Mass spectrometry is an important method for analysis of modified nucleosides ubiquitously present in cellular RNAs, in particular for ribosomal and transfer RNAs that play crucial roles in mRNA translation and decoding. Furthermore, modifications have effect on the lifetimes of nucleic acids in plasma and cells and are consequently incorporated into RNA therapeutics. To provide an analytical tool for sequence characterization of modified RNAs, we developed Pytheas, an open-source software package for automated analysis of tandem MS data for RNA. The main features of Pytheas are flexible handling of isotope labeling and RNA modifications, with false discovery rate statistical validation based on sequence decoys. We demonstrate bottom-up mass spectrometry characterization of diverse RNA sequences, with broad applications in the biology of stable RNAs, and quality control of RNA therapeutics and mRNA vaccines. RNA modifications represent a critical aspect of RNA biology that is not well suited to sequencing methods. Here, the authors provide a software tool for automated analysis of RNA tandem mass spectra with full support of modifications, isotope labelling, and control of false discovery rate. More than 170 post-transcriptional modifications (PTxMs) of various chemical complexity have been identified2 and reported in publicly available repositories such as MODOMICS1 and RNAMDB3. PTxMs are present in transfer RNA (tRNA), ribosomal RNA (rRNA), messenger RNA (mRNA), expanding the chemical diversity afforded by the four canonical nucleotides4. Growing evidence indicates that modified ribonucleosides affect RNA structure and folding, intramolecular interactions5, and stability. They have been associated with translation and decoding6, gene expression control7, bacterial antibiotic resistance8, immunomodulation9, development4 and human diseases10. Further, nucleoside modifications are ubiquitous features of many RNA therapeutics from antisense and siRNA to mRNA11. For example, an outbreak of coronavirus disease 2019 (COVID-19) has speeded up the emergence of Pfizer/BioNTech and Moderna vaccines containing N1-methylpseudouridine modified mRNA as a key element12. Given these considerations, it is of paramount importance to be able to identify and quantify RNA modifications in a broad class of RNA molecules.Ribonucleic acid (RNA) is a biomacromolecule that is decorated with chemical modifications in almost all organisms14. Although NGS-based high-throughput methods are becoming the de facto technique for RNA modification analysis due to the high sensitivity and broad coverage, MS remains an important orthogonal approach that helps to address the existing limitations  and tandem mass spectrometry coupled with liquid chromatography (LC-MS/MS)16. With the exception of a few pioneer programs18, the first automation of the RNA spectra analysis emerged only in 2009, when Ariadne, a software tool, allowed for the first time to match experimental MS/MS spectra against a database of predicted spectra, to identify modified RNA sequences19. However, the recent methodological advances such as improved instrumentation speed, sensitivity21, new isotope labeling schemes, and newly identified ribonucleases with distinct cleavage specificity22, were not supported by a parallel development of data analysis tools23. In fact, the amount of software for large-scale LC-MS/MS analysis of RNA including Ariadne, RNAModMapper24 and most recently NASE25 is very modest compared to number of tools available for proteome MS26, and until now analytical workflows often rely on time-consuming manual curation of RNA spectra.In a typical bottom-up MS experiment, the RNA of interest is cleaved site-specifically with an endonuclease and the digestion products are chromatographically separated before entering the mass spectrometer. The collision-induced dissociation (CID) of the charged RNA oligonucleotides yields sequence-informative product ions (or MS2 fragments) that have been characterized by McLuckey et al. as early as 199227 help to monitor the quality of the database matching process, and to filter out low-confidence identifications. Finally, Pytheas is designed to accommodate custom nucleoside chemistry and isotope labeling and provides a detailed output suitable for downstream applications such as quantitative analysis of RNA modifications.Here we introduce Pytheas, a flexible software package that conveniently encompasses many useful features for automated analysis of RNA tandem MS data. Pytheas performs in silico digestion of the given RNA sequences, then matches theoretical spectra against the acquired MS/MS data, via an empirical scoring function. Pytheas allows the user to visualize the annotated spectra and map RNA modifications on the input sequence. Global statistical analysis of the spectral matches and false discovery rate (FDR) calculation based on a target-decoy approachm/z at the MS1 level), and a list of product ion masses (m/z at MS2 level) comprising a theoretical MS/MS spectrum. Predicted MS/MS spectra are calculated based on rules of RNA fragmentation28, resulting in 9–11 series of sequence-defining fragment ions  score for each candidate RNA sequence. Based on Sp values, the resulting oligonucleotide-spectrum matches (OSMs) are sorted and ranked to choose the RNA sequence that fit the spectrum with the highest confidence  that has been adapted from the SEQUEST algorithm, originally developed for peptide spectra identification29  spectra identifications. In the analysis of rRNA, tRNA and mRNA datasets described in the following sections, the uniform Sp cutoffs are applied, independent of the sequence length, to achieve the desired FDR levels.Pytheas search relies on the empirical scoring function  target sequences are similar between Q-TOF, Synapt and Orbitrap datasets. For the 15N- (heavy) targets, the slightly reduced rate of identification is considered acceptable since part of 15N-labeled entries present in the 16S library were merged during m/z-based consolidation  step performed to account for sequences that are hard to resolve via precursor or fragment ions masses.Between three experiments compared, factors like differences in LC columns and mobile phases used, differences in MS instrument speed and resolution, MS/MS acquisition parameters that define the quality of the tandem spectra , and data pre-processing may directly influence sequence identification coverage. Despite that, summary provided in Table E. coli 16S methylations and that can be identified via T1 digestion, five were successfully detected in the Q-TOF dataset below 1% FDR  is applicable for the automated analysis of complex biological samples, containing over a hundred of nucleolytic oligonucleotides; (b) efficiently discriminates target and decoy sequences within 4–12 nt size limit, present in the theoretical digest of a long RNA; (c) can identify isotopically labeled sequences and sequences containing know methylated nucleosides; (d) performs well on the data acquired via different LC-MS workflows without prior optimization of the search parameters.S. cerevisiae 18S rRNA dataset. The 18S rRNA sample was isolated from yeast cells that were metabolically labeled with 5,6-2H-uracil. Consequently, all the uridines have a +2 Da shift, while the pseudouridines have a +1 Da shift due to the exchange of the pyrimidine C5-2H to C5-1H during the pseudouridylation reaction30.The analysis of bacterial rRNA presented did not include pseudouridines, which are isobaric to uridines and their unambiguous assignment requires either metabolic labeling or chemical derivatization. Pseudouridine is the second most common PTxM after methylations and constitute 30–45% of the modifications in rRNA and 15–20% in tRNA. To confirm that Pytheas supports the identification of pseudouridines, we performed a database search on a S. cerevisiae 18S have been identified at least once and successfully mapped to the 18S sequence by Pytheas  is identifiable at 3.6% FDR level or higher. Based on our findings, Pytheas algorithm is truly capable of assigning pseudouridines in samples through support of user-specified schemes for isotope labeling of RNA.Nine of the thirteen pseudouridines present in 32.Using rRNA we validated Pytheas for search of unmodified RNA and sequences containing methylations and pseudouridines, and our next goal was to evaluate its performance for the identification of chemically complex modifications, such as found in tRNA  in the wobble positions of tRNAGlu and tRNALys. A close inspection of the mcm5s2U spectrum showed good fragment ion coverage and presence of consecutive matches over multiple ion series , N4-acetylcytidine (ac4C), and 5-carbamoylmethyluridine (ncm5U)  are associated with poorer Sp scores due to limited cleavage of the RNA backbone or a non-canonical fragmentation pathway33. To efficiently accommodate identification of these modifications alongside with other modified oligonucleotides, more detailed investigation of their fragmentation behavior and further adjustments to the Pytheas scoring algorithm are required.Markedly, except N6-isopentenyladenosine, few of the chemically complex or rare tRNA modifications Table  were ideAltogether, the results highlight the generally good capability of Pytheas to deal with a wide range of PTxMs, including chemically complex nucleosides. Some of the limitations in the acquired data and in the database search analysis are intrinsic to the abundance, ionization, and fragmentation of tRNA sequences with certain modifications, and do not reflect a flaw in the algorithm.1Ψ) substitution. Incorporation of the unnatural m1Ψ modification has been proven to reduce immunogenic properties of the mRNA vaccines and noticeably increase antigen production in mammalian cells12. Furthermore, 14N- and 15N-labeled versions of SP mRNA were prepared and then mixed in nearly equimolar amounts to demonstrate relative quantitation as well as m1Ψ mapping  has been synthesized via in-vitro transcription, with complete uridine to N1-methylpseudoruridine . The remaining IDs were then used to calculate sequence coverage from a single nuclease treatment or by combining results from two parallel digestions  GFP mRNA constructs, synthesized in vitro using either all U or all m1Ψ 5′-triphosphates, suggests that m1Ψ-RNA sequences can be matched and scored equally well as the corresponding unmodified U-containing analogs , or fragments with unique positional placement  or long (12+ nt) sequences, and sequences with labile modifications. In all these cases, the reduced number of matched sequence-defining fragment ions has negative impact on the Sp scores, often placing these sequence identifications outside of the desired FDR ranges. While training Pytheas to recognize spectral features of the labile modifications would require RNA standards and more high-quality MS/MS data, length biases can be to a large degree mitigated by liming the analysis to the narrower size range of RNA sequences, and furthermore by optimizing parameters of the Sp score.Another important limitation that has emerged is related to the Pytheas scoring function. Notably, Pytheas is based on a dimensionless empirical Given broad interest in PTxMs and the fast-growing speed in developing novel RNA therapeutics, we believe that Pytheas has the potential to become a standard tool for sequence analysis via LC-MS/MS. Characteristics like open-access, transparent development approach, the flexibility of the database search algorithm have been fully explored in Pytheas and substantial improvements in its capabilities that will increase the search speed and facilitate the discovery of unknown modifications are forthcoming.Pytheas is a software package that implements a database search approach for automated RNA tandem MS spectra identification. It has been developed in Python 3 and can be executed via a dedicated graphical user interface (GUI) or in command line (CL) on a personal computer.The in-silico digest library is generated from the input RNA sequences (fasta format) that are cleaved according to a chosen endonuclease base specificity. Pytheas GUI supports the commonly used endonucleases  along with non-specific cleavage and no cleavage options. To foresee the upcoming discoveries that might expand the repertoire of RNA cleavage tools in the near future, an option allowing the user customize cleavage specificity has been added to the CL version of Pytheas. Furthermore, in silico digest library can contain RNA sequences with one or multiple chemistries at the 5′ and 3′ termini such as -OH, -cP  and -P (linear phosphate). In addition, chemical modifications or chemical derivatizations on either the base or the sugar moiety can be added to the library, by editing elemental composition of individual residues, specified in a tabulated input. More than 50 different modifications, identifiable by unique lower- and upper-case letters as well as numbers, can be included simultaneously in a single digest file , plus neutral or charged precursor ion losses of water, phosphate, or a base, altogether identified as a set of candidates MS2 ions  and RNA quantification37. Only decoys with unique sequences are retained in the final target-decoy library. Following the SeqX consolidation step, decoys that cannot be distinguished from target sequences due to uncertainty of the m/z measurements are removed.Lastly, for the purpose of downstream statistical analysis, decoy sequences can be added to the theoretical digest. Decoys are produced directly from target sequences by random shuffling the original sequence excluding the 3′ terminal nucleotideSince RNA contains only four basic building units, decoy sampling can become a problem especially for homopolymeric sequences or short targets that are 3–5 nucleotides long. The issue was addressed by limiting the standard statistical analysis to targets with at least one competing decoy sequence as a default option.m/z and charge values for each acquired precursor ion. Subsequently, the matching between experimental and in silico tandem spectra is performed . MS2 matches found are then used to compute the Spytheas (Sp) score for each oligonucleotide-spectrum match (OSM).Pytheas uses mgf (Mascot Generic Format) files as a standard MS/MS data input. The input file is parsed to retrieve a list of med Fig. , first bn is the number of successfully matched sequence-defining MS2 ions  and precursor ion losses  were added to the theoretical library, targets scores were well separated from the respective decoy scores  evaluation of the overall quality of the database matching; (2) calculation of the accuracy and precision of the mass measurements; (3) control of FDR.Sp (and ΔSp/ΔSp2) score distribution and scatter plots for target and decoy sequences reflect how well RNAs present in the digest library can describe the data, based on the Pytheas scoring algorithm. These automatically generated plots help to highlight adjustments to be made on either the data acquisition process, RNA input, and CID fragmentation, or the parameters of the search. Furthermore, a histogram plot of the m/z matching offsets (in ppm) allows the rapid assessment of the systematic biases in mass measurement for a given dataset, and the instrument drifts can be corrected for via MS1 and MS2 global offset options. FDR is calculated following the concatenated target-decoy search using “simple FDR” defined and broadly used in the protein MS analysis38 and the obtained Sp score cutoffs are extrapolated to maintain the desired FDR value. This enables control of the number of false-positive identifications associated with random matching.The To build a collection of high-quality MS/MS spectra that could be unambiguously assigned to their RNA sequences, mixtures of synthetic RNA oligonucleotides  and in vitro transcribed 16S RNA constructs p1 (202 nt), p2 (250 nt) and p4 (173 nt) were used (Source Data file contains a list of RNAs used). 40–300 pmol of each RNA was digested with either T1 or A nuclease for 1 h at 55 °C in 25 mM ammonium acetate (pH = 6). LC-MS/MS data were collected using an Agilent Q-TOF 6520 LC-MS platform and precursor MS2 scans were averaged across the chromatographic peak to reduce noise. Finally, 95 individual spectra representing 95 distinct sequences 2SO4 or (15NH4)2SO4 as a sole source of nitrogen. 16S RNA was purified using sucrose gradient ultracentrifugation at dissociating conditions30. 14N-RNA and 15N-RNA were mixed in approximately 1:1 molar ratio, heat-denatured, and RNase T1 digested for 1 h at 55 °C in 25 mM ammonium acetate (pH = 6). To test the performance of Pytheas on data collected via different MS platforms, 16S RNA samples were analyzed using Agilent Q-TOF, Waters Synapt G2-S, and Thermo Scientific Orbitrap Fusion Lumos. All systems were equipped with ESI sources and data acquired via negative ionization. Depending on the instrument used, 5–25 pmol of each 14N- and 15N-labeled rRNA were injected and analyzed.S. cerevisiae BY4741 (his3Δ met15Δ ura3Δ leu2Δ) strain has been transformed with two stable centromeric plasmids, pRS413 which carries the HIS3 marker, and with pRS415 which carries the LEU2 marker and into which MET15 has been inserted (Wittenberg Lab at Scripps Research Institute). The resulting BY4741 pRS413, pRS415-MET15 cells were grown at 30 °C in the YNB 2% glucose (w/v) media, in the presence of 20 mg/L of 5,6-2H-uracil (Cambridge Isotope Laboratories) and 5 g/L of either (14NH4)2SO4 or (15NH4)2SO4. Cells were harvested at ~0.8 OD600 by adding the culture to ice, followed by centrifugation at 3000 × g for 10 min. Pellets formed were re-suspended in ~1 mL of the lysis buffer  and cells disrupted via BioSpec mini-beadbeater. 2 U/ml DNase I (NEB) and 0.5 mM CaCl2 was added, and the lysate was cleared from cell debris by two rounds (5 min and 45 min) of centrifugation at 14000 g. Then, layered on top of the dissociating 10–40% (w/v) sucrose gradient, that contained 50 mM Tris–HCl at pH 7.5, 50 mM NH4Cl, and 6 mM 2-mercaptoethanol. Each gradient was centrifuged at 98200 g for 16 h using Beckman SW-32 Ti rotor. Gradient fractionation with A254 trace detection was then used to collect and pull together small and large ribosomal subunit fractions. Combined fractions were TRIzol (Invitrogen) extracted, and isopropanol precipitated to obtain the 18S pellet free of proteins. Pellets were redissolved in Nuclease-Free water (Ambion) and additionally purified via three rounds of spin filtration using Amicon Ultra-0.5 mL with 30 K cutoff. 14N-RNA and 15N-RNA were mixed in approximately 1:1 molar ratio, heat-denatured, and RNase T1 digested. LC-MS/MS data were collected using Agilent Q-TOF.The parental S. cerevisiae, Roche part #10109495001) tRNA were purchased from Roche and used without further purification. 75 µg were digested with ~100 units of RNase T1 for 1 h at 55 °C. LC-MS/MS data were collected using Agilent Q-TOF.Mixture of baker’s yeast . The transcription reaction was incubated at 37 °C for 90 min, followed by a 30-min DNase I treatment to remove the template. RNA was precipitated with an equal volume of 5 M NH4OAc on ice, and the obtained pellet was washed twice with 70% ethanol, dried and resuspended in water. The product was characterized via agarose gel electrophoresis demonstrating a single RNA band of high intensity  encoding the SARS-CoV-2 spike protein (SP) was procured by cloning the full-length protein sequence (GenScript) into the parental cloning vector pcDNA3.4 (NovoPro). A synthetic mRNA that mimics COVID-19 mRNA vaccine was prepared using in vitro transcription, after the linear DNA template containing the entire SP coding region was amplified from the plasmid. 20 µL volume T7 polymerase  in vitro transcription reaction contained 1.8 μg of the linear DNA template, 9 mM of each NTP, and the UTP was replaced with an equal amount of N1-methylpseudouridine-5′-triphosphate (TriLink BioTechnologies). Isotopically labeled SARS-CoV-2 mRNA used as a reference for quantitative analysis was obtained by substituting GTP with m/z) followed by 4–6 successive 1 s MS2 scans (100–1700 m/z). Most intense precursor ions were used for isolation (4 m/z isolation window) and CID fragmentation. Fragmentation collision energies were optimized by direct infusion of 23 RNA oligonucleotide standards , RNA MS data were acquired on an Agilent Q-TOF 6520 ESI instrument coupled to the Agilent 1200 LC system. Mixtures of nucleolytic RNA fragments were resolved on XBridge C18 column  via a 40 min 1–15% of mobile phase B linear gradient elution with 15 mM ammonium acetate (pH = 8.8) as mobile phase A and 15 mM ammonium acetate (pH = 8.8) in 50% acetonitrile as mobile phase B. Data were collected using MassHunter LC/MS Acquisition B.06.01 software in the data-dependent acquisition mode using negative ionization. A typical duty cycle consisted of a single 0.33 s MS1 scan (400–1700 m/z: 545–2000) scans were recorded over 0.5 s, and MS2 (m/z: 250–2000) over 1 s period. The top three MS1 events were selected for MS2, based on abundance. Precursor m/z dependent collision energy ramp  was applied, and the accumulated TIC threshold was set to 3.5 × 105 counts. Dynamic exclusion was switched on to exclude precursors for the 60 s period. Raw data were processed via ProteinLynx Global Server V2.5.2 using operator-specific settings for lock-spray correction, noise reduction, peak picking and centroiding. The resulting mzML file was converted to mgf format using MSConvert from ProteoWizard 3.0.11537.The Waters Synapt G2-S system was coupled to a Dionex Ultimate 3000 RSLNano UHPLC (Thermo Scientific) using an Acquity UPLC BEH C18 column , operated at 5 µl/min flow rate and column temperature maintained at 60 °C. Mobile phase A , and mobile phase B (8 mM TEA and 200 mM HFIP in 50% methanol) were used to elute RNA via a linear gradient (3–55% of B over 70 min). Synapt G2-S data were collected by MassLynx V4.1 software in the sensitivity mode (V-mode), and MS1  was 2 × 105 counts, with a maximum injection time of 100 ms. For MS2 scans, precursor ion intensity threshold was set to 1 × 105 counts, and a 1.6 m/z isolation window was used. The top 12 precursor targets were selected for CID fragmentation at 35% of the normalized collision energies, using precursor dynamic exclusion over a 20 s period. The MS2 resolution was 30,000; AGC set to 1.0 × 105 counts with the maximum injection time of 300 ms. Xcalibur V4.3.73.11 acquisition software was used to record the data, afterwards converted to mgf via MSConvert from ProteoWizard 3.0.11537.The Thermo Scientific Orbitrap Fusion Lumos instrument was coupled to a Vanquish Flex quaternary UHPLC and the HILICpak VN-50 column  operated at 220 µl/min flow rate. Mobile phase A contained 12.5 mM ammonium acetate in 75% of acetonitrile in water, and mobile phase B contained 35 mM ammonium acetate in 30% of acetonitrile in water. Nucleolytic RNA fragments were eluted off the column kept at 50 °C using a linear gradient: 30–56% of B over 30 min. The following MS1 scan settings were used: 220–2000 The theoretical digest library was obtained by using known RNA sequences and nucleoside modification positions  that are C/U positional substitutes were consolidated via SeqX using 16 ppm (MS1) and 40 ppm (MS2) thresholds, corresponding to ~2σ of m/z matching offset distribution for Agilent Q-TOF  and C/U  positional substitutes using 15 ppm (MS1) and 33 ppm (MS2) mass accuracy thresholds. The search against the 18S theoretical digest was performed by setting the parameters of the scoring function β = 0.025 and α = 2, and by enabling precursor ion matching to the m/z of M + 1 and M − 1 isotopologues.18S yeast target-decoy theoretical digest library was generated from the SGD: RDN18-1 sequence, with T1 digestion and up to two missed cleavages. All the modifications previously mapped by Taoka et al.S. cerevisiae S288c that have been retrieved from the GtRNAdb42, identical entries were filtered out and the remaining 55 sequences aligned. T1 digestion, allowing for up to two missed cleavages was used. Except for pseudouridine, 19 different types of tRNA modifications  were applied due to unexpected drifts in Q-TOF mass measurements.mRNA data were analyzed using the target-decoy theoretical digest library prepared from the 3995 nt long sequence (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Mage = 17.59 yrs., SD = 3.67), and 22 amateur  team handball players performed a sport-specific embodied-choice test. Attack sequences (n = 32) were shown to the players, who had to choose between four provided options by giving a respective sport-specific motor response. We analyzed the frequencies of specific choices and the best choice, as well as the respective decision time and decision confidence. Elite and amateur players differed in the frequencies of specific choices , and elite players made the best choice more often. Slower decision times of elite players were revealed in specific choices and in best choices, the confidence of decisions was rated equally high by both player groups. Indications are provided that elite players make better choices rather slower, instead of faster. We suppose this is due to specific sensorimotor interactions and speed-accuracy-tradeoffs in favor of accuracy in elite players. Our findings extend expert decision-making research by using an embodied-choice paradigm, highlighting considerations of decision time and confidence in future experiments.Expert athletes are determined to make faster and better decisions, as revealed in several simple heuristic studies using verbal reports or micro-movement responses. However, heuristic decision-making experiments that require motor responses, also being considered as the embodied-choice experiments, are still underrepresented. Furthermore, it is less understood how decision time and confidence depend on the type of embodied choices players make. To scrutinize the decision-making processes , this study investigated the embodied choices of male athletes with different expertise in a close-to-real-life environment; 22 elite ( In particular, these highly complex situations challenge an athlete to make fast and accurate decisions and to confidently rely on their choices even if alternative options evolve quickly through the dynamic changes.Extensive research in cognitive psychology in sports attributes superiority in decision-making to expert athletes  what choices are made through capturing a sport-specific motor response, and (2) how decision time and confidence differ based on the choices made.In particular, we investigated expertise effects in decision-making with a sports-specific embodied-choice paradigm  with global factors , who came from four different teams with different performance levels. A total of twenty-two players  were part of a professional youth academy of a First League team handball club competing in the highest possible league within their age category. Based on the definition of Swann et al.  were part of local team handball clubs, competing in non-professional, regional leagues. These players are considered amateur players .The total sample consisted of 44 male team handball players  that were prespecified to the contact plates of the SpeedCourt® system were adapted from the previous decision-making test  software. In total, the final test scenario consisted of thirty-two video clips of attack sequences of 2 s each, which were presented to the participants during the measurement procedures.The videos were sized 1,280 × 720 pixels (width × height). The test scenario was implemented by using the The decision-making test was checked for cross-sectional and longitudinal reliability dimensions  as former players and coaches. The rating procedure with the final setup took place before the main experiments. In individual sessions, all four right-hander attack sequences were presented to the experts on the projection screen, and the experts were required to execute a sport-specific motor response on the attacks with one of the four prespecified defense responses. Then, the experts were asked to evaluate the given options for each scene, based on a six-point Likert-type scale . In detail, they were asked about the judgment of the best tactical choice and competition-similarity of the shown attacks.To assess the quality of decisions, we recruited four international team handball experts for a rating of the best tactical choice to finally determine the best choice, a “majority rule” procedure according to Johnson and Raab . The raters' selection demonstrated also satisfying judgments for the perceived competition-similarity . The best choice was coded in the database with the best choice selected (1 = yes) or not (0 = no).To determine the and Raab  was applBefore the start of the single test sessions, written informed consent of the parents of the underage participants was obtained. After an autonomous, game-specific warm-up (about 5 min), explanations about the experimental setting and test procedure were provided by the test staff. Starting with standardized oral instructions, the players were instructed to put themselves in the position of the central block defender in a classic man-to-man defense without teammates, or other opponents than the attacker in the video/situation. Then, four familiarization videos were provided to get used to the test environment. In the main test session, the participants were challenged to execute a direct defense-specific motor response to the attack sequences in the video clips as if they would defend the attacker in a real game situation. Following their first intuition, they were allowed to initialize their motor response whenever required. When a participant left the starting contact plate , the projection screen turned black for the avoidance of a response bias. After each motor response, players had to give a verbal statement about their self-perceived decision confidence regarding the appropriateness of their made response choice with a six-point Likert-type scale . One test session took about 15 min in total.N = 44, we received 352 data points for each variable. Potential outliers were identified based on the absolute deviation around the sample median , measured as the elapsed time from the start of a video trial till leaving the starting contact plate and decision confidence . In preparation for the analysis, all data points from the eight video trials of the decision-making test were extracted. Based on the total sample size of specific choices made [1a], and in the frequencies of making the best choice [1b]. To aid the interpretation of chi-square test results, we calculated the effect size of Cramer's V  between elite and amateur players as well as single between-group differences in each of the specific choices. Cramer's V values for the four specific choices (df = 3) can be interpreted as laid down by Kim  can be interpreted with 0.10 < |ϕ| <0.30 “small”, 0.30 < |ϕ| <0.50 “medium”, and |ϕ| > 0.50 “large” effect  x 4  and a 2 (expertise groupbetween: elite vs. amateur) × 2 (best choicewithin: yes vs. no) with the dependent variables decision time and decision confidence. If significant multivariate main and interaction effects were obtained, those were followed up with subsequent univariate analyses to check which of the obtained dependent variables were affected. The assumption checks revealed that the Box's M-test for homogeneity of covariances was not significant for the specific choice MANOVA, but significant in the best choice MANOVA (p = 0.009). Shapiro-Wilk's tests for multivariate normality were violated (both ps < 0.001), however, we conducted both MANOVAs in accordance with the guidelines of Finch , and sideways left least frequently (5.6%). Amateur players used passive/blocking most frequently (62.9%) and sideways right least frequently (7.1%).specific choices, χ2(3) = 21.501, p < 0.001, Cramer's V = 0.26. In particular, single between-group differences in forward/tackling [p = 0.004] and passive/blocking [p < 0.001] were also revealed, while other choice frequencies  did not differ significantly between expertise groups.Testing for expertise effects in specific choice (Hypothesis 1a), significant chi-square test results showed that elite and amateur players indeed differed in how often they made specific choices made  did not show the main effect of expertise  but revealed a significant main effects of specific choice  and a significant expertise × specific choice interaction .The MANOVA analysis of whether the decision-making processes of elite and amateur players differed based on their specific choice and the expertise × specific choice interaction, subsequent univariate analyses for decision time showed that specific choice  and the sideways left choice . All other specific choice comparisons were not significant. Following up the significant expertise x specific choice interaction revealed only that elite players were significantly slower than amateur players in their forward/tackling choice  while there were no other significant effects of expertise.Following up the significant multivariate main effect for decision confidence revealed only a significant expertise x specific choice interaction . Elite players were less confident in their passive/blocking choice than amateur players . No significance was revealed in the all other specific choice comparisons  in the sport-specific embodied-choice paradigm, we checked the frequency of the best options chosen in both expertise groups (Hypothesis 1b). The respective chi-square tests revealed that indeed elite players  made the best choice more often than amateur players , p < 0.001, Cramer's V = 0.25.For the analyses of the best choices made  did not show a main effect of expertise , but revealed significant main effects of best choice  and the expertise × best choice interaction .The MANOVA analysis of whether the decision-making processes of elite and amateur players differed based on the decision time showed significance in best choice , and elite players showed slower decision times than amateurs when they selected the best choice . No between-group difference was revealed in the non-selected best choice .Following up the significant multivariate main effect and interaction, the subsequent univariate analyses for decision confidence revealed no significant affections of expertise  .specific and best choices players made, and we also scrutinized the decision-making processes  underlying the elites' and amateurs' choices. Against previous assumptions in simple heuristic and decision-making literature . In line with our findings, a previous team handball study did also find higher correctness of responses by expert players  choice lend support to the general tenets of embodied choices  and how (decision time) to act  and best choice of elite players, which stands in contrast to our assumptions of faster and more accurate decisions of better athletes from simple heuristics and decision-making research , however, with the ongoing course of the event, the final choice depends more on additional cues perceived in the unfolding action of the opponent (bottom-up). We assume that the players' embodied choices underly level-dependent corrective interactions during the decision-making processes, leading to slower, but therefore, higher quality decisions of elite players. Corrective interactions, as a dynamic function, may also rely on previous successful experiences , such as simple heuristics.For theory, our results imply that decision-making research should henceforth consider not only In particular, regarding decision time in our study revealed that elite players made better but slower embodied choices. In the sense of a speed-accuracy tradeoff, they seemed to prioritize the quality. This finding has important applied implications: If speed accuracy plays a role in sport-specific embodied choices, players and coaches should focus more on the accuracy of a choice in defense decisions.For the applied field, we recommend that close-to “real-world” experimental setups such as the sport-specific embodied-choice test presented here could be applied as a tactical training tool in team handball and other team sports. Similarly, a row of longitudinal 3D-video-based interventions to train team handball tactics were conducted in the past . The funders had no role in the study design, data collection and analysis, and decision to publish or preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


RNAs not only offer valuable information regarding our bodies but also regulate cellular functions, allowing for their specific manipulations to be extensively explored for many different biological and clinical applications. In particular, rather than temporary hybridization, permanent labeling is often required to introduce functional tags to target RNAs; however, direct RNA labeling has been revealed to be challenging, as native RNAs possess unmodifiable chemical moieties or indefinable dummy sequences at the ends of their strands. In this work, we demonstrate the combinatorial use of RNA-compatible restriction endonucleases (REs) and RNA-extending polymerases for sequence-specific RNA cleavage and subsequent RNA functionalization. Upon the introduction of complementary DNAs to target RNAs, Type II REs, such as AvrII and AvaII, could precisely cut the recognition site in the RNA-DNA heteroduplexes with exceptionally high efficiency. Subsequently, the 3′ ends of the cleaved RNAs were selectively and effectively modified when Therminator DNA polymerase template-dependently extended the RNA primers with a variety of modified nucleotides. Based on this two-step RNA labeling, only the target RNA could be chemically labeled with the desired moieties, such as bioconjugation tags or fluorophores, even in a mixture of various RNAs, demonstrating the potential for efficient and direct RNA modifications. Due to the large variety of sequences, ribonucleic acids (RNAs) play diverse biological roles in all cell-based lives and even RNA viruses . Even thDespite the simplicity and versatility, relying only on hybridization with target RNAs has several drawbacks. For instance, when complex and long RNAs are hybridization targets, short complementary oligos often suffer from off-targeting due to the frequent occurrence of mismatches or wobble pairs . MoreoveAlthough the direct labeling of desired RNAs can be considered an attractive alternative, it is considered challenging as native RNAs have unmodifiable chemical moieties or indefinable dummy sequences at the ends of the strands ,17,18,19To selectively label RNAs, irrespective of modifications and sequence variations at both ends, fully sequence-defined mid-domains of RNAs with no complex folding structures are attractive targets for manipulation. In this work, we explore this possibility demonstrating a novel RNA modification method that combines RNA-DNA heteroduplex-mediated site-specific cleavage of Type II restriction endonucleases (REs) and modified nucleotide-assisted terminal modification of RNA-extending DNA polymerases .There are four types of REs that recognize and cleave specific restriction sites. These sites typically reside in DNA-DNA homoduplexes, but hundreds of certain Type II REs, including AvaII and AvrII, have been revealed to sequence-specifically hydrolyze RNA-DNA heteroduplexes as well . Due to Unless otherwise stated, all reagents used were purchased from Sigma-Aldrich  and Thermo Fisher Scientific . All the DNAs were synthesized by Integrated DNA Technologies, Inc. , and the sequence information is attached to the 2 (75 mM), Dithiothreitol (40 mM), and T7 RNA polymerase buffer  containing 200 mM Tris-HCl, 30 mM MgCl2, 10 mM spermidine, and then DNase I treatment was conducted by using 5 units of DNase I for 15 min at room temperature. The transcribed RNA was electrophoresed on 10% denaturing polyacrylamide gel. Next, as visualized by UV shadowing, the transcript-including gel was cut out with a blade. To recover the transcripts, the collected gel was crushed in a clean tube, and the transcripts were eluted for 12 h in nuclease-free water. The crushed gel was centrifuged for 5 min at 10,000 g, and then the supernatant was collected in a tube, followed by ethanol precipitation. The purity and quantity of transcribed RNAs were measured by a spectrophotometer .To generate RNAs, in vitro transcription reactions were performed with 1 μg of linearized DNAs for 12 h at 37 °C. The reaction volume was 100 μL containing 100 units of T7 RNA polymerase , ATP (10 mM), GTP (10 mM), CTP (10 mM), UTP (10 mM), MgClFor the cleavage reaction by AvrII, a mixture containing RNA (10 pmol), DNA (20 pmol), 10× rCutSmart buffer (1 μL) and 25 units of AvrII with a total volume of 10 μL was incubated at 37 °C for 3 h, and heated at 80 °C for 20 min. For the cleavage reaction using AvaII, water was added to the solution containing RNA (10 pmol), DNA (20 pmol), 10× rCutSmart buffer (2 μL), and 100 units of AvaII up to 20 μL. The reaction was taken at 37 °C for 3 h, and, for the inactivation, the mixture was heated at 80 °C for 20 min. Before running gel electrophoresis, the sample purification was performed using ethanol precipitation by adding 3 M acetate buffer  cold ethanol (66 μL) and glycogen . After placing in a freezer at −80 °C for 40 min, the nucleic acid precipitate was collected by centrifugation for 30 min .Modified nucleotide insertion using different polymerases was conducted in a 20 μL solution with 6-carboxyfluorescein (FAM)-labeled RNA (20 pmol), DNA template (40 pmol), modified nucleoside triphosphate (2 nmol), 2 μL of 10× buffer provided with each enzyme , and 2/2.5/2.5/5 units of Therminator polymerase/Bsu polymerase/Klenow fragment/AMV reverse transcriptase, respectively. The mixture with Thermiantor polymerase was incubated for 2 h at 55 °C, while those using other enzymes were incubated at 37 °C. The sequence information used in this experiment is provided in the Denaturing urea polyacrylamide gel electrophoresis was used for the evaluation of oligonucleotides. To prepare 10% polyacrylamide/7.5 M urea gel, UreaGel diluent  was added to T&I UreaGel 29:1 concentrate , UreaGel buffer , and tetramethylethylenediamine to make a total volume of 30 mL. Then, freshly prepared ammonium persulfate  was added, and the mixture was gently shaken before pouring onto the cassette of 25 × 21 cm (W × H). After the samples were loaded on the gel, the gel was run at 30 Watts for 80 min using PowerPac High-Voltage Power Supply .When a long, transcribed RNA was partially hybridized with a short synthetic cDNA, Type II restriction endonucleases successfully recognized the desired restriction cleavage sites in the RNA-DNA hybrid duplex and subsequently cleaved both DNA and RNA with exceptionally high efficiency A. For va877-956, 80 nt, lane 1 in 926-1005, 80 nt, lane 1 in Depending on the RE, different sites of long RNAs could be cleaved selectively and efficiently B,C. As 12-modified dGTPs with cDNAs with 5′-GAC-3′ overhangs (2 (lane2). Comparably, Bsu polymerase and the Klenow fragment showed poor activities in RNA labeling with an extension rate of 37% (lane 4) and 88.6% (lane 6), respectively. No extension was found when AMV RT was used on the DNA template (lane 8), but it is worth noting that more than half of the RNA fragments were elongated on an RNA template , one modified G would be added for selective and quantitative labeling of the target RNAs A. To thiverhangs B. Thermiverhangs B, lane 1template .4). The replacement of C with T in the DNA overhang caused some degree of G insertion by potential wobble base pairs (TA4), while overhangs with G and A induced no elongation of RNAs by Therminator at all . Even though the yield of nucleotide addition close to the 5′ end of DNA template is reduced, it was confirmed that only one G was inserted with high fidelity and efficiency on either 5′-C-3′ or 5′-AC-3′ overhangs . No RNA elongation was found upon the introduction of scrambled DNA templates (Scrambled), once again demonstrating the importance of correctly hybridized RNA-DNA hybrid sticky ends in RNA labeling. We note that when the same experiments were conducted with the Klenow fragment, the rate of RNA elongation was much lower than that with Therminator, and the nucleotide incorporation was highly influenced by the presence of the adjacent bases . This observation clearly demonstrates the potential of our methodology in multiplexed and biorthogonal RNA functionalization wit hthe use of well-designed multiple cDNAs with varied overhangs.Based on the sequence specificity of cDNA hybridization and RNA cleavage, we further investigated the feasibility of RNA targeting in labeling using a mixture of various RNAs and a target-specific cDNA A. In a sFinally, we examined the possibility of a one-pot procedure in which the RNA cleavage by restriction site-specific RE and RNA functionalization by DNA template-dependent polymerase steps are performed without purification in between B. When tIn this study, we investigated whether simple direct modifications on complex RNAs are feasible, and experimentally validated that the successive application of RNA-compatible REs and RNA-extendable DNA polymerases allows for sequence-specific RNA functionalization with assistance from rationally designed cDNAs. Our RNA labeling technique is readily applicable for native RNAs that often possess unclear terminal domains and unmodifiable capping moieties. Due to the cDNA-guided site-specific cleavage of mid-domain RNAs, the target RNAs expose well-defined 3′ ends that permit direct chemical modifications with high efficiency. Among the polymerases that can catalyze template-directed DNA synthesis using RNA primers, we found that Therminator DNA polymerase has the highest potency for the insertion of various functional nucleotides. We also demonstrated that the Therminator-guided terminal labeling takes place selectively for the desired RNA when other RNAs are present. Moreover, by changing the cDNA through strand displacement, we verified that several functional monomers can be added at the 3′ end of the cleaved RNA, revealing the potential of signal amplification. For the direct labeling of target RNAs, the two consecutive steps of RNA cleavage and enzymatic modification can be simply performed in one pot under mild conditions, but there are still several limitations. For instance, the target RNAs must contain the RE recognition site and the triphosphate form of monomers with desired functional groups must be present. However, more than 3000 REs with varying recognition sites are currently available and it is anticipated that the catalogue of RNA-compatible REs could be further expanded via the engineering of RE variants. Moreover, nucleoside triphosphates with a variety of functional groups, such as fluorescent dyes, quenchers, attachment moieties, and modifiable linkers, have already been developed , and the


Actinobacteria. While Ms1 RNA was identified in Mycobacterium, it is not clear whether Ms1 RNA is present also in other Actinobacteria species. Here, using a computational search based on secondary structure similarities combined with a linguistic gene synteny approach, we identified Ms1 RNA in Streptomyces. In S. coelicolor, Ms1 RNA overlaps with the previously annotated scr3559 sRNA with an unknown function. We experimentally confirmed that Ms1 RNA/scr3559 associates with the RNAP core without the primary sigma factor HrdB in vivo. Subsequently, we applied the computational approach to other Actinobacteria and identified Ms1 RNA candidates in 824 Actinobacteria species, revealing Ms1 RNA as a widespread class of RNAP binding sRNAs, and demonstrating the ability of our multifactorial computational approach to identify weakly conserved sRNAs in evolutionarily distant genomes.Bacteria employ small non-coding RNAs (sRNAs) to regulate gene expression. Ms1 is an sRNA that binds to the RNA polymerase (RNAP) core and affects the intracellular level of this essential enzyme. Ms1 is structurally related to 6S RNA that binds to a different form of RNAP, the holoenzyme bearing the primary sigma factor. 6S RNAs are widespread in the bacterial kingdom except for the industrially and medicinally important  All bacteria contain this housekeeping or primary σ factor, termed σ70 in Escherichia coli, σA in Bacillus subtilis and in Mycobacterium smegmatis  are important regulators of gene expression in bacteria. A majority of sRNAs act by base-pairing to target mRNAs and change mRNA stability or translation but a minor group of sRNAs directly regulates proteins through RNA-protein interaction . The besmegmatis , or HrdBptomyces . In addinitalium  to almoselicolor .E. coli (70 holoenzyme (Eσ70) (6S RNA was first described in E. coli  where ite (Eσ70)  and regue (Eσ70) . 6S RNA e (Eσ70) . Transcre (Eσ70) .70 , industrially important producers of amino acids (Corynebacterium glutamicum) and antibiotics (Streptomyces), bacteria involved in symbiotic nitrogen fixation (Frankia), bacteria utilized for bioremediation (Rhodococcus), probiotic bacteria (Bifidobacterium), and numerous other genera .The intracellular levels of 6S RNAs are high, similar to those of essential non-coding RNAs, such as rRNAs, tRNAs, RNAse P, tmRNA or SRP RNA. Furthermore, 6S RNAs have been found in many bacterial species and are widespread in the bacterial kingdom  with oneActinobacteria, 6S RNAs had been undetected for a long time. In Mycobacterium smegmatis, we identified a putative 6S RNA candidate by the computational suboptimal secondary structure approach    and their secondary structures differ as well—in addition to the central bubble, Ms1 has two additional short hairpins at its 3′ and 5′ ends . An Ms1 23 sRNA) . Other Mocardia) . CurrentStreptomyces coelicolor, a putative 6S RNA (the gene was named ssrS) was identified  and thus it is unclear if the putative 6S RNA sequence is expressed in vivo.In entified , but thed 6S RNA . In addid 6S RNA . Moreove RNA-seq  which isS. coelicolor, which was named “6S-like scr3559 RNA.” It is expressed from the sco3558-sco3559 genomic locus but from the minus strand (the previously published 6S RNA sequence was expressed from the plus strand), with its transcription start site located at position 3,934,888 in the genome  of RNA genes to identify Ms1 candidate genes. First, we applied this approach to Streptomyces coelicolor. Our search identified a Ms1 candidate—the scr3559 sRNA. We validated this result experimentally, demonstrating that scr3559 RNA binds the RNAP core without the primary σ factor HrdB in vivo and, thus, is an Ms1 but not a 6S RNA homolog. Subsequently, we applied our linguistic search to the phylum of Actinobacteria, revealing that Ms1 is present in most orders of Actinobacteria. Ms1 is thus a new type of regulatory RNA associated with RNA polymerase, in addition to 6S RNA, B2 RNA (regulates RNA polymerase II in humans) (To elucidate whether Ms1 RNA is conserved in the phylogenetic group of  humans) , or svRN humans) .Streptomyces well annotated genomes were used (GenBank assembly IDs shown in parenthesis following organism name): S. coelicolor A3(2) (GCA_000203835.1_ASM20383v1), S. ambofaciens ATCC 23877 (GCA_001267885.1_ASM126788v1), S. nodosus (GCA_000819545.1_ASM81954 v1), S. reticuli (GCA_001511815.1_TUE45), S. avermitilis MA 4680 (GCA_000009765.2_ASM976v2), S. griseus NBRC (GCA_000010605.1_ASM1060v1), S. scabies 87 22 (GCA_000091305.1_ ASM9130v1), S. cattleya NRRL8057 (GCA_000237305.1_ASM 23730v1), S. lincolnensis .For the search, 10 selected The search was performed in the following steps:(i)Streptomyces genomes, according to GenBank genomic annotations. IGRs in both DNA strands were searched, hence, an IGR in one strand may overlap with an open reading frame encoded by the other strand.Intergenic regions (IGRs) were identified in the 10 selected (ii)The IGR sequences were sampled with approximate nucleotide lengths of 180 and 300 nucleotides that are specific for 6S and Ms1 RNAs, respectively. The sampling was done in a sliding window, moving sequence window by 5 nucleotides a step through IGR sequences.(iii)P = 1000, W = 1 and X = 10.10 suboptimal secondary structures were predicted for each sampled sequence by UNAfold  with par(iv)B. subtilis 6S RNA The average of three best scores for each sampled sequence was compared to the structural similarity thresholds of 80 and 135 for 6S RNA and Ms1 RNA, respectively. The thresholds were chosen such that we were able to identify known 6S and Ms1 RNAs by the search. To be considered as a candidate for one of the searched for sRNAs, the candidate sequence had to have the averaged similarity score of three most similar suboptimal structures better than the thresholds.–20, and (2) conserved genomic position in the ten searched Streptomyces genomes.To increase reliability of the search, the candidates had to fulfill the following additional criteria derived from genomic properties of known 6S and Ms1 RNAs that were: (1) existence of similar sequences in evolutionarily close species identified by BLAST with BLAST E-values < 10Streptomyces species listed in the previous paragraph. As sRNAs in general are conserved in evolutionarily related species, here in Streptomyces species, the constructed sequences had to be identified by BLAST in multiple Streptomyces species to ensure that they were constructed correctly. Using the superposition of constructed sequences according to their conservation identified by BLAST, we reconstructed probable sRNA sequences in Streptomyces coelicolor. The probable sequences are shown in Streptomyces sRNAs.The full-length sequences were predicted using the approximate length of the transcripts detected by Northern blotting and the genomic positions of oligonucleotide sequences used for sRNA verification by Northern blotting . The seqThe search was based on text phrases in genomic annotations of the genes flanking potential Ms1 RNA genes and sequence similarity search based on BLAST for verification. The search was possible as sRNAs including Ms1 RNA are known to be conserved in evolutionarily close bacteria . The sea(i)Synteny analysis. Specific words in annotations of 5′ and 3′ end flanking genes of known and newly discovered IGR containing Ms1 RNAs are identified. Among them, most repeating specific words are identified. This step is explained in detail in the following section.(ii)Synteny phrases. Text phrases, comprising of at least one most repeating specific word, that are specific for genomic annotation of genes flanking Ms1 RNAs are generated. Synteny phrases used in this work are shown in (iii)Actinobacteria genera which had >4 annotated genomes and in which Ms1 RNA has not yet been discovered. The search is implemented as a sequence of grep LINUX searches, followed by additional text processing implemented in MATLAB computational environment.Text search for Ms1 synteny phrases in genomic annotations of (iv)Synteny hit. An IGR with Ms1 synteny containing a putative Ms1 RNA gene.(v)Evolutionary conservation of the synteny hit. The hit was considered as evolutionarily conserved when there existed IGRs with similar sequence and Ms1 synteny for at least one of the flanking genes.The IGRs with similar sequences were identified by BLAST of the sequence of the synteny hit in NCBI’s nt database with sensitive setting for cross-species exploration Flanking genes annotations of both synteny hit and IGRs homologous to it in new, related species. They were analyzed to get their specific words [step (ii)] and to update old synteny phrases [step (iii)].Annotations of flanking genes were first split into single words and non-specific words were removed. The non-specific words were : ‘hypothetical’, ‘protein’, ‘type’, ‘family’, ‘domain’, ‘putative’, ‘precursor’, ‘component’, ‘subunit’, ‘subfamily’, ‘conserved’, ‘chain’, ‘or’, ‘superfamily’, ‘unknown’, ‘function’, ‘of’, ‘like’, ‘containing’, ‘II’, ‘and’, ‘short’, ‘dependent’, ‘probable’, ‘associated’, ‘to’, ‘that’, ‘the’, ‘predicted’, ‘uncharacterized’, ‘production’, ‘proteases’, ‘fold’, ‘in’, ‘by’, ‘universal’, ‘pathway’, ‘involved’, ‘related’, ‘general’, ‘group’, ‘sequence’, ‘class’, ‘cluster’, ‘accepting’, and ‘determining’.We also removed the so-called questionable non-specific words whose (non)specificity depended on the analyzed context. They were: ‘box’, ‘enzyme’, ‘factor’, ‘secreted’, ‘release’, ‘neighborhood’, ‘solute’, ‘accessory’, ‘peptide’, ‘biosynthesis’, and ‘substrate’.Specific words were left. They characterized specific aspects of gene function. An example is the ‘Fic/Doc family protein’ annotation contained two non-specific words, ‘family’ and ‘protein’, and one specific word ‘Fic/Doc’.M. smegmatis Ms1 RNA identified by sequence similarity by BLAST in 498 species of 8 genera—Mycobacterium, Mycolicibacterium, Rhodococcus, Nocardia, Gordonia, Mycobacteroides, Hoyosella, and Tsukamurella  were grown at 37°C in Middlebrook 7H9 medium with 0.2% glycerol and 0.05% Tween 80, and harvested in exponential (OD600 ∼0.5) or early stationary phase . S. coelicolor A3(2) spore stock expressing HA-tagged HrdB was thawed and inoculated to 2YT medium. Germination was carried out at 30°C for 5 h as described previously , inoculated into Na-glutamate medium supplemented with trace element solution and TMS1 as described previously  at different time points after germination. Rhodococcus erythropolis CCM259 (LK1556) was cultivated at 26°C in 2x TY medium to exponential phase (OD600 ∼2) or stationary phase , Corynebacterium glutamicum ATCC 13032 (LK1557) was grown in 2xTY at 30°C and harvested in exponential (OD600 ∼1) or stationary phase . Bacillus subtilis 168 strain was grown in LB medium at 37°C to exponential phase (OD600 ∼0.3) or stationary phase (OD600 ∼4).eviously . The gereviously , cultivas CCM259  (LK1556)Each frozen cell pellet was resuspended in 240 μl TE (pH 8.0) plus 60 μl LETS buffer  and 600 μl acidic phenol (pH∼3):chloroform (1:1). Lysates were sonicated in a fume hood, centrifuged, the aqueous phase extracted three more times with acidic phenol (pH∼3): chloroform and precipitated with ethanol. RNA was dissolved in water, treated with DNase  and visualized on a 7 M urea 7% polyacrylamide gel by staining with GelRed (Labmark).RNAs were resolved on a 7% polyacrylamide gel and transferred onto an Amersham Hybond-N membrane according to the protocol described in AL645882.The protocol was adopted from The protocol was adopted from Streptomyces coelicolor cells expressing HA-tagged HrdB were pelleted and resuspended in lysis buffer , 0.5 mM phenylmethylsulfonyl fluoride (PMSF), supplemented with Calbiochem Protease Inhibitor Cocktail Set III protease inhibitors), sonicated 15 × 10 s with 1 min pauses on ice and centrifuged. 500 μg (protein) of lysates were incubated for 16–18 h at 4°C with 20 μl of Protein G plus agarose beads (Santa Cruz) coated with 5 μg mouse monoclonal anti-β subunit of RNAP antibody [clone 8RB13] (BioLegend), 2.5 μg of anti-σ70 antibody [clone 2G10] (BioLegend), 1.25 μg of anti-HA antibody [clone HA-7] (Sigma-Aldrich) or 5 μg of mouse non-specific IgG (Sigma-Aldrich) used as a negative control, respectively. The captured complexes were washed 4 times using 20 mM Tris–HCl pH 7.9, 150 mM KCl, 1 mM MgCl2, finally resuspended in 300 μl 20 mM Tris–HCl pH 7.9, 150 mM KCl, 1 mM MgCl2 and divided into two parts. One third of the beads were incubated in SDS sample buffer for 5 min at 95°C and eluted proteins were detected by Coomassie staining and Western blotting. The remaining two thirds of the beads were resuspended in 200 μl 1% SDS, 150 mM KCl, 20 mM Tris–HCl pH 7.9, 1 mM MgCl2 and incubated on a rotating platform with 200 μl acidic phenol (pH∼3):chloroform (1:1) for 15 min. Eluted RNA was precipitated with ethanol, dissolved in water and DNase treated . RNA was reverse transcribed into cDNA  using random hexamers and amplified by quantitative reverse transcription PCR (RT-qPCR) in a LightCycler 480 System (Roche Applied Science) in duplicate reactions containing LightCycler 480 SYBR Green I Master and 0.5 μM primers (each). Primers were designed with Primer3 software and their sequences are in the Supplementary primer list. Negative controls (no template reactions and reactions with RNA as a template to control for contamination with genomic DNA) were run in each experiment, the quality of the PCR products was determined by dissociation curve analysis, and the efficiency of the primers determined by standard curves. The relative amounts of co-immunoprecipitated RNAs were quantified on the basis of the threshold cycle (Ct) for each PCR product that was normalized to input values according to the formula 2∧[Ct(immunoprec)–Ct(input)].Proteins were detected by Western blotting using a rat monoclonal antibody recognizing the HA tag conjugated with HRP [clone BMG-3F10] or a mouse monoclonal antibody recognizing β subunit of RNA polymerase [clone 8RB13] in a combination with secondary antibody conjugated with HRP.Streptomyces genus.First, we conducted a computational homology search for putative 6S RNA/Ms1 RNA in the Streptomyces . Therefore, prior to the experimental validation, we extended the homology search with linguistic synteny analysis approach.Using this computational search, we identified 12 candidate genes for 6S/Ms1 RNAs in yces see . Interesyces see  displayermicutes , making The linguistic approach was based on search for text phrases that were specific to Ms1 RNA synteny and could be found in Ms1 flanking genes annotations. Note that meaning of the term ‘synteny’ in this work is ‘conserved genomic context’. Synteny annotations are annotations of conserved flanking genes of Ms1 RNAs. The phrases were used to identify new IGRs containing putative Ms1 RNAs in other species.Enterobacteriaceae (Enterobacteriaceae (γ-Proteobacteria)  . Althougrmicutes . TherefoMycobacterium, Rhodococcus, and Nocardia were conserved  in annotation of the other flanking gene and the length of IGR between flanking genes larger than 300 nucleotides. This IGR was considered as an IGR containing a putative Streptomyces Ms1 RNA.Intergenic regions with flanking genes with Ms1 RNA-specific synteny phrases (synteny hits) were found in numerous Streptomyces species, analogously to Ms1 RNA from M. smegmatis conserved in other Mycobacteria. Therefore, the sequence of the S. actuosus Ms1 IGR was BLASTed against the NCBI nt database, which produced ∼250 BLAST hits in 188 different Streptomyces species  (ENA ID AL645882.2) were identified at genomic locus 3934559: 3934978. To find out where within this IGR a putative Ms1 RNA was, the 419 nt sequence was sampled with 200–300 nt subsequences in 5 nt steps for which suboptimal secondary structures were predicted using UNAfold  potential 6S/Ms1 RNA candidate genes in S. coelicolor .Next, we used Northern analysis to determine expression of these putative sRNA, probing their expression from both strands. The analysis revealed that several of them were expressed, to various degrees, in exponential and stationary phases in elicolor , 2C. Expsco1791) and it was found in two forms (90 and 120 nt). Additionally, for Str1 we detected a short transcript (100 nt) from the opposite strand, a fragment of the sco1791 (1260 nt) mRNA.Str1 sRNA is an antisense RNA as1791,  to the tsco1761 gene and similarly to Str1, Str3, is also an antisense RNA  excluded them as potential 6S/Ms1 RNAs. 6S RNA/Ms1 RNA must adopt specific secondary structures and one of the shortest known 6S RNAs is from Aquifex aeolicus, which is ∼160 nt long , the candidate with the same synteny as 6S-1 RNA in ssrS gene  or ssrS (putative 6S RNA) is expressed from the 0 nt RNA . This co0 nt RNA . Althoug0 nt RNA , the maj0 nt RNA  or 227 b0 nt RNA .S. coelicolor Ms1 (Str13) RNA - predicted Ms1 starts 126–141 nt downstream from 5′ end of IGR. This 5′ RACE result matches the 5′ end of scr3559 as determined by RNA-seq  is not expressed in detectable amounts in these growth phases.To determine whether scr3559, along with ssrS/6S RNA is perhaps expressed, we used three different probes for Northern blot analysis. Probe “A” should specifically hybridize to ssrS/6S RNA, probe “B” to both sr3559 and ssrS/6S RNA, and probe “C” only to the sr3559 . We detesco3558-sco3559 genomic locus starts at position 3,934,693 and differs from the ssrS/6S RNA sequence that was used to experimentally test the interaction of the putative 6S RNA with the RNAP-HrdB holoenzyme in vitro  that predicted 78 suboptimal structures. The structures were clustered into 5 clusters based on their mutual structure similarity to find structurally representative folds. The clusters represented structural variations of a typical Ms1 fold. The most representative fold was identified in a cluster with most mutually similar suboptimal structures that contained typical Ms1-like structures which resembled Ms1 from M. smegmatis  and Str13 (scr3559) satisfied the criteria for potential 6S/Ms1 candidates and we selected them for further analysis.S. coelicolor. As the commercially available antibody against the primary σ70 (clone 2G10) interacted with HrdB only weakly (S. coelicolor hrdB gene (To answer whether Str11 (scr1505) and/or Str13 (scr3559) are homologs of 6S RNAs or Ms1, we wanted to immunoprecipitate the primary σ factor, HrdB, and RNAP from rdB gene  and immurdB gene . We subsrdB gene .Str11 (scr1505) associated neither with the RNAP core nor with the RNAP-HrdB holoenzyme, similar to four control RNAs that also did not interact with RNAP: 16S rRNA, sco3552, sco3710 encoding membrane proteins, and sco2013 encoding response regulator PdtaR.S. coelicolor  was bound to the RNAP core at 42 h post-germination in elicolor . scr3559NAP core . As we nNAP core , lane 7 NAP core , lane 3, to 42 h  while thmegmatis . Str13 (S. coelicolor and we propose to rename Str13 (scr3559) as Ms1.Therefore, we concluded that Str11 (scr1505) is neither 6S RNA nor Ms1 but a sRNA of unknown function. To the contrary, Str13 (scr3559) is a bona fide homolog of Ms1 in Mycobacterium smegmatis and Rhodococcus erythropolis, Ms1 is an abundant RNA in stationary phase, prominently visible in the gel, similarly to 6S RNA in Bacillus subtilis , there are no prominent bands in the ∼200–300 nt range, suggesting that it might not contain an Ms1 RNA or its expression is below the detection limit of the staining. Therefore, we decided to extend the linguistic search to the whole group of Actinobacteria to reveal how widespread Ms1 RNA is within other Actinobacteria species.In subtilis . In StreS. coelicolor Ms1 RNA using the linguistic search, we applied it to other Actinobacteria. A flowchart of the search procedure is shown in After we identified Mycobacterium, Rhodococcus, Nocardia, Gordonia, Mycobacteroides, Hoyosella, and Tsukamurella . Note that sequences of the synteny hits, i.e., IGRs containing putative Ms1 RNAs, may be dissimilar to each other and therefore could not be identified by sequence similarity searches.For the search, Taxonomy , were usStreptomyces actuosus, we identified IGRs containing Ms1 in 158 species from 46 Actinobacteria genera other than Streptomyces , Brevibacterium , Corynebacterium , Actinomyces , and Bifidobacterium  we found no IGRs that could contain Ms1 RNA , identifying this sRNA as a major class of protein-interacting RNAs. Ms1 RNA associates with the RNAP core as previously demonstrated in Mycobacteria  mycelium, then secondary mycelium is formed and eventually spores  with the direction of transcription oriented toward it .A comparison of genome sizes of the main Ms1 RNAs  reveals Frankiales, Geodermatophilales, and Glycomycetales, where neither the Ms1 synteny nor Ms1 RNA were detected, genome sizes were comparable to Actinobacteria orders with identified Ms1 RNAs. Frankia  genome sizes vary between 4.3 and 10 Mb and this variability is due to the degree of host dependence. Frankia are N2-fixing filamentous plant symbiotic bacteria that can either survive independently in the soil or be entirely dependent on their host plants . Glycomycetales are aerobic bacteria that produce branched vegetative mycelia and aerial mycelia with chains of square-ended conidia  colonies with the individual cubic cells and extracellular matrix forming cauliflower-like aggregates and have been reported to be highly resistant to stresses such as gamma-radiation, UV, and desiccation  the length of the sequence can vary substantially between species or genera, and (2) genomic position of the sequence can be only approximated. In the presented work, the length difference between the known S. coelicolor). The annotation helped us to identify the IGRs that might contain Ms1 RNA even in extremely distant species as represented here by the Actinobacteria genera with predicted Ms1 RNA, in which flanking nucleotide/amino acid sequences might be dissimilar.To overcome this problem, we employed a genomic synteny search. We adopted a linguistics approach based on similarity of genome annotations rather than similarity of nucleotide or amino acid sequences of flanking genes. Ms1 RNA has conserved synteny with a highly specific phrase in one of its flanking genes, the ‘HAD IB hydrolase’. This phrase is relatively rare and occurs only a few times in well annotated genomes , it then proceeded in an iteratively progressive manner within the genus and also in evolutionarily close genera using sequence similarity of the synteny hits. The obtained information was subsequently applied to other genera, expanding the list of identified candidates. Even in this simple form, the computational text search was able to identify Ms1 homologs in The original contributions presented in the study are included in the article/OM, MK, DK, and MJ validated sRNAs expressions. MŠ and JH performed 5′ and 3′ RACE. VVH and MŠ performed immunoprecipitation experiments. PH identified proteins by mass spectrometry. JP and MSc performed the computations. MSc, LK, JHn, and JP wrote the manuscript. JHn and JP designed the study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The degradation process of RNA is decisive in guaranteeing high-fidelity translation of genetic information in living organisms. However, visualizing the single-base degradation process in real time and deciphering the degradation mechanism at the single-enzyme level remain formidable challenges. Here, we present a reliable in-situ single-PNPase-molecule dynamic electrical detector based on silicon nanowire field-effect transistors with ultra-high temporal resolution. These devices are capable of realizing real-time and label-free monitoring of RNA analog degradation with single-base resolution, including RNA analog binding, single-nucleotide hydrolysis, and single-base movement. We discover a binding event of the enzyme (near the active site) with the nucleoside, offering a further understanding of the RNA degradation mechanism. Relying on systematic analyses of independent reads, approximately 80% accuracy in RNA nucleoside sequencing is achieved in a single testing process. This proof-of-concept sets up a Complementary Metal Oxide Semiconductor (CMOS)-compatible playground for the development of high-throughput detection technologies toward mechanistic exploration and single-molecule sequencing. Observing the natural process of RNA degradation in real-time is a significant challenge. Here, the authors develop and use single-exonuclease nanocircuits to reveal the single-base degradation behaviour of PNPase, and demonstrate proof-of-principle RNA sequencing using this approach. It is also decisive in keeping the healthy operation of the physiological environment by the degradation of the aged non-coding RNA4. Comprehensive deciphering of the mechanism of RNA degradation is critical for research on the details of the high-fidelity principle for transcriptome information transmission and precise regulation routine of the physiological environment; it also meaningfully promotes the development of technology for accurate sequencing of RNA. Structural information about, and the dynamic properties of, biological macromolecular systems are of fundamental importance for exploring their intrinsic mechanisms and corresponding functions8. However, traditional macroscopic technologies used to analyze the conformational changes during enzyme–substrate catalytic reactions usually produce collective information about the samples, which can disguise the detailed properties of individual enzymes. To overcome this issue, it is urgently necessary to develop methods that are capable of obtaining intrinsic information at the single-molecule level. The emergence of single-molecule electrical detection provides an advanced way to investigate biological interactions and chemical reactions at the individual event level13. In comparison with optical technologies, the electrical detection approach can directly record the single-event behavior without fluorescent labeling requirements and bleaching problems; it also possesses higher temporal resolution. These advances in single-molecule electrical measurements have enabled in vitro investigations of single-molecule enzyme dynamics, such as adenosine triphosphatase hydrolysis kinetics14, the binding mechanism of the DNA polymerase I with deoxyribonucleoside triphosphate analogs15, peptidoglycan hydrolysis kinetics16, and DNA binding kinetics of WRKY peptides17.RNA degradation is the end of transcriptome information transmission and guarantees high fidelity of genetic information through the degradation of mutational and fragmentary RNA20. This has increasingly been integrated into technologies such as nanopores21, optical tweezers22, and fluorescence resonance energy transfer24, toward developing strategies for high-throughput RNA-sequencing and understanding the enzymatic mechanism of exonucleases. However, a lack of temporal and spatial resolution significantly restricts the implementation of these technologies to decipher the degradation mechanism of the enzyme polynucleotide phosphorylase (PNPase) at single-base resolution.Exonucleases degrade polynucleotides in a stepwise manner in either the 3′- to 5′- or 5′- to 3′-directionEscherichia coli PNPase is a typical homotrimeric exonuclease, composed of two N-terminal RNase PH domains connected by an α-helical linker, two C-terminal domains, and KH/S1 domains. In the presence of phosphoric acids, it can catalyze the step-by-step hydrolysis of RNA, starting from the 3′-terminus, on a millisecond timescale28. Herein, we build silicon nanowire field-effect transistor (SiNW FET)-based single-protein sensors to realize detection of interactions between individual E. coli PNPase molecules and RNA analogs. Integrated with the microsecond temporal resolution technique, this method can directly unveil the detailed degradation process of single-molecule RNAs with high fidelity and single-base resolution in real time. Importantly, using fingerprint mapping analysis, this approach has the capability to efficiently discriminate between different nucleosides in a given RNA sequence, thus constituting the technological basis for future accurate single-molecule sequencing.30. In brief, the pristine SiNW FET formed by a mechanic-sliding method was spin-coated with a thin polymethyl methacrylate layer. Through precise high-resolution electron-beam lithography and wet etching, a nanoscale special area terminated with Si–H and suitable in size for the conjunction with a single protein molecule was generated on the core-shell SiNWs. After removing the polymethyl methacrylate layer with vast acetone, this special surface of SiNWs was successively modified by undecynic acid hydrosilylation, N-hydroxysuccinimide esterification, and maleimide immobilization. Ultimately, by feat of the confinement effect, a single PNPase molecule was conjugated to the molecular bridge on the surface of the SiNW through a thiol–maleimide–Michael addition, relying on a mutated cysteine residue at the bottom of the α-helical domain that has no effect on the activity of the enzyme31  was optimized through regulation of the solution ionic strength  signal of SiNWs was recorded in real time with a high-speed sampling rate  in the blank buffer when a constant source–drain bias voltage (0.3 V) was applied. The same detection was then performed in RNA analog substrate solution, and in RNA analog substrate solution in the presence of H3PO4   Fig. . After inm) Fig. , which c30] into the PNPase molecule ule Fig. , again cule Fig. c and 2a.ase Fig. , which f37. Collectively, the dwell time (τ) distributions of both the PNPase-RNA analog-bound structure and the PNPase RNA-free structure showed a single exponential decay function, indicating that each state has a constant average occupancy rate and is an independent stochastic process  of two adjacent data points. The velocity curves of angular momentum during the transformation process are depicted in Fig. To further explore the binding dynamics of PNPase with RNA analog, the temperature dependence was measured at 31 to 39 °C with intervals of 2 °C based on the ultra-temporal electrical monitoring technique Fig. 27,37. Corve Fig. . In orde40, “Mode 1”  on the structural stability of PNPase bound with RNA analog  and Mgns. Fig. . In addins. Fig. 25,40. Th30 we observed exactly 30 fluctuations  between adjacent low-conductance states at 37 °C , and over a pH gradient individually   value (3.08 ± 0.37), we confirmed that this reaction process was a primary KIE46. The augmentation of the statistical average dwell time and attenuation of the occurrence proportion of the high state hints that H2O/D2O plays a key role in the movement of RNA analog  in the presence of Mgls) Fig. –18. The 2+, Mn2+, or Mg2+ with D2O  could be obtained as shown in Supplementary Figs. τ1 appeared to be almost unchanged, so it can be assigned to the hydrolysis reaction of phosphate diester bonds, while τ2 was closely correlated with the sequence, implicating a process associated with the nucleosides of the RNA analog. Based on the Arrhenius formula, the activation energies (Ea) of hydrolysis with Mg2+ (87.68 ± 2.05 kJ•mol−1), Mn2+ (105.24 ± 4.21 kJ•mol−1), and D2O (142.17 ± 3.23 kJ•mol−1) were obtained through linear fitting between 1000/T and k   Fig. , which wC. crescentus PNPase and archaeal Exosome48. These interactions seem to be responsible for the proper orientation of RNA analog at the catalytic site, thus allowing the cleavage49. Based on the reported Protein Data Bank structure (PDB: 3GME) of PNPase25, we speculate that during the single-base degradation process, there is a stepwise capture action of nucleosides at the 3′-terminus by binding residues R93 and R97 of PNPase near the reaction center  amino acid(s) of PNPase and a nucleoside of RNA analog before hydrolysis. A similar binding action was uncovered using the progressive RNA degradation enzymes ter Fig. 50.52, we hypothesized a model for RNA analog degradation at the PNPase active site 30, poly(C)30, and poly(G)30—were respectively analyzed in identical conditions. Representative data are presented in Fig. th site are more valuable for representation of the hydrolysis reaction rate. In this way, the influence of vibration of the degradation structure without RNA analog movement on statistical analyses is restrained. Taken into the data consolidation of 20 sample volumes, the dwell time dendrograms manifest a good correlation of the dwell time distribution ascribed to the intermediate state on account of a certain sequence, supporting that the dwell time can effectively reflect information about the degradation. Meanwhile, fingerprint maps of the RNA analog-bound structures corresponding to the four types of homogeneous nucleoside sequence exhibit obvious distribution divergencies in the dwell time and current values, confirming the feasibility of distinguishing nucleosides using this technology.To investigate the degradation dynamics for various nucleotides, different homogeneous sequences—poly(A)τ2 corresponding to each sequence were: poly(A)30 = 46.33 ± 3.19 ms, poly(U)30 =54.57 ± 2.99 ms, poly(C)30 = 34.57 ± 3.41 ms, and poly(G)30 = 66.18 ± 4.07 ms. τ1 was almost unchanged for the four sequences . Such dwell time divergences of the binding action for the four nucleosides might originate from steric effects of purines and pyrimidines, as well as the coordination polarity between the nucleoside and positive arginine (R93 and R97).Through biexponential function fitting, we obtained statistical average dwell time values of the bound structures for the four types of homogeneous nucleoside RNA analog  was augmented in contrast to the value  before mutation, showing that the degradation process was delayed after mutation. The reduction of the high-state proportion and the shortening of the step length demonstrated that the stability of single base degradation was also affected Thus, by referring to statistical average dwell time values and the distribution discrepancies in fingerprint maps composed of the dwell time and current values, we could efficiently distinguish four kinds of nucleoside sequences. In view of the fact that the nucleoside is the only difference in these four homogeneous sequences, it is reasonable to infer that the specific nucleoside contributes to the stability of the PNPase binding structure with RNA analog.Because the binding action is the prerequisite of the hydrolysis reaction, and the statistical average dwell time of the binding action is more significant than that of the hydrolysis reaction (see above), the coordination action of the nucleoside at the 3′- terminus of the RNA analog with residues R93 and R97 of PNPase determines the rate of the single-base degradation step. Considering the dwell time divergences of the coordination action in the different hydrogen-bonding modes of the four types of nucleosides with R93 and R97 Fig. , which amccA gene), which is responsible for encoding an oligopeptide of seven amino acids corresponding to the primary structure of microcin C7 in E. coli cells54. The potential secondary structures were shown in Supplementary Fig. 55. After electrical measurements and data analysis, the sequence analysis collections were shown in Supplementary Figs. mccA gene sequence could reach about 79.17% , we found that the complete single base degradation processes were mainly characterized by thirty steps (marked with blue) and 24 steps (marked with brown) as shown in Supplement Fig. In addition, for a more realistic sequencing for transcriptome information relying on this technique, we synthesized a cellular RNA sequence 5′-UACGCAUGACCAUUA CGUUUGAUU-3′  accuracy at single-base resolution. To achieve practical real-time sequencing of RNA, robust and accurate machine learning algorithms for in-depth data mining discrepancy of nucleosides and genetic engineering technologies for mutating amino acids in the RNA analog-binding pocket to prolong the duration of coordination are needed. It will also be important to amplify the signal differences of different nucleosides. In addition, limited by the natural property of the PNPase, which is suitable for the degradation of short-piece RNAs, the current approach has not realized the identification of longer transcripts55. In the future, more practical enzymes and robust analysis algorithms are urgently needed in the realization of sequencing longer transcripts. This single-molecule detection methodology has created an advanced way to investigate the fundamental mechanisms of enzymes, and is naturally applicable to broad DNA, RNA, and peptide identification. This is a profound step in realizing a low-cost de novo sequencing method with the ultimate sensitivity limit.In summary, taking advantage of high temporal resolution, sensitivity, and good biocompatibility, we developed a universal methodology using a SiNW FET-based single-molecule device to realize in-depth investigation of RNA analog degradation dynamics in real time. Statistical analysis fully elicited the degradation process at the single-molecule level, including RNA analog binding, single-nucleotide hydrolysis, and single-base movement. Statistical calculation verified the fact that conformational entropy of PNPase drove the binding action with RNA analog. Importantly, a coordination action before hydrolysis, between the nucleoside at the 3′-terminus of the RNA analog and residues R93/R97 in the active site domain of PNPase, was discovered, and is key to the degradation behavior. By drawing fingerprint maps of every site in an RNA analog sequence, we are able to distinguish nucleosides with ∼80%  and ∼79.17% (2) was used as the p-type dopant. Under the condition of a B/Si ratio of 1/1,00,000 and 7.5 sccm H2 as the carrier gas, boron-doped p-type SiNWs were synthesized at the temperature of 465 °C for about 25 min.Silicon wafers with a 1000 nm‒thick thermally grown oxide layer were selected as growth substrates and gold nanoparticles with an average diameter of 20 nm (Ted Pella) were chosen as catalysts. 2.5 sccm disilane  was used as the reactant source and 0.11 sccm diborane  was used to remove the oxide shell of the nanowires. Then, 8 nm Cr and 80 nm Au were successively deposited on the silicon wafer by thermal evaporation  to form metal electrodes. Subsequently, through electron beam thermal evaporation , another 50 nm thick SiO2 protective layer was deposited to passivate the contact interface of the gold electrodes. Finally, the photoresist as well as the SiO2 protective layer between the electrodes was removed by copious acetone to obtain SiNW FET arrays. Considering the testing condition that the drain current was measured in the buffer solution, a negative resist  was utilized to open a window by photolithography to expose the intact surfaces of silicon wires for following modifications and protect the majority of the surface to prevent a generation of the electrical leakage. The windows between electrodes also functioned as a reactive chamber for the measurement of the enzymatic reaction process in vitro. Through systemic treatments, the high‒density SiNW FET arrays were obtained as shown in Supplementary Figs. SiNWs were transferred to the region designed for electrode patterning on the surface of silicon wafers. Through the photolithographic technique, a positive resist (ARP 5350) stripe was generated to retain SiNWs between the electrodes and remove redundant SiNWs by sonication. After that, the resist was washed with acetone. To generate better Ohmic electrical contacts with metal electrodes, the buffered HF solution  manual probe station. SiNW transistors showed the typical 2 gas stream, the device was immersed in a buffered HF solution (HF (40%): NH4F (40%) = 1:7) for about 7 s to remove the amorphous SiO2 shell and expose the silicon surface that is terminated with Si‒H bonds. Finally, the PMMA layer was cleaned with acetone.A bare SiNW FET device was covered with a polymethyl methacrylate  layer by spin coating and then baked for about 2 min at 180 °C. By precise high-resolution electron beam lithography with a designed CAD file that includes a ~10 nm-wide solid line, a nanogap precursor was generated at the potential position. After that, through sonication at 4 °C for about 1 min, the resist was developed in a mixture of water/isopropanol (V:V = 1:3) for lift-off. As a result, a certain nanogap suitable for the coverage by a single protein in size was generated on the surface of the SiNWs. After the device was washed with deionized water and dried with a N17. The freshly etched device was placed inside the Schlenk bottle and then 3 mg powders of undecynic acid were covered on the device. After 10 h of heating at 90 °C under an argon atmosphere, the device was immersed in dichloromethane. After about 30 s sonication to remove unreacted resides on the surface of the device, the device was dried with a stream of N2 gas. Then, the device was soaked into a mixture solution containing N-hydroxysuccinimide (NHS) (20 mM) and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) (10 mM) for 2 h at room temperature to realize esterification of carboxyl group with NHS (pH = 6.5). The device was thoroughly washed with deionized water and subsequently dried with a stream of N2 gas. The dried device was immediately immersed into a N, N-Dimethylformamide (DMF) solution containing N-(2-Aminoethyl) maleimide Hydrochloride (18 mM) to generate an efficient reaction between activated carboxyl group and maleimide. Then, the device was washed thoroughly with DMF and dried with a stream of N2 gas. Finally, a 50 μL PNPase solution  was added to cover the device surface for 24 h at 4 °C. After the sufficient reaction, the device was washed with buffer solution as well as weak ultrasonic treatment to remove redundant PNPase  to provide synchronous triggering. Once the trigger was switched, the fluorescent and electronic signals of the target location were simultaneously recorded within 5 min. After that, through the reconstruction and analysis of the optical pictures using the Advanced Research software, a single fluorescence spot and an enlarged image without the background were merged17.A buffer liquid layer containing a fluorescent-labeled RNA analog substrate was added to cover the surface of the PNPase-modified SiNW FET device (1.8 cm × 3.5 cm) and then attached with a coverslip. Subsequently, the device was placed on the microscope objective stage. The target electrode pair were caught by two extension probes, which were appended to the signal source and drain sockets of the UHFLI lock-in amplifier, respectively. A Nikon Ni–E microscope with a ×100 objective lens was positioned in close contact with the coverslip on the device by feat of the lens oil. The signal collection area of the stochastic optical reconstruction microscopy was zoomed on the modified portion of the single silicon wire. Then, a 548 nm laser was employed to excite the device and an EMCCD (Andor) was used to receive the feedback emitted light and the fluorescence spectra with a 50 ms exposure resolution. The process control cable of a stochastic optical reconstruction microscopy was connected to the trigger terminal of the UHFLI lock-in amplifier  and resuspended in buffer . After sonication and centrifugation, the cell extract was collected. Subsequently, a 5 mL Ni–NTA column was equilibrated with binding buffer  at a flow-rate of 5 mL•min−1. The cell extract containing the target protein with His-tag was incubated with the equilibrated column packing for 1 h. Then, the products after incubation flowed through the column and were collected. The washing buffer was used to clean off unbound protein and the elution buffer was used to elute the target protein. After that, the purified components were dialyzed into a protein preservation buffer . After dialysis, the protein was concentrated with polyethylene glycol 20,000, filtered using a 0.45-μm membrane, and stored at −80 °C.Protein expression experiments were delegated to Shanghai Sangon Biotech (Shanghai) Co., Ltd. The gene encoding λD was calculated based on the formula36ε is the electrolyte permittivity, kB is the Boltzmann constant, T is the thermodynamic temperature, q is an elementary charge, and c is the solution ionic strength.29. The RNA substrates were kept warm at 65 °C and then quickly cooled to avoid the effect of the changeable structure. Then, a 60-μL RNA analog solution with a specific concentration was subsequently injected into the microchamber. The chamber temperature was precisely controlled with an INSTEC hot/cold chuck, which involved a proportion–integration–differentiation control system (±0.001 °C) and a liquid nitrogen cooling system. Using an HF2LI Lock-in Amplifier (Zurich Instruments), the source–drain voltage was kept at DC 0.3 V throughout all real-time electrical measurements. The current between the source electrode and the drain electrode was amplified through a DLL1211 preamplifier operating at 107 V•A−1 gain and recorded using the HF2LI Lock-in Amplifier equipped with a 5-kHz bandwidth low-pass filter at 28.8 or 7.2 kHz sampling rates.The PNPase-modified SiNW FET device was covered by a polydimethylsiloxane cube, which contained a hole of about 2 mm in diameter as a reaction chamberA low-pass Butterworth filter at 2 kHz was necessary to reduce the circuit signal noise of the raw data. The subsequent data processing was carried out using MATLAB 2016b. A QuB software was used to idealize the filtered data for obtaining the dwell time of each signal event and the number of total events based on the hidden Markov model. In detail, relying on the difference in the conductance of each state, each state was sorted out and fitted to the continuous transformation relationship. Based on the temporal resolution between two adjacent dots, the dwell times of each state were obtained. Finally, the values of the dwell time and current could be extracted as events. Origin 2019b was then used to import and analyze the extracted data. The data of the dwell time were sorted out into seven intervals and plotted in a histogram where the dwell time was in the x coordinate and the count was in the y coordinate. The statistical average dwell time was obtained by relying on the exponential decay function. All statistical data are presented as the mean ± SD.x1 and y1 were coordinate values of the first data points; x2 and y2 were coordinate values of the second data points.HT and ΔST were deduced based on the thermodynamic relationships:HT is the change of enthalpy, equivalent to the negative value of the slope of the fitting curve; ΔST is the change of entropy, equivalent to the intercept of the fitting curve; T is the thermodynamic temperature; KT is the transformation equilibrium constant from the RNA-free-PNPase structure to the PNPase RNA analog-bound structure, As to a first-order dynamic process, KT is the ratio of the concentration belonging to the two structures. The time interval, where data points emerged consecutively in one structure, was chosen as the instantaneous concentration, which is equal to the reciprocal of the occurrence number per unit of time (1/p). Therefore, KT is equal to the value of the ratio of 1/p (bound) to 1/p (free), where p is the occurrence proportion of each structure29.ΔEa was deduced by relying on the Arrhenius equation:A is a pre-exponential factor; R is Avogadro’s constant; k is the hydrolysis reaction rate constant equal to the value of 1/τ1 (hydrolysis reaction); and T is the thermodynamic temperature. Ea can be obtained from the slope of the linear fitting function to ln(k) and 1/RT57. The KIE value was calculated based on the formulas:kH, kD is respectively the hydrolysis reaction rate constant in the solvent of H2O or D2O46.The activation energy mccA gene sequence. All RNA analog sequences for experiments were provided by Synbio Technologies (Beijing) Co., Ltd.Totally, 150 couple values (dwell time and current) at every step were integrated to plot a fingerprint and 30 fingerprints were put together ordinally from the 5′ terminus of the sequence to obtain a collection. Relying on the contrast with the reference fingerprints obtained from the homogenous sequence, we could distinguish several nucleoside information in the sequence. Finally, in the same way, we could obtain ten collections from the bulk data. Through the information overlay of nucleoside sites from 10 collections, we could be competent to identify the information of 24 sites in the artificially designed sequence and 19 sites in a Statistical analysis was conducted relying on the original data without randomization and blinding treatments. The experiments have separately proceeded in three independent devices to manifest the reproducibility.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary