In contrast to 5-methylcytosine (5-mC), which has been studied extensively1–3, little is known about 5-hydroxymethylcytosine (5-hmC), a recently identified epigenetic modification present in substantial amounts in certain mammalian cell types4,5. Here we present a method for determining the genome-wide distribution of 5-hmC. We use the T4 bacteriophage β-glucosyltransferase to transfer an engineered glucose moiety containing an azide group onto the hydroxyl group of 5-hmC. The azide group can be chemically modified with biotin for detection, affinity enrichment and sequencing of 5-hmC–containing DNA fragments in mammalian genomes. Using this method, we demonstrate that 5-hmC is present in human cell lines beyond those previously recognized4. We also find a gene expression level–dependent enrichment of intragenic 5-hmC in mouse cerebellum and an age-dependent acquisition of this modification in specific gene bodies linked to neurodegenerative disorders.

Type 2 diabetes is a serious and common chronic disease resulting from a complex inheritance-environment interaction along with other risk factors such as obesity and sedentary lifestyle. Type 2 diabetes and its complications constitute a major worldwide public health problem, affecting almost all populations in both developed and developing countries with high rates of diabetes-related morbidity and mortality. The prevalence of type 2 diabetes has been increasing exponentially, and a high prevalence rate has been observed in developing countries and in populations undergoing “westernization” or modernization. Multiple risk factors of diabetes, delayed diagnosis until micro- and macro-vascular complications arise, life-threatening complications, failure of the current therapies, and financial costs for the treatment of this disease, make it necessary to develop new efficient therapy strategies and appropriate prevention measures for the control of type 2 diabetes. Herein, we summarize our current understanding about the epidemiology of type 2 diabetes, the roles of genes, lifestyle and other factors contributing to rapid increase in the incidence of type 2 diabetes. The core aims are to bring forward the new therapy strategies and cost-effective intervention trials of type 2 diabetes.

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Recent studies have implicated chemokines in microglial activation and pathogenesis of neuropathic pain. C-X-C motif chemokine 13 (CXCL13) is a B lymphocyte chemoattractant that activates CXCR5. Using the spinal nerve ligation (SNL) model of neuropathic pain, we found that CXCL13 was persistently upregulated in spinal cord neurons after SNL, resulting in spinal astrocyte activation via CXCR5 in mice. shRNA-mediated inhibition of CXCL13 in the spinal cord persistently attenuated SNL-induced neuropathic pain. Interestingly, CXCL13 expression was suppressed by miR-186-5p, a microRNA that colocalized with CXCL13 and was downregulated after SNL. Spinal overexpression of miR-186-5p decreased CXCL13 expression, alleviating neuropathic pain. Furthermore, SNL induced CXCR5 expression in spinal astrocytes, and neuropathic pain was abrogated in Cxcr5-/- mice. CXCR5 expression induced by SNL was required for the SNL-induced activation of spinal astrocytes and microglia. Intrathecal injection of CXCL13 was sufficient to induce pain hypersensitivity and astrocyte activation via CXCR5 and ERK. Finally, intrathecal injection of CXCL13-activated astrocytes induced mechanical allodynia in naive mice. Collectively, our findings reveal a neuronal/astrocytic interaction in the spinal cord by which neuronally produced CXCL13 activates astrocytes via CXCR5 to facilitate neuropathic pain. Thus, miR-186-5p and CXCL13/CXCR5-mediated astrocyte signaling may be suitable therapeutic targets for neuropathic pain.

The nuclear transcription factor c-Myc is a member of the Myc gene family with multiple functions and located on band q24.1 of chromosome 8. The c-Myc gene is activated by chromosomal translocation, rearrangement, and amplification. Its encoded protein transduces intracellular signals to the nucleus, resulting in the regulation of cell proliferation, differentiation, and apoptosis, and has the ability to transform cells and bind chromosomal DNA. c-Myc also plays a critical role in malignant transformation. The abnormal over-expression of c-Myc is frequently observed in some tumors, including carcinomas of the breast, colon, and cervix, as well as small-cell lung cancer, osteosarcomas, glioblastomas, and myeloid leukemias, therefore making it a possible target for anticancer therapy. In this minireview, we summarize unique characteristics of c-Myc and therapeutic strategies against cancer using small molecules targeting the oncogene, and discuss the prospects in the development of agents targeting c-Myc, in particular G-quadruplexes formed in c-Myc promoter and c-Myc/Max dimerization. Such information will be of importance for the research and development of c-Myc-targeted drugs.

BackgroundDrug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance.ResultsSince many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances.ConclusionsThe experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1415-9) contains supplementary material, which is available to authorized users.

The superoxide anion (O2.−) is widely engaged in the regulation of cell functions and is thereby intimately associated with the onset and progression of many diseases. To ascertain the pathological roles of O2.− in related diseases, developing effective methods for monitoring O2.− in biological systems is essential. Fluorescence imaging is a powerful tool for monitoring bioactive molecules in cells and in vivo owing to its high sensitivity and high temporal‐spatial resolution. Therefore, increasing numbers of fluorescent imaging probes have been constructed to monitor O2.− inside live cells and small animals. In this minireview, we summarize the methods for design and application of O2.−‐responsive fluorescent probes. Moreover, we present the challenges for detecting O2.− and suggestions for constructing new fluorescent probes that can indicate the production sites and concentration changes in O2.− as well as O2.−‐associated active molecules in living cells and in vivo.

Motivation Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting drug-drug interaction-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. Results In this paper, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting drug-drug interaction-associated events. DDIMDL first constructs deep neural network-based sub-models by respectively using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug-drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision-recall curve of 0.9208. Availability The source code and data are available at https://github.com/YifanDengWHU/DDIMDL Supplementary information Supplementary data are available at Bioinformatics online.