Fine-tuning Large Language Models for Rare Disease Concept Normalization

Objective

We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO).

Methods

We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms.

Results

When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ~20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy.

Conclusion

Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLM to identify named medical entities from the clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

Integrated Multi-Tissue Transcriptome Profiling Characterizes the Genetic Basis and Biomarkers Affecting Reproduction in Sheep (Ovis aries)

The heritability of litter size in sheep is low and controlled by multiple genes, but the research on its related genes is not sufficient. Here, to explore the expression pattern of multi-tissue genes in Chinese native sheep, we selected 10 tissues of the three adult ewes with the highest estimated breeding value in the early study of the prolific Xinggao sheep population. The global gene expression analysis showed that the ovary, uterus, and hypothalamus expressed the most genes. Using the Uniform Manifold Approximation and Projection (UMAP) cluster analysis, these samples were clustered into eight clusters. The functional enrichment analysis showed that the genes expressed in the spleen, uterus, and ovary were significantly enriched in the Ataxia Telangiectasia Mutated Protein (ATM) signaling pathway, and most genes in the liver, spleen, and ovary were enriched in the immune response pathway. Moreover, we focus on the expression genes of the hypothalamic-pituitary-ovarian axis (HPO) and found that 11,016 genes were co-expressed in the three tissues, and different tissues have different functions, but the oxytocin signaling pathway was widely enriched. To further explore the differences in the expression genes (DEGs) of HPO in different sheep breeds, we downloaded the transcriptome data in the public data, and the analysis of DEGs (Xinggao sheep vs. Sunite sheep in Hypothalamus, Xinggao sheep vs. Sunite sheep in Pituitary, and Xinggao sheep vs. Suffolk sheep in Ovary) revealed the neuroactive ligand-receptor interactions. In addition, the gene subsets of the transcription factors (TFs) of DEGs were identified. The results suggest that 51 TF genes and the homeobox TF may play an important role in transcriptional variation across the HPO. Altogether, our study provided the first fundamental resource to investigate the physiological functions and regulation mechanisms in sheep. This important data contributes to improving our understanding of the reproductive biology of sheep and isolating effecting molecular markers that can be used for genetic selection in sheep.

Integration of Phenotype Term Prioritization and Gene Expression Analysis Reveals a Novel Variant in the PERP Gene Associated with Autosomal Recessive Erythrokeratoderma

Hereditary palmoplantar keratodermas (PPKs) are a clinically and genetically heterogeneous group of disorders characterized by excessive epidermal thickening of palms and soles. Several genes have been associated with PPK including PERP, a gene encoding a crucial component of desmosomes that has been associated with dominant and recessive keratoderma. We report a patient with recessive erythrokeratoderma (EK) in which whole exome sequencing (WES) prioritized by human phenotype ontology (HPO) terms revealed the presence of the novel variant c.153C > A in the N-terminal region the PERP gene. This variant is predicted to have a nonsense effect, p.(Cys51Ter), resulting in a premature stop codon. We demonstrated a marked reduction in gene expression in cultured skin fibroblasts obtained from the patient. Despite the PERP gene is expressed at low levels in fibroblasts, our finding supports a loss-of-function (LoF) mechanism for the identified variant, as previously suggested in recessive EK. Our study underscores the importance of integrating HPO analysis when using WES for molecular genetic diagnosis in a clinical setting, as it facilitates continuous updates regarding gene-clinical feature associations.

Hydration: Tactical and Practical Strategies

Full-spectrum Human Performance Optimization (HPO) is essential for Special Operations Forces (SOF). Adequate hydration is essential to all aspects of performance (physical and cognitive) and recovery. Water losses occur as a result of physical activity and can increase further depending on clothing and environmental conditions. Without intentional and appropriate strategic hydration planning, Operators are at increased risk for degradation in performance and exertional heat illness. The purpose of this article is to highlight current best practices for maintaining hydration before, during, and after activity, while considering various environmental conditions. Effective leadership and planning are necessary for preparing Operators for successful military operations.

The impact of 22q11.2 copy-number variants on human traits in the general population

While extensively studied in clinical cohorts, the phenotypic consequences of 22q11.2 copy-number variants (CNVs) in the general population remain understudied. To address this gap, we performed a phenome-wide association scan in 405,324 unrelated UK Biobank (UKBB) participants by using CNV calls from genotyping array. We mapped 236 Human Phenotype Ontology terms linked to any of the 90 genes encompassed by the region to 170 UKBB traits and assessed the association between these traits and the copy-number state of 504 genotyping array probes in the region. We found significant associations for eight continuous and nine binary traits associated under different models (duplication-only, deletion-only, U-shape, and mirror models). The causal effect of the expression level of 22q11.2 genes on associated traits was assessed through transcriptome-wide Mendelian randomization (TWMR), revealing that increased expression of ARVCF increased BMI. Similarly, increased DGCR6 expression causally reduced mean platelet volume, in line with the corresponding CNV effect. Furthermore, cross-trait multivariable Mendelian randomization (MVMR) suggested a predominant role of genuine (horizontal) pleiotropy in the CNV region. Our findings show that within the general population, 22q11.2 CNVs are associated with traits previously linked to genes in the region, and duplications and deletions act upon traits in different fashions. We also showed that gain or loss of distinct segments within 22q11.2 may impact a trait under different association models. Our results have provided new insights to help further the understanding of the complex 22q11.2 region.

Gene-disease relationship evidence: A clinical perspective focusing on ultra-rare diseases

The ACMG framework for variant interpretation is well-established and widely used. Although formal guidelines have been published on the establishment of the gene-disease relationships as well, these are not nearly as widely acknowledged or utilized, and implementation of these guidelines is lagging. In addition, for many genes so little information is available that the framework cannot be used in sufficient detail. In this manuscript, we highlight the importance of distinguishing between phenotype-first and genotype-first gene-disease relationships. We discuss the approaches currently available to establish gene-disease relationships and suggest a checklist to assist in evaluating gene-disease relationships for genes with very little available information. Several real-life examples from clinical practice are given to illustrate the importance of a thorough thought process on gene-disease relationships. We hope that these considerations and the checklist will provide help for clinicians and clinical scientists faced which variants in genes without robustly ascertained gene-disease relationships.

Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology

Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care.

Quantitative dissection of multilocus pathogenic variation in an Egyptian infant with severe neurodevelopmental disorder resulting from multiple molecular diagnoses

Genomic sequencing and clinical genomics have demonstrated that substantial subsets of atypical and/or severe disease presentations result from multilocus pathogenic variation (MPV) causing blended phenotypes. In an infant with a severe neurodevelopmental disorder, four distinct molecular diagnoses were found by exome sequencing (ES). The blended phenotype that includes brain malformation, dysmorphism, and hypotonia was dissected using the Human Phenotype Ontology (HPO). ES revealed variants in CAPN3 (c.259C > G:p.L87V), MUSK (c.1781C > T:p.A594V), NAV2 (c.1996G > A:p.G666R), and ZC4H2 (c.595A > C:p.N199H). CAPN3, MUSK, and ZC4H2 are established disease genes linked to limb-girdle muscular dystrophy (OMIM# 253600), congenital myasthenia (OMIM# 616325), and Wieacker-Wolff syndrome (WWS; OMIM# 314580), respectively. NAV2 is a retinoic-acid responsive novel disease gene candidate with biological roles in neurite outgrowth and cerebellar dysgenesis in mouse models. Using semantic similarity, we show that no gene identified by ES individually explains the proband phenotype, but rather the totality of the clinically observed disease is explained by the combination of disease-contributing effects of the identified genes. These data reveal that multilocus pathogenic variation can result in a blended phenotype with each gene affecting a different part of the nervous system and nervous system-muscle connection. We provide evidence from this n = 1 study that in patients with MPV and complex blended phenotypes resulting from multiple molecular diagnoses, quantitative HPO analysis can allow for dissection of phenotypic contribution of both established disease genes and novel disease gene candidates not yet proven to cause human disease.

PhenomeCentral: 7 years of rare disease matchmaking

A major challenge in validating genetic causes for patients with rare diseases (RDs) is the difficulty in identifying other RD patients with overlapping phenotypes and variants in the same candidate gene. This process, known as matchmaking, requires robust data sharing solutions in order to be effective. In 2014 we launched PhenomeCentral, a RD data repository capable of collecting computer-readable genotypic and phenotypic data for the purposes of RD matchmaking. Over the past 7 years PhenomeCentral's features have been expanded and its dataset has consistently grown. There are currently 1,615 users registered on PhenomeCentral, which have contributed over 12,000 patient cases. Most of these cases contain detailed phenotypic terms, with a significant portion also providing genomic sequence data or other forms of clinical information. Matchmaking within PhenomeCentral, and with connections to other data repositories in the Matchmaker Exchange, have collectively resulted in over 60,000 matches, which have facilitated multiple gene discoveries. The collection of deep phenotypic and genotypic data has also positioned PhenomeCentral well to support next generation of matchmaking initiatives that utilize genome sequencing data, ensuring that PhenomeCentral will remain a useful tool in solving undiagnosed RD cases in the years to come. This article is protected by copyright. All rights reserved.

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources. This article is protected by copyright. All rights reserved.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

Learnings from a successful transformation to a high-performance organization: a longitudinal case study

An increasing number of organizations embark on a journey to transform themselves into a high-performance organization (HPO) to be able to better deal with the changes in their environment. Unfortunately, in the current literature hardly any approaches are described that can support them during this transformation. One promising approach, the (Waal and Heijtel, Meas Bus Excell 21:101–116, 2017) HPO transformation approach, has only been validated in one instance. This study outlines an additional validated approach. A simplified version of the aforementioned transformation approach (Waal et al. The high-performance finance function, handbook for the future-oriented financial professional, IGI Global, Hershey, 2022) was used at a case company which in 5 year time transformed into an HPO. Based on a questionnaire which collected and evaluated interventions undertaken by the case company, coupled with an extensive interview on the interventions with the manager of the case company, the interventions were categorized in the HPO transformation approach. The HPO transformation approach made it possible to make a structured overview of the interventions, which gave insight into how the case company went about its successful HPO transformation. Thus, the research has both a theoretical and practical contribution. Theoretically, the currently rather limited academic literature on transformation approaches regarding high-performance organizations is extended with another validated case, thus further developing this line of research. Practically, organizations get access to a validated transformation approach, thereby increasing their chance on a successful HPO transformation.

Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity

BACKGROUND

Accurate, detailed, and standardized phenotypic descriptions are essential to support diagnostic interpretation of genetic variants and to discover new diseases. The Human Phenotype Ontology (HPO), extensively used in rare disease research, provides a rich collection of vocabulary with standardized phenotypic descriptions in a hierarchical structure. However, to date, the use of HPO has not yet been widely implemented in the field of inborn errors of immunity (IEIs), mainly due to a lack of comprehensive IEI-related terms.

OBJECTIVES

We sought to systematically review available terms in HPO for the depiction of IEIs, to expand HPO, yielding more comprehensive sets of terms, and to reannotate IEIs with HPO terms to provide accurate, standardized phenotypic descriptions.

METHODS

We initiated a collaboration involving expert clinicians, geneticists, researchers working on IEIs, and bioinformaticians. Multiple branches of the HPO tree were restructured and extended on the basis of expert review. Our ontology-guided machine learning coupled with a 2-tier expert review was applied to reannotate defined subgroups of IEIs.

RESULTS

We revised and expanded 4 main branches of the HPO tree. Here, we reannotated 73 diseases from 4 International Union of Immunological Societies-defined IEI disease subgroups with HPO terms. We achieved a 4.7-fold increase in the number of phenotypic terms per disease. Given the new HPO annotations, we demonstrated improved ability to computationally match selected IEI cases to their known diagnosis, and improved phenotype-driven disease classification.

CONCLUSIONS

Our targeted expansion and reannotation presents enhanced precision of disease annotation, will enable superior HPO-based IEI characterization, and hence benefit both IEI diagnostic and research activities.

Clan genomics: From OMIM phenotypic traits to genes and biology

Clinical characterization of a patient phenotype has been the quintessential approach for elucidating a differential diagnosis and a hypothesis to explore a potential clinical diagnosis. This has resulted in a language of medicine and a semantic ontology, with both specialty- and subspecialty-specific lexicons, that can be challenging to translate and interpret. There is no 'Rosetta Stone' of clinical medicine such as the genetic code that can assist translation and interpretation of the language of genetics. Nevertheless, the information content embodied within a clinical diagnosis can guide management, therapeutic intervention, and potentially prognostic outlook of disease enabling anticipatory guidance for patients and families. Clinical genomics is now established firmly in medical practice. The granularity and informative content of a personal genome is immense. Yet, we are limited in our utility of much of that personal genome information by the lack of functional characterization of the overwhelming majority of computationally annotated genes in the haploid human reference genome sequence. Whereas DNA and the genetic code have provided a 'Rosetta Stone' to translate genetic variant information, clinical medicine, and clinical genomics provide the context to understand human biology and disease. A path forward will integrate deep phenotyping, such as available in a clinical synopsis in the Online Mendelian Inheritance in Man (OMIM) entries, with personal genome analyses.

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.

HPO-driven virtual gene panel: a new efficient approach in molecular autopsy of sudden unexplained death

Background

Molecular autopsy represents an efficient tool to save the diagnosis in up to one-third of sudden unexplained death (SUD). A defined gene panel is usually used for the examination. Alternatively, it is possible to carry out a comprehensive genetic assessment (whole exome sequencing, WES), which also identifies rare, previously unknown variants. The disadvantage is that a dramatic number of variants must be assessed to identify the causal variant. To improve the evaluation of WES, the human phenotype ontology (HPO) annotation is used internationally for deep phenotyping in the field of rare disease. However, a HPO-based evaluation of WES in SUD has not been described before.

Methods

We performed WES in tissue samples from 16 people after SUD. Instead of a fixed gene panel, we defined a set of HPO terms and thus created a flexible “virtual gene panel”, with the advantage, that recently identified genes are automatically associated by HPO terms in the HPO database.

Results

We obtained a mean value of 68,947 variants per sample. Stringent filtering ended up in a mean value of 276 variants per sample. Using the HPO-driven virtual gene panel we developed an algorithm that prioritized 1.4% of the variants. Variant interpretation resulted in eleven potentially causative variants in 16 individuals.

Conclusion

Our data introduce an effective diagnostic procedure in molecular autopsy of SUD with a non-specific clinical phenotype.

Human disease genes website series: An international, open and dynamic library for up-to-date clinical information

Since the introduction of next‐generation sequencing, an increasing number of disorders have been discovered to have genetic etiology. To address diverse clinical questions and coordinate research activities that arise with the identification of these rare disorders, we developed the Human Disease Genes website series (HDG website series): an international digital library that records detailed information on the clinical phenotype of novel genetic variants in the human genome (https://humandiseasegenes.info/). Each gene website is moderated by a dedicated team of clinicians and researchers, focused on specific genes, and provides up‐to‐date—including unpublished—clinical information. The HDG website series is expanding rapidly with 424 genes currently adopted by 325 moderators from across the globe. On average, a gene website has detailed phenotypic information of 14.4 patients. There are multiple examples of added value, one being the ARID1B gene website, which was recently utilized in research to collect clinical information of 81 new patients. Additionally, several gene websites have more data available than currently published in the literature. In conclusion, the HDG website series provides an easily accessible, open and up‐to‐date clinical data resource for patients with pathogenic variants of individual genes. This is a valuable resource not only for clinicians dealing with rare genetic disorders such as developmental delay and autism, but other professionals working in diagnostics and basic research. Since the HDG website series is a dynamic platform, its data also include the phenotype of yet unpublished patients curated by professionals providing higher quality clinical detail to improve management of these rare disorders.

Hypothesis: determining phenotypic specificity facilitates understanding of pathophysiology in rare genetic disorders

In the rapidly growing group of rare genetic disorders, data scarcity demands an intelligible use of available data, in order to improve understanding of underlying pathophysiology. We hypothesize, based on the principle that clinical similarities may be indicative of shared pathophysiology, that determining phenotypic specificity could provide unsuspected insights in pathophysiology of rare genetic disorders. We explored our hypothesis by studying subunit deficiencies of the conserved oligomeric Golgi (COG) complex, a subgroup of congenital disorders of glycosylation (CDG). In this systematic data assessment, all 45 reported patients with COG-CDG were included. The vocabulary of the Human Phenotype Ontology was used to annotate all phenotypic features and to assess occurrence in other genetic disorders. Gene occurrence ratios were calculated by dividing the frequency in the patient cohort over the number of associated genes, according to the Human Phenotype Ontology. Prioritisation based on phenotypic specificity was highly informative and captured phenotypic features commonly associated with glycosylation disorders. Moreover, it captured features not seen in any other glycosylation disorder, among which episodic fever, likely reflecting underappreciated other cellular functions of the COG complex. Interestingly, the COG complex was recently implicated in the autophagy pathway, as are more than half of the genes underlying disorders that present with episodic fever. This suggests that whereas many phenotypic features in these patients are caused by disrupted glycosylation, episodic fever might be caused by disrupted autophagy. Thus, we here demonstrate support for our hypothesis that determining phenotypic specificity could facilitate understanding of pathophysiology in rare genetic disorders.

Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics

The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing human disease phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.

The phenotype-driven computational analysis yields clinical diagnosis for patients with atypical manifestations of known intellectual disability syndromes

BACKGROUND

Due to extensive clinical and genetic heterogeneity of intellectual disability (ID) syndromes, the process of diagnosis is very challenging even for expert clinicians. Despite recent advancements in molecular diagnostics methodologies, a significant fraction of ID patients remains without a clinical diagnosis.

METHODS, RESULTS, AND CONCLUSIONS

Here, in a prospective study on a cohort of 21 families (trios) with a child presenting with ID of unknown etiology, we executed phenotype-driven bioinformatic analysis method, PhenIX, utilizing targeted next-generation sequencing (NGS) data and Human Phenotype Ontology (HPO)-encoded phenotype data. This approach resulted in clinical diagnosis for eight individuals presenting with atypical manifestations of Rubinstein-Taybi syndrome 2 (MIM 613684), Spastic Paraplegia 50 (MIM 612936), Wiedemann-Steiner syndrome (MIM 605130), Cornelia de Lange syndrome 2 (MIM 300590), Cerebral creatine deficiency syndrome 1 (MIM 300352), Glass Syndrome (MIM 612313), Mental retardation, autosomal dominant 31 (MIM 616158), and Bosch-Boonstra-Schaaf optic atrophy syndrome (MIM 615722).

Dysmorphology in a Genomic Era

Dysmorphology is the practice of defining the morphologic phenotype of syndromic disorders. Genomic sequencing has advanced our understanding of human variation and molecular dysmorphology has evolved in response to the science of relating embryologic developmental implications of abnormal gene signaling pathways to the resultant phenotypic presentation. Machine learning has enabled the application of deep convoluted neural networks to recognize the comparative likeness of these phenotypes relative to the causal genotype or disrupted gene pathway.

Application of gas cyclone-liquid jet absorption separator for purification of tail gas containing ammonia

In this experiment, with stainless steel gas cyclone-liquid jet absorption separator as carrier, NH₃ as experimental gas, and water and H₃PO₄ solution as absorbents, corresponding NH₃ absorption rate change is obtained through the adjustment of experimental parameters, such as NH₃ inlet concentration, inlet velocity of mixed gas, injection flow rate of absorbent, temperature of absorbent, and H₃PO₄ absorbent concentration. The NH₃ absorption rate decreases with the increase in NH₃ inlet concentration and inlet gas velocity. The NH₃ absorption rate will increase first and then tends to remain unchanged after reaching a certain degree with the increase in liquid injection flow rate and absorbent concentration. The NH₃ absorption rate will increase first and then decrease with the increase in the absorbent temperature. The maximum NH₃ removal efficiencies of water and H₃PO₄ were 96% and 99%, respectively.

PPR-SSM: personalized PageRank and semantic similarity measures for entity linking

BACKGROUND

Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups. However, as new concepts are introduced, biomedical literature is prone to ambiguity, specifically in fields that are advancing more rapidly, for example, drug design and development. Entity linking is a text mining task that aims at linking entities mentioned in the literature to concepts in a knowledge base. For example, entity linking can help finding all documents that mention the same concept and improve relation extraction methods. Existing approaches focus on the local similarity of each entity and the global coherence of all entities in a document, but do not take into account the semantics of the domain.

RESULTS

We propose a method, PPR-SSM, to link entities found in documents to concepts from domain-specific ontologies. Our method is based on Personalized PageRank (PPR), using the relations of the ontology to generate a graph of candidate concepts for the mentioned entities. We demonstrate how the knowledge encoded in a domain-specific ontology can be used to calculate the coherence of a set of candidate concepts, improving the accuracy of entity linking. Furthermore, we explore weighting the edges between candidate concepts using semantic similarity measures (SSM). We show how PPR-SSM can be used to effectively link named entities to biomedical ontologies, namely chemical compounds, phenotypes, and gene-product localization and processes.

CONCLUSIONS

We demonstrated that PPR-SSM outperforms state-of-the-art entity linking methods in four distinct gold standards, by taking advantage of the semantic information contained in ontologies. Moreover, PPR-SSM is a graph-based method that does not require training data. Our method improved the entity linking accuracy of chemical compounds by 0.1385 when compared to a method that does not use SSMs.

Role of hormonal and inflammatory alterations in obesity-related reproductive dysfunction at the level of the hypothalamic-pituitary-ovarian axis

BACKGROUND

Besides being a risk factor for multiple metabolic disorders, obesity could affect female reproduction. While increased adiposity is associated with hormonal changes that could disrupt the function of the hypothalamus and the pituitary, compelling data suggest that obesity-related hormonal and inflammatory changes could directly impact ovarian function.

OBJECTIVE

To review the available data related to the mechanisms by which obesity, and its associated hormonal and inflammatory changes, could affect the female reproductive function with a focus on the hypothalamic-pituitary-ovarian (HPO) axis.

METHODS

PubMed database search for publications in English language until October 2017 pertaining to obesity and female reproductive function was performed.

RESULTS

The obesity-related changes in hormone levels, in particular leptin, adiponectin, ghrelin, neuropeptide Y and agouti-related protein, are associated with reproductive dysfunction at both the hypothalamic-pituitary and the ovarian levels. The pro-inflammatory molecules advanced glycation end products (AGEs) and monocyte chemotactic protein-1 (MCP-1) are emerging as relatively new players in the pathophysiology of obesity-related ovarian dysfunction.

CONCLUSION

There is an intricate crosstalk between the adipose tissue and the inflammatory system with the HPO axis function. Understanding the mechanisms behind this crosstalk could lead to potential therapies for the common obesity-related reproductive dysfunction.

Text-mined phenotype annotation and vector-based similarity to improve identification of similar phenotypes and causative genes in monogenic disease patients

The genetic diagnosis of rare monogenic diseases using exome/genome sequencing requires the true causal variant(s) to be identified from tens of thousands of observed variants. Typically a virtual gene panel approach is taken whereby only variants in genes known to cause phenotypes resembling the patient under investigation are considered. With the number of known monogenic gene-disease pairs exceeding 5,000, manual curation of personalized virtual panels using exhaustive knowledge of the genetic basis of the human monogenic phenotypic spectrum is challenging. We present improved probabilistic methods for estimating phenotypic similarity based on Human Phenotype Ontology annotation. A limitation of existing methods for evaluating a disease's similarity to a reference set is that reference diseases are typically represented as a series of binary (present/absent) observations of phenotypic terms. We evaluate a quantified disease reference set, using term frequency in phenotypic text descriptions to approximate term relevance. We demonstrate an improved ability to identify related diseases through the use of a quantified reference set, and that vector space similarity measures perform better than established information content-based measures. These improvements enable the generation of bespoke virtual gene panels, facilitating more accurate and efficient interpretation of genomic variant profiles from individuals with rare Mendelian disorders. These methods are available online at https://atlas.genetics.kcl.ac.uk/~jake/cgi-bin/patient_sim.py.

Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders

Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.

Computational Prediction of Position Effects of Apparently Balanced Human Chromosomal Rearrangements

Interpretation of variants of uncertain significance, especially chromosomal rearrangements in non-coding regions of the human genome, remains one of the biggest challenges in modern molecular diagnosis. To improve our understanding and interpretation of such variants, we used high-resolution three-dimensional chromosomal structural data and transcriptional regulatory information to predict position effects and their association with pathogenic phenotypes in 17 subjects with apparently balanced chromosomal abnormalities. We found that the rearrangements predict disruption of long-range chromatin interactions between several enhancers and genes whose annotated clinical features are strongly associated with the subjects' phenotypes. We confirm gene-expression changes for a couple of candidate genes to exemplify the utility of our analysis of position effect. These results highlight the important interplay between chromosomal structure and disease and demonstrate the need to utilize chromatin conformational data for the prediction of position effects in the clinical interpretation of non-coding chromosomal rearrangements.

A comprehensive global genotype-phenotype database for rare diseases

Background

The ability to discover genetic variants in a patient runs far ahead of the ability to interpret them. Databases with accurate descriptions of the causal relationship between the variants and the phenotype are valuable since these are critical tools in clinical genetic diagnostics. Here, we introduce a comprehensive and global genotype–phenotype database focusing on rare diseases.

Methods

This database (CentoMD^®) is a browser‐based tool that enables access to a comprehensive, independently curated system utilizing stringent high‐quality criteria and a quickly growing repository of genetic and human phenotype ontology (HPO)‐based clinical information. Its main goals are to aid the evaluation of genetic variants, to enhance the validity of the genetic analytical workflow, to increase the quality of genetic diagnoses, and to improve evaluation of treatment options for patients with hereditary diseases. The database software correlates clinical information from consented patients and probands of different geographical backgrounds with a large dataset of genetic variants and, when available, biomarker information. An automated follow‐up tool is incorporated that informs all users whenever a variant classification has changed. These unique features fully embedded in a CLIA/CAP‐accredited quality management system allow appropriate data quality and enhanced patient safety.

Results

More than 100,000 genetically screened individuals are documented in the database, resulting in more than 470 million variant detections. Approximately, 57% of the clinically relevant and uncertain variants in the database are novel. Notably, 3% of the genetic variants identified and previously reported in the literature as being associated with a particular rare disease were reclassified, based on internal evidence, as clinically irrelevant.

Conclusions

The database offers a comprehensive summary of the clinical validity and causality of detected gene variants with their associated phenotypes, and is a valuable tool for identifying new disease genes through the correlation of novel genetic variants with specific, well‐defined phenotypes.

The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles

Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

Systematic comparison of the mono-, dimethyl- and trimethyl 3-hydroxy-4(1H)-pyridones - Attempted optimization of the orally active iron chelator, deferiprone

A range of close analogues of deferiprone have been synthesised. The group includes mono-, di- and tri-methyl-3-hydroxy-4(1H)-pyridones. These compounds were found to possess similar pFe(3+) values to that of deferiprone, with the exception of the 2.5-dimethylated derivatives. Surprisingly the NH-containing hydroxy-4(1H)-pyridones were found to be marginally more lipophilic than the corresponding N-Me containing analogues. This same group are also metabolised less efficiently by Phase 1 hydroxylating enzymes than the corresponding N-Me analogues. As result of this study, three compounds have been identified for further investigation centred on neutropenia and agranulocytosis.

PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases

The discovery of disease-causing mutations typically requires confirmation of the variant or gene in multiple unrelated individuals, and a large number of rare genetic diseases remain unsolved due to difficulty identifying second families. To enable the secure sharing of case records by clinicians and rare disease scientists, we have developed the PhenomeCentral portal (https://phenomecentral.org). Each record includes a phenotypic description and relevant genetic information (exome or candidate genes). PhenomeCentral identifies similar patients in the database based on semantic similarity between clinical features, automatically prioritized genes from whole-exome data, and candidate genes entered by the users, enabling both hypothesis-free and hypothesis-driven matchmaking. Users can then contact other submitters to follow up on promising matches. PhenomeCentral incorporates data for over 1,000 patients with rare genetic diseases, contributed by the FORGE and Care4Rare Canada projects, the US NIH Undiagnosed Diseases Program, the EU Neuromics and ANDDIrare projects, as well as numerous independent clinicians and scientists. Though the majority of these records have associated exome data, most lack a molecular diagnosis. PhenomeCentral has already been used to identify causative mutations for several patients, and its ability to find matching patients and diagnose these diseases will grow with each additional patient that is entered.

Molecular characterization, tissue distribution and feeding related changes of NUCB2A/nesfatin-1 in Ya-fish (Schizothorax prenanti)

The protein nucleobindin-2 (NUCB2) was identified over a decade ago and recently raised great interest as its derived peptide nesfatin-1 was shown to reduce food intake and body weight in rodents. However, the involvement of NUCB2 in feeding behavior has not well been studied in fish. In the present study, we characterized the structure, distribution, and meal responsive of NUCB2A/nesfatin-1 in Ya-fish (Schizothorax prenanti) for the first time. The full length cDNA of Ya-fish was 2140base pair (bp), which encoded a polypeptide of 487 amino acid residues including a 23 amino acid signal peptide. A high conservation in NUCB2 sequences was found in vertebrates, however the proposed propeptide cleavage site (Arg-Arg) conserved among other species is not present in Ya-fish NUCB2A sequence. Tissue distribution analysis revealed that Ya-fish NUCB2A mRNA was ubiquitously expressed in all test tissues, and abundant expression was detected in several regions including the hypothalamus, hepatopancreas, ovary and intestines. NUCB2A mRNA expression respond to feeding status change may vary and be tissue specific. NUCB2A mRNA levels significantly increased (P<0.05) in the hypothalamus and intestines after feeding and substantially decreased (P<0.01) during a week food deprivation in the hypothalamus. Meanwhile, NUCB2A mRNA in the hepatopancreas was significantly elevated (P<0.001) during food deprivation, and a similar increase was also found after short-time fasting. This points toward a potential hepatopancreas specific local role for NUCB2A in the regulation of metabolism during food deprivation. Collectively, these results provide the molecular and functional evidence to support potential anorectic and metabolic roles for NUCB2A in Ya-fish.