Soichi Ogishima

The Human Phenotype Ontology in 2017

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals

The Tohoku Medical Megabank Organization reports the whole-genome sequences of 1,070 healthy Japanese individuals and construction of a Japanese population reference panel (1KJPN). Here we identify through this high-coverage sequencing (32.4 × on average), 21.2 million, including 12 million novel, single-nucleotide variants (SNVs) at an estimated false discovery rate of <1.0%. This detailed analysis detected signatures for purifying selection on regulatory elements as well as coding regions. We also catalogue structural variants, including 3.4 million insertions and deletions, and 25,923 genic copy-number variants. The 1KJPN was effective for imputing genotypes of the Japanese population genome wide. These data demonstrate the value of high-coverage sequencing for constructing population-specific variant panels, which covers 99.0% SNVs of minor allele frequency ≥0.1%, and its value for identifying causal rare variants of complex human disease phenotypes in genetic association studies.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

The Tohoku Medical Megabank Project: Design and Mission

The Great East Japan Earthquake (GEJE) and resulting tsunami of March 11, 2011 gave rise to devastating damage on the Pacific coast of the Tohoku region. The Tohoku Medical Megabank Project (TMM), which is being conducted by Tohoku University Tohoku Medical Megabank Organization (ToMMo) and Iwate Medical University Iwate Tohoku Medical Megabank Organization (IMM), has been launched to realize creative reconstruction and to solve medical problems in the aftermath of this disaster. We started two prospective cohort studies in Miyagi and Iwate Prefectures: a population-based adult cohort study, the TMM Community-Based Cohort Study (TMM CommCohort Study), which will recruit 80 000 participants, and a birth and three-generation cohort study, the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study), which will recruit 70 000 participants, including fetuses and their parents, siblings, grandparents, and extended family members. The TMM CommCohort Study will recruit participants from 2013 to 2016 and follow them for at least 5 years. The TMM BirThree Cohort Study will recruit participants from 2013 to 2017 and follow them for at least 4 years. For children, the ToMMo Child Health Study, which adopted a cross-sectional design, was also started in November 2012 in Miyagi Prefecture. An integrated biobank will be constructed based on the two prospective cohort studies, and ToMMo and IMM will investigate the chronic medical impacts of the GEJE. The integrated biobank of TMM consists of health and clinical information, biospecimens, and genome and omics data. The biobank aims to establish a firm basis for personalized healthcare and medicine, mainly for diseases aggravated by the GEJE in the two prefectures. Biospecimens and related information in the biobank will be distributed to the research community. TMM itself will also undertake genomic and omics research. The aims of the genomic studies are: 1) to construct an integrated biobank; 2) to return genomic research results to the participants of the cohort studies, which will lead to the implementation of personalized healthcare and medicine in the affected areas in the near future; and 3) to contribute the development of personalized healthcare and medicine worldwide. Through the activities of TMM, we will clarify how to approach prolonged healthcare problems in areas damaged by large-scale disasters and how useful genomic information is for disease prevention.

Identification of six new genetic loci associated with atrial fibrillation in the Japanese population

Atrial fibrillation is the most common cardiac arrhythmia and leads to stroke. To investigate genetic loci associated with atrial fibrillation in the Japanese population, we performed a genome-wide association study (GWAS) that included 8,180 atrial fibrillation cases and 28,612 controls with follow-up in an additional 3,120 cases and 125,064 controls. We replicated previously reported loci and identified six new loci, near the KCND3, PPFIA4, SLC1A4–CEP68, HAND2, NEBL and SH3PXD2A genes. Five of the six new loci were specifically associated with atrial fibrillation in the Japanese population after comparing our data to those from individuals of European ancestry, suggesting that there might be different genetic factors affecting susceptibility across ancestry groups. Our study discovered variants in the HAND2, KCND3 and NEBL genes, which are relevant to atrial fibrillation susceptibility. The involvement of PPFIA4 and SH3PXD2A in axon guidance also suggested a role in disease pathogenesis. Our findings may contribute to a better understanding of atrial fibrillation susceptibility and pathogenesis.

A Map of Alzheimer's Disease–Signaling Pathways: A Hope for Drug Target Discovery

Alzheimers disease (AD) is a complex neurodegenerative condition, and its drug therapy is challenging. To inform AD drug discovery, we developed the “AlzPathway,” a prototype of a comprehensive map of AD‐related signaling pathways, from information obtained through studies in the public domain. The AlzPathway provides an integrated platform for systems analyses of AD‐signaling pathways and networks.

Analysis of L-glutamic acid fermentation by using a dynamic metabolic simulation model of Escherichia coli.

BackgroundUnderstanding the process of amino acid fermentation as a comprehensive system is a challenging task. Previously, we developed a literature-based dynamic simulation model, which included transcriptional regulation, transcription, translation, and enzymatic reactions related to glycolysis, the pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, and the anaplerotic pathway of Escherichia coli. During simulation, cell growth was defined such as to reproduce the experimental cell growth profile of fed-batch cultivation in jar fermenters. However, to confirm the biological appropriateness of our model, sensitivity analysis and experimental validation were required.ResultsWe constructed an l-glutamic acid fermentation simulation model by removing sucAB, a gene encoding α-ketoglutarate dehydrogenase. We then performed systematic sensitivity analysis for l-glutamic acid production; the results of this process corresponded with previous experimental data regarding l-glutamic acid fermentation. Furthermore, it allowed us to predicted the possibility that accumulation of 3-phosphoglycerate in the cell would regulate the carbon flux into the TCA cycle and lead to an increase in the yield of l-glutamic acid via fermentation. We validated this hypothesis through a fermentation experiment involving a model l-glutamic acid-production strain, E. coli MG1655 ΔsucA in which the phosphoglycerate kinase gene had been amplified to cause accumulation of 3-phosphoglycerate. The observed increase in l-glutamic acid production verified the biologically meaningful predictive power of our dynamic metabolic simulation model.ConclusionsIn this study, dynamic simulation using a literature-based model was shown to be useful for elucidating the precise mechanisms involved in fermentation processes inside the cell. Further exhaustive sensitivity analysis will facilitate identification of novel factors involved in the metabolic regulation of amino acid fermentation.

Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

Alzheimer’s disease (AD), the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs), we identified the PINs expressed in three brain regions: the entorhinal cortex (EC), hippocampus (HIP) and superior frontal gyrus (SFG). Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

Prognostic value of matrix Gla protein in breast cancer

To assess the prognostic value of matrix Gla protein (MGP) expression in cases of breast cancer, 9 samples from patients diagnosed with breast cancer who were followed up for more than 10 years were microdissected and then analyzed using Affymetrix U133 Plus 2.0 Arrays. Genes that exhibited significant differences in expression between patients with a good prognosis and those with a poor prognosis were identified. The MGP gene was among the genes up-regulated in cases where the prognosis was poor, indicating that the mRNA levels of MGP are a potential prognostic indicator of breast cancer. However, immunohistostaining of breast tissue microarrays (n=207) did not reveal a correlation between the protein expression of MGP and overall survival, neither was there a correlation between the protein expression of MGP and ER status or bone metastasis. In breast cancer cases, the mRNA level of MGP may be a marker indicating poor prognosis; however, protein expression determined by immunohistostaining is not.

Genes associated with the progression of neurofibrillary tangles in Alzheimer’s disease

The spreading of neurofibrillary tangles (NFTs), intraneuronal aggregates of highly phosphorylated microtubule-associated protein tau, across the human brain is correlated with the cognitive severity of Alzheimer’s disease (AD). To identify genes relevant to NFT expansion defined by the Braak stage, we conducted whole-genome exon array analysis with an exploratory sample set consisting of 213 human post-mortem brain tissue specimens from the entorinal, temporal and frontal cortices of 71 brain-donor subjects: Braak NFT stages 0 (N=13), I–II (N=20), III–IV (N=19) and V–VI (N=19). We identified eight genes, RELN, PTGS2, MYO5C, TRIL, DCHS2, GRB14, NPAS4 and PHYHD1, associated with the Braak stage. The expression levels of three genes, PHYHD1, MYO5C and GRB14, exhibited reproducible association on real-time quantitative PCR analysis. In another sample set, including control subjects (N=30), and in patients with late-onset AD (N=37), dementia with Lewy bodies (N=17) and Parkinson disease (N=36), the expression levels of two genes, PHYHD1 and MYO5C, were obviously associated with late-onset AD. Protein–protein interaction network analysis with a public database revealed that PHYHD1 interacts with MYO5C via POT1, and PHYHD1 directly interacts with amyloid beta-peptide 42. It is thus likely that functional failure of PHYHD1 and MYO5C could lead to AD development.