Tsuyoshi Hachiya

Genome-wide association study identifies 112 new loci for body mass index in the Japanese population

Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of body mass index (BMI) in Japanese people (n = 173,430), we found 85 loci significantly associated with obesity (P < 5.0 × 10−8), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19+ cells. We also found significant genetic correlations between BMI and lymphocyte count (P = 6.46 × 10−5, rg = 0.18) and between BMI and multiple complex diseases. These findings provide genetic evidence that lymphocytes are relevant to body weight regulation and offer insights into the pathogenesis of obesity.

Accurate identification of orthologous segments among multiple genomes

MOTIVATION The accurate detection of orthologous segments (also referred to as syntenic segments) plays a key role in comparative genomics, as it is useful for inferring genome rearrangement scenarios and computing whole-genome alignments. Although a number of algorithms for detecting orthologous segments have been proposed, none of them contain a framework for optimizing their parameter values. METHODS In the present study, we propose an algorithm, named OSfinder (Orthologous Segment finder), which uses a novel scoring scheme based on stochastic models. OSfinder takes as input the positions of short homologous regions (also referred to as anchors) and explicitly discriminates orthologous anchors from non-orthologous anchors by using Markov chain models which represent respective geometric distributions of lengths of orthologous and non-orthologous anchors. Such stochastic modeling makes it possible to optimize parameter values by maximizing the likelihood of the input dataset, and to automate the setting of the optimal parameter values. RESULTS We validated the accuracies of orthology-mapping algorithms on the basis of their consistency with the orthology annotation of genes. Our evaluation tests using mammalian and bacterial genomes demonstrated that OSfinder shows higher accuracy than previous algorithms. AVAILABILITY The OSfinder software was implemented as a C++ program. The software is freely available at http://osfinder.dna.bio.keio.ac.jp under the GNU General Public License. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Tumour resistance in induced pluripotent stem cells derived from naked mole-rats

The naked mole-rat (NMR, Heterocephalus glaber), which is the longest-lived rodent species, exhibits extraordinary resistance to cancer. Here we report that NMR somatic cells exhibit a unique tumour-suppressor response to reprogramming induction. In this study, we generate NMR-induced pluripotent stem cells (NMR-iPSCs) and find that NMR-iPSCs do not exhibit teratoma-forming tumorigenicity due to the species-specific activation of tumour-suppressor alternative reading frame (ARF) and a disruption mutation of the oncogene ES cell-expressed Ras (ERAS). The forced expression of Arf in mouse iPSCs markedly reduces tumorigenicity. Furthermore, we identify an NMR-specific tumour-suppression phenotype—ARF suppression-induced senescence (ASIS)—that may protect iPSCs and somatic cells from ARF suppression and, as a consequence, tumorigenicity. Thus, NMR-specific ARF regulation and the disruption of ERAS regulate tumour resistance in NMR-iPSCs. Our findings obtained from studies of NMR-iPSCs provide new insight into the mechanisms of tumorigenicity in iPSCs and cancer resistance in the NMR.

Individualized Mutation Detection in Circulating Tumor DNA for Monitoring Colorectal Tumor Burden Using a Cancer-Associated Gene Sequencing Panel

Background Circulating tumor DNA (ctDNA) carries information on tumor burden. However, the mutation spectrum is different among tumors. This study was designed to examine the utility of ctDNA for monitoring tumor burden based on an individual mutation profile. Methodology DNA was extracted from a total of 176 samples, including pre- and post-operational plasma, primary tumors, and peripheral blood mononuclear cells (PBMC), from 44 individuals with colorectal tumor who underwent curative resection of colorectal tumors, as well as nine healthy individuals. Using a panel of 50 cancer-associated genes, tumor-unique mutations were identified by comparing the single nucleotide variants (SNVs) from tumors and PBMCs with an Ion PGM sequencer. A group of the tumor-unique mutations from individual tumors were designated as individual marker mutations (MMs) to trace tumor burden by ctDNA using droplet digital PCR (ddPCR). From these experiments, three major objectives were assessed: (a) Tumor-unique mutations; (b) mutation spectrum of a tumor; and (c) changes in allele frequency of the MMs in ctDNA after curative resection of the tumor. Results A total of 128 gene point mutations were identified in 27 colorectal tumors. Twenty-six genes were mutated in at least 1 sample, while 14 genes were found to be mutated in only 1 sample, respectively. An average of 2.7 genes were mutated per tumor. Subsequently, 24 MMs were selected from SNVs for tumor burden monitoring. Among the MMs found by ddPCR with > 0.1% variant allele frequency in plasma DNA, 100% (8 out of 8) exhibited a decrease in post-operation ctDNA, whereas none of the 16 MMs found by ddPCR with < 0.1% variant allele frequency in plasma DNA showed a decrease. Conclusions This panel of 50 cancer-associated genes appeared to be sufficient to identify individual, tumor-unique, mutated ctDNA markers in cancer patients. The MMs showed the clinical utility in monitoring curatively-treated colorectal tumor burden if the allele frequency of MMs in plasma DNA is above 0.1%.

An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds

BackgroundPrediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored.ResultsWe formulated pathway prediction between a start compound and a goal compound as the shortest path search problem in terms of the number of enzyme reactions applied. We propose an efficient search method based on A* algorithm and heuristic techniques utilizing Linear Programming (LP) solution for estimation of the distance to the goal. First, a chemical compound is represented by a feature vector which counts frequencies of substructure occurrences in the structural formula. Second, an enzyme reaction is represented as an operator vector by detecting the structural changes to compounds before and after the reaction. By defining compound vectors as nodes and operator vectors as edges, prediction of the reaction pathway is reduced to the shortest path search problem in the vector space. In experiments on the DDT degradation pathway, we verify that the shortest paths predicted by our method are biologically correct pathways registered in the KEGG database. The results also demonstrate that the LP heuristics can achieve significant reduction in computation time. Furthermore, we apply our method to a secondary metabolite pathway of plant origin, and successfully find a novel biochemical pathway which cannot be predicted by the existing method. For the reconstruction of a known biochemical pathway, our method is over 40 times as fast as the existing method.ConclusionsOur method enables fast and accurate de novo pathway predictions and novel pathway detection.

Genetic Predisposition to Ischemic Stroke: A Polygenic Risk Score

Background and Purpose— The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk. Methods— We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets). Results— In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33–2.31) and 1.99 (95% confidence interval, 1.19–3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001). Conclusions— The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors.

Meis1 Regulates Epidermal Stem Cells and Is Required for Skin Tumorigenesis

Previous studies have shown that Meis1 plays an important role in blood development and vascular homeostasis, and can induce blood cancers, such as leukemia. However, its role in epithelia remains largely unknown. Here, we uncover two roles for Meis1 in the epidermis: as a critical regulator of epidermal homeostasis in normal tissues and as a proto-oncogenic factor in neoplastic tissues. In normal epidermis, we show that Meis1 is predominantly expressed in the bulge region of the hair follicles where multipotent adult stem cells reside, and that the number of these stem cells is reduced when Meis1 is deleted in the epidermal tissue of mice. Mice with epidermal deletion of Meis1 developed significantly fewer DMBA/TPA-induced benign and malignant tumors compared with wild-type mice, suggesting that Meis1 plays a role in both tumor development and malignant progression. This is consistent with the observation that Meis1 expression increases as tumors progress from benign papillomas to malignant carcinomas. Interestingly, we found that Meis1 localization was altered to neoplasia development. Instead of being localized to the stem cell region, Meis1 is localized to more differentiated cells in tumor tissues. These findings suggest that, during the transformation from normal to neoplastic tissues, a functional switch occurs in Meis1.

COPICAT: A software system for predicting interactions between proteins and chemical compounds

UNLABELLED Since tens of millions of chemical compounds have been accumulated in public chemical databases, fast comprehensive computational methods to predict interactions between chemical compounds and proteins are needed for virtual screening of lead compounds. Previously, we proposed a novel method for predicting protein-chemical interactions using two-layer Support Vector Machine classifiers that require only readily available biochemical data, i.e. amino acid sequences of proteins and structure formulas of chemical compounds. In this article, the method has been implemented as the COPICAT web service, with an easy-to-use front-end interface. Users can simply submit a protein-chemical interaction prediction job using a pre-trained classifier, or can even train their own classification model by uploading training data. COPICATs fast and accurate computational prediction has enhanced lead compound discovery against a database of tens of millions of chemical compounds, implying that the search space for drug discovery is extended by >1000 times compared with currently well-used high-throughput screening methodologies. AVAILABILITY The COPICAT server is available at http://copicat.dna.bio.keio.ac.jp. All functions, including the prediction function are freely available via anonymous login without registration. Registered users, however, can use the system more intensively.

Intraindividual dynamics of transcriptome and genome-wide stability of DNA methylation

Cytosine methylation at CpG dinucleotides is an epigenetic mechanism that affects the gene expression profiles responsible for the functional differences in various cells and tissues. Although gene expression patterns are dynamically altered in response to various stimuli, the intraindividual dynamics of DNA methylation in human cells are yet to be fully understood. Here, we investigated the extent to which DNA methylation contributes to the dynamics of gene expression by collecting 24 blood samples from two individuals over a period of 3 months. Transcriptome and methylome association analyses revealed that only ~2% of dynamic changes in gene expression could be explained by the intraindividual variation of DNA methylation levels in peripheral blood mononuclear cells and purified monocytes. These results showed that DNA methylation levels remain stable for at least several months, suggesting that disease-associated DNA methylation markers are useful for estimating the risk of disease manifestation.

Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols

Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λadjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12–1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λadjusted = 1.00–1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models.