Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori

ABSTRACT: In this study transcriptional start sites (TSS) for H. pylori 26695 were determined To detect the complement of transcripts expressed from H. pylori, we collected three independent biological replicates (B1 – B3) from 26695 wild type strain grown to mid-exponential (OD 600 ~0.6) phase under microaerophilic conditions at 37°C in BHI medium. For all three samples, total RNA was extracted and subjected to differential RNA-seq (dRNA-seq) library preparation for primary transcriptome analysis as described previously (Sharma et al., 2010). Specifically, prior to cDNA library construction half of each RNA sample was treated with 5’ terminator exonuclease (+TEX samples), which degrades RNAs containing a 5’-monophosphate (5’-P) and, thus, enriches for primary transcripts containing 5’-triphosphates (5’-PPP). The other half of each sample was left untreated (-TEX samples) and thus contains both primary transcripts (5’-PPP) and processed RNAs (5’-P).

ORGANISM(S): Helicobacter pylori 26695

SUBMITTER: Konrad Förstner

PROVIDER: E-GEOD-67564 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori.

Bischler Thorsten T Tan Hock Siew HS Nieselt Kay K Sharma Cynthia M CM

Methods (San Diego, Calif.) 20150616

The global mapping of transcription boundaries is a key step in the elucidation of the full complement of transcriptional features of an organism. It facilitates the annotation of operons and untranslated regions as well as novel transcripts, including cis- and trans-encoded small RNAs (sRNAs). So called RNA sequencing (RNA-seq) based on deep sequencing of cDNAs has greatly facilitated transcript mapping with single nucleotide resolution. However, conventional RNA-seq approaches typically cannot ...[more]

PMID: 26091613

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Project description:Background: Prokaryotes have relatively small genomes, densely-packed and apparently dominated by protein-encoding sequences. However, data now generated by high throughput RNA sequencing (RNA-seq) reveal surprisingly more-complex transcriptomes with many previously unrecognized and unanticipated non-coding small and antisense transcripts. To date, such studies have investigated primarily Bacteria. Here, we report the transcripts present in ThermococcusM-BM- kodakarensis, a model hyperthermophilic Archaeon, synthesized under different growth and metabolic conditions. Results: cDNA libraries, generated from RNA preparations isolated from cells growing in media with sulfur or pyruvate, with sulfur to stationary phase, and growing with pyruvate but with sulfur added 20 min before RNA isolation, have been deep-sequenced. The results identify >2,700 sites of transcription initiation, establish a genome-wide map of transcripts, and consensus sequences for transcription initiation and post-transcription regulatory elements in T. kodakarensis. Primary transcription start sites (TSS) are identified upstream of 1,254 annotated genes, including ~78M-BM- % of those predicted by promoter locations, and an additional 644 primary TSS and their promoters have been identified within genes. Most of the mRNAs have a 5'-untranslated region (5'-UTR) between 10 and 50 nt long (median length = 16 nt), ~20 % have 5'-UTRs from 50 to 300 nt long, ~14 % are leaderless with 5'-UTRs M-bM-^IM-$8 nt, and ~50% contain a consensus ribosome binding sequence. The results also identify TSS for 1,018 antisense transcripts, most with sequences complementary to either the 5'- or 3'-region of a sense mRNA. The data confirm the presence of transcripts from all three CRISPR loci, the RNase P and 7S RNAs, all tRNAs and rRNAs and 69 snoRNAs predicted to be encoded in the T. kodakarensis genome. Two transcripts, putatively identified as riboswitches, were present in RNA preparations isolated from growing but not from stationary phase cells. The procedure used is designed to identify TSS but, assuming that the number of cDNA reads correlates with transcript abundance, the data obtained also provide a semi-quantitative overview of global operon expression. They document substantial differences in gene expression under different physiological conditions and are consistent with previous observations of substrate-dependent specific gene expression. Many previously unrecognized and unanticipated small RNAs have been identified, some with relative low GC contents (M-bM-^IM-$50%) and sequences that do not fold readily into base-paired secondary structures, contrary to the classical expectations for non-coding RNAs in a hyperthermophile. Conclusion: We have identified >2,700 TSS that include almost all of the primary sites of transcription initiation upstream of annotated genes, and also many secondary sites, sites within genes and sites resulting in antisense transcripts. The T.M-BM- kodakarensis genome is small (~2.1 Mbp) and tightly packed with protein-encoding genes, but the results reveal the presence of many non-coding RNAs and predict extensive RNA-based regulation in T. kodakarensis. cDNA libraries were generated and sequenced from RNA isolated from T.M-BM- kodakarensis cells growing exponentially (Sexp) and to stationary phase (Sstat) in ASW-YT medium with sulfur, growing exponentially in ASW-YT with pyruvate (Pexp), and from cells growing exponentially in pyruvate but 20 min after sulfur addition (PS). The cDNAs were generated after first incubating the RNA preparations with terminator exonuclease (TEX). TEX does not degrade primary transcripts with a 5'-triphosphate (Sharma et al., 2010) but does digest RNAs generated by transcript processing that have a 5'-monophosphate. As a control and to fully document all transcripts, a cDNA library (C) was also generated and sequenced from an aliquot of an RNA preparation isolated from the cells growing exponentially with sulfur that was not exposed to TEX digestion.

Project description:Gene regulatory networks that govern hematopoietic stem cells (HSC) and leukemia–initiating cells (L-IC) are deeply entangled. Thus, the discovery of compounds that target L-IC while sparing HSC is an attractive but difficult endeavor. Presently, most drug discovery approaches fail to counter-screen compounds against normal hematopoietic stem/progenitor cells (HSPC) to assess therapeutic index. Here, we present a combined in vitro and in vivo strategy to identify compounds specific to L-IC in acute myeloid leukemia (AML). A high-throughput screen of 4000 compounds on novel leukemia cell lines derived from human experimental leukemogenesis models yielded 80 hits, of which most were toxic to normal HSPC. Of the 10 compounds that passed this initial filter, we chose to characterize a single compound, kinetic riboside (KR), on AML L-IC and HSPC. KR demonstrated comparable efficacy to standard therapies against 63 primary AMLs. In vitro, KR effectively targeted the L-IC-enriched CD34+CD38- AML fraction, while sparing normal HSPC enriched fractions, although these effects were mitigated on HSC assayed in vivo, and highlights the importance of in vivo L-IC and HSC assays to measure function. Overall, we provide a novel approach to screen large drug libraries for the discovery of anti-L-IC compounds for human leukemias. The gene expression profile of TEX and M9-ENL1 cells were compared to HL-60 (Series GSE16160, GSE28185) and THP1 (Series GSE28185), to determine if TEX and M9-ENL1 cells were more enriched in stem cell (embryonic, adult, leukemia, and cancer) gene sets using Gene Set Enrichment Analysis (GSEA). TEX and M9-ENL1 cells were treated with DMSO in triplicate. Total RNA was harvested and analyzed with the Affymetrix Human Genome U133A 2.0 Array. Raw data was background corrected with RMA, normalized using quantiles method, corrected using only PM values, and median polished to generate log2 values. Published HL-60 and THP-1 raw data using the same Affymetrix array were processed identically. To account for batch variation, the log2 values were adjusted using ComBat in GenePattern with a parametric Empirical Bayes priors distribution estimation method and without covariate analysis.

Dataset Information

Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori

Publications

Differential RNA-seq (dRNA-seq) for annotation of transcriptional start sites and small RNAs in Helicobacter pylori.

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