The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.     

HPOSim: An R Package for Phenotypic Similarity Measure and Enrichment Analysis Based on the Human Phenotype Ontology

BackgroundPhenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO.ResultsHPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA).ConclusionsHPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (https://sourceforge.net/p/hposim/).     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

Calculating phenotypic similarity between genes using hierarchical structure data based on semantic similarity

Phenotypic similarity is correlated with a number of measures of gene function, such as relatedness at the level of direct protein-protein interaction. The phenotypic effect of a deleted or mutated gene, which is one part of gene annotation, has caught broad attention. However, there have been few measures to study phenotypic similarity with the data from Human Phenotype Ontology (HPO) database, therefore more analogous measures should be developed and investigated. We used five semantic similarity-based measures (Jiang and Conrath, Lin, Schlicker, Yu and Wu) to calculate the human phenotypic similarity between genes (PSG) with data from HPO database, and evaluated their accuracy with information of protein-protein interaction, protein complex, protein family, gene function or DNA sequence. Compared with the gene pairs that were random selected, the results of these methods were statistically significant (all P<0.001). Furthermore, we assessed the performance of these five measures by receiver operating characteristic (ROC) curve analysis, and found that most of them performed better than the previous methods. This work had proved that these measures based on semantic similarity for calculation of PSG were effective for hierarchical structure data. Our study contributes to the development and optimization of novel algorithms of PSG calculation and provides more alternative methods to researchers as well as tools and directions for PSG study.     

MitoPhen database: a human phenotype ontology-based approach to identify mitochondrial DNA diseases

Diagnosing mitochondrial disorders remains challenging. This is partly because the clinical phenotypes of patients overlap with those of other sporadic and inherited disorders. Although the widespread availability of genetic testing has increased the rate of diagnosis, the combination of phenotypic and genetic heterogeneity still makes it difficult to reach a timely molecular diagnosis with confidence. An objective, systematic method for describing the phenotypic spectra for each variant provides a potential solution to this problem. We curated the clinical phenotypes of 6688 published individuals with 89 pathogenic mitochondrial DNA (mtDNA) mutations, collating 26 348 human phenotype ontology (HPO) terms to establish the MitoPhen database. This enabled a hypothesis-free definition of mtDNA clinical syndromes, an overview of heteroplasmy-phenotype relationships, the identification of under-recognized phenotypes, and provides a publicly available reference dataset for objective clinical comparison with new patients using the HPO. Studying 77 patients with independently confirmed positive mtDNA diagnoses and 1083 confirmed rare disease cases with a non-mitochondrial nuclear genetic diagnosis, we show that HPO-based phenotype similarity scores can distinguish these two classes of rare disease patients with a false discovery rate <10% at a sensitivity of 80%. Enriching the MitoPhen database with more patients will improve predictions for increasingly rare variants.     

MicroRNA‐125b in vascular diseases: An updated systematic review of pathogenetic implications and clinical applications

Epigenetic changes, particularly non‐coding RNAs, have been implicated extensively in the pathogenesis of vascular diseases. Specific miRNAs are involved in the differentiation, phenotypic switch, proliferation, apoptosis, cytokine production and matrix deposition of endothelial cells and/or vascular smooth muscle cells. MicroRNA‐125b has been studied in depth for its role in carcinogenesis with a double‐edged role; that is, it can act as an oncogene in some cancer types and as a tumour suppressor gene in others. However, cumulative evidence from the use of advanced miRNA profiling techniques and bioinformatics analysis suggests that miR‐125b can be a potential mediator and useful marker of vascular diseases. Currently, the exact role of miR‐125b in vascular diseases is not known. In this systematic review, we intend to provide an updated compilation of all the recent findings of miR‐125b in vascular diseases, using a systematic approach of retrieving data from all available reports followed by data summarization. MiR‐125b serves as a pathogenic player in multiple vascular pathologies involving endothelia and vascular smooth muscle cells and also serves as a diagnostic marker for vascular diseases. We further provide a computational biologic presentation of the complex network of miR‐125b and its target genes within the scope of vascular diseases.     

Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.     

Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases

Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases.     

An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations

Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.     

Phenotypic switching in fungi

Over the past three decades new fungal diseases have emerged that now constitute a major threat, especially for patients with chronic diseases and/or underlying immune deficiencies. Despite the epidemiologic data, the emergence of stable drug-resistant or hypervirulent fungal strains in human disease has not been demonstrated as seen in emerging viral and bacterial infections. Fungi are eukaryotic microbes that capitalize on a sophisticated built-in ability to generate phenotypic variability. This successful strategy allows them to undergo rapid adaptation in response to environmental challenges, such as individual body locations that may exhibit drastic differences in temperature and pH. Rapid microevolution can also confer drug resistance and protect them from the host’s immune response. This review explores phenotypic switching in pathogenic fungi, including Candida spp and Cryptococcus spp, and how phenotypic switching contributes to the pathogenesis of fungal diseases.     

Isolation and spectroscopic characterization of a recombinant bell pepper hydroperoxide lyase

Fatty acid hydroperoxide (HPO) lyase is a component of the oxylipin pathway and holds a central role in elicited plant defense. HPO lyase from bell pepper has been identified as a heme protein which shares 40% homology with allene oxide synthase, a cytochrome P450 (CYP74A). HPO lyase of immature bell pepper fruits was expressed in Escherichia coli and the enzyme was purified and characterized by spectroscopic techniques. The electronic structure and ligand coordination properties of the heme were investigated by using a series of exogenous ligands. The various complexes were characterized by using UV-visible absorption and electron paramagnetic resonance spectroscopy. The spectroscopic data demonstrated that the isolated recombinant HPO lyase has a pentacoordinate, high-spin heme with thiolate ligation. Addition of the neutral ligand imidazole or the anionic ligand cyanide results in the formation of hexacoordinate adducts that retain thiolate ligation. The striking similarities between both the ferric and ferrous HPO lyase-NO complexes with the analogous P450 complexes, suggest that the active sites of HPO lyase and P450 share common structural features.     

Purification and Properties of Fatty Acid Hydroperoxide Lyase from Green Bell Pepper Fruits

Fatty acid hydroperoxide lyase (HPO lyase) was purified to apparentlyhomogeneity state from immature fruits of green bell pepper(Capsicum annuum L.) by differential centrifugation, ion-exchangechromatography, hydroxylapatite chromatography and gel filtration.The enzymatic activity was separated into two fractions (HPOlyases I and II) during the chromatography on hydroxylapatite.Both the isoforms were deduced to be trimers of 55-kDa subunitsand have similar enzymatic properties. Peptide maps revealedonly slight differences between them. Furthermore, immunoblotanalysis showed that an antibody raised against HPO lyase Ireacted with HPO lyase II as strongly as with the original antigen.These results indicate that there is only limited heterogeneityin terms of amino acid sequence and/or post-translational modification.The activities of both HPO lyases were considerably inhibitedby lipophilic antioxidants, such as nordihydroguaiaretic acidand     

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF,OMIM and PubMed records

Background

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

Results

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

Conclusion

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.     

Hepatopoietin interacts directly with COP9 signalosome and regulates AP-1 activity

Hepatopoietin (HPO)/augmenter of liver regeneration (ALR) is a specific hepatotrophic growth factor, which plays a key role in liver regeneration. Our previous study indicated that HPO executes its function by an inter-reactive network of the autocrine, paracrine and endocrine pathways. Recently, we have demonstrated that intracellular HPO interacts with Jun activation domain-binding protein 1 (JAB1) and leads to potentiation of activating protein-1 (AP-1) activity in a MAPK independent fashion. JAB1 is the fifth subunit of the COP9 signalosome (CSN), which is first identified as a suppressor of plant morphogenesis. A protein complex kinase activity associated with the CSN has been reported but not identified yet. In this report, we investigated further the association of HPO with the whole CSN. HPO exists in a complex with the eight-component CSN, both when purified from glycerol gradient centrifugation and when reciprocal immunoprecipitated from the lysates of transfected COS-7 cells. Intracellular HPO colocalizes with endogenous CSN in nucleus of hepatic cells. In addition, intracellular function of HPO that increases the phosphorylation of c-Jun leading to potentiate the AP-1 activity is inhibited by curcumin, a potent inhibitor of CSN-associated kinase. Taken together, these results elucidate a novel relationship of intracellular growth factor, HPO with large protein complex, CSN, which suggests a possible linkage between CSN and liver regeneration.     

Systematic association of genes to phenotypes by genome and literature mining

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene–phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.