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Advax, a delta inulin-derived microparticle, has been developed as an adjuvant for several vaccines. However, its immunological characteristics and potential mechanism of action are yet to be elucidated. Here, we show that Advax behaves as a type-2 adjuvant when combined with influenza split vaccine, a T helper (Th)2-type antigen, but behaves as a type-1 adjuvant when combined with influenza inactivated whole virion (WV), a Th1-type antigen. In addition, an adjuvant effect was not observed when Advax-adjuvanted WV vaccine was used to immunize toll-like receptor (TLR) 7 knockout mice which are unable to respond to RNA contained in WV antigen. Similarly, no adjuvant effect was seen when Advax was combined with endotoxin-free ovalbumin, a neutral Th0-type antigen. An adjuvant effect was also not seen in tumor necrosis factor (TNF)-α knockout mice, and the adjuvant effect required the presences of dendritic cells (DCs) and phagocytic macrophages. Therefore, unlike other adjuvants, Advax potentiates the intrinsic or in-built adjuvant property of co-administered antigens. Hence, Advax is a unique class of adjuvant which can potentiate the intrinsic adjuvant feature of the vaccine antigens through a yet to be determined mechanism., Highlights • Advax potentiates built-in adjuvant property of vaccine antigens. • Advax does not change the T helper immune bias induced by the vaccine antigen. • Dendritic cells, phagocytic macrophages, and tumor necrosis factor-α play a role in Advax adjuvant activity. Adjuvants are indispensable agent to maximize the efficacy of vaccines. Most adjuvants consistently impart either T helper (Th)1, Th2 or Th17 bias to the vaccine response regardless of the properties of antigen. For example alum adjuvants consistently impart a Th2 bias regardless of the vaccine antigen. Here we show that a delta inulin-derived microparticle adjuvant, Advax, is an additional class of adjuvant that functions as an amplifier of in-built adjuvant activity within the antigens themselves. Advax enhances different types of adaptive immune response dependent on the antigen's own in-built adjuvant properties, confirming Advax's utility as a general purpose vaccine adjuvant.

Evaluation of immune responses in individual immune cell types is important for the development of new medicines. Here, we propose a computational method designated ICEPOP (Immune CEll POPulation) to estimate individual immune cell type responses from bulk tissue and organ samples. The relative gene responses are scored for each cell type by using the data from differentially expressed genes derived from control- vs drug-treated sample pairs, and the data from public databases including ImmGen and IRIS, which contain gene expression profiles of a variety of immune cells. By ICEPOP, we analysed cell responses induced by vaccine-adjuvants in the mouse spleen, and extended the analyses to human peripheral blood mononuclear cells and gut biopsy samples focusing on human papilloma virus vaccination and inflammatory bowel disease treatment with Infliximab. In both mouse and human datasets, our method reliably quantified the responding immune cell types and provided insightful information, demonstrating that our method is useful to evaluate immune responses from bulk sample-derived gene expression data. ICEPOP is available as an interactive web site (https://vdynamics.shinyapps.io/icepop/) and Python package (https://github.com/ewijaya/icepop).

Toxicogenomics focuses on assessing the safety of compounds using gene expression profiles. Gene expression signatures from large toxicogenomics databases are expected to perform better than small databases in identifying biomarkers for the prediction and evaluation of drug safety based on a compound's toxicological mechanisms in animal target organs. Over the past 10 years, the Japanese Toxicogenomics Project consortium (TGP) has been developing a large-scale toxicogenomics database consisting of data from 170 compounds (mostly drugs) with the aim of improving and enhancing drug safety assessment. Most of the data generated by the project (e.g. gene expression, pathology, lot number) are freely available to the public via Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System). Here, we provide a comprehensive overview of the database, including both gene expression data and metadata, with a description of experimental conditions and procedures used to generate the database. Open TG-GATEs is available from https://toxico.nibiohn.go.jp/english/index.html.

Motivation: In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data. Results: We present Toxygates, a user-friendly integrated analysis platform for this database. Toxygates combines a large microarray dataset with the ability to fetch semantic linked data, such as pathways, compound–protein interactions and orthologs, on demand. It can also perform pattern-based compound ranking with respect to the expression values of a set of relevant candidate genes. By using Toxygates, users can freely interrogate the transcriptome’s response to particular compounds and conditions, which enables deep exploration of toxicity mechanisms. Availability and implementation: Toxygates is freely available to the public at http://toxygates.nibio.go.jp. Contact: johan@nibio.go.jp, kenji@nibio.go.jp or y-igarashi@nibio.go.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Limited or regulatory proteolysis plays a critical role in many important biological pathways like blood coagulation, cell proliferation, and apoptosis. A better understanding of mechanisms that control this process is required for discovering new proteolytic events and for developing inhibitors with potential therapeutic value. Two features that determine the susceptibility of peptide bonds to proteolysis are the sequence in the vicinity of the scissile bond and the structural context in which the bond is displayed. In this study, we assessed statistical significance and predictive power of individual structural descriptors and combination thereof for the identification of cleavage sites. The analysis was performed on a data set of >200 proteolytic events documented in CutDB for a variety of mammalian regulatory proteases and their physiological substrates with known 3D structures. The results confirmed the significance and provided a ranking within three main categories of structural features: exposure > flexibility > local interactions. Among secondary structure elements, the largest frequency of proteolytic cleavage was confirmed for loops and lower but significant frequency for helices. Limited proteolysis has lower albeit appreciable frequency of occurrence in certain types of β-strands, which is in contrast with some previous reports. Descriptors deduced directly from the amino acid sequence displayed only marginal predictive capabilities. Homology-based structural models showed a predictive performance comparable to protein substrates with experimentally established structures. Overall, this study provided a foundation for accurate automated prediction of segments of protein structure susceptible to proteolytic processing and, potentially, other post-translational modifications.

Beyond the well-known role of proteolytic machinery in protein degradation and turnover, many specialized proteases play a key role in various regulatory processes. Thousands of highly specific proteolytic events are associated with normal and pathological conditions, including bacterial and viral infections. However, the information about individual proteolytic events is dispersed over multiple publications and is not easily available for large-scale analysis. CutDB is one of the first systematic efforts to build an easily accessible collection of documented proteolytic events for natural proteins in vivo or in vitro. A CutDB entry is defined by a unique combination of these three attributes: protease, protein substrate and cleavage site. Currently, CutDB integrates 3070 proteolytic events for 470 different proteases captured from public archives (such as MEROPS and HPRD) and publications. CutDB supports various types of data searches and displays, including clickable network diagrams. Most importantly, CutDB is a community annotation resource based on a Wikipedia approach, providing a convenient user interface to input new data online. A recent contribution of 568 proteolytic events by several experts in the field of matrix metallopeptidases suggests that this approach will significantly accelerate the development of CutDB content. CutDB is publicly available at http://cutdb.burnham.org.

Cyclodextrins are commonly used as a safe excipient to enhance the solubility and bioavailability of hydrophobic pharmaceutical agents. Their efficacies and mechanisms as drug-delivery systems have been investigated for decades, but their immunological properties have not been examined. In this study, we reprofiled hydroxypropyl-β-cyclodextrin (HP-β-CD) as a vaccine adjuvant and found that it acts as a potent and unique adjuvant. HP-β-CD triggered the innate immune response at the injection site, was trapped by MARCO+ macrophages, increased Ag uptake by dendritic cells, and facilitated the generation of T follicular helper cells in the draining lymph nodes. It significantly enhanced Ag-specific Th2 and IgG Ab responses as potently as did the conventional adjuvant, aluminum salt (alum), whereas its ability to induce Ag-specific IgE was less than that of alum. At the injection site, HP-β-CD induced the temporary release of host dsDNA, a damage-associated molecular pattern. DNase-treated mice, MyD88-deficient mice, and TBK1-deficient mice showed significantly reduced Ab responses after immunization with this adjuvant. Finally, we demonstrated that HP-β-CD–adjuvanted influenza hemagglutinin split vaccine protected against a lethal challenge with a clinically isolated pandemic H1N1 influenza virus, and the adjuvant effect of HP-β-CD was demonstrated in cynomolgus macaques. Our results suggest that HP-β-CD acts as a potent MyD88- and TBK1-dependent T follicular helper cell adjuvant and is readily applicable to various vaccines.

Background Linked Data has gained some attention recently in the life sciences as an effective way to provide and share data. As a part of the Semantic Web, data are linked so that a person or machine can explore the web of data. Resource Description Framework (RDF) is the standard means of implementing Linked Data. In the process of generating RDF data, not only are data simply linked to one another, the links themselves are characterized by ontologies, thereby allowing the types of links to be distinguished. Although there is a high labor cost to define an ontology for data providers, the merit lies in the higher level of interoperability with data analysis and visualization software. This increase in interoperability facilitates the multi-faceted retrieval of data, and the appropriate data can be quickly extracted and visualized. Such retrieval is usually performed using the SPARQL (SPARQL Protocol and RDF Query Language) query language, which is used to query RDF data stores. For the database provider, such interoperability will surely lead to an increase in the number of users. Results This manuscript describes the experiences and discussions shared among participants of the week-long BioHackathon 2011 who went through the development of RDF representations of their own data and developed specific RDF and SPARQL use cases. Advice regarding considerations to take when developing RDF representations of their data are provided for bioinformaticians considering making data available and interoperable. Conclusions Participants of the BioHackathon 2011 were able to produce RDF representations of their data and gain a better understanding of the requirements for producing such data in a period of just five days. We summarize the work accomplished with the hope that it will be useful for researchers involved in developing laboratory databases or data analysis, and those who are considering such technologies as RDF and Linked Data.

Genomic sequencing and structural genomics produced a vast amount of sequence and structural data, creating an opportunity for structure–function analysis in silico [Radivojac P, et al. (2013) Nat Methods 10(3):221–227]. Unfortunately, only a few large experimental datasets exist to serve as benchmarks for function-related predictions. Furthermore, currently there are no reliable means to predict the extent of functional similarity among proteins. Here, we quantify structure–function relationships among three phylogenetic branches of the matrix metalloproteinase (MMP) family by comparing their cleavage efficiencies toward an extended set of phage peptide substrates that were selected from ∼64 million peptide sequences (i.e., a large unbiased representation of substrate space). The observed second-order rate constants [ k (obs) ] across the substrate space provide a distance measure of functional similarity among the MMPs. These functional distances directly correlate with MMP phylogenetic distance. There is also a remarkable and near-perfect correlation between the MMP substrate preference and sequence identity of 50–57 discontinuous residues surrounding the catalytic groove. We conclude that these residues represent the specificity-determining positions (SDPs) that allowed for the expansion of MMP proteolytic function during evolution. A transmutation of only a few selected SDPs proximal to the bound substrate peptide, and contributing the most to selectivity among the MMPs, is sufficient to enact a global change in the substrate preference of one MMP to that of another, indicating the potential for the rational and focused redesign of cleavage specificity in MMPs.

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed. ispartof: Journal of Biomedical Semantics vol:5 issue:5 pages:1-13 ispartof: location:England status: published

Background In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. Findings We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. Conclusions Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available athttp://sagace.nibio.go.jp/en/.

An important technology used in toxicogenomic drug discovery research is the microarray, which enables researchers to simultaneously analyze the expression of a large number of genes. To build a database and data analysis system for use in assessing the safety of drugs and drug candidates, in 2002 we conducted a 5-year collaborative study in the Toxicogenomics Project (TGP1) in Japan. Experimental data generated by such studies must be validated by different laboratories for robust and accurate analysis. For this purpose, we conducted intra- and inter-laboratory validation studies with participating companies in the second collaborative study in the Toxicogenomics Project (TGP2). Gene expression in the liver of rats treated with acetaminophen (APAP) was independently examined by the participating companies using Affymetrix GeneChip microarrays. The intra- and inter-laboratory reproducibility of the data was evaluated using hierarchical clustering analysis. The toxicogenomics results were highly reproducible, indicating that the gene expression data generated in our TGP1 project is reliable and compatible with the data generated by the participating laboratories.

Most known organisms encode proteases that are crucial for constitutive proteolytic events. In the present paper, we describe a method to define these events in proteomes from Escherichia coli to humans. The method takes advantage of specific N-terminal biotinylation of protein samples, followed by affinity enrichment and conventional LC (liquid chromatography)–MS/MS (tandem mass spectrometry) analysis. The method is simple, uses conventional and easily obtainable reagents, and is applicable to most proteomics facilities. As proof of principle, we demonstrate profiles of proteolytic events that reveal exquisite in vivo specificity of methionine aminopeptidase in E. coli and unexpected processing of mitochondrial transit peptides in yeast, mouse and human samples. Taken together, our results demonstrate how to rapidly distinguish real proteolysis that occurs in vivo from the predictions based on in vitro experiments.

ABC (ATP-binding cassette) transporters play an important role in the communication of various substrates across cell membranes. They are ubiquitous in prokaryotes and eukaryotes, and eukaryotic types (EK-types) are distinguished from prokaryotic types (PK-types) in terms of their genes and domain organizations. The EK-types and PK-types mainly consist of exporters and importers, respectively. Prokaryotes have both the EK-types and the PK-types. The EK-types in prokaryotes are usually called "bacterial multidrug ABC transporters," but they are not well characterized in comparison with the multidrug ABC transporters in eukaryotes. Thus, an exhaustive search of the EK-types among diverse organisms and detailed sequence classification and analysis would elucidate the evolutionary history of EK-types. It would also help shed some light on the fundamental repertoires of the wide variety of substrates through which multidrug ABC transporters in eukaryotes communicate. In this work, we have identified the EK-type ABC transporters in 126 prokaryotes using the profiles of the ATP-binding domain (NBD) of the EK-type ABC transporters from 12 eukaryotes. As a result, 11 clusters were identified from 1,046 EK-types ABC transporters. In particular, two large novel clusters emerged, corresponding to the bacterial multidrug ABC transporters related to the ABCB and ABCC families in eukaryotes, respectively. In the genomic context, most of these genes are located alone or adjacent to genes from the same clusters. Additionally, to detect functional divergences in the NBDs, the Kullback-Leibler divergence was measured among these bacterial multidrug transporters. As a result, several putative functional regions were identified, some corresponding to the predicted secondary structures. We also analyzed a phylogeny of the EK-type ABC transporters in both prokaryotes and eukaryotes, which revealed that the EK-type ABC transporters in prokaryotes have certain repertoires corresponding to the conventional ABC protein groups in eukaryotes. On the basis of these findings, we propose an updated evolutionary hypothesis in which the EK-type ABC transporters in both eukaryotes and prokaryotes consisted of several kinds of ABC transporters in putative ancestor cells before the divergence of eukaryotic and prokaryotic cells.

The Proteolysis MAP (PMAP, http://www.proteolysis.org) is a user-friendly website intended to aid the scientific community in reasoning about proteolytic networks and pathways. PMAP is comprised of five databases, linked together in one environment. The foundation databases, ProteaseDB and SubstrateDB, are driven by an automated annotation pipeline that generates dynamic 'Molecule Pages', rich in molecular information. PMAP also contains two community annotated databases focused on function; CutDB has information on more than 5000 proteolytic events, and ProfileDB is dedicated to information of the substrate recognition specificity of proteases. Together, the content within these four databases will ultimately feed PathwayDB, which will be comprised of known pathways whose function can be dynamically modeled in a rule-based manner, and hypothetical pathways suggested by semi-automated culling of the literature. A Protease Toolkit is also available for the analysis of proteases and proteolysis. Here, we describe how the databases of PMAP can be used to foster understanding of proteolytic pathways, and equally as significant, to reason about proteolysis.