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Rho and NusG suppress pervasiveantisense transcription in Escherichia coli

Jason M. Peters,1,2,5 Rachel A. Mooney,1 Jeffrey A. Grass,1,3 Erik D. Jessen,1,2 Frances Tran,1,3,6

and Robert Landick1,3,4,7

1Department of Biochemistry, 2Department of Genetics, 3Great Lakes Bioenergy Research Center, 4Department of Bacteriology,University of Wisconsin, Madison, Wisconsin 53706, USA

Despite the prevalence of antisense transcripts in bacterial transcriptomes, little is known about how theirsynthesis is controlled. We report that a major function of the Escherichia coli termination factor Rho and itscofactor, NusG, is suppression of ubiquitous antisense transcription genome-wide. Rho binds C-rich unstructurednascent RNA (high C/G ratio) prior to its ATP-dependent dissociation of transcription complexes. NusG isrequired for efficient termination at minority subsets (~20%) of both antisense and sense Rho-dependentterminators with lower C/G ratio sequences. In contrast, a widely studied nusA deletion proposed to compromiseRho-dependent termination had no effect on antisense or sense Rho-dependent terminators in vivo. Globalcolocalization of the histone-like nucleoid-structuring protein (H-NS) with Rho-dependent terminators andgenetic interactions between hns and rho suggest that H-NS aids Rho in suppression of antisense transcription.The combined actions of Rho, NusG, and H-NS appear to be analogous to the Sen1–Nrd1–Nab3 and nucleosomesystems that suppress antisense transcription in eukaryotes.

[Keywords: RNA polymerase; Rho-dependent transcription termination; antisense transcription; H-NS; NusG; NusA]

Supplemental material is available for this article.

Received July 2, 2012; revised version accepted October 15, 2012.

Antisense transcription is a common feature of bothbacterial and eukaryotic transcriptomes. Antisense tran-scripts have been identified in diverse bacteria (Georg andHess 2011), including Bacillus subtilis (Rasmussen et al.2009; Irnov et al. 2010; Nicolas et al. 2012) and Escherichiacoli (Peters et al. 2009; Dornenburg et al. 2010; Shinharaet al. 2011). With the exception of a few well-studiedexamples (e.g., l OOP [Krinke and Wulff 1987] and E. coliGadY [Opdyke et al. 2004]), bacterial antisense transcriptsremain largely uncharacterized. Although some antisensetranscripts have specific regulatory functions, others mayresult from ‘‘transcriptional noise’’ generated by nonspe-cific transcription initiation or weak promoters that be-come fixed within genes by evolutionary constraints onthe coding sequence (Struhl 2007). At high levels, however,even spurious antisense transcription could have deleteri-ous effects by interfering with sense transcription, stimu-lating mRNA degradation, or diverting important cellularresources.

The bacterial Rho-dependent transcription termi-nation pathway, which relies on the ATP-dependenttranslocase Rho (Roberts 1969; Banerjee et al. 2006;Boudvillain et al. 2010; Peters et al. 2011), targets un-translated RNAs including tRNAs and sRNAs and at leastsome antisense transcripts (Peters et al. 2009). Rho binds to;80 nucleotides (nt) of C-rich unstructured (G-depleted)nascent RNA segments known as Rho utilization (rut)sites and subsequently dissociates elongation complexes(ECs) via its RNA translocase activity (for review, seePeters et al. 2011). Rho targeting of untranslated RNAcauses the polar effects of nonsense mutations on expres-sion of downstream genes in bacterial operons (Adhyaet al. 1974).

Bacteria contain two general elongation factors—NusAand NusG—that may modulate Rho-dependent termi-nation (Fig. 1). In vitro, NusG enhances Rho termina-tion through direct interactions with Rho and RNApolymerase (RNAP) (Li et al. 1993; Pasman and vonHippel 2000; Mooney et al. 2009b; Chalissery et al.2011). In vivo, NusG is proposed to either aid all Rho-dependent termination (Cardinale et al. 2008) or assistjust a subset of terminators (Sullivan and Gottesman1992). In vitro, NusA exhibits variable effects on Rho-dependent termination (Ward and Gottesman 1981;Burns and Richardson 1995; Cardinale et al. 2008; Saxenaand Gowrishankar 2011a) but is proposed to aid all

Present addresses: 5Department of Microbiology and Immunology,University of California at San Francisco, San Francisco, CA 94158,USA; 6Molecular and Computational Biology Program, University ofSouthern California at Los Angeles, Los Angeles, CA 90089, USA.7Corresponding authorE-mail [email protected] is online at http://www.genesdev.org/cgi/doi/10.1101/gad.196741.112.

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Rho-dependent termination in vivo (Cardinale et al.2008).

The bacterial histone-like nucleoid-structuring pro-tein (H-NS) also can inhibit transcription. H-NS re-presses transcription initiation by binding directly oradjacent to promoter DNA, then oligomerizing intohigher-order structures that occlude RNAP binding ortrap RNAP at promoters (Fang and Rimsky 2008; Graingerand Busby 2008; Dorman 2009). H-NS may also af-fect transcription elongation by binding downstreamfrom ECs and acting as a roadblock, although experi-mental evidence for such effects is minimal. Recentstudies have identified a genetic link between Rho andH-NS-like proteins (Saxena and Gowrishankar 2011b;Tran et al. 2011). It is therefore possible that Rho andH-NS function together to silence transcription at thesame loci. However, a comparison of the Rho termi-nation sites to H-NS-binding locations has not beenreported.

In a previous study, we identified 25 sites in the E. coligenome at which Rho terminated antisense transcription(Peters et al. 2009). These findings raised several impor-tant questions. Is suppression of antisense transcriptiona major, global function of Rho or is it particular to arelatively small set of transcripts? What are the contri-butions of the elongation factors NusG and NusA tosuppression of antisense transcription and Rho termina-tion in general? Finally, if bacterial Rho and eukaryoticSen1 play analogous roles in terminating antisense tran-scripts (Arigo et al. 2006; Brow 2011), does H-NS alsoparticipate in silencing noncoding transcription in a man-ner similar to nucleosomes in eukaryotes?

In this study, we used two strand-specific, globalRNA-profiling techniques—tiling microarray analysis(Perocchi et al. 2007) and RNA sequencing (RNA-seq)(Parkhomchuk et al. 2009)—to obtain high-resolutionmaps of Rho-dependent termination in E. coli and de-termine the functional relationships between Rho, H-NS,NusG, and NusA. Our results establish a major role forRho in widespread suppression of antisense transcription;show that NusG, but not full-length NusA, plays a signif-icant role in Rho-dependent termination; establish thesequence basis for NusG effects on Rho-dependent ter-

mination; and reveal synergy between Rho and H-NS intranscriptional silencing.

Results

A major function of Rho is suppression of antisensetranscription

To investigate the effects of inhibiting Rho on the tran-scriptome of E. coli K-12, we first used tiling arrays tomeasure RNAs from wild-type E. coli grown with orwithout the specific Rho inhibitor bicyclomycin (BCM)at a concentration that reduces Rho function withoutaffecting the rate of cell growth (Ederth et al. 2006). Wedefined sites of Rho termination as positions at whichBCM treatment caused a statistically significant increasein downstream transcript levels (false discovery rate[FDR] # 5%) (Supplemental Table S1). By these criteria,we identified a total of 1264 BCM significant transcripts(BSTs) (Fig. 2A) whose levels or lengths increased whenRho was inhibited. We next used RNA-seq to confirm theidentification of Rho-dependent terminators. The major-ity (91%) of terminators detected using tiling arrays alsoexhibited a twofold or greater increase in RNA-seq-defined readthrough in BCM-treated cells (P < 10�4), whichconfirmed that these terminators were not the result ofmicroarray artifacts.

Analysis of the Rho-dependent terminator data setrevealed a striking connection between Rho terminationand antisense transcription. We divided terminators intothree broad categories based on whether they affectedantisense transcription, sense transcription, or intergenictranscription (Fig. 2B; Supplemental Material), using anupdated E. coli K-12 genome annotation (http://www.ecocyc.org; Keseler et al. 2011). Antisense BSTs werecaused by readthrough of Rho-dependent terminatorseither upstream of an oppositely oriented gene (classes Iand III) or within a gene (class II). Sense BSTs resultedfrom readthrough of terminators at the ends of genes orunannotated transcripts (classes IV and VI) or withingenes (class V). Intergenic BSTs resulted from readthroughof terminators at the ends of genes (class VII) or where nogene is known to exist (class VIII). The vast majority(88%) of Rho-dependent terminators controlled antisensetranscription (Fig. 2B); 52% were class I (e.g., readthroughof grxD transcription into lhr) (Fig. 3A), 35% were class II(e.g., arising within bglF, or within bglH) (Fig. 3B), and afew (;1%) were class III.

To quantify the occurrence of Rho-dependent termina-tion in sense versus antisense transcription, we usedRNA-seq to measure the levels of both sense and anti-sense transcripts for each gene in E. coli (Fig. 2C; Supple-mental Fig. S1). Using a stringent cutoff (FDR # 1%, andfivefold effect), we found that the antisense strands of1555 genes (34% of all genes) were significantly up-regulated by BCM treatment, whereas only 416 geneswere up-regulated to the same degree on the sense strand(Supplemental Table S2). Taken together, our resultsreveal a much greater increase in genome-wide antisensetranscription after Rho inhibition than in sense transcrip-

Figure 1. Regulators of transcript elongation in bacteria. TheEC is comprised of RNAP (b9ba2v subunits), DNA template,and RNA transcript. The Rho hexamer binds nascent RNA inprimary sites on each subunit and secondary site in the centralpore. NusG contacts RNAP via its N-terminal domain (NTD)and Rho via a C-terminal domain (CTD) connected by a flexiblelinker. NusA binds RNAP via an NTD near the RNA exit channeland contains additional domains (KH1, KH2, S1, and a CTDpresent in some bacteria).

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tion and thus establish that a major function of Rho is tosuppress antisense transcription.

Rho-dependent termination of transcription units (TUs)with the potential to generate class I antisense transcriptsrelative to TUs lacking this potential was statisticallysignificant. Of the 2054 genes at the end of TUs (Cho et al.2009), 725 (35%) were terminated by Rho. Of the 1051 genesat the end of TUs at which the next gene is oriented in theopposite direction, 536 (51%) were terminated by Rho (P <10�4), whereas only 189 of 1003 (19%) TUs for which thenext gene is in the same orientation were terminated by Rho.

Sense transcription is not affected by moderateincreases in antisense transcription

The functions of Rho-terminated antisense transcriptswithin genes are unknown. Proposed functions of thefew antisense transcripts characterized to date include

reduction of sense strand gene expression due to eitherpairing-mediated mRNA degradation or transcriptionalinterference (Georg and Hess 2011). In such cases,levels of sense and antisense transcripts should be anti-correlated; in other words, increases in antisense tran-scription would lead to decreases in sense expression. Todetermine whether increased antisense transcription inRho-inhibited cells caused a decrease in sense strandtranscription or an increase in RNA degradation, we cal-culated the correlation between BCM effects on sense andantisense transcription for annotated genes. We found nocorrelation between BCM-induced changes in sense andantisense transcript abundance at the gene level (r =�0.09) (Supplemental Fig. S2A). However, the effects ofantisense transcription might be evident only at specificsites where antisense transcripts are up-regulated (i.e.,BSTs), rather than at the gene level. To address thispossibility, we conducted three additional correlation

Figure 2. Genome-wide analysis of Rho-dependent transcription termination. (A) Distribution of Rho-dependent terminators in theE. coli genome as detected by BSTs. Features on the + strand are shown above the solid black lines, and features on the � strand areshown below the lines. Genes are depicted as black boxes, and transcript abundance detected by tiling arrays is shown as blue bargraphs. Rho-dependent termination sites are shown as colored boxes; the colors correspond to BST annotations from B. (B) Rho-dependent terminator annotations. Terminators are divided into eight classes based on their locations relative to annotated genes. Thediagram immediately to the right of the class number illustrates the orientation of transcripts and termination sites relative toannotated genes. Light-blue arrows represent genes, and the arrows point in the direction of translation of the gene. The blue lineindicates the length of the transcript in untreated cells, and the violet dashed line shows extension of the transcript in BCM-treatedcells. The bar graph to the right of the gene diagram shows the number and percentage (rounded) of terminators in each class. (C) Effectsof Rho inhibition on sense and antisense transcription. The log2 ratios of normalized read counts in BCM-treated versus untreatedconditions are shown for the coding strand (sense) or the strand opposite the coding strand (antisense) for all E. coli K-12 genes inbiological duplicate. Each row represents one gene. Fold increases in transcript abundance due to BCM treatment are shown in yellow,and decreases are shown in blue. The genes are clustered based on similar patterns of effects on antisense and sense transcription intoarbitrarily ordered clusters by centroid linkage clustering using a Euclidean distance metric (see the Supplemental Material; Eisen et al. 1998).

Rho and NusG suppress antisense transcription

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analyses by comparing the transcript levels from bothstrands of (1) all antisense BSTs, (2) antisense BSTs thatoccurred completely within genes (class II BSTs), and (3)BSTs at which the fold increase in antisense transcriptlevels following BCM treatment was greatest (the top

25% most up-regulated). Again, there was no significantcorrelation between sense and antisense transcript levels(r = �0.065, �0.014, and �0.079 for all antisense BSTs,class II BSTs, and most up-regulated antisense BSTs,respectively) (Supplemental Fig. S2B–D). Thus, signifi-cant increases in antisense transcription did not affectsense transcription at either the gene level or the subsetof sites where antisense transcription increased mostsignificantly. We conclude that an increase in antisensetranscription caused by sublethal inhibition of Rho doesnot inhibit sense transcription, consistent with the ideathat most antisense transcription is transcriptional noise.Greater increases in antisense transcription (caused bycomplete, and lethal, inhibition of Rho) could affect levelsof sense RNAs but would be unlikely to reflect a physio-logically relevant state of regulation in the cell.

Rho and H-NS silence transcription at the samegenomic loci

Recent studies have identified genetic interactions be-tween Rho activity and genes encoding H-NS-like pro-teins (Saxena and Gowrishankar 2011b; Tran et al. 2011).These interactions may result from cooperative inhibi-tion of deleterious transcription at certain loci by bothRho and H-NS. To test this hypothesis, we identified thegenome-wide locations of H-NS binding using chromatinimmunoprecipitation (ChIP) on high-density tiling micro-arrays (ChIP–chip) (Supplemental Table S3) under definedgrowth conditions; we then determined the overlapbetween these locations and the sites at which Rhoterminated transcription. We found a strong associationbetween H-NS binding sites and Rho-dependent termi-nators (Fig. 4A–C, cf. the sites of terminator readthroughin BCM-treated conditions and the occupancy of H-NS).Of the 1264 Rho-dependent terminators identified bytiling expression, 1066 (84%) were within 300 base pairs(bp) of sites with significant H-NS ChIP–chip signal.H-NS had a much higher probability of being bound nearRho-dependent terminators than at random locations inthe genome (P < 10�4). Of the 1105 Rho-dependent ter-minators that suppress antisense transcription, 921 (83%)were associated with H-NS-binding sites. The occupancyof H-NS associated with Rho-dependent terminators wasgreatest near the sites of termination, indicating that Rhotermination occurs within H-NS patches (Fig. 4A). In-creased H-NS occupancy near Rho-dependent termina-tors was not simply due to H-NS binding in intergenicregions at the ends of genes; terminators that occurredwithin genes (class II BSTs) were bound to an even greaterextent by H-NS (Fig. 4A). In addition, H-NS was specif-ically associated with Rho-dependent terminators ratherthan transcription terminations sites in general; H-NS wasnot statistically enriched at intrinsic (Rho-independent)terminators (P = 0.55) (intrinsic terminator coordinatesfrom RegulonDB, http://regulondb.ccg.unam.mx). Theseresults establish that H-NS generally occupies DNA atthe sites of Rho termination and are consistent with amodel in which Rho and H-NS act cooperatively to silenceantisense transcription (see the Discussion).

Figure 3. Effects of Rho inhibition at class I and class II Rho-dependent terminators. (A) BCM effects on the class I Rho-dependent terminator at the grxD locus. A statistically significantincrease in transcript levels (magenta dashed brackets) betweenuntreated (blue bars) and BCM-treated (violet bars) cells occursat the 39 end of the grxD gene, indicating that the grxD transcriptis terminated by Rho. (B) BCM effects on two class II Rho-dependent terminators at the bgl locus. Rho terminates antisensetranscripts that arise from within the bglF and blgH genes. Notethat the bglH antisense transcript is essentially undetectable inuntreated conditions.

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The overlap between sites of Rho-dependent termina-tion and H-NS binding suggests that both Rho and H-NSmay suppress transcription at the same genomic loci. Toaddress this possibility, we used ChIP–chip to identifyloci at which RNAP occupancy significantly increased incells deleted for the gene that encodes H-NS (Dhns) (Babaet al. 2006) and compared these loci with sites at whichRho terminated transcription. We found a significantoverlap between loci at which transcription is repressedby H-NS and Rho-dependent termination. Of the 334 lociat which RNAP occupancy increased in Dhns cells (i.e.,loci normally repressed by H-NS) (Supplemental TableS4), 222, or two-thirds, overlapped with Rho-dependentterminators (P < 10�4). The overlap between Rho- andH-NS-repressed loci further connected H-NS with sup-pression of antisense transcription. The majority of locicosuppressed by Rho and H-NS were sites at which Rhosuppressed antisense transcription (198 out of 222, or89%), suggesting that H-NS may have a direct role insilencing antisense transcription at these loci. IncreasedRNAP occupancy in Dhns cells resulted directly from theloss of H-NS repression rather than from pleiotropiceffects caused by Dhns, as the vast majority of loci atwhich RNAP occupancy increased overlapped with sitesof H-NS binding (325 out of 334, or 97%; P < 10�4). Weconclude that Rho and H-NS silence transcription at thesame genomic loci.

Suppression of transcription at the same genomic lociby Rho-dependent termination and H-NS binding mayreflect a functional relationship between Rho and H-NS.For instance, H-NS could slow elongating RNAP andallow time for Rho to terminate transcription of silencedgenes. Functional relationships between genes can oftenbe inferred from the growth phenotypes of double-mutantstrains; a slower-than-expected growth rate for the doublemutant indicates a genetic interaction that suggestsshared function (St Onge et al. 2007). To test for a func-tional relationship between Rho and H-NS, we measuredthe growth rate of strains containing the defective rhoalleles (rho4 [Morse and Guertin 1972] or rho115 [Gutermanand Howitt 1979]) and a deletion of hns (Dhns) (Baba et al.2006) in liquid LB medium (Fig. 4D). Double-mutant rho4Dhns and rho115 Dhns strains grew more slowly thanexpected from a multiplicative model of fitness (Fig. 4D;St Onge et al. 2007), revealing genetic interactionsbetween rho and hns. A third rho allele, rho15(Ts) (Daset al. 1976), was even more detrimental to cell viabilitywhen combined with Dhns. rho15(Ts) allows growth at30°C but not 42°C. We could only construct a straincarrying both rho15(Ts) and Dhns in the presence ofa rho+-complementing plasmid, regardless of the growthtemperature, suggesting that the combination of rho15(Ts)and Dhns is lethal to cells. To test this possibility, wemonitored retention of an unstable plasmid (Koop et al.1987; Bernhardt and de Boer 2004) carrying a rho+ alleleand a lacZ+ reporter gene by the rho15(Ts) Dhns doublemutant on plates containing X-gal at 30°C (Supplemen-tal Fig. S3). The rho15(Ts) Dhns strain formed only bluecolonies, indicating that the rho+ plasmid was requiredfor viability. These genetic interactions confirm co-

Figure 4. Spatial and functional associations between H-NSand Rho-dependent termination. (A) H-NS binding near Rho-dependent terminators. The median H-NS ChIP signal (see theSupplemental Material) is shown at specified distances from the59 end of all BSTs (violet), class II BSTs (black), or randomchromosome positions (gray). (B) H-NS binding at the class IRho-dependent terminator at the yhhJ locus. Colors are as inFigure 3, except that H-NS ChIP–chip data are shown in orange.Readthrough of the Rho-dependent terminator at the end ofthe yhhJ gene (indicated by the appearance of a BST shown asmagenta dashed brackets) corresponds with a peak in H-NSChIP–chip occupancy (orange). (C) H-NS binding at the class IIRho-dependent terminator within the xylG gene. Readthroughof the Rho-dependent terminator on the antisense strand of xylG

corresponds with a peak in H-NS occupancy. (D) Genetic in-teractions between hns and rho. Fitness is expressed as the ratioof the doubling times (D) for wild-type (DWT = 24.75 6 0.33 min)versus mutant cells in liquid LB medium. Wild-type fitness isset at one. Predicted fitness of double mutants is based on themultiplicative model (fitness of mutant #1 3 fitness of mutant#2 = predicted fitness of double mutant) (St Onge et al. 2007).






operative action of Rho and H-NS in transcriptionalsilencing.

NusG principally assists termination at a minoritysubset of Rho-dependent terminators

It is uncertain whether NusG enhancement is a signifi-cant requirement for termination in vivo at all Rho-dependent terminators (Cardinale et al. 2008; Burmannet al. 2010) or a subset of Rho-dependent terminators(Sullivan and Gottesman 1992). We used ChIP–chip todetect RNAP readthrough of Rho-dependent terminatorsin a multiple deletion strain lacking cryptic prophages(MDS42) (Posfai et al. 2006) and also lacking NusG(MDS42 DnusG) (Cardinale et al. 2008). We found a sta-tistically significant overlap between Rho-dependent ter-minators and sites affected by deletion of nusG (P < 10�4).Of the 157 Rho-dependent terminators detectable byChIP–chip (Peters et al. 2009) that are present in MDS42,42 (27%) were within 300 bp of a statistically significantincrease in RNAP occupancy in DnusG cells (Supplemen-tal Table S5). These results indicate that NusG enhance-ment significantly affects termination efficiency at lessthan half of Rho-dependent terminators.

To investigate the contribution of NusG to suppressionof antisense transcription, we performed tiling expressionanalysis on cells deleted for nusG and the rac prophage(Fig. 5A–C). Consistent with our ChIP–chip results, onlya subset of the Rho-terminated transcripts was affectedby deletion of nusG. Of 1264 Rho-dependent terminators,247 (20%) exhibited greater readthrough in DnusG cells.Of the 1105 Rho-dependent terminators controlling an-tisense transcription, 229 (21%) exhibited greater read-

through when NusG was absent. These results demon-strate that NusG acts in concert with Rho to suppressantisense transcription at a minority subset of Rho-dependent terminators rather than at all terminators(Cardinale et al. 2008).

The overlap between sites of Rho termination andeither NusG enhancement or H-NS binding suggests thatRho, NusG, and H-NS may act synergistically at the samesites to suppress transcription. Alternatively, NusG maybe required at terminators lacking H-NS. To distinguishbetween these possibilities, we compared Rho-dependentterminators that require NusG for efficient terminationwith those associated with H-NS. We found that themajority of NusG-dependent terminators were bound byH-NS (210 out of 247, or 85%; P < 10�4). Furthermore,H-NS association with NusG-affected terminators wasstatistically indistinguishable from H-NS association withterminators in general (P = 0.1). We conclude that NusG isnot required at terminators lacking H-NS; instead, ourdata are consistent with the idea that Rho, NusG, andH-NS act coordinately at specific loci.

NusG is required at Rho-dependent terminators withsuboptimal nucleic acid sequences

The features that distinguish Rho-dependent terminatorsthat require NusG for efficient termination from thosethat do not are unknown. The NusG requirement atcertain terminators is not explained by a lack of H-NSbinding. In principle, NusG dependence could be associ-ated with the type or function of the terminated gene(mRNA, tRNA, or sRNA), whether the gene downstreamfrom the terminator is oriented in the same (sense) or

Figure 5. Effects of DnusG and DnusA* onRho-dependent termination. (A) Effects ofDnusG and DnusA* on expression withinMDS42 BSTs. The log2 ratio of medianintensity within MDS42 BSTs in BCM-treated or mutant cells versus untreated cellsis shown in biological duplicate. Each rowrepresents one MDS42 BST. Fold increases intranscript abundance due to either BCMtreatment—DnusG or DnusA*—are shownin yellow, and decreases are shown in blue.The genes are clustered based on similarpatterns of effects on antisense and sensetranscription into arbitrarily ordered clustersby centroid linkage clustering using a Euclid-ean distance metric (see the SupplementalMaterial; Eisen et al. 1998). (B) Effects ofDnusG or DnusA* on the class I Rho-de-pendent terminator at the grxD locus. Nei-ther DnusG nor DnusA* has a significanteffect on transcript abundance at the 39 endof the grxD gene. Colors are as in Figure 3,except that transcripts from DnusG cells areshown in green, and transcripts fromDnusA* cells are shown in red. (C) Effects

of DnusG or DnusA* on the class II Rho-dependent terminator within the bglF gene. DnusG had a significant effect on transcriptabundance of the bglF antisense transcript, but DnusA* had no significant effect. Note that the DnusG effect on termination of the bglF

antisense transcript was not as potent as direct inhibition of Rho with BCM.

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opposite (antisense) direction as readthrough, or nucleicacid sequences present at the terminator. To determinewhether NusG-dependent terminators are enriched atcertain types of genes or classes of terminators, we com-pared gene associations and classes from the total setof BSTs to those BSTs with significant overlapping NusGeffects. We found that NusG-dependent terminators werenot significantly enriched at mRNAs, tRNAs, or sRNAs(P = 0.44). Furthermore, NusG enhancement was notdifferentially represented at any specific class (sense,antisense, within genes, at the end of genes, etc.) of Rho-dependent terminator (P = 0.21) (Fig. 6A). Finally, func-tional annotation analysis using the DAVID BioinformaticsDatabase (http://david.abcc.ncifcrf.gov) (Huang et al.2009) failed to identify any particular set of genes thatrequired NusG for efficient termination. We concludethat NusG stimulation of Rho termination is not associ-ated with gene type or function or the location of theterminator relative to annotated genes.

We next considered whether particular sequences dis-tinguish NusG-dependent Rho terminators. Althoughupstream rut sites are present at some Rho-dependent

terminators (e.g., l tR1 [Chen and Richardson 1987] andE. coli trp t9 [Zalatan and Platt 1992]), sequences associ-ated with Rho termination or NusG dependence have notbeen examined on a genome scale. To identify possiblesequence patterns at Rho-dependent terminators, we cal-culated the C/G ratio in 100-bp windows from �500 bpupstream of to +1000 bp downstream from the first probesignificantly affected by BCM treatment (high C/G ratiosequences will generate rut sites in RNA) (Fig. 6B). Wefound a peak in the C/G ratio centered at approximately+200 bp (P < 10�4) (Fig. 6B). This peak was due to both anincrease in C (from 25%–27%) and a decrease in G (from25%–21%) residues. Because we defined Rho-dependentterminators by the location of the transcript 39 end inuntreated cells and because Rho-terminated transcriptsare typically processed by cellular 39 / 59 exonucleases(Mohanty and Kushner 2007), the elevated C/G ratiodownstream from the mapped 39 ends likely reflectsRho-dependent termination in or near the high C/G ratiosequences followed by processing of these transcriptsto generate RNA 39 ends that map upstream of thesesequences (Fig. 6B). Interestingly, the median C/G ratioremained above the random baseline >1 kb downstreamfrom the initial BCM effect (Supplemental Table S6),suggesting that sites capable of eliciting Rho terminationare arranged consecutively in the genome to increaseoverall termination efficiency.

To investigate whether NusG effects on terminationare related to nucleotide content at Rho-dependent ter-minators, we examined the C/G ratios of 300 terminatorsthat were highly dependent on NusG for efficient termi-nation and 300 that were NusG-independent (see theSupplemental Material). We found that terminators withstrong NusG dependence exhibited a lower median C/Gratio than the total set of Rho-dependent terminators(Fig. 6B; Supplemental Table S6). In contrast, NusG-independent terminators displayed an even greater skewtoward C and away from G than the majority of Rho-dependent terminators. Differences in the median C/Gratio for both NusG-dependent and NusG-independentterminators compared with all terminators were statisti-cally significant within 300 bp of the 59 end of the BST(Supplemental Table S6). We conclude that the NusGdependence of some Rho terminators results from thepresence of sequences less likely to generate rut sites (i.e.,lower C/G ratio sequences).

REP elements also are associated with Rho-dependentterminators

We used the MEME algorithm (Bailey and Elkan 1994)to identify potential motifs in the 300 bp upstream ofRho-dependent terminators (Supplemental Material). Wefound several long (20- to 29-nt) motifs with high in-formation content (E-values from 1.7 3 10�36 to 3.7 3

10�108) at a subset of terminators (Supplemental Fig.S4A). The sequences and locations of the motifs matchedthose of REP elements, which are repetitive sequencesfound in intergenic regions near the 39 ends of genes(Stern et al. 1984). Of the 695 REP elements present in the

Figure 6. Basis for NusG effects on Rho-dependent termina-tion. (A) NusG enhancement of termination is not associatedwith terminator class. All BSTs and BSTs with an overlappingsignificant DnusG effect were not statistically distinguishableby terminator class (Fisher’s exact test; P = 0.21). Colors are asin Figure 2B. (B) NusG enhancement of termination is associ-ated with sequences present at the termination site. The medianC/G ratio (see the Supplemental Material) is shown at specifieddistances from the 59 end of all BSTs (violet), NusG-independentBSTs (black), NusG-dependent BSTs (green), or random chromo-some positions (gray). The orange ‘‘pacman’’ represents putativeprocessing of transcripts by 39 / 59 exonucleases.




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E. coli K-12 genome, 334 (48%) were within 300 bp ofa Rho-dependent terminator (P < 10�4). REP elementscontain palindromic units with dyad symmetry that formhairpin structures when transcribed into RNA (Supple-mental Fig. S4B). REP elements near Rho-dependentterminators could potentially function as modulators oftermination (Espeli et al. 2001) or as stabilizing hairpinsthat prevent RNA decay (Stern et al. 1984) in the absenceof intrinsic terminator hairpins. To distinguish betweenthese possibilities, we determined the distribution of REPelements from �500 bp upstream of to +1000 bp down-stream from the first probe significantly affected by BCMtreatment (Supplemental Fig. S4C). We found that thedistribution of REP elements was centered at approxi-mately �100, ;300 bp upstream of high C/G ratios thatrepresent Rho-binding sites. Thus, the majority of REPelements appear too far upstream to directly affect Rhobinding and subsequent termination but optimally posi-tioned to explain RNA 39 ends present after exonucleasetrimming. We suggest that the major function of REPelements found near Rho-dependent terminators is toinhibit RNA decay by impeding the processivity of 39 /59 exonucleases (Stern et al. 1984) rather than to affectRho termination efficiency.

A classic nusA deletion has no effecton Rho-dependent termination in vivo

The transcription elongation factor NusA has been pro-posed to enhance all Rho-dependent termination in vivo(Cardinale et al. 2008). We used RNAP ChIP–chip toidentify sites of terminator readthrough in a strain thatcarried the same large nusA deletion studied previouslyin either a rho mutant strain containing unmappedmutations that allowed viability or MDS42 (Zheng andFriedman 1994; Cardinale et al. 2008). This deletion,which we term DnusA*, disrupts nusA at codon 128,leaving the NusA N-terminal domain (NTD) intact butremoving the NusA S1, KH1, and KH2 domains. Wefound little or no overlap between Rho-dependent termi-nators and sites affected by DnusA*. Of the 157 MDS42Rho-dependent terminators detectable by ChIP–chip(Peters et al. 2009), only nine (6%) were within 300 bpof a statistically significant increase in RNAP occupancyin DnusA* cells (P = 0.2756) (Supplemental Table S5). Torule out the possibility that our inability to detect amajor effect of DnusA* on Rho termination was due tothe low sensitivity of the ChIP–chip technique, we per-formed tiling expression analysis on DnusA* cells (Fig.5A–C). Again, we found no significant overlap betweenBCM effects and DnusA* effects. Of the 1264 Rho-dependent terminators identified by tiling expression,only four (<1%) overlapped with a transcript significantlyup-regulated in the DnusA* strain (P = 0.4139). Becauseour DnusA* results differed dramatically from those pre-viously published using the same nusA allele (Cardinaleet al. 2008), we confirmed that our strain contained theDnusA* allele by sequencing of the nusA locus (data notshown) and by visual inspection of nusA transcript data(Supplemental Fig. S5). We were unable to explain the

discrepancy between our results and those reported byCardinale et al. (2008). We conclude that the S1, KH1, andKH2 RNA-binding domains of NusA (the domains de-leted in DnusA*) have little effect on Rho termination invivo under standard E. coli growth conditions.

Discussion

Our transcriptomic analysis of Rho termination estab-lishes suppression of antisense transcription as a majorrole of Rho in bacteria. Most antisense transcriptionsuppressed by Rho arises from a large and mostly unchar-acterized set of antisense promoters within genes (in-ternal antisense) (Fig. 7) and from continuation of sensetranscription past the ends of genes into oppositely ori-ented downstream genes (readthrough antisense) (Fig. 7).Both H-NS and NusG contribute to the suppression of

Figure 7. Models of antisense transcription termination byRho. Rho terminates transcription at the end of genes, prevent-ing antisense transcription into downstream genes (readthroughantisense, or class I terminators). Rho also terminates antisensetranscription arising from within genes (internal antisense, orclass II terminators). Termination sites are more C-rich andG-poor than random genomic DNA and thus facilitate Rholoading onto the nascent RNA (violet box labeled ‘‘C>G’’). H-NS(orange ovals) is typically bound adjacent to sites of Rhotermination (H-NS-binding sites are shown as orange boxeslabeled ‘‘H-NS’’) and is functionally synergistic with Rho.NusG enhances Rho termination at termination sites withreduced C and increased G content, which account for less thana quarter of all Rho-dependent terminators (dashed black arrow).The RNA-binding domains of NusA do not affect Rho termina-tion of antisense transcription (black crossout). Class I termina-tors can also be associated with REP elements, which maystabilize the terminated mRNA (light-blue box labeled ‘‘REP’’).

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antisense transcription through apparently independentmechanisms. These findings raise several questions thatmerit discussion and further study. What impact doespervasive antisense transcription have on gene expres-sion, and why has it evolved? Does H-NS affect Rho-dependent termination directly, possibly by slowingelongation by RNAP and allowing more time for Rho toterminate transcription? What is the mechanistic basisfor NusG effects that depend on terminator sequence?Finally, how similar are the bacterial and eukaryoticsystems that control antisense transcription?

Impact of antisense transcription on sensetranscription

We observed no apparent effect on sense transcriptionwhen Rho inhibition caused significantly elevated anti-sense transcription (Supplemental Fig. S2A–E,G). Al-though complete Rho inhibition, possibly causing higherlevels of antisense transcription, could affect sense tran-scription, it is also lethal. This lethality could result inpart from effects on sense transcription, RNA stability, ortranslation of even higher levels of antisense transcrip-tion than observed in our experiments, but such effectsare unlikely to contribute to physiologically relevantregulation. If physiologically relevant effects on sensetranscription occur by Rho modulation in wild-type cells,we should have detected them. This result suggestscounterintuitively that most antisense transcription inwild-type cells has minimal effects on gene expression,even though specific effects of some antisense transcriptson gene expression are well known (Georg and Hess2011).

Apparently, collisions between RNAPs as a result ofantisense transcription are tolerated in normal cells asa form of transcriptional noise (Struhl 2007). The densityof RNAP on most mRNA genes is low (less than one percell) (Bon et al. 2006), so collisions between sense andantisense RNAP molecules are likely infrequent. Evenwhen collisions do occur, transcription by sense but notantisense RNAP molecules will be aided by ribosomestranslating nascent mRNA (Proshkin et al. 2010). SenseRNAPs may thus overpower antisense RNAPs, causingthem to halt or backtrack and thereby facilitate Rhodissociation of antisense transcription complexes. Froman evolutionary perspective, some level of antisensetranscription may simply be tolerated as noise becauseeliminating it would adversely affect other essential pro-cesses (e.g., an antisense promoter may be the unavoid-able consequence of encoding of an optimal proteinsequence in the sense strand) or because the thermody-namic cost of completely eliminating it is too high (Lestaset al. 2010).

H-NS may assist Rho in silencing antisensetranscription

Our findings that H-NS is generally associated with DNAat the sites of Rho termination and that hns and rhogenetically interact suggest that Rho and H-NS act co-ordinately to silence transcription. One explanation for

these synergistic effects is that H-NS increases the timewindow for effective Rho action by slowing transcriptelongation or increasing pausing by RNAP. Several obser-vations support this direct model of H-NS/Rho-synergy,although we cannot rule out indirect effects. First, H-NSoccupancy of DNA increases as RNAP approaches thetermination site, with maximal H-NS binding occurringat the position of termination (Fig. 4A). This is consis-tent with RNAP ‘‘running into’’ a downstream patchof oligomerized H-NS, which may serve as a block toelongation. Second, overexpression of H-NS lacking itsC-terminal DNA-binding domain or of YdgT, whichresembles the N-terminal H-NS oligomerization domain,can suppress defects in Rho-dependent termination causedby mutations in rho or nusG in hns+ cells (Williams et al.1996; Saxena and Gowrishankar 2011b). These resultssuggest that changes to the H-NS nucleoprotein filamentalter the efficiency of Rho termination. These effects arelikely specific to sites of H-NS binding rather than re-flecting general effects of YdgT or H-NS fragments onrates of transcript elongation because neither YdgT noran H-NS fragment altered the rate of mRNA synthesis ina gene (lacZ) that does not bind H-NS (Grainger et al.2006; Oshima et al. 2006; Kahramanoglou et al. 2011;Saxena and Gowrishankar 2011b). Finally, the directmodel of H-NS and Rho synergy requires that sites ofH-NS binding and Rho termination be coincident, butindirect models do not. Testing whether H-NS affectstranscription elongation, pausing, and Rho terminationdirectly will require carefully designed mechanistic andstructure/function experiments.

Mechanism of NusG enhancement and relevanceto polarity models

Our results establish that NusG affects a subset of Rho-dependent terminators and that these effects depend onsequences at the termination site. The simplest mecha-nistic explanation for these results is that NusG isrequired at terminators with suboptimal Rho-bindingsites (rut sites). High C/G sequences that distinguishNusG-independent and NusG-dependent terminatorsshould generate rut sites that bind Rho with high affinity.Furthermore, lower C/G sequences should generate RNAstructures that further diminish unpaired C residuesavailable for Rho binding. Logically, NusG may helpRho bind nascent RNA at terminators with lower C/Gratios by tethering Rho near the RNAP exit channel.

However, NusG stimulation of Rho–RNA bindingchallenges existing ideas about Rho termination. NusGdoes not affect the half-maximal concentration of Rhorequired for termination at a NusG-dependent terminatorin lacZ (Burns and Richardson 1995). Furthermore, Senand coworkers (Chalissery et al. 2011; Kalyani et al. 2011)argued that NusG stimulates the EC dissociation steprather than the RNA-binding step of Rho-dependent ter-mination. Epshtein et al. (2010) argued that Rho is per-manently associated with RNAP, obviating a role ofNusG in tethering Rho to the EC. Conceivably, thesequence dependence of NusG effects on Rho-dependent






terminators could reflect NusG modulation of a stepother than RNA binding that also is favored by Rho–rutsite interaction. Interestingly, Rho-dependent termina-tors that were more sensitive to BCM (i.e., had a higherpercent readthrough in response to BCM) were modestlyless dependent on NusG, suggesting that NusG activitymay partially compensate for suboptimal terminationconditions. Unambiguously determining the mechanisticbasis of termination enhancement by NusG will requirecareful assays of the Rho concentration dependence oftermination at a collection of Rho-dependent terminators.

Our finding that NusG affects a subset of Rho-dependentterminators also has implications for models of transcrip-tional polarity (Peters et al. 2011). In the RNA competi-tion model, ribosomes block Rho binding to rut sites onthe nascent RNA (Adhya et al. 1974). In the NusG com-petition model, ribosomes sequester the NusG C-terminaldomain (CTD) in an interaction with ribosomal proteinS10 (Burmann et al. 2010) and thereby prevent the NusGCTD from activating Rho-dependent termination. Sinceonly a minority subset of Rho-dependent terminatorsrequires NusG, however, most polarity suppression maynot be explained by ribosomes sequestering the NusGCTD. Our results favor a mixed model in which boththe RNA competition and NusG competition play roles.Mutations in the ribosome that disrupt the binding be-tween S10 and NusG will be crucial to test the contribu-tion of the S10–NusG interaction to polarity in vivo.

Analogous control of antisense transcriptionin bacteria and eukaryotes

Although the synergy among chromatin structure andelongation factors in terminating spurious transcriptionis more complex in eukaryotes, the helicase Sen1 plays ananalogous role to bacterial Rho. Together with Nrd1 andNab, which confer both RNA recognition (Carroll et al.2007) and early elongation phase-specific RNAPII CTDrecognition (Vasiljeva et al. 2008), Sen1, like Rho, termi-nates cryptic, untranslated transcripts, including anti-sense transcripts (Arigo et al. 2006; Brow 2011). Bothhistone modifications and nucleosome composition af-fect these processes in eukaryotes (Carrozza et al. 2005;Santisteban et al. 2011), and some histone modificationsappear to aid Sen1 action (Terzi et al. 2011). Furthermore,the evolutionarily conserved Spt5 (NusG) elongationfactor both aids transcript elongation (Hirtreiter et al.2010) and, through its multiple C-terminal KOW domains,recruits elongation factors (Zhang et al. 2005), apparentlyincluding Nrd1 (Vasiljeva and Buratowski 2006; Lepore andLafontaine 2011). Thus, in both bacteria and eukaryotes,nucleoprotein structure and a functionally analogous ter-mination complex increase the overall fidelity of RNAsynthesis by suppressing noncoding transcription.

Materials and methods

Strains, plasmids, and primers

The strains used are listed in Supplemental Table S7. The rho

and hns strains were constructed by P1vir-mediated transduc-

tion (Thomason et al. 2007). The unstable plasmid pJP124 (pRC7-rho+) was generated by cloning a PCR product containing the rho

promoter and rho gene between the ApaI and HindIII sites ofpRC7 (Koop et al. 1987; Bernhardt and de Boer 2004) using thefollowing primers: 59-CTCTCTCTGGGCCCATAAGGGAATTTCATGTTCGG-39 and 59-CTCTCTAAGCTTATGAGCGTTTCATCATTT-39. Transcription of rho+ and lacZ+ was driven bythe rho promoter (Prho-rhoL+-rho+-lacZYA+).

RNA isolation

Cells were grown in MOPS minimal medium (Neidhardt et al.1974) with 0.2% glucose at 37°C in gas-sparged Roux bottles tomid-log phase (OD600 ; 0.3–0.4). Culture samples were trans-ferred directly into an ice-cold ethanol/phenol stop solution(Rhodius et al. 2006), which immediately inactivated cellularRNases. Cells were collected by centrifugation and stored at�80°C until RNA extraction. Total RNA was extracted from cellpellets by hot phenol extraction (Khodursky et al. 2003). Theintegrity of total RNA was determined from agarose gel or micro-channel (Agilent Bioanalyzer) electrophoretograms. RibosomalRNA (16S and 23S) was depleted prior to construction ofRNA-seq libraries using MICROBExpress reagents (Ambion).

RNA-seq, data normalization, processing, and significant

gene identification

RNA-seq was performed by the DOE Joint Genome Institute(Walnut Creek, CA) using the dUTP method (Parkhomchuk et al.2009). Briefly, ribosome-depleted RNA was fragmented in a buff-ered zinc solution (Ambion), then purified using AMPure SPRIbeads (Agencourt). First strand cDNAs were then synthesizedfrom the fragmented RNA using SuperScript II reverse transcrip-tase (Invitrogen), followed by a second bead purification. dUTPwas included in the second strand synthesis reaction in additionto dTTP to chemically mark the second strand. Two further beadpurification steps using different ratios of beads to cDNA (85/100, then 140/100) selected cDNAs in a range between 150 and350 bp. cDNAs were then A-tailed using Exo� Klenow, followedby ligation of sequencing adaptor oligos. Following bead pu-rification, dUTP was cleaved from the second strand usingAmpErase UNG (uracil N-glycosylase, Applied Biosystems),resulting in adaptor ligated single-stranded cDNAs. Deep se-quencing of cDNAs was performed using the Illumina Tru-Seqsequencing platform. RNA-seq reads were mapped to the E. coli

K-12 MG1655 genome (GenBank ID U00096.2) using shortoligonucleotide alignment program (SOAP) (SupplementalTable S8; Li et al. 2008). The RNA-seq data depicted inFigures 2C and 3, A andB, were normalized by dividing eitherthe total number of read counts per sense or antisense strandof genes (Fig. 2C) or the number of read counts at a givenchromosome coordinate (Fig. 3A,B) by the total library size inmillions (i.e., counts per million). Normalized biologicalreplicates showed good agreement (r $ 0.99 at the count pergene level). The log2 ratios for BCM-treated versus untreatedcells shown in Figure 2C were calculated after a constant ofone read per million was first added to each gene to avoid divideby zero errors. These log2 ratios were quantile-normalizedbetween biological replicates of the same strand (sense orantisense) using R (normalizequantiles) (Gautier et al. 2004).To identify genes with significant changes in expression inBCM-treated cells, raw (unnormalized) RNA-seq reads betweenthe start and end coordinates of genes were summed for bothsense and antisense strands. Library normalization and signif-icant gene determination were carried out using an R imple-mentation of the edgeR algorithm (Robinson et al. 2010). Fold

Peters et al.





effect and FDR values for each gene are listed in SupplementalTable S2.

Tiling expression, data normalization, processing,

and significant transcript identification

Reverse transcription and cDNA labeling were performed aspreviously described (Cho et al. 2009), except that Cy3 was usedinstead of Cy5. Microarrays were designed using chipD (Dufouret al. 2010) and contained 378,408 probes that alternate strandswith »12-bp spacing (Roche-Nimblegen). Hybridization andwashing of microarrays were performed according to standardNimblegen protocols (http://www.nimblegen.com). Microarrayswere scanned at 532 nm using a GenePix 4000B scanner(Molecular Devices). Raw probe intensities were normalizedacross samples using robust multiarray (RMA) analysis imple-mented in the NimbleScan software package (Roche-Nimblegen).Data from Drac and MDS42 strain backgrounds were normalizedseparately to avoid problems arising from the lack of probeintensity in MDS42-deleted regions. Normalized biological rep-licates showed good agreement (r $ 0.90 at the probe level).Normalized probe intensities were transformed to log2 and splitinto strands, and biological replicates were averaged. Transcriptsthat were significantly up-regulated in BCM-treated or mutantcells were identified using an R implementation of the CMARRTalgorithm (Kuan et al. 2008). First, data from untreated cells weresubtracted from data from BCM-treated or mutant cells. Second,the CMARRT was used to identify regions of at least three con-secutive significantly up-regulated probes (FDR # 5%) in thesubtracted data. Probe data from MDS42 deletions were dis-carded prior to CMARRT analysis to avoid significant transcriptsthat spanned deletions being called as two distinct transcripts.The significance of overlap between two genome features (e.g.,significantly up-regulated transcripts in BCM-treated cells andsignificantly up-regulated transcripts in DnusG cells) was de-termined by counting the number of overlapping featuresbefore and after rotation of the positions of one set of featuresby 1 Mb. Significance was then determined using the Mann-Whitney U-test.

ChIP–chip, data normalization, processing, and significant

region identification

ChIP–chip was performed as previously described (Mooney et al.2009a). The monoclonal antibody against RNAP (anti-b, NT63)was purchased from Neclone, and polyclonal antisera againstH-NS (Harlan Laboratories) was purified by adsorbsion with cellpower from an E. coli Dhns strain. ChIP–chip data were normal-ized and averaged as previously described (Mooney et al. 2009a).The H-NS ChIP–chip data shown in Figure 4 were smoothedusing two rounds of sliding-window averaging over 300 bp.Significant increases in RNAP occupancy in DnusG and DnusA*cells were identified using CMARRT as previously described forBCM (Peters et al. 2009), except that probe data from MDS42deletions were discarded prior to CMARRT analysis to avoidregions of increased RNAP occupancy that spanned deletionsbeing called as two significant regions. Significant regions ofH-NS occupancy were defined using CMARRT as regions of atleast three consecutive probes that were significantly abovebackground (FDR # 5%).

Data sets

Tiling expression, RNA-seq, and ChIP–chip data sets weredeposited at Gene Expression Omnibus (GEO) with the acces-sion code GSE41940.

Acknowledgments

We thank members of the Landick laboratory, A.B. Banta(University of Wisconsin at Madison), and M.E. Gottesman(Columbia University) for critical reading of the manuscript;B.K. Cho and B.Ø. Palsson (University of California at San Diego)for sharing their tiling expression microarray protocol; D.H.Keating (Great Lakes Bioenergy Research Center), D.L. Court(National Cancer Institute at Frederick), and M E. Gottesman(Columbia University) for strains; the U.S. DOE Joint GenomeInstitute (Walnut Creek, CA) for RNA-seq library preparationand sequencing; and H. Yan (Great Lakes Bioenergy ResearchCenter) for RNA-seq read mapping. This work was supported bythe National Institutes of Health (GM38660). RNA-seq workwas supported by the U.S. DOE Great Lakes Bioenergy ResearchCenter (DOE Office of Science BER DE-FC02-07ER64494) andthe U.S. DOE Joint Genome Institute (DOE Office of Sciencecontract no. DE-AC02-05CH11231).

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