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.
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.
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/.
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.
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/.
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