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State-space models (SSMs) have been widely used for analyzing time-series data in the fields of economics and bioinformatics to express the dynamic behavior of data. Recently, filtering and smoothing algorithms applied to linear discrete SSMs with skewed and heavy-tailed measurement noise have been proposed for a more appropriate model because measurement noise does not often follow a Gaussian distribution. In this paper, we propose a linear SSM with skew-t measurement noise for predicting blood test values, along with a method for estimating their parameter values to ensure consistency with the data when using a generalized expectation-maximization (EM) algorithm. To validate the effectiveness of the proposed model and method, we analyze time-series blood test data using both Gaussian and skew-t measurement noise and compared their prediction accuracy for future values. Then, we predicted future blood test values of the unhealthy participant under his current and improved lifestyles. By comparing these predicted results under different lifestyles, we demonstrate that he will overcome lifestyle-related diseases with the improved lifestyle.

Intestinal microflora has been associated with obesity. While visceral fat is more strongly associated with cardiovascular disorder, a complication linked to obesity, than the body mass index (BMI), the association between intestinal microflora and obesity (as defined in terms of BMI) has been studied widely. However, the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigate the association between intestinal microflora and VFA and BMI using a longitudinal study on Japanese subjects with different VFA statuses (

Amylase genes reside in a structurally complex locus, and their copy numbers vary greatly, especially among agricultural races. Amylase genes seem to shape the metabolic response to dietary starch, and several studies have reported their association with obesity. Besides, the effect of amylase copy numbers seems to depend on lifestyle, and the mechanism of this effect was partially explained by changes in the oral and gut microbiome compositions; however, a detailed mechanism has been unclarified. In this study, we showed their association with diabetes in addition to obesity, and further discovered a plausible mechanism of this association based on the function of commensal bacterial in a northern Japanese population. First, we confirmed that the amylase copy number in the population tends to be larger than that reported in other studies and that there is a positive association between obesity and diabetes (p =1.95E-2 and 3.28E-2). Second, we identified that relative abundance of some genus level microbiome, Capnocytophaga, Dialister, and previously reported bacteria, were significantly associated with amylase copy numbers. Finally, through functional gene-set analysis using shotgun sequencing, we observed that the abundance of genes in the Acarbose pathway in the gut microbiome was significantly decreased with an increase in the amylase copy number (p-value = 5.80E-4), which can partly explain the mechanism underlying obesity and diabetes in populations with high amylase copy numbers.

Background: The oral microbiome, which consists of various habitats, has been shown to be influenced by smoking. However, differences in the tongue microbiomes of current and former smokers, as well as their resultant functional consequences, have rarely been investigated in East Asian populations. Methods: We used 16S rRNA amplicon sequencing of tongue-coating samples obtained from East Asian subjects who were current, former, or never smokers to identify differences in their tongue microbiomes and related metagenomic functions. Two sets of participants from 2016 to 2017 (n = 657 and n = 187, respectively) were analyzed separately. Results: We found significant differences between the overall microbiome compositions of current versus never smokers (p = 0.0015), but not between former versus never smokers (p = 0.43) based on the weighted UniFrac distance. Twenty-nine of 43 investigated genera showed significantly different expression levels in current versus never smokers. Neisseria and Capnocytophaga were less abundant, and Streptococcus and Megasphaera were more abundant in current smokers. Moreover, the abundances of metagenomic pathways, including those related to nitrate reduction and the tricarboxylic acid cycle, were significantly different between current and never smokers. Conclusions: The tongue microbiomes and related metagenomic pathways of current smokers differ from those of never smokers among East Asians.

Background The relationship between chronic kidney disease (CKD) and the gut microbiome, which interact through chronic inflammation, uraemic toxin production and immune response regulation, has gained interest in the development of CKD therapies. However, reports using shotgun metagenomic analysis of the gut microbiome are scarce, especially for early CKD. Here we characterized gut microbiome differences between non-CKD participants and ones with early CKD using metagenomic sequencing. Methods In total, 74 non-CKD participants and 37 participants with early CKD were included based on propensity score matching, controlling for various factors including dietary intake. Stool samples were collected from participants and subjected to shotgun sequencing. Bacterial and pathway abundances were profiled at the species level with MetaPhlAn2 and HUMAnN2, respectively, and overall microbiome differences were determined using Bray–Curtis dissimilarities. Diabetic and non-diabetic populations were analysed separately. Results For diabetic and non-diabetic participants, the mean estimated glomerular filtration rates of the CKD group were 53.71 [standard deviation (SD) 3.87] and 53.72 (SD 4.44), whereas those of the non-CKD group were 72.63 (SD 7.72) and 76.10 (SD 9.84), respectively. Alpha and beta diversities were not significantly different between groups. Based on taxonomic analysis, butyrate-producing species Roseburia inulinivorans, Ruminococcus torques and Ruminococcus lactaris were more abundant in the non-CKD group, whereas Bacteroides caccae and Bacteroides coprocora were more abundant in the non-diabetic CKD group. Conclusions Although gut microbiome changes in individuals with early CKD were subtle, the results suggest that changes related to producing short-chain fatty acids can already be observed in early CKD.

Summary The application of bacteriophages (phages) is proposed as a highly specific therapy for intestinal pathobiont elimination. However, the infectious associations between phages and bacteria in the human intestine, which is essential information for the development of phage therapies, have yet to be fully elucidated. Here, we report the intestinal viral microbiomes (viromes), together with bacterial microbiomes (bacteriomes), in 101 healthy Japanese individuals. Based on the genomic sequences of bacteriomes and viromes from the same fecal samples, the host bacteria-phage associations are illustrated for both temperate and virulent phages. To verify the usefulness of the comprehensive host bacteria-phage information, we screened Clostridioides difficile-specific phages and identified antibacterial enzymes whose activity is confirmed both in vitro and in vivo. These comprehensive metagenome analyses reveal not only host bacteria-phage associations in the human intestine but also provide vital information for the development of phage therapies against intestinal pathobionts.

High visceral fat area (VFA) is a stronger predictor of cardiovascular disease and overall mortality, compared with body mass index (BMI) and waist circumference (WC). Recent reports demonstrate that obesity is related to breath gas, which is produced by the intestinal microflora. However, these studies define obesity using BMI, not VFA. In this population-based cross-sectional study, we investigated the relationship between breath gases (methane and hydrogen) and both VFA and BMI. A total of 1033 participants (62% women; age [mean ± standard deviation] 54.4 ± 14.9 years) in the 2015 Iwaki Health Promotion Project in Japan were enrolled in the study. Breath samples were collected using a breath bag and analyzed by gas chromatography. VFA was measured using a visceral fat meter. The proportion of methanogenic bacteria to total intestinal microbiota was measured by polymerase chain reaction and 16S rRNA gene sequencing analysis. Our analysis revealed a significant association between high VFA and low breath methane, even after adjusting for confounding factors (B = −0.024 and P = 0.004). To identify the association between breath methane and VFA in participants with methane-producing bacteria in their intestinal microflora, participants were divided into two groups based on the presence or absence of methanogenic bacteria in their stool. The Methanogen + group was further divided into two subgroups with breath methane higher (Methane-UP) or lower (Methane-LO) than the median breath methane concentration. VFA was significantly lower in the Methane-UP group than in the Methane-LO group. In participants with methanogenic bacteria, breath methane concentration might be an independent biomarker of visceral fat accumulation.

Cigarette smoking affects the oral microbiome, which is related to various systemic diseases. While studies that investigated the relationship between smoking and the oral microbiome by 16S rRNA amplicon sequencing have been performed, investigations involving metagenomic sequences are rare. We investigated the bacterial species composition in the tongue microbiome, as well as single-nucleotide variant (SNV) profiles and gene content of these species, in never and current smokers by utilizing metagenomic sequences. Among 234 never smokers and 52 current smokers, beta diversity, as assessed by weighted UniFrac measure, differed between never and current smokers (pseudo-F = 8.44, R2 = 0.028, p = 0.001). Among the 26 species that had sufficient coverage, the SNV profiles of Actinomyces graevenitzii, Megasphaera micronuciformis, Rothia mucilaginosa, Veillonella dispar, and one Veillonella sp. were significantly different between never and current smokers. Analysis of gene and pathway content revealed that genes related to the lipopolysaccharide biosynthesis pathway in Veillonella dispar were present more frequently in current smokers. We found that species-level tongue microbiome differed between never and current smokers, and 5 species from never and current smokers likely harbor different strains, as suggested by the difference in SNV frequency.

The gut microbiota is reported to be related to obesity, and visceral fat is reported to be strongly associated with cardiovascular disease and overall mortality. However, the association between the gut microbiota and obesity has mainly been studied using body mass index (BMI) as a proxy for obesity. We investigated the relationship of both visceral fat and BMI with the gut microbiota stratified by sex in a population-based cross-sectional study of Japanese men and women 20–76 years of age (n = 1001). Women with a higher visceral fat area (VFA) harboured a higher relative abundance of the Firmicutes phylum (P for trend P for trend 0.030), whereas men with higher VFA harboured a lower relative abundance of the Firmicutes phylum (P for trend 0.076) and a higher relative abundance of the Bacteroidetes phylum (P for trend 0.013). Similar results were obtained using BMI as an index, but the differences were not significant in men. At the genus level, Blautia was the only gut microbe significantly and inversely associated with VFA regardless of sex. In conclusion, at the genus level we found that Blautia was the only gut microbe significantly and inversely associated with VFA, regardless of sex.

STAG2 encodes a cohesin component and is frequently mutated in myeloid neoplasms, showing highly significant comutation patterns with other drivers, including RUNX1. However, the molecular basis of cohesin-mutated leukemogenesis remains poorly understood. Here we show a critical role of an interplay between STAG2 and RUNX1 in the regulation of enhancer–promoter looping and transcription in hematopoiesis. Combined loss of STAG2 and RUNX1, which colocalize at enhancer-rich, CTCF-deficient sites, synergistically attenuates enhancer–promoter loops, particularly at sites enriched for RNA polymerase II and Mediator, and deregulates gene expression, leading to myeloid-skewed expansion of hematopoietic stem/progenitor cells (HSPC) and myelodysplastic syndromes (MDS) in mice. Attenuated enhancer–promoter loops in STAG2/RUNX1–deficient cells are associated with downregulation of genes with high basal transcriptional pausing, which are important for regulation of HSPCs. Downregulation of high-pausing genes is also confirmed in STAG2–cohesin-mutated primary leukemia samples. Our results highlight a unique STAG2–RUNX1 interplay in gene regulation and provide insights into cohesin-mutated leukemogenesis. Significance: We demonstrate a critical role of an interplay between STAG2 and a master transcription factor of hematopoiesis, RUNX1, in MDS development, and further reveal their contribution to regulation of high-order chromatin structures, particularly enhancer–promoter looping, and the link between transcriptional pausing and selective gene dysregulation caused by cohesin deficiency. This article is highlighted in the In This Issue feature, p. 747

Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because current metagenomic sequencing data consist of a large number of nucleotide sequences, the time required for sequence similarity searches account for a large proportion of the total time. This time-consuming step makes it difficult to perform large-scale analyses. To analyze large-scale metagenomic data, such as those found in the human oral microbiome, we developed GHOST-MP (Genome-wide HOmology Search Tool on Massively Parallel system), a parallel sequence similarity search tool for massively parallel computing systems. This tool uses a fast search algorithm based on suffix arrays of query and database sequences and a hierarchical parallel search to accelerate the large-scale sequence similarity search of metagenomic sequencing data. The parallel computing efficiency and the search speed of this tool were evaluated. GHOST-MP was shown to be scalable over 10,000 CPU (Central Processing Unit) cores, and achieved over 80-fold acceleration compared with mpiBLAST using the same computational resources. We applied this tool to human oral metagenomic data, and the results indicate that the oral cavity, the oral vestibule, and plaque have different characteristics based on the functional gene category.

Motivation: Sequence homology searches are used in various fields. New sequencing technologies produce huge amounts of sequence data, which continuously increase the size of sequence databases. As a result, homology searches require large amounts of computational time, especially for metagenomic analysis. Results: We developed a fast homology search method based on database subsequence clustering, and implemented it as GHOSTZ. This method clusters similar subsequences from a database to perform an efficient seed search and ungapped extension by reducing alignment candidates based on triangle inequality. The database subsequence clustering technique achieved an ∼2-fold increase in speed without a large decrease in search sensitivity. When we measured with metagenomic data, GHOSTZ is ∼2.2–2.8 times faster than RAPSearch and is ∼185–261 times faster than BLASTX. Availability and implementation: The source code is freely available for download at http://www.bi.cs.titech.ac.jp/ghostz/ Contact: akiyama@cs.titech.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Our claim is that so-called "Big Data" will evolve into a new era with proliferation of data from multiple sources such as massive numbers of sensors whose resolution is increasing expo- nentially, high-resolution simulations generating huge data results, as well as evolution of social infrastructures that allow for "opening up of data silos", i.e., data sources being abundant across the world instead of being confined within an institution, much as how scientific data are being handled in the modern era as a common asset openly accessible within and across disciplines. Such a situation would create the need for not only petabytes to zetabytes of capacity and beyond, but also for extreme scale computing power. Our new project, sponsored under the Japanese JST- CREST program is called "Extreme Big Data", and aims to achieve the convergence of extreme supercomputing and big data in order to cope with such explosion of data. The project consists of six teams, three of which deals with defining future EBD convergent SW/HW architecture and system, and the other three the EBD co-design applications that represent di?erent facets of big data, in metagenomics, social simulation, and climate simulation with real-time data assimilation. Although the project is still early in its lifetime, started in Oct. 2013, we have already achieved several notable results, including becoming world #1 on the Green Graph 500, a benchmark to measure the power e?ciency of graph processing that appear in typical big data scenarios.

Metagenomic analysis based on whole genome shotgun sequencing data requires fast protein sequence homology searches for predicting the function of proteins coded on metagenome short reads. However, huge amounts of sequence data cause even general homology search analyses using BLASTX to become difficult in terms of computational cost. GHOSTX is a sequence homology search tool specifically developed for functional annotation of metagenome sequences. The tool is more than 160 times faster than BLASTX and has sufficient search sensitivity for metagenomic analysis. Using this tool, user can perform functional annotation of metagenomic data within a short time and infer metabolic pathways within an environment.

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.

Protein-protein interactions play an important role in a number of biological activities. We developed two methods of predictingprotein-protein interaction site residues. One method uses only sequence information and the other method uses both sequence and structural information. We used support vector machine (SVM) with a position specific scoring matrix (PSSM) as sequence information and accessible surface area(ASA) of polar and non-polar atoms as structural information. SVM is used in two stages. In the first stage, an interaction residue is predicted by taking PSSMs of sequentially neighboring residues or taking PSSMs and ASAs of spatially neighboring residues as features. The second stage acts as a filter to refine the prediction results. The recall and precision of the predictor using both sequence and structural information are 73.6% and 50.5%, respectively. We found that using PSSM instead of frequency of amino acid appearance was the main factor of improvement of our methods.

DNA sequences are translated into protein coding sequences and then further assigned to protein families in metagenomic analyses, because of the need for sensitivity. However, huge amounts of sequence data create the problem that even general homology search analyses using BLASTX become difficult in terms of computational cost. We designed a new homology search algorithm that finds seed sequences based on the suffix arrays of a query and a database, and have implemented it as GHOSTX. GHOSTX achieved approximately 131–165 times acceleration over a BLASTX search at similar levels of sensitivity. GHOSTX is distributed under the BSD 2-clause license and is available for download at http://www.bi.cs.titech.ac.jp/ghostx/. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We offer this tool as a potential solution to this problem.

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

Background: Prediction of protein tertiary and quaternary structures helps us to understand protein functionality. While tertiary structure prediction techniques have been much improved over the last two

Male Sprague-Dawley rats were fed diets containing linseed oil, safflower oil, or cacao butter for 27 weeks. The collagen-induced aggregation of washed platelets decreased only in rats fed linseed oil. Dietary oils did not influence the phospholipid composition of platelets, but did influence the fatty acid composition of platelet phospholipids. Compared with the other diets, the linseed oil diet markedly decreased the arachidonic acid content in platelet phosphatidylcholine and accumulated the eicosa-pentaenoic acid in all phospholipids. A similar change was not found in platelets from rats fed the other diets. These results indicate that the feeding of linseed oil, which has a high linolenic acid (a precursor of eicosapentaenoic acid) content, alters the fatty acid composition of platelet phospholipids similar to the feeding of fish oils, and thereby reduces the aggregation tendency of platelets.

Liquid Chromatography Time-of-Flight Mass Spectrometry (LC-TOF-MS) is widely used for profiling metabolite compounds. LC-TOF-MS is a chemical analysis technique that combines the physical separation capabilities of high-pressure liquid chromatography (HPLC) with the mass analysis capabilities of Time-of-Flight Mass Spectrometry (TOF-MS) which utilizes the difference in the flight time of ions due to difference in the mass-to-charge ratio. Since metabolite compounds have various chemical characteristics, their precise identification is a crucial problem of metabolomics research. Contemporaneously analyzed reference standards are commonly required for mass spectral matching and retention time matching, but there are far fewer reference standards than there are compounds in the organism. We therefore developed a retention time prediction method for HPLC to improve the accuracy of identification of metabolite compounds. This method uses a combination of Support Vector Regression and Multiple Linear Regression adaptively to the measured retention time. We achieved a strong correlation (correlation coefficient = 0.974) between measured and predicted retention times for our experimental data. We also demonstrated a successful identification of an E. coli metabolite compound that cannot be identified by precise mass alone.