Comprehensive Mapping of the Escherichia coli Flagellar
Abstract 
Flagellar synthesis is a highly regulated process in all motile bacteria . 
In Escherichia coli and related species , the transcription factor FlhDC is the master regulator of a multi-tiered transcription network . 
FlhDC activates transcription of a number of genes , including some flagellar genes and the gene encoding the alternative Sigma factor FliA . 
Genes whose expression is required late in flagellar assembly are primarily transcribed by FliA , imparting temporal regulation of transcription and coupling expression to flagellar assembly . 
In this study , we use ChIP-seq and RNA-seq to comprehensively map the E. coli FlhDC and FliA regulons . 
We define a surprisingly restricted FlhDC regulon , including two novel regulated targets and two binding sites not associated with detectable regulation of surrounding genes . 
In contrast , we greatly expand the known FliA regulon . 
Surprisingly , 30 of the 52 FliA binding sites are located inside genes . 
Two of these intragenic promoters are associated with detectable noncoding RNAs , while the others either produce highly unstable RNAs or are inactive under these conditions . 
Together , our data redefine the E. coli flagellar regulatory network , and provide new insight into the temporal orchestration of gene expression that coordinates the flagellar assembly process . 
Introduction
Bacterial flagellar synthesis and motility are highly regulated processes involving positive and negative input at the transcriptional , post-transcriptional , translational , and post-translational levels . 
This complex regulatory system allows for sequential production of flagellar components in roughly the order they are required for assembly . 
One of the primary ways this temporality is established is through the underlying hierarchical transcription network ( reviewed [ 1 -- 3 ] ) . 
Promoters for flagellar genes are divided into three categories ( Classes 1 , 2 , and 3 ) based on their timing and mode of expression . 
The atypical transcription factor FlhDC ( more specifically , FlhD4C2 ) serves as the master flagellar regulator in Escherichia coli , Salmonella enterica , and other related enteric bacteria [ 4 ] . 
The flhDC operon is considered the sole Class 1 transcription unit . 
FlhDC expression and activity is regulated at a number of levels , allowing it to serve as an integrator of environmental , nutritional , and growth-phase signals [ 5 -- 11 ] . 
In turn , FlhDC is responsible for activating the transcription , either directly or indirectly , of all structural and regulatory components of the flagellar machinery . 
Class 2 promoters are directly activated by FlhDC and transcribed by RNA polymerase ( RNAP ) containing the primary s-factor , s [ 2 ] . 
Contacts between FlhDC and the carboxy-70 terminal domain of the a-subunit of RNAP are important for activation , but the precise mechanism is not known [ 12 ] . 
The seven commonly accepted FlhDC-dependent operons ( flgAMN , flgBCDEFGHIJ , flhBAE , fliAZY , fliE , fliFGHIJK , and fliLM-NOPQR ) encode important regulatory factors and the structural components of the membrane-spanning basal body and associated export apparatus [ 1 ] . 
Notably , fliA encodes an alternative s-factor [ 13 -- 15 ] and flgM encodes its cognate anti-s-factor [ 16 ] . 
The interplay between these two factors regulates the transition from early flagellar gene expression to late-stage gene expression . 
When both FliA and FlgM are present in the cytoplasm , FlgM binds FliA , preventing interaction with RNAP , and repressing FliA-dependent transcription . 
Upon assembly of the basal body and secretion apparatus ( from Class 2 gene products ) , FlgM is exported out of the cell , freeing FliA and allowing initiation of FliA-dependent transcription from Class 3 promoters . 
This coupling of transcription and assembly allows for efficient `` just-in-time '' expression kinetics , as has been described for some metabolic pathways [ 17 ] . 
FliA drives transcription of six commonly accepted Class 3 operons ( flgKL , fliDST , flgMN , fliC , tar-tap-cheRBYZ ( meche ) , and motAB-cheAW ( mocha ) ) and can initiate transcription of its own operon , fliAZY . 
These Class 3 operons encode products needed in late flagellar assembly such as the subunits of the flagellar filament ( flagellin ) , components of the motor , and chemotaxis-related regulatory factors . 
In addition to these classical Class 3 operons , FliA has been predicted or shown to drive transcription of genes involved in chemotaxis ( trg [ 18 ] and tsr [ 19 ] ) , aerotaxis ( aer [ 18,20 ] ) , and cyclic-di-GMP regulation of motility ( yhjH and ycgR [ 21 ] ) . 
FliA can also drive transcription of the Class 2 fliLMNOPQR operon [ 22 ] , and FliA-dependent transcription of other Class 2 operons has been suggested [ 1,2,23 ] . 
In addition to their roles in the flagellar transcription network , FlhDC and FliA have been implicated in the regulation of nonflagellar genes . 
Studies have reported a host of non-flagellar genes and processes , such as cell division and anaerobic metabolism , regulated by FlhDC , or by FlhD alone [ 24 -- 28 ] . 
However , the evidence provided for most of these additional target genes , such as changes in gene expression with no information on DNA binding , is far from conclusive . 
The reports of FlhD acting independently to regulate cell division have been directly refuted by another study [ 29 ] and many putative FlhDC targets fail to be repeatedly detected across multiple studies [ 20,24,25,27,28 ] . 
Furthermore , most previous studies fail to accurately distinguish between direct and indirect FlhDC-dependent regulation , as exemplified by the identification of the flagellar-related gene aer as an FlhDC target [ 25 ] when it is actually transcribed by FliA [ 18,20 ] . 
There are a few well-characterized FliA-dependent promoters , such as modA [ 30 ] and flxA [ 31 ] , that have no obvious function in flagellar synthesis or motility . 
Furthermore , there have been various bioinformatic studies that predicted FliA-dependent promoters based on sequence identity , finding hundreds of promoters , many with non-flagellar functions [ 30,32,33 ] ; however , these predictions have not been tested experimentally . 
Though the E. coli flagellar network has been studied extensively over the past few decades , many issues remain to be addressed . 
These include ( i ) the ability of FlhD to have regulatory effects independent of FlhC , ( ii ) the extent of co-regulation by FlhDC and FliA and the relative contributions of each at known complex promoters such as fliAZY and fliLMNOPQR , and ( iii ) the extent and composition of the non-flagellar regulons of FlhDC and FliA . 
Chromatin immunoprecipitation followed by deep sequencing ( ChIP-seq ) is a powerful technique to study the genome-wide localization of DNA-binding proteins . 
ChIP-seq provides highresolution information about binding location and relative binding affinity in vivo [ 34 ] . 
Factors such as local DNA structure and the binding of nucleoid-associated proteins and/or other transcription factors affect binding and regulatory activity , making direct in vivo measurements incredibly valuable [ 35 ] . 
RNA-seq is a highresolution method for assessing the transcriptomic differences between strains or conditions and allows for identification of novel transcripts such as non-coding RNAs [ 36 ] . 
By combining ChIP-seq and RNA-seq , one can assess the regulatory effect of each binding site and comprehensively establish which regulatory effects are direct and which are indirect . 
The combination of ChIP-seq and transcriptome analysis has recently been applied to many bacterial transcription factors [ 37 -- 39 ] and s-factors [ 40,41 ] . 
This powerful combination of techniques has primarily been used to investigate single DNA-binding proteins , but can also be used to build global transcription networks [ 38 ] . 
In this study , we performed ChIP-seq on FlhD , FlhC , and FliA , and RNA-seq on motile wild-type , DflhD , DflhC , and DfliA derivatives of E. coli MG1655 . 
These data reveal new binding sites and regulatory targets for both FlhDC and FliA , including the discovery of two FliA-dependent non-coding RNAs . 
We have comprehensively determined the direct and indirect regulons of these three proteins and demonstrate that this information can be used to build a 
Results
Construction and validation of epitope-tagged strains
In order to perform ChIP experiments , we constructed strains in which FlhD , FlhC , and FliA were chromosomally epitope-tagged with a 36FLAG tag . 
Since both termini of FlhD and FlhC have been implicated in complex formation or DNA-binding [ 42,43 ] , we inserted epitope tags into internal , unstructured regions ( Figure S1A ) . 
FliA was tagged at the N-terminus . 
Tagged strains were constructed from a poorly motile MG1655 derivative , which contained no IS element upstream of flhDC . 
Spontaneous highly motile isolates were recovered following incubation on motility agar , as has been described previously [ 44 -- 46 ] . 
To confirm that all isolates had gained motility through IS element insertion in the region upstream of flhDC , we sequenced the upstream and coding regions of each isolate ( File S1 ) . 
Each motile isolate had an IS element inserted in the region upstream of flhDC although the identity and location of the IS elements varied between strains ( Figure S1B ) . 
No additional mutations were observed in flhD , flhC , or the region between flhD and uspC , in any of the tagged strains . 
To ensure that each of the IS element insertions was responsible for the motile phenotype of the strains , we constructed strains in which a selectable marker gene , thyA , was inserted in each observed insertion location . 
Insertion of the thyA cassette in each of the observed locations resulted in fully motile strains ( Figure S1C ) . 
This experiment confirmed that while the tagged strains used in this study are not strictly isogenic , they are likely functionally equivalent . 
Additionally , it lends insight into IS element-associated motility by showing that motility acquisition is largely sequence - and location - independent . 
This is consistent with the previous hypothesis that IS element insertion up-regulates flhDC by displacing repressors bound in the upstream region [ 44,45 ] . 
It is formally possible that the tagged strains contain additional mutations that confer motility , but this is extremely unlikely given the frequency with which motile strains were isolated . 
While motile isolates were obtained for each tagged strain , all three tagged strains were less motile than a similarly selected wildtype strain , suggesting that epitope-tagging resulted in moderate functional impairment ( Figure S1D ) . 
It is possible that the diminished functionality of the tagged proteins could prevent the identification of very weak binding sites ; however , as discussed in detail below , the tagged proteins resulted in robust ChIP-seq signal at all extensively characterized binding sites and at many novel sites . 
Furthermore , the ChIP-seq signals generated for FlhDFLAG and FlhC-FLAG proteins were nearly identical ( Figure 1A ) although the tags are inserted in distinct locations within the quaternary structure of the FlhD4C2 complex . 
This suggests that whatever the functional defect is , it is not influencing the ability of the tagged proteins to bind DNA . 
Finally , the RNA-seq experiments ( performed with an isogenic set of strains ) do not support the existence of any directly regulated targets not identified by ChIP-seq . 
Genome-wide binding of FlhD and FlhC
We used ChIP-seq to assess genome-wide binding of FlhD and FlhC in E. coli MG1655 grown to mid-exponential phase ( OD600 0.5 -- 0.7 ) in Lysogeny Broth ( LB ) . 
The genome-wide binding profiles of the two proteins are shown in Figure 1A . 
Based on a stringent peak-calling analysis , 10 FlhD binding sites and 8 FlhC binding sites were identified ( Table 1 ) . 
All 8 FlhC binding sites overlapped with FlhD binding sites . 
The 2 additional FlhD binding sites were also associated with substantial FlhC occupancy , barely missing the threshold in the peak calling analysis . 
None of the peaks identified in the FlhD or FlhC ChIP-seq experiments overlapped with regions enriched in control ChIP-seq experiments using untagged control strains . 
The complete colocalization of FlhD and FlhC binding is consistent with the proteins only binding DNA as a heteromeric complex . 
The 10 sites of FlhDC binding include all 5 binding sites associated with the 7 canonical flagellar Class 2 operons . 
An FlhDC binding site was identified upstream of yecR , a gene of unknown function , consistent with previous reports [ 20,24 ] . 
The additional 4 binding sites identified represent novel FlhDC targets . 
Three of the novel FlhDC binding sites are in intergenic regions , upstream of one or more genes ( ampH/sbmA , yciK/sohB , and gntR ) . 
The remaining novel binding site is located inside csgC and immediately upstream of a transcription start site for the adjacent gene , ymdA [ 47 ] . 
To confirm FlhDC binding sites identified by ChIP-seq , targeted ChIP-qPCR was performed for all loci ( Figure 1B ; Table S1 ) . 
Although ChIP-qPCR is not a completely independent validation method , it allows for analysis of more biological replicates , more straightforward comparison with untagged control strains , and reduces common artifacts enhanced by ChIP-seq library amplification [ 48 ] . 
Consistent with the ChIP-seq findings , FlhD and FlhC occupancy was significantly above that detected in an untagged strain ( t-test , p-value # 0.05 ) for all loci . 
ChIP-qPCR was also used to evaluate FlhDC occupancy at previously predicted binding sites [ 24 ] not identified by ChIP-seq ( Figure S2 ) . 
The fliDST promoter was the only site to show significant occupancy by either protein ( Figure 1B , Figure S2 ) . 
This site was not identified by our ChIP-seq peak-calling analysis , but small , correctly shaped peaks are visible in the raw data for both proteins at this locus ( see below ) . 
Hence , we consider the fliDST promoter to be a genuine FlhDC binding site and have included it in all downstream analysis , bringing the total to 11 FlhDC-bound sites . 
The sequences surrounding the FlhDC binding sites identified with ChIP-seq and/or ChIP-qPCR were extracted and analyzed by BioProspector [ 49 ] . 
The resulting motif , found in 7 out of 11 sites , is low-scoring and degenerate ( motif score = 1.49 , Figure 1C ) but corresponds well with previously reported motifs for FlhDC binding [ 20,50 ] . 
Direct and indirect regulation by FlhDC
To determine the effect of the identified FlhDC binding sites on the transcriptome , RNA-seq was performed in the motile wildtype MG1655 strain and isogenic single-gene deletions of flhD and flhC . 
Differential gene expression analysis was performed using Rockhopper , an RNA-seq analysis program optimized for bacterial datasets [ 36 ] . 
Gene expression was almost identical in the DflhD and DflhC strains ( Figure S3 ) , as would be expected since the factors likely regulate the same genes and because the flhD deletion has a minor polar effect on flhC expression . 
Figure 2 shows a genome-wide comparison of normalized expression values in motile wild-type MG1655 versus the average normalized expression of the DflhD and DflhC strains . 
Overall , 228 genes were differentially expressed due to deletion of flhD/flhC ( Figure 2 ) . 
Of those significantly regulated genes , 40 are associated with FlhDC binding sites , indicating direct regulation . 
Of the 11 FlhDC binding sites identified by ChIP , 9 are associated with regulation of one or more adjacent genes ( Figure 3A -- E , Table 1 ) . 
We detected strong , positive regulation of all previously reported Class 2 operons by FlhD and FlhC . 
Additionally , the RNA-seq data demonstrated FlhDC-dependent activation of yecR , fliDST , and the novel target operon ymdABC ( Table 1 , Figure 3B ) . 
The FlhDC binding site upstream of sohB seemed to repress expression by 1.95-fold ( Figure 3C , Table 1 ) . 
The RNA-seq experiments did not provide any evidence for regulation associated with the novel binding sites upstream of ampH/sbmA and gntR ( Figure 3D -- E , Table 1 ) . 
Selected examples of direct FlhDC-dependent regulation , or lack of regulation , were validated using qRT-PCR ( Figure 3F -- G ) . 
The presence or absence of regulation was confirmed for all genes tested , except sohB and the divergently transcribed gene yciK . 
While the RNA-seq data suggested that sohB was repressed 1.95-fold by FlhDC and yciK was not regulated , the qRT-PCR data clearly indicate the opposite : no regulation of sohB and significant , but slight positive regulation of yciK ( 1.5-fold for FlhD and 2.29-fold for FlhC ; Figure 3F -- G ) . 
Positive FlhDC-dependent regulation of yciK is also supported by complementation experiments . 
Small ( less than 4-fold ) but significant changes in ampH and sbmA levels are detected between wild type and DflhC ; however , these changes are not detected in the DflhD strain or supported by complementation experiments ( Figure 3F -- G ) . 
RNA levels were also determined using qRT-PCR in a variety of complemented strains . 
As suggested by the motility assays ( Figure S1E ) , flhD overexpression could only partially rescue the flhD deletion , but overexpression of flhDC restored target gene expression to wild-type or higher levels ( Figure 3F ) . 
Overexpression of flhC alone in the flhD deletion strain did not rescue expression levels at all , demonstrating that the DflhD phenotype was not solely due to the polar effect on flhC expression ( Table S2 ) . 
The flhC deletion could be completely complemented by flhC overexpression , but target gene expression never exceeded wild-type levels , suggesting that the wild-type levels of flhD expression limit the possible pool of active FlhDC complexes ( Figure 3G ) . 
Mechanism of direct regulation of transcription by FlhDC 
To provide further insight into the mechanism of regulation at FlhDC-dependent promoters , ChIP-qPCR was used to evaluate s occupancy at FlhDC-dependent promoters in wild-type and 70 flhD deletion strains ( Figure 4 ) . 
For all positively regulated promoters tested , s occupancy was detected in the wild-type 70 strain ; however , s occupancy was completely undetectable in 70 the DflhD strain , except at the yciK/sohB promoter . 
At that promoter , deletion of flhD significantly reduced , but did not eliminate , s occupancy , consistent with FlhDC-dependent 70 recruitment to the yciK promoter and FlhDC-independent recruitment to the adjacent sohB promoter . 
Our data suggest that FlhDC binding is absolutely required for s : RNAP recruitment 70 to FlhDC-dependent promoters . 
This is consistent with reports that these promoters have poor matches to the consensus 210 and 235 hexamers [ 1 ] . 
Genome-wide binding of FliA
To begin assessing the next level of the transcriptional hierarchy , ChIP-seq was used to determine the genome-wide binding of the flagellar s-factor FliA ( Figure 5A ) . 
Following a stringent peak-calling analysis , 52 regions were identified that did not overlap with regions enriched in untagged controls ( Table 2 ) . 
These 52 FliA binding sites include 7 canonical flagellar Class 3 promoters and one upstream of the fliLMNOPQR operon . 
FliA binding sites were also identified upstream of five other flagellarrelated genes previously shown or predicted to be FliA-dependent : aer , ycgR , yhjH , trg , and tsr . 
Additionally , FliA binding sites were identified at 4 other previously reported FliA-dependent promoters of non-flagellar genes : modA [ 30 ] , ves [ 20 ] , ynjH [ 30 ] , and flxA [ 31 ] . 
The remaining 35 FliA binding sites represent novel FliA promoters for which no previous experimental evidence can be found , although a small fraction have been predicted by various bioinformatic approaches [ 51 ] . 
Strikingly , 28 of these novel binding sites are inside genes , with only 4 of these intragenic binding sites ,300 bp upstream of a start codon for an appropriately oriented annotated gene . 
A subset of the putative FliA binding sites identified by ChIP-seq was also tested using ChIP-qPCR . 
Of the 13 novel FliA binding sites tested , 11 showed significant enrichment in these targeted experiments ( Figure 5B ) . 
The 2 novel sites that could not be validated had ChIP-seq `` fold above threshold '' ( FAT ) scores of 4 or lower . 
Twenty-two other peaks had similarly low peak scores ( # 4 ) but were not tested by ChIP-qPCR . 
Sequences surrounding each of the 52 FliA binding sites were extracted and examined for a motif using MEME [ 52 ] . 
A highly significant motif ( Evalue = 2.4 e ) was identified in all 52 binding regions and 262 matches previously reported FliA promoter motifs well ( Figure 5C , Table 2 ) . 
This MEME analysis identified motifs associated with all low-scoring sites , including the two that could not be validated by ChIP-qPCR . 
To further assess whether low-scoring sites , as a group , show evidence of sequence-specific FliA binding , motifs were identified separately for high-scoring ( FAT .4 , n = 26 ) and low-scoring ( FAT # 4 , n = 26 ) peaks ( Figure 5D ) . 
The two groups yielded very similar motifs , both with highly significant q-values ( E-value = 5.8 e , E-value = 2.9 e 237 210 high low ) . 
This strongly suggests that a majority of low-scoring ChIP-seq peaks represent 
Not only were motifs identified for all ChIP-seq peaks , but these motifs were also localized in a striking pattern relative to the ChIP-seq peaks ( Figure 5E ) . 
For most ChIP-seq experiments , one would expect motifs to be enriched at peak centers [ 53 ] . 
However , FliA motifs were clustered with a median position of 25 nt upstream of the peak center ( relative to the direction of the motif ) . 
This is consistent with the FliA binding to the 210 and 235 hexamer motif while in the context of RNAP holoenzyme [ 54 ] . 
This localization of FliA motifs strongly suggests that FliA only binds in the context of RNAP holoenzyme . 
Furthermore , this analysis allowed us to identify two weak , non-canonical , putative FliA binding sites ( insB-4 / cspH convergent intergenic region and inside proK ) , that have unusually localized motifs , suggesting that they might not be genuine FliA promoters . 
While this manuscript was in preparation , FliA ChIP-chip data was published by another group [ 55 ] . 
Our analysis of this study indicates many false positives and false negatives , and the resolution is far lower than that of our data ( Figure S4 ) . 
Direct regulation by FliA
To detect FliA-dependent changes in gene expression , RNA-seq was performed in a DfliA strain and compared to the isogenic motile wild-type . 
Analysis of differential gene expression was performed with Rockhopper [ 36 ] . 
Overall , 68 genes were differentially expressed between the motile wild-type and a DfliA strain ( Figure 6 ) . 
Most of the strongly regulated genes are associated with FliA binding sites identified by ChIP-seq ( Figure 6 , green points ) , indicating direct regulation . 
Of the 52 FliA binding sites identified by ChIP-seq , 14 were associated with significant gene expression changes under these conditions ( Table 2 , Figure 7 ) . 
These included some , but not all , Class 3 promoters , and 8 other previously reported FliA promoters . 
Interestingly , three of the intragenic FliA binding sites were also associated with significant regulation of surrounding genes . 
The promoters inside flhC , yjdA , and yafY drive transcription of the downstream genes ( motA , yjcZ , and ykfB , respectively ; Figure 7C , Table 2 ) . 
In addition to using Rockhopper to analyze differential gene expression , we visually inspected mapped RNA-seq data to find 
FliA-dependent transcripts that might otherwise have been overlooked . 
We identified an unusual transcript associated with uhpT the novel FliA binding site inside . 
Unlike the previously discussed examples , this promoter drives transcription of a purely intragenic RNA . 
RNA-seq detects a small ( ,50 nt ) RNA encoded in the same orientation as the gene ( Figure 7D ) . 
Visual examination of the RNA-seq data also led to the discovery of an antisense orientation noncoding RNA ( asRNA ) associated with the novel intragenic FliA-promoter inside hypD ( Figure 7E ) . 
This asRNA is contained entirely within the hypD gene and overlaps ,500 nt of the 59 end of the hypD open reading frame ( ORF ) . 
The hypD antisense RNA is too weakly expressed to be detected by 
Rockhopper , despite the program 's ability to identify novel antisense RNAs . 
The remaining novel FliA binding sites were not associated with detectable FliA-dependent changes in gene expression under these conditions ( eg . 
speA , Figure 7F ) . 
A variety of canonical and novel examples of FliA-dependent regulation were further validated using qRT-PCR ( Figure 7G , Table S2 ) . 
In all cases , including the two novel non-coding RNAs , the regulation identified in the RNA-seq experiment was confirmed in these targeted experiments . 
Additionally , overexpression of fliA in the DfliA strain resulted in higher than wild-type levels of expression of all FliA-dependent transcripts tested . 
Dual regulation of complex promoters and overlapping operons 
Complex promoters and overlapping operons were first evaluated using our ChIP-seq and RNA-seq data ( Figure 8 ) . 
The previously described ( fliAZY [ 22 ] and fliLMNOPQR [ 22 ] ) or predicted ( fliDST [ 24 ] ) complex promoters are supported by our findings ( Figure 8A , B , D ) . 
The previously described overlapping flgAMN/flgMN operons are also supported by our data ( Figure 8C ) . 
Finally , our data confirm that flgKL can be transcribed as part of the upstream FlhDC-dependent flgBCDEF-GHIJ operon , in addition to being transcribed from its own Class 3 promoter ( Figure 8C , Table 2 ) . 
While these operons have been previously described in Salmonella [ 56,57 ] , this is the first experimental demonstration of the overlapping operons in E. coli . 
The novel flgBCDEFGHIJKL operon was further confirmed by using RT-PCR to amplify across the flgJ-flgK boundary ( Figure S5A ) . 
In addition to confirming dual regulation of fliDST and discovering that flgKL can be transcribed as part of the upstream operon , the RNA-seq data from this study reveal a surprising pattern for all dual regulated flagellar genes . 
In the investigated conditions and growth phase , deletion of FliA has little or no effect on the expression levels of dual regulated genes ( Figure 8A -- D ) . 
This suggests that , while FliA occupancy is detected at all dual promoters by ChIP-seq , most transcription is coming from the FlhDC-dependent s promoters under our conditions . 
70 We used qRT-PCR to further investigate the regulatory input of FlhDC and FliA for dual regulated genes ( Figure 8E ) . 
As seen in the RNA-seq experiments , deletion of fliA had a much less dramatic effect on gene expression compared to deletion of flhD . 
Overexpression of fliA in the DflhD strain significantly increased RNA levels of all dual targets ( compared to DflhD ) showing that the FliA promoters are capable of transcribing the target genes . 
However , this effect was smaller for fliL than for any other dual regulated gene . 
Furthermore , overexpression of fliA in the DfliA strain ( hashed green bar ) reduced fliL expression by 2.10-fold compared to the empty vector control ( solid green bar ; t-test , pvalue 0.02 ) , suggesting a possible repressive interaction ( Figure 8E ) . 
As dual regulation has been suggested for all Class 2 targets 
[ 23 ] , we performed similar qRT-PCR experiments for these genes . 
Overexpression of fliA in the DflhD strain was not able to significantly increase the expression of any other Class 2 target tested ( Figure S6 ) . 
While not statistically significant , flhB expression does increase with fliA overexpression ( Figure S6 ) . 
Since flhB is downstream of the FliA-dependent meche operon , it is possible that the flhBAE operon is dual regulated and can be transcribed as part of the upstream operon . 
However , there is no evidence of co-transcription from the RNA-seq or RT-PCR targeting the cheZ-flhB intergenic region ( Figure S5B ) so we conclude that this read-through is likely an artifact of extreme FliA overexpression . 
It should also be noted that no FliA ChIP-seq signal was detected in the vicinity of the flhBAE , fliE , or fliFGHIJK operons , nor were any of these Class 2 genes differentially expressed in RNA-seq experiments ( motile wild-type and DfliA strains ) . 
Therefore , with the exception of the dual targets already discussed , our data do not support widespread dual regulation of Class 2 genes . 
Motility assessment of novel FlhDC and FliA targets
To determine if novel FlhDC and/or FliA targets were required for motility , knockouts were generated for ampH , ymdA , sbmA , gntR , ykfB , yjcZ , ynjH , hypD , and uhpT . 
Motility on semi-solid agar was determined for each deletion strain . 
None of the deletions resulted in a substantial change in motility ( Figure S7 ) , suggesting that these genes do not directly contribute to motility under the conditions tested . 
Discussion
FlhDC regulon
To our knowledge , this study provides the first look at genome-wide in vivo binding of FlhD and FlhC . 
We have redefined the direct FlhDC regulon by identifying new targets and showing that many previously reported non-flagellar targets are incorrect or not bound under these conditions ( Table 1 , Figure S2 ) . 
Our data support a surprisingly limited direct FlhDC regulon and show no evidence of either protein binding DNA independently . 
Under the conditions tested , FlhDC binds 11 loci and directly regulates 11 transcriptional units . 
However , it is important to note that some binding sites are associated with regulation of two divergent operons while others are not associated with any detectable regulation . 
In addition to the classical flagellar Class 2 binding sites , our study detects previously reported binding sites upstream of yecR and fliDST , and 4 novel FlhDC binding sites . 
Two of these novel binding sites were associated with detectable FlhDC-dependent regulation of adjacent genes ( ymdA and yciK ) , while the remaining two were not ( gntR and ampH/sbmA ) . 
It is likely that the two latter sites are functional , perhaps under different conditions , because the probability of two spurious sites occurring in intergenic regions is very small ( only ,11 % of the genome is intergenic sequence ) . 
None of the genes surrounding the 4 novel binding sites have an obvious functional connection to flagellar synthesis , and deletion of these genes had no detectable effect on motility ( Figure S7 ) . 
The RNA-seq data provided little evidence of an extensive indirect FlhDC regulon . 
While 183 genes were differentially regulated but not associated with FlhDC or FliA binding , the magnitude of the indirect regulation is significantly smaller than for direct targets ( Figure 2 ; Krustal-Wallis , p , 0.0001 ) . 
FliZ is the only transcription factor regulated directly by either FlhDC or FliA , and we do not see regulation of described FliZ targets [ 58 ] . 
Therefore , we doubt that FliZ-dependent regulation substantially contributes to the indirect FlhDC-depen-dent regulation detected in this study . 
Instead , it is likely that the indirectly regulated genes do not represent an additional level of the FlhDC transcriptional network but result from physiological and metabolic changes associated with flagellar motility . 
Despite having a degenerate consensus motif , FlhDC appears to bind DNA highly specifically - at only 11 sites throughout the genome . 
The presence of a motif alone is insufficient for binding in vivo , even at sites that are bound in vitro ( Figure S2 ) . 
We propose that as-yet unidentified factors such as DNA conformation or competition with nucleoid-associated proteins play a role in the 
Although the presence of a motif and in vitro binding does not always predict in vivo FlhDC behavior , a striking pattern emerges for flagellar Class 2 sites . 
There is a perfect correlation between reported in vitro FlhDC affinities [ 54 ] , expression kinetics [ 59 ] , and in vivo FlhDC occupancy ( this study ) . 
Coupled with the s 7uChIP data presented here ( Figure 4 ) , this suggests a simple mechanism of transcriptional activation : s - RNAP is unable to 70 bind these promoters in the absence of FlhDC , but as its concentration increases , FlhDC binds DNA and recruits s-70 
RNAP to promoters in the order of relative FlhDC binding affinity . 
This model of affinity-based temporal ordering has been commonly predicted in transcriptional networks [ 60 ] . 
Furthermore , this suggests that for transcription factors with a similar recruitment-based mechanism of action , relative in vivo occupancy derived from ChIP-seq can be used to predict temporal 
FliA regulon
By coupling ChIP-seq and RNA-seq , we have comprehensively identified FliA binding sites and matched some of these with FliA-dependent transcripts . 
We identified 52 FliA binding sites , 35 of which are novel . 
Furthermore , we have demonstrated that existing computational approaches [ 30,32,33,51 ] are poor predictors of in vivo FliA binding . 
Our data confirm all the better-characterized members of the FliA regulon while discounting most promoters for which only bioinformatic predictions exist 
Under the conditions used in our study , 14 out of 52 FliA binding sites were associated with significant changes in the RNA levels of one or more surrounding genes ( wild-type vs. DfliA ) . 
This suggests , as will be discussed below , that a large number of promoter sequences bind FliA but are relatively inactive . 
Similar to FlhDC , the RNA-seq data provide little evidence for an extensive indirect FliA regulon . 
Only 40 genes were differentially regulated but not associated with FliA binding . 
The magnitude of indirect regulation was dramatically smaller than direct regulation ( Figure 6 ; Mann-Whitney , p ,0.0001 ) . 
As with FlhDC , it seems likely that indirect regulation is due to secondary physiological and metabolic changes associated with flagellar motility . 
Consistent with this idea , 29 of the 40 indirectly regulated genes are also indirectly regulated by FlhDC . 
Non-canonical FliA binding sites
The most striking finding from our FliA ChIP-seq experiments is that more than half of FliA binding sites are inside genes . 
While intragenic binding has been reported for other s-factors and for many transcription factors , the proportion of intragenic FliA sites is remarkable . 
Similar to our findings , 40 % of the sites bound by the Mycobacterium tuberculosis s-factor , SigF , are intragenic . 
Some of these sites are associated with SigF-dependent transcription in the antisense orientation relative to the overlapping gene [ 61 ] . 
RpoH ( s ) has been reported to bind many intragenic sites , 32 but these sites only account for ,25 % of the total sites bound ( 22 out of 87 , [ 62 ] ) . 
Lastly , a recent study reported that s can bind 70 and initiate transcription at a large number of intragenic sites , but that this phenomenon is repressed at many locations by the nucleoid-associated protein H-NS [ 63 ] . 
Combined with our FliA results , it is clearly important to take intragenic transcription initiation into account when analyzing genome-wide data . 
Of the 30 intragenic FliA sites identified in our study , only 5 are associated with detectable changes in RNA levels . 
Three intragenic promoters appear to drive transcription of canonical mRNAs for the downstream gene ( s ) . 
Two of these promoters have been accurately predicted before ( inside flhC driving motAB-cheAW [ 32,64 ] , and inside yafY driving ykfB [ 20 ] ) , but one is novel ( inside yjdA driving yjcZ ) . 
A FliA promoter upstream of yjdA has been incorrectly predicted , based on expression data [ 20 ] . 
These three sites function as canonical promoters and are likely inside genes simply due to the spatial constraints of a small genome . 
Additionally , two novel intragenic FliA binding sites are associated with detectable small , intragenic RNAs . 
The FliA promoter inside uhpT transcribes a small RNA overlapping the 39 end of the ORF in the sense orientation ( Figure 7D ) , while the FliA promoter inside hypD drives transcription of an antisense RNA ( Figure 7E ) . 
A FliA promoter upstream of uhpT has been predicted based on expression data [ 20 ] , but our data clearly show that the FliA-dependent transcript is not an mRNA . 
While the FliA promoters inside uhpT and hypD are associated with detectable transcription , the functions of the corresponding RNAs are not known . 
Neither intragenic RNA shows evidence of regulating the overlapping mRNA . 
It is possible that one or both of these RNAs regulate trans-encoded targets . 
Most intragenic FliA binding sites are not associated with detectable transcripts . 
Furthermore , most of these putative promoters are greater than 300 nucleotides upstream of the start codon to an adjacent gene making it improbable that they drive transcription of canonical mRNAs . 
More likely , these promoters , if active , transcribe non-coding RNAs . 
However , since no changes in local RNA levels were detected flanking these binding sites , it remains unclear whether these FliA binding sites represent functional FliA-dependent promoters . 
Many studies have reported widespread spurious transcription , most of which is rapidly terminated and produces highly unstable transcripts [ 65 ] . 
Therefore , these promoters may be active but the resulting RNAs are too unstable to detect by standard RNA-seq methods . 
Three of these intragenic promoters were previously predicted in E. coli and two of the three , those inside galK and speA , showed FliA-dependent activity when cloned upstream of the chloramphenicol acetyltransferase reporter gene [ 64 ] . 
This supports the hypothesis that at least some of the intragenic FliA binding sites are functional promoters , even if no transcripts were detectable in our study . 
Alternatively , these binding sites could represent promoter-like sequences where FliA : RNAP can bind but can not initiate transcription under the conditions tested , or potentially , ever . 
Regardless of their transcriptional activity , intragenic FliA sites could serve to alter the available pool of FliA , indirectly affecting transcription from canonical promoters , as has been proposed for some transcription factors [ 66,67 ] . 
Network topology and the consequences of dual regulation
Over the last 15 years there has been a growing interest in modeling cellular processes as networks [ 60 ] . 
Several frequently occurring network motifs have been identified , with feed-forward loops ( FFLs ) being one of the most common . 
In FFLs , one regulator regulates another regulator , and both regulate a common target gene ( or genes ) [ 68 ] . 
A quantitative computational model of the flagellar network has been constructed [ 23 ] and is considered a seminal work in the field of network modeling [ 69 ] . 
A key aspect of the existing flagellar network model is that all Class 2 genes are modeled as FFLs with dual FlhDC/FliA input . 
However , our data clearly indicate that three Class 2 operons ( 10 genes ) are not dual regulated ( Figure S6 , Figure 9A ) . 
This implies that the true behavior of the transcriptional network can not be accurately predicted by the existing model . 
In addition to clarifying the overall topology of the network , our genome-wide and targeted experiments have revealed interesting information about the regulatory inputs for specific dual targets . 
One striking pattern that emerges is that , while FliA binding is readily detected at all dual targets , RNA levels detected by RNA-seq and qRT-PCR only change moderately between wild-type and DfliA . 
In most cases , expression of dual regulated genes is affected less than 4-fold by fliA deletion , but more than 100-fold by flhDC deletion ( Figure 8 ) . 
This suggests FliA-dependent transcription is contributing very little to the overall abundance of dual regulated transcripts under these conditions , despite FliA being present at the associated promoters . 
Despite the seemingly low level of activity of these FliA promoters , when fliA is overexpressed in a DflhD strain , expression of fliD , flgM , and flgK returns to wildtype or higher levels . 
This demonstrates that these promoters do have the capacity to be very active , but it remains to be seen if FliA levels and activity are ever high enough to trigger high-level promoter activity in wild-type cells . 
Interestingly , fliA overexpression results in lower fliL expression in both DflhD and DfliA strains compared to wild-type ( Figure 8E ) . 
Since the FliA promoter is downstream of the FlhDC-dependent s promoter , 70 we speculate that high FliA : RNAP occupancy yields little FliA-dependent transcription and actually competes with s : RNAP at 70 the level of DNA binding to repress transcription from the upstream promoter . 
Overall , our data suggest that flagellar genes likely have diverse temporal expression patterns due to the diversity of regulatory inputs at each promoter ( Figure 9A ) . 
Now that we have systematically identified dual-regulated genes , it is tempting to surmise why certain genes are under dual control , and others are not . 
The correlation between the gene expression class ( Class 2 or 3 ) and the developmental stage at which the resulting proteins are utilized has been described ( reviewed [ 3 ] ) . 
However , dual-regulated gene products show less of a pattern in localization or correlation with a specific phase of assembly ( Figure 9B ) . 
In Salmonella , the Class 3 promoters of fliDST and flgKL have been shown to be required for swarming and for rapid repair of sheared flagella [ 57 ] . 
The authors hypothesized that these genes utilize their Class 2 promoters during de novo flagellar assembly but are transcribed from the Class 3 promoters , independent of FlhDC activity , to repair flagella broken during swarming . 
Wozniak et al. also suggest that flgMN is dual regulated so that FlgM can fine-tune FliA activity throughout flagellar assembly and so that FlgN can assist in FlgK folding and transport during flagellar repair [ 57 ] . 
The final dual-regulated operon identified in our study , fliLMNOPQR , encodes components of the C-ring and secretion apparatus that localize to the proximal portion of the flagella ( Figure 9B ) . 
Our results suggest that high levels of FliA may repress this operon ( Figure 8E ) but it is unclear why these components would be specifically targeted by negative feedback . 
Alternatively , FliA may positively regulate the fliL operon at some stage of flagellar development but it is equally unclear why this would be required . 
The specific roles of the Class 2 and Class 3 promoters of dual-regulated targets should be explored further to determine the physiological importance of the complex temporal gene expression patterns resulting from dual regulation . 
Conclusions
We have identified many new targets of FlhDC and FliA , including many non-canonical FliA binding sites . 
Additionally we have shed new light on the complex topology of the network by systematically defining Class 2 , Class 3 , and dual-regulated targets . 
Finally , we have demonstrated that the combined application of ChIP-seq and RNA-seq to related regulators provides sufficient data to build a transcriptional network model from scratch or redefine even the best-characterized networks , such as the flagellar transcription network . 
Materials and Methods
Strains and plasmids
Bacterial strains used in this work are listed in Table S4 . 
Cells were grown in Lysogeny Broth ( LB : 1 % NaCl , 1 % tryptone , 0.5 % yeast extract ) or Tryptone broth ( TB : 1 % tryptone , 0.5 % NaCl ) . 
Strains harboring plasmids were cultured with the appropriate antibiotic , as listed in Table S4 . 
Primers used in strain construction are described in Table S5 . 
Epitope-tagged strains ( DMF11 , DMF14 , RPB081 ) were generated using the FRUIT method of recombineering [ 70 ] . 
All epitope-tagged strains originate from AMD052 ( MG1655 DthyA ) , which has a poorly motile phenotype . 
FliA was N-terminally tagged by inserting a 36FLAG tag after the third amino acid . 
FlhD and FlhC were tagged by inserting 36FLAG tags into internal loop regions ( Figure S1A ) . 
Internal sites were chosen since the termini of both proteins are known or predicted to participate in proteinprotein and/or protein-DNA contacts [ 42,43 ] . 
Following motility selection ( see below ) , the region upstream of flhDC and the flhDCcoding region were sequenced in each strain ( Wadsworth Center Applied Genomic Technologies Core ; File S1 ) . 
Strains DMF62 -- 65 were also generated using FRUIT . 
In each strain , the thyA cassette was inserted ( facing away from flhDC ) at a precise location upstream of flhDC . 
thyA insertions were confirmed by PCR and sequencing ( Wadsworth Center Applied Genomic Technologies Core ) . 
Deletion strains ( DflhD , DflhC , and DfliA ) were also generated using recombineering . 
First , DMF35 was generated from AMD052 by motility selection , as described below . 
With DMF35 as the common parent strain , FRUIT was used to generate scarless deletions of flhD ( DMF38 ) and fliA ( DMF40 ) . 
The flhC deletion strain ( DMF58 ) was generated by amplifying the flhC : : kan allele from the Keio collection [ 71 ] , electroporating it into DMF35 , and then removing the kan cassette by expressing 
FLP recombinase from pCP20 [ 72 ] . 
Following tag insertion or gene deletion , thyA was reintroduced at its native locus and strains were cured of all plasmids . 
Strains DMF50 -- 57 were also generated from AMD052 , using FRUIT to replace the genes of interest with the thyA gene . 
Note that these strains lack thyA at its native locus , + but have a thyA phenotype . 
Overexpression plasmids were generated by cloning the ORF of interest into pBAD24 [ 73 ] cut with NheI and SphI ( NEB ) using the In-Fusion method ( Clontech ) . 
All deletion strains and plasmids were verified by PCR and sequencing ( Wadsworth Center Applied Genomic Technologies Core ) . 
Motility selection to generate motile isolates
Our lab isolate of MG1655 , and the related strain AMD052 , displayed a poorly motile phenotype . 
PCR using JW3100 and JW5358 demonstrated that these strains lacked an IS element upstream of the flhDC , which is required for high-level motility . 
Epitope tagged strains were generated from AMD052 , and were thus initially poorly motile as well . 
To isolate highly motile derivatives , saturated overnight cultures of strains AMD052 and preliminary epitope-tagged strains were spotted ( 5 mL ) onto soft TB agar ( 0.3 % ) and incubated at 30uC . 
Motile subpopulations began emerging between 20 and 24 hours . 
We typically observed 1 -- 5 motile subpopulations on each plate . 
Motile cells were collected from stabs , yielding motile strains DMF35 , DMF11 , DMF14 , and RPB081 . 
IS element insertion was verified by PCR and sequencing with oligonucleotides JW3100 and JW5358 ( Wadsworth Center Applied Genomic Technologies Core ; File S1 ) . 
Motility assays
Overnight cultures were grown in TB at 30uC . 
Saturated cultures ( 5 mL ) were spotted onto 155 mm TB soft agar ( 0.2 % ) plates and incubated at 30uC . 
Each plate included DMF36 for reference and 1 -- 4 other strains of interest . 
Each assay was performed 5 times from independent overnight cultures . 
Images were taken at hourly intervals from 4 -- 6 hours post-inoculation . 
Representative images are shown in Figure S1 and S7 . 
ChIP-qPCR
Strains DMF11 , DMF14 , RPB081 , and DMF36 were used for all ChIP experiments . 
Subcultures were grown in LB at 37uC with aeration to an OD600 of 0.5 -- 0.7 . 
Cultures were harvested , crosslinked , sonicated , and immunoprecipitated as previously described [ 74 ] , with minor modifications . 
Anti-FLAG ( 2 mL per IP , M2 monoclonal ; 70 Sigma ) or anti-s ( 1 mL per IP ; Neoclone ) antibodies were used for all immunoprecipitations , both for ChIP-qPCR and ChIP-seq . 
ChIP and input DNA was purified using Zymo PCR Clean and Concentrate kit . 
Samples were analyzed using qPCR as previously described [ 74 ] . 
Oligonucleotides used to amplify the bglB control region and regions of interest are described in Table S6 . 
ChIP-qPCR experiments were utilized 3 -- 7 biological replicates per strain . 
Complete ChIP-qPCR data is presented in Table S1 . 
ChIP-seq library preparation and sequencing
Cultures for ChIP-seq experiments were grown as described for ChIP-qPCR . 
ChIP-seq libraries were constructed and sequenced as previously described [ 63 ] with the exception of one replicate of FlhC-FLAG , which was prepared as described [ 37 ] . 
Antibodies used were the same as those used for ChIP-qPCR . 
ChIP-seq libraries were constructed and sequenced for 2 biological replicates per strain . 
Sequencing was performed on an Illumina Hi-Seq instrument ( University at Buffalo , SUNY ) . 
Sequences were aligned to the MG1655 genome ( NC_000913 .2 ) using the CLC Genomics Workbench . 
Mapped reads were piled up and written to a . 
gff file using a custom Python script and viewed in SignalMap ( Nimblegen ) . 
All ChIP-seq images presented in this study are captured from SignalMap and manipulated in the image editing software GIMP to highlight baselines ( zero reads ) and fill gaps in the data resulting from image artifacts . 
Almost all ChIP-seq analysis programs have been designed and optimized for eukaryotic ChIP-seq data and , in our experience , do not perform well with bacterial ChIP-seq data . 
We have generated custom Python scripts to identify peaks in bacterial ChIP-seq data . 
First , all datasets were normalized to 100 million reads . 
Pairs of replicate datasets were considered together . 
For each replicate dataset in the pair , an appropriate threshold was determined . 
The plus and minus strands were considered separately . 
For the first replicate , for a given strand , a value T1 was selected as the threshold . 
For the second replicate , a value T was selected as the 2 threshold . 
Values for T and T were considered between 1 and 1 2 1000 . 
For each combination of values for T and T , the number 1 2 of genome positions with values $ T1 in the first replicate and with values $ T2 in the second replicate was determined . 
The false discovery rate was estimated using the null hypothesis that no regions are enriched . 
The combination of thresholds yielding the highest number of true positive positions , with an estimated false discovery rate of less than 0.01 , was selected . 
Once T1 and T2 were chosen , peak calling was performed as previously described ( Supplementary Material of [ 54 ] ) . 
Briefly , a region was identified as a peak if both replicates showed enrichment above the corresponding thresholds for each strand . 
For a peak to be called there must be a peak on the plus strand within a threshold distance of a peak on the minus strand , as previously described ( Supplementary Material of [ 54 ] ) . 
To identify regions of artifactual enrichment , peaks identified in tagged strains were compared to those called in a control ChIP-seq experiment using an untagged strain ( DMF35 ) . 
For each factor , the calculated T values were adjusted to reflect the total number of reads in control experiment replicates and then applied for peak calling in the controls . 
Any regions for which a peak was called in the true ChIP-seq experiment and in the untagged control experiment within 50 bp of each other were considered potential artifacts and excluded from further analysis . 
RNA isolation and RNA-seq library preparation
Strains DMF36 , DMF38 , DMF58 , and DMF40 were used for RNA-seq experiments . 
Subcultures were grown in LB at 37uC with aeration to an OD600 of 0.5 -- 0.7 . 
RNA was purified using a modified hot phenol method , as previously described [ 37 ] . 
Following isolation , RNA was treated with DNase ( TURBO DNA-free kit ; Life Technologies ) for 45 minutes at 37uC , followed by phenol extraction and ethanol precipitation . 
rRNA was removed using the RiboZero kit ( Epicentre ) and strand-specific DNA libraries were constructed using the ScriptSeq 2.0 kit ( Epicentre ) . 
Sequencing was performed using an Illumina Hi-Seq instrument ( University at Buffalo , SUNY ) . 
RNA-seq data visualization and differential expression analysis
Sequence reads were mapped and visualized as described above . 
Differential expression analysis was performed using Rockhopper [ 36 ] with default parameters . 
As suggested by the developers , changes in gene expression were considered statistically significant if q-value # 0.01 . 
Genes were required to be regulated at least 2-fold to be considered significantly differentially regulated . 
Ribosomal RNAs ( rRNAs ) were excluded from RNA-seq analysis since rRNA was removed during library preparation . 
RNA isolation and qRT-PCR
Strains pDMF9 -- 18 were used for qRT-PCR experiments . 
Subcultures were grown in LB +100 mg/ml ampicillin at 37uC with aeration to an OD600 of 0.4 -- 0.6 . 
Arabinose was added to a final concentration of 0.2 % and cultures were incubated at 37uC for an additional 10 minutes . 
RNA was isolated as follows . 
10 ml of culture were harvested by centrifugation , resuspended in 1 ml Trizol reagent ( Life Technologies ) and incubated at room temperature for 5 min . 
Samples were centrifuged , supernatants were transferred to new tubes , and 200 mL of chloroform was added . 
Samples were mixed well , incubated at room temperature for 3 minutes , and centrifuged . 
The aqueous layers were transferred to new tubes and mixed with 500 mL isopropanol . 
Samples were mixed well , incubated at room temperature for 10 minutes , and centrifuged to collect precipitated RNA . 
Pellets were washed once with 75 % ethanol and then dried . 
RNA was resuspended in H2O and treated with TURBO DNase ( Life Technologies ) as described above . 
RNA was reverse transcribed using SuperScript III reverse transcriptase ( Invitrogen ) with 100 ng of random hexamer , according to the manufacturer 's instructions . 
A control reaction lacking reverse transcriptase ( no RT ) was performed for each sample . 
cDNA and no RT samples were diluted and used as templates for qPCR . 
qPCR was performed with an ABI 7500 Fast real time PCR machine . 
Oligonucleotides used to amplify target genes , and the control ( mreB ) are listed in Table S7 . 
Relative expression values were calculated using a modified 2 method [ 75 ] . 
First target Ct 2DDCt values were normalized to the control ( mreB ) yielding DCt values , then 1.9 ( assuming imperfect PCR efficiency ) was calculated 2DCt for each target in each strain . 
Finally , expression values in all strains were normalized to the average 1.9 value in motile 2DCt MG1655 + pBAD . 
qRT-PCR experiments utilized 3 -- 4 biological replicates per strain and all qPCR reactions were performed in triplicate . 
Supporting Information
locations for internal 3 FLAG-tagging of FlhD and FlhC . 
Black lines represent unstructured regions , red cylinders represent ahelices , and blue boxes represent b-sheets . 
Gold stars represent Zn-binding cysteine residues . 
Insets show amino acid sequence surrounding tag insertion sites . 
( B ) Location , identity , and direction of IS element insertions present in each motility-selected strain . 
The boxes represent regions that are duplicated during insertion . 
( C ) Soft agar motility of motile MG1655 and strains in which a thyA cassette has been inserted in each of the IS element insertion locations described above . 
( D ) Soft agar motility of motile MG1655 and epitope-tagged strains . 
( E ) Soft agar motility of motile MG1655 , isogenic deletions , and complemented strains . 
Note that DflhC + pflhC is non-motile due to disruption of the promoter of the motAB-cheAW operon . 
( PDF ) is not predictive of in vivo binding . 
Stafford et al. [ 25 ] predicted FlhDC binding sites based on the consensus motif of characterized binding sites . 
The sites shown in this figure had good matches to the consensus and demonstrated weak in vitro binding . 
With the exception of fliD and yecR ( not shown ) , none of the predicted sites showed in vivo FlhDC binding in targeted ChIP-qPCR assays ( n = 4 , * p ,0.05 , ** p ,0.01 ) . 
( PDF ) 
Expression of all genes in DflhD versus DflhC . 
Gene expression values represent normalized expression values calculated by Rockhopper . 
( PDF ) and Cho et al. [ 55 ] . 
Motifs were generated from sequence surrounding binding sites identified only in our study ( n = 27 ) or only in Cho et al. ( n = 29 ) . 
( A ) FliA binding sites unique to our study yielded a highly significant motif ( 27/30 sites , Evalue = 1.5e-27 ) similar to that described for FliA . 
( B ) The best-scoring motif for FliA binding regions unique to Cho et al is not significantly enriched , and shows no similarity to described FliA motifs ( best-scoring motif : 10/29 , E-value = 3.5 ) . 
It should also be noted that Cho et al failed to detect some well-characterized FliA promoters such as those upstream of fliAZY and fliC . 
( PDF ) 
RT-PCR using an upstream primer within flgJ and a downstream primer within flgK yielded a band of the expected size in motile MG1655 , but not in DflhD . 
This confirms that flgKL can be transcribed as part of the upstream FlhDC-dependent operon . 
Lanes labeled `` colony '' are a colony ( genomic DNA ) PCR control , `` + RT '' are RT-PCR , and `` 2RT '' are controls in which no reverse transcriptase was added during cDNA synthesis . 
( B ) RTPCR using an upstream primer within cheZ and a downstream primer within flhBAE yielded very little product . 
Product slightly increased , relative to the 2RT control , when fliA was overexpressed . 
This small amount of read-through at very high FliA levels is unlikely to be physiologically relevant . 
( PDF ) 
Expression of flgA , flgB , and fliF can not be rescued by overexpression FliA in DflhD . 
Expression of flhB is moderately increased in the DflhD + pBAD-fliA strain , potentially due to read-through from the upstream FliA-dependent tar-tap-cheRBYZ ( see Figure S4 ) . 
( PDF ) required for motility . 
Soft agar motility of motile MG1655 and single gene deletions of FlhDC and FliA target genes . 
Images are representative of 5 biological replicates per strain . 
( PDF ) 
Acknowledgments
We thank Dave Grainger , James Galagan and Todd Gray for comments on the manuscript . 
We thank Pascal Lapierre for help implementing the ChIP-seq peak-calling algorithm . 
We thank Keith Derbyshire , Randy 
Author Contributions
Conceived and designed the experiments : DMF JTW . 
Performed the experiments : DMF . 
Analyzed the data : DMF JTW . 
Contributed reagents / materials/analysis tools : RPB . 
Contributed to the writing of the manuscript : DMF JTW .