Recent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. We derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations on gene segments, as well as of total nascent RNA on a gene. We also obtain exact expressions for the first two moments of mature RNA fluctuations and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data. Transcription, the production of RNA from a gene, is an inherently stochastic process. Specifically, the interval of time between two successive transcription events is a random variable whose statistics depend on multiple single-molecule events behind transcription  distributions of nascent RNA have been derived from models that do not explicitly model the movement of RNAP along a gene  the movement of RNAP is unidirectional, away from the promoter site and hence left to right, with no pausing or backtracking allowed; (ii) the detachment and elongation rates are independent of the position of RNAP on the gene. Each RNAP has associated with it a nascent RNA tail that grows longer as the RNAP transcribes more of the gene. When the RNAP reaches the last gene segment, termination occurs, i.e. the RNAP–nascent RNA complex gets dissociated from the gene leading to a mature RNA (M) which degrades with rate We consider a stochastic model of transcription that includes the processes of initiation, elongation, and termination, as illustrated in Fig. L is arbitrary, it should be kept in mind that L needs to be sufficiently large for the dynamics to be described at a fine spatial resolution. However, L also has to be small enough for the length of each gene segment to be much larger than the footprint of an RNAP; the latter is needed to ensure the validity of the low-traffic assumption. The elongation time which is the total time T from initiation to termination, that is, conditioning on those realisations for which the RNAP does not prematurely detach, is Erlang distributed with mean Note that, while the choice of mentclass2pt{minimirrespective of their lengths; to shed light on the fluctuations of nascent RNA, in this section we therefore focus on the calculation of statistics of local and total RNAP fluctuations. We define the vector of molecule numbers Note that the total number of RNAPs transcribing the gene is equal to the number of nascent RNA molecules present, ith species when the jth reaction occurs. Given the ordering of species and reactions as described in the tables above, it follows that the matrix Note that In this subsection, we outline the derivation of the steady-state means and variances of local RNAP fluctuations (on each gene segment), as well as of mature RNA. Our results are summarised in the following two propositions.Let Proposition Let Here, we note that an alternative representation of the functions j in Eq. , in termj in Eq.  of ‘AppeAs above, since the underlying propensities are linear in the number of molecules, the CME implies  and (4), respectively, simplify to b denotes the moments in the bursty limit. Moreover, n in Eq. , rather n in Eq. . The reay in Eq.  does noty in Eq.  is simplith to the To test the accuracy of our theory, in Fig. entclass1pt{minimar and The perfect agreement between our exact theory (solid lines) and simulation without volume exclusion (dots) provides a numerical validation of that theory. Our approximate theory (dashed lines) also yields a reasonably good approximation; the mismatch can be decreased if the degree of burstiness is increased, i.e. by increasing the parameters i from the promoter according to a power law, whereas that mean is constant along the gene if there is no premature detachment, as expected; (ii) the size of RNAP fluctuations, as measured by i for small premature detachment rates, but increases with i for sufficiently large values of the detachment rate; (iii) the Fano factor approaches 1—the value of i increases, which is due to the dispersal of the burst as stochastic elongation proceeds; (iv) the correlation coefficient between the local RNAP on two neighbouring gene segments decreases monotonically with i, which is exacerbated by premature detachment and is a direct result of the stochasticity inherent in the elongation process.The following interesting observations can be made from these figures: (i) if the rate of premature detachment is greater than zero, then the mean of local RNAP decreases monotonically with the distance i. When the detachment rate is nonzero, then the same effect is at play; however, the increase in the variance of fluctuations along the gene is now counteracted by the decrease in mean RNAP numbers, which leads to two types of behaviour: for small i, i, since the variance dominates over the mean, while for large i, the opposite occurs and i.The observation in (iii) can be explained in detail as follows. When the detachment rate is zero, a burst of RNAPs rapidly bind to the promoter, leading to large fluctuations near that site; however, thereafter each RNAP moves distinctly from all others due to stochastic elongation. Hence, the burst is gradually dispersed as elongation proceeds, which implies a decrease in the variance of fluctuations with increasing Constitutive limit: The other common parameter regime is that of constitutive gene expression, where the gene spends most of its time in the active state and transcription is continuous, which corresponds to the limit of very small c denotes the constitutive limit. This drastic simplification reflects the fact that, in the constitutive limit, the distributions of mature RNA and local RNAP are Poissonian: as the regulatory network is effectively given by Huisinga .i, independently of the rate of premature detachment, while To further test the accuracy of our theory, in Fig. L is discussed in the next section.Finally, we remark that the accuracy of our expressions for the mean and variance of mature RNA, as given in Eq. (2) and 4), is verified by simulation (SSA) in Fig. , is veriWhile local RNAP fluctuations are measurable in experiment, as discussed in the Introduction, measurements of total RNAP on a gene are typically reported. Hence, in this section, we briefly discuss descriptive statistics of total RNAP fluctuations.ith gene segment, the total number of RNAPs on the gene—arbitrarily divided into L segments—is given by Recalling that L, as we keep the mean elongation time constant; the mean shows very little dependence on L, while the dependence of the variance is more pronounced. We recall that, while the parameter L is arbitrary in principle, it actually determines the size of fluctuations in the elongation time. Since that time is the sum of L independent exponential variables with mean T is Erlang with mean L. Hence, the larger L is, the narrower is the distribution of T and the more deterministic is elongation itself. Thus, Fig. T. It hence follows that models in which the elongation rate is assumed to be exponentially distributed  and (4) reduce ton by Eq.  below. HRelationship between Fano factors of total RNAP and mature RNA: Specifying to the case of no premature detachment, it is interesting to note that in the bursty limit, i.e. for m denotes mature RNA. 14), respSensitivity of coefficient of variation of total RNAP and mature RNA: Since we have found explicit expressions for the first two moments of the distributions of total RNAP and of mature RNA, we can now estimate the sensitivity of the noise in each of those to small perturbations in the transcriptional parameters. Specifically, we calculate the logarithmic sensitivity (LS), which is also known as the relativity sensitivity, of the coefficient of variation (CV) to a parameter s, which is defined as s results in a change of r, and r, and In Table mentclass2pt{minimn et al. ; see Tabmentclass2pt{minimr and r and r is the least sensitive parameter for the genes which are mostly inactive, whereas it is among the most sensitive parameters for genes that are mostly active.The following observations can be made regarding the sensitivity of the noise in total RNAP fluctuations: (i) for the two genes PDR5 and POL1 which spend most of their time in the inactive state due to L) case, the issue remains that the exact solution is not given in terms of simple functions unless promoter switching is slow compared to initiation, elongation, and termination, in which case the solution reduces to a weighted sum of two Poisson distributions  are given by p and q:We approximate the distribution of total RNAP transcribing the gene via a Negative Binomial distribution, as follows. The mean and variance of the Negative Binomial distribution NB or inactive (1). Note that i for n is the number of mature RNAs. The time evolution of the probabilities k is faster than the degradation rate We start by defining s being the characteristic variable. The first characteristic equation gives u, u. Solving Eq. (u is bounded. The constant C in Eq. (F satisfiesP(n) of mature RNA can be found from the formular renormalised to In order to find an analytical expression for the propagator probabilities s in Eq. , we defifrom Eq. , we obtamentclass2pt{minimFenichel , with \d(u). Eq.  which adn in Eq.  and 4f)\document=0), Eq. . Hence, Thus far, we have determined the statistics of the total number of RNAP transcribing the given gene; these are also the statistics of the number of nascent RNA molecules. However, in experiments using single-molecule fluorescence in situ hybridisation [smFISH ; as above, we note the contrasting difference between the statistics of the fluorescent signal and the corresponding statistics of total RNAP—which is the statistics of total nascent RNA—shown in Fig. Similarly, denoting by e in Eq.  in ‘AppeHence, the calculation of the statistics of the number of nascent RNAs from the raw signal intensity presents a challenge and has to be approached carefully. The expressions presented above allow for the inference of transcriptional parameters from the first two moments of the fluorescent signal by means of moment-based inference techniques  state L at constant mean elongation time, We extend the model described in Fig. n. Let Let the number of RNAP molecules in the active state i When the premature detachment rate of RNAP is nonzero and gene expression is bursty, the coefficient of variation of local RNAP fluctuations can either decrease or increase with distance from the promoter. By contrast, when expression is constitutive, the coefficient of variation increases monotonically with distance from the promoter. Other statistical measures such as the mean, Fano factor, and correlation coefficient of local RNAP numbers decrease monotonically with distance from the promoter.(ii)b denotes the mean burst size. An implication is that the telegraph model will result in an overestimate of the mean burst size from nascent RNA data by a factor of 2. Another implication is that deviations from Poisson fluctuations are more apparent in data for nascent RNA than they are for mature RNA. One can further state the following relationship: the Fano factor of nascent RNA equals twice the Fano factor of mature RNA, minus 1. If expression is non-bursty, then the Fano factor of nascent RNA can be larger or smaller than that of mature RNA, as determined by the condition in Eq. For genes characterised by bursty expression, the sensitivity of the noise in total RNAP fluctuations is highest to perturbations in the gene activation rate and the mean elongation time; for constitutive genes, the most sensitive parameters are the initiation rate and the mean elongation time.(iv)A Negative Binomial distribution, parameterised with the expressions for the mean and variance of total nascent RNA derived here, provides a good approximation to the true distribution of total nascent RNA fluctuations on a gene when expression is either bursty or constitutive; the approximation is not accurate when the gene spends roughly equal amounts of time in the active and inactive states. We show that the distribution of nascent RNA is highly sensitive to the distribution of elongation times. In particular, if the elongation time is assumed to be exponentially distributed, as is implicitly assumed by telegraph models of nascent RNA, then the probability of observing zero RNA is much lower than if the elongation time is assumed to be fixed.(v)Using geometric singular perturbation theory (GSPT), we have rigorously proven that, in the limit of deterministic elongation (or fast elongation), no pausing and premature detachment, the steady-state distribution of mature RNA in our model is identical to that in the telegraph model (Raj et al. A summary of the main theoretical results can be found in Table In this paper, we have analysed a detailed stochastic model of transcription. Our model extends previous analytical work (Choubey et al. entclass1pt{minima


Nonsense mutations often result from single nucleotide substitutions that change a sense codon (coding for an amino acid) to a nonsense or premature termination codon (PTC) within the coding region of a gene. The impact of nonsense mutations is two-fold: (1) the PTC-containing mRNA is degraded by a surveillance pathway called nonsense-mediated mRNA decay (NMD) and (2) protein translation stops prematurely at the PTC codon, and thus no functional full-length protein is produced. As such, nonsense mutations result in a large number of human diseases. Nonsense suppression is a strategy that aims to correct the defects of hundreds of genetic disorders and reverse disease phenotypes and conditions. While most clinical trials have been performed with small molecules, there is an increasing need for sequence-specific repair approaches that are safer and adaptable to personalized medicine. Here, we discuss recent advances in both conventional strategies as well as new technologies. Several of these will soon be tested in clinical trials as nonsense therapies, even if they still have some limitations and challenges to overcome. The current genetic code consists of a total of 64 different codons (triplets of nucleotides), of which 61 are sense codons coding for 20 amino acids, and the remaining 3  are nonsense (or stop) codons that signal the termination of protein synthesis oxadiazol-3-yl]-benzoic acid; Ccade ago  and promcade ago ,58,59. AH2A gene . Usher sH2A gene . Lepista inversa, have a significant nonsense suppression activity [The search for new small molecules as potential nonsense suppression drugs continues, even outside the scope of aminoglycosides and oxadiazole derivatives. Recently, it was reported that clitocine and 2,6-diaminopurine, found in extracts from the mushroom activity . More spactivity . As withThe development of small molecules for nonsense suppression therapies can benefit from decades of R&D experience in the pharmaceutical industry. Small molecules are usually administered orally and have a good tissue distribution, achieved by systemic delivery. However, they can form non-specific molecular interactions with undesired targets, even beyond the nonsense suppression scope, leading to toxicity. There is therefore a need for new approaches that combine a simpler rational design with a higher degree of target specificity and potentially a longer half-life for an increased duration of the effect, allowing fewer or even single treatments. The emerging nucleic acid-based technologies are the alternatives to small molecules for nonsense suppression. There are a number of mechanistically different approaches that have been reported, and here, we review some of them, focusing on antisense oligonucleotides, suppressor tRNA, Adenosine Deaminase acting on RNA (ADAR)-catalyzed RNA editing, as well as box H/ACA guide RNA-directed pseudouridylation and CRISPR technology.Antisense oligos with exon-skipping ability were suggested two decades ago as a means to splice out PTC-harboring exons in frame ,64,65. HSeveral labs have used antisense oligos, in the form of gapmers, as a way of downregulating specific protein components of the NMD pathway, such as Upf3b  and SMG1Another nucleic acid-based strategy for nonsense suppression relies on tRNA suppressors that would act by incorporating a cognate amino acid at the PTC position . RecentlEver since Woolf et al.  proposedThis concept of site-specific A-to-I editing has been further advanced by Montiel-Gonzalez et al., as shown in the functional correction of CFTR nonsense mutations  in XenopIt is well established that endogenous RNA editing occurs mostly in the nucleus via the ADAR1 p110 isoform and ADAR2. The other form of ADAR1 is ADAR1 p150, which is interferon-inducible and found both in the nucleus and in the cytoplasm . Some ofIDUA gene (W402X) [Because the NMD pathway dramatically reduces the mRNA substrate level, without inhibiting the degradation of PTC-containing mRNA, the supply of substrate that can be recoded could be limited. Although gRNA-directed ADAR editing can in theory suppress NMD as well, experimental verification remains necessary. Recently, using the editing approach, Qu et al. have restored the full-length iduronidase (IDUA) protein in human skin fibroblasts harboring a common nonsense mutation in the  (W402X) . Editing (W402X) .Finally, it is worth noting that this editing strategy is highly dependent on the sequence and secondary structure contexts of the target site. For instance, there is a preference for certain 5′ and 3′ flanking nucleotides depending on the ADAR type .Karijolich and Yu have shown that the targeted conversion of uridine in the first position of a PTC into pseudouridine, a C-5 glycoside isomer of uridine , results in nonsense suppression in vitro and in yeast cells .Targeted pseudouridylation can be catalyzed by an RNA–protein complex known as box H/ACA ribonucleoprotein (RNP) . Specifically, the RNA component (box H/ACA RNA) serves as a guide that base-pairs with the substrate RNA, specifying the target uridine, which is subsequently modified by Cbf5/NAP57, the catalytic component of the complex. The guide RNA of each box H/ACA RNP folds into a hairpin–hinge–hairpin–tail secondary structure, with one internal loop in each hairpin. The internal loop sequence, also referred to as pseudouridylation pocket, forms base-pairing interactions with the substrate sequence, specifying the target uridine to be recognized and modified by Cbf5/NAP57. In principle, one can change only the guide sequence of a naturally occurring box H/ACA RNA to target a uridine at any position in an RNA. Upon expression in the cell, the guide RNA forms a box H/ACA RNP complex with the endogenous box H/ACA RNP proteins. Indeed, Karijolich and Yu have shown that they were able to design an artificial box H/ACA guide RNA derived from SNR81, a naturally occurring yeast H/ACA RNA, to direct pseudouridylation of a PTC . It seems that this read-through mechanism of action works by promoting unusual interactions between the codon–anticodon pair, which outcompete the release factors in binding to the PTC . FurtherThe guide–substrate base-pairing requirements have been recently analyzed, and it appears that the pseudouridylation pockets are flexible enough to be engineered to recognize virtually any target RNA, while specificity can be achieved with a minimum number of eight base pairs between the guide and the substrate ,90. ThisAnother advantage of this approach is the fact that an artificial guide RNA, upon expression in the cell, complexes with the endogenous proteins to form a functional box H/ACA RNP. Thus, this approach requires the delivery and expression of only one small guide RNA; no co-expression of other gene(s) is necessary. In a therapeutic setting, nonsense-suppressing designer box H/ACA snoRNAs could be administered as chemically modified oligonucleotides  for a short- to mid-term effect. Alternatively, the guide RNA can be inserted into viral vectors, such as adeno-associated viruses, for a more prolonged pharmacological effect in the target tissue. CEP290 gene, which causes the disease. This first treatment is a scientific and technological landmark, as it shows that a groundbreaking and a quite complex treatment for the repair of genetic disorders could reach patients in less than a decade.The advent of genome editing technologies , particuXPC gene with a nonsense mutation . Base editing efficiencies were 10%, and additional functional restoration of the protein was observed for up to one month after treatment. In another study, Cox et al. provided an example of RNA-targeting repair of PTCs. They fused the catalytic domain of ADAR2 with a deactivated Cas13b, a nuclease that can target RNA instead of DNA, to achieve editing in nonsense mutations of two genetic disorders (X-linked nephrogenic diabetes insipidus and Fanconi anemia) in substrates exogenously delivered via transfection [These technologies also hold great promise for the correction of nonsense mutations, whether through an active Cas9-derived nuclease that will directly cut and paste the genome or through the use of base editors, i.e., deactivated versions of the nuclease coupled to an adenosine deaminase. Base editors can either correct the genome without introducing double-strand breaks or specifically target a mutated mRNA. Lee et al. have recently presented an example of a DNA-targeting CRISPR-based tool with a version of Cas9, named CRISPR-PASS, for the correction of therapeutically relevant nonsense mutations . In thissfection . AlthougOver the years, a number of approaches have been developed to suppress nonsense mutations. These approaches can be roughly grouped into two categories: small-molecule-based approaches and nucleic acids-based approaches. Both have advantages and limitations. For instance, small-molecule-based approaches are simple and easy when it comes to cellular delivery. However, given that they presumably target the ribosomes (the mechanisms of action are often unclear), small molecules are probably not PTC target-specific. In contrast, despite the fact that nucleic acids are macromolecules (relatively large) and are therefore not always readily taken up by the cells, nucleic acids-based approaches are developed based on known mechanisms of action, with clear rationale and objectives. Most of the nucleic acids-based approaches specifically target PTC-containing mRNA transcripts (and the PTC) and therefore are relatively sequence-specific.It should be noted that all the different approaches discussed above are capable of suppressing NMD, translation termination, or both. Importantly, many of them have already been shown to be effective in nonsense suppression at least at the cellular level . Thus, they have recently attracted a lot of attention. It is expected that with various approaches being developed in parallel, some will succeed and become effective therapies. The race is on towards a cure for nonsense-associated diseases.


The spontaneous emergence of function from diverse RNA sequence pools is widely considered an important transition in the origin of life. Here we show that diverse sequence pools are not a prerequisite for the emergence of function. Starting five independent selection experiments each from a single RNA seed sequence - comprising a central homopolymeric poly-A (or poly-U) segment flanked by different conserved primer binding sites - we observe transformation (continuous drift) of the seeds into low diversity sequence pools by mutation, truncation and recombination without ever reaching that of a random pool even after 24 rounds. Upon continuous error prone replication and selection for ATP binding we isolate specific ATP- or GTP-binding aptamers with low micromolar affinities. Our results have implications for early RNA evolution in the light of the high mutation rates associated with both non-enzymatic and enzymatic prebiotic RNA replication.  Together with recent discoveries in prebiotic chemistry of varied and robust synthetic routes to the RNA (and DNA) nucleosides, their activation and their non-enzymatic polymerization outline an increasingly plausible path towards the emergence of an early RNA-based biology.Compelling evidence supports the hypothesis of a primordial biology where RNA played a more central role. These include the central catalytic role of RNA in core genetic processes such as translation, RNA processing and splicing as shown by the ribozyme cores of the spliceosome, RNAse P and ribosomal peptidyl-transferase centrein vitro selection (SELEX) experiments, which have succeeded in the isolation of a diverse array of ligands (aptamers) and catalysts directly from diverse sequence pools of RNA, DNA and some unnatural nucleic acid analogues (XNA). Of particular interest in this context are nucleotide binding aptamers, which were among the first RNA aptamers to be isolated and which have been studied extensively as model systems. Such studies have sought to define the necessary and optimal size for binding as well as the sequence parameters necessary for aptamer discovery such as oligomer length, conformational pre-organization, pool complexity and adaptive landscapes. Extrapolation from such selection experiments and analysis of the mutational landscapes of functional oligomers suggests that the frequency of distinct ligand-binding motifs is low (< 10−11), even in highly diverse random sequence pools, although structural pre-organization can increase the chance of ligand discovery. All of these experiments initiate from best-case scenarios regarding the size and sequence complexity of prebiotic oligomer pools, utilizing large, random sequence pools composed of approximately equal proportions of all four RNA nucleotides. However, studies on ab initio non-enzymatic polymerization of activated RNA nucleotides suggest that polymerization of longer RNA oligomers comprising all four RNA nucleotides in comparable proportions is disfavoured compared to the polymerization of homopolymeric or dinucleotide oligomers. This suggests that prebiotic oligomer pools might have been very significantly biased with regards to nucleotide composition with negative impacts on sequence and structural complexity and presumably phenotypic potential, i.e. the probability of functional sequences in a given sequence pool. Indeed, fundamental studies on ligase ribozymes suggest that function  strongly scales with sequence complexity. These findings thus pose a challenge to the conjecture of spontaneous emergence. The probability of the latter would seem to depend critically on pool size and sequence complexity, as these parameters inform both the initial frequency of functional sequences and the ease (the number of evolutionary trajectories) by which functional sequences can be accessed from arbitrary points in sequence space.A key transition along this path is thought to have been the spontaneous polymerization of activated RNA nucleotides into pools of random sequence RNA oligomers from which functional sequences could emerge. The generation of functional sequences from random pools has been recapitulated and studied by Here we sought to critically test these arguments by exploring an extreme scenario of pool complexity: the emergence of functional RNAs starting from single, non-functional, predominantly homopolymeric RNA sequences under a high mutation regime mimicking the poor fidelity of prebiotic RNA replication. We performed five independent selection experiments starting each from a single, but distinct, partially homopolymeric RNA sequence  and subjecting it to cycles of error prone replication and selection for ATP binding. We describe the discovery of both ATP- and GTP-binding RNA aptamers (from two independent selection experiments) and characterize their binding affinity and specificity. Furthermore, such sequences avoid the complications arising from the propensity of poly-G, poly-C sequences to form highly stable non-canonical secondary structures (such as G4 quadruplexes and i-motifs) that can hinder efficient replication.In a typical SELEX  experiment the sequence diversity is high at the beginning and is progressively reduced during each selection step. In contrast, here we start with a single “seed” sequence and introduce diversity before each selection round by error prone replication. Unlike the fully random sequence pool of a standard SELEX experiment, this creates a “cloud of genotypes” connected by mutation to a single “seed” sequence, akin to a quasispecies in viral replication. We sought to perform selections from sequences of minimal informational complexity, but purely homopolymeric sequences are challenging to propagate over multiple rounds of selection. We therefore settled for a compromise of partially homopolymeric seed sequences, whereby a central 39 nt homopolymeric stretch of polyA or polyU sequences is flanked on each side by different 15 nt conserved primer binding sites . We chose poly-A and poly-U seed sequences to initiate selections as these may more accurately reflect a prebiotic scenario, as indicated by the generally increased yield and length distribution of poly-AU RNA polymers obtained by non-enzymatic non-templated polymerization compared to mixed sequences. ). We performed this epPCR step for every selection round including for the initial pre-selection repertoire (R0), to introduce sequence diversity. In contrast to the reported epPCR mutation rate (mean number of mutations per base pair (30 cycles)) of 0.66±0.13%  of transitions/transversions), we observe much higher mutation rates in particular for the generation of the 1st round repertoire (R0). While an accurate mutation rate for R0 and for the other rounds is difficult to calculate due to pervasive slippage, truncation and recombination among amplified sequences (as is evident by the wide length distribution of R0 (and later round sequences)  sequences selection experiments, the homopolymeric sequence context adds additional bias and opportunities for template switching and recombination in PCR.Our protocol includes an error-prone PCR (epPCR) step  , due to quences) . While tGiven these complications, we sought to estimate error rates by calculating the Damerau-Levenshtein distance (LD) of sequence populations R0, R1 etc. to the original “seed” sequence. LD between two nucleic acid strings is defined as the minimum number of operations  required to change one string into the other. Given the high levels of slippage and truncation observed in epPCR of homopolymeric seed sequences, LD provides a more accurate measure of mutational distance than the more commonly used Hamming distance in the sequence pools. We find LDs for the starting pools T5R0 of ~7 (for T5R0) and ~4 for T7R0 and T8R0 suggesting estimated average mutation rates of 10%–18% per base pair  in the homopolymeric stretch . Thus onTo investigate if functional RNA sequences could be isolated from these pools we performed a further 8 rounds of selection for binding to ATP-sepharose and error prone PCR amplification  for T5,  which had previously been observed multiple times in unrelated selections. Said motif comprises a conserved stem-loop-structure with an essential bulged G opposite the recognition loop  by leveraging the search algorithm of Luptak and colleagues. While no unambiguous motif hits were found in the T7R12 and T8R12 selections (despite appearance of the recognition loop sequence), in the T5R8 selection, we obtained > 100 hits with divergent stem sequences    .,17a T5R8/359 suggests that an expanded loop and double G–G-bulge also support formation of a specific ATP binding pocket. This relaxes the search criteria for ATP aptamer motifs and suggest that there may be even more ATP-binding aptamers in the T5R8 pool than the > 100 canonical motifs detected . Binding experiments were performed three times with similar results. The fitted isotherm indicated a Kd of aptamer T5R8/359 binding ATP of around 7 μM . In contbona fide ATP aptamers as judged by the appearance of the canonical ATP aptamer motif  , 4 as weer motif . Thus, t the avian immune system and in hypermutating B-cell lines. Laboratory examples also include the evolution of function (phage infectivity) from an arbitrary soluble polypeptide sequence as well as model experiments in the ribozyme field morphing one ribozyme function into another. However, these examples differ from our work in that new functions emerge from already soluble (or stably folded) and functional protein (or RNA) domains. In contrast, we consciously chose non-functional starting sequences with low informational complexity (39 or T39 (U39), which induced high initial mutation rates due pervasive polymerase slippage on the homopolymeric sequences. Initial diversified R0 pools generated this way comprise a large number of truncated sequences radiating out from the seed by an average Levenshtein (LD) distance of 4–7   (The emergence of new function by iterative cycles of mutation and selection from a single (or very few) starting sequences has precedents in biology e.g. in virology (quasispecies),mplexity  comprisi T7, T8) . As sele T7, T8) . Nevertholution) . and/or towards RNA folds that are both abundant and robust (stable) (such as hairpin and hairpin-like motifs). Such structurally stable motifs can better withstand disruption through high mutation rates even if they do not present the most “fit” species, i.e. best adaptive solutions in the pool, in a process which has been termed “survival of the flattest”. Indeed, in all cases overall selected sequence pools become more structured and more stably folded than starting seed sequences (as judged by secondary structure prediction)  . Howeverdiction) . The foldiction) ) as welldiction) . While s and SAM,26 and was furthermore identified in several bacterial and eukaryotic genomes.,17b This parallels the case of the Hammerhead ribozyme motif and suggests that both of these motifs may represent minimal optimal solutions in RNA sequence space. The isolation of the canonical ATP aptamer motif in the T5 selection may also indicate its resilience to high mutation rates. The isolation of a GTP binding aptamer (T8R16/409) , it clearly binds to the ATP-agarose resin (both as the full-length T8R16/409 aptamer as well as a truncated core motif (T8R16/409core)  suggests that it may constitute privileged molecular solution for ATP binding. Indeed, it has been previously discovered multiple times in independent selection experiments including for binding to ATP containing cofactors such as NADR16/409)  from an 409core) . Indeed,  the starting sequence of the successful T5 selection shows a higher degree of pre-organization ) than the comparable T7 and T8 poly-A seeds . While the purine-rich ATP aptamer loop motif was enriched in all poly-A selections  , the key-A seeds . However-A seeds . Thus, w is more readily accessible from poly-A than poly-U, not only with regards to mutational distance but also due to the increased likelihood of A > G transition vs. U > A/G transversion mutations in mutagenic PCR. Interestingly, this dichotomy is a reflection of the SELEX process comprising transition between RNA and DNA intermediates , whereby only one RNA strand (+) is available for selection. In a prebiotic scenario, where RNA would be replicated from RNA templates , both RNA (+) and (−) strands would potentially be available for selection and poly-A- and poly-U-rich seed sequences would therefore provide comparable starting points for evolution.On the other hand, the failure of the T4 and T6 poly-U seeds to yield high affinity ATP binding aptamers (even after 24 rounds of selection) may simply be a reflection of the radically different mutational distances of the seed sequences from adaptive peaks. Indeed, the canonical purine-rich ATP binding motif and some viruses, genetic information is replicated with high fidelity and the main sources of genetic diversity are drift  and recombination. In contrast, current model systems of prebiotic RNA replication suggest a highly error-prone process both for non-enzymatic RNA replication as well as enzymatic replication by polymerase ribozymes combined with an innate tendency of RNA oligomers for recombination. The high mutation regime approach described herein seeks to mimic such a scenario. Encouragingly, our results indicate that highly error-prone replication does not impede the emergence of simple functional RNA motifs and may aid or even be required for their emergence from the likely compositionally biased prebiotic RNA pools of limited sequence diversity or indeed from single RNA sequences.In biology, with the exception of specialized tissues like the immune systemDNA and RNA oligonucleotides  were PCR amplified  (25 cycles), using the OneTaq hot start master mix (Promega) and corresponding primers , with th2, 20 mM TRIS pH 7.4) to 65 °C for 5 min and cooled down at rt for 20 min. The RNA was added to 80 μl of sepharose (Sigma), that was prewashed with reaction buffer (3×160 μl), in Costar microfilter spin columns , and was incubated for 1 h at room temperature (rt). The flow-through was subsequently added to 40 μl of reaction buffer prewashed (3×120 μl) ATP agarose  in Costar microfilter spin columns and incubated for 1 h at room temperature. The flow-through was discarded and the ATP agarose resin was washed three times with 160 μl of reaction buffer, followed by the addition of 120 μl of 5 mM ATP (Roche) in reaction buffer and incubated for 30 min.The RNA sequences were first subjected to a negative selection step on sepharose, before selecting against ATP agarose. For the selection step 10 pmol of purified RNA was heated in reaction buffer . The obtained DNA was purified and in-vitro transcribed to RNA as described above and the purified RNA was added into the next selection cycle. The sequences defined as round 0 corresponds to one round of standard PCR (25 cycles) and one round of error-prone PCR (20 cycles).For the selection rounds 12–16  and 20–24  the columns were first eluted with 1 mM ATP in reaction buffer, followed by the 5 mM ATP elution. The flow-through was desalted using Vivaspin 500 (3000 MWCO) concentrators (Sartorius). The obtained RNA was reversed-transcribed and PCR-amplified using the Superscript III One Step RT-PCR system (Invitrogen) followed by an additional error-prone PCR step (20 cycles) using standard Taq DNA polymerase (HT Biotechnology) with equimolar concentrations of dNTPs (200 μM) but with the supplement of 1 mM MnClhttps://usegalaxy.org. The occurrence of the ATP aptamer loop motif 5’-GGAAGAAAATG-3’ or 5’-GGNAGANNNTG-3’ was analysed using FIMO  http://meme-suite.org/tools/fimo. RNA secondary structure prediction and display was carried using the ViennaRNA package 2.0, specifically RNAfold either with or without aptamer loop motif constraints.DNA products from different selection rounds were PCR amplified using extended primers including adaptor sequences for Illumina sequencing . The DNAWe measured the mutation rate during error prone PCR (epPCR) through deep sequencing of round 0 (R0), which is the unselected output from epPCR of a poly-A stretch of 39 nucleotides. The mutation rate for each position was calculated with i is the number of correct nucleotides (A) at position i, and Ti is the total number of nucleotides at position i. The average mutation rate is the mean rate of all 39 nucleotides.Where CLevenshtein distance, which was calculated by counting the number of substitutions, deletions and insertions required to convert a given sequence to the reference sequence using the Wagner-Fischer algorithm.To compare converging or diverging sequence populations against a target sequence, we used the  implemented in the openTSNE package v.0.4.0. Here, we used a perplexity of 50, a cosine metric, and principal component analysis for initialisation, with all other parameters as default. We used sequencing reads that were trimmed to contain the full 5’- and 3’-primer sequences, one-hot encoded into a binary triplet encoding, and padded with zeros out to 50 nucleotides. For the projection figures, we merged and collapsed reads across all rounds, fit the t-SNE model as described above, and transformed the reads for each round to draw a mixed scatter and kernel density plot using matplotlib and seaborn (https://doi.org/10.5281/zenodo.592845).To visualise sequence space of our aptamer selection experiments, we chose to reduce the dimensionality and project it into two dimensions using t-Stochastic Neighbour Embedding (t-SNE)# Canonical ATP aptamer motif stricth1 s1 h2 s2 h2’ s3 h1’h1 0:0 NNNN:NNNNh2 0:0 NNNN:NNNNs1 0 GGAAGAAACTGs2 0 N[30]Ns3 0 G# Canonical ATP aptamer motif relaxedh1 s1 h2 s2 h2’ s3 h1’h1 0:0 NNNN:NNNNh2 0:0 NNNN:NNNNs1 0 GGAAGAAANTGs2 0 N[30]Ns3 0 G With the aligned motif sequences (including 5’- and 3’-primer sites), a phylogenetic tree was calculated with MrBayes 3.2.7 single default parameters. Phylogenetic trees (https://itol.embl.de/).Canonical ATP aptamer motifs were identified using RNABOB as described above and a multiple sequence alignment performed with muscle v3.8.1551 using default settings.ic trees  were dis2, 20 mM TRIS pH 7.4). 4 pmol RNA oligonucleotide in 80 μl buffer was heated to 65 °C, cooled down for 15 min, added to the column and incubated for 1 h at rt. After washing with 80 μl buffer, three to five subsequent ATP elutions (80 μl of 5 mM ATP in buffer) were performed, with a delay time of 5 min between each elution and the volume of the different fractions was reduced to ~15 μl by speed-vac. After the addition of 30 μl of 8 M urea/0.1 M EDTA the fractions were added to a 8 M urea PAGE gel and were run at constant power (30 W). The gel was stained by SYBR gold, analysed by a Typhoon scanner and quantified using ImageQuant . Binding assays were repeated 2–3 times for each RNA sequence.RNA Sequences for the column binding assay, were generated by in vitro transcription from DNA templates generated by PCR amplification or by fill-in with primer TxT7  using Go2, pH 7.6 buffer using a Malvern Panalytical both manual and auto iTC200 instruments. The RNA aptamers were loaded into the ITC cell at 6 μM and the nucleotides titrated from the syringe at 100–200 μM depending on affinity. Titrations were performed with 14 injections of 2.8 uL of nucleotide preceded by a small 0.5 uL pre injection that was not used during analysis. After baseline fitting and integration of excess heats the data were corrected using control heats observed for identical titrations of nucleotide solution into buffer. These control heats were small and similar to values obtained for buffer blank titrations. Corrected titrations were fit using Malvern Panalytical PEAQ software using a simple one set of sites binding model.ITC experiments were performed at 25 °C in 50 mM HEPES, 100 mM NaCl, 5 mM MgClSupporting Information


In the last two decades, RNA post-transcriptional modifications, including RNA editing, have been the subject of increasing interest among the scientific community. The efforts of the Human Genome Project combined with the development of new sequencing technologies and dedicated bioinformatic approaches created to detect and profile RNA transcripts have served to further our understanding of RNA editing. Investigators have determined that non-coding RNA (ncRNA) A-to-I editing is often deregulated in cancer. This discovery has led to an increased number of published studies in the field. However, the eventual clinical application for these findings remains a work in progress. In this review, we provide an overview of the ncRNA editing phenomenon in cancer. We discuss the bioinformatic strategies for RNA editing detection as well as the potential roles for ncRNA A to I editing in tumor immunity and as clinical biomarkers. In the following year, Bass and Weintraub [apo-B100. Kim and colleagues [In addition to these uridine-based modifications, other forms of RNA editing were being identified. In 1987, Powell and colleagues  and Cheneintraub , along weintraub , proposelleagues ,12,13.Alu repeats [Identifying the mechanisms responsible for these RNA editing events was initially complicated by the lack of a template RNA for comparison . Consequ repeats . Since t repeats . Moreove repeats ,25. With repeats ,27,28,29Editing events involve either C-to-U or A-to-I base substitutions. Mechanistically, these events are derived from a hydrolytic deamination reaction: C-to-U substitutions are catalyzed by APOBEC, while A-to-I substitutions are catalyzed by ADAR ,8,9,30. The ADAR family of adenosine deaminases catalyzes A-to-I editing ,31 Box . In partADAR1 consists of two isoforms. The interferon-inducible isoform ADAR1L (150 kDa) is involved in immune responses  and it iADAR2 is mainly localized in the nucleus and primarily expressed in brain tissue, where it has a fundamental role in neuronal development and activity . It has ADAR3 is exclusively localized to the brain ,51,52,53Box 1ADAR1 is the INF-inducible isoform, ADAR1L (150 kDa), contains a NES sequence in the N-terminal region, and is mainly located in the cytoplasm. The constitutive isoform, ADAR1S (110 kDa), is localized in the nucleus.ADAR2 is expressed primarily in the brain and is critical for neuronal development and activity.ADAR3 is exclusively expressed in the brain. Its enzymatic activity has not been demonstrated thus far, but it may act as a regulator of RNA modification by competing with ADAR1 and ADAR2.Structurally, the catalytic domain of ADAR is located in the C-terminal. In the well-studied ADAR2, the active site consists of four core amino acids: His394, Glu396, Cys451, and Cys516, which coordinate a zinc ion, and is stabilized by an inositol hexakisphosphate. Up to three dsRNA binding domains (dsRBD) are involved in RNA binding.ADAR1 and ADAR2 catalytic activity requires homodimerization, but this event has not observed for ADAR3, probably explaining its enzymatic inactivity.In 2015, Paz-Yaacov and colleagues determined that ADAR1 and ADAR2 were elevated in cancer as compared with normal tissues in a variety of tumors  . This upThe human APOBEC protein family consists of 11 cytidine deaminases acting on nucleic acids and comprises both primary gene products and their alternatively spliced variants: activation-induced deaminase (AID), APOBEC1A (A1), APOBEC2A (A2), APOBEC3A (A3A), APOBEC3B (A3B), APOBEC3C (A3C), APOBEC3D (A3D), APOBEC3F (A3F), APOBEC3G (A3G), APOBEC3H (A3H), and APOBEC4 (A4) . AID andThe cytidine deaminase catalytic domain is folded to form a structure containing five-stranded mixed β-sheets that are stabilized by six α-helices, where the overall sequence is α1-β1-β2-α2-β3-α3-β4-α4-β5-α5-α6 . The reaMost of the APOBEC proteins contain one cytidine deaminase domain. However, A3B, A3D, A3G, and A3F comprise two domains in tandem, although only the C-terminal domain is catalytically active . Thus, aAID activity is involved in immunoglobulin diversification ,65 and iA1 catalyzes the editing of C-to-U- in position 6666 and 6802 of Apolipoprotein B (ApoB) mRNA in the small mammal intestine and the liver of small species [ species ,69, alth species . Its act species )A2 is expressed in skeletal muscles and cardiac tissue; A2 action is required for muscle development [elopment . Intereselopment .The main function of the A3 subfamily consists of the modification of endogenous and exogenous RNA and DNA  . Indeed,When A4 is overexpressed in yeast and bacteria, this protein does not show any detectable cytidine deamination activity in DNA . In factThe APOBEC protein family plays an important role in cancer development and progression . In 1995Non-coding RNAs (ncRNAs) are untranslated RNA transcripts that are involved in the post-transcriptional and post-translational regulation of gene expression ,25,90. NDuring the last decade, an increasing number of investigators have analyzed the role and effect of A-to-I editing on microRNAs (miRNAs) ,95. MiRNmodus operandi, editing of either the mRNA 3′UTR miRNA consensus site or the miRNA seed region can disrupt or induce miRNA silencing interactions [RNA editing can also target mature miRNAs. To date, 2654 mature miRNA sequences have been identified in humans  . Mature ractions ,104,105.ractions .The altered expression of miRNA editing has been associated with cancer ,107,108.To better distinguish cancer from non-cancer, investigators have proposed miRNA editing patterns as a classifier . These mhttp://www.cs.tau.ac.il/~mirnaed/), allowing for users to upload their small-RNA sequencing data and profile miRNA editing. Recently, another group developed a more comprehensive HTS-based application [https://github.com/mhalushka/miRge), to concurrently profile edited miRNAs and miRNA isoforms, demonstrating that miRNA editing detection is strongly correlated with Alon’s pipeline.At the beginning of the 2000s, only a few RNA editing sites were detectable in miRNAs while using low-throughput methods ,114,115.lication , termed https://darned.ucc.ie/), and subsequently in an improved database that was created by Ramaswami and Li [http://rnaedit.com/). Recently, another resource was created by Picardi and colleagues, named REDIportal (http://srv00.recas.ba.infn.it/atlas/) [As a result of the development of HTS technologies, hundreds of editing events in miRNAs have been compiled in an RNA editing database ,124, cali and Li , named R/atlas/) , in whichttps://ncrnaome.osumc.edu/isotar/), a Web-based application to perform a consensus miRNA targeting prediction and functional enrichment analyses for modified miRNAs, such as edited miRNAs and isomiRs [Distefano et al. recently developed isoTar +, poly(A)−, and small RNA samples and they identified two lncRNAs that are edited at several sites Jpx (41 sites) and Malat1 (31 sites) .Drosophila prune gene) through an ADAR-mediated RNA editing mechanism. The authors also confirmed the co-regulation and RNA editing of PRUNE2 and PCA3 in human prostate cancer specimens [lncRNAs may also interact with ADAR to regulate the expression of the target gene. Salameh and colleagues demonstrated that the prostate cancer antigen 3 lncRNA (PCA3) downregulated its target tumor suppressor gene PRUNE2  . These pAlthough aberrant patterns of RNA editing have been identified in a variety of cancers, the biological consequences of editing events on anti-tumor immune responses remain unclear. Asaoka and colleagues found that APOBEC3-mediated C-to-U RNA editing was common in tissues of The Cancer Genome Atlas (TCGA)-breast carcinoma (BRCA) cohort . In TCGACCNI R75G), the authors showed that edited CCNI peptides activated tumor-infiltrating lymphocytes from human melanoma tumors, suggesting edited CCNI peptides function as antigenic epitopes in vivo. Edited CCNI peptide-specific T cells also facilitated the killing of the HLA-A-expressing lymphoblast cell line T2. Thus, HLA-bound peptides derived from RNA editing can function as tumor antigens to elicit anti-tumor immune responses.Zhang and colleagues designed a proteogenomics screening approach combining liquid chromatography-mass spectrometry and RNA sequencing data from primary human tissue samples and healthy tissue donor samples to identify RNA edited HLA ligands . FocusinThe editing of dsRNA by ADAR1 plays an important role in preventing aberrant and chronic innate immune response activation (reviewed in ). HoweveThese three studies indicate that RNA editing plays an important role in tumor immunity, but its precise role remains uncertain. Both Asaoka and colleagues and Zhang and colleagues show that RNA editing mediated by APOBEC3 and ADAR1 elicits anti-tumor immune responses in breast cancer and melanoma, respectively. In contrast, Ishizuka and colleagues demonstrate that the loss of ADAR1 in melanoma tumors increases tumor inflammation and overcomes resistance to immunotherapy. Thus, additional studies are required to understand the mechanisms, whereby RNA editing influences anti-tumor immune responses and whether RNA editing enzymes may represent novel targets for immuno-oncology therapy.In contrast to general RNA editing, even less is known regarding the role of non-coding RNA editing in tumor immunity. ADAR1, in an editing-independent manner, has been shown to transcriptionally regulate the biogenesis of miR-222 and thereby Intercellular Adhesion Molecule 1 (ICAM1) expression, which consequently affects melanoma immune resistance . HoweverHigh throughput interrogation of the human transcriptome has led to the identification of post-transcriptional modifications that drive cancer biology. Transcriptional modifications, such as editing, have the potential to serve as markers of cancer stage, histology, prognosis and chemotherapeutic response. The dysregulation of A-to-I  and C-to-U editors (AID/APOBEC) has been implicated in prognosis in carcinogenesis and both the innate and adaptive immune system ,150. As ZEB1, ZEB2, and LIFR and, thus, impact downstream signaling. Velazquez-Torres and colleagues demonstrated that selected editing of miR-378-3p resulted in a tumor suppressive phenotype in melanoma [The links between ADARs and AID/APOBEC editing proteins and miRNA are evolving. Both proteins can either alter absolute miRNA expression or indirectly impact miRNA targeting through editing 3′UTR sites. As we have discussed, one of the earliest observations by Choudhury and colleagues demonstrated that within high grade glioma of the brain, miR-376 was primarily in the unedited form correlating with tumor progression. Select A-to-I editing of miR-376 resulted in a shift towards a tumor suppressor like function . This anmelanoma . While mWhile post transcriptional modifications, such as editing impact downstream biology of coding RNA in both malignant and benign human diseases, our understanding for editing in noncoding regions is just starting to emerge. With the advent of high throughput interrogation of the human genome, investigators have uncovered additional complexities of noncoding regions previously unrealized. This relatively new concept allows for us to implicate a new layer for both regulation and patterns of expression of such key genes as miRNAs and lncRNAs that extends beyond conventional approaches of examining absolute expression as biomarkers or linking unedited sequences to downstream targets. The functional mechanisms by which editing in noncoding regions may drive cancer initiation and progression are still in the early phases of investigation. However, it is increasingly evident that the deregulation of central mediators of editing, such as ADAR and APOBEC proteins across many cancers, suggests that editing contributes to many of the hallmarks of cancer. How such aberrations will translate to clinically inform decision making and their role as diagnostic and prognostic classifiers is unknown, but is sure to come to light in the near future.


Numerous publications suggest an association between herpes virus infection and idiopathic pulmonary fibrosis (IPF). These reports have employed immunohistochemistry, in situ hybridization and/or PCR, which are susceptible to specificity artifacts.We investigated the possible association between IPF and viral RNA expression using next-generation sequencing, which has the potential to provide a high degree of both sensitivity and specificity. We quantified viral RNA expression for 740 viruses in 28 IPF patient lung biopsy samples and 20 controls. Key RNA-seq results were confirmed using Real-time RT-PCR for select viruses .We identified sporadic low-level evidence of viral infections in our lung tissue specimens, but did not find a statistical difference for expression of any virus, including EBV, herpesvirus saimiri and HERV-K, between IPF and control lungs.To the best of our knowledge, this is the first publication that employs RNA-seq to assess whether viral infections are linked to the pathogenesis of IPF. Our results do not address the role of viral infection in acute exacerbations of IPF, however, this analysis patently did not support an association between herpes virus detection and IPF. Idiopathic pulmonary fibrosis (IPF) is a progressive disease with insidious onset in older people that progresses relentlessly in the absence of therapy to disability and death . AlthougGiven the fact that alveolar epithelial cells are abnormal and likely contribute to the pathobiology of IPF, we believe that epithelial perturbation may contribute to inducing and maintaining IPF. Herpes viruses can infect many different cell types, including epithelial cells with two infection stages, the lytic stage and the latent stage. Both lytic genes and latent genes could interact with cellular genes to contribute to IPF initiation, progression and/or maintenance. Viral genes may regulate cellular gene expression and induce fibrogenesis. For example, Type I collagen can be induced by adenovirus . HSV-1 sIf viruses are responsible for causing IPF, then viral screening or anti-viral treatment may provide a diagnostic test or a potential treatment. Previously employed techniques, including immunohistochemistry, fluorescence in situ hybridization (FISH), gene array and PCR, are not sufficiently sensitive or specific. Thus, a more sensitive and reliable technique is required to qualify and quantity viruses at the DNA, or RNA levels. Next-generation sequencing offers high sensitivity, specificity and reproducibility in the detection of low levels of gene expression as well as a broad dynamic range afforded by the high sequencing depth. New high throughput technologies have been used to generate comprehensive sequencing data for the identification and quantification of known and novel genes in several diseases . MoreoveLung tissue samples for the first group were obtained from remnants of surgical biopsies from the University of Alabama at Birmingham (UAB) according to IRB . They included 5 control lungs and 12 IPF lungs. The control lung samples were obtained from histological disease-free margins from patients undergoing resection of lung adenocarcinoma. The diagnosis of IPF patients was made by clinicians, pathologists and radiologists according to diagnostic criteria of the American Thoracic Society and the European Respiratory Society . The dethttps://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=326784), and was generated with non-poly(A) selected total RNA [For this report, we analyzed RNA expression from 3 groups that in total were derived from 28 IPF and 20 control lung specimens. The first group included samples from 12 IPF patients and 5 controls, and encompassed patients who underwent lung biopsies at the University of Alabama at Birmingham. Total RNA was prepared using Qiagen’s RNeasy kit (cat#74104). Poly(A) selected RNA-sequencing (RNA-seq) was performed at UAB for the first group (one control (3007) and one IPF (2053) sample was not analyzed by RNA-seq because the aliquot of RNA was considered to be of insufficient quality for these two samples). We repeated RNA-sequencing on this same group at Tulane, but we did not select for polyadenylation because some viral genes are not polyadenylated. Non-poly(A) selected RNA-seq was performed using the Illumina NextSeq 550 located within the NextGen Sequencing Core at the Tulane Center for Translational Research in Infection & Inflammation. Ribosomal RNA was removed from 1 μg total RNA for both poly(A) selected and non-poly(A) selected RNA-seq, and a library was prepared using TruSeq stranded mRNA (polyA+) for poly(A) selected RNA-seq or using TruSeq stranded total RNA ribozero [otal RNA . RNA frootal RNA .Raw RNA-seq data was aligned to a genome reference containing the human genome  plus a library of 740 known mammalian viral sequences that have been documented by the NCBI . Alignments were performed using the transcript aligner STAR (Spliced Transcripts Alignment to a Reference) version 2.3.0 and version 2.5.2a. Uniquely mapped viral and human reads were quantified using in-house computational pipelines. The first script extracts all the reads mapping to virus sequences and writes the output to one file for each sample. The second script takes as input all the viral sequence information from the first script and removes any duplicate reads. The output is a list of all the uniquely mapped viral sequences for each sample. The third script takes as input the uniquely mapped reads for each sample and counts the number of reads mapping to each virus in each sample. The output is a compiled file that contains the virus chromosome name followed by the number of occurrences in each aligned file. As a complementary approach, we also analyzed mapped reads using the metatranscriptomics pipeline, RNA CoMPASS for entire metatranscriptome analysis .tm cDNA Synthesis Kit (cat#170–8891). PCR was performed with 2 μl of 10X diluted cDNA in a 20 μl volume according to the manufacturer’s protocol (BioRAD cat#170–8880). PCR conditions: for EBV we used 3 min at 95 °C, 40 cycles of 15 s at 95 °C and 30 s at 60 °C then 40 s at 72 °C [cDNA was synthesized with 1μg RNA following the manufacturer’s instructions within the Bio-Rad iScripat 72 °C ; to deteat 72 °C ; to deteat 72 °C ; to deteat 72 °C . In brieat 72 °C . The prip < 0.05. Results are expressed as mean ± SEM.RT-PCR was performed in triplicate for each sample. In order to test the significant difference of RT-PCR data and/or RNA-seq reads (RPHM), we used student’s t-tests and F tests that were performed using GraphPad Prism. T-tests were used to compare differences between control and IPF groups, and the F tests were employed to compare variance within the groups (control group or IPF group). Differential expression analysis of RNA-Seq was carried out using the EBSeq statistical package. Scatter plots depict the mean with the standard error of the mean . StatistAlthough we observed that the Shamonda virus averages 10 reads per million human mapped reads (RPHM) in poly(A) selected RNA-seq, this is likely an artifact and not true infection because viral reads were detected in every sample analyzed, including the normal controls. In addition, manual BLAST showed that the actual reads hit to human sequences that were mistakenly being called Shamonda virus, and a few repeat reads have been clonally amplified and resulting in such a high read number. The next most commonly detected virus was human adenovirus C with 1 read per million human mapped reads Table . The mapTo confirm our RNA-seq results, we performed serial RT-qPCR on the first group of specimens (12 IPF and 5 control lung RNAs). This was not performed on the second and third group because we only had the data sets and not the RNA. EBV has two major infection gene expression programs, the latency associated gene expression program and the lytic gene expression program, which are uniquely utilized depending on cell type. Since it is not known which cell type might harbor EBV within IPF lung, and to avoid “lack of detection” errors due to EBV infection status, primers spanning the EBV latent genes, EBNA1, Qp and LMP1, as well as the EBV lytic gene Zta were employed for RT-qPCR. No EBV latent or lytic gene expression was detected using RT-qPCR, suggesting that neither the latent nor the lytic forms of EBV were present in the lungs of IPF patients or the control group (data not shown). However, using primers that span the EBV-encoded noncoding small RNAs, EBER1 to EBER2, we detected a very low level of EBERs expression in both the IPF and control specimens, with cycle threshold  values oOther ubiquitous herpes viruses have also been reported to be associated with IPF, including herpes simplex virus type 1 (HSV-1), HHV-6, − 7 and − 8 and cytomegalovirus (CMV) . Our RNAMore recently, Folcik et al. reported that IPF is associated with herpesvirus saimiri but not with other herpesviruses such as EBV, KSHV, CMV or HSV I/II . They deHERV sequences make up about 4.9% of the human genome. HERV-K research has been assessed in autoimmune disorders and oncogenesis, yet to date we are not aware of any literature to assess its possible role in pulmonary fibrosis. Recently, RT-PCR results have suggested that HERV-K env mRNA was increased in PBMC and skin biopsies of morphea/localized scleroderma . This stQuantitative RT-PCR of the HERV-K env and long terminal repeat (LTR) regions show that the expression levels of env and LTR were higher in IPF than in controls  and non-poly(A) mRNA in the same sample, we divided total RNA extracted from surgical lung biopsies into a poly(A) enriched fraction via oligo (dT) for poly(A)-selected RNA-seq, and a non-poly(A) fraction for non-poly(A)-selected RNA-seq. Similar to eukaryotic mRNAs, viral mRNAs have two main types: poly(A) and non-poly(A) transcripts, based on the presence or absence of a poly(A) tail at their 3′-end. Poly(A) mRNA transcripts represent the majority of viral mRNA, however some viruses express non-poly(A) mRNA including miRNA and lncRNA. Important examples are herpesvirus EBV-encoded non-coding small RNAs (EBER), and adenovirus-encoded non-coding RNA VA  RNAs. Viral-encoded non-poly(A) RNAs have an essential role in a variety of physiological conditions and in several illnesses, including viral life cycle and function, host cell immune evasion and transformation . SpecifiEBV appears to be the most commonly investigated virus in IPF. Previous research has suggested that IPF is linked to EBV, while other studies, using some common techniques such as PCR with primers from the EBV BamHI W repeats or the EBER gene, FISH with an EBER probe, and IHC with antibody against the viral capsid antigen (VCA) or the latent membrane protein 1, have found no link . Here, wHERV-K expression was examined because some reports have indicated that it is elevated in other fibrotic diseases and because conceptually HERV-K could promote fibrogenesis by inducing cellular stress. Moreover, HERV-K expression is reportedly enhanced in response to herpes virus infection. HERV-K protein NP9 can negatively regulate EBV EBNA2 expression by binding to EBNA2 . The envAlthough RNA-seq is highly sensitive technique to detect virus, there are still some limitations in our study. Most of the mapped viral reads detected in our study were low except for HERVs, and this potentially could be due to the quality of RNA-seq or the script used in our research. Additionally, the expression of RNA viruses is difficult to differentiate from genome detection despite the fact that viral RNA may indeed reveal expression of DNA viruses such as herpes. For persistent DNA viruses with very limited expression such as HBV, a strategy restricted to the detection of RNA such as RNA-seq, may miss these viruses. Finally, although a greater number of tissue samples would add to the confidence of our findings, we suggest the lack of significant findings in 28 IPF lungs from 3 different sources is compelling. Although our sample size makes it very unlikely to have missed a significant difference in expression of viral gene expression in common viruses, such as EBV, we are not able to fully exclude that there may be a small percentage of patients with IPF, or a specific IPF phenotype, that have expression viral RNA in lung.This study has bearing as clinical investigators are considering anti-herpesvirus therapy as a treatment for IPF. A similar scenario existed for glioblastoma multiforme (GBM) in which, based on IHC, in situ hybridization, western blotting and RT-PCR, CMV was entertained as a causative factor for this fatal disease . A cliniOur study employs next generation RNA-sequencing to assess whether viral infections are linked to the pathogenesis of IPF for the first time. Although quantification of viral RNAs using RNA-seq in IPF lung specimens does not support the role of viral infection in acute exacerbations of IPF, however, this analysis patently did not support an association between virus detection especially herpes virus detection and IPF.Additional file 1: Table S1. Counts for human and virus mapped RNA-seq reads for IPF and control lung RNA. Virus data is shown if at least one read was detected in at least one sample.Additional file 2: Table S2. Counts for human and HERV-K & HERV-W mapped RNA-seq reads for IPF and control lung RNA. Virus-encoded genes are shown if at least one read was detected in at least one sample.Additional file 3: Table S3. Counts for virus mapped non-poly(A) selected RNA-seq reads for IPF and control lung RNA.Additional file 4: Table S4. Counts for HERV mapped non-poly(A) selected RNA-seq reads for IPF and control lung RNA.Additional file 5: Table S5. Counts for virus mapped SRA RNA-seq reads for IPF and control lung RNA.


RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants. DNA, the genetic blueprint of all organisms, controls all life processes through intermediate RNA molecules that dictate the types and levels of proteins made in cells. From the biogenesis to degradation of RNA molecules they are associated with many proteins. RNA–protein interactions are numerous, widespread, and play diverse biologically important roles in all organisms in many processes associated with gene regulation, including generation of coding and non-coding RNAs, transport, translation, and decay of RNAs, and control of diverse processes associated with development and disease. The proteins that interact with RNAs are collectively referred to as RNA-binding proteins (RBPs), a diverse class of proteins characterized by the presence of one or more RNA binding domains, usually alongside other catalytic or functional domains. Over 1800 candidate RBPs have been identified in plants, with over 800 enriched as RBPs in Arabidopsis ,4,5,6,7RBPs rely on their RNA-binding domains to carry out their biological functions. Several of these classes of domains have been characterized, most notably the RNA-Recognition Motif (RRM), DEAD-box helicases, zinc finger domains, the K homology (KH) domain, the glycine-rich domains, the pentatricopeptide repeats (PPRs), and pumilio/fem-3 binding factors (PUFs) ,7. The RIn this review, we begin by discussing the methods that are currently being used to directly probe RNA–protein interactions, highlighting those that have been used in plants and those that show potential to be used in plants. We discuss the advancements in knowledge contributed to plant biology by each method. We summarize our current understanding of RNA–protein interactions in plants as developed by the earlier use of classical genetics methods and touch on the current uses of RNA–protein interactions for biotechnological applications. Finally, we conclude with an outlook on the study of RNA–protein interactions in plants.RNA-binding proteins have become a target of great interest in recent years, and many new methodologies have been developed to analyze the RNA–protein interactome. However, in plants, most research done in this field before ~10 years ago relied entirely on the use of indirect or in vitro methods to identify RNA and protein interaction, such as gel shift assay, mutant and knockout screening, nucleic acid-binding assay, and other classical genetics and cell biological techniques ,11,12,13These techniques fall into three categories: (i) approaches that focus on identifying RNA targets of a candidate RBP, i.e., protein-to-RNA, (ii) approaches that focus on identifying the proteins interacting with an RNA of interest, i.e., RNA-to-protein, and (iii) global approaches . The vasAmong the first techniques developed to identify direct targets of RBPs in vivo was RNA immunoprecipitation or RIP ,16 Tabl. The basRIP can also involve RNA–protein crosslinking, creating covalent bonds between the protein and its RNA ligands. Reversible crosslinking is accomplished using formaldehyde and reversed via heat treatment . The draCrosslinking and immunoprecipitation (CLIP) builds on RIP by replacing formaldehyde crosslinking with UV-crosslinking to covalently link proteins with RNA molecules within several angstroms distance  . The RNAThus far, CLIP techniques were limited by the fact that reverse transcriptase often terminates prematurely when met with a residual amino acid covalently bound to a nucleotide at a crosslinking site causing such reads to be lost during standard CLIP library preparation. Individual-nucleotide resolution CLIP (iCLIP) was developed to compensate for this problem . iCLIP cThe CLIP technique was also simplified by the development of simplified CLIP (sCLIP), which avoids radiolabeling by biotinylating the RNA for visualization, and uses polyadenylation and random-mer barcoding to uniquely identify RNAs and reduce the requirement for PCR amplification . AnotherWith the advent of HITS-CLIP, many computational tools were developed in order to handle the large datasets produced by HITS-CLIP experiments. One of the most widely used of these is known as dCLIP, a program created to allow comparison of differential binding in different CLIP experiments . dCLIP nCLIP-Seq and its derivatives are powerful techniques but have significant limitations. Namely, CLIP (and its derivative methodologies) are all limited by their reliance on the antibody–antigen interaction; this limits the stringency of washing conditions to those that will not disrupt the antibody–antigen interaction . Thus, tUV-crosslinking and affinity purification (uvCLAP) was developed as a radiolabeling- and immunoprecipitation-free alternative to CLIP methodologies  Figure , uvCLAP.The use of tandem affinity purification in this approach allows confidence that pulldown efficiency will be similar across conditions, experiments, and laboratories, in comparison with immunoprecipitation approaches in which every antibody–antigen interaction has a unique affinity . uvCLAP The TRIBE (targets of RNA-binding proteins identified by editing) and HyperTRIBE approaches were developed in response to the severe limitations of CLIP-based techniques in identifying cell type-specific RNA–protein interactions . TRIBE wThe original TRIBE technique had the opposite problem as most CLIP experiments: it identified only about 25% of the target RNAs identified by CLIP techniques for the same RBP, and is thought to have had a false negative problem, rather than CLIP’s false positive problem. It was found that ADAR’s editing rate was low due to a sequence specificity for UAG and a double-stranded structure surrounding the edited adenosine .To compensate for these weaknesses, hyperTRIBE was developed by introducing the E448Q mutation in ADAR, which lowers ADAR’s sequence and structure preferences and increases editing efficiency . This muThis approach has the advantages of avoiding the use of immunoprecipitation and radiolabeling, requiring only a small amount of starting material, and being simple. Like uvCLAP however, it also requires genetic transformation, and in comparison to both uvCLAP and CLIP techniques, has the drawback of providing no information as to the specific binding site on the RNA (as ADAR edits sites within up to 500 nucleotides of known CLIP sites). CLIP remains the method of choice if information about an RBP’s binding site on an RNA is desired, whereas HyperTRIBE is desirable if interested in RNA–protein complexes in specific cell types or if only small amounts of starting material are available .Among the methods described above, the only protein-to-RNA techniques that have been used in plants to-date are RIP-seq and CLIP-seq. As discussed below, the application of these techniques to several RBPs has revealed their role in several processes.RIP-seq was used to demonstrate that the Arabidopsis Serine- Arginine-rich (SR) protein SR45 directly or indirectly associates with over 4000 RNAs in vivo, regulating constitutive and alternative splicing, post-splicing processing of 30% of ABA signaling genes, and over 300 intron-less RNAs  Table 2Table 2. RIP was used to show that the glycine-rich RBP AtGRP7 modulates alternative splicing in Arabidopsis . A laterNuclear speckle RNA binding proteins (NSRs) have also been shown via RIP-seq to regulate mRNA processing, alternative splicing, and long noncoding RNA (lncRNA) prevalence  Table 2Table 2. HITS-CLIP was used to identify genome-wide targets of HLP1, an hnRNP A/B protein that binds preferentially to A- and U-rich elements around cleavage and polyadenylation sites of transcripts involved in RNA metabolism and flowering to target APA  Table 2Table 2. Using RIP-seq, the pentatricopeptide repeat protein PDM1 was shown to mediate cleavage of a transcript from polycistronic to monocistronic fragments in chloroplasts of Arabidopsis  Table 2Table 2.In rice, RIP-seq was used to show that RNA-binding protein-P (RBP-P) is an RNA-binding protein that plays a role in endosomal trafficking of glutelin and prolamine mRNAs, working to anchor the RBP-bound mRNAs to the endosome via the quaternary complex and transport it to the ER Subdomain for translation, coopting endosomal trafficking  Table 2Table 2. In Arabidopsis, a unique combination of RIP and microarray approaches (RIP-Chip) was used to demonstrate that the cold shock protein 1 (CSP1) acts as an RNA chaperone of polysomes to improve the translation of RNA targets at low temperatures  Table 2Table 2.The RNA-directed DNA methylation effector KTF1 was identified via RIP as an RBP that binds Pol V scaffold transcripts to recruit argonaute 4 (AGO4) and its siRNAs for chromatin remodeling-mediated gene silencing  Table 2Table 2. Nicotiana benthamiana, but to-date this method has not been used for its intended purpose of detecting viral RNAs in plant cells [A modified RIP-seq assay was developed for the detection of RNAs of heterologous origin in plants and applied to transiently expressed nuclear epitope-containing proteins in nt cells .The plastid UMP kinase (PUMPKIN) has been shown via RIP-seq to associate with several RNAs in vivo, altering their metabolism thereby  Table 2Table 2. Despite the breadth of techniques available for use in elucidating RNA–protein interactions in vivo, CLIP and its derivatives remain the most tenable non-global approach for use in plants. RIP has also been used extensively and is suitable for certain experimental purposes. There still remain several techniques used in other organisms to probe interactions between a protein of interest and RNAs that have yet to be successfully adapted, or even tried, in plants. These are opportunities for advancement in plant RNA biology, but if adapted into plants should be modified to include the best features and optimizations of the already-proven RIP and CLIP approaches.Several of these techniques show particular promise; TRIBE, and particularly HyperTRIBE, have not been used in plants as yet, but if viable would overcome the signal to noise ratio problems inherent in CLIP. HyperTRIBE outperforms CLIP when using a small amount of starting material, such as a few cells of homogenous origin. Unfortunately, techniques used to select cells of a single type from a heterogeneous sample in mammalian systems, such as flow cytometry, are not tenable in plants without significantly altering the cell state  . Laser mRNA antisense purification mass spectrometry (RAP-MS) is a technique used to purify long noncoding RNAs and their interacting proteins with complementary, tiled, biotinylated DNA probes bound to magnetic streptavidin beads   is a related technique predating RAP-MS by several years . It alsoThe technique known as PIP-Seq has been used successfully to elucidate important RNA–protein interactions governing the differentiation of root hair cells . PIP-seqRecently, a CRISPR-based system called CRUIS (CRISPR-based RNA-United Interaction System) was developed in mammals . CRUIS uThere are a number of RNA to protein methods that are useful for in vitro studies but are not applicable to in vivo work. Among these is the labeling of RNA with small molecules . In thisAnother exclusively in vitro approach is nucleotide substitution in RNA . Here, RWhereas the uvCLAP approach uses modifications to the protein primary structure, RNA aptamer pulldown  uses modifications to the RNA primary and secondary structures, followed by tandem affinity purification . RNA aptThere are many well-studied RNA aptamers used for such studies; some of the most commonly used are the PP7, S1, D8, tobramycin, streptomycin, Csy4 (H29A), Mango, and MS2 aptamers ,60,61,62Two other RNA-to-protein techniques were developed in the last year in non-plant systems. One of these methods targets engineered peroxidase (APEX) with MS2 or Cas13 to a specific RNA. APEX targeting uses either the MS2-MCP interaction or an engineered CRISPR-Cas13 interaction to target the biotinylation activity of APEX2 to proteins proximal to target RNAs in vivo . After rThe second method is called CRISPR-assisted RNA–protein interaction detection (CARPID). This method was also inspired by APEX-based approaches but uses the engineered biotin ligase BASU instead of APEX2 . Using aPerspective on the Use of RNA-to-Protein in PlantsThere is much room for improvement in the RNA-to-protein direction, particularly considering that none of these techniques have been used in plants to-date. ChIRP-MS in particular would be an attractive technique to attempt in plants for the following reasons: it avoids the need for antibody generation used in RIP and CLIP, it does not use radiolabeling, it permits denaturing conditions and stringent washes, and it does not require genetic transformation. However, as previously described it cannot provide any information regarding the binding site of an RBP.RNA aptamer-mediated pull-down techniques could also be an area for advancement. These approaches do require genetic transformation and could potentially result in altered RNA secondary structure (depending on the aptamer used), but their potential to exceed the antibody–antigen affinity limitations and avoid the antibody generation variabilities of CLIP makes them attractive nevertheless. However, because most of the annotated RNA aptamers in use rely on the binding capabilities of partner proteins , their use limits the stringency of washing conditions; denaturing conditions cannot be used during incubation and washing to prevent the formation of post-lysis ribonucleoprotein complexes without denaturing the aptamer’s binding partner and thereby compromising pull down. Of those that do not rely on protein partners, few match the affinity granted by the polyA-oligo(dT) interactions used in other techniques, such as RNA-interactome capture.It might be advantageous to develop nucleotide-nucleotide RNA aptamers to increase the binding affinity, such as by applying a split RNA aptamer. Split aptamer approaches involve separating out an existing aptamer, such as an RNA that forms a tight stem-loop secondary structure, into two fragments that tightly interact in the presence of a ligand; thus, one fragment of the aptamer is appended to a transcript of interest, and the second is immobilized on a nonreactive bead. For example, the cocaine aptamer has successfully been split and used as a biosensor . AlthougUntil very recently, there was only one currently available global approach to capturing the plant RBPome, called RNA-interactome capture (RIC). RIC uses techniques common to directed RNA–protein interaction studies, beginning with the UV-crosslinking of interacting proteins to their partner RNAs as in CLIP and PAR-CLIP. The cell lysate is then passed over oligo(dT)-magnetic beads under denaturing conditions to pull down polyA RNAs and the denatured proteins covalently bonded to them. After stringent washes to elute any non-covalently interacting proteins, the RNA is enzymatically digested and the protein sample is subjected for proteomics via mass spectrometry  Figure . This teThe RIC technique was adapted into plants several years ago by a trio of studies using cell suspension cultures, seedling leaves, leaf mesophyll protoplasts, and etiolated whole seedlings ,71,72. TThese studies provide us with a perspective on heterogenous plant samples grown under normal conditions and provide a baseline against which future studies may compare the results of experiments using the array of sample types described. Since their publication, the RIC method has been applied to Arabidopsis cell cultures grown under drought stress (using PEG to simulate drought conditions in culture) to identify 150 RBPs responsive to drought stress . SimilarVery recently, a new method for the identification of RNA–protein interactions has been adapted from bacterial and mammalian systems, known as orthogonal Organic Phase Separation, or OOPS. This method uses UV-crosslinking, similar to other techniques, and acidic guanidiniumthiocyanate-phenol-chloroform (AGPC) phase separation to collect RBPs at the interface between the aqueous and organic phases . OOPs haThe study of RNA–protein interactions predates the use of the methods discussed above. Before the advent of techniques capable of directly establishing RNA–protein interaction in vivo, classical genetics and cell biological methods were used to compile evidence supporting the interaction of RNA and protein. To provide a comprehensive understanding of plant RBPs role in diverse cellular processes, we reviewed the literature using classical methods to probe RNA–protein interactions in plants . The totIn all eukaryotes, RNAs are processed with a 5’ methylguanosine cap shortly after RNA polymerase synthesizes the first 25–30 nucleotides; capping is accomplished by the catalytic activities of RBPs, and the cap subsequently increases stability and participates in pre-mRNA splicing via cap binding proteins . Next, eFully transcribed and spliced mRNAs are polyadenylated at the end of the 3’ UTR , by the polyadenylation complex which is composed of a large number of proteins, some of which are RBPs ,84. FinaMedicago truncatula, NSRs are known to partner with the lncRNA early nodulin 40 (ENOD40) involved with root nodulation [Regulation of RNA processing is mediated by RBPs at multiple levels. As discussed in dulation . NSRs ardulation . This phdulation .Splicing of plant resistance genes is controlled by modifier of snc1,12 (MOS12), a cyclin L homolog, and the MOS4-associated complex (MAC), localized to the nucleus . The MACThe splicing modulator AtGRP7, shown by RIP to modulate alternative splicing , is reguIn maize, the nuclear-localized RRM protein defective kernels 42 (Dek42) impacts alternative splicing through its interaction with spliceosome components, including splicing factor 3a subunit 1 (SF3a1) and U1 small nuclear ribonucleoprotein 70 kDa (U1-70k) .Alternative polyadenylation (APA) forms another mechanism of regulation by RBPs, which produces mRNAs with distinct 3’ ends by modifying cleavage and polyadenylation sites in the 3’ UTR . Over 70RBPs ensure that mature RNAs are transported to the correct cellular locale. In rice endosperm, it was shown that two zipcode RBPs binding glutelin mRNA form a quaternary structure with two membrane fusion factors . RBP-P aThe nucleoporin complex participates in the proper apportioning of mRNAs between the nucleus and cytoplasm in response to stresses, mediated by the RBPs MOS2, MOS3, and MOS11. These MOS factors were originally identified as modifiers of snc1 , which encodes a resistance gene involved in basal plant immunity . MOS2 coIn angiosperms, certain mRNAs involved in organ development are translocated through the phloem as long-distance signaling agents in the form of ribonucleoprotein complexes mediated by RBPs . CmRBP50RNA chaperones play a significant role in cold acclimation by binding RNAs nonspecifically and modulating their secondary structure during chilling conditions . These pBrassica rapa) showed that these GRPs participate in response to various abiotic stresses in different tissues according to developmental stage [In cotton, between 32 and 37 GRPs have been identified and subdivided into four families based on the arrangement of glycine repeats and the presence of other motifs . Gene exal stage ,119. A mal stage . GRP8 biArabidopsis thaliana, the universal stress protein UDP-Sugar pyrophosphorylase (AtUSP) acts as an RNA chaperone to bind nucleic acids nonspecifically, showing high nucleic acid-melting activity, to help maintain the RNA secondary structure during cold temperatures and maintain gene expression [In pression .Bruno-RBPs AtBRN1 and AtBRN2 in Arabidopsis bind the 3’ UTR of the flowering pathway integrator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1), which is regulated by FLC, to repress SOC1 via mRNA decay pathways to control flowering time ,121. OveIn chloroplasts and mitochondria, transcripts are protected against degradation by exonucleases by RBPs at their 5’- and 3’-termini ,123. SucThe nuclear-encoded protein termed S1 domain containing RBP (SDP), which is chloroplast-targeted, binds the chloroplast ribosomal RNAs 23S, 16S, 5S, and 4.5S and possesses RNA chaperone activity, indicating that SDP may play a role in ribosomal RNA processing in chloroplasts . SDP knoThe protein SERRATE (SE) was identified by PIP-seq and RNA-chromatography as an inhibitor of root hair cell determination via miRNA biogenesis . SE bindIn rice, 3′-UTR-interacting protein 1 (UIP1) was identified as an RBP through yeast three-hybrid screening . UIP1 inRBPs partner with small RNAs to silence retrotransposons and repetitive DNA elements through the formation of heterochromatin . This siCapsicum annuum, pathogenesis-related protein 10 (CaPR-10) functions as a ribonuclease targeting tobacco mosaic virus (TMV) RNA; CaPR-10 is induced upon TMV infection, after which it is phosphorylated to increase its ribonucleolytic activity and increase its cleavage of viral RNAs [Viral genomes rely on many RBPs of host origin for their replication and dissemination . Consequral RNAs . Thus, ROther plant RBPs or putative RBPs have been identified, from a variety of species, whose impact on the life of their RNA targets has not yet been elucidated. Those discussed briefly hereafter demonstrate the importance of RBPs in regulation of stress response and development, particularly in germination and flowering.In Arabidopsis, the ABA-regulated RNA-binding protein ARP1 was identified as an ABA-responsive protein localized to the nucleus . ARP1 isMalus prunifolia. The glycine-rich protein MpGR-RBP1 was predicted as an RBP that is upregulated during salt, oxidative, or ABA stress [Aeluropus littoralis Stress-Related Gene 1 (AlSRG1) gene encodes a protein involved in abiotic stress response that contains an RRM motif [Another putative RBP involved in seed germination was identified in A stress . An ArabA stress . SimilarRM motif . In tobaRM motif .Capsicum annuum cv. Bukwang) termed CaRBP is also localized to the nucleus—specifically to the nucleolus—and functions in transgenic tomato to delay flowering and alter the expression of various genes related to flowering [A putative RBP isolated from hot pepper -mRNA interacting RBPs in plant cells. The results of these studies have recently been reviewed , so theyTaken together with the studies reviewed above, an image emerges of the intimate relationship between RBPs and RNA, in which RNAs are accompanied by RBPs during every stage of their existence—from the initiation of transcription to their eventual degradation. RBPs have been observed to play important roles in such diverse physiological processes as plant development, immunity, abiotic stress response, and silencing of repetitive DNA elements by interacting with every stage of RNA existence. Indeed, given their ubiquitous and widespread nature in RNA biology, it seems more reasonable to expect that RNA–protein interactions play key roles in the regulation of every biological process. These studies hint at a highly complex regulatory network of ribonucleoprotein complexes whose memberships and activities are constantly in flux in response to developmental stage and external stimuli, each factor regulating and being regulated multiply depending on the status of the network. The conception of a ‘naked’ RNA, sans any RBP companions, may be entirely mythic.What does this mean for the study of plant molecular biology? In short: RNA and protein interactions are everywhere, and they should be expected to be everywhere. For any given gene of interest, probing the RNA interactome of the protein and the protein interactome of the RNA is likely to yield interesting information regarding the gene’s regulation, and both should be considered as key components of routine study. This is currently hindered by the fact that there are no RNA-to-protein methods for individual RNAs that have been used in plants—the application of such methods developed in other systems, such as ChIRP-/RAP-MS or TRAP-MS, should be prioritized, as the current bias toward protein-to-RNA methods likely introduces certain myopias in our knowledge. Moreover, the RNA–protein interactions of most factors should be expected to change in response to developmental stage or environmental stimuli, such as biotic or abiotic stress. These are not static relationships.As nodes of this regulatory network are further illuminated, it will be interesting to discover how the individual regulatory systems currently understood are interlinked with each other to broadly govern plant physiology. The observation of many metabolic enzymes moonlighting as RBPs in both plants and animals likely represents another category of such interlinks, tying metabolic regulatory systems to stress response regulation. The exact functions of these metabolic RBPs, if any, remain to be discovered, but show promise to fulfill the predictions of the REM hypothesis.Beyond the moonlighting of metabolic enzymes themselves, the prevalence of RBPs or candidate RBPs that lack canonical RNA binding domains in the interactome capture studies suggests that we may see similar ‘moonlighting’ activities emerge from other categories of proteins, and that RNA/protein interactome studies might be warranted even in cases where no known connection to RNA processing exists. By all evidence, RNA–protein interactions seem ubiquitous and critical for fine-tuned control of development and environmental response.