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Link to original content: https://doi.org/10.1038/s41594-023-01171-9
Quantitative analysis of transcription start site selection reveals control by DNA sequence, RNA polymerase II activity and NTP levels | Nature Structural & Molecular Biology
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Quantitative analysis of transcription start site selection reveals control by DNA sequence, RNA polymerase II activity and NTP levels

Nature Structural & Molecular Biology volume 31, pages 190–202 (2024)Cite this article

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Abstract

Transcription start site (TSS) selection is a key step in gene expression and occurs at many promoter positions over a wide range of efficiencies. Here we develop a massively parallel reporter assay to quantitatively dissect contributions of promoter sequence, nucleoside triphosphate substrate levels and RNA polymerase II (Pol II) activity to TSS selection by ‘promoter scanning’ in Saccharomyces cerevisiae (Pol II MAssively Systematic Transcript End Readout, ‘Pol II MASTER’). Using Pol II MASTER, we measure the efficiency of Pol II initiation at 1,000,000 individual TSS sequences in a defined promoter context. Pol II MASTER confirms proposed critical qualities of S. cerevisiae TSS −8, −1 and +1 positions, quantitatively, in a controlled promoter context. Pol II MASTER extends quantitative analysis to surrounding sequences and determines that they tune initiation over a wide range of efficiencies. These results enabled the development of a predictive model for initiation efficiency based on sequence. We show that genetic perturbation of Pol II catalytic activity alters initiation efficiency mostly independently of TSS sequence, but selectively modulates preference for the initiating nucleotide. Intriguingly, we find that Pol II initiation efficiency is directly sensitive to guanosine-5′-triphosphate levels at the first five transcript positions and to cytosine-5′-triphosphate and uridine-5′-triphosphate levels at the second position genome wide. These results suggest individual nucleoside triphosphate levels can have transcript-specific effects on initiation, representing a cryptic layer of potential regulation at the level of Pol II biochemical properties. The results establish Pol II MASTER as a method for quantitative dissection of transcription initiation in eukaryotes.

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Fig. 1: A high-throughput system for studying TSS selection.
Fig. 2: Wide range of initiation efficiency measured using MASTER.
Fig. 3: Sequence contributions to Pol II initiation efficiency from positions surrounding the TSS.
Fig. 4: Pol II mutants alter TSS efficiency for all possible TSS motifs while showing selective effects for base at +1.
Fig. 5: Pol II initiation is sensitive to NTP pools.
Fig. 6: Learned initiation preferences are predictive of TSS efficiencies at genomic promoters.
Fig. 7: Logistic regression model of DNA sequence contribution to TSS efficiency.
Fig. 8: Model for TSS sequence preference regulated by multiple mechanisms.

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Data availability

Raw sequencing data generated in this study are available in the National Center for Biotechnology Information BioProject, under the accession number PRJNA766624. Processed data are available in GEO, under the accession number GSE185290. Source data are provided with this paper.

Code availability

Code for analyses in this study is provided at https://github.com/Kaplan-Lab-Pitt/PolII_MASTER-TSS_sequence.

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Acknowledgements

The authors thank Kaplan lab members for helpful comments on the manuscript. We are deeply grateful to C. Qiu for discussions and comments on this project. We acknowledge J. Kinney (Cold Spring Harbor Laboratory) and S. Li (Statistical Consulting Center at University of Pittsburgh) for discussions on modeling. We thank C.D. Johnson, R. Metz (Texas A&M AgriLife Genomics and Bioinformatics Service), A. Hillhouse (Texas A&M Institute for Genome Sciences & Society), W.A. MacDonald and R. Elbakri (the University of Pittsburgh Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh), Y. Pan (the UPMC Genome Center), D. Kumar (the Waksman Genomics Core Facility at Rutgers University) and L. Freeman (Illumina) for discussions and advice regarding deep sequencing strategies. We thank S.J. Mullett and S.G. Wendell (Metabolomics and Lipidomics Core, NIHS10OD023402) for performing NTP measurements. We acknowledge support from National Institutes of Health (NIH) grant R01GM097260 to C.D.K. for the early part of this work and NIH grants R01GM120450 and R35GM144116 to C.D.K. and R35GM118059 to B.E.N. This research was supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483.

Author information

Authors and Affiliations

  1. Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Yunye Zhu & Craig D. Kaplan

  2. Department of Genetics and Waksman Institute, Rutgers University, Piscataway, NJ, USA

    Irina O. Vvedenskaya & Bryce E. Nickels

  3. Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA

    Sing-Hoi Sze

  4. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Sing-Hoi Sze

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  1. Yunye Zhu
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  5. Craig D. Kaplan
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Contributions

Y.Z. designed the project, performed experiments, analyzed data, made figures, and drafted and revised the manuscript. I.O.V. generated libraries for TSS-seq. S-H.Z. analyzed data and discussed the analysis. B.E.N. provided funding and methodology of TSS-seq, and revised the manuscript. C.D.K. conceived and designed the project, guided analyses and interpretation of data, provided funding and revised the manuscript.

Corresponding author

Correspondence to Craig D. Kaplan.

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Nature Structural & Molecular Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Sara Osman was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 High level of reproducibility and coverage depth of library variants.

(A) Schematic of experimental approach. Promoter libraries with almost all possible sequences within a 9 nt randomized region were constructed on plasmids. Libraries were designated ‘AYR’, ‘BYR’, and ‘ARY’ based on randomized region composition. Plasmids were amplified in E. coli and transformed into yeast with wild type or mutated Pol II. DNA and RNA were extracted and prepared for DNA-seq and TSS-seq. (B) Base frequencies at positions within the randomized region of promoter variants demonstrate unbiased synthesis of randomized regions. Bars are mean +/- standard deviation of the mean for promoter variants in WT and four Pol II mutants. (C) Heatmap illustrating hierarchical clustering of Pearson correlation coefficients of reads per promoter variant E. coli libraries and three biological replicates of libraries transformed into yeast. (D) Example correlation plots of DNA reads count of promoter variants for E. coli and yeast WT biological replicates. Pearson r and number (N) of compared variants are shown. (E) Bulk primer extension for RNA produced from promoter variant libraries transformed into WT yeast. ‘No GFP’ control used RNA from an untransformed strain. ‘No RNA’ control used a sample of nuclease-free water. Dots represent three biological replicates. Bars are mean +/- standard deviation of the mean. (F) TSS usage based TSS-seq read lengths from transformed libraries. Dots represent three biological replicates. Bars are mean +/- standard deviation of mean. Distributions are similar to the distributions in E. Note that primer extension will blur usage into adjacent upstream position due to some level of non-templated addition of C to RNA 5’ ends. (G) Heat scatter plots of Coefficient of Variation (CoV, y axis) versus total RNA reads per promoter variant in each Pol II MASTER library. A cutoff of CoV = 0.5 was used to filter higher variance variants. (H) Heat scatter plots of relative expression versus TSS efficiency of major TSSs per promoter variant, with contour lines indicating deciles of data. Number (N) of promoter variants with [−1, +1] relative expression values (log2) and corresponding percentage of total promoter variants are shown.

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Extended Data Fig. 2 Surrounding sequence of TSSs modulates initiation efficiency.

(A) +1 TSS efficiency of all −7 to −2 sequences within each N-8N-1N+1 motif in WT, rank ordered by efficiency of A-8C-1A+1 version shown as a heat map. x-axis is ordered based on median efficiency for each N-8N-1N+1 motif group, as shown in Fig. 2B. Spearman’s rank correlation tests between A-8C-1A+1 group and all groups are shown beneath the heat map. (B) Efficiencies of designed +1 TSSs grouped by base identities between −8 and +1 positions. Statistical analyses by Kruskal-Wallis with Dunn’s multiple comparisons test for base preference at individual positions relative to +1 TSS are shown beneath plots. Lines represent median values of subgroups. ****, P ≤ 0.0001; ***, P ≤ 0.001; **, P ≤ 0.01; *, P ≤ 0.05. (C) Histogram showing the distribution of measured efficiencies for all designed −8 to +4 TSSs of all promoter variants from ‘AYR’, ‘BYR’ and ‘ARY’ libraries in WT. Dashed line marks the 5% efficiency cutoff. (D) A+2G+3G+4 motif enrichment is apparent for the top 10% most efficient designed −8 TSS. A(/G)+2G(/C)+3G(/C)+4 motif enrichment was observed for the top 10% most efficient −8 TSSs but not for the next 10% most efficient TSSs. A(/G)+1 enrichment observed for top 20% most efficient TSSs is consistent with the +1 R preference of TSS. Numbers (N) of variants assessed are shown. Sequence logos were generated using WebLogo 3. Bars represent an approximate Bayesian 95% confidence interval. (E) An A at position −9 results in different sequence preferences at position −8. The dataset of designed +4 TSSs deriving from ‘AYR’, ‘BYR’ and ‘ARY’ libraries was used to detect the −9/−8 interaction. All variants were divided into 16 subgroups defined by bases at positions −9 and −8 relative to designed +4 TSS, and then their TSS efficiencies were plotted. Lines represent median values of subgroups. (F) An A at position −8 results in different sequence preferences at position −7. The dataset of designed +1 TSSs deriving from ‘AYR’ and ‘BYR’ libraries was used to detect −8/−7 interaction. Calculations same as −9/−8 interaction described in E.

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Extended Data Fig. 3 High level of reproducibility of library variants in Pol II mutants.

(A) Histograms showing the distribution of measured efficiencies for all designed −8 to +4 TSSs for MASTER libraries in Pol II mutants. Dashed lines mark the 5% efficiency cutoff with number (N) of TSS variants shown. (B) TSS usage distributions at designed −10 to +25 TSSs for MASTER libraries in Pol II mutants. Dots represent three biological replicates. Bars are mean +/- standard deviation. (C) Hierarchical clustering of Pearson correlation coefficients of TSS efficiencies for major TSSs (designed +1 TSS for ‘AYR’ and ‘BYR’ libraries, +2 TSS for ‘ARY’ library) for WT or Pol II mutants illustrated as a heat map for three biological replicates. (D) Example correlation plots of TSS efficiency of major TSSs between representative biological replicates. Pearson r and number (N) of compared variants are shown. (E) Plots of CoV versus total RNA reads (three biological replicates) for Pol II mutants. The red dashed lines mark the CoV = 0.5 cutoff, an arbitrary cutoff for promoters with reasonable reproducibility across replicates. G1097D replicates contain outliers because this mutant is susceptible to genetic suppressors. A suppressor existing in one biological replicate generates a high CoV allowing filtering.

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Extended Data Fig. 4 Pol II mutants alter TSS efficiency in general.

(A) TSS efficiency distributions of designed +1 TSSs of Pol II mutants for base subgroups at individual positions relative to +1. Identical analysis as in Extended Data Fig. 2B for WT Pol II. (B) Pol II GOF G1097D showed greater increase in efficiency than GOF allele E1103G at upstream TSSs (designed −32 and −8 TSSs), while E1103G showed stronger effects at designed +1 TSS than G1097D. (C) Pol II initiation sequence preference in Pol II mutants. Identical analysis as in Fig. 3B for WT Pol II. (D) Motif enrichment for top the 10% most efficient −8 TSSs for Pol II mutants. Identical motif enrichment analysis as in Extended Data Fig. 2D top panel for WT Pol II. Numbers (N) of variants assessed are indicated. Bars represent an approximate Bayesian 95% confidence interval.

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Extended Data Fig. 5 High of reproducibility of TSS usage and efficiency upon MPA treatment.

(A) TSS usage distributions at designed −10 to +25 TSSs in WT ‘NYR’ library (mixed ‘AYR’ and ‘BYR’ libraries) treated with 100% ethanol or with 20 μg/ml MPA. MPA treatment shifted TSS usage downstream relative to EtOH treatment. Dots represent three biological replicates. Bars are mean +/- standard deviation of the mean. (B) Hierarchical clustering of Pearson correlation coefficients of TSS efficiencies for designed +1 TSS for three biological replicates for MPA or EtOH treatment, illustrated as a heat map. (C) Hierarchical clustering of Pearson correlation coefficients of TSS efficiencies for all genome positions within defined promoter windows with >=3 reads in each replicate, illustrated as a heat map. (D) Correlation plots for combined biological replicates for TSS efficiency upon MPA treatment (y axes) versus EtOH treatment (x axes) for all TSSs ≥ 2% efficiency in the 25%–75% of the distribution for a curated set of 5,979 yeast promoters (see Methods). TSSs are separated into groups depending on base identity at positions −3 (control) or positions +1 to +6.

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Extended Data Fig. 6 Modeling identifies sequence features for TSS selection in WT and Pol II mutants.

(A) Overview of TSS efficiency modeling. (1) TSS efficiencies including designed −8 to +2 and +4 TSSs deriving from ‘AYR’, ‘BYR’ and ‘ARY’ libraries were pooled for modeling. (2) Sequences from −11 to +9 relative to variant TSSs were extracted. (3) To identify robust features, a forward stepwise selection strategy coupled with a five-fold cross-validation for logistic regression was used, with random splitting into training (80%) and test (20%) sets. Stepwise regression starting with a constant term only with stepwise variable addition, until a stopping criterion is met, was performed. Additive terms (sequences at positions −11 to +9) and interactions were tested in stages. Model performance was evaluated with R2. The stopping criterion for adding additional variables was an increase R2 < 0.01. (4) A logistic regression model containing selected robust features was trained using the training set and then evaluated with the test set. (B) Comparison of measured efficiencies and predicted efficiencies. Model performance R2 on entire test set and number (N) of data points shown in plot are shown. (C) Principal component analysis (PCA) for parameters of models trained using individual replicates of WT and Pol II mutants. Close clustering of individual replicates indicates that models are not overfit. The top 15 contributing variables are shown. GOF and LOF mutants were separated from WT by the 1st principal component. GOF G1097D and E1103G were further distinguished by 2nd principal component by additional position +2 information, which is consistent with results in Extended Data Fig. 4D, where G1097D and E1103G differentially altered +2 sequence enrichment. (D) A scatter plot of comparison of measured and predicted TSS efficiencies of all positions within 5,979 known genomic promoter windows21 with available measured efficiency. Pearson r and number (N) of compared variants are shown. Most promoter positions (82%, 1,678,406 out of 2,047,205) showed no observed efficiency, which is expected because TSSs need to be specified by a core promoter and scanning occurs over some distance downstream.

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Zhu, Y., Vvedenskaya, I.O., Sze, SH. et al. Quantitative analysis of transcription start site selection reveals control by DNA sequence, RNA polymerase II activity and NTP levels. Nat Struct Mol Biol 31, 190–202 (2024). https://doi.org/10.1038/s41594-023-01171-9

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