Deletions within genes coding for subunits of the transcription coactivator SAGA caused strong genome-wide defects in transcription and SAGA-mediated chromatin modifications. In contrast, rapid SAGA depletion produced only modest transcription defects at 13% of protein-coding genes – genes that are generally more sensitive to rapid TFIID depletion. However, transcription of these ‘coactivator-redundant’ genes is strongly affected by rapid depletion of both factors, showing the overlapping functions of TFIID and SAGA at this gene set. We suggest that this overlapping function is linked to TBP-DNA recruitment. The remaining 87% of expressed genes that we term ‘TFIID-dependent’ are highly sensitive to rapid TFIID depletion and insensitive to rapid SAGA depletion. Genome-wide mapping of SAGA and TFIID found binding of both factors at many genes independent of gene class. Promoter analysis suggests that the distinction between the gene classes is due to multiple components rather than any single regulatory factor or promoter sequence motif. Efficient in vitro transcription by RNA Polymerase II (Pol II) is initiated with six basal transcription factors (TATA binding protein (TBP), TFIIA, TFIIB, TFIIE, TFIIF, TFIIH), Pol II enzyme and TATA-containing promoter DNA. Cellular mRNA transcription, much of which initiates at non-TATA-containing promoters, also requires one or more coactivator complexes such as Mediator, TFIID, SAGA, NuA4 and Swi/Snf, that either directly contact the basal transcription machinery and/or modify chromatin . Often rThe yeast coactivators SAGA and TFIID share five subunits  but are The genome-wide promoter specificities of SAGA and TFIID have been the subject of much investigation. Early work showed that at least some yeast genes can be activated and transcribed after depletion or inactivation of TFIID (Taf) subunits . PioneerSeveral subsequent findings challenged these initial models. First, mutations affecting genome-wide transcription are now known to alter mRNA stability, making prior steady state mRNA measurements used in earlier studies of TFIID and SAGA specificity problematic . Second,SPT deletions but declines only slowly in the rapid depletion experiments. Finally, we examine features that distinguish the two gene sets and the role of SAGA in maintenance of ongoing activated transcription at the coactivator-redundant genes.We have now investigated the genome-wide specificities of TFIID and SAGA by monitoring levels of newly synthesized RNA after rapid TFIID and SAGA depletion. This approach has greater sensitivity and lower background than methods used in earlier studies and allows reliable quantitation of mRNAs for ≥ 83% of yeast genes after factor depletion. We find that SAGA has two separable functions in gene expression: (1) a general function important for transcription of nearly all genes. This function was revealed by deletions in genes encoding key SAGA subunits. (2) a gene-specific role revealed by rapid depletion of SAGA function, where only 13% of genes are modestly affected. Surprisingly, this smaller gene set is, on average, more sensitive to rapid TFIID depletion. These and other results suggest that TFIID and SAGA function is substantially redundant at this set of co-regulated genes that we term ‘coactivator-redundant’ rather than ‘SAGA-dominated’ or ‘Taf-independent’ as previously proposed. The other 87% of yeast genes that we term ‘TFIID-dependent’ are very sensitive to rapid TFIID depletion but show little or no change upon rapid SAGA depletion. We further found that the role of SAGA in global transcription is strongly linked to SAGA-dependent chromatin modifications, particularly Gcn5-dependent H3 acetylation. This mark, that we find is required for efficient global transcription, is significantly reduced in the To investigate the basis for conflicting reports on the genome-wide roles of SAGA and TFIID, we used 4-thioU RNA-seq to quantitate transcriptional changes that are caused by various SAGA depletion strategies. Yeast were grown in synthetic or rich glucose media and RNAs labeled for 5 min, followed by purification of the labeled RNA and quantitation by sequencing. This method has much higher sensitivity and lower background compared with Pol II ChIP and allows reproducible quantitation of newly-synthesized mRNA from ≥83% of Pol II-transcribed genes . Experiments, except where noted, were performed in triplicate and there was low variation across the set of analyzed genes .spt3Δ, spt7Δ, or spt20Δ or by rapid depletion of several SAGA subunits using the auxin-degron system  and 4-thioU-labeled RNA , contains 87% of analyzed genes (4245 genes) and is most sensitive to TFIID depletion. These genes show a ~ 4.5 fold average decrease in transcription upon depletion of ether Taf13, Taf7 or Taf1 ; see reasoning below), contains 13% of analyzed genes (655 genes) and these genes are more sensitive to deletions in SAGA subunits (~3–4 fold decrease) and less sensitive to TFIID depletion (~1.5–2-fold decrease) compared with the TFIID-dependent genes. , extracted the sequences of all annotated TATA-elements and reclassified promoters as TATA-containing/TATA-less based on the less stringent seven nt consensus (TATAWAW). Although TATA-containing promoters are enriched, the coactivator-redundant class is clearly not equivalent to TATA-containing genes . This analysis found that the binding motifs for 14 transcription factors are preferentially enriched in one of the two gene classes . For exaTo examine these findings in more detail, we directly searched the set of genes used in our transcription analysis  for the Msn2/4 and TBP consensus binding motif  genes have sometimes been analyzed as a distinct category of highly expressed and especially TFIID-dependent genes. From analysis of our new data, we did not find that these genes are obviously distinct from many other genes based on TFIID or SAGA-dependence. Although 95% of the RP genes belong to the TFIID-dependent category they show comparable transcription defects upon TFIID depletion as the rest of the TFIID-dependent genes. However, RP genes are clearly more highly expressed than the average gene, with 83% of the RP genes found in expression quintile 5 . K-meansSPT deletions are mediated in part through changes in chromatin modifications.We next investigated the basis for the difference in transcription phenotypes caused by the SAGA deletion vs degron strains. SAGA functions through direct protein-protein interactions to assist with TBP recruitment and PIC formation as well as via chromatin changes mediated by Gcn5 and Ubp8 . One posgcn5Δ or ubp8Δ), in an spt3Δ strain defective for SAGA TBP-binding function and, in cells after rapid SAGA-depletion (Spt3/7-degron) (gcn5Δ) led to a genome-wide transcription defect in both gene classes that was nearly as strong as that found in the spt3Δ mutant. Elimination of deubiquitination function (ubp8Δ) led to more modest genome-wide defects in transcription for both gene classes.As an initial test of this model, we compared transcription levels in strains lacking specific SAGA-linked enzymatic functions (-degron)  (Supplem-degron) . We founSPT deletion mutants are stronger than that caused by rapid SAGA depletion, we next examined the kinetics of changes in SAGA-dependent chromatin marks in the Spt-degron strains. The SAGA-dependent chromatin marks H3K18-Ac and H2B-Ub , we identified 2938, 3681 and 3723 bound mRNA promoters, with extensive overlap between binding sites mapped with different Taf-MNase fusions  Supplem. PromoteWe next used ChEC-seq to revisit genome-wide binding of SAGA, where Spt3-MNase and Spt7-MNase identified 1601 and 3536 promoter binding sites  Supplem. Figure Importantly, we found widespread promoter binding and ≥ 86% overlap in genes bound by TFIID and SAGA when comparing ChEC signals from Taf7 and Spt7-MNase fusions, supporting our findings above that most genes are regulated by both factors . Furtherspt3Δ or spt7Δ strains before or after addition of sulfometuron methyl (SM), which induces amino acid starvation and induction of Gcn4-dependent genes  that are not Gcn4 targets and three coactivator-redundant genes that are direct Gcn4 targets . Compared with a wild type strain, the SPT deletion strains had ~ 3 fold lower expression levels at all three TFIID-dependent genes , while activation was eliminated in the spt3Δ strain (light vs dark orange bars).Prior work showed that SAGA subunit deletion strains are defective in transcription activation at some genes . To extent genes . TranscrTo determine the consequence of rapid SAGA depletion before transcription was induced, cells containing the Spt3/7 double degrons were first treated with IAA for 30 min to deplete these proteins and then induced with SM for 60 min. This treatment still allowed for greater than normal fold-activation upon subsequent SM addition . Rapid subunit depletion revealed two classes of genes with different dependencies on TFIID and SAGA. The first class, termed TFIID-dependent genes, contains 87% of the analyzed genes and shows strong transcription dependence on TFIID but little or no change in response to rapid depletion of SAGA. The second class, termed coactivator-redundant genes, contains 13% of analyzed genes and shows a modest transcription decrease in response to rapid depletion of either TFIID or SAGA. Importantly, simultaneous depletion of both TFIID and SAGA leads to a severe transcription defect at these coactivator-redundant genes. This latter result is consistent with prior general conclusions that the combination of a SAGA subunit deletion and TFIID ts mutation can result in greater transcription defects compared with either single mutation . TogetheGCN5 or UBP8, the SAGA subunits responsible for chromatin modifications, show genome-wide transcription defects that are much stronger than those caused upon rapid SAGA depletion.We also found that expression of nearly all genes is dependent on another SAGA function that was revealed in cells that had grown many generations without SAGA. Our results link this genome-wide role to SAGA-regulated chromatin modifications - marks that are slow to change upon rapid SAGA subunit depletion. Deletions in either SPT deletion strains and change slowly upon SAGA subunit depletion. Importantly, ChIP-seq analysis found that the levels of promoter-localized H3K18-Ac was much lower in the SPT deletion strains compared to strains depleted of SAGA function for 30 min. Indirect effects, such as decreased expression of general regulatory factors and components of the basal Pol II machinery, etc., likely also contribute to lower genome-wide expression in the deletion strains. The two roles of SAGA seem additive, which readily explains why the coactivator-redundant genes are more sensitive to SPT deletions compared to the TFIID-dependent genes. Our findings show that the coactivator-redundant genes require both SAGA functions while the TFIID-dependent genes are primarily dependent only on the SAGA-mediated chromatin modifications.Western analysis of total chromatin showed that the level of H3K18-Ac and H2B-Ub are reduced in the Yeast cellular transcription is dominated by a small number of exceptionally highly expressed genes and it was proposed that highly expressed genes are biased for Taf-independent expression . We do fResults from mapping the genome-wide locations of TFIID using an improved and more stringent ChEC-seq method are consistent with our findings that TFIID functions at both the TFIID-dependent and coactivator-redundant genes. We observe no bias in promoter type bound by either TFIID or SAGA. In contrast, prior studies have observed a bias in Taf-promoter binding using formaldehyde crosslinking. At this time, the reason for this discrepancy is unknown, although we previously speculated that the observed bias in proximity of TFIID to nucleosomes at TFIID-dependent genes  may playTo examine features leading to the different coactivator responses, we conducted a de novo motif search of promoters in each class. As expected, the TATA consensus sequence is enriched in the coactivator-redundant class, although nearly equal numbers of TATA-containing genes are found in the TFIID-dependent gene set. The results of de novo search found 14 transcription factor motifs that are enriched in one or the other gene class. However, this and more directed searches for these consensus motifs showed that no transcription factor binding site is exclusive for one or the other gene class. For example, while the motif for the Msn2/Msn4 factors is enriched in the CR gene class compared with all other genes, the majority of Msn2/Msn4 binding motifs are found in TFIID-dependent genes. From this combined analysis, we conclude that the distinction between the gene classes is due to multiple components rather than any one factor or promoter sequence. These factors likely include the specific regulatory transcription factors used at each promoter, promoter sequence and perhaps chromatin architecture.From the above results, it is clear that the coactivator-redundant genes are dependent on both coactivators rather than their expression being dominated by SAGA as proposed earlier; when one coactivator is eliminated, the other can at least partially compensate. What is the mechanistic basis for this behavior? Both factors were shown to have TBP-DNA loading activity. Based on earlier studies, we propose that the TFIID-dependent genes exclusively require Tafs for promoter recognition and TBP-DNA loading while the coactivator-redundant genes can use either SAGA or TFIID. Since many CR genes contain TATA elements, we envision that SAGA can recruit TBP more directly, taking advantage of: (1) specific TBP-TATA interactions and (2) direct TBP interactions with other TBP-compatible DNA binding motifs. This model can explain the surprising viability of strains with TBP mutations that are defective in TATA-DNA binding . Some ofSPT deletion strains have defects in gene activation . A XhoI/SacI fragment containing the PBDF1-MNase fragment was inserted to the yeast integrating vector pRS303. The resulting pRD16 plasmid was linearized with BstEII and integrated into strain BY4705. strains  were con strains  was used strains . A straiin pSG79  was exchS. cerevisiae strains were grown as indicated in either rich media  or synthetic complete (SC) media (per liter: 1.7 g yeast nitrogen base without ammonium sulfate or amino acids (BD Difco), 5 g ammonium sulfate, 40 μg/ml adenine sulfate, 0.6 g amino acid dropout mix  and supplemented with two micrograms/ml uracil and 0.01% other amino acids to complement auxotrophic markers). Standard amino acid dropout mix contains 2 g each of Tyr, Ser, Val, Ile, Phe, Asp, Pro and 4 g each of Arg and Thr. S. pombe strains were grown in YE media . Where indicated, S. cerevisiae strains at an A600 of ~ 1.0 were treated with 500 μM indole-3-acetic acid (IAA) dissolved in DMSO  for 30 min. Where indicated, cells were incubated with 0.5 μg/ml sulfometuron methyl (SM) in DMSO (SM) or with DMSO alone for 60 min as described in the text and figure legends, prior to RNA labeling.3. Cells were pelleted, then resuspended in 75 µl SUTEB lysis buffer  and transferred to a microcentrifuge tube, heated to 100°C for 4 min, then an equal volume of 0.5 mm zirconia/silica beads was added. Cells were lysed in a Mini Beadbeater-96 (BioSpec Products) for 30 s followed by 1 min rest on ice and repeated for a total of four cycles. 200 µl additional SUTEB lysis buffer was added, vortexed, and extracts were heated to 100°C for 1 min. Extracts were transferred to new tubes and stored at −70°C until separated by SDS-PAGE. Protein signals were visualized by using the Odyssey CLx scanner and quantified using Odyssey Image Studio software (Li-Cor) by generating a standard curve using a titration from WT extract. Each protein analyzed was normalized to the amount of the TFIIF subunit Tfg2. H2B-Ub was probed with antibody 5546  and H3K18-Ac with antibody 07–354 (EMD Millipore).1 ml cell culture was collected and pelleted from strains after treatment with IAA or DMSO, washed with 500 μl water, then resuspended in 75 μl yeast whole cell extract buffer. After heating for 5 min at 95°C, samples were centrifuged for 5 min at max speed, whole cell extracts were separated by SDS-PAGE and analyzed by Western blot using mouse monoclonal or rabbit polyclonal antibodies. To efficiently visualize histone marks, 5–10 ml of cell culture (adjusted to similar cell count based on OD measurement) was transferred to a tube on ice containing 2 ml 50 mM Tris, pH 7.5 + 10 mM NaNS. cerevisiae or 20 ml S. pombe cells were labeled with 5 mM 4-thiouracil (Sigma-Aldrich) for 5 min, the cells were pelleted at 3000 x g for 2 min, flash-frozen in liquid N2, and then stored at −80°C until further use. S. cerevisiae and S. pombe cells were mixed in an 8:1 ratio and total RNA was extracted using the RiboPure yeast kit  using the following volumes: 480 μl lysis buffer, 48 μl 10% SDS, 480 μl phenol:CHCl3:isoamyl alcohol (25:24:1) per S. cerevisiae pellet + 50 μl S. pombe (from a single S. pombe pellet resuspended in 850 μl lysis buffer). Cells were lysed using 1.25 ml zirconia/silica beads in a Mini Beadbeater-96 (BioSpec Products) for 3 min followed by 1 min rest on ice. This bead beading cycle was repeated twice for a total of 3 times. Lysates were spun for 5 min at 16K x g, then the following volumes combined in a 5 ml tube: 400 μl supernatant, 1400 μl binding buffer, 940 μl 100% ethanol. Samples were processed through the Ambion filter cartridges until all sample was loaded, then washed with 700 μl Wash Solution 1, and twice with 500 μl Wash Solution 2/3. After a final spin to remove residual ethanol, RNA was eluted with 25 μl 95°C preheated Elution Solution. The elution step was repeated, and eluates combined. RNA was then treated with DNaseI using 6 μl DNaseI buffer and 4 μl DNaseI for 30 min at 37°C, then treated with Inactivation Reagent for 5 min at RT. RNA was then biotinylated essentially as described , blocked in 4 ml High Salt Wash Buffer containing 40 ng/μl glycogen for 1 hr at RT, then resuspended to the original volume in High Salt Wash Buffer. After incubation with biotinylated RNA, the beads were washed 3 × 0.8 ml High Salt Wash Buffer, then eluted into 25 μl streptavidin elution buffer  at RT with shaking, then the elution step repeated and combined for a total of 50 μl. At this point, 10% input RNA (4 μl) was diluted into 50 μl streptavidin elution buffer and processed the same as the labeled RNA samples to determine the extent of recovery. 50 μl each input and purified RNA was adjusted to 100 μl with nuclease-free water and purified on RNeasy columns (Qiagen) using the modified protocol as described  and three technical replicates (independent measurements of the same qPCR sample). Values for technical replicates were averaged which gave a single value for each biological replicate. The final result is a mean of biological replicates.Two microliters RNA was used to generate cDNA using Transcriptor (Roche), random hexamer primer, and the manufacturer's instructions. cDNA was used either undiluted, 1/20, or 1/100 for quantitative PCR (qPCR) depending on the gene analyzed. Gene-specific qPCR was performed in triplicate using primers near the 5’ end of the gene. qPCRs were assembled in 5 μl reaction mixtures in a 384-well plate using 2X Power SYBR green master mix (Thermo Fisher Scientific) and reactions were run on the QuantStudio5 Real-Time System (Thermo Fisher Scientific). Relative amounts of DNA were calculated using a standard curve generated from 10-fold serial dilutions of purified genomic DNA ranging from 10 ng to 0.001 ng. Relative amounts of Newly synthesized RNA isolated via 4-thioU labeling and purification was prepared for sequencing using the Ovation SoLo or Ovation Universal RNA-seq System kits (Tecan) according to the manufacturer’s instructions and 1 ng (SoLo) or 50 ng  input RNA. Libraries were sequenced on the Illumina HiSeq2500 platform using 25 bp paired-ends at the Fred Hutchinson Genomics Shared Resources facility.D. melanogaster MNase-digested DNA (1 ng/ml stock concentration) in the amount calculated based on S. cerevisiae culture A600 measurement (volume = A600 x 8 μl).ChEC-seq was performed as previously described  with sevS. pombe (strain 972 hr) chromatin used as a spike-in was prepared the same way as S. cerevisiae samples  and supplied by the Tsukiyama lab (Fred Hutch). Each IP reaction contained 1 μg of S. cerevisiae chromatin, 10 ng of S. pombe chromatin and 20 μl of Protein G Dynabeads  conjugated with 4 μl of anti-H3K18-Ac antibody  and the final volume was brought to 500 μl with FA buffer. 25 μl of the S. cerevisiae/S. pombe chromatin mix was transferred to a separate tube as an ‘input’ sample before addition of beads and combined with 25 μl of the stop buffer. Chromatin-antibody complexes were eluted with two 25 μl volumes of stop buffer. IP and input samples were processed together from this point. After Proteinase K digestion, DNA was purified by phenol extraction (two step extraction for input samples) followed by ethanol precipitation in the presence of 20 μg glycogen. Pelleted DNA was washed with 70% ethanol and resuspended in 15 μl 10 mM Tris-HCl (pH 7.5).ChIP-seq was performed similarly as described  with theSequencing libraries were prepared similarly as described  with sevhttps://jupyter.org) using Python programming language (https://www.python.org) and short Bash scripts. All figures were generated using Matplotlib and Seaborn libraries for Python; . All code snippets and whole notebooks are available upon request.The majority of the data analysis tasks except sequence alignment, read counting and peak calling (described below) were performed through interactive work in the Jupyter Notebook (S. cerevisiae reference genome (sacCer3) and S. pombe reference genome (ASM294v2.20) with Bowtie2 . Signal per gene was normalized by the number of all S. pombe reads mapped for the sample and multiplied by 10000 (arbitrarily chosen number). Genes classified as dubious, pseudogenes or transposable elements were excluded leaving 5797 genes for the downstream analysis. As a next filtering step, we excluded all the genes that had no measurable signal in at least one out of 48 samples including SAGA and TFIID degron experiments and SPT3, SPT7 and SPT20 deletion experiments . The remaining 5158 genes were sorted by the average expression level based on combined results of all WT and DMSO samples . For this analysis, signal per gene was further normalized by the gene length. Results from all relevant samples were averaged and genes were ranked  were averaged. Corresponding samples were compared to calculate log2 change in expression per gene  was used to construct the SPT3 deletion strain) and degron strains are derivatives of SHY1037 . The data for 5888 genes were merged with TSS positions obtained from SAM files for csd.edu,  ‘makeTagS. cerevisiae genome was calculated as the number of reads that mapped at that position divided by the number of all D. melanogaster reads mapped in the sample and multiplied by 10000 (arbitrarily chosen number). Signal per promoter was calculated for each replicate experiment as a sum of normalized reads per base in a 300 bp window around the peak summit. The overall signal per promoter (Coverage at each base pair of the promoter  is the aFASTQ files for the Taf1 ChIP-exo were obtained from the SRA (SRR5511893) . The datS. cerevisiae genome was calculated as the number of reads that mapped at that position divided by the number of all S. pombe reads mapped in the sample, multiplied by the ratio of S. pombe to S. cerevisiae reads in the corresponding input sample and multiplied by 10000 (arbitrarily chosen number). The list of all annotated ORF sequences (excluding sequences classified as ‘dubious’ or ‘pseudogene’) was downloaded from the SGD website (https://www.yeastgenome.org). The data for 5888 genes were merged with TSS positions obtained from SPT7, GCN5 and UBP8 deletion strains, SHY565 (BY4705 with Rpb3-3xFlag::KanMX) was used to construct the SPT3 deletion strain. The final dataset contains log2 change in H3K18-Ac signal values per gene for 5885 genes  were included in the analysis.K-means clustering was performed using ‘KMeans’ function from Python sklearn.cluster library with default settings. Two clusters were found to give the best separation using silhouette analysis (‘silhouette-score’ function from sklearn.cluster library). Loghttp://homer.ucsd.edu) (De novo motif discovery was performed using HOMER (csd.edu)  ‘findMotResulting motifs were screened against the library of known motifs from HOMER database and the best matches were reported . Two mos In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This work elegantly revisits the highly-debated topic of coactivator dependency of different yeast promoter classes. Using acute depletion of TFIID and SAGA subunits and nascent transcript labeling, it reveals that there is a large class of genes that is TFIID dependent and a smaller TFIID/SAGA-redundant class. Importantly, this work clearly shows that acute depletion has different effects on gene expression compared to deletion of the subunits, likely due to the fact that SAGA dependent modifications remain after acute depletion of SAGA, addressing some of the previous confusion in the field as to coactivator dependency of gene classes. The conclusions advance our understanding of the relative roles of SAGA and TFIID in gene regulation in yeast. The work will be of general interest to the transcription/chromatin community and will hopefully lead to future studies identifying the mechanistic basis of the different coactivator dependencies for different gene classes.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your initial submission has been assessed by a Senior Editor in consultation with a member of the Board of Reviewing Editors. Although the work is of interest, we regret to inform you that the findings are not appropriate for further consideration at eLife.Thank you for choosing to send your work, "Two separate roles for the transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA", for consideration at Specifically, based on all the reviews, the paper has some experimental, formatting, and writing issues that are addressable in a reasonable time frame. Importantly, some of these issues, when addressed, could change the conclusions made. For example, if histone modification levels change on the SAGA/TFIID-redundant genes when measured by ChIP, the conclusions on how SAGA depletion affects transcription would need to be modified. Certainly, the concerns about levels of depletion and growth defects of the various mutants needs to be addressed. However, even when all the experimental and writing concerns are corrected, the question remains about whether the paper makes a large enough contribution for our journal. We felt that this topic has been visited and revisited numerous times, and we are not completely certain this paper is giving us the final answer. The impact would be higher if you had included ChEC-seq data, although reading of the accompanying paper indicates that the conclusions about SAGA occupancy from the Molecular Cell paper from your lab would be upheld. We feel that the big remaining question is what mechanistically distinguishes the two gene classes? You have attempted to address this but, in our opinion, the mechanistic distinctions remain unclear. Even though this paper does a better job than many characterizing which promoters go in which category, we still didn't come away with any new insights into why some respond to SAGA and most don't.We do not intend any criticism of the quality of the data or the rigor of the science. Indeed, this is clearly very nice quality work and should be published, but in a more specialized journal, in our opinion.Reviewer #1:This manuscript is the next installment of a series of papers over several years that have attempted to discern which yeast genes are regulated by the two coactivators SAGA and TFIID. As different techniques have been used, the view has changed. In these current experiments, the authors use metabolic labeling with 4-thioU to measure the level of transcription under either mutant conditions or after depletion of SAGA or TFIID components. Interestingly, they obtain very different results for SAGA between the depletion and deletion conditions. From the depletion studies, they conclude that approximately 13% of the yeast genes that they could assay are regulated in a partially redundant fashion by both SAGA and TFIID, while the remainder are primarily regulated by TFIID. The interpretation is reasonable, although their deletion studies show a much greater effect of SAGA mutants and their ChEC studies, in agreement with previous studies, show that SAGA is present at most promoters, as is TFIID. Overall, this is nicely done work that should be of interest to those in the transcription field. Several comments on both the presentation and the experiments are below.1) The first sentence of the Abstract is inaccurate as SAGA does more than chromatin modifications, as the authors reveal in the Introduction. I suggest revising the sentence to the following: "Deletions of genes encoding subunits of the yeast coactivator SAGA reveal a general role in transcription linked to the multiple activities of this complex."2) Please edit throughout the manuscript to correct little writing errors. As one example, in the Abstract, "…87% of expressed genes we term "TFIID-dependent" are highly sensitive to TFIID depletion…" the word "that" should be inserted before "we term." Also, change "contacts" to "contact." Here's another example: – "The genome-wide specificity of SAGA and TFIID have been…" – either change specificity to specificities or change have to has. There are more cases like this throughout the manuscript.3) Introduction section – "relatively weak TBP binding" might be biochemically accurate but it implies a weak activity with respect to the degree of gene regulation, which is not the case for spt3 and spt8 mutants.4) Figure 1 and accompanying results – In the comparisons of deletions versus depletions, it would be helpful if the authors clarified their choices for comparison since they are comparing single gene deletions to multiple subunit depletions. Presumably they chose to deplete multiple SAGA subunits at once to increase the chances of efficient SAGA depletion, but please state that explicitly of that is the case. In an ideal world, they would have compared deletion/depletion of the same subunits, such as Spt3 vs Spt3 or Spt3+Spt7 vs Spt3+Spt7. It's formally possible, for example, that depletion of only Spt3 would have a more severe phenotype than the double depletion, although this is unlikely based on previous studies. If they have any unpublished evidence about this, they might mention it.5) Figure 1—figure supplement 4C – What is being correlated in this table? If each experiment was generally done in triplicate, for each genotype, what was used to general the correlation coefficient? Please explain in the legend.6) Since Tafs are essential for viability, what was done to ensure that the cells were still viable during the RNA measurements, for either Taf depletions or Taf + SAGA depletions?7) Regarding depletion versus deletion, it seems possible that the difference is caused by the level of depletion rather than the time without the protein. Even though the authors have tried to rule this out, I think the formal possibility should be mentioned.8) Subsection “SAGA-regulated chromatin modifications change little during rapid SAGA inactivation” – For SAGA's role in activation, the authors should also cite three other references – Larschan et al., 2001, Bhaumik et al., 2001, and Dudley et al., 1999.9) Figure 4 and subsection “SAGA-regulated chromatin modifications change little during rapid SAGA inactivation” – The authors state that it was unexpected to observe lower H2B-Ub levels in some of the SAGA mutants in panel A. Similarly, it was unexpected to observe lower H3K18-Ac levels in the spt3 deletion strain, based on previous genetic, biochemical, and structural studies that strongly suggest that the HAT module is physically and biochemically distinct from Spt3. Therefore, the same reasoning used for the effect on H2B-UB might apply for the reduction in H3K18-Ac levels in the spt3 mutant. If the authors wish to impose histone modifications as a reason for the depletion vs. deletion difference (Discussion paragraph three), this might be mentioned, as an spt3 deletion has a severe effect on transcription yet in theory it should not affect the level of histone modifications.10) Figure 5, subsection “Rapid SAGA depletion primarily affects maintenance of ongoing transcription rather than gene activation by Gcn4” paragraph two, – This was an interesting experiment. In contrast to what the authors write, the fold induction looks to be greater in the IAA-treated samples in 5B compared to the non-IAA treated cells in 5A, particularly for ARG3 and HIS4.11) Figure 1 and 2 legends and probably elsewhere – SC media is stated as the growth medium but it is not described in the Materials and methods. Please clarify.12) The authors need to provide a list of the yeast strains, including names and genotypes.13) The authors discuss the SAGA-TFIID redundancy in the Discussion but they don't offer an explanation for two aspects of the redundancy. First, why is it partial? If either complex can work at those genes, why does expression decrease at all? Is it due to insufficient levels of the other complex? Second, what does the reduced expression represent? Is it caused by normal levels from a subpopulation of cells in the culture, or is it due to reduced expression from all of the cells? In future experiments, this could be addressed by single cell analysis.Reviewer #2:In this manuscript, Donczew et al. revisit the highly-debated topic of yeast promoter classes, using acute depletion of TFIID and SAGA subunits and nascent transcript labeling. Older studies by the Pugh lab categorized yeast promoters as SAGA-dominated and TFIID-dominated. In general, the SAGA-dominated genes were described as TATA-enriched and stress responsive, while the TFIID-dominated genes lacked consensus TATA boxes. In a 2017 Molecular Cell paper , Hahn and collaborators showed that deletion of SAGA subunits significantly reduced nascent transcript levels at both TFIID- and SAGA-dominated gene classes and also showed that SAGA occupies the promoters of both gene classes by ChEC-seq. This paper threw into question the classification of promoters by coactivator dependencies and suggested that the functions of SAGA are more widespread than previously thought. However, recently, the ChEC-seq profiles reported in the Baptista paper have been disputed by the Pugh lab in a paper in press at Molecular Cell.After acutely depleting SAGA and TFIID subunits and measuring nascent transcription, the authors in this paper conclude that there are two major classes of Pol II promoters with respect to TFIID and SAGA dependencies: a large TFIID-dependent class and a smaller TFIID/SAGA-redundant class, which accounts for 13% of promoters tested. The authors define these classes through measurements of nascent RNA changes in response to factor depletion using the auxin-inducible degron system, followed by k-means clustering. By co-depleting Taf13 and either Spt3 or Spt7, the authors provide strong support for the redundancy model. They also report results from a new ChEC-seq protocol which show widespread TFIID and SAGA occupancy at all genes in line with the Baptista paper. Regarding the mechanistic distinctions between the TFIID-dependent and TFIID/SAGA-redundant promoters, the authors conclude that gene expression levels do not dictate whether a promoter is redundantly regulated by the coactivators. By reanalyzing the TATA sequences in the promoter classes, the authors report that TFIID/SAGA-redundant promoters are not biased toward TATA-containing versus TATA-less. Western data show that two histone modifications regulated by SAGA do not greatly change during the AID depletion time course. From this the authors conclude that some other function of SAGA, such as a protein-protein interaction, is being disrupted by the depletion and leading changes in transcription. Using RT-PCR, the authors test the effect of SAGA depletion on activation of three Gcn4-regulated genes and find that activation is unaffected, whereas ongoing transcription is reduced.The main conclusion from this paper is the redefining of yeast promoters using acute depletion and nascent transcript labeling. The results support a large TFIID-dependent class and smaller class that can utilize either TFIID or SAGA for their regulation. The molecular basis of the two classes defined here remains uncertain.Specific comments:1) The identification of two promoter classes is the main conclusion of the paper and is based on acute protein depletion using the AID system, 4-thioU RNA-seq, and k-means clustering. The paper requires additional controls to show that the AID tags are not disrupting protein function, that auxin or DMSO isn't affecting expression of an untagged WT strain, and that nascent transcripts are enriched (such as by showing browser tracks of intron transcripts). Of note, in Figure 4B, levels of H2B-Ub at the 0 min time point are lower than the WT control. Are the tags affecting the deubiquitination function of SAGA? Details of the clustering analysis are lacking, and a heatmap showing the clusters would be useful. Details on what the authors call an effect on transcription are lacking. Was every gene that passed the filter described in the Materials and methods included in the clustering regardless of the magnitude of the effect on transcription?2) The colors highlighting the two classes of promoters are switched in the middle of the paper. This needs to be corrected.3) Figures 2, 3 and Figure 3—figure supplement 1: The axis label "gene expression rank" is confusing. I assume the #1 gene is that with the highest expression but this is not clearly stated.4) Figure 3A. The authors conclude that the classification of TFIID/SAGA-redundant genes is not based on gene expression levels. This seems to be an overstatement. If the authors included percentages in panel A, it would seem clear that the TFIID/SAGA-redundant genes represent a larger percentage of the top quintile than the lower quintiles.5) Figure 3D. It is unclear how robust the distinction between TATA+ and TATA-less promoters is. Now that the authors have discrete gene sets, why not perform a de novo motif analysis to identify TATA or TATA-like elements as well as other cis-regulatory information that might underlie the coactivator requirements? This type of analysis could provide some mechanistic understanding of the gene classes, which is currently lacking from the field.6) Figure 4. How many repetitions were performed and used to calculate the western signals? In panel B, were the lanes on the left spliced from the same blot, exposed for the same length of time? Ubiquitination is unstable and typically extracts are prepared in denaturing buffers. Information on extract preparation is needed.7) Figure 4. From the western analysis, the authors conclude that loss of SAGA-dependent histone modifications cannot explain the changes in transcription that occur in response to SAGA depletion. However, because only 13% of promoters are dependent on SAGA, it is unclear if changes in SAGA-dependent histone modifications would be visible by western. ChIP experiments are needed to test the histone modifications at promoters in each class upon factor depletion.8) Figure 5. More experimental details are needed.9) Figure 6. Except for changes in color, both of these panels are the same as those included in the Donczew et al. paper on modified ChEC-seq that the authors cite as submitted for publication and was provided to the reviewers of this paper. This is clearly unacceptable. Frankly, I feel that the authors should include the modified ChEC-seq protocol, analysis, and results in this paper and not in a separate paper.10) Figure 1—figure supplement 4C. Correlations between data sets are more standardly shown as xy plots. This graph is confusing. What does "genes left" mean?11) Figure 3—figure supplement 1A. A log scale on the y-axis would be better.Reviewer #3:It's hard to believe we're still arguing about how many and which kinds of genes use TFIID and/or SAGA. This debate goes back to the mid-1990s, when Struhl and Green used TS alleles to question whether TAFs really had a role in activation. In 2004, the Pugh lab classified about 10% of genes as "SAGA-dominant" and the rest as "TFIID-dominant". The arguments continue today, as people use newer methods for factor depletion and for assaying transcription. Most labs have converged on the view that all or nearly all genes use TFIID, and that SAGA probably also localizes to most genes, but that only roughly 10% show a very strong defect in its absence. Combining TFIID and SAGA defects generally leads to much stronger effects.This new paper from the Hahn lab, while extremely rigorous and very well done, generally aligns with the Huisinga and Pugh conclusions. A "TFIID/SAGA redundant" class of genes roughly corresponds with what Pugh called "SAGA dominant". The differences here from Pugh and other earlier papers no doubt reflect the specific systems used, whether the assay is for factor occupancy or function, and how one defines the cutoff for calling a gene affected or unaffected. This paper illustrates the problem by reporting big differences between using SAGA deletion or degron alleles (see my comments below). None of the recent genomics papers explains what makes a small fraction of promoters particularly sensitive to SAGA. This paper does rule out that it's the presence of a consensus TATA box, and finds that TFIID/SAGA redundant genes tend to be more highly expressed, but what that means mechanistically is still unclear. At this point, I suspect only specialists are still following this debate.Major points:1) The effects of spt3 δ are much stronger effect than Spt3 depletion, and the authors use this to propose different functions of SAGA that manifest over different time scales. There are at two far more likely explanations that need to be considered. First, the weaker effects in the degron mutants could simply be due to incomplete degradation, as residual protein is clearly seen in the western blots. Alternatively, the drop in transcription seen in the deletions could be a long term adjustment to slow growth. To rule out indirect effects, have the authors ever assayed a similarly slow growing yeast mutant in a gene that is not directly involved in transcription?2) The authors propose that the long-term gene expression effects of SAGA deletions are due to loss of chromatin modifications: in that case it would make sense to test the gcn5 deletion or, even better, an inactive point mutant that can't acetylate. The Gcn5 point mutant grows pretty well, so this could help rule out the indirect growth effect described above. If H2Bub is involved, testing a ubp8 deletion would also be important.3) Discussion final paragraph. How can SAGA affect "ongoing" transcription but not "activation per se". Does this imply that "ongoing" transcription no longer requires an activator? How would that work? Isn't it simply more likely that the rapid depletion is incomplete?4) What is the difference between a transcription burst and efficient reinitiation? Isn't a burst defined as a series of closely spaced reinitiations? [Editors’ note: The authors successfully appealed and what follows is the authors’ response to the first round of review.]Reviewer #1:This manuscript is the next installment of a series of papers over several years that have attempted to discern which yeast genes are regulated by the two coactivators SAGA and TFIID. As different techniques have been used, the view has changed. In these current experiments, the authors use metabolic labeling with 4-thioU to measure the level of transcription under either mutant conditions or after depletion of SAGA or TFIID components. Interestingly, they obtain very different results for SAGA between the depletion and deletion conditions. From the depletion studies, they conclude that approximately 13% of the yeast genes that they could assay are regulated in a partially redundant fashion by both SAGA and TFIID, while the remainder are primarily regulated by TFIID. The interpretation is reasonable, although their deletion studies show a much greater effect of SAGA mutants and their ChEC studies, in agreement with previous studies, show that SAGA is present at most promoters, as is TFIID. Overall, this is nicely done work that should be of interest to those in the transcription field. Several comments on both the presentation and the experiments are below.1) The first sentence of the Abstract is inaccurate as SAGA does more than chromatin modifications, as the authors reveal in the Introduction. I suggest revising the sentence to the following: "Deletions of genes encoding subunits of the yeast coactivator SAGA reveal a general role in transcription linked to the multiple activities of this complex."2) Please edit throughout the manuscript to correct little writing errors. As one example, in the Abstract, "…87% of expressed genes we term "TFIID-dependent" are highly sensitive to TFIID depletion…" the word "that" should be inserted before "we term." Also, change "contacts" to "contact." Here's another example: "The genome-wide specificity of SAGA and TFIID have been…" – either change specificity to specificities or change have to has. There are more cases like this throughout the manuscript.3) Introduction section – "relatively weak TBP binding" might be biochemically accurate but it implies a weak activity with respect to the degree of gene regulation, which is not the case for spt3 and spt8 mutants.Thank you for your comments. The text and figures of the manuscript have been extensively revised to include our new results and to address reviewer comments. The revisions include the suggested wording and other changes listed above in points 1-3 where still relevant in the revised version.4) Figure 1 and accompanying results – In the comparisons of deletions versus depletions, it would be helpful if the authors clarified their choices for comparison since they are comparing single gene deletions to multiple subunit depletions. Presumably they chose to deplete multiple SAGA subunits at once to increase the chances of efficient SAGA depletion, but please state that explicitly of that is the case. In an ideal world, they would have compared deletion/depletion of the same subunits, such as Spt3 vs Spt3 or Spt3+Spt7 vs Spt3+Spt7. It's formally possible, for example, that depletion of only Spt3 would have a more severe phenotype than the double depletion, although this is unlikely based on previous studies. If they have any unpublished evidence about this, they might mention it.Now in the revised manuscript: “To increase the probability of efficient SAGA inactivation, double degron strains permitted simultaneous depletion of two SAGA subunits, either Spt3/7 or Spt3/20”.FYI, our preliminary experiments using Pol II-ChIP assays (not shown) indicated that single SPT deletions had only modest genome-wide transcription defects. Therefore, in subsequent experiments, we transitioned to using the double degron strains to ensure efficient SAGA depletion.5) Figure 1—figure supplement 4C – What is being correlated in this table? If each experiment was generally done in triplicate, for each genotype, what was used to general the correlation coefficient? Please explain in the legend.2 change in expression values per gene were calculated for each experiment. These log2 change values were used for all relevant plots. The figure legend was changed to provide a more complete description.As described in the Materials and methods, the results of biological replicates (two or three) were averaged and the log6) Since Tafs are essential for viability, what was done to ensure that the cells were still viable during the RNA measurements, for either Taf depletions or Taf + SAGA depletions?New Figure 1—figure supplement 4B shows the viability of strains after 30 min of IAA treatment. Strains are ≥85% viable after incubation with IAA.7) Regarding depletion versus deletion, it seems possible that the difference is caused by the level of depletion rather than the time without the protein. Even though the authors have tried to rule this out, I think the formal possibility should be mentioned.After IAA addition, the levels of most tagged proteins are reduced ~90% but it’s formally possible that the ≥10x depleted levels of intact SAGA are significantly contributing to gene expression. We feel that this is very unlikely for two reasons: (a) The double degron tags have ~10% of each tagged subunit remaining. If each subunit is degraded independently of the other, that means that there is only ~1% of intact SAGA after IAA incubation. (b), The results of the Spt3/Taf13 and Spt7/Taf13 double degron strains show that depletion of only one SAGA subunit is enough to inactivate SAGA . This result shows that the weak effects of the SAGA degrons in otherwise WT cells is due to substantial redundancy with TFIID function at the coactivator-redundant genes rather than incomplete depletion. A short summary of the above points can be found in the final paragraph of subsection “SAGA has two separate roles in transcription”.8) Subsection “SAGA-regulated chromatin modifications change little during rapid SAGA inactivation” – For SAGA's role in activation, the authors should also cite three other references – Larschan et al., 2001, Bhaumik et al., 2001, and Dudley et al., 1999.The references are now included (subsection “SAGA-dependent chromatin modifications change slowly after rapid SAGA depletion”).9) Figure 4 and subsection “SAGA-regulated chromatin modifications change little during rapid SAGA inactivation” – The authors state that it was unexpected to observe lower H2B-Ub levels in some of the SAGA mutants in panel A. Similarly, it was unexpected to observe lower H3K18-Ac levels in the spt3 deletion strain, based on previous genetic, biochemical, and structural studies that strongly suggest that the HAT module is physically and biochemically distinct from Spt3. Therefore, the same reasoning used for the effect on H2B-UB might apply for the reduction in H3K18-Ac levels in the spt3 mutant. If the authors wish to impose histone modifications as a reason for the depletion vs. deletion difference (Discussion paragraph three), this might be mentioned, as an spt3 deletion has a severe effect on transcription yet in theory it should not affect the level of histone modifications.spt3Δ strain still hold – this deletion causes a decrease in H2B-Ub levels. In addition, we show that the spt3Δ has a defect in H3K18-Ac levels.Partly because of another reviewer’s suggestion about using ChIP-seq for H3-Ac mapping, the experiments in this section have been entirely redone to include H3K18-Ac ChIP-seq and a more extensive time course of chromatin modification changes upon activation of the Spt-degrons . The new results are now in line with what is expected for Ubp8 – deletion of this gene has little or no effect on H3K18-Ac. However, the results with the SPT deletions play a large part in the differences between transcription defects in the deletion vs depletion approaches.Combined with our other results, we think that the new experiments make a strong case that changes in chromatin modifications observed in the 10) Figure 5, subsection “Rapid SAGA depletion primarily affects maintenance of ongoing transcription rather than gene activation by Gcn4” paragraph two, This was an interesting experiment. In contrast to what the authors write, the fold induction looks to be greater in the IAA-treated samples in 5B compared to the non-IAA treated cells in 5A, particularly for ARG3 and HIS4.The reviewer is correct, and we have revised the manuscript with numbers indicating the fold induction in all cases (subsection “Rapid SAGA depletion primarily affects maintenance of ongoing transcription rather than gene activation by Gcn4”).11) Figure 1 and 2 legends and probably elsewhere – SC media is stated as the growth medium but it is not described in the Materials and methods. Please clarify.The Materials and methods have been updated to include the definition of YPD and SC media.12) The authors need to provide a list of the yeast strains, including names and genotypes.Yeast strains are listed in Supplementary file 9.13) The authors discuss the SAGA-TFIID redundancy in the Discussion but they don't offer an explanation for two aspects of the redundancy. First, why is it partial? If either complex can work at those genes, why does expression decrease at all? Is it due to insufficient levels of the other complex? Second, what does the reduced expression represent? Is it caused by normal levels from a subpopulation of cells in the culture, or is it due to reduced expression from all of the cells? In future experiments, this could be addressed by single cell analysis.These are all great questions. For some reason, the coactivator-dependent subset of genes can’t efficiently utilize only TFIID or only SAGA to attain normal levels of transcription. At the smaller subset of the exceptionally highly-expressed genes, it might make sense that one or the other complex is limiting, but not at the many other genes in this class that are spread throughout the range of expressed genes. We’ve added a paragraph to the last sentence of the Discussion mentioning this issue. Addressing this question promises to be complex and it will be one of the important issues to investigate going forward with this project.Reviewer #2:In this manuscript, Donczew et al. revisit the highly-debated topic of yeast promoter classes, using acute depletion of TFIID and SAGA subunits and nascent transcript labeling. Older studies by the Pugh lab categorized yeast promoters as SAGA-dominated and TFIID-dominated. In general, the SAGA-dominated genes were described as TATA-enriched and stress responsive, while the TFIID-dominated genes lacked consensus TATA boxes. In a 2017 Molecular Cell paper , Hahn and collaborators showed that deletion of SAGA subunits significantly reduced nascent transcript levels at both TFIID- and SAGA-dominated gene classes and also showed that SAGA occupies the promoters of both gene classes by ChEC-seq. This paper threw into question the classification of promoters by coactivator dependencies and suggested that the functions of SAGA are more widespread than previously thought. However, recently, the ChEC-seq profiles reported in the Baptista paper have been disputed by the Pugh lab in a paper in press at Molecular Cell.After acutely depleting SAGA and TFIID subunits and measuring nascent transcription, the authors in this paper conclude that there are two major classes of Pol II promoters with respect to TFIID and SAGA dependencies: a large TFIID-dependent class and a smaller TFIID/SAGA-redundant class, which accounts for 13% of promoters tested. The authors define these classes through measurements of nascent RNA changes in response to factor depletion using the auxin-inducible degron system, followed by k-means clustering. By co-depleting Taf13 and either Spt3 or Spt7, the authors provide strong support for the redundancy model. They also report results from a new ChEC-seq protocol which show widespread TFIID and SAGA occupancy at all genes in line with the Baptista paper. Regarding the mechanistic distinctions between the TFIID-dependent and TFIID/SAGA-redundant promoters, the authors conclude that gene expression levels do not dictate whether a promoter is redundantly regulated by the coactivators. By reanalyzing the TATA sequences in the promoter classes, the authors report that TFIID/SAGA-redundant promoters are not biased toward TATA-containing versus TATA-less. Western data show that two histone modifications regulated by SAGA do not greatly change during the AID depletion time course. From this the authors conclude that some other function of SAGA, such as a protein-protein interaction, is being disrupted by the depletion and leading changes in transcription. Using RT-PCR, the authors test the effect of SAGA depletion on activation of three Gcn4-regulated genes and find that activation is unaffected, whereas ongoing transcription is reduced.The main conclusion from this paper is the redefining of yeast promoters using acute depletion and nascent transcript labeling. The results support a large TFIID-dependent class and smaller class that can utilize either TFIID or SAGA for their regulation. The molecular basis of the two classes defined here remains uncertain.Thank you for your comments. One misunderstanding above is that we find the coactivator-redundant gene class is enriched for TATA compared with the TFIID-dependent class. Based on your comments below, we have expanded the original analysis on the difference between the two gene classes in Figure 3 and Supplementary file 5 of the revised manuscript. This additional analysis adds to our prior conclusions as to the mechanistic basis for the difference between the gene classes.Specific comments:1) The identification of two promoter classes is the main conclusion of the paper and is based on acute protein depletion using the AID system, 4-thioU RNA-seq, and k-means clustering. The paper requires additional controls to show that the AID tags are not disrupting protein function, that auxin or DMSO isn't affecting expression of an untagged WT strain, and that nascent transcripts are enriched (such as by showing browser tracks of intron transcripts). Of note, in Figure 4B, levels of H2B-Ub at the 0 min time point are lower than the WT control. Are the tags affecting the deubiquitination function of SAGA? Details of the clustering analysis are lacking, and a heatmap showing the clusters would be useful. Details on what the authors call an effect on transcription are lacking. Was every gene that passed the filter described in the Materials and methods included in the clustering regardless of the magnitude of the effect on transcription?Thank you for these comments. Additional controls and experiments are included in the revised manuscript to address these points:a) Figure 1—figure supplement 3A plots levels of gene expression in WT and all degron strains when DMSO is added. We find no major changes in gene expression due to the degron fusions.b) Figure 2A, #1, shows that IAA addition causes no transcription changes in WT cells.c) After purification of labeled mRNA, we routinely test one sample per prep for enrichment of labeled RNA by RT qPCR, probing both total and labeled RNA at 3 transcribed genes. The purified 4TU RNA typically contains 2-10% contamination with unlabeled RNA . We also analyzed the data for enrichment of intron-containing RNA in the purified 4TU-labeled samples as requested . Intron-containing RNA is clearly enriched in the purified labeled RNA as expected.d) The experiment examining degron-mediated chromatin modifications was completely redone to include a longer time course after IAA addition and to examine modifications by ChIP-seq . Before IAA addition, the levels of H2B-Ub in WT and degron strains are nearly identical .e) The revised methods section has additional details on clustering and a heatmap is now included as requested . As stated in Materials and methods and text, every gene that passed our filters  was used in clustering. We did not use the magnitude of the effect on transcription as a criteria for clustering as this would invalidate the analysis by significantly limiting the available gene set.2) The colors highlighting the two classes of promoters are switched in the middle of the paper. This needs to be corrected.Sorry about that – this has now been corrected.3) Figures 2, 3 and S3: The axis label "gene expression rank" is confusing. I assume the #1 gene is that with the highest expression but this is not clearly stated.The figures and legends have been modified to clarify.4) Figure 3A. The authors conclude that the classification of TFIID/SAGA-redundant genes is not based on gene expression levels. This seems to be an overstatement. If the authors included percentages in panel A, it would seem clear that the TFIID/SAGA-redundant genes represent a larger percentage of the top quintile than the lower quintiles.There was a misunderstanding on this point. In the original manuscript we explained that, while the coactivator-redundant genes are found in genes at all levels of expression, this gene class is clearly enriched in the highest expressed genes . This finding is in agreement with the reviewer’s conclusion.5) Figure 3D. It is unclear how robust the distinction between TATA+ and TATA-less promoters is. Now that the authors have discrete gene sets, why not perform a de novo motif analysis to identify TATA or TATA-like elements as well as other cis-regulatory information that might underlie the coactivator requirements? This type of analysis could provide some mechanistic understanding of the gene classes, which is currently lacking from the field.Thank you for the suggestion! This analysis is shown in Figure 3D, E and in Supplementary file 5 and this analysis added important new information. While we find that the motifs for 14 transcription factors are enriched in one vs the other gene class, it’s clear that there is no motif that is exclusive for the TFIID-dependent or coactivator-redundant genes. From this, we conclude that the distinction between gene classes is due to multiple components that could include regulatory transcription factors, promoter sequence, and possibly chromatin architecture.6) Figure 4. How many repetitions were performed and used to calculate the western signals? In panel B, were the lanes on the left spliced from the same blot, exposed for the same length of time? Ubiquitination is unstable and typically extracts are prepared in denaturing buffers. Information on extract preparation is needed.As explained above, this experiment was completely redone to incorporate additional experimental conditions. All experiments were conducted in biological duplicate. Images in Figure 5—figure supplement 1A are from the same un spliced gel. Images in Figure 5—figure supplement 1B are from two gels (too many samples for one gel), with the normalization controls on each gel shown. Extracts were made in Urea-containing buffers and extract preparation details are now in Materials and methods.7) Figure 4. From the western analysis, the authors conclude that loss of SAGA-dependent histone modifications cannot explain the changes in transcription that occur in response to SAGA depletion. However, because only 13% of promoters are dependent on SAGA, it is unclear if changes in SAGA-dependent histone modifications would be visible by western. ChIP experiments are needed to test the histone modifications at promoters in each class upon factor depletion.gcn5Δ and ubp8Δ strains , strengthens our conclusions about the role of H3 acetylation causing the differences between SAGA deletions and depletions.Thank you for the suggestion. ChIP-seq results for Gcn4-dependent H3K18-Ac are now shown in Figure 5C and Figure 5—figure supplement 2. This, along with the more extensive Western analysis and the results of transcription defects in the We also attempted ChIP-seq (and cut-and-run) for genome-wide H2B-Ub mapping, but we found that, with our experimental conditions, the available commercial antibodies are not suitable for these methods due to very low numbers of sequencing reads recovered.8) Figure 5. More experimental details are needed.We have carefully gone over the text, figure legends and methods and feel that the experiment is clearly described. If something specific is not clear, please let us know.9) Figure 6. Except for changes in color, both of these panels are the same as those included in the Donczew et al. paper on modified ChEC-seq that the authors cite as submitted for publication and was provided to the reviewers of this paper. This is clearly unacceptable. Frankly, I feel that the authors should include the modified ChEC-seq protocol, analysis, and results in this paper and not in a separate paper.eLife submission showed how SAGA and TFIID map to the newly defined gene categories “TFIID-dependent” and “coactivator-redundant”. In any case, since Mol Cell has not yet made a decision on our letter, we included the binding data in this revised manuscript as requested and removed it from a revised Mol Cell communication. This binding data is now shown in Figure 6 and Figure 6—figure supplement 1 of the revised eLife manuscript.There was a misunderstanding about what Figure 6 represented. Our letter to Mol Cell in response to Pugh et al. showed ChEC-seq mapping of TFIID and SAGA to genes in relation to the Pugh et al. “TFIID-dominated” and “SAGA-dominated” gene categories. Figure 6 in the original 10) Figure 1—figure supplement 4C. Correlations between data sets are more standardly shown as xy plots. This graph is confusing. What does "genes left" mean?This is now explained in the Figure legend .11) Figure 3—figure supplement 1A. A log scale on the y-axis would be better.We replotted this graph, illustrating the difference between Pugh’s original gene categories and our new ones, on a log scale. However, my coauthors and I felt that this gave a misleading first impression that the differences between the new and old gene categories are huge. Given this, we prefer to leave this plot as a linear scale unless you feel strongly otherwise.Reviewer #3:It's hard to believe we're still arguing about how many and which kinds of genes use TFIID and/or SAGA. This debate goes back to the mid-1990s, when Struhl and Green used TS alleles to question whether TAFs really had a role in activation. In 2004, the Pugh lab classified about 10% of genes as "SAGA-dominant" and the rest as "TFIID-dominant". The arguments continue today, as people use newer methods for factor depletion and for assaying transcription. Most labs have converged on the view that all or nearly all genes use TFIID, and that SAGA probably also localizes to most genes, but that only roughly 10% show a very strong defect in its absence. Combining TFIID and SAGA defects generally leads to much stronger effects.This new paper from the Hahn lab, while extremely rigorous and very well done, generally aligns with the Huisinga and Pugh conclusions. A "TFIID/SAGA redundant" class of genes roughly corresponds with what Pugh called "SAGA dominant". The differences here from Pugh and other earlier papers no doubt reflect the specific systems used, whether the assay is for factor occupancy or function, and how one defines the cutoff for calling a gene affected or unaffected. This paper illustrates the problem by reporting big differences between using SAGA deletion or degron alleles (see my comments below). None of the recent genomics papers explains what makes a small fraction of promoters particularly sensitive to SAGA. This paper does rule out that it's the presence of a consensus TATA box, and finds that TFIID/SAGA redundant genes tend to be more highly expressed, but what that means mechanistically is still unclear. At this point, I suspect only specialists are still following this debate.Thank you for your comments, however, we respectfully disagree with many of the above statements. I think that some of the above comments give an inaccurate picture on the current state of the field and how our work is significantly different from the prior consensus. I believe that the new findings reported in our manuscript will have a significant and lasting impact on understanding the function and gene-specificity of TFIID and SAGA.eLife paper by Struhl and colleagues (PMID: 30681409), which is clearly different from the reviewer’s characterization that “all or nearly all genes use TFIID”. One of the Petrenko et al. conclusions was that many of the most highly expressed genes show minimal use of TFIID. Another recent example is in Randy Morse’s eLife paper  where the results of gene-specific Mediator mutations are interpreted in terms of “SAGA-dominated” and “TFIID-dominated genes”.1) We strongly disagree with the statement that “Most labs have converged on the view that all or nearly all genes use TFIID…” In my experience (based on recently published papers and meeting presentations), most labs are using the terminology “TFIID-dominated” (or Taf-dependent) and SAGA-dominated (or Taf-independent). A recent example of this thinking is a 2019 2) The reviewer goes on to state: “Most labs have converged on the view that ……. SAGA probably also localizes to most genes, but that only roughly 10% show a very strong defect in its absence.” Based on the literature and recent meeting presentations, a widely held view is that SAGA localizes most strongly to the “SAGA” genes and TFIID localizes most strongly to the “TFIID” genes. I believe that our genome-wide results on finding that SAGA and TFIID bind to many genes of both gene classes is not yet (but will soon be) a generally accepted view.In contrast to the above view “that only roughly 10% [of genes] show a very strong defect in [SAGAs] absence”, our new work shows that transcription from nearly all genes is decreased ≥2-fold in the spt3Δ and spt7Δ strains. It’s only when SAGA is rapidly depleted that we observe modest gene-specific transcription defects at ~13% of genes.3) The reviewer states that: “Combining TFIID and SAGA defects generally leads to much stronger effects.” This view, quoted in many papers, is primarily based on Figure 1 in Huisinga and Pugh, 2004; PMID: 14992726. In brief, the basis for this widely quoted statement is not consistent with our recent findings and was likely based in part on measuring steady state RNA instead of newly-synthesized RNA and the technical limitations of RNA microarray assays.(4) We strongly disagree with this general characterization about alignment of our results with the Huisinga et al. conclusions: “This new paper from the Hahn lab, while extremely rigorous and very well done, generally aligns with the Huisinga and Pugh conclusions”. Our manuscript shows that, (a) rather than expression being “dominated” by reliance on one or the other coactivator, there is an important class of genes that can rely on either TFIID or SAGA for their regulation, (b) that the overlapping acute function of the two coactivators is confined to a specific gene set, not all genes, and (c) that the set of the coactivator-redundant genes is >50% different from the Huisinga et al. gene set . This isn’t a trivial difference. As explained in the manuscript, some prior findings in the literature were incorrectly interpreted based on the now outdated gene categories.Major points:1) The effects of spt3 δ are much stronger effect than Spt3 depletion, and the authors use this to propose different functions of SAGA that manifest over different time scales. There are at two far more likely explanations that need to be considered. First, the weaker effects in the degron mutants could simply be due to incomplete degradation, as residual protein is clearly seen in the western blots. Alternatively, the drop in transcription seen in the deletions could be a long term adjustment to slow growth. To rule out indirect effects, have the authors ever assayed a similarly slow growing yeast mutant in a gene that is not directly involved in transcription?A) To rule out indirect effects of the degron tag on SAGA and TFIID subunits and SAGA deletion experiments, we compared our transcription results with the stress-response transcription signature derived from transcriptome profiles of many mutant yeast strains . For all strains assayed in our work, the changes in gene expression showed poor correlation with this stress-response signature, indicating that the changes we observed are not due to a general stress response. Please see paragraph three subsection “SAGA has two separate roles in transcription” and Figure 1—figure supplement 4A.B) After IAA addition, the levels of most tagged proteins are reduced ~90% but it’s formally possible that the 10x lower levels of intact SAGA are significantly contributing to gene expression. We feel that this is very unlikely for two reasons: (a) The double degron tags have ~10% of each tagged subunit remaining. If each subunit is degraded independently of the other, that means that there is only ~ 1% of intact SAGA after IAA incubation. (b), The results of the Spt3/Taf13 and Spt7/Taf13 double degron strains shows that depletion of only one SAGA subunit is enough to inactivate SAGA . This figure shows that the weak effects of the SAGA degrons is due to substantial redundancy with TFIID function at the coactivator-redundant genes rather than from incomplete depletion. A short summary of these points can be found in paragraph six of subsection “SAGA has two separate roles in transcription”.2) The authors propose that the long-term gene expression effects of SAGA deletions are due to loss of chromatin modifications: in that case it would make sense to test the gcn5 deletion or, even better, an inactive point mutant that can't acetylate. The Gcn5 point mutant grows pretty well, so this could help rule out the indirect growth effect described above. If H2Bub is involved, testing a ubp8 deletion would also be important.gcn5Δ and ubp8Δ strains in combination with a longer time course of degron-depletion and H3K18-Ac ChIP-seq . These additional experiments make a strong case for the role of SAGA-dependent chromatin modifications playing a large part in the different transcription phenotypes of SPT deletions and SPT depletion by degron.Thank you for the suggestion! We did 4thioU RNA-seq with 3) Discussion final paragraph. How can SAGA affect "ongoing" transcription but not "activation per se". Does this imply that "ongoing" transcription no longer requires an activator? How would that work? Isn't it simply more likely that the rapid depletion is incomplete?4) What is the difference between a transcription burst and efficient reinitiation? Isn't a burst defined as a series of closely spaced reinitiations?Thank you for pointing out these confusing points. The last section in the Discussion has been rewritten to correct and clarify.


TMPRSS2:ERG (T:E) fusion gene is generally believed to be mainly regulated by the activated androgen receptor (AR) signaling in androgen-dependent prostate cancer. However, its persistent expression in castration-resistant and neuroendocrine prostate cancers implies that other transcription factors might also regulate its expression. Here, we showed that up-regulation of nuclear receptor estrogen-related receptor alpha (ERRα) was closely associated with the oncogenic transcription factor ERG expression in prostate cancer, and their increased coexpression patterns were closely associated with high Gleason scores and metastasis in patients. Both ERRα and ERG exhibited a positive expression correlation in a castration-resistant prostate cancer (CRPC) xenograft model VCaP-CRPC. We showed that ERRα could directly transactivate T:E fusion gene in both AR-positive and -negative prostate cancer cells via both ERR-binding element- and AR-binding element-dependent manners. Ectopic T:E expression under ERRα regulation could promote both in vitro invasion and in vivo metastasis capacities of AR-negative prostatic cells. Intriguingly, ERG expressed by the T:E fusion could also transactivate the ERRα (ESRRA) gene. Hereby, ERRα and ERG can synergistically regulate each other and form a reciprocal regulatory loop to promote the advanced growth of prostate cancer. Inhibition of ERRα activity by ERRα inverse agonist could suppress T:E expression in prostate cancer cells, implicating that targeting ERRα could be a potential therapeutic strategy for treating the aggressive T:E-positive prostate cancer.The  The breakthrough discovery of fusion genes formed by recurrent chromosomal rearrangements in prostate cancer in past decade contributes significantly to our current conceptual understanding on the significance of androgen signaling in prostate carcinogenesis , and alsTMPRSS2) with the translated regions of members of erythroblastosis virus E26 transforming sequence (ETS) family of transcription factors, ERG and ETV1 [TMPRSS2:ERG (T:E) fusion is the most dominant rearrangement and prevalent in majority of prostate cancer patients (40–70%) [ERG is regarded as a key oncogene in prostate cancer [These recurrent fusion genes prevalent in prostate cancer are formed by fusion of the promoter and 5′-untranslated region  of the androgen-regulated prostate-specific transmembrane protease serine 2 gene (and ETV1 . Among t(40–70%) . Associa(40–70%) –13. Clin(40–70%)  and targ(40–70%) , suggeste cancer .TMPRSS2 and SLC45A3), it is thus generally regarded that these fusion genes are mainly regulated by androgen receptor (AR) [Since most prostate cancer-prevalent fusion genes are formed by the fusion with the androgen-responsive promoter region of the androgen-activated genes   and theitor (AR)  and celltor (AR) , metastator (AR) , castrattor (AR) , neuroentor (AR) , 20, andtor (AR) . It stilPreviously, a genome-wide gene expression study on T:E fusion-positive prostate cancer samples performed in two prostate cancer cohorts identifies a specific T:E fusion gene signature that is associated with estrogen receptor (ER) signaling and also the T:E fusion expression could be modulated by ER ligands in AR-negative NCI-H660 cells, implying that the T:E expression can be regulated by an ER-dependent mechanism . In addiEstrogen-related receptor alpha  is a ligand-independent orphan member of the nuclear receptor superfamily. Studies in past decades indicate that together with its coactivator PGC-1α, ERRα functions as a key transcriptional regulator of energy homeostasis and mitochondrial functions . ERRα diIn this study, we uncovered a reciprocal regulatory loop between ERRα and ERG that can promote synergistically the advanced progression of prostate cancer in an AR-independent manner, and suppression of T:E/ETS fusion genes via ERRα targeting could be a potential therapeutic strategy for prostate cancer.low and ARhigh subsets of prostate cancer patients in the same datasets revealed that both ERRα and ERG exhibited a positive correlation in both subsets of patients , we established a CRPC xenograft tumor model VCaP-CRPC based on the castration-relapse growth of AR- and T:E-positive VCaP cells Fig. . AnalysiCRISP3, MMP1, PLA1A, and PLAT  and two androgen-responsive element/ARE-containing sites (P1 and P6 sites located at −600 bp and −13 kb upstream of TMPRSS2 transcription start site). ChIP assays performed in AR-positive VCaP cells validated that among these potential binding sites, ERRα could directly bind to three sites, namely the ERRE-containing P3 site and two ARE-containing P1 and P6 sites  using a T:E fusion expression plasmid pLenti-P-T:E, in which the ERG expression was driven specifically by a fragment of ERRα-regulating T:E promoter containing the P1 ERRα-binding site Fig. . ImmunobESRRA) promoter or regulatory region. To verify this, we performed the ChIP assays in the T:E fusion-positive VCaP cells. Results showed that among the four potential ERG-binding sites, the E1 site located at −635 bp upstream of the ERRα transcription start site was enriched of ERG  . The mutFour human prostate cancer cell lines  and one immortalized prostatic epithelial cell line (BPH-1) were used in this study. 293FT and HEK293, and viral packaging cells PA317 were used in transfection and retroviral packaging, respectively. The culture media and conditions were followed as described previously , 51.TMPRSS2 gene promoter containing an ERRα-binding element  was fused with the full-length coding-domain sequence of ERG by PCR-fusion method as the T:E fusion and cloned into a CMV promoter-deleted pLentiV6 vector as pLenti-P-T:E for lentiviral transduction. pLenti-T:E without TMPRSS2 gene promoter was also generated by insertion of ERG cDNA only into CMV-minus pLentiV6 vector. Expression plasmids of ERRα (pcDNA3-ERRα/ERRα-ΔZF2/ERRα-AF2) and PGC-1α (pcDNA3-PGC-1α/PGC-1α2 × 9) were constructed as described previously [ESRRA) gene promoter (−987 to −1 bp) were PCR-amplified from genomic DNA of VCaP cells and cloned into pGL3 basic vector as pGL3-T:E-Luc and pGL3-ERRα-Luc for reporter gene assay.(a) Expression plasmids: an approximate 2000-bp fragment of eviously . (b) shReviously . (c) RepProcedures on generation of stable T:E fusion- and empty vector-transduced cells, and shT:E- and shERRα-transduced cells were followed as described previously , 52.Assay was performed on T:E- and shERRα-transduced prostatic cells following procedures as described previously , 54.Procedures on PCR and immunoblotting are followed as described previously , 55, andEcoRI was selected for enzyme digestion of chromatin after analysis of digestion map of the −14 kb enhancer/promoter region of T:E fusion gene. ChIP-3C assay of T:E fusion gene was performed in VCaP cells following a procedure as described previously [EcoRI (TaKaRa) overnight and immunoprecipitated with protein A–agarose beads coupled with ERRα-antibody. The beads were precipitated, resuspended in ligation buffer and ligated with T4 DNA ligase (TaKaRa) overnight. The religated protein–DNA complexes bounded to beads were eluted, purified for DNA and analysis by PCR. (b) Luciferase reporter assay. Dual-luciferase assay was performed in HEK293 cells. After 48-h posttransfection, cells were further treated with XCT790 or without for 24 h, followed by measurement of luciferase activity as described previously [(a) Chromatin immunoprecipitation assay (ChIP). ChIP assays of endogenous T:E fusion gene and ERRα gene promoter were performed in VCaP cells following procedures as described previously . Specifieviously . Brieflyeviously .CRPC xenograft model VCaP-CRPC was established based on the castration-refractory growth of tumors formed by subcutaneous implantation of VCaP cells in castrated SCID mice as described previously .+ cells following procedures as described previously [In vivo tumorigenicity and metastasis assays were performed on empty vector-, T:E- or T:E-shERRα-transduced and firefly luciferase-labeled PC-3M-Luceviously .t test was used to compare the means between two groups. One-way ANOVA test was used to compare multiple groups. P < 0.05 was considered statistically significant.Student’s Supplementary Figures. S1–S12Supplementary Figure Legends S1–S12Supplementary Table S1