Development and implementation of an algorithm for detection of protein complexes in large interaction networks

BACKGROUND: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes. RESULTS: This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. CONCLUSION: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Watanabe R., Kawata T., Ueda S., et al. Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information. Molecular Pharmaceutics 20, 419 (2022); https://doi.org/10.1021/acs.molpharmaceut.2c00698.The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics

Research Activities in the Department of Physical Therapy

[Introduction] It is already fifty years since the Japanese law of physical therapists and occupational therapists has been effective. The physical therapist is referred by the law as "the professionals who implements the physical therapy to persons with disabilities under the prescription of medical doctors". In fifty years, however, the target of physical therapy has been significantly expanded. The subject for physical therapy now includes the patients in acute disease just after the surgical operation in addition to those in rehabilitation stage. In other words, the physical therapy is now recognized as the indispensable intervention to the subject with acute as well as chronic disorders. On the other hand, due to a rapid transition of the society into the aged society, prevention of diseases, and decline of activity capacity due to the aging have become major issues for the physical therapy

Transcriptional Activation of Low-Density Lipoprotein Receptor Gene by DJ-1 and Effect of DJ-1 on Cholesterol Homeostasis

DJ-1 is a novel oncogene and also causative gene for familial Parkinson’s disease park7. DJ-1 has multiple functions that include transcriptional regulation, anti-oxidative reaction and chaperone and mitochondrial regulation. For transcriptional regulation, DJ-1 acts as a coactivator that binds to various transcription factors, resulting in stimulation or repression of the expression of their target genes. In this study, we found the low-density lipoprotein receptor (LDLR) gene is a transcriptional target gene for DJ-1. Reduced expression of LDLR mRNA and protein was observed in DJ-1-knockdown cells and DJ-1-knockout mice and this occurred at the transcription level. Reporter gene assays using various deletion and point mutations of the LDLR promoter showed that DJ-1 stimulated promoter activity by binding to the sterol regulatory element (SRE) with sterol regulatory element binding protein (SREBP) and that stimulating activity of DJ-1 toward LDLR promoter activity was enhanced by oxidation of DJ-1. Chromatin immunoprecipitation, gel-mobility shift and co-immunoprecipitation assays showed that DJ-1 made a complex with SREBP on the SRE. Furthermore, it was found that serum LDL cholesterol level was increased in DJ-1-knockout male, but not female, mice and that the increased serum LDL cholesterol level in DJ-1-knockout male mice was cancelled by administration with estrogen, suggesting that estrogen compensates the increased level of serum LDL cholesterol in DJ-1-knockout female mice. This is the first report that DJ-1 participates in metabolism of fatty acid synthesis through transcriptional regulation of the LDLR gene