RNA aptamers readily recognize small organic molecules, polypeptides, as well as other nucleic acids in a highly specific manner. Many such aptamers have evolved as parts of regulatory systems in nature. Experimental selection techniques such as SELEX have been very successful in finding artificial aptamers for a wide variety of natural and synthetic ligands. Changes in structure and/or stability of aptamers upon ligand binding can propagate through larger RNA constructs and cause specific structural changes at distal positions. In turn, these may affect transcription, translation, splicing, or binding events. The RNA secondary structure model realistically describes both thermodynamic and kinetic aspects of RNA structure formation and refolding at a single, consistent level of modelling. Thus, this framework allows studying the function of natural riboswitches in silico. Moreover, it enables rationally designing artificial switches, combining essentially arbitrary sensors with a broad choice of read-out systems. Eventually, this approach sets the stage for constructing versatile biosensors. Non-protein-coding RNAs regulate diverse cellular processes, including the fundamental steps of protein production transcription and translation . Commonlgnesium) . Moreovegnesium)  and pH-vgnesium)  are sensGoing beyond naturally evolved riboswitches, it is highly attractive to design analogous, but artificial, regulatory RNA elements, which respond to arbitrary environmental changes, e.g., sensing small molecules. Aptamers against any small molecule of interest can—at least in principle—be selected utilizing the SELEX  protocol ,6. A ratIn this contribution we first review essential components of riboswitches and read-out mechanisms from experimental perspective. We then focus on thermodynamic and kinetic aspects to de novo design or further investigate riboswitches in silico. Finally, recent design attempts are briefly outlined and graphically summarized.Aptamers are RNA  molecules that specifically bind to target molecules. Over the last 25 years, aptamers for a wide variety of targets have been generated by SELEX. Similar to natural aptamers found in riboswitch elements, those artificial aptamers often have sensitive and specific ligand recognition properties ,17.Suitable aptamers for sensor design must react to ligand binding with sufficient change in structure and/or stability. The basic principle is that the response of the aptamer to ligand binding propagates along the RNA construct and eventually causes structural changes at sites possibly far away from the ligand binding pocket. It can be this distal structural effect that triggers the desired readout. A key problem in the design of synthetic riboswitches is that the necessary prerequisites on the aptamer are by no means fully understood. Therefore, only a few in vitro selected RNA aptamers were successfully integrated in functional riboswitch constructs so far.A frequently used SELEX-derived aptamer in synthetic riboswitches is the theophylline-binding aptamer TCT8-4, isolated by Jenison et al. . This apf 320 mM ,19 and gf 320 mM ,22,23. Wf 320 mM ,25,26.Another aptamer identified by SELEX successfully used in riboswitch-mediated regulation of translation is the tetracycline-binding aptamer ,28,29,30A conformational change of the ligand-bound aptamer structure may be a prerequisite for its riboswitch compatibility in vivo ,35,36. TVibrio vulnificus and a transcriptional OFF switch from Bacillus subtilis, recognizing the queuosine precursor preQAn alternative strategy to generate orthogonal riboswitches is to re-engineer natural aptamer domains. Using randomization by site-directed mutagenesis with genetic selection, the Micklefield lab reprogrammed natural riboswitches to bind non-natural synthetic ligands, while the original intracellular target molecules were no longer recognized. The parental ON and OFF riboswitches were an adenine-sensing translational ON switch from or preQ1 ,41. BothTo monitor the functionality of artificial riboswitches, suitable read-out systems are required. A plethora of different reporter systems are available and are frequently used in the qualitative and quantitative analysis of riboswitch-mediated gene regulation. Consequently, this text can provide only a short overview of the main read-out systems.lacZ gene, encoding for d-galactopyranoside (ONPG), resulting in the cleavage product o-nitrophenol that can be quantified by its absorption at 420 nm.Most frequently, reporter systems based on protein level are used, like the green fluorescent protein (GFP) or variants thereof in both pro- and eukaryotes ,37,42. Atosidase ,44. The tetA gene for positive as well as negative selection modes renders this reporter a very versatile selection system. When tetA is under riboswitch control, cells survive in the presence of tetracycline only if the gene is induced by the regulator. To counter-select for riboswitch-independent gene expression, the surviving cells are cultivated in nickel-containing medium, as the tetA-encoded efflux pump leads to an increased cell permeability to toxic NiFurthermore, genes conferring resistance against antibiotics allow a growth-dependent analysis of riboswitch-mediated regulation. The possibility to use the tetracycline-resistance mediating i2+ ions ,46,47,48E. coli is controlled by an intracellular phosphorylation/dephosphorylation cascade that transfers the signal of an attractant bound to a cell surface receptor to the flagellar motor [A very different, yet effective reporter system for riboswitch activity was introduced in . The chear motor . In the ar motor ,51.As the described read-out systems are all based on the expression of selectable proteins, they are predominantly suited to monitor riboswitches controlling translation. Nevertheless, they can also be used for the analysis of transcriptional riboswitches, although they do not allow for a direct read-out of transcription efficiency. Unexpected side effects have to be considered that can have an impact on translation, leading to an increased read-out signal, without any increase in transcription ,52. HencE. coli, where it was inserted into the corresponding gene. Due to its higher fluorescent efficiency compared to Spinach [A simple direct and frequently used detection method of mRNA expression is Northern blot analysis. Due to a rather elaborate and time-consuming detection, this analysis is only rarely applied in riboswitch research . A more  Spinach , the Man Spinach ,62. Such Spinach  or for S Spinach . In caseThe choice of translation- or transcription-based readout affects the computational design strategy. The control of translation is reversible since the secondary structure at the translation initiation site can switch from accessible to inaccessible conformations many times during the life-time of an mRNA. Translational switches are thus primarily controlled by the equilibrium thermodynamics of RNA structure formation. Control at the transcriptional level, in contrast, determines whether a complete or an incomplete mRNA is produced. In the OFF state of the switch this requires the formation of a pre-mature termination signal to be formed in the nascent transcript. In the ON state, no premature termination is induced and a complete mRNA is transcribed. This mechanism depends crucially on the interplay of transcription and structure formation and thus is controlled to large extent by co-transcriptional kinetic effects of RNA folding.An interesting variation on the theme is to link ligand binding to  splicing rather than the control of translation. This has been observed for instance for the binding of the protein PKR to specific structures in the UTRs of some cytokine mRNAs; this leads to structural changes in the mRNAs that in turn affect splicing . As in tA typical riboswitch consists of a sensory domain  that is linked to a regulatory domain (expression platform). Ligand binding to the aptamer induces a conformational change in the expression platform, which affects translation or transcription of the downstream located gene. In the case of translation regulation, the expression platform contains regulatory motifs, e.g., a RBS or AUG, that—upon ligand binding—are sequestered in base-pairing (OFF switch) or presented and accessible for the ribosome (ON switch). In transcriptional riboswitches, the expression platform consists of a terminator/antiterminator structure that controls RNA polymerization. Similar to the translational riboswitches, ligand binding in the aptamer domain induces a structural rearrangement. This leads to the formation of the terminator structure that shuts off transcription (OFF switch) or an alternative structure (antiterminator) that allows the RNA polymerase to proceed with transcription, resulting in gene expression (ON switch). In such cases, a clear distinction between sensory domain and a regulatory domain is often not possible, see for instance the exemplified designs in While the ribosomal binding site represents a nucleotide sequence that is directly recognized by the small ribosomal subunit, the terminator consists of a combination of secondary structure and sequence. The consensus form of a typical intrinsic terminator has a GC-rich palindromic sequence that—when transcribed—folds into a stable hairpin with 5 to 15 base pairs, carrying a stable tetraloop. This structure is followed by a polyU tract of approximately 8 to 11 residues ,66,67. AE. coli also functions properly in distant species such as Helicobacter or Synechocystis, or Mycoplasma.Taken together, the implementation of a ribosomal binding site as a riboswitch expression platform is rather straight forward, as predominantly masking interactions of this short sequence with the aptamer and the refolding of the mRNA transcript have to be considered. In contrast, the design of transcriptional riboswitches is a more complex task, where—in addition to a defined primary sequence (U stretch)—secondary structure elements (hairpin structure) and the local arrangement of both are critical. It is important to note, however, that the rho-independent termination is not necessarily ubiquitously present in bacteria, and even if this should turn out to be the case, there seem to be substantial variations in the structure and organization of termination signals . ConsequE. coli [B. subtilis) into this ribozyme resulted in an RNA structure that—when inserted into the 3Besides the described cis-regulatory riboswitch elements, there exists a whole variety of systems, where regulatory RNAs act in trans; for example, RNA switches that activate or repress transcription by interaction with an antisense RNA . The desE. coli , where tE. coli ,74. As adent way . The theE. coli, where they could observe a ligand-induced cleavage of the mRNA, regulating the accessibility of the ribosomal binding site in both ON- as well as OFF switch designs. Similar to the above mentioned HHR-based aptazyme, they generated a regulatory RNA cleavage module containing a neomycin aptamer in the 3Recently, also one of the newly discovered catalytic RNA molecules, the twister ribozyme , was sucOT gates .RNA folding is dominated—in good approximation—by the formation of secondary structures, i.e., Watson-Crick and GU base pairs. While tertiary interactions of course play a key role for the actual function, they have less impact on thermodynamics of the folding process. A wealth of empirical data, amassed over the last decades, has shown that the energy of an RNA secondary structure is very well approximated by additive, sequence-dependent contributions of stacked base pairs and looped regions. This is known as the Turner energy model .RNA secondary structures are simple combinatorial objects: they are defined as sets of base pairs satisfying three simple conditions: (i) each base can contribute to at most one base pair, which must be a GC, AU, or GU pair; (ii) base pairs do not cross, i.e., the structure does not contain any pseudoknot; and (iii) hairpin loops comprise at least three unpaired bases. Consequently, secondary structure prediction is phrased as a combinatorial optimization problem: among all secondary structures, find the one that minimizes the energy as determined by the Turner model.n is the length of the RNA sequence. Several widely-used software packages are available for RNA folding, most prominently mfold/UNAfold [This optimization problem can be solved—optimally and efficiently—by dynamic programming in /UNAfold ,80 and t/UNAfold ,82. We r/UNAfold ,84,85 foThe recursive approach allows not only to solve the energy minimization problem, but also to compute partition functions , thus prThe secondary structure model is applicable to predicting the structures of both individual and interacting RNA molecules. In this context the “no-crossing rule” adhered to by the simplest RNA–RNA interaction models  folding trajectories ,99. The d models ,102,103.ractions  incurs s derived : (5)rβ←αFor gradient basins these quantities can be computed by combining the enumeration  or samplier tree ,111,112 ier tree , which ei. This reaction is allowed whenever The kinetic approaches to single structure folding can be extended to RNA–RNA interactions and RNA–ligand binding, in principle. However, since interactions are inherently bimolecular, transition rates become explicitly concentration-dependent, which adds a layer of complexity. More precisely, bimolecular reactions, i.e., the binding steps, require explicit knowledge of concentrations. The situation simplifies again if the ligand is present in excess. In this situation, binding does not relevantly change the ligand concentration: changes ,115. In This analytical approach to RNA–ligand kinetics ,115 enabSo far, the theory assumes a single binding motif. However, ligands are expected to bind to different sub-structures with different binding energies. In addition, there may be multiple binding sites. It is desirable, therefore, to extend the above approach to such more realistic scenarios. A second issue that is not yet solved in a satisfactory manner is the link between re-folding and co-transcriptional folding. The BarMap formalism  allows dA key issue for the design of kinetically controlled switches is the availability of experimental data to dial in simulation time scales. Even where the mathematical formalism has been developed, in practice meaningful results crucially depend on additional experimental measurements of detailed kinetic prefactors and transcriptional speeds, which in turn depend on the specific biochemical environment of the binding process. In principle, such parameters could be measured in targeted experiments, but are currently unavailable for most model systems of interest.The computational models of RNA folding thermodynamics and kinetics, as outlined in the previous sections, make “in silico” design of RNA-based control systems possible. Naturally, computational design requires deep understanding of the working mechanisms in order to develop sufficiently accurate formal models; currently, modeling imperfection limits computational riboswitch design to reliably propose candidates for experimental validation.From the modeling perspective, one distinguishes switches triggered by RNA-molecules and switches triggered by non-RNA ligands. For the former, it is reasonable to model the interaction, including binding motifs and energies, based on the RNA energy model. In contrast, the latter require empirical knowledge about the binding specificity of the ligand to the RNA, since in general this cannot be reliably computed. In the simplest case, one needs information on the sequence and structure of the binding pocket and the according binding energy. In these systems, ligand-specific aptamers are utilized as sensory platforms and are extended to construct the intended expression platform. In a typical scenario, the sequence of the aptamer is constrained and the binding-competent sub-structure is assumed to be known. Based on that, models of the switch’s OFF and ON state, e.g., regions that fold into terminator and anti-terminator structures in a transcriptional switch, are defined.At the foundation of every design process, we require constraints and ranking criteria for riboswitch sequences. Simple hard sequence and structure constraints are, e.g., given by the ligand binding motif, which requires a specific sequence pattern and the possibility to fold into defined structure ‘patterns’. Additionally, there are thermodynamic constraints; for example, switching is observed only if the ligand-bound structure is sufficiently more stable than the ground state of the RNA, since otherwise the ligand can not significantly influence the ensemble of possible structures. Moreover, the binding-competent structure without the ligand must be less stable, such that adding the ligand changes the ensemble.The ranking of designed sequences is particularly challenging, since generally there are multiple criteria for good designs, which cannot be weighted a priori. Moreover, many criteria, like tolerable deviations from the aptamer constraints or a terminator structure, are hard to formalize. For this reasons, such multi-objective optimization is commonly performed in a largely manual, non-automatized process by experts see .The thermodynamic model allows us, thereby enabling automatized rational design, to formalize important design criteria, like the ratios of RNA molecules in the ON and OFF state in the presence and absence of ligand in the thermodynamic equilibrium. For simplicity, assume for an activator system that ON and OFF state, as well as BINDING and NON-BINDING state of the aptamer are described  such that the energies of the constrained subensembles in these states can be efficiently computed. We denote such energies as x and y. These parameters directly control the concentration ratios of molecules in the different states, where values 0 require the ratio (between the states on the left and right side of the inequations) to be at least 1:1; higher values shift the ratios to the left side. More precisely, rewriting from the second inequation showsIn this model, good switching requires that the states BINDING and OFF are disjoint  riboswitches. Consequently, this tells us to minimize the deviation from such differences x and y in our designs, which directly translates to (multiple) objective functions for the switch design. To avoid more complicated multi-objective optimization, one commonly constructs a total objective function as a weighted sum of the single objectives. Finally, this allows to automatize the generation of riboswitch candidates, which is the core idea of tools like MODENA [One can reasonably expect that such energy differences e MODENA  and RNAbe MODENA ; the latIn practice, objective functions are limited to efficiently computable features. Efficient folding algorithms enable applying a multitude of thermodynamic features, e.g., based on the energies—and thus probabilities—of almost arbitrarily constrained structure sub-ensembles in the thermodynamic equilibrium. However, this limits the integration of complex kinetic features like fast re-folding from one state to the other—corresponding to low energy transition paths, into the objective function. Nevertheless, simple kinetic features or fast approximations can be cost-effectively utilized. For instance, Flamm et al.  proposedThe high computational cost of kinetic analysis suggests to consider more complex kinetic criteria only in a generate and test approach for the filtering of optimized candidates. For example, —given sufficiently accurate parameters—the RNA–ligand kinetics model of the previous section can estimate ligand concentration-dependent refolding times; thus detecting kinetically trapped candidates.While refolding speeds influence transcriptional and translational switches, kinetics is potentially decisive for co-transcriptionally controlled transcriptional riboswitches. During the riboswitch synthesis, certain sub-structures, like the aptamer or a terminator, will ‘pop up’ only after the transcription of complementary pairing sequences; in turn, initially stable structures in the 5xtending ,115 to cIn transcriptional switches, where the mRNA is terminated in the inactive state, while read through in the active state, the switching state takes effect exactly once per molecule. In contrast, the molecules of translational switches can be switched forth and back between ON and OFF state—these switches are reversible. This introduces a further design objective exclusively for translational switches: sufficiently fast, reversible switching between the states. Thermodynamically, this means that neither state ligand-bound or ligand-free should be too stable. Kinetically, the refolding times directly impact the response times and must be sufficiently fast.Impressive fold changes based on protein expression measurements have been achieved for recently published designs ,121,122.F is defined as the ratio of the readouts in ON and OFF state, i.e.,Commonly, fold change Generally the fractions For activators, we assume high expression <H> if all molecules were bound, while the low expression <L> results if all molecules are unbound. Since, the OFF state is independent of the trigger, we expect reporter gene expression <L>. However, the ON state depends on the binding efficiency and stability, i.e., on the fractions of the bound and unbound molecules; thus, the gene expression in the ON state is a mixture of high and low expression. Consequently, the activation fold change For repression, the ON state is independent and the OFF state is dependent on the trigger binding efficiency. Moreover, unbound molecules cause expression <H>; bound molecules, <L>. Thus, the repression fold change Varying both ratios and simulating the resulting achievable fold change shows that it is rather easy to achieve high fold changes when activating even if only a small fraction of switches are bound, In the following we outline recent design attempts to regulate transcription and translation using sRNAs and non-RNA ligands as trigger, graphically summarized in Chappell et al.  engineerl et al.  observedE design . Here agonstruct  could bede novo an antisense activation and an antisense repression system [A few years earlier the group of Hervé Isambert designed n system . The desn system . In an iIn the design strategy for ligand sensing synthetic transcription regulating riboswitches, two approaches turned out to be successful, see hairpin) ,69,123. hairpin)  not onlyUsing theophylline and tetracycline aptamers the best performing switches showed a fold change of 6.5 and 3.4, respectively. It appears that gene expression of the functional riboswitches in the ON state is close to maximal, when comparing it to the positive unregulated control, while the OFF state shows rather high leakage. This background level could be significantly reduced, when either multiple copies of the same riboswitch or different ones were placed in front of the reporter gene. The fold change of 6.5 of the best performing theophylline riboswitch could be further improved to 17 and 23, when utilizing two or three copies of it, respectively. For tetracycline the fold change improved from 3.4 to 7.5 and 9.5 for two and three copies. The improved performance directly results from having multiple points during transcription, at which the process can be terminated. However, a higher ligand concentration is necessary to bind multiple aptamer copies. In addition, heterogeneous tandem consisting of the best working theophylline and tetracycline riboswitches were constructed. When the theophylline switch was placed upstream of the tetracycline switch only the addition of both ligands induced gene expression by 10.4 fold as expected for a logical AND gate. However, when the order of the two riboswitches was swapped already theophylline alone induces gene expression. This shows that, even if the design of individual genetic devices was successful, combining them to higher order circuits is not trivial by far.In addition to theophylline and tetracycline, Domin et al.  applied Bacillus subtilis that previously had been reported to function in vivo did not work in the applied in vitro setup. To subsequently demonstrate that observed regulatory effects are a direct result of the designed riboswitches, Ceres et al. [metE and yitJ naturally sensing SAM, and lysC that normally senses lysine, and seven natural as well as artificial aptamers that sense for instance guanine, 2-AP, FMN and theophylline were successfully isolated and assayed. In total 21 mix-and-match combinations have been investigated in detail, of which three represent the natural metE, yitJ and lysC riboswitches. Regulatory efficiency of all constructs was measured by titration. Corresponding in vitro experiments gave three essential parameters: In many natural riboswitches, the aptamer fold is closed by a short helix P1 that separates the sensory and regulatory platform. Inspired by this non-overlapping organization, the Batey lab utilized the P1 helix to combine natural regulatory platforms with a variety of different  aptamers, resulting in functional transcriptional OFF  and ON  riboswits et al.  used pois et al.  tested, pubE adenine switch of B. subtilis. Before decoupling the sensory and regulatory modules the authors improved regulatory efficiency of this riboswitch by deletion of 11 base pairs at the 5’ end of the reported sequence. Decoupling was then done by replacing a sub-sequence, not essential for ligand recognition, of the P1 helix. Again diverse aptamers were tested but only one was able to complement the isolated regulatory platform to a functional switch. All remaining chimeric constructs terminated transcription-independent of ligand concentration. Notably, shortening the terminator U-track by one or two bases made them responsive by increasing the dynamic range but not altering metH SAH riboswitch from Dechloromonas aromatica. This particular riboswitch does by default not follow the structural constraints of the mix-and-match approach as the P1 helix is in the ligand bound state extended by the P4 helix, which forms a pseudoknot. The natural sequence was cut at two positions to remove the pseudoknot and to convert the eight nt long P4 helix into the conceptually essential P1 helix. The tested constructs showed a high read-trough, i.e., In order to apply the mix-and-match design strategy to design chimeric transcriptional ON switches, an explicit decoupling of the sensory and regulatory domains was necessary . Similars et al.  attributs et al. . There, s et al.  used nats et al.  de novo Both approaches, the de novo design by Mörl and colleagues and the mix-and-match strategy of the Batey lab, repeatedly indicated that aptamers or even the designed riboswitches regulate gene expression differently depending on the surrounding sequence and structure context. This makes it particularly difficult to forecast the switch’s performance, regardless of whether the starting point is a naturally occurring riboswitch evolved by nature or a artificial aptamer generated by SELEX.Similar to the work presented in NuPACK. In silico, this tool optimizes the ensemble defect in order to increase the chance of well defined ON and OFF states in vitro as well as in vivo. Green et al. [F) getting up to more than 250 and nearly two thirds had values greater than 10. For a sub-set of 26 switch-trigger pairs they confirmed orthogonal functionality. One aspect of the publication was to investigate how the toehold switch performance varies if the reporter gene is changed. Three selected designs were tested with four different fluorescent reporters, i.e., cerulean, sfGFP, venus and mCherry. As expected, reporter signal strength varies depending on the reporter. Surprisingly, a toehold switch that performs best with venus (In  the switn et al.  initiallde novo, one would like to get best performing candidates only. For this reason, the authors investigated important features of the initial design idea. They varied loop sizes, closing base-pairs of the stem loop structure, toehold length and the length of complementarity between trigger and the 5When designing toehold switches Starting in 2009 Howard M. Salis and colleagues developed step by step a biophysical model to predict the translation initiation rate of a given mRNA sub-sequence, i.e., a 5ng frame ,130,131. package  or NuPAC package . The othg frame [ΔGtotal=ΔThe high predictive accuracy of the biophysical model paved the way to design translation regulating riboswitches by making the translation initiation rate of the ON state high and of the OFF state low , see Figmol/kcal . In a fiublished  theophyli et al.  can be eIn this review we balanced out the theoretical background and practical aspects of riboswitch design. We not only focussed on the classical metabolite binding riboswitches but also broaden the definition to RNA binding and environment sensing structures. The summarized recent design attempts clearly indicate that RNA based switches are attractive biosensors which can be modeled in silico. Combining these individual RNA based sensors into higher order circuits is a challenging task. As described in  it is no


It is the start of a wave of such structural data for viruses, and calls for the development of new analytical tools to describe the results. One approach is Hamiltonian path analysis (HPA) that we introduced to describe repeated, sequence-specific contacts between the MS2 genome and its protein shell3. Here, we describe how HPA is consistent with the new structures and, in turn, how it extends our understanding beyond the structural data alone.Cryo-electron microscopy (EM) is undergoing a revolution, enabling the study of viral pathogens in unprecedented detail. The asymmetric EM reconstruction of bacteriophage MS2 at medium resolution (8.7 Å) by Koning et al.4. TR occurs only once in the MS2 genome, but insights into the roles played by RNA-coat protein (CP) contacts during assembly5 together with normal mode analysis6 and structural studies suggest that many different stem loops (SLs) in the viral genome should be able to bind CP and thus promote virion formation, i.e., act as RNA packaging signals (PSs). HPA has enabled us to identify such sites within the MS2 genome7, which is important, because it underlies the development of a new paradigm for ssRNA virus assembly based on multiple PSs, that seems to occur very widely in nature11.Koning et al.’s and Dai et al.’s reconstructions of MS2 reveal multiple contacts between genomic RNA and the viral capsid. These mimick the contacts seen by crystallography of virus-like particles carrying multiple copies of the high-affinity RNA packaging signal in this virus, the translational repressor (TR) that functions also as assembly initiation signal12). We explicitly assume that the RNA genome is highly branched, since we predict these contacts to be with SLs located within the MS2 genome7, as seen in the new reconstruction. The Hamiltonian path concept is thus an abstraction of discrete 3D contact sites into a linear path that should be understood in the same spirit as the simple lines between atoms in molecular structures are shorthand for the much more complex electronic arrangements of covalent bonds. We note that HPA does not require all possible RNA-CP sites to be occupied. Indeed, testing all possible SLs in the ensemble of putative PS candidates against all complete Hamiltonian paths would be a complex task, and indeed is not always possible as different interaction patterns can occur in the vicinity of asymmetric features such as the A-protein13.HPA is a mathematical abstraction of virus assembly pathways, simultaneously encoding the order in which capsomers are recruited to the growing capsid shell along different assembly pathways. It captures geometric constraints on PS positions in the linear genomic sequence that arise from the relative positions of the RNA-CP-binding sites in the inner capsid surface Fig. . SLs in 7 has revealed that binding sites are differentially constrained across the capsid surface, implying that some (highly constrained) ones are likely to be present in almost every particle, while others (mostly low affinity ones) are more likely to vary across different particles, thus predicting that the RNA conformation in contact with the protein shell will exhibit some similar structural characteristics in every viral particle. This astonishing conclusion is consistent with both Koning et al.’s and Dai et al.’s reconstructions of MS2, identifying a core set of contacts that are present in every particle. Dai et al. explicitly identified 15 RNA SLs in contact with CP and one in contact with the A-protein. Our HPA in Dykeman et al.7 has predicted all of these 15 RNA-CP contacts as shown in Fig. 14An application of HPA to MS218, and in understanding how PSs cooperatively promote efficient virus assembly16. This suggests that HPA should be useful also for the study of the many other viruses, whose complete structures are likely to emerge in the near future from modern EM studies. Such structures will also highlight those viruses that exploit the multiple RNA packaging signal-mediated assembly mechanism.The HPA has been key in identifying the nature of the RNA-CP contacts in MS2, and thus is fundamental to our understanding of the virion structure. It has played a central role in establishing the packaging signal hypothesis


Active nuclear import of Ran exchange factor RCC1 is mediated by importin α3. This pathway is essential to generate a gradient of RanGTP on chromatin that directs nucleocytoplasmic transport, mitotic spindle assembly and nuclear envelope formation. Here we identify the mechanisms of importin α3 selectivity for RCC1. We find this isoform binds RCC1 with one order of magnitude higher affinity than the generic importin α1, although the two isoforms share an identical NLS-binding groove. Importin α3 uses its greater conformational flexibility to wedge the RCC1 β-propeller flanking the NLS against its lateral surface, preventing steric clashes with its Armadillo-core. Removing the β-propeller, or inserting a linker between NLS and β-propeller, disrupts specificity for importin α3, demonstrating the structural context rather than NLS sequence determines selectivity for isoform 3. We propose importin α3 evolved to recognize topologically complex NLSs that lie next to bulky domains or are masked by quaternary structures. Importin α3 facilitates the nuclear transport of the Ran guanine nucleotide exchange factor RCC1. Here the authors reveal the molecular basis for the selectivity of RCC1 for importin α3 vs the generic importin α1 and discuss the evolution of importin α isoforms. This gradient is generated by the asymmetric distribution of Ran effectors: the guanine exchange factor RCC1 (regulator of chromatin condensation 1) is sequestered in the nucleus bound to chromatin, while the GTPase-activating protein specific for Ran, RanGAP, and various accessory Ran-binding proteins (RanBPs) reside in the cytoplasm. The Ran gradient promotes active, signal-mediated trafficking of macromolecules through the permeable barrier formed by nuclear pore complexes (NPCs) and is also essential for mitotic spindle assembly and nuclear envelope formation, all defining features of eukaryotic cells4.Eukaryotic cells maintain an intracellular gradient of the small GTPase Ran that is predominantly bound to GTP in the nucleus and GDP in the cytoplasm5. β-karyopherins like the prototypical importin β6 move through the NPC by interacting with phenylalanine-glycine (FG) repeats exposed by many nucleoporins in a RanGTP-dependent manner7. Importin β can recruit NLS cargos directly8 or via the adaptor importin α9, as in the ‘classical’ import pathway. The association between importin α and β is mediated by the N-terminal importin β-binding (IBB) domain10 of importin α, which also binds NLS cargos via a C-terminal helical core composed of 10 Armadillo repeats (the ‘Arm-core’)11. Two NLS-binding pockets known as ‘major’ and ‘minor’ have been identified on the concave surface of importin α, spanning Arm repeats 2–4 and 6–8, respectively14. Classical monopartite NLSs like the SV40 T-antigen NLS generally bind at the major NLS binding pocket and, to a lesser extent, at the minor site, while bipartite NLSs like the nucleoplasmin NLS or membrane protein NLSs16 span both sites. In addition, several monopartite NLSs have been shown to preferentially17 or exclusively20 bind the minor NLS pocket of importin α. Interestingly, the human genome encodes seven isoforms of importin α, divided into three subtypes (or subfamilies), of which importin α1 functions as the generic adaptor for NLS cargos24. A growing number of specialized cellular NLS cargos like RCC127, STAT128, NF-kB30 as well as viral factors influenza PB231 and Ebola VP2432 enter the nucleus in complex with specific importin α isoforms. These isoforms play an important role in cell differentiation33, disease states, especially cancer21 and viral infections34.Cytoplasmic cargos bearing a nuclear localization signal (NLS cargos) are escorted through the NPC by soluble transport factors of the importin β-superfamily (or β-karyopherins), which share a conserved N-terminal Ran-binding domain35. RCC1 consists of a C-terminal seven-bladed β-propeller36 and N-terminal regulatory tail, which harbors a bipartite NLS37 and a chromatin binding domain40. RCC1 can be imported into the nucleus in a classical Ran-dependent pathway that preferentially uses the isoform 3 of importin α25 and in a Ran-independent pathway that is not dependent on passive diffusion26. The RCC1 tail is post-translationally modified throughout the cell cycle. Methylation of the N-terminal Ser/Pro2 on its α-amino group by the α-N-methyltransferase NRMT41 is important for high affinity binding to chromatin39. Mitotic phosphorylation of Ser11 by Cdc2 kinase reduces interaction with importin α3/β43, elevating the concentration of RanGTP on mitotic chromosomes and promoting binding of the RCC1:Ran complex to mitotic chromosomes, which is required for spindle assembly and chromosome segregation44. RCC1 interaction with chromatin is dynamic: RCC1 continuously binds to and dissociates from chromosomes to promote GTP exchange and this activity is dependent on its N-terminal tail43. Deletion of the RCC1 basic tail prevents RCC1 interaction with chromatin from being stabilized by Ran45. Likewise, Ran association with RCC1 regulates binding to both histones and DNA possibly by inducing an allosteric change in the tail that facilitates association of the binary Ran:RCC1 complex with chromatin46.RCC1 is the founding member of the RCC1 superfamily that includes diverse cell cycle regulators containing one or more RCC1-like domainsIn this paper, we have determined the mechanisms by which RCC1 is selectively recognized and translocated into the cell nucleus by importin α3.26. Using nano isothermal titration calorimetry (ITC), we established that isoform 3 has one order of magnitude higher binding affinity for RCC1 than importin α1 (KD = 0.62 ± 0.1 μM vs. 5.4 ± 0.3 μM)  in complex with importin α3 lacking the IBB-domain. Large crystals containing eight copies of the α3:RCC1 complex in the asymmetric unit were obtained in the triclinic space group. The structure was solved by maximum likelihood molecular replacement using individual atomic models of ΔN-RCC136 and ΔIBB-importin α331. Flexible torsion-angle non-crystallographic symmetry restrains48 among the eight copies in the asymmetric unit were used throughout refinement. This yielded an accurate atomic model (Rwork/Rfree ~27.8/29.6) , including the major NLS box, which is sandwiched between the importin α3 helical groove and the β-propeller  Fig. . To shed6) Table  is shape6) Table . The tai6) Table . It contler Fig. . Signifiler Fig. , althoug−1  and a Porod volume of ~177,403 Å3, consistent with a heterodimer in 1:1 stoichiometry (expected MW ~91.2 kDa)   Fig. . At phys.6) Fig. . The hin.6) Fig. . The SAX2 of the exposed surface of importin α3: 23 residues of RCC1 interact with 39 residues of importin α3, which account for a predicted binding energy of −12.6 kcal mol−1. RCC1 makes contacts with three regions of importin α3, schematically illustrated in Fig. 1′–P3′, plus two additional basic residues (4-KR-5) at P−3′ and P−2′  that occupy the major NLS-binding pocket at positions P1–P45, with K21 at the crucial P2 site. This NLS is somewhat different than classical NLSs52: it contains a basic residue (K19) at position P0 and two non-basic residues at P1 and P4 (S20 and V23) with K24 at P5 making extensive contacts with importin α3 , suggesting the minor NLS-binding site is the main site of interaction with importin α3. Finally, two residues in the β-propeller, E119 and H257 contact R218 and D102, respectively, in importin α3  and after reaching equilibrium, the complex was immobilized on glutathione beads, washed to remove unbound RCC1 and eluted with reduced glutathione. Identical elution volumes were analyzed by SDS-PAGE and quantified in triplicates. As expected, ΔIBB-importin α3 bound RCC1 in a 1:1 molar ratio (or 100% of importin α bound to cargo) ; two deletion mutants that lack either the post-translationally modified N-terminal moiety or the entire minor NLS box (Δ5-RCC1 and Δ10-RCC1), and a phospho-mimetic RCC1-S11E, which mimics a phosphorylation at S1143. We found that point mutations at either P2 or P2′ reduced RCC1-binding to importin α3 by 25% and combining these two mutations had a synergistic effect54 reducing binding by ~85%  and RCC1 were incubated at physiological concentrations  Fig. . Six RCC85% Fig. . A phospbox Fig. . Finally31 reveals both α3-specific cargos have NLSs that run antiparallel to the Arm-core occupying similar positions in the NLS-binding groove  with either the full length GST-RCC1 or just the bipartite RCC1 NLS (GST-RCC1-NLS). A pull-down assay carried out using physiological concentrations of importin αs (~1 μM) and RCC1 (~0.75 μM) α1 Fig. . A stoicITC Fig. . As expeP2′ Fig. . Thus, t−1   function calculated from SAXS has a distinct symmetric peak indicative of a globular domain ~50 Å in diameter flanked by a less ordered moiety  suggests RCC1 looks like a “tennis racket”  simulation using the crystallographic structure of RCC1 as the initial state revealed the N-terminal tail is the most dynamic region of RCC1 with root mean square fluctuation (RMSF) up to 5 Å2  was obtained using a docked ensemble of the eight RCC1 structures observed in the triclinic unit cell  function calculated from SAXS data and computed from the crystallographic model persist in the data . The SAXS envelope is however broader than the crystallographic model, especially in the region where yRCC1 was docked, possibly due to motion of the C-terminal helix that could swing by 180° toward the Arm core . This binding interface is stabilized by 27 residues in yRCC1 that interact with 47 residues of Kap60, which in total account for a predicted binding energy of −12.8 kcal mol−1 . Third, a ‘loop-helix’ motif (residues 24–46) lies immediately C-terminal of the yRCC1 major NLS box and connects it to the first β-strand of the β-propeller (residues 47–51)  Fig. . While t51) Fig. . This in51) Fig.  and H10651) Fig. . Finally51) Fig. .Fig. 7Ka2 and P2′ as well as the double P2/P2′ mutant completely disrupted yRCC1 association with Kap60, while mutations in the α-helix (E34/D35) did not affect binding to Kap60 , followed by pull-down, SDS-PAGE analysis, and quantification Fig. . Strikinp60 Fig. . Likewisp60 Fig. . Thus, b4. Crystallographic and SAXS structures of human and yeast RCC1 in complex with importin α3 and Kap60 reveal the RCC1 NLS makes conserved contacts spanning the entire concave surface of importin α, while the β-propeller is recognized in different ways. It is flexibly wedged against the lateral surface of Arms 1–4 in humans (“backpack” conformation)  Fig. , while son) Fig. . This wo46), importin α   Fig. . Overalltro Fig. , insuffi31 arch-like structure of importin α1 efficiently accommodates NLSs that are stretched in a helical groove by making mainchain and sidechains contacts with importin α1 sidechains14. This recognition is stereochemically possible and energetically favored for cargos that have a linear, bead-on-a-string topology such as the CAP80 complex56, whereby the NLS is neighbored by discrete, non-interacting secondary structure elements   have failed to decipher the mechanisms of NLS-cargo specificity for importin α isoforms, which were identified nearly two decades ago24.Other importin α3-specific cargos such as the influenza PB2 polymerase subunit and the NF-κB p50:p65 heterodimer bear topologically complex NLS sequences, also poorly compatible with importin α1. PB2 is part of a multisubunit polymerase complex that undergoes dramatic conformational changes to expose its NLS and become visible to the import machineryCC1 Fig. . It is r22. An apparent conundrum is how baker’s yeast achieves efficient nuclear import of RCC1 using the generic Kap60, without a dedicated isoform such as α3  or associated with disease states .Saccharomyces cerevisiae (strain S288C) genomic library. Both human and yeast RCC1 genes were cloned in vectors pET28a (Novagen) and pGEX-6P  between restriction sites Bam HI and Xho I. All deletion mutants of human and yeast RCC1  were generated by PCR using loop-out primers. Site-directed mutagenesis was used to introduce point mutations in human RCC1  and yeast RCC1 . GST-RCC1-long, constructed by insertion PCR, contains a 12-residue linker (GSAGSAAGSGEF) between S26 and H27 of wild type (WT) GST-RCC1. GST-RCC1-NLS and GST-RCC1-NLS (P2/P2′) were constructed be introducing a stop codon at position 25 of the full-length GST-RCC1 plasmid. All constructs generated in this study were entirely sequenced to ensure the correctness of the DNA sequence. The nucleotide sequences of all primers used in this study is provided in Supplementary Table 18, human importin α3,31 and yeast Kap6016 (with and without the IBB domain) were cloned in vectors pGEX-6P  and pET28a (Novagen). All plasmids were expressed in BL21(DE3) AI cells (Invitrogen) or regular BL21(DE3). To form stoichiometric complexes of RCC1 and importin α, plasmids encoding human GST-RCC1 and untagged ΔIBB-importin α3 or yeast GST-RCC1 and ΔIBB-Kap60 were co-expressed in BL21-AI cells (Invitrogen). Expression of recombinant proteins was induced with 0.5 mM IPTG (supplemented with 0.2% arabinose in BL21(DE3) AI cells), and grown overnight at 20°C. Cell pellets were suspended in Lysis buffer  and lysed by sonication. Clarified lysates expressing GST-tagged proteins were incubated with glutathione resin (GenScript) and beads were washed with Complex buffer . Clarified lysates expressing His-tagged proteins were incubated with Ni-agarose resin (GenScript) and beads were washed with High Salt buffer  and eluted with Imidazole Elution buffer . The RCC1:importin α and yRCC1:Kap60 complexes were cleaved off beads by overnight digestion with PreScission Protease and further purified by size exclusion chromatography on a Superdex 200 16/60  pre-equilibrated in Complex buffer. All species were concentrated by ultrafiltration using Millipore concentrators.A synthetic gene encoding human RCC1 . Prior to ITC analysis, both GST-RCC1 and ΔIBB-importin αs were dialyzed overnight against Gel Filtration (GF) buffer  at 4 °C. GST-RCC1 (300 µM) was injected in 2.0 µl increments into a calorimetric cell containing 195 µl of ΔIBB-importin α3 or α1 (100 µM). The spacing between injections was 180 s. Titrations were performed in triplicate and data were analyzed using the NanoAnalyze data analysis software (TA Instruments). Heats of dilution were determined from control experiments carried out by injecting GF buffer against importin α3/α1 and subtracted from enthalpies obtained by titrating RCC1 against importin α3/α1. Curve fitting was done in NanoAnalyze data analysis software using a single set of binding sites model. The concentration of samples used for ITC was accurately determined using amino acid analysis, Lowry assay and spectrophotometric determination with the theoretical extinction coefficient 53 for ~60 min at 4 °C. A volume of 700 μl of this mixture was then incubated with 100 µl of glutathione resin beads (GenScript) pre-packaged in a mini spin-column, for 1 h at 4 °C. Beads were then washed three times with 700 μl of complex buffer and GST-Importin α/Kap60:RCC1/yRCC1 complexes were eluted with 100 µl of complex buffer containing 25 mM reduced glutathione. 20 μl of this elution was then analyzed by SDS-PAGE (15%): gels were stained with Coomassie Brilliant Blue-G-250, destained overnight and quantified. An uncropped image of the gels shown in Figs. 2/P2′) with His-tagged ΔIBB-importin α1 or α3, as described above. For quantification of pull-downs in Figs. Pull-down assays in Figs. −1 with crystallization solutions containing 0.1 M sodium cacodylate buffer (pH 6.5), 0.2 M calcium, 8% PEG 8000, galactose 3% and 0.1 M Na cacodylate, 0.1 M NaCl and 1.1 M ammonium sulfate, respectively. Crystals were harvested in nylon cryo-loops, cryo-protected with 27% ethylene glycol and flash-frozen in liquid nitrogen. Crystals were diffracted at LS-CAT Beamline 21-ID-F at Argonne Photon Source on a MARMOSAIC 225 CCD detector and at beamline 14-1 at SSRL on a Rayonix MX325 CCD detector. Data indexing, integration, and scaling were carried out with HKL200062. Initial phases were obtained by molecular replacement using Phaser61 and PDB 3Q5U and 4PVZ as search models. Atomic models were built using Coot63 and refined using phenix.refine64. Final stereochemistry was validated using PROCHECK65. Data collection and refinement statistics are summarized in Table ΔIBB-importin α3:RCC1 and ΔIBB-Kap60:yRCC1 were crystallized by mixing equal volumes of protein complex at 20 mg ml−1. Samples were centrifuged at 16,000 × g for 10 min prior to exposing to X-rays, which was done for 5–30 min in triplicates. The scattering of GF buffer alone was subtracted from all protein sample scattering data. Guinier analysis between the triplicate exposures was used to attentively control for radiation damage and protein aggregation. Data reduction was carried out by circular averaging of the images and scaling to obtain the scattering curve (scattering intensity (I) as a function of the momentum transfer vector q (q = 4π(sinθ)/λ) using the Rigaku Automatic Data Analysis Pipeline software. The program GNOM66 was used to calculate P(r) plots from scattering data. SEC-SAXS data for the Kap60:yRCC1 complex were collected at G1 station at MacCHESS, which usually operates at 9.8 keV and has a 1.5 m camera length and typically reaches q_min = 0.007 to q_max = 0.3. SEC was performed using an AKTA FPLC system equipped with a Superose 12 10/300 GL column . Data were recorded on a Pilatus 100K-S detector. The Kap60:yRCC1 complex at 5.0 mg ml−1 was loaded onto the size exclusion column previously equilibrated in a buffer containing 20 mM Tris-HCl pH 7.5, 150 mM NaCl, 5% glycerol and 3 mM BME. Primary reduction of the SAXS data was performed using RAW67, and ATSAS software68. Frames 710–750 were used to calculate experimental scattering intensities. Ab initio model calculations to generate a scattering envelope were done using DAMMIF69. Twenty solutions obtained from DAMMIF were used to check consistency and averaged together to obtain the final model using the DAMAVER program suite70. This final averaged model was then converted to a surface map using the SITUS program suite71. Theoretical solution scattering curves of all crystallographic structures determined in this work were calculated using the FoXS web server72. SAXS data collection and analysis statistics are summarized in Supplementary Table The monodispersity of all samples used for SAXS analysis was validated using AUC-SV and SEC  at 10 °C. Absorbance values at 280 nm were fit to a continuous sedimentation coefficient (c(s)) distribution model in SEDFIT73.Analytical SEC was carried out on a Superpose 12-column equilibrated with Gel Filtration buffer . AUC-SV analysis was carried out in a Beckman XL-A Analytical Ultracentrifuge out at the Sidney Kimmel Cancer Center X-ray Crystallography and Molecular Interaction Facility at Thomas Jefferson University. Importin α3:RCC1 complex, human RCC1 and Kap60:yRCC1 complex were dissolved at 0.8–2.5 mg ml74 and binding energies between RCC1 and importin α/Kap60 were predicted using PRODIGY75. All figures in the paper were prepared using the program PyMol76.Molecular interactions were analyzed using PISACoordinates and structure factors for the importin α3:RCC1 and Kap60:yRCC1 complexes have been deposited in the protein Data Bank (accession codes 5TBK and 5T94). Other data are available from the corresponding author upon reasonable request.Supplementary InformationPeer Review file


Eukaryotic cells synthesize enormous quantities of RNA from diverse classes, most of which are subject to extensive processing. These processes are inherently error-prone, and cells have evolved robust quality control mechanisms to selectively remove aberrant transcripts. These surveillance pathways monitor all aspects of nuclear RNA biogenesis, and in addition remove nonfunctional transcripts arising from spurious transcription and a host of non-protein-coding RNAs (ncRNAs). Surprisingly, this is largely accomplished with only a handful of RNA decay enzymes. It has, therefore, been unclear how these factors efficiently distinguish between functional RNAs and huge numbers of diverse transcripts that must be degraded. Here we describe how bona fide transcripts are specifically protected, particularly by 5′ and 3′ modifications. Conversely, a plethora of factors associated with the nascent transcripts all act to recruit the RNA quality control, surveillance and degradation machinery. We conclude that initiating RNAPII is ‘surveillance ready’, with degradation being a default fate for all transcripts that lack specific protective features. We further postulate that this promiscuity is a key feature that allowed the proliferation of vast numbers of ncRNAs in eukaryotes, including humans. In the case of messenger RNAs (mRNAs), nascent transcripts are synthesized by RNA polymerase II (RNAPII) as pre-mRNAs consisting of both introns and exons . IntronsThe complexity of nuclear RNA processing makes it inevitable that some fraction of nascent transcripts will fail to mature correctly. The accumulation of aberrant or defective transcripts represents a significant potential problem, because they could saturate the RNA processing machinery and impede the production of functional products. For example, accumulation of cryptic RNAs in yeast mutants with defective RNA degradation reduces the availability of the nuclear cap binding complex, with pleiotropic effects on gene expression . AntisenHistorically, RNA quality control or ‘surveillance’ mechanisms have been difficult to examine in unperturbed systems, as defective transcripts generally constitute only a small proportion of any given endogenous mRNA species. Most studies have, therefore, relied on mutations, either in reporter transcripts or the RNA processing machinery, to artificially trigger surveillance. These analyses suggest that the surveillance machinery monitors a wide range of processing defects, including transcripts with defects in cap structure ; inefficSurveillance pathways also degrade RNAs that result from pervasive transcription (reviewed in ). EukaryIn organisms as diverse as yeast, plants and humans, RNA surveillance depends on the nuclear exosome, a complex with endonuclease and 3′ exonuclease activity. The exosome, in turn, relies on numerous cofactors to guide it to target transcripts and help initiate decay. Interestingly, many of these cofactors also function in RNA maturation, suggesting that the exosome is recruited to nascent transcripts regardless of their processing status. As discussed below, the exosome appears to act as a general scavenger of 3′ ends, potentially degrading nuclear pre-mRNAs and other transcripts by default. Correctly processed transcripts largely escape nuclear surveillance through the deposition of specific RNA binding proteins, particularly at the 3′ end, which sterically hinder exonucleolytic decay.Saccharomyces cerevisiae and human cells, with additional reference to RNAPI and studies in other species as appropriate. We begin with an introduction to the major cellular exonucleases and cofactors, highlighting their parallel roles in mRNA biogenesis and surveillance. Subsequently, we review mechanisms by which the exosome degrades aberrant transcripts, with a particular focus on the connections between transcription termination and surveillance.In this review, we discuss quality control mechanisms for RNAPII transcripts in 2.A striking feature of eukaryotic RNA degradation and surveillance pathways is the preponderance of exonucleases, which degrade RNAs from the 5′ end (5′ exonucleases) or 3′ end (3′ exonucleases), rather than endonucleases that can cleave RNAs internally. As a consequence, accessibility of the 5′ or 3′ ends of the transcript for nuclease attack is likely to be a key feature in determining susceptibility to degradation .Figure 2.1.The major 5′ exonucleases in eukaryotes are the related proteins Xrn1, which is predominantly cytoplasmic, and Rat1 (Xrn2 in humans), which is predominantly nuclear. The activities of both enzymes are largely blocked by the presence of a 5′ triphosphate , which i7G) cap to the 5′ triphosphate end of the RNA. This reaction occurs shortly after transcription initiation, usually within the first 50 nt. Capping defects can be induced by inactivation of the capping machinery itself, or indirectly through mutations in RNAPII [Nascent RNAPII transcripts are further protected by addition of a 7-methylguanosine  and small nuclear RNA (snRNA) are all processed at least in part by the exosome ,31. Subsin vivo targets for this endonuclease activity is unclear; the only confirmed targets are the 5′ETS and pre-5.8S regions of the pre-rRNA. The nuclear exosome can also associate with the distributive 3′ to 5′ exonuclease Rrp6, assisted by its cofactor Rrp47 (C1D in human cells) [The exosome consists of a central hexameric ring comprising six proteins, with an additional three subunits layered on top. These nine proteins are arranged in a barrel surrounding a central channel just wide enough to accommodate single-stranded RNA. Collectively, this complex is referred to as the exosome core, and is structurally conserved in Archaea. In eukaryotes, catalytic activity is supplied by the associated, highly processive 3′ to 5′ exonuclease Dis3/Rrp44. Dis3 is positioned at the base of the barrel, and approximately 30 nt of single-stranded RNA must be threaded through the central channel to reach the active site ,41. Dis3n cells) –44. Dis3n cells) ,33.2.3.S. cerevisiae, TRAMP consists of a poly(A) polymerase (PAP) (either Trf4 or Trf5), a zinc-finger, RNA binding protein (Air1 or Air2) and the RNA helicase Mtr4 [In vivo cross-linking experiments with Dis3 reveal a high fraction of reads with non-templated A-tails, suggesting that TRAMP-mediated polyadenylation generally precedes exosome-mediated decay [iMet [The eukaryotic exosome core is relatively inactive, making its function heavily reliant on cofactors. A well-studied cofactor is the Trf4/5–Air1/2–Mtr4 polyadenylation (TRAMP) complex. In ase Mtr4 ,45,46. Ted decay . Indeed,ay [iMet , defectiay [iMet  and crypay [iMet .The role of Mtr4 and the exosome in ribosome production is complex. Mtr4 acts in ‘constitutive’ pre-rRNA processing steps: 3′ trimming of the pre-5.8S rRNA and turnover of the excised 5′ETS spacer. Specific adaptor proteins, the ribosome biogenesis factors Nop53 and Utp18, interact with the arch domain of Mtr4 during recruitment to the pre-5.8S rRNA and 5′ETS, respectively . NotablyThere is little evidence that the pre-rRNA processing steps involve the TRAMP complexes, which probably function only in pre-rRNA surveillance. Consistent with this, loss of Trf5 was shown to partially rescue ribosome synthesis in several different pre-ribosome assembly mutants ,51. MoreNotably, the processing roles of Mtr4 and exosome take place in the context of a specific, on-pathway pre-ribosomal particle, in which the interactions have presumably been fine-tuned through evolution. During surveillance, in contrast, TRAMP, Mtr4 and the exosome must respond to a multitude of different, off-pathway particles with diverse defects in RNA processing, RNA folding and RNA–protein interactions. Similar issues must arise during surveillance of other RNA classes; the list of defects that could potentially arise seems almost endless. We therefore predict that the surveillance system is keyed to recognize ‘generic’ attributes, and uses general adaptor proteins, rather than recruitment by specific features of any particular, misassembled RNA–protein complex.TRAMP-mediated surveillance of RNAPII transcripts is usually coupled to transcription termination by the Nrd1–Nab3–Sen1 (NNS) complex ,15,53. NThe NNS pathway also participates in the maturation of snoRNAs , promotiThe NNS pathway seems to be unique to budding yeasts; there are apparent human homologues of Nrd1 (SCAF8/RBM16) and Sen1 (Senataxin), but these are not known to form a complex with a Nab3 homologue. By contrast, the TRAMP complex is well conserved , but hum3.3.1.During transcription elongation, the 3′ end of the nascent transcript is buried within the active site of RNAPII and inaccessible to 3′ exonucleases. It seems likely that exosome-mediated surveillance will first require transcription termination and dissociation of the polymerase. Indeed, this appears to be the mechanistic basis for the NNS and NEXT pathways. There is, however, another potential pathway that might allow the exosome access to the 3′ ends of nascent transcripts. When transcription elongation is impeded, the polymerase can backtrack—sliding backwards along the DNA and extruding the 3′ end of the nascent transcript from the catalytic site (reviewed in ). BacktrSchizosaccharomyces pombe results in extended transcripts with elevated RNAPII occupancy downstream of the poly(A) site [S. cerevisiae which implicate the exosome in mRNA transcription termination [In principle, extended backtracking might also lead to the extruded 3′ end projecting from the polymerase to a sufficient extent to allow binding of the surveillance machinery. This could provide an entry point for the nuclear exosome to degrade the nascent transcript, displacing RNAPII in the process . Consist(A) site . RNAPII (A) site ,74. This(A) site . For tramination ,75. Backmination .3.2.Unspliced pre-mRNAs are very rapidly degraded when splicing is delayed, indicating active surveillance. However, the mechanism and factors involved remain obscure. In yeast and humans, splicing generally occurs cotranscriptionally, so introns and splicing factors are usually removed before the 3′ end is exposed by transcription termination. Since excised introns must always be degraded, or trimmed to release internal snoRNAs, it is possible that features or factors associated with splicing would act to specifically recruit the RNA degradation system. When splicing is delayed, the 3′ end is no longer protected by the transcription elongation complex and these factors could facilitate degradation of the entire transcript. Degradation could be induced through direct recruitment of the exosome by one or more splicing factors, or through specific decay-promoting sequences within introns. The clearest example is the binding of Mmi1 to fission yeast introns . Mmi1 re3.3.S. cerevisiae, where rapid depletion of the nuclear poly(A) BP Nab2 dramatically destabilizes newly synthesized, polyadenylated mRNAs [Eukaryotic pre-mRNAs are generally terminated following cotranscriptional endonuclease cleavage downstream of the open reading frame. In nearly all instances, pre-mRNA cleavage is coupled to the addition of a poly(A) tail. In contrast to alternative termination mechanisms, pre-mRNA cleavage and polyadenylation is not clearly associated with exosome-mediated processing or decay. This is usually attributed to the protective effects of poly(A) binding proteins (poly(A) BPs), which coat the nascent poly(A) tail during its synthesis and are thought to fend off exonuclease attack . This hyed mRNAs . This phS. cerevisiae, binding sites for the DNA-binding protein Reb1 are enriched in intergenic regions downstream of poly(A) sites [In contrast to the protective effects of canonical cleavage and polyadenylation, transcripts terminated by alternative mechanisms are generally highly unstable. The clearest example is NNS-mediated termination, which usually facilitates termination early in the transcription cycle. However, even full-length transcripts are destabilized if termination is not coupled to polyadenylation and poly(A) BP binding. In A) sites . DNA-bouA) sites . The nasA) sites ,82. In eUnderlining the importance of 3′ protection, stable but non-polyadenylated RNAPII transcripts all carry specialized 3′ structures and/or specific RNA 3′-end binding proteins . The majIn metazoans, the majority of histone proteins are encoded by replication-dependent histone genes (reviewed in ). These In yeast, 3′ processing of most snRNAs or snoRNAs is initiated by cotranscriptional cleavage by the endonuclease Rnt1, a homologue of RNase III, which cleaves both sides of a stem–loop structure. The cleaved pre-sn(o)RNA is probably initially protected by 3′ binding of the La protein homologue Lhp1 and/or the Lsm2-8 complex to oligo(U) tracts –95, allo3.4.In principle, poorly packaged transcripts which remain bound to chromatin could base pair with the complementary DNA strand. For example, exosome inactivation in human cells leads to the accumulation of enhancer-associated ncRNAs (eRNAs), which generate RNA:DNA hybrids (R-loops) . In both4.NAB2 mRNA by recruiting the TRAMP–exosome complex [Despite its role in mRNA stability (see above), Nab2 has also been implicated in RNA surveillance pathways. Nab2 directs the degradation of pre-mRNAs by Rrp6 ,102 and  complex ,104. Nab complex . Notably complex . These oThe human nuclear protein PABPN1 is not related to Nab2 in sequence, but plays an analogous role in nuclear surveillance, targeting a wide range of nuclear substrates. These include spliced genes that encode snoRNAs but no protein product, cryptic transcripts arising from divergent promoters, intronless transcripts and partially spliced mRNAs ,106–109.In addition, PABPN1 has been reported to recruit the zinc-finger protein ZFC3H1, which acts together with Mtr4 to stimulate exosome degradation of the transcript ,111. ThiThe dual nature of nuclear poly(A) binding proteins in RNA stability and decay may reflect distinct roles during mRNA biogenesis. Following transcription through the cleavage site, mRNAs are rapidly polyadenylated, a process which is coupled to packaging by poly(A) BPs. Delayed poly(A) binding will expose the 3′ end to exosome-mediated degradation before the transcript can be exported to the cytoplasm. However, because nuclear poly(A) BPs also recruit the exosome, any transcript with significantly delayed nuclear export will eventually be degraded by default, albeit with much slower kinetics . In this5.Arguably the most significant role of the nuclear surveillance machinery is the removal of huge numbers of different RNAs that arise from pervasive transcription. This term was initially coined in response to the unexpected finding that a large fraction of the human genome is transcribed into unstable RNAs with little or no protein-coding potential . SubsequGenome-wide studies indicate that eukaryotic promoters are intrinsically bidirectional –11,121. S. cerevisiae, promoter-associated ncRNA transcription is terminated through the NNS pathway, and the resulting transcripts are degraded by the TRAMP–exosome complex. Nrd1 and Nab3 binding motifs are enriched in transcripts generated upstream of bidirectional promoters, also termed NUTs, and depleted within protein-coding transcripts [In nscripts . This asnscripts –56. Morenscripts –56.An additional determinant may be the phosphorylation status of RNAPII. Early transcription is characterized by high Ser5 and low Tyr1, Ser2 and Ser7 phosphorylation levels along the CTD ,123. ThiRemarkably, this transition apparently fails to take place during CUT transcription, with polymerase instead remaining stuck in initiation state , providiAnalogous mechanisms control divergent transcription in humans. Regions upstream of bidirectional promoters are enriched for consensus poly(A) signal (PAS) motifs, aiding early termination by the conventional cleavage and polyadenylation machinery ,125. TheAt first glance, it is surprising that conventional termination should be coupled to decay. However, despite being processed by the canonical cleavage and polyadenylation machinery, uaRNAs are often not polyadenylated . This suAs with yeast, it is unlikely that any one characteristic is sufficient for the mammalian surveillance machinery to distinguish mRNAs from transcriptional noise. Another important feature may be splicing, which has long been associated with RNA stability and surveillance. In mammalian cells, intronless transcripts tend to be weakly expressed and unstable  and, whi6.In addition to CUTs, yeast also produce stable unannotated transcripts (SUTs). Whereas CUTs are only observable when the surveillance machinery is compromised, SUTs are readily detectable in wild-type cells . The greSUT biogenesis appears homologous to nuclear lncRNAs in higher eukaryotes. Mammalian lncRNAs bear a striking resemblance to mRNAs; both classes are capped and polyadenylated, sometimes spliced, and similar in length . HoweverLike SUTs, nuclear lncRNAs are significantly less stable than their cytoplasmic mRNA counterparts . The rel7.RNAs carry out diverse functions within cells, providing both the machinery for protein production and the information to programme its activity. In order to function in these distinct activities, all RNAs undergo elaborate maturation processes within the nucleus. During these complex processing and assembly pathways, defects inevitably arise in some RNA transcripts and RNA–protein complexes, which must be systematically removed by quality control pathways. In this review, we proposed that the identification and degradation of defective RNAs, and enormous numbers of spurious transcripts, does not require recognition of specific ‘bad’ features. Rather the surveillance system will, by default, target almost all RNAs. Transcripts that undergo correct and timely maturation acquire protective features that help them evade the activities of the surveillance system. This model potentially explains how eukaryotes can tolerate the synthesis of huge numbers of diverse ncRNA transcripts, which are constantly cleared by rapid degradation. Moreover, it has been proposed, on thermodynamic grounds, that pervasive transcription is an inevitable feature of eukaryotic genomes . The dev6A and m1A, which in recent years have been implicated in multiple aspects of the mRNA life cycle [Although our understanding of these surveillance pathways is rapidly expanding, many questions remain unanswered. In particular, a quantitative description of RNA surveillance is needed. Discrimination between normal and defective transcripts must require an input of energy, in order to avoid violating the second law of thermodynamics. This energy input could be supplied by ATP-dependent RNA helicases, potentially explaining why helicases such as Mtr4 are critical components of essentially all surveillance pathways. However, a mechanistic understanding of the connection between ATP expenditure and RNA surveillance remains elusive. An additional avenue for future research is the role of RNA modifications such as mfe cycle . Nascent