Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression of intergenic regions of Synechococcus WH 7803 under environmentally relevant stresses

ABSTRACT: We constructed a tiling microarray, covering nearly all of the intergenic regions larger than 50 bp on both strands of the genome of the marine picocyanobacterium Synechococcus WH7803. We analyzed transcript levels from cultures grown under ecologically relevant stress conditions. The investigated stress conditions were cold stress, high light stress, phosphate depletion and iron depletion. We identified several previously unknown small RNAs, partially differentially expressed. The detected RNAs provide a starting point for further investigations on the acclimatisation to different stresses for Synechococcus WH7803. For every applied growth condition the cultures were grown in triplicates as were the respective controls. Respective controls were treated the same as the stressed bacterial cultures in terms of centrifugation and / or dilution. Bacteria were harvested by rapid filtering and directly freezed by liquid nitrogen.

ORGANISM(S): Synechococcus sp. WH 7803

SUBMITTER: Gregor Gierga

PROVIDER: E-GEOD-28263 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:The Myc-Max heterodimer is a DNA binding protein that regulates expression of a large number of genes. Genome occupancy of Myc-Max is thought to be driven by E-boxes (CACGTG or variants) to which the heterodimer binds in vitro. By analyzing ChIP-Seq datasets, we demonstrated that the positions occupied by Myc-Max across the human genome correlate with the RNA polymerase II (Pol II) transcription machinery better than with E-boxes. Metagene analyses showed that in promoter regions, Myc was uniformly positioned about 100 bp upstream of essentially all promoter proximal paused polymerases with Max about 10 bp upstream of Myc. We re-evaluated the DNA binding properties of full length Myc-Max proteins using electrophoretic mobility shift assays (EMSA) and protein-binding microarrays (PBM). EMSA results demonstrated Myc-Max heterodimers have high affinity for both E-box containing and non-specific DNA. Quantification of the relative affinities of Myc-Max for all possible 8- mers using PBM assays showed that sequences surrounding core 6-mers significantly affect binding. Comparing to the in vitro sequence preferences, Myc-Max genomic occupancy measured by ChIP-Seq was largely, although not completely, independent of sequence specificity. Our results suggest that the transcription machinery and associated promoter accessibility play an important role in genomic occupancy of Myc. Two protein binding microarray (PBM) experiments were performed: one for the heterodimer of the human transcription factors c-Myc and Max, and one for the Max-Max homodimer. Briefly, 4x44K arrays (Agilent Technologies; AmadID 015681) containing the M-bM-^@M-^Xall 10-merM-bM-^@M-^Y universal PBM design were used. Arrays were incubated with a PBS buffer based protein mixture of wither 10nM His-tagged Myc-Max heterodimer or 10nM His-tagged Max-Max homodimer, 2% milk, 200ng/M-BM-5L BSA, 50ng/M-BM-5L Salmon Testes DNA, and 0.02% TX-100. Bound protein was tagged with 10ng/M-BM-5L anti-His antibody conjugated to Alexa 488 (Qiagen; 35310) in PBS with 2% milk. Data were analyzed to obtain fluorescence intensities for all 8mers. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence. Three genomic-context protein binding microarray (gcPBM) experiments of human transcription factors were performed. Briefly, the gcPBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occur at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)M-bM-^HM-^RDNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TFM-bM-^HM-^RDNA binding preferences. We used high-throughput proteinM-bM-^HM-^RDNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying re- peat symmetries. Based on statistical mechanics modeling, we iden- tify a new proteinM-bM-^HM-^RDNA binding mechanism induced by DNA se- quence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed gov- erns proteinM-bM-^HM-^RDNA binding preferences. Four custom protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for GTCACGTGAC DNA binding sites flanked by repetitive DNA elements with different symmetries and correlation length scales. Briefly, we represent three categories of 36-bp sequences: 1) 28800 probes centered at a GTCACGTGAC site and flanked by repetitive elements (probe names starting with Ariel_); 2) Unbound probes (or negative controls); and 3) Bound probes, which correspond to randomly selected genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)), which contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot (raw files) as well as the median intensity over the 6 replicate spots (normalized data). The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Three protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 30-bp genomic sequences: 1) ChIP-chip bound probes, 2) ChIP-chip unbound probes, and 3) negative control probes. ChIP-chip bound probes corresponded to genomic regions bound in vivo by Cbf1 or Tye7 (ChIP-chip P < 0.005 in rich medium (YPD) (Harbison et al., Nature 2004, PMID 15343339)) contained at least two consecutive 8-mers with universal PBM E-score > 0.35 (Zhu et al., Genome Research 2009, PMID 19158363). All putative binding sites occurred at the same position within the probes on the array. M-bM-^@M-^\ChIP-chip unboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-chip P > 0.5 and at least two consecutive 8-mers at a more stringent universal PBM E-score threshold of 0.4. Negative control probes corresponded to S. cerevisiae intergenic regions with a maximum 8-mer E-score < 0.3. We also designed probes that contain, within constant flanking regions, all 10-bp sequences that occur within the M-bM-^@M-^\ChIP-chip boundM-bM-^@M-^] probes and contain the E-box CACGTG, but are flanked by synthetic rather than native genomic sequence. Each DNA sequence represented on the array is present in 4 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Dataset Information

Expression of intergenic regions of Synechococcus WH 7803 under environmentally relevant stresses

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure