# G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing

Cyril Esnault, Amal Zine El Aabidine, Marie-Cécile Robert, Anne

Cucchiarini, Talha Magat, Alexia Pigeot, Soumya Bouchouika, Encar

Garcia-Oliver, Kevin Gawron, Eugénia Basyuk, et al.

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► To cite this version:

Cyril Esnault, Amal Zine El Aabidine, Marie-Cécile Robert, Anne Cucchiarini, Talha Magat, et al. G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing. Nature Genetics, 2025, 57 (8), pp.1981-1993. 10.1038/s41588-025-02263-6. hal-05337666

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G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing

Cyril Esnault¹#, Amal Zine El Aabidine¹, Marie-Cécile Robert¹,²,⁸, Anne Cucchiarini³,

Talha Magat¹, Alexia Pigeot¹, Soumya Bouchouika¹, Encar Garcia-Oliver¹, Kevin

Gawron¹, Eugénia Basyuk¹,⁴, Magdalena A. Karpinska¹,²,⁸, Alja Kozulic-Pirher¹,², Yu

Luo³,⁵, Daniele Verga⁵, Raphael Mourad⁶, Ovidiu Radulescu⁷, Jean-Louis Mergny³,

Edouard Bertrand¹,²,⁸ and Jean-Christophe Andrau¹,⁹#

¹ Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS-UMR 5535, 1919 Route de Mende, 34293 cedex 5, Montpellier, France.

² Institut de Génétique Humaine, CNRS-UMR9002, University of Montpellier, 141 rue de la Cardonille, 34396, Montpellier, France.

³ Laboratoire d'Optique et Biosciences, Ecole Polytechnique, CNRS, Inserm, Institut Polytechnique de Paris, 91120 Palaiseau, France.

⁴ Laboratoire de Microbiologie Fondamentale et Pathogénicité, CNRS-UMR 5234, Université de Bordeaux, Bordeaux, France.

⁵ Université Paris Saclay, CNRS UMR9187, INSERM U1196, Institut Curie, Orsay, France.

⁶ LBCMCP, Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France.

⁷ Laboratory of Pathogen Host Interactions, UMR CNRS 5235, University of Montpellier, Montpellier, France.

⁸ Equipe labélisée Ligue Nationale Contre le Cancer, Montpellier.

⁹ Equipe labélisée Fondation pour la Recherche Médicale

# Corresponding authors:

Cyril.esnault@igmm.cnrs.fr and jean-christophe.andrau@igmm.cnrs.fr
## 28 Abstract

Despite their central role in transcription, it has been difficult to define universal sequences associated to eukaryotic promoters. Within chromatin context, recruitment of the transcriptional machinery requires promoter opening but how DNA elements could contribute to this process is unclear. Here, we show that G-quadruplex (G4) secondary DNA structures are highly enriched at mammalian promoters. G4s are located at the deepest point of nucleosome exclusion at promoters and correlate with maximum promoter activity. We found that experimental G4s exclude nucleosomes *in vivo* and *in vitro* while favouring strong positioning. At model promoters, impairing G4s affected both transcriptional activity and chromatin opening. G4 destabilization also resulted in an inactive promoter state and affected transition to effective RNA production. Finally, G4 stabilization resulted in global reduction of proximal promoter pausing. Altogether, our data introduce G4s as *bona fide* promoter elements allowing nucleosome exclusion and facilitating pause release by the RNA Polymerase II.

## 42 Introduction

Initially defined by analogy to bacterial transcription models ¹⁻³ and based on *in vitro* transcription assays from the pre-genomic era, eukaryotic core promoters were defined as ‘the minimal stretch of contiguous DNA sequence that is sufficient to direct accurate initiation of transcription by the RNA polymerase II machinery’ ⁴. However, and unlike in bacteria, eukaryotic promoters require nucleosome exclusion for transcriptional machinery recruitment. Beyond core promoters, eukaryotic promoters include broader sequence contexts that vary across species but commonly generate a well-positioned nucleosome array and open chromatin ⁵,⁶. In mammals, CpG islands (CGIs) contribute to chromatin opening, independently of transcription, due to their high GC and CpG content ⁷,⁸.

Promoters are also highly enriched in potential DNA secondary structures, suggesting that these structures play a role in transcription regulation ⁹. Among these, G-quadruplexes (G4s) are single-stranded and stable structures that consist in the planar arrangement of stabilized guanines that are over-represented in regulatory regions. Their predicting sequences are present at over a million occurrence in human genome and more specifically at promoters. However, and to date, it was unclear how they could contribute transcription since it was described that they either activate or repress transcription depending on promoter contexts ¹⁰⁻¹³. Furthermore, while it was proposed that their formation *in vivo* could be dependent on high level of transcriptional activity ¹⁴,¹⁵ and that mutating of a specific G4 in the Myc promoter could result in a change of TSS¹⁶, the possibility that they could represent promoter elements on their own was never directly tested.

Here, we identify G4s as key mammalian core promoter elements. Using predicted and experimental G4 data, both *in vitro* and *in vivo*, we find G4s located at the deepest
point of nucleosome exclusion at promoters, correlating with maximum promoter activity in the SURE assay. Furthermore, G4s exclude nucleosomes both *in vivo* and *in vitro* and exhibit strong nucleosome positioning potential. Mutating G4s in model promoters affected transcription activity and chromatin accessibility. Destabilizing G4s increased the probability of promoters being in an inactive state (OFF times) and impaired the transition to effective RNA production in live imaging experiments. Conversely, stabilizing G4s using ligands globally reduced promoter-proximal Pol II pausing, aligning with live imaging results.

Altogether, our findings establish G4s as functional promoter elements that enable nucleosome exclusion and facilitate Pol II pause release. These results provide a new perspective on promoter architecture, emphasizing G4s as critical components of transcription regulation.

## G4s are enriched at promoters and correlate with activity

Based on the knowledge that the TFIID general transcription factor (GTF) binds naked DNA *in vitro* in a window frame of 40-50 bp around transcription start sites (TSSs) ¹⁷, sequence analyses of core promoters were often restricted to this window frame. These searches often yielded motifs poorly enriched, lowly conserved in evolution or highly degenerated ¹⁸,¹⁹. Because the most open areas of chromatin extend on average up to 100 bp, we performed a motif search on core promoters associated to open chromatin upstream and downstream (-100/+20) of experimental TSSs in three mammalian cell types (primary T cells, K562 and Raji cells – Fig. 1a and Extended Data Fig. 1; Table S1 describes all data sets). This analysis revealed a prominent G-stretch containing-motif in the 3 cell types, highly compatible with the formation of G4s *in vitro*, using G4Hunter (G4H) predictions ²⁰ at various stringencies. To consolidate this result, we investigated the frequency of predicted G4s (pG4s) or other motifs identified. We found overall that stringent G4 predictions (G4H1.5 and G4H2.0) ²⁰ show very high frequency as well as a strong enrichment above control sequences (observed/expected) ⁸,²¹. Enrichments of pG4s (20-45%) are also higher than Ets and NF-Y and far above the TATA box motifs (Fig. 1a, Extended Data Fig. 1 and Table S2). Their enrichment is similar to BRE and SP1 motifs, both compatible with G4 formation (Extended Data Fig. 2a). Finally, we note that pG4s containing promoters exhibit less of the other motifs (Extended Data Fig. 2b), suggesting that they have more propensity to function autonomously.

A large fraction of mammalian promoters was shown to be intrinsically bidirectional²²,²³. We thus thought to analyse the enrichment of pG4s upstream of sense and antisense experimental TSSs. This showed that pG4s peak on average at positions at -56 and -51 of the TSS, respectively (Fig. 1b). We note that the presence of a canonical or non-canonical TATA box influences a far more directional and focused transcription (Extended Data Fig. 2c) as described previously ⁸,²¹.
To further investigate whether G4s contribute to transcription initiation and promoter activity, we took advantage of four orthogonal approaches for G4 formation assessment, including G4access, a technology we recently developed<sup>24</sup>, which allows mapping G4s globally in the chromatin context (Fig. 1c). We also used G4 ChIP<sup>14,25</sup>, mapping G4s in living cells, G4-seq<sup>26</sup> globally assessing G4s *in vitro* and ssDNA-seq<sup>27</sup>, mapping single-stranded DNA (ssDNA) genome-wide. We performed G4access in Raji and K562 cells and processed published data sets for the other methods whenever available (Extended Data Fig. 2d-e). In this analysis, we ranked the promoters containing pG4s (G4Hunter > 2.0) by increasing G4 signal. G4-seq and ssDNA-seq in Raji cells were used to further validate folded G4s. G4access and G4 ChIP outputs of G4 measurement are very comparable. SsDNA-seq globally confirmed G4 formation in Raji cells while G4-seq validated that selected sequences can form G-quadruplexes *in vitro*. Since G4-seq monitors genomic G4s outside of nuclear context, signals of the 6 groups originally defined from low to high G4 formation in living cells remain largely unchanged. This further indicates that G4access and ChIP do map G4s in the context of chromatin in living samples.

We then compared G4 formation to data sets (Table S1) monitoring nascent transcriptome and large-scale measurement of promoter activity assay (SURE)<sup>28</sup> (Fig. 1d-e). As illustrated at BTG2 promoter, G4s are located at the midpoint of Pol II signals that reflect promoter divergent transcription. They also hallmark maximum promoter activity assessed by SURE (Fig. 1d). To establish this statement globally, we analysed the correlation of G4access at pG4 promoter locations with that of SURE and Pol II and found that both increase with experimental G4 levels (Fig. 1e). In addition, we observed that pG4 locations overlapped with the midpoints between sense and anti-sense transcription initiation mapped by ChIP-exo or ChIP-seq of Pol II, TBP, TFIIB<sup>29</sup> and GRO-CAP<sup>30</sup> (Fig. 1f and Extended Data Fig. 2g). Finally, we also observed that pG4s located at promoter are found upstream of R-loops (Extended Data Fig. 2f) that form where Pol II and GRO-cap signal rise, on the side of the pG4s (Fig. 1f).

Collectively, these analyses show that pG4s represent major motifs of extended core promoters, located on average at a relatively fixed position from TSSs. Moreover, G4s correlate with both promoter activity and midpoints of divergent transcription (Fig. 1g).

## G4s carry an intrinsic ability for nucleosome exclusion

Apparent Nucleosome Depleted Regions (NDRs) are hallmarks of core promoters in eukaryotic cells allowing space for preinitiation complex (PIC) recruitment<sup>18,31</sup>. To understand the link between G4s/pG4s and nucleosome positioning, we performed nucleosome mapping by MNase-seq in Raji cells and re-analysed published datasets, for nucleosomes and active epigenetic marks in our two other mammalian models (Table S1). Strikingly, we found the center of NDRs overlapping with pG4s at a very large fraction of promoters (~91%), including at the BTG2 promoter described above (Fig. 2a). We then investigated all active promoters that contain pG4s and confirmed
that pG4s are found at the deepest points of NDRs globally (Fig. 2b, Extended Data Fig. 2h). By comparing increasing G4 signals to promoter opening, we observed less opening in the absence of G4access signal (group 1). Conversely, deeper NDRs were observed when G4access was present (group 2-5), suggesting a threshold effect in the G4access signal. This effect could at least in part be related to the lower GC/CpG content of group 1 (but not the other groups that are more comparable, Extended Data Fig. 2d). Active histone marks increased together with G4 strength (group 1-6), indicating overall chromatin opening and modifications depending on the G4 formation (Fig. 2b). At inactive promoters, the presence of predicted G4s also hallmarked Polycomb-deposited H3K27me3 inactive chromatin mark (Extended Data Fig. 2i). This set of promoters carries the hallmark of CGIs, with strong GC and CpG content as expected for Polycomb signal.

To further formally exclude that pG4-dependent nucleosome exclusion could depend on sequence or transcription factor binding sites (TFBS) features in vivo rather than the G4 itself, we performed additional analyses. For this, (i) we combined G4 experimental signal and predictions and cell type-specific analyses, (ii) G4 analyses in the presence or absence of NDRs and (iii) the interplay between G4 formation and GC-box motifs containing motifs, allowing us to reasonably conclude to G4 involvement in nucleosome exclusion (Extended Data Fig. 3 and 4; Supplementary note 1).

Next, we tested whether nucleosome exclusion at pG4s was dependent on transcriptional activity. For this, we analysed nucleosome densities at inactive promoters that were separated into two groups, with or without pG4s. Interestingly, only pG4-containing promoters showed significant nucleosome exclusion (Fig. 2c). We confirmed that transcription is not required for nucleosome exclusion at pG4s of active promoters by inhibiting Pol II transcription with triptolide without substantial loss of NDRs (Extended Data Fig. 5a). To further assess whether transcription activation was influencing G4 formation, we performed a serum shock which results in the rapid induction of tens of genes. In these conditions, serum-responsive genes already harbour G4access signals prior activation that increase after 15 min of activation (Extended Data Fig. 5b-d). This result is consistent with the previously described chromatin opening prior activation of constitutively bound SRF sites³².

To further infer the direct link between pG4 sequences and chromatin opening, we made use of nucleosomes assembled *in vitro* with human genomic DNA and recombinant histone³³. As before, promoters were split in two groups, with or without strong pG4s (Fig. 2d and Extended Data Fig. 5e). This analysis revealed that only the pG4-containing group associates with nucleosome exclusion. Thus, pG4 DNA carry the intrinsic ability to exclude nucleosomes since *in vitro*, in absence of any other transcription factor or proteins, we could observe this property. It also indicates that nucleosome occupancy and G4 formation are mutually exclusive. This ability to exclude nucleosome is similarly observed for the SP1 unbound promoters (Extended
187 Data Fig. 4). Together, our data show that at both active and inactive promoters, G-
188 quadruplexes promote intrinsic nucleosome exclusion *in vivo* and *in vitro*.

189 G4 forming sequences are nucleosome organizers

190 Next, we investigated chromatin opening and organisation around G4 predictions at
191 non-promoter locations, including intergenic regions. These locations are mainly not
192 transcribed and yet, pG4s are associated to nucleosome exclusion (Fig. 3a) indicating
193 that these sequences carry this property over the whole genome and not only at
194 promoters. Further investigation of *in vitro* reconstituted chromatin<sup>33</sup> at all genomic
195 locations including promoters, intergenic and intragenic regions demonstrated their
196 intrinsic abilities to evict nucleosome globally (Extended Data Fig. 6a-b). Nucleosome
197 exclusion was found at over 90% for promoter pG4s, and over 80% for intra and
198 intergenic pG4s (Extended Data Fig. 6b). Interestingly, the regions showing the largest
199 nucleosome exclusion areas also showed the highest G4 prediction densities (upper
200 part of the heatmaps), possibly because their presence in clusters increases chances
201 of G4 formation. These observations are consistent with the high stability of G4 single-
202 stranded DNA structures<sup>34,35</sup> which makes them incompatible with nucleosome
203 formation. To test that *in vitro* nucleosome exclusion specifically relies on G4 formation
204 and not just on the GC content, we used DMS-seq and G4-seq data to monitor the
205 ability of a given genomic sequence to form a G4 *in vitro* in either double-stranded
206 DNA (dsDNA) or ssDNA context, respectively<sup>26,36</sup>. Intergenic pG4s (G4H2.0) and
207 controlled regions of exact same GC distributions but with low G4hunter scores
208 (G4H<1) were used in this comparison (Extended Data Fig. 6c). Strikingly, pG4s show
209 high DMS footprinting and G4-seq signals and are largely associated with nucleosome
210 exclusion *in vitro*, while sites unable to fold into G4 are associated with nucleosome
211 occupancy (Extended Data Fig. 6d).

212 To infer how nucleosomes behave around G4 forming sequences, we analysed both
213 nucleosome densities and midpoints around intergenic G4 predictions in our model
214 cell lines. Midpoints analyses allow to better assess if given sequence locations display
215 positioning properties (Fig. 3a). This clearly revealed a high level of positioning
216 associated to pG4s in the model cells. The periodicity of nucleosome positioning is
217 highly similar to nucleosomal organization around specific pioneer transcription factors
218 <sup>37</sup> or CTCF insulator <sup>38</sup> showing an almost identical nucleosomal repeat length (Fig.
219 3b,c), although with a more modest amplitude, in line with previous observations <sup>39</sup>.
220 Hence, our results highlight the association of pG4s with open chromatin regions at
221 promoters and at IGRs where pG4s also associate with nucleosome array
222 organization. Although experimental G4 signals correlate with transcription, pG4/G4
223 driven nucleosome depletion appears independent of transcription. This observation is
224 consistent with results described recently by us and others <sup>40,41</sup>. Our results support a
225 novel and previously unappreciated role for G4s as global chromatin organisers at
226 transcribed and un-transcribed regions.
227 Increased chromatin opening at CpG islands containing pG4s

228 Since CpG islands (CGIs) are able to promote nucleosome depletion at promoters ⁸,
229 we wondered what was the contribution of pG4s in this process. To address this
230 question, we considered all human CGI annotations containing or not strong G4
231 predictions (Fig. 4). We analysed genomic features associated to experimental G4s:
232 nucleosome positioning, active chromatin marks, transcription and promoter activity.
233 As expected, G4access and ChIP-seq show stronger signals at CGIs harbouring pG4s.
234 In addition, active histone marks (H3K4me3 and H3K27ac) and Pol II are also
235 enhanced in this class (Fig. 4a,b). Furthermore, nucleosome occupancy exhibits wider
236 and deeper chromatin opening at pG4-containing CGIs. Finally, the analysis of
237 promoter activity by SURE assay confirmed that G4-containing CGIs have a ~1.5 time
238 higher promoter activity. To further validate our results, we confirmed our analyses on
239 a set of CGIs of same lengths and CG contents (Extended Data Fig. 7a) with a more
240 stringent selection of non-G4 forming sequences. The G4 forming sequences were
241 considered with a G4Hunter score> 1.5 and non-forming sequences for a G4Hunter
242 score< 1.2. This analysis confirmed the association of G4 forming sequences to more
243 open chromatin and active transcription and epigenetic marking within CGIs (Extended
244 Data Fig. 7b,c). Altogether, our observations support the notion that features
245 characteristic of promoters are enhanced in the presence of both experimental and
246 predicted G4s at CGIs and that G4s might represent essential determinants of CGI's
247 ability to exclude nucleosome.

248 G4 promoter mutations decrease transcription in single cells

249 To fully demonstrate that G4s are promoter elements that extend the concept of the
250 core promoter, we mutated G4s in model promoters. We inserted G4-containing
251 mouse promoters in the human HeLa cells using a Flip-in system 42 and upstream of
252 a reporter containing 256xMS2 repeats allowing single cell measurement of
253 transcription by smRNA FISH (Fig. 5a). We chose promoters with a strong G4
254 prediction that was verified experimentally using G4access 40 or G4 Cut&Tag 43 in
255 mouse ES cells (Extended Data Fig. 8a,b). We also observed that most of these
256 promoters displayed a signal in ssDNA-seq experiments *in vitro*, and for some of them
257 *in vivo* in mouse T cells³⁹. For 3 out of 5 models (Taok1, Pkm and Klf6), the sequences
258 did not contain any TATA box, and for 3 of them no SP1/GC box site (Taok1, Pkm and
259 Klf6). We designed G4 mutations that minimally affected the promoter primary
260 sequence while impairing the G4 potential (Fig. 5b, Table S3 and S4, Extended Data
261 Fig. 8c and Supplementary note 2). For Eef1a1, one mutant broke the G stretches with
262 A (*G4mut*), and the other with C (*G4mut2*) respecting the GC content of the sequence.
263 To quantify the transcriptional output of the model promoters, we performed single cell
264 measurements using smRNA FISH over hundreds of cells. The data presented in Fig.
265 5c show G4 mutants' transcription as compared to their WT promoter counterparts.
266 The level of nascent RNA reduction ranked from 23 to 80% in the mutants, indicating
267 that the G4 mutations had a significant effect on transcription of the core promoter.
Interestingly, inverting the G4 orientation in the promoter did not impact on its activity in the case of the Eef1a1 model (Eef1a1inv), suggesting that the G4 functions as promoter element in an orientation independent manner.

Together, our data show that G4 mutations impair transcription quantitatively and establish that G4-forming sequences most likely function as promoter elements in an orientation independent manner.

## 274 Modelling transcription with G4 and TATA box elements

To decode the direct effects of G4s as promoter elements on transcription, as compared to that of the TATAbox, we performed further mutational analyses in the context of the Eef1a1 promoter that contains both a canonical TATA box and a stable G4. We mutated the G4 (using *G4mut*), the TATAbox, or both elements (Fig. 6a). We first analysed their ability to promote transcription, to recruit the pre-initiation complex and to assemble chromatin. Our smRNA FISH of MS2 reporter results show that both G4 and TATA elements are required for full activity (Fig. 6b). We found that pG4 mutation display more pronounced effects (~60%) while less impact of the TATAbox was observed, and an additive or synergic effect seems at play in the double mutant.

To infer how the G4 and TATAbox influence promoter activity and transcription, we then analysed nucleosome organization and PIC assembly at these promoters in bulk assays. MNase-PCR assay revealed that G4 mutation led to increased nucleosome density at the NDR location, that also corresponds to the G4 structure coordinate (Fig. 6c). Remarkably, this effect was not observed in the TATAbox mutant. The double mutant showed synergic effects of the elements, consistent with nascent transcription data. These results suggest that the pG4 is the main element controlling Eef1a1 promoter opening and that the TATAbox influence could only be seen in the *G4mutTATAmut* context. We next monitored PIC recruitment using Pol II and TBP ChIP qPCR assays. Interestingly, while Pol II recruitment was reduced in all mutants, TBP binding was only impaired when the TATAbox was mutated but not in the *G4mut* (Fig. 6d). We conclude that the primary effect of the pG4 mutation is to restrict chromatin and Pol II accessibility but not TBP recruitment, while the TATAbox mutation affects TBP and ultimately Pol II recruitment (Fig. 6a-d).

Next, we analysed the Eef1a1 promoter dynamics and transcription in living single cell by imaging an MCP-GFP fusion protein. We quantified transcription sites in hundreds of cells including permissive (ON) and inactive (OFF) periods (Extended Data Fig. 9a, Fig. 6e and Supplementary note 3). These analyses clearly show that both TATA and G4 mutants displayed shorter ON and longer OFF periods. Moreover, OFF periods were slightly longer for the G4 mutant as compared to *TATAmut* (Fig. 6e,f). To complete this picture, we also recorded short movies at high temporal resolution, to model the entire dynamics of the Eef1a1 promoter (Fig. 6g). By modelling the distribution of initiation events and interpolating the signal intensities (Extended data
Fig. 9b) ⁴⁴, we were able to show that the WT promoter is described by a two states promoter model (ON and OFF). In contrast, both G4 and TATA mutants require three states model to describe the experimental data, with the additional promoter state being a long-lived inactive state (~2h lifetime; Fig. 6h). Given the results of the biochemical analysis of the mutant promoters, the additional state likely corresponds to a TBP unbound state for the *TATAmut*, and a closed chromatin state for *G4mut*. Moreover, examination of transcription initiation rates derived from the models (k3), indicates a slower transition into processive elongation state for the G4 mutant (0.3 vs 0.15 seconds). These live cell kinetic data are consistent with the idea that G4 mutations severely limit chromatin opening. In addition, changes in k3 (Fig. 6h, Extended Data Fig. 9b,c) suggest that G4 mutations also slow down promoter escape and/or Pol II pause release (Fig. 6h). We also analysed Eef1a1 *G4mut2* and observed comparable increase of OFF times (Extended Data Fig. 9d,e).

Our observations on the model Eef1a1 promoter (summarized in Fig. 6h) lead us to propose that the role of G4s at promoters is to promote and maintain nucleosome exclusion as a prerequisite for stable PIC recruitment. In contrast, when the TATAbox is mutated, formation of an active promoter state is also impacted due to defects in TBP and PIC recruitment. Collectively, our reporter experiments in bulk and single cells further support the role of G4s/pG4s as promoter elements, conditioning nucleosome exclusion and the rates of promoter transition toward an active state competent for transcription.

## G4 stabilization results in Pol II promoter pause release

To investigate the influence of G4 stabilization on transcription at the global level, we treated human Raji cells with Pyridostatin (PDS), a well-known G4 ligand ⁴⁵. This ligand stabilizes G4 structures by limiting the transition from folded G4 to unstructured ssDNA or ds B-DNA ⁴⁵. Therefore, since promoters are highly enriched in pG4s, their stabilization could either positively or negatively impact transcription. To address this question, we monitored PDS effects on Pol II densities and nascent transcripts, using ChIP-seq, chrRNA-seq and TT-seq. We used short time points of treatment (10 to 60 min) to avoid indirect effects ⁴⁵,⁴⁶ resulting from the appearance of double-strand breaks at later time points (Extended Data Fig. 10a). In these assays, we observed that PDS led to changes in Pol II profiles after only 10 minutes of treatment, with increased signal in gene bodies while signal at promoters was decreased. Examples in Fig. 7a illustrate this effect for model genes in which Pol II release is illustrated by either promoter density decrease, gene body increase or both. These effects most likely reflect a general promoter proximal pause release of Pol II. We confirmed a global decrease of Pol II pausing by computing apparent pausing scores at the genome-wide level (Fig. 7b and Extended Data Fig. 10b). The effect was found more pronounced for a subset of 602 genes, enriched in stress and RNA processing functions (Extended Data Fig. 10c). Consistent with pause release, Pol II average profiles at this subset show a decrease around TSSs and an increase of Pol II density over gene bodies (Fig. 9d,e).
348 7c-e). This was further confirmed by nascent RNA-seq analyses (Extended Data Fig.
349 10d,e). Because PDS treatment was short and Pol II average speed did not allow to
350 cross all gene bodies during the 10 first min, we analysed Pol II changes on long genes
351 (Fig. 7f). This revealed a clear pause release effect of PDS over the first 27 kb,
352 consistent with an elongation rate around 2.5-3 kb/min as previously described 47.
353 Furthermore, and strikingly, a clear correlation appeared between the different level of
354 pause release and the G4 potential of their corresponding promoters (Extended Data
355 Fig. 10g). At later time points, reduced pausing remains up to 30 min while the impact
356 of PDS is reversed after 60 min over gene bodies (Extended Data Fig. 10f). Finally,
357 G4access and nucleosome dynamics indicated a global G4 increase at all promoters
358 while nucleosome densities were essentially unaffected (Extended Data Fig. 10h,i).
359 Overall, our observations suggest that G4 stabilization results in increased ability for
360 Pol II to escape from pause states. This is also consistent with our mathematical
361 modelling of the Eef1a1 model promoter where G4 mutations affected efficiency of Pol
362 II released from the promoter.

363 Discussion

364 Our study has shown that G4s are highly enriched in mammalian core promoters. We
365 propose a novel function for these structural elements that is to intrinsically exclude
366 nucleosomes. This function could define a previously unappreciated essential property
367 of promoters in vivo. The mechanism of nucleosome exclusion by G4s could be simply
368 explained by the incompatibility of stable single-stranded DNA formation and its
369 incorporation into stable nucleosomes. Since G4s are not significantly present in all
370 eukaryotic promoters 48, other secondary structures or sequence context could have
371 the same role in other organisms, for example AT stretches in yeast 49,50. This G4
372 property is however in contrast to previous observation proposing that it is transcription
373 that stimulates G4 formation based on in vitro transcription assays 15 or transcription
374 activation in vivo 14, but in line with more recent observations 41, including from our
375 laboratory 40. In this work, we show that transcription inhibition results in maintenance,
376 but reduction of G4 signal indicating that transcription does not precede G4 formation
377 at promoter but that it does increase or stabilize its structure. Here, using serum shock,
378 we further show that activated genes often show preformed G4s at their promoters,
379 that become further enhanced during activation. Taken altogether, our data support a
380 model in which G4s are formed prior from/or in the absence of transcription since
381 pG4s/G4s are detected at NDRs in vitro or at silent promoters and since transcription
382 inhibition does not significantly alter NDRs at pG4 locations. The presence of frequent
383 TFBS motifs in addition to pG4 elements also opens the possibility that PIC recruitment
384 in vivo does not directly rely on motifs that recruit GTFs but rather on various already
385 bound TFs, possibly in combination to co-activator, to allow further recruitment of the
386 PIC. Arguing also for this model is the observation that TFIID does not contact DNA
387 on its own upstream of TSSs on TATA-less promoters 51,52, which represent the vast
388 majority of promoters, but is rather recruited through direct chromatin contacts 53-55 or
389 via interaction of TF such as NF-Y 56. Interestingly, recent work has shown that ATRX-
390 DAXX actively suppresses G4 formation and promotes heterochromatin formation,
391 providing a nice duality opposing G4s and chromatin<sup>57</sup>.

392 Our data are consistent with a role of G4s favouring pause release by Pol II. First,
393 modelling transcription at the Eef1a1 G4 mutant promoter indicates one additional
394 limiting step for transcription (OFF1) likely corresponding to chromatin opening in the
395 absence of G4. Another limiting step includes transition to productive elongation, which
396 comprises pausing, also show a significant time increase (k3). Second, stabilizing G4s
397 globally with PDS *in vivo* resulted in pause release of genes containing stronger G4s
398 at their promoter. These results are in apparent contrast with recent work indicating
399 that treatment with another G4 ligand reduces transcription initiation <sup>58</sup>. However, the
400 times of treatments performed in this study were much longer, opening the possibility
401 for more indirect effects, including accumulation of DNA breaks. Overall, our data are
402 in agreement with previous work having shown that G4-containing promoters tend to
403 show less poised Pol II <sup>59</sup>. How could thus G4s facilitate pause release? Because G4s
404 at promoter exclude nucleosome, the presence of one or multiple G4s would favour
405 not only open chromatin but also a pre-melted template for Pol II. Such structures
406 would potentially facilitate the formation and the extension of the transcription bubble.
407 As a consequence, crossing the +1 nucleosomal barrier would become easier for the
408 Pol II complex, resulting in pause release. The comparison of experimental G4 signal
409 (G4access) to ssDNA scored by KMnO4 footprinting supports this hypothesis since it
410 indicates a correlation between G4 formation and open complex at active promoters.
411 Finally, and given previous links made between the action of G4 stabilisation by PDS,
412 pausing and TopII<sup>60,61</sup> that could relax supercoiling locally by promoting DSBs at the
413 promoter level, one could also envision that TopII comes into play during that process,
414 even though our data do not indicate γ-H2AX accumulation at early time points.

415 Another striking property of G4 forming sequences, also visible at experimental G4s
416 determined by G4access, is their ability to position nucleosomes. The nucleosome
417 repeat length observed is in the range of that described for CTCF but also of strongly
418 positioning nucleosome *in vitro* <sup>33</sup>. Because G4s tend to be present as clusters in
419 promoters, at these locations, nucleosome positioning is less visible probably due to
420 the presence of multiple G4 introducing fuzziness in the adjacent nucleosomes and to
421 their additive effects. We do not know at this stage if the positioning property in
422 intergenic regions (IGRs) is inherent to G4 structure or could be explained by the
423 recruitment of G4 binders. In any of these scenarios, the barrier constituted by the G4s
424 would dock the surrounding nucleosomes. It will thus be interesting in future studies to
425 investigate the precise interplay between G4 and CTCF since recent work suggest
426 they could be locally associated <sup>62,63</sup>.

427 Our data point to a role of G4 as driver of CGI's properties, possibly because they yield
428 a more robust and/or constitutive NDR. Over 70% of G4access or G4 ChIP <sup>25</sup> enriched
429 areas are actually located in CGIs and consistently, CGIs also contain large G4
430 clusters, increasing the likelihood of their formation locally. Our analyses suggest that
pG4s could be one essential determinant of the ability of CGIs to exclude nucleosomes, thus adding a novel determinant, besides GC and CpG content ⁷, of these essential areas of the genome. Importantly, while our study shows that G4 forming sequences behave as promoter elements by excluding nucleosome, it does not demonstrate per se that the G4 structures are formed *in situ* in the context of chromatin, nor that other structures are not at play where G4s are observed (see also Supplementary note 4). Nevertheless, the use of various orthogonal techniques to score for pG4s based on different principles and the pG4 ability to exclude nucleosome intrinsically pleads for their direct structural involvement as promoter element rather than protein docking sites on DNA. However, and as suggested by cell-specific and serum-induction analyses, G4-associated NDR formation is likely to be regulated and possibly integrated with other processes. All in all, our work opens a new gate in our understanding and definition of a promoter *in vivo* and readjusts the existing paradigms. It will also support future work on targeting secondary structures to control their activity using specific ligands in cancer therapy.

## 446 Acknowledgements

This work was supported by institutional grants from the CNRS including an 447 80prime2021 'Deciph G4', and a grant from "Agence Nationale de la Recherche" 448 (G4access, ANR-20-CE12-0023), Fondation pour la Recherche Medicale FRM 449 EQU202403018039, and INCA PLBIO20-225. CE was supported by a grant from ARC 450 (retour postdoc), EGO and SB by grants from EpiGenMed labex of excellence and 451 "Agence Nationale de la Recherche" (ANR-18-CE12-0019). JLM was supported by 452 Inserm, CNRS and Ecole Polytechnique. We are grateful to Salvatore Spicuglia, Dan 453 Fisher, Robert Feil and Mounia Lagha for critical reading of the manuscript and to 454 Lucille Esnault, Maxime Esnault, Florian Mueller and Rachel Topno for help with 455 quantification of live cells imaging data. 456

## 457 Author contributions

JCA and CE designed experiments; CE, EGO, TM, KG, SB and AP performed the 458 experiments; CE and AZEA analyzed genomic data; CE, EB, MK, MCR and EB 459 performed and interpreted the microscopy experiments, YL, AC, DV and JLM 460 characterized and interpreted the G-quadruplexes *in vitro*, OR performed the 461 mathematical modelling. AKP and RM contributed to early stage of the project. JCA 462 and CE conceived the project, suggested and interpreted experiments, and wrote the 463 article. All authors reviewed the manuscript. 464

## 465 Competing interests

Authors declare no competing interests.
467 **Table S1: Genomic files**

468 Table summarizing all resources of the genomic analyses presented in this study.

469 The table depicts the cell type, the experiment type and the source of the files.

470 **Table S2: Motif search analysis**

471 Frequency analyses of sequence features in a 120 bp window around experimental

472 TSSs (-100, +20). 14 motifs or features were analysed (BREd, BREu, ETS,

473 G4H1.5, G4H2.0, QP1-7, INR, NF-Y, Half G-quadruplex, SP1 motifs, two TATAbox

474 consensus and two negative control motifs).

475 **Table S3: Table oligonucleotides used in this study in vitro for G-quadruplexes**

476 validation assays.

477 These sequences were used for *in vitro* validation of G4 structure formation using

478 single stranded assays (IDS, TDS, CD, FRET-MC) and double stranded FRET-MC.

479 **Table S4: Sequence of all mouse model promoters used in this study.**

480 These sequences were inserted upstream of the MS2 reporter and inserted in Hela

481 genome using FRT system.

482 **Table S5: Table of qPCR oligonucleotides used in this study**

483 All oligonucleotide pairs used for qPCR are presented; sequences, complementary

484 genomes and genomic location of amplicons are described.

485 **Figure legends**

486 **Figure 1: Predicted G-quadruplexes (pG4s) are highly enriched at promoters and**

487 correlate with maximum of mammalian promoter activity.

488 (a) Promoter motif search around experimental TSSs highlights pG4 motifs in
489 various cells. (Top) Search area around experimental in our 3 model cell
490 types; (bottom) motif distances to experimental TSSs in K562, overlay of motif
491 densities, relative representation at promoters and enrichment over control
492 sequences are shown (INR= initiator). See also Extended data Fig. 1 and
493 Table S2 for detail of motif discovery analyses in the 3 model cells.

494 (b) pG4s mark genomic areas comprised between sense and antisense
495 promoters. Motif densities at K562 promoters show enrichment of pG4s
496 (G4H2.0) 56 bp upstream of sense experimental TSSs (n=8346) and 51 bp of
497 antisense experimental TSSs for genes where divergent transcription
498 initiation is detected (n=5689). See heatmaps of Extended data Fig. 1 for Pol
499 II and short chromatin RNA profiles. Green arrows indicate sense of
500 transcription.

501 (c) G4access principle. Chromatin is digested at moderate MNase level to which
502 G4s are resistant and reed in subnucleosomal fractions. These fractions are
503 purified and subjected to library preparation and high-throughput sequencing.

504 Details of the method are described in ⁴⁰.
505 (d) Example of a pG4 sequence fitting midpoint of upstream/downstream Pol II
506 and nascent transcription (chrRNA and GROcap) at the BTG2 promoter,
507 experimental G4s (G4access and G4 ChIP), and maximum of promoter
508 activity (SURE assay) in K562 cells are displayed. Sequence of the pG4
509 (G4H2.0) is indicated below the gene.

510 (e) Experimental G4 signals correlate with transcription and SURE promoter
511 activity assay. Groups 1-6 correspond to increasing level of G4access signals.
512 Green arrows represent the sense of gene transcription. Metaprofiles were
513 centred on the G4 motif upstream of TSS as shown below the tracks.

514 (f) pG4s are located at the midpoints of GROcap, Pol II and General
515 Transcription Factors using ChIP-exo datasets. See also Extended data Fig.
516 2e for analyses in Raji and Mouse T cells.

517 (g) Model of average pG4 locations as determined by G4Hunter algorithm
518 upstream of TSSs and as a midpoint of upstream and downstream Pol II
519 peaks.

520 See Table S1 for data sets used, references and GEO accession numbers.

<caption>Figure 2: G4s promote nucleosome exclusion at active and inactive promoters, *in vivo* and *in vitro* (see also Extended data Fig. 2-6).</caption>

523 (a) Example of a pG4 sequence fitting the maximum of the nucleosome depleted
524 regions (NDRs) at the Btg2 promoter in K562 cells using MNase-seq, and
525 ChIP-seq of active chromatin marks (H3K4me3, H3K27ac).

526 (b) Experimental G4 signals correlate with levels of nucleosome depletion and
527 with active histone modification marks. Promoters that harbour strong G4
528 predictions using G4Hunter algorithm G4H2.0 were ranked by G4access
529 signal as depicted in the heatmap, corresponding heatmaps of G4 ChIP,
530 G4H2.0, computed G4scores, MNase-seq, H3K4me3 and H3K27ac are also
531 shown (left). Promoters were split in 6 groups, as in Fig.1e. Metaprofiles of
532 nucleosome densities (Nucleosomes (Nucl.), MNase-seq) and of H3K4me3
533 and H3K27ac of all groups are displayed (right). See also Extended data Fig.
534 2h for other cell types. Green arrows represent the sense of gene
535 transcription.

536 (c) pG4 promote nucleosome exclusion at inactive promoters in K562 cells
537 (ENCODE). Transcriptionally inactive promoters (bottom 10% signals of Pol
538 II shown in left panel) were split in two groups with or without G4H2.0
539 prediction. See also Fig. 3 for pG4 influence on nucleosome positioning at
540 inactive intergenic regions and Extended data Fig. 5a after transcription
541 inhibition with triptolide.

542 (d) pG4-containing promoters have intrinsic nucleosome exclusion properties on
543 *in vitro* reconstituted chromatin (analysed from <sup>33</sup>). The promoter selections
544 are based on K562 active promoters shown in Fig. 1. See also Extended data
545 Fig. 6a-b for pG4s effect on nucleosome exclusion properties on *in vitro* at
546 inter and intragenic regions.
547 **Figure 3: pG4s organise nucleosome at intergenic regions (IGRs).**

548 (a) pG4s mark the center of organised nucleosome arrays at IGRs in K562 (ENCODE) and Raji (this study). All G4H2.0 predictions from IGRs are shown.
549 Heatmaps of nucleosome organisation mapped by MNase-seq is displayed.
550 Nucleosome positioning (Nucleosomes (Nucl.), see methods, left), MNase-seq density (right).
551
552

553 (b) Nucleosomal organisation at CTCF sites in K562 cells. Metaprofiles of
554 MNase-seq signal (Top), positioning (Bottom) and CTCF ChIP-seq
555 (GSE30263) at the 13775 identified binding sites in K562 cells.

556 (c) Nucleosomal organisation at CTCF and pG4 show similar nucleosome
557 phasing at IGRs. (Top) Overlay of metaprofiles of MNase-seq at IGRs centred
558 either on pG4s or CTCF ChIP-seq sites in K562 cells; (Bottom) the two
559 nucleosomes surrounding CTCF or pG4 sites (-1/+1) were aligned to compare
560 similarity of nucleosome phasing (observed nucleosome repeat lengths of 193
561 and 194 nt, respectively).

562 **Figure 4: pG4s and experimental G4s hallmark nucleosome exclusion at CpG islands (CGIs) (see also Extended data Fig. 7).**

564 (a) CGIs with pG4s have enhanced chromatin opening, histone modifications and
565 transcription activity. Heatmaps of the 27702 human CGIs were split in two
566 groups with (n=21520) or without (n=6182) pG4 (G4H1.5) annotations.
567 Corresponding signals for pG4, G4access, G4 ChIP, GC and CpG contents,
568 Pol II, nucleosomes (Nucl.) MNase-seq), chromatin marks and SURE
569 promoter activity are shown as indicated.

570 (b) CGIs with pG4s have deeper chromatin opening, increased active histone
571 modifications and promoter activity. Metaprofiles of all marks at CGIs with or
572 without pG4 displayed in the heatmaps from (a) displayed on a 2kb window.
573 Further selection and controls for this analysis are presented in Extended data
574 Fig. 7.

575 **Figure 5: G4 mutations impair promoter activity in single cells (see also**
576 Extended data Fig. 8 a-b and Table S4).

577 (a) Scheme of integrated reporter constructs for the indicated mouse G4-
578 containing promoters used for smRNA FISH. These constructs were
579 integrated in human genome (HeLa) using a Flp-In strategy. Experimental G4
580 signals for the mouse promoters in ES cells and *in vitro* are shown in Extended
581 data Fig. 8a-b.

582 (b) Sequence features and G4 structural assessment in the model promoters
583 indicated in (a). The G4 scores determined by the G4Hunter algorithm,
584 reflecting stability and likelihood of formation are indicated for WT and mutant
585 sequences (see Table S3 for the individual sequences). Three independent
586 assays were performed to conclude for G4 formation *in vitro* on the
587 oligonucleotide (last column) to the exception of Eef1a1 G4mut2 that was only
588 assessed by FRET-MC. Sequences of all oligonucleotides are provided in
589 Table S3.

590 (c) Quantification of smFISH images of MS2 reporter activity of Ee1fa1, Pkm,
591 Klk6, Taok1 and Polr2a WT and mutant promoters. The data are shown as
592 violin plots in which thick bars are medians and thin bars are quartiles. Mann-Whitney two-sided tests were used (ns P > 0.05; **** P ≤ 0.0001). For Polr2a
593 the two mutants have moderate (G4mut) or strong mutations (G4mut2), while
594 Eef1a1 G4mut2 has a balanced GC content as compared to G4mut (see
595 panel b). Sequences of all promoters are provided in Table S4.
596

597 **Figure 6: G4 mutations at Eef1a1 model promoters decrease transcription and**
598 **increase nucleosome density at NDRs, while increasing promoter OFF times in**
599 **single cells (see also Extended data Fig. 9 a-c).**

600 (a) Eef1a1 mouse model promoters inserted in HeLa cells with indicated
601 mutations (see Table S3 for sequences).

602 (b) G4 forming sequences and TATAbox regulate gene expression from Eef1a1
603 model promoter. Quantification of MS2 reporter activity by smFISH of WT and
604 mutant promoters. (Mann-Whitney two-sided tests were used, n=322;401;343
605 and 254)

606 (c) Eef1a1 G4 mutations result in increased nucleosome density at NDR location.
607 MNase signal of WT/mutant promoters was quantified by qPCR (n=2
608 biological replicates independently processed – different cultures, time and
609 experiments; means ±s.e.m). NDR location is highlighted in grey.
610 Quantifications of the nucleosome density signal at NDR are shown on the
611 right together with a GAPDH control (oligonucleotides are in Table S5).

612 (d) Pol II and TBP recruitment at Eef1a1 model promoter and mutants. ChIP were
613 quantified by qPCR (n=2 biological replicates independently processed –
614 different cultures, time and experiments; means ±s.e.m). Pol II is affected in
615 all mutant contexts while TBP is impaired specifically in the TATAmut.

616 (e) Heatmaps of permissive (red) and non-permissive (blue) transcription periods
617 for WT and mutant Eef1a1 promoters in single cell live imaging (Long movies).
618 Each line represents individual cell assessments. Representative movie
619 images and quantification are shown in Extended data Fig. 9a.

620 (f) Violin plots of ON and OFF period duration in WT or mutated Eef1a1
621 promoters measured in long movies. Computed P-values (two-sided Mann-
622 Withney test are as follows: ****<1e-4, **<1e-2, *<5e-2). Boxes represent the
623 median and the first and last quartiles. n= 155;234;633;78;208 and 612.

624 (g) Heatmaps of the polymerases densities for WT and mutant Eef1a1 promoters
625 in single cell live imaging (short movies). Each line represents individual cell
626 assessments.

627 (h) Interpretative scheme of the signal deconvolution and time constant analysis
628 derived from the long and short movies. While WT transcription can be
629 described in 2 main steps both TATA and G4 mutants require at least 3 steps.
630 The G4 mutant has limiting OFF1 and ON state (see Extended data Fig. 9b,c).

631
632 **Figure 7: Stabilization of G4 by ligand results in global pause release by Pol II.**
633 (see also Extended data Fig. 10)

634 (a) Pyridostatin (PDS) treatment (10 min) results in Pol II release from the promoter
635 area at the ACTB, MAT2a, KHLH9 (short genes) and SMC5 (long gene) loci.
636 Tracks of Pol II ChIP-seq (zoomed around TSS) before and after PDS treatment
637 are shown. G4 predictions and the sequence of the pG4 upstream of the TSSs
638 associated with the NDRs are indicated and are shown below the tracks
639 (G4H2.0).

640 (b) Pausing scores are globally reduced at the genome-scale following PDS treatment. Scatter plots comparing pausing scores before and after PDS treatment (10 min) is shown. Genes with enhanced Pol II signal at gene bodies (DESeq, P-value <0.05) are highlighted in dark blue. The light blue slope represents the linear regression curve of the other points. The whole kinetic analysis is presented in Extended data Fig. 10b.

646 (c) Cumulative plots of Pausing scores dynamics following PDS treatment.

647 (d) Metaprofiles of Pol II ChIP-seq signal at selected genes with decreased pausing scores (n=602) depict reduced promoter and increased gene body occupancies.

650 (e) Metaprofiles of Pol II ChIP-seq signal at control genes following PDS treatment. (n=5557).

652 (f) Metaprofiles of Pol II ChIP-seq signal at long genes (10 and 30 min PDS).

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803 54 Bhuiyan, T. & Timmers, H. T. M. Promoter Recognition: Putting TFIID on the Spot. *Trends Cell Biol* **29**, 752-763, doi:10.1016/j.tcb.2019.06.004 (2019).

805 55 Muller, F. & Tora, L. Chromatin and DNA sequences in defining promoters for transcription initiation. *Biochim Biophys Acta* **1839**, 118-128, doi:10.1016/j.bbagrm.2013.11.003 (2014).

808 56 Frontini, M. et al. NF-Y recruitment of TFIID, multiple interactions with histone fold TAF(II)s. *J Biol Chem* **277**, 5841-5848, doi:10.1074/jbc.M103651200 (2002).

811 57 Teng, Y. C. et al. ATRX promotes heterochromatin formation to protect cells from G-quadruplex DNA-mediated stress. *Nature communications* **12**, 3887, doi:10.1038/s41467-021-24206-5 (2021).

814 58 Li, C. et al. Ligand-induced native G-quadruplex stabilization impairs transcription initiation. *Genome Res* **31**, 1546-1560, doi:10.1101/gr.275431.121 (2021).

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828 63 Wulfridge, P. et al. G-quadruplexes associated with R-loops promote CTCF binding. *Mol Cell* **83**, 3064-3079.e3065, doi:10.1016/j.molcel.2023.07.009 (2023).

831 **Methods**

832 Our research complies with French/European ethical policies and did not require a specific board to approve our study.

834 **Cell lines and culture**

835 Data presented in this article were issued from the analysis of human cell lines (K562, Raji, HeLa) or mouse cell lines (primary thymocytes (CD4+ CD8+ (DP) and NIH-3T3). Original data presented concern essentially Raji, HeLa and NIH3T3 cells but all cellular models are described in this section.

839 K562 is a pseudotriploid ENCODE Tier I erythroleukemia cell line derived from a female (age 53) with chronic myelogenous leukemia. The Raji cells are lymphoblast-like cells from a male (age 11) with Burkitt's lymphoma. HeLa Flp-in H9 cells (a kind gift of S. Emiliani) is a cell line derived from the parent HeLa line. The HeLa line is derived from a female (age 31) with adenocarcinoma. Mouse CD4+ CD8+ DP cells were sorted from thymuses of 5 to 6 weeks old mice as described 8,64. NIH-3T3 is a fibroblast cell line that was isolated from a mouse NIH/Swiss embryo.

846 Raji cells were grown in RPMI 1640 medium supplemented with 10% fetal calf serum, penicillin/streptomycin (100 units/L) and glutamine (2 mg/L) at 37 °C and 5% CO₂. For triptolide transcription inhibition experiments (ED Fig. 5a), cells were treated with 1 µM for 2h as described²⁴. For the PDS experiments (Fig. 7 and ED Fig. 10), cells were treated with 10 µM PDS at the indicated times.

851 HeLa Flp-in H9 cells (a kind gift of S. Emiliani) used for reporter assays (Fig. 5-6, ED Fig. 9) were maintained in DMEM supplemented with 10% fetal calf serum, penicillin/streptomycin (100 units/L) and glutamine (2.9 mg/L) at 37°C and 5% CO₂. 852 HeLa cells with integrated constructs were transfected with plasmids using JetPrime (Polyplus), following manufacturer recommendations. 853
856 NIH-3T3 cells were grown in DMEM supplemented with 10% fetal calf serum,
857 penicillin/streptomycin (100 units/L) and glutamine (2.9 mg/L) at 37°C and 5% CO2.
858 Cells at 50% confluence were serum-starved overnight (0.3% FCS) and stimulated
859 with 15% fetal calf serum (FCS) for 15 min prior cell collection for G4access and
860 chrRNA-seq experiments (ED Fig. 5b-d).

861 **Genome-wide data sets**

862 All data sets used in this study including published and original experiments are
863 described in Table S1. All GEO accession numbers are included. The GEO accessions
864 for specific experiments related to this study are recorded under GSE159276.

865 **MNase, MNase-seq and DNase-seq**

866 For sequencing of nucleosomal DNA in Raji cells, 3.5x10⁷ cells were resuspended in
867 350 µl Solution I (150 mM sucrose, 80 mM KCl, 5 mM K₂HPO₄, 5 mM MgCl₂, 0.5 mM
868 CaCl₂, 35 mM HEPES pH 7.4) and NP40 was added to a final concentration of 0.2%.
869 Cell membranes were permeabilized for 5 min at 37 °C. MNase was prepared at 50,
870 25, 12, 6 or 3 units in 0.5 mL of Solution II (150 mM sucrose, 50 mM Tris pH 8, 50 mM
871 NaCl, 2 mM CaCl₂) and incubated with 50 µL of cellular preparation, corresponding to
872 5x10⁶ cells, for exactly 10 min at 37 °C. The reactions were stopped by adding EDTA
873 to a final concentration of 10 mM. The cells were lysed using 1.45 mL of SDS Lysis
874 Buffer (1% SDS, 10 mM EDTA pH 8, 50 mM Tris pH 8), with 10 min incubation at 4
875 °C. 200 µl aliquots were taken for purification and the remaining extracts were stored
876 at -80 °C. An equal volume of TE (200 µl) was added to the aliquots, followed by
877 subsequent 2 h treatments with 0.2 µg/mL of RNase A and Proteinase K at 37 °C and
878 55 °C, respectively. DNA was extracted by two subsequent
879 phenol:chloroform:isoamylalcohol (25:24:1) extractions, further purified using
880 QIAquick PCR purifications columns (Qiagen, Germany). Nucleosomal digestion was
881 verified by running 500 ng of DNA on a 1.5% agarose gel as well as on DNA high-
882 sensitivity 2100 Bioanalyzer chips (Agilent, USA). Digestions showing 75% of
883 mononucleomes (running at 150bp) were selected for library preparations. Fragments
884 below 250 bp were purified with Ampure XP Beads (Beckman Coulter, USA) following
885 manufacturer instructions. Libraries were prepared with TruSeq ChIP Library
886 Preparation Kit (illumina) and sequenced on Hiseq 2000 or 4000 sequencers
887 (Illumina).

888 Chromatin analysis by MNase treatment on HeLa cells (Fig. 6c) were performed as
889 follows, since adherent cells harvested with trypsin tend to clamp using the method
890 described above. HeLa cells were harvested using trypsin and washed twice with ice-
891 cold PBS. Cells were resuspended in 250 µL of ice-cold Nuclei buffer I (15 mM Tris-
892 HCl pH7.5, 300 mM sucrose, 60 mM KCl, 15 mM NaCl, 5 mM MgCl2, 0.1 mM EGTA,
893 0.5 mM DTT, 0.1 mM PMSF, 3.6 µg/mL aprotinin) before addition of 250 µL of ice-cold
894 Nuclei buffer II (15 mM Tris-HCl pH7.5, 300 mM sucrose, 60 mM KCl, 15 mM NaCl, 5
895 mM MgCl2, 0.1 mM EGTA, 0.5 mM DTT, 0.1 mM PMSF, 3.6 µg/mL aprotinin, 0.4%
896 IGPAL CA-630). Extracts were incubated 10 min on ice and layered on 1 mL Nuclei
897 buffer III - sucrose cushion (15 mM Tris-HCl pH7.5, 1.2 M sucrose, 60 mM KCl, 15 mM
898 NaCl, 5 mM MgCl2, 0.1 mM EGTA, 0.5 mM DTT, 0.1 mM PMSF, 3.6 µg/mL aprotinin).
899 Nuclei were isolated by centrifugation at 10,000 g for 20 min at 4°C, and were
900 resuspended in 600 µL of MNase digestion buffer (50 mM Tris-HCl pH7.5, 320 mM
901 sucrose, 4 mM MgCl2, 1 mM CaCl2, 0.1 mM PMSF) and incubated on ice for 3 min.
902 MNase was added for exactly 10 min at 37 °C, using 50, 25, 12, 6 or 3 units of the
903 enzyme. The reactions were then stopped by adding EDTA to a final concentration of
904 10 mM. 100 µL of SDS Lysis Buffer were added (1% SDS, 10 mM EDTA pH 8, 50 mM
905 Tris pH 8) and after 10 min of incubation at 4°C, samples were processed for DNA
906 purification using phenol-chloroform extraction followed by ethanol precipitation. qPCR
907 quantifications were performed by using the primers described in Table S5.

908 DNase I analysis was performed on Raji cells as previously described⁶⁵ with minor
909 modifications. In brief, nuclei were prepared from 5x10⁶ Raji cells per sample and
910 incubated for 10 min at 37 °C with limiting concentrations of the DNA endonuclease
911 DNase I (Sigma) supplemented with Ca²⁺ and Mg²⁺. A dose response experiment of
912 16 samples was performed with 2X dilution of the DNasel between samples in
913 cascade. Digestion was stopped by the addition of EDTA, and the samples were
914 treated with proteinase K. The small fragments (<500 bp in length) were recovered on
915 agrose gel from the sample in which a smear just started to appear using Quiagen gel
916 purification kit and libraries were prepared using illumina Truseq kit.

917 G4access

918 The complete G4access procedure is described in ⁴⁰ and the principle of the method
919 is summarized in Fig. 1c. In short, K562 and NIH3T3 cells were pelleted and rinsed
920 twice in phosphate-buffered saline buffer (PBS). For each experiment, 5x10⁶ cells per
921 titration point were re-suspended in 50 µL of prewarmed permeabilization buffer (150
922 mM of sucrose, 80 mM KCl, 5 mM KH₂PO₄, 5 mM MgCl₂, 0.5 mM CaCl₂ and 35 mM
923 HEPES pH 7.4) supplemented with 0.2% (v/v) NP40 and incubated for 5 minutes at
924 37°C prior digestion. MNase digestions, were then performed by adding a volume of
925 500 µL of prewarmed MNase reaction buffer (150 mM sucrose, 50 mM Tris-HCl pH 8,
926 50 mM NaCl and 2 mM CaCl₂) supplemented with either 3, 6, 12, 25 or 50 U of MNase
927 (Merck, 10107921001). Digestions were incubated at 37 °C for 10 min and stopped on
928 ice and by adding 11 µL of 500 mM EDTA to each reaction. Samples were then
929 incubated 10 minutes on ice with 550 µL of SDS lysis buffer (1% (v/v) SDS, 10 mM
930 EDTA and 50 mM Tris.HCl pH 8). Before DNA purification, 1 mL of water was added
931 to dilute the SDS and the samples were incubated with 5 µL of RNAse A
932 (ThermoFisher, EN0531) at 37 °C for 2 hours and with 8 µL of proteinase K
933 (Euromedex, 09-0911) at 56 °C for 2 hours to complete the lysis. To then quality control
934 the MNase digestions: 125 µL of each sample were cleaned-up using QIAquick PCR
935 Purification Kit (QIAGEN, 28106) and assessed by agarose gel and Bioanalyzer. At
936 this step, for efficient G4access, samples should present ~30% (+/-5%) of mono-
nucleosomes. The remaining of the samples was then purified by phenol-chloroform and ethanol precipitation for subsequent steps. The 0-100 bp size-selected fragments from MNase digestions that have ~30% of mono-nucleosomes were subjected to DNA library preparation. Paired-end libraries were constructed using NEBNext® Ultra™ II DNA Library Prep Kit for Illumina (New England Biolabs, E7645S) using a starting material of 50 ng. DNA fragments were treated with end-repair, A-tailing and ligation of Illumina-compatible adapters. Clean-up of adaptor-ligated DNA was performed by using CleanNGS beads (CNGS-0050) with a bead:DNA ratio of 2:1. The purified products were amplified with 8 cycles of PCR. Finally, samples were cleaned up with a bead:DNA ratio of 0.8:1 to remove the free sequencing adapters. Libraries were sequenced on the Illumina NextSeq-500 Sequencer using paired 50-30 bp reads. The G4access data is deposited to GEO database under GSE159276.

## ChIP-seq and ChIP qPCR

Fifty million cells were used to perform each Pol II ChIP-seq experiment. Cells were crosslinked for 10 min at 20 °C with the crosslinking solution (10 mM NaCl, 0.1 mM EDTA pH 8, 0.05 mM EGTA pH 8, 5 mM HEPES pH 7.8 and 1% formaldehyde). The reaction was stopped by adding glycine to reach a final concentration of 250 mM. After 5 min of formaldehyde quenching, cells were washed twice with cold PBS and resuspended in cold 2.5 mL LB1 (50 mM HEPES pH 7.5, 140 mM NaCl, 1 mM EDTA pH 8, 10% glycerol, 0.75% NP-40, 0.25% Triton X-100) at 4 °C for 20 min on a rotating wheel. Nuclei were pelleted down by spinning at 1350 g in a refrigerated centrifuge and washed in 2.5mL LB2 (200 mM NaCl, 1 mM EDTA pH 8, 0.5 mM EGTA pH 8, 10 mM Tris pH 8) for 10 min at 4 °C on a rotating wheel followed by centrifugation to collect nuclei. Nuclei were then resuspended in 1mL LB3 (1 mM EDTA pH 8, 0.5 mM EGTA pH 8, 10 mM Tris pH 8, 100 mM NaCl, 0.1% Na-Deoxycholate, 0.5% N-lauroylsarcosine) and sonicated using Bioruptor Pico (Diagenode) in 15 mL tubes for 20 cycles of 30 s ON and 30 s OFF pulses in 4 °C bath. All buffers (LB1, LB2 and LB3) were complemented with EDTA free Protease inhibitor cocktail (Roche), 0.2 mM PMSF and 1 µg/mL Pepstatin just before use. After sonication, Triton X-100 was added to a final concentration of 1% followed by centrifugation at 20000 g and 4 °C for 10 min to remove particulate matter. After taking aside a 50 µl aliquot to serve as input and to analyse fragmentation, chromatin was aliquoted and snap-frozen in liquid nitrogen and stored at -80 °C until use in ChIP assays. Input aliquots were mixed with an equal volume of 2X elution buffer (100 mM Tris pH 8.0, 20 mM EDTA, 2% SDS) and incubated at 65 °C for 12 hours for reverse-crosslinking. An equal volume of TE buffer (10 mM Tris pH 8 and 1 mM EDTA pH 8) was added to dilute the SDS to 0.5% followed by treatment with RNase A (0.2 µg/mL) at 37 °C for one hour and Proteinase K (0.2 µg/L) for two hours at 55 °C. DNA was isolated by phenol:chloroform:isoamylalcohol (25:24:1 pH 8) extraction followed by Qiaquick PCR Purification (QIAGEN, Germany). Purified DNA was then analyzed on a 1.5% agarose gel and on Bioanalyzer (Agilent, USA) using a High Sensitivity DNA Assay.
For Pol II ChIP, Protein-G coated Dynabeads were incubated at 4 °C in blocking solution (0.5% BSA in PBS) carrying Pol II N20 (Santa-Cruz sc-899x, lot H3115) and TBP N12 (Santa-Cruz sc-204, lot LO214) specific antibodies (5µg per immunoprecipitation). Sonicated chromatin (1 mL) was added to pre-coated beads (250 µL) and the mix was incubated overnight at 4 °C on a rotating wheel. After incubation with chromatin, beads were washed 7 times with Wash buffer (50 mM HEPES pH 7.6, 500 mM LiCl, 1 mM EDTA pH 8, 1% NP-40, 0.7% Na-Deoxycholate, 1X protease inhibitor cocktail) followed by one wash with TE-NaCl buffer (10 mM Tris pH 8 and 1 mM EDTA pH 8, 50 mM NaCl) and a final wash with TE buffer (10 mM Tris pH 8 and 1 mM EDTA pH 8). Immunoprecipitated chromatin was eluted by two sequential incubations with 50 µL Elution buffer (50 mM Tris pH 8, 10 mM EDTA pH 8, 1% SDS) at 65 °C for 15 min. The two eluates were pooled and incubated at 65 °C for 12 hours to reverse-crosslink the chromatin followed by treatment with RNase A and Proteinase K and purification of DNA, as described above for input samples. Both input and ChIP samples were subjected to Bioanalyzer analysis to check that the major bulk of isolated DNA was in the 250 bp size range.

Samples were analysed by qPCR (Stratagene) in HeLa cells following the manufacturer recommendations. Oligonucleotides pairs used for qPCR in this study are presented in the Table S5. For ChIP-seq experiments in Raji cells, purified DNA was quantified with Qubit DS DNA HS Assay (ThermoFisher Scientific, USA). 5 ng of ChIP DNA were used to prepare sequencing libraries with Illumina ChIP Sample Library Prep Kit (Illumina, USA). After end-repair and adaptor ligation, library fragments were amplified by 12 cycles of PCR. Barcoded libraries from different samples were pooled together and sequenced on Illumina HiSeq2000 platform in paired-end sequencing runs.

## Chromatin RNA sequencing (chrRNA-seq)

Chromatin associated RNAs (ChrRNAs) were isolated from $2 \times 10^7$ Raji cells before and after 10 min of PDS treatment or from $2 \times 10^7$ NIH3T3 cells before and after Serum stimulation (15% Fetal Calf Serum) for 15 min (ED Fig. 5b-c and ED Fig10. d-e) as described previously 66 followed by TurboDNase treatment. In brief, nuclear fractionation was performed by incubating cells in 4 ml of buffer 1 (10 mM Tris-HCl (pH 7.5), 10 mM NaCl, 2.5 mM MgCl2, 0.5% IGEPAL CA-630) on ice for 5 min. Next, we carefully underlaid 1 ml of buffer 2 (10 mM Tris-HCl (pH 7.5), 10 mM NaCl, 2.5 mM MgCl2, 0.5% IGEPAL CA-630, 10% sucrose) before collecting the nuclear fraction at 1400 g for 5 min at 4 °C. Nuclei were resuspended with 125 µl of NUN1 buffer (20 mM Tris-HCl (pH 8), 75 mM NaCl, 0.5 mM EDTA, 50% glycerol) followed by 1.2 ml of NUN2 buffer (20 mM HEPES-KOH (pH 7.6), 300 mM NaCl, 0.2 mM EDTA, 7.5 mM MgCl2, 1% IGEPAL CA-630, 1 M urea). After 15 min of vigorous vortexing, the chromatin was centrifuged at 15,000 g for 10 min at 4 °C and washed with 500 µl of NUN2 buffer. After discarding the supernatant, the chromatin was resuspended in 500 µl of Trizol. At this stage, the chromatin pellet is very tight and needs to be dissolved
1019 in Trizol by repeated pipetting with decreasing volume tips (1 ml-200 µl-10 µl) and
1020 then pushing through very small syringe needles. Then RNA was extracted from
1021 chromatin according to the Trizol manufacturer's protocol and resuspended in 50 µl of
1022 nuclease-free water (Invitrogen, AM9906) followed by TurboDNase (Invitrogen,
1023 AM2238) treatment. Trizol RNA extraction and TurboDNase were performed two more
1024 times, and RNA was resuspended in 20 µl of nuclease-free water. Purified RNAs were
1025 quantified by Qubit, and quality was assessed using the RNA Assay kit (Agilent RNA
1026 6000 Pico reagents) with Bioanalyzer 2100 (Agilent Technologies). ChrRNAs were
1027 then subjected to library preparation using the True-seq stranded total RNA library prep
1028 gold kit (Illumina, 220599) using 1 µg of ChrRNA, with a total of 15 cycles of
1029 amplification and following the manufacturer's instructions (including ribo-depletion.
1030 The datasets are submitted to GEO database and with the manuscript⁴⁰ (Table S1).

1031 **Transient transcriptome sequencing (TT-seq)**

1032 TT-seq experiments were performed as previously described⁶⁷ with minor
1033 modifications. Briefly, experiments were performed in biological duplicates using
1034 47x10⁶ Raji cells treated with 10 µM pyridostatin (PDS; Sigma SML0678) or H₂O
1035 (Control) for 10 minutes. Newly synthetised RNAs were labelled with 500 µM 4-
1036 Thiouridine (4SU; Sigma T4509) for 10 min at 37 °C at the time of the treatment. In
1037 parallel, 60 x106 drosophila S2R+ cells were labelled for 10 min with 500 µM 4SU.
1038 Cells were then harvested by centrifugation and Total RNA was isolated with QIAzol
1039 reagent (QIAGEN) according to the manufacturer's instructions. Drosophila RNA was
1040 used as spike-in and added to Raji TotRNAs (1:40 ratio). TotRNAs were fragmented
1041 (two-times 105 µg in 120 µL) using a Covaris sonicator M220 (7 °C, Power 75 W, Duty
1042 factor 1%, burst 200) to generate RNA fragments of 200 bp to 10 kb. 4SU-labeled RNA
1043 from 210 µg of Fragmented RNA was biotinylated, pulled-down using streptavidin
1044 beads and eluted with freshly prepared 100 mM DTT. 4SU-labeled RNA (TT-seq) were
1045 treated with RNase-free DNase I, purified with miRNeasy MinElute spin column and
1046 eluted in 15 µL Nuclease-free water. Prior to library preparation, samples were
1047 quantified with Nanodrop and quality was assessed by Bioanalyzer Nano RNA ChIP.
1048 Ribosomal RNAs were depleted from samples using NEBNext rRNA Depletion Kit V2
1049 (NEB; E7400) and sequencing libraries were prepared with NEBNext Ultra II
1050 Directional Kit (NEB, E7760) according to manufacturer's instructions. RNA libraries
1051 were then sequenced in 2x50 bp paired-end on a Novaseq 6000 (ED Fig. 10d-e).

1052 **Bioinformatics**

1053 *Motif analysis*

1054 Canonical promoter elements, *de novo* and known motifs (TRANSFAC) were analysed
1055 across all expressed genes in K562, Raji and DP cells (Fig.1 and ED Fig. 1). *De novo*
1056 motif discovery and known motif identification were performed using MEME and
1057 DREME ⁶⁸,⁶⁹ using fragments from -100 to +20 bp of experimental TSSs since this area
encompasses not only the promoter but also the majority of the NDR. Enrichment of
canonical promoter elements were tested using bedtools 'intersect' against all
promoters of expressed genes (-100 to +20 bp of experimental TSSs) and against
10,000 permutations of random genomic areas of 121 bp. Random controls were
generated using bedtools 'random' using or not GC constraints. GC thresholds were
determined to fit exactly the GC biased observed at promoters of expressed genes.
Motifs used for this analysis (Table S2) were the BRE, the canonical or non-canonical
TATAboxes as indicated in ED Fig.1. Additionally, we also used the quadparser QP1-
7 ((Gn>2N1-7)x4) and bed files generated by the G4Hunter algorithm G4H2.0 or G41.5
using a window of 25 bp as described 20,40.

1068 *G-quadruplex predictions, GC and CpG contents*

Most G4 predictions were performed using the G4Hunter²⁰ algorithm, which allows to
attribute a numerical value reflecting G4 propensity, to a sequence of a defined window
length. Predicted G4s (pG4s) at stringencies 1.52 and 2.0 were used throughout this
study, reflecting strong and very strong likelihood of G4 formation *in vitro*. Previous
experiments have shown that these G4Hunter thresholds allow to experimentally
confirm 92 and 100% of the predicted G-quadruplex structures²⁰. In some analyses
(Fig. 2b, 2c, 2d, ED Fig. 10g) G4 scores were extracted from the reference genome
(hg19 with a window of 25 and a minimal score of 0.5) using the G4Hunter algorithm²⁰
and 5 bp bin sized wiggle files were produced for whole genome analyses (G4score
track). GC (G or C) and CpG (CG dinucleotides) contents were defined across the
genome and wiggle files were generated. To compare G4 propensity and PDS effects
on Pol II pause release, genes were segregated in 10 groups depending on the ratio
of pausing indexes before and after PDS treatments. Regression curves and
metaprofiling of G4Hunter scores and predictions (at G4H1.5 and G4H2.0 thresholds)
were then performed in all groups (Ed Fig. 10g).

1084 ChIP-seq, ChIP-exo and MNase-seq analyses

All genomic experiments from this study or re-analysed from available datasets were
processed using our pipeline. Sequencing files were analysed using Bowtie2<sup>70</sup> and
PASHA<sup>71</sup>. Raw sequencing reads were aligned to human Hg19 or mouse genome
(mm9) using Bowtie2. Duplicate reads with identical coordinates (sequencing depth
taken into account) to remove potential sequencing and alignment artifacts. For ChIP-
seq and MNase-seq (nucleosome density) signal analyses, aligned reads were
elongated *in silico* using the DNA fragment size inferred from paired-reads or an
estimated optimal fragment size for orphan reads using Pasha R package. These
elongated reads were then used to calculate the number of fragments that overlapped
at a given nucleotide thus representing an enrichment score for each bin in the
genome. For nucleosome positioning analyses (midpoints) presented in Fig. 3, to
determine the average nucleosome positions, wiggle files representing the central
nucleotides of DNA fragments were also generated. For ChIP-exo, the nucleotide
1098 located at the 5' extremity of the DNA fragments was considered to generate wiggle
1099 files, since it represents the exact points where the nucleases have stopped. Wiggle
1100 files representing average enrichment score every 50 bp or 10 bp were generated.
1101 Sequencing data from Input samples were treated in the same way to generate Input
1102 wiggle files. All wiggle files were then rescaled to normalize the enrichment scores to
1103 reads per million. For ChIP-seq datasets, enrichment scores from input sample wiggle
1104 files were subtracted from ChIP sample wiggle files. This allows removing/reducing the
1105 over-representation of certain genomic regions due to biased sonication, local
1106 duplications, and DNA sequencing. Finally, for MNase-seq, we smoothed the signal by
1107 replacing each 10 bp bin by the average of the 5 surrounding bins on each side.

1108 RNA-seq analysis

1109 All RNA-seq datasets re-analysed in this study were processed using the following
1110 pipeline. Raw sequencing reads were aligned to mouse genome (mm9) or human
1111 genome (hg19) using TopHat2⁷². Alignment files were then treated using PASHA⁷¹ to
1112 generate wiggle files. In Raji and DP cells, experimental TSSs were determined as the
1113 summit in short-RNA-seq signals in a window of 300 bp of annotated TSSs.

1114 Average binding profiles and heatmaps

1115 To generate average binding profiles (Fig. 1-4, 7 and ED Fig. 2-7, 10), R scripts
1116 previously developed were used for retrieving bin scores in defined regions from 10 or
1117 50 bp bin sized wiggle files⁷¹. Heatmaps were generated from produced or published
1118 wiggle files, viewed and color-scaled according to sample read depth using Java
1119 TreeView⁷³ and dedicated Rscripts⁸. Regions were defined either as centered on
1120 experimental TSSs (see above), on the center of predicted G4 from G4Hunter or the
1121 center of the area if no G4 was predicted. In addition, pG4s that where not located in
1122 annotated gene features or further than 200 bp from annotated TSSs were considered
1123 as intergenic. To analyse the relationships between G4 formation, gene expression
1124 and cell specificity, datasets were ranked by the ratio of G4access signals between
1125 Raji and K562 cells (Raji/K562). 3 groups were defined (Raji specific, common and
1126 K562 specific peaks) to perform metaprofiling of transcription and chromatin marks and
1127 to quantify expression using Nascent chrRNA-seq datasets (ED Fig. 3c-e). NDRs and
1128 deepest NDR points were defined using inversed MNase-seq signal. First by creating
1129 an inversed track of the MNase-seq signal (y = 1/x, 0 values were replaced by the
1130 minimal value found in the region -300/+100 of annotated TSSs in the sequencing
1131 signals). Then, peaks and peak summits corresponding to NDRs were determined.
1132 The peak calling was performed using wigpeakcaller⁷⁴ fixing a threshold based on the
1133 peak height and the gap between two adjacent signals (ED Fig. 3f). Metaprofiling on
1134 chromatin remodeler and modifiers (Tables S1), G4Hunter annotations and G4access
1135 were centred at the peak summit of the inversed MNase-seq signal corresponding at
1136 the deepest points of NDRs.
1137 To generate average binding profiles of Pol II, chrRNA and TT-seq (Fig. 7d-f, Ed Fig. 10d-g), hg19 Refseq genes annotations were used to extract values from wiggle files associated with the selected genes. Bin scores inside these annotations and in a region of 5 kb before the TSSs and after 5 kb of annotated termination sites were determined. Based on the gene list selections, bin scores from wiggle files were used to re-scale values between TSSs and transcription termination sites (gene body) of all genes using linear interpolation. In total, 1000 points were interpolated for the gene body of each selected gene in all average profiles presented.

1145 *Identification of inactive promoters*

1146 To select inactive promoters, we selected the bottom 10% of Pol II signal over the defined areas (Fig. 2c, ED Fig. 2i). Hg19 Refseq genes annotations were used to extract values from wiggle files associated with the selected genes and bin scores in a region of 2kb before and after the TSSs were determined.

1150 *Nucleosome arrays:*

1151 We have performed a precise assessment of nucleosome repeat length (NRL) and phasing, comparing pG4s and CTCF⁷⁵ in K562 (Fig. 3c), which yielded an average NRL of 194 and 193 nt calculated over 5 nucleosomes from the docking site and 215 and 216 nt over 10 nucleosomes, respectively.

1155 *Pausing scores*

1156 To analyse how the G-quadruplex ligand pyridostatin (PDS) impacts Pol II pausing (Fig. 7 and ED Fig. 10), we have determined pausing scores based on the ratio of Pol II signals at promoters and in gene bodies⁷⁶. Our approach for pausing scores determination is comparable to the one previously described⁸ with modifications. It takes into account Pol II density on either promoter regions (TSS) or gene bodies (GB). 1160 Promoter regions were considered between -300 and +100 of TSS to define paused Pol II density for calculations. Densities at genes bodies were analysed in the intervals of 50-100% of the length for all genes <25 kb. For longer genes (>25 kb) the interval from 10 kb to 25 kb were considered, assuming an elongation speed of at least, 2.5 kb/min for Pol II. The use of these intervals avoids detecting signal originating from the promoters for short genes or genes with exceptionally large initiation areas and allows detecting more significant signal of elongating Pol II. To avoid interferences between promoter and gene body read counts, only genes larger than 3 kb were considered. 1165 Read count was performed using HTseq⁷⁷, normalized to the length of the genomic regions and expressed as RPKM (reads per kb per millions). Only genes with sufficient read coverage were considered (>10 RPKM at promoters and >2 RPKM at gene bodies n = 6159). Pausing scores were expressed as the ratio TSS/GB. To define a high confidence set of genes with pause release effect and since in our datasets PDS globally affected Pol II pausing (Fig. 7 and ED Fig. 10, linear regression slope T0
1175 versus T10 minutes = 0.43, Wilcoxon test <0.00001, n= 6159), we further selected
1176 genes with significant Pol II signal increase in their gene bodies using DESEQ
1177 (Pvalue< 0.05, n= 602; Pvalue < 0.05, n=6159). The distributions of cumulative
1178 frequencies as a function of pause indexes were established using GraphPad (Fig.
1179 7c).

1180 Plasmids and cloning

1181 The repeats of the 256xMS2 binding sites were cloned from chemically-synthesized
1182 oligonucleotides into pMK123<sup>78</sup>. The MS2 stem loops are separated by a linker of only
1183 three nucleotides and cloned in a pIntro-MS2x256 plasmid, which also contained an
1184 FRT-Hygro cassette for Flp-in recombination<sup>42,79</sup>. pUC57 containing the Eef1a1
1185 (1513bp) or Polr2a (711bp) WT mouse promoters were purchased at genescript;
1186 pGL4.17 containing the Pkm (200bp), Klf6 (400bp) or Taok1 (300bp) or mutagenized
1187 mouse promoters were purchased at genecust (Table S4). All constructs were then
1188 subcloned into pIntro-MS2x256 between SnaBI and MluI sites. To introduce mutations
1189 into the largest promoters Polr2a and Eef1a1, smaller fragments (206 and 219 bp
1190 respectively) were purchased and subcloned to the full-length promoter between NotI
1191 and Nhe1 sites for Eef1a1 and between NotI and MluI for Polr2a. Additionally, for a
1192 second version of Eef1a1 and Polr2a WT promoters and for Eef1a1<sup>inv</sup>, Polr2a<sup>G4mut</sup>
1193 and for Polr2a<sup>G4mut2</sup> promoters an additional luciferase reporter was added
1194 downstream of the MS2 reporter using NEB assembly builder kit and the two following
1195 oligonucleotides

1196 Gibintro-BsrG1-ires-fwd:

1197 GGTTTTCCAGTCACACCTCATGTACAGGCCCCTCTCCCTCCCCCCC and Gibluc-

1198 BsiW1-intro-Rev:

1199 TGTAAGTCATTGGTCTTAAACGTACGTCTAGAATTACACGGCGATC.

1200 Stable expression of MCP-GFP was achieved by retroviral-mediated integration of a
1201 self-inactivating vector containing an internal ubiquitin promoter. The MCP used
1202 dimerizes in solution and contained the deltaFG deletion, the V29I mutation, and an
1203 SV40 NLS24<sup>42</sup>. MCP-GFP expressing cells were grown as pool of clones and FACS-
1204 sorted to select cells expressing low levels of fluorescence. Isogenic stable cell lines
1205 expressing the reporter genes were created using the Flp-In system and a HeLa Flp-
1206 in H9 integrants were selected on hygromycin (150 µg/L). For each construct, several
1207 individual clones were picked and analysed by <i>in situ</i> hybridization. Clones usually
1208 looked similar, and two of them were further selected for the experiments after PCR
1209 and sequencing check.

1210 Circular dichroism (CD) spectroscopy

1211 CD spectra were recorded on a Jasco J-815 spectropolarimeter equipped with a Peltier
1212 temperature control accessory (JASCO Co., Ltd., Hachioji, Japan). Each spectrum was
1213 obtained by averaging three scans at a speed of 100 nm/min. A background CD
1214 spectrum of corresponding buffer solution was subtracted from the average scan for
1215 each sample. The CD profile was monitored between 220 nm and 300 nm using quartz
1216 cells of 5 mm path-length and a volume of 1000 µl. Oligonucleotides were tested at 3
1217 µM strand concentration.

1218 FRET-MC on dsDNA

1219 FRET-MC was performed after hybridizing the two strands (the oligos with predicted
1220 G4s and their complementary strand) in a buffer containing potassium and therefore
1221 allowing G4 formation. To do so, each strand was diluted in FRET-MC buffer (10 mM
1222 lithium cacodylate, 10 mM KCl, 90 mM LiCl, pH7.2) and annealed at 95 °C for 5 min
1223 and let to cool for 2-3 hours until reaching room temperature prior hybridization. In
1224 parallel, the fluorescent oligonucleotide F21T was folded in the same buffer and added
1225 to a 96-well microplate, with a final concentration of 0.2 µM. 3 µM of the formed double
1226 stranded DNA was added in the wells with or without 0.4 µM of PhenDC3. The
1227 experiment was conducted in a CFX96 Real-Time PCR instrument (Bio-rad, CA, USA)
1228 which was programmed to record FAM emission, from 25 °C to 95 °C, with 1 °C per
1229 minute temperature gradient. The fluorescent emission curves were normalized using
1230 OriginPro2019 software (OriginLab, Massachusetts, USA) to determine the melting
1231 temperature (T<sub>m</sub>), corresponding to a normalized fluorescence of 0.5. ΔT<sub>m</sub> is calculated
1232 by the difference in T<sub>m</sub> of F21T with and without PhenDC3. The *S Factor* was calculated
1233 as previously described for each sequence and plotted into a bar graph. A low *S Factor*
1234 value indicates efficient competition and likely stable G4 formation. Experiments were
1235 performed in duplicates in two different plates.

1236 Absorbance spectroscopy

1237 All spectra were recorded on a Cary-300 (Agilent Technologies) spectrophotometer in
1238 10 mM lithium cacodylate buffer (pH 7.2) at 3 or 4 µM oligonucleotide strand
1239 concentration, in the presence or absence of 100 mM KCl.

1240 Thermal difference spectra (TDS) were obtained by taking the difference between the
1241 absorbance spectra of unfolded and folded oligonucleotides that were recorded at high
1242 (95 °C) and low (25 °C) temperatures, respectively, in a buffer containing 100 mM KCl.
1243 TDS provides specific signatures of different DNA structural conformations, provided
1244 that the structure is not too heat-stable (a number of G4 structures do not melt at high
1245 temperatures).

1246 Isothermal difference spectra (IDS) were obtained as described previously⁸⁰ by taking
1247 the difference between the absorbance spectra from unfolded and folded
1248 oligonucleotides. These spectra were recorded at 25 °C before and after potassium
1249 cation addition (100 mM KCl), respectively. IDS provide specific signatures of different
1250 DNA structural conformations.
1251 **Acquisition and analysis of smFISH images**

1252 SmFISH was performed as previously described ⁴², with a mix of 10 fluorescent oligos
1253 hybridizing against the MS2x32 repeat, each oligo containing four molecules of Cy3.
1254 Since each oligo bound eight times across the MS2x256 repeats, each molecule of
1255 pre-mRNA hybridized with 80 oligos, thereby providing excellent single molecule
detection and signal-to-noise ratios. In brief, smFISH images were recorded with an
1256 OMX Deltavision microscope in SIM mode. Acquisition was performed in 3D with a z-
1257 spacing of 0.125 µm, with a × 100 objective, and × 2 intermediate lens and an Evolve
1258 512 × 512 EMCCD camera (Photometrics). To obtain the number of nascent pre-
1259 mRNA per cell, the same images were used without SIM-reconstruction and were
1260 analysed using FISH-quant. Transcription sites (TS) were first identified manually, and
1261 isolated pre-mRNA molecules located in the nucleoplasm were used to define the point
1262 spread function (PSF) and the total light intensity of single molecules, which finally
1263 allowed to determine the intensity of TS. To obtain the number of nascent pre-mRNA
1264 per cell, the transcription sites (TS) were identified manually and isolated pre-mRNA
1265 molecules located in the nucleoplasm were used to define the PSF and the total light
1266 intensity of single molecules, which finally allowed determining the intensity of TS
1267 expressed in number of full-length transcripts.
1268

1269 **Live-cell image acquisition**

1270 Cells were plated on 25 mm diameter coverslips (0.17 mm thick). After 24-48 hours
1271 the coverslips were mounted in the GFP-imaging medium (DMEM-GFP-2, Evrogen)
1272 with rutin in a temperature-controlled chamber with CO2 and imaged on an inverted
1273 OMX Deltavision microscope in time-lapse mode. A x100, NA 1.4 objective was used,
1274 with an intermediate x2 lens and an Evolve 512x512 EMCCD camera (Photometrics).
1275 Stacks of 11 planes with a z-spacing of 0.6 µm were acquired, with one stack collected
1276 every 3 min for 8 h.

1277 **Quantification of short movies**

1278 Short movies were analysed as previously described ⁴²,⁴⁴ (Figure 6G). In short, we
1279 manually defined the nuclear outline and the region within which the transcription site
1280 (TS) is visible, and stacks were corrected for photobleaching using a fitted curve with
1281 a sum of three exponentials. This curve was used to normalize each time-point such
1282 as nuclear intensities were equal to the intensity of the first time-point. We then filtered
1283 the image with a 2-state Gaussian filter. First, the image was convolved with a larger
1284 kernel to obtain a background image, which was then subtracted from the original
1285 image before the quantification is performed. Second, the background-subtracted
1286 image was smoothened with a smaller Kernel, which enhances the SNR of single
1287 particles to facilitate spot pre-detection. TS positions in each frame of the filtered
1288 images were determined as the brightest pixel above a user-defined threshold in the
1289 pre-detected region of the TS. When no pixel was above the threshold, the last known
1290 TS position was used. Then the TS signal was fitted with a 3D Gaussian estimating its
1291 standard deviation σₓz and σ_z, amplitude, background, and position. We performed two
1292 rounds of fitting: in the first round all fitting parameters were unconstrained. In the
1293 second round, the allowed range was restricted for some parameters, to reduce large
1294 fluctuations in the estimates especially for the frames with a dim or no detectable TS.
1295 More specifically, the σₓz and σ_z were restricted to the estimated median value ±
1296 standard deviation from the frames where the TS could be pre-detected, and the
1297 background was restricted to the median value. The TS intensity was finally quantified
1298 by estimating the integrated intensity above background expressed in arbitrary
1299 intensity units. With the live cell acquisition settings, the illumination power was low
1300 and we could not reliably detect all individual molecules. We therefore collected right
1301 after the end of the movies one 3D stack with increased laser intensity (50% of max
1302 intensity, compared to 1% for the movie), which allowed reliable detection of individual
1303 RNA molecules. We also collected slices with a smaller z-spacing for a better
1304 quantification accuracy (21 slices every 300 nm). Quantification of TS site intensity in
1305 the calibration stack was done with *FISH-quant* as follows: (a) when calculating the
1306 averaged image of single RNA molecules, we subtracted the estimated background
1307 from each cell to minimize the impact of the different backgrounds; (b) when
1308 quantifying the TS in a given cell, we rescaled the average image of single RNA
1309 molecules such that it had the same integrated intensity as the molecules detected in
1310 the analyzed cell. To calibrate the TS intensities in the entire movie, i.e. to express the
1311 TS intensity as a number of equivalent full-length transcripts, we used the fact that the
1312 last movie frame was acquired at the same time as the calibration stack. We then
1313 normalized the extracted TS intensity in the movies, *I*<sub>MS2</sub>, to get the nascent counts
1314 *N*<sub>nasc;calib</sub>: *N*<sub>nasc;calib</sub>(t)=*I*<sub>MS2</sub>(t)×(*N*<sub>nasc,final</sub>/*I*<sub>final</sub>), where *N*<sub>nasc,final</sub> stands for the
1315 estimated number of nascent transcripts in the calibration stack and *I*<sub>final</sub> for the
1316 averaged intensity of the last four frames.

1317 **Analysis of long movies**

1318 To quantify long movies acquired at low frames rate (one 3D image stack every 3min),
1319 we used ON-quant, a rapid analysis tool that identifies transcription sites, measures
1320 their intensities, attributes the ON or OFF states of transcription, based on the defined
1321 intensity threshold under which a TS is considered to be silent, and above which a TS
1322 is considered to be active. For the analysis of the Eef1a1 *G4mut2* and re-analysis of
1323 the Eef1a1 lines, Big-FISH a toolbox part of [FISH-Quant](https://github.com/BigFISH-Team/FISH-Quant) package was use due to the
1324 upgrade of our analysis pipeline. The intensity threshold was defined based on the
1325 mean intensity of single molecules <sup>42</sup>.

1326 **Mathematical modelling, short and long movies analysis**

1327 Intermittent transcriptional activity of the promoters is modelled using a Markov
1328 process with one active and multiple inactive epigenetic states. The number of states
1329 and the transition rate parameters are obtained using the algorithms and pipeline first
1330 described in <sup>44</sup>.
1331 For the sake of consistency, we provide a short description of the pipeline. The cells
1332 were imaged live for 30 minutes every 3 seconds (short movies), or for 8-9 h every 3
1333 minutes (long movies).

1334 Deconvolution and position of Pol II initiation events in short movies

1335 The Pol II positions were found by combining a genetic algorithm with a local
1336 optimisation procedure. Before initiation of the analysis algorithm, several key
1337 parameters were established. The Pol II elongation speed was fixed at 67 bp/s⁴²,⁴⁴.
1338 The reporter construct transcript was divided into three sections consisting of the pre-
1339 MS2 fragment (PRE=700 bp), 256xMS2 loops (SEQ=5800 bp), and post-MS2
1340 fragment until the polyA site (POST=1600 bp). An extra time *P*<sub>poly</sub>=100s was added to
1341 POST, corresponding to the polyadenylation signal (during this time the polymerase is
1342 past the polyA site and remains on the DNA, see⁴²). The frame rate of short movies is
1343 sufficient to detect processes that occur on the order of seconds.

1344 In order to find the positions of initiation events via the deconvolution pipeline, all
1345 the possible initiation times were discretized using a step size of 0.45 seconds (or 30
1346 bp at 67 bp/s). This step was chosen as it is smaller than the minimum polymerase
1347 spacing and large enough to still accommodate a reasonable computation time. For a
1348 movie of 30 min length this choice corresponds to a maximum number of 4020
1349 positions. The deconvolution algorithm was implemented in Matlab R2020a using
1350 Global Optimization and Parallel Computing Toolboxes for optimizing Pol II positions
1351 in parallel for all nuclei in a collection of movies. Waiting times were then computed
1352 from the position of each initiation event (Fig. 6g).

1353 Long movies waiting time distribution

1354 For long movies, the low resolution (3 min) does not allow a precise positioning of
1355 initiation events. In this case we binarize the signal by considering that the transcription
1356 site is active or inactive if the measured intensity is above or below a threshold level,
1357 respectively, which is set to be slightly higher than the intensity similar of a single
1358 polymerase The inactive intervals then indicate long waiting times between successive
1359 polymerases. The active intervals are used to estimate the probability that waiting
1360 times are larger than the movie frame rate (3 min), which is one of the parameters
1361 needed for connecting long and short time distributions and obtain a multiscale
1362 distribution (see ⁴⁴).

1363 Multi-exponential regression fitting of the survival function and model reverse
1364 engineering using the survival function

1365 Waiting times were extracted as differences between successive Pol II initiation events
1366 from all the resulting traces and the corresponding data was used to estimate the
1367 nonparametric cumulative short movie distribution function by the Meyer-Kaplan
1368 method. Data from long movies is used to generate the nonparametric cumulative long
1369 movie distribution function. The two distribution functions are fitted together into a
1370 multiscale cumulative distribution function using the total probability theorem and
1371 estimates of two parameters $p_i$ and $p_s$, representing the probabilities that waiting times
1372 are longer than the long movie frame rate, and longer than the length of the short
1373 movie, respectively (see ref<sup>44</sup> for details).

1374 Then, a multi-exponential regression fitting of the multiscale distribution function
1375 produces a set of 2N-1 distribution parameters, where N is the number of exponentials
1376 in the regression procedure (3 for N=2 and 5 for N=3). The regression procedure was
1377 initiated with multiple log-uniformly distributed initial guesses and followed by local
1378 gradient optimisation. It resulted in a best-fit solution with additional suboptimal
1379 solutions (local optima with objective function value larger than the best fit).

1380 The 2N-1 distribution parameters can be computed from the 2N-1 kinetic
1381 parameters of a N state transcriptional bursting model. Conversely, a symbolic solution
1382 for the inverse problem was obtained, allowing computation of the kinetic parameters
1383 from the distribution parameters and reverse engineering of the transcriptional bursting
1384 model. In particular, it is possible to know exactly when the inverse problem is well-
1385 posed, i.e. when there is a unique solution in terms of kinetic parameters for any given
1386 distribution parameters in a domain (Fig. 6h and ED Fig. 9c).

1387 Transcriptional bursting models

1388 The transcriptional bursting models used in this paper are as following:

1389 For a promoter two-state model (N=2), the model corresponds to the well-known ON-
1390 OFF telegraph model. In this case there are 3 distribution parameters and 3 transition
1391 rates parameters.

1392 The distribution parameters are $A_1, \lambda_1, \lambda_2$, defining the survival function

$$
1393 \qquad S(t) = A_1 e^{\lambda_1 t} + (1 - A_1) e^{\lambda_2 t}.
$$

1394 These parameters are obtained by bi-exponential fit of the empirical survival function.

1395 The transition rates parameters of the ON-OFF telegraph model can be obtained from
1396 the distribution parameters using the formulas

$$
1397 \qquad k_3 = -S_1, k'_2 = S_1 - \frac{S_2}{S_1}, k_2 = \frac{S_3 S_1 - S_2^2}{S_1(S_1^2 - S_2^2)},
$$

$$
1398 \qquad S_1 = A_1\lambda_1 + A_2\lambda_2, S_2 = A_1\lambda_1^2 + A_2\lambda_2^2, S_3 = A_1\lambda_1^3 + A_2\lambda_2^3, A_2 = 1 - A_1,
$$

1399 where $k_3, k_2, k'_2$ are the initiation rate, the OFF to ON and ON to OFF transition rates,
1400 respectively.
1401 For a promoter three-state model (N=3), there are 5 distribution parameters and 5
1402 kinetic parameters.

1403 The distribution parameters are A₁, A₂, λ₁, λ₂, λ₃, defining the survival function

1404 $S(t) = A_1 e^{\lambda_1 t} + A_2 e^{\lambda_2 t} + (1 - A_1 - A_2) e^{\lambda_3 t}$.

1405 The corresponding model represented in the Figure has two OFF and one ON state.

1406 The five transition rate parameters can be obtained from the distribution parameters:

1407 $k_3 = -S_1, k_2 = \frac{S_2^2 - S_1 S_3}{S_1(-S_1^2 + S_2)}, k'_2 = S_1 - \frac{S_2}{S_1}$

1408 $k_1 = \frac{L_3(-S_1^2 + S_2)}{S_2^2 - S_1 S_3}, k'_1 = \frac{A_1 A_2 A_3 S_1 (\lambda_1 - \lambda_2)^2 (\lambda_1 - \lambda_3)^2 (\lambda_2 - \lambda_3)^2}{(-S_1^2 + S_2)(S_2^2 - S_1 S_3)}$

1409 where

1410 $S_1 = A_1\lambda_1 + A_2\lambda_2 + A_3\lambda_3, S_2 = A_1\lambda_1^2 + A_2\lambda_2^2 + A_3\lambda_3^2, S_3 = A_1\lambda_1^3 + A_2\lambda_2^3 + A_3\lambda_3^3, A_3 = 1 - A_1 - A_2,$

1412 $L_1 = \lambda_1 + \lambda_2 + \lambda_3, L_2 = \lambda_1^2 + \lambda_2^2 + \lambda_3^2, L_3 = \lambda_1^3 + \lambda_2^3 + \lambda_3^3,$

1413 and $k_3, k_2, k'_2, k_1, k'_1$ are the transcription initiation, OFF2 to ON, ON to OFF2, OFF1 to OFF2, and OFF2 to OFF1 rates, respectively.

1415 *Error intervals*

1416 Distribution parameters result from multi-exponential regression fitting using gradient
1417 methods with multiple initial data. These optimization methods provide a best fit (global
1418 optimum) but also suboptimal parameter values. Using an overflow ratio (a number
1419 larger than one, in our case 2) to restrict the number of suboptimal solutions, we define
1420 boundaries of the error interval as the minimum and maximum parameter values
1421 compatible with an objective function less than the best fit times the overflow.

1422 *Choice of the number of exponentials*

1423 The number of exponentials was determined by a parsimony principle: we have chosen
1424 the smallest N that fits well. More precisely, starting with N=2, we have increased N as
1425 long as the goodness of fit reduced without increase of overfitting. We have used
1426 parametric uncertainty (error intervals) as a proxy for overfitting (ED Fig. 9b).

1427 **Statistics and reproducibility**

1428 **Sample size.** No statistical methods were used to pre-determine sample sizes. This
1429 study mainly used non-parametric tests such as Wilcoxon-Mann-Whitney to the
1430 exception of the extended data figures 10a where data distribution was assumed to be
1431 normal but this was not formally tested and Student t-test was applied.

1432 **Statistics and reproducibility.** No statistical method was used to predetermine the
1433 sample size.

1434 **Data exclusion.** In Extended Data Fig. 3e, genes that are not expressed (RPKM = 0)
1435 were not included.

1436 **Replication.** Experiments were repeated in replicates as indicated in the manuscript
1437 (between 2 and 3 times). All replicates were successful.

1438 **Randomization.** Randomization of genome sequences for motif enrichment analysis
1439 were repeated 10000

1440 **Blinding.** The Investigators were not blinded to allocation during experiments and
1441 outcome assessment

1442 **Data availability**

1443 The GEO accessions for specific experiments related to this study are recorded under
1444 GSE159276. G4 mapping and predictions are available under GSE187007.

1445 **Code availability**

1446 Softwares and codes are all publicly available and have been previously described
1447 8,71,74. Codes used for this study have been deposited in thelinked Zenodo repository:
1448 https://zenodo.org/record/15124924; doi:10.5281/zenodo.15124924 (ref. 82).

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<caption>a</caption>

Human K562 and Raji cells
Mouse primary T cells

<figure><img src="image_3.png" /></figure>

Overlay

<figure><img src="image_4.png" /><figcaption>b</figcaption></figure>

Sense transcription

<figure><img src="image_5.png" /></figure>

Anti-sense transcription

<figure><img src="image_6.png" /><figcaption>c</figcaption></figure>

G4access

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>e</figcaption></figure>

n=1766

G4access

<figure><img src="image_11.png" /></figure>

Pol II

<figure><img src="image_12.png" /></figure>

SURE antisense

<figure><img src="image_13.png" /></figure>

SURE sense

<figure><img src="image_14.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_16.png" /></figure>
<figure><img src="image_17.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>d</figcaption></figure>

Reconstituted chromatin
(Valouev *et al.*)

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
a

<figure><img src="image_30.png" /></figure>

b

<figure><img src="image_31.png" /></figure>
a

Eef1a1, Polr2a, Taok1, Pkm and klf6
promoters

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

b

<table><thead><tr><th></th><th></th><th>Score</th><th>length</th><th>IDS</th><th>TDS</th><th>CD</th><th>Conclusion</th></tr></thead><tbody><tr><td rowspan="4">Eef1a1</td><td>WT</td><td>2.14</td><td>59</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td>G4mut</td><td>1.05</td><td>59</td><td>No</td><td>No</td><td>n.c.</td><td>No G4</td></tr><tr><td>G4mut2</td><td>0.69</td><td>59</td><td>n.d.</td><td>n.d.</td><td>n.d.</td><td>No G4*</td></tr><tr><td>G4inv</td><td>2.14</td><td>59</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td rowspan="3">Polr2a</td><td>WT</td><td>1.62</td><td>37</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td>G4mut</td><td>0.81</td><td>37</td><td>No</td><td>No</td><td>No</td><td>no G4</td></tr><tr><td>G4mut2</td><td>0.43</td><td>37</td><td>No</td><td>No</td><td>No</td><td>no G4</td></tr><tr><td rowspan="2">Taok1</td><td>WT</td><td>2.22</td><td>37</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td>G4mut</td><td>0.65</td><td>37</td><td>No</td><td>No</td><td>?</td><td>no G4</td></tr><tr><td rowspan="2">Pkm</td><td>WT</td><td>1.93</td><td>27</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td>G4mut</td><td>0.44</td><td>27</td><td>No</td><td>No</td><td>?</td><td>no G4</td></tr><tr><td rowspan="2">Klf6</td><td>WT</td><td>2.29</td><td>41</td><td>Yes</td><td>Yes</td><td>Yes</td><td>G4</td></tr><tr><td>G4mut</td><td>0.56</td><td>41</td><td>No</td><td>No</td><td>?</td><td>no G4</td></tr></tbody></table>

C

<figure><img src="image_34.png" /></figure>
<figure><img src="image_35.png" /><figcaption>b. Nucleosomes</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>c. Nucl. density at NDR

EEF1A1</figcaption></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /><figcaption>d. Pol II</figcaption></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /><figcaption>e. Long movies

WT</figcaption></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

n=155
n=234
n=633

n=86
n=208
n=633

ON period

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

OFF period

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>
<figure><img src="image_49.png" /><figcaption>Esnault *et al.*, Figure 7</figcaption></figure>

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<figure><img src="image_51.png" /></figure>

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<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>
**Supplementary note 1**

First, advances in G4 mapping have shown that their formation is cell type specific and that only a portion of pG4s could fold into G4s¹,². Using G4access or G4 ChIP to assess G4 formation, our results show that experimental G4s, but not only the pG4 motifs, were associated with nucleosome exclusion and Pol II signal (Extended Data Fig. 3a,b). When comparing experimental G4s in promoters of Raji or K562 cells, we found that G4s specific for a given cell type showed more active and accessible chromatin, and higher transcription levels than in the other cell type, while common G4s generally sustained a higher and more conserved transcriptional level (Extended Data Fig. 3c-e). Second, we compared a selection of G4s in the presence or relative absence of NDRs (Extended Data Fig. 3f-h). The no NDR selection appeared generally less active and accessible but showed similar chromatin remodeler enrichment. This comparison also indicated that large NDRs generally associate to higher pG4 density. Third, since SP1, and more generally the GC box motifs are also highly enriched at promoters³ and represent half of a pG4 motif⁴, we questioned whether SP1 binding or motif could influence the observed chromatin opening by G4s. We analysed pG4 promoters with or without SP1 binding and observed similar NDR formation, allowing to exclude that SP1 would be responsible for the pG4 property (Extended Data Fig. 4a). We cannot exclude, however, the possibility that SP1 binding yields additional MNase footprinting in G4access. Similarly, promoters that did not carry the canonical or non-canonical SP1/GC box motifs did also show openness at G4 motifs (Extended Data Fig. 4b-c). In this case, we observed residual SP1 binding indicating that SP1 might directly bind G4s in accordance with published observations⁵,⁶.

**Supplementary note 2**

We verified that the structure was abolished *in vitro* in the G4 mutants using 3 independent biophysical assays that included circular dichroism, TDS and IDS (thermal and isothermal differential spectra, Fig. 5b, Table S3). Because these assays monitor G4 structures in ssDNA, we also designed a FRET-MC assay in which we tested model G4s and mutants in a dsDNA context. This confirmed that all model G4s are formed while G4 mutations abolish their structure (Extended Data Fig. 8c, Table S3). Interestingly, most of the G4-prone motifs chosen in this experiment, but not their mutated counterparts, were able to compete with dsDNA formation *in vitro*, even in the absence of supercoiling or stabilizing factors such as molecular crowding. In the case of the Pol2ra and Eef1a1, we performed two independent mutations that affected differentially the G4 potential assessed by the G4Hunter algorithm (Fig. 5b and table S3 and S4).

**Supplementary note 3**

We recorded long movies on the WT, *TATAmut* and *G4mut* Eef1a1 cell lines, by taking 3D image stacks every 3 min during 8 h. Since a single polymerase remains more than 3 min at the reporter transcription sites⁷, and since the sensitivity of this assay allows single polymerase tracking, every single transcription event can be detected. We quantified the brightness of transcription sites in hundreds of cells (see methods) and quantified, from which Pol II regularly initiate transcription, from inactive (OFF) periods (Fig. 6e).

**Supplementary note 4 (Limitations of the study)**

In this article, we have made use of several methods to map G4s in the context of chromatin including G4access, ChIP and Cut&Tag with BG4 and DMS, KMnO4 foot-
printing. However, it is important to mention that none of these approaches allow precise mapping of G4s at the nucleotide resolution, which would likely require a combination of *in vitro* and *in vivo* studies. As mentioned in the discussion, several G4 predictions (or clusters) are often found in promoters, especially in the most open and most active ones. Therefore, it is possible that the formation of these G4s is in equilibrium and frequently transition from one structure to another. Future footprinting studies *in vitro* and *in vivo* and at the single molecule level should help addressing this question.

We also note that the presence of G4s, does not exclude that other secondary DNA structures support chromatin opening. An equilibrium between G4s and other structures could be potentially be at play at some promoters or elsewhere in the genome. Usage of the G4Hunter algorithm⁸ to filter experimental signal however supports more specificity in G4 predictions by limiting structures such as stem loop of triplex DNA (by giving a low score to Cs and C-stretches in G4 predictions).

Finally, while our study largely supports a positive role of G4s in transcription as promoter elements when they are located upstream of TSSs, we can't exclude that they could be inhibitory in some cases or specific activation/repression context (for example in the HIV promoter ⁹). Recent works describe conflicting observations for the Myc G4promoter (Pu27), suggesting an activating or a repressing role ¹⁰,¹¹. However, these two articles have used very distinct approaches and none of them allow to draw a definitive conclusion. It remains therefore a possibility that G4s could be repressive in some context or be in equilibrium with TF binding at G4 forming sequences. In this later case, G4s would not be repressing per se but rather in competition with an activator supporting higher level of transcription. This model remains hypothetical but could directly be tested in the future.

**Supplementary notes references**

1. Hansel-Hertsch, R. et al. G-quadruplex structures mark human regulatory chromatin. *Nat Genet* **48**, 1267-1272, doi:10.1038/ng.3662 (2016).

2. Esnault, C. et al. G4access identifies G-quadruplexes and their associations with open chromatin and imprinting control regions. *Nat Genet*, doi:10.1038/s41588-023-01437-4 (2023).

3. Reed, B. D., Charos, A. E., Szekely, A. M., Weissman, S. M. & Snyder, M. Genome-wide occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional roles and combinatorial regulation of distinct classes of genes. *PLoS Genet* **4**, e1000133, doi:10.1371/journal.pgen.1000133 (2008).

4. Huppert, J. L., Bugaut, A., Kumari, S. & Balasubramanian, S. G-quadruplexes: the beginning and end of UTRs. *Nucleic acids research* **36**, 6260-6268, doi:10.1093/nar/gkn511 (2008).

5. Lago, S. et al. Promoter G-quadruplexes and transcription factors cooperate to shape the cell type-specific transcriptome. *Nature communications* **12**, 3885, doi:10.1038/s41467-021-24198-2 (2021).
6 Raiber, E. A., Kranaster, R., Lam, E., Nikan, M. & Balasubramanian, S. A non-canonical DNA structure is a binding motif for the transcription factor SP1 in vitro. *Nucleic acids research* **40**, 1499-1508, doi:10.1093/nar/gkr882 (2012).

7 Tantale, K. et al. A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting. *Nat Commun* **7**, 12248, doi:10.1038/ncomms12248 (2016).

8 Bedrat, A., Lacroix, L. & Mergny, J. L. Re-evaluation of G-quadruplex propensity with G4Hunter. *Nucleic Acids Res* **44**, 1746-1759, doi:10.1093/nar/gkw006 (2016).

9 Perrone, R. et al. A Dynamic G-Quadruplex Region Regulates the HIV-1 Long Terminal Repeat Promoter. *Journal of Medicinal Chemistry* **56**, 6521-6530, doi:10.1021/jm400914r (2013).

10 Esain-Garcia, I. et al. G-quadruplex DNA structure is a positive regulator of MYC transcription. *Proc Natl Acad Sci U S A* **121**, e2320240121, doi:10.1073/pnas.2320240121 (2024).

11 Qin, G. et al. Targeting specific DNA G-quadruplexes with CRISPR-guided G-quadruplex-binding proteins and ligands. *Nature Cell Biology* **26**, 1212-1224, doi:10.1038/s41556-024-01448-1 (2024).
a

K562 EL cells (n=8346)

<figure><img src="image_56.png" /></figure>

b

Raji B cells (n=8356)

<figure><img src="image_57.png" /></figure>

c

Mouse primary
T cells (n=7947)

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<figure><img src="image_65.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>e</figcaption></figure>

K562 cells

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

Raji cells

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /><figcaption>g</figcaption></figure>

Raji cells

Mouse primary T cells

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

n=1444

<figure><img src="image_79.png" /><figcaption>h</figcaption></figure>

K562 cells

<figure><img src="image_80.png" /></figure>

Raji cells

<figure><img src="image_81.png" /></figure>

Mouse primary T cells

<figure><img src="image_82.png" /><figcaption>i</figcaption></figure>

K562 inactive promoters

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

<figure><img src="image_85.png" /></figure>

pG4 (n=1186)

no pG4 (n=2041)
<figure><img src="image_86.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_87.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_89.png" /><figcaption>e</figcaption></figure>

<figure><img src="image_90.png" /><figcaption>f</figcaption></figure>

<figure><img src="image_91.png" /><figcaption>g</figcaption></figure>

<figure><img src="image_92.png" /><figcaption>h</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>i</figcaption></figure>

<figure><img src="image_94.png" /><figcaption>Remodelers</figcaption></figure>

<figure><img src="image_95.png" /><figcaption>Chromatin and transcription</figcaption></figure>

<figure><img src="image_96.png" /><figcaption>MNase-seq</figcaption></figure>

<figure><img src="image_97.png" /><figcaption>Pol II</figcaption></figure>
a

<figure><img src="image_98.png" /></figure>

b

Without a canonical GCbox (GGGCGGG)

<figure><img src="image_99.png" /></figure>

c

Without any GCbox (GGGNGGG)

<figure><img src="image_100.png" /></figure>
Esnault *et al.*, ED Figure 5

a

<figure><img src="image_101.png" /></figure>

b

<figure><img src="image_102.png" /></figure>

c

<figure><img src="image_103.png" /></figure>

d

<figure><img src="image_104.png" /></figure>

e

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>
<figure><img src="image_107.png" /></figure>

<figure><img src="image_108.png" /></figure>

<figure><img src="image_109.png" /></figure>
<figure><img src="image_110.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_111.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_112.png" /></figure>
Esnault *et al.*, ED Figure 8

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_115.png" /></figure>
a

<figure><img src="image_116.png" /></figure>

G4mut

<figure><img src="image_117.png" /></figure>

TATAmut

<figure><img src="image_118.png" /></figure>

b

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

c

<figure><img src="image_122.png" /></figure>

G4mut

TATAmut

WT

$k_{1A-2} = 1.5 \times 10^{-4}$

$k_{1B-2} = 3.1 \times 10^{-4}$

n.a (s⁻¹)

$k'_{1A-2} = 7.1 \times 10^{-5}$

$k'_{1B-2} = 1.3 \times 10^{-4}$

n.a

$k_2 = 1.2 \times 10^{-3}$

$k_2 = 1.3 \times 10^{-3}$

$k_2 = 4.8 \times 10^{-3}$

$k'_2 = 6.0 \times 10^{-4}$

$k'_2 = 4.9 \times 10^{-4}$

$k'_2 = 2.5 \times 10^{-4}$

$k_3 = 5.1 \times 10^{-2}$

$k_3 = 5.4 \times 10^{-2}$

$k_3 = 11.3 \times 10^{-2}$

d

<figure><img src="image_123.png" /></figure>

G4mut2

n=113

<figure><img src="image_124.png" /></figure>

e

<figure><img src="image_125.png" /></figure>

ON period

<figure><img src="image_126.png" /></figure>
<figure><img src="image_127.png" /><figcaption>b. Pausing scores after PDS treatment</figcaption></figure>

<figure><img src="image_128.png" /><figcaption>c. Gene ontology</figcaption></figure>

<figure><img src="image_129.png" /><figcaption>d. Pause release genes</figcaption></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /><figcaption>e. Other genes</figcaption></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /><figcaption>f. Long genes with pause release</figcaption></figure>

<figure><img src="image_134.png" /><figcaption>g. Pausing scores</figcaption></figure>

<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /><figcaption>G4hunter 1.5</figcaption></figure>

<figure><img src="image_137.png" /><figcaption>G4hunter 2.0</figcaption></figure>

<figure><img src="image_138.png" /><figcaption>h. Pause release genes</figcaption></figure>

<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /><figcaption>i. Other genes</figcaption></figure>

<figure><img src="image_141.png" /></figure>

<figure><img src="image_142.png" /></figure>