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    Note: we included 6 Figures in our response, yet the ReviewCommons system does not appear to support including images as part of the response. These Figures are in the original "Initial Response" file available to ReviewCommons. We requested that Review Commons post our "Initial Response" file that contains these figures so that this information is available.

    Reviewer #1

    *In the paper by Gowthaman et al., the authors aim at better understanding the molecular mechanisms controlling divergent non-coding transcription (DNC). They describe a high-throughput yeast genetic screen using two strains in which two loci consisting of a coding and a divergent non-coding transcription unit (CGC1-SUT098 or ORC2-SUT014) were replaced by a bidirectional fluorescent reporter construct encoding mCherry in the coding direction and YFP in the non-coding direction. The two reporter strains were crossed with the yeast deletion library and mutants leading to increased or decreased YFP signal were selected as potential DNC repressors or activators. The two screens identified a number of common potential repressors and activators. Components of the Hda1C histone deacetylase complex were identified as DNC repressors in both screens. This phenomenon was confirmed genome-wide by performing NET-Seq in WT as well as hda1D and hda3D strains. This experiment allowed to identify 1517 DNC transcripts repressed by Hda1. Further analyses indicate that Hda1C represses DNC genome-wide independently of expression levels and that loss of Hda1 does not substantially affect coding transcription.

    Live-cell imaging of transcription was then used to show that loss of Hda1 increases DNC transcription frequency rather than duration providing novel information on the link between DNC transcription initiation kinetics and chromatin regulation. Finally, using Chip-seq, the authors show that the level of acetylation over the divergent non-coding units is increased in the absence of Hda1 and some experiments suggest that H3K56 acetylation also contributes to DNC regulation, further strengthening the importance of elevated histone acetylation in efficient DNC.

    Importantly, several components of the SWI/SNF chromatin remodeling complex were identified as activators confirming earlier observations (Marquardt et al., 2014). SAGA subunits were also among potential DNC activators, however these effects could not be confirmed through validation experiments. The authors conclude that DNC may be independent of specific activators and mainly due to transcriptional noise resulting from the adjacent NDR.

    Overall this paper is very well structured, clearly written and the experiments are well controlled. The genetic screen identifies novel factors involved in the regulation of DNC. The study clearly demonstrates that the level of acetylation is a key regulator of divergent non-coding transcription and that histone deacetylation by Hda1 reduces the frequency of DNC initiation events. While this conclusion is strongly supported by the Net-Seq and Chip-seq metagene analyses, the fluorescence mCherry and YFP values or qRP-PCR analyses of specific genes do not always behave as expected when looking at absolute values rather than mCherry/YFP or GCG1/SUT098 ratios, which is sometimes disturbing when reading the paper. Therefore, the following points should be clarified.*

    __We are grateful for the kind appreciation of our manuscript and clarify the remaining questions in the revised manuscript. __

    **Major points**

    #1.1:* Figures 2 and S2A: Figures 2C and D show the mCherry/YFP fluorescence and GCG1/SUT098 RT-qPCR gene expression ratios respectively, which are consistent with a repressive effect of Hda1C on DNC transcription and a potential DNC activating effect of SAGA components. However, the absolute mCherry and YFP or GCG1 and SUT098 expression values presented in Figures S2A and S2B show the opposite: loss of Hda1C subunits rather leads to a decrease in mCherry with not much effect on YFP; moreover loss of Hda3 results in decreased SUT098, which is inconsistent with the whole model. The same comment is valid for the SAGA mutants. It would be good to provide some explanation for these a priori contradictory observations, especially for the Hda1c mutants, which are the major focus of the study. The Net-Seq analyses are certainly more reliable since less subject to protein or RNA stability effects, which may underlie some of the inconsistencies between protein and RNA absolute levels. *

    __Thank you for this comment. We offer enhanced clarity in the revised manuscript. __

    __In general, transcription in each direction shows a weak yet highly statistically relevant positive correlation (Spearman rho = 0.26, p-value = 4.94e-24). We are enclosing a plot based on NET-seq data that supports the correlation in each direction of a NDR as part of our response below (RFig.1). To unpick relative effects the ratio captures these effects well, in our experience better than the individual fluorescence measurements or RT-qPCR. Of course, we are ultimately interested in transcription and fluorescence measurements or RT-qPCR of steady-state RNA are only an approximation. Resources and time constraints limit how many mutations we can examine by techniques such as NET-seq, which are arguably most informative. The positive correlation between transcription in each direction has the effect that relative differences can manifest themselves through detectable effects of the other fluorophore. As this reviewer mentions, we can be most confident of results that we could further validate by NET-seq or live-cell imaging. __

    (INSERT Rfig1)

    *RFig1: Scatterplot of NET-seq data for DNC/host gene pairs. Each point corresponds to a bidirectional gene promoter overlapping with a nucleosome-depleted region (NDR). The values represent NET-seq FPKM values in protein-coding (x-axis) vs non-coding (y-axis) directions. These data support a statistically significant correlation (Spearman test: rho = 0.2554876, p-value = 4.939658e-24). *

    #1.2:* Figure 3: this figure examines the effect of Hda1 and Hda3 on the 1517 DNC transcripts. Does loss of this HDAC also increase the expression of all the other 2219 non-coding transcripts identified by Net-Seq, which would make Hda1C a more general repressor of non-coding transcription?*

    __We have performed the analysis for all other non-coding transcripts in Hda1C mutant NET-seq data and added it as part of this response RFig2. Quantification of CUTs, SUTs and other lncRNAs that are not resulting from DNC in Hda1C mutants results in a slight increase in the nascent transcription that is not statistically significant. These data do not offer strong support for the idea that Hda1C represents a more general repressor. We added this plot as novel supplementary figure S3D and adjusted the text of the revised manuscript (line 214). __

    (INSERT Rfig2)

    *RFig2: Metagene plot of NET-seq data for non-coding RNA that are not classified as DNC. Metagene plot shows genomic windows [TSS - 100 bp, TSS + 500 bp] relative to the annotated starts of ncRNA transcripts. *

    #1.3:* Moreover, does loss of Hda1 or Hda3 reveal DNC transcripts that were not detected in wild-type? This may increase even more the number of genes with divergent transcription. *

    __We are grateful for the opportunity to clarify this point. We noticed that the yeast genome shows evidence for much more non-coding transcription than annotated. In this paper, we used TranscriptomeReconstructoR for a data-driven annotation of yeast non-coding transcripts, with an emphasis on the boundaries. See also:____ ( DOI: ____10.1186/s12859-021-04208-2 ____). The set of non-coding transcripts was for example informed by the previously published NET-seq data on wild-type samples (Churchman et al., 2009; Marquardt et al., 2014; Harlen et al., 2016; Fischl et al., 2017). We have clarified relevant Methods sections to make this point more accessible (line 733). The combination of these NET-seq datasets gives a very good sequencing coverage. The Hda1C mutant NET-seq data does not have a better coverage than this combined reference set, so it would be very hard to find new transcripts without prior evidence in our exhaustive set of combined NET-seq data. However, our Supplementary table S3 contains the fold-change values for all DNC transcripts in mutant compared to wild type. Loci with a high fold-change could arguably be regarded as hda-specific. __

    #1.4:* Figures S3A, B, C: are the 3 groups of DNCs derepressed to the same extent by loss of Hda1 or Hda3? This is difficult to judge given the differences in y-axis scales. Figures S3D, E: the authors show the Net-Seq snapshots for the GCG1 and ORC2 loci. It would be good to add the quantifications as presented in Figure 3 for YPL172C and YDRr216C.*

    Thank you for the suggestion. We replaced S3A-C with plots that show the same range of the y-axis in the supplementary figure. Hda1C represses DNC in all three cohorts stratified by DNC expression strength. We also added a quantification boxplot for NET-seq signal in the GCG1 and ORC2 loci in revised S3F-I.

    #1.5:* Figures S4A, B, C and D are not well explained. What does the y axis frequency correspond to? Is it the % of cells showing a signal? Is the intensity of SUT098 higher because the transcription initiation frequency is higher and therefore the transcription site signal is more intense? *

    We improved the annotation for the supplementary figure S4. We clarified in the legend that the y-axis frequency represents the percentage of frames recorded for transcription initiation spots (TS). The bars represent transcription intensity in all the frames recorded, with active transcription ‘ON’ and without TS ‘OFF’. The intensity increases with higher initiation rates and thus the intensity of SUT098 transcription initiation is high.

    #1.6:* Figures S4 A-I should be more specifically cited in the text.*

    We have cited the figures in the text in the revised version.

    #1.7:* Figure 5A: it is really unexpected and unclear why the mCherry/YFP in the WTH3/hda1D and WTH3/hda1D/H3K56mut is increasing compared to WTH3, since DNC is supposed to increase. Similar comment for Figure S5C. This should be clarified in the text.*

    __Thank you for pointing this out. We missed to address this in the text. The isogenic control “H3 wild type” carries only one copy of the two genes coding for H3, which has a general effect on transcription. We added data showing this as part of our response (RFig3.), and explained this part more clearly in the revised text (line 263). Essentially, the genetic background of the yeast synthetic histone mutant collection sensitizes for a decreased ratio of mCherry/YFP (RFig3.). This result is also included in table S2, where deletions of the histone genes HHT2 (H3) and HHF2 (H4) are listed as shared repressors in both screens. Hda1C mutations show the increased ratio in the sensitized “H3 wild type” background, but not in backgrounds we tested that contain a wild-type dosage of histone genes. __

    These data remain valid to support the genetic interaction of hda1D along with the substitution mutants of H3K56.

    (INSERT Rfig3)

    RFig3. Fluorescence signal values of H3WT and BY4741 strains with GCG1pr FPR. The H3WT affects general transcription of coding transcript and decreases the ratio of mCherry/YFP fluorescence.

    #1.8:* More generally, as already mentioned above, the fluorescence data are expressed either as mCherry/YFP ratio or as absolute values. It would be good to systematically show the ratios and the absolute values of mCherry and YFP signal; the same for coding and DNC RT-qPCR as well as Net-Seq values when available. *

    We ensured that the absolute data values for flow cytometry and qPCR have been represented in the supplementary figures S2 and S5. The FPKM values for NET-seq data for individual transcript units are provided in the supplementary table S3.

    #1.9:* Figures S5A and B are not referred to in the text. It should be mentioned and explained how normalization to H3 affects the levels of acetylated H3 over the NDR. *

    We now refer to the figures in the main text and explained the rationale for normalization.

    #1.10:* p. 12 "Our data thus suggest to extend the transcriptional noise hypothesis with activities limiting DNC transcription to account for genome-wide variation in non-coding transcription".

    If DNC is the result of "transcriptional noise", it is surprising that in the case of CGC1-SUT098, the transcription frequency is higher in the non-coding versus the coding direction. Is the SUT098 behaving like the coding unit in this case? The authors should comment on that. *

    This is very interesting point. One interpretation of the “transcriptional noise” hypothesis is indeed that non-coding transcription is at low level. We selected loci with high DNC expression, so these loci are somewhat contradictory to this idea a priori. Nevertheless, identifying a biological function of non-coding RNAs is challenging, and it remains to be tested if SUT098 represents particularly “loud noise” or if the high transcription indicates that it carries a yet unknown cellular function. In theory, this screen is suitable to identify factors that may be required to induce DNC, perhaps even specifically. To identify such factors a locus with high DNC is needed to facilitate detection, since our previous screen using the PPT1/SUT129 system had lower SUT expression and failed to identify such mutants systematically. This is important, since a mutation lowering DNC needs to start from a sufficiently high fluorescence signal to distinguish it from background fluorescence. Since the results presented did not clearly uncover such factors, we favor the hypothesis of DNC arising due to the promoter architecture at NDRs, see also positive correlation plot in RFig1. The many repressive pathways are also acting on highly expressed DNCs, which is certainly an interesting information provided by this manuscript.

    **Minor comments**

    #1.11:* p. 4 should one talk about Hda1C-linked histone acetylation facilitates... (should be deacetylation...??) *

    __Done. __

    #1.12:* The authors should explain why they chose two coding/non-coding pairs that are cac2D insensitive and whether other criteria, such as level of DNC transcription, were also considered, since GCG1-SUT098 represents one of the most highly expressed divergent non-coding transcripts.*

    The GCG1 and ORC2 loci were chosen based on i) high DNC levels, ii) a low fold-change of NET-seq data in the cac2∆__ and iii) a DNC region free from other transcriptional units. However, this was based on the state-of-the-art annotation in 2015 when we started this project. Also, when we categorized genes as affected by cac2, we used a fold-change expression cut-off that suggested that about a third of DNCs are repressed by CAF-I. It appears that we still underestimated the effect of CAF-I, since our data show that the target regions of our new screens are also affected by CAF-I. DNC expression at these loci is high, which would result in a low fold-change in mutants that further increased DNC here. __

    #1.13:* It is hard to understand why both the H3K56A and H3K56Q mutations lead to increased DNC, a result already presented in the Marquardt et al. 2014 paper. It would be helpful to provide a more extensive explanation or hypothesis.*

    __The H3K56 substitution mutant Q is expected to mimic the acetylation state and A is devoid of post-translational modifications. We observe an increase of signal ratio in the mutants because the H3K56ac is both responsible for incorporation and eviction of -1 nucleosomes (Marquardt et al., 2014). Mutations affecting H3K56 can thus result in less -1 nucleosome density and more DNC through reducing incorporation or enhancing eviction. We have improved the revised text to highlight this. We have clarified this in the text (line 271). __

    #1.14:* What defines the level of DNC repression? How does the level of repression correlate with the level of coding transcription?*

    __We have added RFig.1 to address the question about correlation. There is a statistically significant positive correlation between transcription in each direction by NET-seq data in wild type samples genome-wide. However, the correlation is weak (rho = 0.26), which is consistent with locus-specific adjustments of transcriptional strength in each direction. For DNC, several chromatin-based pathways contribute to repression. The resulting level of DNC transcription thus reflects the combined action of several pathways. Here, we characterize Hda1C as a novel player with a genome-wide effect on this phenomenon. Elucidating the mechanistic interplay at specific target DNC loci will be an exciting future research question. __

    *Reviewer #1 (Significance (Required)): *

    This is a very interesting and innovative study using cutting edge genetic approaches, genome-wide sequencing as well as single cell imaging to extend our understanding of non-coding transcription regulation and its potential impact on gene expression. It is a nice continuation and complement of an earlier study from the same author (Marquardt et al., 2014) and will certainly be of interest to a large chromatin biology audience.

    __We are grateful for the appreciation of our research on this topic. __

    __ __

    Reviewer #2

    Promotors are frequently transcribed in both directions. The divergent, *upstream' transcript is frequently unstable. Transcription initiation is regulated through the acetylation of promoter-proximal nucleosomes, where HDAC-dependent deacetylation of histones typically represses transcription initiation.

    *The current manuscript addresses the question whether initiation of coding and divergent, non-coding (DNC) transcription is regulated by the same factors. Previously Marquardt and others showed that H3K56ac-mediated histone exchange has a differential effect on coding and DNC transcription.

    Using a clever reporter system, the authors screened for positive and negative regulators that preferentially affect DNC transcription. They discover the Hda1 deacetylase complex as a DNC-biased repressor and diverse HATs as DNC-biased activators. The role of activators could not be validated, presumably due to high variability of the system.*

    Focusing on Hda1c the authors present data suggesting a larger effect of Hda1c on 'upstream' nucleosomes associated with DNC transcription than in coding transcription. Genome-wide NET-seq mapping was consistent with this differential regulation. Life cell imaging of one specific case argues that Hda1-mediated repression reduced the time between initiation events. The authors employ state of the art methods and in general the data are of very good quality. The effect size is very small, which raises the broader question whether the results, while statistically significant is biological relevant. I have a few comments that the authors may use to revise their manuscript.

    __Thank you for the appreciation of our very good data quality. We hope our revision plan will help to clarify some confusion about the scope and effect size. __

    #2.1)* The differentially regulated coding and DNC transcription are defined by a directionality score. The screen was performed with two reporter loci that are strongly biased for DNC transcription (the idea to detect activators did not work out). Considering that coding and DNC transcription may not be totally independent because of the proximity of target nucleosomes, and sense and antisense transcription may compete for regulators, the question arises how levels of coding transcription affect DNC transcription in wildtype and mutants. The authors stratified their results according to levels of DNC transcription, but discussion and data analysis of the effect of coding transcription on the directionality score may be relevant.*

    __We added the plot in RFig.1 above to address the question of correlation between transcription in each direction. NET-seq data supports a weak but highly statistically significant positive correlation between transcription in each direction genome-wide (rho = 0.26, p-value = 4.94e-24). We agree that it is relevant to discuss the effect of coding transcription on the directionality scores and revised the discussion accordingly (line 315). We have used both the coding and DNC signal values to create the comprehensive quadrant scatter plot in Fig. 1D-E. Analysis of mutants along the diagonal illustrates that many mutations affect coding transcription as well as DNC. The directionality score measures deviations from the axis of positive correlation, which requires us to use the information of both fluorophores. __

    #2.*2) The study is strong where the findings can be generalized. The single-molecule live-cell imaging analysis, while done properly, has only limiting impact, because the corresponding coding transcript could not be detected. This si more an anecdotal finding. *

    There seems to be a misunderstanding, the live-cell imaging measurements of transcription for SUT098 are stand-alone data. SUT098 by itself is a transcription unit, so we measure DNC of this unit independently from GCG1 that has much lower expression. The measurements are specific to SUT098 transcription and the quantification provides new information about the mechanisms involved in the regulation of DNC. We clarified the text in this regard (line 233).

    #2.3) The effect size is small (20%, on average) and the variability is high. The fact that the HATs that emerged as very robust activators of DNC transcription could not be validated and that the Hda2 subunit of the HDAC complex was not found statistically significant show the limitations of the study. To their credit, the authors discuss these limitations appropriately.

    __We have worked on the Methods in the revised manuscript to clarify this confusion (line 712). For the screen, the median signal values represent data from up to 50,000 individual cells. These experiments are remarkably accurate and highly reproducible, especially for molecular biology where n=3 is common. We have uploaded these data to the FlowCore public repository. We encourage any colleague to exploit the opportunity to analyze these data independently to experience the high data quality. With high number of observations, 20% average is a large effect and reflects a rather big shift of the population. As is standard for genetic screens, resource constraints are prohibitive to pursue all hits. In addition, it is expected that only some hits will be affecting transcription of DNC since the fluorescence reporter can be affected by many other cellular events. We focused on the effects on DNC in this manuscript. __

    __There seems to be some misunderstanding, Hda2 is a statistically significant hit in the ORC2/SUT14pr screen; this information is in Fig. 1E. The Hda1C subunits are labeled in purple. __

    #2.4) Figure S3C suggests that the Hda effect is largest at genes that are poorly expressed, and smaller at more average expression levels. Are we looking at a phenomenon that mainly applies to repressed genes?

    __Thank you very much for this suggestion. We replaced S3A-C with revised panels where the data is shown with the same y-axis scale, please see also #1.4. We believe the revised presentation also helps to clarify that the mutations increase DNC for all cohorts stratified by DNC expression. __

    **Minor issues**

    #2.5) The NET-seq study involves two replicates. How well did they correlate?

    The WT and mutant NET-seq replicates have good correlation (Spearman’s correlation coefficient was above 0.6 for WT and above 0.8 for the mutants).

    (INSERT Rfig4)

    RFig4. Correlation scatter plot of individual NET-seq replicates of WT, hda1D and hda3D. Spearman correlation coefficients of WT, hda1D and hda3D are 0.677, 0.8 and 0.825, respectively.

    #2.6) For the live-cell imaging replicates were not mentioned. Were replicate studies performed?

    __We have updated the text to make this important point more accessible (line 230). For live-cell imaging studies, transcription is recorded as movies of cells over time. We took multiple movies, and pooled the data from all the cells to improve statistical power. Data from each movie represent individual repeats. We monitored 130 cells on average for the WT and mutant strains over time. __

    #2.7) Fig 4E is not mentioned in the text (mislabeled as 4D)

    Done.

    #2.8) Fig S5 is not mentioned in the main text.

    __Done.

    __*Reviewer #2 (Significance (Required)): *

    In summary, this is a high-quality study that presents the results of a genome-wide screen that will be of interest to colleagues in the narrower field. Due to the small effects the results may appeal less to a general readership.

    __We are grateful for appreciating our manuscript as a high-quality study. We hope our revisions help to clarify confusion concerning effect size. __

    Reviewer #3

    In this manuscript, Gowthaman et al describe the results and follow up of their screen aimed at identifying regulators of divergent noncoding (DNC) transcription in S. cerevisiae. From this screen, they identify Hda1C as a repressor of DNC transcription, and perform follow experiments to support and detail this finding. In addition to RTqPCR to confirm the reporter and endogenous changes, the authors perform NET-seq to look at global DNC alteration upon Hda1C subunit deletion and identify a number of non-coding transcripts with altered expression levels. In addition, the authors perform live cell imaging to demonstrate that there is a modest restriction of initiation frequency when one of the subunits of Hda1C is deleted. Finally, the authors explore changes to pan-H3 acetylation and the genetic overlap between Hda1C and H3K56ac demonstrating independent genetic pathways, but overall increases in H3 acetylation over DNCs when Hda1C is deleted. Overall, the screen and results are of interest, but the authors overstate some of the conclusions (perhaps most importantly within the title!). I have the following suggestions to improve the manuscript:

    __Thank you for recognizing the interest in our results. We have revised the manuscript to state the conclusions more cautiously. __

    **Major comments**

    #3.1. The title of the manuscript is based on the single molecule live cell imagining experiments presented in Figure 4. While there is a statistically significant decrease in initiation frequency from deletion of one Hda1C subunit, there is no statistical decrease in deletion of the other two. Furthermore, these experiments were performed at one locus. As a result, I find the title to be an overstatement of the findings of the paper and suggest the authors refocus on the more robust findings of the manuscript.

    __Live-cell imaging requires extensive engineering of the target loci. Perhaps this was lost in the Methods, but it is a 5-step process to integrate the stem-loops. We tried to engineer other loci, but this is far from trivial and this technique does not work for all loci tested. The hairpins are also unstable, and need to be carefully checked prior to experimentation, which challenges scaling this approach up to a higher-throughput. It appears that we undersold this point, but the fact that we now provide a locus and strains for the community that makes such studies possible for DNC represent a tremendous achievement. Since hda1D also decreases time between initiations, we generalized the finding to Hda1C. __

    However, we recognized that the reviewer makes a helpful suggestion to choose a more careful title since there is no statistically significant reduction of initiation frequency in some mutants. We have revised the title to “____Hda1C limits divergent non-coding transcription and restricts transcription initiation frequency____” in the revised manuscript to address this point.

    __#3.__2. Relatedly, in Figure 4, the authors present the findings from the single molecule live cell imaging experiments. Within this experiment, the authors include a cac2 deletion (CAF-1 subunit) strain, and observe a modest effect, similar to hda1 deletion. This is surprising as the authors mentioned this location (GCG1/SUT098) was selected as CAF-1 was NOT shown to regulate the DNC previously (Marquardt et al 2014; as mentioned at the beginning of the Results section). The similar decrease in initiation frequency between cac2 deletion and hda1 deletion further concerns me regarding the use of these data as the headlining finding.*

    We believe there is a misunderstanding. We clarify that selection of the *GCG1 *locus was based on a cut-off value for cac2D effect, as is also shown in Fig S1C. The fold-change is small, but since DNC transcription of the chosen loci is high in wild type, an increase in a mutant would not necessarily give a high fold-change. Hence, we need to be cautious to conclude that CAF-I does not regulate DNC at this locus. The fold-change analysis suggested it, but it remained possible. CAF-I appears to affect even more loci than initially identified with the chosen cut-off. We see the same trend as in Hda1C mutants as in cac2, which offers support to the exciting idea that modulation of the initiation frequency may be a shared mechanism by chromatin-based regulators acting on DNC.

    #3.3. It is unclear to me why the change in mRNA expression is included within the screen. Why not solely look at the expression change of the DNC? Importantly, the authors note in the discussion that perhaps the reason the SAGA complex was identified was due to regulating mRNA expression and not DNC expression and therefore was identified in the screen. Could the authors not just present the fold change in DNC expression using their YFP reporter, and not the YFP vs mCherry?

    __The regulation of initiation frequency in each direction is super-imposed on a general positive correlation ____(rho = 0.26, p-value = 4.94e-24) between the coding and non-coding directions____, please see also RFig.1. For the purpose of this study about selective effects on the direction of transcription, it is vital to incorporate both sides of the reporter. Otherwise, we would select for factors that activate or repress the transcription from the target promoter NDR. This point is accessible in Fig.1D-E, where mutations that affect YFP usually also have an effect on mCherry. The aim of this study was to identify mutants that affect the relative expression, and therefore a focus on one fluorophore would not improve the analysis. We clarified this important point more accessibly in the revised manuscript (line 315). __

    Please also note that all the raw data are available, so colleagues are in the position to perform their independent analyses. We believe that it is very valuable for the community to have access to these data since they may be useful for other purposes and could be analyzed in many different ways. In fact, we have tried several methods and approaches over the years and present what we believe is most appropriate in this manuscript. For example, Hda1C comes out as a convincing hit with a range of different approaches to analyze the data, which is also a reason we feel confident about the characterization of Hda1C.

    #3.4. This is absolutely beyond the scope of the paper, but limiting the screen to only nonessential proteins likely misses important regulators. In the future, perhaps the authors could pursue a SATAY screen to look for essential proteins as well? Again, the findings of this paper are appropriate, and the screen is a great undertaking, but I want to suggest this to the authors for potential future projects.

    Thank you for this excellent suggestion. We agree that capturing the role of essential factors would be very informative, and the saturated transposition approach would be promising. However, as the reviewer points out, performing these analyses is beyond the scope of the current manuscript.

    #3.5. The authors perform NETseq experiments in deletion strains and identify ~1500 DNC transcripts with altered expression. Later the authors look into the mechanism and demonstrate an increased H3ac in hda1 deletion strains. The authors could enhance the representation of these datasets by correlating the change in H3ac with the change in DNC transcription - do they correlate?

    __Thank you for bringing up this excellent point. We present the correlation data of change in H3ac and DNC transcription in the hda1D mutant (RFig5.). The ChIP-seq and NET-seq values of hda1D were divided by respective WT values in order to quantify the relative increase of H3 acetylation or nascent transcription in hda1D). The data showed a weak (Spearman rho= 0.23) but significant (pval=3.0e-20) positive correlation between the ratio values. The hda1D-dependent increase in H3 acetylation correlates with hda1D-dependent increase of RNAPII occupancy in DNC transcripts. We enhanced our representation of these data by including this plot as S5D in the revised manuscript as suggested. __

    (INSERT Rfig5)

    RFig5____: Scatterplot of hda1D/WT NET-seq (y-axis) and ChIP-seq (x-axis) ratios. Each point corresponds to a bidirectional gene promoter overlapping with an NDR. The x-axis shows ChIP-seq ratios, and the y-axis shows the NET-seq ratios. These data support Spearman correlation test: rho = 0.234 and a statistically significant p-value = 3.0e-20.

    #3.6. In Figure 5, the authors argue that Hda1C works non-redundantly with K56ac, using point mutants to mutate K56 to A or Q. Did the screen identify anything else in the K56ac pathway? Rtt109 or Asf1, for example? Because Hda1C deacetylates H3, including but not limited to K56, it is a bit surprising the K56 point mutations result in a larger increase in SUT098-YFP levels. The authors discuss within the text that Hda1C has multiple targets; but coming back to my previous point that CAF-I was not supposed to impact this location, I am having a hard time understanding these results.

    __This is an excellent point. We improved the manuscript by highlighting other factors with links to H3K56ac in our scatter plots, for example Rtt109 in Fig 2A. Nevertheless, the reviewer may wish to satisfy his/her curiosity by exploring table S2 in more detail. Table S2 lists the top candidates from both screens. __

    __We hope our answer to point #3.2 helped to clarify the aspect of this comment related to CAF-I. __

    **Minor comments**

    #3.7. The authors follow up the screen using RTqPCR for GCG1/SUT098 in newly made deletion strains. I was surprised the authors choose this locus rather than the ORC2/SUT014 locus, as the screen showed a strong increase for this reporter. While I appreciate generating the deletion strains within the reporter is beyond necessary, assessing the endogenous locus within the deletion strains by RTqPCR seems reasonable.

    We chose GCG1 locus since the fold change in directionality by genetic screen was high for the activator mutants. We will perform this experiment and add the missing validation experiment for the ORC2 locus in the revised manuscript.

    #3.8. The authors tend to show their genomic data as metaplots; it would be nice to see heatmaps where more can be gleaned from the display of all the loci. This applies to the NET-seq data (Figure 3) and the ChIP-seq data (Figure 5).

    We appreciate the suggestion and generated the requested heatmaps using the NET-seq tracks of WT and *hda *mutants (RFig6.). The heatmap represents the same genomic intervals as on the corresponding metagene plot (Figure 3A). We find that the differences between WT and hda samples are more clearly accessible at first glance on the metagene plot rather than on the heatmap. We believe that this could be because the heatmaps do not represent what transcripts have in common and rather underlines the differences. In contrast, the metagene plots reveal the common trends by taking the average of signal. We thus prefer showing metagene plots in the manuscript, as they allow for overlay of multiple tracks on the same plot, thus enhancing visual comparison for the readers.

    (INSERT Rfig6)

    *RFig6. Heatmap representing NET-seq data in WT, hda1D and hda3D. Genomic intervals covering [TSS - 100 bp, TSS + 500 bp] of DNC transcripts (n=1517) are shown. The color indicates the log2-transformed NET-seq values. *

    #3.9. In Figure 5B, the authors present H3ac ChIP-seq data, presented as a ratio of H3ac/total H3. While this is a perfectly acceptable way to present the data, I was surprised to see a decrease in total H3 levels when examining the supplemental data. Has this decrease in H3 occupancy upon hda1 deletion been shown previously? This finding should be discussed within the manuscript.

    __We appreciate that the reviewer noticed this. We do not think this has been explicitly stated before, as the focus thus far had been on the effects towards the mRNA. However, the effect is not statistically significant between the WT and hda1D as observed in S5B. We thus prefer to remain cautious about this conclusion. __

    #3.10. In Supplemental Figure S3, the authors break down the NET-seq data by DNC FPKM, which is very nice. Very minor point that the font here is quite small.

    __Thanks, we improved the font size. Note that we also revised the y-axis scale in response to comment #1.4. __

    *Reviewer #3 (Significance (Required)): *

    ***Significance:** *

    *The regulation of divergent non-coding RNAs is an understudied field. In this paper, the authors perform a screen for all non-essential yeast proteins in regulating the expression of these ncRNAs. The screen results and follow up defining the role of Hda1C in broadly repressing the expression of these ncRNAs is of interest to the field. *

    __We are grateful to the reviewer for highlighting the interest of our work to the field. __

    ***Context:** *

    *This work follows from Marquardt's previous 2014 study that identify Caf1 as regulating DNCs in S. cerevisiae. *

    ***Audience:** *

    *Broadly, the chromatin and transcription field. Anyone interested in how chromatin regulates transcription, regulation of ncRNAs, and functions of histone modifying enzymes. *

    ***Expertise:** *

    I am a member of the chromatin and transcription field, largely performing genomic experiments. We do not perform microscopy, although sufficiently understand the experiments and results presented here.

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    In this manuscript, Gowthaman et al describe the results and follow up of their screen aimed at identifying regulators of divergent noncoding (DNC) transcription in S. cerevisiae. From this screen, they identify Hda1C as a repressor of DNC transcription, and perform follow experiments to support and detail this finding. In addition to RTqPCR to confirm the reporter and endogenous changes, the authors perform NET-seq to look at global DNC alteration upon Hda1C subunit deletion and identify a number of non-coding transcripts with altered expression levels. In addition, the authors perform live cell imaging to demonstrate that there is a modest restriction of initiation frequency when one of the subunits of Hda1C is deleted. Finally, the authors explore changes to pan-H3 acetylation and the genetic overlap between Hda1C and H3K56ac demonstrating independent genetic pathways, but overall increases in H3 acetylation over DNCs when Hda1C is deleted. Overall, the screen and results are of interest, but the authors overstate some of the conclusions (perhaps most importantly within the title!). I have the following suggestions to improve the manuscript:

    Major comments:

    1. The title of the manuscript is based on the single molecule live cell imagining experiments presented in Figure 4. While there is a statistically significant decrease in initiation frequency from deletion of one Hda1C subunit, there is no statistical decrease in deletion of the other two. Furthermore, these experiments were performed at one locus. As a result, I find the title to be an overstatement of the findings of the paper and suggest the authors refocus on the more robust findings of the manuscript.
    2. Relatedly, in Figure 4, the authors present the findings from the single molecule live cell imaging experiments. Within this experiment, the authors include a cac2 deletion (CAF-1 subunit) strain, and observe a modest effect, similar to hda1 deletion. This is surprising as the authors mentioned this location (GCG1/SUT098) was selected as CAF-1 was NOT shown to regulate the DNC previously (Marquardt et al 2014; as mentioned at the beginning of the Results section). The similar decrease in initiation frequency between cac2 deletion and hda1 deletion further concerns me regarding the use of these data as the headlining finding.
    3. It is unclear to me why the change in mRNA expression is included within the screen. Why not solely look at the expression change of the DNC? Importantly, the authors note in the discussion that perhaps the reason the SAGA complex was identified was due to regulating mRNA expression and not DNC expression and therefore was identified in the screen. Could the authors not just present the fold change in DNC expression using their YFP reporter, and not the YFP vs mCherry?
    4. This is absolutely beyond the scope of the paper, but limiting the screen to only nonessential proteins likely misses important regulators. In the future, perhaps the authors could pursue a SATAY screen to look for essential proteins as well? Again, the findings of this paper are appropriate, and the screen is a great undertaking, but I want to suggest this to the authors for potential future projects.
    5. The authors perform NETseq experiments in deletion strains and identify ~1500 DNC transcripts with altered expression. Later the authors look into the mechanism and demonstrate an increased H3ac in hda1 deletion strains. The authors could enhance the representation of these datasets by correlating the change in H3ac with the change in DNC transcription - do they correlate?
    6. In Figure 5, the authors argue that Hda1C works non-redundantly with K56ac, using point mutants to mutate K56 to A or Q. Did the screen identify anything else in the K56ac pathway? Rtt109 or Asf1, for example? Because Hda1C deacetylates H3, including but not limited to K56, it is a bit surprising the K56 point mutations result in a larger increase in SUT098-YFP levels. The authors discuss within the text that Hda1C has multiple targets; but coming back to my previous point that CAF-I was not supposed to impact this location, I am having a hard time understanding these results.

    Minor comments:

    1. The authors follow up the screen using RTqPCR for GCG1/SUT098 in newly made deletion strains. I was surprised the authors choose this locus rather than the ORC2/SUT014 locus, as the screen showed a strong increase for this reporter. While I appreciate generating the deletion strains within the reporter is beyond necessary, assessing the endogenous locus within the deletion strains by RTqPCR seems reasonable.
    2. The authors tend to show their genomic data as metaplots; it would be nice to see heatmaps where more can be gleaned from the display of all the loci. This applies to the NET-seq data (Figure 3) and the ChIP-seq data (Figure 5).
    3. In Figure 5B, the authors present H3ac ChIP-seq data, presented as a ratio of H3ac/total H3. While this is a perfectly acceptable way to present the data, I was surprised to see a decrease in total H3 levels when examining the supplemental data. Has this decrease in H3 occupancy upon hda1 deletion been shown previously? This finding should be discussed within the manuscript.
    4. In Supplemental Figure S3, the authors break down the NET-seq data by DNC FPKM, which is very nice. Very minor point that the font here is quite small.

    Significance

    Significance:

    The regulation of divergent non-coding RNAs is an understudied field. In this paper, the authors perform a screen for all non-essential yeast proteins in regulating the expression of these ncRNAs. The screen results and follow up defining the role of Hda1C in broadly repressing the expression of these ncRNAs is of interest to the field.

    Context:

    This work follows from Marquardt's previous 2014 study that identify Caf1 as regulating DNCs in S. cerevisiae.

    Audience:

    Broadly, the chromatin and transcription field. Anyone interested in how chromatin regulates transcription, regulation of ncRNAs, and functions of histone modifying enzymes.

    Expertise:

    I am a member of the chromatin and transcription field, largely performing genomic experiments. We do not perform microscopy, although sufficiently understand the experiments and results presented here.

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    Referee #2

    Evidence, reproducibility and clarity

    This reviewer considers himself a generalist with insight into chromatin-based gene regulation, but no first-hand experience with the yeast system or single-molecule imaging.

    Promotors are frequently transcribed in both directions. The divergent, *upstream' transcript is frequently unstable. Transcription initiation is regulated through the acetylation of promoter-proximal nucleosomes, where HDAC-dependent deacetylation of histones typically represses transcription initiation. The current manuscript addresses the question whether initiation of coding and divergent, non-coding (DNC) transcription is regulated by the same factors. Previously Marquardt and others showed that H3K56ac-mediated histone exchange has a differential effect on coding and DNC transcription.

    Using a clever reporter system, the authors screened for positive and negative regulators that preferentially affect DNC transcription. They discover the Hda1 deacetylase complex as a DNC-biased repressor and diverse HATs as DNC-biased activators. The role of activators could not be validated, presumably due to high variability of the system.

    Focusing on Hda1c the authors present data suggesting a larger effect of Hda1c on 'upstream' nucleosomes associated with DNC transcription than in coding transcription. Genome-wide NET-seq mapping was consistent with this differential regulation. Life cell imaging of one specific case argues that Hda1-mediated repression reduced the time between initiation events. The authors employ state of the art methods and in general the data are of very good quality. The effect size is very small, which raises the broader question whether the results, while statistically significant is biological relevant. I have a few comments that the authors may use to revise their manuscript.

    1. The differentially regulated coding and DNC transcription are defined by a directionality score. The screen was performed with two reporter loci that are strongly biased for DNC transcription (the idea to detect activators did not work out). Considering that coding and DNC transcription may not be totally independent because of the proximity of target nucleosomes, and sense and antisense transcription may compete for regulators, the question arises how levels of coding transcription affect DNC transcription in wildtype and mutants. The authors stratified their results according to levels of DNC transcription, but discussion and data analysis of the effect of coding transcription on the directionality score may be relevant.

    2. The study is strong where the findings can be generalized. The single-molecule live-cell imaging analysis, while done properly, has only limiting impact, because the corresponding coding transcript could not be detected. This si more an anecdotal finding.

    3. The effect size is small (20%, on average) and the variability is high. The fact that the HATs that emerged as very robust activators of DNC transcription could not be validated and that the Hda2 subunit of the HDAC complex was not found statistically significant show the limitations of the study. To their credit, the authors discuss these limitations appropriately.

    4. Figure S3C suggests that the Hda effect is largest at genes that are poorly expressed, and smaller at more average expression levels. Are we looking at a phenomenon that mainly applies to repressed genes?

    Minor issues

    1. The NET-seq study involves two replicates. How well did they correlate?

    2. For the live-cell imaging replicates were not mentioned. Were replicate studies performed?

    3. Fig 4E is not mentioned in the text (mislabeled as 4D)

    4. Fig S5 is not mentioned in the main text.

    Significance

    In summary, this is a high-quality study that presents the results of a genome-wide screen that will be of interest to colleagues in the narrower field. Due to the small effects the results may appeal less to a general readership.

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    Referee #1

    Evidence, reproducibility and clarity

    In the paper by Gowthaman et al., the authors aim at better understanding the molecular mechanisms controlling divergent non-coding transcription (DNC). They describe a high-throughput yeast genetic screen using two strains in which two loci consisting of a coding and a divergent non-coding transcription unit (CGC1-SUT098 or ORC2-SUT014) were replaced by a bidirectional fluorescent reporter construct encoding mCherry in the coding direction and YFP in the non-coding direction. The two reporter strains were crossed with the yeast deletion library and mutants leading to increased or decreased YFP signal were selected as potential DNC repressors or activators. The two screens identified a number of common potential repressors and activators. Components of the Hda1C histone deacetylase complex were identified as DNC repressors in both screens. This phenomenon was confirmed genome-wide by performing NET-Seq in WT as well as hda1D and hda3D strains. This experiment allowed to identify 1517 DNC transcripts repressed by Hda1. Further analyses indicate that Hda1C represses DNC genome-wide independently of expression levels and that loss of Hda1 does not substantially affect coding transcription.

    Live-cell imaging of transcription was then used to show that loss of Hda1 increases DNC transcription frequency rather than duration providing novel information on the link between DNC transcription initiation kinetics and chromatin regulation. Finally, using Chip-seq, the authors show that the level of acetylation over the divergent non-coding units is increased in the absence of Hda1 and some experiments suggest that H3K56 acetylation also contributes to DNC regulation, further strengthening the importance of elevated histone acetylation in efficient DNC.

    Importantly, several components of the SWI/SNF chromatin remodeling complex were identified as activators confirming earlier observations (Marquardt et al., 2014). SAGA subunits were also among potential DNC activators, however these effects could not be confirmed through validation experiments. The authors conclude that DNC may be independent of specific activators and mainly due to transcriptional noise resulting from the adjacent NDR.

    Overall this paper is very well structured, clearly written and the experiments are well controlled. The genetic screen identifies novel factors involved in the regulation of DNC. The study clearly demonstrates that the level of acetylation is a key regulator of divergent non-coding transcription and that histone deacetylation by Hda1 reduces the frequency of DNC initiation events. While this conclusion is strongly supported by the Net-Seq and Chip-seq metagene analyses, the fluorescence mCherry and YFP values or qRP-PCR analyses of specific genes do not always behave as expected when looking at absolute values rather than mCherry/YFP or GCG1/SUT098 ratios, which is sometimes disturbing when reading the paper. Therefore, the following points should be clarified.

    Major points:

    Figures 2 and S2A: Figures 2C and D show the mCherry/YFP fluorescence and GCG1/SUT098 RT-qPCR gene expression ratios respectively, which are consistent with a repressive effect of Hda1C on DNC transcription and a potential DNC activating effect of SAGA components. However, the absolute mCherry and YFP or GCG1 and SUT098 expression values presented in Figures S2A and S2B show the opposite: loss of Hda1C subunits rather leads to a decrease in mCherry with not much effect on YFP; moreover loss of Hda3 results in decreased SUT098, which is inconsistent with the whole model. The same comment is valid for the SAGA mutants. It would be good to provide some explanation for these a priori contradictory observations, especially for the Hda1c mutants, which are the major focus of the study. The Net-Seq analyses are certainly more reliable since less subject to protein or RNA stability effects, which may underlie some of the inconsistencies between protein and RNA absolute levels.

    Figure 3: this figure examines the effect of Hda1 and Hda3 on the 1517 DNC transcripts. Does loss of this HDAC also increase the expression of all the other 2219 non-coding transcripts identified by Net-Seq, which would make Hda1C a more general repressor of non-coding transcription?

    Moreover, does loss of Hda1 or Hda3 reveal DNC transcripts that were not detected in wild-type? This may increase even more the number of genes with divergent transcription.

    Figures S3A, B, C: are the 3 groups of DNCs derepressed to the same extent by loss of Hda1 or Hda3? This is difficult to judge given the differences in y-axis scales. Figures S3D, E: the authors show the Net-Seq snapshots for the GCG1 and ORC2 loci. It would be good to add the quantifications as presented in Figure 3 for YPL172C and YDRr216C.

    Figures S4A, B, C and D are not well explained. What does the y axis frequency correspond to? Is it the % of cells showing a signal? Is the intensity of SUT098 higher because the transcription initiation frequency is higher and therefore the transcription site signal is more intense?

    Figures S4 A-I should be more specifically cited in the text.

    Figure 5A: it is really unexpected and unclear why the mCherry/YFP in the WTH3/hda1D and WTH3/hda1D/H3K56mut is increasing compared to WTH3, since DNC is supposed to increase. Similar comment for Figure S5C. This should be clarified in the text.

    More generally, as already mentioned above, the fluorescence data are expressed either as mCherry/YFP ratio or as absolute values. It would be good to systematically show the ratios and the absolute values of mCherry and YFP signal; the same for coding and DNC RT-qPCR as well as Net-Seq values when available.

    Figures S5A and B are not referred to in the text. It should be mentioned and explained how normalization to H3 affects the levels of acetylated H3 over the NDR. p. 12 "Our data thus suggest to extend the transcriptional noise hypothesis with activities limiting DNC transcription to account for genome-wide variation in non-coding transcription".

    If DNC is the result of "transcriptional noise", it is surprising that in the case of CGC1-SUT098, the transcription frequency is higher in the non-coding versus the coding direction. Is the SUT098 behaving like the coding unit in this case? The authors should comment on that.

    Minor comments:

    p. 4 should one talk about Hda1C-linked histone acetylation facilitates... (should be deacetylation...??) The authors should explain why they chose two coding/non-coding pairs that are cac2D insensitive and whether other criteria, such as level of DNC transcription, were also considered, since GCG1-SUT098 represents one of the most highly expressed divergent non-coding transcripts.

    It is hard to understand why both the H3K56A and H3K56Q mutations lead to increased DNC, a result already presented in the Marquardt et al. 2014 paper. It would be helpful to provide a more extensive explanation or hypothesis.

    What defines the level of DNC repression? How does the level of repression correlate with the level of coding transcription?

    Significance

    This is a very interesting and innovative study using cutting edge genetic approaches, genome-wide sequencing as well as single cell imaging to extend our understanding of non-coding transcription regulation and its potential impact on gene expression. It is a nice continuation and complement of an earlier study from the same author (Marquardt et al., 2014) and will certainly be of interest to a large chromatin biology audience.

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