CUT&amp;Tag in Bacteria Reveals Unconventional G-Quadruplex Landscape in Mycobacterium tuberculosis: A Novel Defense Mechanism Against Oxidative Stress

doi:10.21203/rs.3.rs-5233864/v1

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CUT&Tag in Bacteria Reveals Unconventional G-Quadruplex Landscape in Mycobacterium tuberculosis: A Novel Defense Mechanism Against Oxidative Stress

https://doi.org/10.21203/rs.3.rs-5233864/v1

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Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, remains a global health threat due to increasing drug resistance and high mortality rates. To combat tuberculosis effectively, novel therapeutic targets are urgently needed. G-quadruplexes (G4s) represent promising candidates for this purpose. In this study, we successfully applied the cleavage under targets and tagmentation (CUT&Tag) technique for the first time in bacteria, mapping the G4 landscape in Mtb under standard and oxidative stress conditions, the latter mimicking the environment Mtb faces within macrophages. We validated the CUT&Tag protocol using an antibody against the RNA polymerase β-subunit, confirming its association with actively transcribed genes. Employing the anti-G4 antibody BG4, we discovered that Mtb G4s, unlike their eukaryotic counterparts, predominantly localize within gene coding sequences and consist of two-guanine tract motifs. Notably, oxidative stress increased G4 formation, correlating with reduced gene expression.

Our findings provide the first evidence of G4 formation in Mtb cells and suggest their potential role in bacterial survival within macrophages. This study demonstrates the successful application of CUT&Tag in bacteria and unveils an unconventional G4 landscape in Mtb, offering new insights into bacterial stress response mechanisms and potential therapeutic targets.

Biological sciences/Microbiology/Bacteria/Bacterial genomics

Biological sciences/Biological techniques/Epigenetics analysis

Biological sciences/Genetics/Epigenetics

Mycobacterium tuberculosis (Mtb) is the etiological agent of human tuberculosis (TB)¹. It is a slow-growing bacillus characterized by a high lipid content cell wall that confers the bacterium protection in hostile environments and virulence properties^2,3.

Mtb is transmitted to susceptible individuals through droplets that are released into the air. Once inhaled, bacilli enter the respiratory tract, reach the lungs and infect alveolar machrophages⁴. Here, as defensive mechanism, the human immune system produces reactive nitrogen and oxygen species that damage bacterial structures like DNA and proteins⁵. Mtb has developed counteractive measures to survive and replicate within macrophages in oxidative conditions thanks to different antioxidant enzymes⁶. Despite global efforts, TB persists as a significant public health challenge, with recent data indicating 1.3 million fatalities and 10.6 million new cases in 2022⁷. The complexities of prolonged treatment regimens, the emergence of drug-resistant strains, and the absence of an efficacious vaccine underscore the imperative for novel therapeutic approaches.

In this scenario, G-quadruplexes (G4s) may represent alternative targets for the development of new therapeutical approaches. With roughly 4,000 genes and an overall GC content around 65.6%⁸, the Mtb genome is likely prone to G4 formation. G4s are alternative nucleic acid secondary structures that arise in single-strand, guanine-rich DNA and RNA sequences from the planar association of four guanines (G-tetrads) through Hoogsteen hydrogen bonds; G-tetrads are linked by loops of different length and nucleotide composition, which contribute to the polymorphic nature of G4 structures^9–11. Monovalent cations like K⁺ or Na⁺ promote G4 stability. G4s have been extensively studied in eukaryotic cells where they have been demonstrated to form in vivo preferentially at genes promoters and in nucleosome-depleted regions¹⁰. They have been implicated in various cellular functions, spanning from DNA replication, transcription and translation to telomere maintenance, gene instability and cancer^9–11.

Conversely, the G4 field in the prokaryotic kingdom is still poorly investigated: G4 formation has never been described in vivo and G4 physiological role in bacteria has been only scantily elucidated so far. The Mtb genome has been predicted by bioinformatic analysis to potentially form more than 10,000 G4 motifs. To date, in silico and in vitro studies have focused on the identification of canonical G4 motifs, composed of at least three guanine per G-tract and preferentially located at gene promoters. G4s have been predicted to form in regulatory regions of genes belonging to specific functional categories, including “cell wall and cell process” and “intermediate metabolism” or in genes that modulate the host-pathogen interaction. Additionally, known G4 ligands, like BRACO-19, c-exNDI 2 and TMPyP4, have demonstrated inhibitory effect on Mtb cell growth^12,13. Mtb encodes for three E. coli ortholog helicases, namely UvrD1,UvrD2 and DinG, which have been demonstrated to unwind G4s^14–16, supporting the presence of folded G4s in Mtb cells.

Here we successfully performed cleavage under targets and tagmentation (CUT&Tag) genome-wide mapping in Mtb to define the G4 landscape. The analysis was performed in the presence and absence of oxidative stress treatment, to reflect the conditions faced by the bacterium when internalized within the host macrophages. We report the CUT&Tag output of two different antibodies that target the β subunit of the RNA polymerase and G4 motifs. We show that the CUT&Tag can be efficiently applied to complex bacteria like Mtb, allowing the in vivo mapping of different targets. We demonstrate that, in contrast to eukaryotes, G4s are mainly located within gene coding sequences and are mostly composed of two guanines tracts. We evidenced that in the oxidative stress conditions more G4s are formed and tied to genes with lower transcript abundance, implicating a new role for G4s in bacteria.

Genome-wide mapping of G4s in Mtb: from ChIP-seq to CUT&Tag

To map the location of G4s in Mtb genome-wide, we initially applied a chromatin immunoprecipitation protocol (ChIP) with BG4, the most used anti-G4 antibody¹⁷, followed by either qPCR or sequencing. Experiments were conducted under standard laboratory growth conditions and under oxidative stress conditions, to compare the genomic distribution of G4s in Mtb cultures in the environments to which the bacterium is exposed during infection of the host.

ChIP is a multi-step antibody-based technique which allows the identification of specific regions within chromatin¹⁸. Initially, bacterial cells are harvested, cross-linked with formaldehyde to preserve biological interactions, and lysed before chromatin shearing. This latter step is critical for the good outcome of the protocol and requires fragments’ distribution to be comprised between 100–500 bp¹⁹. Subsequently, immunoprecipitation with the antibody of interest is performed and the enrichment of the genomic sequences is assessed through qPCR or next generation sequencing. In our set-up, each ChIP experiment consisted of three samples: IP (immunoprecipitated DNA), MOCK and input. The IP was treated with the antibody of interest, whereas the MOCK sample underwent immunoprecipitation in the absence of the antibody, thus serving as control for non-specific background noise. The input sample, consisting of unprocessed sheared chromatin, allows qPCR data normalization and target enrichment evaluation¹⁸. qPCR was thus performed to test the effectiveness of the applied protocol. To this end, three genomic regions were selected as positive controls: two of them included G4 sequences previously shown to fold in vitro, namely zwf1 and mosR¹², and a third one, lpqS, as a further G4-forming region. We performed CD to confirm the G4 folding of the selected positive regions (Fig. S1). As negative control, we identified a genomic segment in the Rv1507A gene that, based on the sequence, could not fold into G4 in vitro. ChIP-qPCR analysis showed remarkable enrichment of IP positive samples with respect to the MOCKs, both in standard growth conditions and upon treatment with H₂O₂, indicating high specificity of BG4. Replicates were highly consistent and positive regions were at least five-fold enriched over the negative control, a value in line with successful ChIP-qPCR data reported in the literature (Fig. 1A-B)²⁰. Encouraged by these results, we proceeded with ChIP-seq analysis. Here, however, the identification of a clear signal above the background proved to be challenging. Despite the presence of a general enrichment in putative G4 positive regions (i.e. zwf1) and no enrichment in the negative region Rv1507A with respect to the input sample (Fig. 1C, S2), broad enriched domains were observed and commonly used peak calling tools struggled to identify true enriched regions above the background. To overcome this issue, the bioinformatic tool EPIC2²¹, specifically designed to identify broad domains, was employed but the identification of few peaks combined with the strong similarity between IP and input samples, made these data unreliable.

RNA Polymerase antibody profiling validate the CUT&Tag protocol in the Mtb genome

To proceed with our goal of identifying the G4 landscape in Mtb, we explored the possibility of applying the CUT&Tag technique, which had never been applied to a bacterial genome before.

In the CUT&Tag protocol cells are first bound to magnetic beads and then mildly permeabilized to allow the antibody of interest and the Tn5 enzyme to reach the cell genome²². We initially optimized the binding of Mtb to concanavalin A (ConA)-coated beads by measuring the optical density upon different incubation times (10 and 20 minutes). Good bead saturation was achieved at 20 minutes of incubation (Fig. S3A). To assess the optimal number of bacteria to be processed, we performed CUT&Tag on three different sample sizes, i.e. 1, 10 and 20 million cells, quantified the bacterial DNA before and after library preparation (Fig. S3B-C) and found that the highest amount of DNA was collected using 10 million cells.

To validate the novel CUT&Tag protocol in Mtb, we initially used the antibody targeting the RNA polymerase subunit β, whose genome-wide data obtained by ChIP-seq were previously reported²³. Experiments were conducted under both standard growth and oxidative stress conditions. Briefly, bacterial cultures were mildly fixed with formaldehyde and the permeabilized cells were incubated with the antibody, tagmented with Tn5 transposase and PCR-amplified fragments were subjected to next-generation sequencing. Each CUT&Tag experiment consisted of three biological replicates that presented high Pearson’s correlation coefficients, indicating robust reproducibility (Fig. 2A), and were clustered according to the growth conditions (standard or oxidative stress growth), as shown in the PCA analysis plot (Fig. 2B). Proper sequencing depth was reached in all samples from both conditions : FRiP values largely exceeded 0.01, a threshold commonly associated with successful experiments (Fig. 2C)²⁴. Peak size distribution showed that the median peak size across all samples was ∼200 bp, as expected²⁰ (Fig. 2D). The sequencing profile of the RNA polymerase subunit β showed peaks across the entire Mtb genome, identified through the MACS2 tool with IgG subtraction. High-confidence peaks, i.e. peaks present in at least two out of three biological replicates, were selected for downstream analysis. Under standard growth conditions, an average of 1101 peaks (replicate 1 = 1059, replicate 2 = 997, replicate 3 = 1373) were identified, while under oxidative stress, an average of 986 peaks (replicate 1 = 1023, replicate 2 = 992, replicate 3 = 1029) were observed. The RNA polymerase peaks predominantly localized around gene start sites (36.7% and 36.1% in standard and oxidative stress condition, respectively), within genes (23.9% and 23.7%, respectively), and upstream of genes (21.8% and 21.4%, respectively) (Fig. 2E-F). As the β subunit constitutes an essential component of the holoenzymatic complex, the predominant localization of peaks near gene start sites indicates that the enzyme is preparing to initiate transcription. The significant abundance of peaks residing within gene bodies or overlapping gene ends indicates that the RNA polymerase complex is either initiating or finishing the transcription process. This observation aligns with the pivotal role of the β subunit, which serves as a fundamental component of the enzyme involved in the entire transcription process, from initiation to termination²⁵.

To further validate our CUT&Tag results, we compared them with the previously published ChIP-seq data for RNA polymerase subunit β from Uplekar et al.²³: 54% of the peaks (595/1101) were obtained with both experimental approaches (Fig. S4). All these data prove the consistency and reliability of our results and support the application of the CUT&Tag protocol in Mtb.

RNA Polymerase peaks are associated with highly expressed genes

Integration of the CUT&Tag datasets with RNA-seq was performed to strengthen the validity of the CUT&Tag analysis within the Mtb genome. The raw read counts were converted into transcripts per million (TPM) for individual genes and associated to their corresponding genetic locus. Gene expression levels were next correlated with the presence or absence of CUT&Tag RNA polymerase peaks. A statistically significant increase in gene expression was observed in regions exhibiting RNA polymerase peaks versus those lacking them, both in standard and oxidative stress conditions (Fig. 3A-B). By dividing genes into three categories based on gene expression interquartile (IQR) values (low, medium, and high), we observed a substantial increase in the expression levels of all RNA polymerase peaks in all categories (Fig. 3C-E and S5). Hence, the presence of RNA polymerase peaks was consistently associated with genes exhibiting increased transcriptional activity.

We next performed differential gene expression analysis (DEG) between standard and oxidative stress conditions. It is known that Mtb treatment with 5 mM H₂O₂ causes a substantial alteration of the bacterial expression profile²⁶. Genes were considered as differentially expressed when meeting criteria of log2-fold change exceeding 1 or falling below − 1 and with adjusted p-value below 0.05. Under these criteria, 732 and 650 genes were found to be upregulated and downregulated, respectively. Within these genes, we identified 211 upregulated and 82 downregulated genes associated with an RNA polymerase peak, constituting 28.8% and 12.6%, respectively, of the differently expressed genes in response to oxidative stress (Fig. 3F). In response to oxidation, the induction of scavenging enzymes becomes crucial for Mtb survival^27,28. For instance, katG, which efficiently reduces reactive oxygen species (ROS) concentrations, was strongly upregulated, along with cysN, responsible for sulfate activation in cysteine biosynthesis, and with enzymes involved in the DNA damage response pathway, all confirming the status of oxidative stress within the bacteria. Other genes that were upregulated upon oxidative stress conditions, included iron-related genes such as the Mtb gene cluster encoding mycobactin, an iron-chelating siderophore²⁶, irtA and ideR involved in iron import and regulation, respectively, DNA repair enzymes, such as recA, radA, and dnaE2^26,27, and protein repair or degradation systems, such as clpC1 and clpP2.

All these results corroborate the efficiency of the CUT&Tag protocol in the Mtb genome, both in exponential growth and oxidative stress conditions.

G4 CUT&Tag reveals a stress-responsive G4 landscape characterized by two guanine tract motifs

We next applied the CUT&Tag protocol to define the G4 landscape in Mtb, using the BG4 antibody. We performed the analysis in standard and oxidative stress growth conditions to assess G4 distribution and possible role in both environments. Analogously to RNA-Pol-CUT&Tag, replicates obtained with BG4 were highly consistent, as shown by Pearson’s correlation (Fig. S6A), with the expected fragment distribution²⁹ (Fig. S6B). Library complexity and sequencing depth exceeded the accepted threshold, confirming the efficiency of the G4-CUT&Tag (Fig. S6C). Visual inspection of G4-CUT&Tag sequencing profiles showed remarkable improvement in signal-to-noise ratio (Fig. S6D) with respect to G4-ChIP data (Fig. 1C), allowing the identification of sharp peaks distributed across the entire Mtb genome. Remarkably, the number of retrieved G4 peaks was higher in the oxidative stress condition than in standard growth (Fig. 4A). In the latter condition, an average of 745 high-confidence peaks (replicate 1 = 836, replicate 2 = 416, replicate 3 = 1580) were identified, while under oxidative stress, an average of 1080 high-confidence peaks (replicate 1 = 1138, replicate 2 = 1061, replicate 3 = 1335) was observed. Annotation of G4 peaks showed an unprecedented distribution of G4s, with almost 60% of peaks in both conditions located in the gene body. The remaining peaks mostly overlapped with gene start and end sites (Fig. 4B). These results strongly differ from the typical eukaryotic distribution, where G4s accumulate at promoter regions^30,31, thus suggesting new regulatory mechanisms. We next performed G4 motif prediction on the sequences included in the high confidence peaks. We screened for motif length up to 30 bp with the following characteristics: i) G-tracts of at least 2 guanines each; ii) loop length up to 7 (short) or 12 (long) nucleotides. Surprisingly, considering guanine abundance in the Mtb genome, ~ 99% of putative G4-forming sequences were characterized by two-guanines tracts and short loops (Fig. 4C), which characteristics are generally associated to weaker G4s.

We selected the twenty top high-confidence peaks and searched for a consensus motif by STREME analysis. Guanine-rich patterns were retrieved in both growth conditions, thus confirming the robustness of our results (Fig. 4D). We used CD spectroscopy to confirm G4 folding of six selected retrieved sequences per condition (Fig. 4E-F). Most sequences showed the typical CD signature of a G4 with antiparallel topology, characterized by two positive peaks at λ ~ 240 and 290 nm and a negative one at λ ~ 260 (STD2, STD3, STD4, STD6, OX1, OX6). The remaining sequences showed a mixed G4 topology, with positive peaks at λ ~ 260 and 290 nm, with different intensities. Overall, all tested sequences were confirmed to fold into G4s.

G4-bearing genes under oxidative stress are associated with lower transcript levels than in standard growth conditions

To elucidate the physiological role played by G4s within the Mtb genome, we performed RNA-seq analysis in both growth conditions and integrated transcripts values with G4-CUT&Tag results. Upon standard growth condition, no statistical difference was observed in terms of transcripts levels between genes bearing or not a G4 peak (Fig. 5A). In the oxidative stress conditions, we observed an overall reduction of gene expression likely induced by the treatment^26,27 and G4-bearing genes showed a statistically significant reduction of transcripts when compared to the non-G4 genes (Fig. 5B). To deepen this aspect, we further subdivided transcript values into high, medium and low categories according to the interquartile range method. Genes harboring a G4 motif and belonging to the high and medium category exhibited a significant reduction in gene expression levels compared to their non-G4 counterparts, while the low category did not show significant differences (Fig. 5C-E). These data suggest a direct correlation between G4 folding and transcription downregulation in oxidative stress conditions. To further corroborate this scenario, we identified the G4 peaks unique for the oxidative condition, i.e. those peaks in which the G4 is not present in standard condition whereas the folding is induced upon H₂O₂ treatment. We associated each G4-bearing gene with the relative transcript level in standard and oxidative growth conditions, and normalized the data on the sigA housekeeping gene, the expression of which is not affected by the oxidative treatment³². By comparing the transcript levels of these genes in both conditions, we observed a significant downregulation upon G4 folding (Fig. 5F), therefore confirming that in the Mtb genome G4 formation in oxidative stress is strictly related to downregulation of gene expression.

We thus performed GO enrichment analysis on the oxidative stress-unique G4 peaks to identify the pathways in which such genes may be involved. G4-bearing genes induced upon oxidative treatment were mostly associated with metabolic and biosynthetic processes and cell wall biogenesis (Fig. 5G). We applied the same enrichment analysis to the unique peaks regrouped based on their expression levels: we divided the dataset into upregulated, downregulated and non-differentially expressed G4-bearing genes, according to the RNA-seq values. We observed that the downregulated group mainly included genes involved in cell wall organization and biogenesis, while the non-differentially expressed genes were mainly associated with biosynthetic pathways (Fig. S7). The up-regulated group included a number of genes that were too small to perform the analysis. These data suggest that Mtb G4s may act as defensive agents that protect the bacterium from the ROS damage, by slowing down the biogenesis of new wall components, hence slowing bacterial replication.

Overall, our findings indicate that the G4-bearing genes recovered during Mtb stress response predominantly engage in various metabolic processes, as well as catalytic and binding functions. Interestingly, the identified enriched pathways are typical of Mtb stress-induced response, both at transcriptomic and proteomic levels³³, suggesting that G4s might be constitutive elements for Mtb response to stress.

In the present work we successfully applied the CUT&Tag technique to a prokaryotic system, like the Mtb genome, for the first time. While CUT&Tag has previously been applied to various species, such as human and eukaryotic cells and plants, it has never been shown to work in a bacterial system before³⁴.

With this technique, we were able to localize the Mtb transcriptional complex and the presence of folded G4s, proving the versatility of the approach. CUT&Tag offers many advantages compared to other immunoprecipitation techniques like ChIP-seq: it allows to streamline and speed up the experimental process thanks to the simultaneous employment of different antibodies, along with the contextual library preparation orchestrated by the Tn5 enzyme during the tagmentation step. Additionally, the elimination of chromatin crosslinking and shearing steps provides a more physiological setting and soft treatment, thus avoiding epitope masking or biases caused by formaldehyde and sonication^22,35. CUT&Tag also requires less starting material, contributing to an overall lower background signal and a higher signal-to-noise ratio, which in turn allows a more reliable identification of peaks above the background. We proved that this aspect is highly critical in Mtb, for which the ChIP-seq approach showed excessive background signal (Fig. 1C), while CUT&Tag provides a much-improved profile (Fig. S6D). Last, CUT&Tag represents a cost-effective technique, requiring low sequencing depths; in our experiments we efficiently mapped the genome sites of the Mtb transcriptional complex with almost 5 million reads per samples, while ChIP-seq needed at least 5 times more sequencing reads for the same purpose. The application of our optimized CUT&Tag with different antibodies will help map the Mtb DNA in the chromatin context and will contribute to disclosing new aspects of Mtb pathophysiology.

All our analyses were performed growing axenic cultures of Mtb under standard laboratory conditions and under oxidative stress treatment, the latter being a condition that mimics the environment faced by Mtb after macrophage phagocytosis. By analyzing the RNA polymerase β subunit, we determined the precise localization of the transcriptional complex genome wide in each tested condition. We observed that genes bearing a peak for the RNA polymerase were generally tied to highly expressed genes (Fig. 3A-B). Upon induction of oxidative stress, a shift in the binding profile of the RNA polymerase was observed, confirming the presence of the transcriptional complex in those genes responsible for detoxification^34,36. RNA-Pol-CUT&Tag efficiently mapped the shift of the transcription complex in response to the oxidative environment, proving to be suitable to identify regulatory elements in Mtb physiology.

Our main goal was to evaluate the possible differences in the G4 landscape as bacteria undergo redox-dependent changes in genome condensation to regulate gene expression and consequently counterbalance this stressful condition^6,37,38. We first had to ascertain whether the thousands of G4 motifs bioinformatically predicted³⁹ were actually folded within the Mtb genome. We found that Mtb G4s were actually folded and were mainly localized within gene bodies, which was surprising as in eukaryotic cells G4s specifically localize at gene promoters and in nucleosome-depleted regions, thus contributing to gene expression regulation^30,31. Our data are in line with a recent in-silico analysis performed on M. smegmatis, a non-pathogenic mycobacterium, that reported higher enrichment of putative G4-forming sequences in mRNA-like strands with respect to promoter regions⁴⁰. This unconventional location suggests that G4s in Mtb, and possibly in other mycobacteria and prokaryotes, have different regulatory roles than those characterized so far in eukaryotic cells. It has been recently shown that bacterial histones bend DNA while compacting it⁴¹. It is possible that this bending allows G4 folding even in closed chromatin regions, such as those in gene bodies of non-transcribed genes, contrary to eukaryotic histones where in fact G4s are observed mainly in open chromatin regions.

G4 prediction on peak sequences evidenced an extraordinary abundance (~ 99%) of G4 motifs composed of two tetrads and linked by short loops (Fig. 4C). Our findings corroborate previous studies which reported that, upon lengthening of the G tracts, from two to five G residues, a reduction in putative G4-forming sequences occurs and that longer loops are detrimental to the stability of G4s formed by two tetrads^12,42. To note that eukaryotic organisms mostly present G4s composed of three or more tetrads⁴², which further suggests a different regulatory role of G4s between prokaryotes and eukaryotes. G4 motifs composed of two-guanines tracts, therefore characterized by an overall lower stability⁴², would be more dynamic, which may be important for their role in Mtb. However, considering the high guanine abundance characteristic of the Mtb genome, it was surprising to find such an abundance of two-tetrad G4s folded in the cells. In this respect, the GC content has been shown to be not the main parameter impacting G4 occurrence within genomes, especially in Archea and Bacteria kingdoms. For instance, Leishmania major and Chlamydomonas reinhardtii, both have 60% GC content, but show an opposite G4 density (low and high, respectively)⁴³.

In Mtb, we found that the number of G4s increased upon oxidative stress treatment, suggesting that G4s are elements responsive to environmental conditions. The role of ROS in the G4 context is disputed. Several studies evidenced the detrimental role of ROS in G4 formation; in particular, ROS have been demonstrated to be responsible for the reduced thermal stability of G4s due to guanine oxidation and thus disruption of the Hoogsteen hydrogen bonds^28,44. In contrast, recent studies have demonstrated that oxoguanines (OGs) can stabilize the G4s⁴⁵. A study on the bcl2 promoter indicated that the presence of OGs led to increased G4 stability, influencing gene transcription regulation ⁴⁶. In the human telomeric G-rich sequence, the presence of oxidative lesions was shown to be overcome by the recruitment of undamaged G-tracts that act as spare tires⁴⁷. A similar mechanism could be advocated in the Mtb genome, considering the abundance of GG-tracts, thus potentially explaining the increased abundance of G4s in the oxidative stress condition. It is worth noting that, upon oxidative stress condition, the bacterial nucleoid undergoes condensation to protect DNA from ROS-induced damage³⁸. Thus, DNA overwinding could contribute to double helix destructuration, promoting G4 formation. To further support this hypothesis, there are some specific proteins, e.g. those belonging to the WhiB family, which act as redox sensors and promote condensation of the mycobacterial genome in response to oxidative stress, through the binding with G/C-rich genomic regions, thus implying a possible recognition and stimulation of G4-forming sequences^38,48. Overall, this evidence suggests that the distinctive prevalence of G4 structures in the Mtb genome may serve as a protective mechanism against the effects of oxidation that Mtb encounters especially in macrophages during infection.

Interestingly, in samples exposed to oxidative stress, G4-bearing genes showed an overall reduction in transcript levels, supporting the hypothesis that G4s could act as sensors of or protectors from environmental stress. Mtb is known to react to environmental stress by slowing down the metabolic and synthesis pathways, thus promoting a dormant state³³. GO enrichment analysis showed that the unique G4 peaks obtained upon oxidative treatment are present in genes involved in these pathways, further supporting the possibility that transcript reduction mediated by G4s leads to a “latent state” that contributes to Mtb survival within macrophages.

We have thus shown that when G4s are embedded in gene bodies, like in Mtb, they mediate repression of transcription, while when at promoters, like in human cells, they increase transcription by enhancing transcription factor binding^31,49–51. Hence, G4s may be considered epigenetic features with opposite effect from DNA methylation, which has been shown to repress and promote transcription when at promoters and gene bodies, respectively⁵².

Considering the number of new TB cases that occur annually, and the alarming emergence of TB drug resistant strains combined with the lack of innovative approaches to cure tuberculosis, there is a pressing need to find alternative therapeutic targets and G4s might serve this function. In fact, G4 motifs could represent a valuable alternative for the development of new therapeutic approaches. This hypothesis is further supported by the evidence that two known G4 ligands, BRACO-19 and a core-extended NDI, have proved to inhibit Mtb growth, with a G4-related mechanism of action¹².

The present work demonstrates that G4s do form in bacterial cells, where they may play a pivotal role in regulating gene expression in the Mtb genome and in sensing or responding to stress condition, thus serving as specific and novel therapeutic targets to efficiently defeat such a deadly pathogen that still threatens mankind.

Bacterial strains and growth conditions

For CUT&Tag and RNA-seq experiments, M. tuberculosis H37Rv virulent strain was employed. Bacteria were grown at 37°C in Middlebrook 7H9 (BD Difco) supplemented with 10% albumin-dextrose-sodium chloride complex, 0.2% glycerol and 0.05% Tween 80 in rolling conditions and were harvested at an early-mid exponential phase. To perform oxidative stress treatment, Mtb cultures in mid-exponential phase (OD₅₄₀ = 0.4–0.6) were exposed to 5 mM H₂O₂ for 40 min and then harvested.

G4-Chromatin immunoprecipitation protocol (G4-ChIP)

Mtb culture was grown at early exponential phase (OD₅₄₀ = 0.4–0.6) and then fixed with 1% formaldehyde (ThermoFisher Scientific, #28906) for 10 min at 37°C rolling. Fixation was quenched by the addition of 125 mM glycine and incubated for 5 min rolling. Bacteria were centrifuged for 5 minutes at 3500 rpm and washed twice with Tris buffered saline (20 mM Tris-HCl pH 7.5, 150 mM NaCl). 300 µL of fixed bacteria were resuspended in immunoprecipitation buffer (50 mM Hepes-KOH pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0,1% SDS) and sonicated on Bioruptor Plus (Diagenode) at 4°C to obtain an average fragment size distribution comprised between 100–500 bp; sonication cycles consisted of 30 sec on followed by 30 sec of recovery. Samples were centrifuged at 13000 rpm for 10 min at 4°C and chromatin aliquots were stored at − 80°C until use.

G4 ChIP experiments were performed upon standard growth conditions and after H₂O₂ treatment. Sheared chromatin batches were derived from independent bacterial cultures. To maximize DNA recovery, each experiment consisted of three IP technical replicates pooled together. 500 ng frozen chromatin was diluted in blocking buffer (25 mM HEPES pH 7.5, 10.5 mM NaCl, 110 mM KCl, 1 mM MgCl₂, 1% BSA). Samples were incubated with 2 µL of RNase A (10 mg/mL Thermo Fisher Scientific) at 37°C for 20 minutes at 800 rpm. After RNase A treatment, INPUT sample was stored on ice until the end of the experiment. 350 ng of BG4 antibody was added to IP samples. IP and MOCK samples were then incubated 1h at 16°C, 1200 rpm. Meanwhile, anti-FLAG M2 magnetic beads (Merck, # M8823) were washed three times with blocking buffer. In the last wash, beads were resuspended in blocking buffer and incubated at 16°C, 1200 rpm along with samples. After 1 h incubation of IP samples with BG4 antibody, 50 µL of magnetic beads were added to IP and MOCK samples, respectively, and incubated for 1 h at 16°C, 1200 rpm. Samples were then washed three times with ice-cold wash buffer (100 mM KCl, 0.1% Tween 20, and 10 mM Tris pH 7.4). Two additional washes were performed at 37°C, 1200 rpm for 10 minutes each. Elution was performed adding 75 µL of TE buffer to the beads. Samples were treated with 2 µL of 10 mg/mL RNase A at 37°C for 30 minutes, 1200 rpm. 0.5% SDS was added to the samples followed by 50 µg proteinase K and DNA decrosslinking was obtained by incubating samples first at 50°C for 2 h and then 8 h at 65°C. Samples were purified using MinElute PCR purification kit (QIAGEN, # 28004) and qPCR was performed using PowerUP SYBR green Master Mix (Thermo Fisher Scientific, # A25741) on ABI PRISM 7000 (Applied Biosystem) using the following thermal conditions: initial denaturation at 95°C for 5 min followed by 40 cycles at 95°C for 10 sec and annealing at 60°C for 30 sec. Four different primer pairs were chosen to target both G4 positive and G4 negative regions. Primers’ sequences were purchased from Sigma-Aldrich and are listed in Supplementary table 1. NEBNext Ultra II Library Prep kit for Illumina (NEB, # E7645S) was used for library preparation of samples. Libraries’ quality and size was checked on Bioanalyzer 2100 (Agilent), with Agilent DNA High Sensitivity Chips (Agilent, #5067 − 4626). Samples were sequenced on Illumina Nextseq 500 platform with single end chemistry, using 150 bp reads.

G4-ChIP-Sequencing data analysis

Raw Fastq reads quality were checked on the Galaxy web platform (usegalaxy.org). Trimmomatic (v 0.39) was used to cut reads at 70bp in length and to remove adaptors. Alignment to Mtb H37Rv reference genome was performed using Bowtie 2 (v2.4.2)⁵³. Bamcoverage (v 3.5.4) was employed to generate bigwig from aligned bam files. RmDup⁵⁴(v 2.0.1) was used to remove duplicate sequences before peak calling and G4 peaks were identified using Epic2²¹ with effective genome fraction and false discovery rate set to 1 and 0.01, respectively.

Circular Dichroism Spectroscopy (CD)

Oligonucleotides used for CD analysis were purchased from Sigma-Aldrich and are enlisted in supplementary table 2. Oligonucleotides were resuspended at a final concentration of 3 µM in 10 mM lithium cacodylate buffer pH 7.4, supplemented with 100 mM KCl and PEG200 40% v/v(Sigma-Aldrich, #88440). Samples were denatured at 95°C for 5 minutes and then cooled down overnight at room temperature (RT). CD spectra were acquired on a Chirascan Plus (Applied Photophysics) using a 5 mm quartz cell. Spectra were recorded at 20°C in the wavelength range comprised between 230 and 320 nm. Data were adjusted for the absorption of their relative buffer.

Clevage under targets and tagmentation (CUT&Tag)

CUT&Tag protocol was optimized, starting from procedures applied to eukaryotic cells^22,55. Mtb cultures were grown until early exponential phase and harvested by centrifugation at 3500 rpm for 5 minutes. Pellets containing approximately 120 million cells were obtained, mildly fixed with 0.1% formaldehyde (ThermoScientific, #28906) for 2 minutes at RT and the fixing reaction was quenched by adding 75 mM Glycine. For bacteria binding, Concanavalin A beads (CliniSciences, #86057-3) were first activated by resuspension in 1 mL of binding buffer (20 mM HEPES pH 7.5, 10 mM KCl, 1 mM CaCl₂, 1 mM MnCl₂). Bacteria were resuspended in 1 mL wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM spermidine) and incubated for 20 minutes at RT on a rotating wheel. The bacterial cell envelope was permeabilized by incubating the bacteria for 1 h with 2 mg/mL of lysozyme (Serva), followed by a 5-minute incubation with 0.1% Triton X-100 (Sigma-Aldrich, #T8787). Beads-bound bacteria were then resuspended in 50 µL of antibody buffer (BSA-wash buffer, 2 mM EDTA), and antibodies were added at the desired concentration. Primary antibodies were incubated for 2 h at 16–18°C on a nutator. Three washes were performed after each antibody incubation step. BG4 samples were incubated with a 1:100 dilution of anti-flag M2 antibody (Merck Life Science, #F3165) for 1 h at RT. Remaining samples were resuspended in antibody buffer an incubated at RT for 1 h with 1:100 dilution of secondary antibodies. RNA polymerase and BG4 samples were incubated with a rabbit anti-mouse IgG antibody (Sigma #06-371) while the negative control was incubated with an anti-rabbit guinea pig IgG (Rockland, #611-201-122). Tagmentation step required first incubation of samples for 1 h at RT in 300-BSA wash buffer (10 mM Hepes pH 7.5, 300 mM NaCl, 0.5 mM Spermidine, 1X proteinase inhibitor, 0.5% BSA) containing 1:50 dilution of pAG-Tn5 (Tebu-bio, #23615 − 1017) followed by beads wash and incubation in tagmentation buffer (300-BSA wash buffer with 10 mM MgCl₂) at 37°C for 1 h. To halt tagmentation, samples were incubated at 63°C for 1 h and then further incubated at 85°C for 10 minutes to inactivate any viable bacteria.

Finally, DNA was purified through phenol-chloroform extraction and subjected to library preparation utilizing the NEBNext High Fidelity 2X PCR Master Mix (Euroclone, #BM0541S) and using universal i5 primer and a uniquely barcoded i7 primer⁵⁶. Libraries were purified using Agencourt AMPure XP beads (Beckman Coulter, #A63881) and sequenced on an Illumina Nextseq 500 instrument using a 75-bp paired-end sequencing strategy.

Bioinformatic analysis

NGS data were analyzed using both the public Galaxy Web platform and R studio (version 4.3.1). Following samples quality control with FastQC (v 0.73) the analysis proceeded as follows: reads underwent trimming using Trim Galore! (Galaxy Version 0.6.7) and were aligned to Mtb reference genome (NC_000962.3) with Bowtie2 (Galaxy Version 2.5.0)⁵³ with -I 10 and -X 700 and default paired-end options. Peaks were called against the IgG negative control using MACS2 (Galaxy Version 2.2.7.1). Peak detection was based on q-value and the minimum cut off to call significant peaks was set to 0.05. All duplicates were retained. BamCoverage (v. 3.5.4) was employed to generate bigwig files which were visualized with Gviz⁵⁷. BEDTools intersect⁵⁸ was employed to retain high confidence peaks among three biological replicates. The R package ‘VennDiagram’⁵⁹ was used to plot shared G4 peaks among standard growth and oxidative stress condition. Peaks annotation was performed using the ChippeakAnno⁶⁰ package on R studio. Fraction of read in peak (FRiP) was calculated using deepTools module CountReadsPerBin.

G4 motif prediction and sequence analysis

G4 peaks in both tested conditions were organized in descending order using R studio and the top 20 MACS2-called G4 peaks were extracted based on their scores. The DNA sequences underlying the peaks were retrieved using Extract Genomic DNA on Galaxy platform (Galaxy Version 3.0.3). G4 sequence motifs were searched with STEME online tool⁶¹. Then, FASTA sequence files from all peaks were employed in the QGRS mapper tool⁴² to predict G4 motifs possessing a minimum of two tetrads and featuring loop lengths ranging from 0 to 7 or 0 to 12, as indicated.

RNA extraction

For RNA sequencing, 5 mL of Mtb culture grown to OD₅₄₀ of 0.4–0.5 were used for RNA extraction. The samples were pelleted at 3200 rpm for 5 minutes, resuspended in 1 mL TRIzol® Reagent (Invitrogen^™ #15596018), and transferred to 2-mL screw cap tubes containing 500 µL Zirconia/Silica beads (0.1 mm diameter, BioSpec Products). To achieve cellular disruption, a Mini Bead Beater (BioSpec Products) was employed, and cells underwent three rounds of 35 second of pulses alternated with 35 seconds incubation on ice. Samples were finally centrifuged at maximum speed for 15 minutes at 4°C. The supernatant was transferred into a new tube containing an equal volume of 96% Ethanol, and RNA extraction was conducted using Direct-zol RNA MicroPrep kit (Zymo Research, #R2060) following manufacturer’s instructions. RNA was eluted using 20 µL nuclease-free water. RNA samples underwent a double treatment with TURBO DNase-free kit (Invitrogen, #AM1907). RNA was quantified on Qubit fluorometer, and RNA quality was evaluated through Tapestation (Agilent). Samples with RIN values above 7 were used. QIAseq FastSelect – 5S/16S/23S (Qiagen, # 335925) was employed to perform rRNA depletion and stranded libraries were prepared using Stranded mRNA Prep (Illumina). Samples were sequenced with 150 bp paired-end sequencing chemistry on Illumina Novaseq platform. Three independent biological samples were produced for each experimental condition.

RNA-seq data analysis

RNA-seq data were analyzed using both the Galaxy Web platform and R studio (version 4.3.1). Quality control on samples was performed using FastQC (v. 0.73) and reads were aligned to the Mtb reference genome (NC_000962.3) using HISAT2 (v. 2.2.1). FeatureCounts⁶² (v. 2.0.3) was employed to count aligned reads with respect to Mtb genomic features and differential gene expression analysis was performed using DESeq2⁶³ package for R studio. Genes exhibiting an adjusted p-value below 0.05 and a log₂fold change above 1 or below − 1 were considered as differentially expressed. Volcano plots were generated using EnhancedVolcano on R studio⁶⁴.

Gene ontology (GO) analysis

The functional enrichment analysis of BG4-immunoprecipitated genes identified uniquely under oxidative stress condition was achieved using the ShinyGO (v0.8) application tool⁶⁵. An FDR cutoff of < 0.05 was used to mark the functional categories as significant.

DATA AVAILABILITY

All genomic data produced in the present project (BG4-ChIP-seq, RNA-pol-CUT&Tag, BG4-CUT&Tag and RNA-seq) have been deposited in the NCBI GEO database under accession numbers: CUT&Tag data GSE272148, ChIP-seq data GSE272149, RNA-seq data GSE272150. ChIP-seq data previously obtained with the RNA-pol antibody were downloaded from GEO at the following accession number GSE40862. All data and custom-made R scripts are available from the corresponding author upon request.

Orcid ID

Ilaria Maurizio 0009-0007-3894-4874

Emanuela Ruggiero 0000-0003-0989-4074

Irene Zanin 0000-0003-2095-4170

Giulia Nicoletto 0000-0002-9428-4205

Roberta Provvedi 0000-0003-1164-9676

Sara N. Richter 0000-0002-5446-9029

Competing interests

The authors declare no competing interests.

AUTHOR CONTRIBUTIONS

Conceptualization, S.N.R. and R.P.; methodology, I.M. and M.C.; investigation, I.M.; data analysis, I.M., E.R., I.Z., G.N.; writing – original draft, I.M. and E.R.; writing - review & editing, E.R. and S.N.R.; funding acquisition, S.N.R. and R.P.; resources, S.N.R. and R.P.; supervision, S.N.R. All authors contributed to data interpretation and editing the manuscript.

ACKNOWLEDGEMENTS

This work was supported by the CaRiPaRo Foundation (grant # 52028).

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CUT&Tag in Bacteria Reveals Unconventional G-Quadruplex Landscape in Mycobacterium tuberculosis: A Novel Defense Mechanism Against Oxidative Stress

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Abstract

Figures

INTRODUCTION

RESULTS

RNA Polymerase peaks are associated with highly expressed genes

G4 CUT&Tag reveals a stress-responsive G4 landscape characterized by two guanine tract motifs

DISCUSSION

METHODS

Bacterial strains and growth conditions

G4-Chromatin immunoprecipitation protocol (G4-ChIP)

G4-ChIP-Sequencing data analysis

Circular Dichroism Spectroscopy (CD)

Clevage under targets and tagmentation (CUT&Tag)

Bioinformatic analysis

G4 motif prediction and sequence analysis

RNA extraction

RNA-seq data analysis

Gene ontology (GO) analysis

Declarations

DATA AVAILABILITY

Orcid ID

AUTHOR CONTRIBUTIONS

ACKNOWLEDGEMENTS

References

Additional Declarations

Supplementary Files

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