Non-invasive clinical diagnostics of bladder cancer is feasible via a set of chemically distinct molecules including macromolecular tumor markers such as polypeptides and nucleic acids. In terms of tumor-related aberrant gene expression, RNA transcripts are the primary indicator of tumor-specific gene expression as for polypeptides and their metabolic products occur subsequently. Thus, in case of bladder cancer, urine RNA represents an early potentially useful diagnostic marker.Here we describe a systematic deep transcriptome analysis of representative pools of urine RNA collected from healthy donors versus bladder cancer patients according to established SOPs. This analysis revealed RNA marker candidates reflecting coding sequences, non-coding sequences, and circular RNAs. Next, we designed and validated PCR amplicons for a set of novel marker candidates and tested them in human bladder cancer cell lines. We identified linear and circular transcripts of the S100 Calcium Binding Protein 6 (S100A6) and translocation associated membrane protein 1 (TRAM1) as highly promising potential tumor markers.This work strongly suggests exploiting urine RNAs as diagnostic markers of bladder cancer and it suggests specific novel markers. Further, this study describes an entry into the tumor-biology of bladder cancer and the development of gene-targeted therapeutic drugs. Human bladder cancer (BCa) is one of the most common cancer types in humans. It is the 6th most common malignancy in men and the 10th most common tumor type worldwide . In 2020For human BCa, the diagnostic gold standard is based on cystoscopy. However, this is invasive, bears a certain health risk, and is related to relatively high costs , 6. AlteThus, there is a need for alternative and powerful diagnostic tools. Ideally, these should provide non-invasive access to tumor markers, increased sensitivity and specificity, and primary human samples should be easily available. These requirements are met by most of liquid biopsies, i.e., body-fluids including blood, plasma, serum, urine, and sputum. In case of BCa, urine is thought to be the most promising source for RNA-based tumor markers as direct physical contact between tumor cells and urine seems to be given. Urine as a source of markers is particularly attractive because urine is easily available and the detection of molecular markers is already established or seems to be feasible , 14. ThiWhile urine contains detectable macromolecular markers, the concentrations are very low when compared to tissue samples or circulating cells in blood , 16. ThiOn the other hand, urine RNA samples contain RNAs which are suitable for cDNA synthesis and RT-qPCR detection . Cell-frThe proof-of-concept of a non-invasive and RNA-based diagnostic of BCa has been described by a number of studies , 20. HowMore recent studies describe a search for RNA-based tumor markers by network analyses which indicate marker candidates  and by aIn this study we describe a systematic, whole transcriptome analysis of urine RNA aiming to identify differentially expressed transcripts between healthy individuals and high risk (HR) BCa patients. This consists of SOP-based acquisition of urine, stabilization of urine RNA, delivery and storage of samples, RNA preparation, and deep transcriptome analyses.The possibility to stabilize urine samples, to ship them to diagnostic laboratories without degradation, and to analyze the composition of cell-free and cell-associated RNA enables one to search for RNA-based markers of tumors, primarily tumors of the urinary tract. In order to bypass the technical problem of very low RNA concentration in urine RNA samples which is not sufficient for standard transcriptome analyses, we decided to pool seven samples of urine RNA (Supplementary Table 1). These samples represent the mean values of a set of markers determined recently in a large set of urine samples, i.e., 47 patients within a control group (C) and 66 samples of the high risk (HR) patient group. Markers include 18S rRNA, Keratin 20 (KRT20), LIM and SH3 protein (LASP1), Oncoprotein 18 (OP18), Uroplakin 1A (UPK1A), and Baculoviral IAP repeat-containing protein 5 (BIRC5)  values comparing C and HR patient groups. Regarding the results of previously analyzed potential tumor markers , the thrA more complex level of criteria is related to the predicted or known splicing pattern within a gene locus and to the exon/intron structure of individual transcripts. The more data we found on these characteristics, the more weight we put on selected transcripts. Finally, we set a length filter for selected transcripts of a minimal length of 150 nts. This was based on the requirement to be able to design appropriate amplicons for qPCR-based detection of selected RNA species.In the following, we describe classes of chosen RNA species of the control group and of the HR group according to the defined criteria. The defined categories include coding sequences (Supplementary Table 3A and 3B), non-coding transcripts (ncRNA) (Supplementary Table 3C and 3D), and circular RNAs (circRNA) (Supplementary Table 3E and 3F). We considered up-regulated transcripts as well as down-regulated transcripts between controls and HR samples for two reasons. First, a marker ratio of either one member of both groups has a higher chance to discriminate between both groups and, secondly, because a ratio of two makers is independent of absolute copy numbers. In the light of almost unmeasurable RNA concentrations in urine samples, this provides robustness to the detection of RNA markers.For downregulated noncoding transcripts (Supplementary Table 3D), we were not able to identify RNAs matching the described criteria. Nevertheless, we showed the best marker candidates for this category. Furthermore, for circular transcripts (Supplementary Table 3E and 3F) we note that algorithms used in this study are not specifically for the identification of circRNA. In addition, no RNase R treatment of urine RNA was performed to ensure the detection of only circular RNA species. However, we include circRNA candidates in the lists shown in this study, because this class could contain RNA species with novel characteristics including their diagnostic value.RNA species for diagnostic purposes require robust and quantitative determination in urine samples. This can be achieved by reverse transcription of urine RNA followed by qPCR-based methodology. Thus, we developed and thoroughly characterised marker-specific pairs of PCR primers for PCR reactions for selected and promising markers described in this study. Because of very limited RNA amounts in human urine and the lack of sufficient patient material, we performed initial studies in the use of a cell culture model system which we recently described . In thisA thorough analysis of transcripts that may serve as tumor marker candidates indicates that the design of amplicons for the PCR-based measurement has to consider the complex potential composition of transcripts expressed from a specific locus as well as the differential expression levels (lg(fc)) and the TSL values. Instructive examples are shown for C-X-C motif chemokine ligand 8 (CXCL8)-, ribosomal protein S27 (RPS27)- and ribosomal protein L23a (RPL23A)-specific RNAs .A list of RNA marker loci and major characteristics of transcripts is shown in It should be noted that the filters we used to identify promising marker sequences seems to be very stringent. For example, for the most promising candidate Glycin-N-Acetyltransferase (GLYATL1)-206 with a lg(fc) value of 1.624 (Supplementary Table 3A) we could not detect a PCR signal in the use of either amplicon and the two urinary bladder cancer cell lines. Similarly, the most promising splicing variant of the potential tumor marker with the lowest log(fc) value in the high risk group, i.e., Zinc finger protein 382 (ZNF382)-208 (Supplementary Table 3B) was excluded, as was Ryanodine receptor 3 (RYR3)-227, which was not detectable in ECV-304 cells nor in RT-4 cells. We cannot tell whether this is due to low TSL values of 4 for ZNF382-208 and 5 for RYR3-227. These examples demonstrate the complexity of developing a specific and sensitive PCR amplicon for a potential marker transcript with regard to the composition of transcripts expressed from a specific locus, the differential expression levels, and the TSL values.Both hits, the potential biomarkers S100 Calcium Binding Protein 6 (S100A6) and translocation associated membrane protein 1 (TRAM1) show an impressive correlation of linear and circular transcripts of these gene loci with the health status of donors . While mThis fits well with the second potential biomarker S100A6 with a correlation of increased linear and circular transcripts in urine pools of patients with high risk BCa . In contFinally, we studied differential gene expression of selected tumor markers in the dual cell culture model system. Differences in gene expression of RT-4 and ECV-304 cells could give a first hint on potential RNA markers in urine of BCa patients. Therefore, we validated the PCR amplicons developed in this study in both cell lines and compared calculated lg(fc) values with the data of transcripts in urine samples of patients .For marker candidates CXCL8 and RPL23A, lg(fc) values are not compatible between the cell culture models versus patient samples. Conversely, for transcripts of RPS27, TRAM1, and S100A6 these values are consistent between pooled urine RNA samples of control and HR patients and RT-4 and ECV-304 cells, respectively. TRAM1 transcripts show decreased expression in urine of high risk BCa patients and in ECV-304 cells which represent the G3 cancer stage. As a potential biomarker with a higher expression in G3 cells and in HR urine pool, we identified S100A6 transcripts. These RNAs exhibit a clear differential gene expression and, thus, they were selected for calculation of RNA ratios .The ratio of S100A6/TRAM1 transcript signals showed a high consistency between cell culture cells and transcriptome analyses of urine pools of human donors. Based on these findings a discrimination of healthy individuals and HR patients seems to be feasible by a factor of 10. Hence, the S100A6/TRAM1 ratio seems to be a significant and highly promising marker for the non-invasive diagnosis of bladder cancer based on urine RNA. With regard to this finding, it might be even more promising to study circular forms of S100A6-specific- and TRAM1-specific transcripts.Whole transcriptome analysis of urine RNA from as little as a few milliliter urine is feasible. This study describes a significant extension of the concept of non-invasive diagnosis of bladder cancer in the use of RNA markers. This molecular concept is attractive for early tumor diagnosis because in case of aberrant gene expression which is linked with malignant cell growth, RNA is the first gene product to be detected even before gene products or their metabolic effects are detectable. It is noteworthy that the technology is robust and reliable. This includes the collection of urine samples at small volumes, the stabilization of RNA, shipping, storage, and the quantitative detection of RNA transcripts via RT-qPCR. All steps are based on established standard operation procedures , 32, 36.With regard to these considerations, we performed a systematic search for RNA-based markers by a whole transcriptome approach. The underlying rational is shown schematically in Here we look at RNA expression data derived from different methodological approaches. Hence, it is a central issue of this study to study whether there is compatibility of marker candidates derived from deep sequencing of pools of patient samples versus individual RT-qPCR-based data of individual marker candidates . This fiOne of the most remarkable findings of this study is related to the ratio of S100A6/TRAM1. This ratio seems to represent an improved potential marker for human bladder cancer  and it iin silico analyses does not necessarily proof that such circRNAs exist in reality. A set of experimental evidence is necessary to enough strongly verify their existence. Methods include PCR-based approaches that can detect backsplicing sites, sequencing of cloned backsplicing sites, and independent orthogonal tests such as RNaseH-based cleavage analyses. However, such assays go beyond the scope of this study although this is one promising way to proceed in order to identify new and potent RNA markers.While all of these hits focus on conventional linear transcripts we also noted experimental evidence supporting the existence of circular RNAs that seem to be able to monitor differences between the healthy state and the cancer state. This is reflected by a number of recent studies describing a link between circular RNA and cancer , 31. Hown = 47; high risk (HR) patients, n = 66) was collected and stabilized immediately with one volume of a lysis buffer  as described recently [This study was approved by the local ethical committee of the Universität zu Lübeck. All urine samples were obtained with written consent of the participants. All bladder cancer patients were classified according to the World Health Organization 2004 grading and risk system. For the investigation of urinary RNA, spontaneously voided urine of donors  was used with minor modifications. Briefly, stabilized urine samples were thawed slowly and adjusted to pH 7.0 by adding 1 M HEPES buffer. Instead of using RLT buffer, recommended volumes of 70% ethanol and mercaptoethanol were added directly to the samples. Subsequent steps were performed following the manufacturer’s instructions. RNA samples were eluted in 320 μl of RNase-free water, lyophylized, and resuspended in 16 μl of RNase-free water. Samples were stored at –80°C.RNA samples were quantified using a NanoDrop ND-1000 spectrophotometer . To measure concentration and integrity of pooled urinary RNA, the Agilent 2100 Bioanalyzer was used in combination with the Agilent RNA 6000 Pico Kit  and the manufacturer’s instructions were followed.For sequencing on Illumina platforms, cDNA libraries were synthesized using the SMARTer Stranded Total RNA-Seq Kit – Pico Input Mammalian . Briefly, 10 ng of total RNA was fragmented according to manufacturer’s instructions and used for first-strand synthesis followed by addition of Illumina adapters. Ribosomal cDNA was depleted and the final double-stranded cDNA library was amplified via PCR according to manufacturer’s instructions. Before depletion of ribosomal cDNA and after PCR amplification, the double-stranded cDNA libraries were purified using the Agencourt AMPure XP PCR purification system . The first purification step was performed according to the manufacturer´s instructions. The second purification step contained minor modifications. Briefly, the amount of beads was increased to 100 μl volume and the cDNA was eluted in 20 μl elution buffer. To quantify the double-stranded cDNA libraries, the Qubit dsDNA HS Assay Kit and the Qubit Fluorometer  were used.Previously synthesized double-stranded cDNA libraries were sequenced by GATC Biotech AG . The Illumina platform was used with a paired-end (PE) mode and a read length of 125 nucleotides.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) by Simon Andrews at Babraham Bioinformatics was used.To check the quality of reads provided by GATC Biotech AG, the software FastQC  in a total volume of 20 μl containing RNA extract and 300 ng of random hexamer primer . Manufacturer’s instructions were followed using the 16 μl of resuspended urinary RNA with an RNA input of 7.5 μl for the RT sample and 7.5 μl for the non-RT control sample. It should be noted that RNA concentrations in urine are extremely low and Nanodrop-based measurements of UV absorption cannot be used. However, the length distribution of urine RNA seems to be suitable [For cDNA synthesis of cellular RNA, RevertAid First Strand cDNA Synthesis Kit  was used and manufacturer’s instructions were followed with 2 μg RNA input of bladder cancer cell lines. For all non-RT control reactions, nuclease-free water was added instead of solutions of RNaseOut or RiboLock RNase Inhibitor and reverse transcriptase.qPCR was performed using SYBR green and TaqMan Systems in the 384-well plate format. Primer concentrations was 200 nM and concentrations of TaqMan probes was 250 nM for a 10 μl total reaction volume with 4 μl template of RT or non-RT sample and 5 μl of SYBR green or TaqMan Master Mix. SYBR green reactions were performed with a SYBR Select Master Mix . The thermal cycler 7900HT  conditions were 50°C for 120 seconds, 95°C for 120 seconds, and 40 cycles consisting of 95°C for 15 seconds, and 60°C for 60 seconds. Melting curve analysis was performed. For the TaqMan system, TaqMan Universal Master Mix II  was used with thermocycler conditions of 50°C for 120 seconds, 95°C for 600 seconds, and 40 cycles consisting of 95°C for 15 seconds and 60°C for 60 seconds. Samples were measured in quadruplicates and negative controls without reverse transcriptase and RNaseOut or without template were included. Data analysis was performed via the SDS 2.1 software . The RNA level of bladder cancer cell lines was normalized to the levels of endogenous 18S ribosomal RNA which served as an internal control. Markers of urinary RNA were quantified and normalized according to serial 10-fold dilutions of plasmid standards.https://www.ncbi.nlm.nih.gov/tools/primer-blast/) with a melting temperature of 57–63°C . The PCR product size was adjusted to a range of 70–150 nucleotides. Three types of primer pairs were created for each transcript: Primer pairs in the same exon, primer pairs separated by one intron, and primer pairs with one primer spanning the exon-exon junction sequence. In the last step, primer pairs were checked for specificity of amplification of the target transcripts and problematic secondary structures as well as self-complementarity were excluded. Divergent primer pairs for the detection of circular transcripts were designed according to “Circular RNA Interactome” [https://blast.ncbi.nlm.nih.gov/Blast.cgi) and the design of primer pairs was performed as described above. TaqMan probes were designed using Primer Express software version 2.0  or Primer3 software . Primer pairs and TaqMan probes were purchased from Metabion  and Eurogentec .Primer pairs and TaqMan probes are shown in Supplementary Table 4. Convergent primer pairs for detection of linear sequences of transcripts were designed using NCBI Primer-BLAST  according to manufacturer’s instructions. The total cellular RNA was isolated using phenol-chloroform extraction followed by ethanol precipitation.The human urinary BCa cell line ECV-304 was cultivated in Medium 199   containing 10% (vol/vol) fetal calf serum . ECV-304 was originally established from an invasive, G3 BCa of an 82 years old Swedish female patient with a mutant p53 in 1970. It is a defined derivative of T24 , 35 whic


In principle, RIC- and CLIP-like methods could be used to globally quantify RBP-RNA interactions in response to perturbations. However, several biases have to be taken into account to avoid misinterpretation of the results obtained. Here, we focus on RIC-like methods and discuss four key aspects relevant for quantitative interpretation: (1) the RNA isolation efficiency, (2) the inefficient and highly variable UV cross-linking, (3) the baseline RNA occupancy of RBPs, and (4) indirect factors affecting RBP-RNA interaction. We highlight these points by presenting selected examples of PTMs that might induce differential quantification in RIC-like experiments without necessarily affecting RNA-binding. We conclude that quantifying RBP-RNA interactions via RIC or CLIP-like methods should not be regarded as an end in itself but rather as starting points for deeper analysis.RNA-binding proteins (RBPs) are key mediators of posttranscriptional gene expression control. However, the links between cell signaling on the one hand and RBP function on the other are understudied. While thousands of posttranslational modification (PTM) sites on RBPs have been identified, their functional roles are only poorly characterized. RNA-interactome capture (RIC) and cross-linking and immunoprecipitation (CLIP) are attractive methods that provide information about RBP-RNA interactions on a genome-wide scale. Both approaches rely on the  Posttranscriptional regulation is an essential part of gene expression control , and RNATens of thousands of PTM sites have been identified in the proteome, but the functional significance of the vast majority of them is currently unknown . A key cin situ followed by “omic” analyses enable identification and quantification of hundreds of RBPs or thousands of RNA-binding sites in a single experiment  is generally assumed to be independent of the bound RNA sequences. In RIC-like assays on the other hand, the pull-down efficiency of RBPs strongly depends on the isolation efficiency of their RNA targets . FeatureOligo(dT)-enrichment is not the only RNA isolation method prone to biases . For insin situ in otherwise unmodified cells or tissues , atoms of the nucleotide are excited to a higher energy state for a short time period. Only during this short time period (microseconds) nucleotides can form covalent cross-links with amino acid residues in close proximity (“zero-distance”) . This isin vitro  and are . Therefore, the baseline occupancy of an RBP  restricts the changes that can be observed in relative quantitative analysis: Low RBP occupancy at baseline allows larger increases, while RBPs with high baseline occupancy are already close to maximal binding, and the opposite is true for decreases in RNA-binding. It is important to consider the baseline global RBP occupancy when studying changes across conditions, as this will affect the biological interpretation of results obtained in RIC-like experiments.in vivo is probably more complex. Most importantly, the actual baseline occupancy of RBPs is not known and also depends on the cellular context. In general, the occupancy depends on the binding affinity, the  concentration of the RBP and the number of available binding sites. For example, low concentration RBPs with a high number of RNA-binding sites are likely to be highly occupied. While the RNA-binding sites of a given RBP can be identified using CLIP-like methods, competition over binding sites between components in the cellular RNA network  complicates estimating actual number of available sites for RBP interactions  show decreased binding in four comparative RIC studies employing different perturbations in distinct mammalian cell lines . Conversractions . Measuriractions . FinallyWhile binding to RNA is the defining feature of all RBPs, important aspects of their cellular function (like protein–protein interaction) do not depend on changes in their interaction with RNA. Conversely, changes in RNA-binding might occur as a secondary effect of other cellular events. For example, cells typically shut down translation in response to stress, which releases mRNAs that would otherwise be bound by ribosomes . CellulaDespite the challenges outlined above, both RIC and CLIP are powerful methods that can provide information about RBP-RNA interactions on a genome-wide scale. A particularly attractive application of RIC-like methods  is to study how RBP function is modulated by PTMs. Since most PTMs have not yet been studied via RIC-like experiments, we instead focus on exemplary cases of RBPs whose regulation by PTMs is sufficiently well characterized to allow us to speculate on their impact, taking the aforementioned biases into account.The most direct way by which PTMs could affect RBP-RNA interaction is to change binding affinities. Amino acids in RBPs that directly contact RNAs are enriched in serine, threonine and tyrosine phosphorylation, lysine acetylation and arginine methylation sites . SeveralIntuitively, higher binding affinities result in increased RNA-binding (and vice-versa). However, due to the biases described above, an increased abundance in RIC-like experiments might not necessarily follow. For example, if the occupancy of the RBP is already close to maximum at baseline, major changes are not expected. Also, changes in the group of transcripts targeted by the RBP might lead to unexpected results in case this group of transcripts shows different isolation and/or UV cross-linking efficiencies. LARP1 might be a good example for the latter case: Upon inhibition of the upstream kinase mTORC1, the abundance of LARP1 in RIC-like experiments increases , suggestRNA-binding proteins are often localized to specific subcellular compartments where they interact with their targets . This isAn important function of many RBPs is to bring target RNAs in contact with core ribonucleoprotein machineries, like the exosome, the ribosome and the spliceosome . SeveralPosttranslational modification of RBPs recently emerged as important regulators of liquid-liquid phase separation and ribonucleoprotein granule dynamics . ParticuThe last decade has seen great advances in the systematic identification of RBPs . In partData presented in this study was directly obtained from the respective references. No new data was generated for this work.CHV-V analyzed data and prepared figures with input from MS. CHV-V and MS wrote the manuscript. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Circular RNAs (circRNAs) are produced by head-to-tail back-splicing which is mainly facilitated by base-pairing of reverse complementary matches (RCMs) in circRNA flanking introns. Adenosine deaminases acting on RNA (ADARs) are known to bind double-stranded RNAs for adenosine to inosine (A-to-I) RNA editing. Here we characterize ADARs as potent regulators of circular transcriptome by identifying over a thousand of circRNAs regulated by ADARs in a bidirectional manner through and beyond their editing function. We find that editing can stabilize or destabilize secondary structures formed between RCMs via correcting A:C mismatches to I(G)-C pairs or creating I(G).U wobble pairs, respectively. We provide experimental evidence that editing also favors the binding of RNA-binding proteins such as PTBP1 to regulate back-splicing. These ADARs-regulated circRNAs which are ubiquitously expressed in multiple types of cancers, demonstrate high functional relevance to cancer. Our findings support a hitherto unappreciated bidirectional regulation of circular transcriptome by ADARs and highlight the complexity of cross-talk in RNA processing and its contributions to tumorigenesis. RNA editing and circRNAs are involved in tumorigenesis. Here the authors report that ADARs regulate the circular transcriptome in a bidirectional manner through and beyond their editing function in multiple cancer cells. However, in the following decades, because of their naturality of low abundance and non-coding feature, the vast majority of circRNAs remained neglected. Only with recent advances in high-throughput sequencing, circRNAs have been characterized as ubiquitously expressed, biologically conserved and tissue-specific RNA molecules3. Diverse functions of circRNAs include competing with linear splicing, sponging microRNA (miRNA), interacting with RNA-binding proteins (RBPs), and producing small peptides3. Importantly, aberrantly expressed circRNAs have been found in many diseases such as neurological diseases, cardiovascular diseases, and cancers4. Since circRNAs are ubiquitous and functional, it is worth further investigation of precise mechanisms underlying the regulation of circRNA biogenesis in cells.Unlike canonical linear RNAs, circular RNAs (circRNAs) are a type of RNA molecules with a covalently closed continuous loop structure. Since the circular form of RNA in the cytoplasm fraction of eukaryotic cells was first observed using electron microscope in 19795. This process requires spatial proximity of non-sequential splice sites, which is usually facilitated by RBPs which bind to flanking introns6 and/or base-pairing formed by reverse complementary matches (RCMs) in flanking introns such as inverted repeat Alu elements (IRAlus)8. RBPs can also facilitate or disrupt the intra-intronic base-pairing. DExH-box helicase 9 (DHX9) negatively regulates circRNA biogenesis by binding to and unwinding the base-paired IRAlus in flanking introns9. On the contrary, nuclear factor 90 (NF90) and its 110 kDa isoform NF110 bind to the base-pairs formed by flanking introns, leading to an increased circRNA production10. However, the role of other RBPs (particularly dsRNA-binding proteins) in regulating circRNA biogenesis remains largely unexplored.CircRNAs are generated by “back-splicing”, which is splicing between a downstream 5′ splice donor and an upstream 3′ splice acceptor11, holds great potential as a potent circRNA regulator. Upon dsRNA binding, ADARs may catalyze adenosine to inosine (A-to-I) editing, the most prevalent type of RNA editing in eukaryotes12, in their bound dsRNAs. Till now, a few studies have reported controversial findings about the effect of ADARs on circRNA biogenesis. It has been suggested that ADARs could suppress the generation of circRNAs by editing and “melting” the dsRNA13. However, another study claimed that ADARs alone had no major effect on circRNA biogenesis, although double knockdown of ADAR1 and DHX9 repressed circRNA biogenesis to a greater extent than the single knockdown of each gene9. Besides, there is still a lack of experimental evidence supporting that ADARs-mediated editing can destabilize (and unwind) dsRNAs formed by IRAlu elements. The role of ADARs in circRNA biogenesis warrants a deeper investigation from the facts that: (1) ADARs preferentially edit A:C mismatches rather than A-U base pairs14, presumably resulting in a more stable secondary structure; (2) through editing, ADARs can strengthen or weaken binding of RBPs by altering RNA sequences of cis-elements and/or creating or destroying RBP binding motifs17; and (3) independent of their editing function, ADARs can also block the access of other RBPs (e.g. U2AF65) to the latter’s original binding sites, contributing to changes in splicing15. These abovementioned facts challenge the common opinion that ADARs function as repressor of circRNA biogenesis, presumably dependent on their editing capability.Adenosine deaminases acting on RNA (ADARs) protein family, known to preferentially bind to dsRNAs formed by IRAlu elements24. Herein, we comprehensively define the regulatory role of ADARs in circRNAs and explore the functional relevance of target circRNAs to cancer. We uncover over a thousand circRNAs either promoted or repressed by ADAR1 and/or ADAR2 via editing-dependent or -independent mechanisms. Next, our mechanistic investigation deciphers editing-dependent mechanisms of action by which editing can stabilize or destabilize secondary structures formed between RCMs within the flanking introns via correcting A:C mismatches to I(G)-C pairs or creating I(G).U wobble pairs, respectively. We also find that editing facilitates the recruitment of RBPs such as PTBP1 to regulate back-splicing. Moreover, we show that these ADARs-regulated circRNAs (ARcircs) are not merely by-products of back-splicing, but indeed influence tumorigenesis. Our findings provide a previously undescribed bidirectional regulation of circular transcriptome by ADARs and highlight a complex crosstalk between RNA editing machinery and circRNA biogenesis and its implications in cancer.It is known that the differentially expressed ADARs and its resultant dysregulation of A-to-I RNA editome are implicated in multiple cancer types such as esophageal squamous cell carcinoma (ESCC), hepatocellular carcinoma (HCC), colorectal cancer (CRC), breast cancer, and gastric cancer25. Untreated or RNase R-treated RNA samples were subsequently sent for total RNA sequencing (RNA-Seq) or circRNA sequencing (circRNA-Seq), respectively  , ADAR2 is most likely to be a potent repressor of circRNAs rather than an enhancer (promoting: 15% vs repressing: 85%)  by either forced expression or silencing in EC109 which is an esophageal squamous carcinoma cell line that has been frequently used for ADARs and A-to-I editing researchely Fig. . We appl5%) Fig. . Among a5%) Fig. . Of note5%) Fig. . Our fincircASS1 and circXPO5, were confirmed to be common targets for both ADARs  region  residing in circRNA flanking introns facilitates circRNA productionata Fig. . To ensu38 or both RNA binding and editing capabilities (EAA mutants)40 were generated. Upon overexpression of each wildtype or mutant form, the ADAR1/2 EAA mutant was incapable of regulating all ARcircs rcs Fig. , suggestorm Fig. . These dCircCHEK2, an editing-dependent ARcirc generated by back-splicing between exon 3 and 9 of its host gene CHEK2  assay, we confirmed the association of ADAR1 with the dsRNA structure formed between the identified RCMs in vivo . As expected, probes with mutations at all three editing sites (edt123) migrated more rapidly on gel -C may enable a more perfect secondary structure, which potentially facilitates circRNA production Fig. . It has gel Fig. , suggestcircASH1L, circANKLE2-1 and circRNF114) and 3 ADAR1/2-repressed circRNAs . From our RNA-Seq data, all 6 ARcircs have editing sites within their RCMs demonstrating ≥ 10% increase in editing frequency upon overexpression of the corresponding ADAR protein. Using the same strategy, we found that the majority of editing sites within RCMs of circASH1L, circANKLE2-1 and circRNF114 locate at A:C mismatches where editing was predicted to lead to more compact dsRNA structures  minigene than the WT counterpart; however, upon knockdown of PTBP1, the difference in the efficiency of circCHEK2 production between edt123 and WT minigene was significantly attenuated , MKN28 (gastric tubular adenocarcinoma cell line), SNU398 (hepatocellular carcinoma cell line), and HCT15 . Intriguingly, we observed the same pattern of editing-dependent or independent regulation of ARcircs in these cell lines as EC109 cells  liver samples as well as 20 matched pairs of primary colorectal cancer (CRC) and NT colon samples. We found that 41% (7 out of 17) and 60% (12 out of 20) of HCC and CRC patients demonstrated a ≥ 2-fold increase in circCHEK2 expression in tumors compared to their NT samples, respectively  against the back-splicing junction sequence of each ARcirc for a specific and efficient knockdown without affecting their host genes expression  systemion Fig. . In 2 diole Fig. . We furtrts Fig. . All the13 or have no major regulatory effect on circRNAs9, our study demonstrates that ADARs are potent regulators of circular transcriptome and they can regulate over a thousand of circRNAs in both directions through and beyond their editing functions. However, it remains unknown what mechanisms determine the direction of circRNA regulation by ADARs. One key factor is the position of the edited adenosine within a dsRNA. Previously proposed model suggested that ADARs destabilize the secondary structure through altering A-U base pairs located in the dsRNA stem, leading to repression of circRNA biogenesis. Here, we provided experimental evidence that adenosines at A:C mismatches, which gain editing preference than those at A-U pairs14, can stabilize the dsRNA structure formed between flanking introns, promoting circRNA biogenesis. It has been known that canonical splicing signals and spliceosomal machinery are required for back-splicing, and editing at cis-acting elements  can result in changes of splicing pattern17. Therefore, the location of editing sites within the host gene transcript may influence circRNA expression. In this work, we demonstrate that ADAR1-mediated A-to-I editing can enhance binding of splicing factor PTBP1 to the flanking intron of circCHEK2, rendering increased expression of circCHEK2. Although PTBP1 is well documented as a pyrimidine-rich sequence binding protein, a previous study also showed that guanosine containing triplets contribute to PTBP1 binding53. This explains our observation that A-to-I (G) substitutions within circCHEK2 RCMs could enhance PTBP1 binding. Moreover, we provided a large-scale prediction of editing-mediated changes on RBP binding motifs on flanking introns of ARcircs and found that upon editing, most analyzed RBPs have altered binding sites in flanking introns of more than 10 editing-dependent ARcircs, further suggesting that editing may regulate circRNA biogenesis through affecting RBP binding in a transcriptome-wide manner. One should note that there may be other editing-dependent and -independent mechanisms underpinning the regulation of circRNAs by ADARs, such as altering the circRNA turnover and splicing.Although several previous studies reported that RNA editing enzymes ADARs function as repressors of circRNA biogenesis54. Interestingly, co-depletion of ADAR1 and DHX9 leads to synergistic effect on circRNA production. This implies the possibility that DHX9 plays as a regulator of circRNA biogenesis by tunning the editing frequency. Besides DHX9, recent studies on other non-ADAR editing regulators55 indicate an additional layer of editing-dependent regulation of circRNA biogenesis.RNA editing, alternative splicing, polyadenylation, and back-splicing are crucial RNA processing steps that expand transcriptome diversity. As each step heavily involves base-pairing , it is not surprising that these processes undergo extensive crosstalk. These dsRNAs recruit RBPs, dramatically increasing the complexity of RNA processing network. One example is DHX9, which regulates RNA editing as a binding partner of ADARs and also suppresses circRNA biogenesis via unwinding dsRNAs formed by IRAlu elements56. Herein, we mapped the identified RNA editing sites by RNA-Seq as well as RCMs of ARcircs to the region near the back-splicing sites. Our results implied the importance of base-pairing of flanking introns in circRNA biogenesis, consistent with previous studies8. However, we found that unlike RNA binding, RNA editing is not always required for ADARs-mediated regulation of circRNAs. Nevertheless, the precise editing-independent mechanisms remain further investigations.Depending on their binding sites along RNA transcripts, ADARs can protect mRNA from degradation, regulate precursor microRNA processing and alter splicing pattern57. However, how ADARs-mediated changes in circRNA production contribute to cancer remain elusive. Herein, we provided extensive evidence supporting that the editing-dependent and/or independent regulation of circRNA expression by ADAR proteins is present in multiple types of cancer cell lines and more importantly, the association between expression levels of ADARs and circCHEK2 could also be found in HCC and CRC patient samples. We further showed these ARcircs are not merely by-products but indeed affect tumorigenesis, which poses an additional important function of ADARs in cancer. Of note, in our study, circSLC39A8 exhibits an oncogenic role; however, the biogenesis of circSLC39A8 could be repressed upon overexpression of ADAR1 which is largely characterized as an oncogene56. In fact, the tumor promoting effect of ADAR1 is most likely arising from functional changes of multiple target genes via ADAR1’s editing-dependent and independent functions. The effect of ADAR1 or ADAR2 on tumorigenesis may differ depending on their target genes and/or cell or tissue types. For example, ADAR1-mediated protein-recoding editing of antizyme inhibitor 1 (AZIN1) promotes hepatocarcinogenesis21; however, ADAR1-mediated editing can also suppress tumorigenesis by recoding Gamma-aminobutyric acid receptor subunit alpha-3 (GABRA3) in breast cancer58. Likewise, each ARcirc may have its distinct cancer-related functions and thus, one should note that contribution of ADARs-regulated circRNA biogenesis to cancer is unlikely to be attributed to one single ARcirc.Circular RNAs were long considered as by-products of aberrant splicing without biological functions. Only until recently, plenty of circRNAs were found to play critical roles in multiple aspects of cellular and physiological functions, and the dysregulated circRNAs have been implicated in tumorgenesisIn sum, by identifying more than a thousand circRNAs regulated by ADARs, we uncover that ADARs could regulate circRNAs in both direction via editing-dependent and editing-independent mechanisms. We provide experimental evidence that ADAR1/2 can edit RCM of ARcirc, altering the secondary structure formed between RCMs within the flanking introns and enhanced binding of RBP to the site of action. Moreover, ADARs-mediated circRNA regulation is most likely to be present in the same manner across different types of cancer cells, including breast, esophagus, liver, stomach and colon. We show that these ARcircs were not merely by-products of back-splicing, but functional molecules influencing tumorigenesis. These findings improve our understanding of the interaction between ADARs and circRNA biogenesis and its biological importance, particularly in the context of cancer.Our research complies with all relevant ethical regulations. All animal experiments were approved by the Institutional Animal Care and Use Committees of National University of Singapore  with the protocol numbers R16-1644 and R20-1586. All human tissue samples used in this study were approved by the committees for ethics review at Sun Yat-Sen University, the National University of Singapore, and the National University Hospital, Singapore. Written informed consent for all patients were provided for the use of their clinical specimens for medical research.2.EC109 cells were kindly provided by Professor TSAO, George Sai Wah ; SNU-398, HCT15 and MB231 cells were purchased from the American Type Culture Collection (ATCC); MKN28 cells were obtained from Japanese Collection of Research Bioresources Cell Bank. MB231 cells were maintained in Dulbecco’s Modified Eagle Medium (Biowest) while the rest cell lines were maintained in Roswell Park Memorial Institute (RPMI) medium (Biowest), all supplemented with 10% fetal bovine serum  and incubated at 37 °C with 5% COThe expression level of ADAR1 or ADAR2 was modulated (forced overexpression or silencing) using a lenti-viral system in EC109 cells. RNA extraction was performed using RNeasy Mini kit (Qiagen) and subjected to rRNA depletion and RNase R treatment to digest all the linear RNAs. Samples were purified with Beckman RNAClean beads, the retrieved RNA was fragmented using divalent cations at an elevated temperature. Libraries were prepared using the TruSeq library preparation protocol (Illumina) using a modified protocol. Fragments were purified with Beckman AMPure beads and resolved in EB buffer for end repair and adding A at the 3’ end. Y-adaptor was added afterwards. The product was then amplified to construct the cDNA library and sequenced on an Illumina HiSeq4000 or NovaSeq6000 instrument.59. First, raw reads were mapped to the reference human genome (hg19) by STAR (v2.5.2a)60 with the chimeric junction reads option on (--chimSegmentMin 20). The gtf file from the GENCODE61 (gencode.v27lift37.annotation.gtf) was used for the gene and junction annotations. The expression of the circRNAs were quantified as read counts that map to the junctions, resulting in a total of 37,916 circRNAs having > = 1 read in at least 1 out of the 3 samples. To identify differentially expressed circRNAs in ADAR1/2 overexpressed and silenced samples compared to the control, the below criteria were applied:High-confidence circRNAs were identified by using an established in-house bioinformatics pipeline1. Total reads in both (EV + ADAR OE) and (Scr + ADAR KD) > = 10,2. Fold change in circRNA expression (ADAR versus EV) < = 0.5 (suppressing) or > = 2 (promoting), and3. Fold change in circRNA expression (shADAR versus shScr) <= 0.5 (promoting) or > = 2 (suppressing).As a result, we identified 1,406 circRNAs which are potentially regulated by ADARs .To conduct a reliable comparison of the performance for circular RNA identification between our in-house pipeline and the other two commonly used benchmark methods- CIRI2 (v2.0.6) and CIRCexplorer2 (v2.3.0)To identify editing-dependent ARcircs using circRNA-Seq datasets of the DeAD mutant or EV control-overexpressing EC109 cells (DeAD or EV), the following criteria were applied:(1) Total reads in (EV + DeAD) ≥ 10;(2) The resultant list of circRNAs was overlapped with ARcircs presented in Fig. 3) We obtained a total of 1,073 ARcircs fulfilling (1) and (2). We next defined “editing-dependent ARcircs” using the following filter criteria: there is no or minor change (0.8 < fold change < 1.25) in expression between the EV and DeAD-overexpressing cells or the pattern of change in expression upon overexpression of DeAD mutants is opposite to that of the corresponding wildtype ADAR1/2 when compared to the EV control. The remaining ARcircs which are 1) regulated by the wildtype or DeAD form of ADAR1 or/and ADAR2 in the same direction and 2) demonstrate ≥1.25-fold change in expression upon overexpression of DeAD mutant versus EV control are defined as “editing-independent ARcircs” 59. For each sample, raw reads were mapped to the reference human genome (hg19) with a splicing junction database generated from transcript annotations derived from UCSC, RefSeq, Ensembl and GENCODE61 by using Burrows–Wheeler Aligner with default parameters 63. To retain high quality data, PCR duplicates were removed 64 and the reads with mapping quality score < 20 were discarded. Junction-mapped reads were then converted back to the genomic-based coordinates. An in-house perl script was utilized to call the variants from samtools pileup data and the sites with at least two supporting reads were retained. The candidate events were filtered by removing the single nucleotide polymorphisms (SNPs) reported in different cohorts  and excluding the sites within the first six bases of the reads caused by imperfect priming of random hexamer during cDNA synthesis. For the sites not located in Alu elements, the candidates within the four bases of a splice junction on the intronic side, and those residing in the homopolymeric regions and in the simple repeats were all removed. Candidate variants located in the reads that map to the non-unique regions of the genome by using BLAST-like alignment tool67 were also excluded. At last, only A-to-G editing sites based on the strand information from the strand-specific RNA-Seq data were considered for all the downstream analyses. The genomic regions of the editing variants and the associated genes were annotated by using ANNOVAR (v2018)68 with the refGene table. As reported by us previously15, ADARs-mediated global editing changes has been confirmed by analysing the same RNA-Seq dateaset of ADARs OE or KD cells.A bioinformatics pipeline adapted from a previously published methodTo identify high-confidence editing events, the editing sites were required to be supported by ≥10 reads in ≥1 sample, and ADAR1/2-overexpressing samples to result in more than 10% change in the editing level compared to the control. This resulted in 41,151 high-confidence editing sites from the RNA-Seq of our EC109 cell lines used in further analyses. To analyze the sequence preference for the neighbour nucleotide surrounding editing sites, the sequence context of these editing sites was extracted using “bedtools getfasta”, i.e. editing site plus 2 neighbour nucleotides on either side in a strand-specific manner. Then the nucleotide frequencies were converted to a position probability matrix and the sequence logo was plotted by using the “seqLogo” package (v1.56.0).8. We used circRNAs exhibiting >2 or <0.5-fold change upon ADAR1 or ADAR2 overexpression with total reads in EV and ADAR1/2 more than 50 in circRNA-seq for RCM identification, which results in 1,118 circRNAs in total  were further removed leaving 886 circRNAs as the final list. The intronic region spanning 1500 bp upstream and 1500 bp downstream of the circRNA back-splicing junctions was considered to plot the distribution of RCM coverage, which is defined as the sum of the number of top-scoring RCMs at each base across all 886 circRNAs.To identify RCMs, we adopted a previously published method8, 41,151 high-confidence RNA editing sites identified from the RNA-Seq data were overlapped with the flanking introns of 886 circRNAs identified from the circRNA-Seq. The density plots showing the distribution of editing sites were generated within the same region as the plot illustrating the distribution of the RCM coverage mentioned above, but we used the density distribution rather than coverage since each editing site indicates a single nucleotide variation whereas RCMs are regions of variable length. In addition, we have also analyzed the distribution of 2,576,459 A-to-I RNA editing sites downloaded from RADAR database36 within the same regions.To investigate if there is an enrichment of RNA editing events within RCMs as previously reportedMinigene fragments were amplified from human placenta genomic DNA (Sigma) (for intronic sequences) or EC109 cDNA (for exonic sequences) using PrimeSTAR Max DNA polymerase (Clontech) with overlapped primers and ligated into one piece of DNA, followed by ligation into pcDNA3.1+ vector. KAPA HiFi polymerase (KAPA Biosystems) was used to introduce point mutations into minigene using primers with corresponding mutation(s).Overexpression plasmids were obtained by cloning coding sequences of protein, which were amplified by PrimeSTAR Max DNA polymerase (Clontech), into pLenti6 vector. ADARs-targeting short hairpin RNAs (shRNAs) were designed using RNAi Platform (Broad Institute) and were cloned into pLKO.1_puro plasmid using AgeI and EcoRI restriction sites.http://n2t.net/addgene:109049, RRID:Addgene_109049; pXR003: Addgene plasmid #109053, http://n2t.net/addgene:109053, RRID:Addgene_109053)52. Guide RNA (gRNA) sequences were designed using sequence of circRNAs around BSJ with a length of 21 bp and cloned into pXR003 plasmid using BbsI restriction sites.CasRX system (pXR001: EF1a-CasRx-2A-EGFP and pXR003: CasRx gRNA cloning backbone) was a gift from Patrick Hsu  or a mixture of 1ug pXR001 and 1ug pXR003 plasmids were transfected into cells a well of 12-well plate using Lipofectamine 2000 (Invitrogen) with a ratio of 1:2 (DNA:reagent).sample. ∆Ct= Cttarget – Ctactin; ∆∆Ct = ∆Ctsample-average∆Ctcontrol. Primers used in RT-qPCR are listed in Supplementary Data RNA was extracted using RNeasy mini kit (Qiagen) with on column treatment of DNaseI. cDNA was synthesized using Advantage RT-for-PCR kit (Clontech) with random hexamer primers and subsequently qPCR was performed with GoTaq DNA polymerase (Promega). Fold change was calculated by 2^-∆∆CtCells were lysed with RIPA buffer (Sigma) supplemented with 1x cOmplete EDTA-free protease inhibitor cocktail (Roche) and concentrations of total protein were quantified using Bradford assay (Biorad). 10% SDS-PAGE were used to separate proteins, followed by transferred onto polyvinylidene difluoride membranes (Millipore) and incubated with primary antibodies  overnight at 4 °C and secondary antibodies  at room temperature for 1 h. Enhanced chemiluminescence  was used to visualize the blots. Primary antibodies used in this study are as listed: anti-PTBP1 , anti-TDP43 , anti-ADAR1 , anti-FLAG-HRP , anti-β actin HRP , anti-mouse IgG HRP-linked , anti-rabbit IgG HRP-linked . ImageJ (1.51J8) was used to measure band density of blots.-∆Ct ×100%; ∆Ct= CtRIP – [Ctinput - dilution factor]. Sequences of primers are listed in Supplementary Data A 10-cm dish of EC109 cells was transfected with 10 µg of FLAG, FLAG-ADAR1, plenti6, or plenti6-ADAR1 plasmid individually. After 48 h culturing, cells were collected and lysed in buffer containing 50 mM Tris, pH7.5, 150 mM NaCl, 1 mM EDTA and 1% Triton X-100 supplemented with cOmplete protease inhibitor (Roche) and SUPERase·In RNase Inhibitor (Invitrogen). For FLAG-RIP, lysates were then incubated with anti-FLAG M2 magnetic beads (Sigma) overnight at 4 °C with rotation followed by six times of washing with 1× TBS buffer . For PTBP1 RIP, the lysates were pre-cleared using 50 μL protein A-agarose suspension (Roche) at 4 °C for overnight. A total of 2 μL anti-PTBP1 antibody was then added into the lysate and incubate at 4 °C for 1 h, followed by overnight incubation at 4 °C with the addition of 50 μL protein A-agarose suspension. The beads were then washed with washing buffer  for 6 times with each time for 10 min at 4 °C. 10% of beads was used for protein elution while the rest was subjected to RNA extraction using RNeasy miniprep kit (Qiagen). Extracted RNA was reverse-transcribed using Advantage RT-for PCR kit (Clontech) with random hexamer and subsequently qPCR was performed. Input indicates 1% of the total cell lysate. %input = 2The region containing editing site(s) was amplified using PCR method, followed by purification using PCR product purification kit (Qiagen). Purified PCR products were then ligated into pGEM-T easy vector (Promega) using T4 quick ligase (Promega). A total of 20–28 individual plasmids were sent for Sanger sequencing. The number of unedited ‘A’ or edited ‘G’ clones was counted, followed by the calculation of the percentage of edited clones by ‘G/(A + G)’. The percentage of edited clones (a readout of ‘editing frequency’) was determined and shown by pie chart (represented by red slice).PCR was used to generate DNA template for in vitro transcription with a primer pair containing T7 promoter sequences (5′- CGAAATTAATACGACTCACTATAGG at forward) and sequence of interest. DNA template was subjected to in vitro transcription with RiboMAX™ Large Scale RNA Production Systems (Promega) according to the manufacturer’s protocol. Synthesized RNA probes were then purified by RNeasy mini kit (Qiagen).32P labelling with γ-32P-ATP (Perkin Elmer) and T4 PNK (New England Biolabs). Labelled probes were then purified by G25 column . 0.5 pmol labelled RNA probe were incubated at 95°C for 5 min and then gradually cool down to help form secondary structure. Probes were then loaded on 4% or 8% native polyacrylamide gel, followed by gel drying and gel exposure to BioMax® MS film (Carestream Kodak). Sequences of probes are listed in Supplementary Data circCHEK2, position of probe with lowest migration rate was labelled as 0, while position of probe with the highest migration rate was labelled as 1. The related migration rate of each probe was measured by: (Distance between the probe with 0)/(Distance between 1 and 0).Each RNA probe 50 pmol) was first dephosphorylated using rSAP (New England Biolabs), followed by 0 pmol waWhole cell extraction was performed with the kit (Active motif) according to the manufacturer’s protocol.2, 50 mM Hepes, 0.5% Igegal CA-630, and pH 7.4) for 30 min at 4 °C with rotation for probe binding to the beads. Beads were then washed washing buffer  for 10 min at 4 °C for three times. 1 mg whole cell extract was supplemented with 4ul 10 mg/ml yeast tRNA (Invitrogen) and SUPERase In (Invitrogen). The mixture was added to the beads and topped up to 300 µl with washing buffer, followed by incubation for 30 min at 4 °C with rotation. After washing three times, beads were subjected to 2× Laemmli buffer (Sigma) at 95 °C to elute proteins followed by western blot (WB). Sequences of probes are listed in Supplementary Data RNA probe was generated as discussed above but only with an addition of 3′-aptamer at reverse primer. For each reaction, 50 µl Dynabeads MyOne C1 (Invitrogen) was used to incubate with 25 µg RNA probe in 300 µl binding buffer  is changed because of an A-to-G mutation, the RBP binding motif is affected by the editing site. The number of circRNAs which have altered RBP binding sites on flanking introns due to editing was calculated using our in-house script.The ±10nt sequences surrounding editing sites were retrieved. The sequence with A or G at each editing site was used as input for RBP motif analysis using RBPmap70. Triplicate independent experiments were conducted with technical triplicates.A total of 1,000 cells were seeded in each well of 6-well plates after transfection and incubated at 37 °C for 7-9 days. Plates were stained with crystal violet solution  to visualize colonies. Colonies were calculated using OpenCFU70. Triplicate independent experiments were conducted with technical triplicates.A total of 2,000 cells (EC109) or 5,000 cells (SNU398) were seeded into 0.4% low-melting agarose (Lonza Rockland) in each well of 6-well plates with 0.6% low-melting agarose at the bottom. Plates were incubated at 37 °C for 2 weeks and stained with crystal violet solution  for visualization. Colonies were calculated using OpenCFU2. All animal experiments were approved by and performed in accordance with the Institutional Animal Care and Use Committees of National University of Singapore .Four-six-weeks-old male and female NOD scid gamma (NSG) mice  were maintained in pathogen–free (SPF) facility in NUS Comparative Medicine Department. Less than 5 mice with same sex were housed in a cage at 20–25 °C and 50% humidity with a 12 h light/dark cycle. A total of 6 female mice (EC109) or 4 female + 1 male mice (SNU398) were used to subcutaneously inject with one million (EC109) or two million (SNU398) cells into the right and left flanks. Tumor growth was monitored by measuring the length and width at indicated day points. Tumor volume was determined by the formula: 0.5 × length × widthA total of 17 matched pairs of primary HCC and adjacent non-tumor (NT) tissues were obtained from the Sun Yat-Sen University Cancer Centre , between 2002 and 2007. A total of 20 matched pairs of primary CRC and adjacent NT colon tissues were obtained from the National University Hospital, Singapore.t-tests using Prism 8 (GraphPad software). For all figures: n.s., not significant; ∗, P  <  0.05; ∗∗, P  <  0.01; ∗∗∗, P  <  0.001.All quantitative data represent the mean ± SD. Statistical significance was accessed with paired or unpaired two-tailed Student’s Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Software 1Reporting Summary


Small extracellular vesicles (SEVs) such as exosomes are released by multiple cell types. Originally believed to be a mechanism for selectively removing unwanted cellular components, SEVs have received increased attention in recent years for their ability to mediate intercellular communication. Apart from proteins and lipids, SEVs contain RNAs, but how RNAs are selectively loaded into SEVs remains poorly understood. To address this question, we profiled SEV RNAs from mouse dendritic cells using RNA-Seq and identified a long noncoding RNA of retroviral origin, VL30, which is highly enriched (>200-fold) in SEVs compared to parental cells. Bioinformatic analysis revealed that exosome-enriched isoforms of VL30 RNA contain a repetitive 26-nucleotide motif. This repeated motif is itself efficiently incorporated into SEVs, suggesting the likelihood that it directly promotes SEV loading. RNA folding analyses indicate that the motif is likely to form a long double-stranded RNA hairpin and, consistent with this, its overexpression was associated with induction of a potent type I interferon response. Taken together, we propose that preferential loading into SEVs of the VL30 RNA containing this immunostimulatory motif enables cells to remove a potentially toxic RNA and avoid autoinflammation. In this way, the original notion of SEVs as a cellular garbage bin should not be entirely discounted. Small extracellular vesicles (SEVs) such as exosomes are extracellular, membrane-bound vesicles that originate from the multivesicular bodies of the cell’s endocytic compartment. Originally considered a mechanism by which cells excrete unwanted materials , SEVs haUsing SEVs to deliver therapeutic RNAs is seen as a particularly promising strategy ,5. HowevTo better understand RNA loading into SEVs, we assessed the mRNA and long noncoding RNA content of dendritic cell (DC) SEVs, which are already being explored therapeutically in clinical trials . Using nMice were bred and maintained in the animal facilities at the Walter and Eliza Hall Institute of Medical Research (WEHI) according to national and institutional guidelines for animal care. All experimental procedures were approved by the WEHI Animal Ethics Committee .2 in DMEM supplemented with 10% fetal calf serum (FCS)  and 10% X-63 (GM-CSF) supernatant to allow the cells to differentiate into DCs. Immortalised cell lines, including a mouse dendritic cell line (DC2.4), a mouse thymoma cell line (EL4), a mouse B cell lymphoma line (WEHI-231), a mouse fibroblast line (NIH/3T3), the human embryonic kidney cell line (HEK293T), and a human neuroblastoma cell line (SH-SY5Y) were cultured in DMEM supplemented with L-glutamine (2 mM), streptomycin (100 μg/mL), and penicillin (100 U/mL), nonessential amino acid (100 μM), 2-mercaptoethanol (supplements purchased from Sigma-Aldrich) and 10% FCS and cultured at 37 °C and 5% CO2.To obtain bone-marrow-derived DCs (BMDCs), femurs from C57Bl/6 mice were flushed with Dulbecco’s modified Eagle’s medium (DMEM) . The resulting bone marrow cells were resuspended, counted, plated at a density of 2 million cells per 10 cm petri dish, and then cultured for 7 days at 37 °C with 5% COg using an SW-28 rotor  for 16 h at 4 °C; removing the supernatant; diluting this 1:1 in DMEM; and filtering the resultant solution with a 0.2 μm filter. To isolate SEVs, cells were first plated and grown in SEV-free medium for 48 h. Supernatant was then collected, centrifuged at 1500 rpm to remove cellular debris, and then filtered with a 0.2 μm filter to remove microparticles. SEVs were pelleted by ultracentrifugation at 100,000× g using an SW-28 rotor for one hour at 4 °C. Then, EV pellets were resuspended in 1 × sterile PBS (Sigma-Aldrich) and spun down at 100,000× g for an hour. Finally washed pellets were diluted with either 50 μL of 1 × PBS for Western blot or 250 μL of 1 × PBS for RNA isolation. For experiments involving RNase treatment of SEVs, 250 μL SEV solution was treated with 5 mg/mL RNAse A  and incubated at 37 °C for 5 min, prior to RNA isolation. SEV pellets were stored at −80 °C until use.SEV-free medium was prepared by: pelleting any SEVs in DMEM/20% FCS via ultracentrifugation at 100,000 SEV size distribution was assessed via tunable resistive pulse sensing using a qNano instrument  according to a previously published protocol . The qNaTotal RNA was extracted from cells using Tri-Reagent (Sigma-Aldrich) as per the manufacturers’ instructions. SEV RNA was extracted using Trizol LS  then purified further using Qiagen RNA Mini kit . RNA yield was quantified using NanoDrop  and RNA quality was assessed using either the TapeStation System 2200 or the Bioanalyser . To remove contaminating DNA, 1 μg RNA was treated with 1 μL (2 U) of DNAse I  in a 20 μL reaction incubated at 37 °C for 30 min and 1 μL DNAse inactivation reagent added.https://www.rstudio.com/products/rpackages/, accessed on 6 July 2021) [https://www.rstudio.com/products/rpackages/, accessed on 6 July 2021). For the cell samples, any gene that did not achieve an average count per million mapped reads (CPM) greater than 0.15 in at least 50% of samples was deemed to not be expressed and subsequently filtered out. For the SEV samples, genes were filtered out if they failed to achieve a CPM greater than 0.05 in at least three samples. Compositional differences between cell and SEV libraries were normalised using the trimmed mean of log expression ratios (TMM) method [2CPM with associated prevision weights using mean-variance modelling at the observational level  [RNA libraries were prepared from 200 ng of total RNA using a TruSeq RNA library preparation kit v.2  and RNA-seq was performed on an Illumina NextSeq550 sequencing platform (Illumina), generating 80 base pair (bp) paired-end reads. The number of reads overlapping each mouse Entrez gene were summarized using featureCounts of the R subread package (ly 2021) . Entrez ly 2021)  and limmly 2021)  software) method , and couly 2021) . Differe15 primers  according to the manufacturers’ instructions. Real-time PCR was performed using AccuPower Greenstar qPCR Mastermix  and run on a LightCycler 480 . The following primers were used: VL30-qRTPCR ; Motif –BamH1-EcorI-XhoI ; VL30-BamHI-EcorI ; mouse β-actin ; human β-actin ; Ifit1 ; Irf7 ; MDA5 ; RIG-I One microgram of DNAse-treated RNA was reverse transcribed using Superscript II reverse transcriptase  and oligo(dT)TEM analysis of SEV samples was performed as previously described . BrieflyPrimers to amplify full length and motif only-containing VL30 inserts were designed and each fragment was amplified by PCR. Primers were designed to include BamHI and EcoRI restriction sites at the ends of each construct and, following restriction enzyme digestion, purified VL30 full length and motif-only containing fragments were ligated into an empty lentiviral plasmid  using T4-DNA ligase (Promega) following the manufacturer’s instructions. Plasmids were then transformed into DH5 alpha competent cells (Sigma-Aldrich). Virus was produced in HEK-293T cells and finally transduced into SH-SY5Y cells and stable cell lines selected using puromycin (Sigma-Aldrich). For doxycycline treatment, VL30-transduced SH-SY5Ys were plated in T175 cm flasks; once cells reached 80% confluency, they were treated with 1 μg/mL doxycycline (Sigma-Aldrich) for 48 h to induce expression of motif only and full length VL30.https://meme-suite.org/meme/, accessed on 6 July 2021) [http://mfold.rna.albany.edu/?q=mfold/download-mfold, accessed on 6 July 2021) [Full length VL30 sequences were uploaded to MEME Suite 5.0.2 (ly 2021) , and motly 2021)  using dev/v) Triton X-100, 0.5% (w/v) sodium deoxycholate) supplemented with protease inhibitor cocktail tablet . Cells were permeabilised in the following chemicals: 0.025% digitonin with 20 mM HEPES-NaOH pH 7.5 (Sigma-Aldrich), 100 nM sucrose (Sigma-Aldrich), 2.5 mM MgCl2 (Sigma-Aldrich), 100 mM KCl (Sigma-Aldrich), supplemented with protease inhibitors  for 10 min and sonicated for 2 min. Homogenates were then spun down at 13,000 g at 4 °C for 15 min. Total protein in the resultant supernatant was quantified using Pierce BCA Protein Assay kit . Forty to fifty micrograms of protein per lane were loaded onto 4–12% NuPAGE precast  gels, separated by SDS-PAGE, and transferred to polyvinylidene fluoride membranes  at 100 volts for an hour. Membranes were then blocked with 5% commercial BSA (Sigma-Aldrich) in 1 × TBS, 0.1% Tween 20 (Sigma-Aldrich) for 30 min then probed with the following antibodies overnight at 1:1000 dilution: FLOT1 , TSG-101 , Transferrin Receptor , GM130 , Bcl-2 , Calnexin  and VDAC-1 . Blots were then washed three times for 10 min, probed with antirabbit secondary-HRP antibody for an hour, washed as before then treated with Luminata Forte Western HRP substrate (Millipore), and visualised using the ChemiDoc MP system .SEV pellets were suspended in a minimum volume of PBS and mixed with lysis buffer containing the following chemicals purchased from Sigma-Aldrich : 150 mM NaCl, 50 mM Tris at pH 7.4, 1% . All histograms show mean values, with error bars indicating standard error of the mean (SEM) unless otherwise stated.Statistical analyses to compare RNA levels in qRT-PCR experiments were performed using unpaired, two-tailed Student’s To first isolate SEVs, we performed ultrafiltration and differential ultracentrifugation of culture supernatant from mouse BMDCs, which are a rich source of SEVs . The res2 fold change <2). Even among the top 20 RNAs that were most enriched in SEVs, there was one RNA known as VL30 (gene symbol: A130040M12Rik) that stood out based not only on its strong enrichment (~200-fold) but also its overall abundance  throughout the mouse genome . Over tiTo test whether this repetitive motif was important for packaging into SEVs, a full-length cDNA clone of VL30 (C730003K16) containing nine tandem copies of the motif was obtained, and a truncated, “motif-only” construct representing ~20% of the full-length sequence and containing the repetitive motif alone was generated a. The fuTo first test whether expression of the full length construct was associated with enrichment of VL30 in SH-SY5Y SEVs, SEVs from SH-SY5Y cells were isolated and the relative abundance of VL30 RNA in cells and SEVs was compared by qRT-PCR b. In theWe next turned our attention to the motif-only construct. Here, we observed that, similar to the full length construct, VL30 containing the repeated motif alone was detected in cells even in the absence of doxycycline and highly enriched in SEVs (~600-fold) c, consisTo investigate this further, we examined the likely secondary structure of our motif-only VL30 RNA using Mfold, and found that this RNA is strongly predicted to form a long dsRNA hairpin a. Given Since the identification of RNA in SEVs  and the In theory, our identification of an RNA sequence that promotes SEV loading could assist efforts to selective package therapeutic RNAs into SEVs. However, overexpression of the repeated motif led to induction of a strong type I IFN response, consistent with its dsRNA structure. If the motif were added to the sequence of a therapeutic RNA as a means of promoting SEV loading, it would therefore be important to avoid this innate immune response; otherwise the health of the parental cells and their SEV production would be compromised, as we observed. In this regard, it is interesting to note that overexpression of the full length VL30 sequence—despite it containing the same repeated motif in its entirety—did not induce a type I IFN response. Why this should be the case is unclear, but one possibility is that the tertiary folding of the full length VL30 RNA either prevents the repeated motif’s dsRNA structure from forming in the first place or else hides it internally so as to prevent recognition by innate immune receptors such as MDA5, RIG-I and TLR3 that recognize dsRNA. Whether adding a therapeutic RNA to the repeated motif enables selective loading of the RNA into SEVs and/or helps to similarly avoid a type I IFN response remains to be seen.Our observation that VL30 RNA was enriched in SEVs from a wide variety of cell types as well as from different species (mouse and human) suggests that the features of VL30 that promote its selective packaging into SEVs utilize a cellular mechanism that is widespread and evolutionarily conserved. What that mechanism might be is something for future study, but we can speculate as to the features of the VL30 RNA that promote its SEV loading. Firstly, by conducting a BLAST search of the 26 nucleotide motif against the NCBI nucleotide database (and excluding inevitable hits to VL30 itself within the mouse genome), we found that the motif matched the viral packaging signal (Psi) that is contained within various retroviral/retrotransposon-based vectors. This signal, originally derived from the VL30 retrotransposon, is believed to efficiently direct the packaging of recombinant RNAs, such as those from the reporter gene lacZ, into virions ,33. At fIf dsRNA is indeed a signal for selective SEV loading, a question that arises is what purpose this might serve. During viral infection, the extracellular transfer of viral dsRNA from infected cells has been proposed as a means of activating the innate immune response within noninfected bystander cells, thus augmenting antiviral immunity in the face of the various immunosuppressive mechanisms that viruses employ within infected cells . ConsistOur study is not without several important limitations. Firstly, we are aware that it would have been helpful to further define the minimum VL30 sequence that facilitates SEV loading. In this regard, it is notable that previous work with the VL30 Psi sequence in relation to retroviral packaging suggests that a Psi sub-sequence of as little as 61 nucleotides is sufficient for promoting RNA encapsidation  . It woulIn this study, we observed that the VL30 RNA is highly enriched in SEVs from multiple cell types, and identified a tandemly-repeated motif that appears to help promote the selective loading of VL30 into SEVs. Further study into whether this repetitive motif can help promote loading of therapeutic RNAs into SEVs is warranted.


Salmonella spp., the presence of cadaverine and putrescine diminished by 62 and 78%, respectively, due to the addition of cava lees. The addition of cava lees phenolic extract also showed an anti-aminogenic effect (21% for cadaverine and 40% for putrescine), although in a lesser extent than cava lees. Cava lees and their phenolic extract were shown to be an effective strategy to control the undesirable accumulation of high levels of biogenic amines during the production of fermented products.Food safety can be compromised by some bioactive compounds such as biogenic amines that can be specially found in fermented foods due to the bacterial decarboxylation of some amino acids by fermentative or spoilage bacteria. Cava lees are a winery by-product rich in fiber and phenolic compounds and previous works have raised their revalorization from a food safety point of view. The aim of the current work was to investigate whether the use of cava lees can help to control biogenic amine formation in bread and fermented sausages. In bread, only very low levels of biogenic amines  were found, whose content did not vary with the addition of different amounts of lees. However, the addition of lees in fermented sausages significantly reduced the formation of tyramine and cadaverine. In sausages spontaneously fermented and inoculated with  Food safety can be compromised not only by food-borne pathogens but also by some bioactive compounds of bacterial origin such as biogenic amines. These microbial metabolites  are formed by a decarboxylation of their precursor amino acids and can be found in almost all types of food in a wide range of concentrations, which may vary even within the same type of product. Histamine and tyramine can have adverse health effects, such as histamine intoxication, histamine intolerance or hypertensive crises caused by the interaction between tyramine and monoamine oxidase inhibitor (MAOI) drugs . In factBesides the food safety issue, because the accumulation of biogenic amines in foods can be associated with the activity of spoilage bacteria, a high content of these compounds is considered a chemical marker of insufficient food freshness or poor hygiene in the manufacturing and/or food preservation processes . AdditioSaccharomyces cerevisiae , lyophilized , ground and stored in tubes protected from light and humidity until use. A lees phenolic extract (LPE) with a total phenolic content of 152.2 ± 3.5 mg GAE/g was obtained from the cava lees using the method described by Cava lees were provided by the winery Freixenet S.A. . To remove any remaining cava, the wet lees were centrifuged at 18,000 × Saccharomyces cerevesiae) and 10 g of salt. Four batches of bread were made: without cava lees (control) and with three different amounts of cava lees . These percentages were selected for showing a growth promoting effect on fermentative lactic acid bacteria (LAB) of the sourdough with total counts determined using MRS medium at 30°C under aerobic conditions during 72 h. The sourdough was prepared following the procedure described by Bread was prepared using 500 g of wheat flour, 150 g of sourdough, 285 g of water, 4 g of lyophilized yeast (The meat batter was prepared by mixing minced pork lean and ground fat (8:2) through a 6 mm plate. The ground meat was then mixed with (g/kg): sodium chloride (25), dextrose (7), black pepper (3), sodium ascorbate (0.5), sodium nitrite (0.15), and potassium nitrate (0.15).in vitro bacterial growth in MRS medium of different strains of starter cultures, such as Latilactobacillus sakei, Lactiplantibacillus plantarum, Latilactobacillus curvatus, Lacticaseibacillus casei, and Limosilactobacillus fermentum  and with 5% (w/w) cava lees. According with previous studies, 5% of cava lees was the most effective amount to enhance the ermentum , as wellermentum . For eacSalmonella enterica were inoculated to the meat batter at a final level of ca. 6 log10 CFU/g (same amount for each strain): CTC1003 (serotype London), CTC1756 (serotype Derby) and CCUG34136  (L. sakei (formerly Lactobacillus sakei) CTC494, producer of the bacteriocin sakacin K  . Portionakacin K , and L. The analyzed samples corresponded to the meat batter (time 0), 8–9 days of ripening (depending on the experiment) and the finished product after 21 days of ripening. Each batch was prepared in triplicate.g, 4°C, 25 min) and the supernatant was collected in a 25 mL volumetric flask. This extraction process was repeated twice more, and the final volume of the extract was adjusted with 0.6 M perchloric acid. The samples were filtered through a 0.22 μm GHP filter. All determinations were done in triplicate.Twelve biogenic amines  were determined in bread (sourdough and baked bread) and fermented sausages (at different time points during fermentation and ripening by triplicate). The biogenic amine content in the cava lees was also analyzed. Ultra-high efficiency liquid chromatography coupled to fluorometric detection (UHPLC-FL) was used according to ortho-phthaldehyde and a fluorometric detection (Ex: 340 nm and Em: 445 nm).Chromatographic separation of the biogenic amines was performed using an Acquity UPLC BEH C18 1.7 μm (2.1 mm × 50 mm) reversed phase column , followed by an on-line post-column derivatization with The aqueous content was determined  and the L. sakei BAP110 and Salmonella spp.) was evaluated using the method described by L. sakei CTC494 was previously established by The amino acid decarboxylase activity of the strains (post hoc test were performed (SPSS software). Differences with p < 0.05 were considered statistically significant.To determine the statistical differences in biogenic amine contents between the different batches of fermented products, a one-way ANOVA and the Tukey HSD S. cerevisiae) and LAB, mainly Lactobacillus spp. and Leuconostoc spp., whose acidifying and proteolytic properties influence the aromatic quality and rheology of bread. Despite this considerable microbial activity, the sourdough was found to contain only low levels of putrescine and spermidine, both naturally present in flour and therefore coming from this ingredient  reduced cadaverine accumulation by more than 76% compared to the control batch.In the control batch, cadaverine was the dominant amine, representing 41% of the total amine content, followed by spermine (23%) and tyramine (18%), whereas putrescine and spermidine were present at the lowest amounts. The effect of cava lees in fermented sausages was only evaluated with the addition of 5% of lees, since this was the amount that showed an antimicrobial effect in this kind of product . In the Based on these promising results, the potential reductor effect of cava lees on biogenic amine formation during the production of fermented sausages was studied in more depth using different batches of fermented sausages from a previous study .Salmonella, a bacterial genus generally linked with a high capacity to form biogenic amines. However, as aminogenic capacity varies among strains, it is necessary to carry out a case-by-case evaluation , and moderate levels of histamine (200 mg/L); the strains were not able to form tyramine. The production of putrescine, cadaverine, and histamine is frequently associated with the activity of Gram-negative spoilage bacteria (Pseudomonas and Enterobacteria) and food-borne pathogenic bacteria  to avoid the concentration effect of the drying process. In the meat batter used in the manufacturing of fermented sausages for both experiments, spermine and spermidine were the only two amines detected, with contents of 89.2 ± 0.2 and 3.4 ± 0.1 mg/kg dm, respectively, which did not change significantly during the fermentation and ripening process in any of the batches. These results confirm the physiological origin of these polyamines from meat. Although the cava lees contained spermidine, the proportion in which the lees were added to the sausages (5%) was too low to create significant differences with the control batches.During the fermented sausage processing, three amines were detected, cadaverine, putrescine and tyramine, the concentrations varying considerably between experiments and/or batches . PhysicoSalmonella strains to synthesize these diamines. Both amines are commonly found in fermented meat products, although usually at relatively low levels  , cadaverine was the major amine, reaching levels of 1,484 mg/kg dm . Signifiby EFSA) . Howeverby EFSA) . The conby EFSA) . For thiby EFSA) . In the p < 0.05). The markedly lower counts of Salmonella (up to 2.7 log10) found in fermented sausages formulated with cava lees (The addition of cava lees (L1) only affected the formation of cadaverine and putrescine, with values in the finished product being 62 and 78% lower than in the control batch C1, respectively , with reductions of up to 91% for putrescine, 88% for tyramine, and 74% for cadaverine. The high efficacy with which the starter culture inhibited amine formation meant it was impossible to observe any synergistic effect with cava lees.Finally, the formation of cadaverine, putrescine, and tyramine was inhibited in the batches added with the starter culture i CTC494 , a highli CTC494 . Numerou strains . Among tmulation . Bover-CpH, formulation, sausage diameter, etc.) and technological (temperature and humidity)  , with levels of 525 and 212 mg/kg dm in the final product, respectively . The forumidity) . Therefo8 in C2) . The effogenesis . TyraminSalmonella than C2 at the end of the ripening (differences of 0.71 log10). Therefore, the reduction in amine levels of E2 could be due to the polyphenols from LPE, as indicated by some other studies performed on different types of meat products. For example, the addition of phenolic extracts from tea, grape, bamboo, or roses induced a significant decrease in levels of cadaverine, tyramine, histamine, and/or putrescine, which was always correlated with lower counts of spoilage bacteria in the polyphenol-enriched products (The LPE added in this experiment (E2) also had a significant aminogenesis reducing effect, 21% lower content for cadaverine and 40% for putrescine compared with the control (C2) . As repoproducts .L. sakei CTC494 as starter culture, both with or without LPE, avoided the formation of the three amines. In contrast, the use of L. sakei BAP110 culture, also without aminogenic capacity in vitro, prevented the formation of putrescine and cadaverine but not totally that of tyramine (As in the case of experiment 1, the use of tyramine . MoreoveIt should be noted that the formation of histamine was not observed in any batch of fermented sausages. According to various studies, histamine is found in less frequency in meat products or only in low quantities. However, the occasional presence of extremely high levels of histamine has been linked to hygienically defective raw materials .The two fermented products considered in this study showed important differences regarding the occurrence of biogenic amines. In the case of bread, only very low putrescine, cadaverine and/or spermidine contents were found, mainly proceeding from raw materials, and without significant differences depending on whether or not cava lees were added. On the contrary, the addition of cava lees or its phenolic extract in the formulation of fermented meat products has been shown to be an effective strategy to reduce the high levels of biogenic amines, such as putrescine and cadaverine, although this effect was not as effective as the use of starter cultures. In view of the obtained results on the anti-aminogenic effect in meat fermented products, it would be convenient to carry out further studies assessing a wider range of concentrations of cava lees or its phenolic extract and also to extend it to other fermented products.The original contribution presented in this study are included in the article, further inquiries can be directed to the corresponding authors.ML-M, MV-C, AJ, and SB-C: conceptualization. SH-M, NF-B, AM-G, OC-B, and ML-M: investigation. SH-M, OC-B, and ML-M: writing—original draft preparation. SH-M, NF-B, OC-B, MR-A, EL-T, ML-M, AJ, SB-C, and MV-C: writing—review and editing. MV-C, AJ, and SB-C: supervision. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.