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.

Rare toxin A-negative and toxin B-positive strain of Clostridioides difficile from Japan lacking a complete tcdA gene.

INTRODUCTION: Clostridioides difficile (C. difficile) produces three kinds of toxins: toxin A (enterotoxin), toxin B (cytotoxin), and C. difficile transferase (CDT), a binary toxin. Some strains show positivity only for toxin B. These strains reportedly possess a gene for toxin A, tcdA. However, toxin A production is inhibited due to a mutated stop codon and/or deletion within the tcdA gene. Here for the first case in Japan, we describe toxin genomes and proteins of a strain possessing only toxin B and lacking a complete tcdA gene, along with clinical manifestations. METHODS: C. difficile was isolated from the bloody stool of a 60-year-old female patient treated with meropenem. Although a rapid detection kit of toxins (C. DIFF QUIK CHEK COMPLETE®, TechLab, Blacksburg, VA, USA) showed positivity, Western blotting detected no toxins. Therefore, we explored the strain's toxin genes and their sequences to determine whether the strain possessed a toxin. RESULTS: Polymerase chain reaction did not identify toxin genes. Whole-genome sequencing analysis showed that a gene for toxin A, tcdA, was completely deleted in the strain. Moreover, 701 mutations and some deletions/insertions were identified on the tcdB gene. CONCLUSIONS: We isolated a rare strain of C. difficile producing only toxin B and lacking a complete tcdA gene herein Japan. The possibility of a false negative needs to be considered with a genetic method for a diagnose of C. difficile infection.

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.

Draft Genome Sequence of the Aspergillus terreus High-Itaconic-Acid-Productivity Strain IFO6365.

Itaconic acid is an important organic acid used in the chemical industry. Aspergillus terreus strain IFO6365 is one of the highest-yielding itaconic acid-producing wild-type strains. Here, we report the draft genome sequence of IFO6365, enhancing the understanding of the role and biosynthesis of itaconic acid in this fungus.

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.

The increased frequency of methicillin-resistant Staphylococcus aureus with low MIC of beta-lactam antibiotics isolated from hospitalized patients.

Methicillin-resistant Staphylococcus aureus (MRSA) causes severe infectious diseases and can be life-threatening in healthcare-settings. MRSA is classified into health-care associated (HA)-MRSA strains and community acquired (CA)-MRSA strains based on genotype and phenotype. CA-MRSA has been reported to show the lower minimal inhibitory concentration (MIC) of some antibiotics as compared to HA-MRSA. Recently, the prevalence of CA-MRSA has been increased in worldwide. CA-MRSA is isolated not only from the healthy individuals in a community but also from the patients in healthcare settings. However, the changing trend in frequency of HA-MRSA and CA-MRSA in the hospital setting is not clear. Therefore, we analyzed the trend of MIC to speculate the frequency of HA-MRSA and CA-MRSA in the facility. Moreover, gene mutations were evaluated on resistant gene loci with next generation sequencer. The frequency of strains with low MIC of beta-lactam antibiotics was gradually increased in isolated MRSA strains from the hospitalized patients. Whole genome analysis revealed the frequency of gene mutation was also decreased in some resistant loci, such as blaZ and blaR1. These findings highlight the changing trend of MRSA strains isolated from hospitalized patients.

Collagen adhesion gene is associated with bloodstream infections caused by methicillin-resistant Staphylococcus aureus.

OBJECTIVES: Methicillin-resistant Staphylococcus aureus (MRSA) causes hospital- and community-acquired infections. It is not clear whether genetic characteristics of the bacteria contribute to disease pathogenesis in MRSA infection. We hypothesized that whole genome analysis of MRSA strains could reveal the key gene loci and/or the gene mutations that affect clinical manifestations of MRSA infection. METHODS: Whole genome sequences (WGS) of MRSA of 154 strains were analyzed with respect to clinical manifestations and data. Further, we evaluated the association between clinical manifestations in MRSA infection and genomic information. RESULTS: WGS revealed gene mutations that correlated with clinical manifestations of MRSA infection. Moreover, 12 mutations were selected as important mutations by Random Forest analysis. Cluster analysis revealed strains associated with a high frequency of bloodstream infection (BSI). Twenty seven out of 34 strains in this cluster caused BSI. These strains were all positive for collagen adhesion gene (cna) and have mutations in the locus, those were selected by Random Forest analysis. Univariate and multivariate analysis revealed that these gene mutations were the predictor for the incidence of BSI. Interestingly, mutant CNA protein showed lower attachment ability to collagen, suggesting that the mutant protein might contribute to the dissemination of bacteria. CONCLUSIONS: These findings suggest that the bacterial genotype affects the clinical characteristics of MRSA infection.

Draft Genome Sequence of Aspergillus terreus High-Itaconic-Acid-Productivity Mutant TN-484.

Itaconic acid is an important organic acid used in the chemical industry. Aspergillus terreus strain TN-484 is a high-itaconic-acid-productivity mutant derived from strain IFO6365. Here, we report the draft genome sequence of strain TN-484, advancing the understanding of the biosynthesis of itaconic acid in filamentous fungi.

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.

Draft Genome Sequence of Saccharomyces cerevisiae Strain Hm-1, Isolated from Cotton Rosemallow.

Saccharomyces cerevisiae strain Hm-1 is a yeast isolated from the flower of cotton rosemallow. This yeast is used for the production of Seishu, a traditional Japanese refined sake. Here, we report the strain's draft genome sequence. With this genomic information, the brewing characteristics of the strain can be better understood.

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.

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.

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.

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.

PLU1 histone demethylase decreases the expression of KAT5 and enhances the invasive activity of the cells.

PLU1 is a candidate oncogene that encodes H3K4 (Lys(4) of histone H3) demethylase. In the present study, we found that ectopic expression of PLU1 enhanced the invasive potential of the weakly invasive cells dependent on its demethylase activity. PLU1 was shown to repress the expression of the KAT5 gene through its H3K4 demethylation on the promoter. The regulation of KAT5 by PLU1 was suggested to be responsible for PLU1-induced cell invasion. First, knockdown of KAT5 similarly increased the invasive potential of the cells. Secondly, knockdown of PLU1 in the highly invasive cancer cells increased KAT5 expression and reduced the invasive activity. Thirdly, simultaneous knockdown of KAT5 partially relieved the suppression of cell invasion imposed by PLU1 knockdown. Finally, we found that CD82, which was transcriptionally regulated by KAT5, might be a candidate effector of cell invasion promoted by PLU1. The present study demonstrated a functional contribution of PLU1 overexpression with concomitant epigenetic dysregulation in cancer progression.

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.