The findings reveal the landscape and potential regulatory mechanism of RNA editing in drug resistance, providing new therapeutic targets for reversing drug resistance.Adenosine‐to‐inosine RNA editing critically affects the response of cancer therapies. However, comprehensive identification of drug resistance‐related RNA editing events and systematic understanding of how RNA editing mediates anticancer drug resistance remain unclear. Here, 7157 differential editing sites (DESs) are identified from 98 127 informative RNA editing sites in tumor tissues, many of which are validated in cancer cell lines. Diverse editing patterns of DESs are discovered in resistant samples, which could not be fully explained by adenosine deaminase acting on RNA enzymes. Some RNA‐binding proteins are identified that potentially regulate these editing events. Notably, the DESs are significantly enriched in 3’‐untranslated regions (3’‐UTRs). The impact of DESs in 3’‐UTR on the microRNA (miRNA) regulations is explored, and some triplets  that may contribute to drug resistance are identified. In addition, it is determined that the functions of genes enriched with DESs are associated with drug resistance, such as apoptosis, drug metabolism, and DNA synthesis involved in DNA repair. An online resource ( The study identifies RNA editing events related to drug resistance through a systematic analysis across 6 types of cancer and 18 drugs. The study highlights that RNA editing is a crucial mediator in the mechanism of anticancer drug resistance, substantially improving the understanding of drug resistance mechanisms regarding transcriptional and post‐transcriptional regulation. Recent studies have indicated that the mechanisms of drug resistance in cancer are multifaceted, such as alterations of drug metabolism and drug targets, DNA damage repair, abnormal tumor microenvironment, dysregulated noncoding RNAs, genetic heterogeneity, epigenetic changes, and RNA modifications.Currently, chemotherapy and molecularly targeted therapies are the primary treatment options to improve the overall survival of patients with cancer.1 Howe In mammals, adenosine‐to‐inosine (A‐to‐I) RNA editing, a widespread post‐transcriptional modification, is catalyzed by adenosine deaminase acting on RNA enzymes (ADARs) that diversifies the transcriptome by altering selected nucleotides in RNA molecules. With the rapid development of high‐throughput sequencing technologies, ≈4.5 million A‐to‐I RNA editing sites have currently been identified. Some studies have revealed that altered editing events have serious effects on human health, such as immune disorders, neurological disorders, cardiovascular disease, and cancer progression. Additional editome‐disease associations have been shown in the Editome Disease Knowledgebase. Collectively, A‐to‐I RNA editing plays critical roles in several biological processes and human diseases.The post‐transcriptional modification of RNA is an important process affecting cellular function as it alters the transcriptional landscape.11 In  Some studies provide evidence that specific RNA editing events could selectively affect the response of cancer therapies, suggesting RNA editing as a predictive marker for therapy response. For example, RNA editing events in the coding region of COG3 and GRIA2 increase sensitivity to MEK inhibitors. In addition, upregulation of DHFR expression could enhance cellular proliferation and resistance to methotrexate. RNA editing destroys the binding sites of miR‐25‐3p and miR‐125a‐3p in the 3’‐untranslated region (3’‐UTR) of DHFR, resulting in elevated DHFR levels. In some cases, A‐to‐I editing could cause aberrant splicing of targeted mRNA resulting in drug resistance. Collectively, A‐to‐I RNA editing has the potential to become a new nucleic acid‐based therapeutic target to reverse drug resistance in cancer. However, because of the limited number of examined RNA editing sites, drugs, samples, or tissues, published studies did not thoroughly analyze RNA editing‐mediated mechanisms in drug response. Thus, systematic analysis of A‐to‐I editing associated with drug response is urgent and necessary.Recently, the roles of RNA editing have been studied for genes associated with pharmacokinetics and pharmacodynamics.19 SomTEP1. Functional analysis of genes enriched with DESs revealed strong functional specificity in different conditions. Finally, we constructed an omnibus repository named REDR, which furnishes a user‐friendly interface for a convenient retrieval of our findings (freely available at http://www.jianglab.cn/REDR/). Our findings highlight that RNA editing is a crucial mediator in the mechanism of anticancer drug resistance, substantially refining our understanding of the RNA editing‐mediated mechanism involved in drug resistance.Here, we aimed to identify RNA editing events related to drug resistance through a systematic analysis across six types of cancer and 18 drugs. There is no significant difference in the overall RNA editing levels (OELs) between resistant and sensitive samples, which was consistent in cancer cell lines. Furthermore, we identified differential editing sites (DESs) in resistant samples relative to sensitive samples. We observed that these DESs were significantly enriched in 3’‐UTRs, whereas a very small fraction were in coding regions. Altered RNA editing patterns (over‐editing or under‐editing) were evidently diverse across conditions (one drug in a specific type of cancer was defined as a condition), which could not be fully explained by ADARs. However, these DESs could share common sequence motifs and were located in binding sites of RNA‐binding proteins (RBPs) related to drug resistance, which indicated that RBPs as upstream regulators may influence the editing activity of these DESs. We also identified RNA editing‐dependent microRNA (miRNA) regulations potentially affecting drug response. In addition, we found that some genes significantly enriched with DESs were associated with drug resistance, such as 22.1p = 2.6 × 10−9, Figure p = 6.8 × 10−2, Figure p = 0.79, Figure  These results suggested that the data are qualitatively good for downstream analysis. We observed that in 96% of the conditions, OELs were not significantly different between resistant and sensitive samples  Data Portal, respectively. Through filtering (see Section 4), we obtained 25 conditions across six types of cancer and 18 anticancer drugs , 14 conditions, same as tumor tissues, were obtained. Each condition includes at least three cell lines in the resistant and sensitive groups . Thereafter, we examined whether DESs related to drug response in tumor tissues could be reproduced in cancer cell lines. As expected, DESs identified in cell lines were significantly enriched with DESs from tumor tissues (Table p < 0.05). We determined that a considerable number of overlapped genes harboring DESs between the identical condition in cell lines and tumor tissues  samples (LUAD‐Cisplatin). A previous study has demonstrated that downregulation of RHOA alone results in Cisplatin resistance.By contrast, the number of DESs in the coding region was relatively limited for both tumor samples (≈0.3%) and cell lines (≈0.1%). There were only 19 unique nonsynonymous DESs and five synonymous DESs across 14 conditions from tumor tissue data. Previous studies have reported that A‐to‐I RNA editing events in transcripts of drug targets, metabolizing enzymes, transporters, and transcription factors may affect the drug response.19a Hea Figure . Moreove2.2.3p < 0.05; Table Figure METTL7A, correlate with poor overall survival of patients with lower grade glioma (LGG)  higher editing levels in patients with LGG resistance to Temozolomide than those that were LGG sensitive   lower editing levels on chr1:1419898  Figure . The same Figure . Reporte tissues.26 In ) Figure . Notably8 Figure . ATAD3B 2.2.4Thereafter, we compared DESs under different conditions by calculating the Simpson's index (as Meet/Min score) between any two conditions  over‐editing in resistant tumor tissues compared to sensitive tumor tissues in head and neck squamous cell carcinoma (HNSC)‐Paclitaxel. In contrast, only 20 (≈3%) DESs showed significant under‐editing in resistant samples  DESs showed significant  in resistant samples relative to sensitive samples. The poor correlation scores indicated that ADAR enzyme levels could not fully explain these altered RNA editing patterns in resistant samples across conditions Figure . Thus, t29 our Notably, we found that some RBPs have been confirmed experimentally to regulate RNA editing sites. For example, overexpression of ILF2 reduced editing levels of 11 sites in HEK293T cells. TARDBP knockdown induced a global reduction in RNA editing levels in HepG2 cells. Moreover, some identified RBPs had been confirmed to be associated with anticancer drug resistance. For example, FXR1, encoding an RNA‐binding protein that interacts with functionally‐similar proteins, was identified under the Cisplatin‐related conditions in this study. A previous study reported that the FXR1/PRKCI complex could activate the extracellular signal‐related kinase signaling to promote cell growth and invasion in Cisplatin‐resistant non‐small‐cell lung carcinoma cells. Collectively, these results suggested that RBP, as an upstream regulator, may mediate drug response by regulating RNA editing.Subsequently, we also explored whether DESs located in these sequence motifs could be bound by any known human RBPs under each condition. A total of 132 RBPs were identified under all conditions  predicted in LGG‐Temozolomide, we observed the over‐editing of chr11:36507636 falling within the miR‐484 binding region in the 3’‐UTR of TRAF6, and the over‐expression of TRAF6 in resistant samples  is known to modulate apoptosis by inhibiting caspases and ubiquitinating target proteins. Transfection with XIAP variants conferred resistance to doxorubicin and increased cellular proliferative capacity in MCF‐7 cells. Additionally, EGFR tyrosine kinase inhibitor resistance and endocrine resistance pathways were significantly enriched, implying that RNA editing possibly mediates drug resistance for molecular targeted and endocrine therapies. Furthermore, we observed that the significantly enriched GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were condition‐specific  has an under‐editing pattern in BLCA‐Cisplatin, where DESs were only located in the 3’‐UTR. Furthermore, the DESs on each enriched gene preferred to be in the same transcribed region across different conditions, especially in the 3’‐UTR and intronic regions  signatures co‐localize with complex disease genome‐wide association studies (GWAS) associations. However, there is a paucity of information on the co‐location of cancer GWAS loci and edQTL signatures. Previous studies have reported the pivotal effects of RNA editing in cancer progression, and several studies regarding cancer GWAS loci were reported. Therefore, it is necessary to explore edQTL signatures co‐localized with cancer loci in specific types of cancer in future studies.The RNA editing events in the exon region could change amino acid. However, there were only 19 unique nonsynonymous DESs identified in the current study. Most (76.5%) of the genes harboring these sites have been reported to be related to cancer progression and drug resistance , which was directly retrieved from RNA‐seq data of tumor samples in TCGA through a customized computational pipeline. Here, the informative RNA editing sites identified in the previous study were used and further analyzed in this study. These informative RNA editing sites derived from tumor samples were re‐annotated using ANNOVAR (version‐Date: 2019‐10‐24). Drug response data of tumor samples in TCGA were obtained from the GDC Data Portal . Thereafter, only samples with RNA editing profiles and drug responses were retained. Samples labeled “Progressive Disease” and “Complete Response” were defined as resistant and sensitive samples, respectively. One drug in a specific type of cancer was defined as a condition. Each condition included at least three tumor samples in the resistant and sensitive groups.RNA editing data were downloaded from Synapse  values was applied across the cell lines used for each compound. Thereafter, a cell line was defined as resistant or sensitive to the compound if the calculated Z‐score was >0.8 or <‐0.8, respectively. Cell lines with Z‐score between −0.8 and 0.8 were considered to be intermediate and were not used in further analyses. In addition, there were at least three resistant and three sensitive cell lines for each compound assayed under each condition, and the conditions in the cell lines had to be consistent with the analysis criterion in the samples. Finally, 14 conditions across five types of cancer and 11 compounds met this criterion, which involved 102 cell lines.Here, It was examined whether the DESs related to drug response in tumor samples were consistent with those in the cancer cell lines. The concentration at which growth was inhibited by 50%  was generally used to measure the drug responses of many cancer cell lines.51 In Thereafter, RNA‐seq raw data of 102 cell lines were downloaded from the CCLE (BioProject accession: PRJNA523380). The quality of the sequences using FastQC software (v0.11.9) was checked. Paired‐end reads were trimmed from the 3’ end to remove low‐quality bases using fastp (v0.20.1). Trimmed paired‐end reads were mapped on the human reference genome (hg19) using the STAR (v2.7.1a) program. Multi‐hits and duplicates were excluded from the read alignment. Aligned BAM files were used (REDItoolDenovo.py) to identify the editing level of RNA editing sites using the REDItools (v2) software with default parameters. Finally, editing profiles were retrieved by filtering out the RNA editing sites with multiple mismatches in each cell line.R2 values and statistical significance  were calculated using linear regressions to assess the correlations between OELs and ADAR expression. The differential analysis of OELs was based on the Wilcoxon rank‐sum test.Here, the average editing levels in all informative RNA editing sites of each sample as the OEL of the sample were calculated. p < 0.05 and |Diff| ≥ 5% are defined. Here, |Diff| represents the difference of the mean editing level between resistant and sensitive samples. The gene expression  data of patients were downloaded from the GDC Data Portal. The differential expression of ADAR enzymes between resistant and sensitive samples was detected using Wilcoxon rank‐sum test.In this study, informative RNA editing sites with no missing values in at least three resistant and three sensitive samples were required. Thereafter, the Wilcoxon rank‐sum test was used to detect RNA editing sites with differential editing activity between resistant and sensitive tumor samples. DESs as statistically significant with p < 0.05 was considered as a significant region enriched with DESs.The transcribed region distribution of the DESs was investigated in an integrative manner and separately under each condition. It was examined whether the DESs were significantly enriched in the particular transcribed regions using the hypergeometric test in an integrative manner and separately for each condition. Furthermore, only the transcribed region with Condi and Condj represent the number of terms of a certain type  under condition i and condition j, respectively.To compare DESs among different conditions, the Simpson's index was calculated, that were separately based on DESs, genes harboring DESs, or GO terms enriched with these genes under each condition as follows:p < 0.05) correlated with patient prognosis was used. The log‐rank test to evaluate the statistically significant difference of prognoses between over‐editing and under‐editing groups was also used. The Kaplan–Meier survival curve from the R (v3.6.3) package survival (v3.2‐11) was used to plot the two groups.It was investigated whether the DESs associated with drug response could distinguish between patients with cancer with favorable or poor outcomes. First, six types of cancer samples with RNA editing levels from Synapse (syn2374375) were collected. The editing level of each site was converted to binary according to the median of editing levels in all samples. The clinical data of patients were downloaded from the GDC Data Portal. The univariate Cox proportional hazards regression model to identify the DESs that significantly  to identify motifs that were notably enriched within these sequences, regardless of their relative location on editing sites. MEME was run using the classic mode and limiting the search to only the top three motifs. The enrichment was measured relative to a random model based on the frequencies of letters in submitted sequences. In addition, FIMO_(v5.4.1) was used to scan a set of sequences for individual matches to the given motifs. Here, Alu was selected as input sequences and the top motif obtained was used as the input motif to detect whether there were significantly matching motifs in Alu repeat elements.Recent studies have demonstrated that RBPs can regulate editing by binding specific sequence motifs around the RNA editing sites.31 Herhttp://rbpmap.technion.ac.il/). The algorithm for mapping motifs on RNA sequences was based on the weighted‐rank approach, which considers the clustering propensity of binding sites and the overall tendency of regulatory regions to be conserved. In this study, a list of motifs obtained in each condition was independently submitted to the RBPmap database to detect motifs enriched in RBP targets.Thereafter, the common sequence motif was used to map binding sites of human RBPs using the RBPmap database (.ac.il/).53 The The edgeR package was a Bioconductor package for examining the differential expression of count data. Finally, significant DEGs were defined as p < 0.05.For the comparison between genes harboring DESs and DEGs in resistant tumor tissues relative to sensitive tumor tissues, differential gene analysis between resistant and sensitive samples was performed. First, the gene count data of patients from the GDC Data Portal were downloaded. Thereafter, DEGs were identified between resistant and sensitive tumor tissues using edgeR (v3.26.8) from R (v3.6.3) packages.54 The 2) If gain‐regulations attributed to the over‐editing of DESs occurred in resistant samples, it would be significantly negative correlation between the expression of miRNA and gene in resistant samples rather than sensitive samples. In contrast, if the loss‐regulations attributed to the over‐editing of DESs occurred in resistant samples, the expression of miRNA and gene would be significantly negatively correlated in sensitive samples rather than resistant samples. Hence, the correlation between expression of miRNA and gene within the identified gain‐ or loss‐regulations above in resistant and sensitive samples, respectively  was calculated. 3) The expression of the gene was lower in resistant samples when the miRNA‐gene relationship was gained in resistant samples. In contrast, the expression of the gene was higher in resistant samples when this miRNA‐gene relationship was lost in resistant samples. Accordingly, the FC of the mean gene expression level between resistant and sensitive samples was computed under each condition. In particular, differential expression analysis of miRNAs between resistant and sensitive samples using the Wilcoxon rank‐sum test was performed. Furthermore, non‐significant differentially expressed miRNAs were only focused on to neglect their impacts on gene expression. Finally, these A‐to‐I RNA editing‐dependent miRNA regulation triplets that met the above three screening criteria were retained.An integrative analysis to identify potential triples that include DESs, miRNAs, and genes Figure  was perf55 2) p < 0.05 was considered a statistically significant gene enriched with DESs. The visualization of the DESs in a particular gene was shown in Figure In this study, genes significantly enriched with DESs beyond what could be expected by chance were identified. In brief, a hypergeometric test was used to examine the over‐representation of DESs within a particular gene. Furthermore, only the gene with n Figure 56 In Metascape, the enrichment of KEGG pathways and GO terms relevant to cellular components, molecular functions, and biological processes using a one‐tailed hypergeometric distribution with Bonferroni's correction were assessed. In addition, all genes that were significantly enriched with DESs under each condition were subjected to functional annotation and enrichment using the R (v3.6.3) package clusterProfiler (v3.12.0). The clusterProfiler package provided two functions, enrichGO and enrichKEGG, to perform enrichment analysis for GO terms and KEGG pathways based on the hypergeometric test. Here, significantly differential GO terms and KEGG pathways with p < 0.05 under each condition were defined.The functional annotation and enrichment of all identified RBPs were performed using Metascape.57 In p‐value less than 0.05 established statistical significance.The RNA editing of cancer cell lines was identified using the REDItools (v2) with default parameters. All statistical analyses were performed using R (v3.6.3). For comparison between two groups, a Wilcoxon rank‐sum test was performed. A univariate Cox proportional hazards regression model was used to identify the DESs that significantly correlated with the prognosis of patient. The statistically significant difference of prognoses between over‐editing and under‐editing groups using the log‐rank test was also evaluated. Genes significantly enriched with DESs beyond what could be expected by chance using the hypergeometric test were identified. A The authors declare no conflict of interest.W.J. and L.W. conceived and designed the study; X.Z. performed the data analysis and website construction and drafted the manuscript; R.M., F.H., and S.Z. discussed the results and revised the manuscript. All authors reviewed, revised, and approved the manuscript.Supporting InformationClick here for additional data file.Supplemental Table 1Click here for additional data file.Supplemental Table 2Click here for additional data file.Supplemental Table 3Click here for additional data file.Supplemental Table 4Click here for additional data file.Supplemental Table 5Click here for additional data file.Supplemental Table 6Click here for additional data file.Supplemental Table 7Click here for additional data file.Supplemental Table 8Click here for additional data file.Supplemental Table 9Click here for additional data file.Supplemental Table 10Click here for additional data file.Supplemental Table 11Click here for additional data file.Supplemental Table 12Click here for additional data file.Supplemental Table 13Click here for additional data file.


Jatrorrhizine reduces myocardial infarction-induced apoptosis and fibrosis through inhibiting p53 and TGF-β1/Smad2/3 pathways in mice, which DOI is: https://doi.org/10.1590/acb370705, published in the journal Acta Cirúrgica Brasileira, 2022;37(07)e370705, page 1, in affiliation:In the manuscript Instead of:2. MD. Shanghai Jiao Tong University – School of Medicine – Ren Ji Hospital – Department of Cardiology – Shanghai, China.Should be:2. MD. Shanghai Jiao Tong University – School of Medicine – Xin Hua Hospital – Department of Cardiology - Shanghai, China.


RNA methylation is a ubiquitous post-transcriptional modification found in diverse RNA classes and is a critical regulator of gene expression. In this study, we used Zika virus RNA methyltransferase (MTase) to develop a highly sensitive microplate assay that uses a biotinylated RNA substrate and radiolabeled AdoMet coenzyme. The assay is fast, highly reproducible, exhibits linear progress-curve kinetics under multiple turnover conditions, has high sensitivity in competitive inhibition assays, and significantly lower background levels compared with the currently used method. Using our newly developed microplate assay, we observed no significant difference in the catalytic constants of the full-length nonstructural protein 5 enzyme and the truncated MTase domain. These data suggest that, unlike the Zika virus RNA-dependent RNA polymerase activity, the MTase activity is unaffected by RNA-dependent RNA polymerase–MTase interdomain interaction. Given its quantitative nature and accuracy, this method can be used to characterize various RNA MTases, and, therefore, significantly contribute to the field of epitranscriptomics and drug development against infectious diseases. RNA methylation is one of over 150 RNA modifications identified, leading to an emergence of a field of study called epitranscriptomics , 3. The in vitro methods used to characterize RNA MTase enzymes. In this study, we used Zika virus (ZIKV) nonstructural protein 5 (NS5) RNA MTase to develop a robust medium throughput microplate assay for enzymatic characterization of RNA MTases.The recent development of transcriptome-wide approaches to capture various RNA modifications has improved our understanding of the role of RNA methylation in fundamental cellular processes essential for normal development . NeverthZIKV is a member of the Flaviviridae family. The viral genome replication is driven by an NS5, containing two active domains . The polIn vitro studies using RNA methylation assays and recombinant MTase enzymes have shown that inhibitors of AdoMet binding, such as Sinefungin and AdoHcy, can suppress NS5 activity  cellulose paper strips, which are washed with buffer and ethanol. The filter strips are dried and soaked in scintillation fluid. The total incorporated radioactivity is then measured in a liquid scintillation counter  RNA substrate that can be immobilized on avidin-coated 96-well plates. In an  enzymes , 35, 36. enzymes , 39, 40.A). The recombinant truncated domain (NS5–MTase) methyltransferase domain is a ∼30 kDa protein known to be active in the absence of the RdRp domain (B) and validated its enzymatic activity. Briefly, methylation reactions were carried out using single-stranded RNA containing 27 adenosine residues as a substrate and [methyl-3H]-AdoMet as methyl donor coenzyme. The conventional DEAE filter binding was used to perform kinetic assays at varying RNA concentrations from 0.035 to 2.0 μM (KM and turnover rate constant (Kcat) values were (7.3 ± 2.4) × 10−7 M and (10.0 ± 0.5) × 10−6 S−1, respectively. The catalytic efficiency was expressed as Kcat/KM ∼14 M−1 S−1 (C). This KM value is similar to what has been reported in previous studies  domain A. The rep domain . We purip domain B and valo 2.0 μM . Michael M−1 S−1 C. This K studies , demonstD). However, the control experiment with [methyl-3H] AdoMet and Bt-RNA in a reaction mix without enzyme confirmed the high background of ∼500 CPM , potentially because of the nonspecific binding of AdoMet to the DEAE filter paper (E). Moreover, each reaction is individually processed on small paper strips, which tend to tear off during washes making the assay cumbersome and inaccurate.The commonly used DEAE filter-binding assay, however, has several disadvantages. For example, the kinetics experiments show high CPM values at the zero time point, suggesting either burst kinetics or a high background level D. Howeveer paper E. MoreovA). In a methylation reaction, ZIKV NS5–MTase transferred the radiolabeled -CH3 group from [methyl-3H] AdoMet to the Bt RNA substrate. The methylation reaction was quenched in the avidin-coated ELISA plate in a quenching buffer containing high salt and unlabeled AdoMet. The Bt-RNA was immobilized on the plate through the biotin–avidin interaction. Next, the unbound reaction components, including unincorporated [methyl-3H]-AdoMet, were washed using the high salt buffer. Finally, scintillation fluid was added to the wells, and radioactivity incorporated with the -CH3 group on the immobilized RNA was measured using a MicroBeta2 plate reader by scintillation counting.We developed a microplate RNA MTase activity assay to circumvent the high background and low processing power of the DEAE filter binding method. A similar method was previously developed for measuring DNA and histone methylation activity , 41. TheFig. 2B). In addition, the Z-factor for the microplate assay is 0.66, compared with −0.32 for the filter assay (C). To test the fidelity of the method, we determined the catalytic constants of NS5–MTase using the microplate assay. We performed kinetic assays at varying RNA concentrations from 0.04 to 1.4 μM (D–F). KM and Kcat values were computed to be (5.1 ± 0.8) × 10−7 M and (4.5 ± 0.3) × 10−6 S−1, respectively. The catalytic efficiency was expressed as Kcat/KM ∼9 M−1 S−1 (G). The values are in the same range as those previously published (C), confirming the method's reliability. The binding capacity of an avidin-coated well for Bt-RNA was estimated to be around 3 pmol, similar to the previously known binding of Bt-DNA , increasing the sensitivity of the assay B. In adder assay  42). A . A B). Ier assay C. To teso 1.4 μM . Reactioo 1.4 μM , D–F. KM M−1 S−1 G. The vaublished  and deteublished C, confirf Bt-DNA . Given tA) (A and B)  × 10−7 M and (3.8 ± 0.2) ×10−6 S−1, respectively (C). The catalytic efficiency was expressed as Kcat/KM ∼6 M−1 S−1. Since these values are very similar to the catalytic constants for the truncated NS5–MTase domain, the data suggest little or no effect of the interdomain interaction on the MTase activity. We performed kinetic reactions at lower enzyme concentrations to ensure multiple turnover conditions. However, no difference in the reaction rates was measured (A and B). These data suggest that under the experimental conditions (D). Moreover, the interface residues are too far to make any direct contact with AdoMeT supporting our experimental results.The NS5 holoenzyme contains the MTase domain fused with the RdRp domain through a linker A 23). W. WA) (23A) , 44. StuA) , 40. HowA) , A and BA and B) . The KM ectively C. The cameasured , A and Bnditions D. Moreov50 of 18 μM, similar to the previously reported value  [methyl-3H]-AdoMet allows direct detection of methyl group incorporation into RNA substrates without secondary reactions detecting products of the reactions. (2) [methyl-3H]-AdoMet is available at a higher specific activity than other radioactive sources like 14[C]-labeled AdoMet, increasing the sensitivity of methyl group incorporation. In addition, [methyl-3H]-AdoMet has a longer half-life, allowing for a more extended detection window. However, because of the high compliance costs associated with radioactivity, alternative nonradioactive methods, such as fluorescence-based assays or mass spectrometry–based techniques, are also being explored for detecting RNA methylation in a high-throughput and quantitative manner  ssRNA viruses like ZIKV and coronavirus . DNA was electroporated into BL21(DE3) (Agilent) cells and plated onto an LB agar media supplemented with 50 μg/ml carbenicillin. Colonies were allowed to grow overnight at 37 °C. A single colony was then used to inoculate 150 ml of LB media supplemented with 50 μg/ml carbenicillin, which was grown overnight at 37 °C while shaking at 200 RPM. The next day, 50 ml of the overnight culture was added to 1 l of LB media supplemented with 50 μg/ml carbenicillin. The 1 l culture was allowed to grow at 37 °C, 200 RPM until the absorbance at 600 nm reached 0.6. The culture was then cooled at 4 °C for 30 min. Protein expression was induced with the addition of 0.25 mM IPTG, and protein expression was carried out for 18 h at 25 °C. Cell pellets were harvested by centrifugation and stored at −80 °C until further use.DNA corresponding to the methyltransferase protein from ZIKV strain g for 30 min at 4 °C to pellet cell debris and unlysed cells. The resulting supernatant was filtered using a 0.45 μm filter before being loaded onto a 5 ml immobilized metal affinity chromatography column  bound with Ni+2 and equilibrated in buffer A. After the supernatant was loaded onto the column, the column was washed with buffer A until the UV280 reading reached baseline. Protein was eluted from the column using a linear gradient from 0% to 100% buffer B  over 100 ml. About 5 ml fractions were collected over the entirety of the gradient. Fractions containing ZIKV MTase, as assessed using SDS-PAGE, were pooled and incubated at a 25:1 ratio with tobacco etch virus protease to remove the histidine tag. Cleavage was allowed to proceed overnight at 4 °C while the protein was simultaneously dialyzed against 2 l of buffer A. The dialyzed and cleaved protein was spun down at 30,000g to remove any protein aggregate, and the resulting supernatant was loaded onto a 5 ml Ni column equilibrated in buffer A. The ZIKV MTase protein was collected, and its purity was analyzed using SDS-PAGE. ZIKV MTase protein was then aliquoted, flash-frozen in liquid nitrogen, and stored at −80 °C until further use.Cell pellets were thawed on ice and resuspended in 5 ml buffer A , supplemented with 50 μg/ml DNase and 200 μg/ml lysozyme per 1 g of cells. Resuspended cells were sonicated on ice using a Branson sonifier for 12 min at 60% amplitude (pulse on for 5.5 s and pulse off for 9.9 s). The lysate was then centrifuged at 30,000Full-length NS5 enzyme was purified as described .About 96-well ELISA plates were coated with a 10 μg/ml avidin solution  made in 0.1 M sodium bicarbonate, pH 9.6. About 100 μl of the avidin solution was transferred in each well, and the plate was stored at 4 °C. The wells were washed with 200 μl of 1× PBS with Tween-20 (PBST) at least five times before use to remove any excess avidin solution not bound to the plate.3H]-labeled AdoMet  and 3′-Bt 27-bp polyadenosine ssRNA substrate. Methylation assays were carried out at various concentrations of RNA and enzyme in methylation buffer . Potential RNA degradation was prevented by supplementing the reaction mix with an RNase inhibitor . Reactions were carried out for 2, 5, 8, and 15 min at 30 °C, and reaction aliquots were transferred at specified time points into avidin-coated wells supplemented with the quenching buffer . The reaction mix was incubated for 30 min on a microplate vortex mixer (Fisherbrand) at 300 RPM to allow the binding of Bt-RNA. Next, the wells were washed five times with 1× PBST and 0.5 M NaCl using a multichannel pipette to remove unused [methyl-3H]-AdoMet and other unbound reaction components. About 200 μl of scintillation fluid was dispensed in the wells, and the incorporated radioactivity was quantified by scintillation counting using a MicroBeta2 plate reader .Methylation assays to determine kinetic parameters of recombinant ZIKV methyltransferase were performed using 0.33 μM [Methylation assays using DE81 filter paper were performed using the previously described standard protocol . BrieflyKcat and KM were determined by fitting the slopes from kinetic experiments to a nonlinear regression using the Michaelis–Menten formula in GraphPad Prism. The Z-factor comparing the performance of the filter and ELISA-plate assays were calculated as 50 values was performed by plotting percent of methylation activity at various AdoHcy (inhibitor) concentrations on the x-axis. Michaelis–Menten equation was used to fit data using variable slopes for enzyme inhibition. As described in the figure legends, errors were calculated as SE for three to six independent experiments. For Methylation activity was measured as CPM by scintillation counting. The CPM was converted to Nm as previously described . Data wehgowher@purdue.edu.All data are contained within the article, and raw data will be shared when requested at This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Fusarium oxysporum f. sp. cubense (Foc) is a devastating plant pathogen that caused a great financial loss in the banana’s source area. Metatranscriptomic analysis was used to determine the diversity of mycoviruses in 246 isolates of F. oxysporum f. sp. cubense. Partial or nearly complete genomes of 20 mycoviruses were obtained by BLASTp analysis of RNA sequences using the NCBI database. These 20 viruses were grouped into five distinct lineages, namely Botourmiaviridae, Endornaviridae, Mitoviridae, Mymonaviridae, Partitiviridae, and two non-classified mycoviruses lineages. To date, there is no report of the presence of mycoviruses in this pathogen. In this study, we demonstrate the presence of mycoviruses isolated from Foc. These findings enhance our overall knowledge of viral diversity and taxonomy in Foc. Further characterization of these mycoviruses is warranted, especially in terms of exploring these novel mycoviruses for innovative biocontrol of banana Fusarium wilt disease. Fusarium oxysporum f. sp. cubense (Foc), is affecting global banana production. Based on the pathogenicity of banana cultivars, Foc is divided into three races, including Foc race 1 (Foc1), Foc race 2 (Foc2), and Foc race 4 , which reduces the virulence of t blight . Roselli disease . Recentl disease . Further disease . Given tMonilinia fructicola , there are 26 families containing over 200 mycovirus species. With the development and widespread use of RNA deep sequencing techniques, the diversity of mycoviruses in Fusarium species is constantly increasing. Mycoviruses have been described in many Fusarium species, such as F. asiaticum  or positive-sense (+) single-stranded RNA (ssRNA) genomes. However, a few mycoviruses with linear negative-sense (−) ssRNA and single-stranded DNA (ssDNA) genomes have also been reported . In receucticola . Based osiaticum , F. andiandiyazi , F. boot boothii , F. circrcinatum , F. coeroeruleum , F. globglobosum , F. gramminearum , F. incacarnatum , F. langgsethiae , F. oxys dianthi , F. poae, a devastating plant fungal pathogen. To fill this gap, we screened and cultured 246 strains isolated from southern and southwestern China  for metatranscriptome sequencing to investigate mycovirus diversity in Foc. This information provides insight into the diversity and taxonomy of mycoviruses of Foc.The majority of  1 FgV1; , Fusariu 1 FgV1; , Fusariu 1 FgV1; , and Fus 1 FgV1; , cause hFoc was isolated and purified from diseased banana plants in Fujian, Guangdong, Guangxi, Hainan, and Yunnan provinces, China. The fungal stock cultures were maintained in a final concentration of 25% (v/v) glycerol at −80°C. All isolates were cultured on potato dextrose agar  at 28°C in darkness.The total RNA of each strain was extracted from 0.5 g of fungal mycelium by using a Plant RNA Extraction Kit . The total RNA was stored at −80°C until used. 246 strains were divided into two groups, group 1 of 146 and group 2 of 100. Approximately 2 ng of RNA was taken from each sample and mixed. A pooled RNA sample was sent to Shanghai Biotechnology Corporation (China) for RNA sequencing (RNA-seq).Ribosomal RNA depletion, library preparations, and Illumina sequencing were performed by Shanghai Biotechnology Corporation (China). Zymo-Seq RiboFree Total RNA Library Prep Kit  was used for ribosomal RNA depletion and library preparations. Illumina sequencing (Illumina NovaSeq 6000) was carried out by Shanghai Biotechnology Corporation (China).The filtered data was spliced together from scratch, and the resulting sequences were then de-duplicated. Subsequently, the virus sequences have been annotated using Diamond software (version 0.9.21.122) and the National Center for Biotechnology Information (NCBI) Non-Redundant Protein database.To confirm the detection of viral-like contigs in the strains tested, the presence of each contig was confirmed by RT-PCR using the appropriate primer pairs. The RNA samples were used as RT-PCR templates and specific DNA bands were amplified using the Vazyme HiScript- II One Step qRT-PCR SYBR- Green Kit  using the corresponding primers listed in the Open reading frames (ORFs) were determined with the aid of the ORF Finder program from NCBI. Then, to determine the reliability of a given branching pattern, phylogenetic trees were constructed from RNA-dependent RNA polymerase (RdRp) sequences using the neighbor-joining (NJ) method and tested with 1,000 bootstrap replicates in the MEGA 11 software. Multiple sequence alignments were performed by ClustalW. Accession numbers of the species for building the evolutionary tree are listed in the Botourmiaviridae, Mitoviridae, and Endornaviridae. The genomes of two -ssRNA viruses were associated with those of viruses in the family Mymonaviridae and a novel, unclassified family of the order Bunyavirales, respectively. The remaining dsRNA genomes were related to those of members of the family Partitiviridae. There is one additional virus, probably representing a member of an unassigned fungal alphavirus-like group.A collection of 246 isolates (group 1 of 146 and group 2 of 100) of Foc isolated from banana samples was used. Among 246 Foc isolates, only 20 in group 1 are Foc1, while all other isolates in two groups are Foc4. A total of 178,543,328 raw reads were generated by Illumina NGS. The assembly of the high-quality reads (QS ≥ 20) yielded about 287,540 contigs . All conBotourmiaviridae includes 12 genera and viruses with positive-sense RNA genomes that infect plants and fungi. Generally, members of this family, except Ourmiavirus, are non-encapsidated fungal viruses with a monopartite and monocistronic genome of 2,000–5,300 nucleotides containing a single ORF encoding the RNA-dependent RNA polymerase . Contig120 was 2,444 nt in length encoding a putative RdRp of 670 aa, namely Fusarium oxysporum f. sp. cubense ourmia-like virus 3 (FocOLV3). The RdRp of FocOLV3 was most homologous to the RdRp sequence of Magnaporthe oryzae ourmia-like virus (MoOLV) with aa sequence identity of 48%. Contig36412 is 2,766 nt, with a complete ORF encoding for 747 aa. This putative protein showed 85% identity with Phomopsis longicolla RNA virus 1 (PlRV1) RdRP, and we named this virus Fusarium oxysporum f. sp. cubense ourmia-like virus 4  method based on the viral RdRp aa sequences  are a large and diverse group of viruses. They infect a very wide range of hosts including vertebrates , invertebrates (mainly arthropods), plants, and fungi. Based on whether their RNA genomes are segmented or not, NSVs are generally classified into two large viral orders: non-segmented negative-sense RNA viruses (nsNSRVs) and segmented negative-sense RNA viruses (sNSRVs). Most nsNSRVs belong to the order families . Within oviridae . Accordioviridae . At leasch virus . Contig2x search . Thus, wx search . Contig1x search . Phylogeaviridae .Partitiviridae contains five genera, Alphapartitivirus, Betapartitivirus, Deltapartitivirus, Gammapartitivirus, and Cryspovirus that are a widely spread group of viruses infecting plants, fungi, and protozoa. Members of each genus have characteristic hosts: either plants or fungi for genera Alphapartitivirus and Betapartitivirus, fungi for genus Gammapartitivirus, plants for genus Deltapartitivirus, and protozoan for genus Cryspovirus. Generally, the genome of partitiviruses consists of two dsRNA segments of 1.3 to 2.4 kb each, the larger of which encodes the replicase and the smaller the structural proteins. Nevertheless, additional dsRNA segments may also be present  with 29% identity . FgALV1,Bunyavirales. This suggests that contig14713 likely represents a novel virus of Bunyavirales, and we have named it Fusarium oxysporum f. sp. cubense negative-stranded RNA virus 2 (FocNSRV2). Therefore, FocNSRV2 may be a new member of the order Bunyavirales  with 57% identity. GaMLV4 belongs to the unclassified avirales .Botourmiaviridae, Mitoviridae, Endornaviridae, Mymonaviridae, and Partitiviridae) and two non-classified mycoviruses lineages.In this study, a metatranscriptome -based strategy was used to investigate the virome diversity and composition associated with the plant-pathogenic fungus Foc that causes banana Fusarium wilt disease. The aim is to identify the mycoviruses which can influence the pathogenicity of the host to provide potential materials for the biological control of Foc. A total of 246 Foc isolates were collected from different diseased banana plants and the isolates were divided into 2 groups for analysis as described by Ourmiavirus and others belonging to the new genus. FocOLV1 and FocOLV4 cluster together while FocOLV3 forms a monophyletic clade. Therefore, we propose the creation of two new genera within Botourmiaviridae. Similarly, mitoviruses are not clustered in monophyletic lineages and there are two mitoviruses in a separate branch, suggesting the creation of a new genus in the Mitoviridae. As shown in Betaendornavirus of the family Endornaviridae. The genome of betaendornaviruses is less than 10.7 kb in length and absence of a site-specific nick near the 5′-end of the coding strand, according to the criteria for the classification of the two genera of Endornaviridae in ICTV , Sclerotinia sclerotiorum mitovirus 1 (SsMV1/HC025) and Sclerotinia sclerotiorum mitovirus 2/KL-1 , Fusarium oxysporum f. sp. dianthi mycovirus 1 (family Chrysoviridae), Fusarium oxysporum alternavirus 1 (family Alternaviridae), Fusarium oxysporum mitovirus 1 (family Mitoviridae), Fusarium oxysporum f. sp. dianthi mitovirus 1 (family Mitoviridae), Fusarium oxysporum f. sp. dianthi hypovirus 2 (family Hypoviridae), Fusarium oxysporum ourmia-like virus 1 (family Botourmiaviridae), Hadaka Virus 1 (family Polymycoviridae), and Fusarium oxysporum mymonavirus 1 , China Agriculture Research System of MOF and MARA (CARS-31), and Guangdong Basic and Applied Basic Research Foundation (2022A1515140114).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1193714/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


The viral structural protein Gag plays a number of central roles in this process, including association with the membrane, selective binding of genomic RNA, and oligomerization and packaging to ultimately produce an immature budded pro-viral particle2. While there have been intensive studies regarding the early stages of Gag assembly, there is a lack of consensus on the mechanism for nucleation and growth of Gag complexes7. Here we show that myristoylated Gag forms a trimer nucleus in a model membrane that can selectively bind a dimeric RNA containing the packaging signal. Subsequent growth of myristoyl-Gag oligomers requires vRNA, and occurs by addition of 1 or 2 Gag monomers at a time from solution. These data support a model where the immature capsid lattice formation occurs by a gradual lattice edge expansion, following a trimeric nucleation event. The dynamic single molecule data that support this model were recorded using mass photometry, involving full length myristoylated protein, RNA, and lipid together. These data are the first to support a lattice edge expansion model of Gag during early stages of assembly in a biological-relevant setting, providing insights to the fundamental models of virus structural protein assembly process.HIV-1 capsid assembly is an essential process in the virus infection cycle. Initiation of capsid assembly involves viral proteins, genomic RNA, and the inner leaflet of the plasma membrane, facilitated by a number of cellular factors The viral structural polyprotein Gag, accounting for more than half of the virus biomass, plays a crucial role in this process, with involvement of four domains that have different but coordinated functions: the matrix (MA) domain mediates binding to the membrane and specifically interacts with phosphatidylinositol 4,5-biphosphate P2) on the membrane, the capsid (CA) domain mediates association with other Gag proteins, the nucleocapsid (NC) domain confers selective binding to gRNA, and the p6 domain is responsible for recruiting cellular host factors2. The general functions of Gag are to oligomerize, selectively bind dimerized viral gRNA, and interact with the inner leaflet of the plasma membrane to form newly assembled immature virus particles.The global impact of acquired immunodeficiency syndrome (AIDS) has made human immunodeficiency virus (HIV) one of the most intensely studied viruses. To infect new cells, HIV must assemble infectious viral particles within the infected host. This assembly process is complicated and carefully choreographed among viral proteins, virus genomic RNA (gRNA), and the inner leaflet of the plasma membrane, involving many cellular host proteins7, there are only a few biochemical studies involving the three key components: myristoylated full-length Gag, viral RNA, and lipid membranes. Recent studies using two of these three components have pointed to the intertwined interactions between Gag, gRNA, and lipids. Recent structural and biochemical results have identified a gRNA dimer that can facilitate Gag-RNA nucleation8, while there is mounting evidence showing that Gag-Gag interactions, via both CA domain and MA domain, are the main driving forces for the final immature lattice formation10. The Gag CA domain forms stable hexamers in the immature lattice, and the Gag MA domain forms a hexamer of trimers on a model membrane system13. The two domains are connected by a flexible linker, possibly allowing both domains to adopt their preferred oligomerization pattern simultaneously13. The interactions between MA domain and the lipid membrane add yet another layer of complexity to the assembly process, and there is no consensus on the mechanism for nucleation and growth of oligomeric Gag complexes. Thus, it is essential to investigate this important viral assembly process with all three components present in the system.Although virus assembly has been studied extensively over the years15. The correlation between the amount of light scattered by a particle and the molecular weight of a particle is linear throughout a wide range, regardless of shape. Mass photometry data can be acquired in two observation modes. In a conventional mass photometry assay (landing assay), interference signals of individual molecules are detected as they adsorb nonspecifically on a slide surface. A histogram is created by observing a sufficiently large number of adsorption events, and the molecular weights of species present in solution can be observed in a histogram form. A second observation mode, called a mass-sensitive particle tracking (MSPT) assay, tracks individual molecules associating with and diffusing across a surface. In this mode, the changes in contrast patterns along a positional trajectory are recorded, allowing both positional dynamics and changes in the molecular weight to be observed directly at the same time  microscopy, where light is scattered by single macro-molecules near a slide surface can be detected in a label-free manner, and transformed into a contrast value by applying a model point spread function (PSF) to the resulting interferomic signal. This signal can then be converted to molecular weight using a mass standard calibrationame time , S116, 118, and a supported lipid bilayer (SLB) containing PIP2. We demonstrated the importance of the presence of lipid and gRNA in Gag oligomerization and showed that proper CA-mediated Gag dimerization is needed for assembly and efficient recruitment of gRNA to the membrane. Our data indicate that a Gag trimer may be the smallest nucleating unit on the membrane, that trimeric Gag associates with dimeric vRNA as a prerequisite for further Gag oligomerization in the membrane, and that the assembly proceeds via an edge-expansion process, where one or two Gag proteins are added at a time from the solution to the growing assembly nucleus.Using mass photometry, we examined the early steps in Gag assembly using myristoylated full-length Gag (myr-Gag), 5’ UTR gRNA containing the packaging signal20, which was confirmed using non-denaturing gel electrophoresis (MP landing assays were used to monitor the oligomerization distribution of myr-Gag or myr-Gag-RNA complexes in binding buffer. Over the concentration range tested (10–100 nM), myr-Gag mainly consisted of monomeric and dimeric Gag populations (~55 and 110 kDa respectively)  When WT phoresis , S2B. Alphoresis . NeitherMSPT experiments were employed to track myr-Gag diffusion on a lipid membrane. Complexes diffusing in the SLB were readily observed and tracking of individual complexes yielded a substantial number of analyzable trajectories . The myr2:Gag3 complex .To investigate lipid-dependent Gag-RNA complexes formation myr-Gag was examined in MSPT experiments on an SLB, alone and with varying amounts of WT 5’ UTR RNA. The results reveal a diverse set of complexes forming in the presence of RNA, with an informative concentration dependence . In the  complex . Small p complex . However2:Gag3 complex appears as the only major Gag-RNA complex when RNA concentration is very low, and it remains a major species across all RNA concentrations tested, making this complex very likely to be the nucleus for assembly.These results demonstrate that the presence of even substoichiometric amounts of 5’ UTR RNA enhances Gag oligomerization on the membrane, and that both RNA concentration and the protein-to-RNA ratio affect the final distribution of Gag-complex species on the SLB. In summary, myr-Gag preferentially forms dimers in solution without and with 5’ UTR RNA, but forms trimers or higher multimers when binding to dimeric 5’ UTR RNA in the context of an SLB. This is clear evidence for the cooperative interaction between protein, RNA, and lipid in the earliest steps of capsid assembly. The RNA10. To investigate the effect of Gag CA dimerization in the assembly process on the membrane, the MWM myr-Gag mutant  was assayed21. In solution, MWM myr-Gag forms a smaller proportion of dimers compared to WT myr-Gag, as expected , and CA-CA interactions are a key feature of the 2-fold interactions in the Gag immature latticeexpected . In the  species . Gag is The mass distribution in the supernatant after SLB incubation was also measured in a landing assay, to determine if the distribution of species in solution was altered by exposure to the SLB. In both cases, there was one broad peak between 55 kDa and 110 kDa, indicating no species larger than a Gag dimer exists in the solution after SLB incubation .4:RNA2, compared to WT myr-Gag (1:Gag2 complex formation (~220 kDa) that also appeared in WT experiments. Since the MWM mutation impairs Gag dimerization, this result indicates RNA1:Gag2 can be maintained via both Gag-RNA interactions and Gag MA-MA interactions in the context of the SLB. It is important to note that while MSPT data can discern the molecular weight of complexes, it cannot discern the conformation or geometry of a given complex. Therefore, complexes may or may not be architecturally homogeneous within a given sample, or across samples.When RNA is added to the mixture, MWM myr-Gag does not substantially bind to RNA in solution, similar to WT myr-Gag . However myr-Gag . The MWMTo investigate how a lipid membrane affects the solution species distribution, supernatants from MSPT assays were removed and re-assayed in a solution landing assay. The composition of the supernatant complexes after SLB incubation exhibited distinct distributions for the wildtype and mutant Gag species. The MWM myr-Gag complexes in supernatant exhibited a much larger portion of RNA dimer after 30 min SLB incubation compared to the WT Gag, indicating that MWM myr-Gag cannot recruit 5’ UTR RNA to the membrane surface as efficiently as the WT myr-Gag .25, allowing the detection of transitions between different species on the SLB. Comparing the results of myr-Gag alone and myr-Gag with WT 5’ UTR RNA, the step size distributions for attachment events are largely similar, though myr-Gag with WT 5’ UTR RNA samples display some larger steps  in the experimental system . We also26. Our data do not support a Gag hexamer being a major assembly step at the concentrations tested, as we did not observe steps containing six Gag proteins, nor did we observe hexameric Gag complexes as abundant intermediates. There has been evidence showing Gag adds to the immature lattice via lower order multimers27, and more recently, patterns of an edge expansion model were identified in the immature lattice of a budded virion and on mica surface in AFM experiments29, but no similar study has been done on a biologically relevant membrane system focusing on the early assembly steps. Our data support an edge expansion model in early-stage Gag assembly where a Gag hexamer is neither the nucleus for assembly, nor added as a whole to the nucleating complex. Rather, assembly proceeds by addition of Gag monomers and dimers to the edge of an assembly core on a lipid membrane surface where trimerization, likely mediated by MA or CA interactions, plays a crucial role.While a Gag hexamer is the smallest repeating structural unit in the immature Gag lattice, there has been no consensus on the potential role of a Gag hexamer as an assembly intermediate, despite decades of studies23. Several molecular models have been developed by superposition of the symmetry axes of the MA and CA lattices13, which are illustrated schematically in The interplay between Gag monomers and dimers in solution with Gag trimers in the membrane may reflect the duality of the immature lattice when viewed from the standpoint of the MA and CA domains. Lattice models exist for hexamers-of-trimers for the MA domain , and tri2:Gag1 has been detected in both WT and mutant Gag experiments. Both of these transition complexes contain an incomplete Gag trimer interface, and T2 also harbors a completed Gag dimer interface 6 fusion, and the yeast NMT1 gene, and transformed into The myristic acid solution was freshly prepared at 5 mM concentration by first dissolving the myristic acid in 5 mL ethanol and then diluted with 50 mL pre-warmed 0.6 mM BSA at pH 9 (adjusted with NaOH). The solution remained at about 50 °C before use to maintain the solubility of myristic acid.Cell pellets were resuspended in Gag sonication buffer  with 1 tablet of protease inhibitor and incubated on ice for 10 min. The mixture was then sonicated at 4 °C on ice  and was then centrifuged at 10,000 g for 30 min. The supernatant was removed and loaded onto a 5 mL HisTrap column equilibrated with Gag HisTrap A buffer . Gag-MBP was then eluted from the HisTrap column by applying an increasing concentration gradient of Gag HisTrap B buffer  and fractions containing Gag-MBP were pooled together. The pooled fractions were then loaded onto a 5 mL Heparin column  equilibrated with Gag Heparin A buffer , followed by gradient elution with Gag Heparin B buffer . Fractions containing Gag-MBP were pooled together and concentrated using a 10K concentrator. The final Gag-MBP solution was exchanged into TEV cleavage buffer  and concentrated by a PD-10 desalting column . TEV (expressed and purified as previously reported) was added to the Gag-MBP solution, and the mixture was incubated at 30 °C for 2 h. The reaction mixture was then loaded onto a Ni-NTA column  equilibrated with TEV cleavage buffer, followed by washing with 4 mL TEV cleavage buffer. All eluates were collected and concentrated at 15 °C to avoid Gag aggregation, as the solubility tag MBP had been removed. Glycerol was then added to the final Gag solution and the protein was stored at −80 °C in small aliquots. The presence of full-length Gag-MBP and its purity were monitored by SDS-PAGE and the molecular weight of the myr-Gag was confirmed by intact mass spectrometry.To express Gag without myristoylation, a Gag construct without NMT1 gene was used and myristic acid was not added in the expression culture. All protein expression and purification procedures were the same aside from the protein encoding sequence.Viral UTR RNA and its variants were made by in vitro transcription (IVT)  using a template containing a T7 promoter made by PCR from a plasmid encoding full-length gRNA sequence. IVT was done at 37 °C overnight, followed by spin-column purification. RNAs were stored at −80 °C in small aliquots. Aliquots of RNA were thawed slowly on ice and then diluted to 1 μM in RNA refolding buffer . The diluted RNA was refolded by heating it up to 80 °C for 5 min followed by gradually cooling to 10 °C. The refolded RNA was kept on ice before use. The integrity of RNA was verified by urea SDS-PAGE and the conformational change through refolding was demonstrated by native agarose gel electrophoresis.SLBs were prepared using defined mixtures of phosphatidyl choline (POPC)  and PIP2  if needed. The lipids were dissolved in chloroform, mixed in the desired ratios, dried under nitrogen and the residual chloroform was removed in a vacuum desiccator overnight at room temperature. The dried lipid “cake” was then resuspended in SLB buffer . The lipid solution was then extruded through a polycarbonate membrane with a pore size of 50 nm using an Avanti mini-extruder  for 49 passes to form lipid vesicles with relatively uniform size. The SLBs were prepared by applying the solution of the vesicles to a cleaned glass slide surface immediately after treatment in a plasma cleaner  at maximum power for 10 min at 0.5 bar air pressure. Unruptured vesicles were removed through extensive washing with SLB buffer after a 15–20 min incubation at room temperature to allow complete membrane formation.Glass slides were washed by distilled water, isopropyl ethanol , water, isopropyl ethanol, and water again before being dried in an air stream. A gasket  was carefully placed on a cleaned slide, which was then placed on the mass photometer holder. Gag binding buffer  filtered by a 0.22 μm filter was added to the well. Sample protein solution or protein-RNA mixture was diluted or mixed in Gag binding buffer and incubated for 5 min before replacing the Gag binding buffer in the well, followed by immediate recording in the mass photometer for 1–2 min at a frame rate of 100 Hz.Glass slides were washed with water, isopropyl ethanol , water, isopropyl ethanol, and water again before drying in an air stream. The slide was then treated in a plasma cleaner  at 0.5 bar air pressure for 10 min at maximum power. A gasket  was carefully placed on the treated slide, and SLB buffer was immediately applied to all wells. Vesicles were then applied to the well to form SLB. Residual vesicles were washed away with the SLB buffer by gently pipetting in the well without touching the membrane . Gag binding buffer was then introduced into the well. Sample protein solution or protein-RNA mixture was diluted or mixed in Gag binding buffer and incubated for 5 min before replacing the Gag binding buffer in the well, followed by recording of five 5-min movies at a frame rate of 100 Hz after 5 min incubation.The contrast of a set of mass standards was measured for both the landing assay and MSPT on SLBs to convert the scattering contrast into molecular weight.For landing assay mass standards, 50 nM bovine serum albumin (BSA) or 50 nM alkaline phosphatase was diluted in Gag binding buffer and equilibrated for 5 min at room temperature. The protein standard solution was added to the well of a landing assay set up and the landing events were recorded for 1 min.For MSPT mass standards, POPC lipid vesicles containing 0.01 mol% biotinylated PE were used to form SLBs. 2.5 nM streptavidin was added to the SLB after the residual vesicles were washed away with Gag binding buffer. Unbound streptavidin was washed away with Gag binding buffer after 10 min incubation at room temperature, followed by the addition of 100 nM biotinylated BSA , 100 nM biotinylated alkaline phosphatase , or 100 nM biotinylated Protein A . The unbound biotinylated protein was washed away after 5 min incubation at room temperature, and movies were recorded for 10 min for each protein.For a landing assay, background removal and contrast-to-mass conversion facilitated by standard curve were done using DiscoverMP software with default parameters.For MSPT experiments, data processing was done using custom Python scripts. The scripts used in background removal and particle identification was a combination of scripts kindly provided by the Kukura lab (University of Oxford) and the Schwille lab (Max Planck Institute of Biochemistry), with specific modifications to meet the current needs.Background processing was done using a median background subtraction method. To detect particles laterally moving on the surface, this method calculates the pixel-wise temporal median image of a 1001-frame window  to extract contrast for each spot.To identify spots in the background corrected movies, for each frame a Laplace filter was applied to filter out shot noise, followed by further thresholding to remove small promiscuous spots. A local maximum was identified for each of the candidate spots and its pixel coordinates were noted as the candidate pixel. A 13×13 pixels (84.4 nm per pixel) region of interest was marked for each candidate spot around the candidate pixel in the original image and fitted by the same PSF used in the DiscoverMP software to extract particle contrast and location. Spots at very close separation were rejected at this step to guarantee good PSF fit. Spots with very large contrast  are excluded as their molecular weight far exceed particles in early assembly process and are more likely to be vesicle residues.After particle identification, trajectories were generated using the TrackPy Python package with minimum length filter set to 20 frames (200 ms), memory set to 1 frame, and maximum allowed step distance set to 5 pixels (422 nm).1:Gag4 (data not shown), suggesting 2 RNA molecules need to exist and dimerize for more than 4 Gag proteins to be incorporated into the complex. All peak assignments regarding Gag-RNA complexes were made based on these three premises.For Gag-RNA complexes species assignment, the following considerations were taken into account: 1) as myr-Gag alone would not form complexes larger than Gag3 on SLB, it was assumed that no larger Gag-only complexes would form when RNA was added to the system; 2) under physiological conditions, HIV-1 would only package 2 copies of gRNAs into the newly assembled virions, thus it was assumed here the maximum RNA copy number in each complex species is 2; 3) when combined with mutated monomer gRNA 5’ UTR and incubated on the SLB, the largest complex was RNAAs there are a variety of complexes differing with each other only by ~55 kDa, some species tend to merge into broader peaks in our experiments, making it difficult for multiple gaussian fits to identify the exact molecular weight of each species. To still provide useful and indicative molecular weight information, in this work we plotted the particle molecular weight distribution in both histogram and kernel density plot, and labeled the expected position of each Gag-RNA complexes, instead of the gaussian fit value of the distribution.To detect mass change steps throughout trajectories, a C language implementation of the Kalafut-Visscher algorithm was used to perform denoising and step detection. The algorithm itself does not require any other input except for the time series itself. Previous studies have reported that the length of the time series and the position of potential change points can affect the step detection result and its accuracy. To address this issue, all individual trajectories from a certain group of experiments were concatenated first, and the data was then divided into a series of n segments of equal length using a sliding window of 1000 frames. Each of these individual 1000-frame segments were used as input to the algorithm for analysis, avoiding bias from step positions and the length of the time series. With this sliding window segmentation procedure, step detection was repeated 1000 times shifting the start point by 1 frame in each iteration. After the step detection was done for every segment, the results were pooled together. For a particular time-spot in a trajectory, it had gone through 1000 iterations of step detection cycle, and the frequency of a returning result being a step was used to decide if it is a real change point identified at that particular location with a threshold of 0.75 .The sliding window length and the threshold are related to each other. A larger window length would result in less sensitive step detection, which can be compensated by choosing a smaller threshold. Considering the noise level of the system and the complexity of the Gag oligomerization process, a rather strict threshold was chosen in this project to avoid over-identification of steps.Supplement 1


The spinning disk technology has previously been utilized to isolate bacterial components from blood in hours instead of days. We hypothesized that this platform could be applied as an alternative approach to isolating plasma RNA from a whole blood sample. We consequently tested the efficacy of the spinning disk technology to extract plasma from whole blood upstream of RNA isolation and analysis. To do so, we collected plasma using either the spinning disk or the typical two-spin centrifuge method. We found that the spinning disk method results in significantly more hemolysis during collection than the conventional two-spin centrifuge method. However, when plasma RNA recovered from both collection methods was quantified using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), we found that the spinning disk method yielded a higher plasma RNA concentration than the two-spin centrifuge method. This suggests that the spinning disk may be an efficient alternative method to recover plasma RNA. Further work is needed to determine whether red blood cell RNA contamination is present in the plasma RNA extracted from spinning disk-processed plasma. The spinning disk method has primarily been used to separate blood components prior to bacterial DNA extraction and high-speed antibiotic resistance profiling Blood Collection and Component Separation:To compare these two plasma separation methods, we collected blood from four donors , in sets of 3 tubes per donor. Each donor set included two Streck RNA Complete BCT (Streck) Tubes and one K3-EDTA (EDTA) tube (A) tube . We paiPlasma Hemolysis Evaluation:To determine the level of background hemolysis in the collected plasma, we used a spectrophotometric-based method, the Harboe Method with the Allen Correction. This method quantifies free oxyhemoglobin levels, a marker of hemolysis which rises in proportion to total hemolysisPlasma RNA Isolation & Quantification:To evaluate the efficacy of the spinning disk plasma separation method, we next determined whether the plasma RNA yield differed between the Streck + Spinning Disk and Streck + Centrifuge groups. To do so, we isolated plasma RNA from all Streck tube samples using the QIAamp Circulating Nucleic Acids Kit (Qiagen), after which we quantified the plasma RNA extracted from each sample using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). To do so, for samples from four donors  with three technical replicates per sample, we measured the ACTB and GAPDH housekeeping genes according to a standard quantification procedure for low-concentration RNA such as plasma RNASample Collection and Experiment Design: IRB approval was obtained before collecting whole blood samples . Blood was drawn into a set of two Streck RNA Complete BCT (Streck) Tubes and one K3-EDTA (EDTA) tube per donor (Becton Dickinson Hemogard™ #366643). Hematocrit was measured from two technical replicates per donor and blood from each donor was diluted with phosphate-buffered solution (PBS) to a standard hematocrit of 32 to ensure uniform performance of the spinning disk. One of the two Streck tubes collected from each donor was processed using the spinning disk method, while the second of the two Streck tubes from each donor was processed using the two-spin centrifuge method. The EDTA tube from each donor was also processed using the two-spin centrifuge method to provide an additional datapoint on tolerated hemolysis levels in typical hospital/clinical lab samples.The centrifugation treatment consisted of a two-spin protocol, with a 15-minute primary spin at 1800 relative centrifugal force (RCF) followed by a 15-minute clarifying spin at 2800 RCF, both in the high-speed centrifuge according to Streck manufacturer protocol as previously reported, after which the plasma layer was carefully collected for further analysis . The spinning disk method we used consisted of a primary spin on the spinning disk and a subsequent clarifying microcentrifuge spin. Before beginning the procedure, the spinning disk and its lid were both sterilized by using a modified version of the protocol used in past workPlasma Hemolysis Evaluation: The plasma was carefully collected for further analysis from the Streck tubes using either the spinning disk method or two-spin centrifugation method, and from the EDTA tubes using the two-spin centrifugation method. Plasma hemolysis was then determined via spectrophotometry by measuring the absorption at wavelengths of 415, 380, and 450 nm in triplicate and applying the Harboe method with the Allen correction according to the following equation: Hb = (1 mg/dL)(167.2(A415) - 83.6(A380) - 83.6(A450)) wherein A415 indicates absorbance at 415 nm, etc.Plasma RNA Extraction and Quantification:Following plasma separation using either the spinning disk or the centrifuge method, plasma RNA was extracted from all Streck tube samples according to the manufacturer’s instructions found in the Cell-free RNA next-generation sequencing workflow for the Streck RNA Complete BCT document . In brief, plasma RNA was extracted from 3 mL of sample using the QIAamp Circulating Nucleic Acids Kit (Qiagen) according to the microRNA protocol for 3 mL samples with following adjustments: 1) extending the incubation step from 30 minutes to 60 minutes at 59°C, and 2) eluting with 87.5 μL of buffer AVE. DNase I digestion was then immediately performed with the RNase-Free DNase Set (Qiagen) according to the Appendix B protocol in the QIAamp Circulating Nucleic Acids handbook, with the adjustment of eluting with 18 μL of RNAse-free water. RNA cleanup was then performed using the RNeasy MinElute Cleanup Kit according to the QIAamp Circulating Nucleic Acids Handbook Appendix C protocol. Following cleanup, the concentration of each plasma RNA sample was checked in triplicate using a Thermo Nanodrop instrument to obtain the 280/260 and 230/260 absorbance ratios, to measure sample purity and estimate RNA concentration. After determining RNA concentrations, purified RNA samples were stored in a -80°C freezer. The RNA samples were prepared for qRT-PCR using the High Capacity RNA-to-cDNA Kit (Applied BioSystems) with random hexamer primers to create first-strand synthesis cDNA libraries prior to storage at -80°C. qRT-PCR was performed on these cDNA libraries on a StepOnePlus Real-Time PCR System (Applied Biosystems) using the SYBR GreenER qPCR SuperMix Universal kit (Invitrogen) and the following primers: GAPDH forward primer 5’- AGCCGAGCCACATCGCT-3’, GAPDH reverse primer 5’-TGGCAACAATATCCACTTTACCAGAGT-3’10.22002/mcr98-ays65Description: The complete dataset of measurements found during this study and analyzed in this paper.. Resource Type: Dataset. DOI:


Therefore, IVT condition should be well optimized to achieve high yield and purity of transcribed RNAs. To this end, real-time monitoring of RNA production during IVT, which allows for fine tuning of the condition, would be required. Currently, light-up RNA aptamer and fluorescent dye pairs are considered as useful strategies to monitor IVT in real time. Fluorophore-labeled antisense probe-based methods can also be used for real-time IVT monitoring. In addition, a high-performance liquid chromatography (HPLC)-based method that can monitor IVT reagent consumption has been developed as a powerful tool to monitor IVT reaction in near real-time. This mini-review briefly introduces some strategies and examples for real-time IVT monitoring and discusses pros and cons of IVT monitoring methods.RNA-based therapeutics and vaccines are opening up new avenues for modern medicine. To produce these useful RNA-based reagents,  Since the global COVID-19 pandemic, RNA vaccines have been hailed as new vaccines to quickly respond to infectious diseases . Furtherin vivo stability of linear mRNA vaccine as an example, 5′-capping (in vitro transcription (IVT) (E. coli poly(A) polymerase  are required for linear mRNAs to have efficient translation (in vivo applications.For efficient translation and -capping  and poly-capping  are requon (IVT)  by co-tron (IVT)  or afteron (IVT)  and cap on (IVT) . Poly(A)lymerase . IVT react yield . In addinslation  and avoinslation  for in vAs an example of other RNA modalities, circRNA also can be generated by IVT  using grTherefore, IVT process would be fundamental for determining the yield and purity of RNA products. To produce large amounts of RNA-based vaccines and therapeutics with high purities, useful IVT monitoring methods are needed to understand molecular mechanisms and to monitor target RNA production, efficacy of the aforementioned enzymatic reactions, and reagent consumption.Traditionally, oligonucleotides and polynucleotides including RNAs have been routinely analyzed by various analytical techniques such as polyacrylamide gel electrophoresis PAGE, , agarosein vitro -like RNAs to visualize RNA by tagging. Therefore, RNA aptamers that can bind and turn on nonfluorescent fluorogens have been developed for RNA imaging and/or detection in vitro , in bactin vitro , and in in vitro . Such liin vitro , which cGenerally, nonfluorescent dye for the light-up aptamer strategy has extremely low quantum yield due to vibrational de-excitation . HoweverAmong various light-up aptamer and fluorescence dye pairs developed so far, Spinach  and relaFor the application of light-up RNA aptamer strategy, the signal from the real-time IVT monitoring method should be independent from the polymerase, ROI, and other components to accurately monitor the transcription activity. To this end, a simple, robust, and universal method has been developed to analyze the quality and quantity of transcribed RNA during IVT . In thisin vivo.Detection speed with real-time IVT monitoring using light-up aptamer and fluorescent dye pair is at least 100 times faster than conventional polyacrylamide gel electrophoresis (PAGE) method , whereasIf transcribed RNAs will not be used for further applications after real-time IVT monitoring using light-up aptamer and dye pair, in other words, if IVT is only monitored to check transcription activity itself for IVT optimization, introduction of ribozyme domain would be unnecessary . For exaFluorescence resonance energy transfer (FRET)-based monitoring strategy using UFM and water-soluble conjugated polymer has also been reported . BrieflyHowever, one concern would be fast fluorescence decay by photoconversion after illumination  during rVarious other strategies have also been employed to monitor IVT in real time. For example, a simple strategy by directly incorporating γ-fluorophore-labeled UTP into RNA during transcription has been used to monitor IVT . HoweverInstead of directly incorporating the fluorophore into RNA target, fluorophore-labeled antisense probes such as a fluorescent DNA probe that can enhance fluorescence on hybridizing with a target RNA have been developed for real-time IVT monitoring. For example, oxazole yellow (YO) is normally non-fluorescent . Howeverin vitro transcription assay  strategy using a DNA probe containing a fluorophore and quencher pair has been applied for real-time IVT monitoring . MB probt manner . The MB A locked nucleic acid (LNA)  probe anHPLC is a powerful and sensitive analytical method that can monitor various reactions and provide qualitative and quantitative information for various materials including nucleic acids . HPLC isRecently, at-line HPLC strategy has been reported for IVT monitoring at a specific time point . In this2+, plasmid, and NTP concentrations) can be optimized by monitoring NTP consumption and RNA production in near real-time. More interestingly, a fed-batch approach for IVT that can extend time and yields by continuously adding IVT components to the reaction mixture for therapeutically relevant mRNA has been demonstrated (By using at-line HPLC method, IVT reaction conditions (e.g., Mgnstrated . Quantifnstrated .2+ by monitoring consumption kinetics with control of various parameters. In addition, quantitative information obtained by HPLC-based IVT monitoring could reduce uncertainty in RNA quantification before downstream purification process (As for future directions, Pregeljc et al. foresee a coupling of at-line HPLC-based IVT monitoring strategy with multi-parallel automated bioreactor systems, enabling controlled and continuous addition of IVT components such as NTP and Mg process .Spectroscopic methods also would be applied to strengthen the real-time IVT monitoring methods in the near future. For examples, raman spectroscopy is likely to be applied for RNA manufacturing also by measuring NTP consumption . CirculaIn this mini review, we briefly introduced real-time IVT monitoring strategies. Among various strategies briefly explained above, light-up aptamer-based method is widely being used for RNA detection and various applications . At-lineEach method has its own pros and cons for IVT monitoring. Researchers need to wisely choose an appropriate method for their research purposes. Real-time IVT monitoring would be more useful for RNA manufacturing by IVT if fluorescence-based strategies and HPLC-based methods are used complementarily. As research on RNA therapeutic and RNA vaccine is receiving a lot of attention, real-time IVT monitoring methods are expected to be rapidly advanced.