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The contribution ratio of metabolic enzymes such as cytochrome P450 to

Medical information is valuable information obtained from humans regarding the phenotype of diseases. Omics data is informative to understand diseases at biomolecular level. We aimed to detect patient stratification patterns in a data-driven manner and identify candidate drug targets by investigating biomolecules that are linked to phenotype-level characteristics of a targeted disease. Such data integration is challenging because the data types of them are different, and these data contain many items that are not directly related to the disease. Hence, we developed an algorithm, subset binding, to find inter-related attributes in heterogeneous data. To search for potential drug targets for intractable IPF (idiopathic pulmonary fibrosis), we collected medical information and proteome data of serum extracellular vesicles from patients with interstitial pneumonia including IPF. Our approach detected 20 proteins linked with IPF characteristics, whose expression intensities were confirmed to be high in fibrotic areas of human lung tissues. Furthermore, ponatinib, which inhibits these proteins, suppressed EMT (epithelial mesenchymal transition) in vitro. This workflow paves the way for data-driven drug target discovery even for intractable diseases whose mechanisms of pathogenesis are not fully understood.

The gut microbiome is an important determinant in various diseases. Here we performed a cross-sectional study of Japanese adults and identified the Blautia genus, especially B. wexlerae, as a commensal bacterium that is inversely correlated with obesity and type 2 diabetes mellitus. Oral administration of B. wexlerae to mice induced metabolic changes and anti-inflammatory effects that decreased both high-fat diet–induced obesity and diabetes. The beneficial effects of B. wexlerae were mediated directly by unique amino-acid metabolism to produce S-adenosylmethionine, acetylcholine, and l-ornithine and indirectly by carbohydrate metabolism resulting in the accumulation of amylopectin and production of succinate, lactate, and acetate and simultaneous modification of the gut bacterial composition. These findings reveal unique regulatory pathways of host and microbial metabolism that may provide novel strategies in preventive and therapeutic approaches for metabolic disorders.

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried-dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.

The integration of heterogeneous data to infer latent relationships across them and find the factors in the relationship is a challenging task. In this regard, various machine learning techniques have provided novel insights through data integration. However, concerns remain regarding their application to biological datasets because the latent consensus information across all views is often limited to partial components that do not have a significant impact on the mutual agreement across views. Advocating the idea of “subset-binding,” which focuses on finding inter-related attributes in heterogeneous data according to their co-occurrence, this study developed a novel algorithm to perform subset-binding by extending fuzzy association rule mining techniques. Our method could detect genes related to liver toxicity caused by acetaminophen in a data-driven manner; the results are consistent with those reported in the literature. This technology paves the way for a wide range of applications, including biomarker detection and patient stratification.

Developing

Multi-omics analyses, combining transcriptomics, genomics, proteomics, and so on, have led to important insights in many areas of biology and medicine. To support these analyses, software that can handle the difficulties associated with multi-omics datasets is crucial. Here, we describe Panomicon, a web-based, interactive analysis environment for multi-omics data. Building on Toxygates, a tool previously created to study single-omics data that features interactive clustering, heatmaps, and user data uploads, Panomicon introduces improvements for the storage and handling of additional omics types, as well as tools for the generation and visualization of interaction networks between different types of omics data. Panomicon is a new type of environment for the collaborative study of multi-omics data, both for users uploading data to our server and for groups wishing to host their own deployment of Panomicon. We demonstrate Panomicon's capabilities by revisiting a microRNA-mRNA interaction networks study in a non-small cell lung cancer dataset., Bioinformatics; Computational Biology; Genetics; gene networks; multi-omics; microRNA; visualization; interactive analysis; gene interactions; lung cancer

The priming, boosting, and restoration of memory cytotoxic CD8+ T lymphocytes by vaccination or immunotherapy in vivo is an area of active research. Particularly, nucleic acid–based compounds have attracted attention due to their ability to elicit strong Ag-specific CTL responses as a vaccine adjuvant. Nucleic acid–based compounds have been shown to act as anticancer monotherapeutic agents even without coadministration of cancer Ag(s); however, so far they have lacked efficacy in clinical trials. We recently developed a second-generation TLR9 agonist, a humanized CpG DNA (K3) complexed with schizophyllan (SPG), K3-SPG, a nonagonistic Dectin-1 ligand. K3-SPG was previously shown to act as a potent monoimmunotherapeutic agent against established tumors in mice in vivo. In this study we extend the monoimmunotherapeutic potential of K3-SPG to a nonhuman primate model. K3-SPG activated monkey plasmacytoid dendritic cells to produce both IFN-α and IL-12/23 p40 in vitro and in vivo. A single injection s.c. or i.v. with K3-SPG significantly increased the frequencies of activated memory CD8+ T cells in circulation, including Ag-specific memory CTLs, in cynomolgus macaques. This increase did not occur in macaques injected with free CpG K3 or polyinosinic-polycytidylic acid. Injection of 2 mg K3-SPG induced mild systemic inflammation, however, levels of proinflammatory serum cytokines and circulating neutrophil influx were lower than those induced by the same dose of polyinosinic-polycytidylic acid. Therefore, even in the absence of specific Ags, we show that K3-SPG has potent Ag-specific memory CTL response–boosting capabilities, highlighting its potential as a monoimmunotherapeutic agent for chronic infectious diseases and cancer.

Caspase recruitment domain family member 14 (CARD14) was recently identified as a psoriasis-susceptibility gene, but its immunological role in the pathogenesis of psoriasis in vivo remains unclear. In this study, we examined the role of CARD14 in murine experimental models of psoriasis induced by either imiquimod (IMQ) cream or recombinant IL-23 injection. In all models tested, the psoriasiform skin inflammation was abrogated in Card14−/− mice. Comparison of the early gene signature of the skin between IMQ-cream–treated Card14−/− mice and Tlr7−/−Tlr9−/− mice revealed not only their similarity, but also distinct gene sets targeted by IL-23. Cell type–specific analysis of these mice identified skin Langerinhigh Langerhans cells as a potent producer of IL-23, which was dependent on both TLR7 and TLR9 but independent of CARD14, suggesting that CARD14 is acting downstream of IL-23, not TLR7 or TLR9. Instead, a bone marrow chimera study suggested that CARD14 in radio-sensitive hematopoietic cells was required for IMQ-induced psoriasiform skin inflammation, controlling the number of Vγ4+ T cells producing IL-17 or IL-22 infiltrating through the dermis to the inflamed epidermis. These data indicate that CARD14 is essential and a potential therapeutic target for psoriasis.

Metagenomic analysis based on the 16S rRNA gene is generally performed to examine the diversity and abundance of commensal bacteria in feces, which is now recognized to be associated with human health and diseases. Guanidine thiocyanate (GuSCN) solution is used as a less onerous way compared with a frozen method to transport and stock fecal samples at room temperature for DNA analysis; however, optimal methods to measure fecal bacterial composition in GuSCN solution remain to be investigated. Here, we examined the influence of various factors such as pretreatment (e.g., removing GuSCN solution and washing feces with phosphate-buffered saline (PBS) before mechanical lysis), fecal concentration in the GuSCN solution, storage time, and position of fecal subsampling on the 16S rRNA-based analysis of fecal bacteria in GuSCN solution. We found that pretreatment and fecal concentration affected the bacterial composition, and a little change was noted with subsampling position. Based on these results, we propose a basic protocol, including fecal sampling, sample storage, and DNA extraction, for the 16S rRNA-based analysis of bacterial composition in feces suspended in GuSCN solution.

With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

Knowing the value of the unbound drug fraction in the brain (fu,brain) is essential in estimating its effects and toxicity on the central nervous system (CNS); however, no model to predict fu,brain without experimental procedures is publicly available. In this study, we collected 253 measurements from the literature and an open database and built in silico models to predict fu,brain using only freely available software. By selecting appropriate descriptors, training, and evaluation, our model showed an acceptable performance on a test data set (R2 = 0.630, percentage of compounds predicted within a 3-fold error: 69.4%) using chemical structure alone. Our model is available at https://drumap.nibiohn.go.jp/fubrain/ , and all of our data sets can be obtained from the Supporting Information.

Adjuvants improve the potency of vaccines, but the modes of action (MOAs) of most adjuvants are largely unknown. TLR-dependent and -independent innate immune signaling through the adaptor molecule MyD88 has been shown to be pivotal to the effects of most adjuvants; however, MyD88's involvement in the TLR-independent MOAs of adjuvants is poorly understood. Here, using the T-dependent antigen NIPOVA and a unique particulate adjuvant called synthetic hemozoin (sHZ), we show that MyD88 is required for early GC formation and enhanced antibody class-switch recombination (CSR) in mice. Using cell-type-specific MyD88 KO mice, we found that IgG2c class switching, but not IgG1 class switching, was controlled by B cell-intrinsic MyD88 signaling. Notably, IFN-γ produced by various cells including T cells, NK cells, and dendritic cells was the primary cytokine for IgG2c CSR and B-cell intrinsic MyD88 is required for IFN-γ production. Moreover, IFN-γ receptor (IFNγR) deficiency abolished sHZ-induced IgG2c production, while recombinant IFN-γ administration successfully rescued IgG2c CSR impairment in mice lacking B-cell intrinsic MyD88. Together, our results show that B cell-intrinsic MyD88 signaling is involved in the MOA of certain particulate adjuvants and this may enhance our specific understanding of how adjuvants and vaccines work.

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance (CL int) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

The priming, boosting, and restoration of memory cytotoxic CD8

Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.

N-terminal tails of H2A, H2B, H3 and H4 histone families are subjected to posttranslational modifications that take part in transcriptional regulation mechanisms, such as transcription factor binding and gene expression. Regulation mechanisms under control of histone modification are important but remain largely unclear, despite of emerging datasets for comprehensive analysis of histone modification. In this paper, we focus on what we call genetic harmonious units (GHUs), which are co-occurring patterns among transcription factor binding, gene expression and histone modification. We present the first genome-wide approach that captures GHUs by combining ChIP-chip with microarray datasets from Saccharomyces cerevisiae. Our approach employs noise-robust soft clustering to select patterns which share the same preferences in transcription factor-binding, histone modification and gene expression, which are all currently implied to be closely correlated. The detected patterns are a well-studied acetylation of lysine 16 of H4 in glucose depletion as well as co-acetylation of five lysine residues of H3 with H4 Lys12 and H2A Lys7 responsible for ribosome biogenesis. Furthermore, our method further suggested the recognition of acetylated H4 Lys16 being crucial to histone acetyltransferase ESA1, whose essential role is still under controversy, from a microarray dataset on ESA1 and its bypass suppressor mutants. These results demonstrate that our approach allows us to provide clearer principles behind gene regulation mechanisms under histone modifications and detect GHUs further by applying to other microarray and ChIP-chip datasets. The source code of our method, which was implemented in MATLAB (http://www.mathworks.com/), is available from the supporting page for this paper: http://www.bic.kyoto-u.ac.jp/pathway/natsume/hm_detector.htm.