Horowitzadkins1927

The genomes of organisms from all three domains of life harbor endogenous base modifications in the form of DNA methylation. In bacterial genomes, methylation occurs on adenosine and cytidine residues to include N6-methyladenine (m6A), 5-methylcytosine (m5C), and N4-methylcytosine (m4C). Bacterial DNA methylation has been well characterized in the context of restriction-modification (RM) systems, where methylation regulates DNA incision by the cognate restriction endonuclease. Relative to RM systems less is known about how m6A contributes to the epigenetic regulation of cellular functions in Gram-positive bacteria. Here, we characterize site-specific m6A modifications in the non-palindromic sequence GACGmAG within the genomes of Bacillus subtilis strains. We demonstrate that the yeeA gene is a methyltransferase responsible for the presence of m6A modifications. We show that methylation from YeeA does not function to limit DNA uptake during natural transformation. Instead, we identify a subset of promoters that contain the methylation consensus sequence and show that loss of methylation within promoter regions causes a decrease in reporter expression. Further, we identify a transcriptional repressor that preferentially binds an unmethylated promoter used in the reporter assays. With these results we suggest that m6A modifications in B. subtilis function to promote gene expression. © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.Secretory cavity, which is formed during schizolysigeny, is a typical structure in Citrus fruit. Previous reports indicate that programmed cell death (PCD) is involved in the degradation of secretory cavity cells in Citrus fruits, and that the spatio-temporal location of calcium is closely related to nuclear DNA degradation in the PCD of secretory cavity cells. However, the molecular mechanism underlying Ca2+ regulation during the nuclear DNA degradation processes in plant secretory cavity formation remains largely unknown. Here, we identified CgCAN, a Ca2+-dependent DNase gene from Citrus grandis 'Tomentosa' fruits, whose function was studied using calcium ion localization, DNase activity assay, in situ hybridization, and protein immunolocalization experiments. Our results suggest that the full-length cDNA of CgCAN contains an open reading frame of 1011 bp that encodes a protein 336 amino acids in length with a SNase-like functional domain. CgCAN digests dsDNA at neutral pH in a Ca2+-dependent manner. In sitgy. All rights reserved. For permissions, please email journals.permissions@oup.com.Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https//idrblab.org/noreva/. selleck inhibitor © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.Increasing evidence indicates that long noncoding RNAs (lncRNAs) have crucial roles in various biological processes. However, the contribution of lncRNAs to β-cell dysfunction and their roles in diabetes therapeutics remain poorly understood. The aim of this study was to identify the lncRNAs dysregulated in diabetic islets and to explore the lncRNAs involved in β-cell function as potential therapeutic targets. By using RNA-sequencing and real-time PCR, we identified thousands of lncRNAs in the islets of db/db mice and db/m littermate mice. Among the differentially expressed lncRNAs, lncRNA-Malat1 (metastasis associated lung adenocarcinoma transcript 1) was reduced in the islets of db/db mice and palmitate-treated MIN6 cells. The results of TUNEL, western blot and flow cytometric analyses and GSIS assays revealed that Malat1 knockdown significantly induced β-cell apoptosis and inhibited insulin secretion. Mechanistically, RNA-immunoprecipitation showed that Malat1 enhanced Ptbp1 (polypyrimidine tract-binding protein 1) protein stability by direct interaction, thereby adjusting the ratio of PKM (pyruvate kinase muscle) isoforms 1 and 2 (PKM1/PKM2). Moreover, luciferase assay and chromatin immunoprecipitation indicated that Malat1 was transcriptionally activated by Pdx1, through which exendin-4 alleviated lipotoxicity-induced β-cell damage. In summary, our findings suggested the involvement of Malat1 in β-cell dysfunction under diabetic conditions via the Malat1/Ptbp1/PKM2 pathway. In addition, exendin-4 ameliorated β-cell impairment by Pdx1-mediated Malat1 upregulation. Hence, Malat1 may serve as a therapeutic target for the treatment of type 2 diabetes. © Endocrine Society 2020. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download.