Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results

The renin-angiotensin-aldosterone system (RAAS) is a regulatory mechanism of the endocrine system and is associated with various diseases, including hypertension and renal and cardiovascular diseases. The gut microbiota (GM) have been associated with various diseases, mainly in animal models. However, to our knowledge, no studies have examined the relationship between the RAAS and GM in humans. The present study aimed to assess the association between the systemic RAAS and GM genera and their causal relationships. The study participants were 377 members of the general population aged 40 years or older in Shika-machi, Japan. Plasma renin activity (PRA), plasma aldosterone concentration (PAC), aldosterone-renin ratio (ARR), and GM composition were analyzed using the 16S rRNA method. The participants were divided into high and low groups according to the PRA, PAC, and ARR values. U-tests, one-way analysis of covariance, and linear discriminant analysis of effect size were used to identify the important bacterial genera between the two groups, and binary classification modeling using Random Forest was used to calculate the importance of the features. The results showed that Blautia, Bacteroides, Akkermansia, and Bifidobacterium were associated with the RAAS parameters. Causal inference analysis using the linear non-Gaussian acyclic model revealed a causal effect of Blautia on PAC via SBP. These results strengthen the association between the systemic RAAS and GM in humans, and interventions targeting the GM may provide new preventive measures and treatments for hypertension and renal disease.

Eight-color multiparameter flow cytometry (EuroFlow-NGF) is as sensitive as next-generation sequencing in detecting minimal/measurable residual disease in autografts of patients with multiple myeloma

The prognostic value of minimal/measurable residual disease (MRD) detection in autografts of patients with multiple myeloma (MM) in an autologous stem-cell transplantation setting has been reported. Next-generation flow (NGF) cytometry has lower sensitivity (2 × 10^-6) to detect MRD than next-generation sequencing (NGS) (<10^-6). We compared the clinical value of high-sensitivity NGF (cutoff: <10^-6) and NGS (cutoff: 10^-6) for the detection of MRD in the cryopreserved autografts of 49 patients with newly diagnosed MM. The sensitivity test using frozen/thawed autografts revealed a strong correlation among MRD levels of 5 × 10^-7 and 1 × 10^-4 (r = 0.9997, p < 0.0001) when an adequate number of cells were analyzed. Autograft MRD levels determined using NGF and NGS were highly correlated (r = 0.811, p < 0.0001). MRD-negative patients identified with NGF (cutoff: <10^-6) showed significantly longer progression-free survival (PFS) than MRD-positive patients (p = 0.026). The PFS of MRD-negative patients determined by NGS (cutoff: 10^-6) was similar to that determined by NGF. These results show that the high-sensitivity NGF method can assess MRD in frozen/thawed autografts, and its prognostic value is comparable to that of NGS.

ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites

Lysine glutarylation is a post-translational modification (PTM) that plays a regulatory role in various physiological and biological processes. Identifying glutarylated peptides using proteomic techniques is expensive and time-consuming. Therefore, developing computational models and predictors can prove useful for rapid identification of glutarylation. In this study, we propose a model called ProtTrans-Glutar to classify a protein sequence into positive or negative glutarylation site by combining traditional sequence-based features with features derived from a pre-trained transformer-based protein model. The features of the model were constructed by combining several feature sets, namely the distribution feature (from composition/transition/distribution encoding), enhanced amino acid composition (EAAC), and features derived from the ProtT5-XL-UniRef50 model. Combined with random under-sampling and XGBoost classification method, our model obtained recall, specificity, and AUC scores of 0.7864, 0.6286, and 0.7075 respectively on an independent test set. The recall and AUC scores were notably higher than those of the previous glutarylation prediction models using the same dataset. This high recall score suggests that our method has the potential to identify new glutarylation sites and facilitate further research on the glutarylation process.

Exhaustive identification of conserved upstream open reading frames with potential translational regulatory functions from animal genomes

Upstream open reading frames (uORFs) are present in the 5′-untranslated regions of many eukaryotic mRNAs, and some peptides encoded by these regions play important regulatory roles in controlling main ORF (mORF) translation. We previously developed a novel pipeline, ESUCA, to comprehensively identify plant uORFs encoding functional peptides, based on genome-wide identification of uORFs with conserved peptide sequences (CPuORFs). Here, we applied ESUCA to diverse animal genomes, because animal CPuORFs have been identified only by comparing uORF sequences between a limited number of species, and how many previously identified CPuORFs encode regulatory peptides is unclear. By using ESUCA, 1517 (1373 novel and 144 known) CPuORFs were extracted from four evolutionarily divergent animal genomes. We examined the effects of 17 human CPuORFs on mORF translation using transient expression assays. Through these analyses, we identified seven novel regulatory CPuORFs that repressed mORF translation in a sequence-dependent manner, including one conserved only among Eutheria. We discovered a much higher number of animal CPuORFs than previously identified. Since most human CPuORFs identified in this study are conserved across a wide range of Eutheria or a wider taxonomic range, many CPuORFs encoding regulatory peptides are expected to be found in the identified CPuORFs.

Comprehensive genome-wide identification of angiosperm upstream ORFs with peptide sequences conserved in various taxonomic ranges using a novel pipeline, ESUCA

Background

Upstream open reading frames (uORFs) in the 5′-untranslated regions (5′-UTRs) of certain eukaryotic mRNAs encode evolutionarily conserved functional peptides, such as cis-acting regulatory peptides that control translation of downstream main ORFs (mORFs). For genome-wide searches for uORFs with conserved peptide sequences (CPuORFs), comparative genomic studies have been conducted, in which uORF sequences were compared between selected species. To increase chances of identifying CPuORFs, we previously developed an approach in which uORF sequences were compared using BLAST between Arabidopsis and any other plant species with available transcript sequence databases. If this approach is applied to multiple plant species belonging to phylogenetically distant clades, it is expected to further comprehensively identify CPuORFs conserved in various plant lineages, including those conserved among relatively small taxonomic groups.

Results

To efficiently compare uORF sequences among many species and efficiently identify CPuORFs conserved in various taxonomic lineages, we developed a novel pipeline, ESUCA. We applied ESUCA to the genomes of five angiosperm species, which belong to phylogenetically distant clades, and selected CPuORFs conserved among at least three different orders. Through these analyses, we identified 89 novel CPuORF families. As expected, ESUCA analysis of each of the five angiosperm genomes identified many CPuORFs that were not identified from ESUCA analyses of the other four species. However, unexpectedly, these CPuORFs include those conserved across wide taxonomic ranges, indicating that the approach used here is useful not only for comprehensive identification of narrowly conserved CPuORFs but also for that of widely conserved CPuORFs. Examination of the effects of 11 selected CPuORFs on mORF translation revealed that CPuORFs conserved only in relatively narrow taxonomic ranges can have sequence-dependent regulatory effects, suggesting that most of the identified CPuORFs are conserved because of functional constraints of their encoded peptides.

Conclusions

This study demonstrates that ESUCA is capable of efficiently identifying CPuORFs likely to be conserved because of the functional importance of their encoded peptides. Furthermore, our data show that the approach in which uORF sequences from multiple species are compared with those of many other species, using ESUCA, is highly effective in comprehensively identifying CPuORFs conserved in various taxonomic ranges.

Draft Genome Sequence of Saccharomyces cerevisiae Strain Pf-1, Isolated from Prunus mume

Saccharomyces cerevisiae strain Pf-1 is a yeast isolated from Prunus mume; it potentially can be used to produce wine and traditional Japanese sake. Here, we report the draft genome sequence of this strain. The genomic information will provide a deeper understanding of the brewing characteristics of this strain.

Factors to preserve CpG-rich sequences in methylated CpG islands

Background

Mammalian CpG islands (CGIs) normally escape DNA methylation in all adult tissues and developmental stages. However, in our previous study we unexpectedly identified many methylated CGIs in human peripheral blood leukocytes. Methylated CpG dinucleotides convert to TpG dinucleotides through deaminization of their cytosine bases more frequently than hypomethylated CpG dinucleotides. Therefore, we wondered how methylated CGIs in germline or non-germline cells maintain their CpG-rich sequences. It is known that events such as germline hypomethylation, CpG selection, biased gene conversion (BGC), and frequent CpG fixation can contribute to the maintenance of CpG-rich sequences in methylated CGIs in germline or non-germline cells. However, it has not been investigated which of the processes maintain CpG-rich sequences of methylated CGIs in each genomic position.

Results

In this study, we comprehensively examined the contribution of the processes described above to the maintenance of CpG-rich sequences in methylated CGIs in germline and non-germline cells which were classified by genomic positions. Approximately 60–80% of CGIs with high methylation in H1 cell line (H1-HM) in all the genomic positions showed a low average CpG → TpG/CpA substitution rate. In contrast, fewer than half the numbers of CGIs with H1-HM in all the genomic positions showed a low average CpG → TpG/CpA substitution rate and low levels of methylation in sperm cells (SPM-LM). Furthermore, a small fraction of CGIs with a low average CpG → TpG/CpA substitution rate and high levels of methylation in sperm cells (SPM-HM) showed CpG selection.

On the other hand, independent of the positions in genes, most CGIs with SPM-HM showed a slightly higher average TpG/CpA → CpG substitution rate compared with those with SPM-LM.

Conclusions

Relatively high numbers (approximately 60–80%) of CGIs with H1-HM in all the genomic positions preserve their CpG-rich sequences by a low CpG → TpG/CpA substitution rate caused mainly by their SPM-LM, and for those with SPM-HM partly by CpG selection and TpG/CpA → CpG fixation. BGC has little contribution to the maintenance of CpG-rich sequences of CGIs with SPM-HM which were classified by genomic positions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1286-x) contains supplementary material, which is available to authorized users.

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Background

Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA.

Results

We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.

We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT.

Conclusions

We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.

Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

BACKGROUND

We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these "differentially expressed GO terms" and have named the algorithm "matrix-assisted identification method of differentially expressed GO terms" (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms.

FINDINGS

We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson's correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated.

CONCLUSIONS

MIMGO is a reliable method to identify differentially expressed GO terms comprehensively.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information

Background

Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.

Results

We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo⁺ and EpicCapo^+REF. Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo⁺ and EpicCapo^+REF outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo^+REF was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.

Conclusions

Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo^+REF is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.

PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors

Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.

Inflammation-induced repression of tumor suppressor miR-7 in gastric tumor cells

Inflammation has an important role in cancer development through various mechanisms. It has been shown that dysregulation of microRNAs (miRNAs) that function as oncogenes or tumor suppressors contributes to tumorigenesis. However, the relationship between inflammation and cancer-related miRNA expression in tumorigenesis has not yet been fully understood. Using K19-C2mE and Gan mouse models that develop gastritis and gastritis-associated tumors, respectively, we found that 21 miRNAs were upregulated, and that 29 miRNAs were downregulated in gastric tumors in an inflammation-dependent manner. Among these miRNAs, the expression of miR-7, a possible tumor suppressor, significantly decreased in both gastritis and gastric tumors. Moreover, the expression of miR-7 in human gastric cancer was inversely correlated with the levels of interleukin-1β and tumor necrosis factor-α, suggesting that miR-7 downregulation is related to the severity of inflammatory responses. In the normal mouse stomach, miR-7 expression was at a basal level in undifferentiated gastric epithelial cells, and was induced during differentiation. Moreover, transfection of a miR-7 precursor into gastric cancer cells suppressed cell proliferation and soft agar colony formation. These results suggest that suppression of miR-7 expression is important for maintaining the undifferentiated status of gastric epithelial cells, and thus contributes to gastric tumorigenesis. Although epigenetic changes were not found in the CpG islands around miR-7-1 of gastritis and gastric tumor cells, we found that activated macrophage-derived small molecule(s) (<3 kDa) are responsible for miR-7 repression in gastric cancer cells. Furthermore, the miR-7 expression level significantly decreased in the inflamed gastric mucosa of Helicobacter-infected mice, whereas it increased in the stomach of germfree K19-C2mE and Gan mice wherein inflammatory responses were suppressed. Taken together, these results indicate that downregulation of tumor suppressor miR-7 is a novel mechanism by which the inflammatory response promotes gastric tumorigenesis.

SUPPORT VECTOR MACHINES FOR PREDICTION AND ANALYSIS OF BETA AND GAMMA-TURNS IN PROTEINS

Tight turns have long been recognized as one of the three important features of proteins, together with α-helix and β-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are β-turns and most of the rest are γ-turns. Analysis and prediction of β-turns and γ-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of β-turns and γ-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict β-turns and γ-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of β-turns and γ-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Computational discovery of miR-TF regulatory modules in human genome

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. They play an important role in several biological processes such as cell development and differentiation. Similar to transcription factors (TFs), miRNAs regulate gene expression in a combinatorial fashion, i.e., an individual miRNA can regulate multiple genes, and an individual gene can be regulated by multiple miRNAs. The functions of TFs in biological regulatory networks have been well explored. And, recently, a few studies have explored miRNA functions in the context of gene regulation networks. However, how TFs and miRNAs function together in the gene regulatory network has not yet been examined. In this paper, we propose a new computational method to discover the gene regulatory modules that consist of miRNAs, TFs, and genes regulated by them. We analyzed the regulatory associations among the sets of predicted miRNAs and sets of TFs on the sets of genes regulated by them in the human genome. We found 182 gene regulatory modules of combinatorial regulation by miRNAs and TFs (miR-TF modules). By validating these modules with the Gene Ontology (GO) and the literature, it was found that our method allows us to detect functionally-correlated gene regulatory modules involved in specific biological processes. Moreover, our miR-TF modules provide a global view of coordinated regulation of target genes by miRNAs and TFs.

Finding microRNA regulatory modules in human genome using rule induction

BACKGROUND

MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes.

RESULTS

We propose a rule-based learning method to identify groups of miRNAs and target genes that are believed to participate cooperatively in the post-transcriptional gene regulation, so-called miRNA regulatory modules (MRMs). Applying our method to human genes and miRNAs, we found 79 MRMs. The MRMs are produced from multiple information sources, including miRNA-target binding information, gene expression and miRNA expression profiles. Analysis of two first MRMs shows that these MRMs consist of highly-related miRNAs and their target genes with respect to biological processes.

CONCLUSION

The MRMs found by our method have high correlation in expression patterns of miRNAs as well as mRNAs. The mRNAs included in the same module shared similar biological functions, indicating the ability of our method to detect functionality-related genes. Moreover, review of the literature reveals that miRNAs in a module are involved in several types of human cancer.

[A prosthetic ring annuloplasty with edge-to-edge repair for a treatment of severe tricuspid regurgitation]

A 59-year-old woman admitted to our hospital with shortness of breath and edema of the lower extremities was diagnosed with right ventricular failure stemming from severe tricuspid valve regurgitation (TR). She had undergone mitral valve replacement (MVR) with a mechanical valve at the age of 42. The approach to the heart was established via a right thoracotomy at the 4th intercostals space. A beating heart cardiopulmonary bypass procedure was performed in which tricuspid valve repair was performed with the edge-to-edge repair and MC3 annuloplasty system. The operative course was uneventful. This technique may be feasible and clinically effective in the treatment of severe TR.

Characterization of a broadly reactive monoclonal antibody against norovirus genogroups I and II: recognition of a novel conformational epitope

Norovirus, which belongs to the family Caliciviridae, is one of the major causes of nonbacterial acute gastroenteritis in the world. The main human noroviruses are of genogroup I (GI) and genogroup II (GII), which were subdivided further into at least 15 and 18 genotypes (GI/1 to GI/15 and GII/1 to GII/18), respectively. The development of immunological diagnosis for norovirus had been hindered by the antigen specificity of the polyclonal antibody. Therefore, several laboratories have produced broadly reactive monoclonal antibodies, which recognize the linear GI and GII cross-reactive epitopes or the conformational GI-specific epitope. In this study, we characterized the novel monoclonal antibody 14-1 (MAb14-1) for further development of the rapid immunochromatography test. Our results demonstrated that MAb14-1 could recognize 15 recombinant virus-like particles (GI/1, 4, 8, and 11 and GII/1 to 7 and 12 to 15) and showed weak affinity to the virus-like particle of GI/3. This recognition range is the broadest of the existing monoclonal antibodies. The epitope for MAb14-1 was identified by fragment, sequence, structural, and mutational analyses. Both terminal antigenic regions (amino acid positions 418 to 426 and 526 to 534) on the C-terminal P1 domain formed the conformational epitope and were in the proximity of the insertion region (positions 427 to 525). These regions contained six amino acids responsible for antigenicity that were conserved among genogroup(s), genus, and Caliciviridae. This epitope mapping explained the broad reactivity and different titers among GI and GII. To our knowledge, we are the first group to identify the GI and GII cross-reactive monoclonal antibody, which recognizes the novel conformational epitope. From these data, MAb14-1 could be used further to develop immunochromatography.

Evaluation of immunochromatography tests for detection of rotavirus and norovirus among Vietnamese children with acute gastroenteritis and the emergence of a novel norovirus GII.4 variant

A prospective study was conducted to evaluate two immunochromatography (ICG) tests for detection of group A rotavirus and norovirus GII, the commercial Dipstick 'Eiken' Rota kit (SA Scientific, USA) and the NV IC-1 stick (Immuno-Probe, Japan). Polymerase chain reaction (PCR) with specific primer pairs (Beg9 and VP7-1', for group A rotavirus; COG2F and G2SKR, for norovirus GII) was used as the reference method. The results of ICG tests were compared with those of reference method. The sensitivity, specificity and agreement between ICG tests and PCR were 87.8%, 93.3% and 89.4%, respectively, for rotavirus ICG test; and 73.7%, 100% and 95.2%, respectively, for norovirus ICG test. The immunochromatography assay for norovirus used in this study could detect not only common noroviruses, but also a novel norovirus GII.4 variant, which emerged in Ho Chi Minh City in 2006. Immunochromatography tests are easy, rapid and useful assays for detection of rotavirus and norovirus among pediatric patients with acute gastroenteritis in Vietnam.

Phylogenetic reconstruction from non-genomic data

MOTIVATION

Recent results related to horizontal gene transfer suggest that phylogenetic reconstruction cannot be determined conclusively from sequence data, resulting in a shift from approaches based on polymorphism information in DNA or protein sequence to studies aimed at understanding the evolution of complete biological processes. The increasing amount of available information on metabolic pathways for several species makes it of greater relevance to understand the similarities and differences among such pathways. These similarities can then be used to infer phylogenetic trees not based exclusively in sequence data, therefore avoiding the previously mentioned problems.

RESULTS

In this article, we present a method to assess the structural similarity of metabolic pathways for several organisms. Our algorithms work by using one of the three possible enzyme similarity measures (hierarchical, information content, gene ontology), and one of the two clustering methods (neighbor-joining, unweighted pair group method with arithmetic mean), to produce a phylogenetic tree both in Newick and graphic format. The web server implementing our algorithms is optimized to answer queries in linear time.

AVAILABILITY

The software is available for free public use on a web server, at the address http://www.jaist.ac.jp/~clemente/cgi-bin/phylo.pl. It is available on demand in source code form for research use to educational institutions, non-profit research institutes, government research laboratories and individuals, for non-exclusive use, without the right of the licensee to further redistribute the source code.

[Open stent-grafting applied with the Matsui-Kitamura stent to a distal arch aneurysm]

An open stent-grafting applied with the Matsui-Kitamura (MK) stent to a distal arch aneurysm is presented herein. The graft using the MK stent at its distal end was successfully inserted into the descending thoracic aorta through a J-shaped sheath-introducer. The major advantages of this stent-graft include its flexibility, shape memory, and small profile when compressed, compared with other devices. This technique may be feasible and clinically effective in the treatment of distal arch aneurysm.

Finding conserved and non-conserved reactions using a metabolic pathway alignment algorithm

Using a metabolic pathway alignment method we developed, we studied highly conserved reactions in different groups of organisms and found out that biological functions vital for each of the groups are effectively expressed in the set of conserved reactions. We also studied the metabolic alignment of different strains of three bacteria and found out several non-conserved reactions. We suggest that these reactions could be either misannotations or reactions with a relevant but yet to be specified biological role, and should therefore be further investigated.

Characteristics of hepatocytes derived from early ES cells and treatment of surgically induced liver failure rats by transplantation

BACKGROUND

Although liver transplantation has become a standard therapy for diseases such as fulminant hepatitis and cirrhosis, the lack of donor organs remains a major problem. One solution is the development of transplantable hepatocytes. The metabolic characteristics as well as function and adaptation of hepatocytes (R-EES-hep cell) derived from rat early embryonic stem cells were examined after transplantation into rats with surgically induced liver failure.

METHODS

Rat hepatocyte cell lines were established from early embryonic stem cells cultured in the presence of embryotrophic factors by colony cloning methods. The cell lines were established from two cell embryos taken from spontaneous dwarf rats using the novel method of Ishiwata et al. Morphologic differentiation as well as albumin and bilirubin production were observed by immunostaining. R-EES-hep cells were transplanted into the spleens of 90% hepatectomized, surgically induced liver failure rats to analyze survival rates.

RESULTS

When cultured in type I collagen gel the cells formed cordlike structures resembling the liver. Both albumin and bilirubin production were observed when transplanted; the spleen was converted into a liver-like structure with prolonged survival of the 90% hepatectomized rats for up to 3 months up to the time of killing.

CONCLUSIONS

R-EES-hep cells showed many of the distinctive metabolic characteristics of the liver. These cells may be efficient for further research and application for hepatic cell transplantation to treat liver insufficiency patients and as biologic artificial organs.

Computational discovery of transcriptional regulatory rules

MOTIVATION

Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. However, although location data obtained from experimental methods are noisy owing to imperfections in the measuring methods, computational approaches suffer from over-prediction problems owing to the short length of the sequence motifs bound by the transcription factors. Also, these interactions are usually environment-dependent: many regulators only bind to the promoter region of genes under specific environmental conditions. Even more, the presence of regulators at a promoter region indicates binding but not necessarily function: the regulator may act positively, negatively or not act at all. Therefore, identifying true and functional interactions between transcription factors and genes in specific environment conditions and describing the relationship between them are still open problems.

RESULTS

We developed a method that combines expression data with genomic location information to discover (1) relevant transcription factors from the set of potential transcription factors of a target gene; and (2) the relationship between the expression behavior of a target gene and that of its relevant transcription factors. Our method is based on rule induction, a machine learning technique that can efficiently deal with noisy domains. When applied to genomic location data with a confidence criterion relaxed to P-value = 0.005, and three different expression datasets of yeast S.cerevisiae, we obtained a set of regulatory rules describing the relationship between the expression behavior of a specific target gene and that of its relevant transcription factors. The resulting rules provide strong evidence of true positive gene-regulator interactions, as well as of protein-protein interactions that could serve to identify transcription complexes.

AVAILABILITY

Supplementary files are available from http://www.jaist.ac.jp/~h-pham/regulatory-rules

Qualitatively predicting acetylation and methylation areas in DNA sequences

Eukaryotic genomes are packaged by the wrapping of DNA around histone octamers to form nucleosomes. Nucleosome occupancy, acetylation, and methylation, which have a major impact on all nuclear processes involving DNA, have been recently mapped across the yeast genome using chromatin immunoprecipitation and DNA microarrays. However, this experimental protocol is laborious and expensive. Moreover, experimental methods often produce noisy results. In this paper, we introduce a computational approach to the qualitative prediction of nucleosome occupancy, acetylation, and methylation areas in DNA sequences. Our method uses support vector machines to discriminate between DNA areas with high and low relative occupancy, acetylation, or methylation, and rank k-gram features based on their support for these DNA modifications. Experimental results on the yeast genome reveal genetic area preferences of nucleosome occupancy, acetylation, and methylation that are consistent with previous studies. Supplementary files are available from http://www.jaist.ac.jp/~tran/nucleosome/.

Reconstruction of phylogenetic relationships from metabolic pathways based on the enzyme hierarchy and the gene ontology

There has been much interest in the structural comparison and alignment of metabolic pathways. Several techniques have been conceived to assess the similarity of metabolic pathways of different organisms. In this paper, we show that the combination of a new heuristic algorithm for the comparison of metabolic pathways together with any of three enzyme similarity measures (hierarchical, information content, and gene ontology) can be used to derive a metabolic pathway similarity measure that is suitable for reconstructing phylogenetic relationships from metabolic pathways. Experimental results on the Glycolysis pathway of 73 organisms representing the three domains of life show that our method outperforms previous techniques.

Detection and Normalization of Biases Present in Spotted cDNA Microarray Data: A Composite Method Addressing Dye, Intensity-Dependent, Spatially-Dependent, and Print-Order Biases

Microarrays are often used to identify target genes that trigger specific diseases, to elucidate the mechanisms of drug effects, and to check SNPs. However, data from microarray experiments are well known to contain biases resulting from the experimental protocols. Therefore, in order to elucidate biological knowledge from the data, systematic biases arising from their protocols must be removed prior to any data analysis. To remove these biases, many normalization methods are used by researchers. However, not all biases are eliminated from the microarray data because not all types of errors from experimental protocols are known. In this paper, we report an effective way of removing various types of biases by treating each microarray dataset independently to detect biases present in the dataset. After the biases contained in each dataset were identified, a combination of normalization methods specifically made for each dataset was applied to remove biases one at a time.

Utilizing weakly controlled vocabulary for sentence segmentation in biomedical literature

Since biomedical texts contain a wide variety of domain specific terms, building a large dictionary to perform term matching is of great relevance. However, due to the existence of null boundary between adjacent terms, this matching is not a trivial problem. Moreover, it is known that generative words cannot be comprehensively included in a dictionary because their possible variations are infinite. In this study, we report our approach to dictionary building and term matching in biomedical texts. Large amount of terms with/without part-of-speech (POS) and/or category information were gathered, and a completion program generated approximately 1.36 million term variants to avoid stemming problems when matching terms. The dictionary was stored in a relational database management system (RDBMS) for quick lookup, and used by a matching program. Since the matching operation is not restricted to a substring surrounded by space characters, we can avoid the problem of null boundaries. This feature is also useful for generative words. Experimental results on GENIA corpus are promising: nearly half of the possible terms were correctly recognized as a meaningful segment, and most of the remaining half could be correctly recognized by some post-processing process, like chunking and further decomposition. It should be remarked that although we have not used term cost, connectivity cost, or syntactic information, reasonable segmentation and dictionary lookup were performed in most cases.

Evidence of complete fusion in the sub-barrier 16O+238U reaction

Evaporation residue cross sections in the 16O+238U reaction were measured for the energy range from above- to extreme sub-barrier. The cross sections are reproduced by a statistical model calculation, for which partial cross sections are calculated by a coupled-channel model taking into account the prolate deformation of 238U. Complete fusion was observed in the collision of the projectile with the tips of the 238U target, in the same way as the side collision.

Mining yeast transcriptional regulatory modules from factor DNA-binding sites and gene expression data

In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Different combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology. In this study, we first defined a new kind of motifs in regulatory networks, transcriptional regulatory modules (TRMs), with the form factorset --> geneset, which emphasizes the combinatorial gene control of the group of factors factorset on the group of genes geneset. Second, we developed an efficient method based on a closed itemset mining technique for finding the two most informative kinds of TRMs, closed inf-TRMs and closed sup-TRMs, from factor DNA-binding sites and gene expression profiles data. The set of all closed inf-TRMs and closed sup-TRMs is often orders of magnitude smaller than the set of all TRMs but does not loss any information. When being applied to yeast data, our method produced results that are more compact, concise and comprehensive than those from previous studies to identify and interpret the transcriptional role of regulator combinations on sets of genes.

AVAILABILITY

Supplementary files: http://www.jaist.ac.jp/~h-pham/regulation/.

Drug interaction ontology (DIO) for inferences of possible drug-drug interactions

Drug Interaction Ontology (DIO) was developed for formal representation of pharmacological knowledge. It provides a fundamental framework for accumulation of reusable knowledge components in molecular pharmacology. Ontology was employed and implemented as a relational model. Some features include: 1) Drug-biomolecule interaction was assumed as a primitive knowledge element. 2) Symbolic representation was developed for drug-biomolecule interaction. Consequences of two conjugated units of interaction were defined by using symbols. These are applied for query development for identification of possible drug-drug interaction. 3) The triadic relationship model was developed as a ground model for bio-logical interactions and/or function, including semantic ones. One application of DIO is to support hypothesis generation of drug interaction by providing new hypotheses from a structured database storing literature information on known drug-biomolecule interactions. A knowledge base using DIO that contains information beginning with anti-cancer drugs is now under development. Detection of possible drug interaction was tested and its capacity to lead clinically known ones was confirmed. The system generated theoretically possible drug-drug interactions, which implies potential usefulness of new drugs to be tested before actual clinical application. In this paper, sorivudine and 5-fluorouracil mediated by dihydropyrimidine dehydrogenase are presented.

[Off-pump coronary artery bypass grafting for restenosis after coronary angioplasty in the patient with left main shock syndrome]

A case of left main shock syndrome due to total occlusion of left main trunk was reported. A 65-year-old man with acute myocardial infarction developed cardiogenic shock shortly after admission. An emergency coronary angiogram revealed total occlusion of the left main trunk without collaterals. Immediately, a stent was implanted in the left main trunk and the lesion was successfully improved. However, the left coronary angiogram revealed 90% restenosis of the left main trunk in a coronary angiogram examined 6 months after stent placement. Since the patient suffered from poor cardiac function, off-pump coronary artery bypass grafting (left internal thoracic artery to left descending coronary artery) was successfully performed using an intracoronary shunt. No complications were observed.

Prediction and analysis of beta-turns in proteins by support vector machine

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Extraction of knowledge on protein-protein interaction by association rule discovery

MOTIVATION

Protein-protein interactions are systematically examined using the yeast two-hybrid method. Consequently, a lot of protein-protein interaction data are currently being accumulated. Nevertheless, general information or knowledge on protein-protein interactions is poorly extracted from these data. Thus we have been trying to extract the knowledge from the protein-protein interaction data using data mining.

RESULTS

A data mining method is proposed to discover association rules related to protein-protein interactions. To evaluate the detected rules by the method, a new scoring measure of the rules is introduced. The method allowed us to detect popular interaction rules such as "An SH3 domain binds to a proline-rich region." These results indicate that the method may detect novel knowledge on protein-protein interactions.