Saccharomyces cerevisiae. Sequence read data from these nuclear run-on libraries revealed that transcriptional regulation in yeast occurs not only at the level of RNA polymerase recruitment to promoters but also at postrecruitment steps. Nascent synthesis signals are strongly enriched at TSS throughout the yeast genome, particularly at histone loci. Nascent transcripts reveal antisense transcription for more than 300 genes, with the read data providing support for the activity of distinct promoters driving transcription in opposite directions rather than bidirectional transcription from single promoters. By monitoring total RNA in parallel, we found that transcriptional activity accounts for 80% of the variance in transcript abundance. We computed RNA stabilities from nascent and steady-state transcripts for each gene and found that the most stable and unstable transcripts encode proteins whose functional roles are consistent with these stabilities. We also surveyed transcriptional activity after heat shock and found that most, but not all, heat shock-inducible genes increase their abundance by increasing their RNA synthesis. In summary, this study provides a genome-wide view of RNA polymerase activity in yeast, identifies regulatory steps in the synthesis of transcripts, and analyzes transcript stabilities.The assessment of transcriptional regulation requires a genome-wide survey of active RNA polymerases. Thus, we combined the nuclear run-on assay, which labels and captures nascent transcripts, with high-throughput DNA sequencing to examine transcriptional activity in exponentially growing  Changes in mRNA steady-state levels reflect the combined effect of changes in RNA synthesis and degradation. Thus, measurement of only steady-state transcript levels provides an incomplete view of transcriptional regulation. Proper control of transcription is achieved by regulation at several steps: (1) preinitiation complex assembly; (2) promoter escape (or early elongation); (3) productive elongation; and (4) transcription termination (The nuclear run-on (NRO) assay traditionally has been used to assess the transcription of individual genes .7 cells. In vitro transcription was performed with 12 µL of biotin-16-UTP  instead of 13 µL of [α-33P]UTP. Total RNA was extracted with hot phenol, treated with Turbo DNase I that was passed through CentriSpin columns . An aliquot of total RNA was used for total RNA library construction . Biotinylated RNA was precipitated from total RNA with Dynabeads MyOne Streptavidin C1 . Binding was performed in 5 mM Tris-HCl (pH 7.5), 0.5 mM ethylene diamine tetra-acetic acid and 1 M NaCl for 20 min at 42° and 2 hr at room temperature with rotation as described  and used for Illumina sequencing library preparation as described  of B16UTP-RNA over nonspecifically bound UTP-RNA on streptavidin beads was calculated on the basis of the differences in their Cp values and adjusted by the differences in Cp values observed in their corresponding inputs:Two different regions of Maq (http://maq.sourceforge.net) with default parameters and filtered to retain high confidence alignments . The quality-filtered alignments were used to compile a strand-specific measure of abundance for transcribed bases in the genome, normalized by the total number of bases acquired per experiment. Alignments from biological replicates were merged by use of the mapmerge function in Maq and processed equally. All reads were 36 bp long.We aligned quality control-filtered reads from the NRO and total RNA libraries to the UCSC sacCer2 reference genome by using To define bases in the genome that can be unequivocally covered by reads, we identified all unique 36 base sequences from both strands of the reference assembly. We defined mappable bases as all positions covered by these sequences.P-value significance cutoff of 0.01 was applied.The normalized per-base read depth from NRO and total RNA libraries was mapped to a collection of previously annotated features that do not overlap in the same strand, including genes with known transcription start and end sites, as well as genes with unknown transcription starts and ends, and noncoding RNAs  was normalized by the total read bases in the transcript model to obtain the fraction (Fci) of the total read depth in the model acquired up to each position i. Transcript model sizes were homogenized to allow comparisons between models of different length. Sizes were adjusted by maintaining the per-base values from the first and last 100 bp of the model and adjusting the intervening values to a 1000-bp segment. Thus, the fractional cumulative NRO read depths along the uniformly sized models represent the amount of transcriptional activity that has occurred as a function of position in the 5′ to 3′ direction of the transcript model.The cumulative read depth along each transcript was calculated to examine the rate of transcription along transcript models. For each position Fci) and non-normalized (Ci) cumulative read depth data and 10,000 simulated AUCs . Simulations were performed by randomly placing the observed number of transcript reads within the model and summing the contributions in cumulative read depth of each alignment to the size-adjusted transcript model. The significance of the observed AUC for each transcript model was assessed against the distribution of simulated AUCs under the null hypothesis that transcription occurs evenly throughout the model. Genes were ranked by their normalized AUC values (i.e. based on Fci curves).For each gene, we calculated the area under the curve (AUC) from uniformly sized models by using normalized (http://yeastgenome.org/) with a P-value significance cutoff of 0.01 and a 5% FDR threshold. Analysis of GO term enrichment near the top and bottom of genes ranked by their ratios of transcription activity and transcript abundance  were performed with GOrilla . The NRO assay conditions were optimized to reduce total RNA background to 4%–11% (Table S1). In addition, we performed NRO reactions spiking in controls consisting of two regions of the Arabidopsis thaliana ELF3 RNA that had been transcribed in vitro either in the presence of UTP or biotin-UTP (Table S2). Together these quantitative PCR experiments demonstrated that total RNA represents between 2.6% and 11% of the NRO libraries and that therefore nascent RNA constitutes between approximately 89% and 97% of the NRO samples. By using a standard curve from known amounts of in vitro synthesized, biotin-labeled RNA, we estimate that the starting amount of biotinylated nascent transcripts in RNA extracts before affinity selection comprises approximately 0.05%. Thus, we calculate that after selection nascent RNA has been enriched by a factor of at least 89/0.05, or approximately 1,800-fold, relative to its starting level.We established a genome-wide NRO assay for yeast consisting of the following steps: (1) permeabilization of yeast cells with sarcosyl to deplete endogenous nucleotides and stall transcription complexes on chromosomes; (2) reactivation of transcription to extend and label newly synthesized RNA transcripts with biotin-modified UTP; (3) isolation of biotin-modified RNA on streptavidin beads; (4) elution of enriched RNA from beads; and (5) construction of strand-specific high-throughput sequencing libraries . To asseTable S3). This combined approach allowed us to measure the amount of nascent transcripts as well as steady-state transcripts in parallel.To examine RNA polymerase II activity and monitor transcript abundance throughout the genome, we constructed both NRO and total RNA libraries. Total RNA libraries were prepared from aliquots of the biotin-UTP-treated NRO sample by excluding the steps of nascent RNA selection on streptavidin beads . Strand-To analyze sequencing results for both NRO and total RNA samples, we defined read depth as the number of sequencing reads acquired per base, normalized by the total bases sequenced for that sample. We summed the read depth values for each mappable, non-intronic base within a transcript model and divided this sum by the total number of these bases within that transcript to calculate read density. Both read depth and read density are presented in units of read depth per million bases acquired to obtain higher scale values.2r > 0.97 and 2r > 0.99, respectively; P < 0.01; see the Materials and Methods). Our measurements of nascent transcription showed moderate correlation with previous genome-wide estimates of transcriptional activity derived from hybridization of radiolabeled nascent transcripts to nylon macroarrays . This correlation is comparable with that between previous measures of transcriptional activity obtained via the use of NRO approaches  .We focused our analysis on a set of 4,285 genes with known TSSs and termination sites . The modest correlation between polymerase occupancy and transcriptional activity has been previously reported . This analysis corroborates that RNA polymerase occupancy is a better predictor of transcriptional activity than of transcript abundance.Because the NRO assay reflects the activity of RNA polymerases on transcribed genes, we compared the transcription data from the NRO sample to the genome-wide distribution of RNA polymerase II in yeast , and this increase was not caused by increased transcript levels. Moreover, splicing was much less abundant in the NRO libraries than in the total RNA libraries , further arguing that the NRO libraries are enriched for nascent transcripts.Although pre-mRNA splicing occurs co-transcriptionally in yeast, we observed greater intronic read depths in the NRO libraries than in the total RNA libraries , increased average read depth of shorter transcripts , and sequencing biases  as possible contributors to the increased read depth near 5′ ends of yeast transcripts.Analysis of RNA polymerase II occupancy data from high-density tiling microarrays demonstrated that only highly expressed yeast genes  have an even distribution of RNA polymerase II throughout their coding region, and that a comparable fraction (~18%) have an enrichment of the polymerase near their 5′ ends . This rela cells . Sarkosyla cells  but prevla cells . Thus, tla cells  likely iNear transcript termination sites, total RNA libraries show greater read depths than NRO libraries , which iIn principle, the capacity to measure transcriptional activity for each base in the genome should allow us to distinguish rate-limiting steps in transcription other than transcription initiation. However, because read depth in high-throughput sequencing of RNA libraries is noisy . For a gP = 7.15 × 10−5), chromatin assembly and disassembly genes (P = 0.001), helicases (P = 0.007), and ATPases (P = 0.008). Histone genes drive this enrichment because seven of the eight core histone genes demonstrated strong evidence of accumulation of polymerases at their 5′ ends, which could reflect promoter-proximal pausing . However, previous chromatin immunoprecipitation analysis of yeast did not provide evidence for widespread polymerase pausing . The middle range of the AUC-ranked list includes transcripts in which the cumulative fraction of reads increased linearly. In addition, the intermediate range includes transcripts with increased read densities in both the promoter-proximal region and near the 3′ end of the transcript . Such transcripts could be targets of regulation at both promoter escape and transcriptional termination. Finally, transcripts in which nascent transcription is concentrated in the middle of the transcript are also found in this range. Although some of the patterns of read depth could be attributable to stochastic variations in fragment ligation, amplification or sequencing, the observation of transcriptional regulation occurring subsequent to initiation, pausing and release from pausing is consistent with recently observed patterns of RNA polymerase II activity derived from polymerase-associated RNAs . The increased prevalence of antisense transcripts in the NRO sample likely relates to their instability and their consequent loss from the pool of total RNA.Because recent genomic analyses have shown substantial antisense transcription in yeast , suggesting that this measure of transcriptional activity accounts for approximately 80% of the variance in transcript abundance , drug transport (P = 5.1 × 10−6), signal transduction (P = 1.0 × 10−4), and protein amino acid phosphorylation . Short-lived transcripts are capable of rapid adjustments in abundance to assure quick responses to stimuli (P = 6.4 × 10−8) and genes involved in nucleosome assembly . Thus, our estimates of the stability of nascent transcripts agree with the expected stabilities of transcripts based on the functions of their encoded proteins, as observed previously . We observed no correlation between the estimated stabilities of nascent transcripts in our study and previously measured mRNA decay rates . These discrepancies could at least in part be attributable to differences in the stability of nascent transcripts vs. that of mRNAs. In addition, the temperature shift to halt transcription might induce changes in RNA folding and thus affect estimates of mRNA half-lives. Finally, technical differences in the methodologies used may be contributing to these differences. For example, microarray measurements in of relative transcript abundance are subject to errors caused by differences in hybridization efficiencies. Similarly, sequencing biases introduce errors into our measurements of both transcriptional activity and steady-state levels of mRNAs and these errors may be compounded in the calculation of ratios.The absolute half-lives of mRNAs have traditionally been measured over a time course after the inhibition of transcription by chemical or thermal inactivation of RNA polymerase II (Table S6).Several genome-wide studies have identified yeast genes that respond to temperature shock by elevating their transcript levels (Table S7). Approximately 65% of those genes showed an increase in nascent RNA synthesis in response to heat shock, indicating that although transcript levels are predominantly regulated at the level of transcription, changes in mRNA stability play a role as well (2r = 0.55) compared with all yeast genes analyzed in this study (Pearson’s 2r = 0.24). However, only 30 of the 304 genes with 2-fold or greater increased total RNA abundance after heat stress are known targets of Hsf1, a master transcriptional activator of the heat shock response, or contain Hsf1 or Msn2/4 recognition sites in their promoters (Table S7). Approximately 87% (26) of these 30 genes showed increased nascent transcript levels in response to heat shock. These findings suggest that additional factors may mediate stress-induced transcriptional activation.To estimate the effect of heat shock on RNA abundance and transcription activity, we calculated the log-transformed ratios of read density values after and prior to heat shock for total RNA  and for nascent RNA (NRO [HS]/NRO). For total RNA, log-space ratios greater than zero represent transcripts with increased RNA abundance upon heat shock treatment, whereas those less than zero indicate decreased RNA abundance. For nascent RNA, log-space ratios greater than zero identify genes whose transcription was up-regulated upon heat shock, whereas those less than zero indicate those down-regulated . This anS. cerevisiae. By coupling the NRO assay to high-throughput sequencing, we assayed the synthesis of nascent RNA in yeast under normal growth conditions and in response to heat stress. We measured RNA abundance and synthesis in parallel, which revealed that cellular RNA abundance in yeast under both growth conditions is predominantly controlled by the rate of RNA synthesis. In accord with previous degradation experiments, we observed distinct functional gene classes between stable and unstable transcripts  that might influence the probability of RNA polymerase II pausing or arrest in yeast. In addition, the elongation factor TFIIS (yeast Dst1) stimulates intrinsic RNA polymerase RNA cleavage activity and thus promotes elongation of arrested RNA polymerase in vivo . Several genome-wide studies revealed that this response involves an increase in the abundance of transcripts from genes that are collectively termed “heat shock-inducible” (at shock . Other sle genes . HoweverIn summary, we used the NRO assay to provide a high-resolution view of transcriptional regulation in yeast. We applied this assay to examine the correlation between synthesis and abundance of transcripts, the distribution of polymerase activity along transcripts, and the genome-wide changes in yeast transcription in response to heat shock treatment. Further investigations may use similar approaches to characterize other modes of yeast transcriptional control under different growth conditions and in response to other environmental perturbations.


Orbitofrontal cortex (OFC) is widely held to be critical for flexibility in decision-making when established choice values change. OFC's role in such decision making was investigated in macaques performing dynamically changing three-armed bandit tasks. After selective OFC lesions, animals were impaired at discovering the identity of the highest value stimulus following reversals. However, this was not caused either by diminished behavioral flexibility or by insensitivity to reinforcement changes, but instead by paradoxical increases in switching between all stimuli. This pattern of choice behavior could be explained by a causal role for OFC in appropriate contingent learning, the process by which causal responsibility for a particular reward is assigned to a particular choice. After OFC lesions, animals' choice behavior no longer reflected the history of precise conjoint relationships between particular choices and particular rewards. Nonetheless, OFC-lesioned animals could still approximate choice-outcome associations using a recency-weighted history of choices and rewards. ► OFC lesions can impair decision making in both changeable and fixed environments ► OFC is critical for assigning credit for a particular reward to a particular choice ► OFC lesions spare system to approximate learning based on choice and reward history ► Role in contingent learning underlies impairments in flexible decision making Learning, tracking, and updating the predictive value associated with environmental stimuli is essential to advantageous decision making. One region consistently implicated in the guidance of such adaptive choice behavior is the orbitofrontal cortex (OFC). It has been suggested that OFC is crucial for processing negative outcomes  or for tTo date, the cardinal tests of flexible reward-guided decision making have been two-option deterministic reversal learning tasks. It has been frequently demonstrated that OFC lesions impair performance following such reversals, even though the initial stimulus-outcome discrimination learning appears unaffected . Both siTherefore, the present study was designed to reexamine the causal function of OFC in decision making in the context of changing reward values . MacaqueTwo key sets of findings were made. First, despite replicating the observation that selective OFC lesions impaired decision-making following reversal in the identity of the best-rewarded option, finer-grained analyses of trial-by-trial choice behavior demonstrated that this was not due to a failure to inhibit previously rewarding responses or insensitivity to negative outcomes as lesioned animals were also just as able as controls to respond to local changes in reward likelihood when the identity of the best stimulus remained the same.The second set of findings demonstrate that the overall pattern of impairments can be explained by considering the OFC as critically concerned with specific contingent learning, the process by which the credit for an outcome becomes assigned to the appropriate previous choice. This process is particularly taxed at times such as when reward contingencies change, where multiple different stimuli might be chosen and different outcomes witnessed .It has long been known that, even during normal behavior, a given outcome can reinforce not just the choice that led to its delivery but also other responses that were made close in time, both preceding and even following this outcome . This “sSuch a facility to learn using recent choice and reward histories would allow animals to make accurate approximations of specific stimulus-outcome associations during periods when the same choice is made repeatedly for a constant rate of reward, but learning would be severely compromised when the pattern of choices and outcomes is variable, such as following a reversal. We demonstrate finally a key implication of this idea: OFC lesions impair learning even in environments where contingencies never change E, so lonTo probe the specific function of the OFC during flexible decision making, we tested six macaques—three controls and three animals given selective OFC lesions after presurgical testing B–1D. At sch) and on estimates each animal's “subjective” value . Importantly, too, there was no effect of testing session , suggesting that animals did not develop a model of the underlying task structure.We analyzed the data based on both the “objective” value associated with each stimulus , where e [RLsee . Preopere [RLsee A, and fosch option following reversal on both schedules , which post hoc tests showed was driven by the fact that the OFC group was only significantly worse at choosing the HRL option than the controls postoperatively during the reversal phase in both conditions (p = 0.001) but not during any other period of testing . This is comparable to several previous lesion studies to have reported effects following reversals along with intact discrimination learning  the lag in trials between the Hsch following surgery , and there was also no significant reduction in the sensitivity of the relationship between Hsch choice performance and Hsch reward rate postoperatively in the OFC group . Similarly, there was no significant reduction in the average range of Hsch choices in the two groups . Taken together, this demonstrates that during the first phase of VRB, the OFC-lesioned monkeys could track local changes in reward rates of the currently selected option, both when there was an increase in negative or positive feedback for selecting the best option, militating against any theory emphasizing the role of OFC in simply responding to negative feedback.There was no difference in how quickly the OFC-lesioned group on average responded to a local change in Hincrease in the rate of switching between the alternatives .Following surgery, the influence of specific choice-outcome associations on behavior was profoundly reduced postoperatively, an effect that was particularly prominent on trials near to the current one  after long history of choice on another stimulus , as is the case in reversal learning. , with these animals post-surgery showing both a significantly increased propensity to switch back to option A after a reward on B and a decreased tendency to switch back to A after not receiving a reward on B , caused by the fact that this effect became more prominent the longer the previous choice history on A.less likely in both groups . The reinforcement after the A choice was strongly affecting the value of a subsequent B choice despite the fact that the reinforcement occurred before the B choice .Control animals were always more likely to select A, and less likely to select B or C, at trial n if a previous A choice had sometime been rewarded than if not, regardless on which previous trial a reward was delivered on A. The credit for a reward delivered after an A choice was predominantly being correctly assigned to stimulus A. Moreover, this effect was cumulative such that their likelihood of returning to an A choice at trial n increased as more of the previous A trials (n-2 to n-5) were rewarded. By contrast, the OFC-group showed a markedly different effect postsurgery such that, compared to when no reward was delivered for a recent A choice, a recent reward on A B choice B. SimilaWhen this effect was broken down, we found that it was mainly driven by a decrease in the OFC-lesioned animals' likelihood of choosing option B again following a recent choice of A that was not reinforced (post hoc test: p = 0.003), although there was also a trend in this group for an increase in the likelihood of persisting with B following a recent reward for A , but rates of persistence became indistinguishable following 3 or more choices of one stimulus .Taken together, the evidence suggests that the choices OFC-lesioned animals make are strongly influenced by their recency- and frequency-weighted history of past choices and of previous rewards. Although OFC-lesioned animals appear unable to make and update specific associations between the stimulus they chose and the outcome they received, they are just as able to use the contiguity between recent choices and rewards to select what responses to make. Such stimulus-outcome approximations would result in the animals learning accurate value representations when either their reward and/or choice histories are relatively constant, such as during the first 150 trials of STB and VRB, but will lead to aberrant learning when reward and choice histories are mixed and changeable, as exemplified by reversal situations where the values of stimuli alter such that the identity of the highest value stimulus changes.fixed schedules of reinforcement where there are never any reversals if it is made more difficult to determine the best option by lowering the reward likelihood of Hsch, therefore requiring animals to integrate across more choices to determine which option was best. To investigate this, we tested the control and OFC-lesioned monkeys on three new fixed three-armed bandit schedules , they were significantly impaired at finding this in FIXED 3 which had the lowest rate of reinforcement, even though the values associated with the stimuli were constant throughout (Mann-Whitney test: p = 0.05). However, although slower at learning, the OFC group did eventually reach a comparable level of performance as controls. When the session was divided up into 2 halves  the OFC group made fewer Hsch responses in FIXED 3 during the first half of the session, when learning the identity of the best stimulus , but they were no different from the control group during the second half . Also as in the changeable schedules, this deficit manifested itself in increased patterns of choice alteration that were particularly evident in FIXED 3 .Using comparable analyses to those employed with STB and VRB, we investigated the speed of learning to choose HOFC has long been associated with enabling animals to alter their behavior in response to changes in reinforcement, particularly during reversals in stimulus-outcome associations . The curIf OFC lesions do not cause difficulties in reward monitoring or inhibiting previous responses, the obvious question arises as to what specific role the OFC plays in guiding adaptive decision making in a changeable environment. Our findings imply that its function is to guide contingent learning, a mechanism that allows rewards received for a particular choice among several alternatives to be correctly credited to that option alone .Such an impairment in appropriate contingent learning may seem to be immediately contradicted by the fact that the large majority of previous studies have implicated OFC as only important following reversals in outcome associations but not during initial discrimination learning  and thatA method of estimating stimulus value based on recent choice and reward histories, if fed into a response selection algorithm, would result in largely appropriate decision making in any situation when reward and choice histories are uniform; in other words when much of the recent choice history comprises of only taking one option and much of the recent reward history comprises of only one type of outcome  that was absent in the local choice pattern. Reward for choosing either A or B made a future selection of C less likely in all circumstances . The speThe claim that the OFC is essential for representing the conjunction of a particular reward with a particular choice having been made is consistent with several strands of evidence from previous studies. First, while there is an extensive literature demonstrating that OFC activity reflects information about anticipated and received reward value  there arOFC is well placed anatomically to mediate specific stimulus-reward learning. OFC, particularly lateral OFC which was the focus of the lesion in the current study A, is theThe conclusion that OFC is crucial for an aspect of stimulus-outcome learning and that this drives its role in reversal learning is consistent with the emphasis placed on OFC in associative learning by other researchers . The preWhile we and others have emphasized that there are many similarities between the structure and function of rodent and primate OFC , it is nIn order for OFC-lesioned animals to be able to approximate associative learning based on recent choices and rewards, it is necessary that these elements should be represented in structures other than the OFC. Such signals have in fact proved to be relatively widespread and present in several brain areas, such as anterior cingulate cortex and striatum . HoweverIn most studies to date, the contingencies between choices and reward have been straightforward, with limited available options and possible outcomes and with the associations between the two remaining stable. However, in many situations outside the laboratory, when there are frequently multiple alternatives and also delays between the consequences of responses and their causal antecedents, it is not straightforward to form appropriate associations. Learning in such complex situations can follow two distinct strategies: either through monitoring integrated choice and reward histories , aged between 4 and 10 years and weighing between 7 and 13 kg were used in these experiments. Three animals acted as unoperated controls, whereas the other three received bilateral aspiration OFC lesions following training and presurgical testing. All animals were maintained on a 12 hr light/dark cycle and had 24 hr ad libitum access to water, apart from when testing. All experiments were conducted in accordance with the United Kingdom Animals Scientific Procedures Act (1986).Six adult male rhesus macaque monkeys . Whether or not reward was delivered for selecting one option was entirely independent of the other two alternatives. Available rewards on unchosen alternatives were not held over for subsequent trials. Each animal completed five sessions under each schedule, tested on different days with novel stimuli each time. For STB and VRB, the sessions were interleaved  and data were collected both pre- and postoperatively. For the FIXED conditions, schedules were run as consecutive sessions, starting with the five sessions of FIXED 1, then five sessions of FIXED 2, and finally five of FIXED 3 and only postoperative data were collected. The changeable schedules comprised of 300 trials per session and the fixed schedules of 150 trials per session.Reward was delivered stochastically on each option according to five predefined schedules: STB and VRB (changeable schedules) or FIXED 1–3  and of HRL . The reward learning rate (α) was fitted individually to each animal's pre-surgery data using standard nonlinear minimization procedures.The data from STB and VRB were analyzed both as a function of HWhere appropriate, data from all tasks are reported using parametric repeated-measures ANOVA see .To establish the contribution of choices recently made and rewards recently received on subsequent choices, we performed three separate logistic regression analyses, one for each potential stimulus . This gave us three sets of regression weights, Surgical procedures and histology for these animals have been described in detail elsewhere . In brie


This paper presents an experimental study of the random incentive mechanisms which are a standard procedure in economic and psychological experiments. Random incentive mechanisms have several advantages but are incentive-compatible only if responses to the single tasks are independent. This is true if either the independence axiom of expected utility theory or the isolation hypothesis of prospect theory holds. We present a simple test of this in the context of choice under risk. In the baseline (one task) treatment we observe risk behavior in a given choice problem. We show that by integrating a second, asymmetrically dominated choice problem in a random incentive mechanism risk behavior can be manipulated systematically. This implies that the isolation hypothesis is violated and the random incentive mechanism does not elicit true preferences in our example. Under a random incentive mechanism (RIM) subjects usually respond to numerous tasks  and at the end of the experiment one of the tasks is randomly selected and played out for real. RIM provides incentives for truthfully responding to all tasks while only paying one of them. This reduces expenditures for experimental studies and excludes wealth effects from paying all choices sequentially during the experiment as well as portfolio effects from paying all choices at the end of the experiment. Due to theses appealing features, RIM has been used in many experimental studies in psychology, e.g However, it was pointed out by Holt risky-dominates the first problem if R′ dominates R and S′ is dominated by S. Analogously, a third problem, consisting of a safe alternative S″ and a risky one R″, safely-dominates the first one if S″ dominates S and R″ is dominated by R. Our hypothesis is that in the presence of a risky-dominating choice problem alternative R (S) looks less (more) attractive, leading to a higher fraction of S choices. The opposite should hold in the presence of a safely-dominating choice problem.This note presents a simple experiment which tests isolation and incentive compatibility of RIM in the presence of asymmetrically dominated choice problems. The literature of context-dependent choice has shown that adding asymmetrically dominated alternatives in the set of options can systematically influence choice behavior Two experiments were run at the University of Kiel with altogether 581 subjects. Subjects gave written consent to participate in the study. As there was no possibility to lose money in the experiments, approval of an ethics committee was neither required nor obtained. In both experiments subjects were randomly assigned to one of five groups, referred to as Groups 1, 2.1, 2.2, 3.1, and 3.2 in the sequel. For Experiment 1 the stimuli received by the groups (in each case printed on a single sheet of paper) are presented in In Group 1 subjects had just to choose between Options A and B. Subjects were told that everybody would receive the payoff of the chosen option in cash directly after the experiment and that the payoff of Option B would be determined by a coin flip. In Groups 2.1 and 3.1 there were two choice problems  and observed a significant violation in four cases. This is rather clear-cut and not mixed evidence because isolation cannot be used to justify use of RIM unless it holds generally; holding 50 percent of the time clearly will not do. Additionally, we ran eight tests of the incentive-compatibility of RIM (fraction of B choices in Groups 2 versus Groups 3 in both experiments) and in seven out of these eight tests responses were significantly different. We can conclude that choice behavior in RIM depends substantially on the other tasks involved and asymmetrically dominated alternatives have an impact in the hypothesized direction.Altogether, the presented results demonstrate that a common methodology in experimental studies may induce distortions. Further research is needed in order to investigate how serious these distortions are in practice.


The central dogma of gene expression considers RNA as the carrier of genetic information from DNA to protein. However, it has become more and more clear that RNA plays more important roles than simply being the information carrier. Recently, whole genome transcriptomic analyses have identified large numbers of dynamically expressed long noncoding RNAs (lncRNAs), many of which are involved in a variety of biological functions. Even so, the functions and molecular mechanisms of most lncRNAs still remain elusive. Therefore, it is necessary to develop computational methods to predict the function of lncRNAs in order to accelerate the study of lncRNAs. Here, we review the recent progress in the identification of lncRNAs, the molecular functions and mechanisms of lncRNAs, and the computational methods for predicting the function of lncRNAs. Mutations in lncRNAs are related with a wide range of diseases, especially cancers and neurodegenerative diseases. Even so, the functions and molecular mechanisms of most lncRNAs are unknown. Though several computational methods have been developed to predict the functions of lncRNAs, it still remains a challenging task, partly owing to the lack of conservation in both the sequence and secondary structures of lncRNAs [Proteins and related protein-coding genes have been the main subject of biological studies for years. However, with the development of RNA sequencing technology and computational methods for assembling the transcriptome, it has become clear that besides protein-coding genes much of the mammalian genome is transcribed, and many noncoding RNA (ncRNA) transcripts tend to play important roles in a variety of biological processes. Understanding the function of ncRNAs has become one of the most important goals of modern biological studies –3. ncRNA lncRNAs –11. In tsense or antisense: lncRNAs that are located on the same strand or the opposite strand of the nearest protein-coding genes [divergent or convergent: lncRNAs that are transcribed in the divergent or convergent orientation compared to that of the nearest protein-coding genes [intronic or intergenic: lncRNAs that locate inside the introns of a protein-coding gene, or in the interval regions between two protein-coding genes [The definition of lncRNA is based on two criteria, the size and the lack of protein-coding potential. In this paper, lncRNA refers to nonprotein-coding RNA longer than 200 nt , 10–12, ng genes ; (2) divng genes . To identify lncRNAs, the first step is to obtain all transcripts including ncRNAs and mRNAs in cells, and then to distinguish lncRNAs from mRNAs and other types of ncRNAs. Traditional technologies, such as microarray, focus on the identification of protein-coding RNA transcripts. New technologies, such as RNA-Seq, are not limited to the identification of protein-coding RNA transcripts, and have led to the discovery of many novel ncRNA transcripts. The discrimination between lncRNAs and other small regulatory ncRNAs depends on their length. However, the length information alone is not enough to separate lncRNAs from mRNAs, and other criteria are needed for this purpose. Below, we will first briefly introduce new technologies in identifying RNA transcripts, especially ncRNA transcripts. Then, we will review current methods to distinguish lncRNAs from mRNAs. Traditional microarray technologies use predefined probes to determine the expression level of mRNA transcripts and are not appropriate to identify lncRNAs. However, it has been found that a few previously defined mRNAs or some probe sequences actually are lncRNAs; thus, former microarray datasets can be reannotated to study the expression of lncRNAs . With moSAGE  technology produces large numbers of short sequence tags and is capable of identifying both known and unknown transcripts. SAGE has been used and proved to be an efficient approach in studying lncRNAs. For example, Gibb et al. compiled 272 human SAGE libraries. By passing over 24 million tags they were able to generate lncRNA expression profiles in human normal and cancer tissues . Lee et C. elegans [With the development of next generation sequencing (NGS) technologies, RNA-Seq  has been widely used for novel transcripts discovery and gene expression analysis. Compared to traditional microarray technology, RNA-Seq has many advantages in studying gene expression. It is more sensitive in detecting less-abundant transcripts, and identifying novel alternative splicing isoforms and novel ncRNA transcripts. The basic workflow for lncRNA identification using RNA-Seq is shown in  elegans . Pauli e elegans . Prensne elegans . RNA-IP (RNA-immunoprecipitation) is a new method developed to identify lncRNA that interacts with specific protein. Antibodies of the protein are first used to isolate lncRNA-protein complexes. Then, cDNA library is constructed followed by deep sequencing of interacting lncRNAs. Using RNA-IP, Zhao et al. discovered a 1.6-kb lncRNA within Xist that interacts with PRC2 . The above-mentioned methods target on RNA transcripts directly. In contrast, chromatin signature-based approach uses chromatin signatures, such as H3K4me3 (the marker of active promoters) and H3K36me3 (the marker of transcribed region), to study actively transcribed genes including lncRNAs. In this approach, ChIP-Seq is used to generate genome-wide profiles of chromatin signatures , and theArabidopsis which encodes a protein of 54 amino acids [Unlike protein-coding genes, the start codons and termination codons in lncRNAs tend to distribute randomly. As a result, the ORF length of lncRNAs can hardly extend to over 100 from a probabilistic point of view. Based on this principle, one way to discriminate lncRNAs from mRNAs is by ORF length. For example, the FANTOM project used a maximum ORF length cutoff of 100 codons to differentiate noncoding RNAs from mRNAs . Howeverno acids . Thus, tno acids . HoweverCompared to protein coding genes, noncoding genes are generally less conservative, meaning they are more inclined to mutate , 67. ThuC. elegans [Owing to the complex identities of lncRNAs, recently an increasing number of machine learning-based methods have been developed to integrate various sources of data to distinguish lncRNAs from mRNAs.  elegans . Xist, a well-known lncRNA, evolves from a protein-coding gene [Although the above-described methods have shown their effectiveness in identifying lncRNAs, exceptional cases still remain. For instance, whether an RNA transcript is translated or not may be changeable during the course of evolution. As an example, ing gene . Besidesing gene . Such amlncRNAs have once been thought as the “dark matter” of the genome, because of our limited knowledge about their functions . With moWith thousands of lncRNAs identified in mammals and other vertebrates , a few lRecent studies have shown the expression of lncRNA is tissue specific. Loewer et al. studied the expression of lncRNA in global remodeling of the epigenome and during reprogramming of somatic cells to induce pluripotent stem cells (iPSCs). They found some lncRNAs have cell-type specific expression pattern , 92. Lostrans-); (2) RNA:DNA hybrid; (3) RNA:RNA hybrid of lncRNA with a nascent transcript; (4) RNA-protein interaction (cis-/trans-). Although only (1), (2), and (4) have been experimentally demonstrated so far [The precise mechanism of how lncRNAs function still remains largely unknown. Currently, there are several hypothesis about it, including (1) RNA:DNA:DNA triplex  or in trans (on distantly located genes). The newly found eRNAs appear to exert their effects in cis by binding to specific enhancers and actively engaged in regulating mRNA synthesis [HOX clusters, and serve as signals of anatomic positions by expressing in cells that have distal and posterior positional identities; they both require the interacting partners to be properly localized to the site of action [HOXA brings HOTTIP into the spatial proximity of multiple HOXA genes, enforcing the maintenance of H3K4me3 and gene activation [Upon interaction with the target molecular, lncRNA may have the ability to guide it into the proper position either in ynthesis , 29. HOTf action . In thistivation . This lotivation , 45.HOTAIR binds the polycomb complex PRC2 to exert its “signal” function. A recent study found that the 3,700 nt of HOTAIR also interact with a second complex consisting of LSD1, CoREST, and REST to antagonize gene activation, further emphasizing its important role as the scaffold of the functional complex [Recent studies found that several lncRNAs have the capacity to bind more than two protein partners, where the lncRNAs serve as adaptors to form the functional protein complexes. The telomerase RNA TERC (TERRA) is a classic example of RNA scaffold, and is essential for telomerase function.  complex , 51. cis- or trans-regulators depending on whether it exerts its function on a neighboring gene on the same allele from which it is transcribed [cis-regulators, as the expression of lncRNA is significantly correlated with their neighboring protein-coding genes [p21 [Sox2 [cis-regulation. This was supported by the fact that knock down of different number of lncRNAs had little effect on the expression of neighboring genes [cis-regulators [trans-regulators [cis-regulating lncRNAs were found to have the capacity to act in trans [lncRNAs can be classified as nscribed . It was ng genes , 98. Hownes [p21  and linc21 [Sox2 ), positi21 [Sox2 ), transcng genes . In genegulators , 100, whgulators , 11, 93.in trans , 102, hiAlthough substantial research progresses have been made since the discovery of lncRNAs, it still remains a challenge to understand the functions of lncRNAs. One reason is, unlike protein-coding genes whose mutations may result in severely obvious phenotypes, mutations in lncRNAs often do not cause significant phenotypes . It is lThe current lncRNA databases are summarized in Computational prediction of lncRNA functions is still at its early development stage. Unlike protein-coding genes whose sequence motifs are indicative of their function, lncRNA sequences are usually not conserved and do not contain conserved sequence motifs , 104. ThAlthough most lncRNAs are not conserved, there are lncRNAs that are conserved across species, indicating their essential functions. Amit et al. identified 78 lncRNAs transcripts conserved in both human and mouse, and found 70 are either located within or close (<1000 nt distance) to a coding gene that is also conserved in the two genomes . They asMany studied lncRNAs play important regulatory roles, and it is likely that lncRNAs regulating a specific biological process may be coexpressed with the genes involved in the same process. Thus, identifying coding genes that are coexpressed with lncRNAs may help to infer the function of lncRNAs. Based on this assumption, Guttman et al. developed a coexpression based method to predict lncRNAs functions at genome scale . For eacLiao et al. furthered the coexpression idea by constructing a coding-noncoding (CNC) gene coexpression network . In contRecent analysis found that lncRNAs share a synergism with miRNA in the regulatory network , 109. ItComputational prediction of lncRNA functions is still at its primary stage. As the sequence and secondary structure of lncRNAs are generally not conserved, function prediction of lncRNAs mainly relies on their relationships with other moleculars, such as protein coding genes, miRNAs, and proteins. However, the molecular mechanism of how lncRNA function by interacting with other molecular remains largely unknown, making it difficult to develop computational methods to precisely predict the functions of lncRNAs. On the other hand, there are currently only a small number of lncRNAs whose functions are well understood, which makes it difficult to validate and optimize computational algorithms for predicting lncRNA functions. Finally, unlike protein-coding genes that have systematic functional annotation systems, there lacks an annotation system for lncRNA functions, making it difficult to evaluate computational algorithms for function prediction. Nevertheless, the success of predicting lncRNAs using the coexpression based approach has shown promises. With more functional genomics data about lncRNAs available in the near future, more powerful and accurate methods will be developed to help decipher the functions of lncRNAs. It has been widely accepted that lncRNAs play important functional roles in cell, though the molecular mechanism of how lncRNAs function remains to be unraveled. In this paper, we have described several currently proposed models about the molecular mechanism of lncRNA functions. One commonality about these models is that lncRNAs function through the interaction with other molecular, including DNA, RNA, and proteins. Given the abundance of lncRNAs in genome, it is likely that the interaction between lncRNAs and other moleculars may be specific. This thus raises the possibility of developing novel methods to target certain lncRNA for gene-specific regulation. However, phenotypic studies of lncRNAs suggested that knockdown of many lncRNAs does not result in obvious phenotypes, making it difficult to understand their functions. Computational prediction of lncRNAs can provide hypothesis about the functions of lncRNAs, and help to design experiments to test them under specific conditions. Yet, it remains a significant challenge to develop effective methods to accurately infer the lncRNA functions, owing to the lack of detailed information about the molecular mechanisms of lncRNAs. In order to develop powerful computational methods, more studies about the derivation of lncRNAs, the molecular mechanism of lncRNAs and tissue-specific, or development-specific expression about lncRNAs are necessary.


The data suggest that either the RNA sequence or the ratio of viral proteins resulting from different levels of frameshifting affects viral replication.In previous studies, differences in the amount of genomic and subgenomic RNA produced by coronaviruses with mutations in the programmed ribosomal frameshift signal of ORF1a/b were observed. It was not clear if these differences were due to changes in genomic sequence, the protein sequence or the frequency of frameshifting. Here, viruses with synonymous codon changes are shown to produce different ratios of genomic and subgenomic RNA. These findings demonstrate that the protein sequence is not the primary cause of altered genomic and subgenomic RNA production. The synonymous codon changes affect both the structure of the frameshift signal and frameshifting efficiency. Small differences in frameshifting efficiency result in dramatic differences in genomic RNA production and TCID Coronaviridae, along with Arteriviridae and Roniviridae, belong to the order Nidovirales. Viruses belonging to these families are large positive strand RNA viruses and are known to infect mammals, birds, fish and arthropods [thropods . Entry ithropods . After ethropods  and refethropods . CoronavThe two largest open reading frames, ORF1a and ORF1a/b, are translated from the full-length RNA. These open reading frames (ORFs) encode polyproteins pp1a and pp1ab which are cleaved by self-encoded proteases. The proteins encoded in ORF1a and ORF1a/b function as the replicase, making subgenomic RNAs and new copies of the genomic RNA . ProductThe SARS coronavirus frameshift signal has a seven nucleotide ‘slippery sequence’ and a stimulatory pseudoknot separated by a spacer region. During programmed -1 ribosomal frameshifting (-1PRF), the tRNAs positioned on the slippery site uncouple from the mRNA and reconnect in the new reading frame. The second stem of the stimulatory pseudoknot is formed by the distal 3’ sequence base-pairing with residues in the loop region of the first stem loop. Unlike other frameshift-stimulating pseudoknots the SARS pseudoknot contains an additional internal stem loop ,9,10. ThIn an effort to further our understanding of how the SARS frameshift signal functions we performed deletion and mutagenesis studies an analyzed the effects on frameshifting efficiency, RNA structure and viral RNA production. There are at least three different types of structures used by the coronaviruses to stimulate frameshifting. Some coronaviruses, such as SARS-CoV, use a three stemmed pseudoknot, others such as human coronavirus 229E have an ‘elaborated pseudoknot’ or kissing stem-loops, while the avian infectious bronchitis virus utilizes a two stemmed pseudoknot ,13. ThatThe individual residues and each stem in the SARS pseudoknot make unique contributions to frameshifting efficiency ,9,10. Foet al., for another coronavirus [Experiments were designed to test the effects of changes to the length of stem 3 while still maintaining a stable structure. Beginning with the wild-type stem 3 containing nine paired bases and a nine residue loop, the following series of mutants were constructed. First, the loop capping stem 3 was replaced with a shorter tetraloop; the L2Tetra construct contains ten paired bases and a four residue loop. This was used as the basis for construction of two additional tetraloop constructs with progressively shorter stem 3 structures: L2Tetra/S3-4bp, containing only four paired bases in the stem, and L2Tetra/S3-2bp, which has only two paired bases. The 5’-uucg-3’ tetraloop sequence used in the L2Tetra/S3-4bp mutant was selected because the last base-pair before the tetraloop affects its formation. The closing pair for UNCG tetraloops is usually C-G compared to the G-C closing base-pair found in CUUG tetraloops. All of the truncated structures promoted frameshifting at levels equal to or greater than the wild-type pseudoknot structure using a dual luciferase reporter assay . Interesonavirus  indicateversus 2.77%, p = 9.3 × 10−19, versus 3.1%, p = 1.1 × 10−19). Interestingly, removal of both adenosines (L2TetraS2ΔS3Δ) partially restored frameshifting with only a 28.2% reduction in frameshifting . Bulged adenosines, which are known to bend helices [versus 1.06%, p = 1.7 × 10−18), similar to that observed with the removal of the stem 2 bulge alone  by mutating three residues in the third codon position so as to retain the amino acid sequence of the frameshift protein pp1ab  analysis . The negative control S2D did not form apseudoknot-like structure (data not shown). The L2-UCC loop is more structured than the wild-type loop and this reduced flexibility appeared to affect the remaining structure of the pseudoknot as shown by diminished reactivity to NMIA at and around the mutated bases in the loop (L2) compared to the wild-type structure B. AdditiHaving established this baseline, S3D and S2ΔS3Δ mutants were evaluated by SHAPE. In contrast to the L2-UUC pseudoknot mutant, the S3D and S2ΔS3Δ pseudoknots were more reactive to NMIA indicating that they are less structured D. In add50 values were below the limit of the assay  of L2-UCC accumulates to wild-type levels four days post-infection C. In con50 and gRNA values as observed for the S3D mutant. Although the efficiency of frameshifting is similar for the L2-UCC and S3D mutants the gRNA levels differ by several log. The stabilization of L2-UCC stem 3 structure and loss of S3D stem 3 structure  procedure of Wilkinson et al. [2O, 4 μL 5M NaCl, 1 μL 20 mg/mL glycogen, 2 μL 100 mM EDTA pH 8.0, and 350 μL ethanol overnight at −80ºC. After centrifugation the RNA was resuspended in 14 μL of 0.5 × TE. The oligonucleotide 5'-GCCGGGCCTTTCTTTATG-3'  was labeled with 30 μCi gamma 32 P-adenosine triphosphate (ATP) using T4 kinase  and purified through a G-25 column . RNA (7 μL) was incubated with 32P-labeled oligonucleotide (3 μL) at 65 ºC for 5 minutes, 35 °C for 5 minutes and cooled to 0 ºC. Reverse transcription reactions were performed using SuperScript III enzyme  at 52 ºC for 20 minutes. Products were separated on an 8% polyacrylamide, 7.5M urea gel in TBE buffer, dried and visualized by PhosphorImage analysis.RNA Structure Probing. The structures of the pseudoknots were probed using the n et al. . SHAPE aDual Luciferase Assays. For each assay cells were transfected with either a test plasmid or a control plasmid (described above) using FuGene6 from Promega . VeroE6 cells were grown overnight in Dulbecco's Modified Eagle Medium supplemented with 10% FBS at 37 ºC. Cells were disrupted using the passive lysis buffer  as per the manufacturer’s instructions. Luminescence reactions were initiated by addition of 10 – 20 μL of cell lysates to 100 μL of the Promega LAR II buffer and completed by addition of 100 μL Stop’n’Glo reagent. Luminescence was measured using a Turner Design TD20/20 instrument. At least three replicates were performed within each assay and all assays were repeated at least three times until the data were normally distributed to enable statistical analyses both within and between experiments. The frequency of frameshifting is expressed as a ratio of firefly to Renilla luciferase from a test plasmid divided by the analogous ratio from the read-through control plasmid multiplied by 100%. Fold change, standard error and estimates of the P-values for ratiometric analyses were performed as described previously .In line with previous studies of coronavirus frameshift signals, data presented here indicate that the sequence and length of the third stem is not critical for frameshifting efficiency ,12,18. HWhile many sequences involved in coronavirus subgenomic RNA synthesis have been elucidated, sequences and structures involved in regulating coronavirus genomic RNA synthesis are less well defined . InternaPackaging signals for coronaviruses are also ambiguous and have been identified in the 5’ untranslated regions and the 3’ end of ORF1a/b ,30. Our The correlation between stem 3 structure and gRNA levels indicates that a structural requirement in stem 3 is required for optimal RNA production but we cannot, at this stage rule out a frameshifting threshold similar to that observed for the yeast double-stranded RNA virus M1 . That isA slight difference in frameshifting efficiency, such as that between the L2-UCC and S3D mutants, would not be expected to have such significant effects on viral RNA levels. Similar to previously described viruses containing mutations in the slippery site of the frameshift signal , here we


In the past few years, RNA molecules have been revealed to be at the center of numerous biological processes. Long considered as passive molecules transferring genetic information from DNA to proteins, it is now well established that RNA molecules play important regulatory roles. Associated with that, the number of identified RNA binding proteins (RBPs) has increased considerably and mutations in RNA molecules or RBP have been shown to cause various diseases, such as cancers. It is therefore crucial to understand at the molecular level how these proteins specifically recognise their RNA targets in order to design new generation drug therapies targeting protein–RNA complexes. Nuclear magnetic resonance (NMR) is a particularly well-suited technique to study such protein–RNA complexes at the atomic level and can provide valuable information for new drug discovery programs. In this article, we describe the NMR strategy that we and other laboratories use for screening optimal conditions necessary for structural studies of protein-single stranded RNA complexes, using two proteins, Sam68 and T-STAR, as examples. For example, recent studies from the ENCODE project (RNA molecules are single stranded in cells and a majority of RBPs recognise and bind short single-stranded RNA (ssRNA) motifs through specific contacts with the nucleic acid bases. The two major techniques to solve structures of macromolecular complexes such as protein-ssRNA complexes are X-ray crystallography and nuclear magnetic resonance (NMR). While overall, NMR contributes to around 10% of the structures deposited into the protein data bank (PDB), the contribution of NMR for structure determination of protein-ssRNA complexes is 53% . This emphasises that NMR is a major technique for the investigation of such complexes. This fact can be explained by numerous intrinsic properties of protein-ssRNA complexes In this article, we will describe the strategy that we and other NMR laboratories commonly use to define optimal protein constructs and RNA sequences for structural studies, using Sam68 and T-STAR proteins as examples.22.1260 using the theoretical extinction coefficient provided by Dharmacon. Typical RNA stock concentrations ranged between 1 and 4 mM.The RNA oligonucleotides used for NMR studies were chemically synthesised at a 1 micromole scale , deprotected according to manufacturer instructions, and lyophilised. RNAs were then resuspended in 100 μl of water and pH was adjusted to 6.5 or 7.0. RNA concentration was measured by OD2.2http://www2.le.ac.uk/departments/biochemistry/research-groups/protex) using the pLeics03 expression vector that contains an N-terminal poly-histidine tag followed by a tobacco-etch virus protease (TEV) cleavage site. All plasmid constructs were verified by DNA sequencing . Recombinant plasmids were transformed into Rosetta BL21 DE3 cells and expressed in 4 litres of 2TY medium or M9 minimal medium supplemented with 15NH4Cl. At an optical density of 0.5, cultures were transferred to an incubator at 20 °C for 1 h and protein expression was induced with 400 μM IPTG for 16 h at 20 °C.Sam68 STAR (amino acids 97-283), KH-QUA2 (150-283) and KH (150-260) domains and T-STAR STAR (1-183), KH-QUA2 (50-183) and KH (50-160) domains  were clo−1 for crystallisation screenings. Protein concentrations were estimated by measuring the OD280 and using a theoretical extinction coefficient (web.expasy.org/protparam/) derived from the protein sequence. RNAse activity was evaluated using Ambion RNAseAlert Lab Test kit according to manufacturer instructions. It is important to note that RNAse inhibitors should not be added to the final NMR sample because the storage buffer contains components with non-labile protons that interfere with the NMR measurements of the proteins and RNAs.The proteins of interest were purified by affinity chromatography using Ni-NTA agarose (Qiagen) followed by TEV cleavage during overnight dialysis in phosphate buffer  at 4 °C. Because short ssRNA oligonucleotides are easily prone to degradation, 5 μl SUPERase IN RNase Inhibitor (Invitrogen) was added to the protein sample that was further purified by size-exclusion chromatography on a Superdex 75 10/300  into the desired buffer for NMR analysis (see Section 3). Selected fractions were pooled and concentrated (Millipore 10 kDa centricon) to approximately 0.2 mM for NMR studies or to approximately 10 mg ml2.32O. NMR measurements were performed using Bruker AVIII-500 MHz, AVIII-600 MHz, AVIII-600 MHz (equipped with a cryoprobe) and Avance-800 MHz (equipped with a cryoprobe) spectrometers. Data were processed using XWINNMR (Bruker) and analyzed with Sparky (http://www.cgl.ucsf.edu/home/sparky/).NMR samples consisted of 330 μl of proteins at concentrations of at least 200 μM in different buffers and 20 μl of D15N–1H HSQC experiments for visualizing the 15N-labeled protein signals, and 2D-TOCSY and 2D-NOESY experiments to visualise the RNA signals and the presence of intermolecular NOEs. TOCSY and NOESY experiments were recorded in D2O with mixing times of 50 and 150 ms, respectively.Optimisation of the buffer and temperature conditions as well as the protein constructs and RNA sequences were evaluated using 2D 2.4−1) using the Douglas Instrument Oryx 4 robot. For optimisation, T-STAR KH crystals were grown by sitting drop vapour diffusion at 4 °C in 200 mM ammonium sulfate, 100 mM HEPES pH 7.5 and 20% PEG 3350. Crystals were flash-frozen in mother liquor containing 15% MPD as a cryoprotectant. The KH–AAAUAA complex was crystallised in 2 M lithium sulfate and 100 mM Tris pH7.0 at 4 °C using the sitting drop vapour diffusion at a protein concentration of 15 mg mL−1 (protein:RNA molar ratio of 1:1.5). Data were collected on single crystals at the diamond synchrotron beamline I04 and microfocus beamline I24 and processed using XDS Six different crystallisation screens  have been used with different protein and RNA concentrations  contains an amide group in its backbone, this spectrum is often referred as the NMR fingerprint of the protein and can be used to optimise the protein construct and the buffer conditions of the sample. Indeed, the quality of the 15N–1H HSQC depends on the folding and stability of the protein, that in turn is dependent on various parameters such as protein concentration, type of buffer, salt concentration, pH and temperature.NMR spectroscopy is limited by the size of the system under study. The upper molecular weight limit for structure determination is currently approximately 50 kDa, which means that it is often not possible to study a full-length protein by NMR. Fortunately, most RBPs are composed of small structurally independent domains that are sufficient for RNA binding. It is therefore possible to subclone RBPs into distinct domains whose size is suitable for NMR studies. If little is known about this RBP, potential functional domains can be identified using multiple sequence alignment algorithms, secondary structure prediction and identification of conserved domains. For well-characterised RBDs, specific constructs can easily be designed. Different protein constructs, although highly homologous, can however behave differently and various expression and purification strategies may have to be attempted in order to obtain a highly concentrated, pure and soluble sample suitable for NMR studies. If protein yield is too low following overexpression in a bacterial host, it may be that the construct is toxic to the cells or prone to aggregation. In this case, adjustment of the domain boundaries, use of an alternative affinity tag or use of a solubility tag may result in a more stable sample l-Arginine and l-Glutamate 15N–1H HSQC. Below pH 5, the acidic condition might induce protein unfolding and aggregation. It should also be noted that the pH should be different from the isoelectric point (pI) of the protein or protein domain by at least 0.5 to avoid problems of solubility. Finally, compounds that are protonated or that increase the viscosity of the buffer, such as glycerol, must be avoided.Various types of buffer are suitable for NMR studies and some parameters must be taken into consideration when optimizing buffer conditions. Ideally, the buffer used should not be protonated. Indeed, the concentration of buffer is generally higher than that of the protein and, if protonated, the buffer NMR signals will interfere with the protein signals. The most commonly used NMR buffer is sodium phosphate at a concentration ranging between 10 and 50 mM. Alternatively, other buffers such as Tris–HCl or HEPES can be used at similar concentrations. As these buffers contain protons, it is preferable to purchase them in a deuterated form. NMR sensitivity is inversely correlated with the ionic strength of the buffer. Typically, the ionic strength should be minimal and not exceed 150 or 200 mM sodium or potassium chloride. If protein solubility requires high ionic strength, a high concentration of salt can efficiently be replaced by low-conductivity salts such as 50 mM 15N–1H HSQC in standard buffer conditions  and estimate the quality of the spectrum. A screen of conditions is then applied where the pH is varied from 5.5 to 7.5 and the salt concentration from 0 to 200 mM NaCl. For each condition, a 15N–1H HSQC is measured at 20, 25 and 30 °C and the quality of the spectrum is estimated based on the number and the line width of the visible crosspeaks , and shorter constructs containing the KH (50–160) and the KH-QUA2 (50–183) domains. In all cases, the protein constructs expressed very well in 15N–1H HSQC experiment at 20 °C. Despite the fact that the protein is soluble in this buffer condition, the 15N–1H HSQC spectrum was poorly defined (15N–1H HSQC spectrum (data not shown). We then expressed and purified a truncated version of the STAR domain, the KH-QUA2, which lacks the QUA1 dimerisation domain but is expected to be sufficient for RNA binding. This construct was also soluble at suitable NMR concentrations in various buffer conditions and the quality of the 15N–1H HSQC spectrum improved dramatically  and that the QUA2 region is flexible since most additional crosspeaks of KH–QUA2 are located in the center of the spectrum and more intense than the crosspeaks corresponding to the KH domain . This experiment is crucial in NMR structure determination for obtaining inter-proton  distance restraints.Chemical shift perturbation experiments performed using in vitro method consisting of a series of selection cycles of interacting RNA from a randomised oligonucleotide library. This generally allows for the identification of a consensus RNA sequence bound by the protein of interest 15N–1H HSQC In order to design a pool of RNA targets for NMR screening, prior knowledge of the protein–RNA specificity is highly desirable. Several biochemical methods allow the identification of specific RNA sequences bound by RBPs, including footprinting, Systematic evolution of ligands by exponential enrichment (SELEX) or Cross-linking immunoprecipitation (CLIP) techniques. Footprinting experiments have been used for decades, using enzymes or chemicals that specifically cleave RNA molecules at certain positions, allowing the investigation of RNA structures and the identification of RNA sequences specifically bound by RBPs Each of these techniques provides useful preliminary information to define a pool of RNA sequences to screen for protein–ssRNA complex structure determination. It should be noted that consensus sequences derived from CLIP, SELEX or SIA are often degenerate and differ from natural sequences bound by RBDs. Nonetheless, the optimal RNA sequence for structure determination is not necessarily found naturally, nor has the highest affinity for the protein. This is due to the fact that a precise structure determination of a protein–RNA complex requires a single and stable conformation of the complex. For example, natural and/or high affinity RNA sequences often contain multiple, similar, and juxtaposed binding sites and are not suitable for structural work because the protein can bind these sequences in multiple registers leading to an inhomogeneity of the sample and a loss of NMR signal. It is therefore crucial to identify the optimal RNA sequence that has reasonably high affinity to obtain a stable complex, specificity to obtain a homogeneous complex, and is still similar to natural sequences to derive biologically relevant structural information.Structure analysis of various KH domains in complex with DNA or RNA showed that the classical nucleotide binding pocket of KH domains accommodates 4 nucleotides and structures of these complexes were solved with DNAs or RNAs varying from 4 to 12 nucleotides in length 3.2.115N–1H HSQC spectra of these domains were of excellent quality. In contrast, T-STAR STAR construct resulted in poor NMR spectra. As it has been shown for other STAR proteins that the KH–QUA2 region is sufficient for RNA binding 15N–1H HSQC experiment of a 0.2 mM sample of the free protein as reference. RNA was then gradually added to the protein sample at different molar ratios (protein:RNA ratio of 1:0.5 and 1:1). In all cases, the pH of the RNA stock solution was adjusted to correspond to the pH of the protein solution and RNAs were prepared at high concentration (up to 4 mM) to restrict the issue of RNA to be added to the protein and avoid a dilution of the protein that could affect the chemical shifts.As mentioned in Section 3.1.1, T-STAR KH and T-STAR KH–QUA2 constructs are highly soluble and stable, and the 15N–1H HSQC spectrum, some peaks disappearing and others changing position, clearly indicating that these RNA sequences are able to bind T-STAR KH–QUA2 . In these conditions, the amide protons of the protein exchange with deuterium and the RNA crosspeaks can easily be analyzed. TOCSY spectra were used to identify the crosspeaks of the uridine H5–H6 bases. As expected, the TOCSY spectra of the AAAUAA and AAAUUU RNAs in complex with T-STAR KH displayed one and three crosspeaks, respectively, indicating that the uridine bases experience a single chemical environment when bound to the protein . Crosspeaks from this region of the spectrum to the RNA sugar region (3–6.5 ppm) arise from RNA base protons in close proximity to RNA sugar protons (intra-RNA NOES) while crosspeaks to other regions of the spectrum (0–3 ppm) arise from RNA base protons in close proximity to protein protons (intermolecular NOES). As shown in TOCSY and NOESY NMR experiments have also been used to investigate the NMR signal quality of the different RNA sequences in complex with T-STAR KH. As most RNA base protons are non-labile and have chemical shift values overlapping with the amide protein protons and with water, these experiments were recorded in 100% D protein A. In con protein A. This i3.2.215N–1H HSQC spectrum of Sam68 KH, indicating complex formation. Furthermore, the same peaks of Sam68 KH were affected by the addition of RNA, suggesting that the same residues are involved in binding. Different RNA sequences led to a combination of intermediate and fast exchange regimes, with many peaks disappearing and others shifting upon RNA addition. Surprisingly, crosspeaks in fast exchange shifted in different directions depending on the RNA sequence used . It was however sufficient to identify changes in the protein spectrum upon addition of increasing amounts of RNA. This suggested that, as for T-STAR, the KH domain of Sam68 is sufficient for RNA binding. We tested different 6mer RNAs designed according to SELEX and published biological data . Interesnce used B–D, indince used E. In thiRecently, we have produced samples of the STAR domain that are stable F. These 3.3−1 in our optimised NMR buffer. We obtained various hits and optimised the conditions in order to obtain protein crystals of sufficient size. Our optimised crystals were rectangular and diffracted to a resolution of 1.6 Å , we set up crystallisation screens for this domain using six commercially available screens and protein concentrations ranging from 10 to 20 mg mlof 1.6 Å A. IntereUsing NMR chemical shift perturbation experiments, we have tested a large number of RNA sequences for binding T-STAR KH (Section 3.2.1) and concluded that the AAAUAA RNA sequence was the most suitable candidate for the structure determination of T-STAR KH in complex with RNA (Section 3.2.1). We therefore initiated a crystallisation trial of T-STAR with various 6-mer RNA sequences. Interestingly, only the complex of T-STAR with the AAAUAA RNA crystallised. In this case, crystals were hexagonal and diffracted to a resolution of 2.0 Å B. Intere4Over the past few years, there has been an increasing interest in RNA biology and RNA binding proteins. Structural studies of protein–RNA complexes are therefore needed if we want to understand how proteins recognise specifically their RNA targets and to derive a general code for RNA recognition


Fasciola hepatica is one of the most important parasites affecting animal health all over the world, causing the so called liver fluke disease (fascioliasis). Infections of mammals can only occur via larvae of the fluke, which live on plants in moistly grassland. Therefore infections of humans are rather rare, but infections of animal stock still lead to large financial losses. The drugs of choice used in the treatment of infected animals are benzimidazoles like triclabendazole, which prevent microtubule formation. During the last decades more and more flukes resistant to these drugs have been found. Beside mutations in the target proteins (β-tubulin), detoxificationmechanisms via ABC (ATP-binding cassette) transporters are thought to be responsible for these resistances. Up to date little is known about proteins of the fluke. We therefore try to isolate yet unknown proteins, focusing mainly on ABC transporters, of the fluke, to investigate their potency as putative new drug targets.The liver fluke Fasciola hepatica was cloned from isolated fluke RNA. Heterologous expression was used for further characterization of the transporter in various cell lines. Polyclonal antibodies were raised to allow localization of the transporter by immunfluorescence microscopy.Adult flukes were collected from bovine liver at a slaughter house in lower Austria. An ABC transporter of Fasciola hepatica ABC transporter showed mainly intracellular localization in various cell lines, including mammalian and insect cell lines. Some members of the B-family of ABC transporters, like the mitochondrial transporters, ABCB10, are known to be expressed intracellularly. We have not determined a substrate of the transporter yet, but if the expression pattern holds true for the subcellular expression of the native fluke transporter, this transporter can not be responsible for resistances to anthelmintic drugs in the liver fluke. We have two other ABC transporters identified by sequence comparison and cloned from fluke RNA which will be analyzed in similar ways.We could clone a full length ABC transporter consisting of twelve transmembrane domains, showing high homology to members of the B-type family of ABC transporters. Heterologous expression of this


Hammerhead ribozymes are small self-cleaving RNAs that promote strand scission by internal phosphoester transfer. Comparative sequence analysis was used to identify numerous additional representatives of this ribozyme class than were previously known, including the first representatives in fungi and archaea. Moreover, we have uncovered the first natural examples of “type II” hammerheads, and our findings reveal that this permuted form occurs in bacteria as frequently as type I and III architectures. We also identified a commonly occurring pseudoknot that forms a tertiary interaction critical for high-speed ribozyme activity. Genomic contexts of many hammerhead ribozymes indicate that they perform biological functions different from their known role in generating unit-length RNA transcripts of multimeric viroid and satellite virus genomes. In rare instances, nucleotide variation occurs at positions within the catalytic core that are otherwise strictly conserved, suggesting that core mutations are occasionally tolerated or preferred. The expanding diversity of noncoding RNA discoveries is revealing a broader spectrum of roles RNA plays in cellular signaling and in biochemical functions. These discoveries in part are being facilitated by the expanding collection of genomic sequence data and by computational methods used to search for novel RNAs. In addition to searching for new classes of structured RNAs, these methods can be used to reevaluate the distributions of long-known RNAs. We have used a bioinformatics search strategy to identify many novel variants of hammerhead self-cleaving ribozymes, including examples from species in all three domains of life. New architectural features and novel catalytic core variants were identified, and the genomic locations of some hammerhead ribozymes suggest important biological functions. This ribozyme class promotes RNA cleavage by an internal phosphoester transfer reaction by using a small catalytic core. The simple sequence and structural architecture coupled with the general utility of RNA strand scission may explain its great abundance in many organisms. Hammerhead ribozymes 2+. Specifically, tertiary interactions form between the loop of stem II and either an internal or terminal loop in stem I that increase activity of the core by several orders of magnitude under low magnesium conditions. Identification of this tertiary substructure in high-speed hammerhead ribozymes Although a minimal three-stem junction constitutes the catalytic core of the ribozyme , additioSchistosoma mansoniArabidopsis thaliana Several searches for new examples of hammerhead ribozymes have been performed previously Using a combination of homology searches we found thousands of new hammerhead ribozyme sequences in all domains of life. These ribozymes are observed in the eubacterial and archaeal domains, as well as in fungi and humans. Moreover, many of the newfound hammerhead ribozymes exploit a pseudoknot interaction to form the tertiary structure necessary to stabilize the positioning of stems I and II. We also identified a number of active sequence variants that suggest the hammerhead consensus is more variable than previously thought.glmS ribozymes Although the biological functions of these hammerhead ribozymes remain unproven, some could be involved in gene regulation based on their genomic contexts, similarly to what has been proposed for the mouse hammerhead and human HDV ribozymes We used a comparative genomics pipeline Dolichopoda (cave crickets) Schistosoma mansoni (parasitic worm) Aedes aegypti (mosquito), Nematostella vectensis (sea anemone), Xenopus tropicalis (frog) and Yarrowia lipolytica (fungus) also appear to have hammerhead ribozymes associated with interspersed repeat elements, which are found in multiple copies in their genomes. Interestingly, we uncovered instances of this motif in humans, and the genetic contexts of two of these are conserved among many mammals.A large number of additional hammerhead ribozymes were identified in metazoans, including mosquitoes and sea anemones. While many hammerhead ribozymes associated with repeated elements were previously found in various species of Our search efforts also extended the range of known hammerhead ribozymes beyond the eukaryotic domain of life. At least three representatives are present in archaea and hundreds are present in bacteria , where mHowever, several novel features for this ribozyme class were observed among the expanded list of representatives. Among the notable variants are the first natural examples of “type II” hammerhead architectures , whereinThe type II hammerhead consensus identified in our bioinformatics search included a putative conserved pseudoknot linking the loop of stem I with the 3′ tail extending from the right shoulder of stem II . Indeed,On many occasions, multiple hammerhead ribozymes are arranged in close proximity to flank individual genes or short blocks of genes in prophage genomes, although the identities of these genes are not constant . Such arClostridium scindens, Azorhizobium caulinodans  for RNA cleavage of greater than 1.2 min−1 under single-turnover conditions and simulated physiological conditions . All deletions or other mutations that are predicted to disrupt the pseudoknot substructure drastically reduce cleavage activity  ribozyme sequence exhibits an observed rate constant  was removed to create a consensus catalytic core undergoes nearly quantitative self-cleavage during transcription (data not shown). Thus, the unusual insertion sequence in this saltern-derived hammerhead ribozyme impairs cleavage activity under standard assay conditions.To examine whether these alterations of the catalytic core reflect adaptations to hypersaline conditions, we prepared wild-type and mutant versions of HHmeta  derived kobs for self-cleavage of HHmeta in a high concentration of monovalent ions alone. HHmeta undergoes self-cleavage with a kobs of 4×10−4 min−1 in 4 M LiCl (data not shown). For comparison, a consensus hammerhead ribozyme catalyzes strand scission with a kobs of 0.17 min−1 under similar conditions kobs that is 425-fold faster than that of the saltern-derived variant.To test whether elevated salt concentrations can rescue this deficiency, we first determined the kobs values over a range of Mg2+ concentrations. The activity of the variant increases with increasing Mg2+ levels .Our homology searches reveal the presence of nine regions in human genomic DNA that conform to the consensus for hammerhead ribozymes  of C10orf118 , a negative regulator of certain metalloproteinases involved in tumor suppression RECK hammerhead also tested positively for cleavage in vitro , suggesting that this ribozyme may be active in vivo.The second human hammerhead we subjected to further analysis, termed “in vitro . AccordiThe application of increasingly powerful bioinformatics algorithms to the expanding collection of DNA sequence data is facilitating the discovery of novel noncoding RNAs and revealing new locations for previously known examples. A recent report Previous in vitro selection studies demonstrated that hammerhead ribozymes are among the first self-cleaving motifs to emerge from random-sequence populations Although the hammerhead consensus is highly conserved, there are rare instances in which the catalytic core is altered. Previous studies have established that mutations at most positions in the core resulted in drastic loss of activity The diversity of structural alternatives observed in our hammerhead collection hints at the inherent difficulty in any effort to comprehensively identify ribozyme representatives. Including more core variations or distal structure variations in search outputs will result in larger numbers of false positives. Given the simplicity of the motif, sequences that conform to the consensus are expected to occur by chance in large sequence databases, even if some of them might be incapable of folding into an active hammerhead.Aedes and Nematostella, organisms in which active hammerheads might require dimeric conformations . For most organisms, this divalent magnesium concentration is not attained. However, for microbes inhabiting certain environments, such as the Dead Sea or high salinity zones of solar salterns, growth has been reported in extracellular MgCl2 concentrations ranging from 0.6 to >2 M 2+, estimates of the intracellular Mg2+ concentrations range as high as 0.42 M For some hammerhead variants such as HHmeta, activity may be facilitated by extreme salt concentrations. In vitro assays reveal that HHmeta requires at least 75 mM MgClAlternatively, it is possible that HHmeta and related variants have been selected to function as gene control elements that modulate the expression of associated genes in response to fluctuating intracellular salt concentrations. HHmeta was identified in a metagenome survey as part of a short sequence fragment, and therefore its genomic context is unknown. However, the structurally analogous hammerhead ribozyme variant HHphage , which rDilution of the environment is highly toxic for obligate extreme halophiles. Accordingly, for certain bacteriophages that infect these organisms, virulence is tightly controlled in response to salt concentrations The discovery of thousands of new hammerheads in all three domains of life provides many opportunities to examine the functions and biological utilities of these ribozymes in their natural contexts. The activities of some representatives may be regulated by RNA folding changes induced by changes in protein, metabolite, or metal ion concentrations, similar to the structure modulation observed with riboswitches. Previous engineering efforts produced numerous examples of allosteric hammerhead ribozymes or other RNAs, establishing a precedent for ligand-mediated regulation of ribozyme function Type II hammerheads were uncovered by a comparative genomics method described previously i.e., N10.1–N11.1, N1.1–N2.1, and A15.1–U16.1) also led us to reject the hit. Finally, multiple mispairs or bulges in short stems resulted in candidate disqualification. The list of rejected hits consisted mainly of cryptic mutant hammerheads that are part of repeated elements, but those occurring in typical gene contexts  were often tested, as they were considered likely functional variants. Initially rejected hits were included in hammerhead alignments if activity could be measured.For final alignments, possible false positives were eliminated based on three criteria. First, any mutation in the core disqualified the hit. For this purpose, the consensus core was considered to be: C3, U4, G5, A6, N7, G8, A9, G12, A13, A14, A15, U16 and H17, where “N” means any nucleotide and “H” means A, C or U. Second, any mispairing directly adjacent to the core in stems I, II or III , Clostridium scindens (ATCC), PaP3 bacteriophage (kind gift of Professor Fuquan Hu) 2, 2 mM spermidine, 40 mM DTT, 2 mM of each ribonucleotide and 40 U µl−1 of purified T7 RNA polymerase. For ribozyme assays in trans, RNA was purified using denaturing PAGE, visualized by UV shadowing, and eluted in 200 mM NaCl, 10 mM Tris-HCl (pH 7.5 at 23°C), and 1 mM EDTA. RNA was then precipitated in ethanol, and the resulting pellet was rinsed in 70% ethanol and resuspended in water. Concentration was measured by UV spectrophotometry with a Nanodrop ND8000 (ThermoScientific).To produce in vitro transcription templates, PCR was performed using genomic DNA isolated from 32P]ATP according to the manufacturer's instructions. Labeled RNA was gel-purified as described, but visualized by autoradiogram.For 5′ labeling, RNA was dephosphorylated with calf intestinal phosphatase (NEB) according to the manufacturer's instructions. Following phosphatase inactivation at 94°C for 3 minutes, 1 pmole of dephosphorylated RNA was typically used for 5′-end-labeling with T4 polynucleotide kinase (NEB) and UTP and [α-32P]GTP. Incubations were continued at 37°C, and 1 µl aliquots were removed at various time points and added to 14 µl of stop buffer. Due to the initially low levels of incorporation of radiolabeled nucleotides, the earliest time point that can practically be assessed is 20 seconds. Note that, due to the requirements of T7 RNA polymerase, the Mg2+ concentrations used in these assays are considerably higher than those used for assays in trans. Note also that HHmeta, because of its requirement for particularly high Mg2+ concentrations, was able to be isolated in precursor form from standard in vitro transcriptions, and was subsequently assayed in cleavage assays in cis.To estimate Figure S1Clostridium scindens. Bands correspond to the expected sizes for hammerhead ribozyme cleavage . (B) Cleavage of internally radiolabeled RNA during in vitro transcription of PCR products from Azorhizobium caulinodans. Full length Azorhizobium caulinodans RNA is not detectable, presumably because of efficient ribozyme cleavage.Activities of RNA transcripts carrying multiple hammerhead ribozymes. (A) Cleavage of internally radiolabeled RNA during in vitro transcription of PCR products from (PDF)Click here for additional data file.Figure S2Agrobacterium tumefaciens multiple hammerhead ribozyme arrangement. (A) Genome context of ribozymes with annotations as follows: “tail” is a structural protein of the phage tail, “ardA” is an Anti-Restriction Defense protein, “marR” is a transcriptional regulation protein and “integrase” is a protein with predicted DNA integration activity. (B) Ribozyme cleavage during in vitro transcription using T7 RNA polymerase to produce internally-radiolabeled RNAs. Bands corresponding to expected sizes and compositions for hammerhead cleavage products are annotated.(PDF)Click here for additional data file.Figure S3Agrobacterium tumefaciens multiple hammerhead ribozyme activity in vivo. (A) Various RNA constructs expressed from plasmids carrying portions of A. tumefaciens hammerheads and expressed in E. coli BL21 cells. Various deletion or mutant constructs are expressed and examined by Northern analysis in the indicated lanes on polyacrylamide gel electrophoresis in panels .  Northern analysis of RNA products express in E. coli BL21 cells from the plasmids (pUC19 with the lac promoter removed) depicted in A. Transcription by T7 RNA polymerase was induced by IPTG and E. coli RNA polymerase was inhibited by rifampicin where indicated to maximize the amount of T7 RNA polymerase transcripts. After 2 h IPTG induction, RNA was extracted with Trizol according to the manufacturer's instructions (in the presence of EDTA). The RNA products were separated by denaturing agarose gel electrophoresis and the resulting gel was used for blotting. Probes for ORF 3, 2 and 1 were successively used to generate the images presented in , respectively. Because probes could not be entirely washed off the membrane between different probing experiments, there is some carry over from the probing in B to C to D. This explains why some bands corresponding to “ORF 2” can be seen in panel D for example.(PDF)Click here for additional data file.Figure S4Pseudoknot interaction between stems I and II. (A) Proportion of each type of hammerhead ribozyme that is predicted to form a pseudoknot. (B) Proportion of hammerhead ribozymes with a pseudoknot versus the length of stem II. (C) Proportion of ribozymes with a pseudoknot versus the difference in length between stems I and II (stem II number of base pairs subtracted from stem I number of base pairs). The highly repetitive type I hammerhead representatives are excluded from the analyses in B and C. Notes: There is some bias in the distributions of pseudoknots with the various hammerhead types and stem lengths. For example, stem length difference disparities between different types of hammerheads is especially striking in type III hammerheads where a two base pair difference between stems I and II commonly are associated with a pseudoknot, while other length differences are not. However, our data indicate that the pseudoknot contact generally is a structurally versatile way to constrain the locations of stems I and II.(PDF)Click here for additional data file.Figure S5Tertiary interactions between stems I and II. (A) Proportion of each type of hammerhead ribozyme that has predicted tertiary interactions noted in the diagrams . Notations: U, uridine; Nm, m number of any nucleotides; Y, pyrimidine; N, any nucleotide; R, pyrimidine; A, adenosine. (B) Proportion of each type of hammerhead ribozyme that have the tertiary interactions depicted in A either with or without a pseudoknot. Note that even when no tertiary interactions are predicted, some unknown interaction or a variation of a known interaction could exist.(PDF)Click here for additional data file.Figure S6Clostridium scindens hammerhead ribozymes are part of larger RNAs transcribed from PCR products. Non-native guanosine residues (lowercase) were added to facilitate transcription in vitro. Gene context is as follows: PaP3 and Bcep176 (phage intergenic regions); Clostridium scindens ; Solibacter usitatus .Complete sequences and secondary structure models of active hammerhead ribozymes that carry core variations. Positions diverging from the core consensus sequence are depicted in red. Variants were tested in trans or in cis as illustrated. Note that the RNA for the PaP3 bacteriophage and (PDF)Click here for additional data file.Figure S7Yarrowia lipolytica . Furthermore, some examples diverged from the consensus at more than one position .Secondary structures of examples of inactive hammerhead-like RNAs. Core nucleotides that differ from the consensus are depicted in red. Except for the (PDF)Click here for additional data file.Figure S8Aedes aegypti (A) and hypothetical dimeric conformation (B). Yellow box highlights loop III that can be completely base paired in a dimeric ribozyme (to form a 10 bp long stem). (B) Blue nucleotides correspond to one ribozyme and black to the other ribozyme. Green nucleotides correspond to one of the most common variants observed. (C) Palindromic character of stem III and loop III sequences for several ribozymes. The vast majority of these sequences would allow base-pairing in a dimeric hammerhead conformation like it has previously been described for ASBVd and newt hammerhead ribozymes Repeat-type putative hammerhead ribozyme arrangement and dimeric conformation. Secondary structures of a putative single hammerhead ribozyme of (PDF)Click here for additional data file.Figure S9C10orf118 expression analysis in human cell lines. (A) The C10 ribozyme is located within the first intron of the C10orf118 gene. The putative start codon of the protein is present at the beginning of the second exon. Several EST sequences have been identified within the first intron of this gene, as shown in the figure (short lines with accession numbers). (B) Expression pattern of EST sequences that map to the C10orf118 gene. %EST designates the proportion of C10orf118 EST sequences that are found in a tissue in comparison to all C10orf118 EST sequences. EST sequences were found in Genbank and GeneCards databases C10orf118 gene. (D) RT-PCR results showing expression of the C10orf118 mRNA in the different cell types indicated. The PCR primers were designed to anneal on the first exon of the gene.(PDF)Click here for additional data file.Table S1Primers used in this study.(PDF)Click here for additional data file.Text S1RNAMotif descriptors for hammerhead ribozymes.(DOC)Click here for additional data file.Dataset S1Multiple sequence alignments of all hammerhead sequences.(PDF)Click here for additional data file.