How functional peptides may have arisen is a significant problem for the scenario of the RNA world. An attractive idea, the direct RNA template (DRT) hypothesis, proposes that RNA molecules can bind amino acids specifically and promote the synthesis of corresponding peptides, thereby starting the RNA/peptides world. To investigate the plausibility of this idea, we modeled the emergence of a “membrane-stabilizing peptide” in RNA-based protocells—such a peptide was suggested to have appeared early in the RNA world based on experimental evidence. The computer simulation demonstrated that the protocells containing the “RNA gene” encoding this peptide may spread in the system owing to the peptide’s function. The RNA gene may either originate de novo in protocells or emerge in protocells already containing ribozymes—here we adopt a nucleotide synthetase ribozyme as an example. Furthermore, interestingly, we show that a “nucleotide synthetase peptide” encoded by RNA  may substitute the nucleotide synthetase ribozyme in evolution, which may represent how “functional-takeover” in the RNA world could have occurred. Overall, we conclude that the transition from the RNA world towards an RNA/peptides world may well have been mediated by the DRT mechanism. Remarkably, the successful modeling on the emergence of membrane-stabilizing peptide in RNA-based protocells is per se significant, which may imply a “promising” way for peptides to enter the RNA world, especially considering the weak interaction between RNA and the membrane in chemistry. IHighlighting the importance of heredity, the RNA world hypothesis is often deemed as a representative idea of “replication first” or “genetics first” during the arising of life, and an “opposing” point of view is referred to as “metabolism first” . The vieIf the RNA world was the primordial living world, a subsequent question is how it could have “invented” DNA and proteins, thus ultimately evolving into the modern living world. In the long run, DNA is more stable and should have taken the place of RNA as the major genetic material; proteins are chemically more versatile and should have taken the place of RNA as the major functional material. Then, “after RNA, which came first, DNA or proteins?” This has long been a controversial issue e.g., ,18); bot; bot18])Consequently, a key concern is: “Are there certain ‘straightforward’ specific interactions between RNA sequences and amino acids?”. RNAs binding sites for amino acids were explored via selection-amplification experiments ,20,21,22Here, we plan to model the DRT mechanism in order to show whether functional peptides can be introduced into the RNA world by this mechanism. Then, as the modeling target, we must choose a functional peptide which possibly appeared early in the RNA world. Interestingly, an experiment study indicated that a sort of hydrophobic dipeptide could decrease the desorption of fatty acids from the membrane of vesicles, and such a “membrane-stabilizing peptide (MSP)” was suggested to have favored protocells in the competition for limited membrane components . So, if APBT, NPBT, and AAPBT, respectively; see 4, ten protocells each containing five copies of MSPG and the same number of a control RNA species are inoculated into the system. The control RNA species can encode another peptide by the DRT mechanism, but the peptide has no function . As time goes on, the MSPG protocells spread in the system  could spread in the system by virtue of the MSPs function. The model’s system is initialized with amphiphile (fatty acid) precursors, nucleotide precursors, and amino acid precursors  is turned down to some extent, the MSPG protocells would decline and finally disappear (AABRP) or the rate of amino acids linking into peptides on an RNA template (AATLP) is turned down to some degree, the MSPG protocells descend correspondingly  would disfavor the spread of MSPG protocells , but both the MSPG protocells and MSPG in the system would decline . This phenomenon should represent a significant effect associated with the labor division between genetic and functional molecules, which may be named as “function-lagging”.Then, interestingly, either too high or too low a probability concerning the degradation of peptides (otocells . It is eAADEP (the probability of amino acid residue decaying at the end of a peptide), PBBP (the probability of peptide bond breaking) is also a parameter associated with the degradation of MSP. However, the parameter analysis showed that the turning down of PPBB has little influence on the spread of the MSPG protocells. That is, the so-called “function-lagging” effect seems to have not occurred. We suspected that it is because the default value of PBBP adopted here (0.01) is significantly lower than that of AADEP (0.1). Then, here the “bottle-neck” concerning the degradation of MSP is AADEP; when PPBB is turned down further, little influence could be observed. To verify this speculation, we interchanged the default values of the two parameters, i.e., PBBP= 0.1 and AADEP= 0.01, and ran the analysis again. Indeed, this time it is the turning down of PBBP(instead of AADEP) that shows the “function-lagging” effect . The RNA template-directed polymerase ribozyme  has long been suggested to be a candidate for the ribozyme emerging in an early RNA world ,30,31,324, ten protocells each containing five copies of MSPG, NSR, and two control RNA species, are inoculated into the system. Wherein, one control species encodes a non-functional peptide by the DRT mechanism, thus as a control of MSPG, and another control species does not encode any peptides, thus as a control of NSR. As time goes on, the protocells containing both the MSPG and NSR spread . Then, we saw that after one of these inoculations, the MSPG-NSR protocells began to rise and ultimately substituted the NSR protocells to become the major protocells in the system ”. The corresponding RNA gene is called “nucleotide synthetase peptide gene (NSPG)”. Indeed, after the initial inoculation of protocells containing MSPG, NSPG, and a control peptide gene, the MSPG-NSPG protocells spread in the system, whereas neither the protocells containing the control peptide gene, nor those containing solely the MSPG or the NSPG, could spread a.4 steps; the MSPG-NSR protocell was chosen randomly for each time of the inoculation). Then, we saw that after one of these inoculations, the MSPG-NSPG protocells began to thrive and ultimately substituted the MSPG-NSR protocells to become the major protocells in the system  could occur. So, in a system with the spread of the MSPG-NSR protocells, we inoculated one copy of NSPG into one of these protocells intermittently  took its place; the ribozyme would then disappear.Here, we noticed that it seems that NSR would fade out because it is less efficient than NSPG, which encodes the same function. Then, we were curious about whether the two “competing” species could co-spread if they have an equivalent efficiency. So, we inoculated protocells containing both the NSR and NSPG into the system in the beginning and attempted to achieve the assumed equivalent efficiency by an adjustment of the parameters. In the model, the catalytic efficiency of NSR is denoted by PNFP see . Howeverd spread . Based oNSR wins . Furtherill wins , but forSPG wins . In otheIn this study, firstly we looked into the plausibility concerning whether the protocells containing an RNA species (MSPG) “encoding” the membrane-stabilizing peptide (MSP) via the DRT mechanism can spread in the system a. After AADEP; In the supposed RNA world, RNA acted as both genetic and functional molecules. However, to walk out of the world, the labor division between genetic and functional molecules was inevitable. There were previous studies modeling the labor division on account of the emergence of DNA in the RNA world, in which genes were carried on DNA and functions were carried out by ribozymes ,49. HereAs already mentioned, for the emergence of DNA in the RNA world, there was a straightforward chemical mechanism to transfer information, i.e., the template-directed copying—either from RNA to DNA or vice versa. The direct RNA template (DRT) hypothesis suggests a potential comparable mechanism for the emergence of peptides in the RNA world ,22,24. IIndeed, the greatest attraction of the DRT mechanism lies in that it formulates a simple but effective way to transfer information from RNA into peptides. In the mechanism, only RNA with specific subsequences can bind corresponding amino acids and ultimately favor the production of a specific peptide. Since the famous Miller–Urey experiment  and manyAnother attractive point of the DRT hypothesis is that it implies a potential way for the arising of the genetic code. The relevant experiments showed that a characteristic RNA sequence binding a specific amino acid tends to contain the anticodons/codons  of the amino acid ,21,22. SRemarkably, here we successfully modeled the takeover of a ribozyme (NSR) by an RNA gene encoding a peptide with the same function (NSPG) b. It seeAs the major modeling target in this study, the membrane-stabilizing peptide was experimentally evidenced to favor the growth of vesicles containing it . In the N × N square grid. Molecules are distributed within the grid rooms, including nucleotides, RNA, amphiphiles , amino acids, and peptides, as well as the precursors of nucleotides, amphiphiles, and amino acids, respectively. Amphiphiles may assemble at the boundary of a grid room and form a membrane, then the grid room is occupied by a protocell.We conducted the computer simulation using a Monte Carlo method similar to that used in our previous work concerning the evolution of the RNA world . A protocell may also move to an adjacent room (MCP) (while it pushes away molecules in that room).In each time step (Monte Carlo step), certain events may occur to molecules and protocells with defined probabilities (see NFP), amphiphile precursors may form amphiphiles (AFP), and amino acid precursors may form amino acids  (AAFP). Nucleotides, amphiphiles, and amino acids may also decay into their precursors .Nucleotide precursors may form nucleotides  (RLP). An RNA molecule may attract substrates (nucleotides or oligomers) (ATP) via base-pairing, with certain fidelity (FPP) and substrates aligned on the template may be ligated (TLP), that is, the template-directed synthesis. The substrates or the full complementary chain may separate from the template (SPP). Phosphodiester bonds within an RNA chain may break (BBP) and thus the RNA molecule turns into two fragments. A nucleotide residue at the end of an RNA chain may decay into a nucleotide precursor (NDEP). An RNA containing a characteristic domain is assumed to be able to catalyze the synthesis of nucleotides  (NFRP).Nucleotides may join to form RNA via random ligation (AML) may accumulate at the boundary of a grid room and form a membrane (MFP), thus “creating” a protocell. Amphiphiles may join or leave the membrane . Nucleotide precursors, amphiphile precursors, and amino acid precursors may permeate through the membrane . Nucleotides, RNA, amino acids, and peptides are assumed to be unable to permeate through the membrane. A protocell may fuse with another protocell in an adjacent grid room (CFP), divide (with an offspring protocell occupying an adjacent grid room) (CDP), and break (CBP), resulting in the disassembly of the amphiphiles.Amphiphiles with a sufficient number (larger than RLP= 1 × 10−6). Third, as for RNA binding amino acids or peptides, we assume that it would adopt a folding conformation and would not experience normal events for free RNA, that is, it could not attract nucleotides/oligonucleotides, would not degrade (due to the protection by amino acids/peptides), and would not conduct the random ligation with other RNA/nucleotides . Then, the new events introduced here are described as follows.The major events described above were introduced as we modeled the RNA-based protocells previously ,49,51,52AABRP) and two amino acids aligned adjacent on an RNA template may be ligated (AATLP), that is, the DRT mechanism. The peptide (or amino acids) may leave the RNA template (PLRP). The peptide bond may break (PBBP). The amino acid residue at the end of a peptide may decay (AADEP); a dipeptide would then turn into an amino acid and an amino acid precursor. Of all the possible dipeptides, only the membrane-stabilizing peptide can join the membrane (PJMP). The MSP may also leave the membrane (PLMP). When modeling the functional takeover of ribozyme by peptides, another kind of dipeptide was assumed to act as an NSP (nucleotide synthetase peptide) (NFPP).An amino acid may bind specifically to an RNA site with a characteristic sequence ; in reality, competitions for materials and energy are both possible in Darwinian evolution.Worth mentioning at this time is that the protocells in the model system are competing for materials but not energy because the nucleotides and amino acids are assumed to be always “activated”, whereas the total materials in the system, including those concerning RNA, peptides, and the membrane of protocells are assumed to be limited , whereas those at the end of the chain decay at a rate lower than that of free nucleotides, i.e., NDEP < NDP; as for the case concerning amino acids and peptides, likewise, AADEP < AADP. Nucleotides and RNA should be easier to degrade outside protocells (due to the higher water activity), so DOF > 1; amphiphiles and peptides within a membrane should be protected, so DWF < 1 (see below for a detailed explanation on the two factors). Because of the self-assembly feature of the membrane, MFP >> CBP, AJMP >> ALMP, and PJMP > PLMP. The movement of molecules should be easier than protocells, so MVP > MCP. Other considerations may include: BBP should be higher than RLP but lower than NDEP, PBBP < AADEP, APPP, and AAPPP > NPPP, etc.The parameters should be set according to some rules. Reactions catalyzed by ribozymes should be much more efficient than corresponding non-enzymatic reactions, so NPB, TAPB,Tand AAPBT), “the lower limit number of amphiphiles to form a membrane” (AML), and the lengths of the characteristic RNA domains for NSR (NSRL) and amino acid-binding sites (AABSL), are set obviously smaller in scale than the corresponding situations in reality. However, such simplifications are believed to be not in conflict with the fundamental rules reflected in the modeling.In consideration of the computational intensity, total materials ; the breaking probability for a single-chain is BBP× DOF, while that for a double-chain is (BBP× DOF)2. The factor DOF also works in the situation of nucleotide decaying and nucleotide residue decaying at the end of an RNA, i.e., NDP×DOF and NDEP× DOF for the case of outside protocells. Likewise, for the degradation of amphiphiles and the membrane-stabilizing peptide within the protocell membrane, another factor is involved (DWF< 1), i.e., ADP× DWF, AADEP× DWF, and PBBP× DWF for the case of within the membrane.With the breaking of phosphodiester bonds, an RNA molecule may degrade into shorter ones (including nucleotides). When the breaking site of the chain is at a single-chain region, the breaking probability is SPrP, where r = n1/2 and n is the number of base pairs in the duplex. When n = 1, the probability would be SPP. When n increases, the separation of the two strands would be more difficult . The introduction of the 1/2 corresponds to the consideration that the self-folding of single chains may aid the dissociation of the duplex.The probability of the separation of the two strands of a duplex RNA is actually assumed to be MFP) x, where x = a − AML + 1 and a is the number of amphiphiles in the grid room. When a is equal to AML (the lower limit of the number of amphiphiles to form a protocell membrane), the probability of membrane formation is equal to MFP. This assumption concerns the consideration that the more amphiphiles in a grid room, the more probable they would assemble to form a vesicle.The probability of membrane formation is assumed to be 1 − (1 − ALM/P(y × z), where y = 1 + i/(b/2)3/2 and z = 1 + MSPF×p. The item y represents the consideration for the “osmotic pressure effect”: a higher concentration of the inner impermeable ions would cause the protocell to be more swollen, and thus amphiphiles on the membrane are less likely to leave, as suggested by experimental work , where i is the quantity of inner impermeable ions and b is the quantity of amphiphiles within the membrane. The element (b/AML) represents the consideration of the constraining effect of the cellular space on the influx of nucleotide precursors. That is, when b increases, meaning that the cellular space increases correspondingly, the probability of a nucleotide precursor permeating into the protocell would become greater. The introduction of the item i/(b/2)3/2, i.e., the concentration of the inner impermeable ions as explained above, represents the consideration of the effect of Donnan’s equilibrium [APPP × (b/AML) and AAPPP × (b/AML), respectively.The probability of a nucleotide precursor permeating into a protocell is assumed to be ilibrium ; simply ilibrium  for a deCDP × (1–2 × AML/b), where b is the quantity of amphiphiles within the membrane. When b is no more than twice that of AML, the probability is no more than 0, i.e., the protocell could not divide. This assumption considers the fact that the larger the protocell, the more probable it would divide, on account of the physical instability.The probability of protocell division is assumed to be MVP/m1/2, where m is the mass of the RNA, relative to a nucleotide. This assumption represents the consideration of the effect of the molecular size on the molecular movement. The square root was adopted here according to the Zimm model, concerning the diffusion coefficient of the polymer molecules in the solution [MVP/m1/2, where m is the mass of the peptide relative to an amino acid. Note that when there are amino acids or peptides binding on an RNA, the total mass would be calculated into m in regard to the movement of the complex. The probability of the movement of an RNA molecule is assumed to be solution . Likewis.


Drosophila melanogaster male and female cells as well as male cells depleted of proteins essential for dosage compensation. We identify two distinct RNA-stability mediated responses involved in regulation of gene expression. The first of these responds to acute and global changes in transcription and thus counteracts potentially harmful gene mis-expression by shifting the RNA stability in the direction opposite to the transcriptional change. The second response enhances inter-individual differential gene expression by adjusting the RNA stability in the same direction as a transcriptional change. Both mechanisms are global, act on housekeeping as well as non-housekeeping genes and were observed in both flies and mammals. Additionally, we show that, in contrast to mammals, modulation of RNA stability does not detectably contribute to dosage compensation of the sex-chromosomes in D. melanogaster.The steady state levels of RNAs, often referred to as expression levels, result from a well-balanced combination of RNA transcription and decay. Alterations in RNA levels will therefore result from tight regulation of transcription rates, decay rates or both. Here, we explore the role of RNA stability in achieving balanced gene expression and present genome-wide RNA stabilities in  The term gene expression is used liberally to refer to anything from the steady state levels of gene products, either mRNA as an intermediate stage or protein as the final effector molecule, to the generation of gene products, thereby describing measures of transcription or translation output. Commonly, the term refers to the steady state levels of RNA. However, the dynamics of gene expression require additional steps that are often overlooked: the decay of the RNAs and the decay of the proteins, since the steady state levels of a molecule are defined by both its production and its decay rate, which are equally important . In factD. melanogaster). The latter form of compensation is generally referred to as buffering, and it can also include compensation for gene mis-expression following, for example, single mutations in transcription factors with broad targets  and Kc167  cell lines were cultivated in Schneider’s medium modified with L-glutamine (Lonza) and supplemented with 10% Fetal Bovine Serum, 100 units/ml penicillin and 100 μg/ml streptomycin. The S2 cells were grown in T-flasks at 25°C and the Kc167 cells were grown in suspension in Erlenmeyer glass flasks in a cabinet at 23°C.Schneider’s msl2 and mle RNAi were obtained by PCR using genomic DNA from wild type Oregon R flies and the following primers:The DNA templates used to make the dsRNA for msl2: TAATACGACTCACTATAGGGAGAGTTGGCTGTGCTGGCTG,Fmsl2: TAATACGACTCACTATAGGGAGATGTTGGCTCGTCACTGTC,Rmle: TAATACGAACTCACTATAGGGGCAACAGGATGGCGAAAAA,Fmle: TAATACGACTCACTATAGGGTCTGGGTAGTCTTTCCGCAC.Ryfp RNAi was obtained by PCR using pEYFP-N1 plasmid DNA (Clontech) and the following primers to introduce the T7 promoter:The DNA template used to make the dsRNA for eYFP: TAATACGACTCACTATAGGGAGAGGTGAGCAAGGGCGAGGAGCT,FeYFP: TAATACGACTCACTATAGGGAGATCTTGAAGTTCACCTTGATGCCG.RThe DNA templates were purified and dsRNA was generated using the T7 RiboMAX Express Large Scale RNA production system (Promega) or the T7 High Yield RNA Synthesis Kit (NEB) according to the manufacturer’s instructions.D. melanogaster cell lines used. Briefly, all RNAs of a cell are labeled with the uridine analog 5-bromouridine (BrU). Next, the cells are washed and resuspended in fresh media without BrU. From this point (t = 0) on, the cells are allowed to continue growing in normal conditions and the RNAs that are degraded will be replaced over time by newly synthesized unlabeled RNAs. Cell samples are collected at multiple time points and the BrU labeled RNAs are separated by immunoprecipitation using an anti-BrU antibody. The BrU labeled RNAs are then sequenced, and the individual RNA half-lives can be deduced from the rate at which transcripts leave the BrU labeled fraction of RNAs.Our BRIC protocol is based on a protocol from  which we6  living cells/ml per time point were seeded in 6-well plates or cell culture flasks and incubated for 15–60 min at 25°C to allow the cells to attach to the surface. For the RNAi experiment, 2 × 106 live cells/ml were collected and resuspended in serum-free antibiotic-free medium. About 20 μg dsRNA/ml were added to the cell culture, followed by 30 min incubation at room temperature. Then the cell cultures were diluted 1:1 with medium containing 20% fetal bovine serum, 200 units/ml penicillin and 200 μg/ml streptomycin and incubated at 25°C in flasks/plates for 4 days. The RNAi treatment was repeated as above on the same cells resuspended at 2 × 106 live cells/ml and incubated for 1 more day. For western blot analysis, protein extracts were run on a 10% SDS–PAGE gel and thereafter transferred to a PVDF membrane for 2 h 30 min at 25 V. Primary and secondary antibodies  were diluted in 1× PBS, 1% BSA, 0.05% Tween-20. Note that MSL2 was not assessed by western blot due to the poor performance of our MSL2 antibodies in that assay. For the labeling, Bromouridine (BrU) (Sigma or Alfa Aesar) dissolved in culture medium was added to the samples to a final concentration of 10 mM every third hour for 24 h .A total of 2 × 106 cells/ml in an Erlenmeyer flask with shaking and incubated for 24 h with a first addition of fresh BrU at a final concentration of 400 μM at the beginning and a second addition of fresh BrU after 12 h.For labeling the Kc167 cells, cells were resuspended at a concentration of 1 × 10After 24 h of BrU labeling, all cells were washed twice with fresh medium (or PBS). Samples of 2 ml for the S2 cells and 10 ml for the Kc167 cells were collected at specific time points  by removing the media and resuspending the cells in appropriate amount of TRIzol LS (Ambion Life Technology) for storage at −80°C.RNA was extracted by the alternative protocol provided by the manufacturer of TRIzol LS (Ambion Life Technology) that uses 1-bromo-3-chloropropane (BCP) instead of chloroform. According to the manufacturer, BCP reduces the risk of DNA contamination of RNA samples and is less toxic than chloroform. The RNA was resuspended in 20 μl of RNAse-free water and incubated at 55–60°C for 10–15 min and the concentrations were measured with a NanoDrop (Thermo Scientific). Immunoprecipitation of BrU labeled RNA from S2 cells was done according to the protocol described in  while weD. melanogaster genome version 6.33 using STAR version 2.7.0e and default parameters. For each gene and each time point, reads were counted using HTSeq version 0.9.1. The BRIC-seq and RNA-seq data reported in this paper have been deposited in the European Nucleotide Archive with accession number PRJEB15335.The sequencing libraries were made with TruSeq RNA Sample Preparation v2 (Illumina) according to the manufacturer’s instructions. The samples were sequenced at different times, all on Illumina sequencing machines (HiSeq2500 or NovaSeq6000) with paired-end sequencing and a read length of 126 and 151 nucleotides respectively. All sequencing data were mapped to t = 0, t = 1 or t = 2 h were filtered out. For genes with rapidly decaying transcripts, the latest time points give very low read counts and the signal is mainly noise, therefore we filtered out all time points with a cpm below 0.5 as well as the period of time coming after a filtered out time point. For the latest time points, in some cases the cpm values increase again due to the fact that the composition is biased toward stable genes. If the increase from one time point to the next is >200%, then the later time point is removed together with all the subsequent time points. In order to correct for biased gene composition at later time points, we calculated and applied normalization factors. To do this, only genes with data for all time points were sequentially fitted to an exponential decay curve using the nls function from the nls2 package in R. Correction coefficients that turn the gene decay curve into a perfect exponential decay curve were collected for each gene and each time point and averaged per time point to form the normalization factors. These factors were applied to the full dataset.To remove genes expressed at low levels from our dataset, genes with a sum of raw read counts below 10 as well as genes with a cpm (count per million as determined by EdgeR) below 2, for time point a*exp(-b*t) and the half-lives were calculated as ln(2)/b. The replicates were pooled together by calculating the average if both values were between 0 and 16 h. Half-life values above 16 h or negative indicate that the RNAs are very stable and these genes were arbitrarily given a 16 h half-life. Of the genes classified as expressed, we were able to determine the half-life for 60–70%. For the remaining genes, the decay curve could not be fitted because either its shape was very different from a first order exponential decay or we did not have enough valid time points due to the very rapid decay of some weakly expressed genes.To calculate half-lives, the available time points for each gene were fitted to an exponential decay curve using nls with the formula GeTMM was chosen to calculate and compare transcript levels because it performs well for both intrasample and intersample comparisons, and the GeTMM values were calculated as presented in . BrieflyThe RNA-seq and ribo-seq data were generated by  and the The poly(A) tail length dataset was generated by  using thGenes with > 6 as the expression level in all 12 FlyAtlas-specified tissue types  were deft = 0 for each sample was used for differential expression analysis because the BrU labeling procedure may affect gene expression slightly and since we compare differential expression with our calculated half-lives that require BrU labeling, the t = 0 time point constitutes a better control than RNA-seq of unlabeled sample. Genes for which the sum of raw counts across all experiments was below 10 were excluded from the analysis. Fold-differences in expression between either msl2 RNAi or mle RNAi and yfp RNAi (control) were calculated using the DESeq2 R software package.For the BRIC experiments, the time point To identify the biological themes enriched in the long and short half-life RNAs, we performed Gene Ontology (GO) term enrichment analysis for the 20% least stable gene transcripts and the 20% most stable gene transcripts for the two different cell lines. We used the Functional Annotation Tool in the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8  to extraIn this study, all data and calculations referring to ‘autosome’ or ‘A’ represent the merged data for the main autosome arms 2L, 2R, 3L and 3R. We exclude chromosome 4 from the autosome group due to its role as an ancestral sex chromosome ,35. The All calculations and statistical analysis were performed using R-4.0.3 and the ggplot2 package was used to generate plots. Adobe Illustrator was used to build the figures. The existing BRIC-seq data in UPF1 depleted mammalian cells  was kindD. melanogaster, we chose a non-destructive method to minimize the disturbance of the natural physiology of the cell. We adapted our protocol from the BRIC-seq method which was developed for mammalian cells by  enrichments for genes encoding the 20% most stable RNAs and the 20% least stable RNAs in male (S2) cells and female (Kc167) cells. The results show that RNAs from genes defined by GO terms like translation and mitochondrion are long-lived while genes defined by GO terms such as transcription regulation are enriched in short lived RNAs, in both male cells (S2) and female cells (Kc167)  Table . An anal7) Table , gave si7) Table  found a U tests returned significant differences between the sexes for both non-housekeeping genes (P = 1.90*10–8) and housekeeping genes (P = 1.19*10–11), indicating significant differences in the distributions. We therefore asked whether RNA stability exhibits gene specificity or cell-type specificity and plotted the gene-wise differences in RNA stability between males and females in increasing order. The curves confirm the statistical test results and show that a large fraction of genes is differentially stable between S2 and Kc167 cells, for both housekeeping  and male (S2) cells Figure . The resg Figure  and non-g Figure . The disWe conclude that RNA stabilities are related to gene functions and the ‘stable’ or ‘unstable’ ranges for RNA stability have been conserved during evolution. However, the observed differential RNA stability of genes between male and female cells suggests that RNA stability is not an entirely intrinsic character based on the RNA sequence but adapts to the cellular and genetic context.S. pombe and S. cerevisiae but not in E. coli  tail length ,45,47,48e Figure . In conte Figure , for mose Figure  and femae Figure . We obseE. coli but not in S. cerevisiae  tail length and RNA stability we divided the genes into 5 bins of increasing poly(A) tail length and with an equal number of genes per bin for males (S2 cells) (poly(A) tail length data for Kc167 cells were not available). We further classified the genes as housekeeping and non-housekeeping Figure . The resWe conclude that RNA stability correlates positively with steady state transcript levels and negatively with RNA length. Intriguingly, RNA stability correlates negatively and positively with poly(A) tail length for housekeeping and non-housekeeping genes, respectively.D. melanogaster even though we did not find any statistically significant difference in RNA half-life between the X-chromosome and the autosomes. To this end we knocked down two components of the MSL complex using RNAi: msl2 encoding the core component MSL2 which is required for the complex to form, and mle encoding the RNA helicase MLE whose absence decreases proper spreading of the complex  and females (Kc167 cells) against their differential RNA stability values Figure  and for By correlating differential half-life with differential RNA levels in different conditions, we draw two conclusions. First, the regulation of RNA stability counteracts induced transcriptional disturbances in both autosomes and sex chromosomes, in both flies and mammals. Second, it contributes to the adapted differential transcript levels between S2 cells and Kc167 cells but also between any two independent human lymphoblastoid cell lines.D. melanogaster, we asked how ribosome density relates to RNA stability. To address this question, we analysed paired ribo-seq and RNA-seq data from  and calculated the average ribosome density per bin for the autosomes and the X-chromosome Figure . We noteGiven that mRNAs from the X-chromosome are on average longer compared to mRNAs from the autosomes and that generally, the ribosome density decreases as the RNA length increases, we asked whether this could explain the lower ribosome density observed for the X-chromosome. We therefore compared ribosome density averages for bins with identical transcript length ranges Figure . Our resP < 0.05) and that there is no obvious correlation between the two variables  tail length participate in translational regulation and that poly(A) shortening acts as a timer for RNA decay . We thers Figure .We conclude that ribosome density is greater on transcripts from housekeeping genes compared to transcripts from non-housekeeping genes and it is consistently lower on transcripts of the X-chromosome compared to transcripts of the autosomes. This decreased ribosome density on transcripts of the X-chromosome is not explained by X-specific gene characteristics. Intriguingly, we observe a significantly higher average ribosome density for the fourth chromosome.Three main conclusions can be drawn from our analysis. First, the modulation of RNA stability counteracts widespread, acute disturbances in transcription output by increasing half-life upon down-regulation of transcription and vice versa both in flies and mammalian cell lines. Second, differential gene expression between two distinct cell lines is partly mediated via modulation of RNA stability, both in flies and mammals. Third, contrary to what has been reported in mammals, there is no evidence for a role of RNA stability regulation in dosage compensation of the sex chromosomes in flies.The BRIC-seq method  is superi.e. housekeeping genes have greater RNA stability compared to genes having functions in e.g. transcription regulation or apoptosis  tail of the transcript that they activate, and thus carry information about the associated stability. Moreover, it has been proposed that short-tailed housekeeping genes could be locked in a closed loop state that promotes translation and stabilizes the RNA, favoring constitutive expression  and Kc167  cells and of seven human lymphoblastoid cell lines of mixed sexes. The RNA stability response shows no sex-chromosome bias and leads to greater stability in the case of genes exhibiting higher RNA levels in a specific cell line. This response (in contrast to buffering that minimizes the differences in transcript levels) will contribute to the differential transcript levels measured here. Finally, we speculate that RNA stability could also be used among other mechanisms to control adaptive expression during speciation.msl2 RNAi, the fourth chromosome has a much, and highly significantly, lower RNA stability compared to the autosomes. It is unclear as to why this happens, and we speculate that elements of the answer lie within one or several of its unique characteristics. The fourth chromosome is enriched in repetitive elements, it is replicated late, and in principle, the entire chromosome can be considered heterochromatic  which confirms previous findings . They hyomosomes . Codon oomosomes . Ribosomomosomes . A recene system , and if Drosophila has a low codon usage bias and a high level of repetitive sequences and is highly heterochromatic (msl2 RNAi (X-chromosome) and a high ribosome density in untreated cells corresponds to decreased RNA stability with msl2 RNAi (chromosome 4). It is plausible that in msl2 RNAi cells, the reduced number of X-chromosome transcripts leads to increased translation initiation rates driven by feedback loop regulation processes, which in turn increases RNA stability. The opposite effect may apply for the fourth chromosome.Additionally, we find a significantly higher ribosome density on the transcripts of the fourth chromosome. The fourth chromosome of hromatic . Accordihromatic , a low cRibosome densities on the X-chromosomal and the autosomal RNAs differ significantly for genes with low to medium transcript levels but not for genes with high transcript levels. This suggests that genes with low to medium transcript levels are more likely to be regulated at the translational level than genes with high transcript levels, because the latter are likely to be saturated at the translational level.Drosophila melanogaster.The chromosome-wise average RNA half-lives are not significantly different from each other; however, the ribosome densities are. Even though ribosome density correlates negatively with gene length, and the X-chromosome has, on average longer genes, for each gene length bin, the X-chromosome still has a lower ribosome density on its transcripts than the autosomes. This suggests that the chromosome specific regulation (dosage compensation) of gene expression is acting mainly at the transcriptional and translational levels rather than through the regulation of RNA stability in In summary, we postulate that the regulation of RNA stability constitutes a versatile tool for the cell, used to control gene expression in several ways. Firstly, it can be used rapidly to buffer shifts in transcription output, provoked by e.g. some mutation that results in broad alterations of transcription such as for general transcription factors or genes involved in dosage compensation, and thus minimize the effects of faulty transcription output. Second, the modulation of RNA stability has a supporting role to establish differential RNA levels between individuals or cell types genome-wide. Lastly, as shown in mammals by , it can The BRIC-seq and RNA-seq data reported in this paper have been deposited in the European Nucleotide Archive with accession number PRJEB15335.gkac208_Supplemental_FilesClick here for additional data file.


Hematopoietic stem cells (HSCs) can differentiate into all mature functional blood cells via hematopoiesis, but can also self-renew to replenish the progenitor cell pool. Both hematopoiesis and HSC renewal are strictly orchestrated processes to guard against dysregulation that could lead to life-threatening blood malignancies, such as leukemia. RNA regulatory networks have essential roles in ensuring normal hematopoiesis. Now, state-of-the-art, high-throughput sequencing technologies, including RNA-seq, CLIP-seq, ChIP-seq, ChIRP-seq, MeRIP-seq, are allowing us to gain comprehensive insights into the contribution of RNA regulatory networks to hematopoiesis and how they might be perturbed to promote malignancies.In this issue, 5 leading scientists involved in RNA research, share with us their insights into the roles of RNA regulation in hematopoiesis and blood diseases. As detailed below, each has extensive experience in dissecting the cellular and molecular basis of RNA regulation and hematopoiesis, with their areas of interest covering RNA-binding proteins (RBPs), noncoding RNAs (ncRNAs), RNA chemical modifications, HSCs, and artificial intelligence (AI).1 Indeed, KHSRP involves in mRNA decay, alternative splicing, and miRNA biogenesis, as well as myeloid differentiation through facilitating miR-129 maturation.2 In this latest study, Prof Yu and colleagues analyze publicly available ChIP-seq, CLIP-seq, and RNA-seq datasets and pinpoint that KHSRP occupies specific genomic sites to regulate the transcription of several hematopoietic genes. Moreover, KHSRP also associates with pre-mRNA intronic regions to modulate alternative splicing during monocytic differentiation. Of note, KHSRP collaboratively controls mago homolog, exon junction complex subunit (MOGOH), and adenosine deaminase RNA-specific B1 (ADARB1) expression at both the transcriptional and posttranscriptional levels. These new data establish a paradigm for analysis of other functional Che-RBPs in different biological systems.Prof Jia Yu is researching RNA regulation and hematopoiesis at the Institute of Basic Medicine Sciences, Beijing, China. Prof Yu’s research article entitled “KH-type splicing regulatory protein KHSRP regulates monocytic differentiation through combining transcriptional and posttranscriptional mechanisms” highlights the dual DNA- and RNA-binding activities of the chromatin-enriched (Che) RBP, KHSRP. Many RBPs are implicated in the transcriptional and posttranscriptional regulation of various biological processes. Among them, Che-RBPs accumulate in the chromatin fractions, where they likely regulate transcription. However, whether other RBPs also have chromatin-binding capacity was previously unknown. In an earlier work, Prof Yu performs a global screen to systemically investigate which Che-RBPs are expressed in hematopoietic cells and identify KH-type splicing regulatory protein (KHSRP) as a Che-RBP.6A modification: Mapping Methods, Roles and Mechanisms in Acute Myeloid Leukemia.” N6-methyladenosine (m6A) is an RNA modification that has important roles in regulating mRNA metabolism, and also implicates in hematologic malignancies, including acute myeloid leukemia (AML). Thanks to the rapid development of high-throughput sequencing technologies, we now have insight into the biological functions of m6A modification at the whole transcriptome level. In their review, Zhang et al summarize the latest advances in understanding m6A biology, with an emphasis on its machinery and the methods permitting m6A mapping. They also discuss the critical roles of RNA m6A in AML pathogenesis. Finally, they outline potential AML inhibitors based on targeting m6A modifiers that have the potential to become a novel therapeutic approach for affected patients.Prof Haojian Zhang is researching the epigenetic transcription regulation of hematopoietic development and blood diseases at Wuhan University, China. Here, Prof Zhang and colleagues compile an interesting review article titled “RNA mProf Pengxu Qian is conducting research at Zhejiang University, China, on the transcription factors and epigenetic modifications involve in HSC self-renewal and pluripotent differentiation. Deep sequencing technologies have uncovered many small ncRNAs, including snoRNAs, miRNAs, tsRNAs, and circular RNAs, which are highly expressed in HSCs. Prof Qian’s review article titles “Small non-coding RNAs in Regulation of Hematopoietic Stem Cell Homeostasis,” updates us on our emerging understanding of how these small ncRNAs regulate HSC homeostasis. Specifically, the authors summarize the regulatory mechanisms of these small ncRNAs in HSC maintenance. They explain how technical advances are enabling us to dissect the cellular and molecular mechanisms of HSC homeostasis at a new resolution, and hope that the resulting advances in our understanding may lead to the development of new therapeutic strategies against hematopoietic malignancies.6A), N1-methyladenosine (m1A), N6, 2′-O-dimethyladenosine (m6Am), 5-methylcytidine (m5C). Since their identification, many researchers have demonstrate that such mRNA modifications are associated with myeloid malignancies. In their review titled “Epitranscriptomics in Myeloid Malignancie,” Prof Su and colleagues explain the function and regulatory mechanism of the most abundant and best-characterized mRNA modification, m6A, combining with other RNA epigenetic marks . They then give their expert insight on how we might better elucidate the nature and roles of epitranscriptomic modifications during leukemogenesis.Prof Rui Su is based at the Beckman Research Institute of City of Hope, Monrovia, where she focuses on epitranscriptomics in myeloid malignancies. “Epigenetics” refers to the chemical modifications that govern heritable changes in gene expression, independent of the DNA sequence. By contrast, “epitranscriptomics” describes the covalent decorations in RNA that help govern posttranscriptional gene regulation. Recent advances in high-throughput sequencing and analytical chemistry techniques have identified >100 chemical modifications in RNAs, including N6-methyladenosine (mProf Dong Wang is working at Southern Medical University, Guangzhou, on the application of computational systems biology techniques to delineate the crucial functions of RNA metabolism in hematopoietic regulation. In their review article, titled “Artificial intelligence and its applications in digital hematopathology,” Prof Dong Wang and colleagues focus on the application of AI to normal and abnormal hematopoietic cell identification. The precise and accurate identification of malignant blood cells is a key step in the diagnosis of blood diseases, such as AML and sickle cell disease. However, the manual morphological examination of blood cells capture on microscope slides is time-consuming and difficult to standardize. AI is an exciting development that is being applied to high-throughput, digital image interpretation; indeed, it is fast becoming the most beneficial technology applied to biomedical imaging. Moreover, the integration of digital images into biological workflows, and the advancement of algorithms and computer vision techniques is expanding the biologist’s horizons beyond the microscope slide. In this review, Prof Wang et al explain key concepts to be aware of in AI, before outlining the recent developments in applying AI to normal or abnormal hematopoietic cell identification. They discuss the great potential of AI over microscopy to improve the resolution, signal and information content of the data obtained, and then speculate on the future directions for the field.


U8 snoRNA, U2 snRNA, and a subset of tRNAs. Mutations in the homodimerization domain of U8 snoRNA impede dimerization in vitro and disrupt zebrafish development in vivo, suggesting an evolutionarily conserved role of this domain. Analysis of virus-infected cells reveals homodimerization of SARS-CoV-2 and Zika genomes, mediated by specific palindromic sequences located within protein-coding regions of N gene in SARS-CoV-2 and NS2A gene in Zika. We speculate that regions of viral genomes involved in homodimerization may constitute effective targets for antiviral therapies.RNA homodimerization is important for various physiological processes, including the assembly of membraneless organelles, RNA subcellular localization, and packaging of viral genomes. However, understanding RNA dimerization has been hampered by the lack of systematic in vivo detection methods. Here, we show that CLASH, PARIS, and other RNA proximity ligation methods detect RNA homodimers transcriptome-wide as “overlapping” chimeric reads that contain more than one copy of the same sequence. Analyzing published proximity ligation data sets, we show that RNA:RNA homodimers mediated by direct base-pairing are rare across the human transcriptome, but highly enriched in specific transcripts, including  The biological functions of RNA molecules depend on their ability to form intra- and intermolecular interactions, often mediated by Watson-Crick base-pairing. Intramolecular base-pairing determines the structure and function of RNA, including rRNA and tRNA; it regulates viral replication; and it influences the efficiency of mRNA translation into proteins. Intermolecular RNA–RNA base-pairing underlies codon–anticodon recognition, splicing, and regulation of gene expression by miRNA and siRNAs. Intramolecular and intermolecular interactions are interdependent, and according to the competing endogenous RNA (ceRNA) hypothesis , intermoAn intermolecular interaction between two identical molecules is known as homodimerization. Although homodimers are common in proteins , relativoskar and bicoid mRNAs in Drosophila embryos  originate from distinct parts of the RNA. When these fragments are ligated, sequenced, and mapped back to the reference, they should never be mapped to the same region of the RNA. The possible arrangements of the two fragments on the source RNA are shown in In contrast, when an intermolecular interaction exists between two copies of the same RNA molecule, the two interacting fragments may or may not originate from the same part of the RNA. When these fragments are mapped to the source RNA sequence, they can be found in any of the arrangements shown in In the following sections, we discuss the suitability of bioinformatic methods to detect the relevant types of chimeric reads; the types of chimeras we identify in various RNA proximity ligation experiments; and the possible origin and interpretation of the interactions we detect.Supplemental Fig. S1). Inspection of the BLAST and Bowtie 2 outputs showed that these chimeras were interpreted by the mapping programs as nonchimeric reads with internal deletions or insertions. We also called chimeras with STAR, a general-purpose mapping tool that has been used in some RNA proximity ligation studies . Both hyb and STAR commonly misidentified the position of the ligation junction between the two arms of the chimera by 1–3 nt, but this did not affect the identification of overlapping chimeras, which typically relies on the mapped position of nonligated ends of reads . Although gapped chimeras could originate from inter- or intramolecular interactions, the near absence of overlapping chimeras suggests that homomeric intermolecular interactions are rare in these data sets. In contrast, RPL and RIC-Seq recovered large numbers of overlapping chimeras. Both RPL and RIC-Seq can plausibly recover indirect interactions: RIC-Seq was specifically designed to detect indirect contacts through protein formaldehyde cross-linking, and RPL might allow for reassociation of RNA:RNA complexes during chemical processing in situ, owing to the absence of a covalent linkage between RNA strands. These results suggest that RNA homodimerization mediated by direct RNA–RNA base-pairing is uncommon in vivo. The results also show that RNA duplexes are generally stable during library preparation, at least in the CLASH, SPLASH, PARIS, and COMRADES methods, because random reassociation of duplexes would lead to the formation of similar proportions of gapped and overlapping chimeras.We focused on homotypic chimeras, that is, those in which both arms are mapped to the same transcript. Among homotypic chimeras, we detected gapped, ungapped, and overlapping chimeras in all data sets, but the relative proportions of these three types varied greatly between data sets . MethodsAn intriguing pattern is the peak at overlap = 0 in Supplemental Fig. S2). We speculate that most such chimeras are derived from the same type of interaction that gives rise to 3′-5′ ungapped chimeras, but the apparent overlap is caused by mapping errors. Although we could not detect similar artifacts in our simulated benchmarking data, the artifacts could arise in experimental data because of sequencing errors or adapter mutations. We thus conservatively exclude chimeras with L < 5 from consideration in the calling of homodimers.We also observed an overrepresentation of overlapping chimeras with very short overlaps (0 < L < 5) in all UV and psoralen data sets . After filtering, gapped chimeras were more common overall, but 84 genes contained overlapping chimeras, including 17 genes that were significantly enriched in overlapping chimeras  . The most highly enriched transcript was mRNA TMEM107, which contained 100 times more overlapping than gapped chimeras  , U8, in its 3′ untranslated region, and almost all TMEM107:TMEM107 chimeras mapped to that region, suggesting that these chimeras represent U8:U8 interactions. The chimeras were concentrated around the 5′ end of U8 , nor have we found homodimer enrichment in other mRNAs from the TMEM family.nd of U8 A,B, and data set , 2022. Pomal RNA  and with RNA . U2 snRNA contained 20 times as many overlapping as gapped chimeras. Regions involved in homodimeric interactions in U1 and U2 are limited to a particular fragment of the RNA, whereas other types of interactions can be found along the transcript . Most overlapping chimeras in U2 included the sequence of stem–loop III, downstream from the Sm binding site, suggesting that in a fraction of U2 molecules found in the cell, stem–loop III is unfolded and forms homomeric intermolecular interactions , and PARIS2 . These transcripts include ribosomal RNA, U1 and U2 snRNA, U3 and U8 snoRNA, 4 tRNAs, and 36 mRNAs. We then compared specific homodimerization events detected by different proximity ligation methods. PARIS and COMRADES showed the largest fractions of homotypic chimeras, most of which were nonoverlapping and likely represented intramolecular interactions. Across all RNA biotypes, rRNAs formed most homodimers, particularly in PARIS and SPLASH, but such homodimers were not statistically enriched, when compared to intramolecular interactions. tRNAs were enriched for homodimers in AGO1 CLASH and SPLASH . tRNA-derived small RNAs (tsRNAs), including tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs), are small regulatory RNAs processed from mature tRNAs or precursor tRNAs (tX(XXX)D, a yeast tRNA similar to serine tRNAs . The tRNA homodimers detected by AGO1 CLASH in human cells  may indicate a miRNA-tRNA network resulting in competition for binding sites and availability for gene silencing, as reported previously , we found 50 transcripts with homodimers found across two or more studies (or tRNAs . tX(XXX)ne tRNAs  formed aeviously .Supplemental Fig. S3). As discussed above, the overlapping chimeras found by RIC-Seq probably represent indirect interactions rather than RNA homodimers. RIC-Seq showed significant enrichment of overlapping chimeras in some mitochondrial mRNAs, with CO1, ND2, and ND4 containing the highest numbers of overlaps . Bidirectional transcription of mitochondrial RNA is known to result in hybridization of complementary strands . YLR154W-E, a possible ncRNA from yeast with a strong enrichment in overlapping chimeras in the RPL data, can be predicted to dimerize through an extended stem structure .The largest ratio of overlapping to homotypic chimeras was recovered by RIC-Seq (7%). RIC-Seq also recovered the highest number of genes with overlapping chimeras (more than a thousand), 17 of which were significantly enriched for overlapping chimeras  to analyze the functional consequences of human U8 mutations . We found that all three mutants fail to complement developmental phenotypes observed in the U8-3−/− embryos . Furthermore, mutations in the homodimerization domain disrupt the formation of slowly migrating conformers by in vitro transcribed U8 RNA in native polyacrylamide gel electrophoresis . Taken together, these results suggest that the U8 homodimerization domain we discovered plays an important biological role that is conserved across vertebrates.We first asked if mutations predicted to affect homodimer formation disrupt  embryos B,C. We aSupplemental Fig. S8). We postulate that compensatory mutations disrupt other important aspects of U8 function, and thus they do not complement function, although they may rescue dimerization. This interpretation is supported by the predicted effect of our compensatory mutations on the U8:28S pre-rRNA interaction . Further biochemical studies are required to dissect the functions of the U8 homodimerization domain and its mutated variants. Altogether, our experiments are consistent with the hypothesis that U8:U8 and U8:28S pre-rRNA interactions are essential for U8 function in vivo.Next, we attempted to rescue homodimerization mutants by compensatory mutations. None of the five double mutants we tested rescued the phenotype of zebrafish embryos .We next turned to COMRADES data from cells that have been infected with SARS-CoV-2 and Zika viruses, to detect possible homodimers of virus RNA. Although Zika RNA is not known to homodimerize, dimerization is an essential step in the packaging of some viruses, including HIV, whereas dimerization of SARS-CoV RNA was suggested to play a role in translational frameshifting . To deteon sites A. RNA foN) gene, and additional peaks in the N, Orf6, and Orf1a coding sequences  , or of an artificial circRNA created in vitro by ligation, producing a concatemeric cDNA. However, the low abundance of circRNAs and low efficiency of RNA ligases makes such events unlikely. We also note that proximity ligation can only identify a subset of possible RNA homodimers, namely, those where both RNAs interact via the same part of their sequence, or via two regions that are close enough in the primary sequence to detect overlaps in chimeric reads. Although many known RNA homodimers are of this type  , interacHomodimerization has now been reported for most major biotypes of RNA, and known roles of homodimers include the packaging of viral genomes, assembly of membraneless organelles, regulation of RNA localization C. Given S. cerevisiae RDN37 gene: NCBI GenBank database (https://www.ncbi.nlm.nih.gov/genbank/) Sequence ID: CP026300.1, range 448,071–448,298, minus strand). We generated all 30-nt substrings of the reference sequence and concatenated all possible pairs of substrings, which yielded 10,871 overlapping chimeras and 28,730 nonoverlapping chimeras.To benchmark methods for detection of overlapping chimeras, we assembled a test data set using an arbitrary RNA sequence :hyb analyse in = input.fasta db = RDN37 format = comp eval = 0.001hyb (BLAST mapping):hyb analyse in = input.fasta db = RDN37 format = comp align = blastall eval = 0.001STAR:STAR ‐‐genomeDir . ‐‐readFilesIn input.fasta ‐‐outFileNamePrefix 06 ‐‐outReadsUnmapped Fastx ‐‐outFilterMismatchNoverLmax 0.05 ‐‐outFilterMatchNmin 16 ‐‐outFilterScoreMinOverLread 0 ‐‐outFilterMatchNminOverLread 0 ‐‐clip3pAdapterMMp 0.1 ‐‐chimSegmentMin 15 ‐‐scoreGapNoncan -4 ‐‐scoreGapATAC -4 ‐‐chimJunctionOverhangMin 15We used the ua.hyb files from hyb and Chimeric.out.junction files from STAR for downstream analysis. To generate the coverage heatmaps of chimeras detected in the test data set, we extracted the coordinates of chimera junctions and plotted them using Java TreeView .S. cerevisiae preribosomal RNA . The lengths of chimeric arms ranged from 20 to 40 nt, to approximate the sizes of chimeric fragments found by CLASH, PARIS, and related methods. We mapped these simulated reads against the entire yeast transcriptome, using hyb with default settings. To estimate sensitivity and specificity for chimera detection, we used L ≥ 5 (see next section) as the cutoff for calling overlapping chimeras.As a second test data set, we used more than 1 million simulated chimeric reads and more than 1 million nonchimeric reads from the 1 represents the end mapping coordinate of the left arm of the chimera (arm 1); e2 represents the end mapping coordinate of the right arm of the chimera (arm 2); s1 and s2 represent the start mapping coordinates of the respective arms. Calculation of L was implemented as a custom awk script, taking the ua.hyb files produced by the hyb pipeline as inputs.To quantify overlap between arms of chimeric reads, we defined the overlap metric, L, ashttps://www.ncbi.nlm.nih.gov/) and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) accession numbers: Escherichia coli RNase E CLASH: GSE77463 . Chimeric reads were called and annotated with the hyb package  with defOverlap statistics across experimental data sets were visualized in R  using thU8 RNA variants were in vitro transcribed from DNA templates containing a T7 consensus sequence as described (U8 variant and 100 pg mKate2 mRNA were microinjected into the yolk of one-cell stage zebrafish embryos through use of the PicoSpritzer III (Parker Instruments) apparatus. Where U8 variants were found to rescue the morphology of U8-3 mutant zebrafish, genotyping was performed as described in Human escribed . pCS2+-mZebrafish embryos were anesthetized using MS-222 (Sigma-Aldrich), embedded in 3% Methyl cellulose (M0387), and imaged on an MZFLIII fluorescent stereomicroscope (Leica) with a MicroPublisher 3.3 RTV camera, using Micro-Manager 1.4.23 software. Embryo length was quantified using images taken at 1× magnification with a 1-mm scale bar as a reference point. Embryo length was measured in Microsoft Powerpoint (Microsoft) by drawing a line from head to tail.P < 0.05 was considered statistically significant (using a student t-test). Statistical methods were not used to predetermine sample size. Experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.Statistical analyses were performed using GraphPad Prism 8.0. Results are presented as violin plots, with bold dashed line representing the median value. All data points are shown. For all analyses, U8 snoRNA. Common reverse primer and primers used to transcribe the wild-type precursor form of U8, and primers used to genotype U8-3 mutant zebrafish are reported in GGGGATCGTCAaGTGGGATAATCCTTACCTG 8G > A sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATgCTTACCTGTTCCTCCTC 19G > C sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGcATAATgCTTACCTGTTCCTCCTC 13C_19G sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCgTTACCTGTTCCTCCTC 20C > G sense: TAATACGACTCACTATAGGGGATCGTCAGGTGcGATAATCgTTACCTGTTCCTCCTC 12G > C_20C > G sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCtTTACCTGTTCCTCCTC 20C > T sense: TAATACGACTCACTATAGGGGATCGTCAGGTGaGATAATCtTTACCTGTTCCTCCTC 12G > A_20C > T sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCCTTAgCTGTTCCTCCTC 24C > G sense: TAATACGACTCACTATAGGGGATCGTCAGcTGGGATAATCCTTAgCTGTTCCTCCTC 9G > C_24C > G sense: TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCCTTAtCTGTTCCTCCTC 24C > T sense: TAATACGACTCACTATAGGGGATCGTCAGaTGGGATAATCCTTAtCTGTTCCTCCTC 9A_24T sense: TAATACGACTCACTATAPrimers unique to this study were mutated nucleotide lowercase, T7 sequence red, bolded Gs to ensure accurate transcription of entire U8 snoRNA (136 nt), U8 snoRNA variants  and a truncated U8 snoRNA (nt 1–26 removed) were produced by PCR from the plasmid pRNA-hU8-GFP . RNA was visualized using SYBR Green II RNA Gel Stain and a LI-COR Odyssey FC imager using the 600 channel. For native gel electrophoresis, U8 snoRNAs were put in 50 mM Tris-Cl at pH 8, 100 mM NaCl, heated to 95°C, and cooled slowly to room temperature. U8 snoRNAs were run at 4°C on a 6% native gel (37.5:1 Acrylamide:Bis-acrylamide) with TBM buffer  at 120 V. RNA was visualized using SYBR Green II RNA Gel Stain and a LI-COR Odyssey FC imager using the 600 channel.In vitro transcription templates for the mature -hU8-GFP  using th hU8_T7_F TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCC mat_hU8_R AATCAGACAGGAGCAATCAGGGTGTTGCAAG phU8T7_19G_F TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATgCTTACCTGTTCCTCCTC phU8T7_20T_F TAATACGACTCACTATAGGGGATCGTCAGGTGGGATAATCtTTACCTGTTCCTCCTC phU8T7_delta-homo_F TAATACGACTCACTATAGGGGATCGTCAccTcccATAATggTTACCTGTTCCTCCTCCGG phU8T7_trunc_F TAATACGACTCACTATAGGGGTTCCTCCTCCGGAGGGCAGhttps://github.com/gkudla/hyb).The software used to perform these analyses can be downloaded from GitHub (https://github.com/gkudla/hyb/blob/master/bin/hyb_overlaps.awk) and as Supplemental Code.The script used to calculate L (the overlap between arms of chimeras) is available at GitHub (


NMR methods, and in particular ligand-based approaches, are among the most robust and reliable alternatives for binding detection and consequently, they have become highly popular in the context of hit identification and drug discovery. However, when dealing with DNA/RNA targets, these techniques face limitations that have precluded widespread application in medicinal chemistry. In order to expand the arsenal of spectroscopic tools for binding detection and to overcome the existing difficulties, herein we explore the scope and limitations of a strategy that makes use of a binding indicator previously unexploited by NMR: the perturbation of the ligand reactivity caused by complex formation. The obtained results indicate that ligand reactivity can be utilised to reveal association processes and identify the best binders within mixtures of significant complexity, providing a conceptually different reactivity-based alternative within NMR screening methods. NMR spectroscopy could be a robust and reliable method for binding detection in drug discovery, but DNA/RNA targets remain challenging to analyze. Here, the authors explore the perturbation of the ligand reactivity caused by complex formation as a binding indicator to identify best binders within mixtures of significant complexity, providing a conceptually different reactivity-based alternative within NMR screening methods. Moreover, RNA has emerged as a key regulatory element in multiple biological processes. Although only a 3% of the human genome codes for proteins, around 85% is transcribed into RNA. A large number of these noncoding RNAs are involved in diseases, both in cancer and non-tumorigenic disorders, providing a wealth of promising and previously unrecognised therapeutic targets2.The specific association of low-molecular-weight ligands to DNA/RNA fragments are molecular recognition events of capital importance in biological and medicinal chemistry. Indeed, nucleic acids are direct targets for most cytotoxic drugs, which are still extensively used in the treatment of a wide range of human cancers16. Among them, ligand-based methodologies have gained popularity in recent years as they are especially suitable for small binders of weak-to-medium affinity, no isotopic labelling of the receptor is required and upper limits for its size are virtually non-existent. In addition, most of these methods can be easily integrated in screening protocols as they are fast and relatively inexpensive. Not surprisingly, ligand-based NMR strategies have become highly popular in the context of hit identification and drug discovery. Unfortunately, despite its undeniable versatility and numerous advantages, NMR also faces some limitations as a detection tool, some of which are especially severe for nucleic acids. First, non-specific association of cationic ligands to the poly-anionic surface of the targets represents a common and serious problem. Furthermore, the short correlation time inherent to the typically employed medium-sized DNA/RNA fragments, together with their reduced proton density and limited chemical shift dispersion as compared to proteins, pose additional challenges for STD-based , or exchange of saturation. Indeed, a variety of strategies have been introduced over the time, many of which have found widespread applications in both academic research and pharmaceutical industry18. In principle, changes in the ligand reactivity associated to binding could be revealed employing the simple strategy represented in Fig. In order to address these issues, herein we put forward an alternative approach, which is based on a fundamentally different binding indicator unexploited by current screening methodologies: the limitation in the ligand reactivity promoted by complex formation (herein referred to as ligand protection). This effect has been shown for the molecular recognition of aminoglycoside antibiotics by RNA aptamers in the context of synthetic applications, and would be expected to be general for different receptors or reactions2 groups with reductive amination reactions. Among the different possible fragments to attach at these sites, methyl groups present several characteristic advantages from an NMR perspective: they comprise three equivalent protons, typically produce sharp NMR signals and, if properly labelled with 13C, they can be detected even at low micromolar concentrations employing HSQC-type (heteronuclear single quantum coherence) experiments. Taking this into account, N-12CH3 and N-13CH3 methyl groups  were selected as NMR-inactive and NMR-active functions, respectively . Second, the sensitivity of the observed protection effects to the ligand/receptor complementarity and the strength of the existing intermolecular contacts is briefly discussed. Finally, protection effects are implemented in actual screening assays, employing an adapted version of the general strategy shown in Fig. The following discussion is structured into three sub-sections. First, we focused our attention on an important family of bio-active RNA binders, the aminoglycoside antibiotics1) was employed as a model compound and its N-methylation kinetics was analysed, both in the absence and presence of the wild-type ribosomal A-site RNA fragment (wt-RNA)30. This can be carried out following the simple NMR-based protocol represented in Fig. 1 (40 μM each) are subjected to a reductive amination reaction with 13C-labelled formaldehyde . This isotopically labelled reagent allows the NMR detection of the reaction products with large sensitivity and for this reason it will be used throughout the manuscript for screening purposes. However, in this section it will be employed just for monitorization of the reaction kinetics. The reaction is triggered by the addition of sodium cyanoborohydride and then left to proceed for a certain labelling-time (2 min–24 h), after which a large excess of unlabelled formaldehyde (>50 mM) was added to complete the reaction, thus yielding a sole per-N-methyl kanamycin derivative. Finally, the RNA receptor was enzymatically digested and both ligand solutions were transferred to NMR tubes for analysis. As shown in Fig. 2 group), whose absolute intensities depend on the fraction of 13CH3 incorporated at every ligand reactive position during the labelling period. Integration of NMR cross-peaks in spectra obtained with increasing labelling-times allowed for the monitorization of the full reductive amination kinetics for each particular amine moiety . The obtained results are represented in Fig. A central hypothesis to this work is that association with the nucleic acid receptor prevents, to some extent, the ligand from accessing to the selected reagent, thus decreasing its apparent reactivity. In order to test this point, kanamycin-B . It is well established that this experimental set up enhances ligand/receptor polar contacts, leading to a significant increase in the complex stability. Indeed, in the absence of any ionic competitors, polar interactions mediated by the ligand –NHiew Fig. . Thus, wiew Fig. , magenta31, this mut-RNA fragment binds kanamycin-B (1) with similar affinity to the wild-type target. However, being devoid of an internal loop motif, it displays a narrower major groove leading to a presumably less exposed binding region. In agreement with our expectations, the mut-RNA receptor promotes a sharper decrease in the kanamycin-B reactivity, whereas the drug reactivity profile is basically maintained  was selected, spanning around >20-fold range in binding affinity for the A-site target . Streptomycin 7, which has a different ribosome target, binds to the A-site non-specifically with Kb < 104 M−133. As shown in Supplementary Fig. 7 as the weakest ligand, the identification of 1 as the best binder is far more problematic. Non-specific association of the three cationic compounds to the RNA target (even at 0.2 M NaCl) render both relaxation-based approaches ambiguous and inconclusive. In our opinion, most of these limitations could be overcome by complementing chemical relaxation with chemical reactivity as a binding indicator.Next we tested the potential of ligand reactivity as a binding indicator in screening protocols, employing relatively simple mixtures of cationic derivatives  contain an identical number of signals. However, in the latter case cross-peak intensities are biased in favour of the stronger binders, which results from the differential protective effect exerted by the receptor during step 1; the best binders are somewhat sheltered from the unlabelled reagent, incorporating larger fractions of 13CH3 after the RNA digestion , which displays positive signals only for the best ligand. In order to evaluate differences in cross-peaks intensity and to facilitate an accurate subtraction of the HSQC data sets, 13C-sodium acetate, present at identical concentrations in all the NMR samples, was employed as internal reference.In summary, differences in intensity between HSQC1 and 2 (mix 1). Fig. −RNA (top) and HSQCdiff (bottom) data sets obtained after applying our protocol to the binary mixture, either with the mutated or the wild-type RNA receptors . It can be observed that, in contrast to the HSQC−RNA experiments, HSQCdiff spectra seem to reflect the presence of a preponderant mixture component, allowing a straightforward identification of the best binder. As expected, the wt-RNA fragment exerts a preferential protection of ligand 2 .Further experiments performed with wt-RNA receptor and ligand solutions containing 5 (mix 2) and 7 (mix 3) aminoglycoside compounds . For particular components we included alternative enantiomers/stereoisomers. As expected, the resulting mixture produced extremely overlapped 1D-NMR spectra which rendered the sample intractable by conventional NMR-based screening approaches. Fortunately, the observed complexity is partially reduced in the 2D-HSQC data-set measured for the 13C-permethylated sample  get protected by the DNA fragment, reflecting their association at relatively buried sites of the junction structure  has a significant influence on most NMR parameters, this interaction mode has a limited effect on ligand reactivity. In this regard, it is important to note that under the employed experimental conditions, the RNA/DNA receptors interact with the components of the ligand libraries in multiple ways, which comprise both strong and weak (non-specific) binding events. Indeed, multiple binding represents a common feature of aminoglycoside/RNA association processes as revealed by ITC studies13C-labelled reagent after the receptor digestion, showing larger cross-peaks in the HSQC+DNA/RNA spectrum. This is the most common situation and the behaviour observed in all the assays included in the manuscript. Conversely, catalysis by the DNA/RNA fragment would translate in an enhanced incorporation of unlabelled N-methyl groups, implying that the corresponding signals would display significantly reduced intensities in the HSQC+DNA/RNA experiment. In summary, any change in reactivity, either inhibition or enhancement, could be equally revealed by comparing the HSQC+DNA/RNA and HSQC−DNA/RNA data sets. These reactivity perturbations could also be taken as evidence of binding for screening purposes.As a final point, it should be noted that the proposed methodology could be equally employed to reveal catalysis by the receptor, an uncommon but not implausible scenario. Thus, according to our protocol, complexed ligands “protected” by the DNA/RNA from the unlabelled formaldehyde will incorporate larger fractions of the To conclude, the obtained results validate the general strategy proposed in this study and set the basis for a conceptually different type of reactivity-based NMR screening methods.42. DNA oligonucleotides 5′-CGCGAAGCATTCGCG-3′ (for the duplex structure), 5′-GGTTGGCGCGAAGCATTCGCGGGTTGG-3′ , 5′-GGGAGGCGTGGCCTGGGCGGGACTGGGG-3′ (for the HIV-LTIII quadruplex-duplex junction) and 5′-CGTGCAGCGGCTTGCCGGCACTTGTGCTTCTGCACG-3′  were purchased in its purified desalted form from IDT (Integrated DNA technologies). Both, RNA and DNA oligos were dialysed in the appropriate buffer. RNA assays were performed in 100 mM NaCl, 10 mM Na2PO4 buffer at pH 7.5. For the DNA duplex and quadruplex-duplex junction structure we employed 20 mM KCl, 20 mM K2HPO4 at pH 7.0. Finally, experiments with the three-way junction structure (TWJ) were performed in 50 mM KCl, 10 mM K2HPO4 at pH 6.5.The 27-mer A-site RNA fragment (receptor wt-RNA) together with a mutated version without internal loop (receptor mut-RNA) were prepared by in vitro transcription as previously describedTo ensure a proper folding of the different RNA or DNA structures the corresponding oligonucleotides were subjected to an annealing protocol. With this purpose they were placed in a water bath at 293 K, heated to 358 K for 5 min and then slowly cooled back down to 293 K over a 2 h period.1, 4–7 and library derivatives l-1 to l-39 (except I-31 and I-32) together with both unlabelled and 13C-labelled formaldehyde samples were purchase from Sigma-Aldrich. Kanamycin derivatives 2–3, I-31 and I-32 were prepared by our group as recently reported35.Compounds 2PO4 buffer at pH 7.5. For the complexed state, lyophilised RNA samples were dissolved in buffer and renatured as previously outlined. Then, ligands 1 or 2 were added from stock solutions. Final aminoglycoside concentration in both solutions was 40 μM, while for the complexed state we employed a slight excess of the RNA fragment (50 μM). According to the literature, binding affinities of 1 and 2 for the employed receptors (wt-RNA or mut-RNA) are in the 106–108 M−1 range, implying that under the employed experimental conditions, the fraction of ligand bound to RNA was, in all cases, larger than 95%40. As a next step, both solutions . The objective of this isotopic dilution step was to stop the incorporation of 13CH3 groups to the aminoglycoside, yielding a single per-N-methyl derivative. Next, we digested the RNA fragment with RNAase-A  and ligand solutions were transferred to NMR tubes for analysis. The fraction of 13C- incorporated at each reactive drug position was evaluated by means of HSQC experiments acquired at pH 10.0. HSQC cross-peaks for per-N-methyl- 1 and 2 derivatives have been previously assigned by means of HMBC spectra43 and were integrated employing 13C-sodium acetate, present in the reaction buffer, as internal reference (100 μM in both samples). This process was repeated employing growing labelling times, ranging from 2 min to 24 h to derive the full reaction curve for every drug reactive position. A protection factor (Pf) was defined for all of them as the ratio between initial reaction rates in the absence and presence of RNA. Uncertainties in their values fall within the following ranges: ±5% for 1 < Pf < 5, ±10% for 5 < Pf < 10 and ±20% for 10 < Pf < 20. Protection factors for compound 1 were derived under a variety of experimental conditions including low ionic strength or acidic pH.Ligand solutions (V = 250 μL each) containing the free and RNA-complexed ligand were prepared in 100 mM NaCl, 10 mM Na12C-formaldehyde and 10 mM sodium cyanoborohydride. Regarding the formaldehyde, different concentrations, in the 0.5–2 mM, were tested depending on the complexity of the ligand mixture. Next, the RNA or DNA fragment is digested with RNAase-A  or DNAase, respectively. Finally  in Bruker Avance 600 MHz or Bruker Avance 800 MHz spectrometers equipped with cryo-probes. Resolved signals in HSQC+RNA/DNA and HSQC−RNA/DNA data sets were integrated employing as internal reference 13C-sodium acetate, present in the reaction buffer (100 μM). Intensity ratios were taken as indicative of the receptor protective influence. Alternatively, HSQC+RNA/DNA and HSQC−RNA/DNA spectra were subtracted to produce a difference HSQC data set (herein referred to as HSQCdiff), which allows a straightforward identification of the protected mixture components.Solutions containing the different ligand mixtures assayed in this work were prepared in the adequate buffer (see above) both in the absence and presence of the nucleic acid receptor. For binary ligand mixtures, total ligand and receptor concentrations were fixed at 100 μM and 30 μM, respectively. For the more complex libraries , total ligand concentrations were in the 300–1000 μM range while that of the nucleic acids was maintained at 30 μM. As a first step in our screening protocol , employing 50 μM concentration for the nucleic acid and 1–2 mM for the individual ligands. STD data sets were acquired with 1024 scans, using a train of 50 ms Gaussian-shaped pulses for the selective saturation of the RNA/DNA protons at s Figure  exhibiteAsensio_PR FileSupplementary Information