Metastatic prostate cancer (PCa) poses a significant public health concern. While radiation therapy (RT) is commonly utilized in the treatment of synchronous oligometastatic hormone sensitive prostate cancer (OM-HSPC), the occurrence of biochemical recurrence still remains. To deepen our understanding and optimize the outcome of OM-HSPC, we conducted this study to investigate the characteristics of PCa progression and explore potential synergistic mechanisms involving carbon-ion radiotherapy (CIRT) and neoadjuvant androgen deprivation treatment (naADT) in OM-HSPC.p value < 0.05 and |FC|>1.5). The impact of CIRT on metabolism were further verified and explored through in vitro and in vivo experiments.Metabolomic analysis was conducted with 72 urinary samples (at different timepoints) from 33 Patients (T2-3N0M0-1b) and 18 healthy volunteers by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). MetaboAnalyst website and R software were employed for metabolomic analysis and visualization (using the criteria of We found that most metabolites (223 out of 233) were upregulated in treatment-naïve PCa samples compared to healthy samples. After naADT, 60 core risk metabolites were still significantly related to PCa’s progression, and the glutamine level which was significantly higher in OM-HSPC compared to other groups. Remarkably, after CIRT treatment, the glutamine levels in OM-HSPC were significantly reduced to the level of healthy samples. Experiments further confirmed CIRT’s ability to suppress glutamine levels in PCa tumors and its potential enhancement with glutamine deprivation intervention.CIRT with naADT might synergistically inhibit HS-OMPC development, progression and even the ADT resistance through glutamine metabolism repression, moreover, the glutamine metabolism might be a novel target to further improved the efficacy of CIRT. Approximately 15% of prostate cancer (PCa) cases were metastatic at diagnosis, with a 5-year survival rate of 31%  and 47 mCarbon ion radiotherapy (CIRT) as a relatively new innovation of irradiation has different killing mechanisms and radiobiological effects than photon irradiation. Numerous studies –7 reportin vivo and in vitro experiments to discern the genuine state of key risk metabolites in tumor tissue or PCa cells post-CIRT. Additionally, we deprived the glutamine in media, aiming to further inhibit tumor cells’ invasion and migration competence. Our results shed light on the metabolic mechanisms underpinning the combination of CIRT and naADT and the treatment responses across different PCa stages to naADT. Remarkably, this research unveils, for the first time, the biomarker and target value of glutamine in PCa CIRT.In this study, we analyzed the metabolic profiles of varying stages and statuses of PCa—including healthy individuals, intermediate-risk PCa, high-risk PCa, and OM-HSPC—using untargeted metabolomics to pinpoint risk compounds. Given the challenges in obtaining PCa tissue from patients, we pursued both In this study, 33 patients were involved who had pathologically confirmed prostate adenocarcinoma staged as T2-3N0M0-1b based on the 2008 AJCC/UICC staging classification. Additionally, 18 healthy volunteers, who were within a similar age range as the patients, participated. Consequently, we successfully collected a total of 72 urinary samples. These samples were categorized into four groups:Healthy Group: This consisted of 18 samples, each from a different healthy volunteer.Treatment-Naïve Group: This group comprised 18 samples from 18 distinct patients  was determined based on the ESTRO ACROP consensus . DependiCIRT was carried out using the Siemens IONTRIS particle therapy system. The prescribed dose was determined using the Syngo planning system , taking into account the local effect model (LEM) 1. For intermediate- and high-risk patients, the CIRT dose was 65.6 Gy (RBE) at 4.1Gy(RBE) per daily fraction. For OM-HSPC patients, the CIRT doses was 65.6 Gy (RBE) at 4.1Gy(RBE) per daily fraction to the whole prostate and the positive bone lesion(s) was treated using metastasis-directed therapy (MDT) with 49.2 Gy (RBE) at 4.1Gy(RBE) per daily fraction. 100% of the CTV was prescribed to be covered by 95% isodose line, while 100% of the planning target volume (PTV) was prescribed to be covered by 90% isodose line. In order to ensure accurate treatment delivery, both bladder and rectum preparation were performed before each treatment.Urine samples were collected from the patients within a time frame of 4 hours before the administration of the initial fraction and within 4 hours after the completion of the final fraction. Following collection, the samples were promptly stored at a temperature of 4°C. To eliminate potential bacterial contamination, a 0.22 µm membrane filter was utilized. In each thawed sample, 80 μL of urine was combined with 160 μL of acetonitrile/methanol  in a 1.5 mL tube and placed in an ice bath. After vortexing for a duration of 30 seconds, the mixture underwent further treatment through ultrasonication for 10 minutes while placed in an ice/water bath. Subsequently, the tube was stored at a temperature of -20°C for at least 1 hour. After centrifugation  at 4°C, 200 μL of supernatant was transferred to a sample vial insert (250 μL) at 4°C, awaiting injection into LC-MS. Simultaneously, a pooled quality control (QC) sample was prepared by combining equal volumes (5 µL) of the supernatant from each sample.Ultrapure water was acquired from the Millipore Milli-Q Integral 5 Ultrapure Water Systems . LC-MS grade acetonitrile and methanol were procured from SCRC . Ammonium hydroxide and ammonium formate were purchased from Merck . All standard compounds were obtained from Tansoole , ThermoFisher , Merck, Aladdin , and Macklin .The UPLC system  coupled with a quadruple time-of-flight mass spectrometer  was used to collect the metabolomics data of PBMC samples. Metabolite separation in both positive and negative modes was performed using an ACQUITY UPLC BEH Amide Column, 130 Å, 1.7 µm, 2.1 mm × 100 mm . The water phase (A) was prepared by mixing 25 mmol of ammonium hydroxide and 25 mmol of ammonium formate with 1 L of water, while the organic phase (B) consisted of acetonitrile. The total liquid flow rate was set at 0.3 mL/min, and the gradient elution program was as follows: 0–1 min: 95% B, 1–14 min: 95% B to 65% B, 14–16 min: 65% B to 40% B, 16–18 min: 40% B, 18–18.1 min: 40% B to 95% B, 18.1–23 min: 95% B. The injection volume was 2 μL, and the column oven temperature was maintained at 40 °C.During the analysis queue, all samples were randomly injected. Mass axis calibration, blank sample injection, pooled QC injection, and laboratory mixed standard compound solution injection were performed sequentially every eight sample injections. For electrospray ionization source parameters, ion source gas 1 and ion source gas 2 were set at 55 psi, curtain gas at 35 psi, temperature at 600 °C, and spray voltage at 5500 V or -4500 V in positive or negative modes, respectively. The TOF MS scan parameters included a mass scan range of 50–1000 Da, an accumulation time of 0.2 s, a declustering potential of 60 V or -60 V in positive or negative modes, respectively, and a collision energy of 10 V or -10 V in positive or negative modes, respectively. The acquisition of MS/MS spectra was triggered using the information-dependent acquisition mode with a maximum of ten candidate ions, and dynamic background subtraction was enabled. For TOF MS/MS scan parameters, the mass scan range was set as 25–1000 Da, the accumulation time as 0.05 s, the collision energy as 35 V or -35 V in positive or negative modes, respectively, and the collision energy spread as 15. The default settings were retained for other items.http://metdna.zhulab.cn/). Subsequently, the statistical significance of unknown peaks was further annotated with available reference standards in our laboratory and web-based resources, including the Human Metabolome Database (HMDB) (http://www.hmdb.ca/), Massbank (http://www.massbank.jp), and PubChem (pubchem.ncbi.nlm.nih.gov).The raw data files (.wiff2 and.wiff.scan) were converted to mzXML format and mgf format using ProteoWizard with the Peak Picking filter to centroid the peaks. The relative quantitative matrixes of all samples were generated using our large-scale metabolomics data processing software based on the R language (Software copyright registration Number: 2023SR0256527). Initially, all metabolite peaks were annotated using MetDNA2 (https://www.gsea-msigdb.org/gsea/msigdb) (14 gene sets of glutamine metabolism were obtained from MSigDB (/msigdb) , 13 via http://xena.ucsc.edu/)   prostatesc.edu/)  public d2 humid atmosphere. Four groups of cells were designed in each in vitro experiment: negative control (NC) with complete medium [NC gln(+)], NC with glutamine-free medium [NC gln(-)], CIRT with completed medium [CIRT gln(+)] and CIRT with glutamine-free medium [CIRT gln(-)].PC3 cells were obtained from the Chinese Academy of Sciences Cell Bank and cultured in 1640 medium  supplemented with 10% fetal calf serum (FBS) , 100 U/mL penicillin, and 100 μg/mL streptomycin  and cultured at 37°C in a 5% COMice were obtained from Hangzhou Ziyuan Laboratory Animal Technology Co., Ltd. The animal ethics committee of Shanghai Proton and Heavy Ion Center approved the use of mice for experiments.6 cells/mL. A 200 μL cell suspension was then subcutaneously injected into the right flank of 10 nude mice. The volume of tumors was measured using the following formula: length × width2 × 0.5 (mm³). Irradiation commenced on day 10 after tumor inoculation when the mean tumor volume reached approximately 100 mm³. Euthanasia was carried out by exposing the mice to carbon dioxide. The percentage of chamber volume replaced by carbon dioxide flow was approximately 50% vol/min.Male BALB/c-nude mice aged 6 weeks were obtained, and PC3 cells were suspended in the culture medium at a concentration of 5 × 10Harvested tumor samples were utilized for further metabolomic analysis, and the steps were as follows: approximately 12 mg (record accurate weight) solid tissue was added to methanol/water  in an ice bath. After homogenization with an automatic tissue separator or other methods, 480 μL homogenate was added to 720 μL acetonitrile/methanol (2:1). After vortexing for 30 s, the mixture was further treated with ultrasonication for 10 min in an ice/water bath. Then, this tube was refrigerated at −20 °C for at least 1 h. After centrifugation  at 4 °C, 1 mL of supernatant was transferred to a new tube. Then, all solvent was removed by a vacuum concentrator at room temperature for 3 h or longer. The obtained metabolite solid could be stored at −80 °C until testing. Before testing with LC-MS, the metabolite solid was redissolved in 100 μL acetonitrile/water (1:1). After vortexing for 30 s, the mixture was further treated with ultrasonication for 5 min in an ice/water bath. Then, the mixture was centrifuged  at 4 °C. The obtained solution was stored in sample vial inserts (250 μL) at 4 °C until LC-MS injection.Transwell assay using 24-well plates with an 8-µm pore chamber , and Matrigel matrix was diluted 1:8 to coat the upper side of the membrane at the bottom of the Transwell chamber. Cells were added to the upper chamber (3 × 10^4 cells/well). Meanwhile, 1640 medium with 10% FBS was added to the lower compartment. Then, the plates were incubated for 48 h in the incubator. After incubation, cells that migrated to the lower surface of the filter membrane were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet. Cells remaining on the upper surface of the filter membrane were gently scraped with a cotton swab. The lower surfaces were captured by an inverted microscope, and the counting was repeated three times.PC3 cells were seeded into a 6-well plate. When the cell confluence reached 80%, we scratched the monolayer cells with a sterile micropipette tip. The floating cells were then washed with PBS. Wound healing within the scrape line was observed at 0 h, 16 h and 24 h. Triplicate wells for each condition were examined.https://metaboanalyst.ca/). A volcano plot was utilized to compare group differences, employing fold change (FC) analysis and the Wilcoxon signed-rank test to identify statistically significant metabolites . Heatmaps were generated to visualize variations in metabolite profiles. Pathway analysis was conducted based on significantly altered metabolites. Box plots were employed to illustrate mean values with standard deviations. Statistical analysis involved the use of the Mann-Whitney test and Pearson’s chi-square test, with a significance level set at p<0.05.The data analysis was conducted using MetaboAnalyst  from TCGA shared 13 genes, indicating that glutamine metabolism may play a risk factor in PCa BCR  Table 3.The results indicate a notable correlation between heightened glutamine-related metabolism and advanced stages of PCa, as well as a reduced BCR-free survival duration. As such, reducing glutamine levels in OM-HSPC patients might enhance their prospects for improved local control.The metabolic profiles of OM-HSPC patients before and after CIRT are depicted in 85% (51 out of 60) risk metabolites were considerably decreased in patients treated with naADT followed by CIRT. In addition, the glutamine levels of OM-HSPC patients were reduced to a level comparable to that of healthy individuals. Based on present conclusions, we depicted the changes among core risk metabolites influenced by treatments in Due to the impossibility of obtaining tissue samples from PCa patients treated with CIRT, an experiment on mouth was conducted to confirm the inhibitory effects of CIRT on glutamine levels in tumor tissue. The mice were divided into two independent groups: normal control (NC) and CIRT. The CIRT group was irradiated with a single fraction of 2.5 Gy carbon ion. NC was the nontreatment control group. Tumor size was measured every three days during this time period, and tumor samples were collected 14 days after irradiation,  , 17. SevThe present study is the first to evaluate the unique radiobiological effect of CIRT from a metabolomics perspective for OM-HSPC. For this phase before widely metastatic PCa, this study provides a deeper understanding of initiation- and progression-related metabolism and the radiometabolic effects of CIRT. The results elucidate the correlation between risk metabolites, specifically glutamine metabolism, and PCa malignancy and preliminarily explain the synergistic metabolic remolding effects of naADT and CIRT.Multiple studies have consistently observed the following in urinary metabolomics of prostate cancer: citrate, glutamine, glutamate, glycine, tyrosine, 2,6-dimethyl-7-octen-2-ol, citrulline, histidine, succinate, kynurenic acid, taurine, leucine, and pyruvate . In thisPrior research has similarly indicated a correlation between glutamine levels and the initiation and progression of PCa . HoweverFurther, an analysis of TCGA PCa RNA-seq data revealed an association between increased expression of glutamine-related metabolism genes and both higher-stage populations and reduced BCR-free survival. Confirming this, other independent studies also underscored the prognostic importance of genes related to glutamine metabolism in PCa , 23. TheIn our previous research, we found that CIRT markedly suppressed urine metabolites in most localized PCa patients . In thisAnother compelling dimension of this study is the heightened efficacy of naADT-CIRT therapy under glutamine restriction. When glutamine was excluded from the cell medium, CIRT’s inhibitory impact on cell invasion and migration intensified, underscoring the significance of glutamine deprivation in augmenting CIRT’s effectiveness. While prior studies have discussed glutamine dependency in PCa, this presents the inaugural evidence that glutamine deprivation can potentiate the effects of CIRT.Our study pinpointed a set of core risk metabolites, with urinary glutamine levels correlating with PCa’s initiation, progression, and response to ADT, positioning it as a potential biomarker and therapeutic target. Furthermore, while naADT and CIRT each have their unique metabolic impacts, their combined effect effectively curtails most core risk metabolites in OM-HSPC, especially glutamine. This provides a mechanistic foundation for their combined therapeutic success in OM-HSPC. Additionally, we unveiled, for the first time, that depleting glutamine amplifies CIRT’s anti-invasion and anti-migration effects on PCa.Bioinformatics datasets and metabolomics data presented in this study can be found in online repositories. The datasets used and/or analyzed during experiments are available from the corresponding author upon reasonable request.The studies involving humans were approved by Ethics Committee of the Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The animal study was approved by Shanghai Proton and Heavy Ion Center approved the use of mice for experiments. The study was conducted in accordance with the local legislation and institutional requirements.ZZ: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. YP: Methodology, Resources, Software, Writing – review & editing. WH: Writing – review & editing. YX: Writing – review & editing. RN: Resources, Supervision, Writing – review & editing. XG: Resources, Supervision, Validation, Writing – review & editing. YS: Formal Analysis, Funding acquisition, Methodology, Project administration, Software, Writing – review & editing. QZ: Conceptualization, Funding acquisition, Resources, Supervision, Validation, Writing – review & editing.


Updates in whole genome sequencing technologies have revealed various RNA modifications in cancer, among which RNA methylation is a frequent posttranscriptional modification. RNA methylation is essential for regulating biological processes such as RNA transcription, splicing, structure, stability, and translation. Its dysfunction is strongly associated with the development of human malignancies. Research advances with respect to the regulatory role of RNA modifications in ovarian cancer include N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G). Numerous studies have demonstrated that epigenetic modifications of RNA can influence the progression and metastasis of ovarian cancer and may provide excellent targets for cancer therapy. This review highlights advances in research on RNA methylation modifications and ovarian cancer prognosis, carcinogenesis, and resistance, which could provide a theoretical foundation for designing therapeutic strategies for ovarian cancer based on RNA methylation modifications. One of the three main gynaecologic malignancies, ovarian cancer (OC), is also the most common cause of death from gynaecologic tumours . AccordiDNA, RNA and protein postsynthetic chemical modifications are involved in alterations at three levels: transcriptional, posttranscriptional and posttranslational, respectively. Of these, posttranscriptional regulation involves RNA modifications and ncRNA, and this mechanism of translational control is known as epitranscriptomics. Over the past five decades, more than 100 RNA postsynthetic modifications have been identified in different RNA classes . All kinhttps://gepia.cancer-pku.cn), the RNA modification proteins YTHDC1/2, HNRNPCA2B1, IGF2BP2/3 and YBX1 were significantly highly expressed and only METTL3/16 expressed at low levels in ovarian cancer tissues; other regulatory enzymes were not significantly differentially expressed. Interestingly, these regulators show inconsistent expression trends and RNA metabolic effects in other tumours. For instance, catalytic m6A-modified methyltransferase-like 3 (METTL3) is highly expressed in malignancies such as cholangiocarcinoma, thymoma, and diffuse large B-cell tumours according to the GEPIA website (https://gepia.cancer-pku.cn). Previous evidence suggests that METTL3 mediates IFIT2 mRNA decay in an m6A-dependent manner to inhibit the progression of intrahepatic cholangiocarcinoma [The dynamic modification process of RNA necessitates the involvement of numerous particular proteins known as RNA modifying proteins (RMPs). Among these, methyltransferases , demethylases (commonly known as erasers), and binding proteins  are involved in the insertion, deletion, and recognition of RNA modifications, respectively ,10. The arcinoma . Aly/REFarcinoma . Similararcinoma . METTL1 arcinoma .Numerous investigations have confirmed that RNA methylation modifications, such as m6A, m5C, m1A, and m7G, are related to various cancers and are involved in their biological processes . HoweverThe methyltransferase complex (MTC), which has METTL3 as its core methyltransferase subunit, is responsible for installation of m6A on mRNA. Regulators such as METTL14/16, Wilms tumor1 associated protein (WTAP), VIRMAKIAA1429), RNA binding motif protein 15(RBM15), and zinc finger CCCHType Containing 13 (ZC3H13) perform different biological functions during m6A modification. The regulator METTL3 is responsible for catalysing m6A modification . METTL3 , RNA binFat mass and obesity-associated protein (FTO), as the first discovered ‘eraser’, was found to delete the m6A modification by using the nuclear RNA form of m6A as a substrate . In addiIdentified ‘readers’ that recognize m6A modification sites include the YTHDF1/2/3 family of YT521-B homologous (YTH) structural domain proteins, the YTH structural domain protein YTHDC1/2, the insulin-like growth factor 2 mRNA binding protein IGF2BP1/2/3 and the heterogeneous nuclear ribonucleoprotein (HNRNP) family. While YTHDF1 enhances mRNA translation and YTHDF2 reduces mRNA stability, YTHDF3 synergizes with YTHDF1 to facilitate protein synthesis and affects YTHDF2-mediated mRNA decay ,38. YTHD5-Methylcytosine (m5C), the methylation of cytidine at position 5, is a posttranscriptional modification. The NOP/SUN RNA methyltransferase (NSUN) family, which includes members NSUN1 through NSUN7, DNA methyltransferase 2 (DNMT2), and tRNA aspartic acid methyltransferase 1, is responsible for catalysing the m5C modification . NSUN2, N1-methyladenosine (m1A) is the methylation of the nitrogen atom at position 1 of adenosine on tRNA. Transfer RNA methyltransferase 10 (TRMT10) and the transfer RNA methyltransferase 6/61 (TRM6/61) complex are currently known methyltransferases. Different studies have shown that m1A is mainly present in the 5’-UTR structural domain associated with high levels of translation, while other m1A sites are mostly concentrated in the CDS and 3’-UTR regions ,55. m1A The majority of 7-methylguanosine (m7G) is located at the 5’ terminal structure  of mRNA, and the “cap” reduces exonuclease degradation to improve mRNA stability . WDR4, RTo explore the clinicopathological characteristics and prognostic values of methylation regulators in ovarian cancer, the cBioPortal tool was used to evaluate RNA-seq and clinicopathological data from TCGA, consensus clustering to distinguish expression, regression models to determine prognostic relevance, ROC curves, and risk scores to predict survival, and the TIMER online database and nomogram were used to integrate data. These analyses suggest that RNA m6A methylation regulators are associated with clinicopathology and prognosis in ovarian cancer . A meta-It is generally known that m6A plays a crucial role in tumours and display different characteristics in different cancers. In the last decade, m5C modifications have been linked to a variety of cancer types, but the precise mechanisms are still unclear. Although the study of m7G and m1A in cancer is not deep and comprehensive, the critical role of m1A and m7G in the biological processes of tumour cannot be ignored ,65. A grin vivo [in vitro and in vivo [vitro, and WTAP targets DGCR8 in an m6A-modified manner to promote maturation of precursor miRNAs [in vivo. Increased NANOG RNA expression by ALKBH5 demethylation leads to enhanced aggressiveness of OC cells [in vitro, and inhibited tumorigenesis and metastasis in vivo. YTHDF1 recognizes the target mRNA EIF3C in an m6A-dependent manner to facilitate its protein synthesis and plays an essential role in ovarian cancer growth and migration [Methyltransferase-like 3 (METTL3) mediates the maturation and upregulation of miR-126-2p to activate the PI3K/Akt/mTOR pathway by directly binding to phosphatase and tensin homologues (PTEN) to promote ovarian cancer progression and tumorigenesis; moreover, knockdown of METTL3 inhibits ovarian tumour formation in vivo . Downregin vivo . In the in vivo . METTL14 in vivo . Interes in vivo . Hypoxiar miRNAs . LncRNA r miRNAs . CocultuOC cells . ALKBH5 OC cells . Knockdoigration . YTHDF2 igration . Recent igration . Codiffeigration . The resigration . Knockdoigration . Moreoveigration   . RNA mod6A on mRNA. It reversibly binds to the FTO or ALKB to form a complex and prevent the recognition of m6A substrates inside cells [6A demethylase activity of FTO [R-2-hydroxyglutaric acid (R-2HG) targets binding to FTO to inhibit demethylase activity and anti-tumour effects of 2HG in inhibiting the proliferation/survival of FTO-high cancer cells by targeting FTO/m6A/MYC/CEBPA signalling [Various studies have shown that RNA methylation is linked to tumour growth and may be used as a prognostic indicator for various cancers. FTO inhibitors have been most abundantly studied. For instance, Rhein was the first identified competitive inhibitor for FTO that can increase the cellular mde cells . MA, a nde cells . Two FTOy of FTO . Similary of FTO . R-2-hydgnalling . The nucgnalling . In addignalling . S-adenognalling . ALKH5  whether the regulator is an oncogene-promoting or oncogene-suppressing gene; 2) the abnormal level of RNA modification in cancer; and 3) the regulatory mechanism of the target mRNA modification. On account of various methylation modification regulators have different biological properties, some small molecule inhibitors or activators targeting RMPs have emerged that can be involved in regulating the level of RNA modification. It is predicted that RNA methylation might serve as a prospective molecular marker for early diagnosis and individualized treatment of cancer. However, RNA modifications show differences in different cancers or cell lines. Although m6A modifications have been extensively studied in various other cancers, research on RNA methylation in ovarian cancer is still in the preliminary stage.Epigenetic transcriptional regulation mainly involves DNA methylation, histone modifications and chromatin remodelling. Dysregulation of DNA methylation and histone deacetylation or methylation are the most intensively studied epigenetic changes in cancer and their dysregulation contributes to cancer progression. DNMTs are the enzymes responsible for DNA methylation, of which DNMT1, DNMT3A and DNMT3B have methyltransferase activity . CurrentHistone lysine methyltransferases (KMTs) label histone lysines and create substrates for histone demethylases. KMTs are similar to DNMTs and catalyse methylation labelling with SUV39H1, DOT1L, and EZH2, a member of the multicomponent protein family. EPZ-5676, a DOT1L inhibitor, targets S-adenosine homocysteine analogues and has been used in clinical trials in AML. Two inhibitors of EZH2, EPZ-6438 and CPI-1205, have both been applied in clinical trials for different tumours. Lysine demethylases (KDM) contain the KDM1 subfamily of lysine-specific demethylases (LSD) and the KDM2–7 subfamily containing the Jumonji C (JmjC) structural domain ,119. StuRNA modifications interact inextricably with epigenetic modifications. DNA methylation directly controls the genetic expression of m6A regulators. In pancreatic duct epithelial cells, cigarette smoke condensate causes hypomethylation of the METTL3 promoter, induces METTL3 expression and promotes PC development and progression . In addiin vivo and in vitro cellular experiments and clinical trials in cancer implies the great potential of RNA methylation in cancer therapy. Perhaps, the development of effective RNA methylation inhibitors or demethylating agents may provide prospective therapeutic strategies for OC and recurrent drug resistance.Unlike DNA methylation and histone modifications, RNA methyltransferases and their inhibitors have not been studied thoroughly enough. Some inhibitors of FTO and several inhibitors of METTL3 and ALKBH5 have been found to exert regulatory effects on RNA methylation modifications. The lack of


Fimoviridae is a newly founded family of plant viruses within the Bunyavirales order that inflicts diverse crop losses worldwide. The fig mosaic virus (FMV), the representative member of the Fimoviridae family, was shown to be a causative agent for the fig mosaic disease. Like all bunyaviruses, FMV has a segmented, negative-sense, single-stranded RNA (ssRNA) genome that is encapsulated by the viral nucleoprotein (N). Here, we present high-resolution crystal structures of FMV N in its RNA-free and RNA-bound forms, revealing a “paper fortune teller” structural transition between the two states. The tightly packed tetramer of FNV N is similar to the structures of other N proteins of different members of the Bunyavirales order. In its RNA-bound form, the tetramer reorganizes to adopt a more open state that allows the accommodation of the RNA. Despite the low sequence similarity to N proteins of animal-infecting bunyaviruses, there is a striking structural resemblance between FMV N and nucleoproteins from members of the Peribunyaviridae, an animal-infecting family of viruses. This structural homology implies that enveloped plant viruses and animal-infecting viruses might have a common ancestor from which they diverged.Enveloped RNA viruses are rare among plant viruses. IMPORTANCE Most insect-born viruses circulate within the Animalia kingdom, whereas plant-infecting RNA viruses are cross-kingdom pathogens. Many plant-infecting viruses cause devastating crop damage that leads to food security endangerment. The evolutionary crossroads of interkingdom circulation and infection are poorly understood. Thus, we took the structural approach to understand the similarities and differences between interkingdom-infecting viruses and viruses that circulate within one kingdom of life. Using our structures of FMV N in its free form and in complex with a single-stranded RNA (ssRNA), we dissected the mechanism by which FMV N binds to the RNA and revealed the conformational changes associated with the binding. The resemblance of our structure to N proteins from members of the Peribunyaviridae family and their recently published ribonucleoprotein (RNP) pseudoatomic resolution assembly model suggests that the FMV genome is similarly encapsulated. Thus, our finding unveils yet another bridge by which plant- and animal-infecting viruses are interconnected. Bunyavirales order, an order of single-stranded, segmented, negative-sense RNA viruses.Even though enveloped plant viruses have been recognized for a century, little is known about their assembly’s molecular mechanisms, particularly their genome encapsulation. Throughout evolution, viruses have developed various strategies to protect their genomes from destruction by host defense mechanisms, leading to large structural diversity in single-stranded RNA (ssRNA) encapsulation . NeverthFicus carica (common fig) that is characterized by multiple symptoms, especially on tree foliage, including chlorotic mottling, vein clearing and banding, ring spots, and small mottled fruits (Acaria ficus) are the transmitting vector of FMV  is a cosmopolitan disorder of d fruits . Only rer of FMV , 5 that e family , 6.Fimoviridae family , FMV shares a similar morphology with other emaraviruses, including the European mountain ash ringspot-associated virus , the glycoprotein precursor, the nucleoprotein (N), and a movement protein, respectively , 9. ReceBunyavirales vary in molecular weight, ranging from 27 to 57 kDa , a negative-sense ssRNA virus family that only recently was included in the Bunyavirales order (17–Hantaviridae) N bound to ssRNA showed a helical filament formation of N upon RNA binding  that enable oligomerization. In contrast, the N proteins of orthonairoviruses adopt a head and stalk architecture , where tes order . Previoues order , 17–19.  binding .As yet, there is no structural information on genome encapsulation in emaraviruses, particularly in FMV. The oligomeric states of their nucleoproteins and the mode of RNA binding are yet to be determined. Here, we present the crystal structures of FMV nucleoprotein in its RNA-free form and in a complex with a single-stranded RNA fragment.NC_029563) in-frame with a removable N-terminal 6×His tag into a pET28 vector for bacterial expression. We expressed full-length N protein (Nfl) in Escherichia coli cells and purified it in its RNA-free form using chromatographic methods . During the first immobilized-metal affinity chromatography (IMAC) step of purification of Nfl, we noticed that the expressed protein was copurified with what seemed to be bacterial RNA, as the A260/A280 absorbance ratio was ~1.5. An additional wash of the IMAC resin with 3 M urea prior to elution removed the nucleic acid contamination. Preparative size exclusion chromatography (SEC) showed that Nfl eluted in two distinct peaks: a void-volume peak, representing a high-molecular-weight Nfl in complex with remnants of the bacterial RNA , and an RNA-free form of Nfl (A260/A280 of ~0.5), representing an oligomeric form of the protein . A size exclusion chromatography with multiangle light scattering (SEC-MALS) experiment exhibited a prominent peak that consisted almost entirely of tetramers, though trimer and pentamer populations were also observed to a much lesser extent .We subcloned the cDNA encoding the fig mosaic virus nucleoprotein . Only the trypsin-treated sample showed a homogeneous proteolytic product. Mass spectrometry combined with N-terminal sequencing analysis of the trypsin-treated N confirmed the elimination of the first 45 N-terminal residues (ΔN45) was then purified and screened for crystallization conditions. Once the crystallization conditions were optimized, we used a single isomorphous replacement with anomalous signal (SIRAS) experiment to calculate phases to 3 Å, and an initial structure was built manually. Finally, the structure was refined against a 2.28-Å-resolution data set with excellent statistics  interacted with two other protomers via its N arm, which interacted with the N−1 protomer, and its C arm, which interacted with the N+1 protomer . Markedly, the interface between the C arm and the N+1 protomer was significantly more conserved than the N arm’s interface with the N−1 protomer .The FMV N tetramer was organized as two dimers with 2-fold symmetry rather than having a 4-fold symmetry . The proaviruses –25. Eachprotomer . Strand fl and NΔN45, we tested their RNA binding characteristics to a short, 42-nucleotide (nt)-long polyribouridylate oligoribonucleotide [poly(rU)] that was previously used to characterize the encapsulation properties of other N proteins from members of the Bunyavirales order (fl or NΔN45 to DK (equilibrium dissociation constant) values of ~5 μM and 6.6 μM, respectively  and obtained crystals that belonged to the P2solution , Fig. 4.0 protomer and continues to the N+1 protomer  complex of single-stranded RNA viruses is formed by encapsulating the viral genomic RNA with multiple copies of the nucleoprotein. The N protein serves as the main building block for the RNP assembly, protects the viral genome from endo- and exonucleases, and helps to evade the host’s innate immune response. Although they have the same function, there is little sequence similarity between N proteins of different members of the es order , 28.Peribunyaviridae virus family ]. FMV N is found exclusively as an asymmetric tetramer within the crystals. The tetrameric form is also the prevalent oligomeric state of N in solution, as determined by SEC-MALS . Such asymmetric tetramer organization was previously observed in the crystal structures of N proteins from members of the s family , 19, 21.Fimoviridae, Tospoviridae, and Peribunyaviridae families share an ancestor. To further investigate the evolutionary link between plant- and animal-infecting bunyaviruses, we modeled a sequence-based phylogenetic tree of Bunyavirales N proteins . However, a dendrogram we generated using a structure-based sequence alignment shows that N proteins from plant-infecting bunyaviruses have a common ancestor with peribunyaviruses . Despite the low sequence homology and the structural differences, the organization of the secondary structures around the RNA binding cleft of FMV N resembles that of the secondary structures around the RNA binding clefts of both peribunyaviruses and tospoviruses . This structural resemblance implies that N proteins from members of the proteins . The twoaviruses . A princaviruses . The resPeribunyaviridae) contained monomers or tetramers in the asymmetric unit and were also found to form a helical assembly throughout the crystal  with no sequence specificity , but the crystal . Electro crystal , and the crystal , top. ThPeribunyaviridae), the native RNP can adopt a highly ordered helical assembly, as observed in the crystal lattice of the recombinant LACV N (Peribunyaviridae) was released, based on cryo-electron microscopy (cryo-EM) with an average 13-Å resolution  was codon optimized for E. coli expression and synthesized at BioBasic. The synthetic product was subcloned into the pET28a expression vector between the NdeI and NotI restriction sites in frame with an N-terminal 6× histidine purification tag and a tobacco etch virus (TEV) protease cleavage site. pET28a-FMV Nfl was transformed into E. coli Rosetta(DE3) strain cells (Novagen). A single colony was used to inoculate 50 mL of LB medium supplemented with kanamycin and chloramphenicol antibiotics (LBKan+Cam). The culture was incubated with shaking at 37°C, 200 rpm, for 16 h. This starter culture was then used to inoculate 1.5 L LBKan+Cam at 37°C, 200 rpm, until it reached an optical density at 600 nm (OD600) of 0.6 to 0.8, when the expression was induced with 1 mM IPTG  at 20°C, 200 rpm, for 14 h. The cells were harvested , and the pellet was resuspended in ice-cold lysis buffer  with 1 mM phenylmethylsulfonyl fluoride (PMSF), 10 units/mL DNase and RNase, 0.2% Triton, 5 mg lysozymes, and cOmplete protease inhibitors (1 tablet per 50-mL suspension). The cell suspension was homogenized and lysed using a cell disrupter (Avestin Emulsiflex C3) for 5 min at 8°C. Cell debris was removed by centrifugation , and the supernatant was loaded onto an immobilized-metal affinity chromatography column (IMAC) . To obtain RNA-free N, we washed the Ni-bound protein with lysis buffer supplemented with 3 M urea and 1 M NaCl prior to imidazole washes. His-tagged Nfl was eluted with 300 mM imidazole in 50 mL binding buffer . Then, the sample was subjected to TEV protease digestion at a 1:20 protease-to-protein ratio for 16 h at 4°C while being dialyzed against the IMAC binding buffer to reduce the imidazole concentration. The NΔN45 was obtained by trypsin digestion  instead of TEV digestion. The digested protein was reloaded onto the IMAC, and the unbound fraction was collected. Washes with 20 mM, 50 mM, and 350 mM imidazole were also collected and analyzed by SDS-PAGE. Digested N proteins (N or NΔN45) were further purified using size exclusion chromatography (SEC)  preequilibrated with 20 mM Tris, pH 7.5, 150 mM NaCl, and 5% glycerol. Peak fractions were analyzed on 15% SDS-PAGE gels. Relevant fractions were collected and concentrated using a 10-kDa-cutoff spin concentrator . The samples at concentrations of 6.5 g/L and 7.3 g/L  were then aliquoted, flash-frozen in LN2, and stored at −80°C.The full-length FMV N gene ] were coincubated for 1 h at room temperature for N-ssRNA complex crystallization. The NΔN45-ssRNA complex at 7 g/L was mixed with a reservoir solution of 20% polyethylene glycol 3350 (PEG 3350), 0.2 M magnesium formate, pH 5.9, at a 1:1 protein-to-reservoir solution ratio. Crystals were observed 3 to 7 days after drop setup and grew to a final size of 200 by 250 by 300 μm. Crystals were harvested and flash frozen in LN2 in reservoir solution supplemented with 2 M l-proline as a cryoprotectant. These crystals belonged to the P212121 space group  with the PHENIX suite . Samples were analyzed using Bruker’s Autoflex Speed matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) instrument.Purified FMV N protein samples were dialyzed overnight against double-distilled water to remove salts. We made serial dilutions of purified Nwww.graphpad.com).A series of 12 2-fold serial dilutions of FMV N protein was made in black opaque Eppendorf tubes. 42-base ssRNA poly(U)–6-carboxyfluorescein (FAM) was added to each sample to a final RNA concentration of 1 μM. Samples were then incubated at 37°C for 20 min , 28. TriΔN45 (10 μM). Both protein and RNA samples were in 20 mM Tris, pH 7.4, 150 mM NaCl, 5% glycerol, and 5 mM β-ME. All measurements were performed at 25°C. The experiment setup included 19 injections of 1.0 μL with an injection duration of 2 s. Data were fitted according to a single-site binding model using the MicroCal PEAQ-ITC Control software, version 1.40  was performed using the MicroCal PEAQ-ITC instrument . The ssRNA 42-mer 25 μM) was titrated into purified N μM was tion 1.40 .Bunyavirales order were obtained from the NCBI archive. The nucleotide sequences were aligned using multiple alignment using Fasta Fourier transform (MAFFT)  were aligned to the globular domain of FMV N using the MUSCLE software (www.graphpad.com).The nucleoprotein sequences of representative members of the  (MAFFT) . The nei (MAFFT) . The den (MAFFT) , 38. Forsoftware , and a p8AX4 and 8AXF .Atomic coordinates and structure factors for the reported crystal structures have been deposited in the Protein Data Bank under PDB IDs


RNA‐based therapeutics have the potential to revolutionize the treatment and prevention of human diseases. While early research faced setbacks, it established the basis for breakthroughs in RNA‐based drug design that culminated in the extraordinarily fast development of mRNA vaccines to combat the COVID‐19 pandemic. We have now reached a pivotal moment where RNA medicines are poised to make a broad impact in the clinic. In this review, we present an overview of different RNA‐based strategies to generate novel therapeutics, including antisense and RNAi‐based mechanisms, mRNA‐based approaches, and CRISPR‐Cas‐mediated genome editing. Using three rare genetic diseases as examples, we highlight the opportunities, but also the challenges to wide‐ranging applications of this class of drugs. This review discusses the latest research on RNA‐based therapeutics, from design to the clinic. The rapid development of mRNA vaccines in response to the COVID‐19 pandemic has led to a resurgence in interest in RNA as a molecule class in diagnostics, prevention, and treatment of diseases. Whether for infectious diseases, cancer, neurodegeneration, metabolic disorders, or rare genetic diseases, RNA holds great promise for combatting human diseases previously intractable to therapy. Much of this promise builds on the fundamental cellular role of RNA – as a template, catalyst, scaffold, or regulator. The unique structural and biochemical properties of RNA allow it to be targeted or applied in a programmable manner since the rules that govern RNA binding affinity and specificity – complementary base pairing – are well understood. While conventional drug development often involves labor‐ and time‐intensive screening to identify lead compounds, RNA drugs can be rationally designed as long as the target is known. As a result, RNA therapies are emerging as platform technologies that can be applied to a variety of diseases. Moreover, delivery strategies can be developed and optimized independently from the RNA component of the drug, which not only speeds up therapeutic development but also opens the possibility to target ultra‐rare diseases.Despite its potential, the use of RNA as a therapeutic faces obstacles, including its poor pharmacological properties, difficulty with intracellular delivery, and immune‐related toxicity. Technological advances in medicinal chemistry and a better understanding of natural antisense RNA phenomena were necessary to make RNA drugs a reality. The scientific breakthroughs that revolutionized basic biological research such as the discovery of microRNAs, RNA interference, and CRISPR‐Cas systems Box  are now Box 1lin‐4 as a regulator of Caenorhabditis elegans development . Their results argued against stochiometric interference with endogenous mRNA and suggested a catalytic component that amplified the process  to inhibit Rous sarcoma virus replication in tissue culture , 2′‐O‐methoxyethyl (2′ MOE), and 2′‐fluoro  linkages, in which one of the non‐bridging oxygen in the phosphodiester is replaced by a sulfur atom. This modification increases nuclease resistance, decreases hydrophilicity and promotes the binding of serum proteins, which, in turn, improves circulation life‐time. It also increases binding to cell surface proteins, thereby facilitating ASO uptake into cells  are the most advanced for mRNA delivery and offer benefits such as ease of formulation, biocompatibility, and a large carrying capacity  from a DNA plasmid using a bacteriophage RNA polymerase. They are structured similarly to cellular mRNA and include elements such as a 5′ cap, a 5′ UTR, an open reading frame (OTR), 3′ UTR, and poly(A) tail. These features are important for mRNA translation and stability and therefore affect efficacy of the mRNA drug. The synthesized mRNA is purified to remove contaminants, reactants, and incomplete transcripts, which is essential to reduce immune‐stimulatory effects  or, more commonly, by error‐prone non‐homologous end joining (NHEJ) or microhomology‐mediated end joining Fig . These ret al, et al, et al, et al, Recently developed precision genome editing strategies that do not rely on DSBs overcome these limitations. Prominent among these are base editors (BEs), which enable single‐nucleotide conversions in genomic DNA  provide additional flexibility to engineer changes beyond single‐nucleotide conversions , adenoviruses or lentiviruses, as well as nanoparticles  . SMA is an autosomal recessive neuromuscular disease caused by deletions or loss‐of‐function mutations in the gene survival motor neuron 2 (SMN2) that encodes an identical SMN protein. But SMN2 contains a synonymous C‐to‐T substitution within exon 7 that weakens the binding of splice activators to the SMN2 pre‐mRNA  have proven more challenging to treat by RNA therapeutics. DMD is a progressive X‐linked muscle‐wasting disease caused by deletions, duplications, or point mutations in the et al, DMD reading frame cause Becker muscular dystrophy, a milder form of the disease with later onset and slower progression . CF is an inherited disorder caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR; Kerem CFTR gene displays mutational heterogeneity; to date, around 2,000 CFTR variants have been identified in patients with CF and CFTR‐related disorders. These mutations are classed into six groups based on how they affect the production, trafficking, function or stability of CFTR (Welsh & Smith, The CFTR mutations that lead to a complete loss of the protein, precluding the effective use of CFTR modulator therapy.For decades, therapies were palliative and focused on medications that reduce symptoms, such as mucolytics, anti‐infectives, and pancreatic enzyme replacement. The development of small molecule modulators of CFTR that promote channel trafficking or function revolutionized CF treatment Cutting, . Nearly CFTR splice modulation are currently in pre‐clinical development (Kim & Krainer, et al, et al, RNA‐based therapies might fill this gap. ASOs with different mechanisms of action such as inhibition of nonsense‐mediated mRNA decay or CFTR as the CF gene (Zabner et al, et al, Gene replacement therapy allows treatment of all CF patients, regardless of the underlying genetic mutation. This approach has been explored since shortly after the identification of CFTR splicing (Maule et al, CFTR gene (Geurts et al, CF is also amenable to CRISPR‐based genome editing approaches. Experiments in patient‐derived organoids have shown that allele‐specific corrections of aberrant et al, Advances in medicinal chemistry, a better understanding of the versatile cellular functions of RNA, and the experiences gained from decades of preclinical and clinical studies have established a strong foundation for the future of RNA therapeutics. Continuous optimization of RNA drug design, be it ASOs, siRNA drugs, mRNAs, and CRISPR‐Cas editing tools, will improve drug efficacies. An impressive illustration of this concept is the improvement in metabolic stability, durability, and potency of siRNA therapeutics achieved through advanced chemical modifications. For example, the prototype GalNAc–siRNA conjugate revusiran, which targeted transthyretin mRNA for the treatment of hATTR, was metabolically labile and therefore required high and frequent dosing. The drug was poorly tolerated in a phase 3 clinical trial and its development was discontinued. Optimized chemistry led to siRNA drugs with a satisfying safety profile and increased potency, facilitating dosing as infrequently as once every 6 months (Ray in vivo delivery. For example, most ASO therapeutics to‐date use local delivery to specific sites, e.g. the eye or spinal cord, or delivery to the liver. It is expected that optimized combinations of RNA chemical modifications, conjugation with cell‐targeting ligands, and improved nanoparticle carrier systems will enhance the efficiency of RNA drug delivery and enable therapeutic molecules to reach previously inaccessible target tissues.Despite the spirit of optimism, RNA therapeutics face remaining challenges, including economic ones. High manufacturing and regulatory costs and the expense of clinical trials combined with the limited size of the potential market in cases of rare diseases might mean that the retail price charged for a treatment, if it is developed, may be unaffordable for most patients. Another major problem that currently stands in the way of achieving broad applications for RNA therapeutics is targeted et al, et al, et al, et al, et al, et al, et al, Apart from the RNA therapeutic modalities discussed in this review, there are others in preclinical and clinical development. These include ASOs or siRNA drugs that activate gene expression (Li et al, et al, While current clinical RNA therapeutics all target eukaryotic cells, RNA drugs can also be applied to prokaryotes. Proof of principle of the efficacy of ASOs in eliminating diverse bacterial species by targeting essential bacterial genes at the mRNA level has been established, including in animal models (Good Looking ahead, we expect to see developments in all areas of RNA medicine in the near future – the possible benefits for patients are too great to ignore.Jörg Vogel: Conceptualization; writing – review and editing. Anke Sparmann: Writing – original draft; writing – review and editing.The EMBO Journal. This has no bearing on the editorial consideration of this article for publication.JV is a member of the Advisory Editorial Board of


Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a  To this end, many protocols couple biochemical isolation, or metabolic labeling, of RNA with high throughput sequencing technologies, thus providing a snapshot of the transcriptome at specific stages of the mRNA life cycle, with high throughput and sensitivity. For example, high-throughput sequencing protocols, when coupled to ribosome isolation, such as in Ribo-seq2, metabolic labeling strategies in SLAM-seq3, immunoprecipitation of RNA binding proteins (RBP) as in CLIP-seq4 and many others, have shed light on many regulatory mechanisms pertaining to different aspects of post-transcriptional gene regulation.The cytoplasmic fate of RNA molecules is impacted their subcellular localization, RNA binding partners, and engagement with the ribosomal machinery. These aspects are strongly interconnected5. Together with its ability to bind initiating ribosomes, DDX3X has been often described as a translation regulator, specifically promoting translation of RNA with structured 5’UTRs7. However, as mentioned above, cytoplasmic processes like translation or mRNA decay are intertwined, and connection between the two processes encompass different molecular mechanisms, such as mRNA surveillance mechanisms like nonsense-mediated decay (NMD)8, ribosome-collision dependent mRNA cleavage9, and others. In order to understand when and how such processes are coupled, it is important to study the dynamics of such mechanisms. For instance, it has been proposed that miRNA can first trigger translation suppression and then mRNA deadenylation and decapping leading to RNA degradation10.DDX3X is a multifunctional RNA helicase that is highly expressed in many tissues and able to unwind structured RNA to influence cytoplasmic post-transcriptional gene regulationDDX3X are associated with a variety of human diseases including cancers and developmental delay11. Variant types are disease selective in DDX3X, with cancers ranging from primarily loss-of-function alleles in NK-TCL and other blood cancers to nearly exclusively missense variants in medulloblastoma11. In DDX3X syndrome, missense variants are phenotypically more severe than loss-of-function. Previously, we used functional genomics approaches to identify mechanistic differences between depletion of DDX3X and expression of missense variants7. We found that DDX3X missense variants predominantly affect ribosome occupancy while DDX3X depletion impacts both ribosome occupancy and RNA levels. However, it is unclear whether the changes in RNA levels constituted a cellular response to translation suppression, often described as “buffering”12.Mutations in 13. To that end, it is important to think about the dynamics of gene expression, as complex dynamics of cell proliferation and differentiation ensure correct developmental patterning.mRNA regulation has been linked to neurogenesis during development, where multiple RNA binding factors, including DDX3X, ensure correct protein synthesis as cells transition between different fates and states14. As in biology many molecular processes are interconnected, large-scale datasets and data amenable to re-analysis are at the very heart of many research efforts15.In order to access such complex interplays of a multitude of factors which shape gene expression, large-scale consortia have provided a great resource for investigations into gene regulation. While historically devoted to promoting investigation into transcriptional regulation, recent efforts started to provide precious information into post-transcriptional mechanisms, with hundreds of RBPs profiled in terms of both binding and function, by means of CLIP-seq, and knockdown followed by RNA-seqHere, we identify how inactivation of DDX3X evolves over time to lead to acute and long-term changes to post-transcriptional gene regulation. We here employ different analytical approaches applied to newly generated experimental data and many previously published studies related to mRNA regulation, to show that GC content is associated with mRNA stability changes following DDX3X depletion. Our analyses indicate that this effect is widespread and is associated with cell cycle changes in mRNA regulation, including RNA stability. This further reinforces roles for DDX3X in RNA stability in addition to translation. Together, our work represents a significant advancement in the understanding of a fundamental regulator, which sits at the very heart of the gene expression cascade.16, where we found near-complete rescue of gene expression changes by DDX3X expression, thus being able to use this tool to monitor DDX3X-dependent changes. We profiled RNA levels and translation using RNA-seq and Ribo-seq along a time-course of DDX3X depletion, at 4, 8, 16, 24 and 48 hours after auxin or DMSO control treatment.  to infer rates of RNA synthesis, processing and decay. Compared to non-regulated mRNAs, regulated mRNAs showed modest changes in transcription rates, suggesting transcription changes are not the major contributor to RNA level changes following DDX3 depletion, In contrast, we found more pronounced changes in mRNA stability as evidenced by TE down transcripts  cell system coupling RNA labeling, scRNA-seq and single-molecule imaging techniques to find extensive decay differences among different transcripts, potentially related to poly-A tail mediated regulation. Despite a lower throughput when compared to sequencing-based experiments, kinetic parameters estimated from their data  mouse model13, were plated in 15cm plates at 20% confluency (~3.5×10 6 cells/plate). 48 hours post plating, when the cells were at ~ 70% confluency, the media was changed and fresh media with 500 μM IAA   or DMSO was added to cells. Cells were harvested at 0, 4, 8, 16, 24, and 48 hours post IAA addition. Cell number did not appreciably increase over the 48 hours of the experiment. To quantify DDX3X protein, we used an anti-DDX3X antibody described in previous work7 normalized to an anti-GAPDH antibody .HCT116 cells with inducible degradation of DDX3X (as previously describedCells were treated with 100 μg/ml cycloheximide (CHX), washed with PBS containing 100 μg/ml CHX, and immediately spun down and flash frozen. Once all time-points were collected, the cells were thawed and lysed in ice-cold lysis buffer  Triton X-100, 25 U/ml TurboDNase (Ambion)). 240 μl lysate was treated with 6 μl RNase I  for 45 minutes at RT with gentle agitation and further digestion halted by addition of SUPERase:In (Ambion). Illustra Microspin Columns S-400 HR  were used to enrich for monosomes, and RNA was extracted from the flow-through using Direct-zol kit (Zymo Research). Gel slices of nucleic acids between 24–32 nts long were excised from a 15% urea-PAGE gel. Eluted RNA was treated with T4 PNK and preadenylated linker was ligated to the 3’ end using T4 RNA Ligase 2 truncated KQ .36 and libraries constructed using a different reverse indexing primer for each sample.Linker-ligated footprints were reverse transcribed using Superscript III (Invitrogen) and gel-purified RT products circularized using CircLigase II . rRNA depletion was performed using biotinylated oligos as describedFor the RNA-seq, RNA was extracted from 25 μl intact lysate (non-digested) using the Direct-zol kit (Zymo Research) and stranded total RNA libraries were prepared using the TruSeq Stranded Total RNA Human/Mouse/Rat kit (Illumina), following manufacturer’s instructions.Libraries were quantified and checked for quality using a Qubit fluorimeter and Bioanalyzer (Agilent) and sequenced on a HiSeq 4000 sequencing system.SLAM-seq was performed at 60–70% confluency for DDX3X-mAID tagged HCT116. Media was changed and fresh media with 100μM 4-thiouridine (4sU) was added to cells and changed every 3 hours for 24 hours. 8 hours prior to collection, growth medium was aspirated and replaced. Uridine chase was performed where cells were washed twice with 1X PBS and incubated with media containing 10 mM uridine and DMSO or 100μM IAA for 0 or 8 hours to induce degradation of DDX3X. At respective time points, cells were harvested followed by total RNA extraction using TRIzol (Ambion) following the manufacturer’s instructions . Total RNA was alkylated by iodoacetamide for 15 min and RNA was purified by ethanol precipitation. 200ng alkylated RNA were used as input for generating 3’-end mRNA sequencing libraries using a commercially available kit .37 using sequences retrieved from UCSC browser and aligning reads were discarded. Remaining reads were mapped to the genome and transcriptome using STAR38 v2.7.9a supplied with the GENCODE v32 GTF file. STAR parameters were: --outFilterMismatchNmax 3 --outFilterMultimapNmax 50 --chimScoreSeparation 10 --chimScoreMin 20 --chimSegmentMin 15 --outFilterIntronMotifs RemoveNoncanonicalUnannotated --alignSJoverhangMin 500 --outSAMmultNmax 1 --outMultimapperOrder Random.Reads were stripped of their adapter, collapsed, and UMI sequences were removed. Clean reads were then mapped to rRNA, tRNA, snoRNA and miRNA sequences using bowtie2--outFilterMismatchNmax 10. Reads containing T>C mutations were extracted from the BAM file using GenomicAlignments and GenomicFiles Bioconductor39 packages.Reads were mapped to the genome and transcriptome using same RNA-seq parameters, except for 17 v1.17, using default settings. Genes significantly changing in their dynamics at a p-value cutoff of .05 were used for subsequent analysis.Reads were mapped to the genome and transcriptome using STAR with same Ribo-seq parameters. Synthesis, processing, and degradation rates were obtained using INSPEcTRiboseQC40. 5’ end coverage values were inspected using Bioconductor39 packages such as GenomicFeatures41 and rtracklayer42. DESeq243 was used to obtain RNA-seq, Ribo-seq, and TE regulation, as described previously7: changes in translation efficiency were calculated using DESeq2 by using assay type (RNA-seq or Ribo-seq) as an additional covariate. Translationally regulated genes were defined using an FDR cutoff of 0.05 from a likelihood ratio test, using a reduced model without the assay type covariate, e.g. assuming no difference between RNA-seq and Ribo-seq counts.Unique counts on different genomic regions were obtained using A similar strategy was used to define significant changes in DDX3X-mediated stability from SLAM-seq: count tables with T>C reads were built and analyzed using labeling (4sU/DMSO) and degron status (8h. vs DMSO) as the two variables of interest; regulation in stability was defined using a reduced model without the degron type covariate, e.g. assuming no difference between DMSO and degron activation.Translationally regulated genes (as defined by Ribo-seq/RNA-seq) and stability regulated genes (as defined by SLAM-seq) were defined using a p-value cutoff of .05.For randomForest44 package with default parameters. Lasso regression was performed on scaled variables using the glmnet45 package. While the entire feature table is available in The Random Forest regression was run using the 18. GC-content at first, second, or third codon position.TPM values using RNA-seq . Baseline TE levels, defined as ratio of Ribo to RNA reads. Baseline RNA mature levels, defined as length-normalized ratio of RNA-seq reads in introns versus exons. GC content, length  and Ribo-seq/RNA-seq density in: 5ʹ UTRs, a window of 25nt around start and stop codons, CDS regions, non-coding internal exons, introns, and 3ʹ UTRs. Codon frequencies. Measures of gene-specific codon optimality, previously calculated from a recent studyFeature importance (measured by mean decrease in accuracy for the random forest model and by the lasso coefficients) and correlation between predicted and measured test data were calculated on a 5-fold cross-validation scheme.21. Genes were partitioned cutting their GCcds values into 3 groups given the low number of quantified genes .Estimated mRNA decay kinetics at cell cycle re-entry were deposited as supplementary files of the original study™ Plus EdU Alexa Fluor™ 647 Flow Cytometry Assay Kit (Thermo Fisher cat: C10634). Per manufacturer’s instructions, FxCycle Violet DNA content stain (Thermo Fisher cat: F10347) was added after the Click-iT reaction at 1:1,000 dilution before quantifying on a BD LSR Dual Fortessa flow cytometer. Alexa Fluor 647 was measured in the 670–30 Red C-A Channel and FxCycle Violet Stain was measured in the 450–50 Violet F-A Channel. Analysis was performed using FACS DIVA and FlowJo V10  software.To measure DNA replication and cell cycle stage, EdU (5-ethynyl-2´-deoxyuridine) was added to cells at 10nM for 1.5 hrs before harvesting. 1 confluent well of a 6-well plate of HCT116 cells were harvested and processed as per manufacturer’s instructions for the Click-iTrtracklayer42. For each differential analysis, we extracted the most 250 stabilized and the most 250 degraded genes ranking P-values from RNA-seq differential analysis. 1500 control RNAs were randomly sampled from non-regulated genes, using p-values >.2 and TPM values > 3. Coverage values were 0–1 (min/max) normalized and the first position at value >.15 was identified as coverage starting position. In addition, a general coverage starting point was selected by pooling all samples, and a window of 250nt around such position was used to calculate average coverage values around the coverage start. Log2 fold change with respect to the control condition were then calculated.Computation on single-nucleotide coverage values was performed using For degron data, starting position and log2fc coverage values were extracted and used as input for linear regression. For coverage values, intercept was omitted, as the first value was 0. Beta coefficients were then extracted and compared between stabilized, degraded, and control mRNAs.For mouse Ddx3x cKO and ENCODE data, differences between starting position (knockdown vs wt) and log2FC (knockdown vs wt) in coverage values were used to compare stabilized, degraded and control mRNAs, bypassing the regression step .DDX3X-mAID tagged HCT116 cells were plated in 6 well plates at 30–40% confluency. 24 hours post plating 500 μM IAA or DMSO was added to cells with or without 200nM Actinomycin D (ActD) for respective conditions. Total RNA was extracted from cells at 60–70% confluency using Direct-zol kit (Zymo Research) at 0 and 24 hours post-ActD and IAA or DMSO treatment. TaqMan probes for JUND, EIF2A, RACK1, LGALS1, and PFN1 were predesigned and purchased from ThermoFisher Scientific. Riboseq degraded (EIF2A) or stabilized genes (JUND) were conjugated with FAM dye while control genes RACK1, LGALS1, and PFN1 were conjugated with VIC dye. For the TaqMan real-time quantitative PCR amplification reactions, we employed an Applied Biosystems QuantStudio 6 Real-Time PCR System instrument. Real-time PCR was conducted using TaqMan Fast Virus 1-Step Master Mix from Applied Biosystems in 384-well plates, following the manufacturer’s protocol. Each well contained either the genes subject to riboseq degradation gene (EIF2A) or stabilization gene (JUND) along with control genes . All reactions were conducted in triplicate. Thermal cycling conditions adhered to the manufacturer’s recommended standard protocol. The quantification of the target input amount was determined using the cycle threshold (CT) value, which corresponds to the point at which the PCR amplification plot crosses the threshold. Expression of ribose degraded and stabilized genes were normalized to each control genes respectively.® real-time PCR assays obtained from ThermoFisher Scientific.RACK1 HUMAN Chr.5: 181236928 – 181243906 on Build GRCh38 Hs00272002_m1 VIC-MGB LGALS1 HUMAN Chr.22: 37675606 – 37679802 on Build GRCh38 Hs00355202_m1 VIC-MGB PFN1 HUMAN Chr.17: 4945650 – 4949088 on Build GRCh38 Hs07291746_gH VIC-MGB JUND HUMAN Chr.19: 18279694 – 18281656 on Build GRCh38 Hs04187679_s1 FAM-MGB EIF2A HUMAN Chr.3: 150546678 – 150586016 on Build GRCh38 Hs00230684_m1 FAM-MGB Details of TaqManSupplement 1Supplementary Table 1. Read mapping statistics for the Ribo-seq RNA-seq DDX3X time course dataset.Supplement 2Supplementary Table 2. Input to the Random Forest model for the DD3X3 time course dataset.Supplement 3Supplementary Table 3. Accession codes for the analyzed ENCODE datasets, with information for each differential analysis. Multiple accession can be technical replicate of a biological replicate.Supplement 4Supplementary Table 4. Input to the Random Forest model for the cKO Ddx3x mouse dataset.Supplement 5


METTL16, a human m6A RNA methyltransferase, is currently known for its modification of U6 and MAT2A RNAs. Several studies have identified additional RNAs to which METTL16 binds, however whether METTL16 modifies these RNAs is still in question. Moreover, a recent study determined that METTL16 contains more than one RNA-binding domain, leaving the importance of each individual RNA-binding domain unknown. Here we examined the effects of mutating the METTL16 protein in certain domains on overall cell processes. We chose to mutate the N-terminal RNA-binding domain, the methyltransferase domain, and the C-terminal RNA-binding domain. With these mutants, we identified changes in RNA-binding ability, protein and RNA expression, cell cycle phase occupancy, and proliferation. From the resulting changes in RNA and protein expression, we saw effects on cell cycle, metabolism, intracellular transport, and RNA processing pathways, which varied between the METTL16 mutant lines. We also saw significant effects on the G1 and S phase occupancy times and proliferative ability with some but not all the mutants. We have therefore concluded that while METTL16 may or may not m6A-modify all RNAs it binds, its binding (or lack of) has a significant outcome on a variety of cell processes. Modification of adenosine to methyl-6-adenosine (m6A) in RNA has been extensively studied in multiple species of prokaryotes, eukaryotes, and viruses for several decades ,2,3,4. TACH” consensus sequence  [AGAGAA” consensus sequence (underlined A is the modified adenosine) [The m6A modification is produced by m6A RNA methyltransferases, colloquially termed “m6A writers”. These m6A writers catalyze the transfer of a methyl group from a S-adenosylmethionine molecule to the target adenosine’s 6th base position . There aenosine) ,31,32,33enosine) ,35. RNAsenosine) ,40,41,42enosine) ,50,51,52Proteins known as “m6A readers” have a high affinity for binding RNAs that are m6A modified. After binding to m6A RNAs, these proteins are directly or indirectly responsible for the effects of m6A modification mentioned previously (such as splicing). Currently known m6A readers include YTHDF1/2/3 ,12,53, YAs an m6A RNA methyltransferase, METTL16 has gained attention as a potential therapeutic for several diseases associated with cellular m6A dysregulation. It has been shown by several groups to methylate MAT2A mRNA and U6 snRNA, and to bind MALAT1 lncRNA ,65,66,67As shown in Because METTL16 studies have shown a wide variety of effects due to knockdown, knockout, mutation, or overexpression, and not all METTL16’s protein domains are fully characterized, we decided to further study the functional effects of individual domains of METTL16 to determine their specific duties. We chose to mutate the N-terminal RNA-binding domain, the methyltransferase domain, or the VCR RNA-binding domain using previously published mutations. We stably expressed the mutated METTL16 in HEK293 cells. The endogenous METTL16 was then removed from these same cells so that only the mutated version of METTL16 remained. These cells lines were established, confirmed, and analyzed for changes in the RNA-binding abilities of METTL16, global RNA expression, protein expression, and cell cycle effects.E. coli cells  via incubation for 30 s at 45 °C, then left for 5 min on ice. E. coli suspension was then adjusted to 1 mL with SOC Outgrowth Media (New England Biolabs) and incubated for 1 h at 37 °C on a shaker at 250 rpm. Bacterial cells were spun down, resuspended in 50 µL of SOC Outgrowth Media, and spread on a Luria Broth (LB) agar plate  containing the appropriate antibiotic for selection. Plates were incubated at 37 °C overnight. Colonies were selected from plates after growth, suspended in 3 mL of LB media with appropriate antibiotic, and grown for approximately 16 h at 37 °C on a shaker at 250 rpm. Plasmids were harvested from bacteria using the GeneJET Plasmid Miniprep Kit  according to manufacturer protocols. Some of the E. coli was kept at 4 °C for later use. The mini-prepped plasmids were sequenced with Sanger sequencing using primers directed at key sequences in the plasmids. Once verified, the stored E. coli cell suspensions were adjusted with another 3 mL of LB media and respective antibiotic, grown overnight at 37 °C, and then added to 250 mL of LB with antibiotic, which was again grown overnight at 37 °C. Plasmids from these cells were harvested using the NucleoBond Xtra Midi Kit  according to manufacturer protocols. When appropriate, a final concentration of 100 µg/mL of ampicillin  or 100 µg/mL of kanamycin  was used for selection. Plasmid concentrations were determined with a Nanodrop 2000 spectrophotometer.METTL16-Myc-FLAG plasmid, METTL16 CRISPR-Cas9 plasmid, and scrambled CRISPR-Cas9 plasmid were purchased from Origene and Genecopoeia . Mutatio2, in Dulbecco’s Modification of Eagle’s Medium 4.5 g/L glucose  supplemented with 10% fetal bovine serum , 2mM L-glutamine , and 1X Penicillin/Streptomycin . When appropriate, G418  was used in culture at 0.5mg/mL for selection and 0.25 mg/mL for maintenance; puromycin  was used in culture at 7.5 µg/mL for selection and 3.75 µg/mL for maintenance.HEK293 cells were obtained directly from American Type Culture Collection (ATCC). Cells were cultured at 37 °C, 5% CO6/well in a 6-well plate  the day before transfection. On the day of transfection, 2 µg of plasmid was transfected with Opti-MEM  and Lipofectamine 2000  according to manufacturer protocols. The plasmid mixture was incubated with cells overnight, and the media were changed the next day. Antibiotic selection was started 24 h after initial transfection time. Cell colonies were selected once visible after approximately 1 week, grown separately, and screened for proper plasmid expression and integration using Western blotting. Cells transfected for transient overexpression were plated in a 10-cm cell culture dish  with 2.5 × 106 cells, transfected with 10 µg of plasmid, and harvested one to two days after transfection.For clone production, cells were plated at 0.5 × 10w/v, 500 mM DTT, 750 µM Bromophenol Blue sodium salt , 50% Glycerol v/v , and 1% β-Mercaptoethanol  into the Western gel. For mini gels , the gel was electrophoresed at room temperature at 250 V for 25 min in 1 Tris-Glycine-SDS buffer . Protein was transferred to an Amersham Protran 0.2-µm nitrocellulose membrane  at room temperature with an ice pack and stir bar at 300mA for 1 h in 1 Tris-Saline +20% methanol  buffer. For large separation gels (20 cm), the gel was electrophoresed at 4 °C at approximately 150 V for several hours in 1 Tris-Glycine-SDS buffer  until proper separation was achieved. Protein was transferred to a nitrocellulose membrane at 4 °C at 300 mA for 1 h in 1 Tris-Saline +20% methanol buffer. After transfer, membranes were handled in the same manner. Membranes were blocked with 5% nonfat milk  in 1 TBS-T ) at room temperature for 1 h on a shaker, and then incubated in appropriate primary antibody suspended in 5% nonfat milk or 5% bovine serum albumin  in 1 TBS-T ) overnight at 4 °C on a shaker , quenched with media, washed with 1X Dulbecco’s Phosphate Buffered Saline , and then lysed with a Whole-Cell Extract buffer , 10 mM Tris pH = 7.6 , 0.1% SDS , 5 mM EDTA , 1 × protease inhibitors , incubated on ice for about 5 min, and stored at −80 °C until use. After thawing on ice, extracts were sonicated, and protein was quantified with the BCA kit  in triplicate according to manufacturer protocols as well as with the Promega GloMax Multi Detection plate reader. Unless otherwise stated, 35 µg of protein was loaded from each sample with 5 loading buffers  and harvested at the time points specified in WCEB with 1 protease inhibitor. Protein concentration was quantified using the BCA kit from Thermo Fisher Scientific Inc. , and Western blots were produced as listed in the Western blot section.Cells were lifted with 0.05% Trypsin , counted with Trypan Blue , and plated in a 6-well dish the day before treatment at 0.4 × 10g for 5 min, rinsed with 1 mL of DPBS, and centrifuged again after moving 100 µL to a separate tube. DPBS was removed. The 100 µL of cells was resuspended in 100 µL of a Total Lysis Buffer (TLB) , 150 mM NaCl, 0.1% SDS, 1% NP40 , 0.5% sodium deoxycholate ) + protease inhibitors, and the 900 µL of cells was resuspended in 100 µL of a Hypotonic Lysis Buffer (HLB) ) + protease inhibitors. Both were frozen at −80 °C overnight to further the lysis. The HLB cells were then thawed on ice, vortexed for a few seconds to lyse, and centrifuged at 1500× g for 5 min. The resulting supernatant was the cytoplasmic fraction which was moved to a separate tube. The pellet was the nuclear fraction and was resuspended in 100 µL of TLB + protease inhibitors. Nuclear and total fractions were sonicated to break up DNA, and equal amounts were loaded for Western blotting after addition of a loading buffer. Western blots were produced as listed in the Western blot section.Cells were lifted with 0.05% Trypsin, quenched with media, centrifuged at 1500× 2O at −80 °C until use.Cells were collected and then lysed with 1 mL of TRIzol . RNA was isolated according to manufacturer protocols and kept on ice thereafter. RNA concentrations were determined with a Qubit 4 Fluorometer  and stored in RNase-free H2, 10 mM HEPES pH = 7 , 1 mM DTT , 10% digitonin , 1× protease inhibitors , and 100 U/mL RNaseOut . Suspension was spun down at 15,000× g for 15 min at 4 °C. This separates organelles, membranes, and DNA from the aqueous fraction of the cells. For each IP, 100 µL of supernatant was used, along with 50 µL of washed IP bead slurry, 10 µL of 100 mM DTT, and 40 µL of 0.5 M EDTA, brought to 1 mL in an NT2 buffer . IP tubes were incubated at 4 °C for at least 4 h on a shaker. IP input and supernatant samples were taken after incubation but before washing, final IP samples were taken after 5 washes with the NT2 buffer to confirm protein attachment. In total, 1 mL of TRIzol was added directly to washed beads, and RNA was isolated according to manufacturer protocols. In total, 10% of the amount of PLB supernatant used in the IP was used to quantify the RNA input and was isolated with TRIzol as well. Anti-FLAG-labeled IP beads were purchased from Sigma-Aldrich .A confluent 10-cm cell culture dish was harvested by gently rinsing the cells off with 1 mL of DPBS. Cells were spun down and resuspended in 125–250 µL of a Polysome Lysis Buffer , 5 mM MgCl2O and used with an iSCRIPT cDNA synthesis kit  and incubated with the thermal cycler according to manufacturer protocols. For RNA resulting from immunoprecipitations, 80% was used in cDNA synthesis to ensure equal amounts were used.Reverse transcription PCR was accomplished with a Bio-Rad Laboratories T100 thermal cycler . For RNA directly isolated from cells, 100 ng polyA RNA was adjusted to 10 µL with H2O when directly isolated from cells, and 2 times with H2O when resulting from immunoprecipitations.Real-time PCR was performed using cDNA described above, primers from Integrated DNA Technologies (See 2O and then quantified using Qubit 4 Fluorimeter. PolyA RNA was isolated from 100 µg of this RNA using the Poly(A)Purist MAG Kit  according to manufacturer protocols. PolyA RNA was quantified with the Qubit 4 Fluorimeter. In total, 200 ng of each cell line’s polyA RNA was used in the Direct cDNA Native Barcoding Nanopore protocol in preparation for sequencing using the MinION Nanopore Sequencer . The protocol was performed according to the manufacturer’s recommendations. Sequences were basecalled using the high accuracy basecalling setting. Passed reads were then processed using the Galaxy platform. Sequences were mapped using MinMap2 to Human Hg38 genome with the Oxford Nanopore read to reference mapping option enabled (minimap2 -x map-ont --q-occ-frac 0.01 -t 6 -a). Reads were quantified using featureCounts and differential expression determined with DESeq2. PCA analysis of individual samples showing grouping by cell line can be found in Cells from a confluent 10-cm dish were harvested with 0.05% Trypsin, quenched with media, and washed with DPBS before resuspension in 1 mL of TRIzol. After RNA isolation according to manufacturer protocols, the RNA was reprecipitated in 3 M sodium acetate  and 100% 2-propanol overnight at −20 °C. The next day, reprecipitation was completed, RNA was suspended in H6) were harvested and washed several times with DPBS and stored at −80 °C. Cell pellets were thawed on ice, resuspended in an ice cold lysis buffer , and subjected to 3 freeze–thaw cycles prior to sonication . The lysate was clarified via centrifugation , and the total protein content of the clarified lysate was determined via a BCA assay . Equal amounts of protein (1 mg/mL) were reduced using 5 mM DTT  and then alkylated with iodoacetamide . Unreacted iodoacetamide was quenched with an additional 15 mM DTT. After reductive alkylation, samples were diluted to a final concentration of 1.5 M urea  and digested with MS grade Trypsin overnight . Samples were acidified with 0.1% formic acid and desalted via solid phase extraction  Peptides were eluted (25–50% acetonitrile + 0.1% formic acid), and the resulting eluant was lyophilized overnight. Peptides were resuspended in 0.1% formic acid, and concentrations were determined via coulometric assays . Concentrations were adjusted to 0.25 mg/mL with 0.1% formic acid.Cells from five independent cell cultures for each group  of cell lines with stable METTL16 expression were lifted with 1 mL of Accutase  and counted using 0.4% Trypan Blue staining. Cells  coupled to a Q Exactive Plus Hybrid Orbitrap mass Spectrometer  via nanoelectrospray ionization as previously described . PeptideThe proteomics data presented in this study are openly available in the ProteomeXchange Consortium via the PRIDE partner repository  with them/z tolerance was ±10 ppm, and fragment ion m/z tolerance ±0.6 Da with three missed cleavages allowed. The search results were filtered by a 1% false discovery rate at the peptide level. Strict parsimony was used to group peptides to proteins. Label-free quantification was carried out using MS1 precursor intensity. Intensities were normalized to both the average intensities and the distribution width of the intensities within the sample. Relative abundances for low sampling proteins were determined via imputation [t-test in GraphPad Prism and considered all proteins with a p-value ≤ 0.05 to be statistically significant.Proteome Discover 2.5 was used for the processing of raw data files and protein identification. Default search parameters included Met oxidation (+15.99492 Da) as a variable modification and Cys carbiomidomethylation (+57.02146 Da) as a fixed modification. Data were searched against the Uniprot Homo Sapiens reference proteome  supplemented with additional sequences for the wild-type, PP185/186AA, N-terminal, and Δ-Linker METTL16 mutant and CRISPR proteins. Precursor ion putation . Assuminp-value < 0.05) was used to establish significance.Functional annotation and pathway enrichment analysis was performed using the PANTHER Overrepresentation Test   was added to each culture and incubated at 37 °C for 1 h, after which the media were replaced with fresh media and incubated for another 3 h. Cells were then lifted with 1 mL 0.05% Trypsin, quenched with media, collected, and washed once with 1% BSA  in DPBS. Cells were then fixed and stained using the Click-iT kit contents and DAPI  according to manufacturer protocols. Cells were analyzed for fluorescence with the Cytek Aurora Flow Cytometer using SpectroFlo and FlowLogic software version 8.4 for analysis.Once cell lines were established and expressions verified, cells were plated at 0.5 × 105 cells were plated in to each well of a 6-well plate. At 24, 48, and 72 h after plating, cells were lifted with 0.05% Trypsin and counted via hemocytometer and/or a LUNA-FL automated cell counter . Media were refreshed on remaining cells every 24 h to avoid nutrient/additive deficiency during the time frame of the experiment and to allow for constant proliferation.Approximately 2.5 × 10To observe the responsibilities of each METTL16 protein domain, we created transgenic HEK293 cell lines expressing a METTL16 protein mutated in one of three domains A. The muThe RNA-binding abilities of all the METTL16 variants were tested using FLAG immunoprecipitation. As expected, the N-terminal RNA-binding mutant showed very little binding ability; however, the methylation mutant (PP185/186AA) also showed stunted RNA binding. We continued with this mutant but also produced a second methylation mutant  which sAfter confirming the expression and RNA-binding ability of METTL16 in each of the clonal overexpression lines, we introduced a METTL16 CRISPR-Cas9 construct targeting exon 2 of the endogenous METTL16 locus, selected for expression via puromycin, and generated clonal lines of each METTL16 mutant, as well as the wild-type overexpression line. Due to the strength of the plasmid’s promoter, a higher expression level compared to the endogenous METTL16 was observed B. ClonesOnce exogenous METTL16 expression was verified, we again tested the RNA-binding ability of the mutants to ensure CRISPR-Cas9 exposure did not alter the intended METTL16 mutation. For this we used METTL16 immunoprecipitation in comparison to the wild-type exogenous METTL16 line as well as the starting HEK293 line (data not shown). Methylation ability was previously shown to be ablated with the PP185/186AA, N184A, and N-terminal RNA-binding METTL16 mutant proteins ,59,76 viPreviously, we had shown that endogenous and exogenous wild-type METTL16 was predominantly cytoplasmic in a number of cell lines . To deteAgain, we determined the half-life of the METTL16 protein in these clones closely mimicked the half-life of the HEK293 endogenous METTL16, which was shown to be more than 16 h. Therefore, we have shown METTL16 to be a long-lived nuclear and cytoplasmic protein, and that these characteristics are not substantially changed when either the methylation or RNA-binding activity of the protein is altered. Given the recent correlations cited between METTL16 expression and cancer progression, we performed an EdU assay with these clonal lines to investigate changes in cell cycle . Using tGiven that the changes to cell cycle occupancy were subtle (yet significant), we also determined the relative proliferation of each cell line over a 48-h time course C. OverexTo further investigate METTL16 mutation-driven changes, RNA sequencing and untargeted proteomics analysis were performed. For RNA sequencing, polyA RNA was extracted from the clones and sequenced as unamplified, barcoded cDNA using the Oxford Nanopore MinION Sequencer. Interestingly, mutating the various domains of METTL16 only resulted in significant changes to about 50–400 transcripts compared to the transgenic wild-type cell line . In addiTo determine whether the mRNA expression changes could be linked to direct interaction with METTL16, we performed FLAG immunoprecipitations followed by real-time PCR normalized to the wild-type METTL16 transgenic line. As the best characterized METTL16 targets, MAT2A mRNA and U6 snRNA were significantly bound by the wild-type transgenic METTL16 as expected B. MutatiTo assess global changes in whole-cell protein abundances, we performed proteomics analysis on all five cell lines . OverallAfter our previous published study in 2020 showing METTL16 protein was localized to both the cytoplasm and the nucleus, and other publications showing lack of complete characterization of the protein’s domains, we hypothesized METTL16, in addition to RNA methylation, had another role related to its RNA-binding ability . To inveFrom our mutated METTL16 cell lines, we have determined first that METTL16’s methylation ability appears to be dispensable for cell survival. Given that m6A methylation is a further fine-tuning of the RNA’s processing or half-life, it is probable that there are other modes of regulation in place that are more influential on the RNA affected by METTL16. This is not to say there are no consequences to removing METTL16’s methylation ability. We saw widespread protein expression changes with our METTL16 N184A mutant cell line which suggests that methylation does indeed play a role in METTL16’s function, perhaps by regulating translation of a subset of proteins.Secondly, we determined METTL16’s RNA-binding ability is reliant on both the N-terminal and C-terminal RNA-binding domains. Mutation of the METTL16 N-terminal RNA-binding domain resulted in lower relative amounts of immunoprecipitated RNA but did not completely abrogate binding. This mutant was adapted from Mendel et al., who showed that mutation of the N-terminal RNA-binding domain completely abrogated binding to the MAT2A hairpin 1 when the C-terminal domain of METTL16 was not present. Interestingly, when we deleted the linker region between the VCRs of the C-terminal RNA-binding domain, it actually increased the RNA-binding efficiency of METTL16. Almost every RNA for which we probed in the immunoprecipitation studies showed increased binding to the C-terminal transgenic METTL16 compared to the wild-type transgenic METTL16. We speculate that removal of the linker region between the vertebrate conserved regions in this mutant slowed and/or limited the range of motion of the vertebrate conserved region domain as a whole and therefore allowed for stronger or longer binding. The region deleted is not evolutionarily conserved and is a disordered region . TherefoBecause we suspected METTL16 to bind and/or influence more RNAs than those currently published, we searched for changes in protein and RNA expression in the clonal lines we produced. We determined more extensive changes in protein expressions compared to RNA expressions but found little correlation between them . This coFrom our RNA expression and immunoprecipitation studies, we have identified ATXN10, CDKN3, PNPLA4, and RTRAF as new potential RNA targets of METTL16. From our analysis, we have determined the RNAs affected by METTL16 alteration belong in one of three groups. Group 1 contains RNAs that bind METTL16 but do not reflect an expression change when METTL16 protein becomes mutated. Among this group is MAT2A mRNA and U6 snRNA. Previous studies have shown the effects of METTL16 manipulation on MAT2A RNA and protein levels, which we did not observe. The lack of change in expression is not necessarily indicative of a lack of effect on the RNA; for instance, U6 snRNA expression is not expected to change, and MAT2A mRNA is more likely to show splicing changes, which we did not investigate in this study. Other RNAs that fall into this group are likely to be imperative to cell survival, such as the two listed. Group 2 contains RNAs that show expression changes but do not seem to bind METTL16. These RNA expressions are most likely changing as an indirect or secondary effect of true METTL16 target changes. Group 3 contains RNAs that both bind METTL16 and show RNA expression changes. Among this group is ATXN10 and PNPLA4 mRNAs. METTL16 may have more than one direct effect on these RNAs, or it is possible they are both directly and indirectly affected by METTL16. Even though the RNAs of groups 1 and 3 are of the most interest, RNAs in group 2 aid in the cell-wide effects observed by altering METTL16.Regardless of the exact mechanisms METTL16 uses to produce the changes we have observed, we have shown that it significantly affects the cell cycle timing. Our EdU assay results show our transgenic METTL16 cell lines spend more time in S phase if METTL16 is mutated in certain domains. Furthermore, we determined CDKN3 to be an RNA-binding target of METTL16, which is well known to affect the G1/S phase transition of the cell cycle. Future studies will determine the effect this binding has on CDKN3 RNA, whether METTL16 methylates it, and its subsequent protein expression.Interestingly, while expression of the mutant METTL16 showed effects on cell cycle progression, it often did not correlate to significant effects on cellular proliferation. However, it does appear that overexpression of wild-type METTL16 did increase proliferation as compared to the parental HEK293T line. This is intriguing as METTL16 overexpression has been implicated in a number of cancer types ,50,51,83Interest in METTL16’s cellular functions continues to grow and more data is becoming available, but at the time of this publication, molecular-based knowledge about METTL16 is still incomplete. This study has added to that knowledge by further revealing RNAs, proteins, and cell processes affected by the METTL16 protein. However, more studies are needed for a better understanding of METTL16. The expression of METTL16 mutants in HEK293 cells under basal cell conditions in this study does limit the view of METTL16’s full potential impact on cell processes. Expressing these mutants in different cell types and subjecting them to environmental and cellular stressors could further reveal how METTL16 contributes to, or combats, cell responses in different states more applicable to human physiology. Once the foundational work is completed, disease-based studies can then be properly performed and interpreted.Overall, the METTL16 mutant cell lines produced for this publication will provide insight into disease states where METTL16 has been documented as mutated or dysregulated. Most diseases associated with METTL16 are currently listed as due to an over- or under-expression of the protein. There is one documented type of large intestinal cancer which has a mutation (in the methyltransferase domain) that abolishes METTL16’s methylation activity . More in