Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Halobacterium sp. NRC-1 ChIP-chip for TFBa, TFBd and TFBf, high resolution array

ABSTRACT: A detailed map of genomic locations where TFs bind DNA and modulate transcription is essential to model mechanisms of gene regulation on a systems-scale. Chromatin immunoprecipitation of transcription complexes coupled to microarray (ChIP-chip (Ren et al, 2000)) or sequencing (ChIP-seq (Robertson et al, 2007)) is a commonly used approach to construct such maps. In ChIP-chip, the resolution to which the protein-DNA binding sites (TFBSs) can be identified is often limited by the genomic spacing of the probes in the array. We utilized the MeDiChI algorithm (Reiss et al, 2008) to estimate precise TFBS locations and their corresponding local false discovery rates (LFDRs) from high-resolution arrays for TFBa, TFBd and TFBf. This regression-based method deconvolves the ChIP-chip enrichment ratios along the genome by fitting them with a 'peak profile' model of binding events, assuming a distribution in enriched DNA fragment lengthsÂ. A comparison of the peak intensities derived from MeDiChI for all three TFs (TFBd, TFBf and TFBa) for which there were biological replicate measurements using 2 different microarray platforms (500 nt resolution spotted arrays, see series GSE7045 vs. 13 nt resolution Nimblegen arrays) provided strong validation (with R2 of 0.66, 0.52, and 0.81, respectively) of most TFBSs. Visualizations comparing the two microarray platforms are available at: http://baliga.systemsbiology.net/regulatory_logic/ IP sample of Cmyc tagged TFBs expressed in Halobacterium NRC-1 cells transformed with pMTFcmyc gene expressing TFBf in standard growth media with 20microgram/ml mevinolin versus a whole cell extract from the same sample. Three biological replicates for TFBd are available.

ORGANISM(S): Halobacterium sp. NRC-1

SUBMITTER: David Reiss

PROVIDER: E-GEOD-15786 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Prevalence of transcription promoters within archaeal operons and coding sequences.

Koide Tie T Reiss David J DJ Bare J Christopher JC Pang Wyming Lee WL Facciotti Marc T MT Schmid Amy K AK Pan Min M Marzolf Bruz B Van Phu T PT Lo Fang-Yin FY Pratap Abhishek A Deutsch Eric W EW Peterson Amelia A Martin Dan D Baliga Nitin S NS

Molecular systems biology 20090616

Despite the knowledge of complex prokaryotic-transcription mechanisms, generalized rules, such as the simplified organization of genes into operons with well-defined promoters and terminators, have had a significant role in systems analysis of regulatory logic in both bacteria and archaea. Here, we have investigated the prevalence of alternate regulatory mechanisms through genome-wide characterization of transcript structures of approximately 64% of all genes, including putative non-coding RNAs ...[more]

PMID: 19536208

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Project description:Gene regulatory networks play an important role in coordinating biochemical fluxes through diverse metabolic pathways. The modulation of enzyme levels enables efficient utilization of limited resources as organisms dynamically acclimate to nutritional fluctuations in their environment. Here we have identified and characterized a novel nutrient-responsive transcription factor from the halophilic archaea, AgmR. Like TrmB, its thermophilic archaeal homolog, AgmR regulates glycolytic and gluconeogenic pathways in response to sugar availability. However, using high throughput genome-scale experiments, we find that AgmR directly governs the transcription of nearly 100 additional genes encoding enzymes in diverse metabolic pathways. Genome-scale in vivo binding site location data reveals that >60% of these are direct targets. Integration of these systems-scale datasets with metabolic reconstruction models suggests that AgmR, a sequence-specific bacterial-like regulator, interacts with the general transcription factor machinery to coordinate nitrogen and carbon metabolism with the de novo synthesis of cognate cofactors and reducing equivalents, achieving system-wide redox and energy balance. Halobacterium salinarum NRC-1 (ATCC700922) ura3 parent and VNG1451C strains were grown in rich medium (CM, 250 NaCl, 20 g/L MgSO4?7H2O, 3 g/L sodium citrate, 2 g/L KCl, 10 g/L peptone) with or without varying concentrations of glucose or glycerol at 37ÂºC under full-spectrum white light. The cells were cross-linked with 1.2% formaldehyde, then lysed and DNA sheared via sonication. Next cleared cell lysate was subject to immunoprecipitation against an anti-myc antibody. Protein-DNA complexes were purified and DNA was isolated after protease and RNAase treatment. DNA was amplified using universal linkers, labeled directly using Kreatech 547 and 647 dyes. Samples were grown in biological duplicate. Each DNA sample was hybridized against 2 microarrays as dyefilps. At least two experiments (including biological duplicates) were carried out.

Project description:Because iron toxicity and deficiency are equally life threatening, maintaining intracellular iron levels within a narrow optimal range is critical for nearly all known organisms. However, regulatory mechanisms that establish homeostasis are not well understood in organisms that dwell in environments at the extremes of pH, temperature, and salinity. Under conditions of limited iron, the extremophile Halobacterium salinarum, a salt-loving archaeon, mounts a specific response to scavenge iron for growth. We have identified and characterized the role of two transcription factors (TFs), Idr1 and Idr2, in regulating this important response. An integrated systems analysis of TF knockout gene expression profiles and genome-wide binding locations in the presence and absence of iron has revealed that these TFs operate collaboratively to maintain iron homeostasis. In the presence of iron, Idr1 and Idr2 bind near each other at 24 loci in the genome, where they are both required to repress some genes. In contrast, Idr1 and Idr2 are both necessary to activate other genes in a putative a feed forward loop. Even at loci bound independently, the two TFs target different genes with similar functions in iron homeostasis. We discuss conserved and unique features of the Idr1-Idr2 system in the context of similar systems in organisms from other domains of life. Data in this GEO archive are linked to the publication: Schmid AK, Pan M, Sharma K, Baliga NS.2011. Two transcription factors are necessary for iron homeostasis in a salt-dwelling archaeon.Nucleic Acids Res.39(7):2519-33. Cultures containing either the gene encoding the Idr1 or Idr2 transcription factors with c-terminal fusions to the myc epitope were grown to mid-logarithmic phase in the presence or absence of 100 uM FeSO4. Cultures were subjected to ChIP-chip as described in Facciotti, MT, Reiss, DJ, Pan, M, Kaur, A, Vuthoori, M, Bonneau, R, Shannon, P, Srivastava, A, Donohoe, SM, Hood, LE and Baliga, NS. General transcription factor specified global gene regulation in archaea. Proc Natl Acad Sci U S A. 2007;104: 4630-4635. Each Sample is based on two arrrays (one with dye-swap).

Project description:Experimentally mapped transcriptome structure of H. salinarum NRC-1 by hybridizing total RNA (including RNA species <200 nt) to genome-wide high-density tiling arrays (60 mer probes with 40 nt overlap between contiguous probes). H. salinarum NRC-1 presents a number of interesting switches in metabolism during growth due to complex changes in EFs including pH, oxygen, nutrition, etc. While most single perturbations (radiation, oxygen, metals, etc.) affect the expression of only ~10% of all genes (Baliga et al, 2004; Kauret al , 2006; Whitehead et al, 2006), the changes during growth resulted in differential regulation of a significantly higher proportion of genes (~63%, 1,518 genes). These conditions enabled the investigation of a wider transcriptional landscape, which includes not only modulation of transcript levels, but also extensive changes in transcriptome structure. We observed altered transcription start sites (TSSs), transcription termination site (TTSs), operon organizations and differential regulation of putative ncRNAs. By integrating hybridization signals with dynamic growth-related changes, we estimated the probability that each tiling array probe was complementary to a transcribed region, mapped locations of putative transcript boundaries and identified 1,574 TSSs and 1,952 TTSs for most genes with some transcriptional variation. In sum, TSSs were assigned to 64% (1,156 singletons and 544 genes in 203 operons) of all annotated genes and TTSs were assigned to 1,114 genes and 202 operons. We were also able to identify 5' and 3' UTRs and revise start sites for 61 genes and 12 operons. H. salinarum NRC-1 growth curve experiments were conducted in CM media, in a water bath incubator at 37oC with agitation of 125 rpm. Reference samples were cultured under standard growth conditions (Baliga and DasSarma, 1999), at mid-log phase (OD600 = ~0.6), as well as all strains used for ChIP-chip experiments (Facciotti et al., 2007). Whole-genome high resolution tiling arrays for H. salinarum NRC-1 were designed with e-Array (Agilent Technologies), using strand specific 60mer probes tiled every 20nt for the main chromosome (NC_002607) and every 21nt for the plasmids pNRC200 (NC_002608) and pNRC100 (NC_001869), consisting a total of 244K probes, including manufacturersâ?? controls. Microarrays were printed by Agilent technologies and hybridized to total RNA, which was isolated using mirVana miRNA Isolation kit (Ambion) and direct labeled with Alexa547 and Alexa647 dyes (Kreatech) (Baliga et al., 2004). We used direct chemical labeling of RNA (Baliga et al., 2004) to avoid enzymatic labeling artifacts (Perocchi et al., 2007) and enable strand-specific signals for transcribed segments. Hybridization and washing were performed according to array manufacturerâ??s instructions. Arrays were scanned in ScanArray (Perkin Elmer) and spot-finding was performed using Feature Extraction (Agilent Technologies). Two biological replicates were sampled and dye-flip experiments were conducted for each sample. Resulting intensities were quantile normalized across all experiments. Log ratios were calculated for each probe (growth curve sample/reference). Reference RNA signals were normalized by sequence content using a linear model similar to that of (Johnson et al., 2006). Interactive visualization of the data was performed in the Gaggle Genome Browser (Bare et al., in preparation), available at http://baliga.systemsbiology.net/regulatory_logic/.

Dataset Information

Halobacterium sp. NRC-1 ChIP-chip for TFBa, TFBd and TFBf, high resolution array

Publications

Prevalence of transcription promoters within archaeal operons and coding sequences.

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