant lists, HPO ID clinical phenotypes, and pedigree files for multiple-sample VCF inputs. QM maps phenotypes to HPO terms before analysis. QM analysis requires 2 to 20 min for 100,000 variants on an 8-vCPU AWS server using Exomiser's "PASS_ONLY" mode for nuclear variants. QM ranks variants based on allele frequency, phenotype-gene association, functional impact, and inheritance mode. Variants are further annotated with multiple data sources such as OMIM, ClinVar, dbNSFP, gnoMAD, MITOMAP, and MSeqDR. In addition to standard Exomiser results, QM generates an Analysis Report and QM Integrated Report with add-on mtDNA-specific analyses, including haplogroup prediction with Phy-Mer, heteroplasmy calculation, and mvTool annotations. We developed the Mitochondrial Disease Variant (MDV) classifier using XGBoost to predict variant pathogenicity for PMD. The MDV classifier was trained on >120 features and performance benchmarking showed that it correctly classified >98% of nuclear gene variants as being pathogenic or benign, and predicted PMD-causing variants with 94% precision. The MSeqDR QM server is an open-access resource for phenotype-driven dual-genome analyses for PMD diagnosis by the global mitochondrial disease community. It is publicly available for non-commercial, non-clinical research use at https://mseqdr.org/quickmitome.php. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Standardizing clinical phenotypes into human phenotype ontology (HPO) terms as the phenotype input for Quick-Mitome (QM) Basic Protocol 2: Prepare the pedigree input for multiple-sample VCF Basic Protocol 3: Quick-Mitome (QM) analysis Basic Protocol 4: Reviewing and understanding the QM Integrated Report and Analysis Report.

Transcription factors in the development and treatment of immune disorders

Immune function is highly controlled at the transcriptional level by the binding of transcription factors (TFs) to promoter and enhancer elements. Several TF families play major roles in immune gene expression, including NF-κB, STAT, IRF, AP-1, NRs, and NFAT, which trigger anti-pathogen responses, promote cell differentiation, and maintain immune system homeostasis. Aberrant expression, activation, or sequence of isoforms and variants of these TFs can result in autoimmune and inflammatory diseases as well as hematological and solid tumor cancers. For this reason, TFs have become attractive drug targets, even though most were previously deemed "undruggable" due to their lack of small molecule binding pockets and the presence of intrinsically disordered regions. However, several aspects of TF structure and function can be targeted for therapeutic intervention, such as ligand-binding domains, protein-protein interactions between TFs and with cofactors, TF-DNA binding, TF stability, upstream signaling pathways, and TF expression. In this review, we provide an overview of each of the important TF families, how they function in immunity, and some related diseases they are involved in. Additionally, we discuss the ways of targeting TFs with drugs along with recent research developments in these areas and their clinical applications, followed by the advantages and disadvantages of targeting TFs for the treatment of immune disorders.

The Medical Action Ontology: A tool for annotating and analyzing treatments and clinical management of human disease

BACKGROUND

Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.

METHODS

MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry. Term development involves a blend of manual and semi-automated processes. Additionally, we have generated annotations detailing diagnostic modalities for specific phenotypic abnormalities defined by the Human Phenotype Ontology (HPO). We introduce a web application, POET, that facilitates MAxO annotations for specific medical actions for diseases using the Mondo Disease Ontology.

FINDINGS

MAxO encompasses 1,757 terms spanning a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. These terms annotate phenotypic features associated with specific disease (using HPO and Mondo). Presently, there are over 16,000 MAxO diagnostic annotations that target HPO terms. Through POET, we have created 413 MAxO annotations specifying treatments for 189 rare diseases.

CONCLUSIONS

MAxO offers a computational representation of treatments and other actions taken for the clinical management of patients. Its development is closely coupled to Mondo and HPO, broadening the scope of our computational modeling of diseases and phenotypic features. We invite the community to contribute disease annotations using POET (https://poet.jax.org/). MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

FUNDING

NHGRI 1U24HG011449-01A1 and NHGRI 5RM1HG010860-04.

Developmental synapse pathology triggered by maternal exposure to the herbicide glufosinate ammonium

Environmental and genetic factors influence synapse formation. Numerous animal experiments have revealed that pesticides, including herbicides, can disturb normal intracellular signals, gene expression, and individual animal behaviors. However, the mechanism underlying the adverse outcomes of pesticide exposure remains elusive. Herein, we investigated the effect of maternal exposure to the herbicide glufosinate ammonium (GLA) on offspring neuronal synapse formation in vitro. Cultured cerebral cortical neurons prepared from mouse embryos with maternal GLA exposure demonstrated impaired synapse formation induced by synaptic organizer neuroligin 1 (NLGN1)-coated beads. Conversely, the direct administration of GLA to the neuronal cultures exhibited negligible effect on the NLGN1-induced synapse formation. The comparison of the transcriptomes of cultured neurons from embryos treated with maternal GLA or vehicle and a subsequent bioinformatics analysis of differentially expressed genes (DEGs) identified "nervous system development," including "synapse," as the top-ranking process for downregulated DEGs in the GLA group. In addition, we detected lower densities of parvalbumin (Pvalb)-positive neurons at the postnatal developmental stage in the medial prefrontal cortex (mPFC) of offspring born to GLA-exposed dams. These results suggest that maternal GLA exposure induces synapse pathology, with alterations in the expression of genes that regulate synaptic development via an indirect pathway distinct from the effect of direct GLA action on neurons.

Rare variants in the sodium-dependent phosphate transporter gene SLC34A3 explain missing heritability of urinary stone disease

Urinary stone disease (USD) is a major health burden affecting over 10% of the United Kingdom population. While stone disease is associated with lifestyle, genetic factors also strongly contribute. Common genetic variants at multiple loci from genome-wide association studies account for 5% of the estimated 45% heritability of the disorder. Here, we investigated the extent to which rare genetic variation contributes to the unexplained heritability of USD. Among participants of the United Kingdom 100,000-genome project, 374 unrelated individuals were identified and assigned diagnostic codes indicative of USD. Whole genome gene-based rare variant testing and polygenic risk scoring against a control population of 24,930 ancestry-matched controls was performed. We observed (and replicated in an independent dataset) exome-wide significant enrichment of monoallelic rare, predicted damaging variants in the SLC34A3 gene for a sodium-dependent phosphate transporter that were present in 5% cases compared with 1.6% of controls. This gene was previously associated with autosomal recessive disease. The effect on USD risk of having a qualifying SLC34A3 variant was greater than that of a standard deviation increase in polygenic risk derived from GWAS. Addition of the rare qualifying variants in SLC34A3 to a linear model including polygenic score increased the liability-adjusted heritability from 5.1% to 14.2% in the discovery cohort. We conclude that rare variants in SLC34A3 represent an important genetic risk factor for USD, with effect size intermediate between the fully penetrant rare variants linked with Mendelian disorders and common variants associated with USD. Thus, our findings explain some of the heritability unexplained by prior common variant genome-wide association studies.

Donor white blood cell differential is the single largest determinant of whole blood gene expression patterns

It has become widely accepted that sample cellular composition is a significant determinant of the gene expression patterns observed in any transcriptomic experiment performed with bulk tissue. Despite this, many investigations currently performed with whole blood do not experimentally account for possible inter-specimen differences in cellularity, and often assume that any observed gene expression differences are a result of true differences in nuclear transcription. In order to determine how confounding of an assumption this may be, in this study, we recruited a large cohort of human donors (n = 138) and used a combination of next generation sequencing and flow cytometry to quantify and compare the underlying contributions of variance in leukocyte counts versus variance in other biological factors to overall variance in whole blood transcript levels. Our results suggest that the combination of donor neutrophil and lymphocyte counts alone are the primary determinants of whole blood transcript levels for up to 75% of the protein-coding genes expressed in peripheral circulation, whereas the other factors such as age, sex, race, ethnicity, and common disease states have comparatively minimal influence. Broadly, this infers that a majority of gene expression differences observed in experiments performed with whole blood are driven by latent differences in leukocyte counts, and that cell count heterogeneity must be accounted for to meaningfully biologically interpret the results.

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human Disease

Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes. MAxO was developed with logical structures that make it compatible with several other ontologies within the Open Biological and Biomedical Ontologies (OBO) Foundry. These cover a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. We have created a database of over 16000 annotations that describe diagnostic modalities for specific phenotypic abnormalities as defined by the Human Phenotype Ontology (HPO). Additionally, 413 annotations are provided for medical actions for 189 rare diseases. We have developed a web application called POET (https://poet.jax.org/) for the community to use to contribute MAxO annotations. MAxO provides a computational representation of treatments and other actions taken for the clinical management of patients. The development of MAxO is closely coupled to the Mondo Disease Ontology (Mondo) and the Human Phenotype Ontology (HPO) and expands the scope of our computational modeling of diseases and phenotypic features to include diagnostics and therapeutic actions. MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method. Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method. Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

Case report: Extending the spectrum of clinical and molecular findings in FOXC1 haploinsufficiency syndrome

FOXC1 is a ubiquitously expressed forkhead transcription factor that plays a critical role during early development. Germline pathogenic variants in FOXC1 are associated with anterior segment dysgenesis and Axenfeld-Rieger syndrome (ARS, #602482), an autosomal dominant condition with ophthalmologic anterior segment abnormalities, high risk for glaucoma and extraocular findings including distinctive facial features, as well as dental, skeletal, audiologic, and cardiac anomalies. De Hauwere syndrome is an ultrarare condition previously associated with 6p microdeletions and characterized by anterior segment dysgenesis, joint instability, short stature, hydrocephalus, and skeletal abnormalities. Here, we report clinical findings of two unrelated adult females with FOXC1 haploinsufficiency who have ARS and skeletal abnormalities. Final molecular diagnoses of both patients were achieved using genome sequencing. Patient 1 had a complex rearrangement involving a 4.9 kB deletion including FOXC1 coding region (Hg19; chr6:1,609,721-1,614,709), as well as a 7 MB inversion (Hg19; chr6:1,614,710-8,676,899) and a second deletion of 7.1 kb (Hg19; chr6:8,676,900-8,684,071). Patient 2 had a heterozygous single nucleotide deletion, resulting in a frameshift and a premature stop codon in FOXC1 (NM_001453.3): c.467del, p.(Pro156Argfs*25). Both individuals had moderate short stature, skeletal abnormalities, anterior segment dysgenesis, glaucoma, joint laxity, pes planovalgus, dental anomalies, hydrocephalus, distinctive facial features, and normal intelligence. Skeletal surveys revealed dolichospondyly, epiphyseal hypoplasia of femoral and humeral heads, dolichocephaly with frontal bossin gand gracile long bones. We conclude that haploinsufficiency of FOXC1 causes ARS and a broad spectrum of symptoms with variable expressivity that at its most severe end also includes a phenotype overlapping with De Hauwere syndrome.

Use of whole genome sequencing to determine the genetic basis of visceral myopathies including Prune Belly syndrome

Objectives/aims

The visceral myopathies (VM) are a group of disorders characterised by poorly contractile or acontractile smooth muscle. They manifest in both the GI and GU tracts, ranging from megacystis to Prune Belly syndrome. We aimed to apply a bespoke virtual genetic panel and describe novel variants associated with this condition using whole genome sequencing data within the Genomics England 100,000 Genomes Project.

Methods

We screened the Genomics England 100,000 Genomes Project rare diseases database for patients with VM-related phenotypes. These patients were screened for sequence variants and copy number variants (CNV) in ACTG2, ACTA2, MYH11, MYLK, LMOD1, CHRM3, MYL9, FLNA and KNCMA1 by analysing whole genome sequencing data. The identified variants were analysed using variant effect predictor online tool, and any possible segregation in other family members and novel missense mutations was modelled using in silico tools. The VM cohort was also used to perform a genome-wide variant burden test in order to identify confirm gene associations in this cohort.

Results

We identified 76 patients with phenotypes consistent with a diagnosis of VM. The range of presentations included megacystis/microcolon hypoperistalsis syndrome, Prune Belly syndrome and chronic intestinal pseudo-obstruction. Of the patients in whom we identified heterozygous ACTG2 variants, 7 had likely pathogenic variants including 1 novel likely pathogenic allele. There were 4 patients in whom we identified a heterozygous MYH11 variant of uncertain significance which leads to a frameshift and a predicted protein elongation. We identified one family in whom we found a heterozygous variant of uncertain significance in KCNMA1 which in silico models predicted to be disease causing and may explain the VM phenotype seen. We did not find any CNV changes in known genes leading to VM-related disease phenotypes. In this phenotype selected cohort, ACTG2 is the largest monogenic cause of VM-related disease accounting for 9% of the cohort, supported by a variant burden test approach, which identified ACTG2 variants as the largest contributor to VM-related phenotypes.

Conclusions

VM are a group of disorders that are not easily classified and may be given different diagnostic labels depending on their phenotype. Molecular genetic analysis of these patients is valuable as it allows precise diagnosis and aids understanding of the underlying disease manifestations. We identified ACTG2 as the most frequent genetic cause of VM. We recommend a nomenclature change to 'autosomal dominant ACTG2 visceral myopathy' for patients with pathogenic variants in ACTG2 and associated VM phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44162-023-00012-z.

The endoplasmic reticulum stress sensor IRE1 regulates collagen secretion through the enforcement of the proteostasis factor P4HB/PDIA1 contributing to liver damage and fibrosis

Collagen is one the most abundant proteins and the main cargo of the secretory pathway, contributing to hepatic fibrosis and cirrhosis due to excessive deposition of extracellular matrix. Here we investigated the possible contribution of the unfolded protein response, the main adaptive pathway that monitors and adjusts the protein production capacity at the endoplasmic reticulum, to collagen biogenesis and liver disease. Genetic ablation of the ER stress sensor IRE1 reduced liver damage and diminished collagen deposition in models of liver fibrosis triggered by carbon tetrachloride (CCl ₄ ) administration or by high fat diet. Proteomic and transcriptomic profiling identified the prolyl 4-hydroxylase (P4HB, also known as PDIA1), which is known to be critical for collagen maturation, as a major IRE1-induced gene. Cell culture studies demonstrated that IRE1 deficiency results in collagen retention at the ER and altered secretion, a phenotype rescued by P4HB overexpression. Taken together, our results collectively establish a role of the IRE1/P4HB axis in the regulation of collagen production and its significance in the pathogenesis of various disease states.

Enriching representation learning using 53 million patient notes through human phenotype ontology embedding

The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.

Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland

BACKGROUND

Pediatric disorders include a range of highly penetrant, genetically heterogeneous conditions amenable to genomewide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits.

METHODS

We conducted a large-scale sequencing study involving more than 13,500 families with probands with severe, probably monogenic, difficult-to-diagnose developmental disorders from 24 regional genetics services in the United Kingdom and Ireland. Standardized phenotypic data were collected, and exome sequencing and microarray analyses were performed to investigate novel genetic causes. We developed an iterative variant analysis pipeline and reported candidate variants to clinical teams for validation and diagnostic interpretation to inform communication with families. Multiple regression analyses were performed to evaluate factors affecting the probability of diagnosis.

RESULTS

A total of 13,449 probands were included in the analyses. On average, we reported 1.0 candidate variant per parent-offspring trio and 2.5 variants per singleton proband. Using clinical and computational approaches to variant classification, we made a diagnosis in approximately 41% of probands (5502 of 13,449). Of 3599 probands in trios who received a diagnosis by clinical assertion, approximately 76% had a pathogenic de novo variant. Another 22% of probands (2997 of 13,449) had variants of uncertain significance in genes that were strongly linked to monogenic developmental disorders. Recruitment in a parent-offspring trio had the largest effect on the probability of diagnosis (odds ratio, 4.70; 95% confidence interval [CI], 4.16 to 5.31). Probands were less likely to receive a diagnosis if they were born extremely prematurely (i.e., 22 to 27 weeks' gestation; odds ratio, 0.39; 95% CI, 0.22 to 0.68), had in utero exposure to antiepileptic medications (odds ratio, 0.44; 95% CI, 0.29 to 0.67), had mothers with diabetes (odds ratio, 0.52; 95% CI, 0.41 to 0.67), or were of African ancestry (odds ratio, 0.51; 95% CI, 0.31 to 0.78).

CONCLUSIONS

Among probands with severe, probably monogenic, difficult-to-diagnose developmental disorders, multimodal analysis of genomewide data had good diagnostic power, even after previous attempts at diagnosis. (Funded by the Health Innovation Challenge Fund and Wellcome Sanger Institute.).

An Integrated Pipeline for Trio-Rapid Genome Sequencing in Critically Ill Infants

Trio-rapid genome sequencing (trio-rGS) can assist the genetic diagnosis of critically ill infants given its ability to detect a broad range of pathogenic variants, as well as microbes, simultaneously with high efficiency. To achieve more comprehensive clinical diagnoses, it is essential to propose a recommended protocol in clinical practice. Here, we introduced an integrated pipeline to detect germline variants and microorganisms simultaneously from trio-RGS in critically ill infants, which provides step-by-step criteria for the semi-automatic processing procedures. With this pipeline in clinical application, only 1 ml of peripheral blood is needed for clinicians to provide both genetic and infectious causal information to a patient. The establishment and clinical practice of the method is of great significance for further mining of high-throughput sequencing data and for assisting clinicians in promoting diagnosis efficiency and accuracy. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Experimental pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms Basic Protocol 2: Computational pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms.

The visualization of Orphadata neurology phenotypes

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds.

Laboratory testing for mitochondrial diseases: biomarkers for diagnosis and follow-up

The currently available biomarkers generally lack the specificity and sensitivity needed for the diagnosis and follow-up of patients with mitochondrial diseases (MDs). In this group of rare genetic disorders (mutations in approximately 350 genes associated with MDs), all clinical presentations, ages of disease onset and inheritance types are possible. Blood, urine, and cerebrospinal fluid surrogates are well-established biomarkers that are used in clinical practice to assess MD. One of the main challenges is validating specific and sensitive biomarkers for the diagnosis of disease and prediction of disease progression. Profiling of lactate, amino acids, organic acids, and acylcarnitine species is routinely conducted to assess MD patients. New biomarkers, including some proteins and circulating cell-free mitochondrial DNA, with increased diagnostic specificity have been identified in the last decade and have been proposed as potentially useful in the assessment of clinical outcomes. Despite these advances, even these new biomarkers are not sufficiently specific and sensitive to assess MD progression, and new biomarkers that indicate MD progression are urgently needed to monitor the success of novel therapeutic strategies. In this report, we review the mitochondrial biomarkers that are currently analyzed in clinical laboratories, new biomarkers, an overview of the most common laboratory diagnostic techniques, and future directions regarding targeted versus untargeted metabolomic and genomic approaches in the clinical laboratory setting. Brief descriptions of the current methodologies are also provided.

Accounting for cis-regulatory constraint prioritizes genes likely to affect species-specific traits

Measuring allele-specific expression in interspecies hybrids is a powerful way to detect cis-regulatory changes underlying adaptation. However, it remains difficult to identify genes most likely to explain species-specific traits. Here, we outline a simple strategy that leverages population-scale allele-specific RNA-seq data to identify genes that show constrained cis-regulation within species yet show divergence between species. Applying this strategy to data from human-chimpanzee hybrid cortical organoids, we identify signatures of lineage-specific selection on genes related to saccharide metabolism, neurodegeneration, and primary cilia. We also highlight cis-regulatory divergence in CUX1 and EDNRB that may shape the trajectory of human brain development.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

BACKGROUND

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

METHODS

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

FINDINGS

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

INTERPRETATION

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

FUNDING

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Genetic diagnosis for adult patients at a genetic clinic

Clinical utility of genetic testing has rapidly increased in the past decade to identify the definitive diagnosis, etiology, and specific management. The majority of patients receiving testing are children. There are several barriers for genetic tests in adult patients; barriers may arise from either patients or clinicians. Our study aims to realize the detection rate and the benefits of genetic tests in adults. We conducted a prospective study of 10 adult patients who were referred to a genetic clinic. Exome sequencing (ES) was pursued in all cases, and chromosomal microarray (CMA) was performed for six cases. Our result is impressive; six cases (60%) received likely pathogenic and pathogenic variants. Four definitive diagnosis cases had known pathogenic variants in KCNJ2, TGFBR1, SCN1A, and FBN1, whereas another two cases revealed novel likely pathogenic and pathogenic variants in GNB1 and DNAH9. Our study demonstrates the success in genetic diagnosis in adult patients: four cases with definitive, two cases with possible, and one case with partial diagnosis. The advantage of diagnosis is beyond obtaining the diagnosis itself, but also relieving any doubt for the patient regarding any previous questionable diagnosis, guide for management, and recurrence risk in their children or family members. Therefore, this supports the value of genetic testing in adult patients.

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.

Transcription factor NFE2L3 promotes the proliferation of esophageal squamous cell carcinoma cells and causes radiotherapy resistance by regulating IL-6

OBJECTIVE

To scrutinize the impact of overexpression and interference of NFE2L3 on radiosensitivity of esophageal squamous cell carcinoma cells (ESCC) and its downstream mechanism and to assess whether NFE2L3 expression alters in vivo radiosensitivity of ESCC by developing a subcutaneous tumor model in mice.

METHODS

Through RNA-Seq, we compared the differentially expressed genes between the ECA-109R cell line and its parent ECA-109 cell line. The differentially expressed genes were selected and verified by qRT-PCR. Transfection of ESCC cell lines with NFE2L3 inhibitor or mimic lentivirus constructs was done to study the activity of NFE2L3. To assess the effect of NFE2L3 on cellular growth and proliferation, clonogenic survival assay, EdU incorporation assay, and CCK-8 assay were done after irradiation. To probe how many irradiated DNA double-strand breaks were produced, the corresponding intensity of γ-H2AX foci were detected by immunofluorescence. Apoptotic cells were assayed by flow cytometry assay after irradiation; To investigate the downstream genes of NFE2L3, we knocked NFE2L3, and RNA-Seq was used to find out the downstream genes. qRT-PCR and western blot ensued to score associated protein profiles. The in vivo ESCC cell radiosensitivity was scrutinized by nude mouse xenograft models.

RESULTS

The differential genes between ECA-109R cells and its parent ECA-109 cells were compared by qRT-PCR to unveil a significant increase in NFE2L3 expression. Functional analysis indicated that NFE2L3 increased radioresistance in ESCC cells. Then, through high-throughput sequencing and bioinformatics analysis, IL-6 was found to be a hub gene that played a role downstream of NFE2L3 and was verified by qRT-PCR, western blot, and double luciferase reporter gene experiment. NFE2L3 could regulate ESCC cell radiosensitivity via the IL-6-STAT3 signaling pathway, and downregulation of IL-6 expression could reverse the effects of highly expressed NFE2L3. In vivo tumor xenograft experiments confirmed that NFE2L3 affects the sensitivity to radiation therapy.

CONCLUSION

NFE2L3 can affect the radiosensitivity of ESCC cells through IL-6 transcription and IL-6/STAT3 signaling pathway. This makes NFE2L3 a putative target to regulate ESCC cell radiosensitivity.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10^-3). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.

Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons

Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson’s disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs. single-cell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort’s transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss. Furthermore, it demonstrates the preclinical relevance of unbiased large transcriptomic assays of reprogrammed patient neurons.

Genome sequencing is a sensitive first-line test to diagnose individuals with intellectual disability

PURPOSE

Individuals with intellectual disability (ID) and/or neurodevelopment disorders (NDDs) are currently investigated with several different approaches in clinical genetic diagnostics.

METHODS

We compared the results from 3 diagnostic pipelines in patients with ID/NDD: genome sequencing (GS) first (N = 100), GS as a secondary test (N = 129), or chromosomal microarray (CMA) with or without FMR1 analysis (N = 421).

RESULTS

The diagnostic yield was 35% (GS-first), 26% (GS as a secondary test), and 11% (CMA/FMR1). Notably, the age of diagnosis was delayed by 1 year when GS was performed as a secondary test and the cost per diagnosed individual was 36% lower with GS first than with CMA/FMR1. Furthermore, 91% of those with a negative result after CMA/FMR1 analysis (338 individuals) have not yet been referred for additional genetic testing and remain undiagnosed.

CONCLUSION

Our findings strongly suggest that genome analysis outperforms other testing strategies and should replace traditional CMA and FMR1 analysis as a first-line genetic test in individuals with ID/NDD. GS is a sensitive, time- and cost-effective method that results in a confirmed molecular diagnosis in 35% of all referred patients.

Integration of Human Protein Sequence and Protein-Protein Interaction Data by Graph Autoencoder to Identify Novel Protein-Abnormal Phenotype Associations

Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets.

Natural history of Myhre syndrome

Background

Myhre syndrome (MS) is a rare genetic disease characterized by skeletal disorders, facial features and joint limitation, caused by a gain of function mutation in SMAD4 gene. The natural history of MS remains incompletely understood.

Methods

We recruited in a longitudinal retrospective study patients with molecular confirmed MS from the French reference center for rare skeletal dysplasia. We described natural history by chaining data from medical reports, clinical data warehouse, medical imaging and photographies.

Results

We included 12 patients. The median age was 22 years old (y/o). Intrauterine and postnatal growth retardation were consistently reported. In preschool age, neurodevelopment disorders were reported in 80% of children. Specifics facial and skeletal features, thickened skin and joint limitation occured mainly in school age children. The adolescence was marked by the occurrence of pulmonary arterial hypertension (PAH) and vascular stenosis. We reported for the first time recurrent strokes from the age of 26 y/o, caused by a moyamoya syndrome in one patient. Two patients died at late adolescence and in their 20 s respectively from PAH crises and mesenteric ischemia.

Conclusion

Myhre syndrome is a progressive disease with severe multisystemic impairement and life-threathning complication requiring multidisciplinary monitoring.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

Whole genome sequences discriminate hereditary hemorrhagic telangiectasia phenotypes by non-HHT deleterious DNA variation

The abnormal vascular structures of hereditary hemorrhagic telangiectasia (HHT) often cause severe anemia due to recurrent hemorrhage, but HHT causal genes do not predict the severity of hematological complications. We tested for chance inheritance and clinical associations of rare deleterious variants in which loss-of-function causes bleeding or hemolytic disorders in the general population. In double-blinded analyses, all 104 patients with HHT from a single reference center recruited to the 100 000 Genomes Project were categorized on new MALO (more/as-expected/less/opposite) sub-phenotype severity scales, and whole genome sequencing data were tested for high impact variants in 75 HHT-independent genes encoding coagulation factors, or platelet, hemoglobin, erythrocyte enzyme, and erythrocyte membrane constituents. Rare variants (all gnomAD allele frequencies <0.003) were identified in 56 (75%) of these 75 HHT-unrelated genes. Deleteriousness assignments by Combined Annotation Dependent Depletion (CADD) scores >15 were supported by gene-level mutation significance cutoff scores. CADD >15 variants were identified in 38/104 (36.5%) patients with HHT, found for 1 in 10 patients within platelet genes; 1 in 8 within coagulation genes; and 1 in 4 within erythrocyte hemolytic genes. In blinded analyses, patients with greater hemorrhagic severity that had been attributed solely to HHT vessels had more CADD-deleterious variants in platelet (Spearman ρ = 0.25; P = .008) and coagulation (Spearman ρ = 0.21; P = .024) genes. However, the HHT cohort had 60% fewer deleterious variants in platelet and coagulation genes than expected (Mann-Whitney test P = .021). In conclusion, patients with HHT commonly have rare variants in genes of relevance to their phenotype, offering new therapeutic targets and opportunities for informed, personalized medicine strategies.

Mutations in the ribosome biogenesis factor gene LTV1 are linked to LIPHAK syndrome, a novel poikiloderma-like disorder

In the framework of the UK 100 000 Genomes Project, we investigated the genetic origin of a previously undescribed recessive dermatological condition, which we named LIPHAK (LTV1-associated Inflammatory Poikiloderma with Hair abnormalities and Acral Keratoses), in four affected individuals from two UK families of Pakistani and Indian origins, respectively. Our analysis showed that only one gene, LTV1, carried rare biallelic variants that were shared in all affected individuals, and specifically they bore the NM_032860.5:c.503A > G, p.(Asn168Ser) change, found homozygously in all of them. In addition, high-resolution homozygosity mapping revealed the presence of a small 652-kb stretch on chromosome 6, encompassing LTV1, that was haploidentical and common to all affected individuals. The c.503A > G variant was predicted by in silico tools to affect the correct splicing of LTV1's exon 5. Minigene-driven splicing assays in HEK293T cells and in a skin sample from one of the patients confirmed that this variant was indeed responsible for the creation of a new donor splice site, resulting in aberrant splicing and in a premature termination codon in exon 6 of this gene. LTV1 encodes one of the ribosome biogenesis factors that promote the assembly of the small (40S) ribosomal subunit. In yeast, defects in LTV1 alter the export of nascent ribosomal subunits to the cytoplasm; however, the role of this gene in human pathology is unknown to date. Our data suggest that LIPHAK could be a previously unrecognized ribosomopathy.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R‐SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web‐accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

Gene-SCOUT: identifying genes with similar continuous trait fingerprints from phenome-wide association analyses

Large-scale phenome-wide association studies performed using densely-phenotyped cohorts such as the UK Biobank (UKB), reveal many statistically robust gene-phenotype relationships for both clinical and continuous traits. Here, we present Gene-SCOUT, a tool used to identify genes with similar continuous trait fingerprints to a gene of interest. A fingerprint reflects the continuous traits identified to be statistically associated with a gene of interest based on multiple underlying rare variant genetic architectures. Similarities between genes are evaluated by the cosine similarity measure, to capture concordant effect directionality, elucidating clusters of genes in a high dimensional space. The underlying gene-biomarker population-scale association statistics were obtained from a gene-level rare variant collapsing analysis performed on over 1500 continuous traits using 394 692 UKB participant exomes, with additional metabolomic trait associations provided through Nightingale Health's recent study of 121 394 of these participants. We demonstrate that gene similarity estimates from Gene-SCOUT provide stronger enrichments for clinical traits compared to existing methods. Furthermore, we provide a fully interactive web-resource (http://genescout.public.cgr.astrazeneca.com) to explore the pre-calculated exome-wide similarities. This resource enables a user to examine the biological relevance of the most similar genes for Gene Ontology (GO) enrichment and UKB clinical trait enrichment statistics, as well as a detailed breakdown of the traits underpinning a given fingerprint.

Diagnostic yield of whole exome data in fetuses aborted for conotruncal malformations

OBJECTIVE

We investigated a custom congenital heart disease (CHD) geneset to assess the diagnostic value of whole-exome sequencing (WES) in karyotype- and copy number variation (CNV)-negative aborted fetuses with conotruncal defects (CTD), and to explore the impact of postnatal phenotyping on genetic diagnosis.

METHODS

We sequentially analyzed CNV-seq and WES data from 47 CTD fetuses detected by prenatal ultrasonography. Fetuses with either a confirmed aneuploidy or pathogenic CNV were excluded from the WES analyses, which were performed following the American College of Medical Genetics and Genomics recommendations and a custom CHD-geneset. Imaging and autopsy were applied to obtain postnatal phenotypic information about aborted fetuses.

RESULTS

CNV-seq identified aneuploidy in 7/47 cases while 13/47 fetuses were CNV-positive. Eighty-five rare deleterious variants in 61 genes (from custom geneset) were identified by WES in the remaining fetuses. Of these, five (likely) pathogenic variants (LPV/PV) were identified in five fetuses, revealing a 10.6% incremental diagnostic yield. Furthermore, RERE:c.2461_2472delGGGATGTGGCGA was reclassified as LPV based on postnatal phenotypic data.

CONCLUSION

We have developed and defined a CHD gene panel that can be utilized in a subset of fetuses with CTDs. We demonstrate the utility of incorporating both prenatal and postnatal phenotypic information may facilitate WES diagnostics. This article is protected by copyright. All rights reserved.