Abstract
The genome-wide architecture of chromatin-associated proteins that maintains chromosome integrity and gene regulation is not well defined. Here we use chromatin immunoprecipitation, exonuclease digestion and DNA sequencing (ChIP–exo/seq)1,2 to define this architecture in Saccharomyces cerevisiae. We identify 21 meta-assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons and transcription by RNA polymerase (Pol) I, II and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We find that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex. Positioned insulators protect core promoters from upstream events. A small fraction of promoters evolved an architecture for inducibility, whereby sequence-specific transcription factors (ssTFs) create a nucleosome-depleted region (NDR) that is distinct from an NFR. We describe structural interactions among ssTFs, their cognate cofactors and the genome. These interactions include the nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator and SWI–SNF. Surprisingly, we do not detect interactions between ssTFs and TFIID, suggesting that such interactions do not stably occur. Our model for gene induction involves ssTFs, cofactors and general factors such as TBP and TFIIB, but not TFIID. By contrast, constitutive transcription involves TFIID but not ssTFs engaged with their cofactors. From this, we define a highly integrated network of gene regulation by ssTFs.
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Data availability
See Supplementary Data 4 for a list of where to find available data and code online. In essence, all raw sequencing data and peak files from this study are available at the NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE147927. Processed data are available at https://doi.org/10.26208/rykf-6050. Additional analyses and data are at yeastepigenome.org. We warn that single-replicate data files are not likely to have meaningful data and should not be used without further replication. All underlying data used to generate composite plots, coordinate files and script parameters for Figs. 2–5, Extended Data Figs. 4, 5, 7, 8b and Supplementary Fig. 1 can be downloaded from https://github.com/CEGRcode/2021-Rossi_Nature. Final composite plot values can be found in Supplementary Data 5.
Code availability
Code is available at https://github.com/CEGRcode/scriptmanager.
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Acknowledgements
This work was supported by National Institutes of Health (NIH) grants ES013768, GM059055 and HG004160 to B.F.P.; National Science Foundation (NSF) ABI INNOVATION grant 1564466 to S.M.; grants from the Pennsylvania State University Institute for Computational and Data Sciences to B.F.P. and W.K.M.L.; and computation from Advanced CyberInfrastructure (ROAR) at the Pennsylvania State University. We thank D. Shao for her role as lead software engineer for the PEGR platform and for support through the Penn State Institute and Computational Data Sciences (ICDS) Research Innovations with Scientists and Engineers (RISE) team. We thank O. Lang for operating EpitopeID.
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Authors and Affiliations
Contributions
M.J.R. designed and conducted experiments; performed library sequencing and data analysis; designed and tested the quality-control pipelines and web page; trained and managed lab personnel to produce data; supervised the project and co-wrote the manuscript. P.K.K. designed, developed and implemented the quality-control pipeline, analysis pipeline and website; organized and maintained data files; and provided bioinformatic support. W.K.M.L. performed high-throughput data processing and analysis, and provided bioinformatic support and scientific discussion. N.Y. provided bioinformatic support and developed the initial quality-control pipeline. N.B. and C.M. performed ChIP–exo and MNase ChIP–seq experiments and provided scientific discussion. G.K. provided bioinformatic support. K.B. and N.P.F. conducted ChIP–exo experiments and performed library sequencing. T.R.B., J.D.M., A.V.B., K.S.M., D.J.R. and E.S.P. conducted ChIP–exo experiments. G.D.K. provided high-performance infrastructure architecture and development, and edge-computing infrastructure design and support. S.M. provided bioinformatic guidance and support. B.F.P. conceptualized the project and conclusions, designed experiments, analysed the data, wrote the main text of the manuscript and co-wrote the remaining parts.
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Competing interests
B.F.P. has a financial interest in Peconic, LLC, which offers the ChIP–exo technology (US Patent 20100323361A1) implemented herein as a commercial service and could potentially benefit from the outcomes of this research. The remaining authors declare no competing interests.
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Peer review information Nature thanks Vishwanath Iyer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
Extended Data Fig. 1 ChIP–exo targets within meta-assemblages.
a, Simplified view of transcriptional regulation. A ssTF (TF; for example, Gal4) binds to its cognate motif (a UAS) within promoters in competition with chromatin/nucleosomes (red bar). The ssTF recruits (pink/green arrow) cofactors (for example, SAGA and Mediator) that assist in the assembly of a PIC (comprising TBP, TFIIB, and so on) and of Pol II at the transcript start site (TSS) of genes. Pol II then traverses the gene to the transcription end site (TES). b, Diagram showing the ChIP–exo assay. Protein targets are crosslinked to DNA, which is then fragmented. Specific proteins are captured through an engineered TAP tag that binds the common Fc region of any immobilized IgG. Near-base-pair resolution is achieved using a strand-specific λ exonuclease that digests each strand of DNA in the 5′–3′ direction up to the point of crosslinking. c, Pie chart showing assayed targets separated by broad GO-based classifications (inner ring), or by UMAP-based clustering of genome-wide binding locations (outer ring). Listed are the common names of ChIP–exo targets that generated significantly enriched locations (with ‘significance’ defined in the Methods section ‘ChExMix locations’), grouped by their UMAP/K-means-derived meta-assemblage abbreviations (along with membership count), which are further grouped by the simplified GO-related categories. See also Supplementary Data 2 (2H).
Extended Data Fig. 2 Data visualization and discovery in yeastepigenome.org.
Shown is an example web browser view at yeastepigenome.org of ChIP–exo occupancy patterns for all targets (for example, Reb1) around predefined genomic features. Rows are sorted by gene or promoter (NFR/NDR) length, or by distance from the indicated reference feature (where x = 0). Promoter classes include (from top to bottom) RP, STM, TFO, UNB and others. See Supplementary Data 1 (1G, 1J, 1C) for the identification numbers and coordinates of respective row features, and for the sort order of features that are constant in all target display windows. The lower right box (when present) provides strand-separated tag 5′ ends distributed around the target’s cognate DNA motif, with the motif’s opposite strand (red) inverted in the composite plot. Corresponding colour-coded nucleotide sequences are shown. All images, underlying data values and datasets can be downloaded through embedded ‘META DATA’ target-specific links at yeastepigenome.org. Each dataset download includes a ReadMe file describing the contents of the download. We warn that targets with only a single replicate did not pass our significance threshold. See Supplementary Data 1 (1C) for sort orders that are not provided in the download.
Extended Data Fig. 3 UMAP granularity.
UMAP projection from Fig. 1c, with zoomed-in inserts. Labels are 40 K-means-based abbreviations (Supplementary Data 2 (1J)). For coordinate values for individual targets, see Supplementary Data 2 (1C, 1D).
Extended Data Fig. 4 Confidence intervals for two example ChIP–exo datasets.
Left, plots showing the ChIP–exo patterns for Orc6 and Mcm5. Bold lines represent means; dotted lines represent the 5–95% confidence interval (CI). The CI was calculated for each base pair in the 1-kb window across all ACSs (n = 253). Right, heatmaps showing ACS occupancy by Orc6 and Mcm5. Blue represents ChIP–exo data on the ACS motif strand; red represents data on the opposite strand.
Extended Data Fig. 5 Protein architecture at regions transcribed by Pol II.
Shown is gene-body occupancy (strands combined) of selected Pol II elongation-associated targets (a and b have different datasets, as annotated). In each panel, data were aligned and orientated by TSS (left) and TES (right). Shown are the top 200 coding genes (middle) and the top 200 noncoding genes (bottom) (based on Sua7 occupancy). See also Fig. 3c and note that the RP panels are identical to Fig. 3c. The y-axis values represent arbitrary linear units (a.u.), and are not comparable across different datasets, but are scaled equivalently across each of the six subpanels for the same dataset (shown with the same fill colour). Individual plotted values can be found in Supplementary Data 5.
Extended Data Fig. 6 Architecture at LTRs.
Heatmaps show the occupancy of the TFIIIB (Bdp1 and TBP/Spt15) components of the Pol III PIC and the TFIIB (Sua7) components of the Pol II PIC at the five Ty LTR classes, along with the nucleotide composition (±100 bp from the LTR start; from yeastepignome.org; Gs, As, Ts and Cs are in yellow, red, green and blue, respectively). All rows are linked and sorted by LTR class, then length.
Extended Data Fig. 7 Properties of inducible (STM), insulated (TFO) and constitutive (UNB) Pol II promoters.
a, NDRs are nucleosomal in vitro, while NFRs are mostly nucleosome-free. Heatmap of in vitro reconstituted MNase H3–ChIP nucleosomes (right), aligned by in vivo +1 nucleosome dyads and sorted by distance between the in vivo +1 nucleosome dyad and the first upstream stable nucleosome (USN) dyad (in vivo, left). b, Insulator ssTFs uncouple divergent PIC assembly. Shown are correlation coefficients between PIC (TFIIB/Sua7) occupancy (100-bp window centred on the TSS) at divergent TSSs that share the same promoter region. Data are presented as means ± s.d., from N = 6 biologically independent experiments, using two-tailed t-tests with no multiple comparisons. RP and STM promoters are merged here. c, Insulation at tandem genes. Shown are composite plots of PIC occupancy (green, TFIIB/Sua7) for promoter regions that share an upstream termination region (that is, tandem genes). Pcf11 (a representative termination factor) is in light brown, along with all 78 ssTFs (cyan), either collectively (‘TF’, top two panels) or individually, as indicated. STM, TFO and UNB composites are shown. The top ten insulator-associated ssTFs are based on the number of TFO promoters bound.
Extended Data Fig. 8 ChIP–exo patterning reveals distinct local ssTF environments.
a, Shown are strand-separated composite plots of 78 ssTFs bound at their cognate sites, and grouped by their meta-assemblage label (coloured borders, as defined in Extended Data Fig. 1c). Plots are oriented and centred by motif, and extend from −100 bp to +100 bp. Patterns were highly penetrant across individual sites for each ssTF (for example, see the lower right panel in Extended Data Fig. 2 for Reb1 and yeastepigenome.org for other ssTFs). b, ChIP–exo composite profiles of individual subunits of the Mediator complex at motifs that are bound by the ssTF Yrr1 (from −500 bp to +500 bp), showing consistency of patterning across Mediator subunits.
Extended Data Fig. 9 Properties of promoters bound by STM cofactors.
a, Venn diagram showing promoters that have overlapping locations of STM cofactors (greater than 0 ChExMix calls in the dataset ‘SampleIDs’ in Supplementary Data 2 (10A)), as well as Z scores for pair-wise overlaps. b, Representative architecture of STM cofactors or PIC components at a consolidated set of ssTF motifs at 984 STM promoters (not strand-separated; see Methods and Supplementary Data 1 (1AI)), oriented by TSS. c, Frequency distribution of promoters having the indicated PIC/TFIID (GTF/Taf2) occupancy ratios. The GTF that was used to measure occupancy is indicated at the top of each panel. These six GTFs were averaged in Fig. 5c. ‘MA’, three-bin moving average.
Extended Data Fig. 10 Circuits involving interactions between ssTFs and cofactors.
Genes encoding ssTFs are capitalized and are connected to their encoded proteins (ovals). Arrows connect ssTFs to other ssTF-encoding genes, to which they are bound via their cognate motif. ssTFs that bind to their own genes or create a loop are indicated with blue arrows (light blue where a motif was not detected for ssTFs binding their own gene). ssTFs are colour-coded on the basis of their meta-assemblage membership (see key). Promoter-bound ssTFs that are also particularly enriched with bound cofactors have coloured halos. Short diagonal black arrows point to the total number of all 5,378 coding genes that are bound by that ssTF to its cognate motif (first number given) or where no motif was detected (second number). The average relative PIC (TFIIB/Sua7) occupancy levels for those sets of genes is indicated by the number of green dots.
Extended Data Fig. 11 Isolated ‘circuits’.
a, Colours demarcate series paths. b, Colours emphasize different meta-assemblages, as defined in Extended Data Fig. 10.
Supplementary information
Supplementary Information
This file contains Supplementary Notes (Evidence that most UNB genes are unlikely to be bound by TFs or STM cofactors), Supplementary Fig. 1, and additional references.
Supplementary Data 1
List of features and genome coordinates used in this study. A description of the information found in each column is described in the tab labeled “Key.”
Supplementary Data 2
Target list, including those that passed and failed QC, and progressed into UMAP analysis. UMAP/Kmeans cluster ID, labels, related biochemical complexes, and derived meta-assemblages for each target are listed. Binary (bound vs not bound) calls are made for target datasets against every genomic feature, along with averages for feature classes. Penetrance of each target into each promoter classes is reported. An overlap between peaks from this study with peaks from MacIsaac et al53 is presented in the tab labeled “MacIsaac Comparison.” -log10 p-values are de-rived from ChiTest function.
Supplementary Data 3
Values report the number of overlapping ChExMix peaks between any two of the 371 targets examined in this study, that is illustrated in Fig. 1b.
Supplementary Data 4
General description of data resources and code availability.
Supplementary Data 5
Values for individual composite traces, as plotted in main and extended data figures.
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Rossi, M.J., Kuntala, P.K., Lai, W.K.M. et al. A high-resolution protein architecture of the budding yeast genome. Nature 592, 309–314 (2021). https://doi.org/10.1038/s41586-021-03314-8
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DOI: https://doi.org/10.1038/s41586-021-03314-8
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