Naoki Nariai

Transposon mutagenesis identifies genes that transform neural stem cells into glioma-initiating cells

Neural stem cells (NSCs) are considered to be the cell of origin of glioblastoma multiforme (GBM). However, the genetic alterations that transform NSCs into glioma-initiating cells remain elusive. Using a unique transposon mutagenesis strategy that mutagenizes NSCs in culture, followed by additional rounds of mutagenesis to generate tumors in vivo, we have identified genes and signaling pathways that can transform NSCs into glioma-initiating cells. Mobilization of Sleeping Beauty transposons in NSCs induced the immortalization of astroglial-like cells, which were then able to generate tumors with characteristics of the mesenchymal subtype of GBM on transplantation, consistent with a potential astroglial origin for mesenchymal GBM. Sequence analysis of transposon insertion sites from tumors and immortalized cells identified more than 200 frequently mutated genes, including human GBM-associated genes, such as Met and Nf1, and made it possible to discriminate between genes that function during astroglial immortalization vs. later stages of tumor development. We also functionally validated five GBM candidate genes using a previously undescribed high-throughput method. Finally, we show that even clonally related tumors derived from the same immortalized line have acquired distinct combinations of genetic alterations during tumor development, suggesting that tumor formation in this model system involves competition among genetically variant cells, which is similar to the Darwinian evolutionary processes now thought to generate many human cancers. This mutagenesis strategy is faster and simpler than conventional transposon screens and can potentially be applied to any tissue stem/progenitor cells that can be grown and differentiated in vitro.

TIGAR2: sensitive and accurate estimation of transcript isoform expression with longer RNA-Seq reads

BackgroundHigh-throughput RNA sequencing (RNA-Seq) enables quantification and identification of transcripts at single-base resolution. Recently, longer sequence reads become available thanks to the development of new types of sequencing technologies as well as improvements in chemical reagents for the Next Generation Sequencers. Although several computational methods have been proposed for quantifying gene expression levels from RNA-Seq data, they are not sufficiently optimized for longer reads (e.g. > 250 bp).ResultsWe propose TIGAR2, a statistical method for quantifying transcript isoforms from fixed and variable length RNA-Seq data. Our method models substitution, deletion, and insertion errors of sequencers based on gapped-alignments of reads to the reference cDNA sequences so that sensitive read-aligners such as Bowtie2 and BWA-MEM are effectively incorporated in our pipeline. Also, a heuristic algorithm is implemented in variational Bayesian inference for faster computation. We apply TIGAR2 to both simulation data and real data of human samples and evaluate performance of transcript quantification with TIGAR2 in comparison to existing methods.ConclusionsTIGAR2 is a sensitive and accurate tool for quantifying transcript isoform abundances from RNA-Seq data. Our method performs better than existing methods for the fixed-length reads (100 bp, 250 bp, 500 bp, and 1000 bp of both single-end and paired-end) and variable-length reads, especially for reads longer than 250 bp.

TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference

MOTIVATION Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other. RESULTS We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods. AVAILABILITY An implementation of our method is available at http://github.com/nariai/tigar CONTACT nariai@megabank.tohoku.ac.jp SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

iJGVD: an integrative Japanese genome variation database based on whole-genome sequencing

The integrative Japanese Genome Variation Database (iJGVD; http://ijgvd.megabank.tohoku.ac.jp/) provides genomic variation data detected by whole-genome sequencing (WGS) of Japanese individuals. Specifically, the database contains variants detected by WGS of 1,070 individuals who participated in a genome cohort study of the Tohoku Medical Megabank Project. In the first release, iJGVD includes >4,300,000 autosomal single nucleotide variants (SNVs) whose minor allele frequencies are >5.0%.

HLA-VBSeq: accurate HLA typing at full resolution from whole-genome sequencing data

BackgroundHuman leucocyte antigen (HLA) genes play an important role in determining the outcome of organ transplantation and are linked to many human diseases. Because of the diversity and polymorphisms of HLA loci, HLA typing at high resolution is challenging even with whole-genome sequencing data.ResultsWe have developed a computational tool, HLA-VBSeq, to estimate the most probable HLA alleles at full (8-digit) resolution from whole-genome sequence data. HLA-VBSeq simultaneously optimizes read alignments to HLA allele sequences and abundance of reads on HLA alleles by variational Bayesian inference. We show the effectiveness of the proposed method over other methods through the analysis of predicting HLA types for HLA class I (HLA-A, -B and -C) and class II (HLA-DQA1,-DQB1 and -DRB1) loci from the simulation data of various depth of coverage, and real sequencing data of human trio samples.ConclusionsHLA-VBSeq is an efficient and accurate HLA typing method using high-throughput sequencing data without the need of primer design for HLA loci. Moreover, it does not assume any prior knowledge about HLA allele frequencies, and hence HLA-VBSeq is broadly applicable to human samples obtained from a genetically diverse population.

Japonica array: improved genotype imputation by designing a population-specific SNP array with 1070 Japanese individuals

The Tohoku Medical Megabank Organization constructed the reference panel (referred to as the 1KJPN panel), which contains >20 million single nucleotide polymorphisms (SNPs), from whole-genome sequence data from 1070 Japanese individuals. The 1KJPN panel contains the largest number of haplotypes of Japanese ancestry to date. Here, from the 1KJPN panel, we designed a novel custom-made SNP array, named the Japonica array, which is suitable for whole-genome imputation of Japanese individuals. The array contains 659 253 SNPs, including tag SNPs for imputation, SNPs of Y chromosome and mitochondria, and SNPs related to previously reported genome-wide association studies and pharmacogenomics. The Japonica array provides better imputation performance for Japanese individuals than the existing commercially available SNP arrays with both the 1KJPN panel and the International 1000 genomes project panel. For common SNPs (minor allele frequency (MAF)>5%), the genomic coverage of the Japonica array (r2>0.8) was 96.9%, that is, almost all common SNPs were covered by this array. Nonetheless, the coverage of low-frequency SNPs (0.5%<MAF⩽5%) of the Japonica array reached 67.2%, which is higher than those of the existing arrays. In addition, we confirmed the high quality genotyping performance of the Japonica array using the 288 samples in 1KJPN; the average call rate 99.7% and the average concordance rate 99.7% to the genotypes obtained from high-throughput sequencer. As demonstrated in this study, the creation of custom-made SNP arrays based on a population-specific reference panel is a practical way to facilitate further association studies through genome-wide genotype imputations.

Wnt3a stimulates maturation of impaired neutrophils developed from severe congenital neutropenia patient-derived pluripotent stem cells

The derivation of induced pluripotent stem (iPS) cells from individuals of genetic disorders offers new opportunities for basic research into these diseases and the development of therapeutic compounds. Severe congenital neutropenia (SCN) is a serious disorder characterized by severe neutropenia at birth. SCN is associated with heterozygous mutations in the neutrophil elastase [elastase, neutrophil-expressed (ELANE)] gene, but the mechanisms that disrupt neutrophil development have not yet been clarified because of the current lack of an appropriate disease model. Here, we generated iPS cells from an individual with SCN (SCN-iPS cells). Granulopoiesis from SCN-iPS cells revealed neutrophil maturation arrest and little sensitivity to granulocyte-colony stimulating factor, reflecting a disease status of SCN. Molecular analysis of the granulopoiesis from the SCN-iPS cells vs. control iPS cells showed reduced expression of genes related to the wingless-type mmtv integration site family, member 3a (Wnt3a)/β-catenin pathway [e.g., lymphoid enhancer-binding factor 1], whereas Wnt3a administration induced elevation lymphoid enhancer-binding factor 1-expression and the maturation of SCN-iPS cell-derived neutrophils. These results indicate that SCN-iPS cells provide a useful disease model for SCN, and the activation of the Wnt3a/β-catenin pathway may offer a novel therapy for SCN with ELANE mutation.

iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data

BackgroundStructural variations (SVs), such as insertions, deletions, inversions, and duplications, are a common feature in human genomes, and a number of studies have reported that such SVs are associated with human diseases. Although the progress of next generation sequencing (NGS) technologies has led to the discovery of a large number of SVs, accurate and genome-wide detection of SVs remains challenging. Thus far, various calling algorithms based on NGS data have been proposed. However, their strategies are diverse and there is no tool able to detect a full range of SVs accurately.ResultsWe focused on evaluating the performance of existing deletion calling algorithms for various spanning ranges from low- to high-coverage simulation data. The simulation data was generated from a whole genome sequence with artificial SVs constructed based on the distribution of variants obtained from the 1000 Genomes Project. From the simulation analysis, deletion calls of various deletion sizes were obtained with each caller, and it was found that the performance was quite different according to the type of algorithms and targeting deletion size. Based on these results, we propose an integrated structural variant calling pipeline (iSVP) that combines existing methods with a newly devised filtering and merging processes. It achieved highly accurate deletion calling with >90% precision and >90% recall on the 30× read data for a broad range of size. We applied iSVP to the whole-genome sequence data of a CEU HapMap sample, and detected a large number of deletions, including notable peaks around 300 bp and 6,000 bp, which corresponded to Alus and long interspersed nuclear elements, respectively. In addition, many of the predicted deletions were highly consistent with experimentally validated ones by other studies.ConclusionsWe present iSVP, a new deletion calling pipeline to obtain a genome-wide landscape of deletions in a highly accurate manner. From simulation and real data analysis, we show that iSVP is broadly applicable to human whole-genome sequencing data, which will elucidate relationships between SVs across genomes and associated diseases or biological functions.

Profiling of microRNA in human and mouse ES and iPS cells reveals overlapping but distinct microRNA expression patterns.

Using quantitative PCR-based miRNA arrays, we comprehensively analyzed the expression profiles of miRNAs in human and mouse embryonic stem (ES), induced pluripotent stem (iPS), and somatic cells. Immature pluripotent cells were purified using SSEA-1 or SSEA-4 and were used for miRNA profiling. Hierarchical clustering and consensus clustering by nonnegative matrix factorization showed two major clusters, human ES/iPS cells and other cell groups, as previously reported. Principal components analysis (PCA) to identify miRNAs that segregate in these two groups identified miR-187, 299-3p, 499-5p, 628-5p, and 888 as new miRNAs that specifically characterize human ES/iPS cells. Detailed direct comparisons of miRNA expression levels in human ES and iPS cells showed that several miRNAs included in the chromosome 19 miRNA cluster were more strongly expressed in iPS cells than in ES cells. Similar analysis was conducted with mouse ES/iPS cells and somatic cells, and several miRNAs that had not been reported to be expressed in mouse ES/iPS cells were suggested to be ES/iPS cell-specific miRNAs by PCA. Comparison of the average expression levels of miRNAs in ES/iPS cells in humans and mice showed quite similar expression patterns of human/mouse miRNAs. However, several mouse- or human-specific miRNAs are ranked as high expressers. Time course tracing of miRNA levels during embryoid body formation revealed drastic and different patterns of changes in their levels. In summary, our miRNA expression profiling encompassing human and mouse ES and iPS cells gave various perspectives in understanding the miRNA core regulatory networks regulating pluripotent cells characteristics.

Identification of acquired mutations by whole-genome sequencing in GATA-2 deficiency evolving into myelodysplasia and acute leukemia

Heterozygous GATA-2 germline mutations are associated with overlapping clinical manifestations termed GATA-2 deficiency, characterized by immunodeficiency and predisposition to myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). However, there is considerable clinical heterogeneity among patients, and the molecular basis for the evolution of immunodeficiency into MDS/AML remains unknown. Thus, we conducted whole-genome sequencing on a patient with a germline GATA-2 heterozygous mutation (c. 988 C > T; p. R330X), who had a history suggestive of immunodeficiency and evolved into MDS/AML. Analysis was conducted with DNA samples from leukocytes for immunodeficiency, bone marrow mononuclear cells for MDS and bone marrow-derived mesenchymal stem cells. Whereas we did not identify a candidate genomic deletion that may contribute to the evolution into MDS, a total of 280 MDS-specific nonsynonymous single nucleotide variants were identified. By narrowing down with the single nucleotide polymorphism database, the functional missense database, and NCBI information, we finally identified three candidate mutations for EZH2, HECW2 and GATA-1, which may contribute to the evolution of the disease.