Enteropathway: the metabolic pathway database for the human gut microbiota.

The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.

Mouse Model of Parkinson's Disease with Bilateral Dorsal Striatum Lesion with 6-Hydroxydopamine Exhibits Cognitive Apathy-like Behavior.

Among the symptoms of Parkinson's disease (PD), apathy comprises a set of behavioral, affective, and cognitive features that can be classified into several subtypes. However, the pathophysiology and brain regions that are involved in these different apathy subtypes are still poorly characterized. We examined which subtype of apathy is elicited in a mouse model of PD with 6-hydroxydopamine (6-OHDA) lesions and the behavioral symptoms that are exhibited. Male C57/BL6J mice were allocated to sham (n = 8) and 6-OHDA (n = 13) groups and locally injected with saline or 4 µg 6-OHDA bilaterally in the dorsal striatum. We then conducted motor performance tests and apathy-related behavioral experiments. We then pathologically evaluated tyrosine hydroxylase (TH) immunostaining. The 6-OHDA group exhibited significant impairments in motor function. In the behavioral tests of apathy, significant differences were observed between the sham and 6-OHDA groups in the hole-board test and novelty-suppressed feeding test. The 6-OHDA group exhibited impairments in inanimate novel object preference, whereas social preference was maintained in the three-chamber test. The number of TH+ pixels in the caudate putamen and substantia nigra compacta was significantly reduced in the 6-OHDA group. The present mouse model of PD predominantly showed dorsal striatum dopaminergic neuronal loss and a decrease in novelty seeking as a symptom that is related to the cognitive apathy component.

TogoID: an exploratory ID converter to bridge biological datasets.

MOTIVATION: Understanding life cannot be accomplished without making full use of biological data, which are scattered across databases of diverse categories in life sciences. To connect such data seamlessly, identifier (ID) conversion plays a key role. However, existing ID conversion services have disadvantages, such as covering only a limited range of biological categories of databases, not keeping up with the updates of the original databases and outputs being hard to interpret in the context of biological relations, especially when converting IDs in multiple steps. RESULTS: TogoID is an ID conversion service implementing unique features with an intuitive web interface and an application programming interface (API) for programmatic access. TogoID currently supports 65 datasets covering various biological categories. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations. AVAILABILITY AND IMPLEMENTATION: The TogoID service is freely available on the TogoID website (https://togoid.dbcls.jp/) and the API is also provided to allow programmatic access. To encourage developers to add new dataset pairs, the system stores the configurations of pairs at the GitHub repository (https://github.com/togoid/togoid-config) and accepts the request of additional pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Heterozygous and homozygous gene knockout of the 5-HT1B receptor have different effects on methamphetamine-induced behavioral sensitization.

The psychostimulant drug methamphetamine (METH) causes euphoria in humans and locomotor hyperactivity in rodents by acting on the mesolimbic dopamine (DA) pathway and has severe abuse and addiction liability. Behavioral sensitization, an increased behavioral response to a drug with repeated administration, can persist for many months after the last administration. Research has shown that the serotonin 1B (5-HT1B) receptor plays a critical role in the development and maintenance of drug addiction, as well as other addictive behaviors. This study examined the role of 5-HT1B receptors in METH-induced locomotor sensitization using 5-HT1B knockout (KO) mice. To clarify the action of METH in 5-HT1B KO mice the effects of METH on extracellular levels of DA (DAec) and 5-HT (5-HTec) in the caudate putamen (CPu) and the nucleus accumbens (NAc) were examined. Locomotor sensitization and extracellular monoamine levels were determined in wild-type mice (5-HT1B +/+), heterozygous 5-HT1B receptor KO (5-HT1B +/-) mice and homozygous 5-HT1B receptor KO mice (5-HT1B -/-). Behavioral sensitization to METH was enhanced in 5-HT1B -/- mice compared to 5-HT1B +/+ mice but was attenuated in 5-HT1B +/- mice compared to 5-HT1B +/+ and 5-HT1B -/- mice. In vivo, microdialysis demonstrated that acute administration of METH increases DAec levels in the CPu and NAc of 5-HT1B KO mice compared to saline groups. In 5-HT1B +/- mice, METH increased 5-HTec levels in the CPu, and DAec levels in the NAc were higher than in others.5-HT1B receptors play an important role in regulating METH-induced behavioral sensitization.

Role for µ-opioid receptor in antidepressant effects of δ-opioid receptor agonist KNT-127.

Previous pharmacological data have shown the possible existence of functional interactions between µ- (MOP), κ- (KOP), and δ-opioid receptors (DOP) in pain and mood disorders. We previously reported that MOP knockout (KO) mice exhibit a lower stress response compared with wildtype (WT) mice. Moreover, DOP agonists have been shown to exert antidepressant-like effects in numerous animal models. In the present study, the tail suspension test (TST) and forced swim test (FST) were used to examine the roles of MOP and DOP in behavioral despair. MOP-KO mice and WT mice were treated with KNT-127 (10 mg/kg), a selective DOP agonist. The results indicated a significant decrease in immobility time in the KNT-127 group compared with the saline group in all genotypes in both tests. In the saline groups, immobility time significantly decreased in MOP-KO mice compared with WT mice in both tests. In female MOP-KO mice, KNT-127 significantly decreased immobility time in the TST compared with WT mice. In male MOP-KO mice, however, no genotypic differences were found in the TST after either KNT-127 or saline treatment. Thus, at least in the FST and TST, the activation of DOP and absence of MOP had additive effects in reducing measures of behavioral despair, suggesting that effects on this behavior by DOP activation occur independently of MOP.

The jPOST environment: an integrated proteomics data repository and database.

Rapid progress is being made in mass spectrometry (MS)-based proteomics, yielding an increasing number of larger datasets with higher quality and higher throughput. To integrate proteomics datasets generated from various projects and institutions, we launched a project named jPOST (Japan ProteOme STandard Repository/Database, https://jpostdb.org/) in 2015. Its proteomics data repository, jPOSTrepo, began operations in 2016 and has accepted more than 10 TB of MS-based proteomics datasets in the past two years. In addition, we have developed a new proteomics database named jPOSTdb in which the published raw datasets in jPOSTrepo are reanalyzed using standardized protocol. jPOSTdb provides viewers showing the frequency of detected post-translational modifications, the co-occurrence of phosphorylation sites on a peptide and peptide sharing among proteoforms. jPOSTdb also provides basic statistical analysis tools to compare proteomics datasets.

jPOSTrepo: an international standard data repository for proteomes.

Major advancements have recently been made in mass spectrometry-based proteomics, yielding an increasing number of datasets from various proteomics projects worldwide. In order to facilitate the sharing and reuse of promising datasets, it is important to construct appropriate, high-quality public data repositories. jPOSTrepo (https://repository.jpostdb.org/) has successfully implemented several unique features, including high-speed file uploading, flexible file management and easy-to-use interfaces. This repository has been launched as a public repository containing various proteomic datasets and is available for researchers worldwide. In addition, our repository has joined the ProteomeXchange consortium, which includes the most popular public repositories such as PRIDE in Europe for MS/MS datasets and PASSEL for SRM datasets in the USA. Later MassIVE was introduced in the USA and accepted into the ProteomeXchange, as was our repository in July 2016, providing important datasets from Asia/Oceania. Accordingly, this repository thus contributes to a global alliance to share and store all datasets from a wide variety of proteomics experiments. Thus, the repository is expected to become a major repository, particularly for data collected in the Asia/Oceania region.

MAPLE 2.3.0: an improved system for evaluating the functionomes of genomes and metagenomes.

MAPLE is an automated system for inferring the potential comprehensive functions harbored by genomes and metagenomes. To reduce runtime in MAPLE analyzing the massive amino acid datasets of over 1 million sequences, we improved it by adapting the KEGG automatic annotation server to use GHOSTX and verified no substantial difference in the MAPLE results between the original and new implementations.

Aniseikonia and Foveal Microstructure in Patients with Idiopathic Macular Hole.

PURPOSE: To quantify the severity of aniseikonia in patients undergoing vitrectomy for idiopathic macular hole (MH) and to examine any relationship between aniseikonia and the foveal microstructure. DESIGN: Prospective, consecutive, interventional case series. PARTICIPANTS: We included 56 eyes of 56 patients who underwent vitrectomy to treat idiopathic MH. METHODS: We examined visual acuity, aniseikonia using the New Aniseikonia Test, and foveal structure using optical coherence tomography (OCT) before and 3, 6, and 12 months after surgery. Based on OCT images, minimum and base diameters of MH, height of MH, and defect lengths of the external limiting membrane (ELM), ellipsoid zone, and interdigitation zone were assessed. MAIN OUTCOME MEASURES: Degree of aniseikonia before and after surgery. RESULTS: The mean aniseikonia was -3.2±4.6%, ranging from -15.5% to +5.0%. Of the patients, 55% had micropsia, 7% had macropsia, and 38% had no aniseikonia. The mean absolute value of aniseikonia improved significantly from 3.8±4.1% before surgery to 1.0±1.5% at 12 months after surgery (P < 0.0001). The preoperative mean absolute value of aniseikonia showed a significant correlation with minimum diameters of MH (P < 0.01), base diameters of MH (P < 0.01), and the defect length of ELM (P < 0.05). In multivariate analysis, preoperative aniseikonia showed a significant correlation with the defect length of ELM (P < 0.05). In contrast, postoperative aniseikonia was not associated with any of the parameters. CONCLUSIONS: Approximately half of MH patients had micropsia. Vitrectomy for MH improved aniseikonia. Preoperative aniseikonia was associated with MH size and the defect length of ELM.

Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs.

Although there are several databases that contain data on many metabolites and reactions in biochemical pathways, there is still a big gap in the numbers between experimentally identified enzymes and metabolites. It is supposed that many catalytic enzyme genes are still unknown. Although there are previous studies that estimate the number of candidate enzyme genes, these studies required some additional information aside from the structures of metabolites such as gene expression and order in the genome. In this study, we developed a novel method to identify a candidate enzyme gene of a reaction using the chemical structures of the substrate-product pair (reactant pair). The proposed method is based on a search for similar reactant pairs in a reference database and offers ortholog groups that possibly mediate the given reaction. We applied the proposed method to two experimentally validated reactions. As a result, we confirmed that the histidine transaminase was correctly identified. Although our method could not directly identify the asparagine oxo-acid transaminase, we successfully found the paralog gene most similar to the correct enzyme gene. We also applied our method to infer candidate enzyme genes in the mesaconate pathway. The advantage of our method lies in the prediction of possible genes for orphan enzyme reactions where any associated gene sequences are not determined yet. We believe that this approach will facilitate experimental identification of genes for orphan enzymes.

DINIES: drug-target interaction network inference engine based on supervised analysis.

DINIES (drug-target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug-target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any 'profiles' or precalculated similarity matrices (or 'kernels') of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.

Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach.

MOTIVATION: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale. RESULTS: In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as 'multistep reaction sequence likeness', i.e. whether a compound-compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways. AVAILABILITY AND IMPLEMENTATION: Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/

Methylone-induced hyperthermia and lethal toxicity: role of the dopamine and serotonin transporters.

Methylone (2-methylamino-1-[3,4-methylenedioxy-phenyl]propan-1-one), an amphetamine analog, has emerged as a popular drug of abuse worldwide. Methylone induces hyperthermia, which is thought to contribute toward the lethal consequences of methylone overdose. Methylone has been assumed to induce hyperthermic effects through inhibition of serotonin and/or dopamine transporters (SERT and DAT, respectively). To examine the roles of each of these proteins in methylone-induced toxic effects, we used SERT and DAT knockout (KO) mice and assessed the hyperthermic and lethal effects caused by a single administration of methylone. Methylone produced higher rates of lethal toxicity compared with other amphetamine analogs in wild-type mice. Compared with wild-type mice, lethality was significantly lower in DAT KO mice, but not in SERT KO mice. By contrast, only a slight diminution in the hyperthermic effects of methylone was observed in DAT KO mice, whereas a slight enhancement of these effects was observed in SERT KO mice. Administration of the selective D1 receptor antagonist SCH 23390 and the D2 receptor antagonist raclopride reduced methylone-induced hyperthermia, but these drugs also had hypothermic effects in saline-treated mice, albeit to a smaller extent than the effects observed in methylone-treated mice. In contradistinction to 3,4-methylenedioxymethamphetamine, which induces its toxicity through SERT and DAT, these data indicate that DAT, but not SERT, is strongly associated with the lethal toxicity produced by methylone, which did not seem to be dependent on the hyperthermic effects of methylone. DAT is therefore a strong candidate molecule for interventions aimed at preventing acute neurotoxic and lethal effects of methylone.

KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters.

The identification of orthologous genes in an increasing number of fully sequenced genomes is a challenging issue in recent genome science. Here we present KEGG OC (http://www.genome.jp/tools/oc/), a novel database of ortholog clusters (OCs). The current version of KEGG OC contains 1 176 030 OCs, obtained by clustering 8 357 175 genes in 2112 complete genomes (153 eukaryotes, 1830 bacteria and 129 archaea). The OCs were constructed by applying the quasi-clique-based clustering method to all possible protein coding genes in all complete genomes, based on their amino acid sequence similarities. It is computationally efficient to calculate OCs, which enables to regularly update the contents. KEGG OC has the following two features: (i) It consists of all complete genomes of a wide variety of organisms from three domains of life, and the number of organisms is the largest among the existing databases; and (ii) It is compatible with the KEGG database by sharing the same sets of genes and identifiers, which leads to seamless integration of OCs with useful components in KEGG such as biological pathways, pathway modules, functional hierarchy, diseases and drugs. The KEGG OC resources are accessible via OC Viewer that provides an interactive visualization of OCs at different taxonomic levels.

GENIES: gene network inference engine based on supervised analysis.

Gene network inference engine based on supervised analysis (GENIES) is a web server to predict unknown part of gene network from various types of genome-wide data in the framework of supervised network inference. The originality of GENIES lies in the construction of a predictive model using partially known network information and in the integration of heterogeneous data with kernel methods. The GENIES server accepts any 'profiles' of genes or proteins (e.g. gene expression profiles, protein subcellular localization profiles and phylogenetic profiles) or pre-calculated gene-gene similarity matrices (or 'kernels') in the tab-delimited file format. As a training data set to learn a predictive model, the users can choose either known molecular network information in the KEGG PATHWAY database or their own gene network data. The user can also select an algorithm of supervised network inference, choose various parameters in the method, and control the weights of heterogeneous data integration. The server provides the list of newly predicted gene pairs, maps the predicted gene pairs onto the associated pathway diagrams in KEGG PATHWAY and indicates candidate genes for missing enzymes in organism-specific metabolic pathways. GENIES (http://www.genome.jp/tools/genies/) is publicly available as one of the genome analysis tools in GenomeNet.

[Genetic vulnerability of methamphetamine dependence].

Methamphetamine (METH) dependence show strong familial and genetic influences in family and twin studies. METH exerts its reinforcing effects by modulating monoaminergic transmission, of which dopamine is supposed to be important. Previously, experimental animals were being used to identify mechanisms of action of METH that are related to its abuse and toxicity, and genetic mouse models have also been used to define genes that may predict risk for the development of drug addiction. We found that genetic variances of dopamine transporter, dopamine receptor, micro-opioid receptor, serotonin 1A receptor, serotonin 6 receptor, and adenosine 2A adenosine receptor could be vulnerability factors for METH dependence or psychosis in the Japanese population. Genetic analysis with a genome-wide association study (GWAS)-based approach has been successful for investigating the genetic influences of METH dependence and other complex features. Collaborative studies with JGIDA and NIDA/NIH have obtained the results that the genetic vulnerability to METH dependence contributes to other major drug addiction. The genetic studies for METH dependence might help to identify the risk of individuals and to develop treatments that take advantage of individual genetic information in the future.

Overexpression of tyrosine hydroxylase in dopaminergic neurons increased sensitivity to methamphetamine.

Drug abuse is one of the great social problems in the world and a major healthcare challenge. It is supposed that sensitivity and reactivity to abuse drugs may vary from person to person, while its molecular basis is largely unknown. Dopaminergic neurons are deeply involved in addiction, and tyrosine hydroxylase (TH) catalyzes the first and rate-limiting step of the biosynthesis of dopamine (DA). We investigated the effects of increased TH expression on the metabolism of DA and reactivity to methamphetamine (METH), a drug of abuse, in mice. Wild-type TH (WT-TH) or the S40E mutant of TH (S40E-TH), which is an active form of TH mimicking phosphorylated TH at the 40th serine, was expressed in midbrain dopaminergic neurons using an adeno-associated virus (AAV) vector. The biochemical analysis showed that the turnover rates of DA in the nerve terminals were increased by the expression of WT-TH and S40E-TH, while there were few changes in the DA contents. Next, we administered METH to TH-overexpressing mice. We found that the S40E-TH-expressing mice responded to lower doses of METH than the control mice and WT-TH mice. The stereotyped behaviors appeared first in S40E-TH mice and then in WT-TH and control mice in this order. These data showed that the TH activity and expression level differentially affect DA metabolism in the nerve terminals from that in the cell bodies and that the TH activity and expression level are one of the determining factors for sensitivity and reactivity to METH. We suggest that TH may be a drug target for ameliorating sensitivity to drugs of abuse.

Evaluation method for the potential functionome harbored in the genome and metagenome.

BACKGROUND: One of the main goals of genomic analysis is to elucidate the comprehensive functions (functionome) in individual organisms or a whole community in various environments. However, a standard evaluation method for discerning the functional potentials harbored within the genome or metagenome has not yet been established. We have developed a new evaluation method for the potential functionome, based on the completion ratio of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional modules. RESULTS: Distribution of the completion ratio of the KEGG functional modules in 768 prokaryotic species varied greatly with the kind of module, and all modules primarily fell into 4 patterns (universal, restricted, diversified and non-prokaryotic modules), indicating the universal and unique nature of each module, and also the versatility of the KEGG Orthology (KO) identifiers mapped to each one. The module completion ratio in 8 phenotypically different bacilli revealed that some modules were shared only in phenotypically similar species. Metagenomes of human gut microbiomes from 13 healthy individuals previously determined by the Sanger method were analyzed based on the module completion ratio. Results led to new discoveries in the nutritional preferences of gut microbes, believed to be one of the mutualistic representations of gut microbiomes to avoid nutritional competition with the host. CONCLUSIONS: The method developed in this study could characterize the functionome harbored in genomes and metagenomes. As this method also provided taxonomical information from KEGG modules as well as the gene hosts constructing the modules, interpretation of completion profiles was simplified and we could identify the complementarity between biochemical functions in human hosts and the nutritional preferences in human gut microbiomes. Thus, our method has the potential to be a powerful tool for comparative functional analysis in genomics and metagenomics, able to target unknown environments containing various uncultivable microbes within unidentified phyla.

PathPred: an enzyme-catalyzed metabolic pathway prediction server.

The KEGG RPAIR database is a collection of biochemical structure transformation patterns, called RDM patterns, and chemical structure alignments of substrate-product pairs (reactant pairs) in all known enzyme-catalyzed reactions taken from the Enzyme Nomenclature and the KEGG PATHWAY database. Here, we present PathPred (http://www.genome.jp/tools/pathpred/), a web-based server to predict plausible pathways of muti-step reactions starting from a query compound, based on the local RDM pattern match and the global chemical structure alignment against the reactant pair library. In this server, we focus on predicting pathways for microbial biodegradation of environmental compounds and biosynthesis of plant secondary metabolites, which correspond to characteristic RDM patterns in 947 and 1397 reactant pairs, respectively. The server provides transformed compounds and reference transformation patterns in each predicted reaction, and displays all predicted multi-step reaction pathways in a tree-shaped graph.

DIRECT ASSIMILATION OF ATMOSPHERIC CARBON BY IMMATURE APPLE FRUITS.

Although fruit development primarily depends on photoassimilation by leaves, immature green fruits can also directly assimilate atmospheric CO2. To elucidate the process of C accumulation due to direct assimilation by fruit, we conducted a 13CO2 exposure experiment in an orchard in late June with immature 'Fuji' apples (Malus domestica). Four fruits from three trees were enclosed in transparent plastic bags and exposed to 13CO2 using an in-situ exposure system. Fruits were collected prior to and immediately following exposure in early July, late September and mid-November, and 13C concentrations in the peduncle, skin, flesh and core (including seeds) were measured. The higher assimilated 13C concentrations measured following exposure indicated that the fruits directly assimilated atmospheric 13C. The 13C concentration in fruit skin was higher immediately after exposure and in early July compared with that prior to exposure. In late September and mid-November, 13C concentrations were close to natural levels.