Project description:MotivationPost-translational modification (PTM) is an important biochemical process. which includes six most well-studied types: phosphorylation, acetylation, methylation, sumoylation, ubiquitylation and glycosylation. PTM is involved in various cell signaling pathways and biological processes. Abnormal PTM status is closely associated with severe diseases (such as cancer and neurologic diseases) by regulating protein functions, such as protein-protein interactions (PPIs). A set of databases was constructed separately for PTM sites and PPI; however, the resource of regulation for PTM on PPI is still unsolved.ResultsHere, we firstly constructed a public accessible database of PTMint (PTMs that are associated with PPIs) (https://ptmint.sjtu.edu.cn/) that contains manually curated complete experimental evidence of the PTM regulation on PPIs in multiple organisms, including Homo sapiens, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae and Schizosaccharomyces pombe. Currently, the first version of PTMint encompassed 2477 non-redundant PTM sites in 1169 proteins affecting 2371 protein-protein pairs involving 357 diseases. Various annotations were systematically integrated, such as protein sequence, structure properties and protein complex analysis. PTMint database can help to insight into disease mechanism, disease diagnosis and drug discovery associated with PTM and PPI.Availability and implementationPTMint is freely available at: https://ptmint.sjtu.edu.cn/.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:BackgroundThe modeling of interactions among transcription factors (TFs) and their respective target genes (TGs) into transcriptional regulatory networks is important for the complete understanding of regulation of biological processes. In the case of experimentally verified human TF-TG interactions, there is no database at present that explicitly provides such information even though many databases containing human TF-TG interaction data have been available. In an effort to provide researchers with a repository of experimentally verified human TF-TG interactions from which such interactions can be directly extracted, we present here the Human Transcriptional Regulation Interactions database (HTRIdb).DescriptionThe HTRIdb is an open-access database that can be searched via a user-friendly web interface and the retrieved TF-TG interactions data and the associated protein-protein interactions can be downloaded or interactively visualized as a network through the web version of the popular Cytoscape visualization tool, the Cytoscape Web. Moreover, users can improve the database quality by uploading their own interactions and indicating inconsistencies in the data. So far, HTRIdb has been populated with 284 TFs that regulate 18302 genes, totaling 51871 TF-TG interactions. HTRIdb is freely available at http://www.lbbc.ibb.unesp.br/htri.ConclusionsHTRIdb is a powerful user-friendly tool from which human experimentally validated TF-TG interactions can be easily extracted and used to construct transcriptional regulation interaction networks enabling researchers to decipher the regulation of biological processes.
Project description:IRESite is an exhaustive, manually annotated non-redundant relational database focused on the IRES elements (Internal Ribosome Entry Site) and containing information not available in the primary public databases. IRES elements were originally found in eukaryotic viruses hijacking initiation of translation of their host. Later on, they were also discovered in 5'-untranslated regions of some eukaryotic mRNA molecules. Currently, IRESite presents up to 92 biologically relevant aspects of every experiment, e.g. the nature of an IRES element, its functionality/defectivity, origin, size, sequence, structure, its relative position with respect to surrounding protein coding regions, positive/negative controls used in the experiment, the reporter genes used to monitor IRES activity, the measured reporter protein yields/activities, and references to original publications as well as cross-references to other databases, and also comments from submitters and our curators. Furthermore, the site presents the known similarities to rRNA sequences as well as RNA-protein interactions. Special care is given to the annotation of promoter-like regions. The annotated data in IRESite are bound to mostly complete, full-length mRNA, and whenever possible, accompanied by original plasmid vector sequences. New data can be submitted through the publicly available web-based interface at http://www.iresite.org and are curated by a team of lab-experienced biologists.
Project description:UnlabelledMany cell lines can be reprogrammed to other cell lines by forced expression of a few transcription factors or by specifically designed culture methods, which have attracted a great interest in the field of regenerative medicine and stem cell research. Plenty of cell lines have been used to generate induced pluripotent stem cells (IPSCs) by expressing a group of genes and microRNAs. These IPSCs can differentiate into somatic cells to promote tissue regeneration. Similarly, many somatic cells can be directly reprogrammed to other cells without a stem cell state. All these findings are helpful in searching for new reprogramming methods and understanding the biological mechanism inside. However, to the best of our knowledge, there is still no database dedicated to integrating the reprogramming records. We built RPdb (cellular reprogramming database) to collect cellular reprogramming information and make it easy to access. All entries in RPdb are manually extracted from more than 2000 published articles, which is helpful for researchers in regenerative medicine and cell biology.Availability and implementationRPdb is freely available on the web at http://bioinformatics.ustc.edu.cn/rpdb with all major browsers supported.Contactaoli@ustc.edu.cnSupplementary informationSupplementary data are available at Bioinformatics online.
Project description:Autoimmune diseases emerge due to several reasons, of which molecular mimicry i.e., similarity between the host's and pathogen's interacting peptides is an important reason. In the present study we have reported a database of only experimentally verified peptide sequences, which exhibit molecular mimicry. The database is named as miPepBase (Mimicry Peptide Database) and contains comprehensive information about mimicry proteins and peptides of both host (and model organism) and pathogen. It also provides information about physicochemical properties of protein and mimicry peptides, which might be helpful in predicting the nature of protein and optimization of protein expression. The miPepBase can be searched using a keyword or, by autoimmune disease(s) or by a combination of host and pathogen taxonomic group or their name. To facilitate the search of proteins and/or epitope in miPepBase, which is similar to the user's interest, BLAST search tool is also incorporated. miPepBase is an open access database and available at http://proteininformatics.org/mkumar/mipepbase.
Project description:BackgroundMany model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources.ResultsWe report here the development of PCAS (ProteinCentric Annotation System) as an online resource of pre-computed proteome annotation data. We applied most available motif or domain databases and their analysis methods, including hmmpfam search of HMMs in Pfam, SMART and TIGRFAM, RPS-PSIBLAST search of PSSMs in CDD, pfscan of PROSITE patterns and profiles, as well as PSI-BLAST search of SUPERFAMILY PSSMs. In addition, signal peptide and TM are predicted using SignalP and TMHMM respectively. We mapped SUPERFAMILY and COGs to InterPro, so the motif or domain databases are integrated through InterPro. PCAS displays table summaries of pre-computed data and a graphical presentation of motifs or domains relative to the protein. As of now, PCAS contains human IPI, mouse IPI, and rat IPI, A. thaliana, C. elegans, D. melanogaster, S. cerevisiae, and S. pombe proteome.PCAS is available at http://pak.cbi.pku.edu.cn/proteome/gca.phpConclusionPCAS gives better annotation coverage for model proteomes by employing a wider collection of available algorithms. Besides presenting the most confident annotation data, PCAS also allows customized query so users can inspect statistically less significant boundary information as well. Therefore, besides providing general annotation information, PCAS could be used as a discovery platform. We plan to update PCAS twice a year. We will upgrade PCAS when new proteome annotation algorithms identified.
Project description:Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).
Project description:Acoustic metamaterials are structures with exotic acoustic properties, with promising applications in acoustic beam steering, focusing, impedance matching, absorption and isolation. Recent work has shown that the efficiency of many acoustic metamaterials can be enhanced by controlling an additional parameter known as Willis coupling, which is analogous to bianisotropy in electromagnetic metamaterials. The magnitude of Willis coupling in a passive acoustic meta-atom has been shown theoretically to have an upper limit, however the feasibility of reaching this limit has not been experimentally investigated. Here we introduce a meta-atom with Willis coupling which closely approaches this theoretical limit, that is much simpler and less prone to thermo-viscous losses than previously reported structures. We perform two-dimensional experiments to measure the strong Willis coupling, supported by numerical calculations. Our meta-atom geometry is readily modeled analytically, enabling the strength of Willis coupling and its peak frequency to be easily controlled.
Project description:The subject of the presented research was a thin-walled composite column made of CFRP (carbon-epoxy laminate). The test sample had a top-hat cross-section with a symmetrical arrangement of laminate layers [90/-45/45/0]s. The composite structure was subjected to the process of axial compression. Experimental and numerical tests for the loss of stability and load-carrying capacity of the composite construction were carried out. The numerical buckling analysis was carried out based on the minimum potential energy criterion (based on the solution of an eigenvalue problem). The study of loss of load-carrying capacity was performed on the basis of a progressive failure analysis, solving the problem of non-linear stability based on Newton-Raphson's incremental iterative method. Numerical results of critical and post-critical state were confronted with experimental research in order to estimate the vulnerable areas of the structure, showing areas prone to damage of the material.
Project description:The involvement of epigenetic aberrations in the development and progression of tumors is now well established. However, most studies have focused on the epigenetic inactivation of tumor suppressor genes during tumorigenesis and little is known about the epigenetic activation of cancer-associated genes, except for the DNA hypomethylation of some genes. Recently, we reported that the overexpression of cancer-promoting genes in ovarian cancer is associated with the loss of repressive histone modifications. This discovery suggested that epigenetic derepression may contribute to ovarian tumorigenesis by constituting a possible mechanism for the overexpression of oncogenes or cancer-promoting genes in tumors. The emerging importance of epigenetic aberrations in tumor initiation and in the regulation of cancer-initiating cells, suggests that epigenetically regulated genes may be promising therapeutic targets and biomarkers. Given that the current challenges in ovarian cancer include the identification of biomarkers for early cancer detection and the discovery of novel therapeutic targets for patients with recurrent malignancies undergoing chemotherapy, understanding the epigenetic changes that occur in ovarian cancer is crucial. This review looks at epigenetic mechanisms involved in the regulation of cancer-associated genes, including the contribution of epigenetic derepression to the activation of cancer-associated genes in ovarian cancer. In addition, possible epigenetic therapies targeting epigenetically dysregulated genes are discussed. A better understanding of the epigenetic changes in ovarian cancer will contribute to the improvement of patient outcomes.