Variability in R-loops levels based on IHC detection

Variability in R-loops levels based on IHC detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Variability in R-loops levels based on IHC detection Nicklas Bassani, Liu Liang, Claudia Wilm, Juliane Braun, Alexander J R Bishop This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4763785/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract R-loops are three stranded nucleic acid structures involving an RNA:DNA hybrid and a displaced single stranded DNA (ssDNA). Though the majority of R-loop studies have investigated their pathological consequences in promoting genomic instability, R-loops also contribute to many physiological processes. In fact, from meta-analysis of R-loop datasets we know that R-loops cover about 3–5% of human genome, with their abundance tightly titrated by different enzymes or helicases; too many or too few R-loops impact normal cell functions. Aberrant R-loop accumulation has been implicated in cancer susceptibility and neurodegeneration, and increased R-loops levels throughout the genome observed in response to oncogenic signaling or mutations results in increased replication stress and DNA damage. Nonetheless, this also confers a vulnerability, and cancer cells harboring high levels of R-loops can be preferentially targeted by drugs that exacerbate R-loop-associated phenotypes. Here, we establish a protocol to detect RNA:DNA hybrids by immunohistochemistry (IHC) using the mouse and rabbit S9.6 antibodies. Using R-loop enhancing drugs, or by genetically manipulate DHX9 and SETX expression, helicases involved in R-loop metabolism, we provide evidence that our protocol is able to detect differences in R-loop levels. Finally, we show that S9.6 IHC is uniquely able to rapidly screen hundreds of cell and tumor samples demonstrating the heterogeneity in R-loop signal that can be observed. We also describe for the first time that R-loop expression determines sensitivity to the active vitamin D metabolite Calcitriol. R-loops Immunohistochemistry Calcitriol AZD6738 Olaparib Drug synergy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction R-loops are three stranded nucleic acid structures involving an RNA:DNA hybrid and a displaced single stranded DNA (ssDNA)[ 1 ]. The majority of R-loops arise co-transcriptionally, in the context of RNA polymerase I or II transcription activity. Though the majority of R-loop studies have been to investigate their pathological consequences in promoting genomic instability [ 2 – 5 ], R-loops also contribute to physiological processes including the regulation of transcription progression, processing and termination, enhancer programs, immunoglobulin diversity, ribosomal RNA transcription and even nucleolar phase separation [ 6 , 7 ]. Meta-analysis of a variety of R-loop datasets has demonstrated that R-loops cover about 3–5% of human genome [ 4 , 7 ], with their abundance tightly titrated; too many or too few R-loops impact normal cell functions [ 8 – 10 ]. Consequently, R-loop abundance is regulated by a variety of proteins including RNase H enzymes, which specifically degrade the RNA moiety of RNA:DNA hybrids, or by helicases, such as senataxin (SETX) or DHX9, which by unwinding RNA:DNA hybrids limit their formation [ 11 , 12 ]. Aberrant R-loop accumulation has been implicated in cancer susceptibility and neurodegeneration. For example, increased R-loops levels throughout the genome are observed in response to oncogenic estrogen signaling [ 13 ] or activated oncogenes like the EWS-FLI1 fusion oncoprotein [ 14 ] or mutations that lead to amyotrophic lateral sclerosis (ALS) such as in senataxin [ 15 ] resulting in increased replication stress and DNA damage. Nonetheless, this abnormal R-loop accumulation also confers a vulnerability, and cancer cells harboring high levels of R-loops can be preferentially targeted by drugs that exacerbate R-loop-associated phenotypes including splicing inhibitors, G4 ligands stabilizers or inhibitors of replication stress response including ATR among others [ 14 , 16 , 17 ]. Since the initial report on S9.6 antibody [ 18 ], multiple techniques, like R-loop mapping by DRIP sequencing, nucleic acid isolation followed by dot-blot, and S9.6 immunofluorescence, have been described for R-loop detection [ 19 – 21 ]. However, even if sequencing-based methods allows locus-by-locus R-loop analysis, they also required expensive equipment and specialized analysis that may not be practical in many clinical settings. Additionally, inconsistency has been described using S9.6 immunofluorescence (IF), with some studies reporting RNase H-resistant staining or prominent cytoplasmic S9.6 signal often attributed to differences in fixation, permeabilization and buffers used before immunolabeling [ 2 , 17 , 19 ]. Therefore, it is evident that there is a need for a protocol to detect and quantify R-loop abundance in the nucleus that can be conducted uniformly across laboratories and clinical settings. In this study, we establish a protocol to detect RNA:DNA hybrids by immunohistochemistry (IHC) using the mouse and rabbit S9.6 antibodies. The specificity of the methods is demonstrated with controls such as RNase H pretreatment or nucleases capable of specifically degrading dsRNA and ssRNA, showing that in paraffin-fixed cells the majority of S9.6 signal stems from the binding of S9.6 to RNA:DNA hybrids. Moreover, using R-loop enhancing drugs, or by genetically manipulate DHX9 and SETX expression, we provide evidence that S9.6 IHC is able to detect differences in R-loop levels. Finally, we show that S9.6 IHC is uniquely able to rapidly screen hundreds of cell and tumor samples demonstrating the heterogeneity in R-loop signal that can be observed, and we describe for the first time that R-loop expression determines sensitivity to the active vitamin D metabolite Calcitriol that express vitamin D receptor as predicted from our prior meta-analysis of R-loop datasets [ 4 ]. Results S9.6 exhibits specificity for RNA-DNA hybrid. S9.6 was raised to be immunogenic to R-loops, showing significant specificity to this structure in vitro [ 1 ] and reasonable specificity by immunofluorescence (IF) [ 19 ]. Here our goal was to establish the utility of S9.6 to detect R-loops by immunohistochemistry (IHC). For this we assessed both mouse and rabbit S9.6 antibodies using breast cancer MCF7 and Ewing sarcoma TC32 cell lines; these cell lines were selected as it has already been established that R-loops preferentially accumulate in breast luminal epithelial cells, such as MCF7 cells [ 26 , 27 ], and because our prior work demonstrating that Ewing sarcoma cells accumulate high levels of R-loops [ 14 ]. In comparison to secondary antibody control slides, both antibodies selectively stained the nucleus of MCF7 and TC32 cells and are free of any nonspecific staining (Fig. 1 A, B). As reported by different studies, S9.6 antibody can recognize dsRNA in vitro under certain conditions [ 19 ]. To test this possibility we pretreated MCF7 and TC32 slides with RNase III and RNase T1 endonucleases, shown to be capable of specifically and efficiently degrading dsRNA and ssRNA without degrading RNA:DNA hybrids [ 19 ]. In both cell lines, RNase III pretreatment decreased the signal detected by mouse S9.6 antibody, decreasing the staining by approximately 34% (35% in MCF7, 32% in TC32), while rabbit S9.6 staining was only reduced by ~ 15% (14% in MCF7, 17% in TC32) (Fig. 1 A, B). Therefore, similar to IF experiments, it appears that dsRNAs can interfere with S9.6 binding to R-loops. The affinity of S9.6 for dsRNAs has been previously reported by others [ 28 ], and can result in mixed signals from R-loops and dsRNAs when DRIP is followed by RNA sequencing, such as in DRIP-seq. RNase T1 pretreatment led to a larger decrease of S9.6 staining in TC32 cells (approximately 30%), while R-loop staining of MCF7 cells were only marginally affected (Fig. 1 A, B). These results indicate that using S9.6 in IHC may also recognize RNA molecules with double stranded structures similar to the cross-reactivity reported by IF and the necessity of including appropriate controls. Finally, RNase H pretreatment, which efficiently digests the RNA strand of RNA:DNA hybrids, completely suppressed mouse and rabbit S9.6 staining (Fig. 1 A, B). Taken together, these observations support that in paraffin embedded formalin-fixed cells the majority of S9.6 signal stems from the binding of S9.6 to RNA:DNA hybrids. Senataxin and DHX9 helicases knockout alter level of R-loops as detected by IHC. To confirm the specificity of the S9.6 antibodies for R-loops in paraffin embedded formalin-fixed cells, we silenced the expression of the Senataxin (SETX) and DHX9 helicases using specific siRNAs (Suppl. Figure 1A). R-loops are not only inherent byproducts of transcription but have programmatic roles controlling numerous physiological processes, and, as a general rule, R-loop homeostasis is tightly controlled by the activities of several enzymes, such as SETX and DHX9 [ 29 – 31 ]. SETX is an RNA:DNA helicase which catalyzes the unwinding of the RNA:DNA hybrid portion of R-loops, promoting their resolution [ 31 ]. As expected, using either mouse or rabbit S9.6 antibodies, we confirmed that SETX knockout led to a statistically significant increase of S9.6 staining in MCF7 cells (Fig. 2 A). In contrast, the helicase DHX9 which is a component of the RNA polymerase II holoenzyme [ 32 , 33 ] and a component of the RNA:DNA hybrid interactome [ 34 ], has been suggested to both prevent and increase R-loops depending on context [ 35 ]. As already reported by others using immunofluorescence and slot blot techniques [ 34 ], we confirmed that siRNA-mediated knockdown of DHX9 caused a global reduction of both mouse and rabbit S9.6 staining in MCF7 cells (Fig. 2 A). Taken together, these results further validate the use of both mouse and rabbit S9.6 antibodies for the detection of R-loops by IHC. Splicing inhibitor Pladienolide B and G4 ligand PDS affect R-loops metabolism. R-loops have been implicated in several human diseases, including repeat-expansion disorders, neurological syndromes, myelodysplastic syndrome and cancer with the utility of different drugs affecting R-loop metabolism being studied in these contexts [ 11 , 36 , 37 ]. Consequently, we asked whether S9.6 IHC can detect changes in R-loop levels in response to treatment with either the splicing inhibitor Pladienolide B (PladB) or G4 ligand binder pyridostatin (PDS). Mechanisms that alter RNAPII progression, such as inhibiting SF3B1, a core component of the U2 spliceosome, can affect R-loop levels and PladB has been reported to increased RNA:DNA hybrids leading to impaired DNA synthesis [ 35 ]. G4 quadruplex structures, particularly in the displaced DNA strand of an R-loop, are thought to promote R-loop stability, thus ligands such as PDS that stabilize G4 ligands have been used to induce R-loop-mediated DNA damage and cell death in human cancer cells [ 17 ]. Overnight exposure to PladB or PDS led to statistically significant increase in R-loops detected by S9.6 IHC in both MCF7 and TC32 (Fig. 2 B), suggesting that S9.6 IHC could be a valuable technique to rapidly detect changes in R-loops. Of note, the doses of PladD and PDS used only marginally affected cell viability (Suppl. Figure 1B, C). Image analysis of R-loop IHC reveals the variation in basal R-loop levels across different human cancer cell lines and tumors. We evaluated by IHC the levels of RNA:DNA hybrids in a panel of human cancer cell lines (CMAhu19-01, CMAhu19-02) (Table 1) and tissue microarrays derived from tumor mouse xenografts (TMA_Xeno15_MB, TMA_Xeno15-2_MB) (Table 2). Since the TMAs were derived from mouse models we only used the rabbit S9.6 antibody to avoid the possibility of cross-reactivity with host tissue that would occur if we had used secondary antibody to detect mouse S9.6. A total of 164 human cell lines and 160 human tissues were stained, and image analysis was performed to evaluate levels of staining. Cell lines and xenografts exhibit an extensive range from high to low rabbit S9.6 signal (Fig. 3 A, 4 A), confirming the variability of R-loop levels that can be detected across different cancers or cell types. In depth analysis confirmed that rabbit S9.6 staining is predominantly located in the nucleus, homogenously distributed across one tissue core, and that the secondary detection antibody itself is free of nonspecific binding (Fig. 3 B, 4 B). Moreover, after pre-treatment with RNase H, rabbit S9.6 did not show any significant binding confirming the specificity of the antibody for RNA:DNA hybrids (Fig. 3 B, 4 B). RNase A pre-treatment resulted in a measurable, though reasonably consistent decrease in rabbit S9.6 signal, without significantly altering the order of cell lines arranged by high to low R-loop signal (Suppl. Figure 1D). In contrast, and unexpectedly, we noted several examples in our xenograft TMAs where RNase A pre-treatment increased R-loop signal, though we also noted that this treatment seemed to correlate with the intensity of the blue hematoxylin counterstain which may indicate differences in sample integrity (Suppl. Figure 1E). R-loop levels predict Vitamin D sensitivity. In our recent meta-analysis of multiple R-loop sequencing datasets, we classified R-loops as being either constitutive or variable, with constitutive R-loops enriched with housekeeping genes [ 4 ]. In that same analysis we found the most consistent enrichment irrespective of variable or constitutive R-loops was with the VDR (vitamin D receptor) target gene set. To our knowledge, the VDR has not previously been associated with R-loop biology. Consequently, we decided to investigate how cell lines with different levels of R-loops as determined by rabbit S9.6 IHC would respond to the active vitamin D metabolite Calcitriol. For this we selected two cells lines with high R-loops (MCF-7, HT-29) or low R-loops (A673, MDA-MB231) for their viability response to Calcitriol exposure (Fig. 3 A). Besides its classic biological effects on calcium and phosphorus homeostasis to preserve bone health, Calcitriol has been shown to play a key role in the prevention and treatment of many extra-skeletal diseases including cancer [ 38 ]. In the context of cancer, Calcitriol regulates cell cycle, induces apoptosis, promotes cell differentiation and acts as anti-inflammatory factor within the tumor microenvironment [ 39 ]; given that these biological response result from induced gene expression changes, they likely are also associated with R-loop inductions. Interestingly, we found that R-loop levels positively correlate with sensitivity to Calcitriol (Fig. 5 A), with cell lines displaying high levels of R-loops (MCF-7 and HT-29) being more sensitive to Calcitriol compared to cell lines with low levels of R-loops (A673, MDA-MB231) (Fig. 5 A, 5 B). Moreover, clonogenic assays revealed that Calcitriol dramatically affects the ability of MCF-7 and HT-29 to form colonies (Fig. 5 C), suggesting that in the context of high R-loop levels Calcitriol may impact R-loop biology ultimately blocking cancer cell stemness. To verify this hypothesis, we measured by S9.6 IHC the effect of Calcitriol on MCF-7 R-loops levels, finding that Calcitriol treatment led to a significantly increase in R-loops detected by both mouse and rabbit S9.6 antibodies (Fig. 5 D). Taken together, these results not only confirm that S9.6 IHC is a fast and reliable method to detect R-loops expression, but also indicate that the use of active vitamin D metabolite may be of therapeutic interest in tumors with high levels of R-loops. R-loop levels predict synergy to ATR/PARP1 co-inhibition. Despite playing physiological roles in specific situations, DNA-RNA hybrids are a major source of DNA damage accumulation and transcription-associated replication stress leading to genome instability [ 40 ]. To prevent these potentially harmful effects, cells have evolved a coordinated cellular response, the DNA damage response (DDR), as an intrinsic barrier to detect and enable the repair of different types of DNA damage [ 41 ]. Consequently, we decided to study how cells that intrinsically harbor different levels of R-loops respond to co-targeting two critical DDR factors, the ATR kinase that responds to replication stress and Poly (ADP-ribose) polymerase 1 (PARP1) that prevents single strand break accumulation that would cause replication stress. Interestingly, we observed that in cells with high levels of R-loops (MCF-7 and HT-29) ATR-PARP1 co-inhibition exponentially exacerbate the effect of the single agents AZD6738 (ATRi) or Olaparib (PARP1i) (Fig. 6 A, B). In cells with low levels of R-loops (A673 and MDA-MB231), this effect is not lost but appeared to be additive and in general correlated to PARP1 inhibition outcome (Fig. 6 A, B). Moreover, additional analysis using the web-application SynergyFinder ( https://synergyfinder.fimm.fi ), we found that in comparison with A673 and MDA-MB231, AZD6738/Olaparib co-treatment resulted a highly synergistic effect in MCF-7 and HT-29 cells (Fig. 6 C). At a minimum, these results indicate that tumors expressing high levels of R-loops may not only be exquisite sensitive to ATR/PARP1 co-inhibition, but that the intrinsic level of R-loops present dictate the extent of AZD6738/Olaparib synergy. Discussion Often associated only with detrimental byproducts of transcription and a basis for genome instability [ 42 ], in the past decade R-loops have emerged to play significant roles in normal cellular physiology. The study of R-loops has revealed their clear role in gene expression regulation, both directly and indirectly, effectively acting as a novel epigenetic mark. However, unregulated R-loop accumulation can have pathological consequences, including cancer, where occurrence of abnormal R-loop formation or a defect in their resolution may drive oncogenesis [ 43 ]. Nevertheless, increased R-loop formation may also offer a novel therapeutic vulnerability by either inducing excessive R-loops or hampering their resolution perhaps resulting in intolerable genomic instability and synthetic lethality depending on context [ 43 ]. Here, we described a fast and reliable method to determine by immunohistochemistry R-loops abundance in cell and cancer samples using the mouse and rabbit S9.6 monoclonal antibodies. Immunohistochemistry has existed since the 1930s and is widely used to diagnosis and predict therapeutic response of tumors [ 44 ]; in the context of R-loops, our results demonstrate that S9.6 IHC is capable of screening and distinguish variability in R-loops levels in hundreds of samples in a highly reproducible and time-sensitive manner. Since its initial report [ 18 ], different subsequent studies have shown that S9.6 antibody can bind off-target double-stranded ribosomal RNA creating pervasive artifacts, making necessary the inclusion of endonuclease enzymes as standards for quality control [ 19 , 28 ]. Despite observations by others using immunofluorescent techniques in cell lines [ 19 , 28 ], we found that with paraffin embedded formalin-fixed cells the S9.6 IHC signal is exquisitely located in the nucleus, and the pre-treatment with RNase A and RNase III endonucleases only marginally reduced with S9.6 binding to RNA:DNA hybrids, and did so in a fairly consistent manner. Moreover, we applied a prior prediction that vitamin D receptor profoundly impacts R-loop levels [ 4 ] by describing for the first time that the vitamin D metabolite Calcitriol adversely impacts cancer cells with high levels of R-loops. Finally, we showed that taking advantage of the variability in R-loops levels by simultaneously co-inhibiting ATR/PARP1, is profoundly synergistic and may be a promising strategy to minimize possible side effects in the treatment of tumors that display high levels of R-loops. In conclusion, R-loop biology is a growing field with many open questions and many aspects of their biology and relative importance yet to be investigated. Clearly there is variation in amounts of R-loops in different tissues, in response to various queues or perturbations or in different genetic contexts. The results of the work presented here support the use of S9.6 monoclonal antibodies to detect RNA:DNA hybrids by IHC, describing a protocol that can be easily duplicate using ordinary laboratory equipment, to investigate a wide variety of contexts to facilitate their comparison. Methods Cell cultures and transfections Ewing sarcoma cell line TC32 was obtained from Children’s Oncology Group. Ewing sarcoma cell line A673, human colorectal adenocarcinoma cell line HT-29, human breast cancer cells MCF-7 and MDA-MB231 were purchased from the American Type Culture Collection (ATCC). A673, HT-29, MCF-7 and MDA-MB231 cells were cultured in DMEM (Corning); TC32 cells in RPMI-40 (Corning); all cultured media were supplemented with 10% heat-inactivated fetal bovine serum (Corning) and 1% antibiotic/antimycotic solution (Corning). All cell lines were maintained at 37°C in a humidified atmosphere with 5% CO 2 and tested for mycoplasma contamination. All transfections were carried out using Lipofectamine RNAiMAX (Invitrogen) following the manufacturer’s instructions, and gene knockdowns performed by reverse transfection. The siRNAs used in this study were DExH-box helicase 9 (DHX9) (ON-TARGETplus, Dharmacon), and senataxin (SETX) (ON-TARGETplus, Dharmacon). All siRNA transfections were accompanied with non-targeting control siRNA (ON-TARGETplus, Dharmacon). Western Blot Whole-cell lysates were prepare using NaCl lysis buffer according to standard protocols. Cell lysates were separated on precast 3–8% gradient gels (Invitrogen) and transferred onto nitrocellulose membrane. All blots were incubated with primary antibodies overnight and developed using enhanced chemiluminescence (Super ECL, ThermoFisher). Antibodies used in this study include DHX9 (Bethyl, A300-855A), SETX (Abcam, ab26271), beta-Tubulin (Cell Signaling, cs2128) and secondary antibody goat anti-rabbit IgG-HRP (Santa Cruz, sc-2030). Western blot experiments were repeated with independent sample preparations three times. Cell viability Cells were seeded at 15–20% confluence in 96-well plates and drug treatment administered on the next day. Calcitriol (Selleckchem, Cat. #S1466), Olaparib (Selleckchem, Cat. #S1060), and AZD6738 (Selleckchem, Cat. #S1060) have been resuspended and properly stored according to manufacturer’s instruction. DMSO treatment was used as control for all the experiments. End point cell viability was evaluated after 72h or 96h using Celltiter-Glo (Promega). Each condition was tested at least in quadruplicate, and every experiment performed in three independent biological replicates. Clonogenic assay Cells were seeded as single cell suspension (3-5x10 3 cells/well according to the cell type) in a 6-well plate and allowed to attached to the plate for 6 to 8h. Drug treatment was added as soon as the cells were attached to the plates before they start replicating to avoid issues of numbers of cells per dish increasing to yield more colonies. The plates were subsequently placed in 37°C humidified incubator for 10 days. Next, colonies were washed with PBS (Corning), fixed for 30 minutes with 10% neutral buffered Formalin (Sigma-Aldrich) on a rocking shaker, and stained with 0.2% Crystal violet (Sigma-Aldrich). For each treatment, colony forming capacity was calculated as: \(\:(Area\:colonies/Area\:colonies\:DMSO\:control\) ) X 100 using ImageJ/Fiji software. Every experiment was performed in three independent biological replicates. Pladienolide B and PDS treatments Cells were seeded at 60–70% confluence in 6-wells plates to generate cell slides or in 96-wells plates to assess cell viability, followed by treatment for 18 hours with Pladienolide B (MedChemEXpress, HY-16399) or Pyridostatin Hydrochloride (PDS) (Sigma-Aldrich, SML2690). Next, cells were processed to generate microarrays as described below, or used for end point cell viability measurements using Celltiter-Glo (Promega). Of note, since average R-loop half-life is 2–30 minutes [ 22 , 23 ], once harvested cells need to be fixed as soon as possible to prevent loss of R-loop signal. Cell microarray and xenograft tissue microarray Cells were washed with PBS (Corning), dissociated using Trypsin-EDTA (Corning), collected using complete culture media and centrifuged for 10 minutes at 2,500 rpm. Cell pellets were washed using PBS and centrifuged for 10 minutes at 2,500 rpm to eliminate any Trypsin-EDTA residue. Supernatant was decanted, cell pellets resuspended in 10% neutral buffered Formalin (Sigma-Aldrich), thoroughly vortexed and fixed for 30–60 minutes at room temperature (Note: the volume of Formalin should be about the same as the cell pellet volume). Specimens were centrifuged for 10 minutes at 2,500 rpm, the supernatant containing Formalin decanted, the cell pellets resuspended in 95% ethanol, thoroughly vortexed and dehydrated for 15 minutes at room temperature (Note: the volume of ethanol should be about the same as the cell pellet volume). Next, specimens were centrifuged for 10 minutes at 2,500 rpm and supernatant containing dehydrant decanted. In parallel, Nutrient Agar slant tube was liquified using a microwave (the gelatin should be warm, not hot), approximately 500µL of liquid agar added to the cell specimen, and the specimen/gelatin sample allowed to solidify for 30 minutes at room temperature. Finally, the specimen is dislodged from the bottom of the tube using a dissecting needle, transferred into a tissue tea bag, placed in a tissue cassette and processed routinely in the histopathology laboratory. Cell line microarrays (CMAhu19-01, CMAhu19-02) comprising of 164 different cell lines was constructed as previously described by Goodman and colleagues [ 24 ]; meanwhile the xenograft tissue microarray (TMA_Xeno15_MB, TMA_Xeno15-2_MB) comprising xenograft tissue from 160 models was constructed using a new-generation tissue arrayer as previously reported by Zlobec and colleagues [ 25 ]. S9.6 Immunohistochemistry In house cell blocks, human cancer cell lines (CMAhu19-01, CMAhu19-02) and human tissue micro arrays (TMA_Xeno15_MB, TMA_Xeno15-2_MB) were subjected to standard histopathology deparaffinization and hydration steps. Antigen retrieval was performed by treating the microarrays with 1 mM EDTA, pH 8.0 for 40 minutes at 95°C followed by a 20 minutes cool down step. To confirm antibody specificity, slides were incubated in a moist humidity chamber with RNase H (overnight in 1xRNase H reaction buffer; New England Biolabs, M0297), RNase T1 (1 hour in 50mM Tris-HCl pH 7.5, 2mM EDTA; ThermoFisher, EN0541), RNase A (1 hour in 10mM Tris-HCl pH 7.5, 0.5M NaCl; ThermoFisher, EN0531), or RNase III (1 hour in 1xShortCut Reaction Buffer, 1xMnCl 2 ; BioLabs, M0245S). Slides were then rinsed in 1X Tris buffered saline (TBS) three times. Following endogenous peroxidase blocking, the slides were incubated with mouse or rabbit S9.6 antibodies (Absolute, Ab01137-23.0, -2.0, 1:10,000) for 2 hours at room temperature in a moist humidity chamber. Anti-mouse and anti-rabbit Powervision-HRP Conjugated Polymer from Leica Biosystems (Cat #PV6114, #PV6119) for 30 min was used for detection. Slides were then developed with DAB for 5 min, rinsed with TBS and counterstained with hematoxylin, dehydrated, cleared and mounted with a synthetic mounting medium. Images were taken on an Olympus sc-100 or a Motic Digital Slide Scanning System. Image analysis For each experiment, all conditions were imaged in parallel taking 5 images in different areas of the slide to capture intrinsic sample variability. Images were analyzed and quantified using ImageJ/Fiji programs. Briefly, images were deconvoluted separating DAB from hematoxylin colors, and DAB intensity quantified. Statistical analysis P values for analyzing cell viability, IC 50 and R-loops intensity were computed using nonparametric Mann-Whitney t test in GraphPad Prism software. P < 0.05 was considered significant, and marked as *. Declarations Availability of data and materials All data generated or analyzed during this study are included in this article and its additional files. Ethical approval and consent to participate : Not applicable. Consent to publication : The authors declare no competing financial interests, and consent to publish the manuscript. Author contribution : N.B. and A.J.R.B conceived and designed the study and wrote the manuscript. N.B. conducted the majority of the research. L.L. contributed to PladB and PDS experiments. C.W. and J.B. provided tissue and cell microarrays slides. Funding NIH/NCI [R01CA152063 and 1R01CA241554], CPRIT [RP150445], SU2C-CRUK [RT6187] to A.J.R.B Acknowledgements We are grateful to Daniel Rebledo and the Histology & Immunohistochemistry Core at UTH-SA. References Bou-Nader, C., et al., Structural basis of R-loop recognition by the S9.6 monoclonal antibody. Nat Commun, 2022. 13 (1): p. 1641. Hamperl, S., et al., Transcription-Replication Conflict Orientation Modulates R-Loop Levels and Activates Distinct DNA Damage Responses. Cell, 2017. 170 (4): p. 774-786.e19. Gan, W., et al., R-loop-mediated genomic instability is caused by impairment of replication fork progression. Genes Dev, 2011. 25 (19): p. 2041-56. 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Castillo-Guzman, D. and F. Chédin, Defining R-loop classes and their contributions to genome instability. DNA Repair (Amst), 2021. 106 : p. 103182. Goodman, S.L., H.J. Grote, and C. Wilm, Matched rabbit monoclonal antibodies against αv-series integrins reveal a novel αvβ3-LIBS epitope, and permit routine staining of archival paraffin samples of human tumors. Biol Open, 2012. 1 (4): p. 329-40. Zlobec, I., et al., A next-generation tissue microarray (ngTMA) protocol for biomarker studies. J Vis Exp, 2014(91): p. 51893. Zhang, X., et al., Attenuation of RNA polymerase II pausing mitigates BRCA1-associated R-loop accumulation and tumorigenesis. Nat Commun, 2017. 8 : p. 15908. Chiang, H.C., et al., BRCA1-associated R-loop affects transcription and differentiation in breast luminal epithelial cells. Nucleic Acids Res, 2019. 47 (10): p. 5086-5099. Hartono, S.R., et al., The Affinity of the S9.6 Antibody for Double-Stranded RNAs Impacts the Accurate Mapping of R-Loops in Fission Yeast. J Mol Biol, 2018. 430 (3): p. 272-284. Yu, K., et al., R-loops at immunoglobulin class switch regions in the chromosomes of stimulated B cells. Nat Immunol, 2003. 4 (5): p. 442-51. Zhao, H., et al., Senataxin and RNase H2 act redundantly to suppress genome instability during class switch recombination. Elife, 2022. 11 . Gatti, V., et al., Senataxin and R-loops homeostasis: multifaced implications in carcinogenesis. Cell Death Discov, 2023. 9 (1): p. 145. Anderson, S.F., et al., BRCA1 protein is linked to the RNA polymerase II holoenzyme complex via RNA helicase A. Nat Genet, 1998. 19 (3): p. 254-6. Nakajima, T., et al., RNA helicase A mediates association of CBP with RNA polymerase II. Cell, 1997. 90 (6): p. 1107-12. Cristini, A., et al., RNA/DNA Hybrid Interactome Identifies DXH9 as a Molecular Player in Transcriptional Termination and R-Loop-Associated DNA Damage. Cell Rep, 2018. 23 (6): p. 1891-1905. Chakraborty, P., J.T.J. Huang, and K. Hiom, DHX9 helicase promotes R-loop formation in cells with impaired RNA splicing. Nat Commun, 2018. 9 (1): p. 4346. Richard, P. and J.L. Manley, R Loops and Links to Human Disease. J Mol Biol, 2017. 429 (21): p. 3168-3180. Boros-Oláh, B., et al., Drugging the R-loop interactome: RNA-DNA hybrid binding proteins as targets for cancer therapy. DNA Repair (Amst), 2019. 84 : p. 102642. Jeon, S.M. and E.A. Shin, Exploring vitamin D metabolism and function in cancer. Exp Mol Med, 2018. 50 (4): p. 1-14. Díaz, L., et al., Mechanistic Effects of Calcitriol in Cancer Biology. Nutrients, 2015. 7 (6): p. 5020-50. Marabitti, V., et al., R-Loop-Associated Genomic Instability and Implication of WRN and WRNIP1. Int J Mol Sci, 2022. 23 (3). Biswas, S., et al., A High Spin Mn(IV)-Oxo Complex Generated via Stepwise Proton and Electron Transfer from Mn(III)-Hydroxo Precursor: Characterization and C-H Bond Cleavage Reactivity. Inorg Chem, 2019. 58 (15): p. 9713-9722. Sollier, J. and K.A. Cimprich, Breaking bad: R-loops and genome integrity. Trends Cell Biol, 2015. 25 (9): p. 514-22. Li, F., et al., R-Loops in Genome Instability and Cancer. Cancers (Basel), 2023. 15 (20). Duraiyan, J., et al., Applications of immunohistochemistry. J Pharm Bioallied Sci, 2012. 4 (Suppl 2): p. S307-9. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1CMAsand2TMAs.pdf Table 1. CMAhu19-01 and CMAhu19-02 cell lines ordered by rabbit S9.6 IHC staining quantification. Table also show 2ry control, RNaseA and RNase H pre-treatment quantifications for all the cell lines. Table 2. XENO15-1, XENO15-2 tumor samples ordered by rabbit S9.6 IHC staining quantification. Table also show 2ry control, RNaseA and RNase H pre-treatment quantifications for all the tumor samples. DHX9WB.jpg SETX303kDAWB.jpg bTubulinWB.jpg SupplementaryFigure1.jpg Supplementary Figure 1. A) Immunoblot showing DHX9 and SETX proteins knock down after 48h of RNA silencing in MCF-7 cancer cells. B, C) End point cell viability after 18h exposure to PladB or PDS in MCF-7 and TC32 cancer cells. D, E) Effect of RNaseA pre-treatment on S9.6 IHC in a panel of human cancer cell lines (CMAhu19-01, CMAhu19-02) and of human tumors (XENO15-1, XENO15-2). Boxplots represent data from one of three independent biological replicates, and P values were determined by nonparametric Mann-Whitney t test. *, P < 0.05. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4763785","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341683022,"identity":"f9fbea21-71e2-45dd-a642-3048f3b046b9","order_by":0,"name":"Nicklas Bassani","email":"","orcid":"","institution":"Greehey Children’s Cancer Research Institute, UT Health San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Nicklas","middleName":"","lastName":"Bassani","suffix":""},{"id":341683023,"identity":"29aca4bd-8869-484c-8642-9d71b5b80a62","order_by":1,"name":"Liu Liang","email":"","orcid":"","institution":"Greehey Children’s Cancer Research Institute, UT Health San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Liang","suffix":""},{"id":341683024,"identity":"d9970917-f242-4ea2-b59c-5c3207d79c5e","order_by":2,"name":"Claudia Wilm","email":"","orcid":"","institution":"the healthcare business of Merck KGaA","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Wilm","suffix":""},{"id":341683025,"identity":"bc7f581e-cf48-4670-88b5-1c94779a6ce8","order_by":3,"name":"Juliane Braun","email":"","orcid":"","institution":"the healthcare business of Merck KGaA","correspondingAuthor":false,"prefix":"","firstName":"Juliane","middleName":"","lastName":"Braun","suffix":""},{"id":341683026,"identity":"c54e37b0-2ec7-40f7-b44a-eca3ab454847","order_by":4,"name":"Alexander J R Bishop","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACA2YGNhAtx8DAAxNLIE6LMQMb0VoYIFoSG4jWYs7O/uzBxx126Rvu9x7+8OOPHQM/e44BXi2WzTzmhjPPJOduOMaXJtnblswg2fMGvxaDwzxs0rxtzEAtPGbMjA3MDAY3CNhicJj9GVBLfbrBMR7jzwx/6hnsCWthMANqOZwA1GIgzcB2mMFAgrBfzCRnnjluOPNYjhnQL8d5JM48K8CrxZz/+DOJjzuq5fkOnzEGhli1HH978ga8WsCAsQHB5sGpCqeWUTAKRsEoGAUYAACOL0Gak0NiPAAAAABJRU5ErkJggg==","orcid":"","institution":"Greehey Children’s Cancer Research Institute, UT Health San Antonio","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"J R","lastName":"Bishop","suffix":""}],"badges":[],"createdAt":"2024-07-18 15:53:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4763785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4763785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62893671,"identity":"e64dfa1c-b290-4790-a609-9ef89c643ec7","added_by":"auto","created_at":"2024-08-20 18:22:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1701880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS9.6 IHC exhibits specificity for RNA-DNA hybrid in MCF-7 and TC32 paraffin-embedded cells. A) \u003c/strong\u003eQuantification and representative images of S9.6 IHC on MCF-7 paraffin-embedded cells that were mock-treated, or pretreated with RNase III, RNase T1 and RNase H before mouse or rabbit S9.6 staining. \u003cstrong\u003eB) \u003c/strong\u003eQuantification and representative images of S9.6 IHC on TC32 paraffin-embedded cells that were mock-treated, or pretreated with RNase III, RNase T1 and RNase H before mouse or rabbit S9.6 staining. Five pictures for treatment were taken and quantified. Boxplots represent data from one of three independent biological replicates, and P values were determined by nonparametric Mann-Whitney \u003cem\u003et\u003c/em\u003e test. *, P \u0026lt; 0.05. **, P \u0026lt; 0.005.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/d4edb8195572b5a3dc920647.jpg"},{"id":62894061,"identity":"6cbe2742-167c-4d75-be69-a2b658e078c1","added_by":"auto","created_at":"2024-08-20 18:30:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1633013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment by S9.6 IHC of R-loops metabolism after SETX and DHX9 helicases perturbation, or PladB and PDS treatments. A) \u003c/strong\u003eQuantification and representative images of S9.6 IHC on MCF-7 cells silenced for SETX or DHX9 helicases expression, paraffin-embedded and stained using mouse or rabbit S9.6 monoclonal antibodies. \u003cstrong\u003eB)\u003c/strong\u003e Quantification and representative images of S9.6 IHC on MCF-7 cells pretreated for 18h with PladB or PDS, paraffin-embedded and stained using mouse or rabbit S9.6 monoclonal antibodies. \u003cstrong\u003eC)\u003c/strong\u003e Quantification and representative images of S9.6 IHC on TC32 cells pretreated for 18h with PladB or PDS, paraffin-embedded and stained using mouse or rabbit S9.6 monoclonal antibodies. Five pictures for treatment were taken and quantified. Boxplots represent data from one of three independent biological replicates, and P values were determined by nonparametric Mann-Whitney \u003cem\u003et\u003c/em\u003etest. **, P \u0026lt; 0.005.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/9e0cbcd13e2f77f73c9b0c16.jpg"},{"id":62893672,"identity":"d3671278-f417-4f40-bd08-7aa4169af7e1","added_by":"auto","created_at":"2024-08-20 18:22:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1580890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePanel of human cell lines shows variability in basal R-loops level detected by S9.6 IHC. A)\u003c/strong\u003e Quantification of S9.6 IHC in a panel of human cancer cell lines (CMAhu19-01, CMAhu19-02). For future experiments we selected cells displaying high R-loops levels (HT-29 and MCF-7, highlighted in blue in the graph) or low R-loop levels (MDA-MB231 and A673, highlighted in orange in the graph). \u003cstrong\u003eB) \u003c/strong\u003eRepresentative images of S9.6 IHC on CMAHu19-01 cell micro array that was mock- or pre-treated with RNase A or RNase H before rabbit S9.6 staining.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/44c2f27af0b1cee94dcf9bdd.jpg"},{"id":62893673,"identity":"1be7d66f-743c-4e0b-b996-220941a3eaec","added_by":"auto","created_at":"2024-08-20 18:22:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1985491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS9.6 IHC reveals heterogeneity in basal levels of R-loops across different human tumors. A)\u003c/strong\u003eQuantification of S9.6 IHC in a panel of human tumors (XENO15-1, XENO15-2). \u003cstrong\u003eB) \u003c/strong\u003eRepresentative images of S9.6 IHC on XENO15-1 tumor micro array that was mock- or pre-treated with RNase A or RNase H before rabbit S9.6 staining.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/7acdff8909d294c79036e1a5.jpg"},{"id":62894064,"identity":"b217bfc4-ae12-4a60-a089-1da8d30a542b","added_by":"auto","created_at":"2024-08-20 18:30:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1725388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActive vitamin D metabolite Calcitriol shows synthetic lethality in cells displaying high levels of R-loops. A)\u003c/strong\u003e End point cell viability for cells with either high (MCF-7 and HT-29) or low (MDA-MB231 and A673) levels of R-loops after 72h exposure to Calcitriol. \u003cstrong\u003eB) \u003c/strong\u003eCalcitriol IC\u003csub\u003e50\u003c/sub\u003e in of cells with high (MCF-7 and HT-29) or low (MDA-MB231 and A673) R-loops levels. \u003cstrong\u003eC) \u003c/strong\u003eQuantification and representative images of Calcitriol effect on the clonogenic capacity of cells with high (MCF-7 and HT-29) or low (MDA-MB231 and A673) levels of R-loops. \u003cstrong\u003eD)\u003c/strong\u003e Quantification and representative images of S9.6 IHC on MCF-7 cells pretreated for 18h with Calcitriol, paraffin-embedded and stained using mouse or rabbit S9.6 monoclonal antibodies. Ten cells/field have been quantified. Boxplots represent data from one of three independent biological replicates, high R-loops expressing cells are highlighted in blue in all the graphs and P values were determined by nonparametric Mann-Whitney \u003cem\u003et\u003c/em\u003e test. **, P \u0026lt; 0.005. ****, P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/452ea251fd8e11dd5fa6a76d.jpg"},{"id":62893678,"identity":"669631bb-77b3-4936-a4ea-1c8cc39d94fe","added_by":"auto","created_at":"2024-08-20 18:22:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1290145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eATR/PARP1 co-inhibition show synergistic lethality in cells with high levels of R-loops. A, B) \u003c/strong\u003eScatter plots and heat maps showing end point cell viability of cells with low (MDA-MB231 and A673) or high (MCF-7 and HT-29) levels of R-loops after 96h exposure to Olaparib alone (black), or to Olaparib/AZD6738 co-treatment (light and dark blue). \u003cstrong\u003eC)\u003c/strong\u003e Heat maps showing Bliss synergy score as calculated using the web-application SynergyFinder in a panel of low (MDA-MB231 and A673) and high (MCF-7 and HT-29) levels of R-loops after 96h exposure to Olaparib/AZD6738 co-treatment.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/51f31b2abefb6213f6f03d24.jpg"},{"id":77618099,"identity":"2b751010-6581-40f6-8a26-f5cf466d6590","added_by":"auto","created_at":"2025-03-03 15:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23109296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/e63a5407-58bc-41bd-925d-4a47141b4d8b.pdf"},{"id":62893674,"identity":"4d279a05-b1d5-4924-9b6e-b8dd87f0c6ff","added_by":"auto","created_at":"2024-08-20 18:22:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":79255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eCMAhu19-01 and CMAhu19-02 cell lines ordered by rabbit S9.6 IHC staining quantification. Table also show 2ry control, RNaseA and RNase H pre-treatment quantifications for all the cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. \u003c/strong\u003eXENO15-1, XENO15-2 tumor samples ordered by rabbit S9.6 IHC staining quantification. Table also show 2ry control, RNaseA and RNase H pre-treatment quantifications for all the tumor samples.\u003c/p\u003e","description":"","filename":"Table1CMAsand2TMAs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/3f01a9b93db90f7d57f15d0a.pdf"},{"id":62893676,"identity":"a20b60fd-734d-4b39-8847-509179f9caac","added_by":"auto","created_at":"2024-08-20 18:22:41","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":951953,"visible":true,"origin":"","legend":"","description":"","filename":"DHX9WB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/a2667f229a100432c73919be.jpg"},{"id":62894753,"identity":"3ecbe5fa-3516-4884-b389-307cc44558e5","added_by":"auto","created_at":"2024-08-20 18:38:41","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":980380,"visible":true,"origin":"","legend":"","description":"","filename":"SETX303kDAWB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/a7b696d42ce0c35c87ff3ad2.jpg"},{"id":62893680,"identity":"ee6fd612-0d72-4ddc-8a0b-12e08193ce4c","added_by":"auto","created_at":"2024-08-20 18:22:41","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":962701,"visible":true,"origin":"","legend":"","description":"","filename":"bTubulinWB.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/d128a8e804861d674b4fbcdc.jpg"},{"id":62893681,"identity":"41d3afb7-5c0e-4ddd-aa28-a383fcb7840c","added_by":"auto","created_at":"2024-08-20 18:22:41","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2552256,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. A) \u003c/strong\u003eImmunoblot showing DHX9 and SETX proteins knock down after 48h of RNA silencing in MCF-7 cancer cells. \u003cstrong\u003eB, C) \u003c/strong\u003eEnd point cell viability after 18h exposure to PladB or PDS in MCF-7 and TC32 cancer cells. \u003cstrong\u003eD, E) \u003c/strong\u003eEffect of RNaseA pre-treatment on S9.6 IHC in a panel of human cancer cell lines (CMAhu19-01, CMAhu19-02) and of human tumors (XENO15-1, XENO15-2). Boxplots represent data from one of three independent biological replicates, and P values were determined by nonparametric Mann-Whitney \u003cem\u003et\u003c/em\u003e test. *, P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4763785/v1/54422855cd5874a1e2ed5140.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Variability in R-loops levels based on IHC detection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eR-loops are three stranded nucleic acid structures involving an RNA:DNA hybrid and a displaced single stranded DNA (ssDNA)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The majority of R-loops arise co-transcriptionally, in the context of RNA polymerase I or II transcription activity. Though the majority of R-loop studies have been to investigate their pathological consequences in promoting genomic instability [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], R-loops also contribute to physiological processes including the regulation of transcription progression, processing and termination, enhancer programs, immunoglobulin diversity, ribosomal RNA transcription and even nucleolar phase separation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Meta-analysis of a variety of R-loop datasets has demonstrated that R-loops cover about 3\u0026ndash;5% of human genome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with their abundance tightly titrated; too many or too few R-loops impact normal cell functions [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, R-loop abundance is regulated by a variety of proteins including RNase H enzymes, which specifically degrade the RNA moiety of RNA:DNA hybrids, or by helicases, such as senataxin (SETX) or DHX9, which by unwinding RNA:DNA hybrids limit their formation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAberrant R-loop accumulation has been implicated in cancer susceptibility and neurodegeneration. For example, increased R-loops levels throughout the genome are observed in response to oncogenic estrogen signaling [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] or activated oncogenes like the EWS-FLI1 fusion oncoprotein [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or mutations that lead to amyotrophic lateral sclerosis (ALS) such as in senataxin [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] resulting in increased replication stress and DNA damage. Nonetheless, this abnormal R-loop accumulation also confers a vulnerability, and cancer cells harboring high levels of R-loops can be preferentially targeted by drugs that exacerbate R-loop-associated phenotypes including splicing inhibitors, G4 ligands stabilizers or inhibitors of replication stress response including ATR among others [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the initial report on S9.6 antibody [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], multiple techniques, like R-loop mapping by DRIP sequencing, nucleic acid isolation followed by dot-blot, and S9.6 immunofluorescence, have been described for R-loop detection [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, even if sequencing-based methods allows locus-by-locus R-loop analysis, they also required expensive equipment and specialized analysis that may not be practical in many clinical settings. Additionally, inconsistency has been described using S9.6 immunofluorescence (IF), with some studies reporting RNase H-resistant staining or prominent cytoplasmic S9.6 signal often attributed to differences in fixation, permeabilization and buffers used before immunolabeling [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, it is evident that there is a need for a protocol to detect and quantify R-loop abundance in the nucleus that can be conducted uniformly across laboratories and clinical settings.\u003c/p\u003e \u003cp\u003eIn this study, we establish a protocol to detect RNA:DNA hybrids by immunohistochemistry (IHC) using the mouse and rabbit S9.6 antibodies. The specificity of the methods is demonstrated with controls such as RNase H pretreatment or nucleases capable of specifically degrading dsRNA and ssRNA, showing that in paraffin-fixed cells the majority of S9.6 signal stems from the binding of S9.6 to RNA:DNA hybrids. Moreover, using R-loop enhancing drugs, or by genetically manipulate DHX9 and SETX expression, we provide evidence that S9.6 IHC is able to detect differences in R-loop levels. Finally, we show that S9.6 IHC is uniquely able to rapidly screen hundreds of cell and tumor samples demonstrating the heterogeneity in R-loop signal that can be observed, and we describe for the first time that R-loop expression determines sensitivity to the active vitamin D metabolite Calcitriol that express vitamin D receptor as predicted from our prior meta-analysis of R-loop datasets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eS9.6 exhibits specificity for RNA-DNA hybrid.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eS9.6 was raised to be immunogenic to R-loops, showing significant specificity to this structure \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and reasonable specificity by immunofluorescence (IF) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Here our goal was to establish the utility of S9.6 to detect R-loops by immunohistochemistry (IHC). For this we assessed both mouse and rabbit S9.6 antibodies using breast cancer MCF7 and Ewing sarcoma TC32 cell lines; these cell lines were selected as it has already been established that R-loops preferentially accumulate in breast luminal epithelial cells, such as MCF7 cells [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and because our prior work demonstrating that Ewing sarcoma cells accumulate high levels of R-loops [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In comparison to secondary antibody control slides, both antibodies selectively stained the nucleus of MCF7 and TC32 cells and are free of any nonspecific staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). As reported by different studies, S9.6 antibody can recognize dsRNA \u003cem\u003ein vitro\u003c/em\u003e under certain conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To test this possibility we pretreated MCF7 and TC32 slides with RNase III and RNase T1 endonucleases, shown to be capable of specifically and efficiently degrading dsRNA and ssRNA without degrading RNA:DNA hybrids [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In both cell lines, RNase III pretreatment decreased the signal detected by mouse S9.6 antibody, decreasing the staining by approximately 34% (35% in MCF7, 32% in TC32), while rabbit S9.6 staining was only reduced by ~\u0026thinsp;15% (14% in MCF7, 17% in TC32) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Therefore, similar to IF experiments, it appears that dsRNAs can interfere with S9.6 binding to R-loops. The affinity of S9.6 for dsRNAs has been previously reported by others [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and can result in mixed signals from R-loops and dsRNAs when DRIP is followed by RNA sequencing, such as in DRIP-seq.\u0026nbsp;RNase T1 pretreatment led to a larger decrease of S9.6 staining in TC32 cells (approximately 30%), while R-loop staining of MCF7 cells were only marginally affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). These results indicate that using S9.6 in IHC may also recognize RNA molecules with double stranded structures similar to the cross-reactivity reported by IF and the necessity of including appropriate controls. Finally, RNase H pretreatment, which efficiently digests the RNA strand of RNA:DNA hybrids, completely suppressed mouse and rabbit S9.6 staining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Taken together, these observations support that in paraffin embedded formalin-fixed cells the majority of S9.6 signal stems from the binding of S9.6 to RNA:DNA hybrids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSenataxin and DHX9 helicases knockout alter level of R-loops as detected by IHC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo confirm the specificity of the S9.6 antibodies for R-loops in paraffin embedded formalin-fixed cells, we silenced the expression of the Senataxin (SETX) and DHX9 helicases using specific siRNAs (Suppl. Figure\u0026nbsp;1A). R-loops are not only inherent byproducts of transcription but have programmatic roles controlling numerous physiological processes, and, as a general rule, R-loop homeostasis is tightly controlled by the activities of several enzymes, such as SETX and DHX9 [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. SETX is an RNA:DNA helicase which catalyzes the unwinding of the RNA:DNA hybrid portion of R-loops, promoting their resolution [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As expected, using either mouse or rabbit S9.6 antibodies, we confirmed that SETX knockout led to a statistically significant increase of S9.6 staining in MCF7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, the helicase DHX9 which is a component of the RNA polymerase II holoenzyme [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and a component of the RNA:DNA hybrid interactome [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], has been suggested to both prevent and increase R-loops depending on context [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As already reported by others using immunofluorescence and slot blot techniques [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], we confirmed that siRNA-mediated knockdown of DHX9 caused a global reduction of both mouse and rabbit S9.6 staining in MCF7 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Taken together, these results further validate the use of both mouse and rabbit S9.6 antibodies for the detection of R-loops by IHC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSplicing inhibitor Pladienolide B and G4 ligand PDS affect R-loops metabolism.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eR-loops have been implicated in several human diseases, including repeat-expansion disorders, neurological syndromes, myelodysplastic syndrome and cancer with the utility of different drugs affecting R-loop metabolism being studied in these contexts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consequently, we asked whether S9.6 IHC can detect changes in R-loop levels in response to treatment with either the splicing inhibitor Pladienolide B (PladB) or G4 ligand binder pyridostatin (PDS). Mechanisms that alter RNAPII progression, such as inhibiting SF3B1, a core component of the U2 spliceosome, can affect R-loop levels and PladB has been reported to increased RNA:DNA hybrids leading to impaired DNA synthesis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. G4 quadruplex structures, particularly in the displaced DNA strand of an R-loop, are thought to promote R-loop stability, thus ligands such as PDS that stabilize G4 ligands have been used to induce R-loop-mediated DNA damage and cell death in human cancer cells [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Overnight exposure to PladB or PDS led to statistically significant increase in R-loops detected by S9.6 IHC in both MCF7 and TC32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that S9.6 IHC could be a valuable technique to rapidly detect changes in R-loops. Of note, the doses of PladD and PDS used only marginally affected cell viability (Suppl. Figure\u0026nbsp;1B, C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImage analysis of R-loop IHC reveals the variation in basal R-loop levels across different human cancer cell lines and tumors.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe evaluated by IHC the levels of RNA:DNA hybrids in a panel of human cancer cell lines (CMAhu19-01, CMAhu19-02) (Table\u0026nbsp;1) and tissue microarrays derived from tumor mouse xenografts (TMA_Xeno15_MB, TMA_Xeno15-2_MB) (Table\u0026nbsp;2). Since the TMAs were derived from mouse models we only used the rabbit S9.6 antibody to avoid the possibility of cross-reactivity with host tissue that would occur if we had used secondary antibody to detect mouse S9.6. A total of 164 human cell lines and 160 human tissues were stained, and image analysis was performed to evaluate levels of staining. Cell lines and xenografts exhibit an extensive range from high to low rabbit S9.6 signal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), confirming the variability of R-loop levels that can be detected across different cancers or cell types. In depth analysis confirmed that rabbit S9.6 staining is predominantly located in the nucleus, homogenously distributed across one tissue core, and that the secondary detection antibody itself is free of nonspecific binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, after pre-treatment with RNase H, rabbit S9.6 did not show any significant binding confirming the specificity of the antibody for RNA:DNA hybrids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). RNase A pre-treatment resulted in a measurable, though reasonably consistent decrease in rabbit S9.6 signal, without significantly altering the order of cell lines arranged by high to low R-loop signal (Suppl. Figure\u0026nbsp;1D). In contrast, and unexpectedly, we noted several examples in our xenograft TMAs where RNase A pre-treatment increased R-loop signal, though we also noted that this treatment seemed to correlate with the intensity of the blue hematoxylin counterstain which may indicate differences in sample integrity (Suppl. Figure\u0026nbsp;1E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eR-loop levels predict Vitamin D sensitivity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn our recent meta-analysis of multiple R-loop sequencing datasets, we classified R-loops as being either constitutive or variable, with constitutive R-loops enriched with housekeeping genes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In that same analysis we found the most consistent enrichment irrespective of variable or constitutive R-loops was with the VDR (vitamin D receptor) target gene set. To our knowledge, the VDR has not previously been associated with R-loop biology. Consequently, we decided to investigate how cell lines with different levels of R-loops as determined by rabbit S9.6 IHC would respond to the active vitamin D metabolite Calcitriol. For this we selected two cells lines with high R-loops (MCF-7, HT-29) or low R-loops (A673, MDA-MB231) for their viability response to Calcitriol exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Besides its classic biological effects on calcium and phosphorus homeostasis to preserve bone health, Calcitriol has been shown to play a key role in the prevention and treatment of many extra-skeletal diseases including cancer [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In the context of cancer, Calcitriol regulates cell cycle, induces apoptosis, promotes cell differentiation and acts as anti-inflammatory factor within the tumor microenvironment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; given that these biological response result from induced gene expression changes, they likely are also associated with R-loop inductions. Interestingly, we found that R-loop levels positively correlate with sensitivity to Calcitriol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), with cell lines displaying high levels of R-loops (MCF-7 and HT-29) being more sensitive to Calcitriol compared to cell lines with low levels of R-loops (A673, MDA-MB231) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Moreover, clonogenic assays revealed that Calcitriol dramatically affects the ability of MCF-7 and HT-29 to form colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), suggesting that in the context of high R-loop levels Calcitriol may impact R-loop biology ultimately blocking cancer cell stemness. To verify this hypothesis, we measured by S9.6 IHC the effect of Calcitriol on MCF-7 R-loops levels, finding that Calcitriol treatment led to a significantly increase in R-loops detected by both mouse and rabbit S9.6 antibodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Taken together, these results not only confirm that S9.6 IHC is a fast and reliable method to detect R-loops expression, but also indicate that the use of active vitamin D metabolite may be of therapeutic interest in tumors with high levels of R-loops.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eR-loop levels predict synergy to ATR/PARP1 co-inhibition.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDespite playing physiological roles in specific situations, DNA-RNA hybrids are a major source of DNA damage accumulation and transcription-associated replication stress leading to genome instability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To prevent these potentially harmful effects, cells have evolved a coordinated cellular response, the DNA damage response (DDR), as an intrinsic barrier to detect and enable the repair of different types of DNA damage [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Consequently, we decided to study how cells that intrinsically harbor different levels of R-loops respond to co-targeting two critical DDR factors, the ATR kinase that responds to replication stress and Poly (ADP-ribose) polymerase 1 (PARP1) that prevents single strand break accumulation that would cause replication stress. Interestingly, we observed that in cells with high levels of R-loops (MCF-7 and HT-29) ATR-PARP1 co-inhibition exponentially exacerbate the effect of the single agents AZD6738 (ATRi) or Olaparib (PARP1i) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). In cells with low levels of R-loops (A673 and MDA-MB231), this effect is not lost but appeared to be additive and in general correlated to PARP1 inhibition outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Moreover, additional analysis using the web-application SynergyFinder (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://synergyfinder.fimm.fi\u003c/span\u003e\u003cspan address=\"https://synergyfinder.fimm.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we found that in comparison with A673 and MDA-MB231, AZD6738/Olaparib co-treatment resulted a highly synergistic effect in MCF-7 and HT-29 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). At a minimum, these results indicate that tumors expressing high levels of R-loops may not only be exquisite sensitive to ATR/PARP1 co-inhibition, but that the intrinsic level of R-loops present dictate the extent of AZD6738/Olaparib synergy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOften associated only with detrimental byproducts of transcription and a basis for genome instability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], in the past decade R-loops have emerged to play significant roles in normal cellular physiology. The study of R-loops has revealed their clear role in gene expression regulation, both directly and indirectly, effectively acting as a novel epigenetic mark. However, unregulated R-loop accumulation can have pathological consequences, including cancer, where occurrence of abnormal R-loop formation or a defect in their resolution may drive oncogenesis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Nevertheless, increased R-loop formation may also offer a novel therapeutic vulnerability by either inducing excessive R-loops or hampering their resolution perhaps resulting in intolerable genomic instability and synthetic lethality depending on context [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we described a fast and reliable method to determine by immunohistochemistry R-loops abundance in cell and cancer samples using the mouse and rabbit S9.6 monoclonal antibodies. Immunohistochemistry has existed since the 1930s and is widely used to diagnosis and predict therapeutic response of tumors [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]; in the context of R-loops, our results demonstrate that S9.6 IHC is capable of screening and distinguish variability in R-loops levels in hundreds of samples in a highly reproducible and time-sensitive manner. Since its initial report [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], different subsequent studies have shown that S9.6 antibody can bind off-target double-stranded ribosomal RNA creating pervasive artifacts, making necessary the inclusion of endonuclease enzymes as standards for quality control [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Despite observations by others using immunofluorescent techniques in cell lines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we found that with paraffin embedded formalin-fixed cells the S9.6 IHC signal is exquisitely located in the nucleus, and the pre-treatment with RNase A and RNase III endonucleases only marginally reduced with S9.6 binding to RNA:DNA hybrids, and did so in a fairly consistent manner. Moreover, we applied a prior prediction that vitamin D receptor profoundly impacts R-loop levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] by describing for the first time that the vitamin D metabolite Calcitriol adversely impacts cancer cells with high levels of R-loops. Finally, we showed that taking advantage of the variability in R-loops levels by simultaneously co-inhibiting ATR/PARP1, is profoundly synergistic and may be a promising strategy to minimize possible side effects in the treatment of tumors that display high levels of R-loops.\u003c/p\u003e \u003cp\u003eIn conclusion, R-loop biology is a growing field with many open questions and many aspects of their biology and relative importance yet to be investigated. Clearly there is variation in amounts of R-loops in different tissues, in response to various queues or perturbations or in different genetic contexts. The results of the work presented here support the use of S9.6 monoclonal antibodies to detect RNA:DNA hybrids by IHC, describing a protocol that can be easily duplicate using ordinary laboratory equipment, to investigate a wide variety of contexts to facilitate their comparison.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCell cultures and transfections\u003c/h2\u003e \u003cp\u003eEwing sarcoma cell line TC32 was obtained from Children\u0026rsquo;s Oncology Group. Ewing sarcoma cell line A673, human colorectal adenocarcinoma cell line HT-29, human breast cancer cells MCF-7 and MDA-MB231 were purchased from the American Type Culture Collection (ATCC). A673, HT-29, MCF-7 and MDA-MB231 cells were cultured in DMEM (Corning); TC32 cells in RPMI-40 (Corning); all cultured media were supplemented with 10% heat-inactivated fetal bovine serum (Corning) and 1% antibiotic/antimycotic solution (Corning). All cell lines were maintained at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e and tested for mycoplasma contamination.\u003c/p\u003e \u003cp\u003eAll transfections were carried out using Lipofectamine RNAiMAX (Invitrogen) following the manufacturer\u0026rsquo;s instructions, and gene knockdowns performed by reverse transfection. The siRNAs used in this study were DExH-box helicase 9 (DHX9) (ON-TARGETplus, Dharmacon), and senataxin (SETX) (ON-TARGETplus, Dharmacon). All siRNA transfections were accompanied with non-targeting control siRNA (ON-TARGETplus, Dharmacon).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot\u003c/h2\u003e \u003cp\u003eWhole-cell lysates were prepare using NaCl lysis buffer according to standard protocols. Cell lysates were separated on precast 3\u0026ndash;8% gradient gels (Invitrogen) and transferred onto nitrocellulose membrane. All blots were incubated with primary antibodies overnight and developed using enhanced chemiluminescence (Super ECL, ThermoFisher). Antibodies used in this study include DHX9 (Bethyl, A300-855A), SETX (Abcam, ab26271), beta-Tubulin (Cell Signaling, cs2128) and secondary antibody goat anti-rabbit IgG-HRP (Santa Cruz, sc-2030). Western blot experiments were repeated with independent sample preparations three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell viability\u003c/h2\u003e \u003cp\u003eCells were seeded at 15\u0026ndash;20% confluence in 96-well plates and drug treatment administered on the next day. Calcitriol (Selleckchem, Cat. #S1466), Olaparib (Selleckchem, Cat. #S1060), and AZD6738 (Selleckchem, Cat. #S1060) have been resuspended and properly stored according to manufacturer\u0026rsquo;s instruction. DMSO treatment was used as control for all the experiments. End point cell viability was evaluated after 72h or 96h using Celltiter-Glo (Promega). Each condition was tested at least in quadruplicate, and every experiment performed in three independent biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClonogenic assay\u003c/h2\u003e \u003cp\u003eCells were seeded as single cell suspension (3-5x10\u003csup\u003e3\u003c/sup\u003e cells/well according to the cell type) in a 6-well plate and allowed to attached to the plate for 6 to 8h. Drug treatment was added as soon as the cells were attached to the plates before they start replicating to avoid issues of numbers of cells per dish increasing to yield more colonies. The plates were subsequently placed in 37\u0026deg;C humidified incubator for 10 days. Next, colonies were washed with PBS (Corning), fixed for 30 minutes with 10% neutral buffered Formalin (Sigma-Aldrich) on a rocking shaker, and stained with 0.2% Crystal violet (Sigma-Aldrich). For each treatment, colony forming capacity was calculated as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(Area\\:colonies/Area\\:colonies\\:DMSO\\:control\\)\u003c/span\u003e\u003c/span\u003e) X 100 using ImageJ/Fiji software. Every experiment was performed in three independent biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePladienolide B and PDS treatments\u003c/h2\u003e \u003cp\u003eCells were seeded at 60\u0026ndash;70% confluence in 6-wells plates to generate cell slides or in 96-wells plates to assess cell viability, followed by treatment for 18 hours with Pladienolide B (MedChemEXpress, HY-16399) or Pyridostatin Hydrochloride (PDS) (Sigma-Aldrich, SML2690). Next, cells were processed to generate microarrays as described below, or used for end point cell viability measurements using Celltiter-Glo (Promega). Of note, since average R-loop half-life is 2\u0026ndash;30 minutes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], once harvested cells need to be fixed as soon as possible to prevent loss of R-loop signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell microarray and xenograft tissue microarray\u003c/h2\u003e \u003cp\u003eCells were washed with PBS (Corning), dissociated using Trypsin-EDTA (Corning), collected using complete culture media and centrifuged for 10 minutes at 2,500 rpm. Cell pellets were washed using PBS and centrifuged for 10 minutes at 2,500 rpm to eliminate any Trypsin-EDTA residue. Supernatant was decanted, cell pellets resuspended in 10% neutral buffered Formalin (Sigma-Aldrich), thoroughly vortexed and fixed for 30\u0026ndash;60 minutes at room temperature (Note: the volume of Formalin should be about the same as the cell pellet volume). Specimens were centrifuged for 10 minutes at 2,500 rpm, the supernatant containing Formalin decanted, the cell pellets resuspended in 95% ethanol, thoroughly vortexed and dehydrated for 15 minutes at room temperature (Note: the volume of ethanol should be about the same as the cell pellet volume). Next, specimens were centrifuged for 10 minutes at 2,500 rpm and supernatant containing dehydrant decanted. In parallel, Nutrient Agar slant tube was liquified using a microwave (the gelatin should be warm, not hot), approximately 500\u0026micro;L of liquid agar added to the cell specimen, and the specimen/gelatin sample allowed to solidify for 30 minutes at room temperature. Finally, the specimen is dislodged from the bottom of the tube using a dissecting needle, transferred into a tissue tea bag, placed in a tissue cassette and processed routinely in the histopathology laboratory. Cell line microarrays (CMAhu19-01, CMAhu19-02) comprising of 164 different cell lines was constructed as previously described by Goodman and colleagues [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; meanwhile the xenograft tissue microarray (TMA_Xeno15_MB, TMA_Xeno15-2_MB) comprising xenograft tissue from 160 models was constructed using a new-generation tissue arrayer as previously reported by Zlobec and colleagues [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eS9.6 Immunohistochemistry\u003c/h2\u003e \u003cp\u003eIn house cell blocks, human cancer cell lines (CMAhu19-01, CMAhu19-02) and human tissue micro arrays (TMA_Xeno15_MB, TMA_Xeno15-2_MB) were subjected to standard histopathology deparaffinization and hydration steps. Antigen retrieval was performed by treating the microarrays with 1 mM EDTA, pH 8.0 for 40 minutes at 95\u0026deg;C followed by a 20 minutes cool down step. To confirm antibody specificity, slides were incubated in a moist humidity chamber with RNase H (overnight in 1xRNase H reaction buffer; New England Biolabs, M0297), RNase T1 (1 hour in 50mM Tris-HCl pH 7.5, 2mM EDTA; ThermoFisher, EN0541), RNase A (1 hour in 10mM Tris-HCl pH 7.5, 0.5M NaCl; ThermoFisher, EN0531), or RNase III (1 hour in 1xShortCut Reaction Buffer, 1xMnCl\u003csub\u003e2\u003c/sub\u003e; BioLabs, M0245S). Slides were then rinsed in 1X Tris buffered saline (TBS) three times. Following endogenous peroxidase blocking, the slides were incubated with mouse or rabbit S9.6 antibodies (Absolute, Ab01137-23.0, -2.0, 1:10,000) for 2 hours at room temperature in a moist humidity chamber. Anti-mouse and anti-rabbit Powervision-HRP Conjugated Polymer from Leica Biosystems (Cat #PV6114, #PV6119) for 30 min was used for detection. Slides were then developed with DAB for 5 min, rinsed with TBS and counterstained with hematoxylin, dehydrated, cleared and mounted with a synthetic mounting medium. Images were taken on an Olympus sc-100 or a Motic Digital Slide Scanning System.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImage analysis\u003c/h2\u003e \u003cp\u003eFor each experiment, all conditions were imaged in parallel taking 5 images in different areas of the slide to capture intrinsic sample variability. Images were analyzed and quantified using ImageJ/Fiji programs. Briefly, images were deconvoluted separating DAB from hematoxylin colors, and DAB intensity quantified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e values for analyzing cell viability, IC\u003csub\u003e50\u003c/sub\u003e and R-loops intensity were computed using nonparametric Mann-Whitney \u003cem\u003et\u003c/em\u003e test in GraphPad Prism software. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant, and marked as *.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthical approval and consent to participate\u003c/u\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent to publication\u003c/u\u003e: The authors declare no competing financial interests, and\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;consent to publish the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor contribution\u003c/u\u003e:\u0026nbsp;N.B. and A.J.R.B conceived and designed the study and wrote the manuscript. N.B. conducted the majority of the research. L.L. contributed to PladB and PDS experiments. C.W. and J.B. provided tissue and cell microarrays slides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNIH/NCI [R01CA152063 and 1R01CA241554], CPRIT [RP150445], SU2C-CRUK [RT6187] to A.J.R.B\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to Daniel Rebledo and the Histology \u0026amp; Immunohistochemistry Core at UTH-SA.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBou-Nader, C., et al., \u003cem\u003eStructural basis of R-loop recognition by the S9.6 monoclonal antibody.\u003c/em\u003e Nat Commun, 2022. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 1641.\u003c/li\u003e\n\u003cli\u003eHamperl, S., et al., \u003cem\u003eTranscription-Replication Conflict Orientation Modulates R-Loop Levels and Activates Distinct DNA Damage Responses.\u003c/em\u003e Cell, 2017. \u003cstrong\u003e170\u003c/strong\u003e(4): p. 774-786.e19.\u003c/li\u003e\n\u003cli\u003eGan, W., et al., \u003cem\u003eR-loop-mediated genomic instability is caused by impairment of replication fork progression.\u003c/em\u003e Genes Dev, 2011. \u003cstrong\u003e25\u003c/strong\u003e(19): p. 2041-56.\u003c/li\u003e\n\u003cli\u003eMiller, H.E., et al., \u003cem\u003eQuality-controlled R-loop meta-analysis reveals the characteristics of R-loop consensus regions.\u003c/em\u003e Nucleic Acids Res, 2022. \u003cstrong\u003e50\u003c/strong\u003e(13): p. 7260-7286.\u003c/li\u003e\n\u003cli\u003eLambo, S., et al., \u003cem\u003eThe molecular landscape of ETMR at diagnosis and relapse.\u003c/em\u003e Nature, 2019. \u003cstrong\u003e576\u003c/strong\u003e(7786): p. 274-280.\u003c/li\u003e\n\u003cli\u003eNgo, G.H.P., J.W. 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S307-9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"R-loops, Immunohistochemistry, Calcitriol, AZD6738, Olaparib, Drug synergy","lastPublishedDoi":"10.21203/rs.3.rs-4763785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4763785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eR-loops are three stranded nucleic acid structures involving an RNA:DNA hybrid and a displaced single stranded DNA (ssDNA). Though the majority of R-loop studies have investigated their pathological consequences in promoting genomic instability, R-loops also contribute to many physiological processes. In fact, from meta-analysis of R-loop datasets we know that R-loops cover about 3\u0026ndash;5% of human genome, with their abundance tightly titrated by different enzymes or helicases; too many or too few R-loops impact normal cell functions. Aberrant R-loop accumulation has been implicated in cancer susceptibility and neurodegeneration, and increased R-loops levels throughout the genome observed in response to oncogenic signaling or mutations results in increased replication stress and DNA damage. Nonetheless, this also confers a vulnerability, and cancer cells harboring high levels of R-loops can be preferentially targeted by drugs that exacerbate R-loop-associated phenotypes. Here, we establish a protocol to detect RNA:DNA hybrids by immunohistochemistry (IHC) using the mouse and rabbit S9.6 antibodies. Using R-loop enhancing drugs, or by genetically manipulate DHX9 and SETX expression, helicases involved in R-loop metabolism, we provide evidence that our protocol is able to detect differences in R-loop levels. Finally, we show that S9.6 IHC is uniquely able to rapidly screen hundreds of cell and tumor samples demonstrating the heterogeneity in R-loop signal that can be observed. We also describe for the first time that R-loop expression determines sensitivity to the active vitamin D metabolite Calcitriol.\u003c/p\u003e","manuscriptTitle":"Variability in R-loops levels based on IHC detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 18:22:36","doi":"10.21203/rs.3.rs-4763785/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cac9995b-71d4-4bb3-a44d-3d47edd8df84","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-03T15:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-20 18:22:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4763785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4763785","identity":"rs-4763785","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}