Background: Circular RNAs (circRNAs) are a new class of RNAs with medical significance. Compared to that of linear mRNA transcripts, the stability of circRNAs against degradation owing to their circular structure is considered advantageous for their use as biomarkers. As systematic studies on the stability of circRNAs depending on the RNA integrity, determined as RNA integrity number (RIN), in clinical tissue samples are lacking, we have investigated this aspect in the present study under model and clinical conditions.Methods: Total RNA isolated from kidney cancer tissue and cell lines (A-498 and HEK-293) with different RIN after thermal degradation was used in model experiments. Further, RNA isolated from kidney cancer and prostate cancer tissue collected under routine surgical conditions, representing clinical samples with RIN ranging from 2 to 9, were examined. Quantitative real-time reverse-transcription polymerase chain reaction (RT-qPCR) analysis of several circRNAs , their corresponding linear counterparts, tissue-specific reference genes, and three microRNAs (as controls) was performed. The quantification cycles were converted into relative quantities and normalized to the expression of specific reference genes for the corresponding tissue. The effect of RIN on the expression of different RNA entities was determined using linear regression analysis, and clinical samples were classified into two groups based on RIN greater or lesser than 6.Results: The results of model experiments and clinical sample analyses showed that all relative circRNA expression gradually decreased with reduction in RIN values. The adverse effect of RIN was partially compensated after normalizing the data and limiting the samples to only those with RIN values > 6.Conclusions: Our results suggested that circRNAs are not stable in clinical tissue samples, but are subjected to degradative processes similar to mRNAs. This has not been investigated extensively in circRNA expression studies, and hence must be considered in future for obtaining reliable circRNA expression data. This can be achieved by applying the principles commonly used in mRNA expression studies. Circular RNAs (circRNAs) are a new class of non-coding RNAs. These RNAs are single-stranded and have a covalently closed circular structure lacking both the 5'-cap and the characteristic 3′-poly(A) tail of linear RNA Meaningful prospective studies have to be performed to translate these initial promising findings of circRNA biomarker research into clinical practice in vivo degradation of circRNAs In addition to high-throughput sequencing, hybridization, and microarray approaches used for the detection circRNAs and expression profiling in samples, quantitative real-time reverse-transcription polymerase chain reaction (RT-qPCR) is an indispensable tool for circRNA research. This approach is essential for validation of circRNAs that have been identified in genome-wide screening using sequencing or microarray technology hsa_circ_0101692, hsa_circ_0001522, and hsa_circ_0007444circEGLN3, circCSNK1G3, and circRHOBTB3 for the circRNAs and linEGLN3, linCSNK1G3, and linRHOBTB3 for the linear counterparts are used in reference to the corresponding host genes to facilitate the presentation of comparisons  how RNA integrity, as a pre-analytical factor, affects the RT-qPCR results of circRNAs, (b) whether the stability of circRNAs differs from those of their linear counterparts, tissue-specific reference genes, and miRNAs, and (c) whether the adverse effects of poor total RNA integrity on the RT-qPCR results of circRNAs can be corrected or reduced by normalizing with the expression of reference genes. Toward these objectives, we first performed model experiments, in which we determined the expression of different RNA species in artificially degraded total RNA samples isolated from two cell lines and a kidney cancer tissue pool. Subsequently, we addressed the clinical situation by determining RIN-dependent expression of different RNAs in total RNA isolated from kidney and prostate cancer tissue specimens. In clinical tissue samples, we examined three circRNAs Table  that areTissue samples were obtained from patients with clear cell renal cell carcinoma (ccRCC) and prostate cancer (PCa) undergoing radical nephrectomy and radical prostatectomy, respectively. The Ethics Committee of the Charité - University Medicine, Berlin, approved the study (EA1/135/12) and informed consent was obtained from all patients. The study was performed in accordance with the Declaration of Helsinki. The clinicopathological characteristics of the patients are summarized in 6 cells were disrupted in 700 µL Qiazol in a TissueLyser (Qiagen) at 30 Hz for 2 × 1 min. The homogenate was processed according to the manufacturer's instructions, with the inclusion of an on-column DNase digestion step. RNA was eluted from the spin column membrane with 30 µL nuclease-free water. RNA yield and purity were controlled by measuring the absorbance on the NanoDrop ND-1000 spectrophotometer . The median RNA concentrations in the tested clinical samples were 1086 (95% CI: 996 to 1172) ng/µL from kidney cancer and 957 (95% CI: 846 to 1031) ng/µL from prostate cancer. The ratio of the absorbance at 260 nm and 280 nm of all isolated RNA samples ranged from 1.89 to 2.01. The RIN was assessed on a Bioanalyzer 2100 with the Agilent RNA 6000 Nano Chip Kit . Isolated RNA samples were stored at -80 °C until analysis. Further details are listed in the checklist  as described previously hecklist  of the MAfter preliminary experiments on artificial heat degradation of total RNA samples, an experimental design was developed. This should take into account all influencing variables and allow measurement of all RNA variables under equal conditions for a representative experiment with matched samples Figure  and 2. TcircEGLN3 and circRHOBTB3, based on the general characteristics of circRNAs regarding their resistance to the RNase R digestion, their lack of a poly(A) tail, the amplification results in complementary DNA (cDNA) and genomic DNA (gDNA) using divergent and convergent primers, and the proof of the backsplice junctions by Sanger sequencing, are compiled in our previous report on circRNAs in kidney cancer circCSNK1G3 measurements, the reaction conditions described in the recently published circRNA landscape of prostate cancer were used The quantification characteristics are listed in the checklist of the MIQE guidelines as mentioned above . Detaile18 primers was used for circRNAs and mRNAs in final reaction volume of 20 µL with 1 µg total RNA 18 primers 18 primers compared with hexamer primers  including a ready-to-use mix of random hexamer and oligo(dT) primers B. The qP primers . These dReverse transcription of miRNAs was performed with the TaqMan microRNA reverse transcription kit  using miRNA-specific stem-looped primers according to the manufacturer's instructions, which is described in circEGLN3, circRHOBTB3, and circCSNK1G3, and their linear mRNA counterparts, the control circRNAs circRNA4 and circRNA9 according to Memczak et al. PPIA) and TATA-box binding protein (TBP) for ccRCC samples ALAS1) and hypoxanthine phosphoribosyltransferase 1 (HPRT1) for PCa samples let‑7a‑5p, miR‑17‑5p, and miR‑210-3p are summarized in qPCR measurements were performed on the LightCycler 480  using white 96-well plates (Roche) in a reaction volume of 10 µL. 40 cycles were used as described previously -ΔCq method. The percentage changes in expression among experiments could be compared as equal amounts of total RNA were used in the RT-qPCR analyses. QBase+ software version 3.2 , which is based on a generalized model of the 2-ΔΔCq approach with correction of amplification efficiency, was used for data evaluation Expression changes with respect to the corresponding starting points due to RNA degradation were calculated using the 2U-test, Wilcoxon test, linear regression analysis, and matched analysis of variance (ANOVA) were performed as indicated in the Results. The significance of the slope was determined based on its deviation from zero and the differences between slopes. Sample size and power calculations to assess the effect of RIN on the expression of circRNAs, their linear counterparts, and reference genes in clinical tissue samples were performed using the MedCalc software. The calculation was based on comparing the mean change in the expression in two sample groups with lower (≤ 6) and higher (> 6) RIN. An effect size  was considered when power was 0.9 and P < 0.05. Twenty-two samples were used to assess the effect of RIN under these conditions. Hence, at least 25 samples of every RIN group were included in this study. P-values < 0.05 (two-sided) were considered statistically significant.GraphPad Prism 8.4.2 for Windows  and MedCalc 19.2.0  were used for statistical analyses. Mann-Whitney To investigate the expression of circRNAs, mRNAs, and miRNAs in total RNA samples as a function of RNA integrity, the RNA samples were artificially degraded by heating. The kinetics of time-dependent RIN reduction as an indicator of changes in RNA integrity is shown in Figure P = 0.0055 to <0.0001). The extent of reduction in expression differed between individual circRNAs and mRNAs. For example, RNA integrity affected the expression of TBP mRNA and circEGLN3 more than that of the PPIA mRNA and circRHOBTB3, respectively  became more evident in total RNA samples with reduced RIN. This effect partially differed depending on the source from which the total RNA was isolated.RT-qPCR showed that the relative expression of individual RNAs after thermal treatment-mediated degradation was different from that before heating of the total RNA samples Figure A-C. Colly Figure A and 2C.TBP and PPIA at the respective RIN values. TBP and PPIA are exemplarily used here as validated reference genes for ccRCC expression studies linEGLN3 and linRHOBTB3 after normalization to the initial expression values prior to degradation depended on the RIN of the degraded RNA samples. The percentage deviations in the expression of the degraded samples were clearly lower than the decrease in expression shown in Figure circRNA4, circRNA9, circEGLN3, circRHOBTB3, linEGLN3, and linRHOBTB3) in the three RNA sources amounted to 95.5% , while the median percentage without this adjustment was only 65.5% . These results support the view that errors due to the use of degraded RNA samples can be partially compensated via adjustment with a combination of suitable reference genes. On the other hand, over- and undercorrections of different RNAs beyond the here selected limit of 15% are observed in dependence on the RIN value of samples . Thus, the contribution of the clinicopathological factors to possible expression differences in the two RIN groups may be ignored. Expression of all circRNAs and mRNAs calculated as relative quantities in both kidney and prostate cancer patients were significantly lower in RNA samples with RIN values ≤ 6 than in those with RIN values > 6  tissue, the most routinely preserved tissue material for diagnostic purposes 18 primers was performed for circRNA validation  under adverse RNA integrity conditions shown in this study  can measurements of circRNAs in degraded RNA samples be corrected to the (probable) initial value and (b) is there a critical limit of RNA integrity that should not be crossed for obtaining reliable results? As circRNA and mRNA expression decrease similarly with reduction in RIN Figure A-C, the y Figure . This ap Figures  and 5, wACTB and GAPDH are used frequently as single normalizers  that excludes type I and II errors as far as possible, and the comparable clinicopathological characteristics of the two different RIN groups support the general validity of the measured and evaluated data. Second, our results of circRNA measurements refer only to the RT-qPCR methodology and did not consider the particularities of other analytical techniques like microarray, hybridization methods, RNAseq, and next generation sequencing. Irrespective of the situation with other methods, it is necessary to consider this issue for RT-qPCR measurements of clinical samples. Moreover, the specific problem of FFPE material needs clarification + (corrective-based approach) In summary, this is the first systematic study comparing the stability of circRNAs with those of their linear mRNA transcripts from the same host gene in clinical tissue samples. CircRNAs showed similar degradation profiles as mRNAs in isolated total RNA samples. The degradation behavior differed between individual circRNAs and was tissue-specific. Based on RNA integrity as the indicator of RNA degradation, the RT-qPCR read-outs of circRNAs were found to be affected similar to those of mRNAs. This has not been sufficiently considered in previous studies on circRNA expression. Based on the observations of RT-qPCR analysis of mRNAs, we concluded that the adverse RNA integrity effect can be partly compensated in an appropriate manner if (a) tissue-specific reference genes are used as normalizers that are validated and recommended in their number by normalization software like geNorm in the software package qbaseSupplementary figures and tables.Click here for additional data file.


Saccharomyces cerevisiae telomerase RNA, TLC1, is rapidly evolving, the central catalytic core is largely conserved, containing the template, template-boundary helix, pseudoknot, and core-enclosing helix (CEH).The telomerase ribonucleoprotein (RNP) counters the chromosome end replication problem, completing genome replication to prevent cellular senescence in yeast, humans, and most other eukaryotes. The telomerase RNP core enzyme is composed of a dedicated RNA subunit and a reverse transcriptase (telomerase reverse transcriptase [TERT]). Although the majority of the 1,157-nucleotide (nt)  Saccharomyces cerevisiae telomerase RNA, TLC1, is rapidly evolving, the central catalytic core is largely conserved, containing the template, template-boundary helix, pseudoknot, and core-enclosing helix (CEH). Here, we show that 4 bp of core-enclosing helix is required for telomerase to be active in vitro and to maintain yeast telomeres in vivo, whereas the ΔCEH and 1- and 2-bp alleles do not support telomerase function. Using the CRISPR/nuclease-deactivated Cas9 (dCas9)-based CARRY (CRISPR-assisted RNA–RNA-binding protein [RBP] yeast) two-hybrid assay to assess binding of our CEH mutant RNAs to TERT, we find that the 4-bp CEH RNA binds to TERT but the shorter-CEH constructs do not, consistent with the telomerase activity and in vivo complementation results. Thus, the CEH is essential in yeast telomerase RNA because it is needed to bind TERT to form the core RNP enzyme. Although the 8 nt that form this 4-bp stem at the base of the CEH are nearly invariant among Saccharomyces species, our results with sequence-randomized and truncated-CEH helices suggest that this binding interaction with TERT is dictated more by secondary than by primary structure. In summary, we have mapped an essential binding site in telomerase RNA for TERT that is crucial to form the catalytic core of this biomedically important RNP enzyme.The telomerase ribonucleoprotein (RNP) counters the chromosome end replication problem, completing genome replication to prevent cellular senescence in yeast, humans, and most other eukaryotes. The telomerase RNP core enzyme is composed of a dedicated RNA subunit and a reverse transcriptase (telomerase reverse transcriptase [TERT]). Although the majority of the 1,157-nucleotide (nt)  In order to overcome the end replication problem and prevent senescence, most eukaryotic organisms require the ribonucleoprotein (RNP) enzyme complex telomerase  and a reverse transcriptase protein component . TERT utilizes a short template sequence in the telomerase RNA to iteratively add telomere repeats to the 3′ end of chromosomes , telomerase is inactivated in vitro , suggest . Switching of the context from Micro-T to Mini-T is necessary since Micro-T lacks features necessary for telomerase function in vivo. Using a circularly permuted RNA is necessary to avoid disrupting the area of required connectivity (ARC) when studying the CEH, which is in the center of the ARC (Our previous results suggested that the core-enclosing helix (CEH) is required for activity o-T(170)  . If the particular sequence or the positioning of bulges in the core-enclosing helix is crucial for function, then deletion of these regions should abolish function. We first deleted the 4 bp nearest to the core Δ8 in . These ain vivo and in vitro ], 19 nucleotides, and 39 nucleotides . All of in vitro  and E. Tin vitro  supportein vitro .in vitro  generate a core-enclosing helix sequence abutting the catalytic core that differs from the wild type, yet all of these alleles function well both in vivo and in vitro telomerase action—thus, the CEH is essential for fundamental telomerase core enzyme activity. Since the core enzyme is composed of the TLC1 RNA and the TERT catalytic protein subunit, a parsimonious explanation for the contribution of CEH to enzyme function is that it is simply required for the RNA to bind to TERT to assemble the core enzyme.What is the mechanistic role of the core-enclosing helix in telomerase RNP function? Our results show that the CEH is required for both in vivo CRISPR-assisted RNA–RNA-binding protein (RBP) yeast (CARRY) two-hybrid RNA-protein interaction assay  . Fi. Fiin viistidine , row 3. in vitro  and 3. WA multisubunit enzyme cannot be active if its core components do not assemble. Despite this basic criterion for function, relatively little is known about the interfaces that govern binding between the subunits at the heart of the biomedically relevant telomerase RNP enzyme. Here, we have demonstrated that the core-enclosing helix of telomerase RNA is essential for binding to the TERT catalytic protein subunit in yeast.in vivo and in vitro, consistent with it being required for binding to TERT. Furthermore, our findings are based on data obtained from studying the CEH within two independent circularly permuted telomerase RNA allele contexts  provide at least basic telomerase activity. Functionality of the CEH in these alleles was despite the sensitized context of being tested within miniaturized TLC1 RNA, which, even when otherwise wild type in sequence, supports rather short telomeres. We conclude from the results that the sequence of the CEH, although making some degree of contribution to telomerase RNA function, is not essential and therefore that the CEH secondary structure is more important than the primary structure.S. cerevisiae CEH are essentially invariant among Saccharomyces species . During this multifaceted interaction of the enzyme with telomeric DNA through the catalytic cycle, the telomerase RNA-TERT interaction must also be maintained. This dynamic interplay should necessitate a multipartite binding interaction between the telomerase RNA and TERT. Telomerase, of course, interacts with the template via its active site, but presumably the highest-affinity binding interaction between the RNA and TERT resides outside the RNA’s template region in order to provide a connection that maintains core RNP integrity during catalysis. It may be important that this TERT-binding site in the RNA is distal to the TERT catalytic site-RNA template part of the RNP so as to avoid the binding site sterically hindering the repeated process of binding, extending, and dissociating from the telomeric DNA substrate. Specifically, as discussed above, we propose that TERT binds most prominently to telomerase RNA nucleotides elsewhere around the RNA’s central hub from the template in the secondary structure model . This isin vivo assays for telomerase function used yeast strain yVL009 (MATatlc1Δ::LEU2 rad52-Δ::LYS ura3-52 lys2-801 ade2-101 trp-1-Δ1 his3-Δ200 leu2-Δ +cir pSD120/pTLC1-URA3-CEN) (TRP1-marked pRS424 vector for in vivo assays or a pUC19-based vector for in vitro assays.All RA3-CEN) . All newTRP1-marked CEN or 2μ plasmid via standard lithium acetate methods. Transformants were selected on medium lacking uracil and tryptophan and then streaked on medium lacking tryptophan and containing 5-fluoroorotic acid (5-FOA) to counterselect for the wild-type URA3-marked TLC1 cover plasmid. Duplicate colonies of yeast containing only the transformed plasmid were then serially streaked 10 times on medium lacking tryptophan at 30°C.yVL1009 yeast strains were transformed with a + membrane  by capillary action, cross-linked with UV irradiation, and incubated with radiolabeled probes for telomeric DNA and a region of chromosome IV. Blots were imaged using a Typhoon 9410 variable mode imager.To determine telomere length, Southern blots were performed as previously described . Briefly+ membrane  and UV cross-linked. The membrane was probed with 1 × 107 cpm of an StuI-NsiI fragment of Mini-T(460) and 1 × 105 cpm of the control RNA, NMEI. Blots were imaged using a Typhoon 9410 variable mode imager.Total RNA was isolated, using a hot phenol extraction method, from log-phase yeast liquid cultures grown in the absence of tryptophan. Thirty micrograms of total cellular RNA was electrophoresed through a 4% polyacrylamide–1× Tris-borate-EDTA–7 M urea gel at 35 W for ∼3 h. Separated RNAs were electrotransferred to a Hybond Nin vitro transcription and translation system as previously described . Purified telomerase was incubated with telomeric DNA oligonucleotide (DZ428: 5′-GGTGTGGTGTGGG-3′), a 5′ γ-32P-labeled internal recovery and loading control, [α-32P]dTTP, and 1 mM each unlabeled dATP, dCTP, and dGTP in standard in vitro telomerase activity buffer. Assembled reaction mixtures were incubated for 10 min at 26°C, and then reactions were stopped by the addition of ammonium acetate and ethanol precipitated. Products were resuspended in formamide loading dye and boiled at 95°C for 5 min. Samples were electrophoresed through a 10% polyacrylamide–1× Tris-borate-EDTA–7 M urea gel for 75 min at 90 W and then dried on Whatman paper and exposed to a phosphorimager screen. Imaging was done with a Typhoon 9410 variable mode imager. As a loading and recovery control during purification of reconstituted telomerase  ura3::(8LexAop-LacZ) leu2::(KanMX6_dCas9)] was transformed with single guide RNA (sgRNA) fusion and GAD fusion plasmids with the vector backbones from TRP1-marked pCARRY2 (LEU2-marked pGAD424 (CARRY two-hybrid assays were carried out as described previously . Briefly pCARRY2  and LEU2 pGAD424 , respect


RNA modifications, long considered to be molecular curiosities embellishing just abundant and non-coding RNAs, have now moved into the focus of both academic and applied research. Dedicated research efforts (epitranscriptomics) aim at deciphering the underlying principles by determining RNA modification landscapes and investigating the molecular mechanisms that establish, interpret and modulate the information potential of RNA beyond the combination of four canonical nucleotides. This has resulted in mapping various epitranscriptomes at high resolution and in cataloguing the effects caused by aberrant RNA modification circuitry. While the scope of the obtained insights has been complex and exciting, most of current epitranscriptomics appears to be stuck in the process of producing data, with very few efforts to disentangle cause from consequence when studying a specific RNA modification system. This article discusses various knowledge gaps in this field with the aim to raise one specific question: how are the enzymes regulated that dynamically install and modify RNA modifications? Furthermore, various technologies will be highlighted whose development and use might allow identifying specific and context-dependent regulators of epitranscriptomic mechanisms. Given the complexity of individual epitranscriptomes, determining their regulatory principles will become crucially important, especially when aiming at modifying specific aspects of an epitranscriptome both for experimental and, potentially, therapeutic purposes.  N7-methylguanosine (m7G), N6-methyladenosine (m6A), inosine (I), ribose methylation (Nm) and poly-A additions, indicating the potential for a regulatory role in messenger RNAs (mRNAs) [For over half a century, chemical RNA modifications have been known to exist in RNA ,2,3,4. F (mRNAs) ,6,7,8.6A-modified DNA but also m6A-modified RNA [6A from RNA [6A(m), m1A) as epitranscriptomic. Others use the term for any RNA modification, be it terminally or internally placed on any RNA including non-coding RNAs. Despite of these semantic issues, determining individual epitranscriptomes (through ‘epitranscriptomics’) is now a rapidly developing field that is (currently) focused on a limited number of RNA modifications  due to the relatively easy detection by DNA and RNA sequencing ,17,18,19fied RNA . This fifrom RNA  providedfrom RNA ,24. Everation in ). This lation in ,26,27,28ation in ,31,32,33ation in ,38,39. IWhile the existence of RNA modifications and their effects on RNA functionality is measurable, our understanding as to how specific epitranscriptomes are established, dynamically modulated and partially erased is very much incomplete. To better define epitranscriptomic mechanisms and to be able to decipher their phenotypic consequences, one basic prerequisite is reproducibly determining individual epitranscriptomes. As of now, many of the published efforts to map specific epitranscriptomes (mapping the same RNA modification transcriptome-wide) have produced contradictory primary data sets. This has resulted in an “uneasy” debate about the limitations of current technologies and, importantly, how to draw conclusions from the existing data sets ,45,46,476A/A, 0.015 to 0.054% for m1A/A, 0.025 to 0.1% for m5C/C, 0.001–0.004% for hm5C/C, 0.2–0.6% for Ψ/U, and only 0.003% for m6Am/all nucleosides  or is every RNA molecule modified, yet is only expressed in a few cells (stoichiometric modification)? State-of-the-art liquid chromatography-mass spectrometry (LC-MS) methods allow the absolute quantification of individual RNA modifications [6A-level and isoform-characterization sequencing (m6A-LAIC-seq) can be used to quantify m6A/A levels at candidate loci and in transcriptome-wide fashion [5C at single nucleotide resolution [5C/C in specific transcripts. However, when using sequencing-based approaches for quantitative RNA modification measurements, it will be crucially important to include unique molecular identifiers (UMIs) during sample preparation [The biological impact of specific RNA modifications likely depends on the percentage of individual transcripts that are modified. For instance, a modification affecting RNA stability is unlikely to have impact if only a few transcripts are modified. On the other hand, a modification such as A-to-I editing at a specific mRNA position resulting in the translation of a particular protein isoform, might have major impact even when produced at low levels. Quantitative measurements revealed that the relative levels of various eukaryotic mRNA modifications (other than RNA editing) were: 0.2 to 0.6% for menced in ). These ications ,63,64,65ications . Therefo fashion ,67. Furt fashion . Also, Rsolution , could bparation , a techn7G at cap structures and non-templated poly-A-tails. Individual tRNAs, rRNAs and snRNAs contain invariant RNA modifications at specific positions with high stoichiometry indicating that these modifications are constitutive . Especially, one RNA modification, the addition of a methyl group onto adenosine resulting in mferation , mRNA spferation ,128,129,feration ,130,131,feration ,133, mitferation , DNA damferation , circadiferation , signaliferation , oocyte feration ,139, matferation , sex detferation ,142, speferation ,144, repferation ,146, steferation ,150,151,feration ,154,155,feration , neural feration , axon referation , neurotrferation , learninferation , CNS myeferation , neural feration ,162, miRferation  and viraferation ,166,167.iewed in ,169), foiewed in ), for Ψ iewed in ) and foriewed in ,173). InThe observed substrate promiscuity of writers and modulators/erasers, the dynamic nature as well as the sub-stoichiometric levels of some RNA modifications suggest that the components of RNA modification systems are subject to regulation. This article will ignore regulatory principles such as gene expression since expression changes cannot explain how different RNA species are modified at varying stoichiometries by the same enzyme within the same cell. Hence, the following paragraphs will focus on the post-transcriptional and post-translational regulation of epitranscriptomic activities through changes in their subcellular localization, interactions with proteins and/or RNAs, protein modifications and the availability of co-factors. 6A . Soiewed in ). Curreniewed in ). The poiewed in ,183,184,iewed in ), which iewed in ). Combiniewed in ) will al6A, Ψ, or A-to-I could be regulated through interacting proteins. While various writers were able to modify RNAs under minimal in vitro assay conditions, not unexpectedly, those proteins acted in multiprotein complexes in vivo. For instance, the core components of the m6A system for modifying mRNAs (METTL3 and METTL14) form a heterodimeric writer complex, which methylate RNAs in vitro. In this complex, METTL3 is the catalytically active subunit while METTL14, unable to bind the essential co-factor S-adenosyl-methionine (SAM), plays a structural role that is critical for substrate recognition [6A deposition on mRNA in vivo (reviewed in [Mutating the most upstream components of various RNA modification systems (writers or modifiers/erasers) has resulted in pleiotropic phenotypes, which are hard to interpret mechanistically. In order to modulate epitranscriptomic signatures in a more targeted fashion, determining the downstream interactions of writers and modifiers/erasers would allow defining points of interference that would not necessarily result in removing an entire epitranscriptome. Besides protein-protein interactions that determine the localization of epitranscriptomic activities (and thereby their substrate choice), specific interactions could also directly inhibit or enhance of their catalytic function. Some efforts have already been made towards determining how the most abundant RNA modifications such as mognition . This suiewed in ). It wasiewed in ,190. Simiewed in  is thougiewed in . Similariewed in ,180,192)iewed in ,193), aniewed in ).RNA-guided processing or degradation of DNAs and RNAs is evolutionary conserved. For instance, bacteria and archaea produce RNAs from genomically encoded “clustered regularly interspaced short palindromic repeats” (CRISPR) ,196, whiTrypanosoma brucei [Importantly, enzymes writing or modulating/erasing RNA modifications can interact with non-substrate RNAs resulting not only in guiding but potentially also in the modulation of their activities. A prominent example for how RNA modification enzymes are guided by RNAs was discovered in a brucei . In trypa brucei ). Later,a brucei ,204. Thea brucei ). Importa brucei . Furthera brucei  suggestia brucei ,208. Thia brucei , proximia brucei ,213,214 6A, can become SUMOylated at a single lysine residue, which promotes FTO degradation thereby affecting the balance between adenosine methylation and demethylation [6A methyltransferase complex (METTL3) can be modified by small ubiquitin-like modifier 1 (SUMO1) at various lysine residues both in vitro and in vivo [6A deposition on mRNAs. However, the impact of many existing PTMs remains unclear. For instance, mutating individual phosphorylation sites in METTL3 or METTL14 did not affect heterodimer formation or the catalytic activity of METTL3 on model substrates [Even though post-translational modifications (PTMs) occur in most proteins including RNA modification enzymes, specific PTMs might be an inroad into experimentally addressing the specificity and dynamic nature of epitranscriptomic activities. To date, a role of PTMs for the turnover, localization and catalytic activity of selected writers and modifiers/erasers has been reported. For instance, ADAR1 isoforms are subjected to context-dependent PTMs such ubiquitination resulting in proteasomal degradation , or phoshylation . Also, dhylation . Further in vivo . These Pbstrates  suggestibstrates ). Once, bstrates ), by usi6A in the 3’ UTR of MAT2A, encoding a ubiquitous mammalian SAM synthetase, and its concomitant upregulation through splicing-dependent mRNA stabilization [NSUN2 mutant cells indicating changes in the output of the methionine cycle [Most known RNA modifications involve enzymatic reactions attaching specific chemical moieties to nucleic acids . Importalization ,232. Furlization . Specifine cycle . In addine cycle ). The rene cycle ,235). Thne cycle ). Some ene cycle . To starne cycle ), and thne cycle ).6A system modulating mRNAs, and reports with regularity on its involvement in anything that resonates with the notion that biology does not work without m6A-modified mRNAs. Another fraction is in “discovery mode” continuously “hunting” for “novel” RNA modifications by mass spectrometry or sequencing-based approaches. In contrast, a minority fraction in the field is conducting experiments aimed at investigating the impact of particular RNA modifications in specific RNAs. Some examples for the latter are studies on the impact of A-to-I editing at specific sites in specific RNAs in particular human disease models [The last 10 years have defined yet another frontier in molecular biology, the existence of epitranscriptomes. Early efforts in epitranscriptomics have been awarded with regular publicity, mostly for developing technologies that allow mapping individual epitranscriptomes at transcriptome-wide fashion. In addition, first insights into the impact of specific epitranscriptomic systems have produced a breath-taking picture of their immense complexity. However, both the accumulation of epitranscriptomic mapping data as well as the multitude of phenotypes resulting from malfunctioning RNA modification systems, have caused another kind of scientific competition. Rather than paving the way for more detailed and in-depth studies of specific (and known) RNA modification signatures, the amount of data and its complexity appears to deter from being used when formulating and testing new hypotheses. This is at odds with the immense amount of compiled mapping data and some of the precision tools now available that would allow “digging into the data”. Rather than doing that, a large fraction of the field focuses on only one particular RNA modification system, the me models ,241,242,e models ), or thee models ,246,247.e models ,252,253 e models ,255,256,e models ) and thre models . A guidie models ,265,266.6A and Ψ circuitry, more efforts will have to be made to determine the structures of other RNA modification enzymes, preferably in combination with their respective RNA substrates. This will result in arriving at a more solid understanding of the structure-function relationships between RNAs and enzymes that determine the complexities of individual epitranscriptomes. Is the use of modified mRNAs in vaccine development all of what RNA modifications can teach us about biology, the ingenuity of human adaptability, technological progress and disease? Likely not. However, in order to move epitranscriptomics from an emerging and experimentally tractable phenomenon to one that can be better understood, the experimental focus needs to divert from counting numbers to addressing the mechanistic details of the modification reactions preferably with atomic scale resolution. To do so, better definitions of the biological, developmental and environmental context in which particular epitranscriptomic systems modulate RNA identity need to be incorporated into experimental designs. Furthermore, the context-dependent regulation of specific RNA modification activities needs to be systematically addressed, preferably by utilizing the great variety of established vertebrate and non-vertebrate model organisms, which offer many advantages over mammalian (cancer) cells constantly (evolving) in culture. Most importantly, structural knowledge will be required as the very prerequisite for an in-depth understanding of any RNA modification system. While some studies have addressed the structural basis for the activity of particular RNA modification enzymes, largely focusing on m


Despite the high economic importance of cacao beans, few RNA-based studies have been conducted on this plant material and hence no optimal RNA-extraction has been reported. Moreover, extraction of high-quality RNA from recalcitrant cacao bean tissue has shown many difficulties and requires optimization. Furthermore, cacao beans are mostly found at remote and under-resourced locations, which pressures the outsourcing of such analysis and thereby demands RNA-stable preservation and transportation of cacao beans. This study aims to select an appropriate RNA extraction and preservation/transportation method for cacao beans. For this purpose, three sample homogenization and five extraction protocols on cacao beans were compared. In addition, 13 preservation conditions—differing in tissue crushing degree, preservation method, duration, and temperature—were compared and evaluated. A comparative analysis revealed that CTAB-based homogenization and extraction outcompeted all tested commercial protocols in RNA yield and integrity, respectively. Preservation at −80°C affected RNA quality the least, whereas freeze-drying was most suitable for transportation at room temperature for maximum 1 week. The cacao bean RNA obtained from the selected methods were compatible for downstream applications. The results of this study will facilitate on-field sampling and transportation of genetically sensitive cacao material prior to cacao bean transcriptomic studies. In addition, valuable insights on sample homogenization, extraction, preservation, and transportation have been provided, which is of interest to every plant geneticist. Theobroma cacao L.) are of high economic importance since they are the raw material for cacao liquor and chocolate  and sample homogenization procedures   .® Plant Mini  shipment—is often the only feasible option. Consequently, preservation methods compatible with long-term transportation should be considered. The gold standard for RNA preservation recommends cryopreservation of the tissue at −80°C or in liquid nitrogen . UnfortuThis work is divided into three phases and offers a detailed comparison of several sample homogenization, extraction, and preservation methods compatible with overseas transportation and wet-lab processing specifically on cacao beans, but of interest to every plant geneticist. For this purpose, three homogenization workflows and five RNA extraction protocols have been evaluated on cacao beans in phases 1 and 2, respectively Figure 1Five mature cacao pods from the R1/257 cultivar were sampled at the Cocoa Research Institute Ghana (CRIG) and transported in a cardboard box to Belgium with DHL. Ten days after collection, pods arrived in-house and were immediately opened. Seeds were removed from the pulp and their seed coat. The beans were partially preserved as a whole (W), while the remainder was cryogenically crushed (C) using a precooled mortar and pestle. Both tissue forms were preserved in sealed eppendorfs or bags at −80°C prior to extraction.Method Validation With RT-qPCR).For independent validation of the selected extraction and preservation method with RT-qPCR, six cacao beans from the CCN 51 variety were sampled on-field in Ecuador (Joya de los Sachas). A mature pod was harvested, opened, and beans were preserved according to optimized methods . The used buffers were either (A) kit-included (RTL), (B) guanidine thiocyanate-based  te-based  , 25 mM ethylenediaminetetraacetic acid (EDTA), 2.5% (w/v) poly-vinyl pyrrolidone (PVP), and 1% (v/v) β-mercaptoethanol], was added to 100 mg of crushed cacao beans. The material was mixed vigorously (30 s to 1 min) prior to addition of 300 µl extraction buffer and 60 µl 20% sodium lauroyl sarcosinate (pH 5.0). Afterwards the solution was mixed again. The mixture (~860 µl) was transferred to a 2 ml collection tube, stripped from the stainless steel beads, and incubated for 10 min at RT (without shaking). The entire volume was then added to the first RNeasy filter and the protocol was continued according to manufacturer’s instructions. In the third workflow (C), 1 ml of preheated (65°C) Sangha buffer, i.e. , was added to 100 mg of crushed cacao beans. Following 30 min incubation at 65°C (with continuous or frequent shaking), an equal volume of chloroform:isoamylalcohol (24:1) was added and mixed for 30 s. Subsequently, the mixture was centrifuged for 20 min at 10,000 g (4°C). Next, the supernatant was transferred to a new tube and the chloroform:isoamylalcohol step was repeated. Then, the mixture was centrifuged for 10 min at 10,000 g (4°C). Finally, the supernatant was transferred to the first RNeasy filter and the protocol was continued according to manufacturer’s instructions. Comparative analysis was based on the resulting RNA yield and integrity retrieved from cryogenically crushed cacao bean samples.The first homogenization workflow (A) was performed as described by the manufacturer . In the second workflow (B), 500 µl of MacKenzie buffer, ® Plant Mini Kit , (B) Spectrum™ Plant Total RNA Kit , (C) InviTrap® Spin Plant Mini Kit , (D) Reliaprep™ RNA cell Miniprep System , and (E) a manual 3% CTAB-based RNA extraction protocol  RNeasyprotocol . Prior tIn total, 13 preservation conditions differing in tissue crushing degree, preservation method, duration, and temperature were evaluated in triplicate Table 1.® 2.0 Fluorometer . RNA quality was measured by A260/280 (protein contamination) and A260/230 (polyphenol and polysaccharide contamination) using the NanoDrop (ND) 1000 UV-Vis Spectrophotometer . RNA integrity was verified using the fragment analyzer™ according to the guidelines detailed in the sensitivity RNA analysis Kit, DNF-472-0500 . The generated data was analyzed using PROsize 2.0® (Agilent), which resulted in RNA quality numbers (RQN). The retrieved chromatograms were in addition scored visually from A to F  was used to validate independently the suitability of the extracted RNA from the most optimal extraction and preservation method proposed by this study. For this purpose, six cacao beans from the CCN 51 variety were sampled on-field in Ecuador and preserved at −80°C. These were freeze dried prior to express transportation (within 4 days) to Belgium and kept at −80°C until further notice.In-house, RNA was extracted with the selected 3% CTAB extraction protocol. The obtained RNA was DNase treated using the RapidOut DNA Removal kit  according to manufacturer’s protocol. The iScript™ Advanced cDNA Synthesis kit  was used to synthesize cDNA by means of 500 ng RNA input.® robot was used to distribute a 1:3 cDNA : SsoAdvanced™ Universal SYBR® Green Supermix  mixture and primermixes (1.25 µM) in each well of a Hard-Shell® 384-well PCR plate . Amplification was performed by a LightCycler® 480 System  by means of the following thermal cycling conditions: incubation at 95°C for 2 min, 40 cycles of 95°C for 5 s; 60°C for 30 s, and 72°C for 1 s. Melting curves for each amplicon were determined between 65 and 95°C, followed by a cooling step to 4°C. Experiments included a negative control (no template), a no reverse-transcriptase (noRTs) control, and a positive control (1 ng cacao leaf DNA and DNA specific primers in a similar setup).Five cacao specific reference genes  were anapost hoc procedures. Normality and homoscedasticity assumptions were verified visually by means of diagnostic plots of the residuals. In case assumption of homoscedasticity was violated, a Welch corrected one-way ANOVA test was performed with Dunett’s T3 post hoc tests. The significance level was set at 0.05. Statistical analysis was performed using IBM-SPSS version 22.The mean RNA quantities and integrities resulting from different extraction conditions were compared between and within groups by Welch corrected t-tests, one-way and two-way ANOVA test using Tukey ® plant mini kit (Qiagen) was used for extraction. Pulverization was increased using stainless steel beads. Three chemical homogenization buffers were tested, namely: (A) RLT buffer (Qiagen), (B) guanidine thiocyanate-based MacKenzie buffer  to 2.08 (Sangha), while A260/230 values ranged from 1.14 (Sangha) to 1.89 (MacKenzie). Overall, both buffers resulted in acceptable purity extracts. However, the Sangha buffer showed higher protein removing reproducibility (A260/280), whereas MacKenzie was more consistent in removing polysaccharide contaminants (A260/230). Overall, the RNA extraction efficiency of the RNeasy® Plant Mini Kit was most improved when using the CTAB-based Sangha buffer.In the first phase Figure 1e buffer , and (C)e buffer . No signpost hoc testing could not reveal which protocol was causing this. Polyphenol and polysaccharide removal efficiencies (A260/230) ranged between 1.15 (RNeasy) and 2.23 (Reliaprep), and showed great consistency within each protocol. The Reliaprep resulted in significant higher polysaccharide removal efficiencies in contrast to the other kits (p < 0.05), with exception of the Spectrum protocol (p = 0.061). Protein removal efficiencies (A260/280), on the other hand, were very similar for all protocols with values ranging from 1.96 (Spectrum) to 2.10 (3% CTAB), reaching optimal removal efficiencies (A260/280 = 2). The measured RNA integrity was overall low ranging from 2.3 (InviTrap) to 4.2 (3% CTAB), with the 3% CTAB protocol showing significant higher integrity (p < 0.03) relative to all other workflows , FD treatment, temperature, and temperature shifts was assessed on RNA integrity and extraction yield. All raw data and statistical studies are documented in Since on-site wet-lab analyses are (mostly) unfeasible in cacao-producing countries, we aimed to optimize preservation conditions compatible with prolonged shipping procedures. In phase 3 Figure 1260/280 and A260/230 values ranged from 1.91 to 2.15, and 0.96 to 2.11, respectively . Besides, the RNA, together with its impurities, is typically diluted to a workable concentration prior to downstream processing. Moreover, the UV spectroscopy based purity measurements lack accuracy and their relevance should be considered carefully. In general, Aversus whole beans—on RNA quantity and integrity was assessed independently from temperature as depictured in vs W” in vs W” in The effect of tissue form—crushed versus 342.07 ng/µl for FD 5w, Since FD transport of samples at RT is generally considered as a good alternative for the more complex and costly shipments at −80°C, we assessed the impact of FD samples stored for 1 (FD RT 1w) and 5 weeks (FD RT 5w) at RT on RNA extraction characteristics (independently from tissue form) 4B. In g4 = 4.12, µ-20 = 4.3, and µ-80 = 5.15, The impact of preservation temperatures, −80, −20, and 4°C, was assessed independently from tissue form 4C. RNA Temperature changes between these conditions did not seem to have a significant effect on RNA integrity  (GADPH) to 28.62 ± 0.85 (ACT-L)  homogenization, (2) extraction, and (3) preservation on the retrieved cacao bean RNA with insights of interest to every plant geneticist. Finally (4), we will select the most optimal cacao bean RNA extraction and preservation workflow suitable for overseas analyses.All RNA extraction protocols start by homogenizing the sample of interest. This entails mechanical (pulverization) and chemical disruption (lysis using extraction buffers) of the tissue. Pulverization by means of a sonicator, a mixer mill, glass and stainless steel beads, or a mortar and pestle, divides the sample into a multitude of smaller chunks, thereby increasing the contact surface of the lysis buffer resulting in higher RNA yields. This is especially important for heavily encased seeds and tough tissues .® plant mini kit based extraction (Phase 1). This kit is commonly used for plant samples, including cacao leaves (e.g. seed and fruit tissue)  buffers were compared in the context of RNA yield and quality upon the RNeasyo leaves , and has tissue) .® protocol, making use of the RLT buffer, seemed incompatible with the complex recalcitrant cacao bean tissue, since it failed to generate measurable RNA quantities. Based on these results and observation in citrus fruits, we hypothesize that guanidine thiocyanate, present in the RLT and MacKenzie buffers, is less efficient in lysing cacao bean cells and is probably unable to dissociate RNA from RNA-polysaccharide complexes  for all protocols, although scores were markedly higher for the Sangha buffer. The low protein contamination is mainly due to the efficient protein degrading properties of β-mercaptoethanol, the complexation by CTAB or guanidium isothiocyanate, and the RNA specificity of the extraction protocol. Protein removal may be further increased by using preheated buffers and additional phenol and chloroform purification steps. However, the latter has proven to be more toxic, damage poly (A)+ RNA and generate lower yields  or guanidium thiocyanate (MacKenzie), prevents polyphenol oxidization and aids in contaminant-removal from nucleic acid extracts . Althougications . If needr yields . Althougr yields . AnotherAltogether, these results suggest that the type of chemical disruption and the level of pulverization during homogenization impacts bean RNA extraction efficiencies. For cacao beans, we recommend Sangha-based homogenization with pulverization, but other methods might prove more efficient for other tissue types or plants.Next, we compared four commercially available RNA extraction kits and a manual 3% CTAB protocol, supplemented with the Sangha-based homogenization workflow (phase 2). Although kits are time-efficient, they are not always designed for polyphenol- and polysaccharide-rich plant tissues. Such contaminants may result in viscous extracts blocking the silica columns drastically influencing extraction efficiencies . The tes260/230 purities seemed compromised (<2.00), except for the Reliaprep protocol. This suggests that A260/230 purities are not influenced by how RNA is separated from additional contaminants (silica gel column filters or pelletation), but how it is homogenized. The higher purity levels observed for the Reliaprep protocol could be attributed to the chloroform:isoamylalcohol based purification used in this kit, instead of the pure-chloroform workflow applied in the others. Furthermore, the Reliaprep protocol uses an additional isopropanol RNA precipitation step before RNA capture on columns. Similar 3% CTAB based approaches have already been applied successfully on cacao tissues  after flash freezing . HoweverAs alternative, we assessed the compatibility of FD with overseas shipment. FD is used for a wide range of applications within the food industry, pharmacy, and biotechnology. It has been used successfully for RNA preservation in grapevine buds , banana i.e. whole or crushed, will most likely also have an impact during transport or preservation  combined with 3% CTAB extraction protocol is the optimal setup to obtain high-quality RNA extracts from cacao beans sampled in countries with limited resources. This procedure is furthermore perfectly compatible with overseas shipping and gene expression analysis. With these findings the complexity of performing genetic studies on cacao beans from foreign countries is reduced. The findings will contribute to cacao bean transcriptome-based studies conducted worldwide, as long as dry ice (−80°C) and a freeze dryer is available and the transportation at RT within a week to a resource-full lab is guaranteed. Moreover, this study provides insights on the importance of RNA extraction and preservation conditions on RNA integrity relevant to the entire plant science society, such as crushing degree, homogenization, extraction method, freeze-drying, preservation time, freezing conditions, and freeze-thaw cycles. Overall, an outline for studies, experiencing troubles with RNA extraction, preservation, and/or transportation, is provided.The datasets used and/or analyzed during the current study are available in supplementary or on reasonable request from the corresponding author.JW wrote the manuscript. JW and SL conceived and designed the study as written in the main manuscript text. JW and DT equally contributed to the practical work conducted for this study. Both HE and HR offered their plant-associated expertise needed for this study, while SL advised on the genetic part. JV assisted on the statistical aspect of the paper. KM and KD supported and guided this study with their essential expertise, network, and resources in the field of cacao. All authors contributed to the article and approved the submitted version.This work was funded by special research fund (BOF) scholarship [BOF16/DOC/338 (JW)] from Ghent University. Mobility to Ecuador and Ghana was funded by the Commission scientific research (CWO) (BOFFF22014000501) at the Faculty of Bioscience Engineering and the Research Foundation Flanders (FWO) (V405218N), respectively.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.


Such accurate inference was further confirmed by a twister ribozyme at 100% precision with only noncanonical base pairs as false negatives. The performance was resulted from analyzing covariation-induced deviation of activity by utilizing both functional and nonfunctional variants for unsupervised classification, followed by Monte Carlo (MC) simulated annealing with mutation-derived scores. Highly accurate inference can also be obtained by combining MC with evolution/direct coupling analysis, R-scape or epistasis analysis. The results highlight the usefulness of deep mutational scanning for high-accuracy structural inference of self-cleaving ribozymes with implications for other structured RNAs that permit high-throughput functional selections.Despite the large number of noncoding RNAs in human genome and their roles in many diseases include cancer, we know very little about them due to lack of structural clues. The centerpiece of the structural clues is the full RNA base-pairing structure of secondary and tertiary contacts that can be precisely obtained only from costly and time-consuming 3D structure determination. Here, we performed deep mutational scanning of self-cleaving CPEB3 ribozyme by error-prone PCR and showed that a library of <5 × 10 The full base-pairing structure of RNA, resulted from the interplay of secondary and tertiary interactions, serves as a preformed frame for final folding of tertiary structure and is evolutionarily conserved to maintain the structural and functional integrity of an RNA . As suchThe most economical method for locating base pairs would be a computational prediction if its accuracy could be assured. Such a computational approach is usually referred to as RNA secondary structure prediction although many base pairs are associated with tertiary interactions . These tLarge improvement in secondary structure prediction can be achieved ,12 when Recently, more direct identification of base pairs can be made by multidimensional chemical mapping methods. Examples are mutate-and-map (M2 ), SHAPE Another powerful technique is deep mutational scanning through function selection in combination with high-throughput sequencing . HoweverUnlike naturally occurring homologous sequences, the quality and quantity of functional mutants can be somewhat controlled in deep mutational scanning. Thus, it is highly desirable if deep mutation data alone can be used to reliably infer complex secondary and tertiary base pairing structures including lone base pairs not detectable by current chemical probing techniques. We demonstrate the feasibility by employing self-cleaving ribozymes that catalyze their own cleavage. These ribozymes are particularly suitable for deep mutational scanning  because Oryza sativa Osa-1–4 ribozyme sequence) . Although the tertiary structure of CPEB3 ribozyme remains to be solved, it will fold into a hepatitis delta virus-like base pair pattern confirmed by mutational studies . This riequence) .The overall procedure for deep mutational scanning of CPEB3 ribozyme is shown in Figure 5 barcoded DNA molecules were amplified by T7prom and M13F primers , 1 U/μl Murine RNase Inhibitor (New England Biolabs), and 5 U/μl T7 RNA polymerase (New England Biolabs) for 5 hours at 37°C. Template DNA was removed by adding 58 μl nuclease-free water, 10 μl of 10× DNase I Buffer (New England Biolabs), and 2 μl of DNase I  to 30 μl RNA product, and incubated at 37°C for 15 min. The active ribozyme mutants were simultaneously cleaved during in vitro transcription and DNase I treatment. The buffer condition was not optimized as we employed the relative activity instead of the cleavage ratio in our analysis. The relative activity over wild type is much less sensitive to different experimental conditions than the cleavage ratio. 2 μl of 0.5 M EDTA  was added to stop both ribozyme and DNase I activities. The transcribed RNAs were purified using RNA Clean & Concentrator-5 kit (Zymo Research) and quantified by absorbance.Then, the mutant DNA library was transcribed Afterward, ∼10 pmol purified RNAs were mixed with 2 μl of 10μM RT_m13f_adp1  and 1 μlNext, DNA-seq libraries were constructed by extending the ribozyme mutant library with P5R1_m13f and P7R2_t7p  in the thttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Raw paired-end reads were merged using program SeqPrep  to generate high-quality full-length reads. For DNA-seq results, reads with a correct match to primers Bar_F and Bar_R were extracted to generate the projection map from barcodes to variants. For multiple variant sequences mapped to one barcode, the partial sequence downstream to the cleavage site was employed as the additional marker for tracking different mutants. If multiple mapping was still an issue, only the consensus sequence with the frequency larger than 50% was used. For RNA-seq results, reads with a correct match to primer Bar_R were extracted. The barcode together with partial sequences downstream to the cleavage site was used to map the full-length ribozyme sequence. Then, we counted the cleaved and uncleaved read numbers for each variant to calculate relative activity (RA), using the equation given by RA(var) = Ncleaved(var)Ntotal(wt)/Ntotal(var)Ncleaved(wt). To compute RA, we set the minimum number of reads to 5.Lastly, the DNA-seq and RNA-seq libraries were sequenced on an Illumina HiSeq X sequencer with 25% PhiX control by Novogene Technology Co., Ltd. The reads were first assessed using FastQC . Moreover, in Batch 2, the cDNA with template switching was used to construct the RNA-seq library directly. In Batches 1 and 3, the cleaved and uncleaved cDNAs were separated by PAGE for RNA-seq library construction and then mixed with a near equal molar ratio for sequencing. The purpose was to improve the sampling of functional sequences. The DNA and RNA sequencing results, the number of mutations, mutation rates, and the median and maximal reads per variant in CPEB3 ribozyme for these batches were summarized in The overview of the method is shown in Figure obs(bi), RAobs (bj), and RAobs, which are RAs of two single mutation variants and the double mutation variant, respectively. Then, these data points are used to generate the SVR model to fit RAobs as a function of RAobs (bi) and RAobs (bj). This SVR model is used to predict the activity for any double mutations as we have 100% coverage for single mutations of twister and CPEB3 ribozymes. Comparison between observed and predicted RA values is shown in Figure 3 and γ = 2.0 to optimize the regression. We found that C from 1 to 104, and γ from 0.01 to 10 did not change the results  by the RAs of two single mutants. However, if two bases form a base pair, the RA of their double mutation to another complementary pair will be higher than two independent single mutations that are disruptive to the wild-type pair. In other words, we can detect base pairs by examining Covariation-induced Deviation of the Activity (CODA) of a double mutant from the independent-mutation model. We constructed this model by using an SVR regression of the RA of all double-mutation variants against RAs of their corresponding single-mutation variants. This was done by organizing RA data into a three-dimensional vector of RA results . We emphobs) and the predicted RA (RApred) from the above regression model normalized by a shifted predicted RA value. CODA = (RAobs – RApred)/(RApred + 0.2). A shift (0.2) is used to avoid the small value of RApred. The results do not depend on this value from 0.1 to 0.5. Here, dividing by RApred is to obtain the relative change in activity.The CODA score is defined as the difference between the actual RA value (RAa1) and standard deviation (sd1) of the first distribution were obtained from all double mutation data, as the effect of the second distribution is negligible. Then, the average (a2) and standard deviation (sd2) of the second distribution were obtained by the subset of data points with a CODA score > a1 + 3sd1. This subset also defines the overall base-pair probability (P(paired)) calculated as the portion of the subset in all double mutations. Varying this 3sd1 cut off from 1 sd1 to 7 sd1 makes minor changes in distributions and the final result  and one centered at a higher value for potential base pairs as illustrated in Figure l result .P(paired) = –0.079. Again, there is no specific assumption about which two bases are paired or not paired.For CPEB3 ribozyme, the distributions for paired and unpaired bases obtained by the above method have mean values at 1.26 and –0.004 and deviations at 0.763 and 0.237, respectively, with the overall base-pair probability (P(paired)) at 0.013. For twister ribozyme, the distributions for paired and unpaired have mean values at 1.71 and –0.116 and deviation at 1.111 and 0.222, respectively, with the overall pair probability The pairing score of a double mutation (Ps) is calculated by a naïve Bayes two-state classifier withWe built a simple secondary structure predictor to examine if it can be used to fill the gap in the region of contiguous base pairs with weak covariation signals. We defined the energy score as follows.tal data . Δ\documE – W*Ps/mean with an optimized weight W, relative to the mean of pairing scores. The simulated annealing procedure was the same as above for using MC alone.After the above energy function was defined, we carried out the CODA-guided base-pairing prediction by using Functional variants with high ribozyme activity were used as the input sequences for covariation analysis by R-scape, EC-RNA and mfDCA-RNA. We have tested different RA cutoffs  for defining functional sequences (1 can be used because many sequences have stronger activities than the wild type). The result was not sensitive to RA between 0.5 and 0.7 See . As a rehttp://eddylab.org/R-scape/. Default parameters except E-value were used in this paper. We changed the E-value significance threshold from 0.05 to 1000 in order to achieve the best performance for our datasets. We assigned a score of 0 to base pairs which did not have a score in R-scape result.R-scape was downloaded from https://github.com/debbiemarkslab/plmc. Due to the high sequence identity of our mutation library, we remove the sample reweighting process by modifying the reweighting parameter theta to 0. The maximum number of iterations was changed from default value 50–500 in our paper, to achieve better performance. mfDCA-RNA was downloaded from http://dca.rice.edu/portal/dca/home. As in EC-RNA, we changed the sequence reweighting factor theta from 0.2 to 0. Average product correction to the final mfDCA score was not employed because it decreases the performance for inferring from mutational libraries.There are many programs for mutational direct coupling analysis. Here we only employed two programs  applied to RNA previously ,36. For et al. (Epi-Rollins)  . The comRollins) .For binary classification of bases paired and unpaired, we measured the performance of different methods by Matthews correlation coefficient (MCC), sensitivity , and precision. MCC, sensitivity, and precision are defined by measure . MCC is  measure  Ntotal(wt)/Ntotal(var)Ncleaved(wt).Twister ribozyme data and processing perl scripts were provided by Dr Yokobayashi . 43 229 in vitro by T7 RNA transcription system as described before. For one 100 μl reaction system, 50 pmol purified CP_E was mixed with 10 pmol CP_S. Prior to the cleavage reactions, the mixed RNAs were denatured at 85°C for 5 min, then annealed in 0.5 mM Tris, 0.05 mM EDTA at pH 7.5 and 37°C for 10 min. Prewarmed 10× RNAPol Reaction Buffer (New England Biolabs) and H2O were added to the annealed RNAs to start the reaction. The fluorescence (Ex 488 nm/Em 520 nm) was measured at 37°C for 6h using TECAN infinite M200 PRO. First-order rate constant of ribozyme cleavage kcat was calculated in a similar way as PAGE-based assay . DNA-seq was used to obtain the full sequences of mutation variants that can be mapped to the reaction products from RNA-seq according to barcodes. This mapping was necessary to calculate the relative activity of each variant based on the fraction of cleaved fragments of the variant from RNA-seq, relative to that of the wild-type sequence. Combining three batches of experiments led to the deep mutation data of CPEB3 ribozyme with a total of 243 single, 11 968 double, 36 214 triple and 62 992 other (>3) mutants, along with measured enzymatic activities. This represents 100% coverage of single mutations but only 41.0% coverage of all possible double mutations. The low coverage of double mutations is in part because error-prone PCR tends to have fewer mutations in the GC than in the AT region  as shown in Figure T region ,53. To ekcat by a fluorescence assay  would lead to the recovery of cleavage activity, disrupted by single mutations. To build a sensitive method, we first employed support vector regression to establish an independent-mutation model in which the relative activity of a double mutant is predicted by the activities of two corresponding single mutants Figure . Then, tE-value was changed to 1000 to optimize its performance. Unlike CODA, all mutants regardless of the number of mutations are employed in R-scape, mfDCA-RNA and EC-RNA. There are many other direct coupling analysis techniques for inferring protein contact maps . We do Figure Figure What makes CODA a better performer in detecting base pairs? Figure However, the helix regions derived from deep mutation data are much shorter than the native ones Figure . Undeteca priori. Only a single adjustable parameter was employed to penalize lone pair and non-AU, GC or wobble GU pairs by setting it to 3 kcal/mol so that the predictor does not produce any lone and base pairs beyond AU, GC and GU in base-pair prediction by Monte Carlo (MC) simulated annealing alone . As shown in Table Ps) from CODA as an additional energetic term with a relative weighting factor over the mean Ps scores. Here, we incorporate Ps scores for all possible pairs of bases without using any threshold to pre-define base pairs or exclude any specific pairs. As the To remedy the problem of underestimating the length of contiguous base-paired regions, we introduced a simple base-pair predictor by using experimentally measured base-pair and stacking energies  and a reIt is of interest to note that Rfam  containsTo confirm the improvement of CODA analysis over R-scape, EC-RNA and mfDCA-RNA and high-accuracy inference of base-pairing structure by using MC simulated annealing, we obtained the raw deep mutation data of a twister ribozyme from Kobori and Yokobayashi . The ribTable The base-pairing plot Figure  shows thMore importantly, CODA+MC determines all 14 canonical base pairs at 100% precision, including lone and non-nested base pairs. This further confirms the complementary power of mutation-derived and experimental base-pairing energetic scores. Similar to CPEB3, the combination of MC with R-scape, EC-RNA and mfDCA-RNA Table  yields s6.5) to 100% (107.8) reads. This result suggests that CODA is more robust against the size variation of the mutation data.One interesting question is how the performance of CODA depends on the total number of reads. For CODA analysis, we have included triple mutants for those position pairs not covered by double mutations. As shown in Base pairing is responsible for stabilizing overall structural fold of RNA structures and the key for understanding functional mechanisms. A full base-pairing structure is more than secondary structure because it contains tertiary contacts such as lone base pairs and kissing loop pseudoknots. This work shows that these challenging tertiary contacts can be inferred from deep mutation data by analyzing covariation-induced deviation of double-mutation activity from the independent-mutation model (CODA). The combination of CODA with a Monte Carlo simulated annealing leads to detection of all WC pairs (except non-WC pairs but including lone WC pairs) for twister ribozyme at 100% precision and all WC pairs plus two non-WC pairs for CPEB3 ribozyme at 96% precision, despite that CPEB3 ribozyme has only a small library of <50 000 single-to-triple mutants and the mutation data of twister ribozyme is limited to the large fragment from self-cleavage. Moreover, two pseudoknots associated with tertiary interactions in CPEB3 and twister were both captured.The CODA analysis developed here has a better performance than R-scape, EC-RNA and mfDCA-RNA. This is reflected from 19% and 5% increases in MCC by CODA over the next best (mfDCA-RNA) for CPEB3 and twister, respectively. More importantly, CODA is more robust against changes in the number of reads  and the Identification of noncanonical, lone and non-nested base pairs is not a trivial exercise. Double-stranded long helical stems with many stacked base pairs are relatively easy to detect because they serve as core structural elements and a mutation at the center of these stems will have a large impact on the overall structural stability. Pseudoknots, on the other hand, are often involved with a few base pairs between two distant hairpin loops  as sA total of three base pairs were missed by the combination of CODA and Monte Carlo simulated annealing in twister ribozyme, all of which are noncanonical pairs. They are A2G39, A22A40 and Hoogsteen base pair U18A23. The backbone orientations of these noncanonical pairs are quite different from Watson–Crick pairs so that all double mutations on these sites yield negative signals in CODA. Moreover, these three noncanonical base pairs are all at the edge of a helical stem Figure , suggestC and γ parameters (C = 2000 and γ = 2.0) where C is a hyperparameter to control how much to penalize the misclassification and γ is a hyperparameter for the non-linear Gaussian kernel. In addition, we define outliers (base pairs) according to 3 standard deviations (SD) away from the fitted model. C from 1 to 10 000, and γ from 0.01 to 10 while fixing the other two values at their default values. MCC values change little in a wide range tested. This result confirms the robustness of the model obtained.This study investigated two self-cleaving ribozymes as proof of concept. There is a question if the parameters used in CODA+MC could have overfitted the two ribozymes tested here. The independent-mutation model in CODA is an unsupervised model generated from non-linear regression between single and double mutations with the parameters optimized to produce the best fit between predicted and actual relative activities of double mutants. We have employed the default For MC simulated annealing, one parameter introduced is the energy penalty that discourages lone pairs and noncanonical pairs by MC alone. The parameters for simulated annealing were chosen to ensure the identification of the global minimum within a reasonable computing time. A value between 2–5 kcal/mol for the energy penalty makes no changes to the outcome of MC alone. Another parameter is the weighting parameter for the CODA score  before mixing with experimentally measured base-pairing energy scores. Obtaining structural clues of self-cleaving ribozymes are important because self-cleaving ribozymes are broadly distributed in genomes of different organisms from viroids to vertebrates . Understin vitro affinity/activity selection . While this number may be a theoretical upper bound, RNAs of 200–300 nucleotides are certainly approachable.Another limitation for deep mutation studies is RNA sequence length. A long RNA would have to overcome an increased difficulty in amplification of RNA libraries with high mutation rates, reduced coverage due to fast increase in the number of possible double mutations, decreased quality of reads for longer RNAs, and the reduced ability to reach the global minimum for long RNAs by MC simulated annealing. We demonstrated that the CODA analysis is capable of detecting nested and non-nested base pairs as well as some noncanonical base pairs even from a small mutant library of <5 × 10fragment correctly. By comparison, the secondary structure predictors RNAfold and IPknot have to use the whole sequence to achieve a reasonable prediction. For example, MCC = 0.22 if using the twister fragment sequence for RNAfold, compared to 0.68 when using the whole twister sequence  even with the data not yet at the single-base-pair level . We haveMoreover, the CODA analysis should be a powerful tool for analyzing deep mutation data of proteins. This transferability is reflected from migration of protein mutational coupling analysis programs (EC and mfDCA) to RNAs although they were originally designed for proteins. Hydrogen-bonded base-pairing interactions in RNA structures are the dominant interaction with the strongest covariation signals. As a result, what emerges from the CODA analysis will be the base-base contact map in term of base pairing. On the other hand, hydrophobic packing with complementary shapes and sizes of side chains is the dominant driving force for protein folding . Thus, Cet al.  and implemented the epistasis program of Schmiedel and Lehner (Epi-SL for short) with minor adaption for RNA. Their results for the Twister fragment, shown in Indeed, after the completion of this work, Rollins et al.  and Schmet al.  publishe .Illumina sequencing data for the CPEB3 ribozyme were submitted to the NCBI Sequence Read Archive (SRA) under SRA accession number PRJNA515794  and a maximum RAM of 128G.All custom scripts needed to repeat the analyses are available at gkz1192_Supplemental_FileClick here for additional data file.