eRAM: encyclopedia of rare disease annotations for precision medicine.

Rare diseases affect over a hundred million people worldwide, most of these patients are not accurately diagnosed and effectively treated. The limited knowledge of rare diseases forms the biggest obstacle for improving their treatment. Detailed clinical phenotyping is considered as a keystone of deciphering genes and realizing the precision medicine for rare diseases. Here, we preset a standardized system for various types of rare diseases, called encyclopedia of Rare disease Annotations for Precision Medicine (eRAM). eRAM was built by text-mining nearly 10 million scientific publications and electronic medical records, and integrating various data in existing recognized databases (such as Unified Medical Language System (UMLS), Human Phenotype Ontology, Orphanet, OMIM, GWAS). eRAM systematically incorporates currently available data on clinical manifestations and molecular mechanisms of rare diseases and uncovers many novel associations among diseases. eRAM provides enriched annotations for 15 942 rare diseases, yielding 6147 human disease related phenotype terms, 31 661 mammalians phenotype terms, 10,202 symptoms from UMLS, 18 815 genes and 92 580 genotypes. eRAM can not only provide information about rare disease mechanism but also facilitate clinicians to make accurate diagnostic and therapeutic decisions towards rare diseases. eRAM can be freely accessed at http://www.unimd.org/eram/.

PedAM: a database for Pediatric Disease Annotation and Medicine.

There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/.

An improved approach to infer protein-protein interaction based on a hierarchical vector space model.

BACKGROUND: Comparing and classifying functions of gene products are important in today's biomedical research. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most widely used indicators for protein interaction. Among the various approaches proposed, those based on the vector space model are relatively simple, but their effectiveness is far from satisfying. RESULTS: We propose a Hierarchical Vector Space Model (HVSM) for computing semantic similarity between different genes or their products, which enhances the basic vector space model by introducing the relation between GO terms. Besides the directly annotated terms, HVSM also takes their ancestors and descendants related by "is_a" and "part_of" relations into account. Moreover, HVSM introduces the concept of a Certainty Factor to calibrate the semantic similarity based on the number of terms annotated to genes. To assess the performance of our method, we applied HVSM to Homo sapiens and Saccharomyces cerevisiae protein-protein interaction datasets. Compared with TCSS, Resnik, and other classic similarity measures, HVSM achieved significant improvement for distinguishing positive from negative protein interactions. We also tested its correlation with sequence, EC, and Pfam similarity using online tool CESSM. CONCLUSIONS: HVSM showed an improvement of up to 4% compared to TCSS, 8% compared to IntelliGO, 12% compared to basic VSM, 6% compared to Resnik, 8% compared to Lin, 11% compared to Jiang, 8% compared to Schlicker, and 11% compared to SimGIC using AUC scores. CESSM test showed HVSM was comparable to SimGIC, and superior to all other similarity measures in CESSM as well as TCSS. Supplementary information and the software are available at https://github.com/kejia1215/HVSM .

Genotype-phenotype correlations of Berardinelli-Seip congenital lipodystrophy and novel candidate genes prediction.

BACKGROUND: Berardinelli-Seip congenital lipodystrophy (BSCL) is a heterogeneous autosomal recessive disorder characterized by an almost total lack of adipose tissue in the body. Mutations in the AGPAT2, BSCL2, CAV1 and PTRF genes define I-IV subtype of BSLC respectively and clinical data indicate that new causative genes remain to be discovered. Here, we retrieved 341 cases from 60 BSCL-related studies worldwide and aimed to explore genotype-phenotype correlations based on mutations of AGPAT2 and BSCL2 genes from 251 cases. We also inferred new candidate genes for BSCL through protein-protein interaction and phenotype-similarity. RESULTS: Analysis results show that BSCL type II with earlier age of onset of diabetes mellitus, higher risk to suffer from premature death and mental retardation, is a more severe disorder than BSCL type I, but BSCL type I patients are more likely to have bone cysts. In BSCL type I, females are at higher risk of developing diabetes mellitus and acanthosis nigricans than males, while in BSCL type II, males suffer from diabetes mellitus earlier than females. In addition, some significant correlations among BSCL-related phenotypes were identified. New candidate genes prediction through protein-protein interaction and phenotype-similarity was conducted and we found that CAV3, EBP, SNAP29, HK1, CHRM3, OBSL1 and DNAJC13 genes could be the pathogenic factors for BSCL. Particularly, CAV3 and EBP could be high-priority candidate genes contributing to pathogenesis of BSCL. CONCLUSIONS: Our study largely enhances the current knowledge of phenotypic and genotypic heterogeneity of BSCL and promotes the more comprehensive understanding of pathogenic mechanisms for BSCL.

Genotype-Phenotype Association Analysis Reveals New Pathogenic Factors for Osteogenesis Imperfecta Disease.

Osteogenesis imperfecta (OI), mainly caused by structural abnormalities of type I collagen, is a hereditary rare disease characterized by increased bone fragility and reduced bone mass. Clinical manifestations of OI mostly include multiple repeated bone fractures, thin skin, blue sclera, hearing loss, cardiovascular and pulmonary system abnormalities, triangular face, dentinogenesis imperfecta (DI), and walking with assistance. Currently, 20 causative genes with 18 subtypes have been identified for OI, of them, variations in COL1A1 and COL1A2 have been demonstrated to be major causative factors to OI. However, the complexity of the bone formation process indicates that there are potential new pathogenic genes associated with OI. To comprehensively explore the underlying mechanism of OI, we conducted association analysis between genotypes and phenotypes of OI diseases and found that mutations in COL1A1 and COL1A2 contributed to a large proportion of the disease phenotypes. We categorized the clinical phenotypes and the genotypes based on the variation types for those 155 OI patients collected from literature, and association study revealed that three phenotypes (bone deformity, DI, walking with assistance) were enriched in two variation types (the Gly-substitution missense and groups of frameshift, nonsense, and splicing variations). We also identified four novel variations (c.G3290A (p.G1097D), c.G3289C (p.G1097R), c.G3289A (p.G1097S), c.G3281A (p.G1094D)) in gene COL1A1 and two novel variations (c.G2332T (p.G778C), c.G2341T (p.G781C)) in gene COL1A2, which could potentially contribute to the disease. In addition, we identified several new potential pathogenic genes (ADAMTS2, COL5A2, COL8A1) based on the integration of protein-protein interaction and pathway enrichment analysis. Our study provides new insights into the association between genotypes and phenotypes of OI and novel information for dissecting the underlying mechanism of the disease.

Systematically Analyzing the Pathogenic Variations for Acute Intermittent Porphyria.

The rare autosomal dominant disorder acute intermittent porphyria (AIP) is caused by the deficient activity of hydroxymethylbilane synthase (HMBS). The symptoms of AIP are acute neurovisceral attacks which are induced by the dysfunction of heme biosynthesis. To better interpret the underlying mechanism of clinical phenotypes, we collected 117 HMBS gene mutations from reported individuals with AIP and evaluated the mutations' impacts on the corresponding protein structure and function. We found that several mutations with most severe clinical symptoms are located at dipyromethane cofactor (DPM) binding domain of HMBS. Mutations on these residues likely significantly influence the catalytic reaction. To infer new pathogenic mutations, we evaluated the pathogenicity for all the possible missense mutations of HMBS gene with different bioinformatic prediction algorithms, and identified 34 mutations with serious pathogenicity and low allele frequency. In addition, we found that gene PPARA may also play an important role in the mechanisms of AIP attacks. Our analysis about the distribution frequencies of the 23 variations revealed different distribution patterns among eight ethnic populations, which could help to explain the genetic basis that may contribute to population disparities in AIP prevalence. Our systematic analysis provides a better understanding for this disease and helps for the diagnosis and treatment of AIP.

Corrigendum: Genotype-Phenotype Association Analysis Reveals New Pathogenic Factors for Osteogenesis Imperfecta Disease.

[This corrects the article DOI: 10.3389/fphar.2019.01200.].

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis.

DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.

Towards efficiency in rare disease research: what is distinctive and important?

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million worldwide. Over the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing (NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.