NSUN5/TET2-directed chromatin-associated RNA modification of 5-methylcytosine to 5-hydroxymethylcytosine governs glioma immune evasion.

Malignant glioma exhibits immune evasion characterized by highly expressing the immune checkpoint CD47. RNA 5-methylcytosine(m5C) modification plays a pivotal role in tumor pathogenesis. However, the mechanism underlying m5C-modified RNA metabolism remains unclear, as does the contribution of m5C-modified RNA to the glioma immune microenvironment. In this study, we demonstrate that the canonical 28SrRNA methyltransferase NSUN5 down-regulates ß-catenin by promoting the degradation of its mRNA, leading to enhanced phagocytosis of tumor-associated macrophages (TAMs). Specifically, the NSUN5-induced suppression of ß-catenin relies on its methyltransferase activity mediated by cysteine 359 (C359) and is not influenced by its localization in the nucleolus. Intriguingly, NSUN5 directly interacts with and deposits m5C on CTNNB1 caRNA (chromatin-associated RNA). NSUN5-induced recruitment of TET2 to chromatin is independent of its methyltransferase activity. The m5C modification on caRNA is subsequently oxidized into 5-hydroxymethylcytosine (5hmC) by TET2, which is dependent on its binding affinity for Fe2+ and α-KG. Furthermore, NSUN5 enhances the chromatin recruitment of RBFOX2 which acts as a 5hmC-specific reader to recognize and facilitate the degradation of 5hmC caRNA. Notably, hmeRIP-seq analysis reveals numerous mRNA substrates of NSUN5 that potentially undergo this mode of metabolism. In addition, NSUN5 is epigenetically suppressed by DNA methylation and is negatively correlated with IDH1-R132H mutation in glioma patients. Importantly, pharmacological blockage of DNA methylation or IDH1-R132H mutant and CD47/SIRPα signaling synergistically enhances TAM-based phagocytosis and glioma elimination in vivo. Our findings unveil a general mechanism by which NSUN5/TET2/RBFOX2 signaling regulates RNA metabolism and highlight NSUN5 targeting as a potential strategy for glioma immune therapy.

Uniform asymptotics for ruin probabilities in a two-dimensional nonstandard renewal risk model with stochastic returns.

In this paper, we consider a two-dimensional nonstandard renewal risk model with stochastic returns, in which the two lines of claim sizes form a sequence of independent and identically distributed random vectors following a bivariate Sarmanov distribution, and the two claim-number processes satisfy a certain dependence structure. When the two marginal distributions of the claim-size vector belong to the intersection of the dominated-variation class and the class of long-tailed distributions, we obtain uniform asymptotic formulas of finite-time and infinite-time ruin probabilities.

Chemical informatics and the drug discovery knowledge pyramid.

The magnitude of the challenges in preclinical drug discovery is evident in the large amount of capital invested in such efforts in pursuit of a small static number of eventually successful marketable therapeutics. An explosion in the availability of potentially drug-like compounds and chemical biology data on these molecules can provide us with the means to improve the eventual success rates for compounds being considered at the preclinical level, but only if the community is able to access available information in an efficient and meaningful way. Thus, chemical database resources are critical to any serious drug discovery effort. This paper explores the basic principles underlying the development and implementation of chemical databases, and examines key issues of how molecular information may be encoded within these databases so as to enhance the likelihood that users will be able to extract meaningful information from data queries. In addition to a broad survey of conventional data representation and query strategies, key enabling technologies such as new context-sensitive chemical similarity measures and chemical cartridges are examined, with recommendations on how such resources may be integrated into a practical database environment.

Feature selection in validating mass spectrometry database search results.

Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced tools using statistical and machine learning approaches may provide faster and more accurate validation than manual inspection and empirical filtering criteria. In this study, we use two feature selection algorithms based on random forest and support vector machine to identify peptide properties that can be used to improve validation models. We demonstrate that an improved model based on an optimized set of features reduces the number of false positives by 58% relative to the model which used only search engine scores, at the same sensitivity score of 0.8. In addition, we develop classification models based on the physicochemical properties and protein sequence environment of these peptides without using search engine scores. The performance of the best model based on the support vector machine algorithm is at 0.8 AUC, 0.78 accuracy, and 0.7 specificity, suggesting a reasonably accurate classification. The identified properties important to fragmentation and ionization can be either used in independent validation tools or incorporated into peptide sequencing and database search algorithms to improve existing software programs.

DB-PABP: a database of polyanion-binding proteins.

The interactions between polyanions (PAs) and polyanion-binding proteins (PABPs) have been found to play significant roles in many essential biological processes including intracellular organization, transport and protein folding. Furthermore, many neurodegenerative disease-related proteins are PABPs. Thus, a better understanding of PA/PABP interactions may not only enhance our understandings of biological systems but also provide new clues to these deadly diseases. The literature in this field is widely scattered, suggesting the need for a comprehensive and searchable database of PABPs. The DB-PABP is a comprehensive, manually curated and searchable database of experimentally characterized PABPs. It is freely available and can be accessed online at http://pabp.bcf.ku.edu/DB_PABP/. The DB-PABP was implemented as a MySQL relational database. An interactive web interface was created using Java Server Pages (JSP). The search page of the database is organized into a main search form and a section for utilities. The main search form enables custom searches via four menus: protein names, polyanion names, the source species of the proteins and the methods used to discover the interactions. Available utilities include a commonality matrix, a function of listing PABPs by the number of interacting polyanions and a string search for author surnames. The DB-PABP is maintained at the University of Kansas. We encourage users to provide feedback and submit new data and references.

The reactive metabolite target protein database (TPDB)--a web-accessible resource.

BACKGROUND: The toxic effects of many simple organic compounds stem from their biotransformation to chemically reactive metabolites which bind covalently to cellular proteins. To understand the mechanisms of cytotoxic responses it may be important to know which proteins become adducted and whether some may be common targets of multiple toxins. The literature of this field is widely scattered but expanding rapidly, suggesting the need for a comprehensive, searchable database of reactive metabolite target proteins. DESCRIPTION: The Reactive Metabolite Target Protein Database (TPDB) is a comprehensive, curated, searchable, documented compilation of publicly available information on the protein targets of reactive metabolites of 18 well-studied chemicals and drugs of known toxicity. TPDB software enables i) string searches for author names and proteins names/synonyms, ii) more complex searches by selecting chemical compound, animal species, target tissue and protein names/synonyms from pull-down menus, and iii) commonality searches over multiple chemicals. Tabulated search results provide information, references and links to other databases. CONCLUSION: The TPDB is a unique on-line compilation of information on the covalent modification of cellular proteins by reactive metabolites of chemicals and drugs. Its comprehensiveness and searchability should facilitate the elucidation of mechanisms of reactive metabolite toxicity. The database is freely available at http://tpdb.medchem.ku.edu/tpdb.html.

Analysis and functional annotation of expressed sequence tags from the fall armyworm Spodoptera frugiperda.

BACKGROUND: Little is known about the genome sequences of lepidopteran insects, although this group of insects has been studied extensively in the fields of endocrinology, development, immunity, and pathogen-host interactions. In addition, cell lines derived from Spodoptera frugiperda and other lepidopteran insects are routinely used for baculovirus foreign gene expression. This study reports the results of an expressed sequence tag (EST) sequencing project in cells from the lepidopteran insect S. frugiperda, the fall armyworm. RESULTS: We have constructed an EST database using two cDNA libraries from the S. frugiperda-derived cell line, SF-21. The database consists of 2,367 ESTs which were assembled into 244 contigs and 951 singlets for a total of 1,195 unique sequences. CONCLUSION: S. frugiperda is an agriculturally important pest insect and genomic information will be instrumental for establishing initial transcriptional profiling and gene function studies, and for obtaining information about genes manipulated during infections by insect pathogens such as baculoviruses.

Support vector machines in HTS data mining: Type I MetAPs inhibition study.

This article reports a successful application of support vector machines (SVMs) in mining high-throughput screening (HTS) data of a type I methionine aminopeptidases (MetAPs) inhibition study. A library with 43,736 small organic molecules was used in the study, and 1355 compounds in the library with 40% or higher inhibition activity were considered as active. The data set was randomly split into a training set and a test set (3:1 ratio). The authors were able to rank compounds in the test set using their decision values predicted by SVM models that were built on the training set. They defined a novel score PT50, the percentage of the test set needed to be screened to recover 50% of the actives, to measure the performance of the models. With carefully selected parameters, SVM models increased the hit rates significantly, and 50% of the active compounds could be recovered by screening just 7% of the test set. The authors found that the size of the training set played a significant role in the performance of the models. A training set with 10,000 member compounds is likely the minimum size required to build a model with reasonable predictive power.

Discover protein sequence signatures from protein-protein interaction data.

BACKGROUND: The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-protein interaction (PPI) datasets. Mining these datasets for underlying biological knowledge has, however, remained a challenge. RESULTS: A total of 3108 sequence signatures were found, each of which was shared by a set of guest proteins interacting with one of 944 host proteins in Saccharomyces cerevisiae genome. Approximately 94% of these sequence signatures matched entries in InterPro member databases. We identified 84 distinct sequence signatures from the remaining 172 unknown signatures. The signature sharing information was then applied in predicting sub-cellular localization of yeast proteins and the novel signatures were used in identifying possible interacting sites. CONCLUSION: We reported a method of PPI data mining that facilitated the discovery of novel sequence signatures using a large PPI dataset from S. cerevisiae genome as input. The fact that 94% of discovered signatures were known validated the ability of the approach to identify large numbers of signatures from PPI data. The significance of these discovered signatures was demonstrated by their application in predicting sub-cellular localizations and identifying potential interaction binding sites of yeast proteins.

Tobacco genes induced by the bacterial effector protein AvrPto.

The type III effector protein AvrPto acts as a virulence factor in susceptible plants lacking a cognate resistance gene but triggers hypersensitive response and disease resistance in tomato plants carrying the Pto gene or in tobacco plants carrying an unknown resistance gene. To assist the characterization of cellular responses caused by AvrPto in the plant, a pathogen-free system was adopted to isolate genes up-regulated 12 h after induced expression of AvrPto. By using subtraction cloning and transgenic tobacco plants expressing avrPto as a transgene, we isolated 125 nonredundant cDNA clones that represent avrPto-response genes (ARG). In addition to genes that are known to be induced by Pto-avrPto recognition, a number of new genes were also isolated. Most of ARG showed a specific induction in tobacco plants challenged with incompatible or nonhost pathogens. The use of an avrPto mutant that selectively eliminated the avrPto recognition in tobacco demonstrated that the ARG were induced in a highly specific manner by the avirulence, instead of the virulence activity of avrPto.