People’s choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network approach that explains a broad set of choice phenomena. We demonstrate that this approach can be used to compare different choice theories and integrates several choice mechanisms from established models. A basic setup implements bounded rationality, loss aversion, and inhibition in a natural fashion, which allows us to predict the occurrence of well-known choice phenomena, such as the endowment effect and the similarity, attraction, compromise, and phantom context effects. Our results show that this network approach provides a simple representation of complex choice behaviour, and can be used to gain a better understanding of how the many choice phenomena and key theoretical principles from different types of decision-making are connected. Traditional theories of choice assume the decision-maker as a homo economicus20, i.e., rational21. For choices to be rational all choice alternatives must be comparable and have transitive preference relations, so they can be ordered by the decision-maker. A second feature, and a central principle of rational choice theory, is that a rational decision-maker consistently chooses the outcome that maximises utility, or expected utility for risky or uncertain choices24. These assumptions clearly fail the scrutiny of everyday experience. To account for the observed inconsistencies, most models nowadays characterise choice as a probabilistic process29.The response behaviour of humans on (discrete) choice problems has been extensively studied in many fields of science, such as economics24 and the multinomial logit model21 for preference, and Bock’s nominal categories model30 for aptitude, are characterised by the following distribution for the choices:x from the set of possible alternatives S as a function of the utility of alternative x, 32 from statistical mechanics. For binary choice problems 34, or as the Rasch model9 in psychometrics:x over option y is strictly increasing in the utility of x and strictly decreasing in the utility of y. Several properties with respect to independence from irrelevant alternatives (IIA) and transitivity follow from simple scalability. The degree to which choices are considered rational from a probabilistic perspective, is often described by the extent to which the choice probabilities for a set of choice alternatives possess these properties37.A prominent group of probabilistic choice models, such as Luce’s strict utility modeli, then a model is strict if 24 for a comprehensive treatment of these properties.For example, the weakest form of IIA is regularity, which implies that the probability of choosing an alternative can never increase by adding more alternatives. A set of preference probabilities is regular if 52. Another example is the endowment effect that describes the tendency of people to perceive an alternative as having increased in value after they have chosen it3. We will discuss these violations and phenomena in more detail later.Although models with simple scalability have statistically desirable properties, their assumptions are often violated in reality. In the preferential choice literature for example, violations of IIA known as the similarity, attraction, compromise, and phantom context effects describe different situations in which the preference relation between two choice alternatives changes when a third alternative is introduced53, for example, is the theory that instead of searching for the alternative with maximum utility, we search until we find the first alternative with satisfactory utility. Loss aversion, which postulates that the perceived utility of not losing something is greater than the perceived utility of gaining that exact same thing, was offered as an explanation for the endowment effect55. Whereas the elimination by aspects model42 provided a first account for the similarity effect only, multi-attribute multi-alternative sequential sampling models, such as multi-alternative decision field theory60, the leaky competing accumulator model64, the multi-attribute linear ballistic accumulator model67, the 69, and the associations and accumulation model70, also account for the attraction and compromise context effect using a range of different mechanisms. The interested reader is referred to three recent papers that offer a comprehensive comparison between the different models72.Over time, theories and models have been adapted or extended to account for these deviations. Bounded rationalityAlthough these approaches are capable of modelling context effects, one drawback is that they are fairly complex. With increasing complexity generalisability often takes a hit, and models that are tuned to account for one type of choice effect fail to account for other choice effects, hence drawing inferences beyond the task-setting becomes challenging. Developing a simple choice model that is capable of connecting a broader spectrum of choice phenomena would thus be a worthwhile effort. For one, unifying distinct phenomena in a collective framework puts them on equal footing and hence can stimulate the development of formal theories that can account for all of them. Also, formalising our theories requires us to be precise and concrete, this in contrast to verbally formulated theories which are easily misinterpreted, often hold hidden assumptions, claim predictions that are not clearly derived from the theory, as well as hide consequences from the model that are not desired. Moreover, formal theories may lead to interesting predictions and new insights, and hence new possibilities to falsify the theory, that might not have been discovered if the phenomena are investigated independently. In this paper we propose such a probabilistic model for choices that conceptualises choice problems as a combination of a choice structure, an alternative evaluation process, and a choice trigger condition.In the remainder of this paper, we start by introducing the choice structure, represented by a network in which the nodes are the cues and alternatives, and the edges describe the relationships between them. We demonstrate a basic setup of our choice model, in which the binary node states follow a distribution known as the quadratic exponential binary distribution, or Ising model, and alternatives are evaluated with single spin-flip dynamics. Sampling choices from the invariant distribution of the configurations in which only one alternative is active, the choice conditions, gives us choice probabilities with the same form as the Boltzmann distribution from Eq. . TriggerHere we introduce the different components of the choice model and derive predictions for choice probabilities and response times.The choice model consists of a structure, a process, and a trigger. The choice structure describes the alternatives available for a choice and the origin of their utilities. The choice process describes how the alternatives are evaluated. The choice trigger describes the condition that stops the evaluation process and prompts a decision.The specific form of these three components allows for some variation depending on the specific setting. For example, in this paper we let the state of cues and alternatives in the choice structure be either active or inactive. While this is reasonable in the case of preferential choice, in the case of modelling an opinion we might want to use three possible states, namely pro, neutral, or against. These types of variations are also possible in the case of the process and trigger elements of the choice model, and we discuss several of them throughout the paper.x’, and alternatives describe the possible choices. An appropriate representation of such a structure is a network in which the nodes correspond to the alternatives and the cues, and the edge between two nodes describes their relation. Figure In their simplest form choices can be structured as a combination of cues and alternatives and the relationships between them. Cues represent the conditions of the choice, e.g., ‘buy a book’, ‘select a present’, or ‘solve for To arrive at predictions about choice behaviour we assume that both the type and strength of a relationship between two nodes can vary, and that nodes outside of the choice subset can also influence a decision through their relationship with nodes that are in the choice subset. In Fig. Do you want a nice and fresh croissant, yesterdays leftover sandwich, or a somewhat dry baguette, for breakfast? enhances the appeal of the croissant through the suggestive phrasing of the cue. A relation between two alternatives signals that the appeal of one is related to that of the other alternative. The next step is formalising the choice structure as a probability distribution.We refer to the experienced magnitude and direction of an alternative’s utility in terms of an alternative’s appeal. Figure n nodes, let i is active i and node j in the choice structure. Let n in which i. A valid probability distribution over the states is obtained by endowing them with the following distribution:i and j, and Z is the normalising constant that sums over all the For a choice structure with 74, a highly popular and one of the most studied models in modern statistical physics75, or as the quadratic exponential binary distribution as it is known in the statistics literature77. Capable of capturing complex phenomena by modelling the joint distribution of binary variables as a function of main effects and pairwise interactions78, it has been used in fields such as genetics79, educational measurement80, and psychology83. In the context of choice it has been applied in sociology in Galam’s work on group decisions in binary choice problems85. In this application each node represents the choice of one person on a specific problem, and the pairwise interactions describe the influence of all people in the group on the individuals choice. Another application is the Ising Decision Maker from Verdonck and Tuerlinckx86, a sequential sampling model for speeded two-choice decision-making. In this model each of the two alternatives is represented by a pool of nodes, inside a pool nodes excite each other, between pools nodes inhibit each other. A stimulus is represented by a change in the external field, after which the node states are sequentially updated. The response process monitors the mean activity per pool, and chooses the first alternative for which this activity crosses a threshold. Both these models use this distribution in a substantially different way compared to the current application, and have not been applied to explain deviations from rationality. As such we will not discuss them in more detail for this paper.The distribution in Eq.  can be rS is the set of all configurations that a particular system can take and A connection between Eq.  and probmentclass2pt{minimg to Eq. . An adva87 in which a proposal configuration is generated at each iteration by sampling one alternative and flipping its state: Let i at random and flip its value Select one node Calculate If If For a choice with m alternatives the evaluation process will thus transition between Although many configurations for the choice process are possible, to illustrate our approach we use a simple stochastic process for interacting particle systems to model the process of alternative evaluation. Specifically, a Metropolis algorithm with single spin-flip dynamicsFrom Eq.  it can bm alternative nodes and k cue nodes. If we let the Markov chain run until convergence, the effect of any interactions between choice alternatives will have worn out and the property of simple scalability will hold for the choice probabilities, guaranteeing that choices are in accordance with the choice axiom. The choice axiom is known to be violated in particular choice problems, however, which leads us to the second choice trigger possibility.The first is that the alternative evaluation process is terminated when the single-spin flip algorithm has converged and a choice is sampled from the invariant distribution of the potential choice configurations:At some moment during the process a potential choice condition is met for the first time. One could say that a choice has effectively been made and there is no need for a decision-maker to continue. This choice trigger implements the idea of bounded rationality and explains various types of irrational choices as we explain after we discuss the consequences of our model setup for rational choices.Although our setup implements bounded rationality, it does not preclude rational choices. However, while choice structures can be made for which even the strongest gradation of rationality holds, finding clear cut rules for when a structure adheres to which gradations of rationality is a different kettle of fish. In the methods section we show that a very simple expression exists for the expected choice probabilities in the single spin-flip algorithm as a function of the transition matrix for the possible configurations of the alternatives. Deriving general rules for the adherence to different types of rationality requires one to express these probabilities as a function of the parameters n alternatives there are 88 can calculate these expected pairwise choice probabilities in reasonable time for choice situations with up to 15 alternatives using the expression from the methods section. For larger numbers of alternatives numerical solutions can be obtained with a simulation approach. Additionally, assumptions that simplify the analytical expression for the expected choice probabilities can also be used to derive rational choice properties.For We define irrational decision-making as those choice situations in which the odds of choosing one alternative over the other, as established by their pairwise choice probabilities, changes as a function of adding other alternatives to the set. We realise that for readers well versed within the choice literature this definition may seem both rather vague, because our definition creates a dividing line somewhere between the choice axiom and regularity, as well as strict, as violating the choice axiom means that the strictest rules and conditions for rationality can still hold for the binary choice probabilities. However, although we touched upon the different gradations of rationality in the previous paragraphs, we think that a more conceptual approach is more appropriate here. We will discuss examples in which it is immediately clear that the choice probabilities as predicted by rational choice theory are conceptually counter intuitive.Context effects are perhaps the most well known and studied violations of IIA and are often described by a situation in which a preference relation between two alternatives, a target and a rival, is established. Then a third alternative is introduced, the decoy, and it is demonstrated that adding the decoy changes the choice probabilities in favour of the target. These effects can range from only increasing the probability for the target while keeping the original order of the preference relations between the alternatives intact, to a full reversal of the preference relation. In our model these effects can be explained by the presence of a relationship between two choice alternatives and its influence on the resting state distribution and the alternative evaluation process.For several types of context effects we provide an example and show how it can be explained in our model. As our explanation of the context effect does not require bias in the presentation of the choice, we assume the relationship between all pairs of a cue and an alternative to be the same across the board 39 describes the situation in which adding a decoy that is highly similar to the rival results in an increased preference for a dissimilar target alternative. The classic example for this effect was given as a thought experiment that provides the choice probabilities, expected under rational choice theory for a choice between three recordings:U have the following three elements:C quartet.B orchestra conducted by F.B orchestra conducted by K.The subject will be presented with a subset of U, will be asked to choose an element in that subset, and will listen to the recording he has chosen. When presented with 38.“Let the set It is clear that these expected choice probabilities are highly implausible. Specifically, in this case one would expect that when presented with 42.The similarity effectOne choice structure that explains the similarity effect does this by introducing a negative association between the two Beethoven recordings, as in shown in Fig. One could argue that the ability to reverse engineer a network structure until the desired choice probabilities are obtained is a weakness of our approach. We believe that this is actually an advantage as, for one, it is possible to check if adaptations of the choice structure will still result in plausible choice behaviour. For example, imagine that you chose 45 describes the situation in which the addition of a decoy alternative that is a substandard version of the target increases the preference for the target. Simonson and Tversky46 investigated this effect by offering two groups a choice between (a subset of) 6 dollar The attraction, or asymmetric dominance, effectFigure a, results in a prediction of choosing b, results in a prediction of still choosing Obtaining the same results from different structures allows us to compare the different theories and predictions that characterise each structure. For example, someone who chose 92. This reversed attraction effect, called the negative attraction or repulsion effect, although not consistently demonstrated, is mostly observed when choices are framed such that the decoy highlights the shortcomings of the more similar target alternative. For example, adding a smaller clementine to the choice between a fruit flavoured candy bar and an orange, might boost the probability of choosing the orange, as the clementine highlights the freshness and health aspects of citrus fruits. However, if the clementine shows some signs of a reduced freshness, e.g. crumpled skin or beginning to mould, it highlights the fleeting freshness of citrus fruit, and might instead boost the probability for the sugar filled candy bars and their long shelf life.In some cases the addition of a substandard version of the target alternative actually decreases the probability of selecting the targetJust as the repulsion effect is the opposite of the attraction effect, so is its explanation, i.e., a positive relation between the rival and decoy alternatives. In the pen example from Fig. 45 describes the situation in which a decoy is added for which the distance to the target mirrors that of the distance between the rival and the target, but in the opposite direction. This boosts the preference for the target alternative by making it seem like the compromise. Distance should in this context be interpreted as the relative position of the alternatives on particular attributes, such as prize and quality in the next example.The compromise effect47 investigated the compromise by offering two groups a choice between (a subset of) cameras of either low (L), medium (M), or high (H), prize and quality. While in the first group, choosing from the subset L and H each with a probability of approximately M was still chosen with a probability of approximately L and M camera in group one, one would expect that both would be also be chosen with an approximately equal probability in group two. Or conversely based on the lower appeal of camera L in group 2, one would expect that in group one camera L would be chosen with a probability of approximately M with a probability of approximately Tversky and SimonsonH and L camera are much more evident than those between the cameras M and L or M and H. Therefore, the weakness of camera L gets highlighted when camera H is part of the choice set, this in turn frames the camera M as the compromise that is of higher quality compared to camera L, but not as expensive as camera H. Once again, as is shown in Fig. L and the decoy camera H.A possible explanation for why this is not the case might be that the (dis)advantages between the cameras So far the similarity, attraction, and compromise effect are each explained in our model by a negative interaction between the decoy and the rival. Whereas in the similarity effect, this relation is assumed to exist because of the large similarities between the rival and decoy alternatives, in the attraction and compromise effects, however, this relation is a function of the large dissimilarities between the two.60. This could suggest that people either focus on similarities or dissimilarities, and hence the choice structure of a person only contains negative relations for one of those types. Whereas the attraction and compromise effect occur when a choice structure contains only negative relations as a function of dissimilarity, a choice structure in which negative relations are the result of similarity will only elicit the similarity effect. Not all context effects can be explained by a (negative) relation between the rival and decoy alternatives alone. In some cases it also manifests itself through the influence of the choice structure on the initial alternative configuration.One explanation for this could be that only when (dis)similarities go into the extreme they are highlighted and start influencing the choice process. Another explanation comes from observed correlations between context effects, i.e., one study found that people who show the attraction effect also show the compromise effect, but not the similarity effect52 describes the situation in which the added decoy alternative is superior to both the target and rival alternatives, yet more similar to the target compared to the rival, but most importantly, unavailable. When it is communicated that the decoy cannot be chosen it subsequently boosts the preference for the target alternative.The phantom decoy effect52 studied the phantom decoy effect by offering two groups a choice between (a subset of) paperclips each with varying degrees of friction and flexibility. The target paperclip (T) and the rival paperclip (R), although different in these properties, were of comparable quality. The decoy paperclip (D) had a quality superior to both T and R but was in terms of friction and flexibility more alike to paperclip T. In the first group, choosing from the subset T with a probability of approximately D was revealed to be unavailable and hence the choice had to made again from the subset Pratkanis and FarquharD. As shown in Fig. D and R paperclips, together with the larger general appeal of paperclip D, reduces the probability for choosing paperclip R. If the unavailability of paperclip D is not communicated before the first choice and all three paperclips appear to be available, the choice structure from Fig. D is most likely to be chosen. At this point, the configuration of the choice structure is known, as only the cue and the node for paperclip D will be active. If at this point one is informed that paperclip D is unavailable, the choice process starts again from the known configuration. Given that node D is active, we can from this moment regard it as an additional cue, as is shown in Fig. D and paperclip R, flipping the R node and hence choosing it become less likely compared to paperclip T.As is shown in Fig. As shown in the 3 describes the situation in which people value an object higher if they possess it compared to when they do not. To illustrate this effect we consider a variation on the Debreu example in which you are given a Beethoven recording (B) and are immediately asked if you want to exchange it for an equally appealing Debussy recording (D). While the choice axiom predicts that you would exchange Beethoven for Debussy about half the time, the endowment effect says that people are unlikely to switch, a prediction that has been experimentally verified93. The endowment effect has been explained with choice-supportive bias94 and loss aversion54.The endowment effectB or node D. From the resulting configurations both choices are then equally likely. Let B for D is then given by:In our model both explanations would translate to an increase in base appeal of an alternative as soon as it has been chosen. With our setup we obtain a new explanation that does not depend on changes in the values of the choice problem but ties into the choice process itself. Having been given the Beethoven makes the choice conditions satisfied, and hence the initial configuration of the alternatives is known when offered to exchange it for the Debussy. Exchanging them requires a sequence of events in the choice process that, due to the equal appeal of both alternatives, has a lower probability compared to keeping the Beethoven. Specifically, the only way that switching becomes an option is when the initial state, the choice condition for the Beethoven, is left in the first iteration by sampling and accepting the flip of either node Response time predictions can be very informative when comparing different choice structures, evaluation processes and trigger conditions. As shown in the methods section, the single spin-flip algorithm provides the expected number of iterations until a choice condition is reached as a proxy for time. This can be used to investigate expected ordering of response times for a particular choice structure. For example, in a simple structure with no relationship existing between alternatives the expected number of iterations before a choice is triggered increases in the number and appeal of the alternatives. Or, assuming that longer response times are indicative of more deliberate decision-making, i.e., requiring more visits to a choice condition before a choice is triggered, we expect that context effects diminish and choices get increasingly rational. With increasing the required number of visits to a choice condition, choice probabilities go to Eq.  if a cho95, which predicts that under time-pressure choices are faster but less accurate, through 97, the attention to an attitude object is represented by 98. Choice expectations under time-pressure can be even more fine-tuned by using The model also allows incorporating response time phenomena such as the speed-accuracy trade-offIn this article we proposed a model for choices in which the choice structure is represented by a network, for which the node states have a distribution known as the quadratic exponential binary distribution or Ising model. Single spin-flip dynamics describe the alternative evaluation process in our basic setup, and potential choice conditions are states in which only one alternative is active. The invariant distribution of this choice process is the same as that of several classic choice models with the property of simple scalability, which guarantees choices to be rational. Stopping when the choice conditions hold for the first time predicts a series of well known violations of rationality known as context effects and several other choice phenomena. This approach allows one to represent choice situations in an accessible way, and can be used to compare different choice structures, alternative evaluation process assumptions, and trigger variations with respect to the choice behaviour they predict. Furthermore, as we show next, it implements or can be extended with features and mechanics used in more complex choice models. We first review the relation between our model and the elimination by aspects (EBA) model, multi-alternative decision field theory (MDFT), the leaky competing accumulator model (LCA), and (simple) 2N-ary Choice Tree (2NCTs), and end with discussing some limitations and prospects of our approach.42. In the EBA model an alternative is characterised by a collection of attributes. At each step in the choice process one attribute is selected proportional to an attention weight, and alternatives without this attribute are eliminated until only one alternative remains. Although the utility of an alternative in the EBA model is a function of unique attributes and those shared between pairs of alternatives, choice probabilities can be calculated independently of the specific attributes. The EBA model can only explain the similarity effect, which occurs when a subset of the alternatives share some attributes that are not shared with the other alternatives. For example, the two Beethoven recordings share attributes that are not shared by the Debussy recording. As such the probability of selecting an attribute that is unique to a Beethoven is smaller when both recordings are in the choice set with the Debussy, compared to when only one is.One of the first models to offer an explanation for the similarity effect was Tversky’s EBA modelMDFT, the LCA, and 2NCTs, are capable of explaining more context effects. They are sequential sampling models, which entails that (noisy) information about the alternatives is integrated in an accumulator for each alternative throughout the choice process. Whereas MDFT and the LCA each assume one accumulator per alternative, in the 2NCTs each alternative has two accumulators, one for positive information and one for negative information. For all models the process stops when either a time limit is reached or one of the (positive) accumulators crosses a threshold, triggering in both cases a choice for the alternative for which the most (positive) information is accumulated. In the 2NCTs the process can also terminate when for all but one of the alternatives the threshold of the negative accumulator is crossed and these alternatives are eliminated.attribute nodes, in which the position of the alternative on the attribute is encoded in the edge between them. During the process one can assume that attributes are always active and function as cues, or let their activity vary over time to incorporate the assumption that attention stochastically switches between attributes.As in the EBA model, an alternative’s appeal is a function of its attributes and attention switches between these attributes over time. The explanation of the context effects in MDFT, the LCA, and 2NCTs rest primarily on some form of asymmetry between, or particular positioning of, the alternatives on attributes and must therefore be specified for all alternative-attribute combinations. In our model we do not need to specify different attributes or assume switching between attributes to predict context effects, as the influence of attributes is captured in the general appeal of the alternative. The influence of different attributes can be made explicit by adding an additional layer of 99, a neural concept in which an exited neuron inhibits its neighbours, is applied with the same magnitude for all alternative pairs in the LCA, and decreasing with the distance between alternatives in MDFT. The LCA and 2NCTs  rely on an implementation of loss aversion. In the LCA accumulators can only take non-negative values and the influence of negative differences between attribute values relative to positive differences is reduced. In the 2NCTs the evaluation process decreases the probability of updating a negative accumulator relative to that of a positive accumulator.The models require several other mechanisms to explain context effects. Lateral inhibition100.Both inhibition and loss aversion are part of our model. Loss aversion is implemented within the process of single spin-flip dynamics, i.e., the probability of activating an appealing alternative is always one, whereas the probability of eliminating an appealing alternative is decreasing in the appeal. Inhibition comes in the form of the negative interactions between the alternatives and plays a vital role in the prediction of context effects. Global inhibition as found in the LCA can be implemented by lowering the interaction between each pair of alternatives with a constant. Otherwise independent alternatives then become negatively related and the evaluation process will move faster to a potential choice condition98.Although our approach has several advantages with respect to the more complex multi-attribute multi-alternative models, it does not provide the same insight in choices times distributions. That being said, our predictions with respect to the ordering of response times are often the same as these models, and the model even provides novel explanations for some response time phenomena. For example, the prediction that context effects strengthen with longer decision times can, in addition to time-pressure, also be explained by the format of the experiment. The study manipulated time-pressure by letting a participants evaluate the characteristics of novel stimuli for 2, 4, 6, or 8 s, after which a choice had to be made immediately. Participants who could look to the stimuli for less than 8 s made choices less consistent with the context effects compared to participants that could look for 8 sWhenever a choice is presented the available alternatives must be encoded to determine their appeal, a process that takes time. For daily choices alternatives are recurring and embedded within a stable choice structure such that decoding takes almost no time. New alternatives must be placed within the global structure and connected to the relevant concepts before their perceived appeal stabilises. As multi-attribute multi-alternative models must specify all alternatives-attribute combinations, studies often use fictional products defined on small numbers of attributes only. Fictional alternatives are new to the participant and time is required to derive the general appeal of, and establish the appropriate relationships between, alternatives. This explanation is consistent with a transition from 101.We discussed our model assuming that all alternatives are known and everyone has the same choice structure. It is clear that choice situations exist in which the alternatives are not necessarily provided, or in such large numbers that evaluating them all might be infeasible. Furthermore, while it is a common assumption that the behaviour of individuals can be described by a set of parameters for the group, it is often rather unrealistic. Future research should focus on extending the model for these situations. For example by introducing initial selection probabilities for alternatives to be included in the choice structure, or interpreting 102, find that for several  persons for whom Tversky asserted that they showed intransitive behaviour, the results are no longer significant105. Although we believe that there is general consensus, based on a large body of research, that humans do not always make rational decisions, some experiments about irrational choice behaviour remain disputed. We therefore want to stress the importance of replicating these studies, or setting up new experiments that investigate the same phenomenon.We also recognise that data and findings in psychology from a few decades ago are sometimes questionable with respect to the current standards of research. For example, papers that used alternative methods to analyse data from a study by Tversky on within-person transitivity104, is that often behaviour that may seem intransitive is actually rational when previously unobserved variables are taken into account. As an example they take a student that assumes that their supervisor’s perceived utility of meeting locations is stable over time, but that this is not the case as the varying teaching location of the supervisor actually determines this utility. As such, while the student judges the choices for meeting locations of the supervisor to be intransitive, this is in reality not the case. This example shows that, particularly for within-person choice effects, it is important to take context variables into account. In our model this could be accounted for by introducing nodes for these context variables, in case of the example a node for each of the teaching locations, that has a positive relation with the closest meeting locations, and which is active if the day of the meeting the supervisor has to teach in that location.Another point, discussed in the paper by Regenwetter et al.Even though very precise quantitative predictions are generally out of reach with behavioural data, there is merit in the prediction of qualitative phenomena, such as the ordering of probabilities, interaction effects, shapes of distributions, and even phenomena such as phase transitions. Formalising our theories about behaviour allows us to obtain these predictions. While we already propose multiple extensions to our model, ideally, it will be formal theories that dictate the assumptions, mechanisms, and structural characteristics to be used in a particular setup. By taking the opportunities to extend, refine, and improve the elements of the choice model, we can hopefully create a broad understanding of how the many phenomena and key theoretical principles from different types of decision-making are connected.We derive the expression for the expected choice probabilities for the single spin-flip algorithm, demonstrate these steps for the attraction effect, and provide the parameter values for all examples used in the main text. We then visualise how variations of k cues and m alternatives there are ith of these m non-zero elements in each row. From For a choice structure with m absorbing states, i.e., those states in which only one alternative is active, and n, and 106.The expected choice probabilities for stopping as soon as the choice conditions are met for the first time are obtained by reformulating the Markov chain with transition matrix t denote the number of iterations of the Metropolis algorithm.Let y is chosen from the set of alternatives S is:yth column of the matrices y is chosen is:The  probability that alternative Random phase (RND) The choice probabilities for choosing the money Strong attraction effect phase (AE+) The probability of choosing Normal attraction effect phase (AE) The probability of choosing Choice axiom phase (CA) The choice probabilities for all options differs only a marginally from those expected under Eq.  The probability of choosing Rational phase (RA+) The probability of choosing The distribution of the different phases as a function of Figure This is also what we see when looking to the distribution of the log mean response times as a function of Looking to both Fig. n alternatives, requires one to write out all the possible paths to the n choice conditions from all x over y in a binary choice problem is given by:x. x is purely a function of the difference between Expressing the expected choice probabilities as a function of the parameters k and alternative x is positive (x is negative (k and alternative y is negative (y is positive (x over y is the sum of all starting configurations except y. Although this situation might not necessarily be encountered in real life, it shows that determining when choices are guaranteed to be even weakly rational is not straightforward.For example, if the relation between cue sing Eq.  we can cSupplementary Information.


Porcine coronavirus SADS-CoV has been identified from suckling piglets with severe diarrhea in southern China in 2017. The SADS-CoV genome shares ~95% identity to that of bat α-coronavirus HKU2, suggesting that SADS-CoV may have emerged from a natural reservoir in bats. Here we report the cryo-EM structures of HKU2 and SADS-CoV spike (S) glycoprotein trimers at 2.38 Å and 2.83 Å resolution, respectively. We systematically compare the domains of HKU2 spike with those of α-, β-, γ-, and δ-coronavirus spikes, showing that the S1 subunit N- and C-terminal domains of HKU2/SADS-CoV are ancestral domains in the evolution of coronavirus spike proteins. The connecting region after the fusion peptide in the S2 subunit of HKU2/SADS-CoV adopts a unique conformation. These results structurally demonstrate a close evolutionary relationship between HKU2/SADS-CoV and β-coronavirus spikes and provide insights into the evolution and cross-species transmission of coronaviruses. Several coronaviruses infecting humans and animals have emerged in recent years. Here, the authors provide structures of the spike proteins of the porcine coronavirus SADS-CoV and closely related bat coronavirus HKU2, providing insights into evolution of coronavirus spike proteins. Nidovirales, family Coronaviridae, and subfamily Coronavirinae, are a large group of viral pathogens with a wide host range1. Their infections in humans, other mammals, and birds can cause respiratory, hepatic, enteric and neurological diseases with varying severity2. The zoonotic transmission of novel coronaviruses into humans poses severe threats to the global health, evidenced by the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and the SARS-CoV-2 that is causing the ongoing pandemic COVID-19 in the world6. In the meantime, coronaviruses infecting domestic animals also bring substantial economic losses7. For example, the swine acute diarrhea syndrome coronavirus (SADS-CoV)  isolated in 2017 caused outbreaks of severe watery diarrhea of suckling piglets with a mortality up to 90% in several commercial pig farms in Guangdong Province of China13. SADS-CoV is an α-coronavirus and other representative members in the α-genus are porcine epidemic diarrhea virus (PEDV), porcine transmissible gastroenteritis coronavirus (TGEV), porcine respiratory coronavirus (PRCV), feline infectious peritonitis virus (FIPV), and human NL63 and 229E coronaviruses (HCoV-NL63 and HCoV-229E)1. Representative members in other three genera include mouse hepatitis coronavirus (MHV), bovine coronavirus (BCoV), SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, and HCoV-HKU1 in the β-genus, avian infectious bronchitis virus (IBV) in the γ-genus, and porcine deltacoronavirus (PdCoV) in the δ-genus1.Coronaviruses, categorized into the order 16. Bats are the natural reservoir of more than 30 different α- and β-coronaviruses that have great potential for interspecies transmission by recombination and/or mutation19. Data on genetic evolution, receptor binding, and pathogenesis have demonstrated that human SARS-CoV and MERS-CoV most likely originate from bats1. Palm civets and dromedary camels are the intermediate hosts of SARS-CoV and MERS-CoV from bats to humans, respectively1. The newly identified porcine SADS-CoV isolates are also found to share ~95% sequence identity with Rhinolophus bat coronavirus HKU2, and this further stressed the severe results of coronavirus spillover from bats to domestic animals13. However, the molecular mechanisms underlying the transmission of SADS-CoV from bats to pigs are still unknown and need to be further explored. Recently it was shown that SADS-CoV is able to infect cells from a broad range of species including mouse, chicken, pig, monkey, and human, indicating a high potential of the SADS-CoV for interspecies transmission20.Cross-species transmission promoted by genetic recombination and/or mutations underlies the host range expansion of coronaviruses22. As a class I viral fusion protein, the spike exists on the envelope of virion as a homotrimer and each monomer contains more than 1000 amino acid residues that can be cleaved into S1 and S2 subunits21. For most coronaviruses, the N-terminal domain (NTD) of the S1 subunit recognizes cell-surface carbohydrates, while the C-terminal domain (CTD) specifically binds to cellular protein receptors23. SARS-CoV, SARS-CoV-2, and HCoV-NL63 utilize CTD to bind human receptor ACE230; MERS-CoV utilizes CTD to bind human receptor DPP432; and TGEV, PRCV, and 229E utilize CTD to bind receptor APN35; HCoV-OC43 utilizes NTD to recognize glycans36; and one exception is MHV, which utilizes the NTD to bind mouse receptor CEACAM1a38. Therefore, the S1 subunit, especially its NTD and CTD, is the most variable region of the spike, and is responsible for different tropisms of coronaviruses. In comparison, the S2 subunit containing the fusion peptide (FP) and heptad repeats (HR1 and HR2) for membrane fusion are more conserved in both sequence and structure22. For the SADS-CoV, receptor analysis indicated that none of the known coronavirus protein receptors including ACE2, DPP4, and APN are essential for cell entry20. There are also no reports regarding the recognition of glycans by the NTD of SADS-CoV.The spike glycoprotein of coronaviruses mediates viral entry by binding host receptor with the S1 subunit and fusing viral and cellular membranes with the S2 subunit, thereby determining viral host range and tissue tropism39, HCoV-229E34, PEDV40, and FIPV41; the β-coronavirus spike structures are determined for MHV43, HCoV-HKU144, HCoV-OC4336, SARS-CoV47, MERS-CoV49, and SARS-CoV-251; and the γ-coronavirus spike structure is determined for IBV52 and the δ-coronavirus spike structure is determined for PdCoV54. The cryo-EM structures of bat coronavirus spike trimers have not been reported, and only crystal structures of the CTD from HKU455, HKU556, and HKU957 were determined.Structural studies of the spike and its binding with glycans and protein receptors have provided important insights into the origin, evolution, and interspecies transmission of coronaviruses. Cryo-EM structures of spike trimer from all four coronavirus genera have been reported: the α-coronavirus spike structures are determined for HCoV-NL6358 and their amino acid identities to other known coronavirus spikes are lower than 28%, indicating the spikes of HKU2 and SADS-CoV are unique59. In this study, we report the cryo-EM structures of the SADS-CoV and HKU2 spike trimers at 2.83 and 2.38 Å resolution, respectively. The HKU2 spike trimer structure is the first one from bat coronavirus. We analyze the HKU2 and SADS-CoV trimer structures and also compare the NTD, CTD, SD1, and SD2 domains of the S1 subunit and the S2 subunit of HKU2 with other spikes from α-, β-, γ-, and δ-coronaviruses. Our results strongly support that HKU2 and SADS-CoV preserve primitive structural features in their spikes that have a close evolutionary relationship with β-coronavirus spikes and provide insights into the evolution and cross-species transmission of coronaviruses.The spikes of SADS-CoV (1130 amino acid residues) and HKU2 (1128 amino acid residues) are among the shortest coronavirus spike glycoproteinsThe cDNAs encoding HKU2 spike YP_001552236) and SADS-CoV spike (AVM41569.1) were synthesized with codons optimized for insect cell expression. HKU2 ectodomains (residues 1–1066) and SADS-CoV ectodomains (residues 1–1068) were separately cloned into pFastBac-Dual vector (Invitrogen) with C-terminal foldon tag and Strep tag. After expression in Hi5 insect cells and purification to homogeneity, the cryo-EM images on these two spike ectodomains were recorded using FEI Titan Krios microscope operating at 300 kV with a Gatan K2 Summit direct electron detector  Fig. , consistThe S1 subunit of the HKU2 spike comprises two major domains, NTD and CTD, which are followed by two subdomains 1 and 2 (SD1 and SD2) connecting them to the S2 subunit Fig. . The S1 23. Therefore, the sequence and/or structural variations indicate that HKU2 and SADS-CoV would also bind different host receptors by NTD and/or CTD of the S1 subunit, although their receptors in bat and pig are unknown and the receptor-binding sites on spike have not been defined.The SD1 and SD2 of the S1 subunit and the S2 subunit are highly similar in amino acid sequence  and structure (Cα r.m.s.d. less than 0.5 Å) between HKU2 and SADS-CoV spikes  is conserved in all subtype I NTDs  connecting β5 and β6 strands and the second one C81–C105 (numbered in HCoV-229E) connecting β2 and neighbor loop  is conserved in both subtype I and subtype II NTDs  of α-coronavirus HCoV-NL63 with an r.m.s.d. of 2.7 Å for 186 aligned Cα atoms Fig. . The NTDTDs Fig. , and theoop Fig. . BesidesTDs Fig. .52. The NTD of γ-coronavirus IBV also resemble the subtype I NTD in the topology, although its disulfide bond positions are not conserved as in the subtype I NTD  in this insertion region responsible for binding their respective protein receptors. In the CTD of HKU2, there is only one short loop between the β5 and β6 strands of the core twisted β-sheet   and δ-coronavirus PdCoV  Fig. . The C56CoV Fig. .The CTD of δ-coronavirus PdCoV has a core of two β-sheets, belonging to the two-layer CTD subtype Fig. . As for 409–C458) connecting its C-terminus to the β1 strand , and another disulfide bond (C524–C533) links the C-terminal loop to the bottom sheet , an FP helix (residues 672–684), a CR (residues 689–747), a 4-helix HR1 (residues 748–836), a CH (residues 837–887), a twisted BH (residues 888–929), and a β-sandwich like SD3 (residues 930–995)  stabilize the folded helices of UH, C696–C706 bends the CR, C884–C895 links the CH and the BH, and C934–C943 is within the SD3  Figs. . Like inSD3 Fig. . The fir696–C706 makes the first helix of CR in HKU2 spike turn upside down. The S2′ cleavage site (between R671 and S672) is then covered by the reversed CR helix and loops, and R671 interacts with E723 in the loop and K697 and K698 in helix 1  was recently identified from suckling piglets with diarrhea in southern China, and its genome sequence was most identical (~95% identity) to that of 39. However, the sequence identity between these two NTDs is only 15.7% in HCoV-NL63 and 12.9% in PEDV. Considering that HKU2 (SADS-CoV) and HCoV-229E have one NTD belonging to either subtype I or subtype II, a more plausible evolution way of the NTD in α-coronaviruses is the recombination of two separate primitive domains into the genome, resulting in the presence of two NTDs in the S1 subunit of α-coronaviruses including HCoV-NL63 and PEDV. Of note, these two NTD subtypes may represent primitive structures that could be the evolutionary ancestors of NTDs of other genera coronaviruses. For example, in the current available spike structures, the NTDs of β-coronavirus are similar to the HKU2 NTD representing the subtype I in both architecture and disulfide bond positions. These β-coronavirus NTDs also have additional loop ceiling over the top sheet, functionally facilitating immune evasion or protein receptor binding such as in MHV38. The NTD of γ-coronavirus IBV is also architecturally similar to the subtype I, although the disulfide bond positions are not conserved  or subtype II (δ-coronavirus NTDs)  or mediates the membrane fusion (S2 subunits), whereas the NTD and CTD are key factors determining tissue tropism and host range of coronavirusesite Fig. . It indi6. We compared the domain structures of HKU2 and SARS-CoV-2 S proteins . The spike ectodomains were purified by StrepTactin beads (IBA) and then purified by gel-filtration chromatography using Superose 6 gel filtration column  pre-equilibrated with HBS buffer . Purified ectodomains were concentrated for electron microscopy analysis.The cDNAs encoding HKU2 spike (YP_001552236) and SADS-CoV spike (AVM41569.1) were synthesized with codons optimized for insect cell expression. HKU2 ectodomains (1–1066) and SADS-CoV ectodomains (1–1068) were separately cloned into pFastBac-Dual vector (Invitrogen) with C-terminal foldon tag for trimerization and Strep tag for purification in the frame of pH promoter. They were expressed in Hi5 insect cell using Bac-to-Bac baculovirus system (Invitrogen). Briefly, the construct was transformed into DH10Bac competent cells and the extracted bacmid was transfected into Sf9 cell using Cellfectin II reagent (Invitrogen). The baculoviruses were harvested after 7–9 days. The high-titer viruses were generated after one more amplification that used to infect Hi5 cells at a density of 1.5 × 10Aliquots of spike ectodomains  were applied to glow-discharged holey carbon grids . The grids were then blotted and then plunge-frozen in liquid ethane using FEI Vitrobot system (FEI).−/Å2 fractionated in 32 frames of 175 ms exposure. Data collection statistics are summarized in Table Images were recorded using FEI Titan Krios microscope operating at 300 kV with a Gatan K2 Summit direct electron detector (Gatan Inc.) at Tsinghua University. The automated software (AutoEMation) was used to collect 7663 movies for HKU2 and 4568 movies for SADS-CoV at ×130,000 magnification and at a defocus range between 1 and 3 μm. Each movie has a total accumulate exposure of 49.784 e60. The final image was bin averaged to give a pixel size of 1.061 Å. The parameters of contrast transfer function (CTF) was estimated for each micrograph using GCTF61. Particles were automatically picked using Gautomatch (http://www.mrc-lmb.cam.ac.uk/kzhang/) and extracted using RELION62. Initially, ~1,400,000 particles for HKU2 and ~900,000 particles for SADS were subjected to 2D classification. After two or three additional 2D classifications, the best class consisting ~750,000 particles (HKU2) and ~320,000 particles (SADS-CoV) were applied for creating 3D initial model, 3D refinement, and 3D classification. A total of 421,490 particles (HKU2) and 152,334 particles (SADS-CoV) of best classes were selected and subjected to 3D refinement with C3 symmetry to generate density map. The reported resolutions based on the gold-standard FSC cutoff of 0.143 criterion were 2.38 Å for HKU2 spike and 2.83 Å for SADS-CoV spike after RELION post processing. Local resolution variations were estimated using ResMap63. Data processing statistics are summarized in Table Whole frames in each movie were corrected for beam-induced motion using MotionCo264 and fit into the map using UCSF Chimera65. The other part of HKU2 spike ectodomains was obtained using Map to Model in PHENIX suit66. As for the SADS-CoV spike model building, the initial model was obtained by fit HKU2 S2, NTD, and CTD separately into the map using UCSF Chimera65. Manual model rebuilding was carried out using Coot67 and refined with PHENIX real-space refinement66. The quality of the final model was analyzed with Molprobity68 and EMRinger69. The validation statistics of the structural models are summarized in Table As for the HKU2 spike model building, the initial model of HKU2 S1 NTD was generated using the SWISS-MODELFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


During particular stress conditions, transfer RNAs (tRNAs) become substrates of stress-induced endonucleases, resulting in the production of distinct tRNA-derived small RNAs (tsRNAs). These small RNAs have been implicated in a wide range of biological processes, but how isoacceptor and even isodecoder-specific tsRNAs act at the molecular level is still poorly understood. Importantly, stress-induced tRNA cleavage affects only a few tRNAs of a given isoacceptor or isodecoder, raising the question as to how such limited molecule numbers could exert measurable biological impact. While the molecular function of individual tsRNAs is likely mediated through association with other molecules, addressing the interactome of specific tsRNAs has only been attempted by using synthetic RNA sequences. Since tRNAs carry post-transcriptional modifications, tsRNAs are likely modified but the extent of their modifications remains largely unknown. Here, we developed a biochemical framework for the production and purification of specific tsRNAs using human cells. Preparative scale purification of tsRNAs from biological sources should facilitate experimentally addressing as to how exactly these small RNAs mediate the multitude of reported molecular functions. Transfer RNAs (tRNAs) are crucial adaptor molecules for the decoding of messenger RNA (mRNA) during protein synthesis. Besides fulfiling this canonical function, tRNAs are also the source of a heterogeneous class of small RNAs, often called tRNA-derived small RNAs (tsRNAs), which have been the subject of intense scrutiny in recent years. An increasing body of work has assigned functional relevance to various tsRNAs because their detection and occurrence are associated with cellular stress and immune responses, cell proliferation and differentiation ,2, but aThe biogenesis of different tsRNA species can be attributed to endonucleolytic activities targeting pre-tRNAs or matured tRNAs in the tRNA loop structures . The best understood mechanism of tsRNA production is stress-induced tRNA fragmentation by anticodon nucleases (ACNases), which is a conserved hallmark of the eukaryotic stress response. Two eukaryotic ACNase protein families (RNase A and T2) specifically cleave matured and full-length tRNAs in response to stress. Mammalian cells express Angiogenin (ANG) –16, an RGCC and tRNA-GluCUC, which are dominantly featured in tsRNA-related literature, were purified from human cells using chromatographic and hybridization-based methods. The post-transcriptional modification status of purified tsRNAs was determined using LC-MS/MS. To show the applicability of this approach, purified tsRNAs were used for RNA affinity capture experiments and for the approximation of actual copy numbers of specific and stress-induced 5ʹ tsRNAs in a human cell line.Here, we set out to develop a biochemical framework to purify large amounts of specific tsRNAs, which might allow addressing the biological function of endogenous tsRNAs. As proof of concept, a scalable purification protocol for specific tsRNAs carrying post-transcriptional modifications was established. To this end, two 5ʹ tsRNA species derived from tRNA-GlyGCC and 5ʹ tsRNA-GluCUC are two tsRNA species representing tRNA halves that are often detected in biological samples as very abundant, especially during stress conditions. To produce these tsRNAs, HEK293 cells were treated with inorganic sodium arsenite (iAs), whose impact on cellular stress responses can be monitored by the amount of phosphorylated eukaryotic initiation factor 2α (GCC and tRNA-GluCUC showed tRNA cleavage during the acute stress response to iAs with increased tsRNA levels being detectable 24 h after removing the stressor (5ʹ tsRNA-Glyactor 2α . Northerstressor . Of noteGCC) to completion, suggesting effective cellular control mechanisms limiting excessive ANG activity.In an attempt to increase tsRNA production, while avoiding excessive stress-induced cell death and overcoming the inhibitory regulation of endogenous ANG, an inducible ANG-expression system was established. Using the Flp-In™-T-REx™ system, human ANG-HA-FLAG (ecANG) was stably inserted into the genome of HEK293 cells. Addition of doxycycline (Dox) induced ecANG expression  and resuin vitro [via its FLAG-tag . Precipitated ecANG was used for tsRNA production on gel-purified total tRNAs. Northern blotting on the cleavage reactions using probes against the 5ʹ half of tRNA-GlyGCC showed that secreted and immuno-precipitated human Angiogenin can be used to produce scalable quantities of specific tsRNAs in vitro  or A37-C38 , respectively  yielded also limited cleavage [GCC and Drosophila tRNA-AspGUC is ApC, this dinucleotide is the least addressable context for DNAzymes according to (46). In addition, C38 in both tRNAs is modified by Dnmt2/TRDMT1 proteins, which might negatively affect cleavage yield as has been observed for other RNA modifications [DNAzymes are short deoxyribonucleic acids displaying RNA hydrolysing activity, which can be designed to cleave RNAs with site-directed specificity. As an alternative to ANG-mediated cleavage of tRNAs, DNAzymes of the 10–23 variant –48 were ectively . Small Re extent . Of noteications . These oGCC  at their 3ʹ ends, which increases their migration behaviour. Importantly, two distinct 5ʹ tsRNA species could sometimes be observed also in vivo, especially during the stress recovery or when exposing cells to exceedingly high iAs concentrations  covalently immobilized on NHS-linked sepharose columns (GCC) showed clear enrichment of 5ʹ tsRNAs after the affinity capture step . Triplicate small RNA sequencing of ecANG-produced 5ʹ tsRNA-GlyGCC revealed that 84.9% of all reads mapped to 5ʹ tsRNA-GlyGCC while 14.3% were derived from the 5ʹ end of the isoacceptor tRNA-GlyCCC , which often interfere with reverse transcription [CCC and GluUUC, respectively), we will, from here on, label tRNA-derived sequences as tRNA or 5ʹ tsRNA for GlyGCC/CCC and GluCUC/UUC.Northern blotting of purified 5ʹ tsRNA-Glycription , therebySaccharomyces cerevisiae  [7 G, m1 G, m22 G, Am, m6A) in targeted RNA species  were very low to non-existent in purified tsRNAs . 5ʹ tsRGCC/CCC)  albeit cCUCUUC purified from Flp-In™ T-Rex™ 293-ecANG cells. To this end, 5ʹ tsRNA-GluCUC/UUC was 5ʹ biotinylated and coupled to streptavidin-coated sepharose beads. Since virtually all iAs-induced 5ʹ tsRNA-GlyGCC/CCC and 5ʹ tsRNA-GluCUC/UUC resided in the cytoplasm . Two replicate experiments probing CPEs collected during steady-state conditions yielded 171 proteins with a positive fold change (log2 ratio ≧ 0) in both, and at least a twofold change (log2 ratio ≧ 1) in one of the replicates when normalized to noRNA control, and 168 proteins when normalized to the scrambled RNA control experiments (2 ratio ≧ 0), and at least a twofold change (log2 ratio ≧ 1) in one of the replicates . Blotting 4.5 μg of total RNA from HEK293 cells before and after iAs exposure against a dilution of purified 5ʹ tsRNAs resulted in signals that could be semi-quantitatively and comparatively quantified. Ensuing calculations indicated that the copy number of tsRNAs in a single HEK293 cell after iAs-induced stress was about 14.000 for 5ʹ tsRNA-GlyGCC/CCC  can be extracted from many small RNA sequencing data sets. The biological significance of these small RNAs has been ignored until recently because their varying abundance and heterogeneity made them likely remnants of tRNA maturation or tRNA degradation intermediates. The potential for biological impact of specific tsRNAs has only been recognized after discovering that stress-induced tRNA fragmentation resulting in tRNA halves is a conserved part of the eukaryotic stress response with particular effects on protein translation and cellular survival ,19,20,55in situ, mapping tsRNA modification patterns and calculating their stoichiometry, as well as determining the molecular interactions of specific tsRNAs. The experimental basis for addressing most of these questions should be the availability of pure tsRNA sequences, preferably from cellular sources and conditions where and under which tsRNAs are being produced. This is important because tRNAs are the most highly modified tRNAs in any cell type, making it likely, but largely untested, that the functionality of specific tsRNAs depends on their modification state. Biochemical attempts for enriching small RNAs including tsRNA have been published [To better understand the mechanistic details of tsRNA function, improved methodology needs to be developed, which would allow measuring actual tsRNA copy numbers, localizing specific tsRNAs ublished . Howeverublished ) therebyublished , which min vivo conditions that promote tRNA fragmentation or after in vitro processing of purified tRNAs. This alternative is rooted in the previous success to purify individual tRNAs and systematically determine their modification patterns after hybridization-based affinity capture of target RNAs from complex samples followed by LC-MS/MS [A potentially viable alternative is the purification of tsRNAs from endogenous sources, especially under LC-MS/MS .in vitro and in vivo strategies for the production of tsRNAs carrying RNA modifications. While some of the in vitro approaches would allow quantitative fragmentation of input RNAs into tsRNAs, use of resulting tsRNAs obtained from these approaches needs to be matched with planned downstream applications. This might become important for planning specific experiments since denaturing and re-folding of tRNAs (serving as substrates for DNAzymes or purified ANG) might result in tsRNA identities that are different from stress-induced tsRNAs produced in a cellular environment and thereby affect experimental outcomes. Furthermore, our findings that particular tRNA positions carrying RNA modifications are not addressable by DNAzymes are informative since use of such DNAzymes would result in tsRNAs with particular modification patterns that are different from the once produced in a cellular context through the activity of particular endonucleases.Here, we report on different in vivo approaches, which employ either endogenous or ectopic ANG activity in living cells, resulted in tsRNAs that were homogeneous in sequence identity but rather heterogeneous in RNA modification content. Quantitative RNA mass spectrometry for a select number of modifications in the HEK293 cells revealed that both purified tsRNAs and co-purified tRNAs were not quantitatively modified (expected mods/detected mods). These results could be explained by biological context but could also be rooted in technical bias introduced during the presented workflow for the purification of tRNA-derived sequences. For example, while database entries list particular mature tRNAs as modified at particular positions [On the other hand, the presented ositions , the extositions ,61, yet ositions  indicatiositions ,64 indicositions ,66. AlteIn addition, also technical bias could have resulted in the affinity purification of a mix of modified, hypo-modified and non-modified tRNA-derived sequences. Since we employed hybridization-based affinity capture methodology with the aim of purifying specific tsRNAs from human cells exposed to stress conditions or after ectopically expressing the mammalian anticodon nuclease ANG, the use of DNA oligonucleotides matching an unmodified and not a modified tRNA-derived sequence might have affected the output of the purification procedure because of different hybridization efficiencies, which can be determined by RNA modifications.in vitro protein capture experiments, which, in combination with experiments using synthetic tsRNA sequences, could help addressing the impact of modifications on protein binding. Furthermore, purified tsRNAs could be used for the development of amplification-independent tsRNA quantification methods, which, in combination with using synthetic tsRNA sequences, might allow determining the effects of RNA modifications on hybridization-based read-outs. The values that we have calculated after northern blotting as to how many individual stress-induced tsRNAs might exist in a single cell fit well to previously obtained values  althougation in ,68. In aar space  could ber miRNAs ,35 will r miRNAs ,71 raiseTaken together, while the described approaches lay the groundwork for the reproducible purification of specific tsRNA species in scalable fashion, these strategies represent basic workflows, which will need to be optimized, adjusted and extended depending on the application and type of experiment that will utilize purified tsRNAs.Click here for additional data file.


The poliovirus type I IRES is able to recruit ribosomal machinery only in the presence of host factor PCBP2 that binds to stem–loop IV of the IRES. When PCBP2 is cleaved in its linker region by viral proteinase 3CD, translation initiation ceases allowing the next stage of replication to commence. Here, we investigate the interaction of PCBP2 with the apical region of stem–loop IV (SLIVm) of poliovirus RNA in its full-length and truncated form. CryoEM structure reconstruction of the full-length PCBP2 in complex with SLIVm solved to 6.1 Å resolution reveals a compact globular complex of PCBP2 interacting with the cruciform RNA via KH domains and featuring a prominent GNRA tetraloop. SEC-SAXS, SHAPE and hydroxyl-radical cleavage establish that PCBP2 stabilizes the SLIVm structure, but upon cleavage in the linker domain the complex becomes more flexible and base accessible. Limited proteolysis and REMSA demonstrate the accessibility of the linker region in the PCBP2/SLIVm complex and consequent loss of affinity of PCBP2 for the SLIVm upon cleavage. Together this study sheds light on the structural features of the PCBP2/SLIV complex vital for ribosomal docking, and the way in which this key functional interaction is regulated following translation of the poliovirus genome. They were originally discovered in members of the picornavirus family that possess a directly translated positive-strand RNA genome  are A genome . ThroughA genome . After dA genome .The poliovirus IRES is one of the best characterized viral RNA elements and exemplifies the type I IRES of the picornavirus family that is also found in coxsackievirus B3 (CVB3), enterovirus 71 (EV71) and human rhinovirus 2 (HRV2) . It consThe essential nature of PCBP2 for poliovirus translation has led to its identification as a key regulator of viral genome translation. When PCBP2 is bound to SLIV of the poliovirus IRES, production of the viral proteins occurs as the first stage of viral replication. This includes the production of the poliovirus 3CD proteinase that, in addition to processing the viral encoded polyprotein, targets a number of cellular proteins for proteolytic cleavage, including PCBP2 . Upon trK homology (KH) domains (Poly(C)-binding proteins (PCBPs) are ubiquitous ssRNA binding proteins that specifically interact with cytosine-rich tracts via their hnRNP  domains . The fam domains ,30. PCBP domains . KH1 and domains ,33. Spec domains  and its  domains . However domains .PCBP proteins bind to many target mRNAs  and serva and loops b, c and d in the current study; Figure b and c as well as a ‘bulge’ in loop c  tetraloop that is positioned at the apex of SLIV loop c, promoting ribosomal docking  of SLIV, stabilized with three extra GC pairs at the stem region  by incubation at 37°C to an OD600 of 0.6. Protein production was induced with 1 mM IPTG for 3.5 h at 25°C. Protein from disrupted cells was bound to Ni-NTA agarose (QIAGEN), washed with buffer containing 20 mM imidazole followed by a 2 M NaCl salt wash and eluted with 60 mM imidazole. After dialysis in a low salt buffer the protein was purified by size-exclusion chromatography using a Superdex 75 10/300 column (GE LifeSciences) followed by anion exchange using a MonoQ 5/5 column. For the production of PCBP2-KH1/2 (residues 13–169), pET15b-PCBP2-KH1/2 was generated, by cloning into the NdeI and XhoI sites of pET15b. The plasmid was transformed into E. coli Rosetta 2 (Merck), and expression, refolding and purification was performed according to Du et al. (PCBP2-FL (full-length) and PCBP2-ΔKH3 (residues 1–253) were expressed using plasmids pET22b-PCBP2-FL and pET28a-PCBP2-ΔKH3 in u et al. . FractioPfu DNA polymerase (Promega) for 30 cycles of 94°C/30 s, 42°C/20 s, 72°C/30 s.The DNA template for SLIVm encoding nucleotides 278–398 of the poliovirus RNA, was produced by PCR using the plasmid pT220-460 (provided by Dildine and Semler) as a template, with a forward oligonucleotide directed against nucleotides 278–290 and encoding the T7 RNA polymerase promotor site at the 5′-terminus (5′TAATACGACTCACTATAGGGCGCGTTGCGCTCAG-3′), and a reverse oligonucleotide designed against nucleotides 380–398 , both oligonucleotides were designed to encode three extra GC pairs beyond the native sequence to stabilize the termini of the RNA. The PCR was performed with et al. (Pfu DNA polymerase (Promega), for 30 cycles of 94°C/30 s, 42°C/20 s, 72°C/30 s.The RNA design for SHAPE  analysis was based on Wilkinson et al. . A 14-meRNA from the PCR amplified DNA template was transcribed using the T7 RiboMAX Express Large Scale RNA Production System (Promega), extracted using phenol/chloroform and precipitated using 0.3 M sodium acetate in 100% ethanol, washed with 70% ethanol, resuspended in TE  and buffer exchanged to remove unincorporated rNTPs using an Amicon Ultra centrifugal concentrator (Merck). To ensure correct folding, RNA was annealed immediately before use in experiments.2, 2 mM DTT, 4% glycerol, 0.1 mM EDTA, pH 7.5) at 30°C for 10 min at ∼3.5 μM and added to the annealed RNA, bringing the concentration of the complex to ∼3 μM. The sample was incubated for a further 10 min at 30°C, then concentrated to 110 μl in a centrifugal concentrator at 21°C. 100 μl samples were injected onto a Superdex 200 10/300 column or S75 10/300 column (GE LifeSciences) in RNA binding buffer at a flow rate of 0.4 ml/min. Elution profiles and molecular masses were estimated using an SEC-MALS instrument consisting of a Shimadzu Prominence UFLC, Shimadzu SPD-20A UV–Vis for UV data collection at 280 and 260 nm, a DAWN HELEO-II and an Optilab T-rEX (Wyatt Technology). Scattering and refractive index data were collected using ASTRA6 (Wyatt Technology).The quality of the protein, RNA and complexes were assessed using size-exclusion chromatography-multi-angle light scattering (SEC-MALS). For complex formation, 50 μg of RNA was diluted to 30 μM in TE, and annealed for 5 min at each of 68°C, 37°C, 21°C. A 1.5 molar excess of protein was incubated in RNA binding buffer . A frozen hydrated specimen was prepared by applying 4 μl of sample on the grid in a Vitrobot Mark IV  maintained at 4°C and 100% humidity. The sample was blotted with a blot force of +3 to +5 for 2.5 s and was plunge frozen into liquid ethane after a drain time of 1 s. Grids were transferred under liquid nitrogen to a Titan Krios transmission electron microscope  operated at 300 kV, equipped with a K2 Summit™ direct detector (Gatan), GIF- Quantum energy filter (Gatan) and a Volta phase plate. Zero loss imaging was performed in nanoprobe EFTEM mode with the slit width set to 20 eV. C2 condenser aperture size of 50 μm and Volta phase plate was used in the objective plane. Movies were recorded at a nominal magnification of 130k corresponding to a calibrated magnified pixel size of 1.06 Å, dose rate of 4e/pixel/second, exposure time of 10 s, and a total dose of 40 e Å−2 which was fractionated into 40 subframes. Data collection was automated using SerialEM software package  were glow discharged in the presence of 20 μl 99%  package . Volta pDose fractionated movies were corrected for beam induced motion and electron dose by the MotionCor2 software package . A non-dParticles were also selected from 100 tilted micrographs in a semi-automated fashion using Relion 2.1. Particles within a 1000-pixel width rectangle along the axis of tilt were then extracted which corresponded to 74 535 particles where then subjected to multiple rounds of reference free 2D classifications and selection. The final subset corresponded to 11 640 particles which were then combined with the un-tilted data and the combined particle set was subjected to 3D classification in Relion 2.1, where two distinct 3D conformations were apparent corresponding to 88 988 and 75 014 particles respectively. These two classes where then further refined and post-processed in Relion 2.1. They refined to a global resolution of 6.1 and 6.4 Å according to the gold standard Fourier shell correlation (FSC) criterion of 0.143 .A significant number of particles recognized as uncomplexed SLIVm RNA were observed on the micrographs. The corrected movie stacks (described above) were imported into CRYOSPARC . ParticlSamples were prepared as described for SEC-MALS and concentrated to ∼100 μM in a centrifugal concentrator. Using a Shimadzu Prominence UFLC, 100 μl samples were injected onto a Superdex 200 10/300 or S75 10/300 column (GE LifeSciences) in RNA binding buffer at a flow rate of 0.4 ml/min in direct connection with the SAXS/WAXS beamline at the Australian Synchrotron. Coflow, a sheath flow capillary system , was empUltrascan Solution Modeler (US-SOMO) . SEC-SAXS data were converted to the time domain with the HPLC SAS module of the program US-SOMO) . TypicalUS-SOMO)  and thenUS-SOMO) . Due to US-SOMO) ,69. The US-SOMO) , DATGNOMUS-SOMO) , and dimUS-SOMO)  was usedDAMCLUST  used to DAMCLUST .ab initio. Coordinates for SLIVm were calculated using RNAexplorer . The MMTSB Toolbox (https://www.ebi.ac.uk/pdbe/pisa/).A complete structure of PCBP2 was modelled using PHYRE  with theexplorer  based up Toolbox  was used Toolbox . The coohttp://www.ks.uiuc.edu/Training/Tutorials/science/mdff/tutorial_mdff-html/tutorial_mdff.html). Prior this the complex model was roughly fitted into the cryoEM map using COLORES from the Situs package (https://situs.biomachina.org/). MDFF utilized the generalized Born implicit solvent model, and, domain restraints were applied using targeted MD (TMD) throughout and included the first two domains of PCBP2 (KH1 and KH2) and the third KH domain, RNA domains were defined in a manner as described above in the rigid body molecular dynamics of RNA alone. Protein termini and domain linkers were not restrained. The elastic constant of the TMD forces was set to 1000 kcal/mol/Å2 and the initial and final TMD rmsds were maintained at 0.1 Å. Interactive molecular dynamics was used to guide the coordinates into the cryoEM maps. The GSCALE parameter was changed from 0.1 stepped through to a value of 10 during the MDFF. NAMD ExtraBonds were exploited to hold the KH domains bound to their cognate RNA binding sites during the MD. A total of 50 ns of MD was performed during the IMD and further 50 ns was performed to allow for equilibration. KH3 of PCBP2 and the linker between KH2 and KH3 were excluded from fitting into the cryoEM map.The coordinates of the poliovirus SLIVm/PCBP2 complex were fitted into the cryo-EM maps using the plugin MDFF as implemented in NAMD2 and outlined in the MDFF NAMD tutorial  and subjected to SEC-MALS. For SHAPE analysis, 8 μl of 60 mM NMIA in 100% DMSO was added to 60 pmol RNA or RNA-protein complexes to a final volume of 80 μl and was incubated for 37°C for 50 min. A no NMIA control containing 10% DMSO was also included and the reactions were quenched by the addition of glycerol to a final concentration of 15%. Hydroxyl radical cleavage was performed according to  were freshly prepared. 60 pmol RNA or RNA–protein complex was prepared in 74 μl of modified RNA binding buffer with 2 μl of each of the Fe(II)/EDTA, H2O2 and ascorbic acid solutions, or 6 μl of nuclease-free water as a no reagent control. The samples were incubated at 37°C for 5 min and the reactions quenched by the addition of glycerol to 15%.SLIVm-SHAPE RNA and PCBP2-FL/SLIVm-SHAPE and PCBP2-ΔKH3/SLIVm-SHAPE complexes were prepared as described above in modified RNA binding buffer  for 5 min at each of 65°C, 35°C and on ice, according to Wilkinson et al.  and incua structure and nucleotide 377 at the 5′ end of the stem of loop d). Default normalization was applied, which resulted in 1.65–1.68% outlier values.QuSHAPE  was usedPCBP2 (80 μg) was prepared with and without an equimolar amount of annealed SLIVm and concentrated to a volume of 70 μl. 0.4 μg of sequencing grade modified trypsin (Promega) was added and the sample incubated at 30°C. 10 μl of sample was removed prior to trypsin addition and at each time point 0, 10, 30, 60, 90 and 120 min, and the digest stopped by the addition of SDS-PAGE loading dye and heating 95°C for 5 min followed by storage on ice. The samples were subjected to electrophoresis on a Mini-Protean TGX Precast gel (Bio-Rad) and visualized using Coomassie Blue. The cleavage products were separated by SDS-PAGE, transferred to PVDF and submitted to the Monash Biomedical Proteomics Facility for N-terminal sequencing by Edman degradation.2, 3.8% glycerol, 20 mM DTT) for 10 min at 30°C. Biotinylated RNA probe and 8 U of RNAsin (Promega) were then added followed by incubation for 10 min at 30°C. The reaction mixture was further incubated with 0.5 mg/ml of bovine serum albumin (Promega) for 10 min at 30°C. Following incubation, 1.25 μl of 80% glycerol was added. The samples were separated by native TBE-PAGE containing 5% acrylamide at 4°C, electrophoretically transferred to a nylon membrane (Roche), UV-crosslinked to the membrane using a Stratalinker UV Crosslinker 1800 . The biotinylated RNA on the membranes was visualized using a LightShift Chemiluminescent RNA EMSA kit (Thermo Fisher Scientific) on a Chemidoc Touch Imaging System (Bio-Rad) and quantified using ImageQuant TL 8.1 software . The fraction bound was calculated as the intensity of bound SLIVm RNA as a fraction of the total intensity of bound and free SLIVm RNA probe.SLIVm RNA was biotinylated using the Pierce RNA 3′ End Biotinylation Kit (Thermo Fisher Scientific) according to the manufacturer's protocol. RNA mobility shift assays were performed as described previously ,82. Purimodified stem–loop RNA representing the apex of the poliovirus SLIV (nt 278–398), henceforth referred to as SLIVm. The T7 RNA polymerase generated 127-nt RNA included loops b, c and d previously established to be the target binding sites of PCBP2 KH domains  and incubated it with a  domains ,56 as we domains . The com domains  and subjc of SLIV , it was possible to resolve the PCBP2-FL/SLIVm complex particles within the thin ice sections of the holey carbon grids for 2D classification . In addi of SLIV ,56, the e Figure .c was built into density arching between the KH1 binding site and the apparent four-way junction of the SLIVm structure. The junction was constructed as a type cH junction  despite these structures being unrelated)  Figure . Loop b  binding . In the d Figure . In thisc, suggesting a bent conformation or a degree of flexibility in the absence of bound protein. There was no evidence of a well-defined GNRA tetraloop as apparent in the PCBP2-FL/SLIVm structures.The cryoEM data also included particles representing RNA alone that could be used to construct a structure for the SLIVm to 9.6 Å resolution Figure . SLIVm adata not shown). We then used this altered version of SLIV in RNA mobility shift assays with increasing amounts of recombinant PCBP2-FL. RNA transcripts harbouring the GNRA substitution (GNRA-Sub) were able to efficiently form a complex with recombinant PCBP2, albeit at reduced levels compared to the wild type SLIV RNA  were tested in cell-free translation assays for their ability to direct viral protein synthesis. As shown by the SDS-PAGE analysis displayed in data not shown). Taken together, these data reveal that although the GNRA tetraloop in SLIV is not essential for PCPB2 binding, it is crucial to the IRES function of poliovirus genomic RNA and, as a result, to virus infectivity.The above-described structures highlight the GNRA tetraloop as a prominent feature of the PCBP2/SLIVm complex and undiscernible in the SLIVm structure alone. To further interrogate the importance of the GNRA tetraloop in poliovirus translation and infectivity, we first analysed a mutated form of SLIV in which the GUGA tetraloop was substituted with the sequence GACG refer to . When thSLIV RNA . DNA seqConcurrent with the cryoEM, we also obtained in-solution structural data using SEC-SAXS for PCBP2-FL, as well as truncated forms, PCBP2-ΔKH3 (residues 1-253) and PCBP2-KH1/KH2 (residues 13–169), before and after complex formation with SLIVm. PCBP2-ΔKH3 represents the inactivated form of PCBP2 after cleavage by 3CD . It contRmax = 132 Å) a more compact globular structure is formed upon complex formation with the SLIVm (Rmax = 115 Å) indicating a shorter maximal dimension and a profile shifted to the right indicating a larger average dimension , suggesting an intrinsically structured region. Reactivity was observed in the double stranded region of loop d (not observed in the whole poliovirus IRES), suggesting that, in the context of our isolated SLIVm construct, loop d has greater flexibility than it does in the entire IRES. Upon repeating the experiment with SLIVm in the presence of bound PCBP2-FL, the SHAPE reactivity pattern remained much the same, as would be expected since RNA reactivity is not known to be sensitive to protein binding. Nevertheless, a few differences were observed in regions of loops b and c (indicated with arrows). This is consistent with the protein engaging with both of these domains of the RNA and impacting the SHAPE reactivity. These differences were not observed for SLIVm in the presence of PCBP2-ΔKH3, indicating that this truncated protein does not have the same impact on SLIVm structure as PCBP2-FL.We performed SHAPE  and hydroxyl radical cleavage experiments to determine the effect of PCBP2-FL (and PCBP2-ΔKH3) binding on the SLIVm RNA flexibility and solvent accessibility at individual nucleotides. For this analysis, the SLIVm RNA sequence was cloned into a cassette incorporating 5′ and 3′ additional sequences for primer extension. The SHAPE method utilized NMIA that reacts with ribose 2′-hydroxyl groups that are flexible in the RNA structure, resulting in adduct formation at specific nucleotides. The RNA adduct formation sites were identified using primer extension followed by capillary electrophoresis and the analysed data are shown in b and c were clearly protected, consistent with direct binding by KH1 and KH3 expected at these sites. Adjacent regions of these loops were also protected, although to a lesser degree. Mapped onto the PCBP2/SLIVm model, these regions of protection are clustered together and potentially indicative of a surface that is protected by the, as yet, unlocated linker region. Several nucleotides became more susceptible to cleavage in the presence of PCBP2. These were in the arched region of stem loop c, indicative of a structural change induced by the protein binding  compared to PCBP2-FL  for SLIVm. Interestingly, while a single shift was observed for PCBP2-FL, two binding transitions were observed for PCBP2-ΔKH3 suggesting a 2:1 protein:RNA stoichiometry for this construct at high protein concentrations. From these findings, it is likely that the PCBP2-ΔKH3 protein concentrations of approximately 0.75 μM utilized for SHAPE and footprinting analyses were insufficient to effect protection of the RNA.RNA footprinting was also carried out in the presence of PCBP2-ΔKH3 to further investigate the impact of PCBP2 cleavage on the PCBP2/SLIVm structure. No significant difference in cleavage pattern was observed compared to the cleavage of RNA alone, indicating no protection by the presence of this protein. Combined with the lack of impact of PCBP2-ΔKH3 on the SLIVm SHAPE reactivity, this likely reflects a lower stability of complex formation by PCBP2-ΔKH3 compared with PCBP2-FL. To quantitate the stability of complexes formed by PCBP2-ΔKH3 versus PCBP2-FL with SLIVm, the binding affinities were determined using RNA electrophoretic mobility shift assay  compared with SLIVm. Again, however, the affinity is diminished in the case of PCBP2-ΔKH3 . While these experiments do not test the binding of PCBP2 and PCBP2-ΔKH3 in the context of the whole IRES and in the presence of other required eIFs and ITAFs, they are consistent with the loss of IRES function being due to the loss of PCBP2 binding affinity upon cleavage.To further explore this possibility, we also tested the binding of PCBP2-FL to the full length SLIV  and compared this to the binding of PCBP2-ΔKH3. We considered that the longer stem of SLIV-FL could contribute to PCBP2-FL binding affinity and potentially also to PCBP2-ΔKH3 binding affinity. RNA electrophoretic mobility shift assays were carried out to compare the binding of both PCBP2-FL and PCBP2-ΔKH3 to SLIV-FL . This anPCBP2 binding to the poliovirus IRES SLIV has been shown to be essential for docking of the 40S ribosomal subunit and initiation of translation ,21. Whenb, c and d in a cruciform arrangement with the stem a, that numerous previous biochemical and biophysical studies have established as the binding sites for the PCBP2 KH domains  comprises loops  domains . In partetraloop ,56. Thisc to enhance PCBP2 binding or that PCBP2 binding presents the GNRA tetraloop in an optimal way for making distal interactions. Data presented in The most compelling feature of the PCBP2-FL/SLIVm structure is the GNRA tetraloop that protrudes from the ‘fist’ of the KH1/KH2 back-to-back dimer. The importance of this tetraloop was also suggested in a study of the analogous stem–loop of cadicivirus (named stem loop 10) . The rolc constrained by PCBP2 towards loop b. This is in contrast to the cryoEM derived shape of the SLIVm RNA alone, in which only the junction can be seen and not the end of the longest loop, likely due to flexibility. This is corroborated by the SAXS data that also show a globular form for the PCBP2/SLIVm complex and an elongated ab initio shape for the SLIVm alone. Thus, PCBP2 binding of the C-rich loop c via KH1 appears to induce an arched dsRNA structure that brings the other KH domains in close proximity to loop b. In the current model KH3 is positioned at the C-rich region of loop b, since KH3 is able to interact with this motif with good affinity and the structural basis for this interaction is also known  that support IRES function until KH3 is lost, but since PCBP2 is the only essential factor, the main role of PCBP2 binding to poliovirus SLIV is likely to be structural. The current work thus provides the first picture of this structural arrangement. It suggests that loop Data supporting the findings of this manuscript are available from the corresponding author upon reasonable request. The cryo-EM map describing the PCBP2/Stem Loop IVm class 1 structure is deposited in the Electron Microscopy data Bank (EMDB) under accession code EMD-21133.SAXS data have been deposited in the Small Angle Scattering Biological Data Bank (SASBDB) under the accession numbers: SASDH55: SLIVm; SASDH65: PCBP2; SASDH75: PCBP2-FL/SLIVm; SASDH85: PCBP2-ΔKH3/SLIVm; SASDH95: PCBP2-ΔKH3; SASDHA5;PCBP2-KH1/2/SLIVm.gkaa519_Supplemental_FileClick here for additional data file.