Complete genome sequence of the Gram-negative probiotic Escherichia coli strain Nissle 1917.Reister M, Hoffmeier K, Krezdorn N, Rotter B, Liang C, Rund S, Dandekar T, Sonnenborn U, Oelschlaeger TA.
J Biotechnol. 2014 Aug 2;187C:106-107. doi: 10.1016/j.jbiotec.2014.07.442.
Escherichia coli strain Nissle 1917 (EcN) is the active principle of a probiotic preparation (trade name Mutaflor®) used for the treatment of patients with intestinal diseases such as ulcerative colitis and diarrhea. It has GRAS (generally recognized as save) status and has been shown to be a therapeutically effective drug (Sonnenborn and Schulze, 2009). The complete genomic DNA sequence will help in identifying genes and their products which are essential for the strains probiotic nature. Genbank/EMBL/DDBJ accession number: CP007799 (chromosome).LINK TO PUBLICATION
APADB: a database for alternative polyadenylation and microRNA regulation events.Müller S, Rycak L, Afonso-Grunz F, Winter P, Zawada AM, Damrath E, Scheider J, Schmäh J, Koch I, Kahl G, Rotter B
Database (Oxford). 2014 Jul 22;2014. pii: bau076. doi: 10.1093/database/bau076. Print 2014.
Alternative polyadenylation (APA) is a widespread mechanism that contributes to the sophisticated dynamics of gene regulation. Approximately 50% of all protein-coding human genes harbor multiple polyadenylation (PA) sites; their selective and combinatorial use gives rise to transcript variants with differing length of their 3' untranslated region (3'UTR). Shortened variants escape UTR-mediated regulation by microRNAs (miRNAs), especially in cancer, where global 3'UTR shortening accelerates disease progression, dedifferentiation and proliferation. Here we present APADB, a database of vertebrate PA sites determined by 3' end sequencing, using massive analysis of complementary DNA ends. APADB provides (A)PA sites for coding and non-coding transcripts of human, mouse and chicken genes. For human and mouse, several tissue types, including different cancer specimens, are available. APADB records the loss of predicted miRNA binding sites and visualizes next-generation sequencing reads that support each PA site in a genome browser. The database tables can either be browsed according to organism and tissue or alternatively searched for a gene of interest. APADB is the largest database of APA in human, chicken and mouse. The stored information provides experimental evidence for thousands of PA sites and APA events. APADB combines 3' end sequencing data with prediction algorithms of miRNA binding sites, allowing to further improve prediction algorithms. Current databases lack correct information about 3'UTR lengths, especially for chicken, and APADB provides necessary information to close this gap. Database URL: http://tools.genxpro.net/apadb.LINK TO PUBLICATION
Massive analysis of cDNA Ends (MACE) and miRNA expression profiling identifies proatherogenic pathways in chronic kidney disease.Zawada AM, Rogacev KS, Müller S, Rotter B, Winter P, Fliser D, Heine GH.
Epigenetics. 2014 Jan 1;9(1):161-72. doi: 10.4161/epi.26931. Epub 2013 Nov 1.
Epigenetic dysregulation contributes to the high cardiovascular disease burden in chronic kidney disease (CKD) patients. Although microRNAs (miRNAs) are central epigenetic regulators, which substantially affect the development and progression of cardiovascular disease (CVD), no data on miRNA dysregulation in CKD-associated CVD are available until now. We now performed high-throughput miRNA sequencing of peripheral blood mononuclear cells from ten clinically stable hemodialysis (HD) patients and ten healthy controls, which allowed us to identify 182 differentially expressed miRNAs (e.g., miR-21, miR-26b, miR-146b, miR-155). To test biological relevance, we aimed to connect miRNA dysregulation to differential gene expression. Genome-wide gene expression profiling by MACE (Massive Analysis of cDNA Ends) identified 80 genes to be differentially expressed between HD patients and controls, which could be linked to cardiovascular disease (e.g., KLF6, DUSP6, KLF4), to infection / immune disease (e.g., ZFP36, SOCS3, JUND), and to distinct proatherogenic pathways such as the Toll-like receptor signaling pathway (e.g., IL1B, MYD88, TICAM2), the MAPK signaling pathway (e.g., DUSP1, FOS, HSPA1A), and the chemokine signaling pathway (e.g., RHOA, PAK1, CXCL5). Formal interaction network analysis proved biological relevance of miRNA dysregulation, as 68 differentially expressed miRNAs could be connected to 47 reciprocally expressed target genes. Our study is the first comprehensive miRNA analysis in CKD that links dysregulated miRNA expression with differential expression of genes connected to inflammation and CVD. After recent animal data suggested that targeting miRNAs is beneficial in experimental CVD, our data may now spur further research in the field of CKD-associated human CVD.LINK TO PUBLICATION
omiRas: a Web server for differential expression analysis of miRNAs derived from small RNA-Seq data.Müller S, Rycak L, Winter P, Kahl G, Koch I, Rotter B.
Bioinformatics. 2013 Oct 15;29(20):2651-2. doi: 10.1093/bioinformatics/btt457. Epub 2013 Aug 13.
Small RNA deep sequencing is widely used to characterize non-coding
RNAs (ncRNAs) differentially expressed between two conditions, e.g.
healthy and diseased individuals and to reveal insights into
molecular mechanisms underlying condition-specific phenotypic
traits. The ncRNAome is composed of a multitude of RNAs, such as
transfer RNA, small nucleolar RNA and microRNA (miRNA), to name
few. Here we present omiRas, a Web server for the annotation,
comparison and visualization of interaction networks of ncRNAs
derived from next-generation sequencing experiments of two
different conditions. The Web tool allows the user to submit raw
sequencing data and results are presented as: (i) static annotation
results including length distribution, mapping statistics,
alignments and quantification tables for each library as well as
lists of differentially expressed ncRNAs between conditions and
(ii) an interactive network visualization of user-selected miRNAs
and their target genes based on the combination of several
miRNA-mRNA interaction databases.
The omiRas Web server is implemented in Python, PostgreSQL, R and can be accessed at: http://tools.genxpro.net/omiras.