CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel driver of metastasis in prostate cancer

CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel driver of metastasis in prostate cancer | 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 Article CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel driver of metastasis in prostate cancer Alvaro Gutierrez-Uzquiza, Maria Rodrigo-Faus, Africa Vincelle-Nieto, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3316991/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 Owing to the inefficacy of available treatments, the survival rate of patients with metastatic prostate cancer (mPCa) is severely decreased. Therefore, it is crucial to identify new therapeutic targets to increase the survival of mPCa patients. This study aim was to identify the most relevant regulators of mPCa onset by performing two high-throughput CRISPR/Cas9 screenings. Furthermore, some of the top hits were validated using small interfering RNA (siRNA) technology, with protein arginine methyltransferase 7 (PRMT7) being the best candidate. Its inhibition, by genetic and pharmacological approaches, or its depletion, via CRISPR, significantly reduced mPCa cell capacities in vitro . Furthermore, PRMT7 ablation reduced mPCa appearance in chicken chorioallantoic membrane and mouse xenograft assays. Molecularly, PRMT7 reprograms the expression of several adhesion molecules through methylation of several transcription factors, such as FoxK1 or NR1H2, which results in primary tumor PCa cell adhesion loss and motility gain. Moreover, PRMT7 is upregulated in advanced stages of Spanish PCa tumor samples and PRMT7 pharmacological inhibition reduces the dissemination of mPCa cells. Thus, here is shown that PRMT7 is a potential therapeutic target and biomarker of mPCa. Biological sciences/Cancer/Cancer screening Biological sciences/Cancer/Metastasis Biological sciences/Cancer/Urological cancer/Prostate cancer Biological sciences/Genetics/Cancer genomics Prostate cancer Metastasis Invasion PRMT7 adhesion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION PCa remains the second most frequent cancer and the fifth leading cause of cancer-related death among men( 1 ). Owing to on-going improvements in tumor diagnostic technologies and treatments, patients who remain in a non-disseminated PCa stage of the disease have a good outcome. In contrast, the mean survival of patients with PCa who develop distant metastases is less than 3 years( 2 ). These patients are commonly administered androgen-castration hormonal therapy along with conventional chemotherapy( 3 ), however, current available treatments are not effective enough to manage metastatic PCa (mPCa) patients. Hence, there is an urgent need to find new targets and biomarkers to establish effective therapies and improve mPCa patient survival. Despite the alarming number of deaths from metastatic cancer each year, the occurrence of metastasis remains poorly understood. Some biological changes have been described as crucial in promoting localized primary tumor progression to a secondary foci dissemination stage. They involve primary tumor cell adhesion loss, degradation of the basement membrane, intravasation to the circulatory system, survival in the bloodstream, extravasation into distant organs, and generation of a new tumor bulk( 4 ). For some types of cancer, primary tumor cells have a preferred niche for colonization, as highlighted by the Stephan Paget “seed-and-soil” hypothesis( 5 ). This is the case for PCa tumor cells, in which approximately 70% of metastases are found in the bone marrow( 6 ). Metastatic tumor cells display high levels of genomic instability and harbor several epigenetic alterations that may empower them with the ability to disseminate and generate a new tumor burden. While the exact mPCa gene signature remains unknown, mPCa cancer cells frequently bear alterations in androgen receptor signaling( 7 ), mutations in TP53 and RB1 loss( 8 ), PTEN loss, overactivated Akt signaling, ETS gene rearrangements, and deleterious mutations in some DNA-repair genes such as BRCA2 ( 9 ). Moreover, some studies have also highlighted the importance of epigenetic factors such as EZH2 , which is a poor prognosis biomarker of PCa ( 10 ). Nonetheless, much remains to be investigated to fully understand the mechanisms that enhance the migratory and invasive abilities of PCa tumor cells. Some single-gene studies have unearthed mPCa regulators such as WNT5A( 11 ), MAP4K4( 12 ) and PPP1CA( 13 ). However, given the complexity of the biological mechanisms implicated in metastasis onset, high-throughput screening assays seem more optimal for uncovering the most relevant regulators of this process. In this context, CRISPR/Cas9 screening methods are becoming increasingly powerful. In vitro and in vivo CRISPR screenings have been successfully performed during the last years in different cancer models, including leukemia and lung cancer( 14 , 15 ). Therefore, the aim of this study was to conduct high-throughput in vitro screening assays using the human GeCKO CRISPR/Cas9 library( 16 ), to identify the most important genes and biological processes involved in metastasis onset in PCa patients. Briefly, protein arginine methyltransferase 7 ( PRMT7 ) was identified as an essential gene in mPCa. Further investigation revealed that PRMT7 induces a switch in the expression of cellular adhesion molecules, at least through FoxK1, NR1H2 and/or NCOA2/3 transcription factor methylation. Moreover, mPCa cells genetically engineered to abolish PRMT7 expression showed reduced metastatic abilities in vitro and in vivo . Furthermore, PRMT7 was overexpressed in a cohort of Spanish primary tumor samples with higher compared to lower Gleason scores (GS) samples. Therefore, our data support the role of PRMT7 in mPCa and its potential application as a novel therapeutic target. RESULTS CRISPR/Cas9 screenings reveal several genes and biological processes essential for mPCa onset Since the progression to metastatic cells in PCa is a complex process, we conducted two unbiased high-throughput CRISPR/Cas9 screenings to identify the most relevant regulators of metastasis onset. The GeCKO V2 Human CRISPR knockout pooled library with three pre-designed sgRNA to target almost 20,000 genes seemed the optimal choice( 16 ). First, mPCa PC3 and DU145 previously engineered in the laboratory to express Cas9 protein, were infected with lentivirus carrying the sgRNA CRISPR library (MOI 0.5) and selected with puromycin. To identify the genes that confer metastatic abilities to prostate cancer tumor cells, we conducted an invasion assay of PC3 and DU145 infected cells, in a Matrigel-coated Boyden Chamber. DNA from the cell population that had lost their invasive abilities, which remained on top of the Matrigel membrane, and DNA from the cells that retained them, at the bottom of the membrane, were isolated separately. Subsequently, sgRNAs were amplified by PCR, sequenced, and analyzed using the MAGeCK algorithm( 17 ), as outlined in Fig. 1 A. The results of PC3 and DU145 CRISPR/Cas9 screening analyses are shown in Fig. 1 B-C and Supplementary Tables 2–3. From the total of 20,000 genes analyzed, the depletion of 990 genes in PC3 and 884 genes in DU145 cells, significantly reduced their invasive capacities. Remarkably, 27 of them were common for both cell lines, suggesting that they could be regulators of the initial steps of mPCa onset, independently of the secondary foci colonized by tumor cells (Fig. 1 D; Supplementary Table 4). Furthermore, multiple gene ontology (GO) biological pathway enrichment were conducted to explore whether any biological pathway was enriched in the CRISPR screening results. GO enrichment analyses of PC3 screening revealed that the genes whose depletion significantly reduced invasion capacities, were mainly involved in the regulation of metabolism, the immune system, cell locomotion, and cell adhesion (Fig. 2 A; Supplementary Table 5A). The same analysis in DU145 screening revealed an enrichment in regulation of proliferation, nucleotide excision repair or the cytoskeleton reorganization pathways (Fig. 2 B; Supplementary Table 5B). Moreover, gene set enrichment analysis (GSEA) revealed an enrichment in pathways related to the epithelial-to-mesenchymal transition (EMT), the negative regulation of stem cell proliferation and the regulation of protein polyubiquitination, among others for PC3 cell screening (Fig. 2 C; Supplementary Table 5C). Meanwhile for the DU145 screening, GSEA analysis showed enrichment in the mRNA nuclear processing, the ion homeostasis and negative regulation of Jun kinase, among others (Fig. 2 D; Supplementary Table 5D). Overall, the biological pathways implicated in mPCa invasion varied between the two cell lines and the algorithm used, over-representation analysis or GSEA, the biological processes varied considerably. Nevertheless, among the significantly enriched GSEA biological pathways of both cell lines, we found four common results, the regulation of calcium ion transport into cytosol, the multicellular organism process, the DNA modification, and the DNA methylation or demethylation. Additional GO enrichment analyses using Reactome and the molecular signature C5 human molecular classifications of Molecular Signatures Database (MSigDB), are shown in Supplementary Fig. 1–2; Supplementary Table 5). PRMT7 ablation reduces metastatic capacities of mPCa cell lines in vitro To validate our initial screening results, we selected some of our best-hit genes based on their association with other types of cancer or metastasis. We studied the in vitro ability of PC3 cells to invade in a Matrigel-coated Boyden chamber using fetal bovine serum (FBS) as chemoattractant, after targeting some of our best hits with siRNA. Downregulation of six of the seven candidates selected for validation led to a reduction in the invasive capacity of PC3 cells being significant for PRMT7 , SYCP3 and TECPR1 (Fig. 3 A). As PRMT7 was one of the 27 common genes found in the initial screening (Supplementary Table 4), we decided to further explore its role in mPCa. Interestingly, PRMT7 is a protein arginine methyltransferase (PRMT) that belongs to a family of other 9 proteins. It is involved in introducing ω-mono-methylation (MMA) marks in several proteins of cells. PRMT7 has been described as an important post-transcriptional regulator in breast ( 18 ) and non-small cell lung cancer metastasis( 19 ). Additionally, PRMT7 is annotated in the biological pathway of DNA methylation and demethylation, which was enriched in both cell lines GSEA analyses. Therefore, the role of PRMT7 in the onset of mPCa was further explored. First, to unequivocally confirm that the effect on invasion was not due to the disruption of an off-target gene, PRMT7 expression was inhibited by siRNA and confirmed that its silencing strongly decreased their invasive properties in both PC3 and DU145 cell lines (Supplementary Fig. 3A-D). Additionally, we analyzed the levels of MMA marks, and observed a reduction of them in the PRMT7 depleted cells (Supplementary Fig. 4A-B). Next, stable PC3 and DU145 CRISPR/Cas9 PRMT7 knock-out cell lines were generated using a different sgRNA from pre-designed GeCKO sgRNA library( 16 ). As shown in Fig. 3 B-C, western blot analyses confirmed PRMT7 depletion in PC3 and DU145 cells without decreasing the expression of PRMT5 , a member of the PRMT family that has been described to be able to methylate several PRMT7 targets( 20 ). Subsequently, invasion capacities of PC3 and DU145 PRMT7 -KO cells were evaluated. As observed in Fig. 3 D-E, depleted cells showed significantly reduced invasive abilities compared to control (CTL) cells, validating the initial screening results. Tumor cells that gain invasive capacities can degrade the extracellular matrix (ECM) and migrate through the body to colonize distant organs. Therefore, to discriminate if PRMT7 was only altering the ability to degrade the ECM or it had also a role in cell motility, a migration assay was conducted. Figure 3 F-G show that PRMT7 depleted cells had significantly lower ability to migrate than their respective controls (Fig. 3 F-G), demonstrating the ability of PRMT7 to influence on both, ECM degradation and cell movement. Previous studies have linked PRMT7 and proliferation( 21 ) so we decided to study the effect of PRMT7 depletion on the viability of mPCa cell lines. As shown in Fig. 3 H-I, a significant decrease in the viability ratio was observed in PRMT7 -KO cells compared to CTL cells at 96h. Altogether, these results indicate that PRMT7 depletion produces a significant reduction in PC3 and DU145 metastatic abilities, particularly in invasion, migration, and viability. In vivo model assays show reduced disseminative capacities of PRMT7 depleted cells PRMT7 has been previously implicated in breast cancer progression in vivo ( 22 ). Thus, two in vivo experimental dissemination assays were performed to validate the role of PRMT7 in mPCa onset. First, a chorioallantoic membrane assay (CAM)( 23 ) was conducted as depicted in Fig. 4 A. PC3-Cas9 PRMT7 depleted and CTL cells were inoculated separately into a vessel of a chicken embryo. Seven days later, the primary tumors generated by PC3 cells and chicken bone marrows, were isolated. As shown in Fig. 4 B and 4 C, PC3 PRMT7 depleted cells generated significantly smaller (Fig. 4 B) and lighter (Fig. 4 C) tumors than control cells. These results are consistent with the decreased viability observed in vitro . Moreover, since PC3 cells display bone marrow tropism( 24 ), the disseminative properties of tumor cells were assessed by qPCR, using primers against Human Alu sequences present in chicken bone marrow DNA. A lower percentage of Human Alu sequences was detected in chicken embryos inoculated with PC3 PRMT7 depleted cells comparted to embryos inoculated with CTL cells, indicating that PRMT7 depleted cells showed a lower capacity to disseminate from the primary tumor to the bone marrow of chicken embryos than their respective controls (Fig. 4 D). Previous studies have shown that upon direct inoculation of PC3 cells in the blood circulation of immunodeficient mice (NOD-SCID), they were able to generate metastatic foci in distant tissues including the femur, tibia, jaws, and ribs( 24 ). Hence, PRMT7 -KO and CTL PC3 cells, engineered to express a luciferase-GFP DNA construct, were inoculated into the left cardiac ventricle of athymic nude mice, as previously described( 25 ). Mice were analyzed using an IVIS system and sacrificed after 28 days, and their tissues (including femur, tibiae, and sternum) were harvested, fixed, and analyzed for the presence of micrometastatic foci, as depicted in Fig. 4 E. Distant metastasis was observed in 50% of mice inoculated with CTL cells compared to 12% or 33% of mice inoculated with PRMT7 -KO1 and PRMT7 -KO2 depleted PC3 cells, respectively (Fig. 4 F, Supplementary Fig. 5A). Unexpectedly, most of the detected macrometastases displayed visceral localization, whereas no bone macrometastases were detected (Supplementary Fig. 5B). To analyze the presence of micrometastases of PC3 cells in the bone marrow, GFP immunohistochemistry of bone marrow from the tibia, femur, and sternum was performed. Quantification of GFP + foci revealed that PRMT7 depletion in PC3 cells severely reduced the formation of bone marrow micrometastases compared to CTL cells (Fig. 4 G). These results highlight the relevance of PRMT7 in controlling biological processes that are directly implicated in PCa metastasis onset both in vitro and in vivo . Expression of relevant cell adhesion molecules is regulated by PRMT7 To elucidate the biological mechanisms controlled by PRMT7 that facilitate distant organ colonization of primary tumor PCa cells, a differential expression analysis of PC3 PRMT7 depleted versus CTL cells, was conducted. Out of the total genes analyzed, the expression of 781 genes was significantly altered (Fig. 5 A; Supplementary Table 6). Among the most differentially expressed genes, we found PNPLA4 , SFMBT2 , and several genes related to cell adhesion such as TUBAC3 and CHST15 . Consecutively, a GO over-representation analysis was performed (Fig. 5 B), which revealed that the cell-substrate adhesion term was over-represented. Hence, the overall expression of genes annotated to the cell adhesion parental ontology term (GO:0007155) was examined (Fig. 5 C). Surprisingly, some genes, such as CDH1 or LAMC3 , were upregulated in PRMT7 depleted cells, while others such as ITGA1, ITGA2 and LAMC2 , were downregulated. To validate our RNA-seq results, the mRNA levels of some of the genes annotated in the cell adhesion process were analyzed by RT-qPCR in independent PC3 PRMT7 -KO and CTL samples (Supplementary Fig. 6). Cell adhesion has been described as crucial in mediating cell motility and colonization of distant organs, with integrin receptors being especially relevant( 26 ). Moreover, the overexpression of some integrins has been associated with poor prognosis in several cancers, and depending on the type of integrin expressed on their membranes, cells can adhere better to specific types of extracellular matrixes( 27 ). Thus, ITGα1 and ITGβ4 protein levels, which mediate binding to collagen IV or laminin matrices, respectively, were studied. Western blot results revealed that ITGα1 protein level was significantly reduced in PRMT7 depleted cells, while ITGβ4 level was significantly increased in comparison their respective controls, in both cell lines (Fig. 5 D-E). Interestingly, overexpression of exogenous PRMT7 ( 28 ) reduced ITGβ4 levels in both control and PRMT7 depleted cells, which is in agreement with previous results (Supplementary Fig. 6). Consecutively, cell adhesion to collagen IV and laminin was evaluated. Overall, PC3 cells, both PRMT7 depleted and control, had a greater ability to adhere to laminin than to collagen IV. However, we observed that PRMT7 depleted cells had a higher adhesion capacity than the control cells. In addition, at longer time points, control cells preferentially adhered to collagen IV, while PRMT7 depleted cells maintained better adhesion to laminin (Fig. 5 F-G). These results indicate that depletion of PRMT7 produces a switch in the type of adhesion molecules expressed by tumor cells, leading to a gain in cell adhesion while decreasing cell motility and, consequently dissemination to distant organs is restrained. Cell adhesion molecule switch is mediated by multiple transcription factors PRMT7 is present in the nucleus and cytoplasm, introducing post-transcriptional modifications to several proteins, including transcription factors( 20 ). To decipher whether any transcription factor methylated by PRMT7 was altering the expression of cell adhesion genes, two in silico approaches were carried out. First, an enrichment analysis of TF binding sites within the promoters of differentially expressed genes in PC3 cells was carried out. It revealed several TF related to Polycomb complex-2, such as EZH2 which was previously associated to PCa progression ( 29 ) (Fig. 6 A). Alternatively, TF activities based on the expression values of their target genes was inferred for each sample, selecting the top 50 TFs with the highest variation across samples independent of their experimental treatment. Our results revealed that the most variable TFs showed high concordance with changes in TF activity between PRMT7 depleted and CTL cells (Fig. 6 B). Surprisingly, out of the 50 TFs identified and according to dbPTM data base, 7 of them had at least an arginine methylation site and intriguingly, 6 of them presented a robust increased activity on PRMT7 depleted samples (Fig. 6 B). Curiously, 4 of those 7 TFs (FoxK1, NR1H2, NCOA2 and NCOA3) were PRMT7s targets as described in their recently published PRMT4, PRMT5 and PRMT7 methylomes( 30 ). Figure 6 C summarizes the number of proteins susceptible of being methylated by the three, two or one of the PRMTs. Therefore, our analysis focused on the TFs controlling the expression of cell adhesion-related genes susceptible to PRMT7 methylation. FoxK1, NR1H2, and NCOA2/3 are the only TF whose activity is changed between PRMT7-KO and CTL cells, which are susceptible to being methylated by PRMT7( 30 ), and also are able to bind to the promoter of cell adhesion genes, including the analyzed CDH1 and ITGA1/2 , where they could promote or inhibit their expression, as summarized in Fig. 6 D and Supplementary Fig. 7. Altogether these results suggest that PRMT7 depletion alters the activity of relevant transcription factors of cell adhesion genes such as FoxK1 , NR1H2 , and NCOA2/3 , leading to reprogramming of the type of cell adhesion molecules expressed on the cell surface. Potential use of PRMT7 as a new therapeutic target or biomarker To further explore the role of PRMT7 in PCa metastasis, TCGA PCa patient data was retrieved and overall survival of patients with high and low expression of PRMT7 , was analyzed. As shown in Fig. 7 A, the survival rate of patients with higher PRMT7 expression was significantly reduced. Additionally, we also explored PRMT7 expression by GS groups in these samples, observing a significant upregulation from GS 6 and above compared to healthy samples (Supplementary Fig. 7B). Moreover, Spanish FFPE-embedded PCa primary tumor samples were recruited from Hospital Clínico San Carlos (Madrid, Spain) to explore mRNA PRMT7 levels. Tumor samples were divided in two groups according to their aggressivity and the presence of metastasis in the patient, (Low group ≤ 6 GS and no metastasis present, High group ≥ 7 GS with metastasis present). Table 1 summarizes characteristics of patients included in the study, and Fig. 7 B shows representative H&E staining images of tumors of both groups. As shown in Fig. 7 C, the samples classified as High showed higher PRMT7 levels than Low group. These results suggest that PRMT7 may be a potential biomarker of PCa malignancy. Table 1 Summary of PCa patient clinical data. In the table are summarized the mean age, Gleason score or OMS group and the percentage of tumor of FFPE-embedded samples from Hospital Clínico San Carlos. Characteristic Low Gleason Score cohort (n = 5) High Gleason score cohort (n = 8) Both groups combined (n = 13) Age at time of treatment, mean (min-max) 67 (53–72) 69 (60–85) 68 (53–85) Gleason score, mean (min-max) 6 (6–6) 8 (7–10) 7 (6–10) OMS group 1 (1–1) 4 (2–5) 3 (1–5) Percentage of tumor in the sample (%), mean (min-max) 7% (0%-20%) 50% (20%-90%) 33% (0%-90%) Because some inhibitors of PRMT family are already in clinical trials( 31 ) and PRMT7 inhibitors were already available commercially but have not been tested to prevent or cure mPCa, we wanted to study the effect of PRMT7 inhibitors on the metastatic abilities. First, we verified that the inhibitor reduced PRMT7 activity by analyzing the MMA levels in cells, observing a reduction of them in both treated cell lines compared to cells treated with vehicle (Fig. 7 D-E). The next step was to check whether pharmacological inhibition was sufficient to reduce the invasive capacity. As shown in Fig. 7 F-G, PRMT7 inhibitor was sufficient to significantly decrease PC3 (Fig. 7 F) and DU145 (Fig. 7 G) invasive abilities compared to control cells. Moreover, we observed a reduction in proliferation and/or viability after treating cells with the inhibitor, mainly at 96h (Fig. 7 H-I). Altogether, these results suggest that pharmacological PRMT7 inhibition could be an effective approach to decrease the metastatic capacity of PCa cells in vitro and open a field of research in in vivo and preclinical studies. DISCUSSION Metastasis is a complex phenomenon involving many biochemical processes that are largely miscomprehended( 6 ), therefore producing effective therapies is highly challenging. However, its elevated incidence highlights the need to study the mechanisms underlying metastasis appearance and to identify new targets for use as prognostic biomarkers and/or therapeutic targets. To our knowledge, our report presents the first two independent large-scale screenings specifically designed to uncover the most relevant gene drivers of mPCa using CRISPR/Cas9 based technology and further validate our best candidate. Our extended in vitro and in vivo characterization of PRMT7 revealed its critical role in PCa tumor progression, being relevant not only for increasing cell viability but also for enhancing the invasive and migratory ability of cells. However, a reduction in viability upon PRMT7 depletion would not explain the decrease in PC3 and DU145 motility solely, as differences in migration and invasion were measured at 24h, while viability differences were observed at longer time frames. Moreover, we validated these results using PRMT7 pharmacological inhibitors, which could be used to prevent the appearance of mPCa. PRMT7 is the only member of the arginine methyltransferase family belonging to subgroup III and is involved in the preferential introduction of MMA( 20 ). Previous studies reported its role in promoting tumor dissemination in breast, non-small cell lung and renal cell cancers. In the context of breast cancer, PRMT7 has been shown to methylate the E-cadherin proximal promoter inducing EMT transition( 18 ), to upregulate the expression of matrix metalloproteinase-9 (MMP9)( 22 ), and to methylate SHANK2 activating endosomal FAK signaling( 32 ); which all together lead to the progression of the disease. In non-small cell lung cancer, PRMT7 contributes to metastasis appearance through interaction with HSPA5 and EEF2( 19 ). In addition, it has been described that in renal cell carcinoma, PRMT7 can methylate β-catenin promoting its stabilization and the induction of c-myc expression, which is an important regulator of cell proliferation and tumor progression( 21 ). Moreover, PRMT7 has been described to methylate the Wnt signaling molecule Dishevelled 3( 33 ), the transcription factor C/EBP-β( 34 ), and the RNA splicing factor hnRNPA1( 30 ), suggesting that it, directly or indirectly, could be implicated in several cellular pathways, both in cellular homeostasis and disease. Therefore, due to the high number of biological processes susceptible to PRMT7 methylation, we decided to conduct a differential transcriptomic analysis to elucidate the most important processes in mPCa onset context. As described for breast cancer, the results showed an CDH1 expression in PRMT7 depleted cells. However, we did not detect significant changes in the expression of other genes, such as MMP9 , HSPA5 , EEF2, CTNNB1 , nor MYC . Remarkably, we observed and enrichment of several cell adhesion biological processes. Cell adhesion is a widely known mechanism that contributes to tumor cell migration and invasion. In fact, changes in the expression of some integrin and laminin receptors, are already considered malignant biomarkers. For example, the alpha 1 subunit of integrin receptors ( ITGA1) is a pre-malignant biomarker that promotes therapy resistance and metastatic potential in pancreatic cancer( 35 ), whereas laminin subunit gamma 2 ( LAMC2 ), has previously been identified as a specific marker for metastatic abilities in lung adenocarcinoma( 36 ). Therefore, our functional validation was focused on demonstrating that PRMT7 depletion reprograms the type of cell adhesion molecules expressed by tumor cells, and at least validated ITGA1 , ITGB4 , LAMC2 , LAMC3 and CDH1. Additionally, here it is demonstrated that the selective adherence of mPCa cells to different types of extracellular matrices, such as collagen IV and laminin, is altered because of cell adhesion protein switch. Interestingly, bone marrow, where 70% of mPCa is found, is an organ rich in collagen IV. Therefore, an upregulation of PRMT7 levels in PCa cells could enhance adhesion to collagen IV rich bone marrow promoting cell survival and metastasis appearance. In this study, TF activity analysis uncovered a new role for PRMT7 as a regulator of several TFs. These results, together with the previously published PRMT7 methylome( 30 ), suggest that either methylation of FoxK1, NR1H2, or NCOA2/3 could regulate their transcriptional activity in mPCa cells. Remarkably, FoxK1, a TF susceptible to methylation by PRMT7 on R161 and R191 residues, is a known regulator of the expression of several adhesion molecules and has previously been involved in the acquisition of mPCa abilities( 37 ) , ( 38 ). In addition, NR1H2 is susceptible of being methylated on R126 and is known to regulate the expression of LAMC2 and CDH1( 39 ). Arginine methylation stabilizes E2F1( 40 ); therefore we speculate that it may have a similar effect on FoxK1, NR1H2, or NCOA2/3. Although further validation is needed, regulation of cellular adhesion molecules via PRMT7-FoxK1/NR1H2/NCOA2/3 may explain the acquisition of migratory abilities shown in PCa cellular model studies. Regarding the use of PRMT7 as prognostic biomarker or therapeutic target, although validation in larger cohorts of samples is required, results are promising. In fact, some studies have already highlighted PRMTs as an emergent group of proteins involved in cancer and metastasis( 41 ). Indeed, PRMT1 and PRMT5 inhibitors are currently under Phase I clinical trials (NCT04676516, NCT03573310, NCT05094336, NCT03854227, NCT04089449, NCT03886831, NCT05275478, NCT04794699, and NCT05245500). In summary, we conducted two large-scale CRISPR/Cas9 screenings to identify the most important gene drivers of PCa cell invasion and validated some of the most robust gene candidates, being PRMT7 our best candidate. Here, we show that its inhibition or depletion significantly reduces the invasion, migration, and viability of mPCa cell lines in vitro and in vivo. Furthermore, at patient level we saw that PRMT7 upregulated levels correlate with a poor survival and progression of the PCa disease. Finally, our transcriptomic analysis indicated that PRMT7 mediates mPCa onset by methylating several TFs, such as FoxK1, NR1H2 or NCOA2/3 which could consequently alter the expression of certain laminins, integrins and cadherin molecules reducing adhesion and promoting cell movement. Taken together, these results highlight PRMT7 as a promising prognosis PCa biomarker and a potential new therapeutic target for the treatment of PCa metastasis. MATERIALS AND METHODS Cell culture and reagents PC3 (CRL-1435) and DU145 (HTB-81) cell lines were obtained from ATCC and cultured in RPMI/HAM’S F12 and RPMI medium supplemented with 10% FBS, respectively. HEK293T (CRL-3216) cells obtained from ATCC, were cultured in DMEM medium supplemented with 10% FBS and were used for lentiviral production. For experiments with SGC3027 inhibitor (Sigma-Aldrich) cells were either pre-teated (invasion) or cultured (viability) with 10 µM of it. CRISPR/Cas9 library preparation The lentiCRISPR v2 GeCKO Human library (Addgene, #52961) was amplified following Sanjana et al., protocol( 16 ). For viral production, see Supplementary Material Methods. PC3-Cas9 and DU145-Cas9 cells were transduced with lentivirus containing pre-designed sgRNAs in the presence of 8 µg/mL polybrene. 48h later, puromycin selection (10 µg/mL) was performed for 5 days. Next, an invasion assay in Matrigel-coated Boyden chambers was conducted separately for both cell lines. MAGeCK screening analysis DNA from the cell population in the upper chamber of the Matrigel-coated Boyden Chamber membrane and the cell population at the bottom of the membrane, was isolated separately. Specific primers designed to amplify all library sgRNAs are included in Supplementary Table 1. Subsequently, next generation sequencing (NGS) and analysis of raw data were conducted at the CNB-CSIC Bioinformatics for Genomics and Proteomics Unit (Madrid, Spain) using the MAGeCK algorithm. Genes with less than 2 sgRNA detected were discarded for further validation. Gene silencing by small interfering RNA Following Lipofectamine RNAiMAX (Invitrogen) manufacturer’s protocol, 2.5 × 10 5 cells were transfected with 50 nM Dharmacon predesigned siRNA (Supplementary Table 1). After 48h, cells were used for invasion or immunobloting assays. PRMT7 CRISPR/Cas9 knock-out production Lenti-sg PRMT7 and lenti-sgControl viruses were produced as previously described( 12 ), using an specific sg RNA for PRMT7 (forward 5’- CACCG AAGGCCTTGGTTCTCGACAT-3’ and reverse 5’- AAA CATGTCGAGAACCAAGGCCTTC-3’) and non-human target sequence for the control (forward 5’- CACCG CGGCTGAGGCACCTGGTTTA-3’ and reverse 5’- AAA CTAAACCAGGTGCCTCAGCCG-3’). PC3-Cas9 and DU145-Cas9 previously engineered in the laboratory, were infected with lentivirus containing sgPRMT7 or sgCTL in the presence of polybrene (8 µg/mL). After 48 days, puromycin selection (10 µg/mL) was performed for 5 days. Single-cell dilutions were performed in 96 multi-well plates to obtain individual clones. PRMT7 knock-out was confirmed by western blot using anti-PRMT7 antibody and by Sanger sequencing (data not shown). Protein extraction and immunoblotting Cells were lysed and western blot protocol was carried out as previously described( 42 ). The primary antibodies used were β-Actin (sc-47778, SCB), PRMT7 (#14762S, CST), mono-methyl arginine (#8015S, CST), PRMT5 (sc-376937, SCB), ITGα1 (sc-271034, SCB), and ITGβ4 (sc-9090, SCB). The secondary antibodies used were anti-mouse IgG (NA931, Cytiva) or anti-rabbit IgG (NA934, Cytiva). Invasion, migration, viability and cell adhesion assays Medium supplemented with 5% of FBS was placed in the lower chamber and used as chemoattractant in both experiments. Invasion was assessed in Matrigel (Corning) ‎coated transwells (30µg/transwell) seeding 7.5 x 10 4 cells in serum free medium and serum-deprived 2.5 x 10 4 cells were seeded in a 48-Well Micro Chemotaxis Chamber (NeuroProbe) migration. After 24h, invading cells were fixed with 4% paraformaldehyde and stained with 0.2% crystal violet or fixed and stained using the Kwik Diff kit (Epredia), respectively. Images were taken with an Eclipse TE300 Nikon microscope and quantified using ImageJ software. Cell adhesion was performed as previously described( 12 ), using pre-covered wells with laminin or collagen IV (5µg/cm 2 ). Cell viability was assessed using the CellTiter 96 Aqueous One Solution Cell Proliferation Assay Kit (Promega) following the manufacturer’s protocol, as previously described( 12 ). Metastasis assays in chicken embryos Experiments were performed as described previously( 23 ). Briefly, 1x10 6 PC3-Cas9 sgCTL or PC3-Cas9 PRMT7 KO1 and KO2 were inoculated into the chorioallantoic membranes (CAMs) of embryonic day 10 (E10) chicken embryos. On day 7 (E17) after inoculation, primary tumors and bone marrow of the embryos were isolated. Primary tumors were sized and weighted, and DNA was extracted from the bone marrow using XNAT2-1KT kit (Sigma Aldrich). The presence of human Alu sequences in the DNA of chicken bone marrow was analyzed by qPCR as described previously. Primer sequences are listed in Supplementary Table 1. Tumor growth and bone metastasis in nude mice Mice studies were carried out in strict compliance with the European Community Council Directive (2010/63/EU) and following guidelines for animal research from the Complutense University of Madrid (UCM) Ethical Committee, approved by the Community of Madrid (Spain) with reference (PROEX 127.0/21). Bone metastasis experiments were performed as previously described( 24 ). Briefly, PC3 cell lines, both control and PRMT7 knock-out, were infected with GFP-Luciferase Lentivirus and sorted. We injected 10 5 cells into the left cardiac ventricle of male NOD SCID J mice (Charles River Laboratories). The appearance of mouse metastasis was monitored by IVIS (IVIS Lumina III, Perkin Elmer) after injecting the animals with luciferine (150 mg/kg, Biotherma). For routine histological analysis, bones were fixed in 10% buffered formalin (Sigma Aldrich), decalcified in 0.5 mol/L EDTA for 7 days, incubated in 30% sucrose, and embedded in paraffin. 2µm paraffin sections were subjected to immunohistochemical analysis using an antibody against GFP (#2956, CST). The tissue slides were scanned using an AxioScan Z1 scanner (Zeiss). Digital images were analyzed, and the number of micrometastases was determined in one or two slides from four randomly chosen mice per group. Microscopy and software calibration for size measurements were conducted using a TS-M2 stage micrometer (Oplenic Optronics). RNA isolation and sequencing Total RNA from three replicates of PRMT7 -KO2 and control PC3 cells was extracted using the NucleoSpin RNA kit (Macherey-Nagel). mRNA quality control checks and NGS for RNA-seq analysis were performed at the NIMgenetics facility (Madrid, Spain). To validate the RNA-seq results, reverse transcription (RT) was performed using Superscript IV Reverse Transcriptase (ThermoFisher) following manufacturer’s protocol. Triplicate samples with their corresponding controls were assessed by qPCR, performed at the UCM Genetic and Genomic Facility. Gene fold changes were determined using the 2- ΔΔ Ct algorithm. All the primer sequences are listed in Supplementary Table 1. GAPDH was used as gene expression normalizer. RNA-Seq data processing and analysis RNA-Seq data in FASTQ format were mapped against the reference human genome GRCh38.p13 using STAR v2.7.9a( 43 ) and GENCODE V38 annotation. Gene-level abundances were counted using HTSeq( 44 ). Downstream analyses were performed using R v4.1.2( 45 ). Genes accounting for less than 15 read counts were removed from across all samples for further experiments. Differential expression analysis was carried out using DESeq2 v1.34.0( 44 ) with the design formula ~ Condition (factor levels, sg PRMT7 -KO, sgCTL). Genes were considered differentially expressed (DEG) using a 5% FDR and an absolute log2 Fold Change > 1 as thresholds. Gene ontology (GO) overrepresentation analysis of biological process terms was performed using clusterProfiler v4.4.2( 46 ) and org.Hs.eg.db v3.14.0( 47 ). Redundant GO terms were excluded for graphical representation using the rrvgo v1.6.0( 48 ). TF enrichments and activity analysis Transcription factor (TF) enrichment was calculated within the promoters of DEGs using ReMapEnrich v0.99.0( 49 ) and promoter regions, established at 2500 upstream and 500 downstream base pairs of the TSS using the GenomicRanges v1.46.1( 50 ). The regulatory activities of TFs were estimated from gene expression data by applying the Weighted Mean method of TF-targets of regulons (confidence levels A-C) provided by decoupleR v2.0.1( 51 ). TF activities per sample were summarized into a statistic corresponding to a normalized weighted mean, and the 50 most variable TFs across samples were selected by their standard deviation for subsequent analyses. Regulatory network of TFs modulated by PRMTs Both experimental and public datasets of TFs with methylation sites regulated by PRMTs and their target genes were handled to outline reported interactions underlying cell-adhesion processes. Proteins with methylation sites regulated by PRMTs were collected from the processed methylomes of PRMT4, PRMT5 and PRMT7( 30 ). Among all the collected PRMTs proteins, only those listed in top 50 TFs with highest variation in estimated activities described above were selected for network building. Additionally, TFs were classified according to the occurrence of arginine methylation sites in their amino acid sequence using public data from dbPTM database ( https://awi.cuhk.edu.cn/dbPTM ). Regulatory interactions between the selected TFs and their target genes were established according to the occurrence of TF binding sites within the promoter of genes annotated to cell adhesion from ChIP-Seq data of ReMap2022 catalog( 49 ). Once all the interactions were retrieved (PRMT – TF – adhesion gene), the gene network was built and represented using Cytoscape v3.9.1( 52 ). Statistical analysis The results are expressed as the mean value ± SEM of 1–9 independent experiments. Statistical analyses were performed using ordinary t-Student tests, Mann-Whitney, one-way ANOVA or two-way ANOVA multiple comparisons test depending on the experiments ( p value < 0.05 was considered as significant). GraphPad Prism version 8.4.2 for MacOS X, GraphPad Software, (San Diego, California USA, www.graphpad.com ) and R statistical environment( 45 ) were used to represent results. GO enrichment of CRISPR/Cas9 library screenings was performed using Metascape( 53 ), GSEA, and Human Molecular Signatures Database C5 as background( 54 ). Venn diagrams were computed using Venny 2.1.0( 55 ). Study approval Publicly available transcriptomic data of TCGA prostate adenocarcinoma (PRAD) patients was downloaded from UALCAN ( https://ualcan.path.uab.edu/ ) ( 56 , 57 ). In the case of validation cohort, slides of FFPE-embedded primary tumor PCa samples were obtained from the Hospital Clínico San Carlos Hospital Biobank (Madrid, Spain) in accordance with protocols approved by the Institutional Review Board. Surgical resection of the primary tumor (PT) from patients with or without de novo metastatic disease were recruited by oncologists Dr. Puente and Dr. Vidal and analyzed by the anatomical pathology unit from Clínico San Carlos Hospital (Madrid, Spain). RNA was extracted using a High Pure FFPE RNA Micro Kit (Roche) following manufacturer’s instructions. RNA samples with less than 0,01 µg/µL of RNA and less than 0,1 of 260/230 absorbance ratio, were excluded from further analysis. Levels of PRMT7 gene expression were assessed by RT-qPCR using specific primers listed in Supplementary Table 1 at Genetic and Genomic facility of UCM. Gene fold changes were determined by the 2 −ΔΔ Ct method. GAPDH was used reference to normalize gene expression. Declarations Author contributions: Conceptualization: AGU, PB, MRF Methodology: MRF, AVN, NV, JP, MSP, ALG, MME, NP, CB, AMC, HQ, HH, MM, ARP Investigation: MRF, AVN, ALG, MM, HQ, ARP, PB, AGU Visualization: MRF, AVN, MME, HQ, ARP, AGU Funding acquisition: AGU, PB, AP, ARP, MM Project administration: AGU Supervision: AGU, PB Writing – original draft: MRF, AGU, PB Writing – review and editing: MRF, AVN, MM, ARP, AP, PB, and AGU Acknowledgments: We would like to acknowledge the computing resources and technical support provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga in the Red Española de Supercomputación (RES). Funding: This work was funded by Comunidad de Madrid under projects “genome-scale screening to identify and validate novel genes essential for prostate cancer metastasis” 2017-T1/BMD-5468 and 2021-5A/BMD-20956. Spanish Government Grants (PID2020-117650RA-I00 (AGU), PID2019-104143RB-C22 (AP) and PID2019-104991RB-I00 (PB)). ARP and AVN have been supported and granted by the Regional Programme of Research and Technological Innovation for Young Doctors UCM-CAM (PR65/19-22460). MM has been granted by the Spanish Government grants PID2021-122797OB-I00. Conflicts of interest/Competing interests: The authors declare no conflicts of interest. Competing interests: Authors declare that they have no competing interests. Data and material availability: Main data are available in manuscript, figures, and supplementary materials. Raw data from CRISPR/Cas9 and RNA sequencing are available under request at ENA repository (CRISPR screenings PRJEB64413, RNA-seq PRJEB64414). References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. Beer TM, Armstrong AJ, Rathkopf DE, Loriot Y, Sternberg CN, Higano CS, et al. 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Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryTable6.xlsx Supplemntaryinformationoncogene.pdf 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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A, \u003c/strong\u003eGraphical scheme of the experimental design of CRISPR/Cas9 screenings\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003eB, \u003c/strong\u003eVolcano plot showing PC3 CRISPR/Cas9 screening results. \u003cstrong\u003eC, \u003c/strong\u003eVolcano plot showing DU145 CRISPR/Cas9 screening results.\u003cstrong\u003eD, \u003c/strong\u003eVenn diagram showing the number of genes significantly associated with PCa invasive process in each line and the number of common genes between both screenings.\u003c/p\u003e","description":"","filename":"1PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/09812fb79e0e02c966c906c8.png"},{"id":43306722,"identity":"a7ef4190-86d9-4619-b5e0-3b7311ca25eb","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological pathway enrichment analyses of PC3 and DU145 screening results. A-B, \u003c/strong\u003eGene ontology biological pathway enrichment analysis (GO:BP) using Metascape of (53) \u003cstrong\u003e(A)\u003c/strong\u003e PC3 and \u003cstrong\u003e(B)\u003c/strong\u003e DU145 results. \u003cstrong\u003eC-D,\u003c/strong\u003e Biological pathways GSEA in \u003cstrong\u003e(C) \u003c/strong\u003ePC3 and\u003cstrong\u003e (D) \u003c/strong\u003eDU145 screening results.\u003c/p\u003e","description":"","filename":"2PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/7baaecce00ae79952e16c3a4.png"},{"id":43312289,"identity":"090a0baf-6621-480d-814f-d6198b3a38d8","added_by":"auto","created_at":"2023-09-18 16:36:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":214741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of top hits by siRNA technology and further \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePRMT7\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e role in mPCa study by CRISPR/Cas9.\u003c/strong\u003e \u003cstrong\u003eA,\u003c/strong\u003eInvasion assay of PC3 inhibited cells using specific siRNA to target our best gene candidates versus control (siCTL) cells.\u003cstrong\u003e B-C, \u003c/strong\u003eRepresentative western blot of PRMT7, PRMT5 and b-actin protein levels in \u003cstrong\u003e(B)\u003c/strong\u003e PC3-Cas9 and \u003cstrong\u003e(C)\u003c/strong\u003e DU145-Cas9 cell lines. The numbers below each lane represent PRMT7/b-actin or PRMT5/b-actin respectively densitometric quantification is referred to control cells. (n=3). \u003cstrong\u003eD-E,\u003c/strong\u003e Invasion assay of \u003cem\u003ePRMT7\u003c/em\u003e depleted versus control (CTL) cells of \u003cstrong\u003e(D) \u003c/strong\u003ePC3-Cas9 and\u003cstrong\u003e(E) \u003c/strong\u003eDU145-Cas9 cells\u003cstrong\u003e \u003c/strong\u003e(mean ± SEM of n=3 biological replicates, by unpaired Student’s t test *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001). \u003cstrong\u003eF-G, \u003c/strong\u003eMigration assay of \u003cem\u003ePRMT7\u003c/em\u003e depleted versus CTL cells of \u003cstrong\u003e(F) \u003c/strong\u003ePC3-Cas9 and \u003cstrong\u003e(G)\u003c/strong\u003e DU145-Cas9 cells (mean ± SEM of n=3 biological replicates, by unpaired Student’s t test *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eH-I, \u003c/strong\u003eViability assay of \u003cem\u003ePRMT7\u003c/em\u003e depleted versus CTL cells of\u003cstrong\u003e (H) \u003c/strong\u003ePC3-Cas9 and\u003cstrong\u003e (I) \u003c/strong\u003eDU145-Cas9 cells (mean ± SEM of n=9 biological replicates, by\u003cstrong\u003e \u003c/strong\u003eTWO-way ANOVA *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"3PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/cc0e278e7cb2798a9c638b91.png"},{"id":43306727,"identity":"4a6ae7a3-346a-42b5-84d1-c704e32da28b","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":323744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePRMT7\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e depletion reduces proliferative and disseminative abilities of PCa cells \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein ovo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eA, \u003c/strong\u003eGraphical scheme of \u003cem\u003ein ovo\u003c/em\u003estudies experimental design. \u003cstrong\u003eB-C, \u003c/strong\u003eGraphs representing primary tumor \u003cstrong\u003e(B) \u003c/strong\u003esize and \u003cstrong\u003e(C) \u003c/strong\u003eweight (mean ± SEM of n=6-8 inoculated chicken embryos, by ordinary ONE-way ANOVA *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eD, \u003c/strong\u003eGraph\u003cstrong\u003e \u003c/strong\u003erepresenting percentage of human Alu sequences amplified by qPCR in chicken bone marrow (mean ± SEM of n=3, Student’s t test). \u003cstrong\u003eE,\u003c/strong\u003e Graphical scheme of \u003cem\u003ein vivo\u003c/em\u003e dissemination assay experimental design\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003eF,\u003c/strong\u003e Left panel shows representative pictures of metastases generated by CTL, \u003cem\u003ePRMT7\u003c/em\u003e-KO cells. Right panel shows the percentage of mice with distant metastasis (n=12). \u003cstrong\u003eG\u003c/strong\u003e, Left panel shows representative immunohistochemistry images of GFP-positive cells in the muse bones. Right panels show the percentage of disseminated tumor cells (DTC) in mouse bone marrow (mean ± SEM of n=9 animals, by Student’s t test).\u003c/p\u003e","description":"","filename":"4PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/30633ff8de0ef00a222365a5.png"},{"id":43306725,"identity":"876ac3bb-8025-4262-8229-5a9fc6748256","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":201874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePRMT7\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e depletion leads to an adhesion molecule switch in PC3 cells. A,\u003c/strong\u003e Volcano plot showing differentially expressed genes (DEGs) (adjusted p-value \u0026lt; 0.05, |logFC| \u0026gt; 1) from the differential gene expression analysis of PC3 control versus \u003cem\u003ePRMT7\u003c/em\u003e depleted cells results, and a barplot representing the number of genes that significantly changed their expression, accounting for the number of genes that were upregulated (red) and downregulated (blue) in \u003cem\u003ePRMT7\u003c/em\u003e depleted cells. \u003cstrong\u003eB,\u003c/strong\u003e Over-representation analysis of biological process GO terms in DEGs (adjusted p-value \u0026lt; 0.05, |logFC| \u0026gt; 1) of \u003cem\u003ePRMT7\u003c/em\u003e depleted cells compared to control cells, representing top 25 terms with highest gene ratio with an adjusted p-value cutoff of 0.05 and redundant GO terms were removed. \u003cstrong\u003eC, \u003c/strong\u003eHeatmap showing the gene expression (Z-score) of the genes found in parental GO term cell adhesion sorted by log\u003csub\u003e2\u003c/sub\u003e fold change. Horizontal lines denote DEG positions, and those of interest are highlighted in red. \u003cstrong\u003eD-E,\u003c/strong\u003e Western blot analysis of ITGa1 and ITGb4 protein levels in \u003cstrong\u003e(D) \u003c/strong\u003ePC3-Cas9\u003cstrong\u003e \u003c/strong\u003eand \u003cstrong\u003e(E) \u003c/strong\u003eDU145-Cas9 cells. The numbers below each lane represent ITGa1/b-actin or ITGb4/b-actin densitometric quantification referred to control cells, respectively. \u003cstrong\u003eF-G,\u003c/strong\u003e Graph representing\u003cstrong\u003e \u003c/strong\u003emean number of cells per field adhered to\u003cstrong\u003e (F) \u003c/strong\u003ecollagen IV or\u003cstrong\u003e (G) \u003c/strong\u003elaminin (mean ± SEM of n=3, by unpaired Student’s t test *P\u0026lt;0.05, **P\u0026gt;0.01).\u003c/p\u003e","description":"","filename":"5PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/796ed4980897dd9192459e6f.png"},{"id":43306731,"identity":"3780cc53-2911-4999-9918-fcafa30621e2","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":147540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRMT7 drives cell adhesion molecule reprogramming through several TF methylation.\u003c/strong\u003e \u003cstrong\u003eA, \u003c/strong\u003eTop 50 most significant enriched TFs in the promoter regions of differentially expressed genes (DEGs). \u003cstrong\u003eB,\u003c/strong\u003e Heatmap showing TF activity estimated from TF regulons expression, representing the top 50 most variable TFs across CTL and \u003cem\u003ePRMT7\u003c/em\u003e depleted cell samples. \u003cstrong\u003eC,\u003c/strong\u003e Venn diagram of the target proteins of PRMT7, PRMT4 and PRMT5 described in Li \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e36\u003c/sup\u003e. \u003cstrong\u003eD, \u003c/strong\u003eRegulatory network of transcription factors (purple hexagons) that are methylated by PRMT7, PRMT4 and PRMT5 (green squares), and can bind to cell adhesion gene promoters (circles).\u003c/p\u003e","description":"","filename":"6PRMT7oncogene.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/ad3f34294cacffe6740d4c2f.png"},{"id":43308355,"identity":"f01c24a9-61e1-4206-9e46-e421097a0238","added_by":"auto","created_at":"2023-09-18 16:20:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":172546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRMT7 clinical approach.\u003c/strong\u003e \u003cstrong\u003eA,\u003c/strong\u003e Kaplan–Meier curves showing the difference in overall survival between patients with high and low expression levels of \u003cem\u003ePRMT7\u003c/em\u003e (Source: TCGA-PRAD UALCAN). \u003cstrong\u003eB,\u003c/strong\u003eHematoxylin and eosin staining of PCa primary tumor slides showing the aggressiveness of tumors measured by Gleason score \u003cem\u003ePRMT7\u003c/em\u003e (scale bars: 50 mM). \u003cstrong\u003eC,\u003c/strong\u003e\u003cem\u003e PRMT7\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003egene expression levels in PCa primary tumor samples with higher (n=8) versus lower (n=5) Gleason score samples (mean ± SEM of n=13 primary tumor samples, by unpaired Mann-Whitney test *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eD-E,\u003c/strong\u003eRepresentative western blot of arginine monomethylation (MMA) levels in \u003cstrong\u003e(D) \u003c/strong\u003ePC3 and \u003cstrong\u003e(E) \u003c/strong\u003eDU145 cells treated with 10mM of SGC3027 (PRMT7 inhibitor). The numbers below each lane represent MMA/b-actin densitometric quantification referred to control cells.\u003cstrong\u003e F-G, \u003c/strong\u003eInvasion assay of PRMT7 inhibited versus vehicle cells of \u003cstrong\u003e(F)\u003c/strong\u003ePC3 and\u003cstrong\u003e (G) \u003c/strong\u003eDU145 cells (mean ± SEM of n=6 biological replicates, by unpaired Student’s t test *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003cstrong\u003e H-I, \u003c/strong\u003eCell viability assay of PRMT7 inhibited versus vehicle cells of\u003cstrong\u003e (H) \u003c/strong\u003ePC3 and \u003cstrong\u003e(I)\u003c/strong\u003e DU145 cell lines (mean ± SEM of n=9 biological replicates, by TWO-way ANOVA *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"7PRMT7Oncogenesurvival.png","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/983f1b06d07b7be4926be8da.png"},{"id":45268205,"identity":"7245732f-f71e-46ac-891b-4039680e5d28","added_by":"auto","created_at":"2023-10-26 14:25:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1959263,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/edf0917f-2b3e-446c-9102-b45aa0e288be.pdf"},{"id":43308352,"identity":"b09d12de-8472-46dc-9e8a-ca8e44744bc9","added_by":"auto","created_at":"2023-09-18 16:20:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/3c807440f6da5d00e59c147c.xlsx"},{"id":43308356,"identity":"b86829ef-1b8d-4c70-a626-83e2914a765f","added_by":"auto","created_at":"2023-09-18 16:20:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4119619,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/341d41f2ddd91bb0b141085a.xlsx"},{"id":43308357,"identity":"b741d968-659e-4003-a635-3a1ddd904703","added_by":"auto","created_at":"2023-09-18 16:20:40","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4373269,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/a66abe40a25c76ba117ad040.xlsx"},{"id":43306726,"identity":"21bb4d51-3154-40ab-93ef-1542065e8d51","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10723,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/36e2e1125731dfbb3b89e8c0.xlsx"},{"id":43306728,"identity":"00b91f70-3fd8-48f1-8657-cb29b8fceb49","added_by":"auto","created_at":"2023-09-18 16:12:39","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":576441,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/fddcdf08bf89b56ba8339e4a.xlsx"},{"id":43306736,"identity":"fc798508-2fd2-4eea-b142-26d31f622f1d","added_by":"auto","created_at":"2023-09-18 16:12:40","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":5404471,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/1b8de306de857f4d19e89fd1.xlsx"},{"id":43306733,"identity":"c05fa67e-0162-4267-8aa0-ea7bc0a4d1c9","added_by":"auto","created_at":"2023-09-18 16:12:40","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1981510,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemntaryinformationoncogene.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3316991/v1/17bf8f116cd7d82814b5b39f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel driver of metastasis in prostate cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePCa remains the second most frequent cancer and the fifth leading cause of cancer-related death among men(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Owing to on-going improvements in tumor diagnostic technologies and treatments, patients who remain in a non-disseminated PCa stage of the disease have a good outcome. In contrast, the mean survival of patients with PCa who develop distant metastases is less than 3 years(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These patients are commonly administered androgen-castration hormonal therapy along with conventional chemotherapy(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), however, current available treatments are not effective enough to manage metastatic PCa (mPCa) patients. Hence, there is an urgent need to find new targets and biomarkers to establish effective therapies and improve mPCa patient survival.\u003c/p\u003e \u003cp\u003eDespite the alarming number of deaths from metastatic cancer each year, the occurrence of metastasis remains poorly understood. Some biological changes have been described as crucial in promoting localized primary tumor progression to a secondary foci dissemination stage. They involve primary tumor cell adhesion loss, degradation of the basement membrane, intravasation to the circulatory system, survival in the bloodstream, extravasation into distant organs, and generation of a new tumor bulk(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For some types of cancer, primary tumor cells have a preferred niche for colonization, as highlighted by the Stephan Paget \u0026ldquo;seed-and-soil\u0026rdquo; hypothesis(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This is the case for PCa tumor cells, in which approximately 70% of metastases are found in the bone marrow(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetastatic tumor cells display high levels of genomic instability and harbor several epigenetic alterations that may empower them with the ability to disseminate and generate a new tumor burden. While the exact mPCa gene signature remains unknown, mPCa cancer cells frequently bear alterations in androgen receptor signaling(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), mutations in \u003cem\u003eTP53\u003c/em\u003e and RB1 loss(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), \u003cem\u003ePTEN\u003c/em\u003e loss, overactivated Akt signaling, \u003cem\u003eETS\u003c/em\u003e gene rearrangements, and deleterious mutations in some DNA-repair genes such as \u003cem\u003eBRCA2\u003c/em\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, some studies have also highlighted the importance of epigenetic factors such as \u003cem\u003eEZH2\u003c/em\u003e, which is a poor prognosis biomarker of PCa (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Nonetheless, much remains to be investigated to fully understand the mechanisms that enhance the migratory and invasive abilities of PCa tumor cells.\u003c/p\u003e \u003cp\u003eSome single-gene studies have unearthed mPCa regulators such as WNT5A(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), MAP4K4(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and PPP1CA(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, given the complexity of the biological mechanisms implicated in metastasis onset, high-throughput screening assays seem more optimal for uncovering the most relevant regulators of this process. In this context, CRISPR/Cas9 screening methods are becoming increasingly powerful. \u003cem\u003eIn vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e CRISPR screenings have been successfully performed during the last years in different cancer models, including leukemia and lung cancer(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Therefore, the aim of this study was to conduct high-throughput \u003cem\u003ein vitro\u003c/em\u003e screening assays using the human GeCKO CRISPR/Cas9 library(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), to identify the most important genes and biological processes involved in metastasis onset in PCa patients.\u003c/p\u003e \u003cp\u003eBriefly, protein arginine methyltransferase 7 (\u003cem\u003ePRMT7\u003c/em\u003e) was identified as an essential gene in mPCa. Further investigation revealed that PRMT7 induces a switch in the expression of cellular adhesion molecules, at least through FoxK1, NR1H2 and/or NCOA2/3 transcription factor methylation. Moreover, mPCa cells genetically engineered to abolish \u003cem\u003ePRMT7\u003c/em\u003e expression showed reduced metastatic abilities \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Furthermore, \u003cem\u003ePRMT7\u003c/em\u003e was overexpressed in a cohort of Spanish primary tumor samples with higher compared to lower Gleason scores (GS) samples. Therefore, our data support the role of \u003cem\u003ePRMT7\u003c/em\u003e in mPCa and its potential application as a novel therapeutic target.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCRISPR/Cas9 screenings reveal several genes and biological processes essential for mPCa onset\u003c/h2\u003e \u003cp\u003eSince the progression to metastatic cells in PCa is a complex process, we conducted two unbiased high-throughput CRISPR/Cas9 screenings to identify the most relevant regulators of metastasis onset. The GeCKO V2 Human CRISPR knockout pooled library with three pre-designed sgRNA to target almost 20,000 genes seemed the optimal choice(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). First, mPCa PC3 and DU145 previously engineered in the laboratory to express Cas9 protein, were infected with lentivirus carrying the sgRNA CRISPR library (MOI 0.5) and selected with puromycin.\u003c/p\u003e \u003cp\u003eTo identify the genes that confer metastatic abilities to prostate cancer tumor cells, we conducted an invasion assay of PC3 and DU145 infected cells, in a Matrigel-coated Boyden Chamber. DNA from the cell population that had lost their invasive abilities, which remained on top of the Matrigel membrane, and DNA from the cells that retained them, at the bottom of the membrane, were isolated separately. Subsequently, sgRNAs were amplified by PCR, sequenced, and analyzed using the MAGeCK algorithm(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The results of PC3 and DU145 CRISPR/Cas9 screening analyses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C and Supplementary Tables\u0026nbsp;2\u0026ndash;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the total of 20,000 genes analyzed, the depletion of 990 genes in PC3 and 884 genes in DU145 cells, significantly reduced their invasive capacities. Remarkably, 27 of them were common for both cell lines, suggesting that they could be regulators of the initial steps of mPCa onset, independently of the secondary foci colonized by tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD; Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eFurthermore, multiple gene ontology (GO) biological pathway enrichment were conducted to explore whether any biological pathway was enriched in the CRISPR screening results. GO enrichment analyses of PC3 screening revealed that the genes whose depletion significantly reduced invasion capacities, were mainly involved in the regulation of metabolism, the immune system, cell locomotion, and cell adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Supplementary Table\u0026nbsp;5A). The same analysis in DU145 screening revealed an enrichment in regulation of proliferation, nucleotide excision repair or the cytoskeleton reorganization pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Supplementary Table\u0026nbsp;5B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, gene set enrichment analysis (GSEA) revealed an enrichment in pathways related to the epithelial-to-mesenchymal transition (EMT), the negative regulation of stem cell proliferation and the regulation of protein polyubiquitination, among others for PC3 cell screening (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Supplementary Table\u0026nbsp;5C). Meanwhile for the DU145 screening, GSEA analysis showed enrichment in the mRNA nuclear processing, the ion homeostasis and negative regulation of Jun kinase, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; Supplementary Table\u0026nbsp;5D). Overall, the biological pathways implicated in mPCa invasion varied between the two cell lines and the algorithm used, over-representation analysis or GSEA, the biological processes varied considerably. Nevertheless, among the significantly enriched GSEA biological pathways of both cell lines, we found four common results, the regulation of calcium ion transport into cytosol, the multicellular organism process, the DNA modification, and the DNA methylation or demethylation. Additional GO enrichment analyses using Reactome and the molecular signature C5 human molecular classifications of Molecular Signatures Database (MSigDB), are shown in Supplementary Fig.\u0026nbsp;1\u0026ndash;2; Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePRMT7\u003c/b\u003e \u003cb\u003eablation reduces metastatic capacities of mPCa cell lines\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo validate our initial screening results, we selected some of our best-hit genes based on their association with other types of cancer or metastasis. We studied the \u003cem\u003ein vitro\u003c/em\u003e ability of PC3 cells to invade in a Matrigel-coated Boyden chamber using fetal bovine serum (FBS) as chemoattractant, after targeting some of our best hits with siRNA. Downregulation of six of the seven candidates selected for validation led to a reduction in the invasive capacity of PC3 cells being significant for \u003cem\u003ePRMT7\u003c/em\u003e, \u003cem\u003eSYCP3\u003c/em\u003e and \u003cem\u003eTECPR1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As \u003cem\u003ePRMT7\u003c/em\u003e was one of the 27 common genes found in the initial screening (Supplementary Table\u0026nbsp;4), we decided to further explore its role in mPCa. Interestingly, PRMT7 is a protein arginine methyltransferase (PRMT) that belongs to a family of other 9 proteins. It is involved in introducing ω-mono-methylation (MMA) marks in several proteins of cells. PRMT7 has been described as an important post-transcriptional regulator in breast (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and non-small cell lung cancer metastasis(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, PRMT7 is annotated in the biological pathway of DNA methylation and demethylation, which was enriched in both cell lines GSEA analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTherefore, the role of \u003cem\u003ePRMT7\u003c/em\u003e in the onset of mPCa was further explored. First, to unequivocally confirm that the effect on invasion was not due to the disruption of an off-target gene, \u003cem\u003ePRMT7\u003c/em\u003e expression was inhibited by siRNA and confirmed that its silencing strongly decreased their invasive properties in both PC3 and DU145 cell lines (Supplementary Fig.\u0026nbsp;3A-D). Additionally, we analyzed the levels of MMA marks, and observed a reduction of them in the \u003cem\u003ePRMT7\u003c/em\u003e depleted cells (Supplementary Fig.\u0026nbsp;4A-B).\u003c/p\u003e \u003cp\u003eNext, stable PC3 and DU145 CRISPR/Cas9 \u003cem\u003ePRMT7\u003c/em\u003e knock-out cell lines were generated using a different sgRNA from pre-designed GeCKO sgRNA library(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C, western blot analyses confirmed \u003cem\u003ePRMT7\u003c/em\u003e depletion in PC3 and DU145 cells without decreasing the expression of \u003cem\u003ePRMT5\u003c/em\u003e, a member of the PRMT family that has been described to be able to methylate several PRMT7 targets(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, invasion capacities of PC3 and DU145 \u003cem\u003ePRMT7\u003c/em\u003e-KO cells were evaluated. As observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E, depleted cells showed significantly reduced invasive abilities compared to control (CTL) cells, validating the initial screening results. Tumor cells that gain invasive capacities can degrade the extracellular matrix (ECM) and migrate through the body to colonize distant organs. Therefore, to discriminate if PRMT7 was only altering the ability to degrade the ECM or it had also a role in cell motility, a migration assay was conducted. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G show that \u003cem\u003ePRMT7\u003c/em\u003e depleted cells had significantly lower ability to migrate than their respective controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G), demonstrating the ability of PRMT7 to influence on both, ECM degradation and cell movement.\u003c/p\u003e \u003cp\u003ePrevious studies have linked \u003cem\u003ePRMT7\u003c/em\u003e and proliferation(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) so we decided to study the effect of \u003cem\u003ePRMT7\u003c/em\u003e depletion on the viability of mPCa cell lines. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-I, a significant decrease in the viability ratio was observed in \u003cem\u003ePRMT7\u003c/em\u003e-KO cells compared to CTL cells at 96h. Altogether, these results indicate that \u003cem\u003ePRMT7\u003c/em\u003e depletion produces a significant reduction in PC3 and DU145 metastatic abilities, particularly in invasion, migration, and viability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003emodel assays show reduced disseminative capacities of\u003c/b\u003e \u003cb\u003ePRMT7\u003c/b\u003e \u003cb\u003edepleted cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePRMT7\u003c/em\u003e has been previously implicated in breast cancer progression \u003cem\u003ein vivo\u003c/em\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Thus, two \u003cem\u003ein vivo\u003c/em\u003e experimental dissemination assays were performed to validate the role of \u003cem\u003ePRMT7\u003c/em\u003e in mPCa onset. First, a chorioallantoic membrane assay (CAM)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) was conducted as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. PC3-Cas9 \u003cem\u003ePRMT7\u003c/em\u003e depleted and CTL cells were inoculated separately into a vessel of a chicken embryo. Seven days later, the primary tumors generated by PC3 cells and chicken bone marrows, were isolated. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, PC3 \u003cem\u003ePRMT7\u003c/em\u003e depleted cells generated significantly smaller (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and lighter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) tumors than control cells. These results are consistent with the decreased viability observed \u003cem\u003ein vitro\u003c/em\u003e. Moreover, since PC3 cells display bone marrow tropism(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), the disseminative properties of tumor cells were assessed by qPCR, using primers against Human Alu sequences present in chicken bone marrow DNA. A lower percentage of Human Alu sequences was detected in chicken embryos inoculated with PC3 \u003cem\u003ePRMT7\u003c/em\u003e depleted cells comparted to embryos inoculated with CTL cells, indicating that \u003cem\u003ePRMT7\u003c/em\u003e depleted cells showed a lower capacity to disseminate from the primary tumor to the bone marrow of chicken embryos than their respective controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have shown that upon direct inoculation of PC3 cells in the blood circulation of immunodeficient mice (NOD-SCID), they were able to generate metastatic foci in distant tissues including the femur, tibia, jaws, and ribs(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Hence, \u003cem\u003ePRMT7\u003c/em\u003e-KO and CTL PC3 cells, engineered to express a luciferase-GFP DNA construct, were inoculated into the left cardiac ventricle of athymic nude mice, as previously described(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Mice were analyzed using an IVIS system and sacrificed after 28 days, and their tissues (including femur, tibiae, and sternum) were harvested, fixed, and analyzed for the presence of micrometastatic foci, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE. Distant metastasis was observed in 50% of mice inoculated with CTL cells compared to 12% or 33% of mice inoculated with \u003cem\u003ePRMT7\u003c/em\u003e-KO1 and \u003cem\u003ePRMT7\u003c/em\u003e-KO2 depleted PC3 cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Supplementary Fig.\u0026nbsp;5A). Unexpectedly, most of the detected macrometastases displayed visceral localization, whereas no bone macrometastases were detected (Supplementary Fig.\u0026nbsp;5B). To analyze the presence of micrometastases of PC3 cells in the bone marrow, GFP immunohistochemistry of bone marrow from the tibia, femur, and sternum was performed. Quantification of GFP\u003csup\u003e+\u003c/sup\u003e foci revealed that \u003cem\u003ePRMT7\u003c/em\u003e depletion in PC3 cells severely reduced the formation of bone marrow micrometastases compared to CTL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). These results highlight the relevance of PRMT7 in controlling biological processes that are directly implicated in PCa metastasis onset both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExpression of relevant cell adhesion molecules is regulated by\u003c/b\u003e \u003cb\u003ePRMT7\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the biological mechanisms controlled by PRMT7 that facilitate distant organ colonization of primary tumor PCa cells, a differential expression analysis of PC3 \u003cem\u003ePRMT7\u003c/em\u003e depleted versus CTL cells, was conducted. Out of the total genes analyzed, the expression of 781 genes was significantly altered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; Supplementary Table\u0026nbsp;6). Among the most differentially expressed genes, we found \u003cem\u003ePNPLA4\u003c/em\u003e, \u003cem\u003eSFMBT2\u003c/em\u003e, and several genes related to cell adhesion such as \u003cem\u003eTUBAC3\u003c/em\u003e and \u003cem\u003eCHST15\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsecutively, a GO over-representation analysis was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), which revealed that the cell-substrate adhesion term was over-represented. Hence, the overall expression of genes annotated to the cell adhesion parental ontology term (GO:0007155) was examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Surprisingly, some genes, such as \u003cem\u003eCDH1\u003c/em\u003e or \u003cem\u003eLAMC3\u003c/em\u003e, were upregulated in \u003cem\u003ePRMT7\u003c/em\u003e depleted cells, while others such as \u003cem\u003eITGA1, ITGA2\u003c/em\u003e and \u003cem\u003eLAMC2\u003c/em\u003e, were downregulated.\u003c/p\u003e \u003cp\u003eTo validate our RNA-seq results, the mRNA levels of some of the genes annotated in the cell adhesion process were analyzed by RT-qPCR in independent PC3 \u003cem\u003ePRMT7\u003c/em\u003e-KO and CTL samples (Supplementary Fig.\u0026nbsp;6). Cell adhesion has been described as crucial in mediating cell motility and colonization of distant organs, with integrin receptors being especially relevant(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Moreover, the overexpression of some integrins has been associated with poor prognosis in several cancers, and depending on the type of integrin expressed on their membranes, cells can adhere better to specific types of extracellular matrixes(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Thus, ITGα1 and ITGβ4 protein levels, which mediate binding to collagen IV or laminin matrices, respectively, were studied.\u003c/p\u003e \u003cp\u003eWestern blot results revealed that ITGα1 protein level was significantly reduced in \u003cem\u003ePRMT7\u003c/em\u003e depleted cells, while ITGβ4 level was significantly increased in comparison their respective controls, in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). Interestingly, overexpression of exogenous \u003cem\u003ePRMT7\u003c/em\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) reduced ITGβ4 levels in both control and \u003cem\u003ePRMT7\u003c/em\u003e depleted cells, which is in agreement with previous results (Supplementary Fig.\u0026nbsp;6). Consecutively, cell adhesion to collagen IV and laminin was evaluated. Overall, PC3 cells, both \u003cem\u003ePRMT7\u003c/em\u003e depleted and control, had a greater ability to adhere to laminin than to collagen IV. However, we observed that \u003cem\u003ePRMT7\u003c/em\u003e depleted cells had a higher adhesion capacity than the control cells. In addition, at longer time points, control cells preferentially adhered to collagen IV, while \u003cem\u003ePRMT7\u003c/em\u003e depleted cells maintained better adhesion to laminin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). These results indicate that depletion of \u003cem\u003ePRMT7\u003c/em\u003e produces a switch in the type of adhesion molecules expressed by tumor cells, leading to a gain in cell adhesion while decreasing cell motility and, consequently dissemination to distant organs is restrained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell adhesion molecule switch is mediated by multiple transcription factors\u003c/h2\u003e \u003cp\u003ePRMT7 is present in the nucleus and cytoplasm, introducing post-transcriptional modifications to several proteins, including transcription factors(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). To decipher whether any transcription factor methylated by PRMT7 was altering the expression of cell adhesion genes, two \u003cem\u003ein silico\u003c/em\u003e approaches were carried out. First, an enrichment analysis of TF binding sites within the promoters of differentially expressed genes in PC3 cells was carried out. It revealed several TF related to Polycomb complex-2, such as EZH2 which was previously associated to PCa progression (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlternatively, TF activities based on the expression values of their target genes was inferred for each sample, selecting the top 50 TFs with the highest variation across samples independent of their experimental treatment. Our results revealed that the most variable TFs showed high concordance with changes in TF activity between \u003cem\u003ePRMT7\u003c/em\u003e depleted and CTL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Surprisingly, out of the 50 TFs identified and according to dbPTM data base, 7 of them had at least an arginine methylation site and intriguingly, 6 of them presented a robust increased activity on PRMT7 depleted samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Curiously, 4 of those 7 TFs (FoxK1, NR1H2, NCOA2 and NCOA3) were PRMT7s targets as described in their recently published PRMT4, PRMT5 and PRMT7 methylomes(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). \u003csup\u003eFigure\u0026nbsp;6\u003c/sup\u003eC summarizes the number of proteins susceptible of being methylated by the three, two or one of the PRMTs. Therefore, our analysis focused on the TFs controlling the expression of cell adhesion-related genes susceptible to PRMT7 methylation. FoxK1, NR1H2, and NCOA2/3 are the only TF whose activity is changed between PRMT7-KO and CTL cells, which are susceptible to being methylated by PRMT7(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), and also are able to bind to the promoter of cell adhesion genes, including the analyzed \u003cem\u003eCDH1\u003c/em\u003e and \u003cem\u003eITGA1/2\u003c/em\u003e, where they could promote or inhibit their expression, as summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and Supplementary Fig.\u0026nbsp;7. Altogether these results suggest that \u003cem\u003ePRMT7\u003c/em\u003e depletion alters the activity of relevant transcription factors of cell adhesion genes such as \u003cem\u003eFoxK1\u003c/em\u003e, \u003cem\u003eNR1H2\u003c/em\u003e, and \u003cem\u003eNCOA2/3\u003c/em\u003e, leading to reprogramming of the type of cell adhesion molecules expressed on the cell surface.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential use of\u003c/b\u003e \u003cb\u003ePRMT7\u003c/b\u003e \u003cb\u003eas a new therapeutic target or biomarker\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further explore the role of PRMT7 in PCa metastasis, TCGA PCa patient data was retrieved and overall survival of patients with high and low expression of \u003cem\u003ePRMT7\u003c/em\u003e, was analyzed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, the survival rate of patients with higher \u003cem\u003ePRMT7\u003c/em\u003e expression was significantly reduced. Additionally, we also explored \u003cem\u003ePRMT7\u003c/em\u003e expression by GS groups in these samples, observing a significant upregulation from GS 6 and above compared to healthy samples (Supplementary Fig.\u0026nbsp;7B). Moreover, Spanish FFPE-embedded PCa primary tumor samples were recruited from Hospital Cl\u0026iacute;nico San Carlos (Madrid, Spain) to explore mRNA \u003cem\u003ePRMT7\u003c/em\u003e levels. Tumor samples were divided in two groups according to their aggressivity and the presence of metastasis in the patient, (Low group\u0026thinsp;\u0026le;\u0026thinsp;6 GS and no metastasis present, High group\u0026thinsp;\u0026ge;\u0026thinsp;7 GS with metastasis present). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes characteristics of patients included in the study, and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB shows representative H\u0026amp;E staining images of tumors of both groups. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, the samples classified as High showed higher \u003cem\u003ePRMT7\u003c/em\u003e levels than Low group. These results suggest that \u003cem\u003ePRMT7\u003c/em\u003e may be a potential biomarker of PCa malignancy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of PCa patient clinical data.\u003c/b\u003e In the table are summarized the mean age, Gleason score or OMS group and the percentage of tumor of FFPE-embedded samples from Hospital Cl\u0026iacute;nico San Carlos.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Gleason Score cohort (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Gleason score cohort (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoth groups combined (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at time of treatment, mean (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (53\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (60\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (53\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason score, mean (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (6\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMS group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of tumor in the sample (%), mean (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7% (0%-20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50% (20%-90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33% (0%-90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBecause some inhibitors of PRMT family are already in clinical trials(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and PRMT7 inhibitors were already available commercially but have not been tested to prevent or cure mPCa, we wanted to study the effect of PRMT7 inhibitors on the metastatic abilities. First, we verified that the inhibitor reduced PRMT7 activity by analyzing the MMA levels in cells, observing a reduction of them in both treated cell lines compared to cells treated with vehicle (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E). The next step was to check whether pharmacological inhibition was sufficient to reduce the invasive capacity. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-G, PRMT7 inhibitor was sufficient to significantly decrease PC3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF) and DU145 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG) invasive abilities compared to control cells. Moreover, we observed a reduction in proliferation and/or viability after treating cells with the inhibitor, mainly at 96h (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH-I). Altogether, these results suggest that pharmacological PRMT7 inhibition could be an effective approach to decrease the metastatic capacity of PCa cells \u003cem\u003ein vitro\u003c/em\u003e and open a field of research in \u003cem\u003ein vivo\u003c/em\u003e and preclinical studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMetastasis is a complex phenomenon involving many biochemical processes that are largely miscomprehended(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), therefore producing effective therapies is highly challenging. However, its elevated incidence highlights the need to study the mechanisms underlying metastasis appearance and to identify new targets for use as prognostic biomarkers and/or therapeutic targets. To our knowledge, our report presents the first two independent large-scale screenings specifically designed to uncover the most relevant gene drivers of mPCa using CRISPR/Cas9 based technology and further validate our best candidate.\u003c/p\u003e \u003cp\u003eOur extended \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e characterization of \u003cem\u003ePRMT7\u003c/em\u003e revealed its critical role in PCa tumor progression, being relevant not only for increasing cell viability but also for enhancing the invasive and migratory ability of cells. However, a reduction in viability upon \u003cem\u003ePRMT7\u003c/em\u003e depletion would not explain the decrease in PC3 and DU145 motility solely, as differences in migration and invasion were measured at 24h, while viability differences were observed at longer time frames. Moreover, we validated these results using PRMT7 pharmacological inhibitors, which could be used to prevent the appearance of mPCa.\u003c/p\u003e \u003cp\u003ePRMT7 is the only member of the arginine methyltransferase family belonging to subgroup III and is involved in the preferential introduction of MMA(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Previous studies reported its role in promoting tumor dissemination in breast, non-small cell lung and renal cell cancers. In the context of breast cancer, PRMT7 has been shown to methylate the E-cadherin proximal promoter inducing EMT transition(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), to upregulate the expression of matrix metalloproteinase-9 (MMP9)(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), and to methylate SHANK2 activating endosomal FAK signaling(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e); which all together lead to the progression of the disease. In non-small cell lung cancer, PRMT7 contributes to metastasis appearance through interaction with HSPA5 and EEF2(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In addition, it has been described that in renal cell carcinoma, PRMT7 can methylate β-catenin promoting its stabilization and the induction of c-myc expression, which is an important regulator of cell proliferation and tumor progression(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Moreover, PRMT7 has been described to methylate the Wnt signaling molecule Dishevelled 3(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), the transcription factor C/EBP-β(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and the RNA splicing factor hnRNPA1(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), suggesting that it, directly or indirectly, could be implicated in several cellular pathways, both in cellular homeostasis and disease. Therefore, due to the high number of biological processes susceptible to PRMT7 methylation, we decided to conduct a differential transcriptomic analysis to elucidate the most important processes in mPCa onset context. As described for breast cancer, the results showed an \u003cem\u003eCDH1\u003c/em\u003e expression in PRMT7 depleted cells. However, we did not detect significant changes in the expression of other genes, such as \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eHSPA5\u003c/em\u003e, \u003cem\u003eEEF2, CTNNB1\u003c/em\u003e, nor \u003cem\u003eMYC\u003c/em\u003e. Remarkably, we observed and enrichment of several cell adhesion biological processes.\u003c/p\u003e \u003cp\u003eCell adhesion is a widely known mechanism that contributes to tumor cell migration and invasion. In fact, changes in the expression of some integrin and laminin receptors, are already considered malignant biomarkers. For example, the alpha 1 subunit of integrin receptors (\u003cem\u003eITGA1)\u003c/em\u003e is a pre-malignant biomarker that promotes therapy resistance and metastatic potential in pancreatic cancer(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), whereas laminin subunit gamma 2 (\u003cem\u003eLAMC2\u003c/em\u003e), has previously been identified as a specific marker for metastatic abilities in lung adenocarcinoma(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Therefore, our functional validation was focused on demonstrating that \u003cem\u003ePRMT7\u003c/em\u003e depletion reprograms the type of cell adhesion molecules expressed by tumor cells, and at least validated \u003cem\u003eITGA1\u003c/em\u003e, \u003cem\u003eITGB4\u003c/em\u003e, \u003cem\u003eLAMC2\u003c/em\u003e, \u003cem\u003eLAMC3\u003c/em\u003e and \u003cem\u003eCDH1.\u003c/em\u003e Additionally, here it is demonstrated that the selective adherence of mPCa cells to different types of extracellular matrices, such as collagen IV and laminin, is altered because of cell adhesion protein switch. Interestingly, bone marrow, where 70% of mPCa is found, is an organ rich in collagen IV. Therefore, an upregulation of PRMT7 levels in PCa cells could enhance adhesion to collagen IV rich bone marrow promoting cell survival and metastasis appearance.\u003c/p\u003e \u003cp\u003eIn this study, TF activity analysis uncovered a new role for PRMT7 as a regulator of several TFs. These results, together with the previously published PRMT7 methylome(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), suggest that either methylation of FoxK1, NR1H2, or NCOA2/3 could regulate their transcriptional activity in mPCa cells. Remarkably, FoxK1, a TF susceptible to methylation by PRMT7 on R161 and R191 residues, is a known regulator of the expression of several adhesion molecules and has previously been involved in the acquisition of mPCa abilities(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In addition, NR1H2 is susceptible of being methylated on R126 and is known to regulate the expression of LAMC2 and CDH1(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Arginine methylation stabilizes E2F1(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e); therefore we speculate that it may have a similar effect on FoxK1, NR1H2, or NCOA2/3. Although further validation is needed, regulation of cellular adhesion molecules via PRMT7-FoxK1/NR1H2/NCOA2/3 may explain the acquisition of migratory abilities shown in PCa cellular model studies.\u003c/p\u003e \u003cp\u003eRegarding the use of \u003cem\u003ePRMT7\u003c/em\u003e as prognostic biomarker or therapeutic target, although validation in larger cohorts of samples is required, results are promising. In fact, some studies have already highlighted PRMTs as an emergent group of proteins involved in cancer and metastasis(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Indeed, PRMT1 and PRMT5 inhibitors are currently under Phase I clinical trials (NCT04676516, NCT03573310, NCT05094336, NCT03854227, NCT04089449, NCT03886831, NCT05275478, NCT04794699, and NCT05245500).\u003c/p\u003e \u003cp\u003eIn summary, we conducted two large-scale CRISPR/Cas9 screenings to identify the most important gene drivers of PCa cell invasion and validated some of the most robust gene candidates, being \u003cem\u003ePRMT7\u003c/em\u003e our best candidate. Here, we show that its inhibition or depletion significantly reduces the invasion, migration, and viability of mPCa cell lines \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo.\u003c/em\u003e Furthermore, at patient level we saw that \u003cem\u003ePRMT7\u003c/em\u003e upregulated levels correlate with a poor survival and progression of the PCa disease. Finally, our transcriptomic analysis indicated that PRMT7 mediates mPCa onset by methylating several TFs, such as FoxK1, NR1H2 or NCOA2/3 which could consequently alter the expression of certain laminins, integrins and cadherin molecules reducing adhesion and promoting cell movement. Taken together, these results highlight \u003cem\u003ePRMT7\u003c/em\u003e as a promising prognosis PCa biomarker and a potential new therapeutic target for the treatment of PCa metastasis.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and reagents\u003c/h2\u003e \u003cp\u003ePC3 (CRL-1435) and DU145 (HTB-81) cell lines were obtained from ATCC and cultured in RPMI/HAM\u0026rsquo;S F12 and RPMI medium supplemented with 10% FBS, respectively. HEK293T (CRL-3216) cells obtained from ATCC, were cultured in DMEM medium supplemented with 10% FBS and were used for lentiviral production. For experiments with SGC3027 inhibitor (Sigma-Aldrich) cells were either pre-teated (invasion) or cultured (viability) with 10 \u0026micro;M of it.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCRISPR/Cas9 library preparation\u003c/h3\u003e\n\u003cp\u003eThe lentiCRISPR v2 GeCKO Human library (Addgene, #52961) was amplified following Sanjana et al., protocol(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For viral production, see Supplementary Material Methods. PC3-Cas9 and DU145-Cas9 cells were transduced with lentivirus containing pre-designed sgRNAs in the presence of 8 \u0026micro;g/mL polybrene. 48h later, puromycin selection (10 \u0026micro;g/mL) was performed for 5 days. Next, an invasion assay in Matrigel-coated Boyden chambers was conducted separately for both cell lines.\u003c/p\u003e\n\u003ch3\u003eMAGeCK screening analysis\u003c/h3\u003e\n\u003cp\u003eDNA from the cell population in the upper chamber of the Matrigel-coated Boyden Chamber membrane and the cell population at the bottom of the membrane, was isolated separately. Specific primers designed to amplify all library sgRNAs are included in Supplementary Table\u0026nbsp;1. Subsequently, next generation sequencing (NGS) and analysis of raw data were conducted at the CNB-CSIC Bioinformatics for Genomics and Proteomics Unit (Madrid, Spain) using the MAGeCK algorithm. Genes with less than 2 sgRNA detected were discarded for further validation.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGene silencing by small interfering RNA\u003c/h2\u003e \u003cp\u003eFollowing Lipofectamine RNAiMAX (Invitrogen) manufacturer\u0026rsquo;s protocol, 2.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells were transfected with 50 nM Dharmacon predesigned siRNA (Supplementary Table\u0026nbsp;1). After 48h, cells were used for invasion or immunobloting assays.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePRMT7 CRISPR/Cas9 knock-out production\u003c/h2\u003e \u003cp\u003eLenti-sg\u003cem\u003ePRMT7\u003c/em\u003e and lenti-sgControl viruses were produced as previously described(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), using an specific sg\u003cem\u003eRNA\u003c/em\u003e for \u003cem\u003ePRMT7\u003c/em\u003e (forward 5\u0026rsquo;-\u003cem\u003eCACCG\u003c/em\u003eAAGGCCTTGGTTCTCGACAT-3\u0026rsquo; and reverse 5\u0026rsquo;- \u003cem\u003eAAA\u003c/em\u003eCATGTCGAGAACCAAGGCCTTC-3\u0026rsquo;) and non-human target sequence for the control (forward 5\u0026rsquo;- \u003cem\u003eCACCG\u003c/em\u003e CGGCTGAGGCACCTGGTTTA-3\u0026rsquo; and reverse 5\u0026rsquo;-\u003cem\u003eAAA\u003c/em\u003eCTAAACCAGGTGCCTCAGCCG-3\u0026rsquo;). PC3-Cas9 and DU145-Cas9 previously engineered in the laboratory, were infected with lentivirus containing \u003cem\u003esgPRMT7\u003c/em\u003e or sgCTL in the presence of polybrene (8 \u0026micro;g/mL). After 48 days, puromycin selection (10 \u0026micro;g/mL) was performed for 5 days. Single-cell dilutions were performed in 96 multi-well plates to obtain individual clones. \u003cem\u003ePRMT7\u003c/em\u003e knock-out was confirmed by western blot using anti-PRMT7 antibody and by Sanger sequencing (data not shown).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction and immunoblotting\u003c/h2\u003e \u003cp\u003eCells were lysed and western blot protocol was carried out as previously described(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The primary antibodies used were β-Actin (sc-47778, SCB), PRMT7 (#14762S, CST), mono-methyl arginine (#8015S, CST), PRMT5 (sc-376937, SCB), ITGα1 (sc-271034, SCB), and ITGβ4 (sc-9090, SCB). The secondary antibodies used were anti-mouse IgG (NA931, Cytiva) or anti-rabbit IgG (NA934, Cytiva).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInvasion, migration, viability and cell adhesion assays\u003c/h2\u003e \u003cp\u003eMedium supplemented with 5% of FBS was placed in the lower chamber and used as chemoattractant in both experiments. Invasion was assessed in Matrigel (Corning) \u0026lrm;coated transwells (30\u0026micro;g/transwell) seeding 7.5 x 10\u003csup\u003e4\u003c/sup\u003e cells in serum free medium and serum-deprived 2.5 x 10\u003csup\u003e4\u003c/sup\u003e cells were seeded in a 48-Well Micro Chemotaxis Chamber (NeuroProbe) migration. After 24h, invading cells were fixed with 4% paraformaldehyde and stained with 0.2% crystal violet or fixed and stained using the Kwik Diff kit (Epredia), respectively. Images were taken with an Eclipse TE300 Nikon microscope and quantified using ImageJ software. Cell adhesion was performed as previously described(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), using pre-covered wells with laminin or collagen IV (5\u0026micro;g/cm\u003csup\u003e2\u003c/sup\u003e). Cell viability was assessed using the CellTiter 96 Aqueous One Solution Cell Proliferation Assay Kit (Promega) following the manufacturer\u0026rsquo;s protocol, as previously described(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMetastasis assays in chicken embryos\u003c/h2\u003e \u003cp\u003eExperiments were performed as described previously(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Briefly, 1x10\u003csup\u003e6\u003c/sup\u003e PC3-Cas9 sgCTL or PC3-Cas9 \u003cem\u003ePRMT7\u003c/em\u003e KO1 and KO2 were inoculated into the chorioallantoic membranes (CAMs) of embryonic day 10 (E10) chicken embryos. On day 7 (E17) after inoculation, primary tumors and bone marrow of the embryos were isolated. Primary tumors were sized and weighted, and DNA was extracted from the bone marrow using XNAT2-1KT kit (Sigma Aldrich). The presence of human Alu sequences in the DNA of chicken bone marrow was analyzed by qPCR as described previously. Primer sequences are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTumor growth and bone metastasis in nude mice\u003c/h2\u003e \u003cp\u003e Mice studies were carried out in strict compliance with the European Community Council Directive (2010/63/EU) and following guidelines for animal research from the Complutense University of Madrid (UCM) Ethical Committee, approved by the Community of Madrid (Spain) with reference (PROEX 127.0/21). Bone metastasis experiments were performed as previously described(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Briefly, PC3 cell lines, both control and PRMT7 knock-out, were infected with GFP-Luciferase Lentivirus and sorted. We injected 10\u003csup\u003e5\u003c/sup\u003e cells into the left cardiac ventricle of male NOD SCID J mice (Charles River Laboratories). The appearance of mouse metastasis was monitored by IVIS (IVIS Lumina III, Perkin Elmer) after injecting the animals with luciferine (150 mg/kg, Biotherma).\u003c/p\u003e \u003cp\u003eFor routine histological analysis, bones were fixed in 10% buffered formalin (Sigma Aldrich), decalcified in 0.5 mol/L EDTA for 7 days, incubated in 30% sucrose, and embedded in paraffin. 2\u0026micro;m paraffin sections were subjected to immunohistochemical analysis using an antibody against GFP (#2956, CST). The tissue slides were scanned using an AxioScan Z1 scanner (Zeiss). Digital images were analyzed, and the number of micrometastases was determined in one or two slides from four randomly chosen mice per group. Microscopy and software calibration for size measurements were conducted using a TS-M2 stage micrometer (Oplenic Optronics).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA from three replicates of \u003cem\u003ePRMT7\u003c/em\u003e-KO2 and control PC3 cells was extracted using the NucleoSpin RNA kit (Macherey-Nagel). mRNA quality control checks and NGS for RNA-seq analysis were performed at the NIMgenetics facility (Madrid, Spain). To validate the RNA-seq results, reverse transcription (RT) was performed using Superscript IV Reverse Transcriptase (ThermoFisher) following manufacturer\u0026rsquo;s protocol. Triplicate samples with their corresponding controls were assessed by qPCR, performed at the UCM Genetic and Genomic Facility. Gene fold changes were determined using the 2-\u003csup\u003eΔΔ\u003c/sup\u003eCt algorithm. All the primer sequences are listed in Supplementary Table\u0026nbsp;1. GAPDH was used as gene expression normalizer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Seq data processing and analysis\u003c/h2\u003e \u003cp\u003eRNA-Seq data in FASTQ format were mapped against the reference human genome GRCh38.p13 using STAR v2.7.9a(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and GENCODE V38 annotation. Gene-level abundances were counted using HTSeq(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Downstream analyses were performed using R v4.1.2(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Genes accounting for less than 15 read counts were removed from across all samples for further experiments. Differential expression analysis was carried out using DESeq2 v1.34.0(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) with the design formula\u0026thinsp;~\u0026thinsp;Condition (factor levels, sg\u003cem\u003ePRMT7\u003c/em\u003e-KO, sgCTL). Genes were considered differentially expressed (DEG) using a 5% FDR and an absolute log2 Fold Change\u0026thinsp;\u0026gt;\u0026thinsp;1 as thresholds. Gene ontology (GO) overrepresentation analysis of biological process terms was performed using clusterProfiler v4.4.2(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) and org.Hs.eg.db v3.14.0(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Redundant GO terms were excluded for graphical representation using the rrvgo v1.6.0(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTF enrichments and activity analysis\u003c/h2\u003e \u003cp\u003eTranscription factor (TF) enrichment was calculated within the promoters of DEGs using ReMapEnrich v0.99.0(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and promoter regions, established at 2500 upstream and 500 downstream base pairs of the TSS using the GenomicRanges v1.46.1(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The regulatory activities of TFs were estimated from gene expression data by applying the Weighted Mean method of TF-targets of regulons (confidence levels A-C) provided by decoupleR v2.0.1(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). TF activities per sample were summarized into a statistic corresponding to a normalized weighted mean, and the 50 most variable TFs across samples were selected by their standard deviation for subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory network of TFs modulated by PRMTs\u003c/h2\u003e \u003cp\u003eBoth experimental and public datasets of TFs with methylation sites regulated by PRMTs and their target genes were handled to outline reported interactions underlying cell-adhesion processes. Proteins with methylation sites regulated by PRMTs were collected from the processed methylomes of PRMT4, PRMT5 and PRMT7(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Among all the collected PRMTs proteins, only those listed in top 50 TFs with highest variation in estimated activities described above were selected for network building. Additionally, TFs were classified according to the occurrence of arginine methylation sites in their amino acid sequence using public data from dbPTM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://awi.cuhk.edu.cn/dbPTM\u003c/span\u003e\u003cspan address=\"https://awi.cuhk.edu.cn/dbPTM\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegulatory interactions between the selected TFs and their target genes were established according to the occurrence of TF binding sites within the promoter of genes annotated to cell adhesion from ChIP-Seq data of ReMap2022 catalog(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Once all the interactions were retrieved (PRMT \u0026ndash; TF \u0026ndash; adhesion gene), the gene network was built and represented using Cytoscape v3.9.1(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe results are expressed as the mean value\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of 1\u0026ndash;9 independent experiments. Statistical analyses were performed using ordinary t-Student tests, Mann-Whitney, one-way ANOVA or two-way ANOVA multiple comparisons test depending on the experiments (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant). GraphPad Prism version 8.4.2 for MacOS X, GraphPad Software, (San Diego, California USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://awi.cuhk.edu.cn/dbPTM\" target=\"_blank\"\u003ewww.graphpad.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R statistical environment(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) were used to represent results. GO enrichment of CRISPR/Cas9 library screenings was performed using Metascape(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), GSEA, and Human Molecular Signatures Database C5 as background(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Venn diagrams were computed using Venny 2.1.0(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStudy approval\u003c/h2\u003e \u003cp\u003ePublicly available transcriptomic data of TCGA prostate adenocarcinoma (PRAD) patients was downloaded from UALCAN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). In the case of validation cohort, slides of FFPE-embedded primary tumor PCa samples were obtained from the Hospital Cl\u0026iacute;nico San Carlos Hospital Biobank (Madrid, Spain) in accordance with protocols approved by the Institutional Review Board. Surgical resection of the primary tumor (PT) from patients with or without de novo metastatic disease were recruited by oncologists Dr. Puente and Dr. Vidal and analyzed by the anatomical pathology unit from Cl\u0026iacute;nico San Carlos Hospital (Madrid, Spain). RNA was extracted using a High Pure FFPE RNA Micro Kit (Roche) following manufacturer\u0026rsquo;s instructions. RNA samples with less than 0,01 \u0026micro;g/\u0026micro;L of RNA and less than 0,1 of 260/230 absorbance ratio, were excluded from further analysis. Levels of \u003cem\u003ePRMT7\u003c/em\u003e gene expression were assessed by RT-qPCR using specific primers listed in Supplementary Table\u0026nbsp;1 at Genetic and Genomic facility of UCM. Gene fold changes were determined by the 2\u003csup\u003e\u0026minus;ΔΔ\u003c/sup\u003eCt method. GAPDH was used reference to normalize gene expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: AGU, PB, MRF\u003c/p\u003e\n\u003cp\u003eMethodology: MRF, AVN, NV, JP, MSP, ALG, MME, NP, CB, AMC, HQ, HH, MM, ARP\u003c/p\u003e\n\u003cp\u003eInvestigation: MRF, AVN, ALG, MM, HQ, ARP, PB, AGU\u003c/p\u003e\n\u003cp\u003eVisualization: MRF, AVN, MME, HQ, ARP, AGU\u003c/p\u003e\n\u003cp\u003eFunding acquisition: AGU, PB, AP, ARP, MM\u003c/p\u003e\n\u003cp\u003eProject administration: AGU\u003c/p\u003e\n\u003cp\u003eSupervision: AGU, PB\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: MRF, AGU, PB\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review and editing: MRF, AVN, MM, ARP, AP, PB, and AGU\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments: We would like to acknowledge the computing resources and technical support provided by the SCBI (Supercomputing and Bioinformatics) center of the University of M\u0026aacute;laga in the Red Espa\u0026ntilde;ola de Supercomputaci\u0026oacute;n (RES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was funded by Comunidad de Madrid under projects \u0026ldquo;genome-scale screening to identify and validate novel genes essential for prostate cancer metastasis\u0026rdquo; 2017-T1/BMD-5468 and 2021-5A/BMD-20956. Spanish Government Grants (PID2020-117650RA-I00 (AGU), PID2019-104143RB-C22 (AP) and PID2019-104991RB-I00 (PB)). ARP and AVN have been supported and granted by the Regional Programme of Research and Technological Innovation for Young Doctors UCM-CAM (PR65/19-22460). MM has been granted by the Spanish Government grants PID2021-122797OB-I00.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e Authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and material availability:\u003c/strong\u003e Main data are available in manuscript, figures, and supplementary materials. 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Nat Commun. 2019;10:1523. \u003c/li\u003e\n\u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eVenny-. Venn Diagrams for comparing lists. By Juan Carlos Oliveros. [Internet]. [cited 2023 Mar 31]. Available from: https://bioinfogp.cnb.csic.es/tools/venny_old/venny.php\u003c/li\u003e\n\u003cli\u003eChandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017;19:649\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eChandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18\u0026ndash;27. \u003c/li\u003e\n\u003c/ol\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":"Prostate cancer, Metastasis, Invasion, PRMT7, adhesion","lastPublishedDoi":"10.21203/rs.3.rs-3316991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3316991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOwing to the inefficacy of available treatments, the survival rate of patients with metastatic prostate cancer (mPCa) is severely decreased. Therefore, it is crucial to identify new therapeutic targets to increase the survival of mPCa patients. This study aim was to identify the most relevant regulators of mPCa onset by performing two high-throughput CRISPR/Cas9 screenings. Furthermore, some of the top hits were validated using small interfering RNA (siRNA) technology, with protein arginine methyltransferase 7 \u003cem\u003e(PRMT7)\u003c/em\u003e being the best candidate. Its inhibition, by genetic and pharmacological approaches, or its depletion, via CRISPR, significantly reduced mPCa cell capacities \u003cem\u003ein vitro\u003c/em\u003e. Furthermore, \u003cem\u003ePRMT7\u003c/em\u003e ablation reduced mPCa appearance in chicken chorioallantoic membrane and mouse xenograft assays. Molecularly, PRMT7 reprograms the expression of several adhesion molecules through methylation of several transcription factors, such as FoxK1 or NR1H2, which results in primary tumor PCa cell adhesion loss and motility gain. Moreover, \u003cem\u003ePRMT7\u003c/em\u003e is upregulated in advanced stages of Spanish PCa tumor samples and PRMT7 pharmacological inhibition reduces the dissemination of mPCa cells. Thus, here is shown that \u003cem\u003ePRMT7\u003c/em\u003e is a potential therapeutic target and biomarker of mPCa.\u003c/p\u003e","manuscriptTitle":"CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel driver of metastasis in prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-18 16:12:34","doi":"10.21203/rs.3.rs-3316991/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":"49beffae-caaa-4614-a155-4c3746dba436","owner":[],"postedDate":"September 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":24591384,"name":"Biological sciences/Cancer/Cancer screening"},{"id":24591385,"name":"Biological sciences/Cancer/Metastasis"},{"id":24591386,"name":"Biological sciences/Cancer/Urological cancer/Prostate cancer"},{"id":24591387,"name":"Biological sciences/Genetics/Cancer genomics"}],"tags":[],"updatedAt":"2023-10-26T14:17:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-18 16:12:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3316991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3316991","identity":"rs-3316991","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}