Bombyx mori is a major economic contributor in the sericulture industry and produces fibroin—the most important silk protein in its posterior silk glands during 5th instar larval stage. The present study investigates the composition of nuclear matrix RNA prepared from the posterior silk glands of Bombyx mori during fifth instar larval stage where maximum silk production occurs. The datasets from which the analysis is carried out are part of data note titled “Nuclear matrix associated RNA datasets of posterior silk glands of Bombyx mori during 5th instar larval development”.The nuclear matrix maintains and regulates chromatin structure. RNA is an integral component of the nuclear matrix and is essential to its structural maintenance. The results showed significant enrichment of nuclear matrix RNA from day 1, to day 5 and day 7. Nuclear RNA showed increased abundance from day 1 to day 5 and day 7. Nuclear matrix RNA exhibited repetitive RNA sequences, of which UGUCC and GCUGGU were the most abundant. Genes involved in metabolic pathways showed significant enrichment correlating with silk production. These results emphasize the role of dynamic, repetitive DNA transcripts in chromatin architecture and further reveal the close association between the nuclear matrix and gene expression.The online version contains supplementary material available at 10.1186/s13104-022-05951-2. Drosophila melanogaster embryos, the NuMat extract retained 30% of RNA compared to DNA (1–2%) and protein (10%)  from a single rearing and were homogenized in nuclear isolation buffer (5 times volume of the weight of the tissue) and processed for nuclei and nuclear matrix isolation by following the standard protocol for isolation through nuclease digestion and salt extraction ) using the ‘BOWTIE2’ tool  was used to perform BLAST of the input nucleotide sequences  using blastx against the Insecta protein sequences downloaded from Uniprot database (subject sequences) (https://www.uniprot.org/uniprot/?query=taxonomy:50557).PSG NuMat RNA datasets of 5th instar day 1 (SG 1), day 5 (SG 5) and day 7 (SG 7) were acquired from NCBI SRA database –13. ThesB. mori reference genome (Bombyx mori (assembly Bmori_2016v1.0), https://www.ncbi.nlm.nih.gov/genome/?term=txid7091[Organism:noexp]) to identify the biological pathways associated with the expressed genes. The biological functions associated with these genes were also determined.The GOs were mapped against expressed transcripts. KAAS (KEGG Automatic Annotation Server) was used and BLASTx  was performed for query sequences (amino-acid sequences of the protein-coding genes) which were aligned against the B. mori, found considerable increase in the expression levels of fibroin RNA during the 5th instar compared to the 4th instar larval stage [B. mori have not been studied. To measure the abundance and size of RNA associated with the nuclear matrix of B. mori, nuclear and NuMat RNA were isolated from PSGs at day 1, day 5, and day 7 of development. Agarose gel electrophoresis was performed without formamide and formaldehyde to check the RNA enrichment. The data showed that NuMat RNA approximately ranged in size from 100 to 400 bp  but no significant increase from day 5 to day 7 (p > 0.05). This correlates with the increase in protein expression observed on day 5 of the 5th instar larval development. The amount of nuclear RNA retained in the nuclear matrix on day 1, 5, and 7 were 33.33%, 50% and, 80% respectively. The NuMat RNA showed a significant increase in concentration from day 1 to day 5 (p < 0.05) and from day 5 to day 7 (p < 0.05)  had molecular functions. These included, metal ion binding—particularly magnesium, calcium and zinc ion binding, nucleic acid binding—specifically DNA binding functions, nucleotide binding, helicase activity, RNA–DNA hybrid ribonuclease activity, RNA directed DNA polymerase activity, ATP binding, sequence-specific DNA binding transcription factor activity, transcription cofactor, coactivator and co-repressor activity, RNA binding, structural constituent of ribosome, translation initiation factor activity, translation elongation and release factor activities, catalytic activity, GTPase activity, oxidoreductase activity, transferase and phosphotransferase activity, hydrolase activity, and transmembrane transporter activity. These functions imply that the NuMat RNA is likely intricately involved in transcription. There is an increase in the number of NuMat RNA linked genes associated with cellular components from day 1 to day 5 that are maintained at day 7  separated on 1.2% agarose gel alongside a 100 bp DNA ladder (M) is provided.Additional file 2: Fig. S2. TapeStation profiling of NuMat RNA. (A) TapeStation Ladder, (B) day 1, (C) day 5 and (D) day 7 NuMat RNA of PSG NuMat RNA during 5th instar development. The NuMat RNA is analysed by TapeStation profiling and the size range is shown.Additional file 3: Table S1. Alignment statistics of SG 1, SG 5, and SG 7 datasets. The percentage of reads mapped against the reference genome is shown.Additional file 4: Table S2. Prediction of SSRs in SG 1, SG 5 and SG 7 PSG datasets. The simple sequence repeats are identified using MISA software from the three nuclear matrix RNA datasets.


Lipids, which are essential for life and one of the most ancient biomolecules, can spontaneously self-assemble to form membranous bilayers, theoretically providing a surface that can serve to concentrate, protect, and regulate RNAs. Here, we show that direct RNA–lipid interactions can modulate ribozyme activity in a lipid-dependent manner. We further demonstrate that RNA–lipid binding is influenced by nucleotide content and base pairing of RNAs. This yields an essential framework for engineering RNA–lipid systems that can be regulated based on sequence specificity and introduces a mechanism for riboregulation in cellular, synthetic, and prebiotic systems. RNA is a ubiquitous biomolecule that can serve as both catalyst and information carrier. Understanding how RNA bioactivity is controlled is crucial for elucidating its physiological roles and potential applications in synthetic biology. Here, we show that lipid membranes can act as RNA organization platforms, introducing a mechanism for riboregulation. The activity of R3C ribozyme can be modified by the presence of lipid membranes, with direct RNA–lipid interactions dependent on RNA nucleotide content, base pairing, and length. In particular, the presence of guanine in short RNAs is crucial for RNA–lipid interactions, and G-quadruplex formation further promotes lipid binding. Lastly, by artificially modifying the R3C substrate sequence to enhance membrane binding, we generated a lipid-sensitive ribozyme reaction with riboswitch-like behavior. These findings introduce RNA–lipid interactions as a tool for developing synthetic riboswitches and RNA-based lipid biosensors and bear significant implications for RNA world scenarios for the origin of life. RNA performs diverse functions, ranging from information storage to regulation of other biomolecules and direct catalysis of biochemical reactions. The functional versatility of RNA has implications for understanding plausible scenarios for the origin of life –3 and fo4713Key to harnessing the functional versatility of RNA is understanding how to spatially and temporally modulate its properties and selectively modulate the activity of different RNAs within one system. The physicochemical environment surrounding an RNA molecule is a central determinant of its structure, stability, and activity. Spontaneous RNA hydrolysis and ligation, as well as catalytic RNA activity, are sensitive to pH , ionic sOne mechanism for modulating RNA activity could be through direct RNA–lipid interactions , 21, 22.23The first functional RNA–lipid interaction was described more than 40 years ago , with su2738This study reports the effect of lipid membranes on RNA catalytic activity. We show that RNA–lipid binding depends on the primary sequence, secondary structure, and length of RNA. Using the transacting R3C ligase ribozyme, we observed that R3C–lipid binding changes ribozyme activity in a concentration-dependent manner. Lipid-binding assays show that the interaction of short RNA sequences with gel membranes depends on guanine content and the presence of double-stranded structures. Lastly, modification of R3C’s substrate sequence increased the tunability of R3C-based reactions through a lipid-dependent mechanism. Our findings demonstrate that membranes can serve as platforms for riboregulation, which could contribute to the development of RNA-based lipid biosensors and lipid-sensitive riboswitches. This approach introduces tools for molecular and synthetic biology and raises the prospect of previously unrecognized roles for RNA–lipid interactions in the origin and evolution of life.The discovery that RNA can catalyze reactions in addition to encoding information , 43 opend) phase [dioleoylphosphatidylcholine (DOPC)], and a liquid disordered–liquid ordered (Ld-Lo) phase-separated system  . These observations allowed us to identify DPPC gel membranes as the most optimal lipid for this study, based on the superior RNA–lipid binding coefficient for DPPC.We took advantage of the observation that RNA can interact with solid crystalline (gel) phase membranes composed of phosphatidylcholine lipids , 42 to t1 ratio) . RNA–lip1 ratio) . As expeLo phase . Thus, Rpeptides . BindingSI Appendix, Fig. S1C). Depletion of divalent cations  or increasing temperature above the lipid melting temperature  reduced RNA–membrane binding and aggregation  that are phase separated into gel and liquid domains and observed the distribution of a random mixture of RNA oligomers stained with SybrGold by fluorescence microscopy A. The flregation A . In subsequent experiments, we focus on the R3C ligase since it is a relatively short single turnover ribozyme with a simple structure and a prebiotically interesting ligation activity  (Bottom native gel), showed continuously increasing and overall higher UV–cross-linking efficiency in the presence of lipids (Bottom graph). These results show that RNA–gel membrane binding can influence the local concentration of RNAs in very different ways depending on the type of RNA.To determine whether local RNA concentration is influenced by interaction with lipid membranes, we relied on ultraviolet (UV)-mediated cross-linking. All small vesicle membranes in this study were composed of DPPC, which is in a solid crystalline (gel) phase at the experimental temperature of 24 °C. Nucleic acid bases absorb UV light, producing chemical changes that yield base–base covalent bonds in a distance-dependent manner, yielding insights into the structure and interactions of nucleic acids –48. We fable S1) . When suable S1) B. In conSI Appendix, Table S1).The contrasting effect of cross-linking for R3C and randomized oligomers suggested that gel membrane binding can enhance RNA–RNA interactions for RNAs with a higher propensity for intermolecular interactions. Indeed, we observed that a mixture of two oligomers with complementary sequences showed enhanced cross-linking relative to a single oligo with lower propensity for inter- and intramolecular interactions C. At higk = 6.4 × 10−4 min−1. Addition of lipid vesicles led to an increased reaction rate , which could plausibly be due to increased ligase substrate interaction on the membrane either through increased concentrations at the membrane surface or through enhanced exposure of the substrate-binding domain of R3C. At the highest lipid concentration, the reaction rate dropped to 8 pM/min (−27%) , both conditions in which RNA–lipid binding is impaired. Thus, despite the relatively small effect on activity, these results provide the proof of principle that RNA–gel membrane binding can enhance the reaction rate of a transacting ribozyme in a lipid concentration–dependent manner.To investigate the functional consequence of RNA–lipid binding, we tested the effect of lipids on the activity of the transacting R3C ligase ribozyme. R3C ligase is a ribozyme that catalyzes ligation of substrate strands to the ribozyme A 10). S. S10). Sn (−27%) B. A decrn (−27%) B. We speTo further understand whether preferential membrane binding of either R3C or its substrate contributes to changes in ligation rates, we analyzed lipid-binding affinities of the ligase and its substrate. The partition coefficient of the short 12-nt substrate was more than three orders of magnitude lower than that of the 84-nt R3C ligase C. Thus, Understanding the features that influence RNA–lipid interactions could allow us to engineer RNAs with higher membrane affinity and thereby enhance activity by concentrating RNAs at the membrane surface. The interaction of RNA with fluid membranes has been shown to be very specific to sequence and structure –40. In c6), oligomers of A, C, or U showed practically no binding. A mixture of 19-nt RNA with randomized sequences showed moderate binding, as did one with AG repeats . These results indicate that G-dependent structures influence RNA–lipid binding and that even low guanine content can significantly increase membrane binding efficiency , controlling for the possibility that higher binding of dsRNA was due to longer intermolecular linkages formed by staggering of repeated complimentary ACUG/CAGU strands. We further observed that dsRNA interacts with fewer lipids than ssRNA, implying different models of binding  and double-stranded RNA (dsRNA) (composed of complementary CAGU and ACUG repeats). We observed that not only was the membrane binding efficiency higher for dsRNA compared with ssRNA species but also that only dsRNA promoted vesicle aggregation A. Bindin binding B. Thus, The correlation between G-content and enhanced membrane binding pointed toward a potential role for G-dependent RNA structures. G-rich RNAs can form structures known as G-quadruplexes, in which G–G interactions lead to the formation of stacked G-tetrads . We therSince G-quadruplexes are physiologically relevant structures with diverse roles in cellular regulation , we askeIn previous experiments, we observed that the binding affinity of the short substrate was less than three orders of magnitude lower than for the R3C ligase and that reaction rates correlated with R3C–membrane binding B and C. SI Appendix, Fig. S6). Therefore, increasing guanine content reduces R3C ligase–substrate binding, likely accounting for decreased activity.We first assessed the effect of modifying the R3C ligase substrate on ligation rate in the absence of membranes A. We obsWe next investigated whether varying guanine content through 5′ modification of the substrate influences membrane binding and, consequently, ligation activity in the presence of membranes. As expected, the longer guanine-containing R3C substrates showed significantly higher membrane binding compared with the shorter variant C. To detIn summary, by modifying the R3C ribozyme substrate with an additional short sequence containing guanine residues, we increased membrane affinity. We had originally hypothesized that enhancing substrate–membrane binding would increase ligation activity by concentrating R3C and its substrate at the membrane surface. The addition of guanines to the substrate, however, introduced an inhibitory effect on ligation activity that was unexpectedly reversed through the introduction of gel membranes to the reaction. While we did not achieve the intended outcome of enhancing activity, we revealed a lipid-dependent allosteric mechanism for tuning ribozyme activity. These observations raise the possibility that ribozyme activity could have been modulated through lipid–RNA interactions in an RNA world and provide the proof of principle for engineering lipid-sensitive riboswitches with a larger dynamic range for synthetic biology.Lipids can spontaneously self-assemble to form membranous bilayers, theoretically providing a surface that can concentrate, protect, and regulate RNAs. Here, we demonstrate that RNA–gel membrane interactions are dependent on nucleotide content and base pairing, providing a means to engineer RNAs with varying membrane affinities. Increasing the guanine content of the R3C substrate was sufficient to enhance its binding affinity to gel membranes and revealed that ribozyme activity can be regulated in an allosteric lipid-dependent manner. This study yields a framework for engineering RNA–lipid systems that can be regulated based on sequence specificity and introduces a mechanism for riboregulation in cellular and synthetic systems.Our finding that guanine is a key factor in promoting RNA–gel membrane partitioning is consistent with previous work indicating that guanine residues might play a role in the binding of RNA aptamers to fluid membranes  and thatThe ability for RNA–lipid interactions to influence ribozyme activity demonstrates a proof of principle that spontaneously self-assembling lipid membranes could provide a mechanism for riboregulation. In the present study, we had hypothesized that gel membranes would enhance R3C ligase activity by increasing concentration of the reactants at the membrane surface. Instead, we discovered that selective guanine–gel membrane interactions had an allosteric effect on the R3C substrate when it was modified with a G-rich tail to enhance membrane binding. This behavior in some ways resembles the behavior of riboswitches with regulatory effects that are derived from sensitivity to physicochemical conditions or through binding with an interaction partner –62. HoweAlthough gel-phase membranes are not widely observed in living cells, RNA–gel membrane interactions could be employed for riboregulation in synthetic biological systems. Gel membranes also plausibly accumulated in ancient prebiotic scenarios, potentially serving as an organizational scaffold, as has been proposed for “ribofilms” on mineral surfaces . Thus, RIn conclusion, our findings reveal that lipids, which are present in every modern cell and were plausibly part of a prebiotic world , 65, 66,672, and CaCl2 were purchased from Carl Roth and were at least >99% pure. All solutions were prepared in MilliQ water, Merck Millipore.DPPC, DOPC, DMPC, and biotinylated phosphatidylethanolamine (18:1 biotinyl cap PE) were purchased from Avanti Polar Lipids and were used without further purification. Cholesterol was purchased from Sigma-Aldrich. DiD was purchased from Invitrogen. Hepes, MgClThe R3C ligase construct  was clonSynthesis of the deaza–G-quadruplex RNA was conducted using T7 transcription on a synthesized DNA template, as described above; however, instead of GTP, 7-deaza-GTP (Trilink Biotechnologies) was used. To enhance transcription efficiency, 1 mM GMP was added to the solution .The 5′-6-carboxyfluorescein (FAM)–labeled R3C substrates, G-quadruplexes, and other short oligomers used in binding assays were obtained from Integrated DNA Technologies and used without further purification.SI Appendix, Table S2.All of the RNA sequences are presented in 2, and 5 mM MgCl2. RNA concentration was determined by measuring absorbance at 260 nm . Before every incubation, RNA was preheated in SafeLock tubes  to ensure unfolded RNA structures. Denaturing PAGE analysis was performed using 8 to 20% 19:1 acrylamide:bisacrylamide gel composition with 8 M urea, whereas native gels were composed of 6 to 10% 29:1 acrylamide:bisacrylamide mix. Before denaturing PAGE, all of the samples were ethanol precipitated.Unless stated otherwise, RNA incubations were prepared in DNA low-bind tubes (Sarstedt) at the constant temperature of 24 °C in buffer composed of 10 mM Hepes (pH 7), 5 mM CaClhttps://rna.urmc.rochester.edu/RNAstructureWeb/).The structure of R3C ligase from 2 and 5 mM MgCl2 were added to the buffer following electroformation and prior to imaging.Gel–liquid phase-separated vesicles were prepared from mixtures of DOPC:DPPC in a 1:1 molar ratio  with 0.5GUVs were visualized in a homemade microscopy chamber with temperature control in isosmotic solution of glucose and buffer. A Leica DMi8 confocal microscope coupled with a camera (Leica DFC9000 GTC) was used for image acquisition. For both confocal as well as camera acquisition a 63× water-immersed objective was used.For confocal image acquisition, 488-nm (SybrGold) and 635-nm (DiD) lasers were used at low power (<0.5%) to avoid photobleaching, and the signal was collected using hybrid detectors (500 to 550 nm for SybrGold and 650 to 750 nm for DiD). Z-scans were acquired as four to eight averaged images per layer and Z-projected as a sum of the slides using FIJI software .Temperature ramps were captured using a camera. Then, 1.5 s of exposure without binning was used to acquire each channel, and measurements were repeated every 5 s. Temperature changes were registered and calculated to be 3.4 °C/min. Image analysis was conducted in FIJI software.2, and 5 mM MgCl2. Lipid vesicle stocks were kept at 4 °C. Stock phospholipid concentrations were confirmed using a phosphate assay  to reduce multilamellarity and achieve a consistent size distribution of vesicles. To obtain palmitic acid vesicles, dry palmitic acid flakes were hydrated in 10 mM Hepes (pH 7) and sonicated in a sonicator bath until all flakes were disrupted in cloudy solution to a final concentration of 5 mM. Heating the solution above the palmitate melting temperature (Tm) was not necessary to generate vesicles. Palmitate vesicles were sonicated prior to each experiment and diluted into reaction buffer containing 10 mM Hepes (pH 7), 5 mM CaClte assay .The size distribution of lipid vesicles was estimated using a ZetaSizer Nano in 173° backscatter with multiple narrow-mode (high-resolution) analysis. The final concentration of lipids was 10 to 25 μM, and the amount of RNA was fixed at a ratio of 10 lipids:nucleotide unless stated differently. Results were plotted as the size distribution in the number of detected species.SI Appendix, Fig. S2). Both values were blanked . To obtain normalized cross-linking, we have divided measured values by the cross-linking efficiency of the lipid-free sample.RNA was incubated for 10 min in the reaction buffer with or without lipids followed by 10 min incubation under UV-B light (300 mW LEUVA77N50KKU00 LED 305 nm) from a distance of 1.5 to 2 cm. After UV treatment, RNA was ethanol precipitated and run on denaturing PAGE. After electrophoresis, gels were poststained with SybrGold dye in order to visualize all of the RNA species. Each cross-linked band was quantified together with whole-lane intensity; we define cross-linked bands as those that are higher in mass than the starting RNA and were not present before UV exposure  were quantified and the concentration of product at time t was calculated asP]t is product concentration and [S]0 initial substrate concentration.For assays, 5 pmol of R3C ligase was mixed with 0.5 pmol of the substrate to ensure saturation of the system (pseudo–first-order reaction) and to decrease any batch-to-batch differences resulting from R3C purification. Denatured (preheated in 90 °C) RNA was added to the reaction buffer with or without lipid vesicles. Incubation was conducted at various concentrations of lipid vesicles, and samples were ethanol precipitated after 30, 60, 90, and 120 min. RNA was analyzed on denaturing PAGE using FIJI software. Product and substrate intensities (To determine reaction rates (M/min), all measured product concentration values were fitted using concatenate linear fit with fixed  intercept. The slope and SE values of the fit were used for statistical comparisons and data plotting.Magnetic beads  were coupled with liposomes doped with 0.5 mol% biotinoyl cap PE. Lipid concentration on beads was estimated using a phosphate assay . Lipid cRNA (10 to 25 pmol) was incubated in the buffer and liposome-coated beads for 30 min in 24 °C. As a control (100% samples), noncoupled liposomes were used. After incubation, the supernatant was separated from the beads using a magnet. The amount of RNA left in solution was estimated either using absorbance or Qubit microRNA (miRNA) quantification kit (Thermo Fisher Scientific).For the absorbance measurements, 20 μL of supernatant was pulled and diluted with 80 μL of MilliQ water and absorbance was measured using a 1-cm quartz cuvette. Because of the significant lipid-based light scattering, 100% sample values were calculated from a theoretical approach; knowing RNA concentration, we calculated theoretical absorbance in the final RNA dilution.For the Qubit miRNA quantification assay, 9 μL of supernatant (out of 30 μL reaction mix) was pulled and incubated with 1.5 μL of 0.5 M ethylenediaminetetraacetic acid (EDTA) . After incubation, 150 μL of 1× Qubit dye was added to the solution, and the amount of RNA was measured using a plate reader .K was calculated according to the following formula:The partition coefficient L] and [W] over RNA concentration, we simplified the equation toLR] is RNA bound to the membranes, [WR] is nonbound RNA, [L] is outer lipid concentration, and [W] is water concentration.Assuming excess of [WR] was calculated as a ratio or readout from the binding assay sample and the 100% sample. [LR] = 1 − [WR]. We assumed that RNA interacts only with the outer membrane leaflet, so the final outer lipid concentration is equal half of the total lipid amount.[g, 40 min, 24 °C). RNA in the supernatant was measured using a Qubit miRNA quantification kit as described for the magnetic beads–based assay. The measured fluorescence intensity values were normalized to the maximum value measured in the supernatants, yielding the fraction of RNA remaining in the supernatant after vesicles were removed. The binding efficiency  was then estimated from normalized values as 1 − F, where F is the fraction of RNA remaining in the supernatant after vesicles were removed by pelleting. Binding efficiencies were plotted as a function of lipid:oligomer or lipid:nucleotide ratios. The data were fitted using Hill’s fit (OriginLab software):Start and End are function plateau values, k is the inflection point of the curve, and n is the cooperativity index. The inflection point of the curve (k) and its SE are parameters that we used for subsequent data analysis.RNA was incubated with various concentrations of DPPC vesicles for 30 min. After incubation, ultracentrifugation was conducted to pellet vesicles from the solution (125,000 × P values in the figures are result of double-sided, unpaired Student’s t test. We assumed that a difference is significant if the P value was lower than 0.05.All of the binding efficiency, partition coefficient, cross-linking efficiency, and R3C activity assays were repeated at least three times. Error bars in figures represent SEM values. Estimated Supplementary FileSupplementary FileSupplementary File


COVID-19 was initially reported in China at the end of 2019 and soon thereafter, in March 2020, the WHO declared it a pandemic. Until October 2021, over 240 million COVID-19 cases were recorded, with 4.9 mln deaths. In order to stop the spread of this disease, it is crucial to monitor and detect any infected person. The etiologic agent of COVID-19 is a novel coronavirus called SARS-CoV-2. The gold standard for the detection of the virus is the RT-qPCR method. This study evaluated two RNA extraction methods and four commercial RT-qPCR assays routinely used in diagnostic laboratories for detecting SARS-CoV-2 in human specimens from the upper respiratory tract. We analyzed a panel of 70 clinical samples with varying RNA loads. Our study demonstrated the significant impact of the diagnostic methods selected by the laboratory on the SARS-CoV-2 detection in clinical specimens with low viral loads. Coronavirus disease 2019 (COVID-19) was initially reported in Wuhan, China, in December 2019 and spread to other countries within a few months. On March 11th, the outbreak was declared a global pandemic . There aCOVID-19 can range from asymptomatic to severe or even critical infection with mortality ,7,8,9. TThe quantitative  reverse transcription-polymerase chain reaction method (RT-qPCR) has been introduced as a gold standard in SARS-CoV-2 diagnostic. There are many commercial RT-qPCR kits for detecting SARS-CoV-2 approved by the WHO and commonly used in diagnostic laboratories worldwide . The easThe primary source of errors leading to improper diagnostic of SARS-CoV-2 was described by Rahbari et al.  and diviOur study demonstrated the impact of the RNA isolation method and the kind of RT-qPCR assay used on the SARS-CoV-2 identification in clinical specimens with a varied load of viral RNA. Two RNA extraction procedures (column- and magnetic-based) and four commercial RT-qPCR tests from different companies were analyzed. Furthermore, we have shown that the lower the viral load, the more difficult it is to interpret the results. The problem with the available diagnostic tests is that they fail to detect the actively infecting virus.Seventy clinical samples collected between May and December 2020 were analyzed. The specimens originated from five hospitals and one drive-thru mobile collection site in six different cities in Mazovia, Poland. All samples were nasopharyngeal cavity swabs transported in different virus-dedicated media. After the diagnostic tests, the samples were stored at −80 °C, pending further studies.Viral RNA was extracted using two complementary methods. The first method was column-based RNA extraction with a Viral DNA/RNA kit . The extraction was performed from 100 µL of samples. The elution volume was 35 µL. The other RNA extraction was performed using a magnetic-based method with a NucleoMag Pathogen kit . This kit is suitable for both automatic and manual extraction. In this case, we used the manual method with magnetic blocks. However, results in both automatic and manual methods should be comparable. The volume of clinical samples was 200 µL, and RNA was eluted in 50 µL elution buffer. Each method was performed according to the manufacturer’s recommendations.Detection of SARS-CoV-2 virus was performed by four commercial RT-qPCR tests commonly used in Poland: Bosphore Novel Coronavirus (2019-nCoV) Detection Kit v3 , DiaPlexQ Novel Coronavirus (2019-nCoV) Detection Kit , Liferiver Novel Coronavirus (2019-nCoV) real-time Multiplex RT-PCR Kit , and MutaPlex Coronavirus real-time-RT-PCR-Kit . The characteristics of the kits are shown in Two of described RT-qPCR kits (DiaPlex and MutaPlex) along with both RNA extraction methods were subjected to external evaluation  and passed the positive verification.5 copies/µL (in-house standard of RNA). The number of E-gene copies was determined using the standard curve, obtained by RT-qPCR of the serial dilution of RNA containing a known number of E-gene copies using the MutaPlex Kit. The duplicate 10-fold dilution series of the in-house standard RNA was run using each RT-PCR kit. The PCR efficiency was determined using the Design and Analysis Quant Studio 6 Pro software. The experiment was performed in three technical replicates.The RT-qPCR efficiency was determined using total human RNA containing RNA of SARS-CoV-2 with an estimated E-gene copy number of 2 × 104 copies/mL of SARS-CoV-2 . The RNA was extracted using the column-based method. The range of a two-fold series dilution (copy number 800-8) in two technical replicates was run using each RT-PCR kit. The lowest detected number of RNA copies was determined as the limit of virus detection.The limit of SARS-CoV-2 RNA detection was determined using RNA extracted from 10RNA from 70 clinical samples were extracted using two complementary methods, as described in p-values less than 0.05.The quality and amount of extracted RNA were estimated based on the Cq value of ISC and IPC measured during the RT-qPCR by MutaPlex kit. The arithmetic mean ± standard deviation of the population was calculated. The mean Cq of RNA extracted by the two tested methods were compared using one-way ANOVA, followed by the Bonferroni–Holm post hoc test . Statistp-value < 0.05 was considered statistically significant. Statistical analysis was performed with Statistica software 9.0 .Comparisons for paired nominal data were performed using chi-square McNemar’s Marginal Homogeneity Test. A Evaluation of a diagnostic test, PQStat software  where confidence intervals were calculated based on the Clopper-Pearson method for a single proportion. As true positives, samples that were positive in at least two tests were considered. The total number of samples tested was 70, of which 53 were True Positives (TP) and 18 True Negatives (TN). The inconclusive samples were classified as positives.The reliability of the studied diagnostic tests was analyzed using the module RNA from various SARS-CoV-2 variants, including B.1.617.1 (one sample), B.1.617.2 (five samples) and B.1.1.7 (seven samples), was isolated using the column-based method and detected by the studied RT-qPCR tests, as described above. The obtained RNA is a part of the National Medicines Institute Collection and has been identified by commercially available SARS-CoV-2 mutation detection assays (data not published). The experiment was performed in three technical replicates.The RNA was extracted from the same samples using two complementary methods, column- and magnetic-based. Each test has a control of reverse transcription inhibition, and an internal control (IC). Thus the influence of inhibitory factors on the result can be monitored. MutaPlex has additional control of the amount of isolated material. It contained primers and probes for β-actin, sample control (ISC). The amount of β-actin correlates with the amount of the genetic material in the sample. These controls allow monitoring the quality and quantity of the isolated RNA.p = 0.44). However, in a few samples, we observed ten times lower RNA concentration of RNA extracted by the column-based method related to the magnetic-based method, which did not affect the detection of the SARS-CoV-2, The result obtained from the RT-PCR by MutaPlex allowed determining no significant difference in the mean Cq for the amount of isolated material in the entire population of the extracted RNA with both methods. The Cq mean for column-based method was 25.82 +/− 2.8, while from magnetic-based method 26.17 +/− 2.7  was run with the studied RT-qPCR assays to evaluate their efficiency. For each kit, the R square was > 0.99, with an efficiency above 90% . The eff3 copies/sample. Following the RNA extraction, we got around 250 gene copies/µL (estimated using a standard curve for E-gene detected with MutaPlex).To determine each kit’s detection limit, the inactivated SARS-CoV-2 whole virus control was used. This control sample contains the whole inactivated SARS-CoV-2 particles, with a viral load of 10The DiaPlexQ and Liferiver kits exhibited a similar detection limit. Both detected at least one target gene, in samples with less than 16 viral copies. Although in samples with a low viral load, only single genes were detected but results were not repetitive.The detection limit for MutaPlex, was 32 gene copies/sample, of which only E-gene was detected in this dilution. In the case of the Bosphore assay, the detection of fewer than 63 copies of E-gene could not be observed .p = 0.00087). The SARS-CoV-2 RNA extracted by the column-based method was detected, using the DiaPlexQ kit, in 52/70 .Comparison of RNA extraction methods demonstrated that the column-based method was more efficient than the magnetic-based method for three of four assays tested . Howeverin 52/70 b, Liferiin 52/70 c, and Muin 52/70 d. In theThe highest differences in the efficiency of RNA extraction methods were observed in samples with low viral load, e.g., less than 500 copies/sample (Cq > 30) . Most saOne of the goals of this work was to compare the SARS-CoV-2 detection sensitivities of four commercial RT-qPCR kits using a panel of 70 clinical samples. The identification rate depends on the RNA isolation method and the RT-qPCR kit .The comparison of the reliability of studied tests is summarized in The Bosphore RT-qPCR kit identified 13 positives and five inconclusive samples with RNA extracted by columns, detecting 26% of samples containing SARS-CoV-2 in the specimen set tested. The detection sensitivity in samples with RNA extracted using the magnetic-based method was higher than the column; 18 positives and 13 inconclusive samples were identified a. HoweveThe DiaplexQ and MutaPlex kits showed similar effectiveness . The MutUsing the DiaPlexQ kit, SARS-CoV-2 RNA was detected in 50 samples with RNA extracted by columns and in 37 samples with RNA extraction by the magnetic method. Additionally, two samples were inconclusive after RNA extraction with columns, which means that only the N-gene was amplified b.The highest sensitivity had the Liferiver kit. In probes with RNA extracted by columns, 54 were identified as SARS-CoV-2 RNA-positive. Eight samples were considered inconclusive because amplification was observed only in one gene. In contrast, 42 positives and four inconclusive samples were identified in the material extracted by the magnetic-based method c.The possibility to detect SARS-CoV-2 variants, including B1.1.7, B.1.617.1 and B.1.617.2 by RT-qPCR was verified. Mutations in the above variants did not influence the detection by the used RT-qPCR kits.This study focuses on applying various RNA extraction methods and RT-qPCR kits to detect SARS-CoV-2. There are three main ways of viral RNA extraction used in diagnostic laboratories worldwide . The fir3 copies of SARS-CoV-2 RNA were correctly identified in both cases. The main problem appeared when the viral load was low, i.e., less than 500 copies (Cq > 30). In this case, more positive samples were identified when RNA was extracted using columns. The influence of the applied RNA extraction method was also demonstrated by Ambrosi et al. [Two manual RNA extraction methods were analyzed. The obtained data suggest that SARS-CoV-2 is more frequently identified in the clinical samples with RNA extracted by the column-based method. All samples with more than 10i et al. . They tei et al. .It is crucial to remember that samples with low viral load  concern two cases. The first relates to specimens from patients coming out of viremia or who have recently had COVID-19. For these patients, failure to detect viral RNA is no more relevant in relative safety. The problem occurs with the second group of patients, those who are just beginning to enter viremia. In them, failure to detect SARS-CoV-2 RNA will result in these individuals being unwitting carriers of the virus and spreading it. Obtaining a negative test will undoubtedly relieve them of the suspicion of having COVID-19, and they will attribute all potential symptoms to other diseases. However, obtaining an inconclusive test result obliges repeating the test after a specified period . After this time, the patient can obtain a conclusive (positive or negative) test result [Four different commercial assays were performed to investigate how the choice of the test influences the identification of specimens with SARS-CoV-2. The three analyzed methods provided comparable results. The RT-qPCR assay from Liferiver showed the highest sensitivity but the lowest specificity. This kit allowed us to identify the most significant number of positive samples, with few inconclusive samples. However, such an increased sensitivity of the test is not an ideal solution in diagnosing the virus. The presence of a few copies of the virus in a sample may be caused by contamination, e.g., during swabbing or sample processing . InactivThe selected diagnostic method is of great importance for the obtained results. Therefore, it is significant to choose a technique that matches the capabilities and equipment in a given laboratory. Many ready-made tests contain information on method validation for a specific RNA extraction method and RT-qPCR equipment. It is possible that the applied RNA extraction method is not compatible with the RT-qPCR kit, as it could be with the RT-qPCR Bosphore kit. Preliminary data with the Bosphore kit showed that it is susceptible to ethanol or protein contamination (unpublished data), commonly inhibiting RT-PCR . TherefoAlmost two years have passed since discovering SARS-CoV-2, and it is still a considerable challenge for patients, physicians, and diagnosticians. New mutations appear almost daily, posing significant difficulties in virus detection . To stop


Least Absolute Shrinkage and Selection Operator, ESL selects only the most important genomic loci to explain a given phylogenetic hypothesis or presence/absence of a trait. ESL models do not directly involve conventional parameters such as rates of substitutions between nucleotides, rate variation among positions, and phylogeny branch lengths. Instead, ESL directly employs the concordance of variation across sequences in an alignment with the evolutionary hypothesis of interest. ESL provides a natural way to combine different molecular and nonmolecular data types and incorporate biological and functional annotations of genomic loci in model building. We propose positional, gene, function, and hypothesis sparsity scores, illustrate their use through an example, and suggest several applications of ESL. The ESL framework has the potential to drive the development of a new class of computational methods that will complement traditional approaches in evolutionary genomics, particularly for identifying influential loci and sequences given a phylogeny and building models to test hypotheses. ESL’s fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci—such as genes, proteins, genomic segments, and positions—as parameters. Using the  Rapid acquisition and assembly of multigene and genomic data sets have fast-tracked the discovery of natural patterns and processes underlying the diversity of form and function. Central to these successes are statistical and computational methods for comparative analysis of molecular sequences, needed for applications ranging from building the tree of life to discovering the genomic loci underlying functional evolution. These methods have revolutionized data-driven discovery and hypothesis testing . The groHere, we introduce supervised machine learning with a sparsity constraint for molecular evolutionary analysis . We refeBy applying sparse learning directly to a multiple sequence alignment, ESL identifies the most important genetic loci (parameters) that predict a phylogenetic hypothesis or the presence or absence of a trait of interest. Alternative models involving different combinations of model parameters are automatically compared, and the model requiring the fewest loci (the “sparse” solution) but offering the highest predictive ability is preferred. Thus, this approach emphasizes building a model with the fewest parameters while maximizing the model fit, similarly to traditional molecular evolutionary analyses investigating genomic features that drive a phylogenetic inference or underlie a trait. However, ESL analysis does not necessitate estimating standard evolutionary parameters, such as branch lengths, substitution rates between nucleotides, or site-wise evolutionary rate variability.The ESL framework introduced here is different from recent machine learning applications in ecology and evolution to classify species , accelerIn the following, we first present the general ESL framework, define several biologically relevant sparsity scores and prediction metrics, and outline several useful potential applications of ESL. We also introduce some examples to demonstrate ESL’s pattern recognition approach and illustrate its modest computational demands that speed up large-scale phylogenomics.ESL builds a logistic regression model that best maps the input multiple sequence alignment  to the probability of output response categories f(Y) =Xβ b. The in outcome . ESL is modeling .The learning part of ESL involves the estimation of importance (A gression  (Least An eq. 1) .Eq. 1L'Here, the strength of association of the genetic variation at position We use logistic regression with  problem . The choenerated .Phylogenomic data sets are often short-and-fat, that is, the number of sequences is hundreds to thousands of times smaller than the number of positions. This results in the curse of dimensionality  because e below) . The proe below) . This isHere, analysis . Here, awell see , but theStatistically, bilevel sparsity is desirable because solutions are invariant under group-wise orthogonal reparameterizations and statistically consistent when the number of groups to be discovered is small, that is, a sparse solution is expected . The spapost hoc gene enrichment approaches frequently employed in evolutionary and functional genomics. In addition, a tree-structured group lasso is also feasible in ESL to relax the common assumption of independence among loci and groups  required by any MSA position in In ESL analysis, one-hot encoded data from different data types can be directly combined for the same set of organisms. For example, one-hot encoded amino acid and nucleotide MSAs can be used together by simple concatenation to build a super-matrixj in In ESL, each sequence elements c. We caniew, see . In a laregularization is needed to obtain stable results. Therefore, one may use stability selection through a subsampling approach to make results robust to the regularization parameters chosen  should be applied for a more reliable estimation of 1-norm) and Ridge (l2-norm) penalties together during the model selection. One may use ElasticNet  belongs to multiple functional categories, it contributes to all relevant Hypothesis Sparsity Score  in a given phylogenetic tree. In this case, 20 genes .starting feature regularization parameter  for high-dimensional data sets. This expectation is realized in the ESL analysis of the Plant data set, which required iy = +1 for sequences in one clade (class) and −1 for sequences in the other clade. In this case, we simply remove all other sequences from the MSA while building the ESL model. The outcome is a list of significant positions and corresponding genes in phylogenomic analyses in which the focus is on phylogenetic inference.In the above example, we partitioned all sequences in a phylogeny into two classes based on our interest in identifying genes and positions that are diagnostic of a group’s monophyly. In the interest of discovering positions that discriminate between sister clades, we can set On the other hand, we seek a model in which positions or domains that distinguish paralogous genes are revealed if the divergence of two clades of interest corresponds to a gene duplication event in a multigene family sequence alignment. This would be useful in functional genomics investigations. In fact, the two classes of sequences in ESL analyses can be specified for any combination of clades, paralogs, and even sequences. So, it is very flexible. For example, we are currently investigating the effectiveness of ESL analysis to identify genes and functional categories in which gene evolution underlies the emergence of convergent traits.We also envision a novel application of the ESL framework in which models are built under two contrasting evolutionary hypotheses. In this case, the relative importance of each locus that distinguishes these hypotheses can be determined based on the difference in their  example .a posteriori estimate when considering a Gaussian likelihood function and a Laplacian prior on j is as much or more correlated with the partial residual than position k  describes a position rather than a single 4- or 20-state substitution model in traditional analysis. This means that the same substitution model is not assumed for all the positions in the alignment or all the positions in a gene, unlike conventional methods. Moreover, the complexity of the model is a function of different base types found at each position. That is, position-by-position consideration of substitution matrices is intrinsic to ESL, but not in the same way or extent as in classical molecular phylogenetics. A major avenue of future research will be to investigate the relationship of ESL, theoretically and empirically, with maximum likelihood and other statistical methods in molecular evolutionary analysis. In particular, it will be interesting to test the robustness of ESL methods as compared to existing approaches to the nonstationarity, nonreversibility, nonindependence, and nonuniformity of substitution models across lineages and positions in multiple sequence alignments, as compared to traditional methods that tend to make these assumptions for analytical tractability.Overall, we expect ESL to complement existing methods of molecular evolutionary analyses because they serve different purposes. For example, one would need classical methods when the goal is to estimate branch lengths in a phylogeny, instantaneous rates of different types of mutations and substitutions, neutrality index, and the degree of heterogeneity of evolutionary rates among sites. They constitute fundamental properties of the evolutionary processes and natural selection, which are best estimated using statistical methods that model those properties. But, we may first use ESL to gain insights about evolutionary relationships and functional loci and then test them using currently standard statistical methods of computational molecular evolution. We also envision hybrid ESL approaches in which the input matrix contains estimates of such properties for genetic loci alongside the sequence alignments. Ultimately, we expect the utility of ESL to be limited only by one’s imagination, as it provides a flexible framework to construct approaches for de novo discovery and hypothesis testing.In conclusion, the power of machine learning in phylogenomics has only begun to be harnessed. ESL brings time-tested mature advances of sparse learning to phylogenomics. It provides a new way of conducting evolutionary analysis and enables a natural combination of heterogeneous data sets. Our simple example establishes the premise of ESL for developing methods for evolutionary analysis, which should motivate theoretical and computational investigations of the powers and pitfalls of ESL.


The ability to immobilize an exoribonuclease enzyme, such as XRN1, to a solid support while maintaining its activity and capability to cleave both the canonical and modified ribonucleotides from an intact RNA molecule can be a viable approach for single-molecule RNA sequencing. In this study, we report an enzymatic reactor consisting of covalently attached XRN1 to a solid support as the groundwork for a novel RNA exosequencing technique. The covalent attachment of XRN1 to a plastic solid support was achieved using EDC/NHS coupling chemistry. Studies showed that the solid-phase digestion efficiency of model RNAs was 87.6 ± 2.8%, while the XRN1 solution-phase digestion for the same model was 78.3 ± 4.4%. The ability of immobilized XRN1 to digest methylated RNA containing m6A and m5C ribonucleotides was also demonstrated. The processivity and clipping rate of immobilized XRN1 secured using single-molecule fluorescence measurements of a single RNA transcript demonstrated a clipping rate of 26 ± 5 nt s With the development of next generation sequencing (NGS), the field of transcriptomics has seen tremendous advancements creating opportunities for improved diagnostics, prognostics, and treatment of diseases such as cancers and infectious diseases ,2. RNA sSingle-molecule nanopore sequencing has been viewed as an attractive alternative to NGS that can address many of the aforementioned issues associated with NGS ,12. Of tNanopore sequencing is currently performed using two approaches, strand sequencing ,14 or ex2+ acts as a cofactor to carry out its function as an exoribonuclease  coupling chemistry.In this report, we lay the groundwork for an exosequencing technique for RNA using solid-state in-plane nanopores fabricated in thermoplastics, with exoribonuclease-1 (XRN1) immobilized onto a solid support . XRN1 isnuclease . The cli5 nt s−1 . Recentld pH 7.9 . However−1), and a significantly higher processivity (∼40 000 bp) compared to the free solution enzyme.To understand the immobilization of XRN1 and its ability to cleave ssRNA into its constituent mononucleotides, we immobilized XRN1 onto pillars poised within a microfluidic device. Microfluidic devices, where enzymes are immobilized for biological reactions, are known as immobilized microfluidic enzymatic reactors, IMERs ,29. Ther3 activated to generate surface confined carboxylic acid groups that can be used to attach biologics containing primary amine groups  for the fabrication of both microfluidic and nanofluidic devices, thermoplastics offer many advantages due to their favorable biocompatibility, good optical properties, ease of surface modification, and the number of well-established fabrication technologies to produce devices . The mose groups ,36.3 activated PMMA device containing micropillars. Attachment consisted of using EDC/NHS coupling chemistry. AFM analysis showed that XRN1 only attached to the PMMA surface where it had been UV/O3 activated and in the presence of EDC/NHS with little or no nonspecific binding. Fluorescence studies, UPLC/MS measurements, and electrophoresis data provided information on the digestion of both modified and unmodified 5′ monophosphorylated RNA by both free solution and immobilized XRN1. Real-time digestion of dye labeled RNA by free solution and immobilized XRN1 was observed using fluorometry and fluorescence microscopy, respectively, allowing deduction of the processivity and clipping rate of both free solution and immobilized XRN1.In this study, we report an IMER containing XRN1 as the immobilized enzyme for the sequential digestion of 5′ monophosphorylated ssRNA for potential applications in single-molecule RNA exosequencing. XRN1 was immobilized onto a UV/O2 and 2.9 μl, respectively  using hot embossing. The IMER contained a single channel that was 24 mm long and 1.4 mm wide. This channel contained 3600 micropillars with each pillar being 100 μm in diameter and 60 μm in height. The surface area and volume of this device were 1.22 cmectively . The devectively  for devi3 modified thermoplastic surfaces  was covalently immobilized onto microfluidic device surfaces using EDC/NHS coupling chemistry, which is used for attachment of primary amine containing biological entities, such as XRN1, to UV/Osurfaces ,38. A sc3 light followed by the addition of a 40 nM XRN1 solution in the presence and absence (negative control) of EDC/NHS coupling reagents. The PMMA surfaces were kept at 4°C overnight and were rinsed with reaction buffer and distilled water and gently air dried prior to AFM imaging.To determine successful covalent attachment of XRN1 to activated PMMA surfaces, AFM analysis  was conducted. The tip used for imaging was a DLC-300 tip with a frequency of 300 kHz and a tip radius <15 nm. Tapping mode was used with a scanning frequency of 1 Hz so that possible damage done by the tapping force applied by the tip to the immobilized enzyme was minimal. PMMA surfaces (1 cm x 1 cm) were irradiated with UV/OPierce™ 660 nm protein quantification assay  was used to determine the amount of XRN1 covalently attached to the microfluidic channel containing micropillars. Absorbance of XRN1 solutions at 660 nm were measured  before and after introduction into the IMER with attached enzyme. The calibration plot and a schematic of the experimental procedure used for protein quantification can be found in The model RNAs  for XRN1 digestion studies were obtained from Integrated DNA Technologies, Inc. . Following synthetic preparation, the 60 nt RNAs were purified using RP-HPLC and purity checked by mass spectrometry, which yielded a purity of 85–90%. The impurities were suspected to consist of truncated 60 nt RNAs lacking the 5′-monophosphorylated end and thus, would not serve as a viable substrate for XRN1 digestion.2+. In the immobilized state, the effective reaction times were achieved by hydrodynamically pumping RNA solutions through the IMERs with a suitable flow rate using a syringe pump . The negative control for the immobilized XRN1 reaction consisted of introducing RNA solutions to the IMERs, which did not contain immobilized XRN1. The pH was set at 7.9 for both solution-phase and solid-phase reactions. For all the XRN1 digestion experiments, ≥3 trials were conducted for each data point and the average value with the standard deviation is reported.Digestion of 5′ monophosphorylated 60 nt RNA was investigated using 2.32 pmol of XRN1 enzyme in both free solution and the immobilized state. In the free solution reaction, EDTA was added to stop the reaction after the desired time. The experimental control for the free solution reaction consisted of adding XRN1 to an RNA solution in the absence of the cofactor MgFollowing the XRN1 digestion, the remaining RNA molecules were labeled with SYTO 82  to assess the extent of digestion. SYTO type dyes show a quantum efficiency of ∼0.4 when bound to RNA and a low quantum efficiency (0.01) in the presence of mononucleotides and the buffer alone . The fluN-acryloylamido ethoxyethanol (AAEE). Gel electrophoresis data were analyzed using the Tapestation data analysis software. More details on the figures-of-merit of the denaturing microchip gel electrophoresis can be found in the SI.The sizes of each ssRNA both before and after digestion by XRN1 were measured using denaturing microchip gel electrophoresis . For the experiments reported herein, we used the high sensitivity RNA Screentape gel, which is a non-rigid plastic device that contains 16 lanes each of which are 25 mm in length, 2 mm in width and 1 mm in height. The denaturing gel (50–75% DMSO) consisted of 3% s CMS mass spectrometry-electrospray ionization system). The experimental procedure is provided in detail in the SI.An analysis of the reaction mixture following the digestion of both unmethylated and methylated 60 nt RNA substrates with XRN1 was conducted using UPLC (Waters Acquity) coupled to a mass spectrometer  and 11 163 nt (DMD), homogeneous ssRNA molecules were synthesized using IVT see  in SI. AO-methylation protocol’ that generates a cap1 structure at the 5′ end of the RNA following the manufacturer's recommended protocol . The capped RNAs were treated with mRNA decapping enzyme (MDE) to remove the 5′ cap structure for subsequent digestion by XRN1  to produce 5′ monophosphorylated RNA, which served as a substrate for XRN1  in SI. A by XRN1  in SI. Met al. was used with slight modifications  in XRN1 buffer with Mg2+  was added to FLuc RNA complexed to XRN1, and the fluorescence intensity of the mixture was measured at 30 s intervals for 30 min with 470 nm excitation and 500 nm emission using the Fluorolog-3 fluorimeter. Data were analyzed using Datamax 2.0 software.For determination of the clipping rate and processivity of XRN1 in free solution, a method described by Han ications . First, et al. was used (2+ to facilitate complexation of DMD RNA with immobilized XRN1. Complexed DMD RNA to XRN1 was determined by monitoring the fluorescence of single RNA molecules to make sure that they were not randomly diffusing (see SI for more details). Once a single DMD RNA molecule was located that was complexed to the immobilized XRN1, enzyme buffer containing Mg2+ cofactor was introduced into the device to initiate digestion and the fluorescence intensity  of the DMD RNA–XRN1 complex was monitored continuously. For these experiments, an epifluorescence microscope was used, which consisted of a NIKON TE 2000 microscope fitted with a 100X/1.4 NA oil-immersion objective and an Andor iXon3 EMCCD camera . For these experiments, three different input concentrations of XRN1 were used  based on the amount of XRN1 needed for a theoretical monolayer coverage of the IMER (1.1 × 1011 molecules), and the lowest XRN1 enzyme concentration that can be measured using the protein quantification assay. The extent of nonspecific adsorption was assessed by introducing XRN1 solutions to IMERs that had not been treated with EDC/NHS coupling reagents following UV/O3 activation of the polymer.We used a protein quantification assay to determine the amount of XRN1 covalently attached inside the micropillared IMER  after 60 s for XRN1 solution-phase reactions.To demonstrate the ability of XRN1 to digest monophosphorylated RNA, 10.6 pmol of a 60 nt RNA substrate was reacted with 2.32 pmol of XRN1 both in solution- and the solid-phase. For solid-phase XRN1 experiments, we used a micropillared IMER consisting of 3600 micropillars  of the 60 nt RNA was digested by the immobilized XRN1 enzyme. However, these numbers should be qualified by the fact that the RP-HPLC purified 60 nt models contained RNA fragments that were not 5′-monophosphorylated making them a non-viable substrate for XRN1.Figure t-test.To assess the effect of surface enzyme density on the activity of immobilized XRN1, the 60 nt RNA substrate was introduced into the XRN1 immobilized IMERs for 60 s with different XRN1 concentrations used for the immobilization reaction. The digestion percentages were >80% for all surface enzyme densities used (Table P = 0.5284).Next, to evaluate the effect of reaction time on the percent digestion of the 60 nt RNA substrate, surface enzyme density and the concentration of RNA solutions were kept constant, while the reaction times were varied by changing the flowrate of the RNA substrate through the IMERs. When a reaction time of 30 s was used, the digestion percentage was ∼35% and when the reaction time was increased to 60 and 120 s, the digestion percentages were >80%  residue  of m6A methylated RNA was digested  of m5C methylated RNA was digested by the immobilized XRN1 (see P = 0.0243). If XRN1 digestion was terminated at the methylation sites, the fluorescence intensity would be closer to that of the negative control due to the fact that the oligomers remaining (56 nt and 51 nt) were close in size to the starting RNA 60mer.To determine the extent of digestion of methylated RNAs by the immobilized XRN1, the IMER digested RNA solutions were stained with SYTO 82 post-digestion and the fluorescence emission spectra were taken from 490 to 700 nm with 480 nm excitation. Peak area analysis of these spectra revealed that after 60 s of reaction, 87.0 ± 4.2% . The Δntave represented the average length of FLuc RNA remaining after the XRN1 reaction, which indicated that the processivity of XRN1 in free solution was 1113 ± 132 nt. Using the fluorescence decay portion of Figure −1  at 25°C  indicating that the labeling had no influence on the activity of XRN1  molecule labeled with SYTO 82 and associated to a single immobilized XRN1 molecule using a high-sensitivity fluorescence microscope equipped with an EMCCD camera. In this case, we used a longer RNA strand compared to FLuc to produce a brighter fluorescence signal from a single RNA molecule. Analysis of the fluorescence emission obtained for DMD RNA labeled with SYTO 82 both pre-digestion and post-digestion did not show a statistical difference at the 95% confidence interval (XRN1 see  in SI.2+) was then flowed into the microchannel and the fluorescence was monitored in real time to determine the processivity and clipping rate. When buffer containing Mg2+ was introduced into the microfluidic device, the fluorescence intensity of the stationary RNA molecules decreased with time . To determine the shortest detectable SYTO 82 labeled RNA fragment, a calibration plot was constructed using DMD RNA and FLuc RNA with SYTO 82 labeling  for immobilized XRN1 at 25°C. We note that the clipping rate and the processivity of both free solution and immobilized XRN1 may not be optimal values as these experiments were conducted at room temperature (25°C) instead of the enzyme's optimum temperature of 37°C.The average clipping rate of XRN1 in the immobilized state was deduced using the decay portion of the fluorescence intensity shown in Figure −4 nt substitutions per year), RNA sequencing will continue to be an important tool for not only detection, but vaccine discovery and determining resistance as well. Thus, new strategies for RNA sequencing that can provide simpler workflow, longer reads, and amplification-free formats would be particularly attractive. We have previously reported a single-molecule DNA sequencing strategy that fits the aforementioned operational criteria  and the MERS virus (∼50% sequence homology) . Due to criteria . BrieflyThere are two categories of ribonucleases, endoribonucleases and exoribonucleases. Endoribonucleases cleave RNA internally whereas exoribonucleases cleave RNA sequentially from either the 3′ or 5′ end . Exoribo7G decapping and dephosphorylation  generates a –COOH group density of 32 pmol/ cm2 as determined by a TBO assay  and 5-hydroxylmethylcytidine (5-hmC). Additional functions of RNA modifications include tRNA stability, cellular stress response (m5C), and microRNA stability (2′-O-methlyation). The ‘epitranscriptome’, which is the term used to describe RNA modifications throughout the transcriptome  modifications contained within an RNA molecule. The antibody crosslinking halts the digestion of RNA by XRN1 at the exact position of m6A/ m6Am thereby indicating the position of these methylations.More than 170 post-transcriptional RNA modifications have been identified to date . Most ofcriptome , has hiscriptome . Recentlmolecule . This teFor the RNA nucleotide modifications we tested, we found that XRN1 could cleave through the m5C and m6A modified nucleotides. This is in agreement with the literature, which has reported that XRN1 is involved in degradation of m6A and m5C containing RNA species . Both m6Our conclusion of XRN1’s ability to cleave through m5C and m6A nucleotides was supported by three lines of independent evidence: (i) Denaturing gel electrophoresis, which showed the disappearance of bands near the intact 60mer RNA model. (ii) Near complete loss of fluorescence specific to intact RNA molecules. (iii) UPLC/MS data obtained that indicated the molar ratios of the ribonucleotides was consistent with complete digestion with the structures of the methylated ribonucleotides preserved after digestion see .in vivo  labelled tRNASer(CGA) variants (Ser(CGA) contained a 2′-O-methylated guanosine and uridine at the 18th and 44th ribonucleotide positions from 5′ end, respectively. When the tRNA variants were reacted with XRN1 the full length tRNASer(CGA) was degraded to completion to give pCp as the end product. Additionally, the digestion of tRNASer(CGA) and tRNAVal(AAC) provided information on the ability of XRN1 to digest through other modified nucleotides such as 1N-methylguanosine (m1G), ψ, dihydrouridine (D), m1A, 2,2,7-trimethylguanosine  and i6A , which digests fully modified mature tRNA species in vivo ,75–77. I and i6A ,77. Thuset al. has demonstrated that Bacillus subtilis RppH (BsRppH), a homolog of RppH, can also be used to successfully remove the NAD cap structure in vitro resulting in 5′ monophosphorylated RNA for subsequent digestion by 5′-3′ exoribonucleases ’, can be used to decap mRNA in vitro to remove both cap0 and cap1 structures  was possibly due to the higher surface area-to-volume ratio coupled with several other factors such as increased stability, and prevention of enzyme aggregation  was completely digested. Therefore, it may be possible to read through the entire SARS-CoV-2 genome (∼30 kb) using our proposed exosequencing approach.In this report, we demonstrated for the first time the covalent attachment of XRN1 onto a solid-support for potential applications in single-molecule RNA exosequencing. The immobilization of XRN1 was carried out using established EDC/NHS coupling chemistry to a plastic support that was activated using UV/O methods ,96. The As noted, we are developing a single-molecule exosequencing approach, which uses a chip-based sequencer consisting of a solid-phase enzymatic bioreactor coupled to a column that measures in real time the electrophoretic mobility of free nucleotides with nanoscale electrophoresis following clipping using an exonuclease ,21,22. Wgkab001_Supplemental_FileClick here for additional data file.


CRISPR-Cas9 expression independent of its cognate synthetic guide RNA (gRNA) causes widespread genomic DNA damage in human cells. To investigate whether Cas9 can interact with endogenous human RNA transcripts independent of its guide, we perform eCLIP (enhanced CLIP) of Cas9 in human cells and find that Cas9 reproducibly interacts with hundreds of endogenous human RNA transcripts. This association can be partially explained by a model built on gRNA secondary structure and sequence. Critically, transcriptome-wide Cas9 binding sites do not appear to correlate with published genome-wide Cas9 DNA binding or cut-site loci under gRNA co-expression. However, even under gRNA co-expression low-affinity Cas9-human RNA interactions  do correlate with published elevated transcriptome-wide RNA editing. Our findings do not support the hypothesis that human RNAs can broadly guide Cas9 to bind and cleave human genomic DNA, but they illustrate a cellular and RNA impact likely inherent to CRISPR-Cas systems. The off-target effects of CRISPR-Cas9 are thought to be mediated by its cognate guide RNA. Here the authors show that Cas9 independently interacts with the human transcriptome, correlating with elevated RNA editing even under guide RNA co-expression. Acquired foreign nucleic acids are stored in genomic memory as part of CRISPR arrays, which are then processed into CRISPR RNAs to which Cas proteins bind for downstream foreign nucleic acid recognition and destruction2.CRISPR-Cas  systems have evolved in bacteria and archaea as adaptative immune systems defending against phage invaders3 has developed into a powerful genome engineering and therapeutic tool4. It has been shown that Cas9 can associate with transcriptome-wide RNAs in bacteria, although such interactions were attributed to CRISPR RNA-mediated binding5. We hypothesized that Cas9 might also bind to endogenous eukaryotic RNA transcripts in a CRISPR RNA/gRNA-independent fashion, and that such interactions would be pervasive and potentially consequential in the far more complex transcriptome environment of human cells.The CRISPR-Cas9 system with its programmable synthetic guide RNA (gRNA)Here we show that Cas9 reproducibly binds to hundreds of human RNAs. These weak interactions in part observe a sequence and structure RNA motif modeled on the Cas9 gRNA. Although under gRNA co-expression such human RNA interactions do not correlate with Cas9 genomic DNA binding or cleavage, they do correlate with elevated RNA editing. Implications of this study for the use of CRISPR-Cas systems in human cells include off-target RNA editing or modification and bound transcript RNA/protein level changes.6. We selected cytoplasm-localized, catalytically dead dSpCas9  between experimental and control conditions. Out of this reproducible eCLIP dataset emerged 478 peaks across 381 human genes, with moderate correlation between V5 and FLAG eCLIP datasets (R2 = 0.548) and CDIP1 identified as the most enriched RNA substrate  with anti-V5 and anti-FLAG antibodies in transfected human HEK 293T cells Fig. , as has as9 Fig. , given tCDS Fig. , frequen10. Whether Cas9 may through RNA interactions activate the stress response pathway to induce apoptotic DNA damage remains an open question. To resolve this question, an examination of the RNAs encoding the 381 human genes with which Cas9 interacts uncovered 66 (17.3% of the eCLIP targets) to be associated with the stress response pathway, with CDIP1, ATF3, and CDKN1A (p21) among the top four enriched genes in our eCLIP experiment  cell lines11. Of the 55 stress response pathway genes that passed this filter, ATF3 (Activating Transcription Factor 3) emerged as the most Cas9-dependent upregulated gene  against an empty vector-negative control . A stress pathway master regulator transcription factor, ATF3 is known to share DNA-binding sites with p53, with which it interacts cooperatively13. Moreover, ATF3 overexpression has been implicated in the acceleration of apoptosis in human HepG2 cells14. Western blot analysis revealed a moderate increase in ATF3 protein level upon cytoplasm-localized dSpCas9 expression without gRNA co-expression over both empty vector and fluorescent protein conditions  in transfected HEK 293T cells, comparing the expression effects of dSpCas9 with and without U6 promoter-driven gRNAs, SpCas9-NLS , and the fluorescent protein UnaG15, identical to that of the gRNA to which the CRISPR RNA recognition domain of SpCas9 binds16. To confirm that this domain binds to the 5′ UTR of CDIP1 mRNA, we performed a competitive electrophoretic mobility shift assay of fluorescently labeled CDIP1 RNA with unlabeled gRNA and non-specific RNA, finding that gRNA—and not non-specific RNA—outcompetes CDIP1 RNA  of SpCas9 for CDIP1 RNA  showed increased performance in predicting Cas9-interacting eCLIP sites over Monte Carlo simulations  of the same nucleotide sequences randomly shuffled  in non-eCLIP genes vs. eCLIP targets showed no statistically significant differences when comparing a no-Cas9/no-gRNA-negative control to four Cas9 conditions with different gRNAs co-expressed . Thus, regions of open chromatin may play the dominant role in Cas9 DNA-binding site preference  datasets in HEK 293T cells24. For this reason, we analyzed a publicly available Cas9 RNA-editing HEK 293T dataset25. In each of two replicates of three different gRNA conditions, a nickase Cas9-APOBEC fusion produced statistically significant  more edit sites in eCLIP target gene transcripts vs. non-eCLIP gene transcripts    Fig. . If nick00) Fig. . This ob27.While the scope of this study concerns the established CRISPR-Cas9 system, Cas protein-human RNA interaction-mediated cellular effects, which we term CRISPR crosstalk, may have far-reaching implications for the CRISPR field  at a ratio of 1:10.HEK 293T cells  were maintained in DMEM (4.5 g/L D-glucose) supplemented with 10% FBS (Gibco) at 37 °C with 5% COpCDNA3.1(-) (ThermoFisher Scientific) by Gibson cloning a protein with upstream Kozak and start codon sequences and downstream stop codon sequence into its EcoRI and BamHI restriction enzyme sites. Catalytically inactive dSpCas9 without an NLS was subcloned from Nelles et al.28. Catalytically dead SpCas9 with an NLS was subcloned from lentiCRISPR v2 (AddGene #52961). A V5 peptide sequence with G linker (5′-GGCAAACCGATCCCGAATCCGCTTCTTGGTCTTGACTCCACGGGG-3′) was cloned upstream of the expressed protein in pCDNA3.1-V5-dSpCas9 and pCDNA3.1-V5-SpCas9-NLS. A 3xFLAG peptide sequence with G linker (5′- GACTACAAAGACCATGACGGTGATTATAAAGATCATGACATCGACTACAAGGATGACGATGACAAGGGG -3′) was cloned upstream of the expressed protein in pCDNA3.1-3xFLAG-dSpCas9. The UnaG protein sequence (Fluorescent Protein Database) was human codon optimized with IDT’s codon optimization tool prior to ordering as a gBlock to clone into pCDNA3.1-UnaG. For U6 promoter and U6 promoter-driven gRNAs conditions, the U6 promoter sequence 5′-AGGTCGGGCAGGAAGAGGGCCTATTTCCCATGATTCCTTCATATTTGCATATACGATACAAGGCTGTTAGAGAGATAATTAGAATTAATTTGACTGTAAACACAAAGATATTAGTACAAAATACGTGAC GTAGAAAGTAATAATTTCTTGGGTAGTTTGCAGTTTTAAAATTATGTTTTAAAATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACC-3′ with a 5′-TTTTTT-3′ terminator sequence was cloned into pCDNA3.1-V5-dSpCas9. The gRNA backbone sequence used was 5′-GTTTAAGAGCTATGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT-3′. Sequences for guides 1 and 2 were 5′-GAGTGTCAGCCAGTATAACCC-3′ and 5′-GGCGCGGGCCGCTCGCTCTA-3′, respectively.Protein-expressing plasmids were constructed from pCDNA3.1-V5-dSpCas9, pCDNA3.1-3xFLAG-dSpCas9, or pCDNA3.1(-). Forty-eight hours post-transfection, biological replicates of confluent 10 cm plates of HEK 293T cells were treated with 400 mJ/cm2 of UV using the Stratalinker 2400, harvested in ice cold PBS and pellets flash frozen in liquid nitrogen and stored in −80 °C until ready to IP with either V5 Tag mouse monoclonal antibody (ThermoFisher Scientific #R960-25) or mouse monoclonal ANTI-FLAG M2 antibody (Sigma-Aldrich #F1804) each at a dilution of 1:3000 in a subsequent protocol exactly as detailed in Van Nostrand et al.6, cutting the nitrocellulose membrane from 115 kDa and up. The size-matched input not subjected to IP was cut from the identical region. Sequencing was performed on Illumina HiSeq 4000 with paired end reads.HEK 293T cells were transfected at 60–80% confluence in 10 cm plates using the jetOPTIMUS transfection kit (Polyplus Transfection) with either https://github.com/YeoLab), aligning reads to the human reference genome hg38. Reproducible peaks were assigned using IDR (Irreproducible Discovery Rate) in which entropy was used to rank replicate peaks, and further evaluated using self-consistency and rescue ratios according to Van Nostrand et al. (2016), where detailed information regarding peak calling used for Cas9 in this study can be found. Bed files representing each of the four sets of eCLIP peak IDR analyses (two for V5 and two for FLAG) were intersected with minimum 50% overlap, intersecting the V5 and FLAG eCLIP peaks separately at first. Maximum eCLIP peak enrichment per gene was taken from the V5 vs. size-matched input or FLAG vs. size-matched input IDR analysis, with R2 statistics performed on these values. Gene regions for each eCLIP peak were determined based on all gene regions represented across all four IDR peaks whose intersection yielded that peak. For the gene region analysis of top three represented regions , region lengths were normalized over average TPM (transcripts per million) across all four no IP/size-matched input eCLIP RNA-sequencing datasets  for genes with TPM > 1. Paired nucleotide probabilities  were region length- and TPM-normalized for genes with TPM > 1.Data were processed through Dr. Yeo’s eCLIP pipeline version 0.4.0  at 1:3000 dilution or mouse monoclonal ANTI-FLAG M2 antibody (Sigma-Aldrich #F1804) at 1:1000 dilution. Slides were washed five times for 10 min with phosphate-buffered saline with Tween 20 (PBST), then incubated for 1 h at room temperature under gentle orbital shaking with secondary antibody: Goat anti-mouse IgG AlexaFluor 488 Superclonal Recombinant Secondary antibody (ThermoFisher Scientific #A28175) at 1:2000 dilution. Slides were washed five times for 10 min with PBST, then washed three more times with PBS before mounting overnight with 4',6-diamidino-2-phenylindole (DAPI). All antibodies were incubated with 5% BSA in 0.1% Tween-PBS. Immunofluorescence images were taken at 63x objective with a Zeiss LSM 780 confocal microscope in 5–10 slices, with maximum intensity projections across the entire image plane generated in Zeiss ZEN 2010 for figures.HEK 293T cells were transfected at 60–80% confluence in Nunc Lab-Tek II Chamber Slides (ThermoFisher Scientific) using the jetOPTIMUS transfection kit (Polyplus Transfection) with either Gene ontology enrichment was performed on the 381 eCLIP genes with Panther, using a background of 11,405 genes derived from size-matched inputs .n = 66) were selected. For each gene, the mean of its log2 RPKM L1000 expression over 165 human cancer cell lines was taken from a Cas9 gene expression dataset in Enache et al.10, filtering out those with <1 log2 RPKM L1000 expression in any of the 165 control cell lines (n = 55 genes).eCLIP genes associated with the Panther GO gene ontology accession GO:0033554 cellular response to stress , or pCDNA3.1-UnaG in three bioreplicates per condition. RNA was extracted from cells with the RNeasy Plus kit (Qiagen). Approximately, 1 µg of RNA was converted into cDNA with the ProtoScript II First Strand cDNA Synthesis kit (NEB) with random primers. qPCR for two technical replicates of each of the three bioreplicates with a distinct pair of PCR primers per gene was performed on a CFX384 Touch Real-Time PCR Detection System (Bio-Rad) with 1/6 diluted cDNA samples at 2 µL input in PowerTrack SYBR Green Master Mix (ThermoFisher Scientific), for 95 °C initial incubation for 2 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Technical replicates were averaged for each of the three bioreplicates per condition. In analysis each gene’s expression was compared to GAPDH housekeeping gene expression to compute Δct values. Then –ΔΔct values were computed for each condition-bioreplicate-gene Δct with respect to the mean gene Δct of the pCDNA3.1(-) bioreplicates. Comparisons among a given gene’s condition-bioreplicate –ΔΔct values were made pairwise with one-way ANOVA. PCR primer pairs for given genes are as follows: GAPDH ; ACTB ; p53 ; CDIP1 ; ATF3 ; CDKN1A (p21) .HEK 293T cells were transfected at 60–80% confluence in six-well plates using the jetOPTIMUS transfection kit (Polyplus Transfection) with either g for 4 min. The supernatant was aliquoted into 100 µL aliquots to be stored at −80 °C to prevent protein degradation. Protein concentration was measured by Pierce BCA Protein Assay (ThermoFisher Scientific #23227). Fifty micrograms of protein was run on NuPAGE 4–12% Bis-Tris Gel (ThermoFisher Scientific #NP0335BOX) at 150 V for 1.5 h. Gels were transferred via iBlot 2 Gel Transfer Device (ThermoFisher Scientific #IB21001), blocked in 5% milk, and put in primary overnight. Florescent antibodies were utilized for multiplexing. For ATF3, primary antibody Recombinant Anti-ATF3 antibody [EPR22610-19] (Abcam #ab254268) at 1:1000 dilution and secondary antibody IRDye 680RD Goat anti-Rabbit IgG Secondary Antibody (Li-Cor #926-68071) at 1:20,000 dilution were used. For alpha tubulin, primary antibody Anti-alpha Tubulin antibody [DM1A] - Loading Control (Abcam #ab7291) at 1:5000 dilution and secondary antibody IRDye 800CW Goat anti-Mouse IgG Secondary Antibody (Li-Cor #926-32210) at 1:20,000 dilution were used. Membranes were visualized using the Azure biosystems c600. Proteins were quantified using ImageJ Version 2.0.0-rc-69/1.52n (https://imagej.nih.gov/).Frozen pellets containing 10 million cells were recovered from −80 °C. Protease inhibitor III (Millipore Sigma #539134) was combined with iCLIP lysis buffer . Cells were lysed with eCLIP lysis buffer and protease inhibitor for 15 min on ice and then sonicated on low for 5 min, 30 s on/30 s off. Lysed cells were centrifuged at 15,000 × 2, 0.1% BSA, 1 mM DTT, 5 mM EDTA, 200 U/mL Superase-In RNase Inhibitor (ThermoFisher Scientific), 5% glycerol, 0.01% Tween 20, 50 µg/mL heparin). RNA was in vitro transcribed with the MEGAscript T7 Transcription kit (ThermoFisher Scientific) and purified with RNA Clean & Concentrator-5 (Zymo). Labeled RNA was 5′ labeled with the 5′ EndTag Labeling DNA/RNA kit (Vector Laboratories) and IRDye 800CW Maleimide (Li-COR Biosciences). After incubating protein and RNA in EMSA buffer for 30 min at room temperature, 10x Orange loading dye (Li-COR Biosciences) was added to samples before pipetting into gels pre-run for 20 min at 120 volts at 4 °C. Gels were resolved by running for 1 h at 120 volts at 4 °C on 6% Novex TBE gels (ThermoFisher Scientific) with 0.5x TBE buffer. Images were taken with the Azure Biosystems c600 imager.All EMSAs were performed with SpCas9-NLS protein (CAS9PROT from Sigma-Aldrich) in EMSA buffer  and either unlabeled in vitro transcribed Cas9 gRNA (5′-AUUAAUCGGUGGGAGUAUUCGUUUAAGAGCUAUGCUGGAAACAGCAUAGCAAGUUUAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCU-3′) or unlabeled in vitro transcribed non-specific N.S. RNA (5′-CUAUGCGGCAUCAGAGCAGAUUGUACUGAGAGUGCACCAUAUGCGGUGUGAAAUACCGCACAGAUGCGUAAGGAGAAAAUACCGCAUCAGGCGCCAUUCGCCAUUCAGGCUGCGCAACUGUUGG-3′) at molar ratios of 1:1 to 1:32 with respect to Cas9 protein.Cas9 protein at 640 nM was incubated with 20 nM 5′-end fluorescently labeled in vitro transcribed N.S. RNA, CDIP1 5′ UTR RNA, CDIP1 5′ UTR RNA (loop G > U), or CDIP1 5′ UTR RNA (loop U > A). GU-loop sequence is emboldened and underlined in the CDIP1 5′ UTR RNA sequence above.Cas9 protein at 0, 198, 296, 444, 667, and 1000 nM was incubated with 20 nM 5′-end fluorescently labeled in vitro transcribed CDIP1 5′ UTR RNA and various GU-loop and 5nt-stem mutants depicted in the figure.In vitro transcribed RNAs were 3′ end labeled with Terminal Deoxynucleotidyl Transferase (ThermoFisher Scientific) and Propargylamino-dCTP-Cy5 (Sigma-Aldrich). Cas9 protein at 0, 148, 222, 333, 500, and 750 nM was incubated with 5 nM 3′ end fluorescently labeled in vitro transcribed Relevant uncropped EMSA gels can be found in Supplementary Figs. RNAfold (Vienna RNA Websuite). A model for SpCas9 RNA binding to eCLIP peaks was developed with RNAplfold (Vienna RNA Websuite), based on the GU-loop:5nt-stem of SpCas9 in complex with its gRNA , under default parameters with 50 nt padding on either side of an input RNA sequence . The prediction performance of this model was compared for eCLIP peak sequences (n = 478) and eCLIP genes with peak sequences (n = 381) against 1000 Monte Carlo simulations of random shuffles of the eCLIP peak sequences (n = 478).The minimum free-energy secondary structure of CDIP1 5′ UTR RNA was predicted in 18 were utilized. For inclusion in the analysis, Cas9-interacting RNA transcripts were required to have (i) an eCLIP peak represented by two valued replicates of icSHAPE reactivities across the entire eCLIP peak, (ii) only one eCLIP peak per transcript, and (iii) a peak interval length of at least 50nt. This quality control filter yielded a total of ten eCLIP peaks. Experimental folds were computed using RNApvmin and RNAfold .In vivo click selective 2-hydroxyl acylation and profiling experiment (icSHAPE) structure probing data of HEK 293T transcripts25 For each of the six replicates, the total number of editing sites per gene  was plotted in a box plot for non-eCLIP genes alongside eCLIP genes. Only genes with at least one editing site were plotted for each cohort. On a per gene basis C-to-U edit site counts were compared to the average TPM (transcripts per million) across all four no IP/size-matched input eCLIP RNA-sequencing datasets , with R2 statistics performed on these values. The mean fraction of edits within W  nt distance of eCLIP peaks was calculated for each unique eCLIP peak whose midpoint mapped to spliced RNA. Briefly, for each eCLIP peak midpoint, the fraction of all C-to-U edit sites on its spliced transcript within W nt distance was calculated. For each of the six replicates, the mean of this value over all eCLIP peaks was then calculated. For the 10,000 Monte Carlo simulations, simulated eCLIP peaks were placed according to a uniform random distribution across their respective spliced RNA transcripts.Editing sites with edit rates for replicates 1 and 2 of EMX, RNF2, and N.T. gRNA were taken from Supplementary Tables 22 Reads were mapped to the human reference genome hg38 and converted to bedgraph file form using bowtie (1.2.2) and bedtools (2.27.1) with read coverage normalized to reads per million. For each of the six replicates, the maximum single-base read coverage per gene  was plotted in a box plot for non-eCLIP genes alongside eCLIP genes. For inclusion in a cohort, genes were required to have at least one mapped read in the CHIP-Seq dataset and TPM > 1, where TPM are the average transcripts per million across all four no IP/size-matched input eCLIP RNA-sequencing datasets . On a per gene basis maximum single-base read coverages were compared to the average TPM across all four no IP/size-matched input eCLIP RNA-sequencing datasets , with R2 statistics performed on these values.CHIP-Seq data for replicates 1 and 2 of gRNAs 1, 2, and 3 were taken from GEO: GSE55887 of Kuscu et al.no Cas/no gRNA negative control and gRNAs 1, 2, 3, and 4 were taken from SRA: SRP050338 of Tsai et al.21 Reads were processed into unique reads from UMIs and then mapped to the human reference genome hg38 using the GUIDE-Seq pipeline (https://github.com/tsailabSJ/guideseq) and BWA (0.7.17), with reads normalized to reads per million. For each of the five conditions, the total number of mapped reads per gene  was plotted in a box plot for non-eCLIP genes alongside eCLIP genes. Only genes with at least one mapped read were plotted for each cohort.GUIDE-Seq data for the Custom scripts written in Python 3.7.7 and MATLAB 2019b were used to analyze and plot data.Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2