Lower expression of DNMT1circle10302690-10302961 in both primary and metastatic tumors was associated with worse prognosis of HGSOC. Taken together, our finding firstly demonstrated the eccDNAs landscape of primary and metastatic tissues of HGSOC. The eccDNA DNMT1circle10302690-10302961 can be considered as a potential biomarker or a therapeutically clinical target of HGSOC metastasis and prognosis.High grade serous ovarian cancer (HGSOC) is the most aggressive subtype of ovarian cancer and HGSOC patients often appear with metastasis, leading to the poor prognosis. Up to date, the extrachromosomal circular DNAs (eccDNAs) have been shown to be involved in cancer genome remodeling but the roles of eccDNAs in metastatic HGSOC are still not clear. Here we explored eccDNA profiles in HGSOC by Circle-Sequencing analysis using four pairs of primary and metastatic tissues of HGSOC patients. Within the differentially expressed eccDNAs screened out by our analysis, eight candidates were validated by outward PCR and qRT-PCR analysis. Among them, DNMT1 High grade serous ovarian cancer (HGSOC) is the most common and lethal subtype of epithelial ovarian cancer, accounting for nearly 70% death of ovarian cancer , 2. The Located outside chromosomes, a group of novel circular DNAs referred as extrachromosomal circular DNAs (eccDNAs) are generated during DNA damage repair, chromothripsis, and other DNA metabolisms –13. Thiscircle10302690-10302961, which has been validated as the most significantly down-regulated eccDNA in metastatic tumors compared to primary tumors of HGSOC. Additionally, we determine the clinical value of DNMT1circle10302690-10302961 in a cohort of HGSOC tissues. The decrease of DNMT1circle10302690-10302961 is associated with a poor prognosis for HGSOC patients. Taken together, our work demonstrate an eccDNA signature of HGSOC, which can be considered as a potential diagnostic strategy for metastasis prevention, and a specific eccDNA DNMT1circle10302690-10302961, which can be a credible clinical therapeutic target for HGSOC metastasis treatments.Here, we characterize the eccDNA expression profiles of primary and metastatic tissues of HGSOC, and identify a novel eccDNA named DNMT1To detect eccDNAs in the paired primary and metastatic tissues (HGSOC-M) of HGSOC, we conducted Circle-Sequencing analysis with four pairs of specimens | ≥2 and .05 Fig. , C.P < 0.05). Besides, we analyzed the Circle-Seq data with the RNA-seq results, and found that 64 altered candidates (P < 0.05 in both sequencing results) were overlapped  failed to be validated due to the low expression levels. The Sanger sequencing result of PRIM2circle57211409-57212590 did not match the corresponding junction sites, indicating that PRIM2circle57211409-57212590 is not a true circular DNA. To further validate our sequencing results, qRT-PCR was performed  between the same primary and metastatic HGSOC tumors by RNA-sequencing analysis Fig. . We idenped Fig. . Meanwhiped Fig. A–B. The 10302961 . To vali118 Fig. . Other tmed Fig.  using ancircle10302690-10302961 was circularized by a segment of DNMT1 gene on the reverse strand of chromosome 19 (chr19: 10302690-10302961). To verify its specific circular structure, outward PCR targeting DNMT1circle10302690-10302961 was performed in SKOV3, A2780 and OVCAR3 cell lines. The results showed that DNMT1circle10302690-10302961 was not presented in genomic DNA  assay using junctional specific Cy3-labeled probes in SKOV3, A2780 and OVCAR3 cells, which were arrested at metaphase spreads beforehand  primary and metastatic tissues of HGSOC by FISH assays and BaseScope assays  stages (P = 0.008), lymph node metastasis (P = 0.047) and postoperative visible residual disease . Further Kaplan-Meier survival analysis showed that lower expression of DNMT1circle10302690-10302961 in primary HSGOC tumors was associated with shorter OS  and lower disease-free survival (DFS) rates . Moreover, the expression of DNMT1circle10302690-10302961 of the metastatic HGSOC tumors were also measured. Among the 71 samples with metastasis, patients with lower DNMT1circle10302690-10302961 expression also had a significantly worse OS  and DFS . In addition, we collected the primary and metastatic tissues of 25 advanced HGSOC patients . The Kaplan-Meier survival analysis showed that low expression of DNMT1 was associated with short overall survival (OS) in ovarian cancer patients Fig. . These dals Fig. . The corcircle10302690-10302961 is significantly down-regulated in metastatic tumors compared to primary tumors of HGSOC, and may potentially serve as a prognostic biomarker for HGSOC patients.Advanced HGSOC are highly metastatic. This is one of the major factors leading to poor prognosis for HGSOC patients . As the Compared with other global sequencing methods, Circle-Seq is a more sensitive method for eccDNA detection, and has been applied in yeast and healthy human somatic tissues , 22, 24.The origins of eccDNAs in HGSOC were mapped to different genomic elements. Our work has shown that eccDNAs are highly enriched in 5′ UTRs and 3′ UTRs region, where R-loop structures are formed , 29. ThiThe roles of eccDNAs have yet to be fully discovered, especially for the small size eccDNAs that are less than 1000 bp , of whiccircle10302690-10302961, as the most significantly down-regulated eccDNA in metastatic tumors, compared to primary tumors of HGSOC. DNMT1circle10302690-10302961 was circularized by a segment of DNMT1 DNA. The host gene DNMT1 is a crucial regulator of genomic methylation, which mediates DNA methylation of various cancer-associated genes in regulation of metastatic propensity [circle10302690-10302961 and DNMT1 mRNA decreased in metastatic tissues of HGSOC. Thus, we deduced that DNMT1circle10302690-10302961 may harbor some similar clinical value in HGSOC. Along with this idea, large amount of clinical samples were utilized to verify the clinical potential of DNMT1circle10302690-10302961. The decreased expression of DNMT1circle10302690-10302961 was associated with metastatic behaviors, as well as an adverse prognosis. It was reported that complete response to NACT was significantly associated with improved OS and DFS compared to partial or no response in HGSOC [circle10302690-10302961 expression in metastatic samples relative to primary tissues in patients who had a partial response to NACT, implying that DNMT1circle10302690-10302961 may also be a prospective marker to evaluate the response to chemotherapy.In the present study, we identified a novel eccDNA, named DNMT1opensity . Loss ofopensity , 37–39. in HGSOC . Intrigucircle10302690-10302961 is expected to be a promising biomarker to predict metastasis, prognosis, and response to NACT for patients with HGSOC. Our Circle-Sequencing results provide valuable insights into the atlas of the HGSOC eccDNA signatures. The identification of eccDNA DNMT1circle10302690-10302961 will contribute to advanced HGSOC treatment therapies.In summary, we have revealed the landscape and characteristics of eccDNAs in primary and metastatic tumors of HGSOC. As the most down-regulated eccDNA in metastatic tumors of HGSOC, DNMT1In this study, 4 pairs of HGSOC primary and metastatic tissues (HGSOC 1-4 and HGSOC-M 1-4) were collected in 2020 and subjected for Circle-Sequencing and transcriptome sequencing. A panel of HGSOC tissues containing 20 primary tumor samples and 20 matched metastatic tumor samples were collected in 2020 and used for validation by qRT-PCR analysis. The FFPE tissues of the same 20 HGSOC patients were also collected for validation by FISH assay and BaseScope assay. Tissue samples were immediately stored in liquid nitrogen after section and then transferred to −80 °C until use. Another panel of FFPE tissues of 80 HGSOC patients were obtained between 2013 and 2020, of whom we had complete clinical and 5-year follow-up data. The FFPE tissues of 25 advanced HGSOC patients who received NACT were obtained between 2015 and 2021.All clinical samples above were pathologically confirmed as HGSOC, and were obtained at Women’s Hospital, School of Medicine, Zhejiang University under the approval of the Hospital Ethical Committee (IRB-20200186-R). Informed consent to use and publish clinical information for research purposes were obtained from all of the patients in accordance with the Declaration of Helsinki.The clinical-pathological information of all patients were listed in Supplementary Table The Circle-Sequencing was used to detect eccDNAs in paired primary and metastatic tissues of HGSOC patients, and the sequencing analysis was provided by CloudSeq Biotech Inc. . The procedures were referred to Circle-Seq methods previously reported and were summarized as below . The samCircle-map software (v1.1.4) was used to detect eccDNAs within all samples. Samtools software (v0.2) was applied to get raw soft-clipped read counts of breakpoint. The differentially expressed eccDNAs between primary and metastatic tissues of HGSOC were filtered using edgeR software (v0.6.9). EccDNAs were annotated by Bedtools software (v2.27.1). The GO and Pathway enrichment analysis were performed based on the differentially expressed eccDNA-associated genes.P value were calculated to identify the differentially expressed mRNA. The GO and Pathway enrichment analysis were further performed according to the filtered differentially expressed mRNA.Total RNA was extracted from paired primary and metastatic tissues of the same four HGSOC patients in Circle-Seq analysis. The library preparation and RNA-sequencing on an Illumina HiSeq 4000 sequencer were carried out at CloudSeq Biotech Inc. . High quality clean reads were aligned to the reference genome (UCSC hg19) with hisat2 software (v2.0.4). Based on gene level FPKM acquired by cuffdiff software, fold change and Human ovarian cancer cell lines SKOV3, A2780, and OVCAR3 were purchased from the American Type Culture Collection  and cultured as described , 42.EccDNA samples of HGSOC tissues, SKOV3, A2780 and OVCAR3 cells were prepared using the Plasmid Mini AX kit (A & A Biotechnology). The SEM analysis was conducted at Center of Cryo-Electron Microscopy (CCEM), Zhejiang University using a field-emission scanning electron microscopy (Nova Nano 450).HGSOC tissues, SKOV3, A2780 and OVCAR3 cells were prepared for TEM analysis in the routine manner. The TEM analysis was conducted at CCEM, Zhejiang University using a cryo-transmission electron microscopy (Tecnai G2 Spirit) at 120KV.The primers for outward PCR were designed across the specific junction sites of each eccDNA candidate. The φ29 amplified samples or genomic DNA were used as templates and the PCR reactions were carried out under standard PCR conditions. Inward PCR was used as a positive control in both linear and circular DNA templates. Primers for outward PCR were designed by CloudSeq Biotech Inc. . The primer sequences were listed in Supplementary Table −ΔΔCt method. The primers for qRT-PCR analysis were provided in Supplementary Table Total RNA was extracted from tissues using Trizol reagent , and RNA was then reverse transcribed into cDNA using the PrimeScript RT reagent kit . QRT-PCR was carried out using TB Green Premix Ex Taq kit . For mRNA validation, GAPDH was used as the internal control. For eccDNA validation, plasmid pGEX-5X-2 was added to the samples prior to eccDNA purification and used as the internal control . The relcircle10302690-10302961 was designed at RiboBio . The probe sequences were available upon request. The cells were first treated with 0.2 μg/mL colcemid for 4 hours to arrest the cells at metaphase stage. The FFPE tissues of patients diagnosed as HGSOC were deparaffinized and rehydrated beforehand. The signals of DNMT1circle10302690-10302961 in prepared cells or tissues were detected using Fluorescent In Situ Hybridization Kit  following the manufacturer’s instructions. The representative images were captured by a laser confocal microscope (TCS SP2 AOBS) at 60× magnification. The relative expression levels of DNMT1circle10302690-10302961 were evaluated and scored based on the intensity and scope of specific signals under blinded circumstance.FISH assays were performed in SKOV3, A2780 and OVCAR3 cells, as well as FFPE tissues of HGSOC patients. Cy3-labeled probe specific to the junction sites of DNMT1circle10302690-10302961 in FFPE tissue of HGSOC was evaluated by BaseScope Assay . A BaseScope probe specifically targeting the junction sites of DNMT1circle10302690-10302961 was designed by Advanced Cell Diagnostics. FFPE tissues of HGSOC patients were prepared following the manufacturer’s instruction. BaseScope assays were conducted using a BaseScope Detection Reagent Kit-RED  according to the manufacturer’s instructions. The BaseScope Fast RED reagent  was used to detect the signals. At 20× magnification, the relative expression levels of DNMT1circle10302690-10302961 were evaluated and scored according to the manufacturer’s instructions.The expression of DNMT1t test, Log-Rank test (for Kaplan-Meier curves), and χ2 test  where appropriate. Differences were statistically significant at *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001.All statistical plots and analyses were executed on GraphPad Prism 8.0 and SPSS 22.0 software in this study. All the data were normally distributed. Variance was similar between the groups that were being statistically compared. Significance was determined using paired Student’s reproducibility checklistSupplementary figures 1-6Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7


Escherichia coli, in which the RNA degradosome is localized to the interior of the cell, has high levels of 20S and 40S particles that are defective intermediates in ribosome assembly. These particles have aberrant protein composition and contain rRNA precursors that have been cleaved by RNase E. After RNase E cleavage, rRNA fragments are degraded to nucleotides by exoribonucleases. In vitro, rRNA in intact ribosomes is resistant to RNase E cleavage, whereas protein-free rRNA is readily degraded. We conclude that RNA degradosomes in the nucleoid of the mutant strain interfere with cotranscriptional ribosome assembly. We propose that membrane-attached RNA degradosomes in wild-type cells control the quality of ribosome assembly after intermediates are released from the nucleoid. That is, the compact structure of mature ribosomes protects rRNA against cleavage by RNase E. Turnover of a proportion of intermediates in ribosome assembly explains slow growth of the mutant strain. Competition between mRNA and rRNA degradation could be the cause of slower mRNA degradation in the mutant strain. We conclude that attachment of the RNA degradosome to the bacterial inner cytoplasmic membrane prevents wasteful degradation of rRNA precursors, thus explaining the reason for conservation of membrane-attached RNA degradosomes throughout the β- and γ-Proteobacteria.RNA processing and degradation shape the transcriptome by generating stable molecules that are necessary for translation (rRNA and tRNA) and by facilitating the turnover of mRNA, which is necessary for the posttranscriptional control of gene expression. In bacteria and the plant chloroplast, RNA degradosomes are multienzyme complexes that process and degrade RNA. In many bacterial species, the endoribonuclease RNase E is the central component of the RNA degradosome. RNase E-based RNA degradosomes are inner membrane proteins in a large family of gram-negative bacteria (β- and γ-Proteobacteria). Until now, the reason for membrane localization was not understood. Here, we show that a mutant strain of  This study shows that the physical separation of early steps in ribosome assembly in the Escherichia coli nucleoid from the RNA degradosome attached to the inner cytoplasmic membrane protects precursor ribosomal RNA in immature particles from degradation. Escherichia coli, ribonuclease E (RNase E) has a central role in the processing of stable RNA (rRNA and tRNA) and in the degradation of mRNA. RNase E homologues are widely distributed in bacteria and the plant chloroplast  end-labelled oligonucleotides specific to the 5′ ends of 5S, 23S, 17S, and 16S rRNA. After extension by reverse transcriptase, the products were separated by denaturing gel electrophoresis. The 5′ end of mature rRNA and that of the prominent precursors are indicated to the right of each panel. Note that in both panels, there is a band located between 5S and p5S rRNA, which is present in 50S, 70S, and polysome fractions, which correspond to the 5S+1 and 5S+2 species. These species are poorly resolved due to a compression artifact in the migration of the cDNA. Uncropped gels of Equal volume of clarified cell lysates from (TIF)Click here for additional data file.S3 FigE. coli rrfB operon. 5′ extensions are indicated in red. Ribosomal RNA processing sites are indicated by arrows: RNase III, blue; RNase E, green; RNase AM, pink. The number of times each sequence was detected is indicated on the right. (A) 5S rRNA, (B) 23S rRNA, and (C) 16S rRNA.RNA from sucrose gradient fractions  was anal(TIF)Click here for additional data file.S4 FigE. coli rrfB operon. 5′ and 3′ extensions are indicated in red; untemplated extension in blue. RNase E processing sites are indicated by the green arrow. The occurrence for each sequence is summarized on the right (3′ RACE) or left side (5′ RACE). (A) 5′ end analysis of 5S* rRNA. 5′ RACE on 5S and 5S* rRNAs from rneΔMTS strain. For comparison, 5S rRNA from rne+ strain was processed in parallel. (B) 3′ end analysis of 5S* rRNA. 3′ RACE on 5S and 5S* rRNAs from rneΔMTS strain. For comparison, 5S rRNA from rne+ strain was processed in parallel. (C) As in (B) except in the ΔpcnB strain background.5S and 5S* rRNA species were extracted from the gel shown in (TIF)Click here for additional data file.S5 FigE. coli rrfB operon. 3′ extensions are indicated in red; untemplated A additions in blue. 5S rRNA (A) and 23S rRNA (B) from the 50S subunit (rne+) and 40S particle (rneΔMTS).RNA from sucrose gradient fractions  was anal(TIF)Click here for additional data file.S6 FigA) In vivo fragments. (B) In vitro fragments. Uncropped gels of Markup of gels shown in (TIF)Click here for additional data file.


Many eukaryotic and prokaryotic organisms employ RNA editing  as a post-transcriptional modification mechanism. RNA editing events are common in these organelles of plants and have gained particular attention due to their role in the development and growth of plants, as well as their ability to cope with abiotic stress. Owing to rapid developments in sequencing technologies and data analysis methods, such editing sites are being accurately predicted, and many factors that influence RNA editing are being discovered. The mechanism and role of the pentatricopeptide repeat protein family of proteins in RNA editing are being uncovered with the growing realization of accessory proteins that might help these proteins. This review will discuss the role and type of RNA editing events in plants with an emphasis on chloroplast RNA editing, involved factors, gaps in knowledge, and future outlooks. RNA is transcribed from DNA using RNA polymerase in the nucleus, mitochondria and plastids and is further processed if needed. RNA editing represents a post-transcriptional modification  mechanism observed in many domains of life, including prokaryotes and eukaryotes . The ediTo begin with, RNA is transcribed normally as per the genetic code of the gene. After this, the RNA is altered in certain ways, which can incorporate uridine at certain loci and/or facilitate a change of cytidine to uridine . These eRNA editing can happen in multiple ways, such as the C-to-U, U-to-C, and A-to-I conversions, as well as insertions and deletions of U or G. However, in plants, RNA editing occurs mainly in the mitochondria and plastids. C-to-U-type RNA editing is primarily found ; howeverThis study gives an overview of transcription and RNA editing in plant chloroplasts, the role of involved proteins, and their mechanism. Additionally, some useful bioinformatics tools are available to study to help the researcher to select a suitable one or a combination of them to study RNA editing in different organisms psaS genes [RPOT genes: RPOTp, RPOTm and RPOTmp [The expression of chloroplast DNA (cpDNA) genes depends on post-transcriptional changes such as polycistronic mRNA processing, intron splicing, and RNA editing . AngiospaS genes . NEP is aS genes . There ad RPOTmp ,22  at the transcript level in organelles such as mitochondria and chloroplasts . It has Selaginella uncinata [Vigna radiata [With the advancement of high-throughput next-generation sequencing technologies, many plant organelle (mitochondria and chloroplast) genomes have been sequenced, and they have shown a very high number of RNA editing events, showing the extensiveness of this phenomenon in the plant genome . A surpruncinata . Using R radiata . An anal radiata . The imp radiata . In addi radiata . The bio radiata .After the generation of each type of RNA, it is processed to remove unnecessary sequences at both ends and introns (in mRNA) and make the final mature RNA. However, these RNAs are also subjected to modifications after their maturation, and RNA editing is one such modification . DependiSelaginella moellendorffii [Pulsatilla patens [The tRNAs ensure proper duty and folding of the mRNA, and tRNA editing involves several base modifications that have been reported in marsupials, splizellomyces, myxomycetes, Leishmania, metazoans, land snails, squids, bacteria, and land plants . This candorffii  and Pulsa patens .RNA editing events could be predicted by comparing the genomic sequences and amino acid sequences for a particular plant. Further validation of editing could be done using quantitative PCR for specific genes. Reports have also shown little (5–12%) RNA editing in the untranslated and intron sequences of the genome .Focused studies done on flowering plants have identified cis-acting genetic elements (20–25 bases upstream of the edit site) near the RNA edit sites, and some putative site-specific proteins have been discovered that interact with these cis-acting elements .Apart from this, trans-acting elements were also identified accidentally, such as chlororespiratory reduction 4 (CRR4) protein, which belongs to the pentatricopeptide repeat (PPR) protein family . PPR proArabidopsis thaliana, a P-type PPR protein known as BFA2 was found to prevent the degradation of atpH/F transcripts within chloroplasts [Brachypodium distachyon [A. thaliana and was vital during its early development [By binding to RNA and preventing its degradation by cellular exonucleases, the P-type PPR proteins are thought to be involved in RNA stabilization. In roplasts . P-type stachyon . Anotherstachyon . The rolstachyon . Similarelopment .A. thaliana [A. thaliana demonstrated that DYW1 functions as an RNA editing factor and interacts with the ndhD-1 gene [As demonstrated in thaliana  and ricethaliana , the addthaliana . The zinthaliana . AlthougD-1 gene . This DYD-1 gene .Although many clues about the factors involved have been found, we still do not fully understand how RNA editing occurs in plants. Recently, a code for PPR-RNA recognition has been described, where each PPR motif’s 6′ and 1’ amino acid positions are involved in RNA recognition . In the Arabidopsis thaliana can be attributed to the process of RNA editing [Arabidopsis thaliana) number of RNA edits, one would assume that multiple RNA editing sites must be edited by the editing PPR proteins [Arabidopsis thaliana, but it might be possible that RNA editing in other plants might happen without the involvement of complex machinery (such as the editosome) and a single PPR-DYW editing protein with a few other unidentified non-PPR altering components might be enough [Apparently, around 200 PLS-type PPR proteins found in  editing ,60. Giveproteins . The mite enough .Studies on loss-of-function mutants for specific PPR proteins suggest that the loss of one PPR protein does not completely inhibit RNA editing but rather attenuates it. This suggests cooperativity between PPR proteins inside a plant for the RNA editing process  For examRapid and advanced genome sequence techniques have provided access to high-resolution and high-throughput data, which has equipped researchers to obtain more insights into RNA editing events . Given tThe first resource is the databases where information about RNA editing in plants has been curated and maintained in a user-friendly format for usage by the researchers. Such resources include a general-purpose database that categorizes based on GO categories and genome annotation databases, but we mention databases that specifically focus on RNA editing events and associated information. One such database is the RNA Editing Site on Protein 3D Structures (RESOPS) , which cThe second resource is prediction tools, which try to narrow down the search for RNA editing sites and provide top hits for these sites. The Predictive RNA Editors for Plants (PREP) suite  is one sThe third resource is the use of data analysis methods to analyze high-throughput sequencing (HTS) data by comparing the cDNA sequences with their respective genomic loci. Many such tools exist, but only two tools, ‘REDItools’ and ‘ChloroSeq’, which are specific to C-to-U and U-to-C edits in plant organelles, have been developed. REDItools uses RNA-Seq and DNA-Seq data as input from the same sample and identifies editing sites by performing a genome-wide search for RNA editing . On the B. napus, O. sativa and A. thaliana), and a weighted scoring model is used to provide scores for the top hits [The fourth resource involves machine learning (ML) approaches, where statistical methods progressively improve the performance of computer systems to predict or analyze RNA editing events . This istop hits .Finally, many future explanations of some events should be clarified, such as how prymidine changes to purines, such as G to C or U to G. Additionally, we should investigate how we can exploit this phenomenon to improve the tolerance of plants to environmental stress after discovering the relationship of RNA editing with endurance to adverse environmental conditions ,12,13.The transfer of genetic data from DNA to RNA and proteins forms the central dogma in biology, but certain deviations exist, and RNA editing is one of the prominent causes. RNA editing has many mechanisms, but nucleotide substitution is the most common in plants and has been found extensively in mRNA, while it is less common in tRNA and rRNA. Although the most common type of RNA editing in flowering plants is C to U, other types have been found, such as U to C or U to G. RNA editing in plants is very important and is evident due to rampant editing events inside mitochondria and chloroplast genes, which are vital for plant embryogenesis, pigment development, growth and coping with stress. Recent advancements in our knowledge of the involved proteins, such as PPR proteins, high-throughput nucleic acid sequencing, and bioinformatics tools to predict and analyze RNA editing events have provided key insights into the importance of RNA editing in plant homeostasis and stress response. The insights derived from studying RNA editing have huge applications in policy design and mitigation strategies useful in scientific research and have translational value for agriculture.


RNA editing is a critical co-/post-transcriptional process perturbing the RNA in eukaryotic cells. RNA editing could occur in coding and non-coding regions, affecting the recoding of proteins, RNA splicing and stability. Adenosine to inosine (A-to-I) editing catalyzed by adenosine deaminases acting on RNA (ADAR) is the most dominant (∼90%) RNA editing event in mammals . DespiteMa and Zhang conducted a consensus clustering analysis of 502 lung adenocarcinoma (LUAD) samples from TCGA based on the expression profiles of 20 m6A regulators. They found two m6A modification patterns with distinct overall survival (OS), activation of signaling pathways and tumor immunity. Furthermore, they identified 213 prognostic m6A-related genes, which were imported into LASSO-cox regression analysis. Next, they developed the m6A risk score and found that patients with low m6A risk score exhibited a prominent survival advantage in an independent dataset. Finally, they established a highly accurate nomogram containing independent prognostic indicators. N6-methyladenosine (m6A) modification plays an important role in regulating the immunity microenvironment of breast cancer (BRCA). Zhang et al. established an m6A-related immune score (m6A-IS) to predict the immune infiltration and prognosis of BRCA accurately. The Low m6A-IS group is associated with enhanced antigen presentation and improved immune checkpoint expression, thus indicating sensitivity to immunotherapy. Furthermore, the m6A-IS can independently predict BRCA patients’ response to immunotherapy.To understand the role of m6A modification in lung cancer, Yang et al. attempted to map the m6A epitranscriptome of neuromyelitis optica spectrum disorder (NMOSD) patients compared to healthy controls. Towards this blood samples of NMOSD patients and healthy controls, the RNA were isolated and subjected to m6A-seq followed by bioinformatic analysis. Authors via bioinformatic analysis highlighted an extensive list of hyper and hypo m6A methylated transcripts and have attempted to connect it to NMOSD pathophysiology.Ma et al. conducted an m6A-related lncRNA prognostic model using TCGA data. Twelve lncRNAs were included and validated in their new model. Also, the model was internally validated and showed good predictive values for ccRCC’s survival.Huang et al. studied breast cancer (BC)-specific m5C-related lncRNAs (m5C-lncRNAs) as potential biomarkers in breast cancer. They made a prognostic risk model, and analyzed the characteristics of tumor-infiltrating immune cells based on the subtypes of the risk model. The study used in silico data from TCGA database and for a validation experiment (sixteen pairs of fresh BC and paracarcinoma tissues) from their own patients. They found that these lncRNAs may serve as prognostic biomarkers in breasr cancer.He et al. downloaded harmonized RNA-seq count data and clinical data for AML from several large study cohorts and analyzed the differential expression of a set of RNA binding proteins (RBPs) using the R package edgeR software together with statistical methods including univariate Cox regression analysis, LASSO-Cox regression analysis and multivariate Cox regression analysis. The authors established a prognostic model of 12-RBPs gene for AML and C-index and calibration diagrams were used to judge the accuracy of the model, and DCA was used to judge the net benefit. They have found that the net benefit and prediction accuracy of the prognostic model and the mixed model based on it was significantly higher than that of cytogenetics which was verified in one of the study cohorts where the data collected from and both of the selected gene set and the LASSO results have high credibility. By that, the authors concluded that their prognosis model of 12-RBPS gene is an optimized biomarker that can effectively stratify the risk of AML patients and the nomogram based on this prognostic model is a reliable method to predict the median survival time of patients.Satir-Basaran et al. explored the role of ncRNA as vector for epigenetic inheritance via paternal germline. In their experimental set-up, the authors use four lines of mice (two susceptible to diet-induced obesity and diabetes and two resistant) and then perform phenotypic analyses in the males directly exposed to the diet, as well as in the following two generations of mice obtained via paternal transmission. To support a role for ncRNA, the authors rely on microinjection of RNA into fertilized oocytes. They used synthetic miR-19b-5p (found previously to induce obesity and diabetes), total sperm RNA, or RNA that was fractionated based on size  or subcellular location (DNA bound and free), which were obtained from males exposed to normal diet or diets with excess of fat. Overall, they conclude that ncRNA plays a role in the inter/trans-generational inheritance of the phenotypes and propose that the DNA-bound RNA may be a critical component.Luo et al. investigated the expression of pyroptosis related genes in colon adenocarcinoma (COAD) and build a risk score model by utilizing the differential expressed genes. The model was found effective in predicting the overall survival and prognosis of COAD patients.Hao et al.gave an introduction about RNA editing, including RNA editing forms and organelles RNA editing occurred, then summarized the factors and mechanism involved in RNA editing in plant organelles, further reviewed RNA editing events identified in plant organelles through deep sequencing data, and finally discussed the functions of RNA editing in plant organelles. Pentatricopeptide repeat (PPR) proteins, one type of RNA-binding protein, are particularly prevalent in land plants. It has been revealed that PPR proteins are involved in the RNA processing of organellar genes and play a vital role in plant development and defense. In this manuscript, Qin et al. summarizes the recent progress in functional studies on PPR proteins, including plant fertility, chloroplast biogenesis, embryogenesis, stress responses, and plant development.This review by Li et al. performed univariate analysis to identify the prognosis-related RNA binding proteins (RBPs). They identified 11 RBPs as prognosis markers of HBV-related hepatocellular carcinoma (HCC). In addition, the authors claimed that their model has better predictive efficacy than the models using other clinical parameters.Although lot of studies of RNA modification and editing in the development and diseases, there are several new aspects need further exploration. Because there are more than 140 kind of RNA modifications in RNA, new methods to detect novel RNA modification systemically are needed to develop. E.g Deep learning based method  and new To summarize, our unique topic covers RNA modifications such as m6A and m5C in disease, the role of RNA binding proteins and non-coding RNAs, and the editing progress of RNA editing in plants. These research studies are complemented by review articles that summarize our current understanding of RNA modifications in human disease and development. We hope our Research Topic will benefit the community of RNA modifications.


The mechanisms by which the tumor behaviors of hepatocellular carcinoma (HCC) support growth and metastasis remain largely unknown, and it has become increasingly apparent that molecular dysregulation is of considerable importance for cellular signaling pathways. Recently, RNA-binding motif protein 47 (RBM47) has been suggested to function as a tumor regulator by acting as an RNA binding protein (RBP), but its role in HCC remains ambiguous. Here, in HCC, we identified that RBM47 had an inhibitory influence on tumor behaviors in vitro and accordingly suppressed the growth and metastasis of xenograft tumors in vivo. Additionally, RBM47 was verified to positively regulate Upframeshift 1 (UPF1), which is a crucial protein involved in the nonsense-mediated RNA decay (NMD) process and was previously determined to be an HCC suppressor. Mechanistically, the stability of UPF1 mRNA was demonstrated to be enhanced with its 3’UTR bound by RBM47, which acted as an RNA binding protein. Meanwhile, RBM47 was also proven to promote the transcription of UPF1 as a transcription factor. Taken together, we concluded that RBM47 functioned as a tumor suppressor by upregulating UPF1, acting as a DNA/RNA binding protein at the transcriptional and posttranscriptional levels. Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies and a principal cause of cancer-related mortality throughout the world . TreatmeThe crucial biological roles of the RNA-binding motif protein (RBMP) family have recently been revealed. As RBPs, RBMPs play important roles in the regulation of RNA editing, RNA modification, alternative splicing (AS), and mRNA stability , 11. MorOn the other hand, as a master regulator of nonsense-mediated mRNA decay (NMD) , Up-framTo determine the regulatory roles of RBM47 in HCC progression, in vitro analyses of oncogenic behavior were performed. Firstly, we examined the expression of RBM47 in different hepatoma cell lines by qRT-PCR and noticed that RBM47 presented higher expression in Huh7 cells and lower expression in HCCLM3 cells  [Flag) fusion protein was constructed in HCCLM3 cells for RIP and subsequent RNA sequencing revealed 11724 valid sequences among 56304 candidates. By comprehensively analyzing the results of RIP-seq, 681 sequences were addressed by Peak Calling analysis, and 30 (4.41%), 193 (28.34%) and 69 (10.13%) of peaks were distributed in 5’UTR, CDS and 3’UTR, respectively  and starBase [RBM47 plays an inhibitory role in HCC progression both in vivo and in vitro, however, the fundamental molecular mechanism underlying these effects remains uncertain. To identify the target gene of RBM47, we predicted target genes using starBase database (edu.cn/)  yieldingely Fig. . Meanwhiely Fig. . GO and ely Fig. . Furtherely Fig. . Taking ely Fig. . To veristarBase , 23. TheWe know that UPF1 behaves as a tumor suppressor in multiple ways through targeting different tumor-associated proteins , 22, 24.153–164th locus from the start site of the 3’UTR)  Fig. . Thus, wTR) Fig. . Finallynot Fig. . Therefohttps://www.proteinatlas.org/) and noticed that RBM47 was distributed in nucleus and cytoplasm  Fig. . We thenCurrent research has determined the role of RBM47 in HCC progression, which presents inhibitory effects on HCC both in vitro and in vivo in multiple ways. Moreover, our results verified that RBM47 inhibited HCC progression by positively regulating UPF1. Mechanistically, RBM47 could bind to the UPF1 3’UTR and its promoter to increase the expression of UPF1. These results clearly indicated that RBM47 promoted UPF1 transcription and enhanced the stability of UPF1 mRNA by DNA/RNA binding to inhibit HCC progression.Human RBM47  is mapped to chromosomal location 4p14 on the reverse strand, and its full-length transcript contains 23 introns and 24 exons. RBM47 was discovered as a differentially expressed gene in the foregut endoderm of the E8.5 mouse embryo  and thenWe provide the first evidence for the role of RBM47 in HCC and made detailed elucidation of its molecular mechanisms by targeting UPF1. UPF1 is an RNA helicase taking part in almost all steps of the NMD procedure, from target discrimination to remodeling the complex of NMD-mRNA and improving nuclease accessibility to the mRNA. Thus, UPF1 is considered to mediate the degradation of the substrates of NMD via a phosphorylation/dephosphorylation cycle , 39. In RBM47 is a multifunctional protein that not just presents an RNA binding effect. According to its cellular distributions and previous investigations, RBM47 may play multiple roles in the process of molecular events , 26. In Taken together, our results demonstrate that RBM47 functions as an effective suppressor of tumors by promoting the upregulation of UPF1. In future studies aimed at obtaining detailed insight into these and other RBM47 functions, other target genes and relative molecular events should be further addressed. Furthermore, it will be essential to scrutinize the links between our outcomes and clinical significance in a large-scale clinical setting.2 at 37 °C.Hepatoma cell lines were obtained from the Cell Bank of Type Culture Collection . Hepatoma cells were cultivated in minimum essential medium (Gibco) supplemented with 10% fetal bovine serum (Gibco), in accordance with recommendations, in an incubator with 5% COFlag fusion protein. All plasmid vectors were transfected with Lipofectamine 2000 (Thermo Fisher) following the protocol of the manufacturer.To knock down RBM47 and UPF1, small interfering RNAs (siRNAs) targeting RBM47 or UPF1 were produced and examined through GeneCopoeia . The cells were transfected with 24 nmol/L siRNA employing Lipofectamine RNAiMAX reagent (Invitrogen) according to the protocol of the manufacturer. Meanwhile, pTT5 plasmid vectors loaded with UPF1 and RBM47 were also generated for overexpression. 3 × Flag tag amino acids (DYKDDDDK) were also fused at the end of RBM47 to construct the RBM47™ Reagent kit (Thermo Fisher) was implemented for the isolation of DNA from cells in accordance with the instructions of the manufacturer. Cells were lysed through agitating the cultivation plate and slowly pipetting the lysate into an assay tube. Following the precipitation of DNA with 100% ethanol, 75% ethanol was employed for DNA rinses. The DNA was air-dried, and in 20 μl of DNase-free water was dissolved, and quantified by applying a NanodropTM spectrophotometer (Thermo Fisher) at 280 and 260 nm. If A260/A280 > 1.8, the DNA was maintained at −20 °C until utilization.The extraction of the total cellular RNA was executed by employing TRIzol reagent (Invitrogen) in accordance with the protocol of the manufacturer. In addition, the isolation and purification of nuclear RNA and cytoplasmic were conducted by employing the Cytoplasmic and Nuclear RNA Purification Kit  in accordance with the instructions of the manufacturer. Extracted total RNA was quantified by implementing a NanodropTM spectrophotometer (Thermo Fisher) at 280 and 260 nm. RNA was employed for reverse transcription if A260/A280 ≥ 2.0. Total RNA was employed for synthesizing first-strand cDNA by implementing random primers and SuperScript II reverse transcriptase (Thermo Fisher) in accordance with the protocol of the manufacturer. Reverse transcription (RT) reactions containing total volumes of 20 µl were conducted by implementing a PrimeScript RT reagent kit , and cDNA was maintained at −20 °C until utilization. The DNAzol−ΔΔCt values were normalized to the GAPDH levels. For intranuclear RNA quantification, U6 small nuclear RNA was employed as the internal control.For quantitative PCR, 2 µl of diluted RT product was blended with 10 µl of 2× SYBR Master mix , 1 µl each of the reverse and forward primers  and transferred onto PVDF membranes . The incubation of membranes was exerted with primary antibodies (Supplementary Table Cell proliferation assessments were conducted by implementing a Cell Counting Kit-8 (CCK8)  in accordance with the protocol of the manufacturer. Hepatoma cells were plated in the 96-well plates in triplicate at a density of 5000 cells per well after transfection. Each well contained 120 μL culture medium and optical density were assessed at 450 nm at the demonstrated time points including 0 h, 24 h, 48 h and 72 h. For the assay of colony formation, 1000 cells were seeded in the plates containing 6 wells for 10 days, and colonies were fixed and stained with crystal violet solution. Each cell line was evaluated in three parallel replicates.2O2 for 12 h before harvested through trypsinization. By taking advantage of Annexin V and propidium iodide , the cultures were double-stained in the dark for 30 min. Cultures were collected and scrutinized for cell apoptosis by employing a flow cytometer (FACScan) equipped with CellQuest 3.3 computer program.For apoptosis analysis, flow cytometric analysis was selected for quantitative comparison. Briefly, the cells after transfection were incubated with H5) were seeded in serum-free milieu and permitted to translocate toward complete milieu supplied with 10% fetal bovine serum. The cells that invaded through the membrane into the lower surface following 24 h were fixed, stained and counted at 37 °C. Microscopically, 200 × representative fields of view were selected for cell quantification.Briefly, quantitative cell migration assessments were exerted by employing the plates containing 24 wells comprising chambers with 8 μm polycarbonate filters. Hepatoma cells (5 × 106). After a 24 h incubation, wound sizes were accessed and imaged. The cell migration area was measured between dashed regions by (0 h–24 h)/0 h as the healing percentage.Wounds were created by employing a 100 μl plastic pipette tip in incubation wells (1 × 10We performed an RNA decay assay by using actinomycin D (ActD) (Sigma-Aldrich) to verify RNA stability. Hepatoma cells were harvested from 6-well plates at 0, 1, and 2 h for qRT-PCR after adding 15 μg ActD. The relative expression levels of each mRNA at 0 h were normalized to 1.Subcutaneous xenograft tissues were sectioned to make 4 μm thick slices. The sections of the tissue were de-paraffinized in xylene and re-hydrated with ethanol, and endogenous peroxidase was inactivated for 10 min by 0.3% hydrogen peroxide. All processes were exerted by employing an UltraSensitiveTM S-P kit  in accordance with the instructions of the manufacturer. An RBM47-specific antibody (Proteintech)  according to the instructions of the manufacturer. The cells were lysed, and the resulting solutions of lysis were subsequently incubated with antibody against Flag or isotype control IgG. The complexes of RNA-protein were immunoprecipitated with protein A agarose beads, and the RNA was extracted and purified for subsequent qRT-PCR or sequencing.After RBM47The HCCLM3 cells were crosslinked for 10 min with 1% formaldehyde by inverting flasks at ambient temperature and quenched for 5 min with 0.125 M glycine. The cell pellets were rinsed for several times in PBS and then kept at −80 °C. The pellets were lysed for 10 min in the buffer of lysis. Following the centrifugation, the supernatant was left, and the pellet was lysed in the buffer of lysis and exposed to sonication. The incubation of sheared chromatin was fulfilled with the primary antibody bound to PierceTM Protein A/G Agarose Beads (Thermo Fisher) during the night hours, succeeded by elution and reverse cross-linking during the night hours at 65 °C. TE buffer (1 mM ethylenediaminetetraacetic acid (EDTA), 10 mM Tris-HCl) was added to DNA elution buffer succeeded by RNase processing (0.5 mg/mL) for 30 min at 37 °C and proteinase K processing (0.3 mg/mL) for 1 h at 51 °C, and subsequently the DNA was isolated and purified for qRT-PCR or sequencing.P values to match to target sequences.After chromatin/RNA immunoprecipitation, DNA/RNA library construction was performed by DNA/cDNA repair, including loading Base A and sequencing connector. Qubit 2.0 (Invitrogen) was used for quality inspection before sequencing on an Illumina sequencer. We compared the raw data to obtain comprehensive transcript information, quantify gene expression, and analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Meanwhile, genome-wide de novo peak was carried out to test the binding preference of proteins on DNA/RNA. Reads ≥ 10 was defined as valid sequence and RPKM was calculated for sequence screening. Homer de novo Motif analysis was performed to predict preference binding sites. Enriched motifs with top 30 significance would be listed and sorted by To verify the activity of the 3’UTR and promoter, a luciferase reporter was used for indirect quantitative analysis. Briefly, luciferase activity was evaluated by applying a Dual Luciferase Assay Kit  in accordance with the instructions of the manufacturer. The wild-type 3’UTR and promoter of UPF1 (containing putative binding sites plus 60 nucleotides upstream and downstream) were embedded into a pGL3-basic vector  downstream or upstream of the luciferase reporter gene, creating the wild-type or mutant plasmid. All fluorescence intensities were assessed implementing a luminometer .The oligonucleotides containing biotin on the 5’ nucleotide of the sense strand were used in the pulldown assays. The UPF1 promoter or its 3’UTR probe (wild type and mutation) was incubated with HCCLM3 cell lysates and streptavidin magnetic beads for DNA or RNA pulldown. The beads were rinsed, and the proteins were eluted and resolved by PAGE. The fragments of the gel were excised and scrutinized through western blotting.6cells/mL. Tumor growth assays were performed by subcutaneously injecting the cells into the armpits of the mice. Tumor growth curves and tumor weights were calculated for quantitative comparison. For the observation of metastases, 1 × 106cells/mL suspensions were injected into the tail veins of 10 nude mice . At the end of the 5th week, the lung fluorescence intensities in vivo were tested by an IVIS® Lumina III System  to quantitatively compare metastasis.Male athymic 4-week-old BALB/c nude mice were acquired from the Animal Center of the Chinese Academy of Medical Sciences  and were kept in a specific pathogen-free facility. HCCLM3 cells stably transfected with RBM47/empty plasmid with fluorescence labeling were harvested from the plates containing 6 wells and suspended at 2–4 × 10t-test was executed. Statistical discrepancies between groups were scrutinized employing SPSS 22.0 computer program , and plots were generated using GraphPad Prism 6.0 .To compare the continuous variables between groups, student’s Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Figure 1Supplementary Figure 2Supplementary Figure 3Original Data File


Metastasis is the predominant cause of mortality in patients with breast cancer. Long noncoding RNAs (lncRNAs) have been shown to drive important phenotypes in tumors, including invasion and metastasis. However, the lncRNAs involved in metastasis and their molecular and cellular mechanisms are still largely unknown.The transcriptional and posttranscriptional processing of LINC00478-associated cytoplasmic RNA (LacRNA) was determined by RT-qPCR, semiquantitative PCR and 5′/3′ RACE. Paired-guide CRISPR/cas9 and CRISPR/dead-Cas9 systems was used to knock out or activate the expression of LacRNA. Cell migration and invasion assay was performed to confirm the phenotype of LacRNA. Tail vein model and mammary fat pad model were used for in vivo study. The LacRNA-PHB2-cMyc axis were screened and validated by RNA pulldown, mass spectrometry, RNA immunoprecipitation and RNA-seq assays.Here, we identified a novel cytoplasmic lncRNA, LacRNA (LINC00478-associated cytoplasmic RNA), derived from nucleus-located lncRNA LINC00478. The nascent transcript of LINC00478 full-length (LINC00478_FL) was cleaved and polyadenylated, simultaneously yielding 5′ ends stable expressing LacRNA, which is released into the cytoplasm, and long 3′ ends of nuclear-retained lncRNA. LINC00478_3′RNA was rapidly degraded. LacRNA significantly inhibited breast cancer invasion and metastasis in vitro and in vivo. Mechanistically, LacRNA physically interacted with the PHB domain of PHB2 through its 61–140-nt region. This specific binding affected the formation of the autophagy degradation complex of PHB2 and LC3, delaying the degradation of the PHB2 protein. Unexpectedly, LacRNA specifically interacted with PHB2, recruited c-Myc and promoted c-Myc ubiquitination and degradation. The negatively regulation of Myc signaling ultimately inhibited breast cancer metastasis. Furthermore, LacRNA and LacRNA-mediated c-Myc signaling downregulation are significantly associated with good clinical outcomes, take advantage of these factors we constructed a prognostic predict model.Therefore, our findings propose LacRNA as a potential prognostic biomarker and a new therapeutic strategy.The online version contains supplementary material available at 10.1186/s12967-023-03967-1. Breast cancer is the most frequently diagnosed cancer in females and the leading cause of cancer-related death worldwide . AlthougMost of the cancer genome is transcribed yielding a complex network of overlapping transcripts that includes tens of thousands of long noncoding RNAs (lncRNAs) with no protein-coding capacity –6. lncRNA significant amount of lncRNAs do not function in the original transcriptional form, but can serve as precursors for shorter and mature lncRNAs, and gain biological functions during this process , 14. LINTo investigate the role of lncRNAs in breast cancer metastasis, we used microarray analysis to establish lncRNA expression profiles in primary breast tumors and paired metastatic lymph nodes. After screening lncRNAs that were annotated in the RefSeq and ENSEMBL databases and excluded pseudogenes, small nuclear RNAs (snRNAs), and small nucleolar RNAs , which was in accordance with previous results   breast cancer dataset to systemically assess the 6 candidate lncRNAs , with different isoforms generated by alternative splicing  than in TNBC cell lines , which were more aggressive and metastatic  to identify the protein interactome of LacRNA. Briefly, biotinylated LacRNA sense and antisense transcripts (negative control), and co-precipitating proteins were isolated with streptavidin-agarose beads. Interestingly, the sense streptavidin labeled LacRNA, but not the antisense or beads only control, specifically precipitated with PHB2, a 32 kD proteinTo verify the association between PHB2 and LacRNA, we analyzed lncRNA-pulldown protein samples by immunoblotting with a PHB2 antibody. Strong signals for PHB2 were observed in proteins pulled down with LINC00478_FL but not in proteins associated with either antisense LINC00478_FL or beads only  assay in which the RNA-PHB2 complex was immunoprecipitated using a PHB2 specific antibody Fig. c. ComparUsing an RNA truncation assay based on the secondary structure of the LacRNA  to inhibit protein synthesis, and found that overexpression of LacRNA could significantly prolong the half-life of the PHB2 protein, so LacRNA may play a role by stabilizing PHB2 Fig. e. To furWe used the autophagy activator rapamycin to treat cells, and overexpression of LacRNA was found to inhibit the rapamycin-induced autophagic degradation of PHB2 Fig. g. ExogenWe then clarified the function of PHB2 and the functional relationship between LacRNA and PHB2. The overexpression of PHB2 inhibited the migration of BT549 and MDA-MB-231 cells  MDA-MB-231 cells using a cycloheximide (CHX) chase assay Fig. d. Overexl-leucyl-l-leucyl-l-leucinal), we found that MG132, a potent proteasome inhibitor, markedly increased the level of c-Myc levels  and disease-free survival (DFS) . LacRNA expression was significantly lower in breast cancer tissues than in normal tissues and was the lowest in positive lymph node tissues Fig. e.p = 0.002) and DFS    . PHB2 exMyc has been widely reported as an oncogene and is seemingly the core of the malignant tumor regulatory networks [Furthermore, the LacRNA-PHB2 complex directly binds to c-Myc and promotes its degradation, ultimately triggers the inhibition of Myc signaling. LacRNA played a critical role in the LacRNA-PHB2-cMyc axis. networks . It was networks . Thus, iImportantly, higher LacRNA levels predicted significantly better DFS and OS in patients with breast cancer, supporting that LacRNA may be a promising prognostic biomarker for breast cancer. Moreover, based on LacRNA and LacRNA-mediated c-Myc signaling, we constructed a model for predicting the survival of patients with breast cancer, which outperformed other clinicopathological risk factors. Overall, the above findings suggest that LacRNA could be a potential new therapeutic method and may facilitating the development of precise diagnosis and treatment strategies for breast cancer.https://cancergenome.nih.gov/). The 827 cases of breast cancer patients' RNA-seq data and OS data were come from TCGA database. 530 cases of breast cancer tissues and 98 cases of patients' plasma were obtained from FUSCC. The use of human clinical specimens in the present study was approved by the Medical Ethics Committee of Fudan University Shanghai Cancer Center. The patients were informed, and they signed consent forms acknowledging the use of their resected tissues for research purposes.Paired primary breast tumor and metastasis lymph node were obtained from the department of breast surgery of Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China. A total of 105 paired primary breast cancer and adjacent normal tissues from TCGA database  according to the manufacturer’s protocol. Samples were analyzed with Affymetrix HTA 2.0 by Shanghai Qiming Biotechnology Co. Ltd.Cell lines used were authenticated by short tandem repeats (STR) profiling. All cell lines were confirmed to be free of mycoplasma (Vazyme). All the cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM) medium  containing 10% fetal bovine serum (FBS) , 100 IU/ml penicillin and 100 μg/ml streptomycin  in a 37 °C incubator with 5% CO2. LM2 was a subline of MDA- MB-231 with high lung metastasis tendency was described previously . Cyclohehttp://www.addgene.org. The single guide RNA (sgRNA) was designed and the sequences of high-scored sgRNAs are presented in Additional file The desired Cas9 cutting site in the LacRNA promoter genomic region were selected, and the target sequences in the region around 100~200 bp flanking the site were searched in The human LINC00478_FL, LacRNA and LINC00478 3′RNA were cloned into the transient expression vector pcDNA3.1 and lentivirus stable expression vector pWPXL to be overexpressed. The PHB2 and c-Myc expression vectors were constructed by inserting the respective open-reading frame sequences into the pcDNA3.1-FLAG vector to generate pcDNA3.1-PHB2-FLAG and pcDNA3.1-c-Myc-FLAG respectively. Additionally, we cloned the sequence of c-Myc into the pcDNA3.1-HA vector to generate pcDNA3.1-c-Myc-HA. The PCR primers are listed in Additional file −ΔΔCT) method.RNA samples from the clinical tissue specimens and cell lines used in this study were extracted with TRIzol® reagent  according to the manufacturer’s protocol. First-strand cDNA was synthesized using the Hiscript III Reverse Transcriptase (Vazyme). Relative RNA levels determined by quantitative Real-Time PCR (qPCR) were measured on a 7900 Real-Time PCR System with the SDS 2.3 software sequence detection system  using the ChamQ SYBR qPCR Master Mix (Vazyme) method. The sequences for the gene-specific primers used are listed in Additional file We used′ 5′- and 3′-rapid amplification of cDNA ends (RACE) assay to determine the transcriptional initiation and termination sites of LacRNA with a SMARTer RACE cDNA Amplification kit (Clontech), following the manufacturer’s instruction. The sequences for the gene-specific PCR primers used for 5′ and 3′ RACE analysis are given in Additional file Cytoplasmic and nuclear RNA fractions of the MDA-MB-231, BT549 and T47D cells were prepared and collected according to the instructions of the PARIS™ RNA Nuclear/ Cytoplasmic Isolation kit . Cytoplasmic and nuclear protein fractions of the MDA-MB-231, BT549 and T47D cells were prepared and collected according to the instructions of the NE-PERTM Nuclear Cytoplasmic Extraction Reagent . β-actin was used as the cytoplasmic endogenous control. U6 small nuclear RNA was used as the nuclear endogenous control. GAPDH was used as the cytoplasmic protein control. Lamin B was used as the nuclear protein control.5/ml for migration assay and 4 × 105/ml for invasion assay. The lower chambers of 24-well plate-sized transwell inserts  contained 600 μl DMEM with 20% FBS for migration assay and 30% FBS for invasion assay. Blow and mix the cell suspension and seeded 200 μl on the top chamber. Incubation for 8–12 h, different culture time was set according to the migration and invasion ability of different cell lines, and the invasion experiment time was appropriately prolonged. After incubating, the inserts were fixed and stained with crystal violet. Cells and dye on the bottom of upper chamber were gently wiped off with cotton swabs. The average confluence of migrated cells was analyzed by ImageJ according to three random fields captured by 100× microscope. Each experiment was conducted in triplicate.For transwell migration and invasion assays, cells in each group were digested, centrifuged and resuspended with serum-free medium, and the cells were counted. The cell density was adjusted to 2 × 105/ml. 200 μl cell suspensions were seeded in 96-well IncuCyte® ImageLock Plates (Essen Bioscience). After 24 h, a wound was made in each well with WoundMaker™. Plates were imaged as described above with 4-h interval. Wound width was measured and analyzed.For wound healing assay, cells were resuspended and adjusted to 1.8 × 10Female NOD/SCID mice, aged 4–6 weeks old, purchased from the Shanghai Slack Laboratory Animal Co., LTD and housed under SPF conditions at the animal care facility of the Experimental Animal Center of Shanghai University of Traditional Chinese Medicine.6 cells (pWPXL-VECTOR and pWPXL-LacRNA stable MDA-MB-231-LM2 cell line) were orthotopically injected directly into the inguinal mammary fat pads of mice. Tumor growth rates and mice body weight were monitored. When a tumor was palpable, it was measured every other day, and its volume was calculated according to the formula volume = length × width × 0.5. After 7 weeks, the tumor in situ was removed and divided into 3 parts: 4% formaldehyde fixation, RNA later preservation and direct freezing in − 80 ℃. After resection of the tumor in situ, the prepared fluorescent substrate dissolved with DPBs to a concentration of 20 mg/ml was injected immediately, and each mouse was injected with 200 μl from the caudal vein. Imaging was performed immediately in a small animal in vivo imaging system. Then, the mouse lung tissue was removed, imaged again, and the lung tissue was preserved.For xenograft models, 200 μl cell suspension containing 2 × 105 cells (Ctrl-dCas9-vector and LacRNA-OE3) were injected into the caudal vein, and the fluorescent imaging was performed after 5 weeks. Mice were killed and the number of metastatic foci in the lung were calculated. All experiments were performed in accordance with relevant institutional and national guidelines and regulations of Shanghai Medical Experimental Animal Care Commission.To further investigate the effect of LacRNA on tumor invasion in vivo, we developed the metastasis model in female athymic BALB/c nude mice. Using the LacRNA activated stable MDA-MB-LM2 cell lines, 200 μl cell suspension containing 2 × 10PCR products were verified by gel electrophoresis and purified by phenol: chloroform extraction. In vitro transcription of LINC00478_FL, LacRNA, LINC00478 3′RNA and different truncated LacRNA was conducted using a HiScribe™ T7 Quick High Yield RNA Synthesis Kit (NEB) with Biotin-16-UTP (Roche). Briefly, the HIScribe T7 Transcription system is completed in a single-tube and transcription reactions expression of genes cloned downstream from the T7 RNA polymerase. 1.0 μg of circular plasmid DNA is added to an aliquot of the Master Mix and incubated in a 40 μl reaction volume for 4 h at 37 °C. Transcription RNA was treated with RNase-free DNase I on column during RNA purification with RNA Clean & Concentrator-25 (Zymo Research). Transcribed and labeled RNA was purified with a Direct-zolTM RNA Miniprep Plus Kit (ZYMO). Next, 1 pmol biotinylated RNA was pretreated with RNA structure buffer  to obtain an appropriate secondary structure formation.For each sample, 3 μg pretreated biotinylated RNAs were incubate with 2 mg protein and rotated overnight at 4 ℃. Then gently mixed with 50 μl washed Dynabeads™ M-280 Streptavidin 11206D) (Thermo Scientific) and incubated on a rotator at 4 ℃ for 2 h. The beads were washed briefly five times in RIPA buffer . The protein complexes were eluted, denatured, and digested with immobilized trypsin (Promega) for mass spectrometry analysis . The retrieved protein was detected by immunoblotting. The primers for PCR and its deletion and mutation fragments for in vitro transcription are provided in Additional file 1206D  for pre-clear. The precleared lysates were incubated with 5 μg indicated antibodies for 12 h at 4 °C with gentle rotation. Then 50 μl Dynabeads Protein G beads (blocked with 1% BSA and 20 mg/ml yeast tRNA for 1 h at 4 °C) were added to the mixture and incubated for another 2 h at 4 °C. The beads were washed thrice with wash buffer containing RNase inhibitor. The RNA–protein complex was eluted with 200 μl TRIzol at room temperature for 10 min. RNA was purified with Direct-zol RNA Microprep (Zymo Research). For qRT-PCR, reverse transcription was performed with Hiscript III Reverse Transcriptase (Vazyme) followed by qRT- PCR analysis. Information on the antibodies are listed in Additional file Cells growing in 10 cm-dishes were lysed in 0.5 ml of lysis buffer containing protease inhibitors and RNase Inhibitor  and centrifuged at 12,000 rpm for 5 min. Suspension 1 × 10g for 15 min at 4 °C, the protein concentrations were determined by the BCA method . Add the proteins with 5 × loading buffer and use 1 × loading buffer to make sure all the samples the same concentration. Heating samples at 100 °C for 10 min for denaturation. The samples were resolved by SDS/PAGE, transferred to PVDF membranes . The membranes were blocked with 5% (w/v) skimmed milk in TBS at room temperature for 1 h, then probing with antibodies at 4 °C overnight. Using immune blotting to analysis by HRP-conjugated secondary antibodies. Information on the antibodies is provided in Additional file Cells were lysed for 30 min in ice with lysis buffer (Beyotime Biotechnology) containing protease inhibitors and phosphatase inhibitors (Bimake). After centrifugation at 16,000×g at 4 ℃ for 5 min and aspirate the supernatant. Lysis cells in NP-40 buffer and incubate with RNase T. RNA was extracted and purified. Run the RNA samples in 1.5% formaldehyde agarose gel to verify that RNAs are digested in 100- to 300-nt range. The supernatants were incubated with 5 μg PHB2 antibody for 12 h at 4 °C with gentle rotation. Then 50 μl Dynabeads Protein G beads were added to the mixture and incubated for another 2 h at 4 °C. After centrifugation at 2000×g for 1 min at 4 °C, wash beads three times with NP-40 lysis buffer. Incubate the pellets with 20 units of RNase-free DNase I in 100 μl NP-40 lysis buffer for 15 min at 37 °C. Then extracted and purify RNA. For qRT-PCR, reverse transcription was performed with Hiscript III Reverse Transcriptase (Vazyme) followed by qRT- PCR analysis.CLIP-qPCR (cross-linking and immunoprecipitation followed by reverse transcription and quantitative PCR) was used to mapping and quantifying PHB2-lacRNA interactions. MDA-MB-231 cells were incubated with DMED culture medium eith 4-Thiouridine (4-SU) added . Irradiate cells with 150 mJ/cm2 of UVA (365 nm), then scrape cells in 5 ml PBS, transfer to 50 ml conical tube, centrifuge at 2000× t-test or one way analysis of variance (ANOVA) were performed to evaluate the differences between two groups or more than two groups, as indicated in the individual Figures. Wilcoxon tests were used to analyze RNA levels in paired cancer and normal samples, and Mann–Whitney tests were used to analyze LacRNA levels in grouped human samples. The univariate and multifactorial Cox regression models were used to determine the independent clinical factors based on the investigated variables. The Kaplan–Meier survival analyses the correlation between the LacRNA levels and survival. Pearson's correlation was performed to analyze the correlation of LacRNA and PHB2, c-Myc and Myc target genes expression. All statistical analyses were performed using SPSS v.23.0  and GraphPad Prism 8.0.The experiment was set up to use 3–5 samples/repeats per experiment/group/condition to detect a difference at the significance level of 0.05 by a two-sided test for significant studies. For RNAscope, northern blotting, RACE analysis, transwell assay, wound healing assay, immunoblotting and immunohistochemical staining, representative images are shown. Data were presented as the mean ± standard error of the mean (SEM) or of at least three independent experiments. Each exact n values are indicated in the corresponding figure legend. Student's Additional file 1. Additional Tables and Figures.


Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients.Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore,  Colorectal cancer (CRC) is one of the most frequent cancers worldwide and has the highest mortality after lung cancer , 2. The Accumulating lines of evidence indicate that genetic factors may play a crucial role in CRC metastasis . HoweverIn recent years, several prediction models were developed for tumor metastasis assessment. Some used conventional clinical pathological characteristics, such as age, race, gender, tumor site, and tumor size, to establish the Cox regression models  to assess metastasis and survival outcomes for CRC patients –20. SomeTumor metastasis is an invasive action of tumor cells, which refers to the process of tumor cells spread to other parts of the body. In principle, metastasis usually progresses in four steps: local invasion, intravasation into the blood circulation system, extravasation into the surrounding tissues, and colonization and proliferation in new locations . Local ivia mining multiple omics data derived from different CRC sources. Lastly, we developed a machine learning model for rapid assessment of early CRC metastasis.In this work, we attempted to mine driver genes/mutations in early CRC metastasis. For this purpose, we elaborately designed an experiment to profile genomic alternation landscapes of cancer, paracancer, and normal tissues simultaneously in a CRC cohort. Upon the genomic mutation profiles, a new statistical parameter was introduced to quantitatively evaluate the contribution of every mutation to local invasion. Subsequently, we identified metastasis driver mutations This study was approved by the Ethics Committee of the Xiamen Xianyue Hospital and was performed in accordance with the Helsinki Declaration. All patients provided written informed consent prior to inclusion in the study. A total of eight CRC inpatients from the Zhongshan Hospital, affiliated to Xiamen University, Fujian Province, China were recruited in this study. They were selected from more than 248 CRC inpatients on the basis of the following criteria: (1) the patients have no blood kinship by medical background review; (2) the patients were diagnosed with rectal differentiated adenocarcinoma of stage II or III; and (3) the patients received a similar chemotherapy regimen and the prognoses were benign. These eight patients were further divided into two groups: the NM group of four patients who had no metastasis till surgery excision, and the LM group of four patients who had local lymphatic metastasis but no distal metastasis. The medical details of the patients are briefly summarized in For every patient in the cohort, three tissue samples were collected from the tumor removal surgery under authorization in advance: the tumor sample was collected at the near edge of the tumor, and the paracancer and normal samples were taken 2 cm and 5 cm away from the tumor, respectively . The DNA concentration was measured by a Qubit Fluorometer and diluted to 50–300 ng/µl. For each sample, 3–5 µg of DNA was applied for quality control, and its integrity was checked by the agarose electrophoresis. The whole exome was captured using the MGIEasy Exome Library Prep Kit  and the library for sequencing was prepared according to the manufacturer’s instruction. The whole-exome sequencing (WES) was performed by the Beijing Genome Institute  using the BGISEQ-500 platform in a 100-base pair (bp) paired-end mode.Before variant calling, quality control was conducted to the sequencing raw data using Trimmomatic  . The cleFor every tumor and paracancer samples, the tissue purity and ploidy were estimated on the basis of genome-wide somatic mutation profiles with Sclust  , taking i if the driving potential outmatches the resisting potential, Mi is considered as the driver mutation to metastasis; otherwise, Mi is the resister mutation. To measure the summarized potential of Mi to local invasion, a novel parameter, namely, local invasion power (LIP), was introduced:Every mutation likely plays dilemmatic roles in metastasis, promotion, or resistance. For a gene mutation, MMi and Mi and where RMTi , VMPi and VMNi stand for the variant allele fraction (VAF) of variant Mi in tumor, paracancer, and normal tissues, respectively. They were determined by dividing reads of alternate allele Mi by total reads at this locus and further normalized by all reads count. LIP > 0 indicated that the variant Mi was prone to promoting invasion than resistance. A larger LIP suggested that the mutation had more power to drive local invasion.where VMoreover, we assume that the tumor invasion is the accumulated consequence of all mutations. Some mutations likely promote tumor cells invading into nearby tissue (paracancer tissue), while some intend to resist the invasion. If the overall promotion effects at the paracancer tissue overwhelm the resistance effects, local invasion is prone to progress; otherwise, invasion unlikely happens  at the selected allele in the metastasis group, respectively. Nm and Nn stand for the number of mutations and non-mutations at the selected allele in the non-metastasis group, respectively. As a result, a list of metastasis-associated variants with OR >5 was determined. (3) The genetic predisposition of metastasis-associated variants to patient survival was examined. For this, the gene expression level interfered by mutation was first determined according to the expression quantitative trait loci (eQTL) information derived from the Genotype-Tissue Expression (GTEx) (p <0.01) variants to either sigmoid or transverse colons were included in the analysis, which were 1,185,110 variants in the GTEx. Having the information of mutations on gene expression levels, we then performed survival analysis subject to high or low gene expression on the basis of 763 CRC patients (including 571 colon and 192 rectum patients) from The Cancer Genome Atlas (TCGA) using the R packages survival (v3.2-3) and survminer (v0.4.8) with default parameters. As a result, we screened out eleven effective variants that could change the host gene expressions and subsequently affect the survival of patients (p < 0.01). These eleven effective variants included ten potential metastasis driver variants that may reduce the survival rate of CRC patients and one resister variant on the opposite.where Mn (GTEx) . Only thTo aid risk assessment of early metastasis, we built a determinant classifier. The core component of classifier was a logistic regression model. The model was constructed on the basis of four exome datasets of this study and three independent CRC cohorts , covering a total 61 CRC patients. The datasets were split into a training set and a testing set in a combinational way Table 3.V, corresponding to the eleven metastasis-associated variants, in which carrying the mutation was defined as 1, otherwise 0.The model took the mutation profiles of eleven metastasis-associated driver variants identified in this study as the input, and output the estimated probability of metastatic risk. In model construction, the input genetic mutation profile was converted into a one-dimension 11-feature binary vector L was prepared for V ,which contained the average LIPs of the eleven metastasis-associated variants determined on the basis of the training dataset.Meanwhile, a weighted vector V and L (V·L) as the accumulated driving force of metastasis contributed by the eleven variants for the patient. For the metastasis issue (y = 1) . the probability of occurrence P (y = 1) can then be determined by the logistic regression:Accordingly, we calculated the dot product of iw is the regression coefficient for the variant and b is the intercept. The regression coefficient iw and intercept b were estimated using the Maximum Likelihood Estimation (MLE) with the glm function of the R package stats (v3.6.0).where The model performance was evaluated by the conventional parameters of accuracy, sensitivity, and specificity, which were calculated with the R function confusionMatrix from the package Caret (v6.0-86) as follows:P and N stand for the positives and the negatives, respectively. The values of TP (true positives), TN (true negatives), FN , and FP  were calculated on the basis of the confusion matrices of the classification model. The area under the receiver operating characteristic curve (AUC) was also determined with the R package pROC (v1.16.2). For evaluation of all models, the leave-one-out cross-validation (LOOCV) strategy was applied to attain unbiased estimation of training. For this purpose, the training dataset was divided 51-fold (corresponding to 51 patients), of which 50 were used for model construction and the remaining one was used for internal evaluation. The LOOCV process was repeated 51 times, and the average parameters were used to evaluate the model performance of the training set.where t-test, p = 7.97e-4). The average purity of tumors and paracancer tissues was 0.52 and 0.33, respectively  produced an average on-target coverage of about 197×, indicating that the sequencing was substantially deep enough for reliable variant calling. Using the matched normal samples as reference, we determined the purities of tumor and paracancer tissue for every patient based on the genome-wide somatic mutation profiles. On average, the purity of tumor samples was significantly higher than that of matched paracancer samples  and 8,275 indels (inserts and deletions). For every mutation, we calculated the LIP; meanwhile, we determined the summation of all mutation LIPs  for every cohort member. Regretfully, both the LIP distribution and sLIPs were unable to differentiate the lymphatic metastasis group (LM) from the non-metastasis group (NM) (p = 0.039) . In the end, we obtained ten potential driver variants to metastasis. These variants can enhance (six variants) or suppress (four variant) their parental gene expression, and all would consequently shorten the lifetime of half survivals for an average of 31.5 months  instead of the whole germline mutation profiles that would be much more costly in practice. The model performance was internally evaluated in a manner of LOOCV, which obtained an average result: accuracy = 0.788, specificity = 0.800, sensitivity = 0.769, and AUC = 0.839. Additional external evaluation also achieved a fairly good performance: accuracy = 0.766, specificity = 0.567, sensitivity = 0.767, and AUC = 0.709. These results affirm that the model is substantially effective for early metastatic assessment.http://www.bio-add.org/AmetaRisk. The AmetaRisk was built upon an architecture of Linux + Tomcat + JSP. To initiate the assessment, the user is required to check the status (yes or no) of eleven metastasis driver/resister variants detected in the tissue samples, which can be determined on tumor, paracancer tissue, or peripheral blood. Upon submission of variant status profile, the server will return a probability value of metastatic risk, ranging from 0 to 1.0 , a surrogate measure of the proportion of DNA molecules in the tissue specimen carrying the variant . The VAFHowever, using sLIP value directly to assess metastatic risk may not be a good solution; many mutations actually contributed little to metastasis . The trevia participation in “phospholipase C signaling”  with a similar experiment design were reported , 78. UnfIn summary, we made a valuable attempt in this study to explore the genetic basis underlying CRC metastasis. Our efforts will provide new insights into the mechanistic understanding of early metastasis, as a complement to current metastasis hypotheses such as “seed and soil”, “big-bang”, and “tumor self-seeding”. Moreover, we constructed a machine learning model for metastatic risk assessment at the early stage of local invasion. This model and its online tool, AmetaRisk, provide a rapid and economic way to assist in prioritizing a precise therapeutic regimen in advance and increasing the survival rate of CRC patients in clinics.https://ngdc.cncb.ac.cn/gvm/ (accession number: GVM000184).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: The studies involving human participants were reviewed and approved by the Ethics Committee of Xiamen Xianyue Hospital. The patients/participants provided their written informed consent to participate in this study.Z-LJ and PY designed and supervised the study. PY, Z-XF, Z-YL, and Z-YZ collected the samples, performed the clinical diagnosis, and prepared the samples for sequencing. R-FD, YZ, and L-YW analyzed the data, and drafted and revised the manuscript. Z-LJ and PY commented on and revised the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Key Research & Developmental Program of China (2018YFC1003601).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 interestAll 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.


Condensates formed by complex coacervation are hypothesized to have played a crucial part during the origin-of-life. In living cells, condensation organizes biomolecules into a wide range of membraneless compartments. Although RNA is a key component of biological condensates and the central component of the RNA world hypothesis, little is known about what determines RNA accumulation in condensates and to which extend single condensates differ in their RNA composition. To address this, we developed an approach to read the RNA content from single synthetic and protein-based condensates using high-throughput sequencing. We find that certain RNAs efficiently accumulate in condensates. These RNAs are strongly enriched in sequence motifs which show high sequence similarity to short interspersed elements (SINEs). We observe similar results for protein-derived condensates, demonstrating applicability across different in vitro reconstituted membraneless organelles. Thus, our results provide a new inroad to explore the RNA content of phase-separated droplets at single condensate resolution. Here, the authors demonstrate that single cell RNA sequencing technology can be leveraged to characterize RNA content of individual membrane-free condensates formed by liquid-liquid phase separation processes such as coacervation. Electrostatic interactions bring the two components together, subsequent entropic release from water and counter ions from around the polyelectrolytes drives phase separation into membrane-free and chemically enriched micron-sized droplets2. These coacervate droplets have been shown to form from a wide variety of different molecules with very little chemical specificity from synthetic polyelectrolytes, to biological polyelectrolytes and small charged molecules3. Consequently, they were hypothesized to play a role in the origin-of-life by bringing together the first molecules to spatially localize the first primitive reactions4. Since then coacervates formed from synthetic polymers have been exploited in a range of industries from food separation to pharmaceuticals5. More recently, it has been shown that the coacervation process plays an active role in the liquid-liquid phase separation of condensates in biological systems. Whilst, the mechanism of formation of biomolecular condensates in cells has now been extensively studied, an understanding of how condensates regulate biochemical processes in time and space is still in its infancy7.In the 1920’s de Jong coined the term coacervation to describe a liquid-liquid phase separation process between two oppositely charged polymers in solution8.Key to unraveling these unanswered questions is deconvoluting the molecular content and physicochemical properties of the condensates. So far, progress in this area has been limited by difficulty in isolating condensates from cells in their dynamic environment. To this end, in vitro reconstitution has been instrumental for in depth droplet characterization9. Despite this progress, there remains no methodology to uncover the heterogenous mixture of molecules and their precise amounts in individual coacervate droplets. To this end, we have exploited single-cell RNA sequencing technology and developed a novel way to determine the amount and sequence of RNA incorporated into individual coacervate droplets. This provides an unprecedented opportunity to determine, for the first time, the RNA content of individual coacervate droplets within a population. Furthermore, we show how this method can be applied to both synthetic coacervate microdroplets and condensates prepared from biological phase separating protein scaffolds such as the human RNA-binding protein Fused in Sarcoma (FUS) and yeast DExD/H-box helicase 1 (Dhh1). We identify that RNA properties are crucial for uptake into synthetic coacervates and demonstrate comparable uptake properties to FUS and Dhh1 droplets depending on the coacervate chemical identity. Thus, our findings strengthen the role for synthetic coacervates as models for biomolecular condensates.Most of the condensate characterization has relied on fluorescence microscopy. Indeed, characterization of the partition coefficients has only recently been optimized using high-throughput microfluidic methods based on fluorescence of single solutes2 at pH 8 or recombinant FUS  or recombinant Dhh1  condensates were prepared in the presence of total RNA (50 ng/µl). The total RNA was isolated from human induced pluripotent stem cells (iPSCs) immediately before each experiment.In order to determine the RNA content of individual coacervate droplets or condensates, we aimed to work with the following four droplet systems: carboxylmethyl dextran (CM-Dex) and Poly (PDDA) (molar ratio: 6:1) or CM-Dex with polylysine (pLys) microdroplets (molar ratio: 6:1) in 10 mM Tris and 4 mM MgCl9. We first measured the partition coefficient of our RNA pool to quantify assembly of the diverse mix of RNA molecules into coacervates   because of its heterogeneity in terms of sequence composition and length providing us with data from a pool of highly diverse RNAs. The abundance of each mRNA molecule present in the coacervate was quantified with the commonly used transcripts per million (TPM) metric which was particularly suitable for sample-to-sample comparisonsAfter we successfully prepared and sequenced libraries from single coacervates we first focused on quality metrics. Bioanalyzer traces were used for quantification and quality control of the amplified cDNA from 0, 1, 10, 100 and 1000 CM-Dex:PDDA coacervate droplets , we observed that coacervates take up more double-stranded RNA composed of Motif 1 and 2  compared to each motif alone or scrambled motifs  display similarly high homology to hominid-specific SINE-VNTR-Alu retrotransposons which also have an Alu element as their main component19.Next, we sequence matched the discovered sequence motifs to match any known genomic features. The motifs showed high similarity to genomic regions annotated as short interspersed elements (SINEs). SINEs belong to the family of transposable elements which have the potential to regulate transcription or generate new transcript isoformsken Fig. . It was Since many of the top motifs that we discovered resemble primate-/hominid-specific elements we tested which motifs enrich in coacervates when we take a transcript input pool isolated from e.g., a murine cell line. When we performed single-coacervate sequencing with coacervates that contain RNA derived from mouse embryonic fibroblasts (MEF), we again observed that most RNAs enter coacervates at high frequency strongly depended on how abundant these RNAs were in the input with the exception of the outlier transcripts as expected from our previous results  also enriched in stress granules and in P-bodies  to compare the results obtained so far to another synthetic coacervate system. Lysine residues are enriched in disordered regions of P-body condensate proteins and its polymer form has been shown to form condensates which support complex enzymatic reactionstes Fig. 7,23.Fig.We first looked at how often any given transcript is detected in the different droplet systems and compared all results Fig. . We foun24. This analysis evaluates how comparable all profiled condensates are to each other with respect to the RNA transcripts they contain. For this analysis, we focused on input independent, enriched transcripts for each condensate type 25.In summary, our data demonstrate for the first time that it is possible to explore the RNA content within single coacervate droplets. We dissected the molecular heterogeneity of a pool of coacervates allowing us to determine molecular differences between them. Thus far, differences between single coacervates could only be described on the phenotypic level by microscopy. Our ability to combine the sequencing data describing the RNA content with the FACS data describing the size and granularity of coacervates enabled us to link genotype and phenotype on the level of individual coacervates. Understanding the genotype-phenotype link is of primary importance toward the generation of artificial cells, the origin-of-life and for modern biology13. Hence, increased charge density as a consequence of RNA double strand formation might be a prevalent feature of RNA content in biomolecular condensates which can be recapitulated in in vitro reconstituted synthetic coacervate systems.A central question regarding the genotype of coacervates is, what types of RNAs it enriches for and which features the RNA molecules are characterized by. We found that RNAs with high sequence complementarity within or across RNA sequences are enriched in coacervates. This finding is reminiscent of the fact that stress granules, which form by liquid-liquid phase separation in cells, enrich non-coding RNAs which are complementary to mRNAs and likely form double-stranded RNA27. Our data therefore indicate that interactions of complementary Alu elements within transcripts could lead to formation of double-stranded RNA. This interaction, rather than overall differences in global RNA structure . Specifically, Poly  or poly-L-lysine  were mixed with CM-Dex sodium salt  at a molar ratio of 6:1  in Tris-MgCl2 buffer (10 mM Tris-HCl pH 8.0 and 4 mM MgCl2). Total RNA was isolated from iPSC cells (409B2) or MEF using the RNeasy Mini Kit (Qiagen). Coacervates were generated by adding CM-Dex, RNA and PDDA in the respective order to the Tris-MgCl2 buffer to achieve a final RNA concentration of 50 ng/µl. For experiments using synthetic RNA, ERCC spike-in mix (Thermo Fisher) was spiked in to total RNA at a dilution of 1:80,000. Coacervates were then incubated for 1 h at room temperature while rotating before FACS sorting. FUS-GFP and Dhh1-mCherry proteins were cloned, purified and respective protein-based condensates were prepared as previously described . Recombinant FUS-GFP in 25 mM Tris-HCl pH 7.4, 150 mM KCl, 2.5% Glycerol and 0.5 mM DTT was used at a final protein concentration of 1 mg ml−1. Dhh1-mCherry in 200 mM NaCl, 25 mM Tris (pH 7.4) and 10% glycerol was used at a final protein concentration of 150 µM. Dhh1 droplets were generated by adding ATP , creatin kinase based ATP recombination system CKM , BSA  and Hepes buffer  to the recombinant Dhh1-mCherry protein in low salt buffer .Synthetic polymer-based coacervates were prepared as previously described  were sorted with a BD FACSAria Fusion flow cytometer using a 150 µl nozzle. Single-coacervates were index-sorted in precooled skirted twin.tec 96-well LoBind Plates (Eppendorf) containing 4 µl of 6 M Guanidine HCl  as lysis buffer. For each plate, one well was sorted with 1000 coacervates and one well was left empty as positive and negative controls respectively. Directly after sorting the plates were briefly spun down (max speed) to collect all FACS-derived droplets in the lysis buffer. The plates were then immediately put on dry ice until all other plates were sorted. Plates were kept at −80 °C until cDNA was prepared.flowcore package. For quantification of RNA incorporation into coacervates, size-matched FAM-labeled RNAs were synthesized (IDT) and incorporated into PDDA-CM-Dex coacervates as described above. Sequences of chemically synthesized oligos can be found in Supplementary Data Coacervates with and without RNA were analyzed on a BD FACSAria Fusion (150 mm nozzle) and data was processed in R using the Image acquisition for experiments involving fluorescent coacervates was performed using a Olympus FV1200 confocal microscope using a ×20 oil immersion objective. For the estimation of the partition coefficients the ratio between [RNA concentration in droplets]/[RNA concentration in supernatant] was determined by quantification of fluorescence intensity of FAM  the template switching oligo was biotinylated on the 5´-end, (2) PCR preamplification was performed for 23 cycles. Size distribution of cDNA obtained from single coacervates was checked for randomly chosen samples to verify success of cDNA preparation. Next, tagmented libraries were prepared and sequenced (100 bp paired-end reads) on an Illumina HiSeq 2500 as described11. Libraries from single coacervates were also compared to input RNA  were equilibrated to room temperature and 2.2× SPRI beads were added to each well. Upon incubation for 5 min at room temperature, beads were washed twice using 80% EtOH as described in the SPRI bead manufacturer’s protocol. EtOH traces were completely removed and beads were dried for 2–3 min (Note: Beads dry out fast after exposure to GuaHCl. Overdrying of beads will lead to significantly lower yields). RNA was eluted by resuspending beads in 3 µl of dNTP/oligodT mix, then beads were magnetically separated from RNA/dNTP/oligodT mix and transferred to a new 96-well plate. For library preparation of total RNA , transcripts with less than 20% read coverage were excluded. Furthermore, since the coacervate size correlates with the number of transcripts detected as a consequence of coacervate size-dependent RNA concentrations we filtered out coacervates with <5% pseudoalignment for sizes FSC > 2e4. Enriched transcripts  using Kallisto (v0.44.0) with standard parameters including -pseudobam flag to obtain read coverage across each transcript. For data obtained from MEFs  were chosen as input for MEME analysis. The background was calculated using the sequences of all input transcripts. Enrichment of discovered motifs for each transcript was calculated using MAST (v5.0.5) with -nostatus -minseqs 21978 -remcorr -sep -ev 0.05 -c 1 parameters. MEME and MAST outputs were parsed for analysis in R using custom python scripts. Distances between every detected motif and its closest 5′-neighbor were calculated for each enriched transcript  with the following parameters: Analysis of minimum free energy for each enriched transcript and the same number of randomly selected non-enriched transcripts was performed using RNAfold (v2.4.12) with standard settings. RNAfold output was parsed for analysis in R using a custom python script.cis complementarity—Fig. trans complementarity—Fig. Biostrings package in R with the following parameters: nucleotideSubstitutionMatrix, pairwiseAlignment. For comparison of enriched sequence motifs with SINEs ; pairwiseAlignment.The presence of Motif 1 and its reverse complement Motif 2 on the same transcript  or P-bodies (isolated from human HEK293 cells) was analyzed for motif enrichment using MEME (v5.0.5). Prior to the analysis, genes with FPKM/CPM values <1 were filtered out. For stress granule data, gene names were converted to transcript IDs by using the most dominant transcript for each enriched gene in human tissuesSeurat R package (v3.1.5). For this analysis, we used transcripts as input which were enriched for each coacervate type as defined by the analysis in Fig. Analysis of transcript-based coacervate heterogeneity  analysis (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary