An efficient and computationally linear algorithm is derived for total leastsquares solution of adaptive filtering problem, when both input and output signalsare contaminated by noise. The proposed total least mean squares (TLMS) algorithmis designed by recursively computing an optimal solution of adaptive TLS problem byminimizing instantaneous value of weighted cost function. Convergence analysis of thealgorithm is given to show the global convergence of the proposed algorithm, provided thatthe stepsize parameter is appropriately chosen. The TLMS algorithm is computationallysimpler than the other TLS algorithms and demonstrates a better performance as comparedwith the least mean square (LMS) and normalized least mean square (NLMS) algorithms. Itprovides minimum mean square deviation by exhibiting better convergence in misalignmentfor unknown system identification under noisy inputs. Ordinary least squares methods are extensively used in many signal processing applications to extract the system parameters from input/output data , 2. ThesO(N3) for an N × N matrix.Total least squares (TLS) method  is an efO(N). However they are sensitive to the correlation properties of input signals and have a drawback of bad performance under correlated inputs.TLS solutions of adaptive filtering problem gained importance after the pioneer work done by Pisarenko . He presO(N) complexity of adaptive TLS algorithms with an additional quality of having steady state convergence under correlated inputs. Convergence analysis is presented to show the global convergence of the proposed algorithm under all kinds of inputs provided the stepsize parameter is suitably chosen.In this paper, an iterative algorithm is presented to find an optimal TLS solution of adaptive FIR filtering problem. A stochastic technique similar to that of least mean squares (LMS) algorithm of adaptive least squares filtering is employed to develop a total least mean squares (TLMS) algorithm for adaptive total least squares problem. Instead of basing the approach on the minimum mean squares error as the LMS algorithm does, the proposed (TLMS) algorithm is based on the total mean squares, obtained by minimizing the weighted cost function for the TLS solution of adaptive filtering problem. The proposed algorithm has maintained the This paper is outlined as follows: we start with a mathematical formulation of adaptive least squares problem in N and response vector wn, at time n, with an assumption that both input and output are corrupted by an additive white Gaussian noise (AWGN). The noise free input vector an ∈ ℛN is formed from the input signals u(n), such thats(n) = wnTan is system's output and Δs(n) an added white Gaussian noise of zero mean and variance σsΔ2.Consider an unknown system to be identified by adaptive FIR filter of length The primary assumption of an adaptive least squares (ALS) problem is that perturbations occur in the output signals only and that the input signals are exactly known. This assumption is not practical enough, because perturbations due to sampling or modeling or measurement errors affect the input signals too. A sensible choice to overcome such situations is to introduce perturbations in input signals in addition to perturbations of output signals. A schematic diagram of an adaptive filter with perturbed input is depicted in an = T ∈ ℛN, denote the perturbations in input vector an, where Δu(n) is an additive white Gaussian noise (uncorrelated from the output noise) of zero mean and variance σuΔ2, then noisy input vector isIf ΔJ = E{e2(n)}, gives a poor estimation of the solution of adaptive filtering problem because of the presence of noise in filter input. Casting adaptive filtering problem as total least squares problem can, however, restructure the poor estimation of solution under noisy input [At this stage the least squares solution, obtained by minimizing the cost function sy input , 11. TheN + 1) × 1 augmented data vector Define an (e(n), in terms of augmented data vector of N + 1) × 1 extended parameter vector.An alternate form of The TLS solution of adaptive filtering problem is an eigenvector associated with the smallest eigenvalue of extended autocorrelation matrix E{e2(n)}, adaptive total least squares problem is concerned with minimizing the total mean square error E{η2(n)} and cost function η(n) is given byInstead of minimizing the mean square error The TLS cost function the form , 11:(9)wopt of the TLS problem  for computation of gradient vector [μ is the stepsize parameter or convergence parameter. Note thatwn+1 is obtained by the following formula:N + 9 multiplications/divisions per iteration. This computational simplicity of adaptive TLMS algorithm makes it a better choice than computationally expensive SVD based TLS algorithm, which requires 6N3 computations per iteration [In adaptive least squares problem, conventional LMS algorithm is a steepest descent method which uses an instantaneous cost function t vector . Using a becomes∇w~nJ~=2|given asw~n+1=w~nituting  to get noisy signal s(n) with an additive white Gaussian noise of SNR 30 dB. Proposed TLMS algorithm is compared with LMS and NLMS algorithms of [N = 10. Least squares misalignment ||wn − wopt|| is compared with the total least squares misalignment A white Gaussian noise of SNR = 30 dB is added in the input signal ithms of  to get aμ, satisfying  complexity, while other algorithms for TLS problems either require higher complexity or are sensitive to correlation properties of data signals.In this paper, an efficient TLMS algorithm is presented for the total least squares solution of adaptive filtering problem. The proposed algorithm is derived by using cost function of weighted instantaneous error signals and an efficient computation of misalignment in terms of mean squares deviation. TLMS algorithm has better ability to tackle with perturbations of both input and output signals, because it is chiefly derived for the purpose. Since in real life problems, both input and output signals are contaminated by noise, therefore TLMS algorithm has great applicability. Convergence analysis shows that the proposed algorithm has global convergence, provided that the stepsize parameter is chosen appropriately. Furthermore, it is computationally simple and requires only


Efficient and precise microRNA (miRNA) biogenesis in Arabidopsis is mediated by the RNaseIII-family enzyme DICER-LIKE 1 (DCL1), double-stranded RNA-binding protein HYPONASTIC LEAVES 1 and the zinc-finger (ZnF) domain-containing protein SERRATE (SE). In the present study, we examined primary miRNA precursor (pri-miRNA) processing by highly purified recombinant DCL1 and SE proteins and found that SE is integral to pri-miRNA processing by DCL1. SE stimulates DCL1 cleavage of the pri-miRNA in an ionic strength-dependent manner. SE uses its N-terminal domain to bind to RNA and requires both N-terminal and ZnF domains to bind to DCL1. However, when DCL1 is bound to RNA, the interaction with the ZnF domain of SE becomes indispensible and stimulates the activity of DCL1 without requiring SE binding to RNA. Our results suggest that the interactions among SE, DCL1 and RNA are a potential point for regulating pri-miRNA processing. These include HYPONASTIC LEAVES 1 (HYL1), a double-stranded RNA-binding protein (dsRBD) containing two dsRBDs, and SERRATE (SE), a zinc-finger (ZnF) domain-containing protein. hyl1- and se-mutant plants accumulate less miRNA and more pri-miRNA than do wild-type plants  processes the pri-miRNA in the nucleus in two steps . Severale plants . Bimolece plants ,8. DCL1 thamiana  and SE athamiana . Besidesthamiana . The forthamiana . The capthamiana ,12. HUA thamiana ,15. HASTthamiana .in vivo in both animals and plants , termed the SE core, adopts a walking man-like topology with the ZnF domain resembling that of an isopentenyltransferase that binds to dsRNA . For DCL1, a silent mutation was introduced by overlapping PCR using primers (5′-GAGAAAGGTCTACTTGTTGAAGCT-3′ and 5′-AGCTTCAACAAGTAGACCTTTCTC-3′). The amplified fragments were digested by restriction enzymes (BbsI and XbaI for DCL1 and BsaI and XbaI for SE) and inserted into a BsmBI-digested pI-Strep-SUMOstar vector, which was created by inserting the Strep-tag II sequence between the hexahistidine and SUMOstar tags in the pI-SUMOstar vector (Lifesensors). For the N-terminally hexahistidine-tagged wild-type and mutant SE proteins, SE cDNA was PCR amplified using primers (5′-CACCATGGCCGATGTTAATCTTCCTCCG-3′ and 5′-TGCAGTCTAGACTACAAGCTCCTGTAATCAATAACGG-3′) and cloned into the modified pENTR-D/TOPO vector (Life Technologies). A nucleotide sequence containing myc tag and Strep tag II was inserted in the NotI site using primers (5′-GGCCGAACAAAAACTCATCTCAGAAGAGGATCTGGCGAGCGCGTGGAGCCACCCGCAGTTCGAAAAAATATCCCG-3′ and 5′-GGCCCGGGATATTTTTTCGAACTGCGGGTGGCTCCACGCGCTCGCCAGATCCTCTTCTGAGATGAGTTTTTGTTC-3′). The fragment was then inserted into the pDEST10 vector (Invtrogen). The SE deletion constructs were created by PCR amplification of the entire plasmid without the nucleotide sequences encoding the N-terminal (SE143–720), C-terminal (SE1–701), or ZnF domains (SEΔ497–547), which were deleted using primers  followed by self-ligation. The resulting vectors were transformed into DH10Bac (Life Technologies) to prepare bacmid DNA.We constructed plasmids to express DCL1 and SE from Sf9 insect cells were cultured at 27°C in Sf-900 III SFM (Life Technologies). Bacmid DNA was transfected to Sf9 insect cells using Cellfectin II (Life Technologies) according to the manufacturer’s instructions, and the resulting supernatant was obtained as the P1 virus stock. It was then amplified to obtain a P2 virus stock, which was further amplified to obtain a P3 virus stock for large-scale expression.9 cells and collected after 48 h. For SUMOstar-SE, the P3 virus stock was added to 5 × 108 cells and collected after 24 h. After collection by centrifugation at 500 g for 10 min, the cell pellet was resuspended in 1 ml per 2 × 107 cells of Buffer A  and disrupted by sonication. After removing cell debris by centrifugation, the supernatant was added to 5 ml of Ni-NTA Agarose (Qiagen) equilibrated with Buffer A and incubated for 30 min. The resin was washed five times with 40 ml of Buffer A, and the bound proteins were eluted with 20 ml of Buffer B . The eluate was loaded directly onto the StrepTrap HP 1 ml , and the bound proteins were eluted with Buffer C . The peak fractions were collected, and the SUMOstar tag was removed by SUMOstar protease (Lifesensors) digestion to generate the native DCL1 and SE. These were passed through Ni-NTA Agarose, and the untagged proteins were collected in the flow-through fractions. These were further loaded on the HiLoad Superdex 200  and dialyzed against Buffer D . The protein concentration was determined by absorbance at 280 nm with the extinction coefficient and molecular weight calculated based on the amino acid sequence of each protein.For SUMOstar-DCL1, the P3 virus stock was added to 2 × 107 cells and disrupted by sonication. After removal of cell debris by centrifugation, the supernatant was added to 1 ml of TALON Metal Affinity Resin (Takara) equilibrated with Buffer E and incubated for 30 min. The resin was washed three times with 10 ml of Buffer E, and the bound proteins were eluted with 2.5 ml of Buffer F . The elution fractions were further loaded on the HiLoad Superdex 200  equilibrated with Buffer F. The protein concentration was determined by the band intensity in the Coomassie blue-stained SDS–PAGE gel with a known amount of untagged SE protein resolved on the same gel.For hexahistidine-tagged SE wild-type and mutant proteins, P3 virus stock was added to an Sf9 cell suspension and collected after 24 h. The cell pellet was resuspended in 1 ml of Buffer E  per 2 × 10in vitro transcription by T7 RNA polymerase by using MEGAshorscript (Life Technologies) according to the manufacturer’s instructions. The DNA template was PCR amplified with primers 5′-AATTTAATACGACTCACTATAGGGATTTCTCCACTTCTTGAGCTTCC-3′ and 5′-AGTCAACTGTGTGCGTTCGGGACT-3′ for pri-miR167b, 5′-TAATACGACTCACTATAGGAACAGAAAAATCTCTCTTTCTCTTTC-3′ and 5′-GAAACAAAGTGTCTTGAAAATCAAAC-3′ for pri-miR167a, 5′-TAATACGACTCACTATAGGTTGAGTTTGAGTTTGAGTTTGAGAG-3′ and 5′-AACCTGAAGAAGATCTGGATGGAATCC-3′ for pri-miR172a, 5′-TAATACGACTCACTATAGGTCGTTTCTTGTTTTCTTTGTTTCATC-3′ and 5′-GAAAGATTGTGTAAGTGATAGAGAG-3′ for pri-miR156a, 5′-TAATACGACTCACTATAGGCTCGGACGCATATTACACATGTTC-′3 and 5′-GTCACTTAGTGGATCAAGCATGTTTTTGTGC-3′ for pri-miR319a). We resolved the products on a 5% polyacrylamide/7.5 M urea denaturing gel and excised the full-length product from the gel. RNA was phenol/chloroform extracted, ethanol precipitated, resuspended in water and stored at −80°C until use. The RNA concentration was determined based on absorbance at 260 nm using a NanoDrop 2000 Spectrophotometer (Thermo Scientific).pri-miRNA transcripts were synthesized by 2, 1 mM ATP unless otherwise stated. Reactions were stopped by adding 10 µl of stop solution  and ethanol-precipitated with GlycoBlue (Life Technologies). It was then dissolved in gel loading dye  and fractionated on a 12% polyacrylamide/7.5 M urea/1× Tris/Borate/EDT (TBE) denaturing gel. The gel was stained with SYBR Gold Nucleic Acid Gel Stain (Life Technologies) and visualized using a Typhoon TRIO . The amount of 21mer and 23mer small RNA was quantified using the software Image J based on the signal intensity of a series of known amounts of the 20mer RNA oligonucleotide (5′- rArGrCrArGrUrGrGrCrUrGrGrUrUrGrArGrArUrU-3′) fractionated on the same gel (RNA was heated at 90°C for 3 min in a buffer containing 20 mM Tris–HCl (pH 7.0) and 50 mM NaCl to denature, cooled down slowly to room temperature to allow renaturation and used for subsequent experiments. Reactions were carried out at 37°C in 10  µl and contained 20 mM Tris–HCl (pH 7.0), 50 mM NaCl, 5 mM MgClsame gel .ligand) and the bound protein  and their stoichiometry were determined based on the following equation:Surface plasmon resonance (SPR)-binding studies were carried out using a Biacore T100 (Biacore). The system was washed twice with 1× HBS-EP buffer before protein or RNA immobilization. Coupling of DCL1 (33 nM) via primary amino groups to the carboxymethyl-5 (CM5) chip was performed at a flow rate of 10 µl/min in 10 mM sodium acetate (pH 4.5) according to the manufacturer’s recommendations. The amount of coupled response unit (RU) is stated in the figure legends. For RNA coupling, a forked substrate was prepared by mixing biotinylated RNA oligo (5′-biotin-rCrArArCrCrCrArArGrArUrArGrArUrCrArUrGrCrArGrGrCrArGrCrUrUrCrArUrCrArGrArUrGrCrCrGrCrUrCrCrCrUrCrUrUrUrUrUrUrUrCrUrCrUrA-3′) with a 5-fold excess of the complementary, nonbiotinylated RNA oligo (5′-rGrCrUrCrCrCrUrCrUrUrUrUrUrUrUrCrUrCrUrArGrGrCrArUrCrUrGrArUrGrArArGrCrUrGrCrCrUrGrCrArUrGrArUrCrUrArUrCrUrUrGrGrGrUrUrG-3′) in a buffer containing 20 mM Tris–HCl (pH 7.0) and 50 mM NaCl and annealed by heating at 90°C for 3 min and slowly cooling down to room temperature. An S-series streptavidin (SA) sensor chip was activated by three regeneration injections of (50 mM NaOH and 1 M NaCl) followed by immobilization of the biotinylated RNA at a flow rate of 10 µl/min; the coupled RU are given in the figure legends. A control flow cell was activated and blocked in the absence of protein or RNA was used to subtract the RU resulting from the non-specific interaction with the sensor chip and the buffer’s bulk refractive index. Before conducting the binding study, the system was primed twice with the corresponding binding buffer. To detect the interaction of DCL1 and SE with RNA at 50 mM and 150 mM NaCl concentrations, each protein at a concentration of 43 nM was injected at a flow rate of 20 µl/min for 70 s in a binding buffer containing 20 mM Tris–HCl (pH 7.0), 50 mM or 150 mM NaCl and 2 mM  β-ME. To detect the interaction of SE and its mutant variants, each protein at a concentration of 20 nM was injected at a flow rate of 20 µl/min for 70 s in a binding buffer containing 20 mM Tris–HCl (pH 7.0), 50 mM NaCl, 0.005% surfactant P20 and 2 mM β-ME. The chip surface was regenerated by three 100 µl of injections of buffer containing 1.5 M NaCl at a flow rate of 100 µl/min. To detect the interaction of SE with the DCL1/RNA complex, DCL1 at a concentration of 16 nM was first injected until ∼1200 RU of DCL1 bound to the RNA, and then wild-type SE protein was injected as described earlier in the text. After the chip surface regeneration, the aforementioned steps were repeated for the mutant SE proteins. Because the RU of DCL1 bound to RNA was slightly different for each SE protein injection, the RU obtained by mutant SE protein injection was normalized based on the ratio of the RU of DCL1 bound to RNA for wild-type SE protein to that for the corresponding mutant SE protein. The relationship between the amount of coupled DCL1 or RNA  from the pri-miRNA was observed under the previously described conditions , significant enhancement in the processing reaction was observed when the MgCl2 concentration was increased to 15 mM, and a further enhancement was observed by decreasing both the MgCl2 and ATP/GTP concentrations while maintaining an excess of Mg2+ over the nucleotides  of bound DCL1 or SE that was reached before the start of the wash with buffer decreased by 4- and 18-fold, respectively, with nearly negligible binding of SE to RNA  on a SA-coated sensor chip and then injected DCL1 or SE proteins. The forked-RNA was designed after pri-miR167a, whose processing was stimulated by SE in vitro C and in   A. By connditions A. These 1–701 and SEΔ497–547 interacted with RNA as did SE, whereas SE143–720 showed a 6-fold reduction in its interaction with RNA , which did not interact with DCL1 or RNA alone, was able to bind to the DCL1/RNA complex (max accumulated during protein injection and the bound SE1–701 dissociated at a faster rate during the wash with buffer. This weaker binding is nonetheless sufficient to stimulate DCL1’s processing activity (Δ497–547 did not bind to DCL1 even in the presence of RNA (Δ497–547 is able to bind to RNA directly (Roughly 1200 RU of DCL1 (214 kDa) remained tightly bound to the 60 RU immobilized RNA (39 kDa) before the injection of SE; we estimated based on  protein C. Surpri complex C. Howeveactivity C and D. e of RNA C. The fadirectly B provideIn the present study using highly purified proteins, we find that SE substantially enhances processing of the pri-miRNA under optimized reaction conditions. We showed that SE interacts directly with DCL1 and with RNA. We further showed that SE interacts with RNA through its conserved N-terminal proline/arginine-rich domain with minimal contribution from other regions and that both the N-terminal region and the conserved ZnF domain of SE are required for its interaction with DCL1 protein alone. However, SE lacking the N-terminal sequence can bind to RNA-bound DCL1 and stimulate processing, but SE lacking its ZnF domain cannot. Thus, although the N-terminal domain of SE appears to be required for RNA binding, the ability of the N-terminally truncated SE protein to stimulate DCL1 activity suggests that it does so directly and that RNA binding is not required for this stimulation.Drosophila Dicer-2 (Caenorhabditis elegans DCR-1 exhibit ATP-dependent cleavage of dsRNA (Drosophila Dicer-1 cleave substrate RNA without a requirement for ATP (We first carried out enzymatic characterization of DCL1 activity alone on several pri-miRNA substrates and identified its optimal reaction conditions. Interestingly, despite the presence of a helicase domain in DCL1, nucleotide binding and hydrolysis is generally not required for DCL1 activity, and when stimulation is observed, it is modest G and H.  Dicer-2  and Caenof dsRNA , whereas for ATP . Plant D for ATP . Conside for ATP ,39, it i for ATP . We alsose mutants display less miRNA and more pri-miRNA accumulation than do wild-type plants. We therefore conclude that SE is integral to DCL1-dependent pri-miRNA processing and suggest that this interaction may regulate the miRNA accumulation in the cell. The substrate-dependent stimulation of DCL1 activity by SE might also provide a mechanism for the selective accumulation of specific miRNAs.The DCL1 processing reaction is highly salt concentration dependent, and at 150 mM NaCl, a physiologically relevant salt concentration, we observed little pri-miRNA cleavage and release of small RNA E and 2D.143–720 (143–720, exhibits only weak affinity for RNA and that it requires a concentration of ∼83 µM to reach 1:1 binding (143–720 was able to recover a significant portion of its interaction with DCL1 in the presence of RNA (143–720 and with the wild-type SE (143–720, but not SEΔ497–547, to stimulate DCL1 processing activity demonstrates that binding of SE to RNA is not required to stimulate DCL1 activity. This is also supported by the observation that SE stimulates DCL1 activity at 150 mM NaCl concentration, where SE does not bind to RNA. Taken together, these observations suggest a model in which DCL1 binding to RNA initiates conformational changes in DCL1 that, in turn, initiate a catalytically critical interaction with the ZnF domain of SE. SE might be oriented such that the ZnF domain and perhaps a portion of the N-terminal region are bound to DCL1, whereas the N-terminal region is bound to RNA. We do not know the identity of the sequence within the DCL1 protein that interacts with the ZnF domain of SE, nor how this interaction stimulates DCL1 activity. DCL1 can bind well to RNA at a high salt concentration (We used deletion mutants of SE to study the mechanism by which SE interacts with RNA and stimulates the activity of DCL1. SE primarily uses its N-terminal region to interact with RNA, as evidenced by the marked reduction in RNA binding by SE143–720 B. Indeed binding . Both th binding A. It is e of RNA C, demons-type SE C and D. -type SE A and C. ntration E; yet, sntration E and 2D.ntration D.se-1 mutant plants, in which the C-terminal domain is disrupted by a 7 bp deletion immediately before the stop codon of the SE gene (in vivo studies suggest to play a role in pri-miRNA biogenesis may be critical for the reconstitution of the full miRNA biogenesis reaction in vitro and a further understanding of its recognition and cleavage mechanisms.There are a number of additional candidate genes that are likely to function during miRNA biogenesis . The mos SE gene , are rem SE gene . Indeed, SE gene . The fun SE gene ,43. InclKing Abdullah University Startup Allowance. Funding for open access charge: Laboratory Base Line Funds.Conflict of interest statement. None declared.


The RNA world hypothesis views modern organisms as descendants of RNA molecules. The earliest RNA molecules must have been random sequences, from which the first genomes that coded for polymerase ribozymes emerged. The quasispecies theory by Eigen predicts the existence of an error threshold limiting genomic stability during such transitions, but does not address the spontaneity of changes. Following a recent theoretical approach, we applied the quasispecies theory combined with kinetic/thermodynamic descriptions of RNA replication to analyze the collective behavior of RNA replicators based on known experimental kinetics data. We find that, with increasing fidelity (relative rate of base-extension for Watson-Crick versus mismatched base pairs), replications without enzymes, with ribozymes, and with protein-based polymerases are above, near, and below a critical point, respectively. The prebiotic evolution therefore must have crossed this critical region. Over large regions of the phase diagram, fitness increases with increasing fidelity, biasing random drifts in sequence space toward ‘crystallization.’ This region encloses the experimental nonenzymatic fidelity value, favoring evolutions toward polymerase sequences with ever higher fidelity, despite error rates above the error catastrophe threshold. Our work shows that experimentally characterized kinetics and thermodynamics of RNA replication allow us to determine the physicochemical conditions required for the spontaneous crystallization of biological information. Our findings also suggest that among many potential oligomers capable of templated replication, RNAs may have evolved to form prebiotic genomes due to the value of their nonenzymatic fidelity. A leading hypothesis for the origin of life describes a prebiotic world where RNA molecules started carrying genetic information for catalyzing their own replication. This origin of biological information is akin to the crystallization of ice from water, where ‘order’ emerges from ‘disorder.’ What does the science of such phase transformations tell us about the emergence of genomes? In this paper, we show that such thermodynamic considerations of RNA synthesis, when combined with kinetics and population dynamics, lead to the conclusion that the ‘crystallization’ of genomes from its basic elements would have been spontaneous for RNAs, but not necessarily for other potential building blocks of genomes in the prebiotic soup. The salient characteristics of life (reproduction and selection) must have therefore emerged either gradually or abruptly from inanimate chemical processes some time in the early history of the Earth. Our ever-increasing knowledge on the biochemical and genetic basis of modern life forms should guide the quest to understand this transition, in addition to the chemistry of potential building blocks Conceptual difficulties to this scenario include the need for the existence of sufficiently concentrated and pure building blocks  and the necessity to explain subsequent evolutions of multi-chemical autocatalytic systems Our focus in this paper, in particular, is the transition from the first RNA molecules formed, which must have been pools of near-random RNA sequences, to the first genomes coding for RNA ribozymes. Crucial in understanding such an emergence of the first RNA genomes is the error threshold predicted by the quasispecies theory The prebiotic evolution in the RNA world is in the opposite direction of the error catastrophe transition, and may thus be referred to as ‘crystallization.’ In an equilibrium fluid, whether one observes melting or crystallization is determined by the changes in temperature and pressure. Can we find analogous conditions for the emergence of the first genomes? Addressing this question requires connections to thermodynamics of RNA synthesis. Recent developments in the theory of nucleotide strand replication To what extent these theoretical predictions are applicable to the actual prebiotic evolution that occurred in the past must ultimately be judged based on quantitative empirical data from existing and new experiments. Here, we extend our previous work The thermodynamic theory of molecular evolution rium see . We may One may seek the origin of the observed high-fidelity of polymerization reactions In quantitative descriptions encompassing both the high kinetic selectivity and this marginal stability difference, it is important to fully take into account the reversibility of the reactions e base ; . In realIn general, the overall elongation reaction of a single nucleotide goes through a transition state, whose activation energy is differentially affected by the action of polymerases. If one ignores the reverse reaction under the condition of To characterize this dual aspect of enzyme-catalyzed polymerization reactions, we adopt a ‘reduced’ description involving two key characteristics of forward rates: the mean base incorporation rate eory see :2a), a given external pressure can be balanced by the mean force of a state (gas), where density is low and molecules rarely interact. The equilibrium density is then roughly proportional to pressure and inversely proportional to temperature. At low enough temperatures, a given external pressure can also be matched by a different phase (liquid) with a much higher density held together by intermolecular attractions. Both gas and liquid phases are characterized by the lack of long-range order. The sharp boundary between them appears when temperature goes below the critical value because the effect of molecular interaction renders a certain range of pressure values unstable.Analogously, an RNA molecule replicating in a chemical reservoir is driven by the external thermodynamic force given by Eq. (1), which matches the average entropy production per monomer addition. For large  L phase  in whichFor the population as a whole, random drifts in negative . Once a We next relaxed the assumption that We tested this simulation algorithm using the special case of the exponential growth of a population with no degradation [Eq. (23a) only], uniform error rate ((18) see . Systemse values  predicteWe used the constant-lication  in Eqs. We also considered an alternative setup where a population growth occurs in a closed system, which leads to an evolutionary change we refer to as the ‘starvation process.’ Similar situations were also considered in Ref. We introduce the fractional population size rium see . A singl, , and  . In Figulication , thermodossible  .The conclusion that there was an underlying driving force biasing fidelity increases in the absence of genomes is particularly powerful because it is independent of the physical mechanisms implementing it. A likely mechanism for such changes is the evolution of error correction with the necessary increases in genome length Together, our findings in An elementary elongation reaction can be written asA useful physical insight to Previously, we derived a mean field theory for the templated replication and showed with numerical tests that it was exact for the two-parameter Jukes-Cantor model rates ulations . The parConsidering the elongation of RNA depicted in A special case of particular interest is the symmetric template models, for which the set of rates would be specified completely by at most onstants .Using Eqs. (34), (35) and summing both sides of Eq. (32) over Equation (36) is valid for any Equations (37b), (38), (40), and Without the symmetry of kinetic rates with respect to template base identity, the main difficulty in the analytical treatment is that Eq. (34) is no longer valid, and the velocity In applying the mean field theory expressions, all forward rates are first scaled with iminated  [62].Equilibrium is reached when Numerical simulations of single-molecule RNA replication kinetics were performed with the Gillespie algorithm In the stochastic form of the Eigen model given by Eqs. (23), the total rate of transformation at a given time For testing the Gillespie simulation of RNA population dynamics, we used the single-peak Eigen landscape 18) without back-mutation, for which the quasispecies dynamics (15) can be easily integrated. Although more advanced methods pioneered by Saakian and coworkers  without During an idealized starvation process, a single genotype is placed inside a medium containing


Coevolving residues in a multiple sequence alignment provide evolutionary clues of biophysical interactions in 3D structure. Despite a rich literature describing amino acid coevolution within or between proteins and nucleic acid coevolution within RNA, to date there has been no direct evidence of coevolution between protein and RNA. The ribosome, a structurally conserved macromolecular machine composed of over 50 interacting protein and RNA chains, provides a natural example of RNA/protein interactions that likely coevolved. We provide the first direct evidence of RNA/protein coevolution by characterizing the mutual information in residue triplets from a multiple sequence alignment of ribosomal protein L22 and neighboring 23S RNA. We define residue triplets as three positions in the multiple sequence alignment, where one position is from the 23S RNA and two positions are from the L22 protein. We show that residue triplets with high mutual information are more likely than residue doublets to be proximal in 3D space. Some high mutual information residue triplets cluster in a connected series across the L22 protein structure, similar to patterns seen in protein coevolution. We also describe RNA nucleotides for which switching from one nucleotide to another (or between purines and pyrimidines) results in a change in amino acid distribution for proximal amino acid positions. Multiple crystal structures for evolutionarily distinct ribosome species can provide structural evidence for these differences. For one residue triplet, a pyrimidine in one species is a purine in another, and RNA/protein hydrogen bonds are present in one species but not the other. The results provide the first direct evidence of RNA/protein coevolution by using higher order mutual information, suggesting that biophysical constraints on interacting RNA and protein chains are indeed a driving force in their evolution. A primary cause of coevolution between residues is biophysical interactions in the corresponding folded structure. Commonly a sequence slowly changes over evolution while the native fold is maintained, and coevolving positions have been observed in residues proximal in the 3D structure Mutual information (MI) is one of a handful of different methods used to characterize coevolution and has been successful in identifying coevolving pairs of positions in multiple sequence alignments (MSAs) Due to the biophysical interactions between RNA and protein present in joint complexes, it is natural to hypothesize that protein and RNA have coevolved in these complexes. However, to date no study has been able to demonstrate RNA/protein coevolution in the sequence record. Due to the wealth of sequence and structural data, the ribosome , is more complex than mutual information between two variables, MI. MI measures the dependence between two variables. Two dependent variables have high MI, and two independent variables have MI of zero. M measures the interaction between three variables. MI between three variables can be defined in the following way: Shannon  joint enWhy study this system? The functions of ribosomal proteins are poorly understood. Ribosomal proteins may play a role during ribosome assembly, contribute to structural stability, and/or aid in translation. Nascent chains translated by the ribosome encounter a beta hairpin in L22 immediately after the formation of a new peptide bond at the catalytic site, and L22 likely plays a regulatory role in this process Increased coevolution between RNA/protein triplets at close 3D space suggests the influence of RNA/protein biophysical interactions in evolution, and we use our data in conjunction with ribosome crystal structures to gain insight into this process. For two RNA nucleotides, their respective high MI triplets cluster in a series connected across the structure, similar to patterns seen in protein coevolution MI was calculated between one nucleotide and two amino acids from bacterial MSAs for the L22 protein and the large subunit RNA chain that binds to the L22 protein, the 23S  and classify them as “in contact” if their 3D coordinates are within a distance threshold High MI RNA/protein triplets are likely to be close in 3D space , squaresLow signal to noise is a known problem when looking for coevolution in MSAs. In our dataset, there are far fewer triplets at close distances than there are at far distance due to the relative orientations of the nucleotides and amino acids . In addition to differences in sample size at different distance bins, noise can occur because of insufficient number of sequences. It has been shown that at least 125 protein sequences are needed to compensate for background noise in MI E.coli standard numbering is used throughout. U519 and C487 are both base paired to other residues in the 23S RNA chain, and thus their interaction with L22 is via backbone atoms. L22 has two domains; an extended region that is buried in the large subunit of the ribosome, and a globular domain that is partly solvent exposed. The globular domain has alpha helices and antiparallel beta sheets, and both clusters are in this globular domain. The U529 high MI cluster is at the extended region end of the globular domain, and the C487 high MI cluster is at the solvent exposed end of the globular domain. Many of the amino acids in these clusters are at the edges of secondary structure elements, a structural link that has been previously described in coevolving amino acids High MI, proximal triplets have different amino acid distributions with different RNA bases, and a triplet with RNA U519 is shown as an example. U519 is adjacent to helix 1 and beta strand 2 in the L22 protein, and all four nucleotides are represented in our MSA . In the majority of sequences, U519 maintains a base pair irrespective of its value .The most proximal high MI triplet with U519 is U519/R18/D22 and the triplets from the MSA are shown in e.coli crystal structure E.coli, the values of U519/R18/D22 are U, Arg (R) and Asp (D), respectively. The distance between the three residues is 7.7 Å and MI/H 0.08  and Arg (R), respectively. A structural alignment of the crystal structure from this species E.coli indicates a structural shift in which the hydrogen bonds are broken and the residues farther apart . Adding to the complexity is the fact that MI between triplets can be negative, zero, or positive. MI between doublets is either zero or positive. For positive MI, the higher the number the greater the dependence between the variables. The equation for MI between triplets can be written in a few different forms, including the followingWe see high MI, proximal RNA/protein triplets as well as two nucleotides with top ranking triplets that form a connected series of residues in the 3D structure, consistent with two types of coevolution previously described in proteins Characterizing sequence covariation is a predictive tool as well as an important methodology to further fundamental understanding of evolution and the relationship between primary and 3D structure, and this is the first study to establish statistical evidence for the coevolution between RNA and protein. There are no simple rules governing RNA/protein interactions and a wide variety of interactions have been observed http://www.ncbi.nlm.nih.gov/genomes/MICROBES/microbial_taxtree.html), which had 998 species at time of download, and using clustalw MSA for the 23S was taken from The Comparative RNA Web Site MI between three variables is calculated asThe distance between three residues is calculated as the square of the sum of distances between each point and the center of mass of the three points.Figure S1The top ten highest MI triplets for 23S RNA U519 (red) and C487 (yellow), G2009 (green), and C2815 (blue).(TIF)Click here for additional data file.Figure S2Residue distributions for the most proximal high MI triplet with G2009 G2009/T39/K42.(TIF)Click here for additional data file.


Phosphorus (P) in RNA accounts for half or more of the total non-storage P in oxygenic photolithotrophs grown in either P-replete or P-limiting growth conditions. Since many natural environments are P-limited for photosynthetic primary productivity, and peak phosphorus fertilizer production is inevitable, the paper analyses what economies in P allocation to RNA could, in principle, increase P-use efficiency of growth (rate of dry matter production per unit organism P). The possibilities of decreasing P allocation to RNA without decreasing growth rate include (1) more widespread down-regulation of RNA production in P-limited organisms, (2) optimal allocation of P to RNA, both spatially among cell compartments and organs, and temporally depending on the stage of growth, and (3) a constant rate of protein synthesis through the diel cycle. Acting on these suggestions would, however, be technically demanding. Optimal allocation and the Growth Rate Hypothesis are then revisited in the context of variations in growth rate in relation to RNA content, including temporal and spatial variations in net protein synthesis rate per unit RNA, and then the significance of protein turnover in the total rate of protein synthesis per unit RNA. The paper concludes with consideration of how plant P-use efficiency could be increased by maximizing protein synthesis rate per unit RNA at all times and in all parts of the organism.RNA is an essential component of all living organisms, and the phosphorus (P) it contains cannot be substituted by any other element. The core role of RNA is in protein synthesis, involving mRNA, rRNA, and tRNA, but it also has (presumably) derived functions in regulation of gene expression. RNA-P is often the major non-storage form of P in photosynthetic organisms, and any attempts to increase the P-use efficiency of growth of algae and plants must include a consideration of economizing on the use of RNA. The paper first deals with the general background of the molecular biology, biochemistry, physiology and biogeochemistry, including optimal allocation of P within the organism in the context of the Growth Rate Hypothesis  is an essential element for all of the organisms that have been examined  and two-thirds of the mass of 70S ribosomes , the rest being protein. Ribosomes catalyze the translation of mRNA via the addition of amino-acyl units from amino-acyl tRNA to growing polypeptide chains. mRNA is the intermediary in converting the DNA nucleotide sequence in genes into the amino acid sequence in proteins. Intron removal from mRNA transcripts by splicing is an essential part of translation, and alternative splicing means that two or more different functional proteins can be produced from a single gene  algae and cyanobacteria Table . The mosMicrocystis  to the Growth Rate Hypothesis and optimal allocation of P is that a higher (specific) growth rate requires a higher rate of protein synthesis, and hence either a higher specific reaction rate of polypeptide elongation (amino-acyl units added per ribosome per unit time) or more ribosomes per unit biomass. The maximum specific reaction rate is apparently set by a trade-off between the rate of peptide-chain elongation and the accuracy of translation  are the specific growth rate of the organisms and the protein and RNA content in g per organism. The g protein per organism multiplied by the specific growth rate gives the instantaneous rate of protein synthesis as g protein per organism per day; division by g RNA per organism gives g protein synthesized per day per g RNA. Conversion to units appropriate for calculating the PER requires correction of the total RNA content for the fraction which is rRNA (assumed to be 0.8 in the absence of specific measurements for the organism concerned: see discussion above), and the fraction of the ribosomes that are active : Karpinets et al.  more relevant to the growth conditions for many photosynthetic organisms. The rates obtained in this way are up to 27 g protein per g RNA per day (range of 0.3–6.5 amino acids per ribosome per second) for Bacteria and up to 17.5 g protein per g RNA per day (range of 1.4–5.7 amino acids per ribosome per second) for the ascomycotan Fungi. The highest values are the most appropriate for this analysis, since the focus is on the maximum catalytic rates, which can be achieved by the two sorts of ribosomes. It should be noted that the Q10 value of 2 may be an under-estimate for in vitro protein synthesis by ribosomes from teleost muscle, albeit over a lower temperature range than that considered for microorganisms. However, work on Escherichia coli  80S ribosomes, respectively, will not be regarded a significantly difference.The conclusion from this work is that the maximum PER, adjusted to 20°C assuming a Qin vivo protein synthesis. The cells were grown on a 12L:12D light-dark cycle with temperatures of 23°C in the light and 20°C in the dark. The PER for these organisms can be calculated as follows.More relevant to work on photosynthetic organisms are the data compiled from Lourenço et al. , 2004 inMr of ribonucleotide is 340 g/mol . The mean Mr of aminoacyl units in protein is 110 g/mol , and only about 0.8 of ribosomes are active  on ropes in coastal waters. They examined the seasonal variation in external P, blade elongation rate (corrected for erosion) and the P, RNA and DNA concentration of the basal, meristematic, part of the blade between late February (sea temperature 2°C) and early September (sea temperature 19°C). The external inorganic plus organic P concentration varied from 2.8 μ M in early March to 0.4–0.6 μ M in April–September. There is a general correlation of RNA concentration with total intracellular phosphorus concentration. However, the elongation rate in early June, when RNA concentration is at its lowest, is five times the minimum value, which is in September when the RNA concentration is 2.5 times the minimum value in June. There is the complicating factor of the greater than 15°C temperature increase between the time of the maximum elongation rate in February and the minimum elongation rate in September; also, there is a 3° temperature increase between June (minimum RNA concentration) and September (minimum growth). After normalizing the elongation rate data to 20°C assuming a Q10 of 2, these data do not support the Growth Rate Hypothesis. Any correlation of growth rate and RNA concentration with external P availability is complicated by buffering by internal P stores, and the large change in temperature is a significant complication. Furthermore, it is not certain that P, rather than N, limits growth in the summer.Turning to macroalgae, Mizuta et al.  used a pReef et al.  measuredPinus contorta and Pinus muricata, and found that nutrient-replete plants had three times the RNA:protein ratio in their needle biomass than did low-nutrient plants, but that the plant relative growth rate for the nutrient-replete plants was less than twice that of the low-nutrient plants. Lower protein:RNA values were also found for two other treatments, which resulted in faster growth, but with no data given for relative growth rate. Matzek and Vitousek  growth rate and RNA per unit biomass (or RNA:protein or RNA:DNA) in photosynthetic organisms, although in a number of cases there are reasons why such a correlation might not occur, e.g., the work of Mizuta et al.  and Reef2 assimilation in photosynthesis minus 24 h respiration, andnet import of soil-derived nutrients through the xylem, over a 24 h diel cycle, the elemental content is kept constant from day to day under constant environmental conditions by export of photosynthate and the derivatives of soil-derived nutrients  in the phloem to growing and storage zones. The RNA in mature leaves is involved in protein turnover, including synthesis to replace photodamaged components of photosystem II  in P-replete cultures of the green microalga Dunaliella was almost as high as that in the light. Lancelot et al.  and the marine prymnesiophycean Emiliania huxleyi  ribosomes, so that about 40% of the rRNA is in the 70S ribosomes. Similar change in cytosol and plastid had previously been found by Loening and Ingle  protein per cell as a function of mesophyll cell age. Total ribosomes per cell peaked before protein per cell, and 80S ribosomes peaked before 70S ribosomes.For terrestrial embryophytes the available data cover a longer time period than the diel period addressed for microalgae. This accords with the longer generation of the embryophytes than of the microalgae. For embryophytes there are data for DNA, RNA, with some studies of rRNA distinguishing between monosomes and polysomes and/or whether ribosomes are free or protein-bound, and studies of protein synthesis during leaf development  of the organism which is encoded by organelle genomes  and the resulting proteins   than are needed for protein turnover in maintenance processes [discussed above, and in Raven  and VeneMaintain protein synthesis at a constant rate throughout the diel cycle, as discussed above.Minimize the extent of protein turnover, without incurring a corresponding increase in RNA use in providing, for example, additional photoprotection to decrease photodamage to PSII, or restriction of oxygen damage to nitrogenase.The available data on oxygenic photosynthetic organisms lends significant support to the Growth Rate Hypothesis, although a substantial number of well-conducted investigations show no continuous increase in RNA concentration with increasing growth rate determined by the supply of external P or other environmental factors; further well-designed experiments are needed. In specific relation to limitation by P availability, the few available data sets do not give a clear picture on whether decreased RNA concentration is relatively greater than that of the other major non-storage pools of P, i.e., low-molecular-mass phosphate esters and phospholipids, and more focussed experimentation is needed. However, it would appear that in cases where the spatial and temporal allocation of RNA does not follow optimality, there could be economies in RNA and hence P. Examples are modification of the spatial allocation of RNA in relation to the required rate of protein, and of the timing of protein synthesis (including protein turnover) so as to give the same relative rate of protein synthesis during exponential growth, or the same rate of protein synthesis in linear growth; If achievable, these modifications would require less RNA per unit biomass to achieve the same growth rate; however, achieving such reallocations would not be simple. Furthermore, as with all such manipulations, there could be unintended consequences, so any resulting genotype should be examined under a very wide range of environment conditions.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Archaebacteria and Lycopodiaceae to higher eukaryotes, display phenotypes based on genetic information transfer contained within, and regulated by, hierarchic orders of RNA. While DNA is capable of storing large amounts of genetic information over long periods of time, RNA-based signaling represents a transient, highly selective, ancient, and still evolving information transfer mechanism. The information-storage capability of RNA, its solubility and the mobility of small non-coding RNA (ncRNA) species, ability to self-replicate, to catalyze chemical reactions, to direct the amino acid polymerization into peptides, and to variably regulate the expression of highly selective genetic information underscores RNA as an important, and perhaps pre-eminent, driver in the evolution of genetic complexity and information transfer  are 18–25 nucleotide, conserved, non-coding ssRNAs that represent the smallest known carriers of highly selective genetic regulatory information in plants and animals. Mature miRNAs are typically derived from larger, double-stranded pre-miRNAs transcribed by RNA polymerase II (RNA Pol II) and processed by at least 2 RNA cleavage steps in which the activities of the RNAses Drosha and Dicer are involved, resulting in the generation of mature miRNA species Ambros, . While os Orgel, . Diverses Orgel, . Interess Orgel, , 2013. Ss Orgel, . InteresArabidopsis thaliana, Caenorhabditis elegans, Mus musculus, and Homo sapiens  miRNA-854 commonly targets an oligouridylate binding protein 1b mRNA 3′-UTR that normally encodes a member of a heterogeneous nuclear RNA binding protein family, suggesting a common origin of miRNA-854 as an ancient regulator of basal eukaryotic transcription . Smaller than any known ssRNA viruses are minimalist plant pathogens known as viroids. It remains debatable whether viroids are a biological oddity, an evolutionary fossil of pre-cellular evolution, and/or a highly novel and evolving, self-perpetuating pathogenic entity   or the chloroplast . After replication, viroid precursors exit the nucleus or chloroplast via an Exportin-5 or related transport mechanism (in a fashion similar to miRNA translocation); viroids then traffic from cell to cell to establish systemic infection. Recent findings indicate that like miRNAs, viroid infection and pathogenic activity is associated with the appearance of a small viroid-specific ssRNA (vsRNA), 21–24 nucleotides in size, processed by an RNaseIII of the family of Dicer-like proteins from a double stranded RNA (dsRNA) viroid precursor , with a 3.5 × 10 Diener, . The hos Diener, . Comprisds Ding, . Viroidsds Ding, . The fir) Figure . Viroidsr Figure . Mature r Figure . Indeed r Figure .in vivo; in fact they are probably physically too small to do so, and in in vitro experiments usually require nucleotide-linkers to effectively copy miRNAs using RT-PCR-based systems. Interestingly, circularization of pre-viroids, pri-miRNAs or miRNAs and/or the formation of complex higher order secondary and/or tertiary structures may further stabilize them (Rocheleau and Pelchat, That 21–24 nucleotide viroid-generated vsRNA are sufficient to induce diseases in plants is highly reminiscent of the finding that miRNAs, through analogous genetic mechanisms, have capabilities to contribute to disease processes in both plant and animal species Diener, . Very muinformation transfer mechanism” it is highly preserved, and this design is used repeatedly in diverse biological applications across diverse species. These biological applications range from the post-transcriptional modulation of gene expression to the potential for disease induction in both the plant and animal kingdom. Such conservation appears to lie in the molecular-genetic mechanism of miRNA and viroid ribonucleotide sequence structure and ensuing complementarity-based ssRNA-mRNA interaction. Intriguingly, ssRNA miRNA and viroid ribonucleotide sequences contain fingerprints for conservation across many diverse species, and these fingerprints represent some of the most highly conserved nucleic acid sequences known (Triboulet and Gregory, Intriguing similarities between the structure and function of miRNAs and viroids underscore the idea that during evolution, once nature has found and tested a successful molecular design and “


Caulobacter crescentus PNPase. In the RNA-free form, the S1 domains adopt a ‘splayed’ conformation that may facilitate capture of RNA substrates. In the RNA-bound structure, the three KH domains collectively close upon the RNA and direct the 3′ end towards a constricted aperture at the entrance of the central channel. The KH domains make non-equivalent interactions with the RNA, and there is a marked asymmetry within the catalytic core of the enzyme. On the basis of these data, we propose that structural non-equivalence, induced upon RNA binding, helps to channel substrate to the active sites through mechanical ratcheting. Structural and biochemical analyses also reveal the basis for PNPase association with RNase E in the multi-enzyme RNA degradosome assembly of the α-proteobacteria.Polynucleotide phosphorylase (PNPase) is an exoribonuclease that cleaves single-stranded RNA substrates with 3′–5′ directionality and processive behaviour. Its ring-like, trimeric architecture creates a central channel where phosphorolytic active sites reside. One face of the ring is decorated with RNA-binding K-homology (KH) and S1 domains, but exactly how these domains help to direct the 3′ end of single-stranded RNA substrates towards the active sites is an unsolved puzzle. Insight into this process is provided by our crystal structures of RNA-bound and apo  PNPasenterica ,4 and hoinia sp. . In Eschtory RNA –11. More[E. coli . Deinoconditions . Human Pnditions  and to tnditions . In the nditions .a. Two domains, both resembling closely the phosphorolytic exoribonuclease RNase PH, almost certainly have originated from duplication and fusion of an ancestral gene. While the C-terminal RNase PH-like domain catalyses phosphorolytic attack of RNA, the N-terminal domain has lost this capacity; instead, it contributes to the ring-like quaternary structure of the trimeric PNPase assembly [Streptomyces antibioticus, E. coli and human PNPase reveal how these domains are spatially organized [All known PNPase homologues share the same modular organization of conserved structural domains, depicted schematically in figure 1assembly . Two recassembly . Finallyrganized ,19. Forerganized .Figure S. antibioticus structure [E. coli PNPase S1 domain [E. coli structure [Access to the PNPase active sites is through the central channel, which can accommodate single-stranded RNA with some structural adjustment of a constricted aperture at the channel entrance. The S1 and KH RNA-binding domains are located on the surface of the trimer near the aperture, but they are highly dynamic so that their detailed structures have been difficult to resolve. Recently, the KH domains were defined in the crystal structure of human PNPase , but onltructure , guided 1 domain  and the tructure .E. coli PNPase with manganese ion bound corroborates the key role of conserved acidic residues in the catalytic mechanism [Structural studies have explained many of the enzymatic properties of PNPase. First, the enzyme cleaves single-stranded RNA in the 3′–5′ direction with ‘processive’ behaviour, meaning that once the first cleavage is initiated, cutting continues along the same substrate until it becomes stalled by RNA secondary structure . Processechanism .Structural data also reveal that the constricting aperture at the entrance to the central channel is formed by a loop containing highly conserved phenylalanines that make aromatic stacking interactions with the bases of engaged RNA . The proCaulobacter crescentus, which has been established as a model system to investigate cell cycle progression, because populations can be readily prepared that are synchronized in the cell cycle [C. crescentus PNPase was shown to form part of a multi-enzyme RNA degradosome assembly, to which PNPase is recruited through interaction with a short conserved motif at the C-terminus of the endoribonuclease RNase E [C. crescentus PNPase in complex with the conserved peptide motif explains how PNPase interacts with RNase E to form an assembly involved in post-transcriptional regulation, and mutational analysis in vitro and in vivo corroborates the importance of two conserved tryptophan residues at the C-terminus of RNase E for this interaction.The PNPase studied here is from the bacterium ll cycle . Recentl RNase E . The cry3.3.1.C. crescentus PNPase strain NA1000 using a GenElute bacterial genomic DNA kit (Sigma). The pnp gene (CC0034) was amplified from the isolated genomic DNA using the forward primers: ccPNP_pGEX6p1.fw or ccPNP_6xHIS.fw, and the reverse primer: ccPNP.rev  and alanine mutations of the tryptophan codons were synthesized and annealed.Genomic DNA was isolated from pnp gene was ligated into pGEX-6p-1  and pET-DUET (Merck Biosciences) expression vectors to generate N-terminal GST and 6xHis-tagged versions of PNPase, respectively, rne gene fragments encoding GWW peptides were ligated into pGEX-6p-1. Transformed E. coli cells were grown in 2x YT media at 37°C until OD 600 nm reached 0.4, and then induced with 1 mM isopropyl beta-d-1-thiogalactopyranoside (IPTG) at 18°C for 15 h (PNPase) or 37°C for 3 h (GST–GWW peptides). Cells were harvested and resuspended in either pGEX-lysis buffer  supplemented with 5 mM EDTA, or 6xHis-lysis buffer  supplemented with complete EDTA-free protease inhibitor (Roche). Resuspended cells were lysed with an Emulsiflex cell disruptor (Aviston) and clarified by centrifugation at 30 000g for 30 min at 4°C. For GST–PNPase and GST–GWW peptides, the lysate was then loaded onto a glutathione sepharose column, washed with lysis buffer and eluted with lysis buffer supplemented with 50 mM reduced glutathione. GST–GWW peptides were stored at −80°C. GST–PNPase was incubated with Precission Protease  at 4°C for 15 h, concentrated and loaded onto a Sephacryl 200  and eluted in GST-lysis buffer. For 6xHis–PNPase, the lysate was loaded onto a 5 ml HisTrap HP column , washed with 6xHis-lysis buffer, then eluted in 6xHis-lysis buffer supplemented with 500 mM imidazole. Fractions enriched in PNPase were supplemented with 50 mM sodium phosphate at 37°C for 1 h to drive phosphorolysis of the co-purifying RNA, and then reapplied to Sephacryl 200. The RNA content of the purified sample at this stage was estimated to be 15 per cent by UV spectroscopy (260 : 280 nm ratio of 1.55). This is consistent with profiles from analytical ultracentrifugation, suggesting a mass excess above the expected trimer corresponding to approximately 20 nucleotides. Avidly bound RNA was removed by heparin column equilibrated with loading buffer (20 mM Tris–HCl pH 7.5) and developed with a linear gradient of elution buffer .The 3.2.3.2.1.GST–GWW binding experiments were performed in a buffer composed of 20 mM Tris–HCl, pH 7.5 and 200 mM NaCl. An approximate fourfold molar excess of GST–GWW, GST–GWA, GST–GAW or GST–GAA was mixed with PNPase and incubated at room temperature for 10 min prior to analysis by 8 per cent acrylamide non-denaturing polyacrylamide gel electrophoresis (PAGE).3.3.−1. The C. crescentus PNPase recognition peptide from RNase E  was mixed at a 2 : 1 molar excess with apo-PNPase immediately prior to crystallization. The protein–peptide and protein–RNA complexes were mixed with an equal volume of crystallization buffers. Crystals were grown by sitting droplet vapour diffusion at 18°C and appeared after approximately one week for all crystal forms. Crystals for RNA-bound PNPase appeared in 24 per cent w/v PEG 3350, 0.1 M bis–Tris (pH 5.5), 0.1 M ammonium acetate. Hexagonal crystals of apo-PNPase appeared in 17 per cent w/v PEG 3000, 0.1 M trisodium citrate pH 5.5. Rhombohedral crystals of apo-PNPase appeared in 19 per cent w/v PEG 3350, 0.15 M dl-malic acid. Crystals were harvested in reservoir solutions supplemented with 25 per cent v/v glycerol as cryoprotectant, then flash frozen in liquid nitrogen. Diffraction data were collected at 100 K using stations IO2 and I24 at Diamond Light Source, Didcot, UK. The crystal used for the RNA-bound dataset was treated by rapid temperature annealing. Data were processed with MOSFLM [RNA-free and RNA-bound PNPase were concentrated to approximately 15 mg mlh MOSFLM  and mergh MOSFLM .3.4.E. coli PNPase as an initial search model (PDB code 3GME [C. crescentus PNPase KH domain using the FUGUE server (http://tardis.nibio.go.jp/fugue) [C. crescentus KH domain in the clearest density of the early electron density maps. The models were improved iteratively with refinement using REFMAC [E. coli PNPase S1 domain [All PNPase structures were solved by molecular replacement with the program Phaser  using a ode 3GME ). For thp/fugue) . The humg REFMAC  and BUSTg REFMAC , followeg REFMAC . For theg REFMAC , and the1 domain  was used3.5.rne), a triple-flag sequence, and the beginning of the rne gene were PCR amplified using the primer pairs 3713_SpeI-700upcc1877-fw and 3714_Upcc1877-3xflag-rv, 3715_Upcc1877-3xflag-fw and 3746_3xflag-cc1877-rv, or 3716_3xflag-cc1877-fw and 3718_700cc1877-NheI-rv, respectively. The three fragments were connected via overlap extension PCR, and the product was digested using SpeI and NheI restriction enzymes. Ligation of this insert with pNPTS138, digested with the same restriction enzymes, resulted in pIH79. For the construction of pIH84, the DNA flanking the sequence encoding the PNPase-binding site of RNaseE (last 10 amino acids) were amplified using the primer pairs 3719_SpeI-700cc1877-fw and 3736_2664cc1877-stop-down-rv, or 3737_2664cc1877-stop-down-fw and 3724_700downcc1877-NheI-rv, respectively. The fragments were connected by overlap extension PCR, the product was cut with SpeI and NheI restriction enzymes, and ligated with the pNPTS138 vector.DNA sequences upstream of cc1877 . The three KH domains are positioned over the entrance to the catalytic core like an umbrella, with the RNA-binding GSGG loops (residues 572–575) forming a secondary entrance aperture. We will return again to discussions of this loop in §4.3. The S1 domains adopt a splayed conformation, and the conformation is constrained by the crystal packing . We suggest that the S1 domains are flexibly tethered to the KH domains, and that they potentially could manoeuvre to capture RNA substrates as part of a molecular fly-casting mechanism. The RNA-binding surfaces of the S1 domains face the molecular threefold axis at the centre of the PNPase trimer, and it is envisaged that S1 domains could clamp down in concert on RNA targets.The structure of the complete PNPase reveals how the KH and S1 RNA-binding domains are spatially arranged in relation to the RNase PH-like catalytic core b. The th4.2.C. crescentus PNPase, it was found that oligonucleotide originating from the E. coli expression host remained avidly associated with the protein during chromatographic purification. Extensive incubation with phosphate to promote phosphorolysis, followed by gel filtration, liberated nucleoside diphosphate; however, the limit digest product still remained associated with the PNPase. On the basis of absorbance at 260 nm, an estimated 20–30 nucleotides were bound to each PNPase trimer. It was possible to liberate a fraction of the protein from RNA by chromatography, and this material was used to prepare the apo-form crystals described in §4.1. Crystals of the RNA-bound material were obtained in space group P22121 that contain a PNPase trimer in the asymmetric unit (a). The KH domain is structurally similar to the human poly(C) binding protein, and the GxxG loop makes similar contacts with nucleic acids .In the course of preparing recombinant ric unit . The strric unit a. The KHAlthough purines could be distinguished from pyrimidines in the modelled RNA, it was not possible to definitively identify the base sequence. The refined crystal structure revealed 12 RNA nucleotides, and while it was anticipated that 20–30 oligonucleotides would be present in the structure based on spectroscopic absorbance, the RNA may have been partially degraded during crystallization or may be extensively disordered in some places. The S1 domains were not visible in this structure, although the presence of these domains in the crystals was confirmed by SDS–PAGE of harvested crystals. The S1 domains are therefore apparently disordered in the RNA-bound structure.E. coli MicC small non-coding RNA. The protein:synthetic RNA complex crystallized in the same unit cell and space group as the endogenous RNA complex crystals. The structure with the synthetic RNA corroborates the interactions seen with the endogenous RNA; however, the resolution and map quality were marginally better for data collected from crystals of PNPase in complex with endogenous RNA, and the following analysis is based on data from the endogenous RNA co-crystal.To evaluate the interactions with a defined RNA species, crystals of RNA-bound PNPase were obtained by co-crystallization of apo-PNPase with a short synthetic RNA (27mer 5′-GUUUAUUGCCGUUCUUGUUAUAUGCCUU), corresponding to a portion of 4.3.a). A key set of interactions is made with the phosphate backbone of RNA by a glycine-rich loop  separating two α-helices, as shown in figure 2a and summarized schematically in figure 2b. At the most distal contact with the RNA, S573 of the middle GSGG loop forms a hydrogen bond to the 2′-OH of the first base, whereas the amide of G574 (upper) interacts with phosphate of the second nucleotide. The base of the second nucleotide interacts with S573 of the lower KH domain and, in turn, G574 of the middle domain contacts the phosphate of the third base. Finally, the fourth and fifth bases are buried against an exposed hydrophobic patch of the lower KH domain, whereas G574 contacts the phosphate of the fifth base. The RNA follows a spiralling trajectory, with bases on the outside and phosphate backbone innermost. Portions of the density indicate disorder in the backbone, and this is also apparent in an omit electron density map of the entire RNA chain, which corroborates the key contact points with the protein (figure 2c).The three KH domains (residues 556–619) interact with the RNA in the manner of three overlapping hands grasping a length of rope. These contact sites will be described as upper, middle and lower positions according to the distance from the RNase PH-like core a. A key C. crescentus (grey) and E. coli  with bound RNA shows that the interaction is similar in both enzymes. Some density could be seen at a protomer interface adjacent to the FF loop and may be a binding site for one nucleotide base. Some residual density could be seen at all three active sites, but it was not of sufficient quality to enable modelling. The pathway of the RNA continuing from the narrow FF-loop aperture to the active site is not resolved in the electron density maps, but broken density indicates that a disordered polymer might bridge those two points.The path of the RNA continues from the KH ensemble to the aperture of the central channel, where a base stacks on the aromatic side chain of the conserved FF loop . Here, partial density for stacked bases is seen at each FF loop, and probably results from averaging of three separate conformations of the RNA, rather than having three separate chains, because the opening is not sufficiently large to accommodate more than one polynucleotide chain. The overlay of the FF loops of 4.4.b). These interactions are reinforced cooperatively through the spatial organization of the KH domains, mediated by favourable inter-domain contacts.The question naturally arises how the RNA is oriented so that the 3′ end is directed towards the PNPase aperture. We do not observe any contacts with the 3′ end of the RNA that might contribute to directionality of the substrate, and because our structure represents effectively a snapshot of a late stage of substrate entrapment, we cannot comment on whether the 3′ end is explicitly recognized during the early capture stage. However, the structure suggests that the 3′–5′ polarity of the substrate originates from the interactions of the GSGG loops with the RNA sugar–phosphate backbone by the KH domains b. These 4.5.a). The ‘lower’ KH domain is packed against the proximal face of the RNase PH core, with interactions between D590 (KH domain) and G361 (RNase PH core), as well as an interaction between S600 (KH domain) and T360 (RNase PH core). Non-equivalent interactions between the KH domains and catalytic core are seen in the KH domains at the ‘middle’ and ‘upper’ positions, namely the I557/P555 (KH domain) and Y425 (RNase PH core) contact. In the reference frame of the RNase PH trimer, the ensemble of KH domains is displaced from the central threefold axis, and defines three different states (a). Analysis of the displacement of the KH domains by DynDom (http://fizz.cmp.uea.ac.uk/dyndom/) [figure 3a; the rotation axes are represented in the electronic supplementary material, figure S4). Assuming all three KH domains can alternate between the three different orientations, circularly permuting through these states gives the impression of rotary displacement of the KH ensemble .The KH domains adopt non-equivalent orientations with respect to the RNase PH-like core, and each domain contacts the RNA at different points a. The ‘lt states a. Analysdyndom/)  reveals C. crescentus structures reveals that they adopt strikingly different conformation with respect to the catalytic core . The RNA-binding GxxG loops are spatially distant in the human structure, presenting an apparent ‘open’ conformation for the KH aperture . Perhaps such presentation may aid substrate capture.The hypothesis that the KH domains can undergo marked movement is supported by the recent X-ray crystal structure of human PNPase . In the 4.6.c; electronic supplementary material, movie S2). With respect to the fixed subunits in the reference frame of the RNase PH fold, the centre of mass of the neighbouring subunits is displaced by roughly 2 Å. Inspection of the interfaces between the protomers suggests that there is a continuum of small local adjustments involved and not a simple binary switch, as found, for example, in the quaternary structural transition of haemoglobin [Structural non-equivalence is also observed in the RNase PH-like core, which can be seen most clearly by circularly permuting the subunits in a common reference frame of one of the subunits (figure 3moglobin . The mov4.7.C. crescentus PNPase interacts with the endoribonuclease RNase E to form part of an RNA–degradosome complex [b). GST fused to the PNPase recognition peptide (TAPPEKPRRGWWRR) was mixed at an approximate fourfold molar excess with PNPase, and the mixture was then resolved on a native gel. The band corresponding to free PNPase was completely shifted to a slower migrating species in the presence of the wild-type GST–GWW peptide. However, when either tryptophan was mutated to alanine the formation of the complex with PNPase was severely disrupted.It has previously been shown that  complex . This inin vivo. For these experiments, a strain of C. crescentus was generated with a flag tag fused to the N-terminus of genomically encoded RNase E and a deletion of the putative PNPase recognition GWW motif (c). When the GWW motif is absent from RNase E, the amount of PNPase retrieved from the pull-down experiment is greatly reduced, if not totally absent, whereas the other degradosome components  are present at levels similar to pull-downs from the wild-type strain.The impact of the mutations of RNase E:PNPase interactions was evaluated WW motif c. When ta(i)). In the hexagonal crystal form, the GWW peptide additionally forms crystal packing contacts with the S1 domain of a neighbouring molecule . For the E. coli homologues, the interaction involves the RNase E peptide becoming an additional strand with the exposed edge of a sheet from PNPase [E. coli PNPase is spatially distant from the corresponding location of the GWW peptide recognition site on the surface of C. crescentus PNPase (figure 4a(ii)).Crystals of RNA-free PNPase with recognition peptide (KPRRGWWR) revealed clear density for the peptide in both the hexagonal and rhombohedral crystal forms of PNPase. For the rhombohedral crystal form, eight residues of the peptide could be modelled into this density. The GWW motif occupies a hydrophobic pocket formed by the loop connecting helix α2 and strand β7, and the linker region between helices α5 and α6 on the surface of PNPase , which is absent from the other two KH domain interfaces. The path of the RNA can be traced from the KH domains to the FF loop at the entrance to the central processing core of PNPase; however, there is no clear density to connect the active site and the RNA at the aperture formed by the FF loops.The KH domains are flexible in the bacterial PNPase structures and can cooperate through RNA interactions consolidated by a few weak salt bridge contacts. Notably, there is a salt bridge between the conserved residues D593 (‘upper’ domain) and R579 . In the RNA-free structure of PNPase, the S1 domains were resolved but were poorly ordered and could not be resolved in the RNA-bound structure, and as such their role in substrate recognition and processing remains to be elucidated. In the model shown in E. coli RNase E as a guide to speculate on the path of the nucleic acid from one extreme end of the PNPase trimer to the active site.We have prepared a speculative model for the path of single-stranded RNA by combining the crystal structures of the aperture b. In theb). The up–down movement of RNA substrates through the central channel could be favoured by binding and displacement of the bases at the FF-loops (c) and at a small pocket adjacent to the aperture inside the channel. The threading mechanism is thus envisaged to be a biased ratchet, in which the RNA moves along because of the slightly favoured binding energy of the 3′ terminus for the active site [One salient feature of the structure is the asymmetry of the RNA-bound PNPase. We suggest that the asymmetry in PNPase is functionally important, and enables substrate channelling and communication between the active sites. We envisage that substrate is channelled by a coupling mechanism, which would link RNA binding by the RNA-binding domains with displacement of the polynucleotide through a constricting aperture to thread to the active sites b. The upFF-loops c and at ive site , or a stRatcheting of the substrate would require the bottom KH domain to disengage from the RNA chain following each phosphorolytic event and re-engage at the upper position for the process to continue. The relatively weak interactions between the KH domains and the RNA may allow for such rapid association and dissociation. Given the knobbly nature of the contacting surfaces, it seems that the entire chain must be displaced from the surfaces before it could move along. In this ratcheting-like movement, the end of the RNA could visit one of the neighbouring catalytic sites. Whether the mechanism involves obligate movement between active sites with a defined rotational sense or random movement between sites remains to be investigated. However, communication between active sites of the RNase PH-like core could orchestrate their activities, so that product from one active site shuttles to one of the adjacent free sites. The mechanical displacement of the strand may be linked with substrate binding at one of the vacant active sites, which would be either inorganic phosphate in the case of the degradative direction, or NDPs in the case of the reverse (random polymerization) direction. Accordingly, the energy for the displacement would originate from the free energy of binding of phosphate or NDP at one of the vacant active sites.The model for mechanical coupling in PNPase might explain the puzzling observation that PNPase cleaves RNA at similar rates regardless of length over a certain threshold size of six to nine nucleotides, and that cutting of the longer substrates is inhibited by short RNA (such as 6-mers) ,40. ThisE. coli PNPase with RNase E has been the subject of several previous studies, and indeed the structural basis of this interaction was highlighted by the crystal structure of the complex [C. crescentus is markedly different from that seen in E. coli. In our C. crescentus structure, it is clear that the GWW motif is buried into a hydrophobic pocket on the surface of PNPase, at a site approximately 60 Å from the position corresponding to the RNase E recognition site in E. coli. This is a fascinating example of convergent evolution, where different bacterial classes have arrived at distinct means of recruiting PNPase into a complex with RNase E.The interaction of PNPase with RNase E plays an important role in the formation of an RNA–degradosome complex in bacteria. The interaction of  complex , where tThe structural data presented here reveal how the versatile KH fold can be used for RNA recognition and translocation, achieved through the spatial arrangement and cooperative interplay between independent KH domains. The conformational adjustments we observe in PNPase associated with RNA binding provide further insight into the mechanism of this fascinating molecular machine.


Nucleophosmin (NPM1/B23) is a nucleolar protein implicated in growth-associated functions, in which the RNA binding activity of B23 plays essential roles in ribosome biogenesis. The C-terminal globular domain (CTD) of B23 has been believed to be the RNA binding domain because the splicing variant B23.2 lacking the CTD binds considerably less efficiently to RNA. However, the recognition of target RNAs by B23 remains poorly understood. Herein, we report a novel mechanism by which B23 recognizes specific RNA targets. We observed that the nucleolar retention of B23.3 lacking the basic region of B23.1 was lower than that of B23.1 because of its low RNA binding activity. Circular dichroism measurements indicated that the basic region and adjacent acidic regions of B23 are intrinsically disordered regions (IDRs). Biochemical analyses revealed that the basic IDR alone strongly binds to RNA with low specificity. The excessive RNA binding activity of the basic IDR was restrained by intra-molecular interaction with the acidic IDR of B23. Chemical cross-linking experiments and fluorescent labeling of bipartite tetracysteine-tagged proteins suggested that the inter- and intra-molecular interactions between the two IDRs contribute to the regulation of the RNA binding activity of CTD to control the cellular localization and functions of B23. The biological activities of proteins are often exerted by structurally ordered domains; therefore, structure-function analyses have been central in understanding several biological processes. However, recent studies have documented that the structurally disordered flexible domain in the native state [intrinsically disordered regions (IDRs)] play crucial roles in the regulation of protein–protein and protein–nucleic acid interactions . The IDRThe nucleolar phosphoprotein NPM1/B23 is involved in the regulation of pre-ribosome RNA (rRNA) transcription, processing and pre-ribosome transport . In addirRNA gene chromatin to stimulate transcription . A. ArRNA gcription . Becausecription . It was cription . Severalcription . We prevcription . HoweverIn this study, we examined the bIDR involved in the regulation of the RNA binding activity of B23.1. We observed that B23.3 lacking a part of the bIDR showed significantly lower RNA binding activity than B23.1. Further analyses strongly suggested that the association between acidic and bIDRs of B23.1 allowed its recognition and binding to target RNAs.HeLa and 293T cells were maintained in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum. Transient transfection of plasmid DNA was performed using GeneJuice (Novagen) according to the manufacturer’s instructions.Antibodies Anti-B23 serum was generated in rabbit using the His-B23.1-CR protein as an antigen. Anti-Flag tag antibody  and anti-His tag antibody  were commercially available.Plasmids pEGFPC1-Flag-B23.1, pGEX2T-B23.1, and pET14b-B23.1, pET14b-B23.1-ΔC1 (used as B23.2), pET14b-B23.1-ΔC2 (used as B23.1-C188), pET14b-B23.1-ΔC3, and pEGFPC1-Flag-B23.2, pET14b-B23.2, and pGEX2T-B23.2 and pET14b-B23.1-T4sD were described previously . To consEscherichia coli were grown at 37°C until OD600 reached 0.4. Expression of the recombinant proteins was induced by addition of isopropyl β-D-thiogalactopyranoside at 30°C for 3 h or at 18°C for 16 h. For purification of GST-tagged proteins, sonicated bacterial cell lysates were incubated with Glutathione sepharose  in buffer A  containing 150 mM NaCl. The resin was washed with buffer A containing 500 mM NaCl, treated with micrococcal nuclease (MNase) , and extensively washed with buffer A containing 500 mM NaCl. GST-tagged proteins retained on the resin were eluted in a buffer containing . His-tagged proteins were purified with HIS-Select® Nickel affinity gel according to the method suggested by the manufacturer (SIGMA-ALDRICH). Purified proteins were dialyzed against buffer H . His-tagged proteins used in proteinase digestion assays and FlAsH assays were further purified by separation on SDS–PAGE followed by extraction in a buffer containing  and precipitated with acetone. Precipitated proteins were denatured in a buffer containing , refolded by dialysis against a buffer containing  and dialyzed against buffer H. The protein concentrations were estimated by comparison with proteins in a standard molecular mass marker (Bio-Rad) on SDS–PAGE stained with Coomassie Brilliant Blue (CBB). Identity of a part of recombinant proteins was confirmed by matrix-assisted laser-desorption time-of-flight mass spectrometry (MALDI-TOF-MS), and the purity of the proteins was also analyzed by SDS–PAGE . The phosphorylation of recombinant proteins was performed as described in  and MNase  were added, and proteins were incubated for additional 30 min followed by purification using GST-tag as described earlier in the text. Phosphorylation status was examined by phospho affinity gel electrophoresis using phos-tag®   was transformed with pGEX2T or pET14b plasmids containing B23.1 mutant cDNAs. The transformed ribed in , using mustries)  in the p2 at 37°C and analyzed as previously described . For fluorescence recovery after photobleaching (FRAP) analysis, HeLa cells were transiently transfected with EF-B23.1, EF-B23.2 and EF-B23.3, and grown on 35-mm glass base dishes (IWAKI). The dish was set on inverted microscope  in an air chamber containing 5% COescribed . The dat293T cells were transiently transfected with EF, EF-B23.1, EF-B23.2 and EF-B23.3 with Gene Juice (Novagene). Sixteen hours after transfection, the cells were collected and sonicated in buffer A containing 150 mM NaCl. The cell lysates were incubated with anti-Flag M2 affinity gels (SIGMA-ALDRICH) in buffer A containing 150 mM or 300 mM NaCl and 35 units RNase inhibitor . The resins were washed with the same buffer, and treated with 10 units of DNase I (Invitrogen), and washed extensively with the buffer same. The proteins bound with the resin were eluted with buffer A containing 150 mM NaCl and Flag peptide (SIGMA-ALDRICH), separated by SDS–PAGE and analyzed by western blotting. RNAs co-precipitated with EF-tagged proteins were treated with proteinase K, purified with phenol-chloroform extraction and ethanol precipitation and separated by 6% denatured PAGE. RNAs were visualized by Gel Red staining (Biotium).GST-tagged B23 proteins (5 µg) preincubated in the absence or presence of total RNA purified from 293 T cells (10 µg) or total RNA alone (10 µg) (100 µl) were loaded on 15–45% sucrose density gradient in the buffer containing  (2.1 ml). The samples were centrifuged at 54 000 rpm for 2 h at 4°C in S55S rotor , and fractions (150 µl) were collected from the top. Proteins in each fraction were separated by SDS–PAGE and visualized by CBB staining. RNAs in each fraction were purified, separated by 1% denatured agarose gel and visualized by Gel Red staining. The distribution patterns of proteins and RNAs were analyzed using CS analyzer (ATTO).2 dmol−1. The concentration of His-tagged B23 proteins was 5 µM in 50 mM sodium phosphate buffer (pH 7.0).CD spectral analyses were performed using a JASCO spectrometer J-710 instrument (JASCO Inc.) at room temperature in the wavelength interval 190–260 nm. Data were collected at 0.2 nm resolution, 20 nm/min scan speed, 1.0 nm bandwidth and 2 s response using 0.1 cm path-length quartz cuvette. Data for each protein collected from four scans were averaged. The signal was converted to mean molar residue ellipticity in units of mdeg cmFilter binding assays were performed as described . RNAs usGST-B23.1-A was incubated with GST-tagged B23.1-CR1, B23.1-CR1.5 and B23.1-CR in 10 µl of buffer H for 30 min at room temperature. Incubated samples were separated on 6% native-PAGE in 0.5× TBE and visualized by CBB staining.His-B23.1 and His-B23.1 -ΔA1A2 (2 µg) were incubated in 10 µl of 50 mM phosphate buffer (pH 7.6) containing Trypsin for 2 min at 37°C. The proteolysis was stopped with the addition of 3.5 µl of 4×SDS sample buffer and incubation for 5 min at 95°C, and proteins were separated by SDS–PAGE followed by western blotting using anti-His tag antibody.2  and 0.5 µM EDT  in a buffer containing  in 384-well plate (Corning). The fluorescence emission at 535 ± 5 nm was detected by Varioskan (Thermo Fisher Scientific).Recombinant proteins containing Cys-tag  were mixed with 10 µM FlAsH-EDTHis-B23.1 -ΔN and His-B23.2 -ΔN (15–45 pmoles) were incubated in the buffer  containing 0.1% glutaraldehyde (10 µl) for 10 min at room temperature. The reaction was stopped by the addition of 1 µl of 1M Tris–HCl (pH 7.9) and 3.5 µl of 4 × SDS–PAGE sample buffer, and incubation at 95°C for 5 min. Cross-linked proteins were separated by SDS–PAGE and visualized by CBB staining.t1/2) of EF-B23.1 and -B23.2 recovery was 5.48 ± 1.80 s and 2.33 ± 0.96 s, respectively. This finding was consistent with the previous observation that CTD plays a crucial role in the nucleolar retention of B23 (t1/2 = 2.11 ± 1.02 s).Three splicing variants of NPM1/B23, B23.1, B23.2 and B23.3 are expressed in HeLa cells A. The thn of B23 ,26. DespSupplementary Figure S2A). EF-B23.1 co-precipitated with 28S, 5.8S and 5S rRNAs as previously reported . To investigate the direct RNA binding activity, we performed sucrose density gradient centrifugation assays using GST-tagged recombinant proteins and total RNAs purified from 293T cells , GST-tagging did not affect the RNA binding activity of B23 proteins. These results indicate that not only CTD but also the region lacking in B23.3 play important roles in the RNA binding activity of full-length B23.1 in cells and in vitro.Localization and chromatin association of B23 are closely related to its RNA binding activity ,28. Ther3T cells E. All th3T cells C. In conhttp://mbs.cbrc.jp/poodle/poodle-i.html) . In all cases, the overall pattern of the spectra was similar, although the peak at ∼200–210 nm of B23.1, B23-CR.1, B23.1-ΔN was slightly shifted to the left when His-tag was added. Thus, we concluded that His-tag did not significantly affect B23 protein structure.Recent studies showed that the IDRs regulate the functions of structured regions, such as protein–protein or protein–nucleic acid binding activity . The aci-i.html)  showed t-i.html) A. To con-i.html) B. Consis-i.html) , the spe-i.html) . The spe-i.html) , the speTo address the function of bIDR of B23, we ivestigated the RNA binding activity of this region. Recent studies indicated that in addition to CTD of B23.1, a part of bIDR was required for its stable G-quadruplex DNA binding activity . These rSupplementary Figure S5A). 18S and 28S rRNAs were recovered in fractions 4–9 and 6–10, respectively. In addition, 5.8S rRNA and a small but distinct level of 5S rRNA clearly cofractionated with 28S rRNA. To test whether B23.1 directly binds to 28S but not to 18S rRNA, both 28S and 18S rRNAs were purified. 18S and 28S rRNAs alone were primarily found in fractions 4–6 and 7–9, respectively. We next examined the 28S and 18S rRNA binding activity of B23.1 by sucrose density gradient centrifugation assay . Surprisingly, we found that GST-B23.1 was cofractionated with both purified 18S and 28S rRNAs. However, when total RNAs were mixed with purified 18S rRNA and examined for B23.1 binding, GST-B23.1 recovery in the 18S rRNA peak fractions (fractions 4–7) decreased and those in the 28 S rRNA peak fractions (fractions 7–10) increased. These results suggest that B23.1 has some preference for RNA structure or sequence found in RNAs in the 28 S peak fractions such as 28 S and 5.8 S rRNAs.B23.1 was recovered primarily in the 28S rRNA peak fractions, but we did not know whether B23.1 directly binds 28S rRNA. To investigate the same, we examined the RNAs contained in the 28S peak fractions. Total RNAs were fractionated by sucrose density gradient centrifugation assay, and the RNAs were separated by agarose gel and polyacrylamide gel electrophoresis . CD spectral analyses of the three mutant proteins showed peaks at ∼222 and 208 nm, the typical pattern of α-helix . This finding suggests that a specific structure of bIDR is not induced on binding to RNA.Previously, we have demonstrated that the phosphorylation of B23 by cdc2/cyclin B kinase reduces its RNA binding activity . The pho α-helix G and H,  α-helix B was notbIDR of B23 showed strong RNA binding activity, whereas B23.2, containing identical amino acid sequences in this region, did not efficiently associate with RNA, suggesting that the RNA binding activity of bIDR is modulated by full-length B23 protein. Because B23 has an aIDR, it is likely that negative charge of the aIDR neutralizes the positive charge of bIDR. To examine this possibility, we studied the effect of aIDR deletion on B23 RNA binding activity using B23.1 -ΔA2, B23.1 -ΔA1A2 and B23.1-ΔAC1 A. SucrosFurthermore, GST-B23.1-ΔAC1 associated with RNAs, as did GST-B23.1-ΔA1A2, indicating that CTD was not required for RNA binding when aIDR was deleted. From these results, we concluded that the RNA binding activity of bIDR is negatively regulated by aIDR and that aIDR confers RNA binding specificity on B23.1.Supplementary Figure S8). These results indicate that aIDR regulates the non-specific RNA binding activity of bIDR through direct association. Moreover, phosphorylation in bIDR diminished the RNA binding activity and the interaction with aIDR. To assess the association between aIDR and bIDR in full-length B23.1, we tested the sensitivity of His-tagged B23.1 and B23.1-ΔA1A2 toward trypsin (We subsequently tested whether the two regions associated with each other. GST-tagged B23-A (amino acids 120–188) was mixed with increasing amounts of B23.1-CR1, B23.1-CR1.5, B23.1-CR, B23.1-CRA or B23.1-CRD A and B,  trypsin D. DigestP < 0.0005). This result suggests that CTD of B23.1 restricted the access of FlAsH molecule to the tetracysteine sequence located between bIDR and CTD. When increasing amounts of B23.1-Nc, B23.1-Cc, B23.2-Nc and B23.2-Cc harboring a single Cys–Cys pair were mixed with FlAsH, very weak fluorescence was detected  in B23.2 was suppressed. Therefore, we hypothesized that the interaction mode of acidic and bIDRs in B23.1 was different from that in B23.2. In addition, it was unclear whether the interaction between the two IDRs was inter- or intra-molecular. To address these points, we tested the interaction between the two IDRs of B23 proteins using FlAsH labeling A–D. FlAsdetected D. A mixtdetected D. In condetected E. His-tadetected D. Therefdetected F.FigureSupplementary Figure S9).In this study, we investigated the molecular mechanism by which B23.1 associates with RNA. CD spectral analysis revealed that the central acidic and basic regions of B23 were intrinsically disordered . Our resDK > 1 × 10−5 M), but it increased on addition of flanking sequences (DK was ∼2 × 10−6 M)  , and the 10−6 M) . In this 10−6 M) , and B23 10−6 M) . These r 10−6 M) ,37. In h 10−6 M) . It is sSupplementary Figure S5B). Of interest, 18S rRNA binding of B23 decreased on total RNA addition. These results suggest that the RNA structure or sequence in 18S rRNA targeted by B23 is disrupted or hindered by other RNA species. It is also possible that B23 associates with 28S rRNA complexed with other RNA species with much higher affinity than that with18S rRNA or 28S rRNA alone. It is well established that 28S rRNA is strongly bound to 5.8S rRNAs by hydrogen bonds .B23.2 lacking CTD did not efficiently associate with RNA . We assuB23 is known to be subjected to various post-translational modifications including phosphorylation, acetylation and sumoylation ,42–47. OrRNA gene  and Takeda Foundation. NIMS Molecule & Material Synthesis Platform in “Nanotechnology Platform Project” operated by the MEXT, Japan. Funding for open access charge: Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan [21113005 to M.O.].Grant-in-Aid for Scientific Research from the Conflict of interest statement. None declared.