ChIP-seq was performed on cells growing on LB or LB+arabinose. Chipseq results were validated with ChIP-qPCR. Motif discovery was performed in the ChIP enriched regions and a motif matching the known consensus was identified. RNAseq was also used to analyze expression patterns on w-t and AraC mutants in presence or absence of arabinose. Expression changes >4 fold between w-t and mutant upon arabinose were deemed significant. The conservation of identified sites was evaluated with multiple sequence alignments against several related species.
ChIP assay conditions
S. enterica: 40 ml cells expressing C-terminally FLAG-tagged
AraC (CB005; Table 3) were grown in LB + 0.2% arabinose at 37 °C to an OD 600 of 0.6-0.8.
ChIP notes
Cells were crosslinked for 20 minutes with formaldehyde (1% final concentration), pelleted by
centrifugation and washed once with Tris-buffered saline (TBS). Cell pellets were resuspended
in 1 ml FA lysis buffer (50 mM Hepes-KOH, pH 7, 150 mM NaCl, 1 mM EDTA, 1% Triton X-
100, 0.1% sodium deoxycholate, 0.1% SDS) with 2 mg/ml lysozyme and incubated at 37 °C for
30 minutes. Samples were then chilled and sonicated for 30 minutes in a Bioruptor sonicator
(Diagenode) with 30 s on/30 s off pulsing at maximum amplitude. Samples were pelleted in a
microcentrifuge to remove debris and supernatants (“chromatin”) were saved, 1 ml FA lysis
buffer was added, and samples were stored indefinitely at -20 °C. For each immunoprecipitation
(IP), 500 μl chromatin was incubated with 300 μl FA lysis buffer, 20 μl Protein A Sepharose
slurry (50%) in TBS and either 1 μl anti-β (RNA polymerase subunit) antibody (NeoClone) or 2
μl M2 anti-FLAG antibody (Sigma) for 90 minutes at room temperature with gentle mixing on a
Labquake Rotisserie Rotator (Thermo Scientific). For ChIP of AraC-TAP, Protein A sepharose
and antibody was replaced with IgG sepharose. Beads were then pelleted at 1,500 x g in a
microcentrifuge for 1 minute. The supernatant was removed and the beads were resuspended in
750 μl FA lysis buffer and transferred to a Spin-X column (Corning). Beads were then incubated
for 3 minutes with gentle mixing on a rotisserie rotator before being pelleted at 1,500 x g in a
microcentrifuge for 1 minute. Equivalent washes were performed with FA lysis buffer, high salt
FA lysis buffer (50 mM Hepes-KOH, pH 7, 500 mM NaCl, 1 mM EDTA, 1% Triton X-100,
0.1% sodium deoxycholate, 0.1% SDS), ChIP wash buffer (10 mM Tris-HCl, pH 8.0, 250 mM
LiCl, 1 mM EDTA, 0.5% Nonidet-P40, 0.5% sodium deoxycholate) and TE (10 mM Tris-HCl,
pH 7.5, 1 mM EDTA). After the TE wash, beads were transferred to a fresh Spin-X column and
eluted with 100 μl ChIP elution buffer (50 mM Tris-HCl, pH 7.5, 10 mM EDTA, 1% SDS) for
10 minutes at 65 °C with occasional agitation. Eluted samples were centrifuged at 1,500 x g in a
microcentrifuge for 1 minute. Supernatants were decrosslinked by boiling for 10 minutes and
cleaned up using a PCR purification kit (Qiagen).
Further wash steps involved incubation of samples with the wash solution for 3 min with gentle mixing on a
rotisserie rotator before pelleting beads at 1,500 x g in a microcentrifuge for 1 min. Following
the 90 min incubation, beads were washed twice with FA lysis buffer and twice with 10 mM
Tris-HCl, pH 7.5. Beads were resuspended in 100 μL 1X Quick Blunting Buffer (NEB)
containing dNTPs (concentration specified by NEB Quick Blunting kit) and 1 μL Quick
Blunting enzyme mix (NEB), and incubated at room temperature for 30 min with gentle mixing.
Beads were washed twice with FA lysis buffer and twice with 10 mM Tris-HCl, pH 8.0. Beads
were resuspended in 100 μL Buffer 2 (NEB) containing 2 mM dATP and 2 μL Klenow DNA
polymerase (NEB), and incubated for 30 min at 37 °C with gentle mixing. Beads were washed
twice with FA lysis buffer and twice with 10 mM Tris-HCl, pH 7.5. Barcoded adapter
oligonucleotides JW2870 + JW2881 or JW2876 + JW2887 (two different barcoded pairs) were
annealed by boiling a mixture of 100 μM each in 10 mM Tris, pH 7.5, 50 mM NaCl, 1 mM
EDTA, and cooling slowly. The adapter oligonucleotide mix was then diluted 10-fold in water.
Beads were resuspended in 1X DNA Quick Ligase buffer (NEB), 1 μL adapter oligonucleotide
mix and 2 μL Quick DNA Ligase (NEB), and incubated at room temperature for 15 min with
gentle mixing. Beads were washed twice with FA lysis buffer, and once each with high salt FA
lysis buffer, ChIP wash buffer and TE. After the TE wash, beads were transferred to a fresh
Spin-X column and eluted with 100 μl ChIP elution for 10 minutes at 65 °C with occasional
agitation. Samples were decrosslinked by boiling for 10 minutes. DNA was extracted with
phenol/chlorofom/isoamyl alcohol, ethanol precipitated, and resuspended in 11 μL H 2O. 1 μL
DNA was used for real time PCR amplification with oligonucleotides JW1169 + JW2327 using
an ABI 7500 Fast real time PCR machine. The number of cycles required to reach a Delta Rn
score of 0.1 was recorded (“X”). 8 μL of the remaining DNA was amplified using conventional
PCR with oligonucleotides JW1169 + JW2327 for X+3 cycles with an annealing temperature of
60 °C. PCR products were purified using Ampure XP magnetic beads (Ampure) and
resuspended in 20 μL H2O. Purified PCR products between 200 bp and 600 bp were gel purified
from an 8% non-denaturing polyacrylamide gel, eluted overnight in 0.4 M NaCl, and ethanol
precipitated to generate the final ChIP-seq libraries. Libraries were resuspended in 10 μL H2O
and quantified using a Qubit fluorimeter (Invitrogen). Libraries were sequenced using a HiSeq
2000 sequencer (Illumina; University at Buffalo Next Generation Sequencing Core Facility).
Sequence reads were aligned to non-repetitive sequences in the S. enterica subsp. enterica
serovar Typhimurium 14028s genome using the CLC Genomics Workbench and overall
coverage was determined using custom Python scripts. AraC-bound regions were called for any
genomic coordinate with >200 sequence reads mapping to both strands. ChIP-seq peaks were
identified as the position with the most number of mapped sequence reads within each AraC-
bound region.
Regulated genes for each binding site are displayed below. Gene regulation diagrams
show binding sites, positively-regulated genes,
negatively-regulated genes,
both positively and negatively regulated
genes, genes with unspecified type of regulation.
For each indvidual site, experimental techniques used to determine the site are also given.
ChIP-chip (and to a lesser degree ChIP-Seq) results are often validated with ChIP-PCR, in which a PCR with specific primers is performed on the pulled-down DNA. As in the case of RNASeq, there are many variations of these main techniques.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.
ChIP-chip (and to a lesser degree ChIP-Seq) results are often validated with ChIP-PCR, in which a PCR with specific primers is performed on the pulled-down DNA. As in the case of RNASeq, there are many variations of these main techniques.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.
ChIP-chip (and to a lesser degree ChIP-Seq) results are often validated with ChIP-PCR, in which a PCR with specific primers is performed on the pulled-down DNA. As in the case of RNASeq, there are many variations of these main techniques.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.
ChIP-chip (and to a lesser degree ChIP-Seq) results are often validated with ChIP-PCR, in which a PCR with specific primers is performed on the pulled-down DNA. As in the case of RNASeq, there are many variations of these main techniques.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.
ChIP-chip (and to a lesser degree ChIP-Seq) results are often validated with ChIP-PCR, in which a PCR with specific primers is performed on the pulled-down DNA. As in the case of RNASeq, there are many variations of these main techniques.
ChIP-Seq is equivalent to ChIP-chip down to the last step. In ChIP-Seq, immunoprecipiated DNA fragments are prepared for sequencing and funneled into a massively parallel sequencer that produces short reads. Even though the sonication step is the same as in ChIP-chip, ChIP-Seq will generate multiple short-reads within any given 500 bp region, thereby pinning down the location of TFBS to within 50-100 bp. A similar result can be obtained with ChIP-chip using high-density tiling-arrays. The downside of ChIP-Seq is that sensitivity is proportional to cost, as sensitivity increases with the number of (expensive) parallel sequencing runs. To control for biases, ChIP-seq experiments often use the "input" as a control. This is DNA sequence resulting from the same pipeline as the ChIP-seq experiment, but omitting the immunoprecipitation step. It therefore should have the same accessibility and sequencing biases as the experiment data.
In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.
In RNA-seq, RNA is extracted from the cell at a given time and reverse transcribed to obtain cDNA. This cDNA is then sequenced. This provides a snapshot of the "transcriptome" of an organism at a given time.